esa-sceva/satcom-chunk-collection huggingface dataset:
# Productivity Assessment of the Yolo V5 Model in Detecting Road Surface Damages
## 3 Presenting the Yolo V5 Network Architecture Model and Introducing Other Detection Models
### Yolo Introduction to Yolo Network Architecture Model
#### 3.3.2 Yolo V5's Structure  
Yolo's structure consists of four main parts: input, backbone, neck, and head. Below is a detailed description of each part.  
Input: This is the first part of the Yolo model and is responsible for input processing. Normally, the input image will be divided into (grid) blocks and converted into tensors to be fed into the neural network.  
Backbone: This is the most important part of the model; it performs feature extraction from the image through a neural network architecture. In Yolo V5, EfficientNet architecture is often used as the backbone to learn complex features from images.  
Neck: Neck is the middle part of the model and is responsible for combining features from previous layers to create common features, which improves the ability to detect and localize objects. In Yolo, the neck is usually not used, and features are passed directly from the backbone to the next part.  
Head: This is the final part of the model and performs object prediction on the image. Head includes two main components, Dense Prediction and Sparse Prediction; specifically, Dense Prediction: Dense Prediction predicts detailed information about the position, size, and class of objects in each grid cell of an image. Through several convolutional layers and up-sampling techniques, it produces highly detailed and dense predictions.  
Sparse Prediction: Sparse Prediction uses convolutional layers with larger kernels to generate sparse predictions for larger regions in the image. These predictions help in detecting larger objects that may span multiple grid cells.  
Combining both Dense Prediction and Sparse Prediction, Yolo V5 is capable of detecting and classifying objects on images quickly and accurately.  
The Working Process of Yolo V5 in RTI IMS Software Development to Automatically Detect Road Surface Damage  
Application of Yolo V5 Model to Develop RTI IMS Software to Automatically Detect Road Surface Damage  
In this study, the Yolo V5 platform model was used to develop RTI-IMS software that can automatically detect road surface damage. To accomplish the above purpose, the training data for the RTI IMS software use a dataset of 9053 images provided Maeda, Sekimoto, Seto, Kashiyama and Omata [16]. The dataset used to train the machine learning data model is the road surface damage images in the studies in Taiwan, Japan, and India mentioned above. In addition, in order to make the RTI-IMS software improve inference without affecting inference speed, this study uses the Mosaic method in the Yolo V5 platform to help the training model not be overfit with input data in order to reduce many false predictions and make the model quality better on test data. This Mosaic method converts the input data to triple the variation, resulting in a more diverse and intelligent dataset.
## 4 Experiments and Results  
To assess the performance of the proposed method for IR, restoration, and enhancement using learning enriched features, experiments have been conducted separately for denoising and super-resolution tasks. Experiments are performed on the publicly available Diverse IR Image Datasets SIDD [70] and DND [71], which contain real-life captured degraded IR images under different types of environments.  
Before enhancing the image, we will denoise the noisy images first. This is because noise artifacts always cover many details of the image, and noise may also cause some non-existing artifacts to appear in the image. Hence, we must filter out the noise first. By doing this, we can keep only the important details and then enhance the important details in the low-resolution image to a higher resolution. The dataset comprises a large number of real-world images with various forms of degradations. The dataset's images are from noisy environments, as in real applications. The dataset exhibits various noise-level images from different scenes and objects. Thus, the dataset can represent almost all the possible noise level conditions in the real world.
## 5 Experiments and Data Analysis
### 5G Experimental Base Station Testing
#### 5.1.2 Bilateral Rotating Target  
In the bilateral rotating target experiment, data measurements are conducted on two types of bilateral targets as shown in Figure 16a,b. Taking the counterclockwise speed of 0.375 rps as an example for analysis, the time-frequency analysis results are shown in Figure 17a when the model has the same radius. The radii of the two targets are both 0.3 m. The result of frequency offset extraction is shown in Figure 17c and the spectrum is shown in Figure 17e. The obtained speed is 0.757 rps, which is twice the actual speed. The reason for this is that, when the radii are the same, the frequency offset curves of the two targets are the same, with only half a cycle of time delay. When the radii of the two targets are 0.3 m and 0.15 m, respectively, the time-frequency analysis results are shown in Figure 17a.  
