esa-sceva/satcom-chunk-collection huggingface dataset:
# Cross-Layer Energy Optimization for IoT Environments: Technical Advances and Opportunities
## 2 Significance of Energy Efficiency in IoT Use Cases
### Case Study: Smart Fabric  
During the 1990-2000 period the smart clothes concept merging ICT capabilities (sensing, actuating, computing and communication) with clothes and textiles to augment their appeal and develop protective textiles was explored by several companies (Philips, Infineon, DuPont, etc.). Multimodal sensor, human-computer interface, continuous Internet access, long distance communication and unobtrusive packaging functionalities are available in smart phone apps for smart textile systems. In applications such as sports, healthcare, protection and maintenance, a larger area and closer contact with skin than possible with sensors or smartphones is required. Textile electrodes provide better, continuous and direct skin contact for recording skin conductance for stress evaluation, measuring ECG or estimating local pressure to prevent ulcers [17]. Several smart textile technologies to scale up weaving of integrated circuits into textiles have been proposed. Strain, humidity, and temperature sensing, light transport and fluid monitoring functionalities have been enabled in threads. Because of the smaller surface and limited functional complexity achievable in a textile thread, several wearable threads equipped with sensors and transceiver-integrated CMOS circuits provide higher functional density, but smart cloth is still a very new research area in IoT even before researchers consider various issues such as efficient energy usage, reliability, less cost, etc. Because of the smart devices such as sensors, smartphone, CMOS circuits used in smart clothes systems, energy consumption has become the crucial issue for the future.
# Improvement of Wheat Grain Yield Prediction Model Performance Based on Stacking Technique
###### Abstract
The elastic net algorithm is a type of neural network first proposed by [PERSON] and [PERSON] [42]. Recently, the elastic network algorithm has been proven to achieve excellent results in machine learning, which can effectively avoid over fitting of the model prediction results due to small sample size and high dimension. For example, based on the elastic network regression model, [PERSON] et al. [43] constructed the transmission and distribution cost allocation model of a power grid project to realize a reasonable allocation of operation and maintenance cost. [PERSON] et al. [44] proposed a dam deformation prediction model based on the extreme learning machine and elastic network. The elastic network algorithm effectively alleviated the over fitting phenomenon, which remains challenging for the extreme learning machine, and the prediction results showed better stability. These results indicate that elastic networks can be used to solve regression problems. To our knowledge, no studies have applied the strategy of combining the ENET algorithm and stacking techniques to stack LAI and hyperspectral data from multiple growth cycles to construct an ensemble learning framework for GY prediction. The main objectives of this study are as follows: (1) To assess the ability of winter wheat canopy hyperspectral vegetation indices and LAI to be used for grain yield prediction based on the ENET algorithm; (2) to study the effect of Stacking technology in improving the accuracy of grain yield prediction based on ensemble learning; (3) to analyze the influence of each individual growth stage on the development of an accurate integrated learning model. To achieve these goals, the correlations between 15 vegetation indices and LAI from four growth stages and yield were analyzed. Then, yield prediction models for individual growth stage of winter wheat were constructed based on the ENET algorithm in two cases of vegetation indices without or with coupled LAI, respectively. Finally, stacking techniques were used to integrate the yield prediction capabilities in the four growth stages, develop an ensemble learning framework to improve the robustness of the model, and analyze the weights attributed to each growth stage based on the prediction results.
## 6 Experimental Evaluation
### Categorization of the Alerts  
The entire process of categorization has already been discussed in Section 5.2, where it is reported that the frequency of words has been computed according to each type of component and overall.  
The reason to distinguish between these two cases is that certain words should occur more often in certain types of components. For example, we expect that the word \"LEAK\" is important in those types of components that may have fluid leaks, such as pumps, and it will be less important in those components that are not subject to this type of phenomenon.  
Figure 4: Example of how the polynomial integration works.  
By carrying out the categorization for each alert, we were able to categorize only 3641 alerts, which corresponds to 0.34% of the overall number of them. This low percentage strongly depends on how the descriptions of the warnings are written. As already mentioned, they suffer from the problem of having too little information. As an example, the description \"RT2 MP5B NO WATER CIRCULATION TXT\", in the second-to-last row of Table 9 and related to a pump, has the expression \"NO WATER CIRCULATION\", which is too general to be assigned to one of the categories of Table 8. It may be associated with faults related to DAMAGE, FUNCTIONALITY, REGOLATION, LEAK, REPAIR, or REPLACEMENT.  
