esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Autonomous Driving Sensor Data-Based C-ITS Environment Architecture  
### C-ITS Environment  
The goal of the proposed C-ITS environment is to incorporate the utilization of vehicle big data. By transmitting vehicle big data from the platform to the central LDM, the accuracy and quality of information can be improved.  
Figure 4a is a C-ITS environment based on the existing central LDM that processes the collected data and transmits them to the vehicle LDM. The central LDM detects unexpected situations on the road through the RSU and receives information on the road conditions from the traffic information central. However, the fixed RSU can only gather information on a limited area of about 1 km, making it difficult to assess the road conditions accurately. Another disadvantage is that additional equipment is required to utilize the RSU for C-ITS. However, vehicles are also equipped with sensors similar to the  
RSU, but unlike the RSU, only simple data such as vehicle speed and direction angle are transmitted to the central LDM.  
Figure 4b describes the C-ITS environment that compensates for the disadvantages of the existing C-ITS environment shown in Figure 4a with sensor data from an autonomous vehicle, a moving IoT equipment. The data collected from the vehicle are transmitted to the central LDM after recognizing a broader scope than the infrastructure. In addition, when the big data platform recognizes information on weather and traffic situation of the area where the vehicle is driving in real time, it transmits the data to an external affiliated organization to supplement the real-time information.  
The internal structure of the big data-based LDM linkage platform for autonomous cooperative driving is composed mainly of three systems, as shown in Figure 5. The vehicle system collects data from the equipment installed in the vehicle and vehicle driving data and drives autonomously through real-time processing. It also communicates with surrounding vehicles and the infrastructure in the vehicle system to provide the basis for cooperative driving. The platform is the core system of the proposed C-ITS environment, consisting of several modules to store, process, and analyze big data collected from the vehicles. Analysis results are transmitted to the vehicles and users again using a messaging system or are converted into a structure suitable for utilization and provided to affiliated organizations outside C-ITS. The service system consists of a messaging system that provides analysis results to the central LDM and a search module that allows users to access the server and check data directly. The search module provides a function to check and download data suitable for various research purposes to utilize the collected data.  
Figure 4: (**a**) Existing C-ITS environment diagram; (**b**) C-ITS environment with the proposed platform diagram.
## 4 Method
### Hypotheses  
This study aims to investigate the capacities of LLMs in predicting medical diagnoses based on Spanish textual descriptions of clinical cases. We hypothesize that LLMs demonstrate a high level of adaptability and generalization to diverse and complex cardiology cases. We explore the impact of different prompt techniques and temperature settings on the diagnostic accuracy of LLMs. Specifically, we hypothesize that few-shot prompting techniques will enhance diagnostic accuracy (H1.1) and that RAG will further improve predictions by incorporating external knowledge sources (H1.2). Additionally, we expect that lower temperature settings will result in more deterministic and focused predictions (H2.1), while higher temperature settings will introduce variability and creativity (H2.2). Finally, we aim to compare the performance of different LLMs, hypothesizing that models with advanced techniques such as MoE and fine-tuning will show superior performance (H3.1, H3.2).  
Impact of Prompt Techniques.
* H1.1: Few-shot prompting techniques will enhance the diagnostic accuracy of LLMs by providing contextual guidance and reducing the need for extensive training data.
* H1.2: RAG will further improve diagnostic predictions by incorporating external knowledge sources, particularly in cases with ambiguous or complex symptoms.  
Influence of Temperature Settings.
* H2.1: Lower temperature settings will result in more deterministic and focused diagnostic predictions, leading to higher accuracy in straightforward cases.
* H2.2: Higher temperature settings will introduce variability and creativity in the predictions, which may be beneficial for exploring alternative diagnoses in ambiguous cases but could also lead to less consistent results.  
Variability Among LLMs.
* H3.1: Different LLMs (e.g., Gemini Pro, Mixtral 8x22b, Medichat Llama3) will exhibit varying performance levels due to differences in their architectures, training data, and parameter settings.
* H3.2: Models with advanced techniques such as MoE and fine-tuning will show superior performance in handling the complexities of cardiology diagnoses.
# An Intelligent Classification Model for Surface Defects on Cement Concrete Bridges
## 6 Conclusions  
This paper mainly develops a surface defect detection model for cement concrete bridges based on transfer learning and the convolutional neural network (CNN). The collected images were preprocessed by a morphology-based weight adaptive denoising method. The VGG-16, which is a classic CNN, was fine-tuned by reducing the three fully connected layers into two and making the Softmax layer  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Recognition** & \multirow{2}{*}{**Normal**} & \multirow{2}{*}{**Cracks**} & **Plate** & **Corner** & **Edge/** & **Skeleton** & **Repairs** & **Mean** & **Top 5-** \\
**Model** & & & **Facturing** & **Rupturing** & **Exfoliation** & **Exposure** & **Rupairs** & **Accuracy** & **Accuracy** \\ \hline BPNN & 59.72 & 60.01 & 57.65 & 61.23 & 58.96 & 60.82 & 58.75 & 59.59 & 70.68 \\ SVM & 61.54 & 62.37 & 60.23 & 64.58 & 61.66 & 64.39 & 61.36 & 62.30 & 80.56 \\ AlexNet & 79.32 & 81.51 & 78.31 & 82.96 & 79.89 & 81.65 & 82.16 & 80.83 & 92.32 \\ GoogleNet & 78.46 & 84.65 & 80.24 & 83.61 & 80.52 & 80.88 & 81.53 & 81.41 & 95.49 \\ ResNet & 81.64 & 84.99 & 80.55 & 84.02 & 81.64 & 81.36 & 81.65 & 82.33 & 97.85 \\ Our model & 82.98 & 84.96 & 81.06 & 84.67 & 83.03 & 85.45 & 84.17 & 83.76 & 98.96 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of the six models on surface defect detection.  
Figure 12: The accuracy and loss of the model.  
with seven tags as the final classification layer. The fine-tuned model was trained and tested with characteristic images of seven classes of defects including normal, cracks, fracturing, plate fracturing, corner rupturing, edge/corner exfoliation, skeleton exposure, and repairs. Lastly, our model was compared with other NNs, using the same ratio between training and test sets. The comparison demonstrates the excellence of our model in defect detection, generalization, and robustness.  
Conceptualization, [PERSON] and J.S.; methodology, J.Z.; writing--original draft preparation, J.Z.; writing--review and editing, J.S. All authors have read and agreed to the published version of the manuscript.  
The National Key Research and Development Program of China (Grant No.: 2018 YFB1600300 and 2018 YFB1600301), the National Science Foundation of China (Grant No.: 51578370), and the Transportation Science and Technology Development Planning Project, Tianjin, China (Grant No.: G2018-29) supported this work. Any opinions, findings, conclusions, and recommendations mentioned in this work are of the authors and do not necessarily reflect those of the sponsors.  
The authors declare there is no conflicts of interest regarding the publication of this paper.
# Correct Pronunciation Detection of the Arabic Alphabet Using Deep Learning
###### Abstract  
Automatic speech recognition for Arabic has its unique challenges and there has been relatively slow progress in this domain. Specifically, Classic Arabic has received even less research attention. The correct pronunciation of the Arabic alphabet has significant implications on the meaning of words. In this work, we have designed learning models for the Arabic alphabet classification based on the correct pronunciation of an alphabet. The correct pronunciation classification of the Arabic alphabet is a challenging task for the research community. We divide the problem into two steps, firstly we train the model to recognize an alphabet, namely Arabic alphabet classification. Secondly, we train the model to determine its quality of pronunciation, namely Arabic alphabet pronunciation classification. Due to the less availability of audio data of this kind, we had to collect audio data from the experts, and novices for our model's training. To train these models, we extract pronunciation features from audio data of the Arabic alphabet using mel-spectrogram. We have employed a deep convolution neural network (DCNN), AlexNet with transfer learning, and bidirectional long short-term memory (BLSTM), a type of recurrent neural network (RNN), for the classification of the audio data. For alphabet classification, DCNN, AlexNet, and BLSTM achieve an accuracy of 95.95%, 98.41%, and 88.32%, respectively. For Arabic alphabet pronunciation classification, DCNN, AlexNet, and BLSTM achieve an accuracy of 97.88%, 99.14%, and 77.71%, respectively.  
deep learning (DL); artificial neural network (ANN); deep convolution neural network (DCNN); recurrent neural network (RNN); bidirectional long short-term memory (BLSTM) +
Footnote †: journal: Applied Sciences
# Review of Unmanned Aerial Vehicle Swarm Communication Architectures and Routing Protocols
## 3 Routing Protocols
### Topology-Based Routing Protocols
#### Hybrid Routing Protocols  
To overcome the huge overhead of control messages in PRP and the large delay of the route discovery process in RRP, Hybrid Routing Protocols (HRP) were introduced. HRP divides large-scale Ad hoc networks into zones. PRP is used between adjacent nodes in the same zone, while RRP is used between nodes in different zones. Adjusting the routing strategy according to network characteristics can not only reduce the routing overhead but also decrease the large delay in route discovery. However, the highly dynamic nature of UAV ad hoc networks makes it difficult for HRP to obtain and maintain information. The most representative HRPs are Zone Routing Protocol (ZRP) and Temporarily Ordered Routing Algorithm (TORA), as described below.  
(1) Zone Routing Protocol (ZRP) [77; 78] is a routing protocol that makes full use of routing zone topology. The application range of PRP is limited to a zone measured by the number of hops. Accordingly, the number and propagation of routing control packets is reduced. When communicating with nodes outside the zone, the RRP is selected. Compared with the simple PRP, ZRP can greatly reduce routing overhead. When compared with RRP, its time route convergence and transmission delays are relatively small. In the case of a large network load, the performance of ZRP does not decrease much, which makes it a routing protocol with great development potential. At present, the zone radius in ZRP is generally specified in advance, which limits the adaptability of the protocol. Therefore, improving ZRP with adaptive zone radii has been a research hotspot. [PERSON] et al. [56] proposed a novel clustering algorithm for UAV networks. First, the UAVs are clustered on the ground. Then each cluster head is selected in the zone based on geographic information and each cluster structure is adjusted according to the mission information. [PERSON] [79] et al. also focus on the clustering algorithm and they proposed another mobile prediction clustering algorithm. To solve the cluster update problem, the tree structure, the link failure time and the nodes' movement model are used to predict the movement of UAVs and further to update the network topology.  
(2) Temporarily Ordered Routing Algorithm (TORA) [80] is a hybrid distributed routing protocol with high adaptability. The distributed algorithm is embodied in the maintenance of the routing information. Each node only updates the routing information of neighboring nodes. In response to the high mobility of UAVs, TORA minimizes the sensitivity to topology changes to limit the spread of control messages. It is worth noting that the shortest path algorithm is not the choice of TORA. Instead, it usually uses longer routes to quickly discover new routes in the event of a link outage. In TORA, different \"height\" values are used to mark the UAV nodes. Based on this, a directed acyclic routing structure is constructed to achieve effective traffic forwarding.
## 3 Proposed System Model
### Proposed Methodology and Q-Value Updates
#### 3.4.3 Target Network Update  
Periodically updating the target network in accordance with the Q-network helps maintain the stability of the training process. By correlating the weights of the Q-network with those of the target Q-network, this method enables the calculation of target Q-values using a more reliable reference. Infrequent updates mitigate instability or divergence during training.
## 3 Results
URT and LRT did not lead to a significant change in AorPWV compared to the baseline value (Figure 1B). ArmPWV showed a tendency to decrease following URT, but this tendency did not reach statistical significance (\(P\) = 0.06; Figure 1C). No change was observed in the LegPWV following LRT (Figure 1D). \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Group** & **Exercise** & **Pre-Intervention** & **Post-Intervention** & \
\begin{tabular}{c} _t-Test_ \\ _P Value_ \\ \end{tabular} &
\begin{tabular}{c} _\(\Delta\)1 RM (\%)_ \\ \end{tabular} \\ \hline \multirow{4}{*}{URT} & Chest press, kg & 66.8 \(\pm\) 3.0 & 81.7 \(\pm\) 2.9 & 0.0001 & 22.3 \\  & Shoulder press, kg & 58.5 \(\pm\) 8.4 & 71.2 \(\pm\) 11.1 & 0.006 & 21.7 \\  & Seated row, kg & 20.2 \(\pm\) 1.2 & 23.3 \(\pm\) 0.5 & 0.004 & 15.3 \\  & Barbell curl, kg & 24.3 \(\pm\) 2.8 & 30.2 \(\pm\) 2.7 & 0.0001 & 24.3 \\ \hline \multirow{4}{*}{LRT} & Leg press, kg & 182.7 \(\pm\) 14.0 & 208.3 \(\pm\) 14.8 & 0.001 & 14.0 \\  & Leg extension, kg & 78.4 \(\pm\) 5.6 & 93.8 \(\pm\) 4.3 & 0.0001 & 19.6 \\ \cline{1-1}  & Lying leg curl, kg & 15.4 \(\pm\) 1.7 & 18.8 \(\pm\) 1.7 & 0.0001 & 22.1 \\ \cline{1-1}  & Hip extension, kg & 13.9 \(\pm\) 1.7 & 16.4 \(\pm\) 1.7 & 0.0001 & 18.0 \\ \hline \hline \end{tabular} Data are mean \(\pm\) SE. Abbreviations: RM, repetition maximum; URT, upper-limb resistance training group; LRT, lower-limb resistance training group.  
\end{table} 
Table 2: Changes in maximal strength (1 RM) after resistance training. Figure 1: Change in central (AIx and AorPWV, panels (**A**) and (**B**), respectively) and peripheral (ArmPWV and LegPWV, panels (**C**) and (**D**), respectively) artery stiffness in response to the intervention. AIx, augmentation index; AorPWV, aortic (carotid–femoral artery) pulse wave velocity; ArmPWV, arm (brachial–radial artery) pulse wave velocity, LegPWV, leg (femoral–posterior tibial artery) pulse wave velocity; CON, nontraining control group; URT, upper-limb resistance training group; LRT, lower-limb resistance training group. Plasma epinephrine concentration tended to decrease in the three groups but showed no statistical significance (\(P\geq\) 0.07; Table 1). The norepinephrine level was significantly reduced in all groups (\(P\leq\) 0.001; Table 1). In the resistance training groups, changes in Alx and LegPWV were significantly associated with changes in serum triglyceride levels and leg LBM, respectively (r = 0.57 and \(-\)0.61, \(P\) = 0.02 and 0.01; Figure 2). The changes in AorPWV and trunk LBM in response to resistance training seemed related to each other but did not reach statistical significance (r = 0.41, \(P\) = 0.06). Figure 2: Relationship between the change in triglycerides and the change in augmentation index in response to resistance training (panel (**A**)). Relationship between the change in leg lean body mass and the change in leg pulse wave velocity in response to resistance training (panel (**B**)). Alx, augmentation index; PWV, pulse wave velocity; LBM, lean body mass.
