esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Research Design and Methods
### The Simulation Transport Model
#### 2.3.2 Input Data to the Simulation Model  
The OD matrix, which contains mutual transport relations between centroids, is needed to perform the simulation on the model created above. As mentioned above in this article, traditional methods for constructing an OD matrix involve extensive traffic research, which is done only once in a while, and it is very expensive and time-consuming [61]. There are also situations where such research cannot be carried out due to financial or pandemic reasons. An example is a current situation where research could not be carried out as a result of the various restrictions caused by the COVID-19 pandemic, such as curfews, ordered work from home, etc. As a result, we could not provide accurate results due to reduced mobility.  
Fortunately, OmniTRANS allows for estimating the OD matrix from old research. In our case, research conducted in 2015 can estimate the OD matrix [68]. The methodology and algorithm for estimating the OD matrix are solved by several authors in their studies and are not interested in this article.  
To estimate the OD matrix, we will use studies conducted in 2015 as part of the last national traffic census. The values of traffic intensity at all entrances and exits to/from the town's internal traffic circuit were subtracted from the traffic survey provided in 2015. Based on these data, the so-called historical OD matrix for both Variant A and Variant B. Table 8 shows the historical matrix for the current state, i.e., bidirectional traffic on the inner circuit (Variant A) and in Table 9, the historical matrix for Variant B is compiled. The OD matrix for variant C is created from the historical source-target matrix for Variant B, with the number of vehicles reduced by 10%. The numbers of vehicles in the source-target matrices are rounded up to integers. The number of vehicles and the number of public transport funds was recorded.  
Figure 8: Illustration of the signal plan of the Palackeho–Stefanikova intersection for Variant A (**a**) and Variants B and C (**b**) (source elaborated by authors).  
Individual simulation experiments, conducted separately for each variant, were performed based on these input values. The simulation results are presented in the following chapter.
## 5 Experiments
### Case Study  
Table 6 shows two examples that compare the responses generated with the response weight and those without the attention. If the response weight is removed from RGRW, RGRW becomes equivalent to a sequence-to-sequence model with a BERT-encoder and a GPT-2 decoder. Thus, the responses in 'Without RW' in this table are the outputs of the GPT-2 decoder, while those in 'With RW' are the outputs of RGRW. In each example, the attention by the BERT-encoder (blue line) and the proposed response weight (red line) are depicted as graphs. In these graphs, the attention weights are expressed per a word unit for easy interpretation, where the attention weight of a word is obtained as an average of the attention weights for the subwords of the word. Note that the values for the response weight are \(a_{i}\)s in Equation (4).  
\begin{table}
\begin{tabular}{l l l} \hline \hline Query \& & & \\ Attentions & & & \\ Golden & & & \\ Response & All right. I’ll give it a try. Do you have it somewhere I can download it? \\ \hline W/o RW & It would be nice to have them. \\ W/RW & I’ll give it a shot and see what happen! \\ \hline Query \& & & \\ Attentions & & \\  & & \\ Golden & & & \\ Response & Too bad. They taste awful. \\ \hline W/o RW & I think it’s a good idea. \\ W/RW & Oh yeah! I’ve always wanted to try them! They taste great! \\ \hline \hline \end{tabular}
\end{table}
Table 6: The examples that show the effectiveness of the response weight in RGRW.  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Model** & **Bleu-2** & **Dist-2** & **Entropy** & \(R_{a}\) \\ \hline RGRW & **1.70** & 25.04 & 11.42 & **1.26** \\ - EDA & 1.59 & **25.60** & **11.45** & 1.23 \\ - RW & 1.52 & 20.45 & 8.96 & 1.08 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Ablation study on two attention layers. Scores in bold are the highest score for each metric.  
According to the table, 'With RW' generates responses more similar to the ground-truth ones and less general than the 'Without RW' ones. In the first example, '_love_' and '_gave_' have high BERT attention weights. As a result, the GPT-2 decoder generates the word '_have_' in the response of 'Without RW', and the response is semantically unrelated with the ground-truth one. On the other hand, all words in the phrase of '_gave it a shot_' have high \(a_{i}\) values. Since RGRW pays high response weight to 'gave it a shot', the response in 'With RW' follows the ground-truth one more semantically.  
