esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction  
Ferromagnetic pipelines are the primary means of transporting gas, oil, and other liquids, especially in the industrial sector. Pipeline maintenance and inspection are necessary to ensure proper functioning conditions. The manual assessment of the internal conditions of small diameter pipes by workers and/or in hazardous conditions is unfeasible. To prevent the exposure of operators to these conditions, in-pipe inspection robots could potentially replace the human-conducted tasks.  
In the literature, in-pipe inspection robots are categorized into five common systems based on robotic locomotion. First, wheeled robots [1; 2; 3; 4] have simple structural designs. These systems typically have good maneuverability and can be controlled easily. Wheeled systems are also extensively combined with other locomotion types [5]. Second, inchworm-like robots [6; 7; 8; 9; 10] can perform both horizontal and vertical locomotion, but still have difficulties in traveling along a curved pipe. Third, wall-pressed robots [11; 12; 13; 14; 15; 16] are the most popular system used for the application of in-pipe inspection robots [5]. These systems require a frictional force between the leg or wheel of the robot and the inner wall of the pipe to travel along the pipe. These robots have difficulties in passing through elbow and tee connectors. Due to the structure of the wall-pressed robots, the range of accessible pipe diameters is restricted. Fourth, snake-like or train-like robots [17; 18; 19; 20; 21; 22] are modular robots that have a strong adaptability to complex configurations of pipelines and fittings. However, due to the modular design, these robots may experience unreliable motion control problems. Fifth, magnetic systems [23; 24; 25; 26; 27] can be applied in vertical, horizontal, and curved ferromagnetic pipes. These systems are also applicable to a wide range of pipe sizes. [PERSON] et al. [27] designed a robot to move in vertical pipes using 2 magnet wheels. The robot can move in both longitudinal and lateral directions by rotating the wheels, but it is difficult to control the robot to move steadily in the lateral direction.  
From the literature review, our proposed in-pipe robot is inspirationally designed based on a combination of the easy-to-control characteristics of wheeled robots, the flexibility characteristics of snake-like or train-like robots, and the advantageous ability of a magnetic system that can be applied to the various sizes of ferromagnetic pipes. In this article, we present the design and implementation of an in-pipe inspection robot that is applicable to a wide range of ferromagnetic pipe sizes from 5-inch (127 mm) diameter pipes to flat plates. The proposed robot can travel in any direction and pass through 90-degree elbow fittings. The robot can also provide real-time visual feedback and operate in waterlogged conditions.
## 3 Reduced-Complexity ICFO and CIDS Detection Method
### Detection Algorithm
 \tag{16}\]  
Under the hypothesis that \(D_{\tilde{w}}(m)\) is a perfect match with the \(\tilde{v}\)-th shifted sequence of the received NR-PSS, equivalently, \((\vartheta,\tilde{w})=(v,w)\), (16) can be rewritten as  
\[ \begin{split}\Omega_{red}(\vartheta,\tilde{w})&= \sum_{l=1}^{M_{\oplus}}\sum_{m\in\mathcal{D}}|G_{q_{l}}(m)|^{2}E_{X}^{2}+\sum_{ l=1}^{M_{\oplus}}\sum_{m\in\mathcal{D}}\bar{I}_{q_{l}}(m)D_{0}^{*}(m)B_{ \oplus}^{*}(m)\\ &+\sum_{l=1}^{M_{\oplus}}\sum_{m\in\mathcal{D}}\bar{Z}_{q_{l}}(m )D_{0}^{*}(m)B_{\oplus}^{*}(m),\end{split}  \tag{17}\]  
where \(E_{X}=\mathbb{E}\{|X_{q_{l}}(m)|^{2}\}\) represents the symbol energy, and \(\bar{I}_{q_{l}}(m)\) and \(\bar{Z}_{q_{l}}(m)\) are considered as zero-mean complex Gaussian random variables (RVs) with variances \(\sigma_{l}^{2}\) and \(\sigma_{Z}^{2}\), respectively. As the amount of frequency selective fading increases, the first component on the right-hand side (RHS) of (17) contributes to multipath diversity. When \((\tilde{v},\tilde{w})\
eq(v,w)\), assuming a flat fading channel, we have  
\[ \begin{split}\Omega_{red}(\vartheta,\tilde{w})&= \sum_{l=1}^{M_{\oplus}}|G_{q_{l}}(m+\vartheta-v)|^{2}\sum_{m\in\mathcal{D}}D_{ w}(m+\vartheta-v)D_{0}^{*}(m)B_{\oplus}^{*}(m)\\ &+\sum_{l=1}^{M_{\oplus}}\sum_{m\in\mathcal{D}}\bar{I}_{q_{l}}(m )D_{0}^{*}(m)B_{\oplus}^{*}(m)+\sum_{l=1}^{M_{\oplus}}\sum_{m\in\mathcal{D}} \bar{Z}_{q_{l}}(m)D_{0}^{*}(m)B_{\oplus}^{*}(m).\end{split}  \tag{18}\]  
In this case, as the first component of the RHS in (18) becomes negligible due to the nearly orthogonal property of differential m-sequence at all other lags, \(\Omega_{red}(\tilde{v},\tilde{w})\) can be approximated as  
\[\Omega_{red}(\tilde{v},\tilde{w})\approx\sum_{l=1}^{M_{\oplus}}\sum_{m\in \mathcal{D}}\bar{I}_{q_{l}}(m)D_{0}^{*}(m)B_{\oplus}^{*}(m)+\sum_{l=1}^{M_{ \oplus}}\sum_{m\in\mathcal{D}}\bar{Z}_{q_{l}}(m)D_{0}^{*}(m)B_{\oplus}^{*}(m). \tag{19}\]  
Please note that (19) can be treated as a zero-mean complex Gaussian RV with variance \(\sigma_{1}^{2}\approx M_{v}(M_{s}-1)E_{X}^{2}(\sigma_{I}^{2}+\sigma_{Z}^{2})/2\).
# Joint Embedding of Semantic and Statistical Features for Effective Code Search
## 3 Approach
### Feature Extraction with Different Granularities
#### 3.1.1 Feature Extraction at the Method Level  
For code search with method as the granularity as a parameter, the semantic information at the method level can be reflected in the method name, API invocation, string, variable name, method comment, and other information. This study uses the triplet, \(<\)method name, API invocation, token collection\(>\) to model the method [9]. The specific information extraction method is as follows:  
* method name: For each Java method, we extract its method name and parse it according to camel case. For example, the method name \"ContentAddExecutor\" will be resolved as \"content, add, executor\".
* API invocation: A sequence of API invocations can represent the semantics of the source code [24]. We present the detailed methods of feature extraction through the following examples: (a) For each constructor invoking \(new\)\(C()\), \(C\).\(new\) is generated and appended to the API sequence. (b) For each method invoking \(o.m()\), where \(o\) is an instance of class \(C\), \(C\).\(m\) is generated to append to the API sequence. (c) For method invocations passed as parameters, the method is attached before invoking the method. For example, \(o_{1}.m_{1}(o_{2}.m_{2}()\), \(o_{3}.m_{3}())\), we generate a sequence \(C_{2}.m_{2}\)-\(C_{3}.m_{3}\), \(C_{1}.m_{1}\), where \(C_{i}\) is the class to which the instance \(o_{i}\) belongs. (d) For the statement sequence \(s_{1}\), \(s_{2}\),..., \(s_{N}\), the API sequence \(a_{i}\) is extracted from each \(s_{i}\) and concatenated to the API sequence \(a_{1}\)-\(a_{2}\)-...-\(a_{N}\). (e) For conditional statements such as if(s\({}_{1}\))s\({}_{2}\);else\(s_{3}\); we create a sequence from all possible branches, i.e., \(a_{1}\)-\(a_{2}\)-\(a_{3}\), where \(a_{i}\) is the API sequence extracted from statement \(s_{i}\). (f) For loop statements such as while(s\({}_{1}\))s\({}_{2}\), a sequence \(a_{1}\)-\(a_{2}\) is generated, where \(a_{1}\) and \(a_{2}\) are API sequences extracted from statements \(s_{1}\) and \(s_{2}\).
* token collection: According to the hump rule, the various names in the entire method entity are split. The words, after filtering the stop words and keywords are added to the token collection. For example, stop words (e.g., _and_ and _in_) and keywords in Java language are removed because they do not distinguish semantically in source code.  
Figure 2: Feature extraction for Java source code.
## Wind Turbine Model
### Cost of Energy (COE) Model  
The study [17] has shown the design objective of maximizing annual energy production or using sequential aerodynamic and structural optimization to be significantly suboptimal compared to the aero-structural integrated methods. For variable rotor diameter and hub height, the optimal rotor diameter may be misleading because the tower mass dominates the total mass of turbine. Thus, minimizing COE is a much better metric for the full-scale wind turbine optimization objective than minimizing the ratio of turbine mass to annual energy production. The main objective is to minimize the COE of a wind turbine used in low wind speed areas. Following the study [15], COE is calculated as:  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Stack ID** & **Materials** & **E1 (GPa)** & **E2 (GPa)** & **G12 (GPa)** & **v12** & **\(\rho\) (kg/m\({}^{3}\))** &
\begin{tabular}{c} **Thickness** \\ **(mm)** \\ \end{tabular} \\ \hline
1 & Unidirectional & 39.18 & 11.69 & 3.5 & 0.3 & 1940 & 0.892 \\
2 & Bi-axial & 11.5 & 11.5 & 10.8 & 0.48 & 1970 & 0.555 \\
3 & Triax & 25.8 & 13.59 & 7.36 & 0.36 & 1842 & 0.906 \\
4 & Gelcoat & 3.44 & 3.44 & 1.38 & 0.3 & 1235 & 0.6 \\
5 & Balsa & 1.321 & 0.03 & 0.006 & 0.39 & 151 & 25.4 \\
6 & Polyvinyl chloride (PVC) & 0.035 & 0.035 & 0.022 & 0.3 & 60 & 5 \\ \hline \hline \end{tabular}
\end{table}
Table 4: The material properties.  
Figure 1: Distribution of section’s layup layers along the blade radial coordinate.  
\[\text{COE}=\frac{\text{FCR}\times\text{ICC}+\text{AOE}}{\text{AEP}} \tag{1}\]  
where FCR is the fixed charge rate, ICC is the initial capital cost, AOE is the annual operating expenses, and AEP is annual energy production.  
As wind turbines become more sophisticated in terms of aerodynamic shape and structural configuration with increasing size, the design of each wind turbine component is not always clear. The COE model is used to assess the true impact of the technical change and various designs. The FCR, ICC and AOE are obtained from the National Renewable Energy Laboratory (NREL) wind turbine design cost and scaling model [15]. The BEM code and the Weibull probability distribution with annual average wind speed of 6 m/s at hub height are used to calculate AEP. The blade stiffness and mass distributions are computed by the classical laminate theory code named PreComp [18]. The total cost of a wind turbine is defined by adding the value of the initial capital cost multiplied by the fixed charge rate to the annual operating expenses. The fixed charge rate is the annual amount per dollar of initial capital cost used to cover the capital cost and other fixed charges. The FCR value is set to 0.1158 per year as suggested in reference [15].
## 3 Mathematical Model
### Model Validation  
The numerical model was validated by comparing the simulation results of the No. 4 exterior surface without gabion structure with the experiment results [41] under three wind speed levels: 1.5 m/s, 2.0 m/s and 4.5 m/s. The CHTCs of the simulation result, the experimental data and the corresponding fitted curves are shown in Figure 5. The maximum difference between the simulation results and the experimental data is 0.968 W/(m\({}^{2}\cdot\)K) (7.10%). Therefore, it can be concluded that the CHTCs from the simulation results agree well with the experimental data, demonstrating that this numerical model could predict the heat transfer between exterior surface and external environment well.
# Integration of a Mobile Node into a Hybrid Wireless Sensor Network for Urban Environments +
## 5 Conclusions
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Item**} & **NH3** & **Temp** & **Humidity** & **O\({}_{2}\)** & **CO\({}_{2}\)** & **AP** & **VOC** & **GPS Coordinates** \\  & **(ppm)** & **(\({}^{\circ}\)C)** & **(\%)** & **(\%)** & **(ppm)** & **(kPa)** & **(ppm)** & **GPS Coordinates** \\ \hline
1 & 1.459 & 24.19 & 68.2 & 18.891 & 332.310 & 131.44 & 2.058 & 36.713976, \(-\)4.482990 \\
2 & 1.480 & 22.90 & 77.4 & 19.278 & 332.153 & 131.83 & 1.464 & 36.713168, \(-\)4.457434 \\
3 & 1.486 & 23.06 & 77.4 & 19.182 & 332.271 & 132.39 & 1.598 & 36.709676, \(-\)4.427887 \\
4 & 1.472 & 21.94 & 78.8 & 19.182 & 332.193 & 132.02 & 1.428 & 36.720616, \(-\)4.403189 \\
5 & 1.482 & 22.74 & 76.1 & 19.278 & 331.448 & 132.07 & 2.999 & 36.720149, \(-\)4.368288 \\
