esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction  
Increased pressure within the society to use hydrogen technologies in the transport segment necessitates the search for solutions to partial problems regarding hydrogen production, as well as reduction of the energy consumption in the hydrogen production process. Along with these requirements, it is also necessary to solve hydrogen purification from undesired impurities [1] and the transformation of the chemical energy of the gas into electrical energy, for example, in fuel elements [2]. An important part of hydrogen technologies is also the efficient storage thereof at acceptable pressures [3].  
For the purpose of hydrogen storage in the form of metal hydrides, the absorption pressure is decreased below 1 MPa. Compression of pure hydrogen or a hydrogen-containing mixture is carried out using positive-displacement compressors or centrifugal compressors.  
Compressor failures frequently occur in the real operation of compressors. Such failures are usually caused by insufficient cooling or by a fabrication-related fault of any of the compressor's components. For several reasons, it is necessary to know the limits of a particular compressor in terms of its cooling capacity. The first reason is the manufacturer's reputation with regard to marketing high-quality products that meet all of the operator's requirements. The second reason is the financial loss that is incurred by the manufacturer if the device is faulty.  
The diagnostics of these machines and their parts is carried out using various apparatuses and technology. Currently, non-destructive measuring techniques are predominantly used; they are based on ultrasound waves [4] and vibrations [5; 6]. Compressors are usually subjected to simplified testswith a shorter duration [7]. The examination of flow conditions, heat transfer, and the effect thereof on a compressor's efficiency is carried out while applying, to a large extent, numerical methods that are based on the Finite Element Method or the Finite Volume Method [8; 9; 10]. Attention is also paid to oil retention in the compressor pipes, depending on the thickness of the oil film on the compressor surface [11]. The methods applied when dealing with non-equilibrium compressor states and while using the equation of the Lagrangian velocity-gradient correlation are discussed in [12]. With regard to the stable compressor operation, the issues regarding the hydrogen embrittlement on the surface of individual steel parts that is caused by hydrogenation are very important [13].  
Following chapters describe the compression of a mixture of H\({}_{2}\) and N\({}_{2}\) by a two-stage compressor with the aim to quantify real heat transfer necessity for maintaining the stable operation of the device.
# Coordinated Multi-UAV Reconnaissance Scheme for Multiple Targets
###### Abstract  
This study addresses dynamic task allocation challenges in coordinated surveillance involving multiple unmanned aerial vehicles (UAVs). A significant concern is the increased UAV flight distance resulting from the assignment of new missions, leading to decreased reconnaissance efficiency. To tackle this issue, we introduce a collaborative multi-target and multi-UAV reconnaissance scheme. Initially, the multitasking constrained multi-objective optimization framework (MTCOM) is employed to optimize task allocation and reconnaissance time in static scenarios. Subsequently, in case of emergency, we iteratively refine the outcomes of static task allocation through an enhanced auction-based distributed algorithm, effectively reducing UAV flight costs in response to new missions, UAV withdrawal, or damage. Simulation results demonstrate the efficacy of our proposed multi-UAV and multi-target cooperative reconnaissance scheme in resolving dynamic task allocation issues. Additionally, our approach achieves a 5.4% reduction in UAV flight distance compared to traditional allocation methods. The main contribution of this paper is to consider a dynamic scenario model involving UAV damage and the emergence of new reconnaissance areas. Then we propose an innovative collaborative multi-target and multi-UAV reconnaissance scheme to address this issue and, finally, conduct experimental simulations to verify the effectiveness of the algorithm.  
dynamic task allocation; distributed algorithm; collaborative reconnaissance
## 4 Methodology
### Background: Reinforcement Learning
#### 4.1.1 Model Selection  
Model selection is a critical preliminary step before building and tuning a model's hyperparameters. RL algorithms can generally be classified into value-based, policy-based, or hybrid methods, such as actor-critic algorithms [27]. The key distinction among these categories lies in whether the algorithm learns a state-action value function or a stochastic policy, each with varying degrees of modeling flexibility. Actor-critic methods combine the strengths of both value- and policy-based approaches. An overview of various model-free RL algorithms is presented in Figure 4 for the reader's reference. As illustrated in Figure 4, RL algorithms can be categorized into value-based, policy-based, and actor-critic methods. The Maximum a Posteriori Policy Optimization (MPO) algorithm, an actor-critic method, was selected over alternative actor-critic algorithms for the following reasons [28]:  
1. Stable EM-Style Updates: MPO employs a two-step update procedure (E-step and M-step), reminiscent of the Expectation-Maximization algorithm. This structure provides smooth and stable policy updates, which is especially advantageous in both low- and high-dimensional action spaces by mitigating the risk of destructive gradient updates.