In the second example, '_them_' and '_salt_' are stressed on in the BERT attention, which leads to the generation of a very general answer in the response of 'Without RW'. On the other hand, RGRW focuses on the words '_cucumber_', '_them_', and '_much_' with high response weight. In particular, the focus on '_much_' results in the generation of '_try_' and '_taste_' in the response of 'With RW'. In addition, the generated answer is much less general and semantically similar to the ground-truth response, though the sentiment polarity of the answer is opposite to that of the ground-truth one.
###### Abstract
However, the original Mindlin plate theory assumes that thickness variations of the defects are symmetric with respect to the mid-plane of the plate. While this assumption can approximate wave interactions with non-symmetric defects to some extent, for sharp and deep defects, additional considerations are necessary to accurately predict mode conversion and wave scattering [32; 33]. The simpler theory is more attractive for solving tomographic inverse problems; however, it is important to understand how it describes corrosion defects, which in general are smooth, but the thickness contrast increases for small diameter yet deep defects compromising the validity of the theory. In this context, the Finite Difference Method (FDM) is employed to simulate and analyze the scattering of the A\({}_{0}\) mode from defects with varying depth, width, and smoothness in a plane strain setting, while the Pseudospectral Method (PsM) is implemented to study more complex defect geometries in the 3-D domain. The results are compared with those obtained from Finite Element Method (FEM) simulations, ensuring a robust validation of our approach. This PsM incorporation also sets the foundation for future application in GWT algorithms, further demonstrating the novelty of this work. The article is structured as follows: In Section 2, the governing equations of Mindlin plate theory are presented, along with the solution procedures, sample problems, and analysis methods. The results of the scattering from different defects are provided in Section 3, followed by a discussion of the model's potential in GWT and final conclusions in Section 4.
# A Review on the Thermal Hazards of the Lithium-Ion Battery and the Corresponding Countermeasures
## 4 Thermal Hazards of Lithium-Ion Battery
### Thermal Hazards of Typical Battery Components
#### 4.1.1 Thermal Hazards of the Electrolytes  
Due to its relatively high content and liquidity, the combustion behavior of electrolyte is quite complicated. According to previous research, the initial decomposition temperature of electrolyte is approximately 80 \({}^{\circ}\)C [72; 73], and large quantities of heat and gas are released, which easily lead to LIB leakage. In addition, the electrolyte will react with electrodes with the increasing temperature. At the meantime, leaked electrolyte will increase the liquid fire risk once ignition occurs. If the internal temperature of the LIB is higher, it will lead to a jet fire, and then noticeably accelerate the spread of fire.  
Currently, among the commonly used commercial electrolytes, lithium salt is mainly used as lithium hexafluorophosphate (LiPF\({}_{6}\)) or lithium tetrafluoroborate (LiBF\({}_{4}\)). For the functional mechanism of the two kinds of lithium salts in electrolyte combustion process, [PERSON] et al. compared and analyzed the pyrolysis process of LiPF\({}_{6}\) and phosphorus pentafluoride (PF\({}_{5}\)) in ethylene carbonate/dimethyl carbonate (EC/DMC), and found that the products were almost the same [73]. [PERSON] et al. analyzed the thermal decomposition process of LiPF\({}_{6}\) in methyl ethyl carbonate (EC/EMC), and then put forward the pyrolysis mechanism of the LiPF\({}_{6}\) under high temperature [74; 75; 76]:  
1. Lithium salt undergoes thermal decomposition to form PF\({}_{5}\);
2. PF\({}_{5}\) reacts with water to generate hydrogen fluoride (HF);
3. PF\({}_{5}\) can also react with carbonate solution to form methyl fluoride (CH\({}_{3}\)F) and other substances.  
In addition, it is generally regarded that LiBF\({}_{4}\) is superior to LiPF\({}_{6}\) with respect to factors such as high-temperature performance, reaction characteristics with water and chemical stability [77; 78]. However, the anion of LiBF\({}_{4}\) is very small and it can be easily combined with lithium ions in solution, resulting in its weak ion transport ability. To overcome this weakness, researchers chelated lithium salt with oxygen to synthesize new large anionic lithium salts, such as lithium diethylborate (LiBOB) and lithium fluoroethylborate (LiDFOB) [79; 80].  
After a long period of development, some researchers believe that liquid electrolytes have inherent safety disadvantages. No matter how good the electrolyte additives and solutions are, they cannot prevent the formation of lithium dendrites. Therefore, researchers have started to develop solid-phase electrolytes [81; 82; 83; 84].
