esa-sceva/satcom-chunk-collection huggingface dataset:
# Hybrid Cooperative Cache Based on Temporal Convolutional Networks in Vehicular Edge Network
## 4 Cooperative Caching Decisions Based on Temporal Convolutional Networks and Hybrid Content Values
### Content Popularity Prediction Based on Temporal Convolutional Networks
#### 4.2.1 Content Feature Predictor  
The purpose of this module is to build input-output maps based on TCN, thereby predicting the popularity of future content through historical request information, to help THCS make effective caching decisions. Specifically, the input vector of TCN is a content popularity feature, and the expected output is a collection of content popularity features over the next \(K\) time periods. This article uses TCN to predict content popularity because it can transform the problem of content popularity prediction into a time series problem; moreover, compared with the commonly used recurrent neural network (RNN), TCN is based on a parallel architecture and takes up less memory. In addition, TCN can prevent gradient explosion and disappearance; the architecture is shown in Figure 2.  
Specifically, the input vector of TCN is a content popularity feature, and the expected output is a collection of content popularity features over the next \(K\) time periods. TCN uses a one-dimensional full convolution network (FCN) structure for prediction, and due to the  
Figure 2: TCN prediction for caching.  
causal relationship between convolution operations, future information will not leak into the past. Next, we will explain the fusion process of TCN and convolution structure in detail.  
Dilated causal convolution: In order to establish long-term memory, causal convolution can introduce dilated convolution to ensure that the convolutional receptive field can be increased without changing the number of parameters. The principle can be simply understood as that dilated convolution can generate some new kernels, and then use these kernels to perform ordinary convolution. For the filter \(Y=\{y_{1},y_{2},\ldots,y_{k}\}\), the dilated casual convolution \(b_{t}\) of \(V=\{v_{1},v_{2},\ldots,v_{T}\}\) at \(v_{t}\):  
\[b_{t}=\sum_{k-1}^{K}y_{k}v_{t-(K-t)d} \tag{15}\]  
where \(d\) represents the dilated factor and \(k\) is the size of the filter.  
Residual block: After normalizing the weight of the dilated causal convolutions each time, the ReLU function is used to increase the nonlinear relationship between layers, and then a dropout term is added to achieve regularization. In addition, in order to prevent the TCN output from being inconsistent with the length of \(X\), a \(1\times 1\) convolution is performed before the output, so that the TCN can output in the desired dimension.
# Dissolved Gas Analysis for Fault Prediction in Power Transformers Using Machine Learning Techniques
## 1 Introduction
In [14], the authors' proposed system mixes various traditional DGA techniques in one expert system to reliably and consistently identify fault types. A combination of machine learning and correlation coefficients was proposed by the authors of [15]; this method of study can automatically cluster different fault types using DGA. In another study [16], a DGA technique was proposed that combined five DGA methods, yielding an excellent accuracy of 93.6%. Many studies have used RF to determine the condition of power transformers and to facilitate diagnostic monitoring [17]. Results were obtained and compared between the defect diagnostic performances utilizing the gas dataset and the input dataset provided by conventional methods. The structure of this study is as follows. Details on the DGA approach and the interpretation method are provided in the Section 2. The Section 3 provides information on the machine learning techniques, application processes, and a brief explanation of the MLMs employed in this study. The findings of the defect diagnosis and the content of the dataset collection are presented in the Section 4. The results of this investigation are presented in the Section 5. The major contribution of this study is the introduction of a new dissolved gas analysis technique with better evaluation criteria for fault identification in transformers using six MLMs.
