esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experiment Setup
### Evaluation Metrics  
In the experiments, the quality metrics we employed are as follows: Perceptual Evaluation of Speech Quality (PESQ, wide-band version in ITU-T P.862.2, from \(-0.5\) to \(4.5\)) [36], Mean Opinion Score (MOS) prediction of speech distortion (CSIG, from 1 to 5) [37], MOS prediction of background noise interference (CBAK, from 1 to 5) [37], MOS prediction of overall effect (COVL, from 1 to 5) [37], Segmented SNR (SSNR) [38], and the Short-Time Objective Intelligibility (STOI) [39]. Note that all these six metrics are better if higher.
## 3 Results and Discussion
### Effects of Temperature and Time  
Polymer decomposition such as that for PS has long been studied [16; 17; 18; 19; 20; 21; 22]. Since the polymer has low thermal conductivity, a preliminary heating time is necessary to achieve the target temperature for decomposition [21]. The generated SOs has an extremely low value of less than \(\mu\)g kg\({}^{-1}\) thus making analysis difficult in this study, this difficulty was overcome by a new decomposition using PEG as the heat medium. Among the various factors governing plastic degradation in the nature, we focused thermal effect on PS decomposition in the experiments. We assume first-order reaction of PS to form SOs. The rate of ST formation was calculated at each reaction temperature. In \(k\) and the reciprocal of the absolute temperature (T\({}^{-1}\)) are shown in Figure 3, which shows a linear relationship in the entire temperature range of 30 to 150 \({}^{\circ}\)C. PS activation energy of 45 kJ mol\({}^{-1}\) was obtained from the slope of the line by using the Arrhenius equation. A low activation energy 45.0 kJ mol\({}^{-1}\) indicated a secondary reaction, a backbiting reaction [24] to be a dominant factor in low temperature decomposition.  
Figure 3: Arrhenius plot of styrene trimer (ST) formation through PS-decomposition at 30 to 150 \({}^{\circ}\)C.  
SO composition did not change in the range 30 to 150 \({}^{\circ}\)C. The largest component was ST followed by SD and SM. The SO composition ratio of SM:SD:ST was about 1:1:5. Trace amounts of ethylbenzene, propyl benzene and benzaldehyde were observed along with secondary reactions when temperature reached 200 \({}^{\circ}\)C and higher. After the reaction, the PS was recovered and prepared into a film and analyzed by IR (infrared absorption spectrum). The IR analysis showed no change in the PS main chain (2924, 2850 cm\({}^{-1}\) aliphatic stretching vibration, 1450 cm\({}^{-1}\) aliphatic vibration). Alternatively Gel Permeation Chromatography (GPC, Column: Asahipak GF-7 MHQ, Detector: RI, eluent: THF 0.6 mL/min, Sample: 2 mg in 100 mL THF, 20 \(\mu\)L injection, 30 \({}^{\circ}\)C) detected number-average molecular weight decreased by 20% when reaction conditions were between 200-280 \({}^{\circ}\)C for 30 min or more [21].
## 1 Introduction
Further, CC/CV control was proposed in [28] by switching capacitors on the secondary coil. In [29], switching capacitors were proposed for both primary and secondary coils. In [30], a single switching capacitor and a variable operating frequency were proposed. However, these methods are not economically viable. In [24], the addition of a coil was required, and [25; 26; 27; 28; 29] are based on high-order topology, which requires additional components. In [26; 27; 28; 29], more switches were required to change the charging mode, while a more complex controller was required in [30]. As these systems invariably cause an increase in cost or weight, they are unsuitable for low-cost WPT battery chargers. Thus far, there has been no reported study on the application of control strategies without including additional components. In this study, a novel low-cost IPT compensation design method based on SS topology has been proposed. The proposed design method has resonance elements similar to those of the conventional SS design, but the resonance requirements are designed differently. Unlike the CC or CV characteristics of most conventional topologies, the proposed resonance design has a nonlinear converging current and voltage curve. Further, the proposed design method does not need additional control functions and components to prevent current/voltage fluctuations when an open or short state occurs on the secondary side. This paper is organized as follows: A fundamental circuit analysis is presented in Section 2. The output characteristics are explained according to the load variation in Section 3. A prototype of the proposed design method is experimentally validated on a 400 W prototype. A control method for compensating for changes in output characteristics due to misalignment or tolerance is presented in Section 4. The comparison results of the proposed and conventional SS design methods, and the battery output characteristic results are presented in Section 5. The conclusions of the study are presented in Section 5.
## 2 Data and Sensor System
### Modeling: Identifying Relations among Sensors
This paper uses time-averaged values as the input parameters instead of simple time-series data, which represent feature for some duration of the observed data. The relation between two (sensor) nodes \(i\), \(j\) is learned using the four types of data, three days average and standard deviation of the two sensor data. The parameter \(T_{ij}\) is determined by evaluating the relation between the two nodes based on the profiles generated by the SVM. If any of the four types of data, e.g., standard deviation of the sensor node \(j\), deviates, then the relation from node \(i\) to node \(j\) is \(T_{ji}=-1\); otherwise \(T_{ji}=1\). For prediction purposes, an imaginary sensor node \(E_{24}\) represents a high-energy electron flux 24 h ahead. We have two types of data, normal data without the event and abnormal data with the event having occurred. The normal data contain the event in which the high-energy electron flux possibly keeps a safety level 24 h ahead. The relation from a node to the node \(E_{24}\) is trained to give \(-1\) when the event occurred after 24 h and \(+1\) when the event did not occur after 24 h. There can be no arc from the imaginary sensor node \(E_{24}\).
## 4 A Case Study of the LW7434
### High-Energy Seismic Events Identification Results  
The seismic data from the LW7434 during the mining period were used to calculate the SSPs and their relationships with the seismic energy are fitted to obtain the correlations between each SSP and the seismic energy. The results are shown in Figure 10 and Table A7 of Appendix B. The correlation coefficients between each SSP and the seismic energy are calculated and shown in Table A8 of Appendix B.  
Figure 10: _Cont._  
The results of the correlation coefficient calculations for each parameter indicate that the seismic moment, stress drop, and apparent stress are strongly related to the seismic energy and thus can serve as key mechanical parameters for subsequent dynamic hazard evaluations. In contrast, the apparent volume and source radius exhibit low responses to the seismic energy, qualifying them as non-key mechanical parameters for further evaluations. Additionally, the influence weights of each parameter on the dynamic seismic hazard are calculable using Equation (13), with the calculated results presented in Table 10 of Appendix B.  
In addition, we compare and analyze the identification results of hazardous high-energy seismic events based on the energy and _CEI_ described in Section 3.3, which considers various source parameters. The analysis is conducted with data from 100 seismic events monitored during the near-fault mining of LW7434.  
Seismic data from LW7434 during 21 October 2018 and 1 February 2019, were analyzed. The distributions of hazardous high-energy events as determined by energy and _CEI_ are depicted in Figure 11a,b, respectively. In the energy-based discrimination result (Figure 11a), all seismic events are categorized as weak hazard class. Figure 11b,c show that 95 events were classified as weak hazards, and five as moderate hazards by the _CEI_. Furthermore, Figure 11d reveals that out of 100 seismic events, those categorized as hazardous high-energy events by the _CEI_ have higher values in source parameters like energy, seismic moment, stress drop, source radius, and apparent volume, with the exception of apparent stress. This suggests that the rupture intensity, size of disturbance, and stress adjustment at the source of hazardous high-energy events are significantly more pronounced than in general seismic events. Relying solely on one source parameter, such as apparent stress, for classifying hazardous high-energy events could lead to misclassification.  
Figure 10: The relationship between source energy and other SSPs. (**a**) Scaler seismic moment vs. seismic energy. (**b**) Stress drop vs. seismic energy. (**c**) Apparent stress vs. seismic energy. (**d**) Source radius vs. seismic energy. (**e**) Apparent volume vs. seismic energy.
## 2 Research Area and Data Selection  
### Selection of the Research Area  
In this paper, we selected a region in Northeast Asia within the longitude and latitude ranges of 105\({}^{\circ}\) E-145\({}^{\circ}\) E and 38\({}^{\circ}\) N-58\({}^{\circ}\) N, respectively, located at the convergence of the Eurasian, Pacific and North American plates. This region is seismically active and covers China's only deep seismic zone, encompassing the Northeast Deep Seismic Zone, Tanlu Seismic Zone, Shanxi Seismic Zone, Yanshan Seismic Zone and Hebei Plain Seismic Zone, where several Ms \(>\) 5.0 earthquakes have occurred in recent years, including the 1679 Sanhe-Pinggu Ms 8 earthquake, 1976 Tangshan Ms 7.8 earthquake, and 1975 Haicheng Ms 7.5 earthquake, which caused enormous economic losses and casualties [28].
## 2 Experimental
### Single Element Batch Isotherm Adsorption Tests  
A series of single-element batch isotherm adsorption experiments were conducted using individual solutions of Nd(III), Y(III), Ce(III), Er(III) and Fe(III) in 3 M HCl with concentrations ranging from 93.2 mg/L to 846 mg/L, 87.9 mg/L to 718 mg/L, 126 mg/L to 802 mg/L, 124 mg/L to 813 mg/L, and 30.4 mg/L to 502 mg/L, respectively. Before the adsorption tests, the resin was allowed to swell using 3 M HCl (2-5 mL) as outlined previously. After swelling, 5-8 mL of the REE solution was added into the flask, with the volume of solution dependent on the swelling solution used to ensure a total volume of 10 mL. The pH of the solution was measured before and after adsorption.  
The batch adsorption test was initiated by placing the flask in a thermostat water bath shaker at 25 \({}^{\circ}\)C and 120 rpm. The samples were collected after reaching apparent equilibrium at 360 min, which was determined in the batch kinetic adsorption tests. The filtrates were collected by filtration, and residual REE concentration was analyzed using ICP-AES. Meanwhile, the loaded resin was collected, washed thoroughly with deionized water, dried in a vacuum oven at 40 \({}^{\circ}\)C, and analyzed using SEM-EDX and ATR-FTIR.  
The adsorption data were fitted with Langmuir and Freundlich isotherms to determine the adsorption mechanisms of REEs onto TK221, and the best R-squared value was used to select the most appropriate isotherm model.  
The Langmuir isotherm assumes single monolayer adsorption and is given by:  
\[q=(q_{max}\times K_{L})\times\left(\frac{C}{1+K\times C}\right)\]  
where:  
\(q\)--is the amount of solutes adsorbed per unit weight of adsorbent  
C--is the equilibrium concentration of the solutes in solution  
\(q_{max}\)--is the maximum amount of solutes that can be adsorbed per unit weight of adsorbent  
\(K_{L}\)--is the Langmuir constant related to the energy of adsorption  
The linear form of the Langmuir isotherm equation is:  
\[\frac{1}{q}=\left(\frac{1}{q_{max}}\right)+\left(\frac{1}{K_{L}\times q_{max}} \right)\times 1/C \tag{3}\]  
The Freundlich isotherm assumes that multilayer adsorption occurs and is given by:  
\[q=K_{f}\times C^{\frac{1}{n}}\]  
where:  
\(q\)--is the amount of solute adsorb per unit weight of adsorbent  
C--is the equilibrium concentration of the solutes in solution  
\(K_{f}\)--is the Freundlich constant related to the adsorption capacity  
\(n\)--is the Freundlich exponent related to the adsorption intensity  
The linear form of the Freundlich isotherm equation is:  
\[\ln(q)=\ln\Bigl{(}K_{f}\Bigr{)}+\left(\frac{1}{n}\right)\times\ln(C) \tag{4}\]
## 5 Experimental Verification Based on a Toroidal Transformer
### Methods of Degaussing and Magnetization Before Experiments  
In order to ensure the accuracy of the experiment, it is necessary to degauss and magnetize the toroidal transformer before each experiment, degauss the transformer by generating a constant frequency variable voltage signal through the signal generator, and measure the excitation inductance value of the transformer with the RCL digital bridge instrument as the basis for judgment to determine the completion of the degaussing of the  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Name** & **Parameter** \\ \hline Rated power & 200 W \\ Number of turns on the high-pressure side & 80 turns \\ Rated voltage & 30 V/10 V \\ Inner and outer diameters & 50 mm/100 mm \\ \hline \hline \end{tabular}
\end{table}
Table 3: Toroidal transformer parameters.  