## 4 Methodology  
### Dataset  
In this study, we used a subset of the UNBC-McMaster shoulder pain expression archive dataset [10]. To develop an automatic pain assessment system based on facial expressions, researchers at McMaster University and the University of Northern British Columbia recorded 200 sequences for 25 adult participants (12 males and 13 females) suffering from shoulder pain. The recording sessions were conducted while the participants were performing a series of active and passive physical tests on their affected and unaffected limbs. The UNBC-McMaster shoulder pain expression archive dataset consists of 200 sequences with a total of 48,398 colored frames of 320 \(\times\) 240 pixels.  
The UNBC-McMaster shoulder pain expression archive dataset is highly imbalanced; 82.71% represents the \"no pain\" class with 40,029 images and only 17.29% represents the different levels of pain with 8369 images. Thus, using the full dataset may result in a biased classification, as shown in [9; 33]. To avoid such potential bias, we used a subset with 6000 randomly selected frames of 24 participants. We classified the subset dataset into four classes: no pain, low pain, moderate pain, and severe pain. Each had 1500 frames. The no pain class included images with PSPI equals zero. The low pain class included images with PSPI equals 1. The moderate pain class included images with a PSPI equal to 2. The severe pain class included images with a PSPI equal to or greater than 3. Table 1 shows details of our subset data.  
The no pain class represents the normal situation, whereas the low pain, moderate pain, and severe pain classes represent attention-drawing situations. Thus, the alarm should be activated for these three classes. Additionally, 80% of the subset data was used for training and 20% was used for validation. Figure 3 shows samples of the UNBC-McMaster shoulder pain expression archive dataset.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**The Class** & **Label** & **PISP Code** & **Number of Images** \\ \hline
1 & No Pain & 0 & 1500 \\
2 & Low Pain & 1 & 1500 \\
3 & Moderate Pain & 2 & 1500 \\
4 & Severe Pain & 3\({}^{+}\) & 1500 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Our subset data description.  
Figure 3: Samples of the UNBC-McMaster shoulder pain expression archive dataset with different levels of pain. Reprinted/adapted with permission from Ref. [10]. Copyright (@)[PERSON]).  
To test the data, we used all frames that belonged to the unseen participant. They were coded as \"064-ak064\" and totaled 1611 frames.  
To validate the FEAPAS, the consecutive frames in the unseen testing sequences were merged using OpenCV library to create 6 test videos (one for each sequence) with 4 frames per second each. Table 2 shows details of each video.
# Intelligent Environment-Adaptive GNSS/INS Integrated Positioning with Factor Graph Optimization
## 5 Further Analysis and Evaluation
### Analysis between Fixed and Adaptive Integrations  
As mentioned above, considering all 920 s, adaptive uncertainty solutions (i.e., AKF and AFGO) have a lower RMSE and STD than the corresponding fixed uncertainty solutions (i.e., KF and FGO), which indicates that adaptive solutions have more accuracy and smoothness. This could be explained, incorporated with Figure 16, that adaptive solutions provide uncertainty (marked in blue) that are tailored by the fast-changing urban environment and are closer to the actual 2D error or truth uncertainty (marked in green). These can potentially handle more scenarios than solutions with a single fixed uncertainty (marked in red) throughout the period.  
Figure 17 presents the adaptive, fixed, and truth uncertainty for all discussed receivers. The curves are sometimes discontinuous due to the lack of available GNSS epochs and the uncertainties without CNN prediction due to extreme feature values are set to be the \"Max uncertainty\", while \"Min uncertainty\" is employed as the allowable lowest value of uncertainty. A typical short duration from second 162 to 167 in Figure 17 has  
Figure 16: GNSS solution uncertainties of fixed integration (red) and adaptive integration (blue) and the true 2D positioning error indicating the truth uncertainty (green) throughout the time frame for the three receivers. For each receiver, the uncertainties during the GNSS-unavailable period are omitted on all three curves, and only INS propagation is processed. Moreover, the epochs without CNN predictions are particularly omitted on the truth uncertainty curve.  