## 2 Takagi-Sugeno Fuzzy Model  
The TS fuzzy modeling method's primary idea is to split nonlinear system dynamic behavior into numerous individually linearized systems, such that the entire nonlinear system behavior may be represented by fuzzy mixing of such systems. As a result, the fuzzy rule associated with the \(i\)-th linear system is specified as the \(i\)-th rule:  
IF \(z_{1}(k)\) is \(M_{i1}\), and \(z_{2}(k)\) is \(M_{i2}\),..., and \(z_{p}(k)\) is \(M_{ip}\) THEN  
\[\begin{split}\mathbf{x}(k+1)&=A_{i}\mathbf{x}(k)+B_{ i}\mathbf{u}(k),\quad i=1,2,\ldots,r,\\ \mathbf{y}(k)&=C_{i}\mathbf{x}(k),\quad i=1,2,\ldots, r,\end{split} \tag{1}\]  
where \(\mathbf{x}(k)\in\mathcal{R}^{n}\) is the state vector, \(\mathbf{u}(k)\in\mathcal{R}^{M}\) is the input vector, \(\mathbf{y}(k)\in\mathcal{R}^{N}\) is the output vector and \(A_{i}\in\mathcal{R}^{n\times n}\), \(B_{i}\in\mathcal{R}^{n\times M}\), \(C_{i}\in\mathcal{R}^{N\times n}\). Here \(\left\{z_{1}(k),z_{2}(k),\ldots,z_{p}(k)\right\}\) are some nonlinear functions of the state variables obtained from the original nonlinear equation and \(M_{ij}(z_{i})\) are the degree of membership of \(z_{i}\) in a fuzzy set \(M_{ij}\). Whenever there is no ambiguity, the discrete time variable \(k\) in \(z(k)\) is ignored.  
For affine local models, the previous i-th rule remains the same and Equation (1) has the following form:  
\[\begin{split}\mathbf{x}(k+1)&=A_{i}\mathbf{x}(k)+B_ {i}\mathbf{u}(k)+\boldsymbol{\alpha}_{i},\\ \mathbf{y}(k)&=C_{i}\mathbf{x}(k)+\boldsymbol{\beta} _{i}.\end{split} \tag{2}\]  
The overall output of the TS fuzzy system is computed as  
\[\mathbf{x}(k+1)=\frac{\sum_{i=1}^{r}w_{i}(z)\{A_{i}\mathbf{x}(k)+B_{i}\mathbf{ u}(k)\}}{\sum_{i=1}^{r}w_{i}(z)}=\sum_{i=1}^{r}h_{i}(z)(A_{i}\mathbf{x}(k)+B_{i} \mathbf{u}(k)), \tag{3}\]  
\[\mathbf{y}(k)=\frac{\sum_{i=1}^{r}w_{i}(z)C_{i}\mathbf{x}(k)}{\sum_{i=1}^{r}w_ {i}(z)}=\sum_{i=1}^{r}h_{i}(z)C_{i}\mathbf{x}(k), \tag{4}\]  
for the model with linear consequents and  
\[\mathbf{x}(k+1)=\frac{\sum_{i=1}^{r}w_{i}(z)\{A_{i}\mathbf{x}(k)+B_{i}\mathbf{ u}(k)+\boldsymbol{\alpha}_{i}\}}{\sum_{i=1}^{r}w_{i}(z)}=\sum_{i=1}^{r}h_{i}(z)(A_ {i}\mathbf{x}(k)+B_{i}\mathbf{u}(k)+\boldsymbol{\alpha}_{i}), \tag{5}\]\[\mathbf{y}(k)=\frac{\sum_{i=1}^{r}w_{i}(z)\{C_{i}\mathbf{x}(k)+\mathbf{\beta}_{i}\}}{ \sum_{i=1}^{r}w_{i}(z)}=\sum_{i=1}^{r}h_{i}(z)\{C_{i}\mathbf{x}(k)+\mathbf{\beta}_{i}\}, \tag{6}\]  
for the model with affine consequents, where \(\mathbf{\alpha}_{i}\in\mathcal{R}^{n}\) and \(\mathbf{\beta}_{i}\in\mathcal{R}^{N}\) are the biases of the \(i\)-th local model, and  
\[w_{i}(z)=\Pi_{j=1}^{p}M_{ij}(z_{j}),\quad h_{i}(z)=\frac{w_{i}(z)}{\sum_{i=1}^ {r}w_{i}(z)}. \tag{7}\]  
It also holds true for every \(k\) that \(\sum_{i=1}^{r}w_{i}(z)>0\), \(w_{i}(z)\geq 0\), \(i=1,2,\ldots,r\).
