DSpace community: 太空科學研究所

PEARL-1A/1B 立方衛星姿態判定與控制次系統地面驗證與模擬;Ground Verification and Simulation of Attitude Determination and Control Subsystem for the PEARL-1A/1B CubeSats

title: PEARL-1A/1B 立方衛星姿態判定與控制次系統地面驗證與模擬;Ground Verification and Simulation of Attitude Determination and Control Subsystem for the PEARL-1A/1B CubeSats abstract: PEARL (Propagation Experiment using Kurz-Above-band Radio in Low earth orbit) -1A 與 1B 為兩顆 6U XL 大小立方衛星，由中央大學負責整合測試，立方衛星主要任務目標為在台灣上方進行太空與地面及衛星間通訊實驗。兩顆立方衛星上皆搭載三個酬載，分別為 Inter-Satellite Link (ISL)、Compact Ionospheric Probe (CIP)、Perovskite Solar Cell (PSC)。ISL 將使用 Ka 波段進行跨地平通訊實驗測試，以達成星間通訊與衛星對地通訊功能；CIP 則為中央大學太空酬載實驗室自製小型電離層探測儀酬載，可進行全球電離層電漿溫度、速度與密度探測；而 PSC 則由鈣鈦礦太陽能電池及其他太陽能電池組成，其透過量測太陽能電池在太空中的電流和電壓之間的關係曲線 (I-V curve)，驗證其太陽能電池的發電功率。為確保立方衛星能符合各酬載所需指向， PEARL-1A、PEARL-1B 使用的姿態判定與控制次系統 (Attitude Determination and Control Subsystem, ADCS) 為 CubeSpace 所生產之模組，透過 LabVIEW 整合亥姆霍茲線圈控制、太空環境模擬與 ADCS 通訊，進行地面硬體迴路測試，以了解實際衛星在軌指向變化，以及模擬在軌磁力計校正等測試。;PEARL (Propagation Experiment using Kurz-Above-band Radio in Low earth orbit) -1A and PEARL-1B are two 6U XL CubeSats integrated by National Central University (NCU). Their main purpose is to perform space -to-earth and inter-satellite radio propagation channel experiments. Each CubeSat is equipped with three payloads, an Inter-Satellite Link (ISL) payload, a Compact Ionospheric Probe (CIP), and a Perovskite Solar Cell (PSC) payload. The ISL enables over-the-horizon radio communication experiments, utilizing the Ka-band for both inter-satellite communication and space-to-ground communications. The CIP is an all-in-one in-situ ion sensor developed by NCU to measure global ionospheric ion concentration, velocity, and temperature especially to monitor ionospheric plasma irregularities resulted in radio scintillations. The PSC consists of perovskite solar cells designed to verify power efficiency by measuring their characteristics of I-V curves in space. Depending on the pointing requirements of the payloads, PEARL-1A and PEAR-1B using CubeSpace ADCS modules as their Attitude Determination and Control Subsystem (ADCS). To verify that the ADCS meets mission requirements, we use LabVIEW to control Helmholtz coil for space magnetic environments and communicate with ADCS. Through these setups, we test ADCS control performance in different operating modes and simulation CubeSats behavior in orbit.

利用全天空影像之雲追蹤預測短期太陽輻射;Cloud Tracking Using All-Sky Images for Short-Term Solar Irradiance Prediction

