esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Proposed Method
### Codeword Embedding Layer  
The input of frame-wise tasks for low-bit-rate speech streams is made up of numerous codewords. The values of these codewords are discrete. Such discrete codewords are encoded as one-hot vectors, resulting in feature spaces with unreasonably high dimensionality for large vocabularies. The embedding layer [38] is used to solve this problem. These codewords are transformed into codeword vectors by the embedding layer, allowing the DNN to learn more complex codeword features and semantic codeword properties.  
The transformed embedding vector \(e_{i}\) for the \(i\)-th codeword \(x_{i}\) is obtained via  
\[\mathbf{e}_{i}=\mathbf{W}_{i}\mathrm{F}_{\mathrm{one-hot}}(x_{i}) \tag{7}\]  
where \(\mathbf{W}_{i}\) is the embedding matrix for the \(i\)-th codeword \(x_{i}\) and \(\mathrm{F}_{\mathrm{one-hot}}\) denotes the one-hot representation. The dimension of \(\mathbf{W}_{i}\) is \((f,n)\), where \(n\) is the maximum value of the \(i\)-th codeword of \(x_{i}\) and \(f\) is the dimension of the transformed embedding vector.  
As each codeword has unique characteristics, there are \(k\) embedding matrices for \(k\) codewords in the speech frame. By transforming each codeword in the speech frame into a feature vector, we obtain  
\[\mathbf{E}=[e_{1},e_{2},\ldots,e_{k}] \tag{8}\]  
where \(\mathbf{E}\) is the output of the embedding layer, with a dimension of \((B,k,f)\), and \(\mathbf{e_{i}}\) denotes the transformed embedding vector of the \(i\)-th codeword \(x_{i}\).  
The mask-gating attention [36] mechanism aims to perform some operations on global elements; thus, the output of the embedding layer \(\mathbf{E}\) is reshaped as \(\mathbf{V}\), with a dimension of \((B,f\times k)\).
# Phased Antenna-Array Synthesis Using Taylor-Series Expansion and Neural Networks
### Appendix A Synthesized excitations (weights).  
This second table shows the synthesized excitation weights for each element at different sidelobe levels (\(-20\) dB, \(-30\) dB, \(-40\) dB, and \(-50\) dB), where the Taylor method tailors the amplitude distribution across the array. As the sidelobe levels are reduced, the weight distribution becomes more concentrated toward the central elements, further enhancingthe array's ability to focus energy and reduce interference, thus optimizing the radiation pattern for applications like radar, communication, and satellite systems.
# Compressive Sensing-Based IoT Applications: A Review
## 4 CS-Based IoT Applications
### Processing Layer
quantified the performance of OMP implementation on FPGA Vertex-7 for ECG signal [103] and image data [104]. Recently, [PERSON] et al. presented an efficient solution for the FPGA implementation of OMP [105]. The proposed approach uses fast Fourier transform (FFT) to implement the coefficient selection step which is represented by a correlation operation. Additional efforts for FPGA-based implementation of OMP can be found in [106; 107; 108; 109]. Beside FPGAs, the hardware implementation of CS reconstruction algorithms on other computing platforms such as graphics processing units (GPUs) and application specific integrated circuits (ASICs) have been presented in the literature. A hardware implementation of three CS recovery algorithms on ASIC have been proposed in [110] for channel estimation in wireless network. The authors provided a comparative study of the architecture, the complexity and the cost of MP, OMP and gradient pursuit. [PERSON] et al. [111] presented a GPU implementation of OMP where they proposed a matrix inversion update method to minimize the computation complexity of the matrix inversion problem which leads to speed up the reconstruction. In [112], the authors presented a comparative study between the implementation of the OMP for image reconstruction using a high parallel computation with an LU decomposition for solving the LS problem. The proposed architecture has been implemented on different platforms including general purpose CPUs, GPUs, a Virtex-7 FPGA and a domain-specific many-core. The implementation results showed that reconstruction time on FPGA is improved by two times compared to the results in [99]. In addition, the GPU based implementation has shown to be three times faster than results obtained in [111]. Table 3 expatriates on the comparative results between several works presented in the literature on the implementation of OMP on different platforms.
