esa-sceva/satcom-chunk-collection huggingface dataset:
# Adaptive Solar Power Forecasting based on Machine Learning Methods
## 4 Adaptive Solar Power Forecasting Algorithm
### The Algorithm of whole System  
Based on the above analysis, we now summarize the complete steps of the algorithm to realize the ASPF in Algorithm 2.  
**Step 1**: The historical data is divided into different groups by Algorithm 1, and corresponding clustering centers \(C_{k}^{*}\) are confirmed.  
**Step 2**: LARS is used to each group to get its important predictors, and BPNN is employed to learn the features in every group, subsequently, according the principle in Section 3.4, \(\Delta_{1}\) and \(\Delta_{2}\) is setup.  
**Step 3**: For a new \(P_{ori}\) and its important predictors, compares with \(C_{k}^{*}\), if the maximal error is less than \(\Delta_{1}\), go to Step 4; otherwise, go to Step 5.  
**Step 4**: \(P_{ori}\) and its important predictors compares with every data in _group_\(k\), and finds the most similar day as \(P_{rev}\), and update \(\Delta_{1}\) according formulation Equation (16), then go to Step 6.  
**Step 5**: \(P_{ori}\) is revised by BPNN, and the day-ahead PV power \(P_{rev}\) is obtained, go to Step 6.  
**Step 6**: \(\Delta=|P_{act}-P_{rev}|\), if any element in \(\Delta\) is larger than \(\Delta_{2}\), update the BPNN, storage \(P_{act}\) and \(P_{ori}\) in EMS, then go to Step 3; otherwise, go to Step 7.  
**Step 7**: According to formulation Equation (17) update \(\Delta_{2}\), and go to Step 3.  
**Algorithm 2** Adaptive Solar Power Forecasting Algorithm
## 4 Experimental Results
\begin{table}
\begin{tabular}{c c c c} \hline \hline \(|\mathbf{E}|\) & \(\mathbf{W}\)_p_-Value j30 & \(\mathbf{W}\)_p_-Value j60 & \(\mathbf{W}\)_p_-Value j90 \\ \hline
3 & 105,452, 0.0233 & 97,082, 0.0000 & 109,940, 0.2208 \\ \hline
4 & 101,898, 0.0019 & 92,158, 0.0000 & 108,898, 0.1424 \\ \hline
5 & 88,290, 0.0000 & 90,745, 0.0000 & 107,896, 0.0890 \\ \hline
6 & 69,670, 0.0000 & 84,909, 0.0000 & 106,188, 0.03591 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Non-parametric test using the Mann-Whitney U test between ADP and ADP–DCS for ACO priority. \begin{table}
\begin{tabular}{c c c c} \hline \hline \(|\mathbf{E}|\) & \(\mathbf{W}\)_p_-Value j30 & \(\mathbf{W}\)_p_-Value j60 & \(\mathbf{W}\)_p_-Value j90 \\ \hline
3 & 110,909, 0.3179 & 115,377, 0.9439 & 114,834, 0.9321 \\ \hline
4 & 108,934, 0.1447 & 112,690, 0.4877 & 113,998, 0.7796 \\ \hline
5 & 95,083, 0.0000 & 111,910, 0.3816 & 112,744, 0.5675 \\ \hline
6 & 77,254, 0.0000 & 110,352, 0.2162 & 112,499, 0.5296 \\ \hline \hline \end{tabular}
\end{table} 
Table 11: A non-parametric test using the Mann–Whitney U test between ADP with EDA and ADP–DCS with ACO is their priority. \begin{table}
\begin{tabular}{c c c c} \hline \hline \(|\mathbf{E}|\) & \(\mathbf{W}\)_p_-Value j30 & \(\mathbf{W}\)_p_-Value j60 & \(\mathbf{W}\)_p_-Value j90 \\ \hline
3 & 105,902, 0.0304 & 110,006, 0.1879 & 110,887, 0.3154 \\ \hline
4 & 103,263, 0.0055 & 108,726, 0.1066 & 110,142, 0.2391 \\ \hline
5 & 89,583, 0.0000 & 107,760, 0.06605 & 209,112, 0.1565 \\ \hline
6 & 68,310, 0.0000 & 105,614, 0.01949 & 107,070, 0.05843 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: A non-parametric test using the Mann–Whitney U test between ADP and ADP–DCS for EDA priority. Table 13 shows the mean difference between ADP-DCS with ACO and ADP-DCS with EDA as their priorities. For ADP-DCS, the choice between EDA and ACO is not meaningful as the mean differences are not statistically significant. Finally, Table 14 shows the mean difference between ADP with ACO and ADP-DCS with EDA as their priorities. The mean differences are statistically highly significant for all job sets. Based on Table 3, we can conclude that ADP-DCS with EDA is better than ADP-ACO. It is important to recognize that for each cardinality of \(|\)E\(|\), a total of 480 distinct projects were analyzed for each project set (i.e., j30, j60, and j90). The average values of the NPV corresponding to four different parameters are shown in Tables 3-6. To gain further insights of the models' performances, bar charts of the mean values of the NPV in terms of four parameters for all possible models for various project sizes are shown in Figures 2-9. For different values of \(|E|\), after performing normality test, we compared the mean difference using the Mann-Whitney U test in seven different scenarios. ADP-DCS leads to better results than ADP alone. This makes sense as DCS further improves the solution of ADP. Using EDA and ACO for MC as a priority also improved the solution. Although the choice between EDA and ACO is difficult, EDA performed relatively better, and its performance improves with increasing \(|E|\).
