esa-sceva/satcom-chunk-collection huggingface dataset:
# Algorithms for Various Trigonometric Power Sums
## 2 Background
In this work, we will be frequently generating rational values for the trigonometric power sums, but when we wish to compare them with those generated by \begin{table}
\begin{tabular}{c l} \hline \(v\) & \(S_{v,2}(m)\) \\ \hline
1 & \(\frac{2}{3}(m^{2}-1)\) \\
2 & \(\frac{4}{45}(2m^{4}+5m^{2}-7)\) \\
3 & \(\frac{8}{945}(8m^{6}+21m^{4}+42m^{2}-71)\) \\
4 & \(\frac{16}{14175}\,(24m^{8}+80m^{6}+147m^{4}+270m^{2}-521)\) \\
5 & \(\frac{32}{9355}(32m^{10}+132m^{8}+286m^{6}+451m^{4}+792m^{2}-1693)\) \\
6 & \(\frac{64}{638512875}\,(44224m^{12}+218400m^{10}+558558m^{8}+993850m^{6}+14384 37m^{4}+2457000m^{2}\) \\  & \(-5710468)\) \\
7 & \(\frac{128}{27368375}\,(3840m^{14}+22112m^{12}+65520m^{10}+133419m^{8}+211640m^{6 }+289926m^{4}\) \\  & \(+486000m^{2}-1212457)\) \\
8 & \(\frac{256}{38825429375}\,(1388928m^{16}+9139200m^{14}+30824128m^{12}+71073600m^{ 10}+126249123m^{8}\) \\  & \(+185684200m^{6}+244813158m^{4}+404838000m^{2}-1074010337)\) \\
9 & \(\frac{512}{194896477600625}\,(112299520m^{18}+831273408m^{16}+314514140m^{14}+8 109940848m^{12}\) \\  & \(+16038788310m^{10}+25993141083m^{8}+36233012730m^{6}+46442359908m^{4}\) \\  & \(+76014288000m^{2}-212920335247)\) \\
10 & \(\frac{1024}{371579187711031525}\,(3754834944m^{20}+30882368000m^{18}+129540106080m^{16}\) \\  & \(+369483576000m^{14}+806213290740m^{12}+1435972970250m^{10}+2178408236235m^{8}\) \\  & \(+2917253715750m^{6}+3659175925020m^{4}+5941116720000m^{2}-17471801743019)\) \\ \hline \end{tabular}
\end{table} 
Table 2: Values of \(S_{v,2}(m)\) for \(v\) up to and including 10.  
the standard instructions in Mathematica, we will need to convert them to number or decimal form. For these situations, any number of decimal places can be specified by the user, but here they will be restricted to 16 decimal places, since those evaluated by standard means are limited by the byte size for the computing machine, especially when \(m>5\). Therefore, whilst it is possible to obtain much higher decimal precision from the computed values of the new results in this work, they will only be expressed to 16 decimal places in order to make a fair comparison with the machine-dependent numbers arising from the standard instructions in Mathematica. To complete this section, it should also be mentioned that the untwisted Dowker sums can be obtained in Mathematica simply by typing in the \(l=1\) version of (10) as follows:  
\[\text{Sv}1[v_{-},\text{m}_{-}]\text{:= Sum[Sc[k }\pi/\text{m}]^{\wedge}(2\text{ v}),\text{ [k,1,m$--$1$]}].\]  
With \(v=6\) and \(m=m\) in the instruction, Mathematica prints out  
\[\text{In[7]:= Sv1[6, m]}\]  
\[\text{Out[7]}=(1/638512875)(-92427157+78624000\text{ m}^{2}+11507496\text{ m}^{4}+1987700\text{ m}^{6}+279279\text{ m}^{8}+27300\text{ m}^{10}+1382\text{ m}^{12}).\]  
The same approach, however, does not apply to the Gardner-Fisher case, but can be applied to the alternating version of the untwisted Dowker sum as described in the next section.
## 2 Site, Data, and Methods
### Data  
Using the CDWL, a long-term stable observation experiment was conducted in the TD from July 2021 to May 2023. The wavelength and pulse energy of the CDWL are 1.5 \(\upmu\)m and 130 \(\upmu\)J, respectively. During the experiment, the scanning elevation angle was fixed at 70\({}^{\circ}\), the azimuth interval angle was set to 12\({}^{\circ}\), and the velocity azimuth display (VAD) scanning mode with period of 1 min was continuously performed [(29)]. The key parameters of the CDWL are listed in Table 1. In the preprocessing stage, when the carrier-to-noise ratio (CNR) is below \(-\)18 dB, the lidar data involved in the MLH calculation are eliminated. The instrument calibration and potential data bias were described by [PERSON] and [PERSON] [(30; 31)]. According to the average sunrise and sunset time, the daytime period is defined as 08:00\(\sim\)21:00 Beijing time (BJT). The amount of MLH data included in the statistics reached 440 thousand groups.  