# Energy Management in Microgrids with Renewable Energy Sources: A Literature Review
## 3 Microgrid Energy Management with Renewable Energy Generation
### Energy Management Based on Dynamic Programming Techniques  
Shuai et al. [60] proposed an energy management system for a MG based on dynamic programming and mixed-integer non-linear programming optimization. The MG is interconnected to the grid and decisions are made using the Bellman equation. Historical data are used off-line, while considering the power flow and battery storage as constraints. Using the algorithm in multiple MGs simultaneously is a feasible possibility.  
Almada et al. [61] proposed a centralized system for energy management of a MG either in the stand-alone or interconnected modes. In the stand-alone mode, the fuel cell only works if the battery is less than 80%. In the interconnected mode, a 60% threshold is required to ensure reliable behavior.  
Wu et al. [62] proposed an algorithm based on dynamic programming for the management and control of stand-alone MGs. The deep learning algorithm works in real time, which permits intra-day scheduling to obtain a control strategy for MG optimization, while sending information from local controllers within the framework of centralized management.  
Zhuo [63] proposed an energy management system using dynamic programming to manage a MG with renewable generation sources and batteries. The objective was to maximize the benefits from the sale of renewable energy and minimize the cost required to satisfy the energy demand. The author used a non-regulated energy market where electricity prices fluctuate and the battery control actions are determined by dynamic programming.  
Choudar et al. [64] presented an energy management model based on the battery state of charge and ultra-capacitors. The hierarchic structure of optimal MG management has four states or operating modes: Normal operating mode, photovoltaic limitation mode, recovering, and stand-alone modes.  
Marabet et al. [65] proposed an energy management system for a laboratory scaled hybrid MG with wind, photovoltaic, and battery energy. The control and data acquisition system are operated in real time. The energy management system is based on a set of rules, and optimizes the MG performance by controlling and supervising the power generation, load, and storage elements.  
Luu et al. [66] presented a dynamic programming method and methodology based on the rules applied to a stand-alone MG containing diesel and photovoltaic generators, and a battery. The constraints are governed by the power balance between generation and consumption, along with the capacity of each distributed generator. Dynamic programming is used to minimize the operational and emission costs. The constraints are the power balance between offer and demand, along with the operating capacity of each distributed generator.
## 4 Experiment
### Data Processing  
Data processing mainly processes the collected results to obtain the K\({}_{\text{s}}\) value of each frequency, and the results are arranged according to time. The flow chart is shown in Figure 8. Firstly, the measured data are preprocessed. Preprocessing is the average processing of multi-cycle data. Then, the values of standard sampling points are extracted. The next step is divided into two parts. One is obtaining the static model using the lowest frequency data. The other is obtaining K\({}_{\text{s}}\)(m,1). First, K\({}_{\text{1}}\)(m,1) was calculated. Second, K\({}_{\text{2}}\)(m,1) and K\({}_{\text{2}}\)(m,2) are calculated. Then, the subsequent frequency is calculated until the data of all frequencies are processed. Then, all K\({}_{\text{s}}\)(m,1) are arranged according to time, input, and rotation point. The probability distribution of conversion times can be obtained by parabolizing K\({}_{\text{s}}\)(m,1) relative to the change of the static model at each standard sampling point.  
Figure 7: Experimental process.  
When estimating the output, the flow chart is shown in Figure 9. Firstly, the output variation of the static model is calculated according to the input. Then, according to the slope of the input data, the corresponding time of the standard sampling point is calculated. The probability is obtained according to the rotation point, standard sampling point, and time corresponding to the standard sampling point. Finally, the probability is used to modify the output variation of the static model; then, the output of the dynamic model was obtained.  
Figure 8: Data process.  
Figure 11: Results of sensor 2 at 3 kHz.  
Figure 10: Results of sensor 1 at 3 kHz.  
