esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Experiment  
### Experimental Description  
Experiments were carried out on two different brands of LIB (Sanyo ternary and Sony LiFePO\({}_{4}\) battery). The detailed parameters of the batteries were shown in Table 1. The experimental conditions of the battery in extreme conditions (inside the closed aluminum tube), natural convection, pure PCM, and PCM combined with the heat dissipation fins were set up. The experiments were carried out in the carton box (length \(\times\) width \(\times\) height, \(35\times 20\times 20\) cm) for the thermal isolation from environmental influences, and each experimental condition is shown in Figure 1. The aluminum tube size with an inner diameter of 36 mm, the height of 65 mm, and thickness of 6 mm (provided by Yuezhong Metal Material Co., Ltd., Dongguan, China) was used, and its thermal conductivity is 237.2 W/m-K. Figure 1d shows that the fin is composed of two circular fins and three vertical fins. Its material is tin sheet and the thermal conductivity is 150 W/m-K. The design of the fin structure is followed by: (1) The ring is set at the bottom of the battery and in the middle of the battery respectively as the temperature of the middle and low part of the battery is higher. (2) The vertical fin protruding is designed to disperse the generated heat in time and reduce the temperature effect. (3) According to previous studies, the best results are obtained when the distance between the radiator and the battery is 0.2 times the diameter of the battery [39]. So, the circular heat dissipation fin size (outer diameter \(\times\) inner diameter \(\times\) height) is \(\varphi 26\times\varphi 22\times 4\) mm, vertical heat dissipation fin size (length \(\times\) width \(\times\) height) is \(4\times 2\times 80\) mm.  
The high thermal conductivity phase change composites with phase change temperature of 52 \({}^{\circ}\)C (provided by Zhongjia New Material Technology Co., Ltd., Guangzhou, China) were selected due to the higher thermal conductivity than the pure paraffin. In this experiment, the phase change material is wrapped around the battery with a thickness of 9 mm and a weight of about 9.9 g. The main components are phase change wax and expanded graphite, which have the following advantages: solid-solid phase change, high phase change latent heat, insulation, good cycle stability, non-corrosive, and non-toxic. Detailed thermal physical properties are shown in Table 2.  
\begin{table}
\begin{tabular}{c c c} \hline
**Battery Type** & **Sanyo Lithium-Ion Battery** & **Sony Lithium-Ion Battery** \\ \hline Capacity & 2600 mAh & 1100 mAh \\ Voltage (V) & 3.7 V (Discharge cut-off voltage 3.0 V, & 3.2 V (Discharge cut-off voltage 2.0 V, \\  & The maximum charging voltage is 4.2 V & The maximum charging voltage is 3.6 V) \\ Sizes & \(\Phi 18\times 65\) mm & \(\Phi 18\times 65\) mm \\ Life (seconds) & 1000 & 1000 \\ Anode material & Ternary materials & LiFePO\({}_{4}\) \\ \hline \end{tabular}
\end{table}
Table 1: Detailed battery parameters.
# _remote sensing_
## 3. HORUS Multiangle and Multispectral Observation Module
### Spatial Resolution  
The achievable performances of the HORUS instrument in terms of spatial resolution, are strongly dependent on the camera view angle and observation wavelength, are shown in Table 7, with reference to the NASA-JPL MISR view angles, in the configuration described in Figures 3 and 5.
# Solving Optimal Electric Vehicle Charger Deployment Problem
## 6 Case Study 2: Region with High EV Density
### Case-Specific Refinement of Solving Method
#### 6.1.3 Training Methods
(c) Buffer Size: We investigate the influence of the buffer size, which determines the scope of the reference data, and the resulting level of data duplication on consistency. To find the optimal buffer size, the previously used 1.13 km buffer size is set as the maximum value, and the buffer size is changed to measure the change in consistency. Figure 8 represents the buffers with a radius of 1.13 km (gray), 500 m (yellow), and 50 m (red).
## 3 Results
### Experimental and Numerical Results Obtained from CFD Simulation  
Tables 3 and 4 summarize the values of the aerodynamic forces and coefficients obtained from the tunnel tests and CFD simulations for various angle \(\theta\) (Figure 9) for gantry cranes with horizontal box and truss girders, respectively.  