The MinFeng weather station provides SMEs from July 2021 to July 2023, including near-surface ten-minute average wind speed (WS), near-surface ten-minute average wind direction (WD), atmospheric temperature (T), relative humidity (RH), and horizontal visibility (VIS). The temporal resolution of SMEs is 1 h, and the sample size is 9883 groups.
## 2 Field Sites
### Demokeya, Sudan  
Demokeya (13.282\({}^{\circ}\)N, 30.478\({}^{\circ}\)E) is located in the North Kordofan state. Mean annual air temperature is 30 \({}^{\circ}\)C and mean annual precipitation is 320 mm (Table 1). The landscape around the site is a typical Sahelian savanna, comprised of sparse trees, mainly _Acacia senegal_ and _Acacia nilotica_, with a canopy cover of approximately 7%. The ground cover comprises annual grasses, primarily _Cendrus biforus_, _Eragrostis tremula_, and _Aristida pallida_. The sandy soils are poor in nutrients, have low soil organic C, and low cation exchange capacity [39]. A complete description of Demokeya is provided in [PERSON] et al. [36].
# Multi-Label Extreme Learning Machine (MLELMs) for Bangla Regional Speech Recognition
## 3 Results and Discussion
### Comparison Among Datasets  
The number of Convolutional Autoencoders in the SCAE-MLELMs model is varied to evaluate the accuracy of the prediction of the class label for the test data as well as the impact the input data format has on the suggested system. MFCCs and MFECs are selected as input data types. They are the most commonly used data formats in audio recognition studies. The dataset mentioned earlier is used to analyze Bangla's speech. While for English Speech, freely available datasets from Google AudioSet and VoxCeleb is utilized [34]. Table 8 shows the categorization accuracy in the specifics of the experiment with MFCCs as data input format and Table 9 for MFECs data format.  
Model 1 employs only one CAE network with MELMs to evaluate the efficiency of the suggested methods; Model 2 employs three CAE networks with MLELMs. In models 2, 3, and 4, three, four, and six CAE networks are followed by MELLM networks; a comprehensive architectural description can be found in this paper's proposed model section. For all datasets in the MFCCs data format, Model 4 gives the highest classification accuracy for all class labels for both types of speeches. It requires more convolutional autoencoders to detect the prominent aspects of an audio stream in an MFCCs spectrogram. The MFECs data has the highest classification accuracy when using Model 2, since its log-mel-energy properties are easier to discern. As the number of CAE networks for MFECs input data format increases, the model tends to overfit as well. The accuracy of Model 3 and Model 4 is superior to Model 2. Across all datasets of Bangla and English speech, dialect and type of audio have the greatest accuracy in prediction.
# Malware Collusion Attack against SVM: Issues and Countermeasures
## 3 Motivation
### Formal Definition of Collusion  
A malicious payload is a set of actions that must be executed in a certain order. Actions are operations that are specified by Android API (e.g., getDeviceID(), sendTextMessage()). A malicious behavior can be performed by a single app or multi apps. In this paper, we model the behavior by a ordered set \((A,\leq)\), where \(A\) is the set of actions and \(\leq\) defines the order that must be followed by actions.  
Definition 1: Given an action sequence \(a_{1}\cdots a_{n}\), the extend action sequence of which is defined as \(Extend(a_{1}\cdots a_{n})=a_{1}\cdots b_{1}\cdots a_{i}\cdots b_{n}\cdots a_{n}\). i.e., \(Extend(a_{1}\cdots a_{n})\) is obtained by adding some other actions between \(a_{1}\) and \(a_{n}\). On the contrary, \(a_{1}\cdots b_{1}\cdots a_{i}\cdots b_{n}\cdots a_{n}\) is the subsequence of \(a_{1}\cdots a_{n}=Sub(a_{1}\cdots b_{1}\cdots a_{i}\cdots b_{n}\cdots a_{n})\).  
