esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Related Work
### Distributed and Parallel Spatiotemporal Data Processing Methods
Reference [5] proposes an in-memory distributed indexing method for moving objects based on Apache Spark. The basic technique of Reference [5] is a simple gird index. Reference [5] adds new transformation operators and output operators such as bulkLoad, bulkInsert, splitIndex, search to index, and query moving objects in real-time. The input stream is the location data of moving objects that are transmitted periodically from vehicles. Spark Streaming transforms the input stream into D-Streams. Reference [6] proposes distributed an in-memory moving object management system based on Spark. It consists of a data and query collector, an index manager, and a data manager. Data and query collectors which are designed based on Apache Kafka receives location data and time from vehicles and queries from users. Index manager creates grid-based spatiotemporal index structures, and it is an enhanced version of that in Reference [5], which is based on Spark Streaming to consider the case of the full of main memory. Also, the indexing method of this paper provides snapshot isolation level of transactional processing with multi-version concurrency control techniques based on RDD(Resilient Distributed Dataset)s of Apache Spark. Data manager is to store old index structures to HBase and to load index structures. GeoMesa [12] provides spatiotemporal indexing using space-filling curves to transform multidimensional spatiotemporal data into the one-dimensional data. It is designed to run based on distributed storage systems such as HDFS, Apache Accumulo, and so on. GeoMesa creates indices on the geospatial attributes (point and spatial) of spatiotemporal data. These indices are implemented by creating a space-filling curve based on a Geohash index. GeoMesa uses Zcurve and XZ space-filling curve, respectively, for point data and spatial data.
# QoS Aware and Fault Tolerance Based Software-Defined Vehicular Networks Using Cloud-Fog Computing
## 3 Problem Formulation  
As reflected from the Literature Review [5; 9; 10; 11; 12; 13; 14; 15; 16; 17; 18; 19; 20; 21; 22; 23; 24; 25; 26; 27; 28; 29; 30; 31], they used the Cloud for data execution and storage, which has the following limitations, as shown in Figure 1:  
1. Response time is not provided for the safety and non-safety messages of vehicles
2. Cloud is used for task execution, which leads to higher response time
3. Safety messages are given priority based on deadline and size, but not given priority based on message nature, as some messages are large in size, but have the highest priority.  
Furthermore, it is observed that no fault-tolerance method is used in [27; 28]; when any highest priority message does not deliver to the destination, then it may create an efficiency problem of the proposed model.
# The Effect of Retinal Eccentricity on Visually Induced Motion Sickness and Postural Control
## Results
### Postural Sway  
Movement data from the head, torso, and hip are presented in Figure 2 as SD (top panel), velocity (middle panel), and range (bottom panel) in the AP (left panel) and ML (right panel) axes, respectively. In general, postural sway was exaggerated in both peripheral and control groups. The ANOVAs confirmed the main effects of group for several parameters, but only in the AP axis. A significant group effect for SD was confirmed for torso movement, \(F(2,33)=4.89\), \(p<0.05\), \(\eta_{\text{p}}{}^{2}=0.23\). For velocity, head, \(F(2,33)=0.364\), \(p<0.05\), \(\eta_{\text{p}}{}^{2}=0.18\), torso, \(F(2,33)=9.75\), \(p<0.001\), \(\eta_{\text{p}}{}^{2}=0.37\), and hip movements, \(F(2,33)=6.50\), \(p<0.01\), \(\eta_{\text{p}}{}^{2}=0.28\), were all statistically significant. For range, torso, \(F(2,33)=4.27\), \(p<0.05\), \(\eta_{\text{p}}{}^{2}=0.21\), and hip movements, \(F(2,33)=4.67\), \(p<0.05\), \(\eta_{\text{p}}{}^{2}=0.22\), were statistically significant. Tukey post hoc tests showed that the differences between the central vision and control groups contributed to the significant effects for all measures except for velocity of hip movement where both peripheral vision and control groups differed from the central vision group.  