title: 利用全天空影像之雲追蹤預測短期太陽輻射;Cloud Tracking Using All-Sky Images for Short-Term Solar Irradiance Prediction abstract: 隨太陽能發電佔比逐年增加，如何準確預測太陽能發電量已成為關鍵議題，而太陽輻射量的短期預測是其中的核心挑戰。現有方法多高度仰賴運算能力或大量訓練資料，限制其在即時運用之可行性。本研究針對雲遮蔽太陽所引起的輻射量劇烈變化，提出一套低運算成本的即時預測方法。此輕量化的雲配對及雲預測演算法，採用外延法推估未來 1∼10 分鐘後雲厚度分布，進而預測輻射值。因架構簡潔、演算法複雜度低，可在消費級中階運算設備上運行，平均可於 2.23 秒內完成一次預測。透過實測資料評估，本方法能有效預測未來 10 分鐘內之輻射量趨勢，預測與實際影像輻射值呈現高度正相關 ( 1 分鐘預測： R > 0.91， nRMSE 為 10.04%； 10 分鐘： R=0.84， nRMSE 為 13.39%）。整體表現勝過既有模型，本方法在不依賴複雜訓練模型與高效能硬體的情況下，仍能提供高準確度的短期太陽輻射預測，提供一實用的短時間預測方案。;The share of solar power generation in the energy mix is steadily increasing. Accurate forecasting of solar output has therefore become essential. A key challenge in this task is short-term prediction of solar irradiance. Existing approaches often depend on high computational capacity or large training datasets, which limits their applicability in real-time operations. Rapid irradiance fluctuations caused by cloud obstruction is a significant challenge for solar energy modulation. This paper proposes a novel, lowcost, real-time prediction method to address this issue. The proposed method utilizes a lightweight cloud-matching and irradiance-predicting algorithm. An extrapolation approach is employed to estimate cloud thickness distribution 1– 10 minutes ahead, which is then used to predict irradiance values. Due to its simple architecture and low algorithmic complexity,the method can be implemented on consumer-grade mid-level computing devices, with each forecast taking an average of 2.23 seconds. Experiments with measured data show that the method accurately forecasts irradiance trends. For a 1-minute forecast, the method achieves a high correlation (R>0.91) with a normalized root mean square error (nRMSE) of 10.04%. A 10-minute forecast yields R=0.84 and nRMSE=13.39%. Compared with existing models, our approach avoids complex training procedures and reliance on high-performance hardware. This work presents a practical and accurate solution for short-term solar irradiance predicting, demonstrating a clear advantage in implementation complexity and computational cost.

使用 SA U-net 個人平台深度學習網路辨識正/斜射電離圖軌跡與參數

title: 使用 SA U-net 個人平台深度學習網路辨識正/斜射電離圖軌跡與參數 abstract: 電離層位於地球表面約 50 至 1000 公里高度，因太陽輻射電離大氣的中性粒子而形成，對高頻（HF）無線電波的傳播特性具有重要影響，並在遠距離通訊、GPS 導航、軍事與太空天氣預報等領域有重要應用。電離圖（Ionograms）由電離層觀測系統網資料生成，記錄了無線電波反射訊號的虛擬高度與頻率關係。其中，普通波（O-mode）軌跡對提取 F2 層的電離層參數—臨界頻率（foF2）與虛擬高度（ℎ’F2）至關重要。中央大學的原始自動分析方法使用模糊邏輯理論處理，例如 Tsai 等人提出的模糊辨識與連通演算法。然而，這方法在背景噪音高或多跳軌跡複雜的情況下仍會有誤差。近年，隨著深度學習的發展，卷積神經網絡已被應用於電離層資料分析。例如：Huang 等人使用 Spatial Attentional U-net（SA U-net）成功從花蓮站電離圖中識別出 F2 層的普通波和雜散 Es 層訊號；本研究採用 SA U-net 深度學習網路模型，結合空間注意力機制，自動檢測垂直與斜射電離圖中的 O-mode 軌跡，並與中央大學 Fuzzy 算法進行性能對比。為了實現這一目標，使用中央大學蔡龍治教授的花蓮站 VIPIR 的電離圖數據，建立本地環境進行 SA U-net 模型訓練，並在 NVIDIA Tesla P100 GPU 與 Docker 容器環境下試著優化模型參數。實驗結果顯示，SA U-net 在垂直電離圖中能夠較好識別 F2 層普通波軌跡，但在處理雜亂多跳或 X-mode 訊號時效果不如預期；使用垂直電離圖訓練之模型預測斜射電離圖中，僅在少部分情況下成功辨識斜射電離圖軌跡，多數斜射特徵未被完整捕捉。可見，深度模型對原始電離圖具有一定的辨識潛力，但對複雜場景仍需更大規模數據與本地環境改進。;The ionosphere, located approximately 50 to 1000 km above Earth′s surface, is formed by the ionization of neutral atmospheric particles due to solar radiation. It significantly influences the propagation of high-frequency radio waves and plays a critical role in applications such as long-distance communication, GPS navigation, military operations, and space weather forecasting. Ionograms, generated from Hualien VIPIR, record the relationship between virtual height and frequency of reflected radio signals. The ordinary wave (O-mode) trace is essential for ionospheric parameters of the F2 layer, namely the critical frequency (foF2) and virtual height (ℎ’F2).The original fuzzy logic–based approach adopted by National Central University (NCU), including the fuzzy identification and connectivity algorithms proposed by Tsai et al., may still produce recognition errors when analyzing ionograms with high background noise or complex multi-hop trace conditions. In recent years, advancements in deep learning have led to the application of convolutional neural networks in ionospheric data analysis. For instance, Huang et al. employed a Spatial Attentional U-net (SA U-net) to successfully identify F2-layer O-mode and sporadic Es-layer signals in ionograms from the Hualien station. This study adopts the SA U-net deep learning model with a spatial attention mechanism to automatically detect O-mode traces in both vertical and oblique ionograms, comparing its performance with NCU fuzzy algorithms. To achieve this objective, using ionogram data from the Hualien VIPIR station, provided by Professor Lung-Chih Tsai of National Central University (NCU), a local environment was established to train the SA U-net model, with optimization performed on an NVIDIA Tesla P100 GPU within a Docker container. Experimental results reveal that SA U-net effectively identifies F2-layer O-mode traces in vertical ionograms but exhibits suboptimal performance in handling complex multi-hop or X-mode signals. When the model, trained on vertical ionograms, was applied to predict oblique ionograms, it successfully identified oblique traces in only a few cases. Most oblique features not fully captured. The findings reveal that deep learning models possess notable potential for interpreting raw ionograms, yet their effectiveness in complex conditions is constrained by limited data and insufficient accommodation of local environmental factors.