# Development of Screen-Printable Nafion Dispersion for Electrochemical Sensor
## 2 Materials and Methods
### Characterization of Nafion  
The rheological properties of the modified Nafion inks were characterized using a Discovery HR-20 rheometer (TA Instrument, New Castle, DE, USA). Shear-dependent viscosity was measured at frequencies ranging from 0.1 to 500 s\({}^{-1}\). Three-level flow method was used to measure thixotropy, with shear rate starting at 0.1 s\({}^{-1}\) for 2 min, followed by increasing to 100 and 500 s\({}^{-1}\) for 10 s and 5 s, respectively, and then recovering back to 0.1 s\({}^{-1}\) for 5 min. Yield stress was tested by dynamic strain sweep method. All three measurements were performed with a 40-mm parallel plate geometry at 25 \({}^{\circ}\)C.  
X-ray photoelectron spectroscopy (XPS) was conducted using a Thermo Scientific Nexsa XPS with a micro-focused monochromated Al K\(\alpha\) (1486.6 eV) X-ray radiation source. All spectra were collected using a 400-\(\upmu\)m spot size in a -5 \(\times\) 10\({}^{-8}\) mbar vacuum environment. The survey scans were collected at a pass energy of 200 eV and binding energy (BE) steps of 0.10 eV, and the high-resolution scans were collected at a pass energy of 50 eV and BE steps of 0.01 eV. The commercial and modified Nafion membranes were stencil coated onto silicon substrates and subsequently cured on a hot plate at 55 \({}^{\circ}\)C for 24 h.  
X-ray diffraction (XRD) was conducted using a Bruker D2 phaser XRD to characterize the crystallinity of the Nafion membranes. Both commercial and modified Nafion were stencil coated onto zero background silicon wafers.  
Film thickness and surface roughness characterizations of the post-cured Nafion films were conducted using an Olympus LEXT laser scanning confocal microscope (LSCM) equipped with a 405 nm ultraviolet (UV) laser.
## 4 Model Verification and Comparison Based on 1 DCNN-BiLSTM-AdaBoost  
### Evaluation Indicators  
In this study, the model's performance is comprehensively evaluated using metrics such as the confusion matrix, cross-entropy loss function, precision, accuracy, recall, and F1-score. These metrics are widely used in deep learning and pattern recognition, as they not only reflect the model's convergence during training but also provide a multifaceted assessment of its effectiveness in real-world fault detection tasks. For instance, the cross-entropy loss function measures the discrepancy between the predicted probability distribution and the true labels, while the confusion matrix visually represents the model's classification performance across different categories. The following section will provide a detailed explanation of each evaluation metric, including their definitions and computation formulas, followed by an in-depth discussion of the experimental results in subsequent chapters.
## 4 System Overview
### Infrastructure  
A minimal infrastructure is required for the operation of the system. This infrastructure consists of multiple elements that sustain the autonomy of the swarm, enable the communications among the agents, and allow the human-swarm interaction.  
As mentioned above, autonomy is a major challenge for applying drone swarms to firefighting missions. Some of the tasks imply continuous flights over target areas, such as fire surveillance and monitoring, whereas some others require a rapid deployment there, such as fire investigation and firefighter support. Therefore, the drones must be able to charge their batteries in the scenario to increase their availability during the missions. For this purpose, charging stations can be distributed throughout the scenario, even using the ground vehicles involved in the mission.  
Adaptive and immersive interfaces can improve the situational awareness and reduce the workload of operators in the considered mission. These results have been validated in similar missions, such as the control of multiple robots to perform complex missions [59] and the analysis of the information collected by a drone swarm from a smart city [57].  
These interfaces adapt their displays to the mission state and operator preferences, in order to reduce the amount of information and the workload of operator. For this purpose, they can integrate mission and operator models. The first ones allow following the state of the mission and selecting the relevant information according to it, whereas the second ones allow adapting the interface to the operator preferences. The adaptation can be performed through artificial intelligence models like neural networks.  
