esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Effect of Process Parameters
#### 3.1.2 Effect of Ultrasonic Frequency  
The effect of ultrasonic frequency on the glycyrrhizic acid yield at different extraction times was illustrated in Figure 2. Similar to the effect of ultrasonic power on the extraction yield, the glycyrrhizic acid yield increased rapidly in the first 3 min and then showed a slow increase from 7 to 16 min before reaching equilibrium. Moreover, the glycyrrhizic acid yield significantly increased along with the increase of the ultrasonic frequency from 20 kHz to 55 kHz. After reaching equilibrium, the glycyrrhizic acid yield at 20 kHz was 133.8 mg/g while the corresponding yields increased to 217.7 mg/g at 55 kHz. It was noteworthy that a further increase in the ultrasonic frequency to 90 kHz,  
Figure 1: Evolution of extraction yields of glycyrrhizic acid with different ultrasonic powers at an extraction temperature of 25 \({}^{\circ}\)C and ultrasonic frequency of 20 kHz.  
however, the glycyrrhizic acid yield did not increase, but slightly decreased. The results suggested that the frequency of 55 kHz was an optimal ultrasonic frequency for the extraction of glycyrrhizic acid. Similarly, our previous studies [34; 35] also reported that the highest extraction yield was obtained at an optimal ultrasonic frequency during the extraction of rutin from _Sophora japonica_ and hesperidin from tangerine peels. These results were mainly attributed to the resonance mechanism of cavitation bubbles generated by ultrasound. Actually, in the UAE process, not all bubbles can cause significant cavitation effects. Only when the ultrasonic frequency is equal to the resonance frequency of cavitation bubbles, the greatest coupling of the ultrasonic energy will occur, which is favorable for cell to swell and enlarge the surface area of the cell wall. The increased area and sound swelling can improve the rate of mass transfer, which further leads to a reduction in the extraction time and increase in the extraction yield [36]. Therefore, the recommended ultrasonic frequency was 55 kHz in the rest of experiments.
## 4 Experiments and Analysis
### Model Performance Evaluation
#### 4.2.1. Function Similarity Detection
Furthermore, it introduces attention mechanisms at both intra-function and inter-function levels rather than limiting to one. Lastly, GMN+ significantly enhances semantic extraction from binary \begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{3}{c}{**ARM-MIPS**} & \multicolumn{3}{c}{**ARM-X86**} & \multicolumn{3}{c}{**MIPS-X86**} & \multicolumn{3}{c}{**ARM-MIPS-X86**} \\ \cline{2-13}  & **Recall** & **Precision** & **F1** & **Recall** & **Precision** & **F1** & **Recall** & **Precision** & **F1** & **Recall** & **Precision** & **F1** \\ \hline FIT & 0.9042 & 0.8479 & 0.8752 & 0.9160 & 0.9124 & 0.9142 & 0.9383 & 0.9012 & 0.9194 & 0.9186 & 0.8719 & 0.8946 \\ GMN & 0.8573 & 0.8636 & 0.8604 & 0.9122 & 0.8638 & 0.8873 & 0.8798 & 0.8984 & 0.8891 & 0.9161 & 0.8491 & 0.8813 \\ SAFE & - & - & - & 0.8854 & 0.8451 & 0.8647 & - & - & - & - & - & - \\
**GMN+\(\mathrm{(ours)}\)** & **0.9943** & **0.9215** & **0.9565** & **0.9727** & **0.9549** & **0.9637** & **0.9822** & **0.9375** & **0.9594** & **0.9835** & **0.9321** & **0.9571** \\ \hline \hline \end{tabular} Note: Values in bold font are the optimal values for each column.  
\end{table} 
Table 7: Results of the similarity detection task across architectures. Figure 5: Comparison of ROC curves for different methods across architectures. code by integrating instruction type information into instruction embeddings, providing a marked improvement over the FIT method.
## 3 Results and Discussion
### Environmental Impact of Dust Storms and Wind Erosion  
#### 3.7.1 Key Observations of Wind Erosion and Dust Deposition in Al-Baha Region  
Location S11 exhibited the highest dust deposition across the study period, peaking at 0.00616 ton ha\({}^{-1}\) on 20 July. This suggests significant susceptibility to wind erosion, likely due to a combination of strong winds and vulnerable soil conditions. The data indicate a seasonal trend with higher dust deposition in mid-July, possibly linked to specific weather patterns, like dry winds or temperature changes, that enhance soil erosion. The general decline in dust collection toward late August suggests natural soil stabilization, possible vegetation growth, or a reduction in wind speeds, a trend consistent across almost all locations.  