## 5 Evaluation
### Evaluation of the Final Architecture Design
#### 5.3.2 Efficiency  
Latency of prediction was quite high. The factors behind the low performance were studied further. It was discovered that parsing of a JSON-request to a Python Dataframe took \(\sim\)40-80 ms (an input request contained 600 data samples with a timestamp and temperature (Appendix A)). The rest of the delay (\(\sim\)6-9 s) consisted of getting predictions from the SBN-model [23]. Within the chosen research context, the observed latency was feasible, because the frequency of energy consumption data from the best smart meters has been 1 min [46]. Thus, predictions can be provided for realising energy consumption optimisation (in the future) within the performance requirement (1 min). However, SBN-model prediction latency should be reduced in future work.  
With the presented model switching mechanism (Figure 7) predictions could be provided simultaneously from the old model during the loading of a new model. Performance results (Figure 9) indicated that model switching increased prediction latency by \(\sim\)2.1-3.7 s. However, the increased latency is below the performance requirement (1 min). Without the implemented model switching mechanism, serving of prediction requests would have been interrupted (at least) for the duration of model loading in the new predictor (due to closing of Keras/Tensorflow session of the old predictor; see Appendix D; phase 3). Further tests with Jetson Nano indicated \(\sim\)21-22 s model loading latency.  
In reality models may be switched much less often (e.g., few times per day/week), because the frequency of the best smart energy meters is 1 min [46]. Thus, the results present an extreme situation, where models are switched more often than may actually be needed.
# The Use of Earth Observation Data for Railway Infrastructure Monitoring--A Review
## 3 EO-Based Railway Infrastructure Monitoring: Literature Review
### Comparative Analysis of EO Data Use in Railway Infrastructure Monitoring
Satellite image data and machine learning algorithms were used for supervised, pixel-based classification of vegetation on the French railway network [35]. Pictures from the track-monitoring train were used in addition to the Pleaides satellite images to create training samples. The pretrained models were reused to classify other images from the same region and month. The result was a vegetation map with four classes: trees, shrubs, grass, and no vegetation. [PERSON] et al. [54] analyzed the ecological environment quality along the Beijing-Tianjin intercity high-speed railway using Landsat satellite images from 2008 to 2020. The research utilized a revised remote sensing-based ecological index to monitor and assess the vegetation transformation and quality over time. [PERSON] et al. [48] reported pioneering efforts to update existing railway monitoring systems in Italy by developing an innovative system for the supervision of railway areas to identify hydrogeological and anthropogenic hazards, ensuring safety and efficiency for railway traffic. The system uses SAR and optical images for image acquisition and Artificial Neural Networks (ANNs) for processing and feature extraction in order to identify anomalies, such as vegetation encroachment and new buildings along the railway. The systems are designed to allow both periodic and on-demand analyses, offering strong scalability for sharing with other European infrastructure managers to establish a common standard. The system aims to reduce analysis time and costs, eliminating the need for third-party services in monitoring and maintenance. Accurate monitoring of flood risks along railway corridors is essential for ensuring infrastructure resilience and operational safety, with advanced remote sensing technologies playing a crucial role in WL assessment. [PERSON] et al. [16] used commercial off-the-shelf Unmanned Aerial System (UAS) DJI Matrice 300 RTK combined with satellite optical imagery in four sites at risk of flooding along the railway line between the cities of Ottawa and Brockville in Canada. UAS-based Structure from Motion (SfM) photogrammetry was used to measure changes in vertical water level at the centimeter level, while the UAS-based high spatial detail orthomosaics were used to measure the extent of the surface water. Classification of the surface water and wetlands was performed from UAS orthomosaics and satellite optical images of areas larger than those recorded by the UAS.