## 4 Problem Definition
### Formulation of UAV Placement Optimization as an MOP
#### 4.2.2 Minimizing the Degree of Dissatisfaction of the Required Data Rate  
Consider a set of transmission modes \(\mathcal{B}\) comprising the possible bit rates \(b_{i}\). We denote the transmission modes in use by a UAV and requested by a GN as \(b_{i}^{u}\) and \(b_{i}^{v}\), respectively. We define the degree of dissatisfaction as follows:  
\[\gamma^{v}=\begin{cases}\frac{|b_{i}^{u}-b_{i}^{v}|}{b_{i}^{v}}&\text{if }(b_{i}^{u }-b_{i}^{v})<0\\ 0&\text{Otherwise}\end{cases} \tag{8}\]  
We consider that the use of a \(b_{i}\) depends on the SNR. Usually, GNs experiencing a relatively low SNR will have their receiver interface tuned to a robust (with lower BER when compared with other modes under the same channel conditions) transmission mode with a lower data rate. On the other hand, if the SNR is relatively high, the receiver may be tuned to a transmission mode that offers a higher data rate. Essentially, from Equation (8), we measure the difference between the required data rate by the GN and the one that is delivered by the serving UAV given the current distance between them. As previously stated, LoS links are assumed between GNs and UAVs, and orthogonal channel codes are used to avoid interference between concurrent transmissions. Thus, if the distance between the UAV and GN is within the admissible distance for the required transmission mode \(b_{i}^{v}\), then the GN can transmit at the required data rate, i.e., the degree of dissatisfaction is zero. In this work, we try to minimize the maximum dissatisfaction value as follows:  
\[min\ (max_{v\in\mathbf{V}}\ \gamma^{v}) \tag{9}\]
## 5 Discussion  
### Time Series Analysis of Feature Points  
To analyze the temporal and spatial characteristics of subsidence distribution in Shanghai better, this section randomly selects six feature points (i.e., PSI-PS6) scattered in different areas of Shanghai for specific analysis. The specific distribution of feature points is shown in Figure 3, and the accumulated subsidence amount of six time series points is shown in Figure 6.  
PS1 is located in the vicinity of Sichuan North Road Park in Hongkou District, which was constructed earlier. The area is the location of the old city of Shanghai, and the subsidence here has basically stabilized. Therefore, the subsidence of PS1 is relatively stable during the study period, and no excessive subsidence phenomenon occurred.  
PS2 is located near Xinglian Central Village, Jiuting Town, Songjiang District. In the subsidence area, the subsidence fluctuation is relatively small, and no obvious subsidence phenomenon occurred.  
PS3 is located near the Pailoutou Building in Minhang District. The point shows a slow subsidence trend with an accumulated subsidence of \(-\)14.4 mm. Many industrial parks and factories are found in this area. Moreover, the industrial water is extracted from groundwater, which is the main cause of subsidence.  
PS4 is located near Fei Shangwangjia in Fengxian District. There are a large number of large factories such as aluminum products and machinery parts production plants. Industrial production and industrial water using become one of the main factors for subsidence. The maximum accumulated subsidence in this area is \(-\)44.6 mm, and the subsidence phenomenon is more obvious.  
PS5 is located near Shanghai Keen Machinery Manufacturing, Fengxian District. The surface of this area shows a continuous subsidence trend, and many machinery part production factories are found in this area, which becomes one of the main reasons for the subsidence in this area.  
PS6 is located near Kangqiao Peninsula in Pudong, and the subsidence in this area is basically stable without obvious subsidence.  
Figure 6: Subsidence time series with respect to PS points labeled as PS1–PS6 in Figure 3.  
In general, the temporal and spatial inhomogeneity of subsidence in Shanghai is obvious, which shows uplift in the east and decline in the west and uplift in the north and decline in the south. The surface subsidence in Shanghai is nonlinear because of the over-exploitation of groundwater in the region.
# A New Method for Detecting Onset and Offset for Singing in Real-Time and Offline Environments
## 3 The Proposed Algorithm
### Estimating F0s  
Since the algorithm is based on the fundamental frequencies, the F0s must be estimated correctly. However, as mentioned in [35; 36; 37], the current real-time pitch detection algorithms are unreliable when applied to singing phrases. Therefore, according to the study by [PERSON] and [PERSON] [35], a more reliable offline algorithm, pYin [38], was employed to avoid a compounding effect in this analysis if any real-time pitch detector algorithm would be used. Thus, it was possible to evaluate the accuracy of the onset algorithm without any adverse effects caused by the pitch detection algorithms. A Python library, Librosa [22], was used for pYin.  
The main difference between the real-time and offline algorithms is the amount of data they need for the calculation. Therefore, real-time algorithms are only based on the previous data points and/or a few later data points meaning that only a short buffer delay is required. On the other hand, offline algorithms require a long buffer delay to have sufficient data to perform their calculations. Using the pYin algorithm does not mean the proposed algorithm needs a long buffer delay to obtain a large amount of data, but the algorithm can work with a very short buffer delay, as explained below.