Most locations reached their highest wind surface values around 20 July, indicating a period of intense wind erosion activity, possibly linked to specific seasonal weather patterns. However, significant spatial variability was observed in wind surface values, with locations like S11, S1, and S6 showing consistently higher values, highlighting the need for targeted wind erosion-control measures in these areas. A clear temporal pattern was observed where dust deposition increased in the mid-summer period (late June to mid-July) and decreased toward the end of August. This suggests that dust deposition is closely linked to seasonal weather patterns and wind activity. The decline in dust deposition toward late August across most locations, such as S1 (dropping to 0.00494 ton ha\({}^{-1}\)), indicates a reduction in dust-transporting wind events, likely due to seasonal changes that reduce soil erosion and dust mobilization.  
Furthermore, the S11 location also showed a particularly high peak in dust deposition (0.02056 ton ha\({}^{-1}\)) on 10 July, possibly indicating a localized event or conditions that significantly increased dust transport and deposition. The high dust deposition observed in locations such as S11 may be influenced by specific land-use practices, such as agriculture, overgrazing, or construction activities that disturb the soil. These practices can leave soil surfaces more vulnerable to wind erosion. The topographical conditions, such as steep slopes or uneven terrain in these areas, may also contribute to higher dust mobilization by facilitating wind erosion. Additionally, intense weather conditions, like strong winds, prolonged dry spells, and high solar radiation, can exacerbate soil erosion and dust deposition, particularly during the mid-summer period, when these factors are most pronounced.
# Edge-Guided Hierarchical Network for Building Change Detection in Remote Sensing Images
## 3 Results
### Training Parameter Settings and Compared Methods  
The network is implemented based on the deep learning framework PyTorch 1.0.1 using a NVIDIA GTX 3090 GPU (NVIDIA Corporation, in Santa Clara, CA, USA). During training, the network uses Adam as an optimizer. The encoder network was initialized using the ResNet-18 model pre-trained at ImageNet, and the rest was initialized using the Xavier method. The initial learning rate for both datasets is 0.001. The size of the convolutional kernel used for the convolutional layer in the decoder is set to 3, and the weight balance factor is set to 5.  
In the experiments, the evaluation metrics use five evaluation metrics computed based on the confusion matrix: overall accuracy (OA), precision rate (PRE), recall rate (REC), and F1-score (F1).  
To validate the effectiveness of our proposed EGHNet, we compare our method with multiple deep learning-based methods, including FC-EF [31], FC-Siam-Conc [31], BIT-CD [32], FCN-PP [33], UNet++MSOF [20], DSIFN [34], ChangeFormer [35], HSAA-CD [36] that are described as follows:  
1. FC-EF: Image-level fusion method, where the bi-temporal images are concatenated as a single input to an FCN;
2. FC-Siam-conc: Feature-level fusion method, which employs a Siamese FCN to extract multi-level features and fuses bi-temporal information using concatenation.
3. FCN-PP: An FCN with a pyramidal pool, which widens a wider receptive field and fuses the multi-scale features;  
Figure 4: Samples of the LEVIR-CD dataset and WHU. (**a–c**) LEVIR-CD dataset. (**d–f**) WHU dataset.  
* UNet++MOSF: A densely connected \"U-shaped\" convolutional network with multiple side-outputs fusion;
* DSIFN: An encoder-decoder architecture that extracts multi-scale features using a VGG-based encoder and fuses features through an attention-based decoder;
* EGRCNN: An encoder-decoder architecture that contains two prediction branches, with one branch predicting the change area and the other predicting the edges of the change area;
* BIT-CD: Transformer-based change detector, which fuses bi-temporal convolutional features using transformer encoders and decoders;
* ChangeFormer: A transformer-based Siamese network architecture for Change Detection from a pair of co-registered remote sensing images;
* HSAA-CD: A dual-branch CNN and transformer-based Siamese network.
## 2 Experimental Procedure
### Instruments and Metrics  
We gathered data from five different sources: (1) a registration form with students' demographic information, (2) pre-test utterances, (3) log files, (4) utterances of users' interactions with Japanol, and (5) post-test utterances. Personal information included name, age, gender, L1, academic level, and final consent to analyze all gathered data. Log files gathered all low-level interaction events with the CAPT tool and monitored all user activities with timestamps. From these files, we computed a CAPT score per speaker which refers to the final performance at the end of the experiment. It includes the number of correct answers in both perception and production (in which we used gASR) tasks while training with Japanol [19]. Pre/post-test utterances consisted in oral productions of the minimal pairs lists provided to the students.  
A set of experimental variables was computed: (1) WER values of the train/test set models for the specific-purpose kASR system developed in a [0, 100] scale; (2) the student's pronunciation improvement at the segmental level comparing the difference between the number of correct words at the beginning (pre-test) and at the end (post-test) of the experiment in a [0, 10] scale. We used this scale for helping teachers to understand the score as they use it in the course's exams. This value consists of the mean of correct productions in relation to the total number of utterances. Finally, we used (3) the correlation values between gASR and kASR systems of the pre/post-test utterances and between the CAPT score and both ASR systems at the end of the experiment (post-test) in a [0, 1] scale.  
By way of statistical metrics and indexes, Wilcoxon signed-rank tests were used to compare the differences between the pre/post-test utterances of each group (intra-group), Mann-Whitney U tests were used to compare the differences between the groups (inter-group), and Pearson correlations were used to explain the statistical relationship between the values of the ASR systems and the final CAPT scores.
## 4 Design and Realization of an Automated Guided Vehicle  
The main objective of the innovative AGV, which was developed at the University of Applied Sciences Ravensburg-Weingarten, was the realization of nearly unlimited maneuvering possibilities with a rather simple mechanical design (Figure 1).  
In earlier work, a production platform with a patented steering principle was developed [24]. This production platform exhibited very good maneuvering possibilities [25], but required eight dedicated drive motors and was restricted to flat floors. The innovative design allows driving in uneven environments and only requires four drive motors. The AGV's main frame consists of four sprung arms that each dispose of one drive motor. The arms can freely rotate, but the front arms and the back arms are connected by belts. Each drive motor disposes of an angle encoder. This allows for determining the angle, the angular velocity and the angular acceleration of each wheel. Another set of two angular encoders measures the steering angle of the front wheels and the back wheels, respectively. The use of four individual motor controllers (electronic position control EPOS--communicating via the CANopen protocol) enables independent use of each drive motor. The ability to use four independent motors leads to a steering system that is already registered as a patent and is based on the concept to use the torque differences between wheels to steer the axles of a vehicle. The AGV can drive directly in any direction without time- and space-consuming turning maneuvers and is able to turn around its own center. Especially in narrow spaces that are common in production settings, this characteristic isvery desirable. Still, the mechanical construction is rather simple, because no dedicated steering motors are necessary. This leads to a high robustness. Apart from the six angular sensors mentioned above, the robot is equipped with global acceleration, velocity and yaw rate sensors, whose measurements are available via the Bluetooth protocol. Figure 2 shows the main parameters of the steering system of the AGV with all the considered forces and the parameters are additionally listed in Table 1.  
Figure 1: Automated guided vehicle (AGV).  
Figure 2: Steering system. COG: center of gravity.  
Each arm that holds a drive wheel disposes of a spring suspension intended to absorb impacts from uneven terrain and to balance possible different heights of the terrain (Figure 3).  
For controlling the AGV different modes can be chosen: manual driving and an autonomous driving mode. A central control unit (PC ALIX) determines the required steering angles for the front and the back wheels and sends (different) angular velocity commands to the four drive motors in order to achieve the desired steering angle, the desired driving direction, the desired instantaneous center of rotation ICR (which is often infinitely far away) and the desired vehicle speed.
