esa-sceva/satcom-chunk-collection huggingface dataset:
# Fusing Local and Global Information for One-Step Multi-View Subspace Clustering
## 5 Experiments
### Experimental Setting  
In this paper, LGOMSC is compared with twelve relevant methods including  
* **FeatConcate**: Concatenate the features of different views into a vector and utilize k-means to acquire the clustering result. It is regarded as the baseline method.
* **Co-reg_c** and **Co-reg_p**: Centroid-based co-regularization [52] and pairwise co-regularization [52].
* **LMSC**: Latent multi-view subspace clustering [35].
* **FMR**: Flexible multi-view representation learning for subspace clustering [36].
* **MLRSSC**: Multi-view low-rank sparse subspace clustering [17].
* **RMKMC**: Robust multi-view k-means clustering [53].
* **mPAC**: Multiple partitions aligned clustering [25].
* **LMVSC**: Large-scale multi-view subspace clustering in linear time [26].
* **PMSC**: Partition level multi-view subspace clustering [22].
* **COMVSC**: Consensus one-step multi-view subspace clustering [24].
* **GMC**: Graph-based multi-view clustering [43].
* **MVSCLG**: Multi-view subspace clustering with local and global information [45].  
We conduct these comparison methods from corresponding open-source codes and follow their papers to set the optimal parameters. LGOMSC contains two parameters \(\lambda\)  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Data Set** & **Point** & **Class** & **View** & **d1** & **d2** & **d3** & **d4** & **d5** & **d6** \\ \hline
3 sources & 169 & 6 & 3 & 3560 & 3631 & 3068 & & & & \\
100 leaves & 1600 & 100 & 3 & 64 & 64 & 64 & & & \\ BBC & 685 & 5 & 4 & 4659 & 4633 & 4665 & 4684 & & \\ Caltech101 & 1474 & 7 & 6 & 48 & 40 & 254 & 1984 & 512 & 928 \\ COIL-20 & 1440 & 20 & 3 & 1024 & 3304 & 6750 & & & \\ NottingHill & 4660 & 5 & 3 & 6750 & 3304 & 2000 & & & \\ Webkb & 1051 & 2 & 2 & 1840 & 3000 & & & & \\ Cornell & 195 & 5 & 2 & 195 & 1703 & & & & \\ Wikipedia & 693 & 10 & 2 & 128 & 10 & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Summary of nine multi-view benchmark data sets (\(d_{v}\) denotes the dimensionality of the \(v\)-th view).  
and \(\lambda_{1}\). For \(\lambda\), it is first set with a proper value in a heuristic way, then in each iteration, \(\lambda\) is divided by two if the number of zero eigenvalues of \(L_{U}\) is greater than \(c\) and multiplied by two if it is smaller than \(c\). For \(\lambda_{1}\), we adopt the grid search method to empirically choose it in the range of \(\{1,10,20,\ldots,100\}\). In this paper, we use L2 norm for data normalization, i.e., \((x_{i}^{v})^{\prime}=\left.\frac{x_{i}^{v}}{\left\|x_{i}^{v}\right\|_{2}}\right.\).
## 6 Numerical Results and Discussion
### Average MV-MOS Performance  
Figure 6 shows the average MV-MOS performance comparison between the P3 DVQC scheme and the five baseline schemes. BFS is the ideal upper bound algorithm of the experiment, it is obtained by traversing all the solution spaces and requires knowing all the system information, which requires a lot of time overhead to search the solution space, hardly realized in the actual scene. The CES scheme does not use any caching technology, so its performance is the lowest. Secondly, the RCS chooses video content caching by the random algorithm, resulting in the performance being very unstable. The FOCS only caches the lowest bit rate video. Because the lowest bit rate video file is small, the FOCS can cache more different video content varieties, but it cannot effectively provide high-definition video. ROCS caches high-bitrate HD videos as much as possible, so vehicle users can achieve high average MV-MOS if the required HD video is pre-cached in MECS. However, the size of high bitrate video content is larger, so the higher storage overhead leads to a lower cache hit rate and performance. The P3 DVQC proposed by this paper comprehensively considers the multiple bitrate video quality, backhaul bandwidth, and caching capacity to maximize objective. Therefore, the caching strategy obtained by the P3 DVQC is that it caches a mixture of low and high bitrates. Figure 6 shows that the proposed can converges to the upper bound, and its performance is better than other benchmark algorithms.  