In the case of the box girder, the aerodynamic drag force \(F_{x}\) slightly increases with increasing the angle theta. Contrary to drag force \(F_{x}\), the aerodynamic lift force \(F_{y}\) decreases. However, the variations in the lift force \(F_{y}\) are much more significant. In Figure 13a,b, the selected results of CFD analysis for \(\theta=0^{\circ}\) are shown.  
On the other hand, the wind direction impact in the studied angle range is negligible for the truss girder. The drag force \(F_{x}\) values are almost four times less than the box girder. The lift force \(F_{y}\) values are comparable with zero.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Experiment**} & \multicolumn{3}{c}{**CFD**} \\ \hline \(\theta\) (\({}^{\circ}\)) & \(F_{x}\) (N) & \(F_{y}\) (N) & \(C_{x}\) & \(C_{y}\) & \(F_{x}\) (N) & \(F_{y}\) (N) & \(C_{x}\) & \(C_{y}\) \\ \hline \(-6\) & 12.050 & 0.082 & 2.54 & 0.02 & 10.487 & 0.310 & 2.21 & 0.07 \\ \(-4\) & 11.269 & 0.137 & 2.37 & 0.03 & 10.372 & 0.320 & 2.18 & 0.07 \\ \(-2\) & 11.110 & 0.037 & 2.34 & 0.01 & 10.303 & 0.343 & 2.17 & 0.07 \\ \(0\) & 10.351 & 0.740 & 2.18 & 0.16 & 10.256 & 0.430 & 2.16 & 0.09 \\ \(2\) & 10.606 & 1.007 & 2.23 & 0.21 & 10.248 & 0.561 & 2.16 & 0.12 \\ \(4\) & 10.991 & 1.628 & 2.31 & 0.34 & 10.267 & 0.713 & 2.16 & 0.15 \\ \(6\) & 12.176 & 1.564 & 2.56 & 0.33 & 10.335 & 0.846 & 2.17 & 0.18 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Aerodynamic forces and coefficients of the truss girder section model.  
Figure 13: Results of CFD analysis (\(\theta=0^{\circ}\)): (**a**) distribution of the pressure for the box girder; (**b**) distribution of the pressure for the truss girder.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Experiment**} & \multicolumn{3}{c}{**CFD**} \\ \hline \(\theta\) (\({}^{\circ}\)) & \(F_{x}\) (N) & \(F_{y}\) (N) & \(C_{x}\) & \(C_{y}\) & \(F_{x}\) (N) & \(F_{y}\) (N) & \(C_{x}\) & \(C_{y}\) \\ \hline \(-6\) & 36.698 & 15.918 & 1.76 & 0.76 & 40.869 & 14.573 & 1.96 & 0.70 \\ \(-4\) & 37.621 & 13.905 & 1.80 & 0.67 & 40.157 & 16.494 & 1.92 & 0.79 \\ \(-2\) & 38.421 & 15.572 & 1.84 & 0.75 & 39.358 & 18.345 & 1.88 & 0.88 \\ \(0\) & 36.087 & 20.032 & 1.73 & 0.96 & 38.491 & 20.035 & 1.84 & 0.96 \\ \(2\) & 36.108 & 23.338 & 1.73 & 1.12 & 37.562 & 21.501 & 1.80 & 1.03 \\ \(4\) & 36.870 & 23.505 & 1.77 & 1.13 & 36.638 & 22.642 & 1.75 & 1.08 \\ \(6\) & 34.171 & 25.197 & 1.64 & 1.21 & 35.733 & 23.401 & 1.71 & 1.12 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Aerodynamic forces and coefficients of the box girder section model.
## 2 Preliminaries
### Some Useful Definitions and Results  
In this section, we give some results and definitions which are useful for our analysis later.  
