esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Soil water and drought monitoring for early-warning applications
### Model Inputs
#### 4.1.2 Potential Evapotranspiration (PET)  
USGS/EROS calculates daily PET values for the globe at 1.0-degree (\(\sim\)100 km) spatial resolution from 6-hourly numerical meteorological model output fields of GDAS (Global Data Assimilation System) using the standardized Penman-Monteith equation ([PERSON] et al., 1998). Recent validation of GDAS-PET using PET derived from station parameters in the US has demonstrated the usefulness and reliability of GDAS-based PET for crop water balance studies ([PERSON] et al. 2007a).
## 3 Results and Discussion
### Comparative Analysis of In Situ and Laboratory Direct Shear Results  
Figure 11 presents shear stress-shear displacement relationships from in situ direct shear tests, conducted only at natural (\(w=2.45\%\)) and saturated (\(w=7.66\%\)) moisture levels due to field constraints. Differing normal stress settings between in situ and laboratory tests prevent direct strength comparisons, so this study focuses on shear strength parameters (\(c\) and \(\phi\)) derived from the Mohr-Coulomb criterion, summarized in Figure 12 for both conditions.  
The original gradation specimens in laboratory tests exhibit notably higher peak strengths than in situ results at both \(w=2.45\%\) and \(w=7.66\%\). This gap widens at higher normal stresses, with laboratory-derived \(c\) and \(\phi\) also exceeding in situ values. For  
Figure 11: Shear stress vs. shear displacement curves from in situ direct shear tests.  
Figure 12: Fitted shear strength parameters for CGS with different gradations under varying moisture contents.  
a 350 mm \(\times\) 250 mm shear box, using original gradation specimens with \(d_{\rm max}=60\)mm thus overestimates shear strength parameters due to size effects. Scaled specimens yield peak strengths closer to in situ outcomes: ESM specimens' \(\phi\) aligns well with in situ results, though their \(c\) is overestimated, while SGM specimens show slightly lower \(\phi\) and higher \(c\). Overall, laboratory and in situ results differ regardless of \(d_{\rm max}\) or scaling method, but SGM outcomes most closely match in situ peak strengths and parameters.
# Framework Based on Simulation of Real-World Message Streams to Evaluate Classification Solutions
## 3 Case Study: Spam Email Detection  
This section presents a case study, Spam Email Detection, where an implementation ([[https://github.com/Cujae-IF/FrameworkToEvaluateMessageClassification.git](https://github.com/Cujae-IF/FrameworkToEvaluateMessageClassification.git)]([https://github.com/Cujae-IF/FrameworkToEvaluateMessageClassification.git](https://github.com/Cujae-IF/FrameworkToEvaluateMessageClassification.git)) accessed on 16 January 2024) of the proposed framework is used to evaluate solutions in spam detection scenarios. The Spam Email Detection case study aims to measure the influence of email streams on the quality (accuracy, precision and recall) of spam email detection. For this purpose, the proposed framework will be implemented to generate mail streams from the SpamAssassin corpus [25] using the Message Stream Simulation module, to classify them using a built-in test classifier (LearningAntiSpamServer) in the Classification module, and finally to evaluate the quality of the classification using the Evaluation module.  
While email recipients may have historically viewed spam as nothing more than an annoying intrusion, unwanted advertising or a waste of time, they now commonly associate it with complex and potential threats to their online security, integrity and trustworthiness [26]. Approximately 50% to 85% of the world's daily email traffic is now generated by spam [6], although spam is not exclusive to email and can also be found in other contexts such as social networks [27]. The negative impact of spam has resulted in billions of dollars of economic loss every year. Several proposed spam detection techniques have been developed to determine the authenticity of the emails [6; 10; 28]. To evaluate the performance of the filters, various corpus, measures and evaluation methods have been published [6; 10]. Although the evaluation of spam filters seems to be quite consolidated from an academic point of view, the current methods do not simulate the real environment correctly. Among the factors that are often not taken into account when evaluating spam filters are the combination in the stream of email clusters with different characteristics (e.g., social networking, marketing and informational), the arrival frequency of the emails to be filtered, the size of the emails, the number of emails per arrival, and the obfuscation of the email body by spammers.
