esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Remaining Challenges and Open Research Questions
The authors recommend the correction of the BRDF for reliable and consistent estimations, as it accounts for the surface reflectance anisotropy and varying solar and instrument-view angles. Although the authors of [61] used an imaging sensor, the same principle applies to the UAS-mounted point spectrometers [62] since the BRDF effect is inherent to remote sensing information [63]. [PERSON] et al. [63] studied the SIF directionality according to the surface, inclination, and orientation of leaves, as well as the sensor and sun angles. Nonetheless, the authors worked with single and static measurements, therefore a full characterization of the BRDF was not possible due to the limited range of illumination and viewing angles. In this respect, hypothetically, the implementation of a detailed canopy slopes map computed from a UAS-derived crop surface model might contribute to upscale and improve the results found by [63]. Moreover, considering that surface elevation differences of 0.50-1.50 m AGL (common crop canopy height range) represent 5%-15% of the sensor-surface distance in low altitude (e.g., 10 m AGL) UAS flights, we can also hypothesize that the incorporation of a crop surface model into the UAS-based methods for SIF assessment might help to better account for the ChIP re-absorption effect. Accordingly, more scientific questions arise: does the implementation of accurate surface elevation models significantly improve the quality of SIF estimations? Could a slope map derived from a crop surface model represent the canopy architecture complexity? Might this be useful for a full canopy BRDF characterization thus precise SIF retrievals from UAS-based methods?
# InnCube--A Wireless Satellite Platform to Demonstrate Innovative Technologies
## 2 Mission  
The goals of the CubeSat project can be divided into four categories:  
* Long-term goals;
* Technological goals;
* Scientific goals;
* Educational goals.  
The long-term goal is to increase both universities competencies and to raise TRL of used components for further development. This is reflected in the technology goals to demonstrate SKITH in an operational environment and have the first wireless satellite bus with respect to data harness. A further goal is the assessment of the functionality of a new battery concept of a carbon-reinforced plastic structural battery in a relevant space environment. In addition, a concept of an on-orbit navigation laboratory using a software-defined Global Navigation Satellite System (GNSS) receiver and Laser Ranging Reflector Experiment (LRR) is flown. The scientific goals comprise the validation of the initial theoretical assumption and to validate the GNSS experiment using the accuracy of a miniaturized laser retro reflector for CubeSats. Additionally, if possible the LRR measurements analyses should be used for improvement in positioning accuracy applying in orbit updates to the navigation algorithms. With the anticipated highly accurate orbit data, propagators developed at IRAS [1; 2], can be improved. Works in the field of space situational awareness, rendezvous, and docking for active debris removal missions can be advanced. In addition to the scientific goals, one of the main objectives (educational goals) of the project is the training of students. This includes not only component development and technical applications, but also satellite operations. In summary, the design, fabrication, launch and operation of a small satellite with the following boundary conditions are to take place within 4 years:  
* Fabrication, validation and verification of a CubeSat flight model within a maximum of 3 years;
* Successful launch with up to three months of Launch and Early Orbit Phase (LEOP) and payload calibration;
* Mission life expectancy of 12 months (in-orbit);
* Verify and validate science experiments and record science data through end of mission(active) and through end of life (passive-LRR);
* Verify and validate technologies (SKITH and WALL#E);
* Hardware and software testing;
* Satellite operations with students.
## 4 The Proposed Method
### Step 1: System Engineering Fundamentals Establishment and UCAs Identification
#### 4.2.1 System Engineering Fundamentals Establishment  
As mentioned in Section 3, the consequences of hazardous events in ISO26262 could be determined by integrating accidents in STPA with operating modes and operational situations. A \"hazard\" of STPA could be mapped to a \"hazardous event\" of ISO26262. In order to distinguish the \"hazard\" of STPA from the \"hazard\" of ISO26262, we use \" the system-level hazard\" here to present the \"hazard\" of STPA, and the \"vehicle-level hazard\" to present the \"hazard\" of ISO26262 in the rest part of this paper.  
The first step of STPA includes:  
* Define the \"loss\" of the system accident in STPA with limitation to the scope of harm defined by ISO26262 to be analyzed.
* Identify system-level hazards leading to the accidents and corresponding operating modes and operational situations from the descriptions of identified system-level hazards.
