esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Methodology
### Methods
#### 3.2.1 Preprocessing Steps  
Since text documents are unstructured by nature, to properly use them in NLP, suitable preprocessing is needed to transform and represent those text documents in a more structured way so they can be used later on [28]. In addition to that, it increases the quality of the final results when applied to classification problems, clustering, and other types of issues [29; 30].  
Online user-generated content, such as forums and social media discussions, is increasingly important, since it can provide essential knowledge to companies and organizations. However, this type of content has lots of noise, such as abbreviations, non-standard spelling, a specific lexicon of the platform, and no punctuation. These problems reduce the effectiveness of NLP tools, hence, why data preprocessing is needed [31].  
Multiple steps can be taken in the preprocessing task, such as sentence segmentation, also known as sentence boundary detection and sentence boundary disambiguation, which consists of segmenting large paragraphs and documents into the fundamental unit of text processing, that is, a sentence [32]. Other operations, such as lowercasing text data [33; 34] and stop word removal, are usually applied in preprocessing step. Stopwords are a type of word that does not have any linguistic value. Since they are considered low information, removing them allows for focusing on the essential terms of a text document [33]. This task requires a list of stopwords to remove specific words for each natural language, and this list is already compiled [34]. Stemming consists of reducing inflection in words to their base form, which can help deal with sparsity issues and standardizing the text document's vocabulary [33]. Lemmatization works similarly to stemming, in terms of reducing inflected words into their root form but varies in the fact that it tries to do it correctly without crude heuristics, making sure the word that resulted from the lemmatization (lemma) belongs to the language [33; 34].  
Furthermore, tokenization and normalization are used to break a text document into tokens, commonly words, for more accessible text manipulation [29]. It includes all sorts of lexical analysis steps, such as removing punctuation, number, accents, extra spacing, removing or converting emojis and emoticons, spelling correction, removal of URLs and HMTL characters, etc. [28; 33; 34]. In this work, a custom-designed intra-sentence segmentation tool was used. In addition to the standard sentence segmentation tool features, this tool works inside the sentence level. It detects comparisons, using the annotations to improve the results when assigning them back to the row. It then applies lowercasing of the text data, tokenization, removal of punctuation, and stopwords. Lemmatization of the tokens is the last preprocessing step taken.
## 4 Packet Scheduling Schemes
### MLWDF Scheduler  
The Modified Largest Weighted Delay First scheduler was developed to support multiple RT services with different Quality of Service (QoS) requirements [36]. This scheduler has been used in Satellite High Speed Data Packet Access networks [37] and Satellite Long Term Evolution networks [38]. The scheduler uses the waiting time of the Head of Line packet to shape the behavior of the Proportional Fair (PF) scheduling scheme. The MLWDF scheduler defines its metric as follows:  
\[m_{i,j}=\frac{R_{i,j}(t)}{T_{i}(t)}*\frac{(-\log\delta_{i})}{\tau_{i}}*D_{HOL,j }(t) \tag{5}\]  
where \(\tau_{i}\) is the delay deadline for UE/IoRT device \(i\), \(D_{HOL,i}(t)\) is the waiting time of the Head of Line packet in UE/IoRT queue \(i\), \(R_{i,j}(t)\) is the instantaneous data rate of UE/IoRT device \(i\) for resource block \(j\) at current Transmission Time Interval (TTI) \(t\), \(T_{i}(t)\) is the average data rate of UE/IoRT device \(i\) before TTI \(t\), and \(\delta_{i}\) is the maximum probability that the \(D_{HOL,i}\) exceeds \(\tau_{i}\) in the form:  
\[Pr(D_{HOL,i}>\tau_{i})\leq\delta_{i} \tag{6}\]
# A Presentation of Ionic Liquids as Lubricants: Some Critical Comments
## 6 Perspectives
### Some Attention to Avoid the Possibility of Corrosion  
The chemical composition of employed ILs is crucial when dealing with corrosion problems. In fact, the use of ILs containing halogen (i.e., fluoride) can form metal halides as decomposition byproducts that may cause corrosion in steel [129; 130], aluminium [131; 132], bronze [133], and titanium [134] materials. To prevent damage due to the corrosion process and to avoid excessive reactions between halogen and metal, halogen-free ILs should be utilized, even if the boundary lubrication ability of halogen-free ILs can be slightly lower than those that contain halogen. For example, in 2017, [PERSON] and collaborators proposed a study on the lubrication of sliding materials, such as _diamond-like carbon_ (DLC) and _hydrogen-free diamond-like carbon_ (H-free DLC), that, recently, have been widely used as ecofriendly coatings with superior mechanical and tribological properties [135]. In particular, the lubricant activity of the halogen-free ionic liquids 1-butyl-3-methylimidazolium dicyanamide and 1-butyl-3-methylimidazolium tricyanomethane ([BMIM][DCN] and [BMIM][TCC], respectively, whose structures are reported in Figure 18, were tested on H-free DLC materials.  
