esa-sceva/satcom-chunk-collection huggingface dataset:
# Recent Progress on Irradiation-Induced Defect Engineering of Two-Dimensional 2H-MoS2 Few Layers
## 3 Electromagnetic Irradiation
### Terahertz Wave Irradiation  
MoS2 multi-layers were fabricated into terahertz modulators [148] applied in high-speed communications. Terahertz conductivities of MoS2 few-layers were experimentally measured [149; 150] from THz spectroscopy. However, there was no report on terahertz damage on MoS2. Theoretical simulations indicated that the THz absorption of mono-layer MoS2 was very low and the maximum THz absorption of mono-layer MoS2 was approximately 5% [151]. The sum of reflection and absorption losses of mono-layer MoS2 was lower than that of graphene by one to three orders of magnitude.
## 1 Introduction
Further, applications that require standardized data integration, such as ERP/MES, can search and collect data without having to install a separate network. This study proposes a VR/AR collaboration design environment based on CPS communication platform technology and quality function development. This study makes the following contributions:  
* We extract VR/AR rendering and data of 3D design files through data exchange and distribution through CPS servers with OPC UA and propose a model specialized in manufacturing collaboration. * While referring to the OPC UA specification of international industry standards, the system is constructed by connecting both OPC UA modeling and CPS Node modeling with technical convergence with CPS. * The CAD Data Import Interface allows designers to insert CAD data into the CPS and reads the CAD data interface to a VR Parser and converts it into a 3D object. * BOM (Bill of Material) is extracted from CAD or migrated from legacy DB to refer the contents of the BOM to the related node in the Address Space of the CPS server to enhance the functionality of the physical engine based on the BOM. The structure of this paper explains the necessity of collaboration in Industry 4.0 and the necessity of this study in Section 1. In Section 2, we examine the related studies of OPC UA-based CPS and VR/AR systems. In Section 3, we describe the composition and role of the VR/AR collaboration model and VR engineering process architecture proposed in this paper. In Section 4, we measure the address space conversion accuracy and optimization of the BOM of the actual factory equipment, and measure the rendering speed. Section 5 summarizes the proposed architecture, implementation, and test results.
# Open-Set Recognition of Pansori Rhythm Patterns Based on Audio Segmentation
## 5 Experiment Setup
### Dataset Acquisition  
In our experiments, we employed the dataset of Pansori rhythm patterns to undertake an open-set audio segmentation task. However, the versatility of our proposed framework extends beyond this specific dataset, making it applicable to a broad array of acoustic datasets, not solely limited to Pansori rhythm patterns. To demonstrate the adaptability and efficacy of our model, it was also evaluated using three additional audio event datasets. Specifically, the Pansori rhythm, Synthetic Instrument Sound, and GTZAN datasets were each divided into three subsets, namely training, validation, and testing, to facilitate model training and optimization. The Sound Event Detection dataset followed the official split rules to ensure adherence to standard evaluation protocols. The process for dataset acquisition is detailed as follows:
# Traffic-Aware Optimization of Task Offloading and Content
## 4 Joint Optimization for Offloading and Content Caching with Traffic Stream Prediction
### FOST Enabled Traffic Stream Prediction  
#### 4.1.1 Data Preprocessing Requirements  
Before using the data model for prediction, it is necessary to provide a dataset format adapted to the model, including train and graph, where train involves time and target values, and graph involves the spatial relationship weights between nodes.  
According to the first law of geography, \"Everything is related to everything else, but near things are more related than distant things\". In order to reflect the spatial dependence of multiple regions, we define \(W\) is the spatial weight matrix, which describes the spatial dependence between \(i\times j\) regions.  
\[\begin{bmatrix}w_{11}&w_{12}&\cdots&w_{1j}\\ w_{21}&w_{22}&\cdots&w_{2j}\\ \vdots&\vdots&\ddots&\vdots\\ w_{i1}&w_{i2}&\cdots&w_{ij}\end{bmatrix} \tag{15}\]  
where \(w_{ij}\) represents the degree of influence of the region \(i\) on the region \(j\) in the space. If \(w_{ij}=1\), region \(i\) and region \(j\) are adjacent. Otherwise, \(w_{ij}=0\).  