Figure 2: Movement data from the head, torso and hip as standard deviation (SD) (top panel), velocity (middle panel), and range (bottom panel) in the anterior-posterior (AP) (left panel) and radio-lateral (ML) (right panel) axes for three experimental groups. Error bars represent \(\pm 1\) SE of the mean. * \(p<0.05\).
## 2 Methodology  
### Problem Statement  
We consider the problem of counting cells as a regression task. Let \(x\in\mathbb{R}^{C\times H\times W}\) denote an image with \(C\) channels (e.g., RGB), height \(H\) and width \(W\). Furthermore, let \(y\in\mathbb{R}\) be several cells present in \(x\). The regression model \(F\) transforms the extracted features from \(x\), \(\phi(x)\), to the number of cells, i.e.,:  
\[y=F(\phi(x;a);\theta), \tag{1}\]  
where \(a\in\mathbb{R}^{D}\) denotes the parameters of a feature extractor, and \(\theta\) stands for parameters of the regression model \(F\).  
For given \(N\) datapoints, \(\mathcal{D}=\{(x_{n},y_{n})\}_{n=1}^{N}\), we aim to minimize a given loss function \(\mathcal{L}\) with respect to \(a\) and \(\theta\), i.e.,:  
\[\mathcal{L}(a,\theta;\mathcal{D})=\frac{1}{N}\sum_{n=1}^{N}\ell(a,\theta;x_{i },y_{i}), \tag{2}\]  
where, typically, \(\ell(a,\theta;x_{i},y_{i})=\left(y_{i}-F(\phi(x_{i};a);\theta)\right)^{2}\), and \(\mathcal{L}\) is then known as the _Mean Squared Error_ (MSE) loss.  
We consider two approaches for automatic cell counting, namely a machine learning pipeline and a CNN-based regressor. In the following sections, we explain the components of these two approaches. For the machine learning pipeline, we outline different feature extractors and nonlinear regressors. Next, we present our proposition of the CNN-based regressor with a specific architecture of the CNN, namely xResNet [47; 48].
# Creating a Remote Choir Performance Recording Based on an Ambisonic Approach
## 4 Realization of Remote Music Recording
### Postproduction of the Visual Layer  
When producing a video of a classical virtual choir, much attention needs to be paid to planning all the shots and transitions between them. For this recording, it was decided to use a static 360\({}^{\circ}\) image, on which the recordings of the choristers would be \"suspended\". The same people are visible throughout the recording, as transitions between them would be unsightly. Moreover, in a 360\({}^{\circ}\) recording, the creator has no control over where the viewer is looking at any given moment. Therefore, the image in the entire sphere was left unchanged. Instead, a refined model was used to generate the background. For that purpose, the 3D model of the cathedral was created, and then Blender was used to render the interior of the cathedral from a given point--a specific camera setting was employed. The camera was set to spherical mode, or more precisely, to cylindrical equirectangular projection. In this way, the resulting visual background could be used as a background for the video timeline in the nonlinear editing software--DaVinci Resolve [82]. Figure 6 presents an arrangement of choristers arranged on the background of the developed graphics.  
The model created in this way can then be combined with the audio in B-format. In the case of first-order ambisonics, this is a 4-channel audio track. The FB360 Encoder program [83] provided by the Facebook platform can be used for this purpose, as it also supports audio-video encoding for the YouTube platform. After uploading to this platform, the resulting file is automatically recognized as a 360\({}^{\circ}\) video with ambisonic sound based on the metadata contained in the file. Once processed by the platform, it is ready for playback.