In the figures, blue is the error of the Preisach model, red is the error of the probability model, and yellow is the error of the modified probability model. It can be observed that the error of the probability model is much smaller than that of the Preisach model. Still, the error caused by accumulation induces a linear shift in the results of the probability model. The modified model solves this problem well. The error data of the two sensors are shown in Table 2.  
Figure 12: Errors of sensor 1 at 3 kHz.  
Figure 13: Errors of sensor 2 at 3 kHz.  
0.0019. Compared with the Preisach model, the maximum error of the probability model was reduced by 67.6%, variance was reduced by 93.4%, and the average value was reduced by 78.4%.  
We can see that the errors are greatly reduced, especially the variances. This shows the effectiveness of the probability model.
# Demystifying MLOps and Presenting a Recipe for the Selection of Open-Source Tools
## 3 Workflow of DevOps and MLOps
### Challenges of Traditional ML Workflow  
In comparison to conventional software development, ML has unique challenges that need to be handled differently. The major challenges encountered in the manual ML pipeline are as follows.  
* ML is a metrics-driven process, and in order to select the best model, multiple rounds of experiments are performed. For each experiment, one needs to track the metrics manually which increases the overall complexity.
* Manual logging is an inefficient and error-prone process.
* Data are not versioned. This further adds to the complexity as to reproduce results not only code, but data is also required. Similar to code, data also evolve.
* No model versioning or model registry is available. It is harder to reproduce a model from the past.
* Code reviews are not performed in ML. Testing is missing, such as unit or integration tests are not performed, which are commonly seen in traditional software development. The code development is limited to individual development environments, such as jupyter notebooks, on a data scientist's local workstation.
* The end product of the manual process is a single model rather than the whole pipeline.
* Collaboration between team members is a headache as it is difficult to share models and other artifacts.
* There is neither CI nor CD in the workflow as operations and development are considered as two distinct branches of the whole process.
* There is a lack of reproducibility as the experiments and the models produced (artifacts) are not tracked. There is no concept of maintenance of experiments.
* The deployed Model can not be retained automatically, which might be required due to a number of reasons such as model staleness or concept drift. As the process is manual, deploying a new model takes a lot of time and there are less frequent releases.
* Changing one particular parameter (for example, a specific hyperparameter that impacts the dimensions in a deep neural network) changes the total pipeline and influences the pipeline stages afterward and may lack versioning or may contradict.
# A Survey on Intrusion Detection Systems for Fog and Cloud Computing
## 6 Intrusion Detection Overview
### Intrusion Detection in SDN for Fog and Cloud Computing  
Software-defined networking manages networks that aim to provide dynamic, programmability, and efficient network configuration to enhance network performance and monitoring. In other work [51], the authors explore how the use of SDN technology can be utilized to improve intrusion detection technology through the concept of detection as a service (DaaS) in the SDN environment. The research focuses on how the combination of the programmability features in SDN can be combined with intrusion detection systems to make a redundant, reliable, and scalable anomaly detection system that can be used for mobile network operators. The paper focuses on multiple approaches for the implementation of the DaaS technology. The first approach considers where the traffic originates from a subscriber and aims towards the internet--it uses DaaS units to analyze the traffic and differentiate between anomalies and maliciousness in traffic. The second approach mentioned uses a clustering algorithm combined with the SDN application and controller to forward incoming to the respective DaaS node--the cluster information is sent to the SDN application to formulate forwarding rules (also known as flows) for different DaaS nodes and for load balancing. The platform consists of an application layer, a management layer, and a data layer. The architecture is evaluated throughout the research in that it protects SDN environments from being destroyed in cyber-attacks, focusing on resource overload and destructive attacks.
## 2 Materials and Methods
### Pre-Processing
#### 2.4.3 Calibration and Geocoding  
Using the associated orbital information, TSX images were calibrated and geocoded in WGS84--UTM 32N. Then, intensity images were obtained. This pre-processing was carried out using (\({}^{\text{\textregistered}}\)SARScape Analytics Toolbox (version 5.5). All the pre-processing steps were integrated into the Time Series tool (version 5.5). Also, this dataset was masked on AOI, water, and no-crop areas.