# Gathering Big Data in Wireless Sensor Networks by Drone +
## 4 Modeling the GBD Problem
\(TG=(P_{grid},F)\), where \(P_{grid}\) is the set of all _PDPs_ and \(F\) is a set of edges that represents the shortest distance between every pair of two _PDPs_. The Euclidean distance between two _PDPs_ is the weight of each edge. \(T_{trip}\) is calculated by using \(TG\). The _Connectivity Graph_ represents the data paths between the drone hovering over each _PDP_ and every sensor node. \(CG=(V,E)\), where \(V\) is a set of vertices that represents the sensor nodes and the _PDPs_, such that \(V=S\cup P_{grid}\). \(E\) is a set of edges. Each edge represents the data path between the drone over a _PDP_ in \(P_{grid}\) and a sensor node. Each vertex representing a _PDP_ is connected to every vertex representing sensor nodes by an edge, which has a weight representing the distance in hops between them (One hop is a link of direct communication between two sensor nodes or between a sensor node and the drone. A path with two hops has three sensor nodes: a source, a destination, and a node between these two to relay packets.). There is no edge connecting vertex representing sensor nodes. Figure 3(a) shows an example of a WSN and Figure 3(b) the correspondent _CG_. Figure 3(a) presents a WSN composed of three sensor nodes (\(S=\{s_{1},s_{2},s_{3}\}\)) and four _PDPs_ (\(P_{grid}=\{PDP_{1},PDP_{2},PDP_{3},PDP_{4}\}\)). Figure 3(a) also shows all possible links of communications between two sensor nodes and between the drone over every _PDP_ and the sensor nodes. The _CG_ presented by Figure 3(b) has weights on each edge. These weights represent the distance in hops between the drone over a _PDPs_ and a sensor node. Let us take the sensor node \(s_{3}\) as an example. It has three edges, two of them have weight \(1\). These edges represent possible direct communication between \(s_{3}\) and the drone hovering over _PDP_\({}_{3}\) and _PDP_\({}_{4}\). The other edge of \(s_{3}\) has weight \(3\), representing a data path with \(3\) hops linking \(s_{3}\) and _PDP_\({}_{1}\) (\(s_{3}\longrightarrow\)\(s_{2}\longrightarrow\)\(s_{1}\longrightarrow\)_PDP_\({}_{1}\)). Figure 3: Finite set of points, named Possible Drone Points (_PDPs_), forming a grid. The drone can hover over these points for data gathering.
# Diagnosing Phosphorus Limitation in Subtropical Forests in China under Climate Warming
## 3 Results
### The Latitudinal Distribution of Additional Phosphorus Demand in Subtropical China  
We define the zones in the north of 30\({}^{\circ}\) N, between 25\({}^{\circ}\) N and 30\({}^{\circ}\) N, and in the south of 25\({}^{\circ}\) N in the study area as northern, central and southern subtropical China, respectively. The NPP trends averaged over northern, central and southern subtropical China for EBF are 1.63 gC\(\cdot\)m\({}^{-2}\)-a\({}^{-1}\), 2.02 gC\(\cdot\)m\({}^{-2}\)-a\({}^{-1}\) and 1.79 gC\(\cdot\)m\({}^{-2}\)-a\({}^{-1}\), respectively, and those for ENF are 1.23 gC\(\cdot\)m\({}^{-2}\)-a\({}^{-1}\), 1.38 gC\(\cdot\)m\({}^{-2}\)-a\({}^{-1}\) and 1.27 gC\(\cdot\)m\({}^{-2}\)-a\({}^{-1}\), respectively. For both EBF and ENF, the highest NPP trends occurred in central subtropical China, and the lowest in northern subtropical China, and the NPP trend for EBF was higher than that for ENF in the whole subtropical China.  
Figure 6: Changes of additional phosphorus demand averaged over EBF, DBF and ENF for: (**a**) S1 and (**b**) S2. EBF, DBF and ENF are the abbreviations for evergreen broad leaf forest, deciduous broad leaf forest and evergreen needle leaf forest, respectively. S1 and S2 are the scenarios for the low and high mineralization rates, respectively.  
The AAPD trends over 1951-2010 for EBF in northern, central and southern subtropical China are 3.44 mg\(\cdot\)m\({}^{-2}\)\(\cdot\)a\({}^{-1}\), 4.25 mg\(\cdot\)m\({}^{-2}\)\(\cdot\)a\({}^{-1}\) and 3.91 mg\(\cdot\)m\({}^{-2}\)\(\cdot\)a\({}^{-1}\), respectively, and those for ENF are 2.00 mg\(\cdot\)m\({}^{-2}\)\(\cdot\)a\({}^{-1}\), 2.14 mg\(\cdot\)m\({}^{-2}\)\(\cdot\)a\({}^{-1}\) and 1.91 mg\(\cdot\)m\({}^{-2}\)\(\cdot\)a\({}^{-1}\), respectively. The latitudinal distribution of AAPD trends is consistent with that of NPP trends. For both EBF and ENF, the highest AAPD trend occurs in central subtropical China, and the lowest in northern subtropical China. While the AAPD trends for EBF are higher than those for ENF in the whole subtropical China. Therefore, the central subtropical forests, especially the EBF, have the highest increasing trends of NPP and P demands over 1951-2010 due to climate warming.
