esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Methods
### Fuzzy AHP
The association between the TFN numerical values and linguistic characteristics were illustrated  
Figure 3: Linguistic variables used for determining importance weight of criteria. Figure 2: A triangular fuzzy number (TFN), \(\widetilde{A}\). by adopting the fuzzified [PERSON]'s scale. When \(\widetilde{A}=(l,m,u)\) was considered to be a TFN, the reciprocal of TFN, \(A^{-1}\), was indicated as \((1/u_{i,1}/m_{i,1}/l_{i})\). The next goal was to develop a fuzzy pairwise comparison matrix. Decision-makers (DMs) use linguistic terms for developing a pairwise comparison matrix of criteria. To achieve this, we used the nine-point conversion scale developed by [PERSON] et al. [41] to transform responses into fuzzy numbers (See Table 2). The following is the resultant comparison matrix:  
\[\widetilde{A}=\left[ \begin{array}{cccc}1&\widetilde{a}_{12}&\ldots& \widetilde{a}_{1n}\\ \widetilde{a}_{21}&1&\ldots&\widetilde{a}_{2n}\\ \ddots&\ddots&\ddots&\ddots\\ \widetilde{a}_{m1}&\widetilde{a}_{m2}&\ldots&1\end{array} \right]\]  
The aggregation of the fuzzy pairwise comparison matrix was completed. When it comes to group decision-making, the DMs' opinions are summarized as follows:  
\[l_{ij}=(\prod_{k=1}^{k}\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\  
Determination of normalized weight valuesThe following equation gives the normalized weights for the \(j\)th row of the crisp comparison matrix:  
\[\widetilde{W}_{i}=\frac{\widetilde{GM}_{i}}{\sum\limits_{i=1}^{n}\widetilde{GM} _{i}} \tag{4}\]  
The fuzzy comparison values' geometric mean is represented by \(\widetilde{GM}_{i}\), where the criteria weights are represented by \(\widetilde{W}_{i}\). **Step 3. ** Check for consistency  
Determination of the consistency ratio (\(CR\)) with respect to each pairwise comparison matrix was done to control the outcomes of the AHP technique, and the value had to be less than 0.1 to consider a matrix consistent. Further, when a crisp comparison matrix was found to be consistent, the fuzzy pairwise comparison matrix was also in consistent state [43]. The equations below describe the consistency index (\(CI\)) and \(CR\) related to a comparison matrix:  
\[CI=\frac{\lambda_{max}-n}{n-1} \tag{5}\]  
\[CR=\frac{CI}{RI} \tag{6}\]  
where, \(\lambda_{max}\)\(\rightarrow\) largest Eigen value, \(n\)\(\rightarrow\) matrix size, and the standard value of \(RI\) is obtained using the matrix order. **Step 4. ** Defuzzification of the fuzzy weights  
Because the weight is a fuzzy number, the center of area (COA) approach was used to defuzzify it, using the equation below:  
\[W_{i}=\frac{(low_{i}+mw_{i}+uw_{i})}{3} \tag{7}\]  
**Step 5. ** Normalize the crisp weights  
Crisp criteria weights were determined by normalizing the obtained crisp values via the following equation:  
\[W_{c}=\frac{w_{i}}{\sum_{i=1}^{n}w_{i}} \tag{8}\]
# A Deep Learning Method for DOA Estimation with Covariance Matrices in Reverberant Environments
## 6 Computational Complexity
### Dpd-Music  
Ref. [46] shows the run-time complexity of the narrowband signal estimation using SH-MUSIC method in the spatial domain. The complexity is \(O({(N+1)}^{6}+{(N+1)}^{2}N_{c})\), where \(N_{C}\) is the total number of the DOA search grids. In terms of DPD-MUSIC, each rectangular window of time-frequency bins, whose size is \(J_{\tau}\times J_{k_{h}}\), needs the eigenvalue decomposition. The number of the windows is \((N_{T}-J_{\tau}+1)\times(N_{F}-J_{k_{h}}+1)\). The \(J_{\tau}\) and \(J_{k_{h}}\) are typically small compared to \(N_{T}\) and \(N_{F}\)[21; 28]. Thus, the amount of the rectangular windows is \(O(N_{T}N_{F})\). Thus, the complexity of DPD-MUSIC is defined as \(O(N_{T}N_{F}(N+1)^{6}+(N+1)^{2}N_{\theta}N_{\phi})\).
