esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Data Processing  
As mentioned above, each supply line is equipped with flow meters that can measure volumetric flow rates, as well as pressure and temperature. The actual fuel pressure and temperature are measured with the sensors integrated into the mass flow meter. Importantly, the recorded fuel pressure, temperature and fuel volumetric rate for each test are synchronized to enhance data precision. The volumetric flow rate provided via the flow meter is converted to mass flow rate  
\[\dot{M}_{ox}=\rho_{ox}v_{ox} \tag{1}\]  
where \(\rho_{ox}\) is the oxidizer density and \(v_{ox}\) is the oxidizer volumetric flow rate. The oxidizer density is determined using the pressure \((p_{ox})\) and temperature (\(T_{ox}\)) measured using sensors incorporated in the mass flow meter, according to the following equation  
\[\rho_{ox}=\frac{p_{ox}}{R_{ox}T_{ox}} \tag{2}\]  
where \(R_{ox}\) is the gas specific constant. The same approach is extended to the fuel component, mirroring the procedure followed for the oxidizer. Once both the fuel and oxidizer mass flow rates are determined, the equivalence ratio \(\phi\) is calculated based on the following:  
\[\phi=\frac{\dot{M}_{fl}\dot{M}_{ox}}{\left(\dot{M}_{fl}\dot{M}_{ox}\right)_{ st}} \tag{3}\]  
The total specific impulse  
\[I_{sp}=\frac{T}{g\left(\dot{M}_{fl}+\dot{M}_{ox}\right)} \tag{4}\]  
The fuel specific impulse  
\[I_{sp}=\frac{T}{g\dot{M}_{fl}} \tag{5}\]  
The total specific impulse, as defined above, is the typical figure of merit used for launchers or in-space propulsion systems where both the fuel and oxidizer are stored onboard, thus entering directly into the mass budget of the propulsion system. The fuel specific impulse is reported in the current paper too to be compared with the current airbreathing technology. Moreover, the fuel specific impulse, as defined above, is employed to directly characterize the performance of the combustor from the point of view of mixing and energy harvesting from the available chemical energy.
# Classification of Event-Related Potentials with Regularized Spatiotemporal LCMV Beamforming
## 2 Materials and Methods
### Classification  
#### 2.6.1 Cross-Validation Scheme per Subject  
We use a variation of grouped \(k\)-fold cross-validation per subject to evaluate the classifiers. We applied four-fold cross-validation by splitting the blocks of each subject into four continuous folds. Unlike regular cross-validation, we only used a single fold to train the classifiers while using the other three folds for validation. This scheme resulted in a training set of 9 blocks of 135 epochs each. We chose this approach since we are primarily interested in the performance of the classifiers in the case of low data availability. The classification task was to determine the cued target for each block. The fraction of correctly predicted cues provided the accuracy of a classifier. Data from all trials were used in the training stage, whereas classifier validation was performed multiple times per fold, each time using an increasing amount of trials (i.e., using the first trial, using the first two trials, etc., until all 15 trials have been used). For each of the 9 stimulated targets, the averages over the corresponding epochs across the utilized trials were used to predict the cued target in that block. The target with the maximum classifier score was then chosen as the predicted cued target. Before training the classifiers, a Z-score normalization transformation was developed on the training data to scale all EEG channels to unit variance. This transformation was then applied to the validation data.
# Vehicle Delay Model Applied to Dynamic and Static Traffic Impact Analysis of Large Parking Lots
## 2 Establishment of Vehicle Delay Model
### Establishment of Delay Model
#### 2.2.2 Delay Model of Leaving Vehicle  
The driving process of vehicles leaving the parking lot is as follows. The vehicle decelerates or stops in front of the gate, waits for the pole to be lifted and accelerates through, then seeks acceptable clearance and decelerates into the road, and finally accelerates away. The delay caused by leaving the vehicle is shown in Figure 2.  
It can be seen from the above driving process that the vehicles leaving the parking lot are also affected by the fixed delay \(d_{lf}\), the delay of the exit gate \(d_{lg}\), and the delay of leaving the vehicles and driving into the road \(d_{lc}\).  
(1) Delay of the exit gate \(d_{lg}\)[16].  
\[d_{lg}=\frac{1}{\mu_{l}-q_{l}}\times 3600 \tag{4}\]  
where \(d_{lg}\) is the average delay of leaving vehicles passing through the exit gate, and \(\mu_{l}\) is the service rate of the export gate, and \(q_{l}\) is the number of vehicles leaving the parking lot.  