## 6 Evaluation and Security
### Security Analysis  
The protocol achieves robust security primarily through the use of authenticated encryption with the Ascon algorithm. This method guarantees data confidentiality by ensuring that all sensitive information is encrypted before transmission. Even if an adversary manages to intercept the communications, the encrypted data remain unintelligible without the corresponding decryption key. This approach is crucial for preventing unauthorized access, particularly in environments where data are exchanged over open networks.  
Data integrity is secured by leveraging two key mechanisms. First, the blockchain's inherent immutability ensures that all metadata--such as transaction records and access logs--cannot be altered once recorded. This provides a reliable audit trail and prevents any unauthorized modifications. Second, IPFS employs content-based addressing, where every file is identified by its cryptographic hash. Any attempt to alter a file results in a different hash value, thereby making tampering immediately detectable.  
Access control is enforced using smart contracts deployed on the blockchain. These contracts are programmed to execute strict permission rules, ensuring that only authorized users can access or modify resources stored in the system. The combination of these measures creates a robust barrier against unauthorized access while maintaining a transparent and verifiable record of all interactions.  
Furthermore, the decentralized architecture of both blockchain and IPFS contributes significantly to the protocol's resilience. By eliminating single points of failure, the system remains operational even if parts of the network are compromised or subjected to distributed denial-of-service (DDoS) attacks. This distributed design not only improves reliability but also enhances the overall security posture of the protocol.
# Comparative Study of Energy Savings for Various Control Strategies in the Tunnel Lighting System
## 3 Materials and Methods
### Hardware Structure of Tunnel Lighting Control System
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Tunnel Section** & \multicolumn{2}{c}{**Luminance (cd/m2)**} & \multicolumn{2}{c}{**Distance (m)**} \\ \hline \multirow{3}{*}{Threshold zone 1} & \multirow{3}{*}{\(L_{\text{th1}}=\)} & \(0.00050-0.013)\times L_{\text{20}}\) & \(N\leq 350\) & \multirow{3}{*}{\(D_{\text{th1}}=0.5(1.154D_{\text{s}}-(h-\)  
\((0.3555+0.0002N(y-29)-9.03)\times L_{\text{20}}/850\)} & \multirow{3}{*}{\(350<N<1200\)} & \multirow{3}{*}{\(D_{\text{th1}}=0.5(1.154D_{\text{s}}-(h-\)  
\((0.0007v-0.0188)\times L_{\text{20}}\) & \(1.5)/tan10^{\circ}\))} \\  & & & \(L_{\text{th2}}=0.5\times L_{\text{th1}}\) & & \(D_{\text{th2}}=D_{\text{th1}}\) \\ \multirow{3}{*}{Threshold zone 2} & \multirow{3}{*}{\(L_{\text{th1}}=0.15\times L_{\text{th1}}\)} & \multirow{3}{*}{\(D_{\text{th1}}=(D_{\text{th1}}+D_{\text{th2}})/3+v_{\text{t}}/1.8\)} & \multirow{3}{*}{\(L_{\text{th2}}=20.7/1.8\)} \\ Transition zone 2 & & \(L_{\text{th2}}=0.05\times L_{\text{th1}}\) & & \(D_{\text{th3}}=3v_{\text{t}}/1.8\) \\ \multirow{3}{*}{Transition zone 3} & \multirow{3}{*}{\(L_{\text{th3}}=0.02\times L_{\text{th1}}\)} & \multirow{3}{*}{\(D_{\text{th3}}=3v_{\text{t}}/1.8\)} \\  & & \(0.0007v^{2}-0.0693v+2.6\) & & \(N\leq 350\) \\ \multirow{3}{*}{Interior zone} & \multirow{3}{*}{\(L_{\text{in}}=\)} & \(0.0005v^{2}-0.0207v+0.9\) & \(350<N<1200\) & \multirow{3}{*}{\(D_{\text{th2}}=L-D_{\text{th1}}-D_{\text{th2}}-D_{\text{tr1}}-D_{\text{ th2}}-D_{\text{tr1}}-D_{\text{th2}}\)} \\  & & \(0.0012v^{2}-0.0732v+2.1\) & & \(N\geq 1200\) & \\ \multirow{3}{*}{Exit zone 1} & \multirow{3}{*}{\(L_{\text{ex1}}=3\times L_{\text{in}}\)} & \multirow{3}{*}{\(D_{\text{ex1}}=30\)} \\  & & \(L_{\text{ex2}}=5\times L_{\text{in}}\) & & \(D_{\text{ex2}}=30\) \\ \hline \hline \end{tabular}  
* _Note:_ (\(v\)) is vehicle speed (km/h); (\(N\)) is traffic volume (veh/(h-h)); (\(D_{\text{s}}\))is the stopping sign distance (SSD, m); \(h\) is the tunnel clearance height (m); (\(v_{\text{t}}\)) is the design speed (km/h). In this study, \(v_{\text{t}}=80\) km/h, \(D_{\text{s}}=100\) m, \(h=6.0\) m.  