* Determine corresponding vehicle-level SCs which could be used to support the SGs determination subclause of ISO26262, as the SGs are high-level requirements too. However, the SGs in ISO26262 are more specific than the SCs of the same level in STPA [9].
* Draw the safety control structure of the system to identify the potential UCAs leading to the system-level hazards. The control structure diagram shows the boundary of the system under analysis and its interface, and information it contains can be used to define an item.
# A Semantically Aware Multi-View 3D Reconstruction Method for Urban Applications
## 2 Materials and Methods  
In the field of 3D semantic reconstruction, the ultimate goal is to achieve target point clouds enriched with semantic information. There are primarily two methodologies to acquire such 3D semantic data: one method involves semantic segmentation of point clouds, and the other method entails mapping semantic segmentation from 2D images onto 3D point clouds. In this study, we adopted the latter approach. Our main workflow encompasses three key stages: the acquisition of semantics from 2D images, followed by sparse and then dense reconstruction based on these semantics. Incorporating semantic information during the reconstruction process not only enhances the precision of the reconstruction but also yields dense point clouds with high-level semantic information. Considering the unique aspects of urban targets, we developed a comprehensive 3 Dsemantic reconstruction technique specifically designed for complex urban settings. The detailed process of this technique is illustrated in Figure 1.  
The main idea of our proposal is to semantically optimize the two steps of multi-view 3D reconstruction, sparse reconstruction and dense reconstruction, as illustrated in Figure 1. In the sparse reconstruction process, we propose semantic consistency-based feature matching and dynamic object separation algorithms to achieve semantic-based SfM. In the dense reconstruction process, we propose the sSGM reconstruction method to implement semantic-based MVS. Combined together, the semantic SfM and the semantic MVS constitute our proposed semantic-based 3D reconstruction pipeline.  
In our implementation, the SfM module utilizes the OpenMVG platform [27], while for semantic dense reconstruction, OpenMVS is employed [28].
# Pose Measurement for Unmanned Aerial Vehicle Based on Rigid Skeleton
## 3 Research Methodology
### 3D Reconstruction Stage  
This study uses a semi-global matching algorithm to solve the disparity problem of input image pair [26]. The algorithm idea is to form a disparity map by selecting the disparity of each pixel. The global energetic function associated with the disparity map is set to minimize the energetic function to achieve the optimal goal of each pixel. The energetic function that depends on the disparity image \(D\) is as follows:  
\[E(D)=\sum_{p}\left(C\big{(}p,D_{p}\big{)}+\sum_{q\in N_{p}}P_{1}T\big{[}\big{|} D_{p}-D_{q}\big{|}=1\big{]}+\sum_{q\in N_{p}}P_{2}T\big{[}\big{|}D_{p}-D_{q} \big{|}>1\big{]}\right) \tag{6}\]  
where \(p\) and \(q\) represent pixels. \(D\) represents the disparity image. \(D_{p}\) and \(D_{q}\) represent the disparity between \(p\) and \(q\), respectively. \(C\big{(}p,D_{p}\big{)}\) is the matching cost of pixel \(p\) with respect to disparity \(D_{p}\), which can be solved by the sum of absolute differences. \(q\) is in the neighborhood \(N_{p}\) of \(p\), and its disparity changes slightly (that is, 1 pixel). The operator \(T[\big{|})\) is the probability distribution of the corresponding intensity, which is 0 if its argument is false and 1, otherwise. \(P_{1}\) and \(P_{2}\) represent a penalty coefficient of 1 and a disparity difference greater than 1, respectively.  
Solving using the energetic function in the Formula (6) is an NP-complete problem. It is approximated as multiple one-dimensional problems and solved by dynamic programming [27]. For example, considering the direction from left to right, each pixel is only related to its coordinate pixel. The formula is as follows:  
\[L_{r}(p,d)=C(p,d)+\min\left(\begin{array}{c}L_{r}(p-r,d),L_{r}(p-r,d-1)+P_{ 1},\\ L_{r}(p-r,d+1)+P_{1},\\ \min_{i}L_{r}(p-r,i)+P_{2}\end{array}\right)-\min_{k}L_{r}(p-r,k) \tag{7}\]  
where \(L_{r}(p,d)\) represents the minimum cost of the current pixel when the disparity of the current pixel is \(d\). Here, \(r\) can be understood as the neighboring pixels of the pixel \(p\) in this direction.  