(2) Delay of vehicles leaving the parking lot to enter the first lane of the road \(d_{lc}\).  
The first lane is regarded as the main road, and the exit channel of the parking lot is regarded as the secondary road. Based on the delay formula [17], the capacity of vehicles leaving the parking lot to drive into the first lane is corrected. Finally, the average delay of vehicles leaving the parking lot and driving into the first lane of the road is obtained, as shown in Equation (5).  
\[d_{lc}=3600\times\frac{1-e^{-((q_{l}t_{lc}+q_{l}t_{lf})/3600)}}{3600\times\eta _{lc}\times\frac{1}{t_{lf}^{2}}e^{-(q_{l}t_{lc}/3600)}-q_{l}} \tag{5}\]  
where \(d_{lc}\) is the average delay of vehicles leaving the parking lot and driving into the first lane of the road, and \(q_{l}\) is the number of vehicles leaving the parking lot, and \(q_{r1}\) is the traffic volume of the first lane, and \(t_{lc}\) is the critical gap between the parking lot and the first lane of the road, and \(t_{lf}\) is the following time of vehicles leaving the parking lot, and \(\eta_{lc}\) is the capacity correction factor for vehicles leaving the parking lot and driving into the first lane.  
Therefore, the average delay of vehicles leaving the parking lot is shown in Equation (6).  
\[d_{l}=d_{lf}+d_{lg}+d_{lc} \tag{6}\]  
where \(d_{l}\) is the average delay of vehicles leaving the parking lot, and \(d_{lf}\) is the fixed delay of vehicles leaving the parking lot, and \(d_{lg}\) is the average delay of leaving vehicles passing through the exit gate, and \(d_{lc}\) is the average delay of leaving the vehicle into the first lane of the road.  
Figure 2: Delay of Leaving Vehicles.
# Towards Robust Object Detection in Floor Plan Images: A Data Augmentation Approach
## 4 Dataset  
The prior literature on floor plan images reflects that there is a scarcity of datasets in this area. SESYD [3], CVC-FP [7], and ROBIN [8] are the 3 most widely used publicly available datasets in this area. SESYD [3] contains synthetic floor plan images with furniture objects placed in different rooms randomly. It has 10-floor plan layouts and, in total, contains 1000 images. Although this idea is fascinating to put furniture objects randomly, the overall number of images is less. It has 16 different furniture classes.  
In the CVC-FP [7] dataset, we only have 122-floor plan images. These floor plan images are distributed amongst four different floor plan layouts. Overall, the total number  
Figure 5: Internal working of deformable convolution [16]. The light green grid on the input feature map shows the conventional \(3\times 3\) convolutional operation, whereas the blue boxes on the input feature map show the effective receptive field of a \(3\times 3\) deformable convolution.  
of furniture classes is also less and limited to 4 classes. This dataset is not suitable for the training of the deep neural network [20].  
In the ROBIN [8] dataset, we have different hand-drawn as well as synthetically generated images. In this dataset also, we have only a limited number of images 510. Floor plan layouts and furniture object classes are also limited.  
To train a deep detector, we need a dataset with sufficient images and variety in the floor plan layouts to generalize the network for realistic images. Moreover, the number of furniture classes should be large enough to identify different varieties of furniture objects. To address this, we create our custom dataset, which is based on the SESYD [3] dataset. We named our custom dataset SFPI (Synthetic Floor Plan Images). From this point onward, we will describe our custom dataset with the name SFPI.  
In Figure 6, we have a sample floor plan image from the SESYD [3] dataset. It has various furniture objects present in different rooms. It is visible from the image that the scale of different furniture classes among them is almost the same and rarely varies; we observed this behavior in the full dataset as well. It is also noticeable that the orientation of different furniture objects also does not vary a lot. Moreover, we observe that few furniture objects are available in specific rooms, which is good if we want to identify room types but not for our purpose of detecting furniture objects. We want to generalize the model so that it can identify the objects available in any room. Keeping all these shortcomings of the SESYD [31] dataset in mind. we propose our dataset SFPI.  