**Definition 8**.: _Let \(P\) and \(Q\) are two point sets in \(\mathbb{R}^{n}\). The **Minkowski sum** of \(P\) and \(Q\), denoted as \(P\oplus Q\), is the point set \(\{p+q:p\in P,q\in Q\}\)._  
**Lemma 3**.: _Let \(B_{1}=B_{n}^{(p)}(\mathbf{0},a)\) and \(B_{2}=B_{n}^{(p)}(\mathbf{v},a)\) such that \(\|\mathbf{v}\|_{p}=\lambda_{1}^{(p)}\) and \(\lambda_{1}^{(p)}<2a\). Let \(D=B_{1}\cap B_{2}\). If \(|D|\) and \(|B_{1}|\) are the volumes of \(D\) and \(B_{1}\), respectively, then_  
1. _[_64_]_ \(\frac{|D|}{|B_{1}|}\geq 2^{-n}\Big{(}1-\frac{\lambda_{2}^{(p)}}{2a}\Big{)}^{n}\) _if_ \(1\leq p<\infty\)_._
2. _[_26_]_ _When_ \(p=2\)_, further optimization can be done such that we get_ \(\frac{|D|}{|B_{1}|}\geq\Big{[}1-\Big{(}\frac{\lambda_{1}^{(2)}}{2a}\Big{)}^{2} \Big{]}^{n/2}\)_._
3. _[_49_]_ _When_ \(p=\infty\) _then_ \(\frac{|D|}{|B_{1}|}\geq\Big{(}1-\frac{\lambda_{1}^{(\infty)}}{2a}\Big{)}^{n}\)_._  
**Theorem 1** (**Kabatiansky and Levenshtein [65]**).: _Let \(E\subseteq\mathbb{R}^{n}\setminus\{\mathbf{0}\}\). If there exists \(\phi_{0}>0\) such that for any \(\mathbf{u},\mathbf{v}\in E\), we have \(\phi_{\mathbf{u},\mathbf{v}}\geq\phi_{0}\), then \(|E|\leq 2^{cn+o(n)}\) with \(c=-\frac{1}{2}\log[1-\cos(\min(\phi_{0},62.99^{o}))]-0.099\)._  
_Here, \(\phi_{\mathbf{u},\mathbf{v}}\) is the angle between the vectors \(\mathbf{u}\) and \(\mathbf{v}\)._  
Below, we give some bounds which work for all \(\ell_{p}\) norms. We especially mention the bounds obtained for the \(\ell_{2}\) norm where some optimization has been performed using Theorem 1.  
**Lemma 4**.: _[_47_]_ _Let \(c_{c}=\log(1+\frac{2}{7})\). If \(\mathcal{C}\) is a set of points in \(B_{n}^{(p)}(R)\) such that the distance between two points is at least \(\gamma R\), then \(|\mathcal{C}|\leq 2^{c_{c}n+o(n)}\)._
2. _[_26_,27_]_ _When_ \(p=2\)_, we can have_ \(|\mathcal{C}|\leq 2^{c_{c}^{(2)}n+o(n)}\) _where_ \(c_{c}^{(2)}=-\log\gamma+0.401\)Since the distance between two lattice vectors is at most \(\lambda_{1}^{(p)}(\mathcal{L})\), we obtain the following corollary.  
Corollary 1: _Let \(\mathcal{L}\) be a lattice and \(R\) be a real number greater than the length of the shortest vector in the lattice._  
1. _[_64_]_ \(|B_{n}^{(p)}(R)\cap\mathcal{L}|\leq 2^{c_{b}n}\) _where_ \(c_{b}=\log\left(1+\frac{2R}{\lambda_{1}^{(p)}}\right)\)_._
2. _[_26,28_]_ \(|B_{n}^{(2)}(R)\cap\mathcal{L}|\leq 2^{c_{b}^{(2)}n+o(n)}\) _where_ \(c_{b}^{(2)}=\log\frac{R}{\lambda_{1}^{(2)}}+0.401\)_._
## 3 Effects of Joints on the Tensile Properties
### Effects of Parallel joints on the Tensile Properties
#### 3.2.2 Effect of Joint Density of Parallel Joints  
In a certain unit of rock mass, the number of joints is often referred to as the fracture intensity [36; 37]. It is well addressed that the mechanism of deformation and failure of rock masses varies with fracture intensity, as well as the engineering properties such as cavability, permeability, and fragmentation characteristics. In this study, the effect of joint intensity on \(\sigma_{t}\) and \(E_{t}\) is investigated by means of the number of joints (\(n\)) inside the specimen. Four sets of models with different dip angles (\(\alpha\) = 20, 30, 40, 50\({}^{\circ}\)) were built (as illustrated in Figure 6) under the condition of \(d\) = 5 mm, and for each set of the model, six subsets with different numbers of joints (\(n\) = 1, 2, 3, 4, 5, 6) were analyzed. The test program specifics are shown in Table 6 and the stress-strain curves under uniaxial tensile load are shown in Figure 7. The results are shown in Table 7.  