Figure 9: Schematic diagram of the experimental platform.  
transformer. According to multiple measurements, when the excitation inductance of the transformer tends to the value of 18.944 mH, it will gradually stop changing, which can be regarded as the end of degaussing. The degaussing waveform is shown in Figure 10.  
For the primary winding, a variety of amplitude DC pulses with a width of 9 ms are provided, the magnetic flux of the remanence preset is obtained by integrating the voltage waveform on the secondary side, and then the size of the remanence preset \(B_{\mathrm{r}}\) is obtained according to Equation (17). The magnetization results are shown in Figure 11.  
\[B_{\mathrm{r}}=\frac{\varphi}{S} \tag{19}\]  
where \(\varphi\) is the preset magnetic flux, and \(S\) is the magnetic flux cross-sectional area. The inner and outer diameters of the toroidal transformer are 50 mm/100 mm, and the height is 45 mm, so the magnetic flux cross-sectional area \(S\) is 0.001125 m\({}^{2}\).
# A Multitask Learning Framework for Abuse Detection and Emotion Classification
## 4 Experiment
### Datasets  
The different datasets are described and the statistics of all datasets are shown in Table 1. To be fair, we treated all the datasets uniformly, removing the URL field containing \"http\" and removing the \"#\" from the tags on datasets from Twitter. After that, we converted all the characters to lowercase and treated all the samples to a uniform length of 50. For samples greater than 50, we truncated them; for those less than 50, we added \"[PAD]\" at the end.  
* HatEval [42], a Twitter-based hate speech dataset released in the SemEval-2019 ([[https://www.reddit.com/r/datasets/comments/3](https://www.reddit.com/r/datasets/comments/3) bxlg7/i_have_every_publicly_available_reddit_comment/]([https://www.reddit.com/r/datasets/comments/3](https://www.reddit.com/r/datasets/comments/3) bxlg7/i_have_every_publicly_available_reddit_comment/)) (accessed on 27 March 2022) mission. Its English portion provides a sentence-level commentary on hate speech against immigrants and women. Only hateful messages targeting migrants and women belong to the positive class, leaving any other message (including offensive or abusive messages against other targets) to the negative class. The training set is composed of 9000 messages and the test contains 3000 messages.
* Davids [43], a Twitter-based online abusive language detection dataset, which includes three classes, Hate, Offensive or Neither based on the hate speech lexicon from Hatebase.org. Complying with [44], we took Hate and Offensive as the positive class, Neither as the negative class.
* OffEval [45]; it contains three subtasks A, B, and C, and we focused on subtask A, which is about offensive language identification. It contains 13,240 tweets, and each tweet is classified as to whether it is offensive (33%) or not (67%). It has 13,240 pieces of training data, and 840 pieces are dedicated to testing.
* FNUC [46], a lightweight hate speech detection dataset collected from complete Fox News discussion threads, and annotated with the general level categories Hateful or Non-hateful. It contains 1528 records.
* StormW [47], a Stormfront-based hate language detection dataset with general-level labels Hate and NoHate. Stormfront is a supremacist forum that promotes white nationalism and antisemitism.