Costs in the eight directions are accumulated, and the smallest accumulated cost is selected as the final disparity of the pixel. The cumulative formula is expressed as follows:  
\[S(p,d)=\sum_{r}L_{r}(p,d) \tag{8}\]  
A disparity map of the entire image can be obtained by performing the above operation for each pixel in image.
## 3 Nutritional and Bioactive Components of Sprouted Grains
### Macnontrient Modifications During Sprouting  
#### 3.1.1 Modification of Protein and Amino Acid Contents  
Sprouting activates endogenous proteases that initiate the hydrolysis of storage proteins, resulting in the breakdown of large polypeptides into smaller peptides and free amino acids [31]. This proteolytic activity not only alters the protein structure but also enhances protein solubility and digestibility, thereby improving the functional and nutritional properties of grains [44]. While the total crude protein content may remain relatively unchanged in some grain types, the increase in water-soluble proteins and free amino acids is consistently observed across various studies [32; 45]. For example, in soybean varieties ZD41, J58, and JHD, germination for 36 h significantly increased water-soluble protein content by 30.52%, 9.34%, and 10.97%, respectively, despite minimal changes in total protein content [31]. Similarly, a seven-day germination process at 25 \({}^{\circ}\)C led to a modest 4% increase in protein concentration in other cereal
## 4 Discussions
### Limitations and Future Works
#### 4.5.5 Limitations of Layer-2 Solutions  
While Layer-2 solutions provide a pathway to scale blockchain networks, they introduce critical challenges in terms of security, interoperability, and decentralization [95; 96; 97].  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Criteria** & **Traditional Centralized Storage** & **Blockchain-Based Storage** \\ \hline Cost & Lower cost, cloud-based storage (AWS, Google Cloud) has flexible pricing models [103]. & Higher costs due to mining fees, redundancy, and data replication [97]. \\ \hline Performance & High performance, real-time processing [104]. & Lower throughput due to consensus mechanisms [110]. \\ \hline Scalability & Easily scalable with cloud services [103]. & Limited scalability, high transaction fees [97]. \\ \hline Security & Centralized security measures (firewalls, access control) [105]. & Cryptographic security, tamper-proof records [102]. \\ \hline Data Integrity & Prone to data breaches and insider threats [104]. & Data immutability ensures integrity [110]. \\ \hline Examples & SQL Databases, AWS S3, Google Cloud Storage [103]. & Filecoin, IPFS, Arweave [97]. \\ \hline \hline \end{tabular}
\end{table}
Table 15: Comparison of Traditional Centralized Storage vs. Blockchain-Based Storage.  
First, security concerns stem from off-chain transaction processing, where Layer-2 mechanisms do not fully inherit Layer-1 security guarantees. For example, Optimistic Rollups rely on fraud proofs, which introduce delays and potential risks in dispute resolution [96]. Similarly, Plasma chains require regular interactions with the main chain, which can lead to vulnerabilities in chain synchronization and fund withdrawals [97].  
Second, Layer-2 solutions lack standardization, making cross-platform integration difficult. Since each Layer-2 framework operates differently (e.g., ZK-Rollups vs. State Channels), interoperability remains a key challenge for broader adoption [95].  
Lastly, the computational cost of cryptographic techniques used in Layer-2 solutions remains high. ZK-Rollups, while offering high security, require complex proof-generation processes, making them resource intensive and potentially expensive for large-scale blockchain deployments [95].  
The following table presents a detailed comparison of Layer-1 and Layer-2 solutions, highlighting their trade-offs in security, decentralization, and scalability. In Table 16, a comparison of Layer-1 and Layer-2 blockchain solutions is given.  
These challenges suggest that while Layer-2 solutions provide significant scalability improvements, their limitations must be considered when designing blockchain-based storage models [95; 96; 97].
# Privacy-Preserving Outsourced Artificial Neural Network Training for Secure Image Classification
## 5 Analysis and Evaluation
### Comparison of Execution Time  
For the purpose of evaluating the efficiency of privacy-preserving image classifiers based on neural network algorithms, we compare the execution time of _IPPNN_ and NPMML training different datasets and different network architecture under different size of security parameters, respectively. The training time of the two executing algorithms _IPPNN_ and NPMML, respectively, is shown in Table 1.  