2. KL-Constrained and Off-Policy Learning: MPO enforces a Kullback-Leibler (KL) divergence constraint to limit how far the updated policy can deviate from the current one. This constraint, combined with off-policy learning via experience replay, enables efficient learning in complex environments without requiring new data at each iteration.
3. Strong Performance in Continuous Control Tasks: Empirical studies have shown that MPO performs well in continuous and high-dimensional tasks such as robotics and simulated control environments. Its robust theoretical foundation and modular architecture also allow it to generalize effectively to simpler problems.  
Figure 4: Taxonomy of RL algorithms, categorized into model-free and model-based methods [27]. Common algorithm names are abbreviated.
## 3 Implementation of the System
### Client Configurations and Functionalities
Furthermore, it is important to evaluate the performance of the newly trained local model using a local test data set and send the obtained metrics to the server. Finally, the client should transmit the parameters of the trained model back to the server. Figure 3, provides a detailed experimental setup used during the implementation of FL with the help of Raspberry Pi nodes, a virtual machine, and an FL server installed on a Windows PC. Figure 3: Experimental Setup Used During the FL Implementation. Figure 2: Cooling Mechanism for Raspberry Pi Devices.
# WTA: A Static Taint Analysis Framework for PHP Webshell
In recent decades, the role of taint analysis [17] in program analysis has attracted extensive attention from researchers. Static taint propagation analysis, also called static taint analysis [18], is the analysis of the data dependencies between variables to test whether data can be propagated from the taint source to a point of the taint sink without running or modifying them. The object of static taint analysis is generally the source code or intermediate representation of the program. RIPS [19] uses taint analysis to analyze PHP codes in a static way, which is based on AST derived from syntax analysis. [PERSON] et al. [20] proposed a detection platform named Shellbreaker. They extracted eight new source codes and AST syntactic and semantic features. Two of the features are _explicit data flow_ and _implicit data flow_, and they are extracted by taint analysis. Then, the eight features are fused into a vector. Finally, a statistical classifier is used to analyze the feature vector. However, this method has a limited detection effect on one-sentence webshells [21], because several types of features extracted by this method are aimed at self-adaptive webshells. The performance of the dynamic feature detection method is poor, and the construction of the environment is complex. Traditional static feature detection methods have difficulties in detecting unknown webshells and lack the capability to perform interprocedural analysis. In addition, there has been some research that has used taint analysis for webshell detection with limited effect. To address the above challenges, this paper proposes a webshell detection method based on static taint analysis. The main contributions of this paper are as follows:  
1. We applied the ZendVM instruction set to the field of taint analysis for the first time and defined the taint propagation rules and taint sink rules of the instruction set;
2. We proposed a novel static detection method based on taint analysis for PHP webshells. The method can carry out interprocedural analysis and detect more unknown webshells;
3. We implemented a taint analysis prototype system named WTA for webshell detection and evaluated the effectiveness of our method by comparing it with existing tools through a benchmark dataset consisting of ten webshell datasets and six CMSs. The remainder of this paper is organized as follows. Section 2 describes the background information on PHP. Section 3 introduces the include-type webshell, the user-defined function-type webshell, and the unknown webshell, which bring challenges to webshell detection. The overview of the proposed approach is described in Section 4. Section 5 describes the details of the three key steps in our method. Section 6 evaluates our method. We summarize the related work in Section 7 and provide our conclusions in Section 8.
