esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Device-to-Device (D2D) communications, which enable direct communication between nearby user devices over the licensed spectrum, have been considered a key technique to improve spectral efficiency and system throughput in cellular networks (CNs). However, the limited spectrum resources cannot be sufficient to support more cellular users (CUs) and D2D users to meet the growth of the traffic data in future wireless networks. Therefore, Long-Term Evolution-Unlicensed (LTE-U) and D2D-Unlicensed (D2D-U) technologies have been proposed to further enhance system capacity by extending the CUs and D2D users on the unlicensed spectrum for communications. In this paper, we consider an LTE network where the CUs and D2D users are allowed to share the unlicensed spectrum with Wi-Fi users. To maximize the sum rate of all users while guaranteeing each user's quality of service (QoS), we jointly consider user access and resource allocation. To tackle the formulated problem, we propose a matching-iteration-based joint user access and resource allocation algorithm. Simulation results show that the proposed algorithm can significantly improve system throughput compared to the other benchmark algorithms.  
D2D-U; user access; resource allocation; matching theory Article
## 4 Discussion, Future Work, and Conclusions  
The measurement system used was designed to reflect and determine the conditions of lamp operation in various atmospheric conditions. It is much more difficult to perform measurements for all conditions, not to mention changing the geometry of the lamp to optimize its parameters. Therefore, the numerical model was developed, and its parameters and the boundary conditions used in the equations were adjusted in such a way that the results were consistent with the measurements. The experimentally verified numerical model was used for further analysis by changing the ambient temperature and wind speed values.  
Based on on situ measurements, it was found that the temperature of the lamp stabilizes after approximately 7 h of operation, which corresponds to the average night-time operation in a moderate climate. However, the numerical model of the lamp reaches the steady state already after 3 h of operations, which was due to the great simplifications of the numerical model and the individual elements of the lamp having not been taken into account. Nevertheless, the steady state of both the computational and physical models agree in terms of the temperature distribution on the lamp surface. Moreover, the performed theoretical analyses allowed determining the heat emitted by the currently produced lamp at high wind speeds (up to 10 m/s) and for high ambient temperatures (up to 44 \({}^{\circ}\)C).  
Figure 8: Results of simulations performed using the numerical model depicting the calculated temperature values in dependence from wind speed (log scale) and ambient temperature (linear scale) values at location point P2 (see Figure 4b).  
Figure 9: Results of simulations performed using the numerical model depicting the calculated temperature values in dependence from wind speed (log scale) along the lamp edge (see Figure 4b) by ambient temperature \(T_{\text{amb}}=24\)\({}^{\circ}\)C.  
As part of future work, it is planned to analyze and change the shape of the heat sink in order to achieve better cooling results for high ambient temperatures and low wind velocities.  
The advantage of the software used in research work enables the integration of various physical phenomena. In addition to the analysis of the interdependencies between air movement and heat emission, it is possible to extend the model in the future to include radiation emission and perform studies on the influence of heat emission on the propagating rays of light.  
Conceptualization, D.W., A.B., and P.W.; methodology, D.W. and P.W.; software, D.W.; validation, D.W., A.B. and P.W.; formal analysis, A.B.; investigation, D.W.; resources, A.B.; writing--original draft preparation, D.W.; writing--review and editing, A.B. and P.W.; visualization, D.W.  
This research received no external funding.  
The authors declare no conflict of interest.
## 3 Materials and Methods
### ECG Data Processing  
For the ECG data, given its lower complexity compared to EEG and the limited data size per individual (140 data points), we omitted the use of DWT, inverse DWT, downsampling, and upsampling techniques. Instead, the data were directly input into the WGAN-GP model, as outlined in Section 3.3, with only an adjustment to the learning rate (set to 0.0001). All other model parameters remained unchanged:  
1. num_epochs = 100
2. batch_size = 100
3. gradient_penalty_coef = 10
4. betas = 0.5, 0.999  
The synthetic data generated via WGAN-GP often exhibit sudden, unnatural spikes or abrupt transitions, contrasting with the consistent, rhythmic patterns typically seen in real ECG data. To address this, a smoothing process with a window size of 10 is applied to the synthetic ECG output, reducing spikes and enhancing its resemblance to the original dataset. This step is specifically required for ECG data, as the periodic, smooth pattern is essential for accurate representation. Conversely, for EEG data, this smoothing step is unnecessary due to the inherent presence of spike-wave patterns in natural EEG signals. Ultimately, smoothing the synthetic ECG data enhances the quality of the generated output, aligning it more closely with real ECG patterns and thereby making it more suitable for subsequent analysis or training applications.