Figure 6: Sample image from SESYD [3] dataset. Minimal variation in the furniture objects. It is visible from the red highlighted objects of Table class that these objects do not vary in orientation or scale.
# Modeling Undrained Shear Strength of Sensitive Alluvial Soft Clay Using Machine Learning Approach
## 3 Results and Discussion
These results indicate that the formulated model is reliable and accurate enough to be employed for forecasting the USS of soft clay soil. Figure 6 demonstrates assessment plots of the predicated undrained shear strength against the actual one for the modeled dataset. As depicted in the figure, most values of the measured samples lie close to the line of equality with the predicted values. Furthermore, Figure 7 represents the correlation between the actual and the predicted data as regression curves. As can be observed, the model's predictability is good, especially for the testing dataset. As a result, the use of an SVR model to forecast soil USS is achievable with high accuracy and low error. Based on the above results and analysis, accurate ML prediction of the undrained shear strength of the alluvial sensitive soft clay has an important and positive impact on the field of geotechnical engineering. The current ML model achieved more accuracy compared with other previous studies [6, 19]. Precise determination of USS for the sensitive clay is a challenging task, being time-consuming, requiring great effort, and incurring high financial costs. The input data of the basic soil properties effectively and directly influence the USS values with positive or negative impacts. The selected features in the present model are essential properties of the studied soil that can be easily determined in a short time. The collected realistic and feature-enriched datasets, as well as the selection of the appropriate ML algorithm, resulted in a significant improvement in the prediction accuracy of the presented model. The accurate ML prediction of USS of the alluvial sensitive clay contributes toward solving many problems, such as saving time, effort, and costs. This is achieved by reducing the number of samples and tests that are essentially required using traditional methods. \begin{table}
\begin{tabular}{c c c c} \hline \hline \(C\) & \(\epsilon\) & **Kernel Function** & **Kernel Scale** \\ \hline
11.8255 & 0.02199 & Gaussian & 8.6869 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: List of hyperparameters of the optimized model. Figure 6: Comparison results of the trained model, predicted versus actual output data of vane-tested USS. The straight line denotes to line of equality.
## 3 Discussion and Analysis
### Influence of Environmental Parameters on the Ice Shape Evolution
#### 3.2.2 Seawater Temperature  
The seawater temperatures were selected as \(0\)\({}^{\circ}\)C, \(-1\)\({}^{\circ}\)C, \(-2\)\({}^{\circ}\)C, \(-3\)\({}^{\circ}\)C and \(-4\)\({}^{\circ}\)C, respectively, while the other environmental conditions remained unchanged. The evolution of ice shape in the freezing process of ballast water under changes in the seawater temperature was analyzed. Figure 18 shows the evolution of the ice shape in the ballast tank at typical moments at different seawater temperatures. Figure 19 shows the curve of the ice volume fraction of the ballast tank with time at different seawater temperatures.  
Combined with Figures 10 and 18, it is evident that changes in the seawater temperature significantly influence the freezing characteristics of ballast water. When the seawater temperature is at \(0\)\({}^{\circ}\)C, as shown in Figure 18a, the temperature change within the ballast tank is minimal, resulting in a slow ice growth rate, a low freezing degree, and the formation of almost no ice layer. As the seawater temperature decreases to \(-1\)\({}^{\circ}\)C, the temperature near point a, located at the center of the ballast water level, fluctuates slightly. At this point, the ice formation at the ballast water level takes on a distinct curvature. When the seawater temperature drops to \(-4\)\({}^{\circ}\)C, the temperature at the liquid level of the ballast water reaches the freezing point, creating a thicker freezing temperature zone on the right side of the bulkhead, as illustrated in Figure 18d. Therefore, changes in seawater temperature have  
Figure 17: The variation curve of the ice phase volume fraction in the ballast tank under different cold air temperatures.  
a substantial impact on the growth of ice formations on the right bulkhead. The greater the decrease in seawater temperature, the more pronounced the growth rate of the ice formations on the right bulkhead.  
