esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Proposed Approach
### Functional Analysis and Requirements Definition
#### 3.1.1 Product Performance  
Several researchers have developed methodologies and analysis tools to improve assembly during the development phase of mechanical systems. According to the predefined principle, the product performance indicators are used to quantify the quality of the proposed solution and indicate whether the latter needs improvement. For each indicator, a limit value is assigned. These values serves to allow their comparison to those calculated for each designed solution. The indicators used for the product performance analysis are: maximum number of parts (N), cost (C), efficiency (D\({}_{\text{e}}\)) and are calculated using Equations (1)-(3). These equations as follows:  
\[\text{N}=\text{A}+\text{B} \tag{1}\]  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Indicators** & **Specifications** & **Limit Values** \\ \hline \multirow{3}{*}{Product performance} & Number of parts (N) & \\  & Cost (C) & Defined by SE in accordance \\  & Design efficiency (D\({}_{\text{e}}\)) & with the working group \\ \hline \multirow{3}{*}{Process performance} & Total assembly time (T\({}_{\text{AT}}\)) & \\  & Insertion ratio (I\({}_{\text{R}}\)) & \\ \cline{1-1}  & Handeling ratio (H\({}_{\text{R}}\)) & \\ \cline{1-1}  & Assemblability ratio (A\({}_{\text{R}}\)) & \\ \cline{1-1}  & Complexity factor (C\({}_{\text{F}}\)) & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Assemblability indicators.  
\[C=\sum_{k=0}^{N}C_{k} \tag{2}\]  
\[D_{e}=100\ \frac{A}{A+B} \tag{3}\]  
where  
* A: Essential components;
* B: Non-essential components;
* N: total number of parts;
* C\({}_{k}\): Part assembly cost.  
It is noticed that all parts are classified into essential (A) and non-essential parts (B) according to the working group and based on the answer to the subroutine shown in Figure 3.  
Based on an in-depth needs study of each mechanical system under consideration, the SE defines limit values for the product performance indicator in a requirement diagram. These values serve to judge the validity of the design solution later on. The product performance requirements of the speed reduc  
Figure 3: Algorithm for classifying components into essential and non-essential.
# Detecting the Absence of Lung Sliding in Lung Ultrasound Using Deep Learning
## 4 Discussion  
Our architecture selects temporal slices for the detection of lung sliding based on tissue segments of the lung, pleura, and ribs. The U-Net for lung and rib segmentation outperforms the U-Net for pleura segmentation by almost every measure; see Table 1. These performance differences result from different tissue-to-background ratios and possible mislabelling during the process of semi-automated production of semantic segmentation masks. The lung tissue and rib shadows are well constrained. The lung tissue is constrained by the pleura and the anechoic shadows of the ribs, but the contours of the pleura tissue are fuzzier.  
We compare two types of slice selection: Lung slice selection and centroid slice selection. While lung slice selection uses different sample amounts for each volume, centroid slice selection extracts three slices per volume. Only the 8-frame classifier performs significantly worse with centroid slice selection; see Figure 6. However, our best-performing classifiers--the 16-, 32-, and 64-frame versions--do not display significant differences between the two slice selection methods.  
Lung sliding is a dynamic phenomenon. We must take into account the frame rate of the video and physiology of the process that we are trying to detect. Videos from our dataset contain 30 frames per second. In our case, the best-performing classifiers were the ones for 16-, 32-, and 64-frame input time windows, that is, periods of 0.53-2.13 s. Therefore, for detection of the absence of lung sliding, we recommend using time windows between 0.53 and 2.13 s.  
The dataset used in this study consists of LUS samples taken with a single device from patients after thoracic surgery in a single institution. While we do not think that this invalidates the presented research results, the dataset is affected by this bias. It is reasonable to assume that the classifiers would perform slightly differently if the samples were taken with several US devices of varying imaging quality.  
Figure 7: The mean difference of balanced accuracy for five classifiers with varying number of frames and the chosen slice selection method against the best performing classifiers (64 f. ctr.) are shown in the above Cumming estimation plot. The raw data is plotted on the upper axes. On the lower axes, mean differences are plotted as bootstrap sampling distributions. Each mean difference is depicted as a dot. Each 95% confidence interval is indicated by the ends of the vertical error bars. (_f._: frames; ctr.: centroid slice selection; lung: lung slice selection.)
