esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Conclusions  
In this paper we presented an architectural scheme for cooperative clustering which employs hierarchical routing featuring cooperative communication among the sensor nodes. The novelty of our scheme lies in the fact that our scheme saves energy by addressing three different critical design considerations. Firstly, as compared to state-of-the-art CMIMO, our clustering algorithm does not necessitates clustering of the whole network in case the responsibility of clusterhead has to be shifted to another node. Secondly, the clustering algorithm also results in a near optimal number of clusters in the network, a property that state-of-the-art CMIMO lacks. Thirdly, by employing cooperative MIMO in hierarchical routing, our algorithm gives better performance in comparison to existing routing techniques. Most of the time, clusters use 2 \(\times\) 2 MIMO for communication. The clustering algorithm selects clusters on the basis of four factors, namely: geographic location, link quality, residual energy of nodes and boundary constraints on the number of nodes in a cluster. Each cluster is led by a clusterhead which selects the cooperative node such that the energy required to communicate with the cooperative node is minimum. As circuit energy dominates the transmission cost for shorter distances, therefore, a node in a close geographic vicinity of the clusterhead is selected as the cooperative node. Experimental results show 15%-20% energy gain as compared to CMIMO and more than 50% energy savings as compared to traditional SISO for large distances. Network lifetime also shows a significant increase when compared with CMIMO. Number of hops to CCS in hierarchical MIMO also shows improvement as compared to previously proposed schemes.  
Since the underlying clustering scheme is distributed, performance in terms of energy expenditure can be further improved by using a centralized clustering approach for cooperative communication. However, this may not be practical since nodes may not have complete knowledge of the network. Secondly, even if nodes acquire this knowledge through exchange of messages among each other, the cost incurred in exchange of such messages, cluster formation and reclustering in a centralized clustering approach may surpass the energy conserved during cooperative communication of data.  
Considering implementation in real life scenarios, a few applications that can benefit from our proposed scheme can be monitoring of mines and oil and gas installations. Since our scheme gives improved results for large networks, we believe that implementing this scheme on real sensors for detecting and reporting faults and anomalies in above applications can be highly effective. This work lays foundations for such implementations while ensuring energy efficiency and enhanced network lifetime. Our subsequent investigations in WSN domain focus on utilizing the proposed scheme for such applications.
# Mapping Rice Fields in Urban Shanghai, Southeast China, Using Sentinel-1A and Landsat 8 Datasets
## 3 Results
### Optimal Polarization Images
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Class Pairs** & **VH Polarization** & **VV Polarization** \\ \hline Rice and Water * & 1.1 & 1.0 \\ Rice and Built & 1.6 & 1.6 \\ Rice and Trees * & 1.9 & 1.0 \\ Rice and Others * & 0.4 & 0.1 \\ Water and Built ** & 1.9 & 2.2 \\ Water and Trees * & 2.3 & 2.1 \\ Water and Others * & 1.1 & 0.7 \\ Built and Trees ** & 0.9 & 1.3 \\ Built and Others * & 1.3 & 1.2 \\ Trees and Others * & 1.1 & 0.6 \\ \hline \hline \end{tabular}  
* Class pair in which VH has better separability than VV, and ** class pair in which VV separates better than VH.  
\end{table} 
Table 4: Comparison of two-class separability results at VH and VV polarizations. Figure 5: Temporal backscatter profiles of Rice subclasses and the other land-cover at VH polarization. Data points correspond to Sentinel-1A images of Shanghai acquired on 9 June, 8 July, 1 August, 25 August and 18 September in 2015, respectively. In Figure 5, it can be clearly seen that, apart from the two Rice subclasses, the other land-cover classes show linear temporal backscatter profiles during the paddy rice growing season. Therefore, based on the observed increase in the backscatter of paddy rice at the first two months of growth and its steady decline thereafter relative to the almost stable temporal backscatter profiles of the other land-cover classes, and the spatial distribution of single-date NDVI and MNDWI values, we have presented results from two mapping algorithms; one obtained only from the temporal VH images of Sentinel-1A and the other derived from the synergistic application of temporal Sentinel-1A images and single-date Landsat indices. The latter algorithm (described in Section 2.7) is, however, proposed in this study as it produced much better classification accuracy.
# Real-Time Moving Ship Detection from Low-Resolution Large-Scale Remote Sensing Image Sequence
## 2 Low-Resolution Sequence Target Detection Algorithm
### Basic Framework  
The basic framework of the sequence target detection algorithm is shown in Figure 1, including four steps: preprocessing, feature discrimination, association discrimination, and S-YOLO discrimination.  
