esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Simulation Analysis of Deformation and Stress Characteristics of Rock Bolts
### Simulation Analysis of Rock Bolt Force  
The force conditions of the rock bolts are shown in Figures 12 and 13. The axial force of the 1\({}^{\#}\) rock bolt is the smallest, which is 84.5 kN, indicating that the subsidence of the surrounding rock within its support range is small. The axial force of the anchorage section increases greatly at the interface between the roof and the coal seam, indicating that there is a separation layer at this interface. The axial force of the 2\({}^{\#}\) rock bolt is the largest, which is 204.8 kN, indicating that the surrounding rock within its support range has a large subsidence and obvious separation layer. The axial force of the 3\({}^{\#}\) rock bolt is slightly lower than that of the 2\({}^{\#}\) rock bolt, which is 180.3 kN. There is no obvious change in the axial force of the anchorage section at the interface between the roof and the coal seam, indicating that the rock layers at this location have coordinated subsidence and the separation layer at the interface is small. The 1\({}^{\#}\), 2\({}^{\#}\), and 3\({}^{\#}\) rock bolts are all subject to a large shear force at the interface between the roof and the coal seam. The shear force of the 1\({}^{\#}\) rock bolt is the largest, which is 105.3 kN. It is not subject to shear force at the gangue surface and the interface between the anchorage and the free section, indicating that the coal seams above and below the gangue surface deform coordinately and there is no dislocation. Therefore, the force conditions of the 1\({}^{\#}\), 2\({}^{\#}\), and 3\({}^{\#}\) rock bolts correspond to the deformation characteristics of the bolt bodies. In addition, according to the technical specifications of rock bolt support for coal mine roadways, the maximum bearing capacity of the roof rock bolts in the roadways of Changing Mine is 125 kN. Therefore, according to the simulated actual force conditions of the rock bolts, the safety factors of 1\({}^{\#}\). 2\({}^{\#}\), and 3\({}^{\#}\) rock bolts during operation are calculated to be 1.48, 0.61, and 0.69, respectively. Under normal circumstances, the safety factor of rock bolts should be between 1.5 and 2.5. Within this range, the stability requirements of coal mine roadways can be met. Therefore, the above-mentioned rock bolts all undergo obvious deformation and lose their bearing capacity during actual operation, which is consistent with the on-site monitoring results of rock bolt deformation, once again verifying the reliability of the simulation method in this paper.  
Figure 11: Simulated Deformation Results of Roof Rock Bolts in 41072 Roadway.
# PPDC: A Privacy-Preserving Distinct Counting Scheme for Mobile Sensing
## 4 Performance Evaluation
### Accuracy Evaluation of PPDC  
In PPDC, \(q\) is an accuracy controlling parameter, the length of encoded bit string in _Step 2_ of Section 3.2. Figures 4 and 5 show the relationship between the value of \(q\) and the accuracy rate when the length \(  
Since there is a significant error in applying only one FM sketch, \(d\), the number of FM sketches, must be discussed. From Figure 6, it is observed that the error rate of estimated data decreases dramatically along with the increase of the number of repeat times in the beginning, then keeps relatively stable after a specific threshold, like \(d=4\) in this experiment. In the face of different sizes of datasets, the threshold will be different. We set experiments with different numbers of users participating in the program. In the meantime, the corresponding number of distinct counting in each dataset is independent and irregular since the elements which present users' sensing data are generated entirely randomly. The calculated values of PPDC are contrasted with true values in Figure 7. There is a difference between the two values, and as the size of the dataset improves, the overall difference tends to decrease but still shows fluctuation. The fluctuation is associated with the result of the FM sketch which has a close relationship with the multiplies of 2.  
According to the results in Figure 7, we calculate the accuracy rate of PPDC presented in Figure 8 to evaluate the correctness of PPDC more intuitively. It is obvious that the accuracy rate of PPDC is gradually raising close to 100% along with the increase of the size of datasets, where, even in the case of a small dataset, the accuracy rate can still reach 97%. When the amount of users is huge and corresponding cardinal number is big, PPDC can perform much better.  
Figure 5: Accuracy rate of estimated data influenced by the value of \(q\), where \(n=25\),000.  
Figure 6: Error rate of estimated data affected by the value of \(d\), where \(n=20\),000.  
Figure 7: The contrast of the numbers of distinct counting coming from the true value and the calculated value.
## 4 Results
### Performance Evaluation
It is defined as follows:  
\[MCC=\frac{TP\times TN+FP\times FN}{(TP+FP)\times(TP+FN)\times(TN+FP)\times(TN+ FN)}. \tag{4}\]  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multicolumn{6}{c}{**Simple Embedding Layer**} & \multicolumn{3}{c}{**Pre-Trained**} \\ \hline
**MT Model** & \multicolumn{2}{c}{**82 Features**} & \multicolumn{2}{c}{**86 Features**} & \multicolumn{2}{c}{**82 Features**} & \multicolumn{2}{c}{**86 Features**} \\ \hline  & **Precision** & **Recall** & **Precision** & **Recall** & **Precision** & **Recall** \\ \hline \multicolumn{6}{c}{**Language pair: EN-EL**} \\ \hline  & \multicolumn{4}{c}{**NN model with 2687 segments for C1 and 2022 segments for C2**} \\ SMT C1 & 70\% & **89**\% & 70\% & **92**\% & 70\% & **77**\% & 68\% & 77\% \\ NMT C1 & 67\% & 37\% & **69**\% & 31\% & 54\% & 40\% & 50\% & 45\% \\ SMT C2 & 62\% & 59\% & 63\% & 60\% & 60\% & 58\% & 62\% & **64**\% \\ NMT C2 & 58\% & 60\% & 55\% & **59**\% & 56\% & **59**\% & 57\% & **62**\% \\ \hline \multicolumn{6}{c}{**NN model\_SMOTE with 3024 segments for C1 and 2276 segments for C2**} \\ SMT C1 & 68\% & 72\% & 68\% & **78**\% & 65\% & 67\% & 65\% & 75\% \\ NMT C1 & 48\% & 41\% & 48\% & 42\% & 40\% & 37\% & **54**\% & 46\% \\ SMT C2 & 58\% & 49\% & 60\% & 52\% & 66\% & 45\% & 65\% & **73**\% \\ NMT C2 & 60\% & 59\% & 60\% & 64\%When using 10-fold CV, \(C1\) outperforms \(C2\) for both language pairs. When the percentage split method (70% training-30% testing) is used, a small performance improvement is observed for the \(C2\) corpus. Moreover, \(MCC\) achieves higher value for the \(C2\) corpus, when pre-trained embeddings are used. Figure 2 shows the accuracy performance according to training speed and batch size. Increasing the batch size can increase the model's accuracy. As seen above, the training speed decays more quickly for the simple embedding layer compared to the pre-trained embedding layer model. Moreover, the accuracy of the pre-trained embeddings is consistently higher for corpus \(C2\). The best performance has been consistently obtained for batch size 512. It is important to analyze the correlation with human-performed evaluations [47]. In this work, the correlation of the predicted scores with human judgments is reported using Kendal \(\tau\). Kendall \(\tau\), is a coefficient that measures the agreement between rankings produced by human judgments, and rankings produced by the classifier. The WMT'12 (Workshop of Machine Translation) definition of [PERSON]'s \(\tau\) is used, and it is calculated as follows:  
\[\tau=\frac{(concordantpairs-discordantpairs)}{totalpairs} \tag{5}\]  
where 'concordant pairs' is the number of times the human judgment and the predicted judgment agree in the ranking of any two translations that belong to the same \(SSE\), and 'discordant pairs' is the opposite.
# Application of HY-2B Satellite Data to Retrieve Snow Depth on Antarctic Sea Ice
## 2 Data
### HY-2B SMR and GCM-W1 AMSR-2 Brightness Temperature Data  
The passive microwave brightness temperature data used in this study were obtained from two sources: HY-2B SMR L2A brightness temperature data from the National Satellite Ocean Application Center (NSOAC) and GCM-W1 AMSR-2 L3 brightness temperature data from the National Snow and Ice Data Center (NSIDC). Both datasets have a spatial resolution of 25 km with the polar stereographic grid. The HY-2B SMR L2A brightness temperature data include five frequency channels: 6.93, 10.65, 18.7, 23.8, and 37.0 GHz.  
Figure 1: **(a)** Five sectors in the Southern Ocean: the blue line represents the R/V XueLong routes during the 35 th Chinese National Antarctic Research Expedition (CHINARE-35), and the red dots indicate the measurement points of snow depth on sea ice conducted by the ship, **(b,c)** are the brightness temperature measured by HY-2B SMR at 18.7 GHz on 1 January and 1 July, 2019, respectively, and **(d,e)** are the GCM-W1 AMSR-2 snow depth products released by the NSIDC on 1 January and 1 July, 2019, respectively.  
Except for the 23.8 GHz channel, which has only vertical polarization mode, each frequency channel has both vertical (V) and horizontal (H) polarization modes (Table 1). Prior to using the HY-2B SMR L2A brightness temperature data, geolocation correction, reprojection, and daily synthesis preprocessing were performed. All brightness temperature data measured over one calendar day were processed and mapped onto the polar stereographic grid provided by the NSIDC. (Figure 1b,c). The GCM-W1 AMSR-2 L3 brightness temperature data consist of six frequency channels: 6.93, 10.65, 18.7, 23.8, 36.5, and 89.0 GHz, all of which have both vertical (V) and horizontal (H) polarization modes. The GCM-W1 AMSR-2 L3 brightness temperature data are used to analyze the influence of brightness temperature differences observed by different sensors on the retrieval of snow depth on sea ice.
## 4 Compressive Behavior
### Effects of Beam Radius and Node Yield Strength  
Figure 11 shows the normalized plastic energy dissipation against strain of 3D AH at low-speed (V = 5 m/s) impact and high-speed (V = 50 m/s) impact. It is noticed in both impact scenarios that the energy absorption performance deteriorates due to the increase in beam radius. This can be explained by the deformation and failure mode of 3D AH under dynamic compressive loadings. When it is subjected to low-speed impact (such as v0 = 5 m/s), the 3D AH converts impact stroke into rotation and lateral collapse, which is rather a feature of rigid body motion with the increase in beam radius. The 3D AH with larger beam radius corresponds to higher plastic energy absorption capacity as the stronger dynamic effect, where the strain rate effect plays a dominant role in the plastic energy absorption.  
Figure 12 compares the performance of plastic energy absorption of the chiral structures corresponding to different yield strengths of the nodes. It is observed that the yield strength of the node has a marginal effect on the energy absorption capability of 3D AH.
###### Abstract  
Detecting alcohol intoxication is crucial for preventing accidents and enhancing public safety. Traditional intoxication detection methods rely on direct blood alcohol concentration (BAC) measurement via breathalyzers and wearable sensors. These methods require the user to purchase and carry external hardware such as breathalyzers, which is expensive and cumbersome. Convenient, unobtrusive intoxication detection methods using equipment already owned by users are desirable. Recent research has explored machine learning-based approaches using smartphone accelerometers to classify intoxicated gait patterns. While neural network approaches have emerged, due to the significant challenges with collecting intoxated gait data, gait datasets are often too small to utilize such approaches. To avoid overfitting on such small datasets, traditional machine learning (ML) classification is preferred. A comprehensive set of ML features have been proposed. However, until now, no work has systematically evaluated the performance of various categories of gait features for alcohol intoxication detection task using traditional machine learning algorithms. This study evaluates 27 signal processing features handcrafted from accelerometer gait data across five domains: time, frequency, wavelet, statistical, and information-theoretic. The data were collected from 24 subjects who experienced alcohol stimulation using goggle busters. Correlation-based feature selection (CFS) was employed to rank the features most correlated with alcohol-induced gait changes, revealing that 22 features exhibited statistically significant correlations with BAC levels. These statistically significant features were utilized to train supervised classifiers and assess their impact on alcohol intoxication detection accuracy. Statistical features yielded the highest accuracy (83.89%), followed by time-domain (83.22%) and frequency-domain features (82.21%). Classifying all domain 22 significant features using a random forest model improved classification accuracy to 84.9%. These findings suggest that incorporating a broader set of signal processing features enhances the accuracy of smartphone-based alcohol intoxication detection.  