These interfaces apply immersive technologies like virtual reality (VR), augmented reality (AR), and mixed reality (MR) to introduce the operator in the scenario, improving their perception of the environment where the robots are working. VR reproduces virtual environments and allows interacting with their elements; AR enhances real environmentswith virtual elements with which the operator can interact, and MR combines real and virtual elements and allows interacting with them [60].  
In this work, we consider VR interfaces for the mission commander and AR interfaces for team leaders and members. The mission commander works away from the scenario, so they can focus on the information from the mission. A VR interface can reproduce the scenario, incorporating the real-time information of the swarm and its environment, allowing the operator to move around the scene searching for the best point of view. Meanwhile, team leaders and members work in the scenario, so they must pay most of their attention to the mission. In this case, an AR interface can provide them with relevant information about the mission while keeping their attention in their environment.
## 4 HFAJAYA for the Multi-Constraint OPF Problems
### OPF without Stochastic Renewable Energy
#### 4.1.2 Case 2  
Thermal generators may use various fuel sources, including oil, natural gas, and coal, depending on the power system's operation circumstances. As a result, the theoretical analysis for these generators' fuel cost curves (units 1 and 2) may be considered a collection of restrictions [18].  
\[F(P_{Gi})=\left\{\begin{array}{cc}a_{i1}+b_{i1}P_{Gi}+c_{i1}P_{Gi}^{2};\ P_{Gi}^{min} \leq P_{Gi}\leq P_{Gi1}\\ a_{i2}+b_{i2}P_{Gi}+c_{i2}P_{Gi}^{2};\ P_{Gi1}\leq P_{Gi}\leq P_{Gi2}\\ \ldots\\ a_{ik}+b_{ik}P_{Gi}+c_{ik}P_{Gi}^{2};\ P_{Gik-1}\leq P_{Gi}\leq P_{Gi}^{max} \end{array}\right. \tag{26}\]  
The \(i\)th generator's cost coefficients are characterized by \(a_{ik}\), \(b_{ik}\), and \(c_{ik}\) for the \(k\)-th fuel type. The other single fuel source units' cost coefficients are unchanged and have similar values as scenario 1. As a result, the goal function for modeling the gasoline cost features can be depicted as:  
\[J_{2}=\left(\sum_{i=1}^{2}a_{ik}+b_{ik}P_{Gi}+c_{ik}P_{Gi}^{2}\right)+\left( \sum_{i=3}^{NG}a_{i}+b_{i}P_{Gi}+c_{i}P_{Gi}^{2}\right) \tag{27}\]  
It is important to note that the optimization technique was chosen. The unit data were linked to the type of objective function and the restrictions [41]. Table 4 compares the minimal fuel cost (USD/h), emission (t/h), power losses (MW), and V.D. (p.u.) of the FA and HFAJAYA algorithms and gives a comparison study between these results and the  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Optimizer** & **Fuel Cost** & **Emission (t/h)** & **Power Losses (MW)** & **V.D. (p.u.)** \\ \hline PSOSA [58] & 800.49859 & - & 9.0339 & 0.12674 \\ \hline JAYA [13] & 800.4794 & - & 9.06481 & 0.1273 \\ \hline MSA [9] & 800.5099 & 0.36645 & 9.0345 & 0.90357 \\ \hline ARCBBO [59] & 800.5159 & 0.3663 & 9.0255 & 0.8867 \\ \hline FPA [9] & 802.7983 & 0.35959 & 9.5406 & 0.36788 \\ \hline GWO [42] & 801.41 & - & 9.30 & - \\ \hline IEP [60] & 802.46 & - & - & - \\ \hline TS [16] & 802.29 & - & - & - \\ \hline MICA-TLA [61] & 801.0488 & - & 9.1895 & - \\ \hline MGBICA [62] & 801.1409 & 0.3296 & - & - \\ \hline ABC [17] & 800.660 & 0.365141 & 9.0328 & 0.9209 \\ \hline SFLA-SA [63] & 801.79 & - & - & - \\ \hline MFO [9] & 800.6863 & 0.36849 & 9.1492 & 0.75768 \\ \hline MHBMO [42] & 801.985 & - & 9.49 & - \\ \hline AGSO [20] & 801.75 & 0.3703 & - & - \\ \hline DE [64] & 802.39 & - & 9.466 & - \\ \hline SKH [65] & 800.5141 & 0.3662 & 9.0282 & - \\ \hline EP [66] & 803.57 & - & - & - \\ \hline MPSO-SFLA [48] & 801.75 & - & 9.54 & - \\ \hline FA & 800.7502 & 0.36532 & 9.0219 & 0.9205 \\ \hline HFAJAYA & **800.4800** & 0.3659 & 9.0134 & 0.9047 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The obtained optimal results in the current works for Case \(1\).  