The preprocessing steps include morphology processing, adaptive threshold segmentation, and connected domain area elimination of false alarms. Morphology processing is often used to enhance the edges of breakable objects. This framework adopts multiple iterative morphology processing to improve the adaptability of the enhancement processing to different complex scenes. The adaptive threshold segmentation uses the ratio of the target region to the mean of the noise region to judge the front and rear scenes, which has good adaptability to low resolution and large fields of view. Finally, the connected domain was labeled for the binary results, the area of each region was calculated, and the larger or smaller parts were eliminated.  
For the remaining suspected targets, the features were identified. Considering that the parts are relatively simple and to ensure the real-time performance of the calculation, S-HOG (simplified HOG algorithm [13]) was used for analysis. After removing the gamma correction step, the obtained first-level suspected target image was divided into 16 blocks, and eight angular direction histograms were calculated, respectively. The 9X32 S-HOG feature result was obtained by merging 2 \(\times\) 2 adjacent blocks. Input S-HOG to binary nonlinear SVM [14] to generate feature discrimination results and eliminate false alarm targets.  
Target sequence association, through the position prediction and search of multi-frame sequence images, the suspected target, which cannot form trajectory information in each frame, was eliminated, and the trajectory information of the third-level suspected target was obtained.  
In the S-YOLO identification process, the secondary suspected target is further refined through feature labeling and CNN network identification. Considering the small scale of the third-level suspected target slice, the simplified four-layer YOLO network [15] was adopted to identify the associated sequence slice. The series was judged invalid when the number of false alarms in the sequence exceeded the limited threshold.
## 4 Network Interference Probability Analysis
### Heterogeneous Node Deployment Probability Analysis
#### 4.2.2 The Node Distance is \(0\leq d\leq r/2\)
: As shown in Figure 9, when \(0<r_{1}<0.343r\), the value range of \(d_{1}\) is \((r-r_{1},r)\); when \(0.84r<r_{1}<r\), the value range is \((r-r_{1},0.343r_{1}+\sqrt{r^{2}-r_{1}}{}^{2})\); when \(0.343r<r_{1}<0.84r\), the value range of \(d_{1}\) is \((r-r_{1},0.445\cdot\sqrt{4.5r^{2}-4r_{1}}{}^{2}+0.31r_{1})\). Therefore, according to the probability density function (PDF) (18), the integral of the PDF shown in (18) on the value range of \(d_{1}\) represents the probability of interference-optimality under specific transmission range, which can be calculated as:  
\[P_{\rm hets}=\left\{ \begin{array}{l}\int_{r-r_{1}}^{r}2\pi\rho r_{1}\cdot e ^{-\pi\rho r_{1}}{}^{2}dr_{1},\left(0<r_{1}<0.343r\right)\\ \int_{r-r_{1}}^{0.445\cdot\sqrt{4.5r^{2}-4r_{1}}{}^{2}+0.31r_{1}}2\pi\rho r_{1} \cdot e^{-\pi\rho r_{1}}{}^{2}dr_{1},\left(0.343r<r_{1}<0.84r\right)\\ \int_{r-r_{1}}^{0.343r+\sqrt{r^{2}-x^{2}}}2\pi\rho r_{1}\cdot e^{-\pi\rho r_{1 }}{}^{2}dr_{1},\left(0.84r<r_{1}<r\right)\end{array} \right., \tag{37}\]  
**Corollary 2**. : _In heterogeneous networks, when the transmission range is 0.84r, the probability of interference-optimal will get the maximum value._  
Proof. : As shown in Equation (37), when \(0<r_{1}<0.84r\), \(P_{\rm hets}^{\prime}>0\); when \(0.84r<r_{1}<r\), \(P_{\rm hets}^{\prime}<0\). So when \(r_{1}=0.84r\), the probability of interference-optimality in the heterogeneous WSNs will get the maximum value.
# An Approach for Deployment of Service-Oriented Simulation Run-Time Resources
## 6 Experiment Evaluation
### Benchmark Policies  
Referring to the experimental evaluation in [8; 10; 13; 35], we provide the following five representative baselines for comparative studies.  
* K-Means Clustering + NSGA-II (KMNSGA): This strategy first deploys edge servers on \(k\) BSs using the K-Means clustering algorithm. On this basis, it uses NSGA-II to determine how other software resources should be deployed.
* NSGA-II: This approach is to directly use NSGA-II to solve the deployment problem for SRR we are facing.
* K-Means Clustering + Top-\(k\) Service Placement (TServiceD): This algorithm first deploys \(k\) edge servers using the K-Means clustering algorithm. Then, it greedily deploys micro-services by first deploying the MS with the highest user demand, then the service with the second highest user demand, and so on. Finally, it randomly deploys the simulation middleware and constructive members.