# Application of Improved LightGBM Model in Blood Glucose Prediction
## 3 Experimental Results and Discussion  
### Experimental Results  
After the parameter optimization of the LightGBM model by the Bayesian hyper-parameter optimization algorithm, the parameters of the LightGBM model obtained were set as follows: the learning_rate was 0.052 and the number of basic learners n_estimators was 376. The minimum row tree that the leaf may have, the tree min_data_in_leaf, is 18. The ratio of data used in each iteration bagging_fraction was 0.9, and the ratio of randomly selected features in each iteration feature_fraction was 0.5. This set of optimal parameters of the LightGBM models were input into the LightGBM model to predict the blood glucose value. The experimental results are shown in Table 3. The scatter diagram of the actual blood glucose values and the predicted blood glucose values is shown in Figure 10.  
It can be found from Table 3 that the HY_LightGBM model had a mean square error of 0.5961, a root mean square error of 0.7721, a determination coefficient of 0.2236, and a training time of 26.7938 s in the prediction of the blood glucose values.
## 3 Methodology
### UTM Monitoring Application Setup  
The UTM monitoring application based on the network Remote ID was developed in Python language programming version 3.7 with the Bokeh module version 2.4.3 for visualization. The inverted teardrop detection algorithm was implemented in 3D space as explained in the previous section. The UTM application can be viewed in a web browser with the user interface as shown in Figure 6. The most challenging process in the UI design of our UTM monitoring is how to draw the inverted teardrop shape because there is no built-in teardrop shape in the Bokeh module used. However, the best option found was using multiple circles with different radii arranged in the direction of the UAV flight to form an inverted teardrop shape. Each UAV was surrounded by a teardrop shape as the detection volume and a circle as the near-collision volume. A teardrop shape in orange indicates a conflict was detected, while green indicates no detection. For the near-collision shape, red indicates a near-collision event, while yellow indicates no near-collision event.  
The UTM monitoring application was installed on a laptop computer with a core i3 Intel processor and Python programming language version 3.7. The computer was connected to the internet via a WiFi connection from a smartphone that used the FarEasTone 4G cellular network.
## 1 Introduction
### Home Area Network  
HAN is applicable for home automation. It is used for the consumer domain and consists of electronics appliances and wireless sensor networks [8; 9; 10; 11]. These consumer electronics appliances communicate their energy consumption statistics to central home monitor and regulator or smart meter. Central regulator or smart meter sends it to the central electricity grid for monitoring, control, fault detection and billing purposes. Smart meters and intelligent electronic devices receive the commands from central power grid and they control the home appliances based on the received commands. Home Area Networks (HANs) have the coverage area of few meters. IEEE 802.15.1 (Bluetooth), IEEE 802.15.4 (Zigbee), IEEE 802.11 (WLAN/Wi-Fi), IEEE 802.3 (Ethernet), Narrowband PLC (Power line communication), etc. standards can be used for Home area networks [10; 11; 12; 13; 14; 15; 16].
# Quantification of the Effective Detectable Period for Concrete Voids of CLP by Lock-In Thermography
## 3 FEM Modeling and LIT Simulation
### FEM Model  
The commercial software SpaceClaim 2022 R1 and ANSYS Workbench 2022 R1 were used to generate the FEM model and perform thermal analysis to be used for the LIT simulation, and the designed shape in Figure 2 was applied. Figure 3a is a 3D model created by SpaceClaim, and Figure 3b is an image of the finite element mesh shape created by ANSYS viewed from the front of the CLP. The element size of the model was set from a minimum of 2.5 mm to a maximum of 5 mm, and the grid of the CLP surface to perform the LIT simulation was fixed to 2.5 mm in both width and length and had three elements in the thickness direction.  
Figure 2: CLP mockup design with concrete voids (units: mm).  