As can be seen, for each subset with the same \(\alpha\), the peaks of the curves are almost identical, while the slopes are notably effected by \(n\). Taking the model set with \(\alpha\) = 40\({}^{\circ}\) as an example, as listed in Figure 7, when \(n\) increases from 1 to 6, \(E_{t}\) significantly drops from 0.81 to 0.52 GPa, while \(\sigma_{t}\) remains constant. The influencing pattern of \(n\) on \(E_{t}\) is identical for different values of \(\alpha\). These results indicate that the fracture intensity of a rock mass has a great effect on \(E_{t}\) but a negligible effect on \(\sigma_{t}\).  
The relationship between \(n\) and \(E_{t}\) with different values of \(\alpha\) after fitting is shown in Figure 8. As can be seen, \(E_{t}\) is negatively correlated to \(n\), and the relationship varies with different values of \(\alpha\). The change in \(E_{t}\) with \(n\) will be less significant in cases of higher values of \(\alpha\).  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Sets of models** & \(\alpha\) & **Subsets with respect to \(n\)** \\ \hline \multirow{4}{*}{1} & \multirow{4}{*}{20\({}^{\circ}\)} & \(n=2\) \\  & & \(n=3\) \\  & & \(n=4\) \\  & & \(n=5\) \\ \hline \multirow{4}{*}{2} & \multirow{4}{*}{30\({}^{\circ}\)} & \(n=2\) \\  & & \(n=3\) \\  & & \(n=4\) \\  & & \(n=5\) \\ \hline \multirow{4}{*}{3} & \multirow{4}{*}{40\({}^{\circ}\)} & \(n=2\) \\  & & \(n=3\) \\  & & \(n=4\) \\ \hline \multirow{4}{*}{4} & \multirow{4}{*}{50\({}^{\circ}\)} & \(n=2\) \\  & & \(n=3\) \\ \cline{1-1}  & & \(n=4\) \\ \cline{1-1}  & & \(n=5\) \\ \hline \hline \end{tabular}
\end{table}
Table 6: Model designs for rock specimens with multiple parallel joints.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \(\alpha\) & \(\sigma_{t}\) **(MPa)** & \(E_{t}\) **(GPa)** \\ \hline \(20^{\circ}\) & 0.88 & 0.71 \\ \(30^{\circ}\) & 0.91 & 0.71 \\ \(40^{\circ}\) & 0.93 & 0.73 \\ \(50^{\circ}\) & 1.05 & 0.73 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Tensile properties of double-jointed rock specimens with different joint dip angles.
# Priority-Based Machine-To-Machine Overlay Network over LTE for a Smart City
## 3 PM2M Overlay Network over PRACH
### Physical and MAC Layer  
The physical layer of the M2M overlay network is implemented by selecting \(N_{M}=8\) of the 54 sequences available for contention-based access and dedicating them to M2M access. Data bits from the M2M stream are multiplexed on M2M preambles so that each sequence carries a single bit from each data byte. To transmit a single M2M data bit, \(N_{b}=16\) preamble elements are used as a kind of 'chipping' sequence, the size of which affects the performance as SINR increases by \(10\log_{10}N_{b}\). Higher SINR is needed for the detection of M2M data bits since an H2H preamble is detected based on the SINR over its entire duration, while only a portion of that duration is used by M2M packets.  
Performance is also affected by the number of ZC codes dedicated to M2M, as these are bundled together so that each code carries \(\frac{8}{N_{M}}\) bits of each user's byte. The physical layer data rate is, then, \(R_{M}=\frac{RN_{M}}{N_{b}}\), where \(R\) is the preamble element rate.  