We choose Queen adjacency to construct the spatial relationship weight matrix, as shown in Figure 2. The specific meaning of Queen adjacency is that two geographical entities are considered adjacent if they are located next to each other horizontally, vertically, or diagonally within a given context. The creation of a Queen adjacency weight matrix serves to represent these spatial relationships, supplying fundamental data for subsequent spatial analyses. For instance, in Figure 2, if entity B is linked to entity A, then for entities A and B, their spatial weight is set to 1. Conversely, if entity C is distant from A, then for entities A and C, their spatial weight is designated as 0. The weight matrix derived from Queen adjacency has been proven as a valuable tool for a wide array of spatial analysis and modeling tasks.
## 1 Introduction
The proposed e-HPS electrically drives the hydraulic pump that equips the hydraulic system of the compactor truck, rather than the traditional ICE-driven hydraulic pump systems (ICE-HPSs) for similar applications. In this study, the power consumption during the garbage collection cycle was first defined in the case of the ICE-HPS and later experimentally measured in the case of the proposed e-HPS. The different components of the e-HPS are described and the battery pack was modelled using the kinetic battery model (KiBaM), implemented in the Simulink MathWorks environment. The KiBaM was first used to estimate the state of charge (SOC) of the battery pack during the garbage collection cycle and later to evaluate the trend of the battery state of health (SOH) as a function of the number of charge/discharge cycles. The end-of-life of the battery pack was determined to determine the economic feasibility of the proposed e-HPS for the whole lifespan of the compactor truck. The immediate advantage of the proposed e-HPS is that the hydraulic pump can be driven by the electric motor, at a high efficiency operating point [28] and zero CO\({}_{2}\) emissions, without using the ICE. In addition to the expected fuel saving, switching off the ICE also offers the additional advantage to reduce acoustic emissions with respect to conventional compactor trucks, particularly for garbage collection in urban centers undertaken at night. The contribution of this study, with respect to the present literature [15; 27], is to provide an implementation method to build an electrical e-HPS system and to analyze the economic feasibility of this technology compared to conventional ICE-HPS. The aim of this paper is twofold: first, to provide a deep insight into the practical realization of the e-HPS for the retrofit of a conventional waste compactor truck, addressing the knowledge gap in the scientific literature and the lack of technical datasheets; and, second, to assess the economic feasibility of the proposed retrofit, and investigate the fuel consumption and reduction of CO\({}_{2}\) emissions during its use phase. Finally, the insight from this study could be extended to a broader range of vehicles that use similar hydraulic systems, such as ICE forklifts and dump trucks, laying the foundations for more economic and greener logistic operations. This paper is organized as follows. After a general description of the e-HPS, Section 2 describes the garbage collection operations, the experimental measurements and battery simulation, and the vehicle consumption and carbon emissions. Section 3 discusses the results of the electric power measurements, the KiBaM simulations, the estimations of both the battery's end-of-life and the operating costs and emissions. Sections 4 and 5 present the discussion and the conclusion, respectively.
## 6 Results and Discussion
### Thermal Hydraulic Performance Optimization
#### 6.1.1 Single Objective Optimization  
The \(j\) factor is an index of thermal performance of the heat exchanger. The \(f\) factor is directly related to the pressure drop in the fresh air channels and exhaust air channels. The \(JF\) factor is the heat transfer enhancement comprehensive performance index.  
The optimal values of the design parameters with single objective optimization presented in Table 5 are the best optimization results of the 50 operations. The best heat transfer enhancement comprehensive performance is obtained by fresh air channel length \(L_{\text{f}}\) = 0.3673 m, exhaust air channel length \(L_{\text{e}}\) = 0.3662 m, apex angle \(\theta\) = 0.7553 rad, channel height \(H\) = 0.0032 m, plate thickness \(\delta_{\text{plate}}\) = 0.0006 m, and heat exchanger height \(L_{\text{h}}\) = 0.5299 m.