## 2 Noise and Noise Equivalent Power Calculations
Furthermore, the influence of the MMW/THz radiation, \(\Delta f_{MMW}\), on the output frequency can be measured the same way if we subtract two successive measurements where the first one is with MMW radiation and the second one is without MMW radiation. The LTF converts input optical irradiance, \(E_{e}\), to the electric TTL levels frequency, \(f_{o}\). \(R_{e}\) is the irradiance responsivity. The dark frequency of the LTF is \(f_{D}\) is 2 Hz. The _NEP_ of the suggested sensor can be calculated according to:  
\[NEP=\frac{(\Delta f_{n}+f_{D})}{Re\sqrt{B}} \tag{4}\]  
where \(B\) = 1 MHz is bandwidth of the LTF [26]. Note that the GDD modulation bandwidth is very high, on the order of several GHz [12; 18], attesting to the GDD speed of response. In these speed measurements, speed was limited by the optoelectronic detector and not by the GDD plasma.
## 4 Experiments and Results
### EEG-TCNet with Different Hyperparameters
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline  & **S1** & **S2** & **S3** & **S4** & **S5** & **S6** & **S7** & **S8** & **S9** \\ \hline \(F_{1}\) & 6 & 10 & 6 & 6 & 8 & 10 & 10 & 10 & 8 \\ \(K_{E}\) & 48 & 48 & 32 & 32 & 32 & 48 & 16 & 48 & 32 \\ \(F_{T}\) & 12 & 12 & 14 & 14 & 10 & 12 & 12 & 14 & 14 \\ \(K_{T}\) & 6 & 6 & 6 & 6 & 8 & 8 & 4 & 6 & 4 \\ \hline \end{tabular}
\end{table} 
Table 4: Variable hyperparameters of EEG-TCNet. \begin{table}
\begin{tabular}{c c c c c} \hline \(F_{1}\) & \(K_{E}\) & \(F_{T}\) & \(K_{E}\) & **Average Accuracy (\%)** \\ \hline
6 & & & & & 75.38 \\
8 & 32 & 12 & 4 & 76.55 \\
10 & & & & & **78.04** \\ \hline  & 16 & & & & 76.66 \\
8 & 32 & 12 & 4 & 76.55 \\  & 48 & & & & **77.05** \\ \hline  & & 10 & & & 77.99 \\
8 & 32 & 12 & 4 & 76.55 \\  & & 14 & & & **78.58** \\ \hline  & & & & 4 & 76.55 \\
8 & 32 & 12 & 6 & **79.96** \\  & & & & 8 & 78.59 \\ \hline \end{tabular}
\end{table} 
Table 3: Comparison of different numbers of filters and kernel sizes in the EEGNet module and the TCN module in average accuracy. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Subject** & **Baseline** & **Fixed Model** & **Variable Model** \\ \hline
**S1** & 82.92 & 86.12 & **88.26** \\
**S2** & 61.13 & 71.02 & **72.79** \\
**S3** & 91.58 & 94.87 & 97.07 \\
**S4** & 64.91 & **80.26** & 80.26 \\
**S5** & 76.45 & 76.81 & **84.06** \\
**S6** & 59.53 & **69.30** & 69.30 \\
**S7** & 91.34 & 87.00 & **94.58** \\
**S8** & 81.92 & 81.18 & **87.82** \\
**S9** & 79.17 & 85.23 & **87.12** \\
**Average** & 76.55 & 81.31 & **84.59** \\ \hline \end{tabular}
\end{table} 
Table 5: Accuracy of the EEG-TCNet baseline model, fixed model, and variable models (%). Figure 9 shows a column chart of a comparison in accuracy between baseline EEG-TCNet, EEG-TCNet with the best tuning hyperparameters (optimised fix model), and EEG-TCNet with the best hyperparameters of each subject from different trainings. This experiment aims to research the impact of different hyperparameters on EEG-TCNet. The variable model is obviously better than the baseline and fixed model because each subject is the best hyperparameter from numerous training experiments.