# Performance Analysis of Maximal Risk Evaluation Formulas for Spectrum-Based Fault Localization
## 2 Background  
### Spectrum-Based Fault Localization (SBFL)  
Spectrum-based fault localization (SBFL) uses two significant pieces of information to help localize faults, if any, in a program during the software testing and debugging processes. The first piece of information is the testing result of the program with respect to a test suite. It basically indicates whether a program passes or fails on each individual test case. The second piece of information is the program spectrum, which contains the information about a program entity (e.g., statement, branch or basic block) and its coverage information, such as whether it has been executed or not; how many test cases that execute or do not execute in the program entity such that the program passes; and how many test cases that execute or do not execute in the program entity such that the program fails [3]. The program spectrum information, together with the test results, provide a behavioral signature for program execution with respect to a test suite [1]. It also describes the characterized information of the program entities obtained from program runtime profiling when the program executes on the test suite. From software testing perspective, a program entity could be a statement, branch, path, function, or basic block, among which statement is the most widely used because of its analysis simplicity [19]. The characterized information of a program entity could be the number of times that the program entity has been executed, entity coverage information, and program state before and after the execution etc. [11]. Testers and debuggers can make use of the test results and program spectra to identify the program entities that are more likely to cause program failure [1; 20]. An example is to combine the test results and statement coverage information [21; 22].  
Given a program \(PG\) with \(n\) statements (\(s_{1}\), \(s_{2}\),..., \(s_{n}\)) and a test suite \(TS\) with \(m\) test cases (\(t_{1}\), \(t_{2}\),..., \(t_{m}\)), Figure 1 depicts the relationships among all the essential information for SBFL [14]. The \(n\times m\) matrix \(MS\) records the coverage information for each statement in \(PG\) with respect to each test case in \(TS\). If statement \(s_{i}\) is executed by test case \(t_{j}\), the entry in the \(i\)-th row and \(j\)-th column of \(MS\) will be marked as \"1\". Otherwise, it will be marked as \"0\". The \(1\times m\) matrix \(RE\) represents the testing result of individual test case in terms of pass (\(p\)) or fail (\(f\)). The \(1\times 4\) matrix \(A\) represents four important quantities for each statement in the program. These four quantities are (1) the number of test cases that execute the statement and fail the program, denoted as \(e_{f}\); (2) the number of test cases that execute the statement and pass the program, denoted as \(e_{p}
# A New LoRa-like Transceiver Suited for LEO Satellite Communications +
## 2 Synchronization Issues in LoRa
### Analysis of Imperfect Synchronization on Symbol Estimation
As shown in (13), when the timing alignment of the  
Figure 1: Time desynchronization illustration. received signal is not performed, ISI occurs. In the following subsections, we analyze the impact of the CFO, the Doppler shift and the STO on the symbol estimation.
## 2 Materials and Methods
### Sliding-Window Segmentation Methods
#### 2.3.2 Peak-Detection Windowing  
The human gait contains peak acceleration and angular velocity signals when fall and near-fall activities occur. Hence, the need to accurately detect the fall of senior subjects requires us to differentiate the peak signals of near-falls from those of fall events. Due to the alternation in the signal values, we take the highest signal values of the acceleration as the basis to define the observation window of the detector.  
Therefore, to use these advantages, we start the engineering of sliding-window features from the maximum magnitude of the acceleration signal (as shown in Algorithm 1) rather than using the conventional engineering of sliding-window features. This kind of windowing helps us to include the important peak signal value in a single window, besides paying great attention to the maximum-value signal. If we used conventional windowing based on the raw signal, the important information may be shared in different windows. For our proposed method, the maximum values of the acceleration and angular velocity simulated in the accelerometer and gyroscope datasets, consisting of 3D acceleration data \(acc_{t}(t)=[acc_{x}(t),acc_{y}(t),acc_{z}(t)]\) and 3D angular velocity \(gyro(t)=[gyro_{x}(t),gyro_{y}(t),gyro_{z}(t)]\)[22], are calculated using the magnitude of these vectors for the \(i^{th}\) sample as follows:  
\[\|acc_{i}(t)\|=\sqrt{acc_{xi}^{2}(t)+acc_{yi}^{2}(t)+acc_{zi}^{2}(t)}. \tag{2}\]  
\[\|gyro_{i}(t)\|=\sqrt{gyro_{xi}^{2}(t)+gyro_{yi}^{2}(t)+gyro_{zi}^{2}(t)}. \tag{3}\]  
Next, the maximum magnitude of the signal calculated above is determined as follows.  