\end{table} 
Table 5: Luminance and length of tunnel zones shown in Figure 5.  
Figure 6: Schematic of the hardware of the SCADA tunnel lighting system.
# Quantitative Assessment of the Environmental Impacts of Dredging and Dumping Activities at Sea
## 3 Results
### Calibration and Validation of Hydrodynamic in 2D Model  
Calibration and validation of hydrodynamics in the 2D model used an observed water level tide at Quy Nhon station during 1-30 September 2016 and 1-31 October 2016, respectively. In this study, Nash-Sutcliffe efficient (NSE), Percent bias (PBIAS), and RMSE-observations standard deviation  
Figure 4: Calibration and validation of the wave height in 2D SW model at Ly Son station.  
Figure 5: The results of wave field: (**a**) low tide and (**b**) high tide.  
ratio (RSR) were used to evaluate the observed and simulated water levels [49; 50]. The results of the calculated and observed water levels were in good agreement in terms of the vibration amplitude, absolute value, and tidal phases during both the calibration and validation processes (Figure 6). The NSE value for calibration and validation of the water level at Quy Nhon station ranged from 0.84-0.86. The maximum tide peak error in calibration and validation were 0.14 m and 0.16 m, respectively. According to the guidelines for model evaluation [50], the 2D model simulated the streamflow trends very well, as demonstrated by the statistical results, which were in agreement with the graphical results. The RSR values ranged from 0.32-0.44 for both calibration and validation. The PBIAS values ranged from \(-4.7\) to \(-3.5\) for both calibration and validation. The average magnitude of the simulated values had a good performance rating (PBIAS \(<\pm\) 10%) for both calibration and validation. The simulation results for the streamflow regime using the 2D model were very good in terms of performance ratings, as revealed by NSE, RSR, and PBIAS. The calibration parameters used in the model validation process were the bed resistance Manning coefficients, which varied from 52-57 (m\({}^{1/3}\)/s), the eddy viscosity coefficient (Smagorinsky) [51; 52; 53], which ranged from 0.28-0.32 (m\({}^{2}\)/s), and wind friction, which ranged from 0.0018-0.002. The time step was 72 s. The last parameters were used to simulate mud transport in the dumping zone of the 2D model under different scenarios.  
The model results showed that the flow field in the study area was strongly impacted by topography, flow in the river, wind and wave fields (Figure 7). The flow field has tremendous fluctuations in both direction and speed. This means that the flow field can impact the sedimentation process differently in the various scenarios. To avoid this, the study decoupled the hydrodynamic module and mud transport module in the 2D model to simulate the sedimentation of the submerged zone.  
Figure 6: Calibration and validation of water level at Quy Nhon station.
# HCTC: Hybrid Convolutional Transformer Classifier for Automatic Modulation Recognition
## 8 Conclusions  
The HCTC model, referred to as the Hybrid Convolutional Transformer Classifier model, is proposed in this work for modulation classification and shows significant classification performance. The proposed HCTC model explores the use of a convolutional layer, a transformer encoder, and a DNN for the extraction of spatial and temporal features from I/Q sequences. The proposed model was evaluated using the benchmark RadioML database, and its performance was compared with those of 14 widely used algorithms. The performance evaluation metrics included recognition accuracy across SNRs, confusion matrices, and the numbers of parameters used for training and FLOPs. The experimental results demonstrate that the proposed HCTC model achieves a better performance in the classification of the modulation signal. In addition, the proposed HCTC method was studied for different batch sizes and model configurations. In future studies, ensemble hybrid DL methods and image representation models for modulation recognition should be developed.  
Conceptualization, J.D.R. and H.-N.K.; methodology, J.D.R. and H.-N.K.; software J.D.R.; writing--original draft preparation, J.D.R.; writing--review and editing, J.D.R., D.-H.P. and S.-Y.K. All authors have read and agreed to the published version of the manuscript.  