###### Abstract  
The accurate monitoring of crop areas is essential for food security and agriculture, but accurately extracting multiple-crop distribution over large areas remains challenging. To solve the above issue, in this study, the Pixel-based One-dimensional convolutional neural network (PB-Conv1D) and Pixel-based Bi-directional Long Short-Term Memory (PB-BiLSTM) were proposed to identify multiple-crop cultivated areas using time-series NaE (a combination of NDVI and EVI) as input for generating a baseline classification. Two approaches, Snapshot and Stochastic weighted averaging (SWA), were used in the base-model to minimize the loss function and improve model accuracy. Using an ensemble algorithm consisting of five PB-Conv1D and seven PB-BiLSTM models, the temporal vegetation index information in the base-model was comprehensively exploited for multiple-crop classification and produced the Pixel-Based Conv1D and BiLSTM Ensemble model (PB-CB), and this was compared with the PB-Transformer model to validate the effectiveness of the proposed method. The multiple-crop cultivated area was extracted from 2005, 2010, 2015, and 2020 in North China by using the PB-Conv1D combine Snapshot (PB-CDST) and PB-CB models, which are a performance-optimized single model and an integrated model, respectively. The results showed that the mapping results of the multiple-crop cultivated area derived by PB-CDST (OA: 81.36%) and PB-BiLSTM combined with Snapshot (PB-BMST) (OA: 79.40%) showed exceptional accuracy compared to PB-Transformer combined with Snapshot and SWA (PB-TRSTSA) (OA: 77.91%). Meanwhile, the PB-CB (OA: 83.43%) had the most accuracy compared to the pixel-based single algorithm. The MODIS-derived PB-CB method accurately identified multiple-crop areas for wheat, corn, and rice, showing a strong correlation with statistical data, exceeding 0.7 at the municipal level and 0.6 at the county level.  
multiple-crop classification; multi-temporal data; deep learning; remote sensing; snapshot; SWA; ensemble classifier +
Footnote †: journal: Applied Sciences
# Tensile Properties and Tensile Failure Criteria of Layered Rocks
## 5 Conclusions  
This study focuses on the tensile behavior of a transversely isotropic rock with varied layer dip angles subjected to direct and indirect tension tests. The following conclusions can be drawn:  
Figure 18: The result comparisons of laboratory test, simulations, and theoretical analysis [40].  
1. The layered rock specimens display an anisotropic mechanical behavior when subjected to direct tension load. The tensile stress-strain behavior of the layered rocks depends on the direction of the bedding planes with respect to the tensile load. However, the direct tension test of layered rocks in the laboratory needs to be studied in more depth in the future.
2. The numerical results show that the dip angle has a significant influence on the fracture characteristics during the progressive failure, such as peak strength, failure patterns, and deformational behavior. The failure modes of the layered rock specimens are characterized by tensile failure along the bedding plane, progressive saw-toothed failure, and tensile failure of the rock material under direct tension.
3. Based on the failure modes of the layered rocks, the SPW failure criterion is modified. The theoretical results of the modified SPW (MSPW) failure criterion show a good agreement with the experimental and the numerical results. The MSPW failure criterion can accurately describe the tensile strength when the dip angle of the bedding plane is close to 45\({}^{\circ}\).
4. Based on the MSPW criterion, a method to determine the tensile strength of the layered rocks is proposed, which can simply predict the tensile strength of the layered rocks.  
Conceptualization, S.J.; Data curation, J.Z.; Formal analysis, M.G.; Funding acquisition, M.G.; Investigation, M.G. and J.Z.; Methodology, Z.L.; Resources, S.J.; Software, Z.L.; Supervision, Z.L.; Writing--original draft, M.G.; Writing--review & editing, Z.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Natural Science Foundation of Anhui Province (2108085 QE208), the China Postdoctoral Science Foundation Funded Project (2021M700753), the Natural Science Research Project of Universities in Anhui Province (KJ2021A0463), and the National Natural Science Foundation of China (51779031, 41977219).  
Not applicable.  
The results reported in this study were obtained using our in-house code. The experimental data were obtained from measurements and experiments on RMT-150C. The data for producing the reported results will be made available upon request.  