# Simultaneous Indoor Tracking and Activity Recognition Using Pyroelectric Infrared Sensors
## 4 Localization
### Data Fusion  
In our deployment, there are five sensor nodes attached on the ceiling, forming a WSN as shown in Figure 8. For the \(k\)th window, \(\mathbf{z}_{k}=(x_{k},y_{k})\) is defined as the position estimation of the human target. The monitored space of each sensor node is overlapped to enhance the localization precision by data fusion. The data fusion strategy is based on the number of sampling cells triggered within the same time window:  
* Two sampling cells: the midpoint of these two centers is regarded as \(\mathbf{z}_{k}\), as shown in Figure 9a;
* More than two cells: the maximum likelihood estimation algorithm is used to figure out \(\mathbf{z}_{k}\), as shown in Figure 9b.  
However, this strategy will fail in some situations, as shown in Figure 9c. The distances between \(\mathbf{z}_{k}\) and three triggered sampling cells are the same, but this is obviously not correct. Thus, we have to judge the relationship between \(\mathbf{z}_{k}\) and the sampling cells: if \(\mathbf{z}_{k}\) is outside the convex region formed by the centers of sampling cells, the geometric center of these cell centers will be the \(\mathbf{z}_{k}\) estimation.  
Figure 8: Deployment of the sensor nodes. The red circles are the locations of sensor nodes.  
Figure 7: Data frame and window. The size of each data frame is 30 sampling points, and the size of each window is 15 sampling points. The overlap between two successive data frames is 15 sampling points.
## 2 Related Research
### GAN-Based Data Enhancement  
Generative adversarial networks (GAN) [10] are a new data generation method that mainly utilizes discriminators and generators similar to convolutional neural network models to learn the data distribution of real data. This method is commonly used to generate image data. Figure 1 shows the architecture of GAN, which mainly consists of a discriminator and a generator. The primary role of the discriminator is to distinguish whether the real data are similar to the fake data generated by the generator and feed the judgment result to the generator, thereby optimizing the training of the generator model to generate data. The primary role of the generator is to learn the data distribution of the real data, generate the fake data using the random input noise, and then input the generated fake data into the discriminator for the discrimination process. Therefore, when the discriminator cannot correctly distinguish the generated fake data from the real data, it implies that the generator has learned the data distribution of the original real data. Therefore, the final generated fake data will be very close to the real data distribution.  
[PERSON] et al. [11] were the first group to report using the GAN model to generate network traffic data. They used two GAN variants, WGAN [12] and WGAN-GP [13], to generate network traffic data. Their results demonstrated that GAN techniques could efficiently generate network traffic data. [PERSON] et al. [14] used an autoencoder combined with WGAN  
Figure 1: Generative adversarial network (GAN) architecture.  
and WGAN-GP to generate traffic data for the IoTs. The results of this study revealed that the generated data have very similar data distribution characteristics to those of the real data.  
Although the abovementioned related research used new data generation technologies such as an autoencoder and GAN to expand the overall network intrusion detection dataset, it improved the recognition rate of the comprehensive dataset. However, because of the severe data imbalance problem in the network intrusion dataset, the improvement of the recognition rate for rare attack data is still not significant.
###### Abstract  
Electric power load forecasting is essential for the efficient operation and strategic planning of utilities. Decisions regarding the electric market, power generation, load management, and infrastructure development all rely on accurate load predictions. This work presents a novel methodology for day-ahead load forecasting. The approach employs a long short-term memory neural network (LSTM NN) trained on representative load and meteorological data from the region. Before training, the load dataset is grouped by its statistical seasonality through K-means clustering analysis. Clustering load demand, along with similar-day data management, enables more focused training of the LSTM network on uniform data subsets, enhancing the model's ability to capture temporal patterns and reducing the complexity associated with high variability in demand data. A case study using hourly load demand time-series data provided by the Centro Nacional de Control de Energia (CENACE) is analyzed, and the mean absolute percentage error (MAPE) is calculated, showing lower MAPE than traditional methods. This hybrid approach demonstrates the potential of integrating clustering techniques with neural networks and representative meteorological data from the region to achieve more reliable and accurate regional day-ahead load forecasting.  
day-ahead forecasting; LSTM NN; hourly electrical load forecasting; similar days; clustering; K-means; seasonality +
Footnote †: journal: Applied sciences  
Article
# A Novel Fractional Sine Chaotic Map and Its Application to Image Encryption and Watermarking
## 5 Analysis of Image Encryption and Decryption
### Statistical Analysis
#### 5.1.4 Information Entropy  
The uncertainty of an image or information transmission is described with information entropy. The entropy of an image is calculated using the distribution of pixels in the histogram. The value can be evaluated by  
\[Entropy=-\sum_{\mu}F_{\mu}\log_{2}(F_{\mu}), \tag{15}\]  
where the number of pixels is denoted by \(\mu\) and the probability of a pixel is \(F_{mu}\). An ideal image entropy value is 8 and images with entropy values close to 8 are considered to be random and better encrypted. The values in Tables 6 and 7 illustrate the entropy of the different input images and their respective encrypted image. Entropy values of different encrypted images are very close to 8. Our results of the encrypted image entropy reveal higher levels of ambiguity and an uncertain pixel distribution.