As a result of model meshing, 827,054 nodes and 177,660 elements were formed, and the CLP surface had a total of 25,921 nodes in a square lattice arrangement of 161 \(\times\) 161. In addition, #1 and #3 void surfaces have 31 \(\times\) 31 nodes, and #2 and #4 void surfaces have 43 \(\times\) 43 nodes. Table 1 shows the size and material properties of each component.
###### Abstract  
Phosphofructokinase (PFK) plays a pivotal role in glycolysis. By deletion of the genes _p/RA_, _p/fkB_ (encoding the two PFK isoenzymes), and _zwf_ (glucose 6-phosphate dehydrogenase) in _Escherichia coli_ K-12, a mutant strain (GL3) with a complete block in glucose catabolism was created. Introduction of plasmid-borne copies of the _fsaA_ wild type gene (encoding _E. coli_ fructose 6-phosphate aldolase, FSAA) did not allow a bypass by splitting fructose 6-phosphate (F6P) into dihydroxyacetone (DHA) and glyceraldehyde 3-phosphate (G3P). Although FSAA enzyme activity was detected, growth on glucose was not reestablished. A mutant allele encoding for FSAA with an amino acid exchange (Ala129 Ser) which showed increased catalytic efficiency for F6P, allowed growth on glucose with a \(\upmu\) of about 0.12 h\({}^{-1}\). A GL3 derivative with a chromosomally integrated copy of _fsaA\({}^{\text{A129S}}\)_ (GL4) grew with 0.05 h\({}^{-1}\) on glucose. A mutant strain from GL4 where _dhaKLM_ genes were deleted (GL5) excreted DHA. By deletion of the gene _glpK_ (glycerol kinase) and overexpression of _gldA_ (of glycerol dehydrogenase), a strain (GL7) was created which showed glycerol formation (21.8 mM; yield approximately 70% of the theoretically maximal value) as main end product when grown on glucose. A new-to-nature pathway from glucose to glycerol was created.  
**Keywords:**_Escherichia coli_; phosphofructokinase; _p/fAB zwf_ triple deletion; fructose 6-phosphate aldolase; point mutation; new-to-nature pathway; metabolic bypass; dihydroxyacetone; glycerol dehydrogenase; glycerol formation
# A Petri Net Approach for Business Process Modeling and Simulation
###### Abstract
Its explicit graphical representation allows the visualization of the dynamic behavior of the results in the process simulation. Generalized Networks (GN) are a tool for describing adaptive, flexible, and structured business models of complex systems with many different and interacting components that are not necessarily homogeneous and may have parallel and simultaneous arrangements. Generalized nets represent a significant extension and generalization of the concept of Petri nets. While PNs and GNs both contain places, transitions, and tokens, the positions are of a more complex structure in GNs. The input and output in the transitions of the GN contain the moment of activation. When entering a new place, the token changes its characteristics. This constitutes a very important difference, as the movement of the token represents the change of state of the network; thus, GNs allow more complex systems to be modeled [10]. Regarding the theory of Service Systems, in which a service can be considered as the provision of assistance and experience through a supplier-client interaction to create and capture value in business, in terms of resources, services can be understood as a series of activities in which resources of various kinds (employees, physical resources, goods, service provider systems) are used in interaction with the customer to find a solution to a problem or need. From this perspective, a service system is not simply the sum of its parts; rather, the interactions form a higher-order construct. The service can be understood as the results of value co-creation under a win-win logic within interrelated processes. One approach to the results of process mining has revealed that it is possible to obtain a system of transitions in which these are comparable to the activities of the processes. Essentially, to derive a process in transitions means to obtain a PN. In this context, a well-known area of PN theory is called Petri net synthesis [11]. Among the advantages of using Petri nets as a business process modeling tool are the graphic way in which they are handled (i.e., squares, transitions, arcs), which makesthem a very light tool, the high precision of the mathematical syntax, and the possibility of simulating states due to the implicit handling of tokens. This article begins with an introduction to the conceptual framework of business process modeling highlighting the advantages of utilizing PNs for modeling purposes. The second section provides an overview of the current state of the field and describes the fundamental form of PNs. In the third section, a case study is presented, specifically focusing on the workflows of a Water Operating Agency and employing elementary PN structures to model them. Finally, the article concludes by summarizing the findings and discussing how business process modeling with PNs can enhance the competitiveness of organizations.