Figure 14: (**a**) Time–frequency analysis results of channel response without interference suppression; (**b**) time–frequency analysis results after suppressing random phase noise; (**c**) time–frequency analysis results after suppressing zero frequency component; (**d**) smoothed frequency offset curve of unilateral target.  
Figure 15: (**a**) The error between the actual measurement value and the theoretical value; (**b**) spectral plot of frequency offset curve for unilateral targets; (**c**) frequency offset curves of unilateral targets with different rotation directions.  
Figure 17b,d, and the corresponding spectrum is shown in Figure 17f. The obtained speed is 0.378 rps, with an error of 0.8%.  
Figure 16: (**a**) Bilateral rotating target model with the same radius; (**b**) bilateral rotating target models with a different radius.  
Figure 17: (**a**) Time–frequency analysis when rotating targets have the same radius; (**b**) time–frequency analysis when rotating targets have different radii; (**c**) frequency offset curve when rotating targets have the same radius; (**d**) frequency offset curve when rotating targets have different radii; (**e**) spectrum plot of frequency offset curve when rotating targets have the same radius; (**f**) spectrum plot of frequency offset curve when rotating targets have different radii.
# Stiffness-Oriented Structure Topology Optimization for Hinge-Free Compliant Mechanisms Design
## 5 Conclusions  
### Conclusions  
A stiffness-oriented structure topological synthesis method is proposed to design a hinge-free compliant mechanism, in which both flexibility and stiffness requirements are considered. The formulation of the compliant mechanism optimization problem presented here is to balance these two conflicting objectives simultaneously by combining the mutual potential energy and eigen-frequency into one objective. Numerically obtained hinge-free designs show that required output displacements are achieved through structure elastic deformation; meanwhile, the required stiffness of the specified eigenmode is maximized by weighting corresponding eigen-frequency, which demonstrates the validity of the presented method and offers an alternative method for hinge-free compliant mechanisms design. We notice that the proposed synthesis method can be easily extended to 3D problems or more complicated compliant problems, for instance, with the integration of a piezoelectric actuator for an actual compliant application.
###### Abstract  
The identification and classification of network traffic are crucial for maintaining network security, optimizing network management, and ensuring reliable service quality. These functions help prevent malicious activities, such as network attacks and illegal intrusions, while supporting the efficient allocation of network resources and enhancing user experience. However, the widespread use of traffic encryption technology, while improving data transmission security, also obscures the content of traffic, making it challenging to accurately classify and identify encrypted traffic. This limitation hampers both network security maintenance and further improvements in service quality. Therefore, there is an urgent need to develop an efficient and accurate encryption traffic identification method. This study addresses three key challenges: First, existing methods fail to explore the potential relationship between flow load features and sequence features during feature extraction. Second, there is a need for approaches that can adapt to the diverse characteristics of different protocols, ensuring the accuracy and robustness of encrypted traffic identification. Third, traditional deep learning models need large amounts of labeled data, which are expensive to acquire. To overcome these challenges, we propose an encrypted traffic recognition method based on a CLSTM model (a combination of 2-conv CNN and BiLSTM) and Mean Teacher collaborative learning. This approach detects and integrates traffic load features with sequence features to improve the accuracy and robustness of encrypted traffic identification while reducing the model's reliance on labeled data through the consistency constraint of unlabeled data using Mean Teacher. Experimental results demonstrate that the CLSTM-MT collaborative learning method outperforms traditional methods in encrypted traffic identification and classification, achieving superior performance even with limited labeled data, thus addressing the high cost of data labeling.  
encrypted traffic classification; convolutional neural network (CNN); Bidirectional Long Short-Term Memory (BiLSTM); semi-supervised learning; deep learning +
Footnote †: journal: Applied sciences  
Article
# Study and Design of Distributed Badminton Agility Training and Test System
## 4 System Communication Design
### Front-End Communication Design  
The ESP8266 is a module that uses a serial port to communicate with an MCU (or other serial devices) and has a built-in TCP/IP protocol stack that enables conversion between the serial port and WiFi [27]. The communication module supports three data transmission modes: AP mode, STA mode and STA+WIFI&AP coexistence mode. Each mode also includes three sub-modes: TCP server, TCP client and UDP mode [28].  
