esa-sceva/satcom-chunk-collection huggingface dataset:
# A Credit-Based Congestion-Aware Incentive Scheme for DTNs
## 5 Performance Evaluation
### Performance Comparison
#### 5.2.2 Number of Failed Transmissions  
From Figure 8, we can see that in SMART, Multicenter, and GTA, about 20 nodes have some messages fail in transmission due to a shortage of credits. However, in CBCAIS, only about 10 nodes have some failed transmissions. In addition, the total number of failed transmissions due to a shortage of credits in CBCAIS is much smaller. In the SMART, Multicenter, and GTA schemes, since most messages will be forwarded to the nodes with a high connection degree, many will be discarded. As a result, previous forwarding nodes or destination nodes will lose their credits. In addition, many nodes with larger delivery delay but free buffer will have no chance to earn credits, so these nodes will not have enough credits to pay for messages, leading to an increase in failed transmissions. In CBCAIS, the check and punishment mechanism can prevent nodes from discarding messages deliberately, so the nodes will have a better credit balance. Therefore, the number of failed transmissions will be small.
# An Ensemble Tree-Based Model for Intrusion Detection in Industrial Internet of Things Networks
## 4 Experimental Results and Discussions
### Experimental Results Based on the Proposed Model
Summarily, it is observed from the outcomes presented in Sections 4.2 and 4.3 that the XGBoost classifier gave a superior performance score for all assessment indicators across all the datasets. In contrast, the Adaboost ensemble classifier performed worst based on the assessment indicators across all datasets. Based on the recall rate of all models across all datasets, the XGBoost classifier performed the least on the 'Motion_Light\" dataset, with the least value of 0.8957. Based on the baseline study, the proposed study also evaluates IoT/IIoT-based dataset by combining all of the individual datasets for each device into the collective IoT dataset. Since most real-time apps save their data in a single location, this may represent some real situations. The combined IoT dataset assessed the proposed ensemble classifiers for binary and multi-class classification issues. It is worth mentioning that most studies that use the datasets combine the whole datasets to become one before applying classifiers to them. Table 11 shows the results of the binary classification of the ensemble classifiers enabled with feature selection mothed. Both the overall IoT dataset and the per-device IoT dataset were used to test the ensemble classifiers. The proposed ensemble classifiers, enabled with the feature selection model, were also evaluated using the performance measures. The findings are summarized in Table 11: XGBoost receives the maximum accuracy rating of 1.0 and scores about 98% across the other performance metrics. From the results obtained, it was noted that all the classifiers scored about 97% across all the performance metrics except Adaboost, which scored less than 70% across all the performance metrics. The performance can be attributed to the application of feature selection to remove the irrelevant features before the classification of the dataset using the ensemble classifiers.
## 2 An SSVEP-Based BCI with Multi-Domain Feature and Multi-Task Learning
### Neural Network Structure for Feature Extraction  
Two convolutional blocks are designed for extracting features from the SSVEP signals. Each block consists of a convolution layer, a batch normalization layer, and an exponential  
Figure 1: The proposed neural network architecture with MDFs and MTL for SSVEP-based BCIs. The proposed neural network consists of 5 different blocks: the input, convolution, dropout, multi-task learning, and the output.  
linear unit. For a convolutional block, let z be an input, which is the output of previous block, and then the convolution, \(u()\), of the input with the kernel \(w\) can be defined as follows:  
\[u(z)=\sigma(w*z), \tag{2}\]  
where * is the convolution operation. The activation function, \(\sigma()\), is an exponential linear unit and defined as  
\[\sigma(v)=\begin{cases}v&v\geq 0\\ \alpha(e^{v}-1)&v<0\end{cases}, \tag{3}\]  
where \(\alpha\) is a hyperparameter to be tuned with the constraint \(\alpha\geq 0\). A dropout layer, which is a mask that nullifies the contributions of some neurons, is also included in each block to reduce the number of parameters in the neural network.  
The first block is responsible for extracting embedding features from the time or frequency domain of the SSVEP signal. The process of the first block is similar to extract features using spectral and cepstrum processing, and its first convolutional layer has a kernel size of \(1\times L_{1}\). The convolutional layer in the second block convolves across the dimension of EEG channels with a kernel size of \(L_{2}\times 1\), and then it performs spatial filtering.