# Recurrent Neural Network-Based Hybrid Localization for Worker Tracking in an Offshore Environment
## 3 Materials and Methods
### Positioning Module of RHLS  
The positioning module of RHLS is composed of ANNs, as shown in Figure 5. The positioning module is divided into an input layer that processes input values, a middle layer composed of artificial neurons, and an output layer that converts the resulting values into metric coordinates through scaling.
## Materials and Methods
### NIR Hyperspectral System and Image Calibration  
Hyperspectral images were collected by spectral camera (Specim, Spectral Imaging Ltd., Oulu, Finland). The camera was equipped with a cryogenically cooled Mercury-Cadmium-Telluride (MCT) detector. Individual images consisted of 288 spectral bands with the range from 921 to 2529 nm at 5.6 nm resolution. The matched Lumo software (Specim, Spectral Imaging Ltd., Oulu, Finland) was for data acquisition and camera parameters setting. Eight images (DC, D\({}_{1}\), D\({}_{2}\), D\({}_{3}\), D\({}_{4}\), D\({}_{5}\), D\({}_{6}\), D\({}_{7}\)) were collected at identical times (the eighth day from the first inoculation, and control group DC samples were prepared at the seventh day) after all inoculations from 1 to 7 days. Each image consisted of the above-mentioned two uncovered Petri dishes. To reduce the influence of illumination and dark current of the charge-coupled-device (CCD) detectors, white and dark references were captured prior to sample images for image correction and calibration. A white reference was acquired based on a standard Teflon white board, and a dark reference was obtained by closing the shutter. The calibrated images were calculated according to Equation (1):\[R=\frac{R_{0}-D}{W-D}, \tag{1}\]  
where \(R\) = calibrated images; \(R_{0}\) = raw images; \(D\) = dark reference; \(W\) = white reference.
## 2 Related Work
In the realm of collaborative distribution mechanisms, while cache optimization and multi-path transmission techniques have improved distribution efficiency, the absence of global joint scheduling frameworks for computational, storage, and communication resources across satellite-terrestrial nodes represents a critical gap. Centralized decision-making paradigms dominate current implementations, introducing scalability limitations and compromising real-time responsiveness in dynamic environments. Collectively, these challenges underscore the need for more sophisticated models that can unify fine-grained demand understanding with adaptive network management and distributed resource orchestration to achieve truly efficient and intelligent remote sensing data dissemination in next-generation integrated networks.
# Optimization of Processing Parameters to Increase Thermal Conductivity of Rice Straw Fiber Film
## 1 Introduction
Response surface methodology (RSM) was employed to optimize the processing variables (basis weight, neutral sizing agent, charcoal addition ratio, wet strength agent, and beating degree of the reaction mixture) that affect dry tensile strength, wet tensile strength, and thermal conductivity.
## 5 TSN Scheduling Problem Formulation
### TSN Scheduling Problem Formulation Assumptions
#### 5.3.8 Assumptions on Traffic Characteristics  
Focusing on the HTS traffic, [119, 120] assumed a maximum size of the frames (e.g., MTU = 1500 bytes), and [41, 73, 74, 87, 118] assumed that every stream has only one frame per cycle. Such assumptions may lead to multiple problems if the talker does not perfectly arrange the sending of the frames. Any gap between frames could result in the last frame always missing the allotted time slot. One way to avoid this issue is to control the sending offset of the frames. Refs. [71, 91] required a zero offset at the talker and [41] assumed a controllable offset. In general, legacy talkers do not support TSN mechanisms (e.g., off-the-shelf sensors). Consequently, this assumption is applicable only to specific use cases where talkers support controlling transmission offsets. Nevertheless, for sporadic traffic, controlling the offset is not possible. Refs. [22, 113] assumed a minimum time gap between two consecutive transmissions of sporadic traffic. This assumption is very specific to the use case (e.g., the emergency red button is sporadic and the button could be pressed at any time). However, it could be justified in some scenarios. For example, in the case of interstream dependency caused by tasks chaining, the task should be given enough time to process an incoming stream before sending its response; [78] assumed that talkers send and receive only at the end of the task. Other assumptions include considering only unicast streams, as assumed in [41, 51, 72, 76, 84, 86, 102]. While this may be true for some specific use cases, it may lead to suboptimal solutions in the presence of multicast streams. Multicast streams sourcing from one talker need to traverse a switch where frames need to be replicated on multiple ports for forwarding along a multicast tree. Casting multicast stream allocation to unicast streams may result in additional resource reservation or inconsistency in network configuration.