outcomes reported in the recent literature. It can be observed that HFAJAYA had a suitable optimization influence compared to the other algorithms.
## 5 Case Study
### Blasting Damage Simulation of Surrounding Rock with Six Slot Bores  
The model with the length, the width and the height all 4.0 m was established using LS-DYNA3D to simulate the blasting damage of surrounding rock with six straight slotting bores (see Figure 13).  
We investigate the generating process of slot cavity and the blasting damage law of surrounding rock using the proposed model, Lagrange algorithm and co-node algorithm. Explosive elements and rock elements are directly with point contact. The absorbing boundary conditions are used for the lateral and bottom of the model. The front of the model (Plane XY) is a free surface, and other surfaces of the model are non-reflective.  
The blasting bore inside rock mass is a complete cylinder with a diameter of 42 mm and a depth of 2 m. The grids surrounding explosive products were refined.  
The explosives length of every blasting bore is 2.0 m. The stemming length of every blasting bore is 0 m. The bores in the model are all filled by the explosives and are detonated beginning from the bottom of the blasting bores at the same time.  
The main parameter values of explosives were the same as those in Table 2. The main physical and mechanical parameter values of surrounding rock were listed in Table 3.
# The Analysis of Experimental Deployment of IGLUNA 2019 Trans-Ice Longwave System
###### Abstract  
An experimental longwave system operating in the broadcasting spectrum with horizontal magnetic loop transmitting antennas is presented as an element of simulated lunar astronaut mission of the IGLUNA program of Swiss Space Center (ESA_Lab demonstrator) in June 2019 on the Klein Matterhorn glacier in Switzerland. The parameters of the antennas, the environment, the transmitter design, and propagation tests are presented. The best-suited propagation model is developed. As the system, using low powers, provided coverage of maximal distance of 2077.06 km, a single radio station of this type would cover about 36% of the Moon's surface and allow in situ ground-penetrating research.  
longwave; LF; propagation; Moon; IGLUNA; simulation; habitat; ESA; SSC; ground sensing
## 4 Discussion and Comparison with Experiments
### LFC of Thick Plates  
For LFC thick materials (i.e., with a thickness \(L\) of several millimetres or even centimetres), it may be necessary to create correspondingly many times longer filaments. As we saw above in the examples for LFC sapphire and tempered glass, the total length of the filaments \(H_{\mathrm{\Sigma}}\) for separation was from \(\approx 0.13~{}L\) to almost \(L\). If this ratio is maintained for the LFC of thick materials, for example, with a thickness of \(L=10\) mm, this corresponds to the total length of about \(H_{\sum}\) =1.3-10 mm of filaments with one or more of their arrays in the material depth. The methods discussed above in the Introduction, which use axicon focusing, Bessel beams, and multi-focusing, make it possible to obtain such long filaments of several millimetres--see, for example, [4, 17, 18]. Then scaling of the results obtained for \(\lambda\) = 0.52 \(\upmu\)m calculations with an increase in the filament length from 45 \(\upmu\)m to, for example, \(H_{\sum}\) = 10 mm for thick plates, i.e., by 222 times, according to the obtained formulas, if we assume that the filament has the same radius \(r_{f}\) = 0.244 \(\upmu\)m, leads to an increase in the same number of times of the pulse energy in the material, i.e., from 1.21 \(\upmu\)J to \(E_{1}\) = 269 \(\upmu\)J, and the average power in the material from the above value of 196 mW to \(P\) = 43.5 W at the same \(s_{1}\) = 1.16 \(\upmu\)m, \(f\) = 162 kHz and \(u\) = 0.19 m/s.  