Figure 15: Summary of integration methods in terms of the 2D RMSE and STD for the three receivers during all 920 epochs.  
been particularly selected as the example to showcase the differences between fixed and adaptive settings on the solution uncertainty. It can be seen in the subfigures of Figure 17a about U-Blox and Figure 17c about NovAtel that adaptive uncertainty is closer and shares a similar decreasing trend with the truth compared with the curve of fixed uncertainty. As for Xiaomi, shown in Figure 17b, the missing CNN prediction from second 164 to 167 indicates the poor data quality. The fixed uncertainty will not change according to the GNSS context, while the adaptive uncertainty surges intelligently to the \"Max uncertainty\" 130 m, which is assumed to be more approximated to the true uncertainty.
# Preparation of an Asymmetric Membrane from
## 3 Results and Discussion
### Characterization of Cellulose Acetate
#### 3.2.3 1H-NMR Spectroscopy  
1H-NMR spectroscopy was used to determine the distribution of acetyl among the three hydroxyl groups of the anhydroglucose, and to determine the DS value. The 1H-NMR spectra for synthesized CA samples recorded in CDCl\({}_{3}\) are presented in Figure 3. The chemical shifts have been reported in parts per million (8) from tetramethylsilane as an internal standard. Peaks between 1.9 and 2.2 ppm are assigned to three methyl protons of the acetyl groups. The presence of these peaks demonstrates that the extracted cellulose was successfully acetylated. The reaction order of three hydroxyl groups at C\({}_{2}\), C\({}_{3}\) and C\({}_{6}\) positions is C\({}_{6}\)-OH \(>\) C\({}_{2}\)-OH \(>\) C\({}_{3}\)-OH, which is in good agreement with the previous reports [32]. In the 1H-NMR spectrum of CA-1 (Figure 3a), the integrals for the peak areas are 2.82, 2.64 and 2.54 for C\({}_{6}\), C\({}_{2}\), and C\({}_{3}\), respectively. The total peak integral for all protons on the NMR spectrum for acetyl groups is 8.00. Peaks at 3.5-5.2 ppm relate to the seven protons of the anhydroglucose. The total peak integral for all protons on the NMR spectrum for the anhydroglucose is 6.99. Similarly, from the 1H-NMR spectrum of CA-2 shown in Figure 3b, the total peak integral for all protons in the acetyl groups and for all protons in the anhydroglucose is 8.88 and 7.37, respectively. DS values are calculated from the 1H-NMR spectroscopy results following Equation (10) [33]:  
\[\text{DS}=\frac{7\times I_{acetyl}}{3\times I_{H,AGU}} \tag{10}\]  
where \(I_{acetyl}\) is the peak integral of methyl protons of acetyl groups, and \(I_{H,AGU}\) is the peak integral of all protons of an anhydroglucose unit. The calculated DS values are 2.67 for CA-1 and 2.81 for CA-2, which are well consistent with the obtained values by the titration.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Sample**} & **\% Acetyl** & **Degree of** & **Viscosity \(\eta\)** & **Average** & **Yield (\% Dry** \\  & **Group (AG)** & **Substitution (DS) b** & **(mL/g)** & **M\({}_{\text{w}}\) (g/mol)** & **Weight) a** \\ \hline CA-1 & 41.68 & 2.65 & 103.2 & 43,760.8 & 45 \\ CA-2 & 43.05 & 2.80 & 112.8 & 48,685.9 & 51 \\ \hline \hline \end{tabular}  
* Increase weight of the sample. b DS values from titration.  
\end{table}
Table 2: DS, % AG and average molecular weight of as-prepared cellulose acetate.  
Figure 3: _Cont._
# New Optimal Design of Multimode Shunt-Damping Circuits for Enhanced Vibration Control
## 3 Results and Discussion
### Comparison between the Three Models  
An overall comparison between the three models is given in this section. For this reason, the tip FRFs using optimal solutions of all models are illustrated simultaneously in Figure 12. It can be observed from this figure that the optimal solution of the proposed model 1 gives greater amplitude reduction than both the other models. This means that the proposed model 1 yields better results than all the other models. The same conclusion can be obtained by comparing the amplitude reduction for the second and the third mode for all models given in Table 8.  
Figure 11: Iteration process of the fitness function using PSO—model 3.  
Figure 12: Simulated frequency response of the three models.  