## 3 Experimental Analysis of the Reactive Power in Nowadays Power Distribution Lines  
### Experimental Analysis of the Reactive Power in PDL Loaded by LED Lighting Devices  
In order to investigate the reactive power provided by LED light devices, an experiment was performed with the most commonly used LED light bulbs on the Lithuanian market. Different color temperature and different power LED light bulbs were selected. The effect of not only individual LED light bulbs but also their groups on the reactive power of PDL was investigated. The experimental analysis of the reactive power in the LED lighting devices loaded PDL was performed with a METREL MI 2892 power quality analyzer [2].  
The experimental setup, for the reactive power provided or drawn out by diode rectifier supply-side device analysis, consists of (Figure A1):  
1. Power distribution line connection;
2. METREL MI 2892 power quality analyzer;
3. 806 lm 2700 K LED light bulb;
4. 1850 lm 4000 K LED light bulb;
5. 1500 lm 3000 K LED light bulb;
6. 1350 lm 3000 K LED light bulb;
7. 1350 lm 3000 K LED light bulb;
8. 1055 lm 3000 K.  
A photograph of the experimental setup is presented in Figure A2.  
The following PDL parameters were investigated:  
* \(P_{C}\)--combined active power;
* \(N_{C}\)--combined reactive power;
* \(S_{C}\)--combined apparent power;
* \(PF\)--combined effective power factor;
* \(P_{Fund}\)--fundamental harmonic active power;
* \(Q_{VFund}\)--fundamental harmonic reactive power;
* \(S_{VFund}\)--fundamental harmonic apparent power;
* \(PF_{VFund}\)--fundamental harmonic displacement factor.  
The results of
## 4 Hybrid Thematic Analysis: Key Findings from NLP and Expert Review
### Digital Twins: Transforming Predictive Maintenance and Asset Management  
#### 4.3.1 Digital Twins and Predictive Models in Asset Management  
Digital twins and predictive models have revolutionized asset management by enabling real-time simulations for fault diagnosis. The integration of digital twins with deep learning has facilitated precise vibration monitoring and wear prediction in axial fans, as demonstrated in iron manufacturing [58; 59]. Similarly, simulations of low-voltage motors have been utilized to optimize reliability [57], while hydraulic models have contributed to improved industrial pump management [61].  
Recent investigations highlight that the combination of digital twins with advanced AI models is accelerating the transition from reactive to predictive maintenance across multiple industrial domains, including maritime power systems [68], nuclear energy [64; 76], and large-scale conveyor belt systems in the mining sector [2; 3]. By leveraging both physics-based simulations and real-time sensor data, these hybrid digital twin frameworks facilitate a more comprehensive approach to diagnosing failures and scheduling maintenance operations [1; 6; 84]. In particular, the synergy between big data analytics, explainable AI, and digital twin infrastructures has emerged as a key enabler for robust asset lifecycle management, scaling from small rotating machinery to entire production chains [55; 65; 85].  
Advancements in multi-physics digital twins [64; 80] have provided enhanced representations of fluid-structure interactions in leaching and tailing pipelines, enabling early anomaly detection before potential ruptures occur [36; 37]. This integration has significantly reduced water loss and prevented environmental hazards, reinforcing sustainability objectives in large-scale mining operations.  
Hybrid approaches that integrate neural networks with finite element analyses have been applied to classify gearbox cracks [37], allowing for the detection of structural flaws without requiring costly downtime. Additionally, applications in wind turbines and refrigeration systems have demonstrated the effectiveness of merging digital twins with machine learning for real-time performance optimization, leading to reduced unexpected failures and minimized environmental impacts.
## 2 Validation of FLAC Modeling on Wave Transmission across Rock Joints
### Normally Incident P Wave Transmission through a Joint Set  
When a wave propagates through a rock mass with multiple parallel joints, multiple reflections occur between joints, which makes the problem more complex [21]. The joint set involved is assumed to have the same mechanical parameters and physico-mechanical properties of the rock matrix as those in Table 1. The FLAC model is also the same as in Figure 1 except that multiple joints exist with different spacing. In this model, the incident and transmitted measure point are located at (X, Z) = (250, 50) and (250, 456), respectively.  