# A Study on Design and Control of the Multi-Station Multi-Container Transportation System
## 5 Conclusions and Future Developments
### Conclusions
Finally, the solution of using counters in combination with the conveyor has solved the problem of energy savings, especially when expanding the system to a larger scale with even more conveyors. This solution ensures that a conveyor will only be activated when the number of containers it carries is more than one, regardless of how many conveyors there are in the entire system. In conclusion, this research has contributed a new solution for freight as well as logistics. A particularly important achievement of this research is the integration of the line switching feature along with the appropriate logic algorithm into the conveyor system, making the transportation process more flexible and space-saving, while still ensuring reasonable costs. The potential of this system is a commercial application in automatic transportation of a variety of products in many different environments, such as factories, office buildings, hospitals, etc. The potential solution is feasible. This proposed system is developed considering all the available conditions in Vietnam, with the key element being the control algorithm at the location of the line switching module to serve the process of moving containers between stations.
## 2 Integrated Navigation System of SINS/DVL/MCP/PS
### Error Propagation Model of DVL  
In DVL, Doppler effects are used to measure the velocity. With four acoustic beams in fixed directions, named the Janus configuration, three velocities along the body frame axes can be measured. Generally, the accuracy of the upward velocity is lower than those in the level direction. The precision of DVL is affected by several factors, such as installation error, scale factor error, and frequency measurement errors. For the purposes of this paper, the constant errors are assumed to be well-compensated. The random error in the DVL caused by acoustic scattering and environment changing is the focus. In engineering, random error can be described by a one-order Markov process or white Gaussian noise. To simply the analysis, white Gaussian noise is selected, and the statistical characteristics are as follows [5; 6]:  
\[\mathbf{w}_{\text{DVL}}^{b}\sim\left(0,\mathbf{R}_{\text{DVL}}^{b}\right), \tag{9}\]  
where \(\mathbf{w}_{\text{DVL}}^{b}\) is the random errors in DVL, and \(\mathbf{R}_{\text{DVL}}^{b}\) is the variance matrix of white noise corresponding to \(\mathbf{w}_{\text{DVL}}^{b}\).
# An Intelligent Control Method for Servo Motor Based on Reinforcement Learning
## 4 The Proposed Intelligent Control Method
### The Structure of the Control Method
#### 4.1.1 Structure and Loss Function of Value Network  
As shown in Figure 4, the inputs of the value network are the motor running state \(s_{t}\), target speed or torque \(g_{t}\), and the duty ratio output of the agent \(a_{t}\). Its output is \(Q(s_{t},g_{t},a_{t})\), denoting the reward of the duty cycle in a given state.  
Figure 3: The structure of the control method.  
To solve the overvaluation problem of the duty ratio, the algorithm uses two value networks with identical structures. When estimating the duty cycle value, the output values of the two networks are compared, and the minimum value is taken as the final output. In the training process, the objective function of value network \(J(\theta)\) can be expressed as Equation (5).  
\[J(\theta)=E_{(s_{t},g_{t},a_{t})\sim\mathcal{D},a_{t+1}\sim\pi}\frac{1}{2}[Q(s_ {t},g_{t},a_{t})-r(s_{t},g_{t},a_{t})-\gamma(Q^{*}(s_{t+1},g_{t},a_{t+1})- \alpha log\pi(a_{t+1}|s_{t+1},g_{t}))]^{2} \tag{5}\]  
where \(\mathcal{D}\) represents the experience buffer that stores the interactive data with the environment, \(\gamma\) is the discount factor, \(\alpha\) is the temperature coefficient, and \(\pi(a_{t}|s_{t},g_{t})\) is the policy function that will be described next.  
Equation (5) represents the update objective function of the value network. By sampling in the replay buffer D, the calculation method used is the negative of the absolute value of the difference between the target of the control trajectory and the actual control result.