The wireless communication module is connected to the communication serial port of the controller. By programming the program in the controller, the wireless communication module sends out corresponding AT commands to achieve the functions of initialization, network connection and controlling the touch front-end system as a client to connect to the server side. The lower computer sends data to the server via the communication module and received data from the service port of the upper computer. The ESP8266 WiFi module operation instructions are shown in Table 1.  
The interaction of information between the WiFi module and the controller is important in the design of the lower computer. This is achieved by creating the function TxData(), which sends a single byte, implementing the function send_string_com(), which sends a string through the function TxData(),implementing the function send_string_com(), which sends ATSEND (send command) and appends the length of the sent character through the function send_string_sendcom(), and finally, wrapping the three functions together to form a function that sends characters through the serial port. The above functions can be used to directly encapsulate a function to connect to an AP site by specifying the AP site name and password. In practice, it is possible to connect to an AP site with any name and password  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Command Type** & **Grammar** & **Description** \\ \hline Setup commands & AT + CWMODE = 2 & AP mode \\ Reboot command & AT + RST & Restart the communication module \\ Query commands & AT + CWLAP & Currently available APs \\ Connection commands & AT + CWJAP = “Name”, “Password” & AP connection command \\ Sending orders & AT + CIPSEND = \textless{}nnum\textgreater{} & Sending bytes \\ Connection commands & AT + CIPSTART = TCP, IP address, port number & Connecting to a TCP server \\ \hline \hline \end{tabular}
\end{table}
Table 1: ESP8266 WiFi module operation command.  
by simply modifying the specified string parameters. The same can be carried out for the function to send a command to connect to a TCP server using the function wrapped in the above implementation, which can then be called directly to connect to the specified TCP server site with the default parameters. It is only necessary to modify the IP address and port number in the specified string parameters to connect directly to the TCP server using this function.
# A Survey of Wireless Sensor Network Based Air Pollution Monitoring Systems
## 3 Air Pollution Monitoring Equipment
### Gas Sensor
#### 3.1.2 Electrochemical Gas Sensor [78]  
The working mechanisms of the electrochemical ambient gas sensors are electrochemical reactions (oxidation-reduction reactions, to be specific) within the sensors. The reaction between the sensor and the ambient gas molecules produces an electrical signal (current) proportional to the concentration of the ambient gas.  
An electrochemical sensor consists of a Working Electrode (WE) and a Counter Electrode (CE). For sensors requiring an external driving voltage, a Reference Electrode (RE) is needed. These two or three electrodes are separately deployed into the electrolyte within the sensor (see Figure 4).  
Figure 3: Chip-type Sensor.  
Figure 2: Bead-type sensor.  
Different sensors may use different types of selective membranes, electrolyte and working electrodes in order to improve the sensor's selectivity to a specific kind of ambient gas. To allow enough amount of ambient gas to react with the sensor while preventing electrolyte leakage, the ambient gas first goes through a capillary-type opening and a hydrophobic barrier. When the ambient gas reaches the working electrode, the oxidation-reduction reaction occurs. The specifically developed electrode for an interested ambient gas catalyzes these reactions. By measuring the current between the Working Electrode (WE) and the Counter Electrode (CE), researchers are able to deduce the concentration of the target ambient gas. For sensor with Reference Electrode (RE), the reference electrode is used to control the oxidation and reduction reactions and reduce the potential drift on working electrode due to deterioration (may not work when the electrodes are fouled).  
Note that, most of the electrochemical ambient gas sensors require a small amount of oxygen and humidity to function properly. Also, wind velocity influences the chemical equilibrium on the sensor's surface and further influences the sensor's readings [85].
## 3 Proposed Optimal Orchestration Process
### Algorithm Performance Analysis  
The evaluation of machine learning algorithms through LazyPredict revealed significant performance variations across different models. Random Forest emerged as the superior algorithm, achieving 94.8% accuracy, with an average inference time of 12 ms. This  
Figure 6: Simulation setup.  
performance notably surpassed other tested algorithms, with XGBoost achieving 93.2% accuracy (15 ms inference), Neural Networks at 91.7% (25 ms inference), and SVM at 89.5% (18 ms inference).  
Resource prediction accuracy demonstrated strong performance across different time horizons, with RMSE values of 3.2%, 5.8%, and 8.9% for short-term (5 min), medium-term (1 h), and long-term (24 h) predictions, respectively. This progressive increase in error rates with prediction horizon length aligns with expected behaviour in time-series forecasting applications.