# A Secure and Efficient Three-Factor Authentication Protocol in Global Mobility Networks
## 3 Review of [PERSON] et al.'s Protocol
### Login and Authentication Process  
In [PERSON] et al.'s protocol [16], they considered a scenario in which the \(MU\) associated with the \(HA\) visits a foreign network from the foreign agent \(FA\) and attempts to access the roaming service. A \(MU\) who requests roaming service must send a login request message to the \(HA\). The \(MU\), \(FA\), and \(HA\) then perform mutual authentication with each other, then \(MU\) and \(FA\) share the session key. Figure 2 indicates the login and authentication process of [PERSON] et al.'s protocol [16]. The process is described in detail as follows.  
Figure 1: The user registration process of [PERSON] et al.’s protocol.  
**Step 1:**: The _MU_ retrieves the authentication data stored in the mobile device and enters \(ID_{MU}\) and \(PW_{MU}\). After that, the mobile device computes \(K^{*}_{MU}=h(ID_{MU}||PW_{MU}||R)\) and checks whether \(K^{*}_{MU}\stackrel{{?}}{{=}}K_{MU}\). If this condition holds, the _MU_ generates a random number \(R_{MU}\) and computes \(U=R\oplus R_{MU}\), \(V=(C_{MU}\oplus h(R)||ID_{FA})\oplus R_{MU}\) and \(W=(U||K||C_{MU}\oplus h(R))\). Then, _MU_ sends the login request message \(M_{1}=\{U,V,W\}\) to _FA_ through an insecure channel.
**Step 2:**: After obtaining the \(M_{1}=\{U,V,W\}\), _FA_ selects a random number \(R_{FA}\) and encrypts \(M_{1}\) and \(R_{FA}\) using the shared secret key. The _FA_ sends the \(M_{2}=\{ID_{FA},E_{KFH}(M_{1},R_{FA})\}\) to the _HA_.
**Step 3:**: Upon reception of \(M_{2}=\{ID_{FA},E_{KFH}(M_{1},R_{FA})\}\), the _HA_ checks the identity \(ID_{FA}\) of _FA_ and retrieves the secret key corresponding to \(ID_{FA}\). After that, the _HA_ decrypts \(D_{KFH}(E_{KFH}(M_{1},R_{FA}))\) and computes \(a=h(d)\), \(g^{a}\) mod \(p\), \(R^{*}_{MU}=V\oplus((g^{a}\) mod \(p))||ID_{FA})\) and \(R^{*}=U\oplus R^{*}_{MU}\). The _HA_ then checks whether there exists \(R^{*}\stackrel{{?}}{{=}}R\) in a secure database. If the condition is valid, the _HA_ computes \(W^{*}\)=(\(U||K||(g^{a}\) mod \(p))\) and checks whether \(W^{*}\stackrel{{?}}{{=}}W\). If it is correct, the _HA_ computes \(SK\)=\(h(g^{a}\) mod \(p)\oplus R_{MU}\oplus R_{FA}\) and sends \(M_{3}=\{E_{KFH}(SK)\}\) to the _FA_.
**Step 4:**: After obtaining the \(M_{3}=\{E_{KFH}(SK)\}\), the _FA_ decrypts \(D_{KFH}(E_{KFH}(SK))\) and computes \(X=h(SK||R_{FA})\). Finally, the _FA_ sends \(M_{4}=\{X,R_{FA}\}\) to the _MU_.
**Step 5:**: Upon reception of \(M_{4}=\{X,R_{FA}\}\), the _MU_ computes \(SK^{*}=C_{MU}\oplus h(R)\oplus R_{MU}\oplus R_{FA}\) and \(X^{*}=h(SK^{*}||R_{FA})\). After that, the _MU_ checks whether \(X^{*}\stackrel{{?}}{{=}}X\). If this holds, the _MU_ and _FA_ achieve the \(SK\) successfully.