However, if we use the parameters calculated above for irradiation at the wavelength \(\lambda\) = 1.04 \(\upmu\)m, which were obtained when comparing the model with the experiment [5, 6] for a smooth LFC of chemically tempered Corning\({}^{\circledR}\) Gorilla\({}^{\circledR}\) Glass 5 (see Table 6), then with the same increase in the filament length to 10 mm (by 222 times), we obtain for the same \(w\) = 270 kJ/cm\({}^{3}\), \(s_{1}\) = 5.9 \(\upmu\)m, \(f\) =153-191 kHz and \(u\) = 0.90-1.13 m/s, that the pulse energy in the material increases by the same number of times from the previously obtained value of 11.5 \(\upmu\)J to \(E_{1}\) = 2550 \(\upmu\)J. The average laser power in the material increases from the above value of 1.76-2.20 W to \(P\) = 390-490 W, if we assume that the filament has the same radius \(r_{f}\) = 0.49 \(\upmu\)m as in the experiment [5, 6]. This average laser power for \(\lambda\) = 1.04 \(\upmu\)m is by the order of magnitude greater than \(P\) = 43.5 W obtained above for \(\lambda\)= 0.52 \(\upmu\)m.
# S2p: Provenance Research for Stream Processing System
## 6 Provenance Management
### Provenance Data Management  
Provenance data refers to the data generated and stored for provenance purposes, including parents' ID list in each stream data, operators' states, and intermediate data in the offline provenance phase.  
**Intermediate data of interest**: In the offline provenance phase, we will cache all the intermediate data derived from the object source data. In s2p, the intermediate data is expressed in the form as \(<OP_{name},D_{build},D_{value},\{P_{build}\},Flag>OP_{name}\) representing the operator's name, \(D_{build}\) as the ID of this data, \(D_{value}\) as the actual value, \(\{P_{build}\}\) as the ID set of upstream data that is related to, and \(Flag\) denoting it as input data, output. All of these intermediate data are stored locally and managed transparently by our provenance management system.  
Since we track the object data and their dependent data only, we filter out other data using a tagging method as follows. We tag the data whose upstream data is object data to be tracked--starting from source operators. This tag operation continuous until to sink operators. The other untagged data are filtered out without consideration.  
**Parents' ID list of stream data**: All stream data, including the temporary data within the transformation, are appended with a parents' ID list, which comes from their upstream data. In the online provenance phase, since we only focus on the source data contributing to the final results, the parents' ID lists for stream data are the source IDs, leading to these data, instead of their upstream data' IDs. However, parents' ID lists in the offline provenance phase are aggregating IDs of all related upstream data.  
**Provenance data transmission and storing**: We adopt a server-client architecture, where each operator runs with one **ProvClient,** which interacts with **ProServer** and each physical machine runs one **ProServer** for provenance-related data collecting and storing. **ProServer** also reacts to query commands from **ProvManagerQueryService**, which manages the provenance data from different **ProServers** and responds to provenance queries. Consider one tiny Apache Flink application as an instance, which consists of two nodes with one task manager per node. Our provenance management system works for this application, shown as in Figure 9. This Flink application is decomposed into parallel tasks that are running on task slots ([[https://ci.apache.org/projects/flink/flink-docs-stable/concepts/flink-architecture.html](https://ci.apache.org/projects/flink/flink-docs-stable/concepts/flink-architecture.html)]([https://ci.apache.org/projects/flink/flink-docs-stable/concepts/flink-architecture.html](https://ci.apache.org/projects/flink/flink-docs-stable/concepts/flink-architecture.html))). Each task works with one **ProvClient**, through which it interacts with **ProServer**. The ProvServer contains provenance data locally in one node and talks with **ProvManagerQueryService** transparently.