_applied sciences_  
_Article_  
**Lattices-Inspired CP-ABE from LWE Scheme for Data Access and Sharing Based on Blockchain**  
**[PERSON] 1,2, [PERSON] 1,3,*, [PERSON] 1,3, [PERSON] 4 and [PERSON] 5**  
1 School of Information, Yunnan University of Finance and Economics, Kunming 650221, China; [EMAIL_ADDRESS] (T.C.); [EMAIL_ADDRESS] (Y.Y.)  
2 Yurman Key Laboratory of Blockchain Application Technology, Kunming 650223, China  
3 Institute of Intelligent Application, Yunnan University of Finance and Economics, Kunming 650221, China  
4 Division of Science & Technology Administration, Yunnan University of Finance and Economics, Kunming 650221, China; [EMAIL_ADDRESS]  
5 School of Logistics and Management Engineering, Yunnan University of Finance and Economics, Kunming 650221, China; [EMAIL_ADDRESS]  
*Correspondence: [EMAIL_ADDRESS]  
**Abstract:** To address the quantum attacks on number theory-based ciphertext policy attribute-based encryption (CP-ABE), and to avoid private key leakage problems by relying on a trustworthy central authority, we propose a lattice-inspired CP-ABE scheme for data access and sharing based on blockchain in this paper. Firstly, a CP-ABE-based algorithm using learning with errors (LWE) assumption is constructed, which is selective security under linear independence restriction in the random oracle model. Secondly, the blockchain nodes can act as a distributed key management server to offer control over master keys used to generate private keys for different data users that reflect their attributes through launching transactions on the blockchain system. Finally, we develop smart contracts for proving the correctness of proxy re-encryption (PRE) and provide auditability for the whole data-sharing process. Compared with the traditional CP-ABE algorithm, the post-quantum CP-ABE algorithm can significantly improve the computation speed according to the result of the functional and experimental analysis. Moreover, the proposed blockchain-based CP-ABE scheme provides not only multi-cryptography collaboration to enhance the security of data access and sharing but also reduces average transaction response time and throughput.  
**Keywords:** post-quantum cryptography; blockchain; ciphertext policy attribute-based encryption; data sharing; privacy protection
# Doppler Shift Tolerance of Typical Pseudorandom Binary Sequences in PMCW Radar
## 4 Comparative Performance Analysis
### Investigated Pseudorandom Binary Sequences
#### 4.1.1 m-Sequences  
Also known as maximum-length sequences (MLSs), PN sequences, or pseudorandom sequences, m-sequences [25] are a type of PRBS generated by linearly recursive LFSRs [26] of \(N_{\text{bit}}^{\text{MLS}}\in\mathbb{N}\) bits. In such LFSRs, every \(n_{\text{bit}}^{\text{MLS}}\)th bit, \(n_{\text{bit}}^{\text{MLS}}\in\{0,1,\ldots,N_{\text{bit}}^{\text{MLS}}-1\}\), is determined by a linear combination of the previous \(n_{\text{bit}}^{\text{MLS}}\) bits, so that the resulting m-sequence has an odd period or length equal to \(N_{\text{PRBS}}=2^{N_{\text{bit}}-1}\). For m-sequences, it holds that the PACF usable length is equal to the PRBS length, i.e., \(N_{\text{PRBS,usable}}=N_{\text{PRBS}}\).
## 3 System Model and Problem Formulation  
### Network Model  
This section considers a multi-UAV-assisted edge computing system for M-IoT devices, which consists of an LEO satellite, multiple UAVs, and multiple M-IoT devices, including buoys, ships, and various sensors, as shown in Figure 1. Each UAV is equipped with an edge computing server to provide task offloading services for M-IoT devices, and both the UAVs and the M-IoT devices are powered by their own energy reserves. It is assumed that the total M-IoT devices are evenly distributed in the designated sea area, and all the M-IoT devices are within the coverage of the UAVs and the LEO satellite. There are UAVs in the specified sea area, and the number of M-IoT devices and UAVs can be represented by sets \(\mathcal{N}=\{1,2,\cdots,N\}\) and \(\mathcal{M}=\{1,2,\cdots,M\}\). The entire continuous time is divided into equal time slots.