In Figure 5, the percentages of occurrences of the fault categories for all the components are indicated. Figures 6 and 7 show the frequency distribution of the fault categories of compressors and pumps, respectively.  
Clearly, the two graphs show a different distribution of the fault categories. This is expected, as different component types may be subject to different failures. More specifically, we notice that the pumps are more subject to fluid loss phenomena (LEAK), perhaps due to the rupture of pipes. Conversely, the compressors are more subject to failures related to cleaning (CLEANING), perhaps linked to the clogging of pipes or filters.
# A Survey on Penetration Path Planning in Automated Penetration Testing
## 5 Planning Methods in Penetration Path Planning
### Reinforcement Learning  
RL is a significant branch of machine learning that focuses on how agents should take actions in an environment to maximize cumulative reward [84]. Unlike supervised learning, which uses labeled data, or unsupervised learning, which finds patterns in unlabeled data, RL involves an agent learning through trial and error by receiving feedback from its actions.  
In the penetration path planning problem, RL treats the task as a sequential decision-making problem, modeling it using MDPs. In this framework, the RL agent takes the network environment's state as input, outputs the PT actions, and receives rewards based on the results of these actions. By continuously interacting with the environment, the agent learns to develop an optimal strategy, identifying the best penetration paths and effectively planning network penetration. This method closely mirrors the actual PT processes used by experts and allows RL to adapt to the dynamic nature of PT. It optimizes decision-making and pinpoints the most effective attack paths. By leveraging intelligent learning, RL enablesthe agent to create adaptive strategies that improve over time, offering a more robust and efficient solution to complex penetration path planning challenges. This section categorizes RL-based penetration path planning methods into four main types.
## 4 Specific Prenylquinones
### Tocopherol  
#### 4.3.1 Biosynthesis and Distribution of Tocopherol  
Also known as vitamin E, tocopherol contains a chromanol as an aromatic head group bound to a phytyl chain (Figure 8). Several forms of tocopherol have been described as intermediates of \(\alpha\)-tocopherol biosynthesis (\(\gamma\), \(\beta\), \(\delta\)-tocopherol), and the biosynthesis of this compound can differ greatly depending on the organism [189]. Tocopherols are mainly found in the thylakoids of chloroplasts of photosynthetic organisms, but the biosynthesis of tocopherols was also detected in _P. falciparum_ and _Leishmania L. amazonensis_[64, 190]. These compounds have several functions; however, the most well studied function is protection against lipid peroxidation [191, 192, 193]. Other studies indicate that in photosynthetic organisms, tocopherol specifically interacts with photosystem II (PSII) components and cytochromes [194, 195]. In humans, \(\alpha\)-TQ appears to play an important role as a hydrogen acceptor for fatty acid desaturation [196]. Moreover, the irreversible reduction of \(\alpha\)-TQ in alpha-tocopherol quinone by b-cytochromes in the bc(1) complex suggests that \(\alpha\)-TQ in mitochondrial membranes causes downregulation of respiratory activities [197]. In most photosynthetic organisms, the biosynthesis of tocopherol, PQ, PQ derivatives (for example PQ-B and PQ-C) and plastochromanol begins when chorismate is converted to 4 HPP and consecutively to homogenistate by the enzyme HPPD [198]. The homogenistate is prenylated by the enzyme homogenistate phytyltransferase (HPT) and PQ by the enzyme homogenistate solanesyltransferase (HST) in plants. Finally, various enzymes, such as VT1, VT3 and VT4, catalyze different modifications of \(\alpha\)-tocopherol or \(\gamma\)-tocopherol. However, an alternative route for PQ biosynthesis using 4-hydroxybenzoate as an aromatic donor has been discovered in cyanobacteria [45, 198].  
Figure 8: Chemical structures of tocopherols. (**a**) Chemical structures of alpha-tocopherol, (**b**) beta-tocopherol, (**c**) gamma-tocopherol and (**d**) delta-tocopherol.
## 4 Measurement Results
### Overall Comparisons across the Three Four-Way Classifiers  
**Intrasubject classification**  
Figure 33 confirms further that ML_CSP_OVR performs better than the other two models in terms of precision, sensitivity, and accuracy. The measurement data are obtained from participant 12.  