Figure 18: Chemical structure of [BMIM][DCN] and [BMIM][TCC].  
Accurate surface analyses by Raman spectroscopy, atomic force microscopy (AFM) and time-of-flight secondary ion mass spectrometry (TOF-SIMS) highlighted a superior protection activity of the tested contacting surfaces (i.e., a smoother surface and a lower friction) by using [BMIM][TCC] when compared with [BMIM][DCN]. The probable explanation of this phenomenon is due to the major adsorption of CN groups inside and outside the wear tracks of the tested surface, which for the [BMIM][TCC] is four times higher than the [BMIM][DCN]. The friction probably causes the thermal decomposition of [BMIM][TCC], generating isolated CN anions covering the H-free-DLC surface.  
In 2019, to overcome the wear and corrosion problems due the corrosive hydrogen fluoride formed by the degradation of the fluorinated ILs in the presence of water, an ionic liquid formed from a fluorinated phosphonium cation and a dicyanamide anion (tributyl-3,3,4,4,5,5,6,6,7,7,8,8,8-tridecafluorooctylphosphonium dicyanamide (see structure in Figure 19)) was studied at room temperature and at various levels of relative humidity [136].  
The results showed that the simultaneous presence of fluorine and phosphorus on the IL cation provided good lubricant properties conferred from the halogen presence and high corrosion-inhibiting properties due to the phosphonium group. To be clear, water was actually found to be a fundamental key to promoting the formation and growth of a tribolayer between steel contacts, because tests carried out in the presence of dry air showed a film limited only to the border of the contact area.
## 3 System Analysis
### Electromagnetic System Modeling  
The equation that describes the resultant force on the neodymium disc is given by [28; 30]:  
\[m\frac{\mathrm{d}^{2}z(t)}{\mathrm{d}t^{2}}=mg-k\frac{i(t)}{z(t)^{4}}, \tag{1}\]  
where \(m\) is the mass of the levitating object, \(g\) is the acceleration due to gravity, and \(k\) is a magnetic constant related to turn ratio and cross sectional area of the electromagnet. As shown, it is the sum of the gravitational pull on the disc and the magnetic force. The force applied by the magnetic field generated by the electromagnet on the disc is influenced by the current that is generating the magnetic field, and the distance of the disc to the electromagnet [28; 30].  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Value** & **Units** \\ \hline Operating Voltage & 5 & V \\ \hline Clock Frequency & 16 & MHz \\ \hline Flash Memory & 256 & KB \\ \hline SRAM & 8 & KB \\ \hline EEPROM & 4 & KB \\ \hline \hline \end{tabular}
\end{table}
Table 2: Specifications of the ATmega2560 [29].  
Figure 5: Electromagnetic system.  
The equation of the electromagnet circuit is:  
\[v(t)=R_{e}i(t)+L_{e}\frac{\mathrm{d}i(t)}{\mathrm{d}t}, \tag{2}\]  
where \(R_{e}\) and \(L_{e}\) are the resistance and inductance of the electromagnet, respectively.  