Additionally, in comparison to model 2, the numerical results demonstrate the beneficial impact of the third design variable, \"inductance of the shunting branches\". Another benefit of the approach introduced in model 1 is that of reducing the required inductance values of the multimode shunt circuits. Indeed, comparing the inductance values required for shunt damping control in all models (see Tables 5 and 6), the total inductances in model 1 have the lower values. It seems that considering the capacitances as design variables to be determined and tuning their values via optimization method is an alternative way to reduce the inductance requirements of piezoelectric shunt-damping systems [16]. However, in contrast to the method of [16], the proposed approach is not performed at the expense of damping performance.
# Handover Decisions for Ultra-Dense Networks in Smart Cities: A Survey
## 2 Background
### Sixth-Generation Networks  
Sixth-generation networks are the future of present-day wireless technologies with much greater data transmission speeds, ultra-low latency, and an extensive range of new service support. Sixth-generation networks will utilize the terahertz frequency range, which will bring an incredibly large bandwidth increment. Deep artificial intelligence integration,  
Figure 1: Organizational structure of the survey paper.  
decentralized computing, and support for quantum communications are a few of the key characteristics of 6G [13].  
Furthermore, 6G is envisioned to fulfill the needs of future technologies, holographic communication, the tactile internet, and full convergence of the physical and digital worlds. These networks will be able to adapt as per the environment and even predict the needs of users to utilize the resources effectively [14; 15]. The 6G networks will feature advanced multiple access techniques, such as reconfigurable intelligent surfaces and ultra-massive MIMO (multiple-input multiple-output) for higher spectral efficiency and connectivity in dense environments [16]. These advancements will enable ubiquitous communication in highly dynamic contexts, for example, smart cities, autonomous transport, and industrial automation. Also, in 6G, security and privacy will be in the spotlight with the embrace of blockchain-based decentralized authentication, quantum-resistant cryptography, and Al-driven anomaly detection mechanisms [17; 18]. Such safeguards will secure user data and ensure the trustworthiness of mission-critical apps. Figure 2 shows the key components of the 6G network [19].
## 2 Selected Wireless Sensor Networks Standards: State-of-the-art
### 802.15.4e Factory Automation MAC Layer
#### Network Elements  
The network is composed of _sensors_ and _actuators_ organized in star topology around a _gateway_ (Fig. 10). The _network manager_ configures each end device via the gateway and allocates the dedicated time slots. After the configuration phase, sensor to gateway communication is unidirectional, whereas actuator/gateway communication is bidirectional.
# Reliability Evaluation for Clustered WSNs under Malware Propagation
###### Abstract  
We consider a clustered wireless sensor network (WSN) under epidemic-malware propagation conditions and solve the problem of how to evaluate its reliability so as to ensure efficient, continuous, and dependable transmission of sensed data from sensor nodes to the sink. Facing the contradiction between malware intention and continuous-time Markov chain (CTMC) randomness, we introduce a strategic game that can predict malware infection in order to model a successful infection as a CTMC state transition. Next, we devise a novel measure to compute the Mean Time to Failure (MTTF) of a sensor node, which represents the reliability of a sensor node continuously performing tasks such as sensing, transmitting, and fusing data. Since clustered WSNs can be regarded as parallel-serial-parallel systems, the reliability of a clustered WSN can be evaluated via classical reliability theory. Numerical results show the influence of parameters such as the true positive rate and the false positive rate on a sensor node's MTTF. Furthermore, we validate the method of reliability evaluation for a clustered WSN according to the number of sensor nodes in a cluster, the number of clusters in a route, and the number of routes in the WSN.  
wireless sensor network; reliability evaluation; malware propagation; epidemic theory; continuous-time Markov chain; reliability theory +
Footnote †: journal: Sensors
## 5 Data Communication Phase
### Intra-Cluster Transmission  
The limited transmission ranges in intra-cluster scale makes both single-hop and multi-hop communication feasible for sensor networks. Some researchers [53; 57; 114] focus on the advantages of single-hop schemes such as real-time data transmission and less complexity in implementation. On the other hand, some others [124; 126; 127] count up the disadvantages of using single-hop transmission in large network fields where long distance communication of the nodes located near the boundaries of the cluster may cause radio interference with neighbouring clusters and waste the energy resources of the nodes. However, transmission delay generated in relaying process and construction and maintenance of chains created by the routing protocols in the cluster are two issues that should be addressed by the schemes using multi-hop transmission.