Figure 33: Performance comparisons of the three models, doing intrasubject classification on participant 12.  
Figure 32: Validation accuracy of the classification using CSP-OVR.  
Figure 34 also shows that ML-CSP-OVR performs better than the other multiclass classifier models. Even though there is a dip in sensitivity and precision for RL and RV, the overall trend of ML-CSP-OVR still performs better than the other two. For accuracy, ML-CSPOVR manages to achieve approximately a 20-40% better result than the other two models.
## 3 Results and Discussion
### Battery Pack Model Development
#### 3.2.3 Response Surface Model (RSM) Development  
An RSM comprises regression surface fitting over a bounded design space to predict approximated responses for input variable combinations not accounted for during a physical or simulation experiment [26]. Usually, a design of experiment method is used to obtain the minimum number of experiments needed to develop an RSM. In the RSM method implemented in this study, the approximation function used in developing the response surface illustrated in Figure 25 is a second degree polynomial. Most response surfaces functions are generated with polynomials depending on the number of data points provided from an experiment.  
The RSM developed for the battery model was done using a MATLAB curve fitting tool box. Fitting parameters R\({}^{2}\) adjusted were used to determine the goodness of fit, and sum of square error (SSE) and mean square error are used to determine the predictability of the model. The R\({}^{2}\) adjusted values lie between 0 and 1 for which a good response surface has a value closest to 1 [26; 27]. On the contrary, an SSE and Mean Square Error (MSE) value closer to zero is desired for a good response surface. An SSE value closer to zero indicates a model has smaller random error components hence higher prediction accuracy [27]. Just as for SSE, an MSE value closest to zero is desired for a good response model.  
After various variations of the fit method applied on the variables were tested to obtain the best adjusted R\({}^{2}\) and SSE values, the model was developed with the polynomial function with robustness \"off\" as this trail produced the most suitable fitness parameters results. Table 7 provides a comparison of various selection criteria values obtained during the iterative training process.  
Figure 25: Response surface model (RSM) developed for charge and discharge experiment.  
The model equation of the RSM developed for the charge and discharge experiments performed for the battery pack and module in this study is presented as:  
\[f(x,y)=P_{0}+P_{1}x+P_{2}y+P_{3}x^{2}+P_{4}xy \tag{1}\]  
where \(x\) and \(y\) are designed variables air flow rate and current rate respectively, \(P_{0}-P_{4}\) are coefficient constants and \(f(x,y)\) is the design objective function. Table 8 presents coefficient values for the developed model equations.
# Research on Distributed Autonomous Timekeeping Algorithm for Low-Earth-Orbit Constellation
## 1 Introduction
Additionally, the optimization of the weight calculation method in the KPW algorithm has been shown to improve long-term stability and provide a novel approach to time scale algorithms [12]. Further exploration of satellite clock data processing methods and time synchronization models has demonstrated the feasibility of establishing a space-based time reference [13; 14]. Most of these studies focus on GNSSs, where the number of satellites involved in timekeeping is relatively small. Consequently, they employ Kalman filtering algorithms [15] or their improved versions [16; 17; 18; 19; 20] to establish the time reference. However, in low-orbit systems, challenges such as the large number of satellites and complex noise characteristics arise. Traditional Kalman filtering algorithms, which require extensive matrix operations and parameter configurations, are unsuitable for low-orbit constellation scenarios. While the weighted average algorithm [21; 22; 23] is computationally simple, its noise suppression capability is limited, preventing full utilization of satellite clock characteristics. In this paper, we first propose a parameter estimation method for the noise matrix of the Kalman filter to ensure accurate and reasonable parameter estimation. Then, to solve the problem that a large number of satellites participate in the synthesis, a D-KPW algorithm is proposed, which estimates each set of the clock error independently and calculates the weights using Allan variance. Finally, to optimize the utilization of clock characteristics, the D-KPW (Control) algorithm is introduced as an enhancement of the original D-KPW framework. Two independent time references with distinct characteristics are constructed: one exhibiting superior short-term stability and the other demonstrating enhanced long-term stability. The short-term stable reference is calibrated by the long-term stable reference to produce the system's unified time reference. The validity of the proposed algorithm is corroborated through a comparative experimental analysis.