# Dynamic Multi-Compartment Vehicle Routing Problem for Smart Waste Collection +
## 2 Literature Review
[31] (2022) introduced a Chance-Constrained Multi-Compartment Electric Vehicle Routing Problem (CCMCEVRP) for separate and intelligent waste collection and solved by Diversity Enhanced Particle Swarm Optimization with Neighborhood Search and Simulated Annealing (DNPSPOSA). The authors proved that using electric vehicles is more economical than using fuel vehicles and the saving rate is increased with the increase of the vehicle compartment. Dynamic vehicle routing models have rarely been applied to waste collection problems. We cite three articles in which the authors take into account historical data (i.e., without considering real-time information): [PERSON] et al. [32], [PERSON] et al. [33] (2019), and [PERSON] et al. [34] (2019). [PERSON] et al. solved a dynamic optimization model for creating the waste collection route. As a reference, we note that there is only one definition regarding smart waste management; it is based on sensors installed inside bins to communicate information wirelessly to collection decision-makers  
The contributions of this paper are summarized as follows:  
1. We propose a new scheme based on separate waste collection in the smart city through compartmentalized vehicles. The objective of this problem is to minimize total transport costs, including crossed distance and penalties caused by bin overflowing. 2. The problem is modeled as a dynamic vehicle routing problem, in which we divide the day into periods of equal duration; in each time slice, we consider the requests known at the beginning of the day and the new requests that have arrived and which can be visited. Contrary to the classic dynamic vehicle routing problem, which considers each visited customer must be removed from the list of customers to be visited for the rest of the day, we assumed that if a waste type in a station has triggered the alarm more than once in the same working day, this station can be visited more than once on the same day in order to avoid the overflow of the bins. 3. The effectiveness of the proposed approach is validated with a used a real dataset collected by Valorsul with some modifications to adapt these data to our problem.
# An Effective ELECTRA-Based Pipeline for Sentiment Analysis of Tourist Attraction Reviews
## 2 Background and Related Works  
### Travel Review Sentiment Analysis  
Text sentiment analysis (TSA) is the process of extracting users' opinions, sentiments and demands from unstructured subjective texts in a specific domain and distinguishing their polarity. Existing TSAs roughly fall into three main categories: sentiment lexicon and rule-based methods [5; 6; 7]; traditional machine learning-based methods [8; 9; 10; 11]; and deep learning-based methods [12; 13; 14; 15; 16; 17; 18; 19; 20]. Category I has proved to perform poorly when the texts are rich in new words, contextual words or multilingual words. Category II focuses on extracting sentiment features and the combination of different classifiers (e.g., KNN, SVM and Naive Bayes, etc.). Without fully utilizing the contextual information of the text, their classification accuracy is affected to a certain extent. In order to obtain better classification results, category III introduces deep neural networks--such as CNN, RNN and LSTM networks, Attention networks and Transformer networks--to automatically extract sentiment features and make good use of contextual semantic information.  
In recent years, research on sentiment analysis of review texts in tourism has started to receive more and more attention. It is driven by helping tourism stakeholders to better understand and to more quickly grasp relevant tourism information, providing a reliable basis for their decision-making process. Sentiment analysis is considered a key step in restaurant or hotel recommendations for tourists in many works [21; 22; 23]. However, only a handful of studies on the sentiment analysis of attraction review can be found [24; 25; 26]. It is worth noting that there is no existing work applying a pre-trained model for sentiment analysis of tourist attraction review texts. Pre-trained language models (PLMs) can achieve comparable or even SOTA (State-of-the-Art) results on NLP tasks with a small number of supervised corpora.
# Application of Gas Foil Bearings in China
## 2 Applications of Gas Foil Bearings
### Application in Oil-Free Turbocharger  
In the vehicular industry, an engine's output power can be greatly improved by increasing the air-fuel ratio of a turbocharger. Up to now, oil lubricated bearings have mostly been used in automotive turbochargers. Due to the high working temperature, there will be oil quality deterioration and coking, which may lead to catastrophic failure. These problems can be solved effectively by foil bearings. At the same time, the weight, size and complexity of the devices are greatly reduced. In addition, fuel economy and exhaust emissions are greatly improved. At present, domestic studies on turbochargers have also been carried out.  
In order to improve turbocharger performance and solve problems, such as oil leakage, Hunan University conducted theoretical and experimental research on oil-free turbochargers supported by air foil bearings, as shown in Figure 7. Subsequent acceleration and deceleration test showed that the rotors exhibited large sub-synchronous vibrations at 20-60 krpm, but the sub synchronous vibration of the rotor was significantly reduced at 68 krpm. The rotor trajectory and FFT analyses showed that the main vibration of the rotor at a steady speed of 68 krpm originated from the synchronous vibration, and the turbocharger could operate stably at 68 krpm [54; 55].  
Figure 6: Centrifugal blower by the Institute of Machinery Manufacturing Technology, CAEP [53].  