## 2 System Model  
### Network Architecture  
This study considers a downlink power allocation model based on a cell-free massive MIMO network. In conventional cellular networks, each base station (BS) independently transmits signals and serves its own user equipment within a fixed coverage area. However, UEs located near the cell edge often experience poor communication quality due to strong inter-cell interference and imbalanced resource allocation. To address these issues, cell-free massive MIMO eliminates the concept of fixed cells and allows multiple distributed access points to cooperatively serve all UEs. Each AP is connected to a CPU by a wired backhaul, enabling centralized coordination and efficient signal processing (Figure 1).  
The network model considered in this study consists of \(M\) APs and \(K\) UEs, with all APs operating under the control of the CPU. Unlike cellular systems, in which each BS serves its own UEs independently, cell-free massive MIMO adopts a cooperative downlink transmission strategy in which multiple APs jointly serve each UE. This coordination enhances signal quality while mitigating interference. Instead of independent transmission, APs follow centrally optimized transmission parameters determined by the CPU, improving the overall SINR and resulting in better system performance. Unlike idealized models that assume a uniform UE distribution, this study considers a more realistic scenario in which UEs are densely clustered in certain regions. In practice, UEs are often concentrated in urban centers, stadiums, or business districts, creating localized traffic hotspots. Within these clusters, the UEs are randomly distributed to reflect the stochastic nature of user mobility and spatial behavior. These nonuniform yet random user distributions significantly impact network load, and should be considered when designing power allocation strategies. The CNN-based model proposed in this paper explicitly accounts for such spatial patterns in order to ensure efficient power utilization under real-world deployment conditions [15].  
In a cell-free massive MIMO network, AP cooperation plays a vital role in minimizing interference and enhancing both SE and EE. Unlike conventional systems where each BS handles power control independently, the CPU in cell-free networks can coordinate global power allocation across all APs. Moreover, UEs benefit from constructive signal combination from multiple APs through joint downlink beamforming, which improves the effective SINR and data rate. To address the challenge of downlink power allocation under this cooperative architecture, a CNN-based model is proposed that jointly optimizes CPU-to-AP and AP-to-UE power distribution. By enabling centralized power control, the proposed method can flexibly distribute power budgets across APs depending on their spatial demand and channel conditions. This joint optimization of CPU-to-AP and AP-to-UE stages enhances power efficiency and overall system performance compared to traditional rule-based schemes such as equal power allocation and max-min power allocation [16].
## 3 Results
### Comparison of Input Variable Combinations Based on RF
#### 3.2.3 RF Performance in RO1 Site While Trained in NE1, NE2, and NE3 Sites  
RF performance was further validated in the RO1 site by training it with all data from the three NE sites (_mtree_ = 500 and _nrty_ = 3). The growing environments, including soil texture and moisture, weather, etc., were significantly different between the RO1 site and the three NE sites. It will further demonstrate whether the RF method trained by three NE sites could maintain the effectiveness when it was applied to other unknown sites. The validation results of four input variable combinations (Table 2) are shown in Figure 7. Although the estimated GPP of all combinations had a good linear relationship  
Figure 6: Scatter plots of A0 and A3 combinations, respectively, for NE1, NE2, and NE3 sites. **(a,b)**: training in NE1 site and testing in NE2 site; **(c,d)**: training in NE1 site and testing in NE3 site; and **(e,f)**: training in NE3 site and testing in NE2 site. The red line is the fitting line between estimated GPP (GPPm) and ground-measured GPP (GPPg), and the dashed line is the 1:1 line.  