## 3 Proposed Method  
In this study, we aim to detect schizophrenia by analyzing brain activity through EEG signals using a method that involves minimal computational complexity. Traditionally, neurologists review EEG signals manually, which is time-consuming and subjective. Our approach simplifies this by using a type of deep learning model known as a convolutional neural network (CNN), specifically designed to handle the data format of EEG signals, which has shown promising results over traditional methods based on hand-crafted features [11; 15; 25].  
We utilize a lightweight CNN, which means it operates effectively even with smaller datasets--like those commonly available in medical settings--without losing accuracy. The model analyzes small segments of EEG data, ensuring it can handle data of any length without increasing computational demands.  
Furthermore, we employ data augmentation to enhance our model's learning capability from limited data. This technique artificially expands the dataset by slightly altering existing data points to generate new ones, providing our model with more examples to learn from. Our method aims to provide a reliable, automated alternative to manual EEG analysis, offering the robust and accurate detection of schizophrenia, which could greatly assist in clinical practice.  
In this section, the problem formulation and an overview of the design of the proposed method are given first. Then, the architecture of the backbone CNN model is presented. After that, the data augmentation technique is discussed.
# The Electric Vehicle Routing Problem with Time Windows, Partial Readers, and Parcel Lockers
## 5 Experimental Results
### Parameter Tuning
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Parameter** & **Description** & **Value** \\ \hline \(\eta^{max}\) & The maximum number of iterations implemented in ALNS & 30,000 \({}^{*}\) \\ \(\eta^{noimp}\) & The maximum number of consecutive iterations without improvement & \([1500,3000,4500,6000]\) \\ \(\eta^{proba}\) & The number of iterations of a learning period in _updateSelectionProb_() & \([50,75,100,125]\) \\ \(\eta^{penalty}\) & The number of iterations for _updatePenaltyWeights_() & \([5,10,15,20]\) \\ \(\omega^{proba}\) & Smoothing factor in _updateSelectionProb_() & \([0.7,0.75,0.8,0.85]\) \\ \(\omega^{penalty}\) & Reaction factor _updatePenaltyWeights_() & \([1.1,1.3,1.5,1.7]\) \\ \(\epsilon_{f}\) & A reward when obtaining a new best feasible solution & \([11,13,15,17]\) \\ \(\epsilon_{b}\) & A reward when obtaining a new best (infeasible) solution & \([5,7,9,11]\) \\ \(\epsilon_{i}\) & A reward when obtaining a better current solution & \([2,4,6,8]\) \\ \(\epsilon_{w}\) & A reward when obtaining a worse solution & \([1,3,5,7]\) \\ \(\delta_{L}\) & The probability for applying _lockRemovalInsertion_() & \([0.1,0.3,0.5,0.7]\) \\ \(\phi_{1}\) & The weight of travel distance criteria in Shaw removal & \(6\)\({}^{**}\) \\ \(\phi_{2}\) & The weight of earliest time criteria in Shaw removal & \(4\)\({}^{**}\) \\ \(\phi_{3}\) & The weight of demand criteria in Shaw removal & \(5\)\({}^{**}\) \\ \(\phi_{noise}\) & The randomness level used for removal operators & \(6\)\({}^{**}\) \\ \(\rho_{w}^{min}\), \(\rho_{w}^{max}\), \(\rho_{w}^{0}\) & The weights associated with the time window penalty & see Section 4.2 \\ \(\rho_{w}^{min}\), \(\rho_{w}^{max}\), \(\rho_{w}^{0}\) & The weights associated with the battery penalty & see Section 4.2 \\ \(\rho_{lo}^{min}\), \(\rho_{lo}^{max}\), \(\rho_{lo}^{0}\) & The weights associated with the load penalty & see Section 4.2 \\ \(T_{0}\) & The initial SA temperature of the acceptance criteria & see Section 4.5 \\ \(\alpha\) & The cooling rate of SA temperature over the iteration & see Section 4.5 \\ \hline \hline \end{tabular}  
Notes: \({}^{*}\) The maximum number of iterations is fixed at 30,000 to ensure reasonable computation time for comparison with the state-of-the-art algorithms. \({}^{**}\) These values are taken from [53].  
\end{table} 
Table 1: ALNS parameters and their domains.
# The Role of Trees in Sand Dune Rehabilitation: Insights from Global Experiences
## 3 Results
### Tree Species Used in the Afforestation of Sand Dunes  
A significant number of tree species are cited in studies concerning their use on sand dunes. Several of these species are presented in Table 3.  