Figure 7: Oil-free turbocharger host machine by Hunan University [54].
## 5 Error Vector Magnitude Calculation  
After the equalization of the distorted constellation in Matlab, the EVM of the equalized constellation diagram is computed. Figure 8 depicts the computation of the EVM; the equation used to compute the EVM is given by [42; 43].  
\[\text{EVM}=1/\text{N}\left[\frac{\sum_{n=1}^{\text{N}}(\left|\text{S}_{\text{ reference,n}}\right.\left.-\text{S}_{\text{measured,n}}\right|)^{2}}{\sum_{n=1}^{\text{N}}( \left|\text{S}_{\text{reference,n}}\right.\left|\right.)^{2}}\right]^{1/2} \tag{15}\]  
where Sreference,n is the ideal nth symbol, and Smeasured,n is the nth measured symbol.
## 4 Results  
### Experimental Setup  
To evaluate the performance of RK-SLAM, we conducted experiments on the Replica dataset [41] (eight sequences), the TUM-RGBD dataset [42] (three sequences), and the ScanNet dataset [43] (five sequences). The Replica dataset consists of high-quality synthetic RGB-D images with a resolution of \(1200\times 680\). And the TUM-RGBD dataset and ScanNet dataset contain low-quality real-world images captured by cameras and with a resolution of \(640\times 480\), characterized by significant noise and blurriness. In particular, the depth images in the TUM-RGBD dataset and ScanNet dataset often suffer from information loss. We evaluated the effectiveness of our method on both types of datasets.  
We compared RK-SLAM with state-of-the-art NeRF-based SLAM and 3 DGS-based SLAM, including NICE-SLAM [36], Point-SLAM [38], Co-SLAM [37], SplaTAM [27], GS-SLAM [26], MonoGS [28], and CG-SLAM [39]. For RK-SLAM, the results were derived as the average of three experimental runs.  
We evaluated the tracking and mapping performance of RK-SLAM separately. For tracking, we used the average absolute trajectory error (ATE RMSE) as the evaluation metric. For mapping, we employed the peak signal-to-noise ratio (PSNR), structural similarity index measure (SSIM), and learned perceptual image patch similarity (LPIPS) [44].  
In the experiments, the maximum size of the sliding window \(W_{\text{s}}\) was set to 3, as discussed in detail in Section 4.3. Additionally, inspired by MonoGS [28], we limited the maximum size of the related frame window \(W_{\text{r}}\) to 7 to prevent excessive computational overhead caused by an increasing number of keyframes. This ensured that the total keyframe window (\(W_{\text{s}}+W_{\text{r}}\)) size remained at a maximum of 10, consistent with MonoGS. Meanwhile, the maximum size of the random keyframe window \(W_{random}\) was set to 2. For loss computation, \(\gamma\) was set to 0.9, \(\eta\) to 0.05, and \(\lambda\) to 10. RK-SLAM was implemented in Python (v.3.7) using the PyTorch (v.1.12.1) framework, incorporating CUDA code for 3 DGS. We ran our SLAM on a server with AMD EPYC 7V12 and NVIDIA GeForce RTX 3090 (Santa Clara, CA, USA).
# Proxy-Based Adaptive Transmission of MP-QUIC in Internet-of-Things Environment
## 3 Proposed Proxy-Based Adaptive MP-QUIC Transmission
### Path Management  
The proxy employs the path manager that is used to monitor each available path between proxy and server. For this purpose, the proxy measures the RTT for each path by exchanging Path Request and Response messages with the server periodically.  
From these RTT measurements with the server, the proxy will maintain the path management table, as shown in Table 1. In the table, RTT represents the RTT value that has  
Figure 2: The overall operation of the proposed MP-QUIC transmission.  
been measured and updated for each available path (or IP address). On the other hand, the _'hits'_ represent the number of clients who are using this network path. This value will be referred to and updated in the connection management operation. The QUIC connection of the client with the same priority packets will use the network path with the lowest _hits_ value.  
In the path management operations, the proxy monitors all candidate paths to the server in the backbone network by measuring the RTT for each candidate path 'periodically' (e.g., 30 s). This is because the measured RTT values can vary over time, and thus we continue to measure the recent RTT values periodically. Among many candidate paths, the proxy will choose the best path with the smallest RTT as the data transmission path.