* Top-V Server Deployment + Top-V Service Deployment (TSD\({}^{2}\)): This method first selects the \(k\) most heavily used BSs to deploy edge servers. Then, it greedily deploys micro-services by prioritizing the MS with the highest user demand, followed by the second highest, and so on. Finally, like TServiceD, it also randomly deploys the simulation middleware and constructive members.
* Random: This policy randomly determines the deployment scheme while ensuring its feasibility.
# Conception of a System-on-Chip (SoC) Platform to Enable EMG-Guided Robotic Neurorehabilitation
## 4 Experimental Results
### Resource Utilization  
Resource consumption on the TE0727 (ZZ-10) SoC-FPGA was minimal, using 15.9% of LUTs, 11.9% of registers, and 5% of available RAM blocks, leaving capacity for additional processing logic as can be summarized in Table 2. However, it is important to note that using a higher sampling frequency, such as the indicated 16 kSPS and 32 kSPS, results in increased FPGA resource consumption. This is because, to achieve the same capture time, the amount of generated data is two or four times greater, respectively.  
One identified limitation was that the higher transfer speed of DMA compared to UART transmission caused data overwrites in DDR memory. Increasing allocated DDR mitigated this issue, enabling continuous signal acquisition for up to 7 min and 38 s, deemed sufficient for volunteer evaluations. A permanent solution is proposed for future work.  
Figure 4: EMG signals obtained by the AFE in time **(top)** and frequency **(bottom)** at gain 12. Cap. 1 and 2 were taken at different times, but they represent the acquisition of the Rectus Femoris. The red box indicates the frequency range displayed in the zoomed inset.
## 2 Background
### Other Notations  
\(\overline{\mathbf{A}}\) and \(\mathbf{A}^{H}\) denote the complex conjugate and the conjugate transpose of a matrix \(\mathbf{A}\), respectively. \(\mathbf{A}^{T}\) denotes the transpose of a matrix \(\mathbf{A}\). The support set of a vector \(\mathbf{x}=[x_{1},\cdots,x_{N}]\) is defined by \(\mathrm{supp}(\mathbf{x})=\{i|x_{i}\
eq 0\}\). \(\mathcal{CN}(\mu,\sigma^{2})\) denotes the complex Gaussian distribution with mean \(\mu\) and standard deviation \(\sigma\). The Fourier matrix \(\mathbf{F}_{q}\) is given by \(\mathbf{F}_{q}=[\omega^{(i-1)(j-1)}|1\leq i,j\leq q]\), where \(\omega=e^{-2\pi j/q}\). The Hadamard matrix \(\mathbf{H}_{n}\) satisfies \(\mathbf{H}_{n}\cdot\mathbf{H}_{n}^{T}=n\mathbf{I}\)[46]. As an example, the Hadamard matrix from [PERSON]'s construction [47] is given by  
\[\mathbf{H}_{2n}=\begin{bmatrix}\mathbf{H}_{n}&\mathbf{H}_{n}\\ \mathbf{H}_{n}&-\mathbf{H}_{n}\end{bmatrix}, \tag{5}\]where \(\mathbf{H}_{1}=[1]\) and \(\mathbf{I}\) is an \(n\times n\) identity matrix. For a matrix \(\mathbf{A}\in\mathbb{C}^{m\times N}\), the operator \(\mathrm{vec}(\mathbf{A})\) returns a vector \(\mathbf{a}\in\mathbb{C}^{mN\times 1}\) by stacking the columns. The operator \(\mathrm{circ}(\mathbf{a})\) returns a matrix by circularly shifting a input vector \(\mathbf{a}=[a_{1},\cdots,a_{N}]^{T}\), i.e.,  
\[\mathrm{circ}(\mathbf{a})=\begin{bmatrix}a_{1}&a_{N}&a_{N-1}&\cdots&a_{2}\\ a_{2}&a_{1}&a_{N}&\cdots&a_{3}\\ a_{3}&a_{2}&a_{1}&\cdots&a_{4}\\ \vdots&\vdots&\vdots&\ddots&\vdots\\ a_{N}&a_{N-1}&a_{N-2}&\cdots&a_{1}\end{bmatrix}\in\mathbb{C}^{N\times N}. \tag{6}\]  
The operator \(\mathrm{diag}(\mathbf{a})\) returns a diagonal matrix that has the vector \(\mathbf{a}\) as its diagonal entries. The operator \(\bigcirc\) denotes a column-wise multiplication operator, i.e.,  
\[\mathbf{a}\bigodot\mathbf{b}=[a_{1}b_{1},\cdots,a_{m}b_{m}]^{T} \tag{7}\]  
where \(\mathbf{a}=[a_{1},a_{2},\cdots,a_{m}]^{T}\in\mathbb{C}^{m\times 1}\) and \(\mathbf{b}=[b_{1},b_{2},\cdots,b_{m}]^{T}\in\mathbb{C}^{m\times 1}\). The operator \(\mathcal{M}(\mathbf{A})\) returns the maximum magnitude of a matrix \(\mathbf{A}\).
