esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion  
Using the proposed time-scale varying finite difference method (TSFDM), we calculated the safety factor of the bank slope of a tailing pond from 1 April to 27 July 2019, considering the rainfall and pond water level change (Figure 9). On 1 April 2019, under the impacts of both rainfall and water level changes, the safety factor was 0.914, and the potential sliding surface was in the middle and lower part of the slope, across the strongly weathered carbonaceous slate and gravel soil (Figure 9a).  
Figure 8: The coupled impacts of rainfall and pond water level on the safety factor and the pore water pressure at the monitoring points.  
On 27 July 2019, affected by rainfall alone, the safety factor was 0.9089 (Figure 9b). A new shear strain surface was formed on the weathering interface of carbonaceous slate. The rear part of the slope was in tension [17]. The strongly weathered carbonaceous slate and gravel soil front above the weathering zone have an obvious downward trend. This is because the rock and soil above the weathering zone were deformed under the effects of rainwater seepage, softening, and suspension. The safety factor is smaller than that in April, which requires attention [18].  
On 27 July 2019, impacted by the pond water level change alone, the safety factor of the bank slope was 0.9068 (Figure 9c). The water level changes softened the rock-soil mass on the weathering zone. The shear strain rate of the weathering zone increased, and some parts of the front edge of the bank slope became unstable [19; 20].  
As Figure 9d shows, at the end of the observation period, under the combined impacts of rainfall and water level change, the slope safety factor was 0.9062. The weathering zone had softened seriously, and the shear strain rate of the weathering zone increased significantly. Some parts of the front edge of the bank slope became unstable, requiring treatments [21].  
The safety factor calculated by TSFDM was generally smaller than that calculated by LEM. This is because the TSFDM underestimates the shear strength parameters of the saturated slope, which is in line with reality, but the LEM uses the parameters of unsaturated slope, generating larger shear strength parameters, and a higher safety factor. The program automatically reads the slope seepage field file by batch, performs finite difference calculation, and realizes the intelligent calculation of the geological model. Using TSFDM to study the slope stability of a tailings pond under heavy rainfall and water level fluctuations obtains the evolution features of the stress and strain of the weathering zone and gravel belt [22].  
Figure 9: The bank slope safety factor calculated by the TSFDM and the displacement vector (**a**) under the initial conditions, (**b**) under the rainfall impact alone at the end of the observation, (**c**) under the pond water level change impact alone at the end of the observation, and (**d**) under the impacts of both conditions at the end of the observation.
## 3 Proposed Methodology
### Bert Model Structure
#### 3.2.3 The Fully Connected Layers  
The fully connected layers within the text classification model play a pivotal role in the extraction and transformation of the encoded text features generated by preceding layers, such as the Bert encoding layer. An in-depth examination of their functionalities is follows:  
* Multi-scale Feature Extraction: The incorporation of multiple fully connected layers enables the model to extract text features at varying scales or levels of abstraction. Each fully connected layer can capture distinct aspects of the input text, ranging from fine-grained details to higher-level semantic information. This multi-scale feature extraction facilitates the model's ability to discern intricate patterns and semantic nuances present in the text across different levels of granularity.
* Dimensionality Transformation: Fully connected layers facilitate the transformation of feature vector representations from one dimension to another. This transformation serves two primary purposes. Dimensionality Expansion: In some cases, fully connected layers increase the dimensionality of the feature vector representation, allowing it to capture more complex patterns and relationships within the input text.
* Dimensionality Reduction: Conversely, fully connected layers can also reduce the dimensionality of the feature vector representation, condensing it into a lower-dimensional space while retaining essential information. Dimensionality reduction techniques, such as principal component analysis (PCA) or feature selection, are employed with the objective of preserving the most salient features of the input text while reducing computational complexity and mitigating the risk of overfitting.
* Nonlinear Transformation: Fully connected layers typically incorporate nonlinear activation functions, such as ReLU (Rectified Linear Unit) or sigmoid functions, to introduce nonlinearity into the feature transformation process. This nonlinearity enables the model to capture complex relationships and interactions between different features, enhancing its capacity to model intricate textual patterns and semantic structures.
* Feature Fusion and Abstraction: As the feature vectors propagate through the fully connected layers, they undergo successive transformations that fuse and abstract information from the input text. By iteratively combining and refining features across multiple layers, the model learns to extract hierarchical representations of the input text, gradually transitioning from raw textual data to high-level semantic abstractions.
## 3 Results and Discussion
### Microbiological Analysis of the Tested Beef Hams
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Parameter** & **Treatment** & \multicolumn{3}{c}{**Storage Time**} \\ \cline{3-5}  & & **0** & **3 Months** & **8 Months** \\ \hline \multirow{3}{*}{LAB (log CFU/g)} & C & 4.99 \(\pm\) 0.01 \({}^{\text{A}}\) & 7.15 \(\pm\) 0.01 \({}^{\text{Ba}}\) & 6.25 \(\pm\) 0.09 \({}^{\text{Ca}}\) \\  & A & 4.92 \(\pm\) 0.01 \({}^{\text{A}}\) & 7.48 \(\pm\) 0.03 \({}^{\text{Bb}}\) & 5.83 \(\pm\) 0.03 \({}^{\text{Cb}}\) \\  & AW & 4.97 \(\pm\) 0.20 \({}^{\text{A}}\) & 6.45 \(\pm\) 0.07 \({}^{\text{Bc}}\) & 6.24 \(\pm\) 0.01 \({}^{\text{Ca}}\) \\ \hline \multirow{3}{*}{TVC (log CFU/g)} & C & 3.89 \(\pm\) 0.01 \({}^{\text{Ab}}\) & 7.42 \(\pm\) 0.20 \({}^{\text{Ba}}\) & 7.13 \(\pm\) 0.03 \({}^{\text{Ca}}\) \\  & A & 4.39 \(\pm\) 0.01 \({}^{\text{Ab}}\) & 7.31 \(\pm\) 0.02 \({}^{\text{Ba}}\) & 6.27 \(\pm\) 0.10 \({}^{\text{Cb}}\) \\  & AW & 3.53 \(\pm\) 0.02 \({}^{\text{Ac}}\) & 6.45 \(\pm\) 0.07 \({}^{\text{Bb}}\) & 7.36 \(\pm\) 0.03 \({}^{\text{Cc}}\) \\ \hline \multirow{3}{*}{ENT (log CFU/g)} & C & \textless{}1.00 & \textless{}1.00 & \textless{}1.00 \\  & A & \textless{}1.00 & \textless{}1.00 & \textless{}1.00 \\  & AW & \textless{}1.00 & \textless{}1.00 & \textless{}1.00 \\ \hline \multirow{3}{*}{E. COLI (log CFU/g)} & C & \textless{}1.00 & \textless{}1.00 & \textless{}1.00 \\  & A & \textless{}1.00 & \textless{}1.00 & \textless{}1.00 \\  & AW & \textless{}1.00 & \textless{}1.00 & \textless{}1.00 \\ \hline \multirow{3}{*}{SA (log CFU/g)} & C & \textless{}1.00 & \textless{}1.00 & \textless{}1.00 \\  & A & \textless{}1.00 & \textless{}1.00 & \textless{}1.00 \\  & AW & \textless{}1.00 & \textless{}1.00 & \textless{}1.00 \\ \hline \multirow{3}{*}{SAL} & C & nd & nd & nd \\  & A & nd & nd & nd \\  & AW & nd & nd & nd \\ \hline \multirow{3}{*}{LIST} & C & nd & nd & nd \\  & A & nd & nd & nd \\ \hline \hline \end{tabular}  
* Sample: AW—1.5% salt and 5% acid whey, A—1.5% salt and 5% apple vinegar, C—1.5% salt; LAB—lactic acid bacteria, TVC—total viable count; ENT—bacteria from the _Enterobacteriaceae_ family, E. COLL—_Escherichia coli_, SA—coagulase-positive staphylococci (_Staphylococcus aureus_ and other species), SAL—_Salmonella_ spp., LIST—_Listeria_ spp. including _L. monocytogenes_; \textless{}1.00—counts below the detection limit of the plating method; nd—not detected in 25 g of product; means in the same row with different uppercase letters (A–C) differ significantly, means in the same column with different lowercase letters (a–c) differ significantly (\(p<0.05\)); means \(\pm\) standard deviations; n = 4.  
\end{table} 
Table 2: Microbiological quality of studied hams.