# On-the-Fly Machine Learning for Improving Image Resolution in Tomography
## 2 Background and Notation
### Deep Convolutional Neural Networks  
A recently proposed strategy to improve the resolution of photographic images is the use of deep _convolutional neural networks_ (CNNs) [17]. This class of machine learning algorithms processes the image by applying multiple convolution kernels and saves the intermediate results in _layers_. Each layer consists of several images, and is computed by convolving the previous layer with learned kernels, adding a scalar bias term, and applying a nonlinear activation function to each pixel. The network  
\[\text{Net}_{C,\varphi}:\mathbb{R}^{C\times N\times N}\rightarrow\mathbb{R}^{N \times N} \tag{6}\]  
is _trained_ to find a set of parameters \(\hat{\varphi}\in\Phi\) such that it best transforms a stack of \(C\) images into a desired image of \(N\times N\) pixels. In modern CNN architectures, the number of parameters can range up to millions.  
The training procedure applies the network to a _training set_ of _input data_, \((x_{i})_{i=1}^{n}\), and compares the output to _target data_\((z_{i})_{i=1}^{n}\), where the goal is to minimize a user-specified _empirical loss function_\(L\). In this paper, we use the pixel-wise mean square error  
\[L(\varphi)=\frac{1}{n}\sum_{i=1}^{n}\bigl{\|}\text{Net}_{C,\varphi}(x_{i})-z_{i }\bigr{\|}_{2}^{2}. \tag{7}\]  
Training is not usually carried out to completion, i.e., the training is stopped before the absolute minimum of \(L\) is attained on the training set. This is done to prevent _overfitting_ the network to the training data. When the network is overfit, the empirical loss is low for data in the training set, but high for data not included in the training set. The performance of a network that is being trained is commonly tested on a _validation set_ that is not part of the training set. Once the performance on the validation set stops improving, training is stopped. An alternative to using a validation set is _early stopping_, where training is stopped at some predetermined time.  
Most existing state-of-the-art CNNs process 2D images. To process 3D volumes, we can subdivide the input and target voxel grids \(\widetilde{\mathbf{x}}^{\text{input}}\) and \(\widetilde{\mathbf{x}}^{\text{target}}\) into 2D horizontal _slices_. As input, the network is provided with a _slab_ containing not just a single input slice but also surrounding slices, supplying the network with _quasi-3D_ information. Although network architectures exist that process entire 3D volumes at once, their memory requirements are challenging when applied to voxel grids with sizes that are common in tomography.
# Durability and Safety Performance of Pavements with Added Photocatalysts
## 2 Materials and Methods  
### Materials  
A pilot scale demonstration platform with slabs of at least 1 m\({}^{2}\) each makes up the experimental setup. Fourteen different paving materials were used, 10 of which had photocatalytic functionality and 4 were the corresponding reference materials without the photocatalyst. They were stored outdoors at IETcc-CSIC headquarters, located in Madrid, Spain. The photoactive material in all of them was a TiO\({}_{2}\)-based photocatalyst, and several configurations were chosen to cover the main commercially available photocatalytic solutions in construction materials. This includes in-situ applications of slurries and emulsions over two different matrixes: open-graded asphalt (24.6 percent of air voids as measured by UNE-EN 12697-08:200) and/or urban concrete tiles), as well as tiles in which the photocatalyst was incorporated when casting. The emulsion coatings were sprayed on both asphalt and tiles, the cementitious slurries were spread on open asphalt substrate using a cleaner wiper, and the photocatalytic tiles were glued to a concrete basis. All of them were applied by the producers of the materials or by staff of the IETcc following the instructions given by the producers. More details on the experimental setup configuration are given in [14; 27; 51].  
Table 1 shows the labelling of the materials, as well as their initial efficiency in NOx degradation (RNOx (%)) measured on laboratory samples according to ISO 22197-1:2007.
# A Fast Identification Method of Gunshot Types Based on Knowledge Distillation
## 4 Experimental Verification
### Experimental Results and Interpretation
#### 4.3.2 Comparison of the Combined Performance of the Teacher Network and the Student Network  
Knowledge distillation networks, composed of different teacher networks and student networks, were analyzed for their performance of gunshot recognition. Three networks, from resnet32 \(\times\) 4 [22], wrn40 \(\times\) 2 [23] and ours, were used as the teacher network to compare with the proposed network. Resnet8, Resnet8 \(\times\) 4, WRN16 \(\times\) 2, MobileNetV2 [24] and ShuffleV1 [25] were used as the student network to compare with the proposed network. Table 4 summarizes the comparison.  
As can be seen from Table 4, the gunshot recognition ability of the entire network is not only dependent on the individual teacher network and student network, but also on their combinations. The teacher-student network combination proposed in this paper registered the highest accuracy of gunshot recognition, reaching 83.49%.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Teacher Network** & **Student Network** & **Accuracy Rate (\%)** & **Model Size** \\ \hline \multirow{8}{*}{resnet32 \(\times\) 4} & Resnet8 & 5.64 & 337.8 KB \\  & Resnet8 \(\times\) 4 & 80.44 & 4.9 MB \\  & WRN16 \(\times\) 2 & 56.27 & 5.5 MB \\  & MobileNetV2 & 61.24 & 3.0 MB \\  & ShuffleV1 & 79.12 & 3.7 MB \\  & Ours & 68.16 & 2.5 MB \\ \hline \multirow{8}{*}{wrm40 \(\times\) 2} & Resnet8 & 6.18 & 264 KB \\  & Resnet8 \(\times\) 4 & 39.92 & 4.6 MB \\  & WRN16 \(\times\) 2 & 71.03 & 5.5 MB \\  & MobileNetV2 & 79.52 & 4.0 MB \\  & ShuffleV1 & 82.16 & 3.5 MB \\  & Ours & 75.56 & 2.5 MB \\ \hline \multirow{8}{*}{Ours} & Resnet8 & 5.14 & 264 KB \\  & Resnet8 \(\times\) 4 & 41.21 & 4.6 MB \\ \cline{1-1}  & WRN16 \(\times\) 2 & 75.24 & 5.5 MB \\ \cline{1-1}  & MobileNetV2 & 72.25 & 4.0 MB \\ \cline{1-1}  & ShuffleV1 & 81.24 & 3.5 MB \\ \cline{1-1}  & Ours & 83.49 & 2.5 MB \\ \hline \hline \end{tabular}
\end{table}
Table 4: Recognition accuracy of different teacher network and student network combinations.  
Figure 9: Training set and validation set accuracy of knowledge distillation network training.
# Insights into the Issue of Deploying a Private LoRaWAN
## 4 Experimental and Analytical Results
### Performance Evaluation  
#### 4.1.1 Impact of Channel Selection on Network Performance  
We measured in front of the building H (approx. 35 m in front of the building H and 60 m from the building E). We selected two frequency plans (see Table 2): (i) the default LoRaWAN channel frequency plan (i.e., the three default LoRaWAN channels in the 868 MHz sub-band); and (ii) the extended channel frequency plan (in the 867 MHz sub-band, including additional channels). The results are displayed in Table 3 (LR = loss rate).  
Each value represents an arithmetic mean of 100 messages, which were transmitted over the day for each scenario for the public and private network. The default channel settings showed a higher loss rate (40% higher in the public and 20% higher in the private network). The noise level, when using the default channels, was \(>\)14 dB higher than that for the extended frequency plan (the RSSI difference was \(>\)26 dB). The measurement sufficiently proves our claim that it is possible to at least partially evade interferences by  
\begin{table}
\begin{tabular}{c c c c c} \hline Channels, f [MHz] & 868.1 * & 868.3 * & 868.5 * & 867.1 \\ \hline Channels (cont-d), f (MHz) & 867.3 & 867.5 & 867.7 & 867.9 \\ \hline BW (KHz) & \multicolumn{4}{c}{125} \\ \hline MOD/SF & \multicolumn{4}{c}{LoRa with Multi-SF} \\ \hline \end{tabular}
* Default LoRaWAN channels from the newest specification [10].  
\end{table}
Table 2: Frequency plan for experimental measurements (both public and private networks).  
choosing the right channels to extend the frequency plan. The growing number of devices will, in the future, create an environment with increased noise. Therefore, the LoRaWAN standard will need to evolve together with extending the recommended frequency plan, and give a methodology for choosing the right channels. Therefore, the private network has an advantage (given that these are updated regularly) over the public network as there might be a fully customized frequency plan that allows minimizing the interferences with other systems. Notably, the smaller scale of these networks supports using the different frequency plans in different regions.