## 2 Single-Leg Kinematics and Dynamics Modelling
### Single-Leg Dynamic Modeling
Subsequently, based on the Newton-Euler formula, the inertial forces and active forces of each link are obtained as follows:  
\[F_{C_{i}}= \begin{bmatrix}\mathbf{\tau}_{C_{i}}\\ \mathbf{f}_{C_{i}}\end{bmatrix} = \begin{bmatrix}-\mathbf{{}^{0}}\mathbf{I}_{C_{i}}\mathbf{ \kappa}_{C_{i}}-\mathbf{\omega}_{C_{i}}\times\mathbf{{}^{0}}\mathbf{I}_{C_{i}}\mathbf{\omega}_ {C_{i}}\\ -m_{C_{i}}(\mathbf{\alpha}_{C_{i}}-\mathbf{g})\end{bmatrix}  \tag{5}\]  
where \(\mathbf{{}^{0}}\mathbf{I}_{C_{i}}\) is the inertia matrix of the link in the base coordinate system \(\{0\}\), and \(\mathbf{{}^{0}}\mathbf{I}_{C_{i}}=\mathbf{{}^{0}}\mathbf{R}_{C_{i}}\mathbf{L}_{C_{i}}\mathbf{{}^{0}} \mathbf{R}_{C_{i}}^{\text{T}}\). \(\mathbf{I}_{C_{i}}\) is the inertia matrix of the link in the centroid coordinate system \(\{C_{i}\}\). \(\mathbf{{}^{0}}\mathbf{R}_{C_{i}}\) is the rotation matrix of the centroid coordinate system \(\{C_{i}\}\) relative to the base coordinate system \(\{0\}\). Based on the inertial forces and active forces of each link calculated above, in the ideal situation, the dynamic model of the single leg with a closed-loop chain can be obtained as follows:  
\[\delta\mathbf{\theta}^{\text{T}}\mathbf{\tau}+\delta\mathbf{P}^{\text{T}}\mathbf{F}_{P}+\sum \limits_{i=1}^{7}\delta\mathbf{\xi}_{C_{i}}\mathbf{F}_{C_{i}}=0 \tag{6}\]  
where \(\delta\mathbf{\theta}\) is the virtual displacement of the active pair, \(\mathbf{\tau}\) is the driving torque of the active kinematic pair, \(\delta\mathbf{P}\) is the virtual displacement of the foot-end, \(\mathbf{F}_{P}\) is the touchdown force, \(\delta\mathbf{\xi}_{C_{i}}\) is the virtual displacement of each link, and \(\mathbf{F}_{C_{i}}\) is the inertial force and active force of each link. For non-zero virtual displacement \(\delta\mathbf{\theta}\), by integrating and eliminating Equation (6) with Equations (3) and (4), it can be obtained that:  
\[\mathbf{\tau}+\mathbf{J}_{P}^{\text{T}}\mathbf{F}_{P}+\sum\limits_{i=1}^{7}\mathbf{I}_{C_{i}}^{ \text{T}}\mathbf{F}_{C_{i}}=0 \tag{7}\]After integration into the standard form, the single-leg dynamic modeling is thus obtained:  
\[\mathbf{M}(\theta)\ddot{\mathbf{\theta}}+\mathbf{V}(\theta,\dot{\mathbf{\theta}})+\mathbf{G}(\theta)=\mathbf{\tau} \tag{8}\]  
where \(\mathbf{M}\), \(\mathbf{C}\), and \(\mathbf{G}\) represent the generalized inertia matrix, the Coriolis, and centrifugal force matrix, and the potential energy matrix, respectively.