# Using Graph Embedding Techniques in Process-Oriented Case-Based Reasoning
## 3 Neural Networks for Graph Embedding
### Aggregator and Graph Similarity  
The next step after the propagation layer is the aggregator. The aggregator aggregates all node representations from graph \(G\) that result from the \(K\)-th iteration of propagation to form a single whole-graph representation \(h_{G}\).  
\[h_{G}=\text{MLP}_{\text{G}}\Bigg{(}\sum_{v\in N}\Big{(}\sigma\big{(}\,\text{ MLP}_{\text{gate}}(h_{v}^{(K)})\big{)}\odot\text{MLP}_{\text{state}}(h_{v}^{(K)}) \Big{)}\Bigg{)} \tag{6}\]  
The aggregation approach in Equation (6) originates from [PERSON] et al. [49]. It describes a weighted sum that uses gating vectors as a method to differentiate relevant from irrelevant node representations. At the single node level, the function \(\text{MLP}_{\text{gate}}\) transforms the node representation \(h_{v}^{(K)}\) to a gating vector. This gating vector is then activated with the softmax function \(\sigma\) (similar to Equation (4)). \(\text{MLP}_{\text{state}}\) transforms the node representation into a new vector that can then be used in an element-wise multiplication (indicated by the symbol \(\odot\)), resulting in the final single-node representation. The representations of all individual nodes are afterwards combined by an element-wise sum (indicated by the sum symbol \(\Sigma\)) to form a single vector. This single vector finally acts as an input for \(\text{MLP}_{G}\), leading to the whole-graph representation \(h_{G}\). \(h_{G}\) is an element of \(\mathbb{R}^{l_{g}}\), whereas the vector length \(l_{g}\) can be parameterized and contributes to a trade-off between representation quality (i.e., increased \(l_{g}\)) and computation time (i.e., decreased \(l_{g}\)).  
To obtain a single scalar from the whole-graph representation of the two graphs, represented as the final similarity \(fsim(h_{G_{1}},h_{G_{2}})\), it is necessary to apply a vector similarity measure on the graph vectors (see Equation (7)).  
\[fsim(h_{G_{1}},h_{G_{2}})=vsim(h_{G_{1}},h_{G_{2}}) \tag{7}\]  
[PERSON] et al. [23] do not specify the type of vector similarity measure. Common candidates are cosine similarity, dot product, and Euclidean distance. To have a similarity value that is bounded between \([0,1]\), it is recommended to use the cosine similarity in combination with a ReLU activation of \(\text{MLP}_{G}\).
# Fatigue Life Analysis of Cylindrical Roller Bearings Considering Elastohydrodynamic Lubrications
## 4 Result Calculation and Analysis
### Effect of Radial Load Variations on Bearing Fatigue Life  
Variations in radial loads exert significant impacts on bearing fatigue life. This study calculates bearing lifespan using both the proposed theoretical and De Mul (neglecting lubrication conditions) models, while also determining the modified reference rating life under different load conditions in accordance with ISO/TS 16281:2008.  
Figure 10: Load distribution diagram of the outer ring.  
Figure 9: Load distribution diagram of the inner ring.  
As revealed in Table 5, (1) bearing fatigue life decreases rapidly with increasing radial loads, and (2) the comparative analysis demonstrates substantial discrepancies between the De Mul model (lubrication-free assumption) and the proposed theoretical model. These differences arise because the consideration of lubrication conditions amplifies roller-bearing ring contact loads, thereby reducing calculated bearing life. Furthermore, when comparing the ISO/TS 16281:2008-calculated modified reference rating life with other models' results, the ISO-based lifespan is smaller due to additional parameters considered in the standard, including the contamination factor, operational clearance, and misalignment effects.  
To further investigate the lubrication state's impacts on roller bearing system fatigue life, this study computes relative deviations between system lifespans derived from the De Mul model (non-lubricated condition) and the proposed theoretical model, as well as between ISO/TS 16281:2008 calculations and the theoretical model results. The lifespan calculation outcomes and relative deviations are illustrated in Figure 11.