Letting \(\mathcal{A}^{*}\) denote the set of all actions, \(\mathcal{M}^{*}\) denotes the set of all malicious behavior. In the scope of this paper, \(\mathcal{M}^{*}\) only refers to the behavior caused by single apps. Note that one may obfuscate a malicious behavior by executing it with meaningless or unrelated actions. Then, \(Extend(\mathcal{M}^{*})\) denotes all the possible implementations of malicious behavior. Similarly, the collusion behavior can be defined as follows:  
Definition 2: A set of app \(\mathcal{Z}\) is colluding if they can execute a sequence of actions \(A=a_{1}\cdots a_{n}\) such that:  
* There are communication links between the apps \(z_{m},z_{n}\in\mathcal{Z}\). The \(i_{th}\) link is denoted by \(L_{i}(z_{m},z_{n})\). The actions of app \(z\) is denoted by \(Action(z)\). The union action conducted by the \(i_{th}\) link of app \(z_{m},z_{n}\) is denoted by \(UnionAction_{i}(z_{m},z_{n})=a_{i}\cdots a_{j}L_{i}(z_{m},z_{n})a_{j+1}\cdots a _{k}\), where \(a_{i}\cdots a_{j}\in Action(z_{m})\) and \(a_{j+1}\cdots a_{k}\in Action(z_{n})\).
* There exists an extend action sequence \(UnionAction(z_{1},\cdots,z_{n})\in Extend(\mathcal{M}^{*})\).
# Federated Learning-Based Service Caching in Multi-Access Edge Computing System
## 2 Related Work
### Centralized Service Caching  
Service caching, data admission, and resource allocation problems of the MEC system were jointly investigated [8]. This study aimed to optimize the average service throughput of servers within the limitations imposed by bandwidth and memory restrictions. The central server gathered information regarding the arrival of data and channels. It transformed the data into a two-dimensional knapsack problem. Finally, this issue was resolved using dynamic programming to make optimal decisions.  
Ref. [9] solved the problem of optimizing service caching by modeling four types of costs in an entire edge network system. The ITerative Expansion Moves (ITEM) method was proposed to address this problem. In each iteration, the ITEM transformed the optimization problem into a graph-cut problem and then solved it using the max-flow method.  
Ref. [10] considered minimizing the load on a centralized cloud server in an MEC system. The simultaneous modeling of service caching and request routing problems was conducted considering practical constraints. The proposed method was based on a randomized rounding algorithm shown to approach optimal performance asymptotically. Moreover, this approach demonstrated adaptability in efficiently addressing changes in customer demand profiles.  
The study [20] addressed the problem of optimizing service caching inside an MEC system comprising a single base station (BS) and multiple users. Specifically, the central BS determined the caching of services, whereas system users determined the offloading of tasks. The formulation of the optimization problem took the form of a Stackelberg game. Subsequently, a heuristic algorithm optimized the service caching decision at the BS.  
In [21], the authors examined problems with service caching and request distribution inside the MEC systems. The system had a central cloud center that was interconnected with many BSs via the core network. A multi-objective optimization problem was formulated to minimize response time deadline violations. The authors proposed a genetic algorithm that proved close to the Pareto optimal front.  
Centralized cache replacement systems use a central server that collects information and performs calculations for all MEC servers in the system. The central server uses heuristic algorithms, or RL-based models, to make optimal decisions for the entire system. As mentioned in Section 1, this not only imposes pressure on the system [12; 13], but also raises issues about security and privacy [14]. In contrast to these methodologies, the proposed strategy is constructed in a distributed manner. MEC servers have the capability to independently use data and make individual decisions. This approach not only evenly distributes the workload throughout the system, but also reduces the resources required for system operation due to the compactness of the model weights in comparison to the original data. Moreover, the proposed approach also tackles the security concerns of the system.
## 4 Discussion
### Factors Influencing Exoskeleton Effectiveness  
Several key factors appear to influence the effectiveness of wearable robotic exoskeletons in construction settings. Understanding these factors is crucial for optimizing the implementation and maximizing the benefits of this technology.  
Task characteristics played a significant role in determining exoskeleton effectiveness. Tasks involving repetitive bending and lifting (such as the masonry task at ground level) showed the greatest productivity improvements. This aligns with the mechanical assistance provided by the exoskeleton, which is particularly beneficial for reducing the strain associated with repetitive bending movements. Conversely, tasks requiring more dexterous upper-body movements showed more modest improvements, suggesting that current exoskeleton designs may be particularly suited for lower-body intensive tasks.  
Work intensity emerged as another critical factor. The consistent finding across all analyses was that high-intensity conditions showed greater relative improvements with exoskeleton assistance. This pattern suggests a potential application priority for exoskeleton technology in construction settings. Implementing exoskeletons in the most physically demanding tasks may provide the highest return on investment in terms of both productivity gains and potential injury prevention.  
Adaptation time also significantly influenced exoskeleton effectiveness. The continuous increase in productivity advantage over the 2 h work period (reaching an average of 40.1% in the final interval) indicates that workers require time to optimize their movements with the exoskeleton and that fatigue mitigation benefits become more pronounced with extended use. This finding has important implications for training programs and expectations when implementing this technology. Organizations should account for adaptation periods and avoid making performance judgments based solely on initial usage.
# Special Issue on Electroporation Systems and Applications
## 1 Introduction  
High pulsed electric fields (PEF) trigger the phenomenon of a transient increase in the permeability of the plasma membranes of biological cells, which is known as electroporation [1, 2]. In the case of high-intensity PEF, the process can be irreversible, resulting in cell death. The capability to employ electroporation for drug and gene delivery [3, 4], food processing [5], tissue ablation [6] and biorefinery [7] ensures the high relevance of the topic and the method, which will persist decades. Due to a variety of biological phenomena triggered by PEF and its wide array of applications, the field is highly transdisciplinary, joining together electrical and electronics engineers, medical doctors (cardiology, oncology, immunology, dermatology, etc.), microbiologists, chemists, molecular biology specialists and many others. More than 17,500 publications are indexed in Clarivate Analytics Web of Science (access date: 26 February 2023) on the topic; the summary of keyword co-occurrence is shown in Figure 1.  
Figure 1: The co-occurrence of electroporation-based topics and keywords in scientific publications (source: Clarivate Analytics Web of Science; keyword: “electroporation”; access date: 26 February 2023). Processed using VOSviewer, version 1.6.18 (Leiden University). Larger circle size corresponds to a higher rate of occurrence of a keyword.  
It can be seen that, in recent years, the applied aspects of the methodology have been the central focus (i.e., irreversible electroporation and cancer treatment applications), which indicates the successful transition from fundamental science into clinical practice. In the early 2000s, the applied aspects of the electroporation methodology were mainly limited to non-viral transfection of microorganisms as a biotechnological step in routine research.
## 1 Introduction
However, these methods focus merely on identifying the modulation format of the received signal and do not provide any information about the quality of signal in terms of OSNR. Joint OSNR monitoring and modulation format identification in digital coherent receivers using deep neural network (DNN) was performed in [18]. The excellent experimental effect of DNN was considered, but the problem of high complexity was neglected. In this study, a joint modulation format identification and OSNR monitoring algorithm is proposed and experimentally demonstrated using the k-nearest neighbor (KNN) algorithm for IMDD OFDM systems. A modified amplitude histogram (AH) of received signal is employed to serve as the classification feature to simplify the computation. Experimental results show that five common QAM modulation formats including 4-QAM, 16-QAM, 32-QAM, 64-QAM and 128-QAM can be identified under 100% accurate estimation at the received optical power of \(-11\) dBm. Robustness of the proposed scheme to constellation rotation is also experimentally assessed. At the same time, system OSNR monitoring can also be achieved with an average prediction MSE of 0.69 dB\({}^{2}\), which is similar to what is achieved using an artificial neural network. Computational complexity assessment demonstrated that similar performance but less computing resource consumption can be achieved by using the proposed scheme rather than the artificial neural network (ANN)-based scheme. AMOOFDM without transceiver negotiations can also be achieved using the proposed scheme, showing its good potential for intelligent transceivers in elastic optical networks.
## 3 Fuzzy Logic Controller  
As an MPPT algorithm keeps perturbing the PV operation point, the incremental changes in PV power and the previous perturbation offset can produce some non-quantitative information, i.e., the PV operation point is either far away from the MPP, or not, and the PV operation point is either on the left-hand side of the MPP or not. Although, the non-quantitative information may not directly contribute to calculations, those can still assist MPPT algorithms in terms of providing correct timings for changing perturbation offset. In consequence, the PV operation could be pushed to the MPP with few trials. For example, as seen in Figure 7, in the \"PB\" zone, the slope is relatively constant because the points in this zone are distant from the MPP. Thus, the perturbation offset can be enlarged for quickly pushing the operation point out of this zone, and the PV power can increase rapidly. As the operation point moves within the \"PS\" area, the incremental changes in PV power decrease towards zero. Hence, the perturbation offset needs to be diminished in order to carefully increase the power point to the peak value. Otherwise, the operation point may easily miss the MPP. If the operation point shifts into the \"PZ\" and \"NZ\" zones, where points within in these zones are \"extremely close\" to the MPP, the ideal perturbation offset should be extremely weak for suppressing power oscillations around the MPP.  