# Lightweight Insulator and Defect Detection Method Based on Improved YOLOv8
## 3 Improved YOLOv8 Lightweight Object Detection Network
### Lightweight YOLOv8s Enhancement  
The basic YOLOv8s consists of three main components, namely, Backbone, Neck, and Head. Each component plays a critical role in the overall functionality of YOLOv8s, contributing to its efficacy in object detection and classification tasks.  
The Backbone is mainly used for feature extraction, which consists of a convolutional neural network with pre-training parameters and extracts feature maps at different levels through multi-layer convolution and pooling operations on the input image, which contains rich information of the image and is the basis for subsequent processing.  
The main function of the Neck part is to fuse the multi-layer features extracted by the backbone part. Through feature fusion, the Neck part can retain and enhance the key information of the image, which makes the subsequent target detection and classification more accurate. The Neck part usually adopts structures such as Feature Pyramid Network (FPN) [37] or Path Aggregation Network (PANet) [38], in order to realize multi-scale feature fusion.  
The Head part is responsible for using the fused features obtained from the Neck part for predicting the types and locations of the targets in the Bounding Box. It performs the target detection task by generating the class probability and location information of each detection frame through a series of convolutional and fully connected layers.  
In optimizing YOLOv8s network, this paper introduces the BNeck module part of MobileNetv3 to replace the convolutional and C3 modules in the original backbone part. The network structure after the optimization is shown in Figure 4. The red line in the figure represents the multi-scale fusion operation performed, which further improves the network's ability to detect small targets by fusing features at different scales. This multi-scale feature fusion combines feature information from different scales so that the network can maintain high accuracy in detecting targets of different sizes; particularly, the detection of small targets is significantly improved.
# Enhancing Target Speech Based on Nonlinear Soft Masking Using a Single Acoustic Vector Sensor
## 4 Experiments and Results
### Experiments on Recorded Data in an Anechoic Chamber
#### 4.2.1 Experiment 4: The Performance versus Angle Difference with Recorded Data  
In this experiment, the performance of our proposed method has been evaluated versus the angle difference between the target and interference speakers (\(\Delta\phi=\phi_{s}-\phi_{i}\) and \(\Delta\theta=\theta_{s}-\theta_{i}\)). The experimental settings are as follows. (1) PESQ is taken as the performance measurement metric. (2) The parameters of algorithms are set as the same as that of _Experiment 1_. (3) Considering page limitation, here, we only consider the changing of azimuth angle \(\phi_{s}\) while \(\theta_{s}\) = 90\({}^{\circ}\). The interfering speaker \(s_{1}(t)\) is at (\(\theta_{1}\),\(\phi_{1}\)) = (90\({}^{\circ}\),45\({}^{\circ}\)). \(\phi_{s}\) varies from 0\({}^{\circ}\) to 180\({}^{\circ}\) with 20\({}^{\circ}\) increments. Then, there are 13 recorded datasets. The experimental results are shown in Figure 10. It is noted that the \(x\)-axis represents the azimuth angle \(\phi_{s}\). It is clear to see that the overall performance of our proposed AVS-SMASK algorithm is superior to that of the comparing algorithm. Specifically, when \(\phi_{s}\) approaches \(\phi_{1}\) = 45\({}^{\circ}\), the PESQ degrades quickly for both algorithms. When the angle difference \(\Delta\phi\) is larger than 30\({}^{\circ}\) (\(\phi_{s}\) is smaller than 15\({}^{\circ}\) or larger than 75\({}^{\circ}\)), the PESQ of our proposed AVS-SMASK algorithm goes up quickly, and is not sensitive to the angle difference.  
Figure 10: (Experiment 4) The performance versus \(\phi_{s}\). (**a**) PESQ results and (**b**) LSD results.  
Figure 9: (Experiment 3, Case 2) The performance versus the \(\partial\theta_{s}\). (**a**) SINR results and (**b**) LSD results (Case 2).