Figure 4: A comparison of P3 DVQC and DVQC.  
Figure 5: Convergence speed.
# Experimental Test of Continuous Wave Frequency Diverse Array Doppler Radar +
## 2 Data Collection I: Far-Field Beampattern
### Temporal Periodicity of the Far-Field Pattern  
#### 2.5.1 RF Hardware Collection  
For a fixed collection location \((R,\theta)=(R_{o},\theta_{o})\) and an FDA transmitting a linearly progressive frequency offset \(\Delta f\), [PERSON] [24] showed that the expected periodicity in time of the far-field pattern is \(1/\Delta f\). This result is also seen in [4; 5]. To demonstrate that this behavior continues at baseband in our system setup, a bench test was conducted using three signals fed into a power combiner whose output was then sampled by an X310 SDR. The frequency spacing between the signals followed a linear frequency offset scheme to mimic the coherent summation of the FDA beampattern in the far field. The temporal periodicity results for frequency offsets of 10 kHz, 12 kHz, 20 kHz, 50 kHz, 100 kHz, 250 kHz, and 500 kHz were recorded and averaged over 100 peaks at each frequency offset. These results are shown in Table 4.
## 2 SIR-Based Performance Analysis  
In the next sections, the performance of a wireless system in the presence of BX fading and \(\kappa\)-\(\mu\) CCI will be determined. In order to mitigate the effects of fading and CCI, a multi-branch SC diversity receiver is used. The model of this receiver is shown in Figure 1.  
The SC receiver operates by feeding the user the signal from the highest value input.  
We marked the input signals with: \(x_{i}\), \(i=1\), \(2\), \(\ldots\), \(L\); \(L\geq 2\), and the output signal with \(x\). The input envelopes of CCI are: \(y_{i}\), \(i=1\), \(2\), \(\ldots\), \(L\), with output value \(y\). Given the presence  
Figure 1: Model of multi-branch SC diversity receiver.  
of CCI, performance will be determined based on the output SIR, denoted by \(z\) and equal to \(max(z_{1},z_{2},\ldots,z_{L})\), where \(z_{i}\) are the input SIRs equal to the ratios of the useful signals and the CCI at the input antennas: \(z_{i}=x_{i}/y_{i}\).
# QoS Aware and Fault Tolerance Based Software-Defined Vehicular Networks Using Cloud-Fog Computing
## 4 Proposed Solution
### VANET Vehicles
#### 4.3.1 SDN Controller  
The SDN controller is used for information/messages prioritization and forwarding to destination. The controller is connected to the nearby SDN nodes and, from time to time, updates routing tables of the received information. The controller is placed in edge fog nodes to get the processing power and short storage. For heavy computation and permanent storage, the controller sends information to the cloud.
## 3 Simulation Results
### Pipeline Response to a Lightning Event Striking a Power Line Conductor  
Once the impulse response of the conductors of a given system is available, it can be used to obtain the transient response to any given applied waveform. Let us consider the following physical situation: a lightning event striking Phase Conductor \(a\) of the power line described in Figure 1 and Table 1 of the previous section. The resulting voltage waveform on the phase conductor \(\Delta\varphi^{\prime}_{L}\) is shown in Figure 4. The applied per-unit length voltage \(\Delta\varphi^{\prime}_{L}\) was taken from the measurements performed by [PERSON] and [PERSON] on a scaled power line in [29]. The phase conductor current due to the lightning strike, \(I_{phase\;a}\), can be obtained with the following convolution integral:  
\[I_{phase\;a}(t)=\int_{-\infty}^{\infty}\Delta\varphi^{\prime}_{L}(t-\tau)I^{ \prime}_{phase\;a,\delta}(\tau)\,\mathrm{d}\tau\,, \tag{8}\]  
where \(I_{phase\;a,\delta}\) is the current impulse response of Phase Conductor \(a\). Analogously, the transient pipeline current response to the lightning event can be obtained using \(I^{\prime}_{pipeline,\delta^{\prime}}\), i.e., the impulse response of the buried pipeline:  
\[i_{pipeline}(t)=\int_{-\infty}^{\infty}\Delta\varphi^{\prime}_{L}(t-\tau)I^{ \prime}_{pipeline,\delta}(\tau)\,\mathrm{d}\tau\,. \tag{9}\]  
It is worth noticing that \(I^{\prime}_{phase\;a}\) and \(I^{\prime}_{pipeline,\delta}\) in the two former expression have units of \(\mathrm{A}\,\mathrm{m}\,\mathrm{V}^{-1}\,\mathrm{s}^{-1}\), since they were obtained as the response to a per-unit length voltage impulse with a time-integral equal to one. The computed currents on the pipeline and on Phase Conductor \(a\) are shown in Figure 5.  