The power oscillations around the MPP will be surpressed, not eliminated. The reason is that due to enviornmental changes, such as irradiation or temperature variations, photovoltaics demostrate various P-V characteristics (shown in Figure 1). The real MPP of photovoltaics may shift at any time[25; 26; 27]. As a result, the MPPT algrotihm can detect the potential change of the P-V curve by continously pertubing the operation point with extremely small offsets.
# Wireless Traffic Prediction Based on a Gradient Similarity Federated Aggregation Algorithm
## 3 Proposed Framework and Methods
### Enhanced Data Strategy Based on Global Incremental Integrations  
The variability of base station traffic patterns and the mobile characteristics and communication behaviors of users within the base station range further expand the model  
Figure 2: FedGSA Overall Architecture.  
diversity of wireless services, and the wireless traffic data from different base stations are highly heterogeneous and non-IID in nature. It is shown that Non-IID client-side data leads to a degradation of the performance of the federation learning algorithm, since the weight differences of the client-side model parameters should be considered when performing model aggregation at the server side. Therefore, this paper uses an augmented data strategy based on global incremental integration to overcome the traffic data heterogeneity challenge by creating a small augmented dataset using the original wireless traffic dataset and generating a global shared dataset.  
The augmentation strategy in this paper is as follows. The dataset is first partitioned into weekly slices based on temporal indexes. For weekly traffic, statistical averages are calculated for each time point and the obtained results are considered as augmented data, and finally, the augmented data are normalized as shown in Figure 3.  
As can be seen from Figure 2, the employed enhancement strategy is easier to implement and generate enhanced data than the traditional time-domain or frequency-domain time-series data enhancement strategies [15]. It has been experimentally proven to provide an effective solution to the problem of data heterogeneity.  
During the training process, each base station sends a small fraction of its augmented dataset, say \(\beta\%\), to the central server to eventually generate a global dataset that obeys the original client data distribution. The size of the augmented data is much smaller compared to the size of the original data. Based on this augmented dataset, a quasi-global model can be trained and used as prior knowledge for all clients, and the model is trained using the augmented data for all clients rather than the original data. Even so, due to the high similarity between the augmented data and the original data, the model can still be used as prior knowledge for all clients.
## 4 Results
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Classifier** & **Class** & \(\mathbf{Pr}\) & \(\mathbf{Se}\) & \(\mathbf{Sp}\) & \(\mathbf{F1}\) & \(\mathbf{Acc}\) & \(\mathbf{AUC}\) \\ \hline \multirow{3}{*}{DT} & DR & 0.7000 & 0.7700 & 0.8334 & 0.7368 & 0.8148 & 0.8055 \\  & Glaucoma & 0.8000 & 0.7273 & 0.8750 & 0.7619 & 0.8148 & 0.8011 \\  & Normal & 0.7000 & 0.7000 & 0.8334 & 0.7000 & 0.7857 & 0.7666 \\ \hline \multirow{3}{*}{\(k\)NN} & DR & 0.5000 & 0.5555 & 0.7619 & 0.5263 & 0.7000 & 0.6587 \\  & Glaucoma & 0.8000 & 0.6667 & 0.8667 & 0.7272 & 0.7778 & 0.7666 \\  & Normal & 0.8887 & 0.8889 & 0.8667 & 0.8421 & 0.8752 & 0.8777 \\ \hline \multirow{3}{*}{NB} & DR & 0.4000 & 0.5000 & 0.7142 & 0.4444 & 0.6551 & 0.6071 \\  & Glaucoma & 0.8001 & 0.5714 & 0.8467 & 0.6667 & 0.7037 & 0.7087 \\  & Normal & 0.7000 & 0.8750 & 0.8000 & 0.7778 & 0.8260 & 0.8375 \\ \hline \multirow{3}{*}{LDA} & DR & 0.8000 & 0.7273 & 0.8947 & 0.7619 & 0.8334 & 0.8110 \\  & Glaucoma & 1 & 1 & 1 & 1 & 1 & 1 \\  & Normal & 0.7000 & 0.7776 & 0.8571 & 0.7368 & 0.8334 & 0.8178 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Classification performance per classifier for every class in the HRF dataset. Table 10 shows a recently published system proposed by [PERSON] [17], which is based on automatic deep learning and performs five-class classification, in contrast to our system which is based on hand-crafted features, conventional ML algorithms, and three-class classification. The common factor between the two systems is the complete results provided for every individual class in terms of precision, sensitivity, specificity, \(F1\) score, and accuracy.