In order to obtain a stable levitation of the neodymium disc, the magnetic force of attraction needs to be equal to the gravitational pull so that the resultant force in the disc is null. So in the equilibrium point the system obeys the following conditions:  
\[\frac{\mathrm{d}^{2}z(t)}{\mathrm{d}t^{2}}=0, \tag{3}\]  
\[z(t)=z_{eq}, \tag{4}\]  
\[i(t)=i_{eq}. \tag{5}\]  
Therefore, in the equilibrium point, the magnetic constant is given by:  
\[k=mg\frac{z_{eq}^{4}}{i_{eq}}. \tag{6}\]  
As can be seen, the above equations represent a nonlinear system, and to design a linear controller the system must be linearized around an equilibrium point [31], \((i_{eq},z_{eq},v_{eq})\), in which \(\Delta i(t)\), \(\Delta z(t)\), and \(\Delta v(t)\) denote small variations around it, such as:  
\[i(t)=i_{eq}+\Delta i(t), \tag{7}\]  
\[z(t)=z_{eq}+\Delta z(t), \tag{8}\]  
\[v(t)=v_{eq}+\Delta v(t). \tag{9}\]  
Applying the Taylor series expansion in (1) and (2) around \((i_{eq},z_{eq},v_{eq})\), we obtain the linearized equations:  
\[m\frac{\mathrm{d}^{2}\Delta z(t)}{\mathrm{d}t^{2}}=mg-k\frac{i_{eq}}{z_{eq}^{ 4}}+k\frac{4i_{eq}}{z_{eq}^{5}}\Delta z(t)-k\frac{1}{z_{eq}^{4}}\Delta i(t), \tag{10}\]  
\[\Delta v(t)=R\Delta i(t)+L\frac{\mathrm{d}\Delta i(t)}{\mathrm{d}t}. \tag{11}\]  
Replacing (6) into (10), we get:  
\[\frac{\mathrm{d}^{2}\Delta z(t)}{\mathrm{d}t^{2}}=\frac{4g}{z_{eq}}\Delta z( t)-\frac{g}{i_{eq}}\Delta i(t). \tag{12}\]  
With Equations (11) and (12) linearized, the Laplace transform is used to obtain the transfer function between the supplied voltage and the distance of the disc from the electromagnet, as:  
\[\frac{\Delta Z(s)}{\Delta V(s)}=\frac{-\frac{g}{i_{eq}}}{(Ls+R)(s^{2}-\frac{4 g}{z_{eq}})}. \tag{13}\]  
Note that (13) is an unstable system since one of the poles is in the right-half of the \(s\)-plane.
## 3 Fatigue Tests at Low Temperature
### Loading  
Fatigue tests were carried out using a testing machine, PWS-250, with a maximum dynamic load of 200 kN. The specimen was rigidly clamped at one end and loaded at the other end. The loading force was a sinusoidal tensile force with a stress ratio of 0.1, and the loading frequency was 9.5 Hz. The loading conditions of fatigue tests are shown as Table 4.  
Figure 4: Microstructure of cruciform welded joints (the spherical particles in the figures are residual polished sand): (**a**) WZ, (**b**) FZ, (**c**) HAZ, and (**d**) BM.  
The fatigue tests were performed at \(-60\)\({}^{\circ}\)C (\(\pm 0.5\)\({}^{\circ}\)C), and the temperature was created by a low environmental chamber (Figure 7a). During the cooling process from room temperature to \(-60\)\({}^{\circ}\)C, the temperature changed dramatically, and the strain gauge with its own temperature compensation could produce a large temperature drift [32] after reaching the specified temperature, which would also cause a certain temperature drift, as shown in Figure 7b. The temperature drift affected the data measured by the strain gauges. Therefore, the strain gauges needed to be temperature-compensated in the low-temperature environment. The method used was to add a strain gauge (the strain gauge can be called a compensated strain gauge, Rd) that was the same as the working strain gauge. Then, the compensated strain gauge was attached to blocks of the same material as the specimen and formed a half-bridge circuit (Figure 7c), such that the temperature drift generated by the specimen could offset the temperature drift generated by the sample, as shown in Figure 7d.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Mean Stress Levels/MPa**} & \multicolumn{2}{c}{**The Number of Specimens**} \\ \cline{2-3}  & **T-Welded Joints** & **Cruciform Welded Joints** \\ \hline
120 & 3 & 3 \\
140 & 6 & 6 \\
160 & 6 & 6 \\ \hline \hline \end{tabular}
\end{table}
Table 4: The loading condition of fatigue tests.  