## 7 Review of Current Design Practices  
Current design practices of the connections for the CBFs system vary across the globe. Different types of connections with diverse design methodologies are recommended through various codes. AIJ [34] (Japanese code) provides guidelines for DC and OOPB braced frame systems. It recommends only the brace axial force for the design of the interface weld, termed the AIJ method. EN 1998-1-1 [35] (Eurocode), AS 1170.4 [36] (Australian code), and CSA-S16 [37] (Canadian code) recommend semirigid and partial-strength connections for the braced frame systems without any guidelines on the design and detailing of end connections. NZS 3404:1997 [38] (New Zealand code) implicitly discusses all three types of connections (e.g., DC, OOPB, and IPB). It incorporates the frame action by accommodating the opening and closing of beam- column joints. However, the design method to evaluate the interface stress and rotational rigidity of the minor axis bending of the gusset is absent. The recent version of ANSI/AISC 341 [26] implicitly includes all three types of brace connections. However, the design check to prevent out-of-plane buckling is lacking.  
In the absence of a design guideline, the designer either neglects the possible mode of failure, compares the buckling load of the IPB braced system for the two different planes based on the effective length of the respective plane, or minimizes the radius of gyration along the in-plane buckling direction of the brace frame system [10]. However, it is observed that both methods do not provide efficient design criteria, and premature failures have been observed.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Reference Study** & **Specimen** & **Section** & **Description** & **Observed Mode** & **Failure Mode** \\  & & & & **of Buckling** & \\ \hline \multirow{2}{*}{Lumpkin [31]} & \multirow{2}{*}{L-3S} & \multirow{2}{*}{HSS 125 \(\times\) 25 \(\times\) 9} & Third story of 3-story & \multirow{2}{*}{Out-of-plane} & \multirow{2}{*}{Brace fracture} \\  & & & single bay frame \# & & \\ \hline \multirow{2}{*}{[PERSON] et al. [27]} & \multirow{2}{*}{S-2S} & \multirow{2}{*}{HSS 127 \(\times\) 127 \(\times\) 9.5} & First story of 2-story single & \multirow{2}{*}{In-plane} & \multirow{2}{*}{GP weld fracture} \\  & & & bay frame \# & & \\ \hline \multirow{2}{*}{[PERSON] et al. [10]} & \multirow{2}{*}{S-1A} & HRS 52.4 \(\times\) 101.6 \(\times\) 9.5 & \multirow{2}{*}{1-story single bay frame \%} & In-plane & GP weld fracture \\  & & & HRS 152.4 \(\times\) 101.6 \(\times\) 9.5 & \multirow{2}{*}{Out-of-plane} & GP weld fracture \\  & & & HS 127 \(\times\) 127 \(\times\) 9.5 & & Out-of-plane & GP weld fracture \\ \hline \multirow{3}{*}{[PERSON] and [PERSON] [24]} & \multirow{2}{*}{P-1A} & HCS 76.1 \(\times\) 2.9 & \multirow{2}{*}{1-story sub-assemblag} & In-plane & Brace fracture \\  & & & HS 76.1 \(\times\) 2.9 & & In-plane & Brace fracture \\ \cline{1-1}  & & & HS 88.9 \(\times\) 3.2 & \multirow{2}{*}{system \%} & In-plane & Brace fracture \\ \cline{1-1} \cline{5-5} \cline
# Is Running Technique Important to Mitigate Hamstring Injuries in Football Players?
###### Abstract  
(1) Background: Among the myriad of injuries affecting football players, hamstring strains have emerged as a persistent and debilitating concern, underscoring the need for novel approaches to reduce strain rates. The current study aims to analyze the impact of improved running technique on reducing hamstring injuries in football players. (2) Methods: Twenty-two male \(<\) 19 years old footballers were randomly assigned to an intervention and a control groups (\(n\) = 14 vs. 8), the former performing a running technique program of six weeks. Three-dimensional kinematics, using a markerless motion capture system, assessed linear and angular variables during touchdown and toe-off instants to evaluate pre- and post-intervention. Independent and repeated measure \(t\)-tests and effect size calculations were employed. (3) Results: A decrease in hip flexion and thorax external rotation during the touchdown (\(-2.39^{\circ}\) and \(-2.02^{\circ}\), \(p\leq 0.05\)) and a decrease in pelvic external rotation and an increase in stride length (\(-3.22^{\circ}\) and \(0.06\) m, \(p\leq 0.05\)) during toe-off for the players that engaged in the running technique development was observed. (4) Conclusions: These findings emphasize the significant impact of improved running technique and sprint mechanics, suggesting a potential decrease in the risk of hamstring injuries during high-speed running in football players.  