In this scheme, each PRACH resource holds a single PM2M superframe, which makes CSMA-CA access possible. For the Medium Access Control (MAC) layer, PM2M access is configured as a CSMA-CA overlay. The time for preamble transmission becomes the superframe time for the PM2M overlay network. We consider the IEEE 802.15.6 beacon mode with beacon period boundaries where at the beginning of every superframe, a beacon is transmitted on the medium. The superframe time after the beacon is divided into slots each containing 20 bits. The superframe is divided into an Access Phase (AP) and Random Access Phase 1 (RAP1); all other access phases allowed by the standard will have zero length. We do not use the RTS/CTS handshake, nor the four-way handshake of LTE, as they would easily overload the PRACH.  
CSMA-CA access in the superframe resembles the one used in the beacon-enabled IEEE 802.15.6. In the CSMA-CA overlay, one backoff period has 20 sequence elements, i.e., \(tboff=20/R=18.51\)\(\upmu\)s. To implement the overlay superframe, we use Preamble Format 2, where the preamble duration is 1.6 ms. Access in Format 2 is achieved by repeating the same preamble twice. Finally, to achieve a continuous superframe sequence without coordinated node sleeping, we assume configuration index \(c_{f}=5\).  
Figure 5: LTE and Priority-based Machine-to-Machine (PM2M) overlay superframe with PF = 2 (2-ms subframe).
# Research on Sentiment Analysis of Online Public Opinion Based on RoBERTa-BiLSTM-Attention Model
## 6 Conclusions  
This study proposed a framework for the sentiment classification of public opinion comments that completes the whole process of sentiment classification from the crawling and preprocessing of public opinion data to constructing and annotating the supplemental sentiment lexicon and identifying and evaluating the sentiment of deep learning models.  
In terms of sentiment dictionary annotation, a supplementary event dictionary was constructed based on the SO-PMI algorithm, which was merged with the commonly used dictionaries to annotate the sentiment of public opinion comments and initially judge the sentiment tendency of the text, thus saving the cost and time of manual annotation and simplifying the feature engineering and annotation process.  
In terms of deep learning model recognition, combining RoBERTa's dynamic word vector feature extraction capability, BiLSTM's contextual semantic information processing capability, and the attention mechanism's feature focusing capability, the RoBERTa-BiLSTM-Attention model was constructed, and the annotated corpus was used for sentiment training, validation, and testing. The model accuracy, precision, recall, and F1-score reached 91.56%, 90.87%, 91.07%, and 91.17%, respectively, and the model had superior performance and was able to effectively distinguish between positive, negative, and neutral emotions. In addition, the model performed better in the training and validation sets, with the loss reduced to a low level and no overfitting.  
Based on the sentiment classification results, the event was analysed and the following was found: 1 Situational briefings, expert responses, and regulatory developments trigger significant fluctuations in public opinion volume and public sentiment. Among them, incorrect information notifications cause a negative public opinion backlash; expert responses can guide negative public opinion, but this does not last long; continuous announcements of regulatory processes can appease the public, but late announcements of final investigation results weaken the effect of public opinion management. In addition, the emergence of secondary public opinions disturbs public sentiment again, but the impact is small. 2 Regarding different types of emotional focus, in this tudy, positive emotional focus was on the truth, expert appraisal, appraisal by the investigation team, disposal methods, etc.; negative emotional focus was on food safety, ineffective supervision, and leadership responsibility.
## 4 Assessment
### Radio Layers' Packet Granularity  
Given the diverse nature of the data sources, a wide range of heterogeneous temporal granularities can be found. Experiments can be performed with a sampling period within the range of minutes. At the same time, the radio interface provides updates with  
Figure 10: RSRQ boxplot comparison.  
Figure 9: RSRP boxplot comparison.  
the logs approximately every second. Therefore, the variation over time is an essential consideration for the correlation between samples and overlapping the time series of the different parameters.  
The Figure 11 shows an example of how the temporal granularity varies in the case of the packets contained within the Mobile Insight traces. The bars show the average time between packets captured, with the arrow indicating the standard deviation.  