## 4 Model Validations
### Fixed-Pad Hydrodynamic Journal Bearings  
Figure 4 compares the predicted static load versus eccentricity ratio for an sCO\({}_{2}\)-lubricated plain journal bearing to the predictions in Ref. [16] with incompressible and compressible fluid flows. The analyzed bearing has an axial length and a diameter of 40 mm (L/D = 1) with a radial clearance of 40 \(\upmu\)m, and the rotating speed is 60 krpm [16]. CFD model predictions using ANSYS FLUENT software are also compared with both predictions. In the CFD model, the ANSYS design modeler is used to create the fluid film  
model. The fluid film is meshed with 10 divisions in the radial direction and 900 divisions in the circumferential direction, and it has an element face size of 0.4 mm in the axial direction, resulting in 900,000 hexahedron elements. The interfaces attached to the bearing and journal sides are specified as stationary and rotating no-slip walls, respectively. Three-dimensional (3D) compressible Reynolds-averaged Navier-Stokes (RANS) with an SST k-omega turbulence model is solved using a coupled numerical scheme until the predefined convergence criteria are reached. Note that the CFD tool also uses Equations (11) and (12) to model the density and viscosity of sCO\({}_{2}\), respectively. The static load increases nonlinearly with the eccentricity ratio for all predictions. Compressible fluid flow models show higher load capacity than the incompressible ones, particularly for high eccentricity ratios, because the density and viscosity of compressible fluids (sCO\({}_{2}\)) increase with an increase in hydrodynamic pressure under heavy loads (Figure 3). The current bulk flow model predictions agree well with those obtained using CFD. For the compressible fluid flow model, the discrepancies between the current model predictions and those reported in Ref. [16] can be attributed to the difference in the density and viscosity models. Reference [16] treats sCO\({}_{2}\) as an isoviscous ideal gas, whereas the proposed model considers it a real gas with temperature- and density-dependent viscosity. The mean square errors for the current predictions and those in Ref. [16] are 0.13% and 0.35% for incompressible and compressible fluids, respectively.
## 3 Results
### Classification Model
#### 3.1.2 Dataset 2: Vegetation Index (VI)  
As shown in Figure 4, the highest separation gap of the average reflectance between healthy and infected plants for the red region was obtained at wavelength number 630 nm with 0.61%. For NIR region, wavelength number 934 nm was selected to be used to develop the NDVI since it was identified as the band with the highest separation gap of average reflectance value between healthy and infected seedlings.  
Figure 3: Scatter plot of SVM classification models. (**a**) Original dataset before classified using (**b**) linear SVM and (**c**) cubic SVM.  
As shown in Table 8, the accuracy scores obtained from both the vegetation index (SR and NDVI) were compared to identify which vegetation indices were better at detecting _G. boninense_ infection in oil palm seedlings. For SR, the highest accuracy was obtained from the Linear SVM with 57.3%. Meanwhile, cubic SVM obtained the lowest accuracy at 47.6%. In terms of sensitivity, the coarse Gaussian SVM had the highest score (97.8%) in correctly identifying infected seedlings as infected. However, it has the lowest specificity (4.3%). Therefore, most of the healthy seedlings were identified as infected. Meanwhile, cubic SVM had a better performance in identifying the healthy seedlings correctly with 65.6% specificity. Overall, the linear SVM could balance its performance in detecting infected and healthy seedlings. It obtained the highest score of AUC with 0.59. The NDVI has demonstrated similar trends of model performance for accuracy, sensitivity, specificity, and AUC. It obtained the same highest accuracy score as SR, which was from the linear SVM. Therefore, the classification results show that a simple linear separation between two classes would be more suitable to classify between infected and healthy seedlings.
# A Scientometric Review of Obstructive Sleep Apnea and Obesity
## 4 Discussion
### Cluster #5: Childhood Obesity and OSA and Cluster #4: Pediatric Obstructive Sleep Apnea
For example, the Children Adenotonsillectomy Trial (CHAT) [58] reported an overall success rate of 79% for surgery, although there was a lower likelihood for children with obesity, certain ethnicities, and more severe OSA. Results from CHAT have helped to inform evidence-based clinical practice guidelines [153], where notably, tonsillectomy is considered a first-line treatment for children with adenotonsillar hypertrophy.
## 3 Classifications of Routing Protocols in UAV Networks  
UAV networks require distinct routing strategies based on the network structure and the system deployment environment [13]. When UAVs move rapidly, they will cause temporary connection interruptions among nodes. Therefore, routing protocols must be able to detect and restore connections quickly while maintaining stable network performance. However, in the process of developing research on this issue, it was found that most traditional routing protocols are not suitable for UAV swarm communication [22]. This leads to two parallel development directions for UAV swarms. These are improvements of existing dynamic routing protocols to suit the application environment, and the emergence of protocols specifically designed for each specific application [19].  
In general, these protocols are classified as network topology-based routing, location-based routing, hierarchical routing, and swarm-based routing. In addition, the application of artificial intelligence (AI) to UAV networks based on learning capabilities is also a potential research direction for developing intelligent routing protocols [20]. Emerging research on AI-based routing protocols will be described in detail in the following section of this paper. Figure 8 illustrates the classification of UAV network routing protocols based on the methods mentioned. The following is a discussion of some prominent routing protocols and their use cases.  
Figure 8: UAV network routing classification.
# Efficient Expiration Date Recognition in Food Packages for Mobile Applications
## 4 Results
### Detector Results  
In this evaluation, a confusion matrix serves as the primary metric. This matrix is an effective means of assessing a classification model's performance, including its application in detection scenarios. It supplies a detailed comparison of the model's predictions versus the true labels, enabling the identification of areas where the model may falter. This particular study focuses on a classification task involving a single class, the expiration date.  
Figure 5: Expiration date recognition application flow chart.  
Figure 6: Application interface.  
To illustrate the impact of model conversion and quantization, the configurations remain unchanged. Although the IoU metric might not ensure that all the digits are encapsulated within the detected region--since it can be considered a true positive even when missing some digits, potentially leading to varied date interpretation--it serves as a useful parameter for assessing the performance of different models.  
The same datasets are used by this work and [2] regarding the evaluation of the _Products-Real_ dataset. It is worth noting that the date label is only selected since the other labels would be redundant. The numerical results are taken from [2] for comparing the differences in our proposed work with theirs. From Table 3, the precision, recall, and F1-score are 0.9850, 0.8814, and 0.9303, respectively, in this work; by contrast, the metrics are 0.9758, 0.9905, and 0.9831, respectively, in the previous work. While our model excels in precision, it sacrifices recall, which results in an overall F1-score that is lower than the previous work's. The precision, recall, and F1-score are calculated as follows,  
1. Precision: \((\frac{TP}{TP+TP})\);
2. Recall (Sensitivity): \((\frac{TP}{TP+FN})\);
3. F1-Score: \((2\times\frac{Precision\times Recall}{Precision+Recall})\).  
The tests were conducted in the same environment on a single GeForce GTX 1080 GPU. The detection model of [2] took 239 ms per image on average, while the proposed detection model can infer with 53 ms per image on average. The Torch Script model offers the best overall performance in terms of precision and F1-score but is also the largest in size for the PC platform. The FP32 and FP16 models have similar performance with slightly reduced precision and F1-score but are significantly smaller in size for edge devices. The INT8 model has a significant drop in precision and F1-score.
###### Abstract
Recent ISAC studies have primarily focused on performing communication and radar functions on the same time and frequency resources, with the goal of maximizing spectral and hardware efficiencies [10; 11]. In [12; 13], novel approaches integrating MIMO and ISAC systems have been proposed. In [12], a computer vision (CV) technique has been investigated for a location-based beam focusing method in ISAC environments. Movable antennas can be exploited to enhance the performance of ISAC systems by boosting beam gain at specific angles [13]. In addition, the beamforming accuracy for the radar target is also crucial. Since RSMA can flexibly manage these interferences along with enhancing the sensing accuracy of ISAC, the sensing accuracy can be increased and communication service connectivity can be ensured in RSMA-assisted ISAC networks. In [14], the concept of RSMA-assisted ISAC was introduced, which was extended to satellite communications in [15]. Subsequently, in [16], an RSMA-assisted ISAC system incorporating a digital-to-analog converter (DAC) at the transmitter was investigated in low Earth orbit (LEO) satellite systems. In [17], the hybrid beamforming is applied in RSMA-assisted ISAC systems. The main objective of this paper is to provide an up-to-date overview of various aspects related to RSMA-assisted ISAC systems. These include the evolution of MA techniques, key features of RSMA and ISAC, and potential research directions for RSMA-assisted ISAC systems. The main contributions of this paper are summarized as follows:  
* We highlight the evolution of mobile communication with existing MA technologies from 1G to 6G. Then, we compare the key features between RSMA and conventional MA techniques such as SDMA and NOMA in terms of both message construction and resource allocation. * We account for the basic principles of RSMA-assisted ISAC systems, along with key challenges and considerations for implementing RSMA-assisted ISAC networks. * We put forth the potential research directions of prime candidate technologies that can boost the RSMA-assisted ISAC systems, including non-terrestrial networks (NTNs), reconfigurable intelligent surfaces (RISs), millimeter wave (mmWave) and terahertz (THz) technologies, and vehicle-to-everything (V2X).