6 & 1.500 & 23.39 & 74.1 & 19.182 & 332.193 & 132.00 & 1.428 & 36.715815, \(-\)4.346487 \\
7 & 1.475 & 24.03 & 71.5 & 18.794 & 332.114 & 132.10 & 1.762 & 36.720230, \(-\)4.349773 \\
8 & 1.485 & 26.13 & 70.7 & 18.504 & 332.193 & 132.24 & 1.849 & 36.720692, \(-\)4.356785 \\
9 & 1.489 & 23.55 & 71.0 & 19.182 & 332.153 & 132.15 & 1.273 & 36.723351, \(-\)4.392885 \\
10 & 1.502 & 23.71 & 74.3 & 19.085 & 332.114 & 132.00 & 1.784 & 36.717411, \(-\)4.423333 \\
11 & 1.513 & 25.48 & 71.5 & 18.891 & 331.996 & 132.41 & 1.017 & 36.715752, \(-\)4.444772 \\
12 & 1.519 & 24.68 & 72.2 & 19.085 & 332.193 & 131.71 & 1.103 & 36.713402, \(-\)4.470782 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Summary of the data obtained by the Mobile node in a route in the city of Malaga, working in networked mode. **Author Contributions:** The mobile node was conceived and developed by [PERSON], [PERSON] and A.G.-C. The integration to form a hybrid wireless sensor network were developed by [PERSON], [PERSON] and [PERSON]-R. The design and analysis of experiments were done by [PERSON], [PERSON], and [PERSON]. The manuscript was written by [PERSON], [PERSON] and [PERSON]. The work presented in the paper was conceived within research projects led by A.G.-C.  
**Funding:** This article was partially supported by Spanish project DPI2015-65186-R.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Dynamic Observer Modeling and Minimum-Variance Self-Tuning Control of EDM Interelectrode Gap
## 2 EDM Interelectrode Gap Model Based on Plate Capacitor
### Determination of \(\beta\)  
Figure 4 is obtained by amplifying the voltage signal of region A in Figure 3. According to the characteristics of the voltage signal across the flat-plate capacitor of the \(RC\) circuit, when \(t=\tau\)  
Figure 3: EDM discharge waveform: (**a**) The overall waveform; (**b**) The locally amplified waveform.  
\(u_{c}(t)=0.63\) U. When the interelectrode gap \(d\) changes, the capacitance \(C_{g}\) of the panel capacitor also changes accordingly, resulting in a change of the time constant \(\tau\). Therefore, in the single-pulse discharge test of EDM, it was only necessary to detect the time \(t\) during which the voltage across the plate capacitor rose from 0 to 0.63 U in order to determine the constant \(\tau\).  
The rod copper electrode was used in the experiment and the electrode diameter was 10 mm. The workpiece material was p-type single-crystal Si, and the parameters are shown in Table 1. The no-load voltage of the pulse power supply was \(U=100\) V, and the pulse width and pulse interval of the power supply were 100 us and 400 us, respectively. Considering the randomness in EDM, each set of tests was conducted three times, and the test data is shown in Table 2. \(\overline{\beta}\) is the average value of \(\beta\) in three trials.  
A least-squares fit was performed on the experimental data in Table 2, and the fitting curve is shown in Figure 5. The \(R^{2}\) of the least-squares fit between \(\overline{\beta}\) and the interelectrode gap \(d\) was 0.9977. The closer the value is to 1, the higher the correlation between the fitted value and the experimental value. The correction coefficient (Adj \(R^{2}\)) was 0.9971, which means that 0.29% of the test data cannot be explained by this fitting expression. The root-mean-square error (RMSE) was 0.4453, which indicates a high degree of fitting. The least-squares fitting expression between \(\overline{\beta}\) and the interelectrode gap \(d\) is as follows:  
\[\overline{\beta}=0.0002044d^{3}-0.03767d^{2}+2.282d-2.74 \tag{8}\]  
According to Equations (7) and (8), the model expression of the EDM interelectrode gap \(d\) can be obtained as  
\[d=-50.4\tau^{3}+190.6\tau^{2}-281.6\tau+182.6 \tag{9}\]  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**P-Type Monocrystalline Silicon** & **Diameter (1 in)** & **Growth Mode** & **Straight Pull Single Crystal (CZ)** \\ \hline Thickness of Si wafer & 5 mm & Doping type & Boron doping \\ Crystal orientation & \(<\)111\(>\) & Resistivity & 0.1 \(\Omega\) cm \\ Melting point & 1410 \({}^{\circ}\)C & Boiling point & 2355 \({}^{\circ}\)C \\ Density & 2.33 g/cm\({}^{3}\) & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameters of p-type monocrystalline silicon.  
Figure 4: Voltage rising edge waveform.