running technique; hamstring injury; football; prevention; sprint Article
# Federated Learning for Surface Roughness
## 2 Related Technologies
### Mini-GPT with Time-Series Data  
The model architecture in this study is a modified version of Mini-GPT [24] tailored for real-time surface roughness prediction in wire electrical discharge machining (WEDM). The original Mini-GPT was designed primarily for text generation and natural language processing tasks, incorporating an embedded layer for vectorizing textual data. However, considering the nature of industrial data--specifically, time-series numerical data--this study replaced the embedded layer with a fully connected (dense) layer. This adjustment allows the model to directly process continuous numerical inputs, enhancing both computational efficiency and prediction accuracy.  
The input data are structured as a three-dimensional time-series matrix defined as input = [a, b, c], with time measured in seconds. The three dimensions represent the following:  
* a (measured value dimension): actual sensor readings, such as current, voltage, or vibration intensity;
* b (data type dimension): distinguishes data from different sources or sensor types;
* c (sampling frequency dimension): reflects the number of samples taken per second by the sensors.  
This structure ensures the model effectively captures temporal patterns and multivariate relationships critical for accurate surface roughness prediction.  
As a practical example, this study collects data using current sensors installed on three axes (X, Y, and Z). Each time the sensors perform a measurement, three types of current data are obtained: average current (avgCurrent), maximum current (maxCurrent), and minimum current (minCurrent). Therefore, the resulting data matrix, with a dimensionality of b, can be explicitly represented as a set of current measurements across the three axes, as illustrated in Figure 2.  
The data sampling frequency dimension c is adjusted based on the actual configuration of the sensors. For instance, the current sensors used in this study operate at a sampling rate of approximately 20 data points per second, meaning that 20 (_a,b_) matrices are generated every second. However, this frequency can be dynamically modified according to practical needs, depending on the machining task or sensing scenario. With the above improvements, the proposed model is capable of effectively handling high-frequency, multi-dimensional machining data and capturing subtle variations in current across eachaxis that influence surface roughness during machine operation. In addition, by removing the original embedding layer of Mini-GPT and directly using a dense layer to receive continuous numerical inputs, the model avoids unnecessary data transformation and computation, thereby reducing model size, increasing training speed, and saving both energy and computational resources.
## 1 Introduction
### On Secure Communications
#### 1.1.3 Unconditional (Information-Theoretic) Security  
In order to achieve unconditional (information-theoretic) security at the key exchange, proper laws of physics with a special hardware are utilized. Two major classes of physics-based schemes have emerged for unconditional security:  
(i) Quantum key distribution (QKD) [3; 4] concepts assume single photons and utilize quantum physics. The underlying laws of physics are [PERSON]'s uncertainty principle and the related quantum no-cloning theorem [5]. Even though there are serious debates about the actual level of unconditional security a practical QKD can offer (see e.g., [6; 7; 8; 9; 10; 11; 12; 13; 14; 15; 16; 17; 18; 19; 20; 21; 22; 23; 24; 25; 26; 27; 28; 29]), most scientists agree that QKD is unique in its offering information-theoretic security via (a dark) optical fiber and also through air at night, provided the visibility is good.  
(ii) The Kirchhoff-Law-Johnson-Noise key distribution method that is based on the statistical physical features of the thermal noise of resistors [30; 31; 32; 33; 34; 35; 36]. The related law of physics is the fluctuation-dissipation-theorem (FDT). Some of its advantages are: It works via wire connections including power, phone, and internet lines, which can be used as information channels [31; 32] to connect all homes and other establishments. It can be integrated on a chip, which implies excellent robustness, low price, and applicability in bankcards, computers, instruments, and physical unclonable function (PUF) hardware keys [33; 34]. Its low price allows general applications such as unconditional security for the control of autonomous vehicular networks [35; 36].