行星際震波特性對離子加速效應影響

title: 行星際震波特性對離子加速效應影響 abstract: 行星際震波是太空中高能粒子加速的重要來源之一。本研究探討行星際震波的物理特性對離子加速效應的影響，分析 Solar Orbiter 衛星於 2021 至 2023 年間觀測到的 43 起快速震波事件。研究結合磁流體力學（MHD）理論，並運用 Rankine-Hugoniot 關係式進行擬合與統計，定量評估震波參數（如 ρ_d⁄ρ_u、θ_Bn、V_S、M_f 及 β ）與離子通量變化之相關性。分析結果顯示，在 60 keV 至 6 MeV 能量範圍內，V_S 與 M_f 與離子通量峰值高度正相關，ρ_d⁄ρ_u 則表現為中度正相關，而 θ_Bn 與 β 的相關性則較不顯著。針對不同事件型態（如穩定型、尖峰型、階梯型、預增型）以及不同日心距離（近距離、中距離、遠距離）下的分布進行細部分析，發現各型態事件在參數相關性與能量分布上有顯著差異：尖峰型事件多與 θ_Bn 較大的準垂直震波相關，階梯型與預增型事件則更符合震波擴散加速（DSA）理論預測。隨著日心距離增加，震波能量逐漸耗散，離子加速效率下降，事件型態也發生變化：近太陽區域多為明顯加速特徵（尖峰型、階梯型、預增型），而遠距離則以穩定型事件為主。但無論距離遠近，V_S 越快、M_f 越高的事件，其離子通量峰值提升幅度越大，顯示強震波具有更高能量轉移與粒子加速能力。 ;Interplanetary shocks are one of the most important sources of energetic particle acceleration in space. In this study, we investigate how the physical properties of interplanetary shocks influence ion acceleration by analyzing 43 fast shock events observed by the Solar Orbiter spacecraft between 2021 and 2023. The analysis incorporates magnetohydrodynamic (MHD) theory and employs the Rankine-Hugoniot relations for fitting and statistical evaluation, quantitatively assessing the correlations between shock parameters (such as compression ratio, shock angle, shock speed, fast-mode Mach number, and plasma beta) and variations in ion flux. The results show that, within the energy range of 60 keV to 6 MeV, shock speed and fast-mode Mach number exhibit a strong positive correlation with the peak ion flux, while the compression ratio shows a moderate positive correlation. In contrast, the correlations involving shock angle and plasma beta are less significant. A detailed analysis of different event types (including stable, spiky, step-like, and pre-enhanced types) and their distribution at different heliocentric distances (near, middle, and far) reveals pronounced differences in parameter correlations and spectral distributions. Specifically, spiky events are mostly associated with larger shock angle (i.e., quasi-perpendicular shocks), while step-like and pre-enhanced events are more consistent with predictions from diffusive shock acceleration (DSA) theory. As the heliocentric distance increases, the energy of the shocks dissipates and the efficiency of ion acceleration decreases, resulting in changes in event types: clear acceleration signatures (such as spiky, step-like, and pre-enhanced events) are mainly observed near the Sun, whereas steady events become predominant at larger distances. Nonetheless, regardless of distance, events with higher shock speeds and larger fast-mode Mach numbers exhibit greater enhancements in peak ion flux, indicating that strong shocks have a higher capacity for energy transfer and particle acceleration.