It can be observed from Figure 19 that as the seawater temperature decreases, the volume fraction of the ice phase in the ballast tank increases correspondingly. At a seawater temperature of \(0\)\({}^{\circ}\)C, the ice volume fraction is approximately 24.57% at 14,000 s, indicating a low degree of freezing. In contrast, at a seawater temperature of \(-4\)\({}^{\circ}\)C, the ice volume fraction rises to about 91.53% at 14,000 s, reflecting a high degree of freezing, which represents an increase of approximately 272.52%. This phenomenon occurs because the seawater is in direct contact with the ballast water. A decrease in seawater temperature directly impacts the ballast water, enhancing its heat transfer efficiency and promoting the formation of ice on the right side of the bulkhead. Consequently, at a seawater temperature of \(-4\)\({}^{\circ}\)C, the volume fraction of ice in the ballast water tank reaches its maximum.
## 4 Results
### Simplification Model Evaluation  
The four distribution envelopes for 17 May, 6 June, 9 July, and 5 August 2021 were simplified based on the SM-ADSL. The simplified distribution envelopes are shown in red in Figure 8, which have very little deformation compared to the original envelops shown in black.  
The number of original envelope vertices (NOEV), number of simplified envelope vertices (NSEV), area of the original distribution envelope (AODE), and area of the simplified distribution envelope (ASDE) were calculated (see Table 2). The simplification rate, overall error, and algebraic error were calculated based on the evaluation methods in Section 3.2.1. The results are shown in Table 2. Overall, the simplification rates for these four dates were higher than 150, the overall errors were less than 0.75%, and the absolute values of algebraic errors were less than 0.13%.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Date**} & **Vertex** & **Vertex** & \multirow{2}{*}{**Simplification**} & **Area of** & **Area of** & **Overall** & **Algebraic** \\  & **Original** & **Simplified** & & **Rate** & **Envelope/km\({}^{2}\)** & **Envelope/km\({}^{2}\)** & **Error** & **Error** \\ \hline
17 May 2021 & 34,704 & 218 & 159 & 8216 & 8212 & 0.75\% & \(-\)0.05\% \\
6 June 2021 & 58,830 & 391 & 150 & 43,708 & 43,729 & 0.25\% & 0.05\% \\
9 July 2021 & 101,881 & 602 & 169 & 42,501 & 42,558 & 0.42\% & 0.13\% \\
5 August 2021 & 35,510 & 225 & 158 & 8615 & 8606 & 0.65\% & \(-\)0.10\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: Simplification rate and errors of the simplified envelope produced by this method.  
Figure 7: Mapping flow chart of green tide distribution and drift prediction initial field (x and y represent the azimuth difference and side length, respectively).
## 3 Passive Phased Arrays with Integrated Metallic and Dielectric Waveguide Phase Shifters
### Fully Dielectric Beam-Steering Rod-Antenna Array  
Very recently, some investigations are also focused on an electrically tunable dielectric delay line phase shifter, inserting an LC section inside the dielectric core [78; 80; 82; 83; 100; 177; 181]. They offer low-loss propagation at very high frequencies and potentially low-cost fabrication by using processing technologies such as 3D printing, injection molding, or milling, since no metallic component is required for it. Thus, in [83], a continuously tunable subwavelength fiber, partially filled with LC G7 exhibitsa FoM of about 130\({}^{\circ}\)/dB (with 22\({}^{\circ}\)/mm) in the frequency range from 85 to 110 GHz. Four of them had been integrated into a fully dielectric \(1\times 4\) rod antenna array, including the feeding network. Similar to a metallic waveguide phase shifter, dielectric ones require high voltages above 100 V (up to 200 V), and response times are slow. Thus, the switch-on response time is about 17 s, and the switch-off response time takes a few minutes. A summary of these new LC-tuned dielectric components such as filters, phase shifters, RF switches, multi-mode interference power divider networks, arrays, and lenses is given in [258].
## 3 Proposed OCC IoMT Platform (Design and Implementation)
### YOLOv8 Object Detection  
YOLO [39] is an artificially intelligent model that employs object detection tasks. Object detection is a computer vision task in which the position and location of objects in 2D images are predicted using a certain algorithm, which is identified through class representations such as animals, persons, cars, and traffic signs. YOLO adopts one-stage detection [40], signifying that the bounding box and object identification are performed directly in a singular feed-forward fully convolutional network. Meanwhile, two-stage detection [41] divides the detection process into two phases: the first involves the proposal of object candidates through the region of interest (RoI), and the second phase conducts the object identification of the proposed RoI candidates. The two-stage approach is considered to result in more accurate detection than the one-stage approach. However, due to the pre-processing in the first stage of the two-stage technique, this approach is also considered slower than the latter approach. Hence, a one-stage method is more convenient for use in mobile scenarios. YOLO has had several versions since its release in 2016. At the time of writing, YOLO has been updated to Version 8 (YOLOv8). In this study, YOLOv8 is employed as the object detection model. YOLOv8 proposes several new techniques compared with previous versions. Anchor-free detection is introduced to reduce the number of squares that need to be predicted by directly calculating the position of the object's centroid. Mosaic data enhancement is also proposed by merging several items into one entity and using it as model input. This technique results in less time required with training for a certain amount of data.  