The FLAC results and analytical solutions are compared in Figure 4. The horizontal coordinate is the dimensionless fracture spacing \(\xi\), which is defined as the ratio of joint spacing d to incident wavelength \(\lambda\), where \(K\) = 1. The vertical coordinate is the magnitude of transmission coefficient across a joint set for P wave incidence \(T_{N}\), which is defined as:  
\[T_{N}=\max|\upsilon_{TP}|/\max|\upsilon_{IP}| \tag{5}\]  
where \(\upsilon_{IP}\) is the particle velocity of incident P wave and \(\upsilon_{TP}\) is the particle velocity of transmitted P wave through a joint set. From Figure 4, it can be found that \(T_{N}\) from the FLAC results agrees well with the analytical solutions. Therefore, FLAC is applicable to study wave propagating through a rock mass with multiple parallel joints.  
Figure 3: Relations between transmission coefficient \(T_{1S}\) and normalized joint stiffness \(K\) for normally incident Sv wave transmission across a single joint.
The authors integrated the modified perpendicular drought index (MPDI) as a soil moisture indicator into the SEBS fed with HJ-1 images to obtain accurate ET estimation at the field scale in Ganzhou irrigation districts. The principal objectives of this study were to: (1) provide an effective way to overcome the shortage of overestimation of ET under water-limited conditions by integrating MPDI into the SEBS model only using HJ-1 and meteorological data as inputs; (2) evaluate the performance of the MPDI-integrated SEBS model for irrigated oases in Northwestern China; (3) analyze the spatiotemporal variability of daily ET for different crops during the crop growing season to gain an understanding of the irrigation water requirement in the studied region.
## 4 Proposed Estimation Scheme
### Estimation of Desired Power, CCI Power, and COR
#### 4.1.1 Signal Recovery  
Chirp demodulation is performed for one packet. Accordingly, the chirp pattern selected by the transmitter is estimated and denoted as \(\widehat{\mathbf{\phi_{l}}}\). The \(N_{pkt}\) chirp patterns are estimated when the number of chirp modulation symbols per packet is defined as \(N_{pkt}\). When the frequency spectrum of the received signal is obtained using the fast Fourier transform (FFT), the discrete partial frequency spectrum, \(X_{n,k}\), is obtained. In accordance with \(\widehat{\mathbf{\phi_{l}}}\), the two types of estimated powers, \(\eta_{l,A},\eta_{l,B}\), are obtained as follows:  
\[\eta_{l,A}=\sum_{(n,k)\in\widehat{\mathbf{\phi_{l}}}}|X_{n,k}|^{2}/N_{\eta_{A}}, \tag{7}\]  
\[\eta_{l,B}=\sum_{(n,k)\in\left\{\mathbf{V}\backslash\widehat{\mathbf{\phi_{l}}}\right\} }|X_{n,k}|^{2}/N_{\eta_{B}}, \tag{8}\]  
where \(\eta_{l,A}\) and \(\eta_{l,B}\) are the combined results of the power whose spectrum includes--and does not include--the components of the desired signal in the \(l\)th chirp pattern, respectively. The partial frequency spectra of the former and latter are referred to as partial occupied bands (PODs) and partial unoccupied bands (PUDs), respectively. The numbers of partial frequency bands in PODs and PUDs are defined as \(N_{\eta_{A}},N_{\eta_{B}}\).  
In the first chirp-modulated symbol in Figure 3, the estimated chirp pattern is \(\phi_{l=2}\); thus, the combined results of power in PODs and PUDs, \(\eta_{2,A},\eta_{2,B}\), are as follows.  
\[\eta_{2,A}=(|X_{1,2}|^{2}+|X_{2,3}|^{2}+|X_{3,4}|^{2}+|X_{4,1}|^ {2})/4, \tag{9}\] \[\eta_{2,B}=(|X_{1,1}|^{2}+|X_{1,3}|^{2}+|X_{1,4}|^{2}+|X_{2,1}|^ {2}+|X_{2,2}|^{2}+|X_{2,A}|^{2}+|X_{3,1}|^{2}+|X_{3,2}|^{2}+\] \[|X_{3,3}|^{2}+|X_{4,2}|^{2}+|X_{4,3}|^{2}+|X_{4,4}|^{2})/12. \tag{10}\]  
Figure 4: System flow of the power estimation process (PEP) in the proposed estimation scheme.