Figure 4: Response of the phase conductor voltage to a lightning event from [29].
# Action Classification for Partially Occluded Silhouettes by Means of Shape and Action Descriptors
## 3 Proposed Approach for Extraction and Classification of Action Descriptors
### Extracting Motion Information and Adding Occlusion  
Preprocessed binary images are used to obtain the centroid trajectory. The centroid coordinates are calculated as the average coordinates of all pixels belonging to the silhouette's area. The trajectory length (relative to the bottom edge of the video frame) is used as a condition for dividing the database into two subgroups. This step is referred to as coarse classification. Actions are often distinguished into periodic and non-periodic ones, but in case of physical activity all exercises can be repeated periodically. Therefore, here the database is divided automatically into actions performed in place (a trajectory is short, e.g., bending) and actions during which a person changes location across the video frame (a trajectory is long, e.g., walking). For a single video sequence, centroid positions are calculated for each silhouette and accumulated in a separate image whose size is equal to the size of a video frame (144 \(\times\) 180 px). The trajectory length is measured relative to the bottom edge of the video frame and if it is shorter than 20 pixels an action is classified as performed in place. Longer trajectories refer to actions with changing location of a silhouette. Coarse classification allows for the use of different features and parameters for each subgroup.  
The assumed application scenario requires the input data to be occluded in the specific manner; therefore, the foreground masks from the [PERSON] database were modified in two different ways to occlude the upper and lower parts of the camera's field of view. This type of occlusion simulates a situation in which a person is too close to the camera, and either the person's legs or head with neck and shoulders do not appear in the video frame. Foreground silhouettes on the original masks were located in slightly different places vertically. Therefore, in order to determine the cut-off point, the centroid coordinates of all silhouettes in the given sequence were averaged. The average value was then decreased or increased to experimentally indicate the final cut-off point, which translates to the size of the occlusion. For example, if the averaged centroid row coordinate equals 50 (the  
Figure 3: Examples depicting various artefacts resulting from the background subtraction.  
Figure 2: Exemplary foreground masks from the [PERSON] database [21].  
image matrix origin is in the upper left corner), then it was decreased by 20 for upper occlusion and increased by 20 for the lower occlusion. The cut-off point differs only between sequences due to changes in centroid location, but it is constant for all frames within a given sequence (a simulation of a static camera). Figure 4 depicts several examples of occluded silhouettes.
###### Abstract  
Nowadays, more and more complex methods of signal modulating and processing are actively used for organizing underwater acoustic communication with and between submerged mobile vehicles due to harsh underwater conditions. In this research, the method that is based on multi-frequency signals forming (OFDM) with the constant envelope is applied to the problem. It is based on multi-frequency FM-OFDM signals forming with Quadrature Phase Shift Keying (QPSK) modulated subcarriers and FM spectrum spreading coefficients of 1, 2, 4, and 10. The proposed solution was modeled in a software simulator, which implements a noisy underwater acoustic multipath channel, changing the bit error rate (BER) from 0.15 to 10\({}^{-3}\). In addition, it was tested during the full-scale data transmission experiments at 25 km distance using a low frequency (400 Hz) underwater acoustic apparatus under conditions of strong impulse noises and quasi-non-stationary channel. The results of in-situ experiments were similar to the ones that were obtained during the simulation.  
OFDM; autonomous underwater vehicle; underwater acoustic communication; long-haul underwater acoustic communication 2018 8 402 doi:10.3390/app8030402
## 2 Study Area and Data
### Data  
A total of 3.32 million Weibo blogs geotagged with Beijing were crawled from the Sina Weibo platform. All posts were published from June to September 2017, a period that Beijing receives most of the rains all over the year. Every blog comes with the information of its user, publishing location, publishing time, and texts.  