# Effect of Fibers on Mixture Design of Stone Matrix Asphalt
###### Abstract  
Lignin fibers typically influence the mixture performance of stone matrix asphalt (SMA), such as strength, stability, durability, noise level, rutting resistance, fatigue life, and water sensitivity. However, limited studies were conducted to analyze the influence of fibers on the percent voids in mineral aggregate in bituminous mixture (VMA) during the mixture design. This study analyzed the effect of different fibers and fiber contents on the VMA in SMA mixture design. A surface-dry condition method test and Marshall Stability test were applied on the SMA mixture with four different fibers (i.e., flocculent lignin fiber, mineral fiber, polyester fiber, blended fiber). The test results indicated that the bulk specific gravity of SMA mixtures and asphalt saturation decreased with the increasing fiber content, whilst the percent air voids in bituminous mixtures (VV), Marshall Stability and VMA increased. Mineral fiber had the most obvious impact on the bulk specific gravity of bituminous mixtures, while flocculent lignin fiber had a minimal impact. The mixture with mineral fiber and polyester fiber had significant effects on the volumetric properties, and, consequently, exhibited better VMA over the conventional SMA mixture with lignin fiber. Modified fiber content range was also provided, which will widen the utilization of mineral fiber and polyester fiber in the applications of SMA mixtures. The mixture evaluation suggested no statistically significant difference between lignin fiber and polyester fiber on the stability. The mineral fiber required a much larger fiber content to improve the mixture performance than other fibers. Overall, the results can be a reference to guide SMA mixture design.  
stone matrix asphalt; volume parameters; Marshall Stability; flocculent lignin fiber; polyester fiber; mineral fiber; fiber content +
Footnote †: journal: Applied Sciences
## 3 Proposed Method
### Graph-Based Label Space Information  
In semi-supervised learning, the information contained in the data space is generally well used, while the information that may be encoded in the label space is rarely used to obtain a general representation of the data. For example, in the face beauty problem, it is easy to understand that if two face images have similar beauty scores in the label space, they are likely to have strong similarity in the data space, i.e., the label space contains similarity information between two samples (cluster hypothesis [45]). In the complex face beauty prediction problem, two images with different image descriptors may have similar scores. Therefore, in this particular case, a graph based on the similarity of the scores can complement the data graph and contribute to a better score propagation among all images.  
Indeed, we considered the label space as a new view of the data that could be described by a new similarity graph. The nodes of the graph were the samples and the weight of each edge was calculated by the Pearson correlation coefficient involving scores only. Since the face beauty score is not a vector, the Pearson correlation coefficient between the labels of two images cannot be calculated. To overcome this issue, for each image \(I_{i}\), we define a new label vector \(\mathbf{s}_{i}=[s_{1};s_{2};\ldots;s_{K}]\) with the purpose of making use of the label (score) information. Here, \(s_{1},s_{2},\ldots,\) and \(s_{K}\) are the \(K\) nearest scores to the score of the image \(I_{i}\). For two images \(I_{i}\) and \(I_{j}\) with label vectors \(\mathbf{s}_{i}\) and \(\mathbf{s}_{j}\), the similarity between them is calculated using the Pearson correlation coefficient, yielding the correlation similarity matrix \(\mathbf{W}_{corr}\) with elements given by:  
\[W_{corr}(i,j)=corr(\mathbf{s}_{i},\mathbf{s}_{j})=\frac{\sum_{m=1}^{K}(s_{i,m }-\bar{s}_{i})(s_{j,m}-\bar{s}_{j})}{\sqrt{\sum_{m=1}^{K}(s_{i,m}-\bar{s}_{i}) ^{2}}\sqrt{\sum_{m=1}^{K}(s_{j,m}-\bar{s}_{j})^{2}}} \tag{7}\]  
where \(\bar{s}_{i}\) is the average of the label vector \(\mathbf{s}_{i}\) over the \(K-\)nearest neighbours of the image \(I_{i}\).  
The computation of the score graph is based on the scores \(s_{j}\). If the image \(I_{j}\) is in the labelled part of the data, then \(s_{j}\) is set to the ground-truth score of that image. In the casethat the image does not belong to the labelled part of the data, then \(s_{j}\) is set to the soft score \(f_{j}\) estimated in the iterative process.  
Afterward, we assign zero to the negative value obtained through Equation (7), and finally a sparse graph is generated by retaining the \(K^{\prime}\) highest correlations values. In our work, \(K\) and \(K^{\prime}\) are set to 10.  
We add the obtained correlation graph to the previously mentioned data space graph by linearly adding it to the data graphs with the same weights as follows:  
\[\hat{\mathbf{L}}=\mathbf{L}_{data}+\mathbf{L}_{corr}=\sum_{k=1}^{M}\mathbf{L }_{k}+\mathbf{D}_{corr}-\mathbf{W}_{corr} \tag{8}\]  
where \(\mathbf{L}_{corr}=\mathbf{D}_{corr}-\mathbf{W}_{corr}\) is the Laplacian of the correlation graph.