# Enhancing Methyl Orange Degradation with Laser-Generated ZnO and Ce-Doped ZnO Nanoparticles
## 2 Materials and Methods
### Synthesis of Ce-Doped ZnO Nanoparticles by CO\({}_{2}\) Laser  
Figure 1 presents a schematic representation of the typical setup employed for generating the Laser-Assisted Chemical Bath Synthesis (LACBS) technique using a CO\({}_{2}\) laser. The laser operates at a wavelength of 10.6 \(\upmu\)m with a power output of 60 watts. Notably, a concave mirror is strategically positioned to focus the laser beam, which is directed at an inclined angle towards the solution under test, contained within a ceramic boat and isolated within a Perspex box. Subsequently, the solution, comprising undoped ZnO (preparation is in Section S1), undergoes irradiation for a duration of 10-15 min. The reaction occurs under atmospheric pressure and room temperature conditions, leading to the formation of ZnO nanoparticles (NPs) after approximately 10 min of irradiation [16]. The same experimental sequence is applied for the synthesis of doped ZnO, where the 1% Ce-doped ZnO NPs are produced following a similar procedure (see Section S2). The obtained ZnO and 1% Ce/ZnO powders, both with a concentration of 0.5 M, exhibited a milky-white appearance. These powders were then separated by centrifugation, followed by a thorough washing process using deionized water (DI) and ethanol to eliminate any existing impurities. Afterward, the powders were subjected to drying in an oven at 80 \({}^{\circ}\)C for one hour and subsequently calcined inside a furnace at 400 \({}^{\circ}\)C for a duration of 30 h.
# A Platform of Unmanned Surface Vehicle Swarms for Real Time Monitoring in Aquaculture Environments
## 3 Aquatic Platform
### IoT Wireless Sensor Kit
#### 3.2.2 Electronic Setup  
Due to the fact that there is no commercial sensor kit suited to the system's needs, the entire sensor kit was designed from scratch, implementing hardware-appropriate firmware. Each USV, DTS and MC is composed by a Raspberry Pi model 3b+, as illustrated in Figure 3(a), with _buster_ Raspbian OS ([[https://www.raspberrypi.org/blog/buster-the-new-version-of-raspbian/](https://www.raspberrypi.org/blog/buster-the-new-version-of-raspbian/)]([https://www.raspberrypi.org/blog/buster-the-new-version-of-raspbian/](https://www.raspberrypi.org/blog/buster-the-new-version-of-raspbian/))) and ROS Melodic ([[https://www.ros.org/](https://www.ros.org/)]([https://www.ros.org/](https://www.ros.org/))). Moreover, the Buoy (illustrated in Figure 3(b)) is composed by a Compute Module Raspberry Pi version 1, to be able to accommodate more sensors, due to the expanded IO. The USV's also have space to add more sensors, but the bottleneck is the central processing unit capabilities (memory and power). All devices equipped with LoRa communication also includes a SX12772 LoRa module and Multiprotocol Radio Shield manufactured by Libelium [28].  
In order to lower the cost of each device, each USV has different types of sensors, instead of each one having all sensors integrated. A printed circuit board (PCB) was designed, as illustrated in Figure 5, to minimize mechanical requirements and facilitate the assembly process. However, this solution makes the platform vulnerable, due to the unprotected connections. Figure 5 represents the different devices that can be assembled using the same PCB, easing the process of changing to different sets of sensors, not changing any connections or code. Moreover, the Buoy also integrates another PCB to connect from the compute Module IO to the Raspberry Pi 3 IO.  
Figure 4: Overall prototype design.  
Figure 3: Architecture overview.  
Each PCB can integrate the sensors presented and described in Table 1. The Liquid Level sensor and the Dissolved Oxygen are only used in Buoys and in these devices we do not use the Ultrasonic sensor to measure obstacles. Each device has one power bank to feed the Central Processing Unit and the sensors. However, the USVs have another battery directly connected to the motors. Figure 2a shows that, close to the motors there is a LED light and a switch on opposite ends. The LED will turn on when the battery reaches critical levels and the switch acts as an emergency button to the motors. When flipped to the OFF position, the motors stop. The chosen hardware provides a low-cost device that has a modular structure, making it easy to repair.