# Dynamic Performance Optimization of Circular Sawing Machine Gearbox
## 2 Theoretical Basis
### Multi-Objective Optimization Based on the Response Surface  
The response surface method (RSM) is a response surface model that uses experimental design theory to test a set of designated design points to obtain the objective function and constraint function. It is widely used in complex mechanical structure optimization problems, such as the transmission gearbox. RSM is derived from the design of the experiment and the response surface assessment, which has the advantage of low resource utilization. Based on the complexity of the structure of the saw machine gearbox response surface optimization is an optimization method of the multi-objective genetic algorithm (MOGA) based on the Kriging model. The Kriging model is based on sample points and response values, and uses the method of fitting response surface to predict the response values of non-sample points [37; 38; 39; 40].  
In this report, the Kriging model was used to fit the response surface function, and the functional relationship between the response value and independent variable [41; 42] can be described as follows:  
\[\left.\begin{array}{l}\hat{\gamma}(x)=F(\beta,x)+Z(x)\\ F(\beta,x)=f(x)^{T}\\ \beta=[\beta_{1},\beta_{2},\cdots,\beta_{p}]^{T}\\ f(x)=[f_{1}(x),f_{2}(x),\cdots,f_{p}(x)]^{T}\end{array}\right\}, \tag{13}\]where \(x\) is the m-dimensional input variable and \(y(x)\) is the l-dimensional output response. The value \(F(\beta\),\(x)\) is the regression model; \(\beta\) is the coefficient of the regression model; \(f(x)\) is the \(p\)-order linear polynomial regression model set; and \(Z(x)\) is a random process. In the process of optimum design of the gearbox, the dimension variables calculated based on the sensitivity are taken as sample points, and the modals of each order are taken as the response values.  
MOGA is often used to solve multi-objective optimization problems, and ultimately, a set of optimal solutions is obtained. Considering the insufficient stiffness of the gearbox and the requirement of lightweight design, the mathematical model can be expressed as follows:  
\[\left.\begin{array}{l}\min(y_{1}(\mathbf{x}),y_{2}(\mathbf{x}),\cdots y_{ n}(\mathbf{x}))\\ -m(\mathbf{x})\geq-m_{0}(\mathbf{x})\\ \mathbf{x_{L}}<\mathbf{x}<\mathbf{x_{U}}\end{array}\right\}. \tag{14}\]  
In Equation (14), \(y_{1}(\mathbf{x})\), \(y_{2}(\mathbf{x})\), and \(y_{3}(\mathbf{x})\) are the first, the second, and \(n\)-modes of the gearbox, respectively; \(m(\mathbf{x})\) is the total mass of the gearbox; \(m_{0}(\mathbf{x})\) is the upper limit value of the gearbox; \(\mathbf{x_{U}}\) and \(\mathbf{x_{L}}\) are the upper and lower limits of the design variable vectors, respectively; and \(\mathbf{x}\) is the design variable vector (decision vector). By combining the Kriging model with the MOGA algorithm, the multi-objective function can be solved, and the response surface model of the design variables and the objective function of the gearbox can be determined.
# Elderly Fall Detection in Complex Environment Based on Improved YOLOv5s and LSTM
## 2 Related Works
### Fall Detection Based on Video  
The work in [9] employed a camera, sensors, positioning module, heart rate module, and narrowband Internet of Things (NB-IoT) module to collect and transmit information, achieving outdoor and indoor fall detection of elderly people. Threshold detection and support vector machine (SVM) classification were used to improve the fall detection accuracy. The monitoring app was used to query status and location information on the elderly, and this method can effectively detect elderly people's falling behavior and send alarm information and positioning information to the guardian, realizing the intelligent care of the elderly.  