## 3 System Model
### PSO Model Review
#### 3.4.3 Particle Sorting  
Particle optimization begins through the generation of n random particle coordinates, from which the coordinates of the top k particles are preserved as the superior set. A new set of n particle coordinates is then synthesized for subsequent comparison against the prior set on a one-to-one basis utilizing a fitness function.  
This methodology involves the preservation of coordinates for the leading k particles, distinguished by their superior fitness values. After m iterations, the process culminates in the retention of the k particle coordinates that exhibit the closest proximity to the designated target position. A PSO optimization schematic is shown in Figure 4. In Figure 4a, PSO particles which are labeled in blue are scattered in the region. The three particles closest to the target are labeled in green. The target position is labeled in red. In Figure 4b, the PSO particles are updated, and one particle is closer to the target location than one of the previous particles, so this particle replaces it as the optimal particle, and the replaced one is labeled in yellow. In Figure 4c, a new particle also replaces one of the previous particles. Finally, in Figure 4d, with the continuous iteration of particles, three optimal green particles are finally retained, which is the complete process of particle screening. In the actual process, the optimal number of particles, k, can set to other values. In the particle sorting procedure, k is set to 3.
## 1 Introduction
To date, various post-processing algorithms have been introduced to increase the performance of distributed vibration sensors. These algorithms are designed to process one- and two-dimensional datasets, for instance, integrated principal component analysis [18], curvelet denoising [19], empirical mode decomposition [20], fast wavelet transform [21], CEEMDAN technique [22], power spectrum analysis [23], two-dimensional edge detection [24], and two-dimensional adaptive bilateral and filtering [25]. In this paper, a novel noise-reduction technique is proposed to enhance the SNR of the distributed vibration sensor using weighted singular value decomposition (WSVD). WSVD removes low-frequency amplitude fluctuation and other random noises from the two-dimensional image matrix made by the number of raw signal-traces encoded with time- and spatial-domain information. The technique shows great potential in retrieving the location information and frequency of the vibration event precisely under the condition of strong background noise.
# Unbridled Integons: A Matter of Host Factors
## 2 The Unique Integon Genetic System
This selectivity for the bottom strand of the structured _attC_ site is essential for the correct orientation of the cassette upon integration at the _attl_ site, allowing for its expression by the \(\mathrm{P_{C}}\) promoter [14]. Figure 1: Organization of the integration system. Integrons include a stable platform and a variable cassette array. The platform is composed of the integrase gene (_intl_) under the control of the integrase promoter (P\({}_{\text{intl}}\)) and encoding the integron integrase (Intl). The platform also contains the cassette promoter (P\({}_{\text{C}}\)), which directs transcription of proximal cassettes and the _attl_ recombination site in which cassettes are inserted. The cassettes in the variable cassette array are represented by successive arrows. Their color intensities reflect their level or expression. Integrase catalyzes both excision (_attC_\(\times\)_attC_) and insertion (_attl_\(\times\)_attC_) of cassettes, leading to cassette shuffling. Figure 2: _attl_ recombination sites. (**A**) Sequence of the double-stranded _attl_ site. Direct repeats (DR1 and DR2), and both left and right (L and R) imperfect inverted repeats are indicated by grey arrows. Putative integrase binding sites are represented by green boxes. The cleavage site (CS) is indicated by a red arrow. (**B**) Schematic representation of the double-stranded (ds) and single-stranded (ss) bottom strand (bs) _attC_ sites. Only the conserved nucleotides are indicated. Inverted repeats (R”, L”, L’, and R’) are indicated by grey arrows. Green boxes show putative Intl binding sites, and the cleavage site (CS) is indicated by a red arrow. R, purine; Y, pyrimidine; N, any bases; bs, bottom strand; and ts, top strand. For the _attCbs_, the structural features, namely, the unpaired central spacer (UCS), the extrahelical bases (EHBs), and the variable terminal structure (VTS), are indicated. The dotted lines represent the VTS length variability among _attC_ sites. In this review, we will focus on the extensive and exquisite interconnectivity of the integron system. We will particularly focus on the host factors that bridge their functioning to the biology of the host. We will first examine how the expression of the cassettes is connected to the host cell physiology. We will then detail how the observed integrase expression regulation is relevant in both natural and clinical contexts. We will also discuss the mechanistic aspects of their recombination reactions, considering the host biological processes and molecular actors involved to achieve those recombination reactions. Finally, we will examine how any interconnection between the integron and the host comes at the cost of constraints in terms of dissemination. This apparently led to the emergence of the MI quasi species, which probably owe their high success to their limited need of specific HF, but which, however, rely on the much more controlled SCI cassette reservoir to provide them with an unlimited source of cassettes to be able to spread in an unbridled fashion.