## Abbreviations and Expansions  
\begin{tabular}{l l} Abbreviations & Acronyms \\ ARIMAX & Autoregressive integrated moving average with exogenous variables \\ ARIMA & Autoregressive integrated moving average \\ SPF & Safety performance functions \\ LSTM & Long short-term memory \\ GBRT & Gradient boosting regression trees \\ MVNB & Multivariate negative binomial \\ NB & Negative binomial \\ INAR & Integer-valued autoregressive \\ PIT & Probability integral transform \\ ACF & Autocorrelation \\ PACF & Partial Autocorrelation \\ ANN & Artificial neural network \\ CNN & Convolutional neural network \\ SVM & Support vector machine \\ SVR & Support vector regression \\ RBF & Radial basis function \\ ReLU & Rectified Linear Unit \\ AUC & Area Under Curve \\ ROC & Receiver operating characteristic curve \\ RMSD & Root Mean Squared Deviation \\ MARS & Multivariate adaptive regression splines \\ GARCH & Generalized AutoRegressive Conditional Heteroskedasticity \\ \end{tabular}
# Prediction of Air Quality Combining Wavelet Transform, DCCA Correlation Analysis and LSTM Model
## 2 Materials and Methods
### Long Short-Term Memory Neural Network  
LSTM (long short-term memory neural network) is based on the recurrent neural network, adding a forgetting layer mechanism so that its unit state is influenced by the output of the previous unit, the information of the hidden layer, and the memory information of the forgetting layer, which, together, determine the information of the unit at this moment, effectively resolving the sequence length dependence problem and gradient explosion problem; it is widely used in the predisposition prediction field [29]. LSTM is built on a recurrent neural network with an input gate, output gate, and forgetting gate structure that decides the information passed by the unit. The structure of the LSTM cell is shown in Figure 1.  
As shown in Figure 1, The LSTM model operates as described below:  
1. Determine the informational state of each neural unit. The forgetting gate receives the \(h_{t-1}\) and \(x_{t}\) information and outputs a value between 0 and 1 for each element in cell state \(C_{t-1}\), with 1 indicating complete information retention and 0 indicating complete information discard, where \(C_{t-1}\) is the value of the cell stored at \(t-1\). The forgetting gate's equation is: \[f_{t}=\sigma\Big{(}W_{xf}x_{t}+W_{tf}h_{t-1}+W_{f}C_{t-1}+b_{f}\Big{)}\] (9)
2. Determine the information that needs updating via the input gate. The calculating formula for the input gate is as follows: \[i_{t}=\sigma(W_{x}x_{t}+W_{k}h_{t-1}+W_{d}C_{t-1}+b_{i})\] (10)
3. The calculation for the output gates is as follows: \[o_{t}=\sigma(W_{xo}x_{t}+W_{ho}h_{t-1}+W_{co}C_{t-1}b_{o})\] (11) where \(\sigma\) is a sigmoid function with values in the interval; the door is opened when the value is 1 and closed when the value is 0. The activation function's value is: \[\sigma(x)=\frac{1}{1+e^{-x}}\] (12)
4. Calculate the memory gate cell, select the data to be stored in memory, and then calculate the temporary cell state (the value of the candidate memory cell). \[C_{t}^{\prime}=\tanh(W_{xc}x_{t}+W_{hc}h_{t-1}+b_{c})\] (13) where \(W_{xc}\) represents the weight between the input data and the storage cell and \(W_{hc}\) represents the weight between the hidden layer and the storage cell.
5. The formula for calculating the value of a memory cell is as follows: \[C_{t}=f_{t}C_{t-1}+i_{t}C_{t}^{\prime}\] (14)
6. The output at time \(t\) of the LSTM structural cell. \[y_{t}=o_{t}\text{tanh}(C_{t})\] (15)  
Figure 1: LSTM cell structure diagram.
# Adaptive Super-Twisting Control for Mobile Wheeled Inverted Pendulum Systems
## 4 Experimental Studies
### Experimental Results
#### 4.2.1 Case1: Regardless of Disturbances  
In this case, no additional disturbances are considered. The experimental system in this case is shown in Figure 3a.  
The experimental results of the adaptive STA and adaptive SMC are illustrated in Figures 4-7. Figure 4 compares the inclination angle of the MWIP system by employing the adaptive SMC and adaptive STA, and Figure 5 depicts the value of the sliding surface. One can conclude from the above observation that both strategies can balance the MWIP system effectively. It can be seen from Figure 4 that the variation in the inclination is obviously large when the adaptive SMC algorithm is employed,while the inclination angle is relatively stable and stays near zero when the adaptive STA algorithm is applied. From the perspective of the sliding surface, as shown in Figure 5, both the adaptive SMC and adaptive STA can drive the system's states to slide on the sliding surface. Moreover, the adaptive STA effectively suppressed the occurrence of chattering better than the adaptive SMC. Figure 6 shows the variations in the adaptive coefficients of the adaptive SMC and the adaptive STA, respectively. It can be seen that the adaptive coefficient changes greatly at the initial stage, and its rate of change gradually decreases until it reaches a suitable value, which indicates that the parameters are sufficient to stabilize the system. Additionally, we plotted the control torque in Figure 7. The control torque of the adaptive STA turns out to  
The experimental results prove that it is effective to turn discontinuous control into continuous control by hiding the high-frequency switch as the second derivative of the sliding mode variable. At the same time, adaptive gains are used to precisely learn the disturbance bound. As a result, the chattering caused by excessive gain is significantly reduced.  