This research received no funding.  
The RadioML database used in this work was downloaded from the website [[https://www.deepsig.ai/Datasets/](https://www.deepsig.ai/Datasets/)]([https://www.deepsig.ai/Datasets/](https://www.deepsig.ai/Datasets/)) (accessed on 17 October 2023).  
The authors declare no conflicts of interest.
# Quadratic Clustering-Based Simplex Volume Maximization for Hyperspectral Endmember Extraction
###### Abstract  
The existence of intra-class spectral variability caused by differential scene components and illumination conditions limits the improvement of endmember extraction accuracy, as most endmember extraction algorithms directly find pixels in the hyperspectral image as endmembers. This paper develops a quadratic clustering-based simplex volume maximization (CSVM) approach to effectively alleviate spectral variability and extract endmembers. CSVM first adopts spatial clustering based on simple linear iterative clustering to obtain a set of homogeneous partitions and uses spectral purity analysis to choose pure pixels. The average of the chosen pixels in each partition is taken as a representative endmember, which reduces the effect of local-scope spectral variability. Then an improved spectral clustering based on k-means is implemented to merge homologous representative endmembers to further reduce the effect of large-scope spectral variability, and final endmember collection is determined by the simplex with maximum volume. Experimental results show that CSVM reduces the average spectral angle distance on Samson, Jasper Ridge and Cuprite datasets to below 0.02, 0.06 and 0.09, respectively, provides the root mean square errors of abundance maps on Samson and Jasper Ridge datasets below 0.25 and 0.10, and exhibits good noise robustness. By contrast, CSVM provides better results than other state-of-the-art algorithms.  
endmember extraction; quadratic clustering; spectral purity analysis; maximum simplex volume
## 5 Simulation Verification
### Experimental Simulation  
According to the parameters of PUMA560 in Table 1, the start and final points of the manipulator are designed as shown in Equation (29):  
\[\begin{array}{ll}q_{start}=[10,-10,-30,-25,20,0](^{\circ})\\ q_{final}=[-20,25,90,20,-60,120](^{\circ})\end{array} \tag{29}\]  
The population size \(N=100\), the number of iterations \(gen=300\), the learning rate \(\alpha=0.06\), the discount rate \(\gamma=0.85\), and the greediness rate \(\varepsilon=0.06\). The improved algorithm was used to solve for the time-optimal trajectory of the PUMA560 manipulator, and the path points and time are shown in Table 8. Based on the data in Table 8, the angular displacement, angular velocity, angular acceleration, and angular jerk of the manipulator are plotted in Figure 6.  
Figure 5: The PUMA560 model.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Joint** & **1** & **2** & **3** & **4** & **5** & **6** \\ \hline Angular velocity V/(\({}^{\circ}\)/s) & 100 & 95 & 100 & 150 & 130 & 110 \\ Angular acceleration A (\({}^{\circ}\)/s\({}^{2}\)) & 45 & 40 & 75 & 70 & 90 & 80 \\ Angular jerk J (\({}^{\circ}\)/s\({}^{3}\)) & 60 & 60 & 55 & 70 & 75 & 70 \\ \hline \hline \end{tabular}
\end{table}
Table 7: PUMA560 kinematic constraints.  
As shown in Table 8 and Figure 6, the optimal time for the manipulator is 5.899 s, and the resulting angular displacement, angular velocity, angular acceleration, and angular jerk curves are smooth and continuous and all within the constraint range.
## 4 Experimental Results and Comparisons
### Comparative Analysis  
Many existing conventional ML and DL approaches use various data balancing methods to enhance the classification performance in the case of imbalanced dataset. In contrast, in this research, we used the imbalanced malware families of the benchmark Malimg database to classify malware images with high accuracy and without employing any data augmentation or any other balancing techniques.  
Therefore, this section compares the performance of the proposed multi-classification framework with numerous well-established ML and DL classifiers; to prove its efficiency in detecting and classifying visualized malware images even when applied to same datasets with imbalanced malware families.  
Table 9 summarizes the performance comparison in terms of the accuracy, precision, recall, and F1-Score metrics of the proposed multi-classification framework using fine-tuned VGG16 CNN model and the baseline-related ML and DL classifiers. The comparison results confirm that the developed multi-classification framework outperforms all other conventional approaches in all tested evaluation metrics. The classification accuracy of the proposed framework reached 99.97% after applying fine-tuned VGG16 which is considered superior compared to traditional classifiers. This is due to the use of an efficient FT process and well-developed CNN-based TL models.