The authors declare no conflict of interest.
# Joint Radar and Communications Waveform Design Based on Complementary Sequence Sets
## 3 JRC-CWG and DR-JRC-CWG Design
### JRC-CWG Design with Low Sidelobe Level
#### 3.1.3 Computational Complexity Analysis  
For the proposed algorithm to design the JRC-CWG in Algorithm 1, the computational complexity of step 2 is \(O(M_{r}(M+1)K\log_{2}(K))\). The computational cost of step 3 is \(O(NM_{r})\). In step 4, the computational complexity is \(O\big{(}\bar{N}MK\log_{2}(K)\big{)}\), where \(\bar{N}\) is the search time to obtain \(\lambda_{k_{0}}\) with the line search method. In step 5 (2), the computational complexity is \(O(M_{r}(M+1)K\log_{2}(K))\), and the computational cost of step 5 (3) is \(O(NM_{r})\). The total computational complexity of each iteration of the JRC-CWG design algorithm is approximately \(O\big{(}M_{r}(M+1)K\log_{2}(K)+\bar{N}MK\log_{2}(K)+2 NM_{r}\big{)}\). Hence, the computational complexity of the JRC-CWG design algorithm in Algorithm 1 is approximately \(O\big{(}\bar{L}M_{r}(M+1)K\log_{2}(K)+\bar{L}\bar{N}MK\log_{2}(K)+2\bar{L}NM_{r} \big{)}\), where \(\bar{L}\) is the number of iterations.
## 3 Results
### Calculated Torso Air Gap Volume and Distance  
Using the raw torso volume and surface area data, torso air gap volume and air gap distance were calculated for the BL, TS, and TS w/SCBA configurations. These findings are presented in Figure 3. The TS and TS w/SCBA configurations had similar torso air gap volume and distance measures, regardless of the SCBA. Differences between garment configurations were statistically significant (\(p<0.001\)) for the TS and TS with SCBA when compared to the BL. However, donning the SCBA on top of the turnout suit did not significantly change the torso air gap volume or distance within the clothing microclimate (\(p>0.5\)). The addition of the SCBA decreased the torso air gap volume for five of the nine participants by an average of 12.1%. Conversely, the other four participants exhibited an increase in torso air gap volume of 15.3%, on average. This variability in air gap volume between participants could be indicative of individual body composition differences, variations in PPE sizing, and fit preferences (e.g., a looser fit versus slim fit). Future research should expand the sample size and data collected to include participant body composition, turnout suit size, and a subjective fit survey for further analysis.  
While these findings are the first of their kind to quantify air gaps in firefighting PPE when donning an SCBA, it should be noted that three-dimensional body scanning limitations still exist. Many scanners lack the ability to adequately distinguish the SCBA tank from the clothed form. Therefore, to provide further insights, garment ease analysis of 10 individual upper front torso body measurements was performed.
## 2 Stress Distribution and Deformation Law
### Initial Stress Field  
The initial stress field refers to the original stress field existing in rock mass before tunnel excavation; it is formed by long-term geological tectonic movement and is closely related to the physical mechanics and structure of the rock. Generally, the initial stress field is very complex, and is divided into two parts in geomechanics: the gravity stress field and the tectonic stress field.  
The formation of the initial stress field is closely related to the rock mass structure, properties, burial conditions and history of tectonic movement. The problem is complex as it is also very difficult to distinguish the gravity stress field from the tectonic stress field and quantitatively determine the tectonic stress field. At present, it is common to divide the initial stress field into vertical stress and horizontal stress for simplification. The vertical stress is derived from the gravity stress, while the horizontal stress is proportional to the gravity stress, and the magnitude is the lateral pressure coefficient multiplied by the horizontal stress [46; 47].  
\[\sigma_{h}=\gamma\cdot h \tag{1}\]  
\[\sigma_{v}=K_{0}\cdot\sigma_{h} \tag{2}\]  
where \(\gamma\) is the bulk density of rock mass (N/m\({}^{3}\)) and \(K_{0}\) is the coefficient of lateral pressure.