## 2 Materials and Methods
### Methods
#### 2.2.2 Selection of Independent Variables Based on Geographical Detector Model  
The geographical detector model proposed by [PERSON] et al. [44] is a widely used geospatial model for spatial stratified heterogeneity analysis, since this method with no linear hypothesis has an elegant form and a definite physical meaning [45]. A factor detector, one of the sub-detectors in used in geographical detector models, was used in our study. First, 3000 sample points were randomly selected in the study area. Second, the population density of sample points was chosen as the dependent variable of the factor detector, while the 24 influence factors' values of sample points were chosen as independent variables. Next, the q-statistic values of the 24 influence factors, which denoted the power of determinant, were calculated using the factor detector tool in GeoDetector. Finally, all the influencing factors with q-statistical values greater than 0.1 and \(p\) values significant at the level of 0.05 were selected as independent variables for the subsequent regression analysis.  
2.3 Spatial Decomposition of Demographic Data Using Different Regression Algorithms Machine Learning Training Method  
The values of nineteen independent variables for each subdistrict were obtained by using the zonal statistics function of ArcGIS 10.8 software. The independent variables values were rescaled to the range of [0,1] using the formula of min.-max. normalization. The population density of each subdistrict, which was calculated using demographic data, was taken as a dependent variable for the regression analysis. The open-source framework for the implementation of the regression model used in our study was scikit-learn 0.24.0, which is a well-known free machine learning software library for the Python programming language [46]. In terms of machine learning, the data from all the subdistricts were used as a training set and a grid-scale of the data from all the grid units was used as a test set during the process of spatial decomposition. The amount of training sets was 106 and the amount of test sets was 46,569. First, twelve regression models were trained to search the best model parameters by using the GridSearchCV method of regression. The neg_mean_squared_error, which denotes the negative value of the mean square error, was chosen as the cost function of the GridSearchCV method. Ten-fold cross validation (CV) was used to improve the model accuracy and avoid overfitting [47,48].
# The Rise of Quantum Information and Communication Technologies
## 4 Next Steps
### Quantum Hardware Abstraction Layer (Quantum-HAL)  
In the long term, the evolution of QKDN towards the quantum internet will intersect with quantum computing technologies aiming at the functional integration of quantum computing and networking. One major obstacle towards these evolutionary developments and exploitations is that, today, industry choices have not yet consolidated one type of quantum hardware.  
In order to mitigate this, a quantum hardware abstraction layer (Quantum-HAL) would allow application and service developers to use the abstractions of the quantum hardware underneath (even if this belongs to different technologies); this would accelerate the development of quantum platforms, services, and applications.  
In fact, a Quantum-HAL would provide northbound quantum application programming interfaces for the higher application layers, decoupling from the different types of quantum hardware technologies for quantum computing and networking. This is for further study.
## 2 Algorithm
### Acquisition of Initial Coordinates by DPL  
Thanks to the large number of GNSS satellites, excellent quality of signals, and stable and strictly synchronized clocks, it is easy to obtain the initial position using SPP. However, due to the constellation of pseudolites, multipath effect, noise, and the instability of the clocks, it is difficult to obtain the initial information by SPP in indoor positioning. As mentioned in the previous section, most of the research institutes prefer to use KPI to achieve high-precision positioning by the DD carrier phase model. However, it is difficult to obtain a fixed point in an engineering application, and it is not convenient to operate in the dynamic measurement. Therefore, we are going to eliminate the limitation of the KPI.  