More quantitatively, we compared the RMS (root mean square) errors of the inclination angle and sliding mode variable. We ignored the first two seconds for the adjustment of the adaptive gains and calculated the RMS errors for the next eight seconds:  
\[D(x_{i})=\sqrt{\frac{\sum\limits_{n=201}^{n=1000}x(n)^{2}}{800}}, \tag{33}\]  
where \(x=[q_{2},S_{1}]\). The RMS error of the inclination angle indicates the equilibrium performance of the MWIP system, and the RMS error of the sliding mode variable is used to describe the chattering degree of the system. The results of the comparison are shown in Table 3.  
Figure 6: Comparison of the adaptive gains in Case 1.  
Figure 7: Comparison of the control torque in Case 1.
# Genetic Algorithms Optimized Adaptive Wireless Network Deployment
## 1 Introduction
The network deployment problem is divided into two phases to deal with two different problems: (1) the search phase, for searching users on the ground by deploying a static network, and (2) the service phase, to reconfigure a deployed network adaptively to better serve users. The proposed two-phase approach deploys networks in real-time and adapts to users' requirements. However, this does not guarantee generalizability and robustness. To obtain generalizable solutions (a set of potential field parameters) that work for unknown user distributions, four different training scenarios with varying user distributions (uniform and non-uniform) were generated. The aim is to tune the potential field parameters to be distribution-agnostic and enable UAVs to better serve users. To measure the generalizability and robustness of the proposed approach, the performance of the best evolved solution was measured on 100 different test scenarios with varying user distributions. These test scenarios were designed to simulate real-world scenarios and provide a comprehensive evaluation of the approach's performance. The experimental results show that the networks deployed using the best solution evolved using GANet on four training scenarios outperformed the networks deployed using the current best network deployment algorithm, adaptive triangular network deployment (ATRI) [15], on training scenarios. More importantly, it was found that this performance advantage also holds over the never before seen 100 test scenarios. Furthermore, different levels of information exchange between UAVs have been considered to measure the differences in the performance of deployed networks. Specifically, we compared the performance of networks deployed considering one-hop (neighbor) UAV-to-UAV communication with two-hop (neighbor's neighbors) communication. The results show that the performance of two-hop GANet is statistically significantly better than one-hop GANet and ATRI. This work makes two significant contributions. First, we propose a unified approach that can be used for both static and dynamic network deployment. Second, our proposed approach is adaptive, generalizable, and demonstrates a superior performance compared to the current state-of-the-art network deployment algorithms when deploying dynamic networks. The remainder of this paper is organized as follows. Section 2 describes prior work in mesh networks and potential fields-based UAVs movement. Section 3 sets up the problem and Section 4 describes the UAV's movement model under the influence of potential fields. Section 5 explains the two-phase proposed algorithm. The experimental setup and results constitute Section 6 and the last section provides conclusions.
# Ultrasound-Guided Procedures in Common Tendinopathies at the Elbow: From Image to Needle
## 3 Common Elbow Pathologies
### Distal Biceps Tendingupathy
#### 3.3.1 Essential Anatomy  
Biceps brachii muscles originate in two heads. The short head originates in the coracoid process of the scapula. The long head originates from the supraglenoid tubercle and partly from the superior part of the glenoid labrum. A DBT is a conjoint tendon of two heads. The long head inserts into a radial tuberosity proximal aspect, whereas the short head inserts into the radial tuberosity's distal aspect (Figure 8). Attachment of the DBT to the radial tuberosity has a spreading area of approximately 3 cm\({}^{2}\)[72]. Bifurcation of the distal biceps tendon allows functional independence and isolated pathological processes of each portion [73]. The fibers of the tendon form a spiral formation [74]. Proximal radioulnar joint was identified as a potential site of impingement of the DBT, potentially influencing the DBT vascular zone alterations [75]. The bicipitoradial bursa is located in between the DBT and the anterior part of the radial tuberosity. Enthesopathy of DBT could be an accompanying factor in the evolution of DBT tears [72]. At the elbow joint, the bicep muscle forms the biceps aponeurosis, known as the lacertus fibrosus (LF). The LF originates from the short head of the DBT and transverses medially over the CFT [38]. The biceps brachii muscle is innervated by the musculocutaneous nerve.  