accelerometer sensor; alcohol consumption; artificial intelligence techniques; time series analysis; smartphone; blood alcohol content; gait analysis +
Footnote †: journal: Applied sciences
# Optimized Clustering Algorithms for Large Wireless Sensor Networks: A Review
## 2 Routing on Wireless Sensor Networks
### Group Based Routing  
Having the location of each node within a field is not easy when the amount of sensors increases considerably. A more easy approach consists of deploying sensor nodes in groups. In this kind of routing, nodes in the same group are most of the time closed to each other [18; 19]. In group-based routing solutions, each group is able to perform its own application independently. Take, for example, the measurement of the environmental impact of an area made up of a small forest, a sandy place, and a marine reef. In this example, three groups can be deployed according to the measurements of each application (forest, sand, underwater). However, the deployment of group-based protocol within the study field needs to be meticulous. As explained by [PERSON] et al. [19], in a group-based solution, each sensor node is assigned to its group before the deployment. There are several algorithms based on group routing. [PERSON] et al. [18] proposed the Group-Based Protocol for Large Wireless Ad Hoc andSensor Networks called GBP-WAHSN. Another group-based algorithm is called Group based Mobile Agent Routing (GMAR) [20], which uses a mobile agent in order to aggregate data in each group.
## 3 Materials and Methods
### Hybrid Sensing Platform for IoT-Based Precision Agriculture
#### 3.1.1 Ground-Based Node  
The ground-based node comprises two main components that, respectively, collect plant data observations and environmental conditions. The plant observation component includes a SparkFun variable resistor soil moisture sensor, an AMG8833 grid-eye infrared (IR) sensor that is used to detect the target plant temperature, and an AS7265x spectrometer sensor that monitors the light spectrum reflectance from the target plant. The spectrometer sensor provides 18 channels that detect light from 410 nm to 940 nm wavelengths. The plant observation component is powered with a 10,000 mAh power bank, and its data are (1) recorded on a SD card as backup via an Arduino Mega microcontroller, (2) transferred to a Raspberry Pi 3 (i.e., a single board computer used as an edge server via its serial communication port), and (3) sent to the IoT platform at the cloud node via a Long-Term Evolution (LTE) module connected to the Raspberry Pi. Figure 2 shows the design of the plant observation component.  
The second component of the ground-based node is an Ecowitt WH2910 weather station. The weather station records ambient data including humidity, temperature, wind speed, wind gust, wind direction, solar radiation, and rainfall. The weather station data are transferred to the cloud using WiFi communication. Both components of the ground-based node are waterproof to prevent any environmental condition's impact on the data collection process.
# Cyclic Consistent Image Style Transformation: From Model to System
## 5 Experiment
### Visualization Comparison against SOTA Methods  
This experiment aimed to evaluate the performance of different image translation methods, particularly in converting aerial images into maps. To ensure the objectivity of the volunteers' evaluations, we designed a series of measures to minimize subjective bias.  
First, the experiment selected multiple types of aerial images, converting them into map images and then converting the maps back into photos. All generated images were presented to the volunteers in random order to avoid any sequence effects that might influence the evaluation results. Additionally, each volunteer could only see one original image and its generated counterpart at a time, preventing them from making comparative judgments with other original or generated images. This design helps mitigate reference bias in their evaluations.  
The experiment was conducted on the AMT crowdsourcing platform, recruiting a large number of volunteers. Each volunteer was asked to evaluate multiple pairs of images and identify the one they believed to be the most realistic. The volunteers' ratings were calculated using a weighted average approach, further reducing the impact of individual assessment biases. The results of these ratings are shown in Table 1.  
In the map-to-photo task, our C3 GAN model was rated as producing the most realistic images, with 24.63% of volunteers believing the images were real. This was notably higher than other methods, such as CoGAN (16.65%) and L1+CNN (20.88%). Similarly, in the photo-to-map task, C3 GAN again outperformed the others with a score of 22.90%, compared to SimGAN's 18.56% and L1+CNN's 20.24%. These results demonstrate C3 GAN's superiority in generating realistic images, with a clear advantage of 4-8 percentage points over the other methods.
# Development and Evaluation of a LiFi-Transceiver Module for TMTC Intra-Satellite Communication +
## 3 Transceiver-Design  
SatelLight mainly addresses LiFi communication for small satellites and CubeSats. For this reason, one of the main criteria is the size and mass of the transceiver. Complexityshould be reduced to a minimum but still have the possibility to implement redundancy mechanisms. Furthermore, the power consumption should be minimized.  
In this section, we first present the development of the hardware of the transceiver. Following this, the software architecture is shown.  
Figure 1 shows a block diagram that provides an overview of the architecture of the developed transceiver module.
# Exploiting Mobile Edge Computing for Enhancing Vehicular Applications in Smart Cities
## 2 Literature Review
### Architecture
#### 2.2.3 The Remote Cloud Server Layer  
This layer presents high computational power and storage capabilities of traffic data in addition to a wide range of services and applications. Network access can be provided on-demand to network edges and mobile vehicles with an Internet connection. To facilitate urban traffic management, learning algorithms can be employed to offer driver convenience services such as travel time estimation, congestion detection and route guidance.
## 1 Introduction  
Many methods have been used to achieve rotation invariant pattern recognition [1]. Recent methods are based on deep learning techniques [2; 3]. The wedge-ring detector method is one approach that also exhibits scale invariance properties [4; 5]. An analysis of the different techniques for recognizing and detecting objects under extreme scale variations were presented by [6]. One of the frequently used filters is the circular harmonic filter (CHF). The correlation using such a filter is invariant under the rotation of the scene image but suffers from the defects that result from only one circular harmonic component (CHC) being used. As a result, the correlation peak is not sufficiently sharp.  
To relax such a limitation, various methods have been proposed. These attempts are still the object of active research. However, one should emphasize that the critical parameters of optical efficiency, the sharpness of correlation peaks, the peak-to-sidelobe ratios, resistance to noise, and circular harmonic techniques yield as good or better results than competitive methods. This paper presents a rotation invariant approach based on the optical lossless implementation of one circular harmonic component by means of a diffractive optical element. Both the scene image and reference are subjected to a projection onto one CHC. For clarity, we will use the zero-order CHC, but other CHCs can be used as well.  
The diffractive element sums information over each radius of the scene image and projects the result onto one point of a line located at a certain distance behind the image. The method is, to a large extent, flexible and might include parallel pattern recognition and classification as well as further geometrical invariance. The database of references (of fingerprints of subscribers in a bank, for example) is compressed.  
The contributions of this paper may be summarized as follows.  
* Compression is performed in a way that the rotated images give the same compressed data.  
In contrast to CHC filters, rotation invariance is ensured without any significant energy loss.
* By maintaining rotation invariance, the image compression technique allows parallel data processing.
* The proposed method might be used to further add geometrical invariance, such as scale invariance, given that during the compressing task a scale factor can be easily included by means of the diffractive compressing element.  
The remainder of the paper is organized as follows. Section 2 provides an overview on related works. Section 3 presents a mathematical analysis of the issue of rotation invariance. Section 4 proposes a possible optical implementation. An extension of the proposed architecture is given in Section 5. Results are presented in Section 6. Finally, Section 7 presents concluding remarks.
## 3 Experiments
### Experimental Configuration  
According to the data format requirements of the discriminator, the reference and defect rectangles were uniformly transformed to 224 \(\times\) 224 resolution by linear interpolation. The defect boxes were not classified during training, and all types of defect boxes and corresponding reference ones were classified into one category. The hardware environment was configured with an Nvidia GeForce GTX 1080 8 GB graphics card, Intel Core i5-9500F CPU, and 16G memory. Paddle2.1 was employed as the deep learning framework, batch_size was set to 16, each category selected eight images, a total of 100 epochs were trained, and the initial learning rate was set to 0.0025. The learning rate decreased by 0.5 at 40, 60, and 80 epochs, respectively. The momentum optimizer was selected, and the momentum factor was set to 0.9. Based on Equation (6), we set the hyperparameters s = 30, m = 0.15, \(\alpha=\beta=1\); then loaded the pre-training parameters of MobileNetV3_large_x1_0 on ImageNet as the initial weight of the backbone. During verification and testing, the 512-D feature vector was taken from the neck part of the discriminator, calculating the cosine distance according to Equation (7) and we set a threshold equal to 0.5 to determine whether det_boxes was a defect. The comparator, detector, and discriminator were integrated to detect three kinds of CTs with different textures on the test set. The threshold of the detector ref_boxes is set to 0.95, and the threshold of defect is 0.5.