Figure 5: T-welded joint (unit: mm).  
Figure 6: Cruciform welded joint (unit: mm).
## 5 Analysis of Other Non-AWGN Nuisances
### The Influence of the Code Delay Error  
The peak column selected in the search space (marked in Figure 4) also depends on the resolution of the search space in the code delay domain. The mentioned quantization error \(\Delta\tau_{q}=\overline{\tau}_{p}-\tau\) affects the CAF samples with an amplitude scale factor \(|R(\Delta\tau_{q})|\) as shown in Equation (21), where now \(\Delta\tau=\Delta\tau_{q}\). This factor is expected to affect the performance of the estimators C\({}_{n}\)-3, and R\({}_{n}\)-3.  
Figure 12: Influence of the quantization error on the RMSE as a function \(\Delta_{p}\).  
Figure 13: The strategy used in improved algorithm R\({}_{n}\)-3.  
The influence of such a code delay error can be quantified by modifying the expression of the signal-to-noise ratio \(\rho\) in the CRLB Equation (49), so as to take into account the term \(R(\Delta\tau_{q})\). Thus, defining the modified SNR \(\tilde{\rho}=\frac{A^{2}[R(\Delta\tau_{q})]^{2}}{2\sigma_{TF}^{2}}\), Equation (49) becomes  
\[\mathrm{var}(\widehat{f}_{d})\geq\frac{12}{(2\pi)^{2}\;\tilde{\rho}\;f_{s}T_{ d}^{3}} \tag{56}\]  
From Equation (56), because \(R(\Delta\tau_{q}\leq 1)\), we can conclude that the CRLB value increases as the code delay \(\Delta\tau_{q}\) increases; this increase will be also experienced by the RMSE of the proposed algorithms. In conclusion the effect of the quantization error \(\Delta\tau_{q}\) is an attenuation, which does not modify the results of Figures 8 and 9, except for a scaling factor in the abscissa.
## 3 Identification of Critical Nodes base on Controllability Theories of Complex Networks
### Identification Process  
The controllability index is used to identify the critical nodes, and it is defined in the previous section. In this section, the identification process is shown in Figure 2.  
Figure 2: Identification process based on network controllability theory.
## 3 Methods
### Performance and Sensitivity Assessment  
Performance, as expressed by accuracy and precision, was measured using the reserved second half of the modelled brightness temperature values from the coefficient retrieval. Thereby, for each profile and view angle, LST was calculated using the algorithms on the basis of the brightness temperature, and the atmospheric information from the profiles for parameter class selection (columnar water vapour and initial guess LST). Statistics was summarized for each class of view angle, initial guess LST, and columnar water vapour by comparing the initial LST and the output LST from the split-window algorithms. Such, the difference between the initial LST and the output LST from the split-window algorithms for each class was calculated. From these differences then statistical values were derived. The comparison was performed in form of mean absolute difference (MAD), root mean square (RMS), standard deviation (STDEV), and correlation coefficient (\(r^{2}\)).  
The sensitivity analysis was undertaken by artificially altering the input parameters columnar water vapour, initial guess LST, mean band emissivity, and difference of band emissivity. In case of  
Figure 2: Schematic overview of split-window coefficient retrieval.  
the columnar water vapour and the initial guess LST, the wrong input values affect the choice of the parameter class and led therefore to a lower accuracy of LST. In case of wrong mean emissivity and wrong differences of band emissivity, the correct parameter classes are chosen, but the algorithm does not foresee such wrong values and therefore renders incorrect LST values. The magnitude of the over- or underestimation of LST is expressed in boxplot figures. Furthermore, total sensitivity is given for each algorithm.