Figure 7: Prone position for the ultrasound-guided golfer’s elbow injection with the elbow flexed and forearm pronated while supported on the examination bed.
# Human Factors Analysis of Air Traffic Safety Based on HFACS-BN Model
## 2 Methodology and Materials
### Research Framework  
In order to clearly describe how HFACS and BN effectively combine to predict the performance of ATC, Figure 4 presents the research framework of this paper.  
Phase 1. First, an HFACS framework was established for the specific analysis of human factors related to ATC in civil aviation safety. To this end, based on the existing mature HFACS structure, and aviation accident report, we also interviewed the opinions of experts and engineers in relevant fields to select the most important ATC human factors that may lead to aviation accidents. Detailed data information for phase 1 will be provided later in this article.  
Phase 2. Next, human factors from the HFACS framework are selected as the node variables of the BN model to establish the influence relationship between different variables involved in the HFACS framework. When the BN model is used for prediction, the prior probability of the root node in the model should be given first. According to the data in the aviation accident reports, and the \"Skybrary\" database, as well as the results of the questionnaire survey, the conditional probability tables (CPT) among the BN nodes can be generated. CPT calculation results of phase 2 are shown in Section 3.2.  
Figure 4: Research framework.  
Phase 3. Based on the HFACS-BN model established in the above steps, BN inference and sensitivity analysis were conducted to predict and analyze the most important factors affecting the performance of ATC in the model. Finally, it gives corresponding risk factors intervention measures, and verifies the model to help managers predict aviation safety performance ability.
## 2 Materials and Methods
### Methods
#### 2.5.3 Model Construction  
To conduct the anomaly detection experiments, the models are constructed with the training set including normal welds only. All the abnormal welds and the remaining normal welds are used for model verification, as shown in Figure 7. Each weld in the test set is classified as 'normal' or 'abnormal'. The Python packages from scikit-learn [31] are used, and the major parameters are listed in Table 5.  
Four performance measures are applied in this study: recall (also termed sensitivity), specificity, processing time, and the area under the receiver operating characteristic curve (AUC) [20]. Recall and specificity are calculated by Equations (8)-(9). True positive (TP), false positive (FP), true negative (TN) and false negative (FN) are four types of predicted results in binary classification. In general, minority class (abnormal weld) is regarded as positive case and majority class (normal weld) is termed  
Figure 7: Model construction process.  
as negative case. AUC is widely used to analyze the performance of a binary classifier. It is in the range [0, 1], and a good classifier has an AUC score near 1.  
\[\text{Recall}=\frac{TP}{TP+FN} \tag{8}\]  
\[\text{Specificity}=\frac{TN}{TN+FP} \tag{9}\]  
As common for preprocessing in machine learning, the features \(\Delta Q_{10}\) and \(\Delta S_{10}\) are scaled into the interval of [0, 1] by normalization. The normalized value \(\Delta Q_{10\text{N}}\) is calculated by Equation (10), and \(\Delta S_{10\text{N}}\) is equal to \(\Delta S_{10}\), where \(\text{Lim}_{\text{L}}\) is the lower limit and \(\text{Lim}_{\text{U}}\) is the upper limit used for \(\Delta Q_{10\text{N}}\). Compared with the previous 10 welds, the welds with small changes in \(Q_{E}\) have \(\Delta Q_{10\text{N}}\) near 0.5, and \(\Delta S_{10\text{N}}\) near 0 reflects small change in the \(R_{t}\) profile.  
\[\Delta Q_{10\text{N}}=\left\{\begin{array}{cc}0&,\Delta Q_{10}\leq\text{ Lim}_{\text{L}}\\ \frac{\Delta Q_{10\text{-Lim}_{\text{L}}}}{\text{Lim}_{\text{U}}-\text{Lim}_ {\text{L}}}&,\Delta Q_{10}\in(\text{Lim}_{\text{L}},\text{Lim}_{\text{U}}) \\ 1&,\Delta Q_{10}\geq\text{Lim}_{\text{U}}\end{array}\right. \tag{10}\]