# An Adapter and Segmentation Network-Based Approach for Automated Atmospheric Front Detection
## 2 Data and Method
### Method
#### 2.2.3 The RPN (Region Proposal Network) Module for Obtaining Frontal Candidate Bounding Boxes  
The RPN (region proposal network) is used to extract high-quality ROI (region of interest) bounding boxes for atmospheric frontal candidate regions, which are then utilized in subsequent stages. It consists of four main components:  
1. Anchor Generator--Generates initial anchor boxes based on feature scales and aspect ratios. The scales are set to 8, 16, and 32, while the aspect ratios are 0.5, 1, and 2, resulting in a total of 9 anchor boxes.
2. Anchor Target Generator--Determines which anchor boxes are positive samples (fronts) and which are negative samples (non-fronts) by computing the IoU (Intersection over Union) between anchors and ground truth.
3. RPN Loss--Computes the classification and regression losses during training.
4. Proposal Generator--Produces refined candidate regions through post-processing steps, such as clipping out-of-bound boxes and removing excessively small boxes, to obtain high-confidence ROI proposals. As illustrated in Figure 3b, the RPN operates via two parallel branches:  
1. The upper branch classifies anchors into positive/negative samples using a softmax function.
2. The lower branch predicts bounding box regression offsets to adjust anchor positions for better localization. This structure enables efficient and accurate extraction of frontal candidate regions for further analysis.
###### Abstract
In summary, this study presents a novel adaptive control method that improves the speed-tracking performance of HDVs during long-haul operations and in dynamic environments by improving the existing MRAC. Specifically, a state predictor is introduced to address the trade-off between fast learning and smooth control signals. In addition, a control framework that considers input constraints is proposed to suppress actuator saturation. This research leverages the benefits of MRAC, such as disturbance compensation and adaptability to system changes, to enable more precise and stable speed control for autonomous driving systems in HDVs. The proposed closed-loop control system was developed in MATLAB/Simulink R2022b and the vehicle dynamics simulation software IPG TruckMaker 12.0.1. The validation scenario involved repeatedly changing the driving speed on a straight road with varying slopes and headwinds. Four different scenarios were considered based on the vehicle's mass. The efficiency of the proposed model is explained by comparing the tracking performance of the target speed and the stability of the control inputs with those of two control systems, LQR and conventional MRAC. The paper is structured as follows: Section 2 provides a detailed description of the vehicle's longitudinal dynamics, which forms the foundation for the proposed control system. Section 3 introduces the conventional MRAC framework and presents the enhanced design. Section 4 describes the closed-loop control system, incorporating the proposed longitudinal adaptive controller. Section 5 discusses the simulation environment, validation scenarios, and analysis of the simulation results. Finally, Section 6 concludes the paper by summarizing the key contributions and outlining directions for future research.
# TRSDL: Tag-Aware Recommender System Based on Deep Learning-Intelligent Computing Systems
## 1 Introduction
In summary, we propose a tag-aware recommender system based on deep learning (TRSDL) for rating prediction task. Firstly, the model uses pre-trained word embeddings to represent user-defined tags so that it not only addresses problems caused by tags but also makes full use of tags' semantic information. Secondly, it constructs item and user profiles based on the item's tags and the user's tagging behaviors, and then utilizes deep neural networks (DNNs) and recurrent neural networks (RNNs) to extract the latent features of the item and the user, respectively. Finally, we use a forward neural network to simulate matrix factorization (MF) [11] to predict ratings from latent features. Relative to other tag-aware recommendation methods, the main contributions of the proposed model are as follows:  
1. We use the pre-trained Word2 Vec to represent tags, instead of the bag-of-words (BOW) model that is used in many TRS. By using word embeddings of tags, TRSDL alleviates problems of tag redundancy and ambiguity, and takes advantage of tags' semantic information at the same time. Besides, it reduces the dimensionality of BOW-based representations so that it speeds up learning process and uses less memory resources. 2. Users' preferences evolve over time and are influenced by their historical behaviors. By organizing the user's tagging records chronologically, and using the excellent ability of the recurrent neural networks to process temporal sequence, we obtain more useful user dynamic preferences to enhance RS's performance. Interpret ability of deep structure methods is more powerful than shallow structure in the face of complex data. We model items characteristics and users preferences through deep learning algorithms, which captures the complex nonlinear relationships within the data, and extracts high-level abstract features. The rest of this paper is organized as follows. Section 2 reviews related work. In Section 3, the proposed model is described in detail, and we present the results of our experiments in Section 4. Finally, a conclusion and future work are given in Section 5.