Figure 4 shows the YOLOv8 object detection architecture, which comprises three main layers: the backbone, neck, and head (prediction) [42]. The backbone serves as the initial processing layer for image data, incorporating various convolutional neural networks. Notably, YOLOv8 uses pre-trained models such as DarkNet and ResNet as part of its backbone. The neck layer acts as a bridge between the backbone and the head, featuring concatenation and sample layers, along with regular layers such as C2f and convolutional layers in the YOLO context. The head section incorporates three detection modules, and the final output is constructed by aggregating the results from these three detection modules. YOLOv8 has several size variants: YOLOv8n, YOLOv8s, YOLOv8m, YOLOv8l, and YOLOv8x. Although a larger version provides more depth image pre-processing and better results, this study selected and fine-tuned YOLOv8s for mobility and lightweight purposes to fulfill the requirements of the proposed IoMT system.
# Audio Fingerprint Extraction Based on Locally Linear Embedding for Audio Retrieval System
## 4 Experimental Results and Analysis
### Results and Analysis
#### 4.2.5 Multiple Reduction Result Compared with Other Algorithm under Different SNR  
The combination of central and horizontal group with the largest reduction is selected for the experiment which has a reduction of six dimensions and fingerprint size of 16 bits. The comparison methods are still Philips, Shazam, and MASK algorithms, and the results are shown in Figure 9.  
The retrieval performance of the reduction combination in this paper is slightly lower than that of the Shazam and the original MASK, which indicates that the combination of groups aggravates the feature loss. But its performance is basically the same as that of Philips and Shazam algorithms, indicating that the method in this paper can meet the retrieval requirements to some extent. For example,  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline \multirow{2}{*}{**HorizontalCentral**} & \multicolumn{2}{c}{**Reduced Dimension = 1**} & \multicolumn{2}{c}{**Reduced Dimension = 2**} \\ \cline{2-5}  & **Recall (\%)** & **Precision (\%)** & **Recall (\%)** & **Precision (\%)** \\ \hline Reduced dimension = 1 & 98.6 & 97.2 & 97.2 & 96.2 \\ Reduced dimension = 2 & 96.8 & 94.6 & 95.4 & 93.8 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Central and vertical reduction results.  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline \multirow{2}{*}{**HorizontalCentral**} & \multicolumn{2}{c}{**Reduced Dimension = 1**} & \multicolumn{2}{c}{**Reduced Dimension = 2**} \\ \cline{2-5}  & **Recall (\%)** & **Precision (\%)** & **Recall (\%)** & **Precision (\%)** \\ \hline Reduced dimension = 1 & 99.2 & 98.6 & 99.0 & 98.5 \\ Reduced dimension = 2 & 98.9 & 98.3 & 98.7 & 98.1 \\ Reduced dimension = 3 & 98.8 & 98.0 & 98.5 & 97.8 \\ Reduced dimension = 4 & 98.5 & 97.7 & 98.2 & 97.4 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Central and horizontal reduction results.  
Figure 9: Comparison of multiple reduction with other methods.  
when dealing with small- or medium-sized audio retrieval, the multiple reduction proposed in this paper reduces the amount fingerprint data to the greatest extent and has basic retrieval efficiency. In large-scale audio retrieval, the performance of multiple reduction is difficult to maintain the accuracy, but single reduction can still be used for it.  
To present the compression potential more clearly, a table of the fingerprint size of each method is shown below.  
Table 7 includes the fingerprint size of three comparison methods and two proposed methods. All the methods have same length of the frame and extract a fingerprint from each frame, so the experiment only needs to compare the size of a single fingerprint. The multiple reduction has the smallest fingerprint size which is half that of the Philips and Shazam. The fingerprint size of proposed method is also a significant reduction compared with the MASK.