We also collected data of rainfall amount, points of interest (POIs), and water ponding sites. We used two precipitation data sets in this study. The hourly precipitation data at meteorological stations were collected from the China Meteorological Data Network ([[http://data.cma.cn/](http://data.cma.cn/)]([http://data.cma.cn/](http://data.cma.cn/))). The 1-h cumulative precipitation dataset was generated from the meteorological radar in Daxing, a town located 13 km south of the city. The radar covers the entire Beijing area and provides precipitation data with a spatial resolution of \(1.051\times 1.051\) km (After image processing and registration processing). In this paper, we unified the study of grid scale to this resolution, and the other data sources used are also processed to this resolution for further analysis and processing.  
The POIs include the locations of businesses, educational institutions, residential areas, transportation facilities, open spaces, and others. The data set was produced mainly for navigation by the Beijing NavInfo Co., Ltd, Beijing, 110000. Each POI comes with its coordinates (latitude and longitude), type, name, address and flag (show its importance level). In this paper, we categorized the POIs into five classes (Table 1).
## 6 Results and Discussion
### Similarity Score  
In this study, the similarity test was applied to the Word2 Vec and FastText models.  
As shown in Figure 11, the FastText models achieved an optimal similarity score of approximately 8 out of 10. The models displayed a clear performance trend, with scores consistently improving as the vocabulary size decreased. Notably, the Wiki skip-gram models outperformed the CBOW models in the majority of cases. Additionally, the models consistently yielded their best results when employing a vector size of 200 with a higher window size. This indicates that a smaller vector size can have a positive impact on the models' performance.  
Conversely, the Word2 Vec models displayed distinct performance characteristics, as illustrated in Figure 12. They achieved an optimal score of approximately 7.3. Notably, models trained on the Wiki corpus consistently surpassed all others in performance. Unlike FastText, the Word2 Vec models delivered their best results when utilizing the CBOW architecture in most cases. Additionally, employing a smaller window size also had a positive impact on their performance.  
The strong performance observed in the Wiki corpus models can be attributed to the fact that Wiki models have a larger vocabulary size. This particular factor seems to have had a notable impact on the FastText models trained on the Books corpus. Hence, reducing the window size and vector size had an impact on the outcomes for both the Word2 Vec and FastText models. The size of the vocabulary affected the FastText models, whereas the quality of the vocabulary affected the FastText models.  
To conclude, our observations revealed that the size of the corpus and the values of the hyperparameters had varying impacts on each test. In the analogy test, we found that employing a large vocabulary size had a positive influence on the results. Additionally, the FastText models with skip-gram approaches proved effective in solving analogy test questions. For sentiment analysis, we discovered that vocabulary size played a crucial role. Furthermore, for the FastText models, applying CBOW architecture led to improved accuracy, whereas for Word2 Vec, applying skip-gram architecture enhanced accuracy. Additionally, smaller window size had a positive effect on accuracy. Finally, in the similarity score test, the FastText models delivered the highest scores with the Wiki corpus and skip-gram architecture. Smaller window sizes and smaller vector sizes positively impacted the results. Hence, since the impact of the hyperparameters varies for each test, we can infer that there is no universally superior hyperparameter setting that guarantees high accuracy across all NLP tasks. This implies that each test has its own specific criteria that need to be met [1].
## 3 Thermal Analysis of the WCLL COB Segment
### Thermal Analysis of the Equatorial Radial-Toroidal Cell of Each of the 7 COB Poloidal Regions
#### 3.1.3 Results  
A steady-state thermal analysis was performed for the equatorial cells of each of the 7 COB poloidal regions with the aim of investigating their thermal behaviour under the loads and boundary conditions they undergo. For the sake of brevity, the maximum temperature predicted in each region is reported in Table 3, where the temperatures exceeding the suggested limit value of 550 \({}^{\circ}\)C are reported in red. Looking at the temperature values reached on SPs and FW, as the baffle plates do not play a structural role, only O1 and O7 regions did not overtake the Eurofer structural material limit temperature, set to 550 \({}^{\circ}\)C, whereas all the others exceeded this value. Hence, for the latter regions, an optimization of the DWTs layout was necessary. Moreover, looking at the results reported in Table 3, the O6 region showed a temperature higher than the temperature limit on the FW too. This result suggests that this poloidal region must be equipped with 6 channels, as in O7. Hence, as to O6 region, a further optimization of the FW channels was necessary.  