These experiments illustrate that the running time of NPMML is roughly 25 times longer than that of _IPPNN_. To summarize the results: (_i_) the training time will more than double if the number of neurons in hidden layer become a double while other settings remain fixed. (_ii_) the training time will slightly less than double if the bits of security parameter become a double while other settings remain fixed. The above experimental  
Figure 3: Comparison between _IPPNN_ and Baseline with [PERSON] approximation in model accuracy (batchsize = 60). (**a**) Accuracy on digits-MNIST (\(b\) = 256). (**b**) Accuracy on digits-MNIST (\(b\) = 128). (**c**) Accuracy on fashion-MNIST (\(b\) = 256). (**d**) Accuracy on fashion-MNIST (\(b\) = 128).  
results manifest that the _IPPNN_ is more suitable for training large-volume image data sets with higher bits of security parameter and more neurons.
# A Review: Image Processing Techniques' Roles towards Energy-Efficient and Secure IoT
### Image Processing's Role in the Development of IoT Platforms and Systems  
With the support of many areas, the successful implementation of IoT-related services has grown rapidly [23]. Digital image processing is one of these areas [24]. For example, image processing has contributed to the improvement of IoT application services [25; 26]. Day-by-day, images have been significantly employed owing to their specific traits and properties. IoTSs have used images as a tool to execute a variety of tasks and functions. Images have been recorded by sensors in order to, for example, monitor a faraway site or zone. Some additional IoTSs have utilized the image processing field to identify static or moving objects. Based on the identified item, a choice might be subsequently taken appropriately to the nature of the IoTS. A step beyond object detection is object recognition, which is helpful for a variety of tasks, including text recognition based on color, texture, and form attributes [27]. Object identification technique has been leveraged additionally by a variety of IoTSs to perform, for example, remote guiding and support to visually-challenged people after things have been detected without the need for a physical supporter. Objects in front of the intended individual are collected by a visual sensor and transferred to a cloud or other distant processing unit for decision-making. Another possibility is that, in case the visually-challenged individual has met unsafe things, the remote assistance center will be alerted where recognized objects would be transported through IoT platforms.  
Image processing and the IoT have been integrated to improve our quality of life and a wide range of industries, including healthcare, manufacturing, technology, home security, and entertainment, among others [28; 29].
# A Data-Driven Battery Degradation Estimation Method for Low-Earth-Orbit (LEO) Satellites
## 2 Background on LEO Satellite Power System and Methods
### LEO Satellite System Characteristics
If the SOC is incorrectly estimated, the satellite may experience power  
Figure 4: KOMPSAT-5 satellite battery voltage telemetry with redundancy. Figure 3: Outliers and data periodicity example in the KOMPSAT-5 satellite. shortages during critical tasks such as data collection, communication, or attitude control. This could result in mission failure, entry into safe mode triggered by fault management logic, or, in the worst-case scenario, catastrophic failure. LEO satellite missions are planned for future operations under the assumption that the SOC will not fall below the critical threshold (typically 25-30%) during execution. However, as the battery in the satellite ages, the increased internal resistance and reduced capacity degrade the battery's charge and discharge efficiency, compromising the accuracy of the SOC predictions. Ignoring battery aging in mission planning increases the risk of power shortages during actual operation, potentially jeopardizing mission success [20]. For example, the start of a mission requires a specific SOC to satisfy its power consumption demands. If the SOC decreases more rapidly than planned during the mission or is initially at a level lower than expected, the satellite is at a greater risk of performance degradation or mission failure. Therefore, the success of mission uploads and planning for LEO satellites depends heavily on the satellites' ability to accurately predict battery SOCs and account for aging effects. Precise forecasting of these factors ensures that a satellite can sustain its operations without compromising its performance or risking failure.