The DD pseudorange positioning model is used to eliminate or weaken the clock difference and other errors. Gauss-Newton iteration is employed for non-linear LSE (least square estimation) and the measurement equations can be written by a general non-linear vector function as [25]:  
\[y=h(x)+v \tag{2}\]  
where \(h(x)\) is a measurement vector function of a parameters vector \(x\). The equation can be extended by using Taylor series around an initial vector \(\mathbf{x_{0}}\) as:  
\[h(x)=h(x_{0})+B(x-x_{0})+\ldots \tag{3}\]  
where \(B\) is a partial derivatives matrix of \(h(x)\) with respect to \(x\) at \(x=x_{0}\):  
\[B=\left.\frac{\partial h(x)}{\partial x}\right|_{x=x_{0}} \tag{4}\]  
If the initial parameters are adequately near the true values and the second and further terms of the Taylor series can be ignored. The equation follows:  
\[y-h(x_{0})=B(x-x_{0})+v \tag{5}\]  
By applying linear weighted LSE for Equation (5), the normal equation for non-linear LSE can be obtained:  
\[B^{T}PB(\hat{x}-x_{0})=B^{T}P(y-h(x_{0})) \tag{6}\]  
The estimated unknown parameter vector can be obtained by:  
\[\hat{x}=x_{0}+\left(B^{T}PB\right)^{-1}B^{T}P(y-h(x_{0})) \tag{7}\]Due to the initial parameters \(\mathbf{x_{0}}\) (default initial value is (0, 0)) are not near the true values, the estimated parameters can be iteratively improved like:  
\[\begin{array}{l}\overset{\wedge}{x_{0}}=x_{0}\\ \overset{\wedge}{x_{i+1}}=\overset{\wedge}{x_{i}}+(B^{T}PB)^{-1}B^{T}P(y-h( \overset{\wedge}{x_{i}}))\end{array} \tag{8}\]  
If the iteration is converged, the final estimated parameters can be obtained as: \(\overset{\wedge}{x}=\underset{i\rightarrow\infty}{\lim}\overset{\wedge}{x_ {i}}\).  
The iterated LSE is often called the Gauss-Newton method [25]. Note that such iterations are not always converged by the simple Gauss-Newton method [25]. In order to improve the correctness and efficiency of the estimation, the estimated parameters of previous epoch is passed to the later epoch as initial values in our pseudolite positioning system. Then continue to calculate until the difference of coordinates between the epochs is minimal. The initial approximate coordinates can reach the decimeter level through the above steps.
## 1 Introduction
Real-time healthcare services have been boosted by using IoT technology and assisted in the development of communication protocols such as CoAP [19], MQTT [20], next-generation IPv6 version [21] and AMQP [22] of inter-network. The IoT has enhanced the home-centric environment by extending healthcare services. IoT sensors help in expanding the acquisition of health data, and various technologies such as data mining results in automatic analysis of health conditions without the presence of a concerned doctor. Furthermore, the large use of fog and cloud computing results in providing enhanced services in healthcare applications, which includes time-sensitive data management and analysis. In the modern world, improved IoT and fog-cloud computing [23] serves many applications in real-time healthcare. The Firefly Algorithm (FA) [24] has increased importance in swarm intelligence [24] and is used in the area of both optimization and engineering. FA and its variants are used to solve various problems from different areas. The ideal behaviour of FA is its flashing feature [25], which helps in efficient feature selection. Though the basic Firefly Algorithms are comparatively efficient, the outcomes have been changed when it approaches the optima. The success rate of FA depends on the balance that exists between exploitation and exploration. CGFA (Chaotic Gaussian Firefly Algorithm) possesses improved numerical stability and an enhanced convergence rate [26]. The present study has used MFB-FA (Modified Flashing Behaviour-based Firefly Algorithm) for feature selection by utilizing IoT in a fog environment. Gaussian Chaotic Maps (GCMs) in FA have been used to reveal the optimal fit that helps in exploring chaotic maps. Gauss Map (GM) avoids the trapping of local optima by moving towards the optimal solution and possesses better optimization ability in comparison to swarm intelligent approaches. Hence, the proposed method utilizes GM for attaining improved optimization ability. The present study has used MANN-AM (Modified ANN with Attention Mechanism) for classification. ANN possesses more advantages such as parallel processing, the ability to perform with partial knowledge, storing data within the network and possessing memory distribution and fault tolerance. Similarly, the attention mechanism imitates cognitive attention. MANN-AM performs so that the network has to devote high focus to low but significant data parts. The present study aims to detect UTI by using MFB-FA and MANN-AM based on the following objectives:  
* To perform feature selection by using MFB-FA (Modified Flashing Behaviour-based Firefly Algorithm) for improvising classification. * To perform classification by using MANN-AM (Modified Artificial Neural Network with an Attention Mechanism) to efficiently prognosticate the existence/absence of UTI. * To evaluate the efficacy of the proposed model with standard-performance metrics for exposing its performance in predicting UTI.