As an example, the thermal field obtained within the structural material of the O4 region, corresponding to the equatorial region of the whole COB segment, is depicted in Figure 5, whereas in Figure 6 the spatial temperature distribution within Eurofer of the O6 equatorial cells is depicted.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **O1** & **O2** & **O3** & **O4** & **O5** & **O6** & **O7** \\ \hline HTC\({}_{\rm{CC}}\) & 31,368 & 33,609 & 34,490 & 35,570 & 35,518 & 38,895 & 36,167 \\ HTC\({}_{\rm{DWTs}}\) & 16,965 & 19,901 & 19,258 & 19,905 & 19,847 & 20,033 & 13,576 \\ HTC\({}_{\rm{DWTs,rec}}\) & 26,545 & 31,139 & 33,530 & 34,656 & 34,556 & 31,346 & 21,243 \\ \hline \hline \end{tabular}
\end{table}
Table 2: HTC values used for cooling channels and DWTs coolant.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **O1** & **O2** & **O3** & **O4** & **O5** & **O6** & **O7** \\ \hline Tmax on FW [\({}^{\circ}\)C] & 478.2 & 503.3 & 519.0 & 526.6 & 525.9 & 566.4 & 479.2 \\ Tmax on SPs [\({}^{\circ}\)C] & 546.7 & 580.5 & 603.1 & 612.9 & 612.1 & 585.2 & 501.0 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Maximum temperature per region equipped with v06b layout.
## 4 Algorithm Design
### Workflow  
The algorithm framework consists of two main components, support logic and control logic, which work together to implement a queue scheduling algorithm designed to achieve airtime fairness, as illustrated in Figure 5.  
The support logic does not directly handle data frame transmissions; instead, it provides the necessary calculations and services for the algorithm's operation. Specifically, it determines the airtime resources (discussed in Sections 3.2 and 3.3) and quotas (detailed in Section 4.2) for each flow. The airtime resources for each flow depend solely on its own state and must be recalculated whenever the wireless rate changes. Conversely, the quotas that ensure fairness are influenced by the states of all flows. Thus, when a flow joins, leaves the network, or experiences a rate change, its airtime resources must be recalculated based on the current network state, while quotas are updated to reflect the conditions of all flows accessing the AP.  
The control logic is responsible for selecting specific frames from the sending queue for transmission. When a wireless frame is ready to be sent, its type is identified by its header field. The proposed queue scheduling algorithm focuses on TCP and UDP data frames and does not affect wireless control frames. The algorithm regulates the number of data packets sent by each flow according to the quotas calculated by the support logic, using a DRR-basedapproach. Ultimately, the product of the total data transmitted by each flow and its respective airtime resource results in their total airtime allocation, thereby improving airtime fairness.
# Accelerometers in Monitoring Systems for Rail Vehicle Applications: A Literature Review
## 1 Introduction
### The Applications of Accelerometers Used in the Industry
#### 1.5.1. Vehicle Engineering  
Accelerometers measure vehicle acceleration, speed, and vibrations experienced by cars or motors. In [23], MEMS accelerometers measure vibration parameters related to different vehicle locations, such as the hood above the engine, the hood above the radiator fan, the exhaust pipe, and the dashboard. The data collected can be used for vehicle diagnostics, safety, and comfort, as shown in Figure 4.  
Figure 4: Deployment of MEMS sensors in potential smart vehicles—reprinted from [23].  
Figure 3: Example modules with MEMS accelerometers—reprinted from [19].  
Figure 2: The functional principle of MEMS accelerometer: 1. seismic mass, 2. flexible electrode, 3. fixed electrode, 4. substrate, 5. spring, 6. anchor, \(\Delta\)d displacement, d0 median position, C1 and C2—capacitance.  
Paper [24] investigates using accelerometers mounted on rotors to measure steady and transient vibrations induced in the rotors. A new topology and method are used to detect rotor imbalance directly from accelerometer data.  
The authors of [25] propose a new filtering method for data obtained from accelerometers. This method combines an adaptive least mean squares function with a low-pass finite impulse response filter. The experiments show better signal-to-noise ratio and signal attenuation results than regular low-pass filters.  
The paper's authors [26] describe designing and optimizing a vibration-to-electric energy harvester based on a magnetic structure consisting of a levitated moving magnet and two fixed magnets, as shown in Figure 5.  
In paper [27], the IPMSM structure is designed and simulated based on the ANSYS Workbench 2023 multi-physical field coupling platform to optimize the operation of the motor. This study looks at how the vibration noise from the first-order tooth harmonic occurs in space and time. To reduce this noise, we can make slots in the rotor.