# Research on Passive Ranging Technology of Moving Ship Based on Vertical Hydrophone Array
## 4 Experimental Analysis  
### Experiment Situation  
In 2016, our research group conducted a sea trial in the South China Sea. The experimental configuration diagram is shown in Figure 9. This experiment employed a 16-element vertical hydrophone line array. A moving ship was chosen as the sound source, and the hydrophone array received the broadband-radiated noise of the vessel. The concrete experiment parameters were set as follows:  
Figure 8: The estimated ranges of all elements and after fusion.  
Figure 7: The simulation result for a single element. (**a**) Signal radiated from the source; (**b**) DEMON spectrum analysis; (**c**) Comparison between the estimated range and true moving range; (**d**) Relative error of the range estimation.  
* The vertical hydrophone line array was composed of 16 elements with an interval of 5 m. The depth of the top hydrophone was 370 m, and that of the bottom sensor was about 445 m. The depth of water over the experimental area was about 1800 m.
* The position where the heavy block attached below the array finally entered the water was regarded as the real array position recorded by the Global Positioning System (GPS) during the experiment. The moving vessel had GPS modules to record its real-time spatial positions. The corresponding trajectory map has been shown in Figure 10a. The real range from the ship to the array was calculated by GPS data and was then shown in Figure 10b. From this figure, we can see that the range and time of the CPA point were 344 m and 130 s, respectively. The moving velocity of the ship was about 5 m/s.
* The array may have a small tilt due to the disturbance of ocean currents. However, this small angular offset has little effect (only within 10%) on the final ranging result.
* The sound velocity profiler (SVP) was used to measure the sound speed profile (SSP) at the beginning of the experiments. The measured SSP is shown in Figure 11.
* The sampling rate was 16 kHz. The sea condition was level 3, and no other vessels passed during the measurement time.  
Figure 10: Results calculated by GPS data. **(a)** Ship trajectory map (The green diamond represents the location of the 16-element vertical hydrophone line array. The yellow circle and the five-pointed star stand for the starting and ending positions of the trajectory, respectively. The solid red line is the trajectory, and the solid blue line in the middle is that of the estimation segment. The color of the color bar stands for the water depth, and the contour has been marked.); **(b)** Real moving range variation with time.  
Figure 9: Experiment configuration diagram.
## 3 Digital Beamforming General Considerations
### Amplitude and Phase Errors Estimation
Current values used in the algorithm are \(T_{\mathit{max}}=450\) K and \(T_{\mathit{med}}=370\) K.  
Assuming that the amplitude and phase errors drift linearly and knowing that the estimated errors are noisy, a one-dimensional Kalman filter is applied to the calibration values [24]. The aim of using this recursive estimator lies in the minimization of the noise impact to fulfill the residual calibration errors requirements. A Kalman filter is an efficient recursive filter that estimates the state of a linear dynamic system from a series of noise measurements. This is a well-known and practiced technique in control engineering; indeed it is used in remote sensing applications especially on radar. The implementation of the Kalman filter and the experimental results has been reported in [11]. Using this technique, the calibration requirements are fulfilled with a residual root mean square (rms) error of \(\sim\)0.09 dB in amplitude and \(<\)0.5 \({}^{\circ}\)in phase. It is important to realize that this really good performance is not the whole system array, it is for one single receiver. More effects have to be considered when the whole array performance is taken into account. The main ones, further detailed in the next sections, are: The whole array has four receivers; the performance of the RF power combining network; and the estimation of the difference between the CNS and the antenna signal path, which is the higher error source.