\[\|acc_{max}\|=max(\{\|acc_{i}\|:i\in[1:M]\}), \tag{4}\]  
\[\|gyro_{max}\|=max(\{\|gyro_{i}\|:i\in[1:M]\}), \tag{5}\]  
where M is the number of samples. Using the above two equations and the size of the window (which is 0.2 s in our case), the sliding window around the peak signal is as shown in Figure 1.  
After we estimate the maximum acceleration and decide on the size of the sliding window, the algorithm above gives us the input features and their labels, which are formed by simply concatenating the six features of the accelerometer and gyroscope, \(\{Acc_{ij},Acc_{ji},Acc_{zj},Gyro_{xj},Gyro_{yj},Gyro_{zj}|j\in[r_{o}-\frac{w}{2}f_{s },r_{o}+\frac{w}{2}f_{s}]\}\), where \(w\) is the duration of the observation window (0.2 s) and \(f_{s}\) the sampling rate of the sensor (200 Hz) [22]. Finally, the size of the input features (\(N_{i}\)) that depends on the duration of the observation window (\(w\)) is \(N_{i}=6*(w*f_{s}+1)\).
# A Collision Risk Assessment Method for Aircraft on the Apron Based on Petri Nets
###### Abstract  
The airport apron is a high-risk area for aircraft collisions due to its heavy operational load and high aircraft density. Currently, existing quantitative models for apron collision risk provide limited consideration and classification of risk areas. In response, this paper proposes a Petri net-based method for assessing aircraft collision risk. The method predicts the probability of aircraft reaching different areas at different times based on operational data, enabling the calculation of collision risks within the Petri net framework. This approach highlights areas with potential collision risks and provides a classification evaluation. Subsequently, aircraft path re-planning is carried out to reduce collision risks. The model simplifies the complex operations of the apron system, making the calculation process clearer. The results show that, during the mid-phase of aircraft taxiing, there is a significant deviation between the actual and ideal positions of aircraft. Areas with high taxiway occupancy are more prone to collision risks. On peak days, due to relatively high flight volumes, the frequency of collision risks is 14% higher than on regular days, with an average risk increase of 23.3%, and the risks are more concentrated. Therefore, reducing collision risks through path planning becomes more challenging. It is recommended to focus attention on areas with high taxiway occupancy during peak periods and carefully plan routes to ensure apron safety.  
apron operation; collision risk; risk assessment; trajectory prediction; risk area; Petri net +
Footnote †: journal: Applied  
The second section of the paper primarily analyzes the current state of research. The third section focuses on the development of the model, including the establishment of the apron operation Petri net, calculation of aircraft potential movement space, calculation of aircraft collision risk based on potential movement space and classification and evaluation of aircraft collision risk. The fourth section presents the simulation experiment data analysis, where the identified collision risks are analyzed and classified using XGBoost algorithms, and path re-planning is performed to reduce risks. The fifth section is the discussion, which highlights the practical implications of the model, its advantages and limitations, and outlines future research directions. Finally, the conclusion section summarizes the entire paper, providing an overview of the work and a brief explanation of the results.
## 3 Data Acquisition
### Landslide Conditioning Factors  
Based on the literature, data availability, and our experience, we selected 18 landslide conditioning factors for modelling: Slope angle, slope aspect, elevation, distance to road, topographic wetness index (TWI), normalized difference vegetation index (NDVI), lithology, land use/land cover, rainfall, distance to fault, plan curvature, profile curvature, slope length-angle index (LS), solar radiation, stream power index (SPI), distance to the river, river density, and fault density. The factors are described briefly in the following subsections: