esa-sceva/satcom-chunk-collection huggingface dataset:
# Control of Flexible Robot by Harmonic Functions
## 3 Results
### Model of Flexible Robot  
A flexible robot is modelled as a double pendulum with flexible arms. It consists of two flexible beams, interconnected by a revolute joint, and coupled to the base frame with the second revolute joint. Every beam has flexibility modelled by three modes. There are actuators in each revolute joint. The model covers the mass of the actuator, and the endpoint is loaded by extra mass. The system is situated in the gravity field, see Figure 1.  
Eigen equations of motion of the flexible double pendulum are obtained using the procedure derived in [38]. The implementation is presented in [40]. The Eigen equations of motion are in the following form:  
\[\mathbf{M}(\mathbf{x})\dot{\mathbf{x}}=\mathbf{F}(\mathbf{x},\dot{\mathbf{x}} )+\mathbf{B}(\mathbf{x})\begin{bmatrix}u_{1}\\ u_{2}\end{bmatrix} \tag{20}\]  
where  
\[\mathbf{x}=\begin{bmatrix}\phi_{1}\\ \phi_{2}\\ \delta_{11}\\ \delta_{12}\\ \delta_{13}\\ \delta_{21}\\ \delta_{22}\\ \delta_{23}\end{bmatrix}\quad\mathbf{B}=\begin{bmatrix}1&-1\\ 0&1\\ 0&0\\ 0&0\\ 0&0\\ 0&0\\ 0&0\end{bmatrix}\]  
Furthermore, matrix \(\mathbf{M}\) is an inertia matrix, vector \(\mathbf{F}\) covers velocity-dependent forces, gravity forces, and damping forces, and \(u_{1}\), \(u_{2}\) are actuator moments. Beam \(k\) endpointposition is determined by angular rotation \(\varphi_{k}\) and beam deflection \(d_{k}\). Beam deflection is obtained according to the following equation:  
\[d_{k}=\sum_{i=1}^{3}d_{ki}=\sum_{i=1}^{3}\Phi_{ki}\delta_{ki} \tag{21}\]  
where \(\delta_{ki}\) are dimensionless modal coordinates and \(\Phi_{ki}\) are coefficients of modal parameters beam deflection at particular point. The angular deflection of the first beam endpoint is needed for the determination of the second beam position. This endpoint angular deflection is indicated as \(\theta\), and generally  
\[\theta_{k}=\sum_{i=1}^{3}\Psi_{ki}\delta_{ki} \tag{22}\]  
where \(\Psi_{ki}\) are coefficients of modal parameters, beam angular deflection at particular point. Angle \(\theta_{1}\) is the relative angle between the un-deformed shape of the first beam and its endpoint angular deflection (or endpoint tangent to deformed beam shape).
# Comparative Analysis of Skew-Join Strategies for Large-Scale Datasets with MapReduce and Spark
## 3 Data-Skew-Handling Algorithms
### Multi-Dimensional Range Partition
#### 3.4.1 Partitioning Matrix  
The algorithm considers two datasets \(R\) and \(L\) with a join key of \(k\). Suppose that there are \(p\) partitions, and the input datasets will be divided into \(p\) sub-ranges, i.e., \(\{R_{1},R_{2},\ldots,R_{p}\}\) and \(\{L_{1},L_{2},\ldots,L_{p}\}\). Creating sub-ranges of the two datasets is similar to creating sub-ranges with the range-based partition algorithm. For dataset \(R\), the sub-range will include the entire domain of the join key \(k\) from \(\alpha\) to \(\beta\), \(\alpha<R.k<\beta\). Two special cases \(R_{1}\) and \(R_{p}\) will include sub-ranges \([-\infty,\alpha]\) and \((\beta,\infty]\), respectively.  
As in the previous example, we have \(R.k=\{1,1,2,2,2,2,4,4\}\) and \(L.jk=\{1,1,2,2,2,3,4,4\}\) with \(p=4\) partitions. We get two split vectors \(\{1,2,2\}\) and \(\{1,2,3\}\) from the two datasets \(R\) and \(L\). In the partitioning matrix \(M\), as shown in Figure 11, the \(i\)th row represents the sub-range \(R_{i}\) and the \(j\)th column represents the sub-range \(L_{j}\). The cell \((R_{i},L_{j})\) is classified into one of the following two groups: candidate and non-candidate. Candidates are cells that produce join results and non-candidates are cells that do not generate join results.  
Figure 11: Mapping between cells and partitions.  
Figure 10: Data flow of multi-dimensional range partition algorithm.  
\(M(i,j)\) is the value representing the workload of a candidate cell \((R_{i},L_{j})\) to be processed. In the MDRP algorithm, \(M(i,j)=|R_{i}\bowtie L_{j}|\), where \(R_{i}\) and \(L_{j}\) are records that belong to the same sub-range. In the partitioning matrix \(M\), \(M(2,2)=12\) because we have four records whose join key is 2 in dataset \(R\) and three records with a join key of 2 in dataset \(L\), which belong to the same sub-range \((R_{2},L_{2})\). Similarly, \(M(1,1)=4\) because we have two records \(\{r1,r2\}\) in dataset \(R\) and two records \(\{11,12\}\) in dataset \(L\) that have join keys belonging to the same sub-range \((R_{1},L_{1})\). \(M(4,4)=4\), since we have two records in dataset \(R\) and two records in dataset \(L\) that have join keys belonging to the same sub-range \((R_{4},L_{4})\). The partition matrix will help to ensure that the total number of cells \((R_{i},L_{j})\) and the total workload in each partition are relatively equal.
# An Energy-Efficient Approach to Enhance Virtual Sensors Provisioning in Sensor Clouds Environments
## 3 Problem Definition  
In this section, we first describe some basic assumptions considered in this work, followed by the mathematical formulation of the problem. Table 1 shows the notations and their descriptions used in the rest of the paper.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Notation** & **Description** \\ \hline \multicolumn{3}{l}{**A. Sets and Variables**} \\ \hline \(SC\) & Sensor cloud: a set of Wireless Sensor Networks (\(WSN\)) \\ \(WSN_{i}\) & Set of sensor nodes (\(SN\)) of the \(i_{th}\)\(WSN\) \\ \(SN_{i,j}\) & The \(j_{th}\) sensor node of the \(i_{th}\)\(WSN\). A \(SN\) comprises a set of sensor (\(S\)) \\ \(S_{k}^{i,j}\) & The \(k_{th}\) sensor of \(SN_{i,j}\). It defines a set of \(M^{k,ij}\) measurements gathered by the sensor \\ \(C_{c,t}\) & Set of sensor nodes (\(SN_{i,j}\)) inside the \(c\)-\(th\) cluster of type \(t\) \\ \(\Omega\) & Set of selected sensor nodes (\(SN\)) to be provisioned \\ \(\Omega_{t}^{a}\) & Solution found by the \(a_{th}\) at the end of the \(i_{th}\) iteration \\ \(type_{i}\) & a specific type/dimension that represents a monitored physical condition \\ \(TYPE\) & The set of all dimensions monitored by \(SC\) \\ \(QUERY\) & The set of all queries triggered by sensor cloud’s users. \\ \(T_{i}\) & Deployed area of \(WSN_{i}\), denoted by \(H_{i}\times W_{i}\). \(H\) and \(W\) means height and width \\ \(R\) & Region of interest \\ \hline \hline \end{tabular}
\end{table}
Table 1: Notations used in the work.
## 3 Design Optimization
### Design Optimization Based on Metamodel  
Optimal design was carried out using the metamodels generated using an MLP and Kriging, respectively. HMA was used as the optimal design algorithm, and the ratio of the weighting between the average torque and the THD of the back EMF was set to 0.5:0.5. The HMA, a global optimization algorithm, was used for the optimal design based on the metamodel. The HMA was proposed in 2016 by [PERSON] [24]. The HMA can determine the global optimum faster than other global optimizers owing to the combined advantages of improved constrained differential evolution (rank-iMDDE) and modified cuckoo search (MCS). Table 3 shows the optimum design results. When Kriging was used, the average torque was improved by 1.3% compared to the initial model, while when MLP was used, it was improved by 2.2%. For the THD of the back EMF and constraints, the Kriging and MLP results were almost equal. Using MLP required somewhat more function calls than using Kriging, but convergence time decreased by five seconds. The optimal design results confirm that the proposed metamodeling-based optimal design results obtained using MLP had better prediction performance than the conventional Kriging model. In addition, FEA for the optimal model was conducted to verify the optimal design results. Therefore, the validity of the optimal design result was verified. Figure 9 shows the torque waveforms of the initial model and the optimal model. The average torque of the optimal model was improved by 2.2%, but the torque ripple was equivalent. The improvement of the torque characteristics may be due to the smooth change of the magnetic flux flow, as shown in Figure 10. The improvements in the magnetic flux flow and the change in reluctance can be attributed to the increase in the back EMF and torque [19]. Owing to the improvements in the flux flow, the back EMF of the optimum model was 34.0 V, which represents an improvement of 4.3% compared to the 32.6 V of the initial model, as shown in Figure 10(a). The results of harmonic analysis of the back EMF of both models were almost similar, as shown in Figure 10(b), and its THD values were also almost the same, as described in Table 3.
## 3 Results
### Data Correlation  
In this paper, seven parameters were compared to one another, including the occurring flood heights, in the two rivers to identify their correlation to the Mn content of soils. Table 2 shows the correlation table of each parameter, particularly flood height and Mn concentration.  
Table 2 presents the correlation matrix plot of each parameter with one another. Physicochemical properties of water show the following correlation with the Mn concentration in surface water: EC (\(r\) = 0.258), TDS (\(r\) = 0.231), pH (\(r\) = \(-\)0.704) and Temp (\(r\) = \(-\)0.073). Further, Mn concentration in soils was inversely proportional with pH (\(r\) = \(-\)0.438) and Temp. (\(r\) = \(-\)0.3). On the other hand, this parameter was positively correlated with EC (\(r\) = 0.196), TDS (\(r\) = 0.186), Mn in SW (\(r\) = 0.381), CN (\(r\) = 0.142) and flood heights (\(r\) = 0.458). Based on the statistical analysis, there is a significant and moderate positive correlation between flood heights and the Mn content of soils. In contrast, a significant and moderate negative correlation was observed between pH and Mn content of soils. There is also a significant and strong negative correlation between pH and Mn concentration in surface water.
## 2 Related Works
### Time Series Methods for Anomaly Detection  
Time series approaches for network anomaly detection also exist. For example, a study used Attention Mechanism-CNN-LSTMs for time series analysis and anomaly detection of Industrial IoT (IIOT) network data, such as edge device failure detection [16]. The authors noted that existing Deep Anomaly Detection (DAD) solutions could not be directly applied to IIOT with distributed edge devices due to privacy concerns and inflexibility. A Federated Learning (FL) technique was instead used in which the training algorithm was distributed across decentralized edge devices holding local data samples. The AMCNN attention mechanism was highlighted as a means to improve focus on important features in the time series, as well as aid in feature extraction. Another study also focused on time-series-analysis-based anomaly detection, suggesting an Exponential Weighted Moving Average (EWMA) model, or CNN-based time series analysis to learn filters capturing repeating patterns in the time series, which is treated as a one-dimensional image [17]. Further creative approaches to time series detection also exist. A study contributed a novel time series detection approach called Series2 Graph based on first representing time series subsequences as vectors and embedding them into a vector space [18]. In the vector space, overlapping trajectories correspond to recurrent patterns in the time series data, and a graph can be constructed in such a way that nodes are based on intersecting trajectories and whose edges are based on transitions among subsequences. The graph model encodes the normal behavior of the time series and allows for differentiating between frequently occurring patterns and anomalous behavior in time series data.
###### Abstract  
**Objective**: Our objective was to investigate the biomechanical and neuromuscular adaptations of the lower limbs during the landing phase of running under fatigue conditions. **Methods**: A controlled fatigue protocol was used to induce running-related fatigue in participants. Data were collected using a three-dimensional motion capture system, force platform analysis, and surface electromyography (sEMG). Kinematic variables, such as hip, knee, and ankle joint angles and range of motion, were analyzed alongside kinetic parameters, including vertical ground reaction forces (vGRFs) and joint moments. sEMG was used to measure the muscle activation levels of the rectus femoris, biceps femoris, tibialis anterior, and gastrocnemius, and to calculate antagonist coactivation ratios. Statistical analyses were performed to assess the differences in pre- and post-fatigue using paired t-tests, with a significance level set at \(\alpha\) = 0.05, and FDR correction was applied to control for multiple comparisons. **Results**: Post-fatigue, hip and knee flexion angles at initial contact decreased by 4.5% and 4.8%, respectively (FDR-adjusted \(p\) = 0.023, 0.0157), while their range of motion increased significantly by 10.4% and 11.1% (FDR-adjusted \(p\) = 0.0115, 0.0063). The second vGRF peak increased by 2.1% post-fatigue (FDR-adjusted \(p\) = 0.0086), with no significant changes in the first vGRF peak (\(p\) > 0.05). Muscle activation levels significantly increased in the rectus femoris (10.7%), biceps femoris (8.3%), tibialis anterior (9.1%), and gastrocnemius (10.2%) (FDR-adjusted \(p\) < 0.05). The antagonist coactivation ratio significantly decreased in the early and late landing phases (FDR-adjusted \(p\) = 0.0033, 0.0057), reflecting neuromuscular adjustments to fatigue. **Conclusions**: Fatigue-induced adaptations in joint kinematics, muscle activation, and coactivation strategies optimize performance and stability but may increase mechanical stress on lower-limb joints, highlighting a need for targeted interventions to mitigate injury risk.  
**Keywords:** running fatigue; kinematics; kinetics; sEMG; neuromuscular adaptation; coactivation +
Footnote †: journal: Applied sciences
## 4 Proposed Protocol Design
### Data Sensing and Compression  
The deployed sensor-based detector gathers data on the sensed data, the position of the explosive material, and an exact location picture. Sensor nodes compress the sensed data to effectively handle the sensor node resource and prolong the network lifetime. The Discrete Cosine Transform (DCT) or Discrete Wavelet Transform (DWT) is used in the image compression techniques to improve the image quality. However, both transformations reveal an increased complexity that requires more time, memory, and processing power. As a result, it has a constraint to apply in WSN since it consumes more energy.The image compression method based on LBT and Golomb + Multiple Quantization (MQ) coders described in [44] has a lower computational cost and memory need, making it perfectly adapted for distributed mobile WSNs. Thus, the compression method described in [44] is used to compress the image in the proposed work.
## 2 Materials and Methods
### Data
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Factors** & **Datasets** & **Data Source** & \begin{tabular}{c} **Spatial/Temporal** \\ **Resolution** \\ \end{tabular} \\ \hline Rainfall activity & \begin{tabular}{c} GPM\_3 IMERGDL \\ v06 \\ \end{tabular} & [PERSON], NASA & 0.1\({}^{\circ}\)\(\times\) 0.1\({}^{\circ}\) Daily \\ \hline Soil erodibility & \begin{tabular}{c} Soil structure \\ \end{tabular} & \begin{tabular}{c} European Soil \\ Database (ESDB) \\ \end{tabular} & - \\ \cline{2-4}  & Soil properties & \begin{tabular}{c} LUCAS topsoil \\ database \\ \end{tabular} & \\ \hline Slope length and & ALOS PALSAR DEM & \
\begin{tabular}{c} Alaska Satellite \\ Facility (ASF) \\ \end{tabular} & 12.5 m \\ \hline Cover management & Sentinel-2 images &
\begin{tabular}{c} “Copernicus” \\ program \\ \end{tabular} & 10 m \\ \hline Support practice & LUCAS observation & Eurostat & 2018 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Input datasets used in the Revised Universal Soil Loss Equation (RUSLE). For landscape metrics analysis, the land cover maps for the years 2010 and 2020 were derived from satellite imagery. For the year 2010, Landsat-5 TM Collection 1 Tier 1-calibrated top-of-atmosphere (TOA) reflectance images were accessed on Google Earth EngineS (GEE) provided by USGS. These images were classified into different Corine Land Cover (level 3) classes. For the year 2020, the classified land cover map already derived for the cover management factor (C-factor) in RUSLE model were used to estimate the landscape metrics. Concerning morphometric parameters analysis, ALOS PALSAR DEM was used to derive the basic morphometric parameters. The linear, shape, and relief parameters were extracted from the basic parameters.
## 3 Experiments and Analysis
### Qualitative Comparison
#### 3.4.5 Other Methods (FKRW, UIUNet)  
In the experiment, FKRW performs poorly, generating numerous false alarms and failing to identify any true targets. The deep learning-based method, UIUNet, generally produces fewer false alarms, but, as seen in results like Figure 8(i1-i6), it suffers from significant missed detections.
A disadvantage is that the transformer should manage the total converter power, and the number of bulky capacitors will be two times the number of inputs. Finally, a multi-input DC-DC topology for renewable energy applications with battery backup is proposed in [25]. The converter is based on the standard flyback topology, but additional diodes and an auxiliary switch are included to allow a safe switch between the renewable energy inputs and the batteries. In this paper, a N-modules flyback interleaved power converter with parallel connected inputs and a single output is proposed for a PV generation system. The circuit configuration is built by using a single transformer (one core) with N primary windings and one secondary winding. This represents an advantage in terms of cost and volume compared to the conventional interleaved IPOS topology [12], where the number of individual transformers (individual cores) equals the number of flyback modules. With this structure, the output diode operates N times more in a cycle (than conventional interleaved converters), and the output capacitor can therefore be smaller. For the classical problems of flyback topologies (leakage inductance) [15], active snubber circuits are added to provide protection to the main switches against \(dv/dt\) during commutation. The snubber circuits return the energy of the leakage inductance to the input capacitor of the PV, then increasing the efficiency of the topology. The proposed power converter is depicted in Figure 1 (with N = 3). More than three PV panels can be implemented in applications where it is required to increase the system power. The proposed converter is intended for applications with low input voltage and high current; the DC output voltage can be used to supply single-phase grid-tied inverters or hydrogen production with photovoltaic cells [26; 27; 28; 29]. On the other hand, a maximum power point tracking (MPPT) algorithm is implemented to extract the maximum power available in the PV modules [30]. The rest of the work is organized as follows. Section 2 thoroughly describes the operating principle of the converter. Section 3 presents the implemented MPPT scheme. In Section 4, guidelines about the transformer's core design are presented, whereas in Section 5 a description of the converter input capacitor design is reported. Simulations results are shown in Section 6, and Section 7 presents the obtained experimental results. In Section 8, efficiency results are show. The conclusions of the work are stated in Section 9.
# Distributed Detection of Large-Scale Internet of Things Botnets Based on Graph Partitioning
## 4 Proposed Method  
In this section, we describe a new botnet community detection method, which is constructed by Cluster-GCN. Figure 5 illustrates the overall forward propagation process. It involves the following steps:  
Step 1: Incorporate the initial embedding of nodes into the input model. Deploy network monitoring probes at specific locations to collect data traffic information from monitoring devices. Abstract the devices and traffic information as nodes and edges, respectively, constructing a graph representation of botnet traffic and performing data preprocessing.  
Step 2: Employ graph partitioning algorithms to partition the graph. Use the METIS graph partitioning algorithm while generating a matrix to store the removed edges during the partitioning process. During this partitioning process, a matrix is generated to meticulously record the edges that are removed. This careful partitioning enables the handling of large-scale data by breaking it down into smaller pieces that can be more easily processed and analyzed.  
Step 3: Each machine can independently train the segmented subgraphs. Prior to distribution, the server holds all the subgraphs and edge information. Start by randomly selecting a number, q. Combine every q subgraphs into one subgraph and distribute them to IoT devices for training. After completing one round of training, update the node embeddings in the server and select the next random integer.  
Step 4: Restore the training results from each device into the large graph and perform botnet detection on the nodes.
# An Overview of FIR Filter Design in Future Multicarrier Communication Systems
## 2 Multicarrier Modulations and FIR Filter  
By splitting the transmitting data into several components and sending each of these components over separate carrier signals, multicarrier modulation (MCM) has numerous advantages compared to single carrier modulation, including the relative immunity to multipath fading, less susceptibility to interference caused by impulse noise, and enhanced immunity to ISI. In the following subsection, five important MCM waveforms--OFDM, FBMC, GFDM, UFMC and F-OFDM are introduced.
# A Phase Correlation Anti-Noise Displacement Estimation Method Based on PSD Phase Screening
## 2 Methods  
Based on the traditional phase correlation, this paper introduced the PSD to evaluate the clarity of the cross-power spectrum matrix interference fringe, and prescreens the cross-power spectrum matrix according to the evaluation results, so as to automatically remove the areas with severe disturbances from the cross-power spectrum matrix. The workflow of the proposed method is shown in Figure 1.  
The input of the workflow is sequence images that show possible displacement between nearby continuing images. Firstly, the two-dimensional discrete Fourier transform (DFT) is used to convert the two images before (image 1 in Figure 1) and after displacement (image 2 in Figure 1) to the frequency domain. To prevent spectral leakage, the two images were windowed after converting to the frequency domain. In addition, in order  
Figure 1: The workflow of the proposed method.  
to concentrate the areas that are not disturbed by noise, we performed frequency domain centering on the two frequency domain images, and then the two frequency domain images are interfered with to obtain the cross-power spectrum matrix. Secondly, the PSD of the cross-power spectrum matrix is calculated to screen out the clear area of the cross-power spectrum matrix. Then, SVD is used to convert the cross-power spectrum matrix to one dimension, and one-dimensional phase unwrapping is performed. Finally, the displacement is solved by fitting a straight line based on the unwrapping phase. The details are explained as follows.