## 5 Applications of THz-FSO Communication
### Secure Communication  
The operating frequencies of these systems are inherently of a narrow beam width; hence, the signal leakage is very low, which is effective for military and financial communication. The small dispersion of THz beams also guarantees that all signals are channeled to proceed on a certain path, thus enhancing the security of the data that are being transmitted. However, new approaches involving sophisticated single-phase detection methods also improve the security of THz-based systems by allowing the accurate identification of the occurrence of eavesdropping on the system [6; 7; 8].  
THz-FSO communication systems are also immune to jamming and interferences since they operate in short wavelengths and are highly directive; hence, they can be employed in military communication networks. Optical feeder link models have shown better predictions in terms of accuracy and reliability and are important in bidirectional links where the applications of portions of the link may require high levels of security [3; 4; 6; 9]. In addition, these systems utilize quantum encryption schemas to enhance the privacy of the user as well as to mitigate possible cyber threats [25; 33; 42].  
In financial sectors, THz communications systems allow link networks to transfer high-value data between financial institutions and prevent possible attacks on data. The application of hollowness in fiber systems for THz transmission has allowed the highest data rate observed to date, as these systems are free from latency and immune to security threats, which the current financial networks require. Furthermore, abstraction has also improved the performance and stability of waveguide-based systems in coupled THz communication systems, guaranteeing their relevance for highly sensitive uses [37; 45].  
The next step in developing these systems is integrating terahertz-wave generation using periodically inverted gallium arsenide, which has increased the reliability and security of these systems, enabling their use in creating secure communication systems. For instance, photonic crystal fibers have been used to identify instances of violation of the signal integrity within holistic communication lines [36; 101]. THz-FSO systems are expected to maintain a steady increase in demand for use in new communication solutions, where significant security-related challenges have emerged in recent years and where innovative technologies are constantly being sought for both civilian and military applications [78; 100].
###### Abstract  
We examine Land Use Land Cover Change (LULCC) in the Dedza and Ntcheu districts of Central Malawi and model anthropogenic and environmental drivers. We present an integrative approach to understanding heterogenous landscape interactions and short- to long-term shocks and how they inform future land management and policy in Malawi. Landsat 30-m satellite imagery for 2001, 2009, and 2019 was used to identify and quantify LULCC outcomes based on eight input classes: agriculture, built-up areas, barren, water, wetlands, forest-mixed vegetation, shrub-woodland, and other. A Multilayer Perceptron (MLP) neural network was developed to examine land-cover transitions based on the drivers; elevation, slope, soil texture, population density and distance from roads and rivers. Agriculture is projected to dominate the landscape by 2050. Dedza has a higher probability of future land conversion to agriculture (0.45 to 0.70) than Ntcheu (0.30 to 0.45). These findings suggest that future land management initiatives should focus on spatiotemporal patterns in land cover and develop multidimensional policies that promote land conservation in the local context.  
1
# Evaluating LoRaWAN Connectivity in a Marine Scenario
## 2 Material and Methods
### LoRa and LoRaWAN
Hence, there is no need for managing the communication between two gateways, and, moreover, it is possible to manage the problems related to any collisions directly on the central server. Each end-node that is part of the LoRaWAN network is uniquely identified by a 64-bit code (DevEUI) and it is associated with an application also defined by a 64-bit identification code (AppEUI). As soon as an end-node wants to join the LoRaWAN network, it receives from the server a unique 32-bit address shared with the network and the server where the application resides. Moreover, it receives a security key (NwkSKey) consisting of an \"Advanced Encryption standard\" encryption code (AES) of 128 bits which is shared between the node and the server and guarantees the integrity of messages on the network. Before an effective communication is established between the end-node and the network, the end-node must be activated by means of a key (AppSKey), consisting of 128 AES encrypted bits as well. This is functional to the encryption and decryption of the information content of the node and guarantees the confidentiality of the information. The request for joining the network from the end-node takes place with \(SF\) equals 12, thus implying minimum data transmission speed and higher transmission time interval of the same amount of information. As soon as the acknowledgement that identifies the end-node as belonging to the network is received from the LoRaWAN server, the transmission of information takes place with reduced \(SFs\) depending on the noise over the channel and the distance between gateway and node estimated by the server. The end-node can register to the network using two different methods, called \"Over The Air Activation\" (OTAA) or \"Activation By Personalization\" (ABP). The activation via OTAA requires that the end-node transmits a join request to the server containing its DevEUI, the AppEUI, and the AppKey and receives a response from the server containing the address of the sensor (DevAddr), the security network (NwkSKey), and the application security key (AppSkey) required to be part of the network. The activation via ABP does not involve the negotiation phase of the address through join request, since the values of the keys (DevEUI, AppEUI, and AppKey) are previously coded within the end-node itself. This procedure is far less secure than the OTAA method.