### Tree Species Used in the Afforestation of Sand Dunes  
A significant number of tree species are cited in studies concerning their use on sand dunes. Several of these species are presented in Table 3. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Ctrl. No.** & **Tree Species** & **Region** &
\begin{tabular}{c} **Risk Level/Invasiveness** \\ **on Sand Dunes** \\ \end{tabular} & **Citing Article** \\ \hline \(1\) & _Ahies__sachalinensis_ F-Schmidt & Japan & Low–Native, non-invasive & [PERSON] et al., 2004 [46] \\ \hline \multirow{2}{*}{\(2\)} & _Acacia auriculiformis_ & \multirow{2}{*}{Malaysia} & Medium–Fast-growing, naturalized; potentially invasive & [PERSON] et al., 2006 [47] \\ \hline \multirow{2}{*}{\(3\)} & _Acacia cyanophylla_ [PERSON]. & \multirow{2}{*}{Marocco} & High–Invasive in Mediterranean regions & [PERSON] et al., 2022 [48] \\ \hline \multirow{2}{*}{\(4\)} & _Acacia mangium_ [PERSON]. & \multirow{2}{*}{Malaysia} & Medium–Known for invasiveness in some tropical zones & [PERSON] et al., 2006 [47] \\ \hline \multirow{2}{*}{\(5\)} & _Acacia nilotica_ (L.) [PERSON]. & Niger & Low–Native in Africa & [PERSON] [49] \\ \hline \multirow{2}{*}{\(6\)} & _Acacia radiazura_ ([PERSON].) & \multirow{2}{*}{Niger} & Low–Native, adapted to arid zones & [PERSON] [49] \\ \cline{2-2}  & [PERSON] \& [PERSON] & & High–Known invasive in Mediterranean ecosystems & [PERSON] et al., 2023; [PERSON], 2017 [50; 51] \\ \hline \multirow{2}{*}{\(7\)} & _Acacia saligana_ ([PERSON].) & \multirow{2}{*}{Israel} & Medium–Generally low risk, but can alter ecosystems & [PERSON] et al., 2024 [52] \\ \cline{2-2}  & H.L.Wendl. & & & \\ \hline \multirow{2}{*}{\(8\)} & _Anocardium accidentale_ L. & \multirow{2}{*}{Brazil} & Medium––Generally low risk, but can alter ecosystems & \\ \cline{2-2}  & & & Low–Native to & \\ \hline \multirow{2}{*}{\(9\)} & _Angephora costata_ & \multirow{2}{*}{Australia} & Low–Native to Australian coasts & [PERSON], 1986 [53] \\ \cline{2-2}  & & & Medium–Cultivated; potential invasiveness in drylands & [PERSON] et al., 2009 [54] \\ \hline \multirow{2}{*}{\(10\)} & _Baulinia rafescens_ Lam. & Niger & Low–Native to Sahel & [PERSON] [48] \\ \hline \multirow{2}{*}{\(11\)} & _Caragana microphylla_ Lam. & China & Low–Native, used for dune stabilization & [PERSON] et al., 2025 [55] \\ \cline{2-2}  & & & & \\ \end{tabular}
\end{table} 
Table 3: Tree species used on sand dunes. Figure 10: Annual distribution of keywords related to trees on sand dunes.
## 2 Legal Status of Mega-Constellations under International Space Law
### International Telecommunications Union in Regulating Mega-Constellations
#### 2.2.3 Application of Military Satellites within ITU Framework  
The ITU framework involves the licensing and allocation of radio frequencies, which applies to both civilian and military satellites, regardless of whether they are dual use or not [31]. Furthermore, ITU radio regulations are internationally binding for all ITU member states. This has been emphasized by the ITU's Report on Satellite Regulation in Developing Countries [32]. As a result, even if Article 48 of the ITU Constitution allows member states to retain their freedom regarding military radio installations, they are bound to abide by the principles and provisions of the ITU Constitution and are encouraged to observe the provisions of the ITU Administrative Regulations concerning the types of emission and the frequencies to be used [33].  
In summary, the ITU's role is fundamental to the legal status of mega-constellations. By managing the allocation of radio frequencies and satellite orbits, the ITU helps to mitigate the risks of interference and ensures compliance with international space law principles. However, the rapid growth of mega-constellations and the varying priorities of national regulators present ongoing challenges that require coordinated international efforts in order to be addressed effectively [2; 4; 5; 34; 35; 36].