An apparent disparity between the packets in the case of the physical layer, and for example, those collected in the RRC layer can be appreciated. For the correlation analysis with the NetTest service, the PDSCH LTE Packet's content has been chosen, since it corresponds to the highest granularity of all the information collected by Mobile Insight traces. It also contains the key PHY layer parameters, such as MCS, transport block size (TBS) and carrier index used (coded as primary or secondary component carrier).
# An Approach for Detecting Feasible Paths Based on Minimal SSA Representation and Symbolic Execution
## 3 Related Works
Initially, a CFG is created as a C function that comprises the source code, including the inputs, maximum iteration count for loops, and the coverage conditions. Feasible test sequences are then determined using the backtracking technique, a Z3 satisfiability modulo theory (SMT) solver and symbolic execution for traversing the CFG. This technique's primary drawback is that it is time-intensive when addressing the constraints under a CFG possessing numerous decisive vertices but many fewer infeasible sequences. [PERSON] et al. [12] proposed the SDART tool, which improved upon the DART [23] tool, along with their prior research [34] by integrating these techniques with a static test data-producing technique that could determine feasible paths using relatively fewer iterations. The proposed static analysis scheme creates several partial test paths that traverse unvisited branches. Subsequently, test data creation is attempted by traversing these test paths. Traversed branches are marked in order to prevent revisiting the branches more than once. Table 1 summarizes the most popular symbolic execution studies that aim to detect feasible paths, the methods and tools that their approaches are based on, the limitations of their studies and the programming language used to implement and evaluate their approaches. To overcome the limitations of previous studies in detecting feasible paths, our proposed approach aims to decrease the time required to detect feasible paths, which was the main issue in the previous studies that used the LLVM compiler [7; 28; 29; 30; 31]. Some of the previous studies [23; 24; 25] also missed some feasible paths because of the use of the Simple Theorem Prover (STP). Therefore, the Z3 solver [36], as a high-performance theorem prover, could be used to solve different complex theorems [37]. In addition, we present a method to avoid generating duplicated feasible paths by calculating the weight of each path and avoiding the repetition of other paths with the same weight. Based on our knowledge, there is no available study that has been conducted to detect feasible paths in PHP. Therefore, we implemented and evaluated our approach in a PHP environment, which is considered the most common web technology with which to build a web application.
# The Sensitivity Analysis of Parameters in the 1D-2D Coupled Model for Urban Flooding
## 2 Method
### Construction of the Coupled Model
#### 2.1.2 Overview of the Telemac 2D Model  
Telemac (Version 8.5) was developed by Electricite de France (EDF) in its Laboratoire National d'Hydraulique et Environnement (LNHE) in the 1990s and has since evolved into an open-source, multifunctional hydrodynamic simulation software. Telemac employs the finite element or finite volume method to solve the shallow water equations by generalizing the surface of the study area to multiple triangular grids and performing water and momentum balance calculations for each grid.  
In its computational process, Telemac primarily involves the continuity equation and the momentum equations.  
The 2D continuity equation is written as follows:  
\[\frac{\partial h}{\partial t}+\frac{\partial(uh)}{\partial x}+\frac{\partial( yh)}{\partial y}=0 \tag{3}\]  
where \(h\) is the water depth and \(u\) and \(v\) are the horizontal velocity components in the \(x\) and \(y\) directions, respectively.  
The momentum equations in the \(x\) and \(y\) directions are expressed as follows:  
\[\frac{\partial(uh)}{\partial t}+\frac{\partial\left(u^{2}h\right)}{\partial x}+ \frac{\partial(wh)}{\partial y}+gl\frac{\partial h}{\partial x}=-ghS_{fx}+ \textit{viscous terms} \tag{4}\]  
\[\frac{\partial(vh)}{\partial t}+\frac{\partial(wh)}{\partial x}+\frac{ \partial\left(v^{2}h\right)}{\partial y}+gl\frac{\partial h}{\partial x}=-ghS_{ fy}+\textit{viscous terms} \tag{5}\]  
where \(g\) is the gravitational acceleration, \(S_{fx}\) and \(S_{fy}\) are the friction slopes in the \(x\) and \(y\) directions, and \"viscous terms\" represent the diffusion effects and may include turbulence closure models (such as the \(k-\varepsilon\) model) to account for turbulence.