# Dealing with Deadlocks in Industrial Multi Agent Systems
## 3 \(P\)-Invariant-Based Method of Prevention Deadlocks
This deadlock can be reached by following paths:  
\[\mathbf{M}_{0}\xrightarrow{t_{1}}\mathbf{M}_{1}\xrightarrow{t_{1}} \mathbf{M}_{3}\xrightarrow{t_{4}}\mathbf{M}_{9}\xrightarrow{t_{4}}\mathbf{M}_{ 17} \tag{19}\] \[\mathbf{M}_{0}\xrightarrow{t_{1}}\mathbf{M}_{1}\xrightarrow{t_{4}} \mathbf{M}_{5}\xrightarrow{t_{1}}\mathbf{M}_{9}\xrightarrow{t_{4}}\mathbf{M}_{ 17}\] (20) \[\mathbf{M}_{0}\xrightarrow{t_{1}}\mathbf{M}_{1}\xrightarrow{t_{1}} \mathbf{M}_{3}\xrightarrow{t_{2}}\mathbf{M}_{8}\xrightarrow{t_{1}}\mathbf{M}_{ 14}\xrightarrow{t_{4}}\mathbf{M}_{23}\xrightarrow{t_{3}}\mathbf{M}_{9} \xrightarrow{t_{4}}\mathbf{M}_{17}\] (21) \[\mathbf{M}_{0}\xrightarrow{t_{1}}\mathbf{M}_{1}\xrightarrow{t_{1}} \mathbf{M}_{3}\xrightarrow{t_{2}}\mathbf{M}_{8}\xrightarrow{t_{4}}\mathbf{M}_ {16}\xrightarrow{t_{1}}\mathbf{M}_{23}\xrightarrow{t_{3}}\mathbf{M}_{9} \xrightarrow{t_{4}}\mathbf{M}_{17} \tag{22}\]  
Hence, we can see that in RT the node \(M_{9}\) immediately precedes the node \(M_{17}\) representing the deadlock. These nodes are marking vectors \(\mathbf{M}_{9}=(2,\,0,\,1,\,0,\,0,\,1,\,2)^{T}\) (the 10 th column of \(\mathbf{X}_{t}\) because the first column is \(\mathbf{M}_{0}\)) and \(\mathbf{M}_{17}=(2,\,0,\,2,\,0,\,0,\,0,\,1,\,1)^{T}\) (the 18 th column of \(\mathbf{X}_{t}\)). After comparing entries of these vectors, from (19) follows the restriction \(M(p_{1})+M(p_{3})<2\) (i.e., only one of them can be marked), i.e., \(\mathbf{L}=(1,\,0,\,1,\,0,\,0,\,0,\,0)^{T}\) and \(\mathbf{b}=(2)\). Consequently, \(\mathbf{N}_{s}=(-1,\,1,\,0,\,-1,\,1,\,0)\) and \(\mathbf{s}\mathbf{M}_{0}=(2)\) because in (16) \(\mathbf{L}.\mathbf{M}_{0}=0\). Hence, with respect to (11)-(13) we have the augmented PN \(N_{a}\) (i.e., the original uncontrolled net \(N\) together with the net \(N_{s}\) representing the supervisor) and the augmented vector \({}^{a}\mathbf{M}_{0}\) of the initial marking as follows  
\[\mathbf{N}_{a}=\left( \begin{array}{cccccc}1&-1&0&0&0&0\\ 0&1&-1&0&0&0\\ 0&0&0&1&-1&0\\ 0&0&0&0&1&-1\\ -1&1&0&0&-1&1\\ 0&-1&1&-1&1&0\\ -1&0&1&0&0&0\\ 0&0&0&-1&0&1\\ -&-&-&-&-&-\\ -1&1&0&-1&1&0\end{array} \right)\quad{}^{a}\mathbf{M}_{0}=(\mathbf{M}_{0}^{T}|^{ s}\mathbf{M}_{0}^{T})^{T}=\left( \begin{array}{c}0\\ 0\\ 0\\ 0\\ 2\\ 3\\ -\\ 2\end{array} \right) \tag{23}\]  
Thus, we have the supervisor represented by the single PN place \(p_{9}\) (monitor) interconnected with the original PN model as it can be seen in Figure 3.  
After computing RT of the augmented net, we can find that it contains 28 nodes and no deadlock exists there. It means that the augmented net works correctly. Figure 2: The principal fragment of the RT.