# Energy Harvesting over Rician Fading Channel:
## 4 Numerical Results and Discussion  
For the purpose of validation, the correctness of the derived outage probability and SER expressions as well as investigation of the effect of various parameters on the system performance, a set of Monte Carlo simulations are conducted and presented in this section. For each simulation, we first provide the graphs of the outage probability and throughput obtained by the analytical formulas. Secondly, we plot the same outage probability and throughput curves that result from Monte Carlo simulation. To do this, we generate \(10^{5}\) random samples of each channel gain, which are Rician distributed. Using these random samples, the SNR at destination node \(S_{1}\) is calculated and compared with the threshold value \(\gamma\). The outage probability occurs if this SNR falls below the threshold. By taking the number of cases that \(SNR<\gamma\) divided by the number of samples, we can estimate the outage probability and then the throughput of system. The analytical curve and the simulation one should match together to verify the correctness of our analysis.  
The hardware impairment parameters are chosen as \(\kappa=\kappa_{r}=0.1\). The ideal hardware impairment situation (\(\kappa=\kappa_{r}=0\)) is also considered as a benchmark performance for simulation. The channel gains are considered as Rician fading with \(\lambda_{1}=\lambda_{2}=0.5\) and with the Rician K-factors equal to 3 for both channels. The transmit power are set to the same value \(P_{1}=P_{2}=P\) for both two sources, so that the ratio \(P/N_{0}\) varies in the range from 0 to 50 dB. The energy harvesting efficiency is set to be 0.7. The source transmission rate is chosen as 1.5 bps/Hz. From the [PERSON]'s theorem on capacity of the channel, we can calculate the \(SNR\) threshold as \(\gamma=2^{2R}-1\). All simulation parameters are listed in Table 1.
## 3 Results  
The proposed setup recorded a set of holographic images in the following sequence: holograms in the absence of helium in the observation chamber (with only air inside),  
Figure 7: Configuration of the digital holographic interferometry setup for the study of plasma flow in the pulsed generation mode (photograph).  
holographic images when helium was supplied, images under plasma generation, and finally, the images with helium but when the plasma was turned off.  
Therefore, it was possible to compare the following phase changes for refractive indices, for \(\varphi_{a}\)--phase in air, \(\varphi_{h}\)--phase in helium, and \(\varphi_{hp}\)--phase in helium in the presence of a plasma pulse, namely, the observation of the phase difference \(\Delta\varphi_{hn}=\varphi_{h}\)--\(\varphi_{a}\) (helium--air), \(\Delta\varphi_{hp}=\varphi_{hp}\)--\(\varphi_{a}\) (helium + plasma--air), and \(\Delta\varphi_{p}=\varphi_{hp}\)--\(\varphi_{h}\) (helium + plasma--helium), provided that the image acquisition system was properly synchronized.  
Figure 8 shows interferograms of gas flow development in air, where the white frame highlights areas in which phase changes were identified. Figure 8a depicts the phase difference with the initial state of helium flow before the appearance of plasma (\(\Delta\varphi_{hu}\)). The highlighted region in Figure 8b shows changes associated with the onset of plasma formation (\(\Delta\varphi_{hp}\)). Further on until plasma generation was completed, the distribution of fringes corresponding to that process is shown in Figure 8c. After the completion of plasma generation, phase difference distribution returned to the initial state as shown in Figure 8d.  
It is important to note that the changes in interference patterns associated with the appearance of plasma correspond to the duration of the plasma jet (\(t_{plasma}=750\) ns).  
In order to show the influence of the plasma on the refractive index, the phase difference \(\Delta\varphi_{p}\) was evaluated. Figure 9a shows the phase difference for the helium flow only. The phase distribution is uniform, and no other processes appear. Figure 9b shows the phase difference between the moments of gas with plasma and in its absence. This interferogram demonstrates the phase change that is distributed along the direction of plasma jet formation. Figure 9c shows an interferogram of the phase difference at \(700\) ns after the end of plasma generation. The figure shows an asymmetric intensity distribution, which may be associated with the residual transient processes in the gas after the plasma  
Figure 8: Interference patterns of the refraction observation in the registered sequence of holographic images: (**a**)—helium flow; (**b**)—start of plasma jet generation; (**c**)—plasma generation; (**d**)—end of plasma generation.  
decay. Based on the phase distribution and without taking into account neutral atoms, we estimated the peak electron concentration at 0.9 \(\times\) 10\({}^{16}\) cm\({}^{-3}\).