## 4 Conclusions  
We investigated the influence of impurity Fe atom on magnetism and electronic structures of Heusler compound Ti\({}_{2}\)CoSi by using the first-principle calculations within density functional theory (DFT). Ti\({}_{2}\)CoSi is a spin gapless semiconductor (SGS) with an indirect closed spin down energy gap. It lost its SGS property when the impurity Fe atom intervened, and when the concentration of impurity Fe atom increases to x = 0.875, the compound shows gapless metal character. At the doping concentration of x = 1, TiA atoms are completely occupied by impurity Fe atoms, and the compound converts to TiFeCoSi, which is a half-metallic ferromagnet (HMF). During this SGS\(\rightarrow\)HMF transition, the total magnetic moment linearly decreases with the concentration of impurity Fe atom increasing, which follows the Slate-Pauling rule well. When the impurity Fe further increases from y = 0.125 to y = 0.625, the doping compounds maintain their half-metallicity. While, when the doping concentration increases up to y = 1, the compound converts to semiconductor Fe\({}_{2}\)TiSi. During this HMF\(\rightarrow\)nonmagnetic semiconductor transition, when the concentration of impurity Fe atom ranges from y = 0.125 to y = 0.625, the magnetic moment of Fe atom is positive and linearly decreases, while that of impurity Fe and TiB are negative and linearly increase. When all Co atoms are substituted by Fe atoms, the magnetic moments of Ti, Fe, and Si return to zero, and the compound, therefore, is a nonmagnetic semiconductor.  
Methodology, Z.C.; software, M.-s.W. and S.A.; writing--original draft preparation, B.W.; writing--review and editing, Y.F. This work was partly supported by the National Natural Science Foundation of China (11747114), Doctor Foundation of Jiangsu Normal University (16 XLR022). The authors declare no conflict of interest.  
Figure 9: The calculated total magnetic moment and atom-resolved spin magnetic moments (AMMs) of the Co, Fe, TiB, and impurity Fe (Fe\({}^{\prime}\)) atoms of the TiFeCo\({}_{1-y}\)Fe\({}_{y}\)Si as a function of impurity Fe concentration y.
# A Secure Key Aggregate Searchable Encryption with Multi Delegation in Cloud Data Sharing Service
## 5 Security Analysis
### Informal Analysis
#### 5.1.9. Session Key Disclosure Attacks  
An adversary tries to obtain sensitive information by calculating a legitimate session key \(SK\). However, as discussed in Sections 5.1.4, 5.1.7 and 5.1.8, the adversary cannot compute \(SK\) because of the computationally infeasible problem. Therefore, our proposed scheme is safe against session key disclosure attacks.
## 5 Engineering Applications
### Comparison of Numerical Simulation and Machine Learning Method Results  
Table 8 gives the calculation of the FOS of the slope based on the ANN machine learning method and the input parameters of the slope. These data sets are used to verify the ANN method. The ANN method performs well in the calculation of the safety factor of the slope. Compared with the safety factor in Table 7, two of the results are very close to the numerical simulation results, namely, (1.05, 1.40) and (1.08, 1.35). With the increase in water content, the slope stability shows a trend of strengthening and then weakening, which is the same as the calculation result of numerical simulation.  
This result difference is closely related to the historical data set. The existing training set does not cover the historical data of the slope close to the geometric parameters in this verification case, especially the slope height is quite different. The prediction results of the model can be further improved by more widely distributed data. Meanwhile, since the surface sandstone soil layer and the strongly weathered rock layer are considered, as a whole, to determine the FOS of the slope in the process of numerical simulation, the difference of the analysis perspective will also lead to the deviation of the calculation results.