The authors of [10] designed an innovative architecture using consumer webcams to detect typical abnormal behaviors in the elderly, such as falling, attacking, wandering, etc. A deep learning model was designed to detect and classify abnormal behaviors by robust bone joint extraction with consideration of the spatio-temporal background. Abnormal images were taken with a web camera fixed to the typical activity points of the elderly, which were then fed to a local server, equipped with a Graphic Processing Unit (GPU), for online monitoring and alarm. In addition, a camera-based human posture analysis method using an Intel RealSense Depth Camera D435 was introduced in [11]. This method integrated the OpenCV, MediaPipe, and Numpy Python libraries to analyze human posture in three-dimensional space. This depth-based analysis included calculating the depth of the pose sign, estimating the body's center of mass depth, and tracking the speed of motion.  
With the development of target detection algorithms, which show fast processing speeds, some researchers have recently investigated human posture detection based on YOLO [12], achieving some impressive results. However, the YOLO model's performance is not perfect enough in fall detection applications, so improvements to the model's detection accuracy are necessary. For example, the multi-target detection layer of YOLOv5s was improved to make the model more accurate on small-sized datasets. Further, the loss function can be replaced or improved to obtain faster and higher-quality detection results [13]. Alternatively, the performance of YOLOv5 can be improved by changing some of the modules [14], such as by adding attention models [15], among others [16].  
The video-based fall detection method has high precision and strong anti-interference ability, but it often makes mistakes in the case of target occlusion. However, the passive monitoring system based on wireless signals solves this problem well, and human body pose estimation based on the WiFi channel state has become an application of human body pose estimation algorithms.
# A Multimodal Framework for Advanced Cybersecurity Threat Detection Using GAN-Driven Data Synthesis
## 4 Experimental Analysis and Results
### Evaluation of GAN Models for Data Synthesis
#### 4.3.2 Evaluation of ZDGAN for Botnet Data Synthesis  
Following the logic of Section 4.3.2, to evaluate ZDGAN's applicability beyond zero-day intrusion synthesis, the model was applied to a separate dataset focused on botnet attack traffic. The goal was to determine whether the generator could generalize effectively across different threat categories. The same architecture, training configuration, and evaluation pipeline were reused, and only the dataset and class schema were modified.  
As shown in Table 12, the generated samples retained strong alignment with the original botnet traffic data. A mean cosine similarity of 80.3% indicated solid feature-level consistency, while the average log-mean deviation was calculated at 9.3%, suggesting only minor dispersion across synthetic instances. The Jensen-Shannon divergence remained relatively low at 11.7%, reinforcing the statistical proximity between real and synthetic distributions. The diversity index of 74% confirmed that the model did not collapse into repetitive patterns, and a t-SNE overlap score of 85% demonstrated that synthetic clusters were meaningfully embedded within the real feature space.  
Figure 4: Log Mean Deviation per Feature for Zero-Day Data.  
Figure 3: PCA Visualization Plot for Zero-Day Data.
## 1 Introduction
Using a mixed inventory (mainly slides, debris flows and falls) triggered by a 2013 earthquake in Lushan, China, [PERSON] et al. [6] tested the effect of 2.5, 5, 10, 20, 40 and 80 m resolutions in statistical landslide susceptibility evaluation, concluding that \"the results show that the satisfactory result could be achieved with high-resolution DTMs but the accuracy of landslide prediction becomes increasingly worse with coarser resolutions\". By contrast, for shallow landslides, [PERSON] et al. [7] tested very coarse resolutions (30, 300, 1000, 2000 and 3000 m), but better performances were obtained from a very low resolution (2000 m). In this research, a robust inventory including three different types of landslides was exploited to explore the effect of changing the cell size of mapping units on the inner structure of the three derived susceptibility models (i.e., the involved dominant controlling factors) and on their predictive performance (accuracy and reliability). In fact, this analysis of the effects of cell sizing on the model accuracy using the same source data and different slope failure types offered a key to the interpretation of any predictive performance changes, also in light of the geomorphological suitability of the related factor selection. Other types of landslides were excluded from the analysis (such as falls, topples and deep-seated landslides) due to the fact that, generally, a direct and/or physical-based approach is employed for their susceptibility evaluation (e.g., [8; 9; 10; 11; 12; 13]). LS modelling was performed by applying Multiple Adaptive Regression Splines statistical modelling to regress the three-type landslide inventory and a set of controlling factors by using five (5, 8, 10, 16, and 32 m) mapping units.