# Non-Intrusive Load Disaggregation Based on a Multi-Scale Attention Residual Network
## 4 Result
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**Index** & **Function** & **Air** & **Fridge** & **Microwave** & **Washing Machine** & **Dish Washer** \\ \hline \multirow{2}{*}{MAE} & Relu & 46.029 & 10.799 & 6.954 & 3.403 & 4.539 \\  & Leaky-Relu & 36.388 & 10.440 & 4.862 & 2.161 & 2.013 \\ \hline \multirow{2}{*}{SAE} & Relu & 0.015 & 0.034 & 0.234 & 0.287 & 0.157 \\  & Leaky-Relu & 0.014 & 0.025 & 0.143 & 0.152 & 0.052 \\ \hline \end{tabular}
\end{table} 
Table 3: Comparison of activation function of Household 25 of the WikiEnergy dataset. Table 5 shows the judgement results of \"on\" and \"off\" states of Household 5 in the UK-DALE dataset. The thresholds of the kettle, the fridge, the microwave, the washing machine, and the dishwasher were set to 100 W, 50 W, 200 W, 20 W, and 100 W, respectively. Table 5 shows that the Recalls of the washing machine and the dishwasher using the CNN s-s and the CNN s-p are low, the number of positive samples is small, and its ability to predict the \"on\" state is poor. If the task of judging the electrical state is considered as classification, appliances with a high utilization rate have better classification results. Figure 10 shows load disaggregation comparisons of these five methods over a period of time. It can be seen from the figure that, compared with other algorithms, the MSA-Resnet could better disaggregate equipments, whereas the KNN and the DAE have the worst decomposition abilities. For the low-frequency washing machine and dishwasher, the MSA-Resnet could still well fit the power curve, because of its network structure. It uses multi-scale convolutions to obtain rich load characteristics, and it improves the performance of the network through the attention mechanism and the residual structure. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Index** & **Method** & **Kettle** & **Fridge** & **Microwave** & **Washing Machine** & **Dish Washer** \\ \hline \multirow{4}{*}{MAE} & KNN & 1.413 & 2.407 & 0.378 & 4.032 & 3.274 \\  & DAE & 8.867 & 8.218 & 1.226 & 14.920 & 12.756 \\  & CNN s-s & 8.829 & 3.866 & 1.125 & 20.696 & 9.101 \\  & CNN s-p & 4.002 & 4.517 & 1.159 & 23.881 & 9.747 \\  & MSA-Resnet & 0.804 & 2.136 & 0.906 & 3.618 & 2.601 \\ \hline \multirow{4}{*}{SAE} & KNN & 0.076 & 0.015 & 0.054 & 0.018 & 0.001 \\  & DAE & 0.377 & 0.021 & 0.748 & 0.006 & 0.340 \\ \cline{1-1}  & CNN s-s & 0.522 & 0.032 & 0.880 & 0.315 & 0.213 \\ \cline{1-1}  & CNN s-p & 0.242 & 0.024 & 0.845 & 0.302 & 0.154 \\ \cline{1-1}  & MSA-Resnet & 0.001 & 0.013 & 0.720 & 0.0007 & 0.050 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison of the load disaggregation indexes of Household 5 of the UK-DALE dataset. Figure 9: Comparison load disaggregation results of Household 5 in the UK-DALE dataset.
## 4 Full Dimensioning of V2G Energy Resource
### Social Dimension  
* Direct jobs: As it is an unconsolidated technology, there are still no figures corresponding to the generation of jobs in the manufacture of V2G modules. Considering the operation part of the installed system, the customers (consumers) or the energy concessionaire themselves can be considered to be in charge of this configuration/operation of the modules. High demand for skilled labor is expected for maintenance [26].
* Quality and safety: V2G modules are produced such that they have a friendly interface for the customer to handle this device.
* Perception of Comfort
* Noise pollution: No noise pollution is caused.
* Visual pollution: No visual pollution is caused.
* Olfactory pollution: No olfactory pollution is caused.