# Performance Evaluation of GPS Auto-Surveying Techniques
## 2 Materials and Methods
### GPS Observables
#### 2.1.2 Carrier Phase  
Another observable is the phase of the GPS carrier signal, which can be measured with a resolution on the order of millimeters but is ambiguous by an integer number of cycles, also called integer ambiguity, \(N_{r}^{s}\). By multiplying the carrier phase by the wavelength of the GPS signal and adding the same errors as the pseudorange model, the full model of the carrier phase observable, in units of meters, can be written as [13]  
\[\Phi_{r}^{s}=R+c\delta t_{r}+c\delta t^{s}+\lambda N_{r}^{s}+T^{i}-I^{i}+M ^{i}+\epsilon_{\phi} \tag{3}\]  
where \(\Phi_{r}^{s}\) is the carrier phase observable in meters, the modeled errors are equal to those in Equation (1), and \(\epsilon_{\phi}\) denotes the unmodeled errors in the carrier phase.
# Construction of a Cutting-Tool Wear Prediction Model through Ensemble Learning
## 4 Results and Discussion
### Machine Learning
#### 4.5.2 Machine Learning and Ensemble Learning Results  
Similarly, the machine learning metric scores are based on the results from the test dataset in this section. The ensemble voting regression model, composed of the basic regression models, including DT, RF, extra trees, XGB, LGBM, AdaBoost, SGD, SVR, LSVR, and MLP, is constructed to predict the test dataset. It is compared with the best-performing model in all metrics from the machine learning models in Section 4.5.1, which is the extra trees model. As shown in Figure 19 and Table 8, the performance of the voting regression model surpasses that of the extra trees model. The extra trees model is the best-performing model among the ten basic regression models, indicating that the voting regressor outperforms other machine learning models. The metric scores for this model are an R\({}^{2}\) of 0.94576, RMSE of 0.24348, MSE of 0.05928, and MAE of 0.18182. The residual plots in Figure 20 demonstrate that different models have distinct interpretations and levels of significance for the data. By combining multiple models, a better prediction of data characteristics can
# QoSComm: A Data Flow Allocation Strategy among SDN-Based Data Centers for IoT Big Data Analytics
## 5 Performance Evaluation  
We evaluated the data flow transfer performance among two hosts. In the first phase, we used two applications to test network performance. IPERF and D-ITG were used to generate TCP and UDP traffic between two hosts since its behavior would be similar to a BDA transferring data between hosts. D-ITG was used to generate simultaneous data flow transfers between two hosts, as well as packet size variation for each transfer, simulating applications behavior transferring data flows over the Internet. In the second phase, we configured an MPI cluster in a Mininet simulator to test MPI application performance. We used an MPI application to transfer messages between two nodes and measured the job completion time. In both test phases, we compared controller default and QoSComm data flow forwarding methods. For both phases, application network QoS requirements for end-to-end paths were: bandwidth \(\geq\) 85 mbps, delay \(\leq\) 30 ms, PLR \(\leq\) 0.008 and jitter \(\leq\) 10 ms.  
In H1 to H6 tests, the controller used Path 2 (S0, S1, S3, S10, S11, S13) and QoSComm selected Path 14 (S0, S2, S1, S3, S10, S11, S13) referred in Table 3. For H2 to H5 tests, Controller used Path 9 (S2, S1, S3, S10, S8, S9) and QoSComm selected Path 13 (S2, S1, S4, S3, S10, S8, S9) referred in Table 4. For H3 to H4 tests, Controller used Path 16 (S6, S4, S3, S10, S8, S7) and QoSComm selected Path 8 (S6, S5, S4, S3, S10, S8, S7) referred to in Table 5. Controller and QoSComm host-to-host selected paths have different network metrics. QoSComm calculated and configured selected paths considering application network QoS requirements.