with ground-measured GPP (slope = 1.008\(\sim\)1.070 and NSE = 0.709\(\sim\)0.758), they showed a slight overestimation of GPP for the RO1 site (Bias = 0.609\(\sim\)1.464). Ground-measured GPP values of the three NE sites ranged from 0 to 33 gC\(\cdot\)m\({}^{-2}\)-day\({}^{-1}\) while ground-measured GPP values of the RO1 site ranged only between 0 and 22 gC\(\cdot\)m\({}^{-2}\)-day\({}^{-1}\). The maximum GPP value in the NE sites was higher than that in the RO1 site. This might be the reason why the estimated GPP of the RO1 site tended to be overestimated when GPP data from the three NE sites were used for model training. In Figure 7, it can be seen that SLN and NMP have boosted the model precision in terms of NSE, RMSE, and CV. Therefore, the RF method considering SLN and NMP had potential for an unknown site's GPP estimation. However, it was noted that the results of both the A1 and A2 combinations were superior than that of the A3 combination, particularly for the A2 combination. It was speculated that there was redundancy in input data considering the explicit polynomial function between SLN and NMP (Figure 3).
## 3 Results and Discussion
### Atomic Vacancy Effect of Ti\({}_{4}\)N\({}_{3}\) Nanosheet
Moreover, in Table 2 the formation energy \(E_{\text{form}}\)[48] was calculated by the following formula:  
\[E_{\text{form}}=E_{\text{def}}-E_{\text{id}}-\Sigma n_{i}u_{i} \tag{3}\]  
where \(E_{\text{def}}\) and \(E_{\text{id}}\) were the total energy of the defective and \"ideal\" system, \(n_{i}\) is the number of removed or added atoms and \(u_{i}\) is the chemical potential of the corresponding atoms. The positive formation energies imply that the \"ideal\" Ti\({}_{4}\)N\({}_{3}\) system is more stable than the defective systems. A relatively small value in Ti\({}_{4}\)N\({}_{3}\)-Ti system indicates that eliminating a surface Ti atom is easier than losing a subsurface N atom. In Figure 4, the total TDOS in a unit cell and PDOS of the neighbor Ti or N atom for Ti-vacancy and N-vacancy were plotted dramatically in Ti\({}_{4}\)N\({}_{3}\)-Ti and Ti\({}_{4}\)N\({}_{3}\)-N nanosheets. From Figure 4a, a relatively large spin-up peak appeared near the Fermi level in the Ti-vacancy system. The peak was mainly derived from spin-up \(d\)-orbitals of surface Ti neighboring vacancy. The high spin polarizability in Ti\({}_{4}\)N\({}_{3}\) was damaged. The subsurface N atom and the next subsurface Ti atom underwent a little change owing to the missing surface Ti atom. For the N-vacancy system, as shown in Figure 4b, the TDOS near Fermi level was contributed by the surface or the next subsurface Ti. A spin polarizability of more than 50% was still detected. In the two atomic vacancy systems, the middle layer N atom was not polarized visibly. Figure 4: TDOS and PDOS of vacancy defected Ti\({}_{4}\)N\({}_{3}\) nanosheet: (**a**) Ti\({}_{4}\)N\({}_{3}\)-Ti and (**b**)Ti\({}_{4}\)N\({}_{3}\)-N. \(\uparrow\) and \(\downarrow\) indicate the spin-up and spin-down orbitals, respectively. The Fermi level (dashed vertical line) is set to zero. The TDOS is transited to a primitive cell.
# Intelligent Monitoring of Tunnel Fire Smoke Based on Improved YOLOX and Edge Computing
## 4 Model Deployment of Edge Computing Based on Knowledge Distillation
### Knowledge Distillation
Finally, we obtained the parameters and computations for both the standard model and the CKD model, as presented in Table 3.  
Evidently, the compact model generated through CKD has significantly fewer parameters compared to the standard model, with only a minor loss in accuracy. Furthermore, the computational load is merely around 18.5% of the standard model's, greatly improving the feasibility of deploying the model on edge computing devices that have limited computational capabilities and storage space. This advancement creates opportunities for the integration of edge computing and deep learning, making it practical to effectively apply them in the intelligent monitoring of tunnel fire and smoke.
## 4 Experimental Results and Analysis
### Analysis of Model Performance Metrics
The Prophet model has certain methods in dealing with conventional seasonal time series, but it is not adaptable enough in the face of complex changes. The SARIMA model has a relatively good fitting effect, but the requirement for data stability limits its prediction accuracy in some months. Due to their own problems, it is difficult to fully and accurately grasp the data characteristics, and there will be large errors and poor fitting effects in different months, as the models cannot perfectly match the complex changes in the data. In practical applications, it is necessary to comprehensively consider the selection of appropriate models or combine the advantages of multiple models for prediction based on specific data characteristics and application scenarios.