* Thermal pollution: No thermal pollution is caused.
* Impacts on Development
* Impacts on infrastructure and economic activities: Need to install energy meters with technology for distributed generation. Businesses related to the sale of technology will be created. It opens several possibilities related to the sale of energy.
* Impacts on Human Development: It can indirectly impact knowledge concerning electricity, economy and environment. This is due to the users' greater awareness of how much they will spend and how much energy they can sell to the grid. There is no relevant work on this topic about the V2G technology.
* Social effects due to environmental imbalance
* Impacts on health: Considering the removal of automobiles, the combustion within the studied city would contribute to enhanced health due to a reduction in greenhouse gas emissions. The impact solely attributed to V2G technology becomes less significant with the transition from combustion vehicles to electric ones [26].
* Impacts on agriculture: This type of technology does not directly affect agriculture because it takes place in urban centers.
# Energy Efficient and Reliable Routing Algorithm for Wireless Sensors Networks
## 4 Ds-Eera
### Weight Assignment  
The entropy weight method is used to determine the index weight by the amount of information provided by the entropy value of each index [30]. The size of the entropy weight is directly related to the evaluated object. From the perspective of information, entropy weight indicates how much information the index contributes to the problem, i.e., the greater the difference within the index value, the greater the corresponding index information, the more important the index is. In this paper, the entropy weight method is used to determine the weight of each index, which is divided into the following steps:(1) Standardized decision matrix  
For any node \(i\), suppose its forward neighbor node to be the evaluation object set \(M=(M_{1},M_{2},\ldots,M_{m})\), the set of three indexes in this paper is \(D=(D_{1},D_{2},D_{3})\), and the value of the evaluation object \(M_{a}\) in index \(D_{b}\) is denoted as \(x_{ab}(a=1,2,\ldots,m\,;b=1,2,3)\), then the decision matrix \(X\) is established as follows:  
\[X=\left(\begin{array}{ccc}\text{x}_{11}&\text{x}_{12}&\text{x}_{13}\\ \text{x}_{21}&\text{x}_{22}&\text{x}_{23}\\ \vdots&\vdots&\vdots\\ \text{x}_{m1}&\text{x}_{m2}&\text{x}_{m3}\end{array}\right) \tag{8}\]  
In order to eliminate the different dimensions of each index, the standardized decision matrix \(V=[v_{ab}]_{m\times 3}\) is obtained. According to the index property [31], the indexes that we established are all benefit indexes, i.e., the larger the index value is, the better the performance is. It is normalized as follows:  
\[v_{ab}=\frac{x_{ab}-\text{min}x_{b}}{\text{max}x_{b}-\text{min}x_{b}} \tag{9}\]  
(2) Characteristic weight of evaluation object \(a\) under index \(b\)  
In this paper, sensor nodes are used as an evaluation object. The smaller the entropy value is, the greater the weight is, i.e., the larger the information content of the corresponding evaluation index is, the more important the index is. According to the definition of relative entropy [32], the increase of information means the decrease of entropy value, so entropy can be used to measure the size of this information.  
The characteristic weight of node \(a\) under index \(b\) is:  
\[\theta_{ab}=\frac{v_{ab}}{\sum\limits_{a=1}^{m}v_{ab}},0<a\leq m\,\ 0<b\leq 3 \tag{10}\]  
Thus, obtain the entropy value \(EN_{b}\) of index \(b\):  
\[EN_{b}=-\frac{1}{\ln m}{\sum\limits_{a=1}^{m}\theta_{ab}\ln\theta_{ab}} \tag{11}\]  
(3) The entropy weight of each index  
It can be known from Equations (10) and (11) that for a certain index, the greater the difference of \(v_{ab}\), the smaller \(EN_{b}\) is i.e., the larger the amount of information reflected by this index, the more weight should be given, thus the entropy weight of index \(b\) is defined as follows:  
\[w_{b}=\frac{1}{1-\frac{EN_{b}}{\sum\limits_{b=1}^{3}\left(1-EN_{b}\right)}} \tag{12}\]