esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Controller Design
### Terminal Sliding Mode Control with Sliding Perturbation Observer (TSMCSPO)  
The new dynamics of \(\hat{s}_{j}\) for TSMCSPO is defined as follows:  
\[\hat{s}_{j}=\hat{e}_{j}+\beta_{j}\big{|}\hat{e}_{j}\big{|}^{r}sign(\hat{e}). \tag{19}\]  
where \(\hat{e}_{j}=\hat{x}_{1j}-x_{1 dj}\) is the estimated tracking error, and \(\beta_{j}\) is positive constant. \(\hat{s}_{j}\) can be computed as:  
\[\hat{s}_{j}=\alpha_{3j}\overline{u}_{j}-[\frac{k_{2j}}{\varepsilon_{0j}}-( \frac{k_{1j}}{\varepsilon_{0j}})^{2}]\overline{x}_{1j}-(\frac{k_{1j}}{ \varepsilon_{0j}})\overline{x}_{2j}-\ddot{x}_{1 jd}+\beta_{j}\frac{d}{dt}( \big{|}\hat{e}_{j}\big{|}^{r}sign(\hat{e}))+\hat{\Psi}_{j}. \tag{20}\]  
The control input \(\overline{u}_{j}\) of TSMCSPO is defined as:  
\[\overline{u}_{j}=\frac{1}{\alpha_{3j}}\Bigg{\{}\begin{array}{l}-\Gamma_{j}R _{maxj}(\frac{k_{1j}}{\varepsilon_{0j}})sign(\hat{s}_{j})-\eta_{j}\big{|}s_{ j}\big{|}^{r}sign(\hat{s}_{j})\\ +[\frac{k_{2j}}{\varepsilon_{0j}}-(\frac{k_{1j}}{\varepsilon_{0j}})^{2}] \overline{x}_{1j}+\ddot{x}_{1 dj}-\hat{\Psi}_{j}-\beta_{j}\frac{d}{dt}(\big{|} \hat{e}_{j}\big{|}^{r}sign(\hat{e}))\end{array}\Bigg{\}}. \tag{21}\]  
where \(\hat{e}=\hat{x}_{2j}-(\frac{k_{1j}}{\varepsilon_{0j}})\overline{x}_{1j}-\dot{ x}_{1 dj}\) is used in practical applications, \(\eta_{j}\) is constant and positive, \(R_{maxj}\) and \(\Gamma_{j}\) are defined in Assumptions 1 and 2. After reaching the sliding surface \(\hat{s}_{j}=0\), the above function will be a non-singular function. However, if \(s_{j}\
eq 0\), \(\hat{e}_{j}\
eq 0\), and \(e=0\), a singularity will occur [30]. To avoid the singularity problem, the following condition can be given as:  
\[\frac{d}{dt}\big{(}\big{|}\hat{e}_{j}\big{|}^{r}sign(\hat{e})\big{)}=\left\{ \begin{array}{l}r|\hat{e}|^{r-1}\hat{e},\;if\;\hat{e}\
eq 0,\hat{e}\
eq 0\\ r|\Delta|^{r-1}\hat{e},\;if\;\hat{e}\
eq 0,\hat{e}=0\\ 0\quad,\;if\;\hat{e}=0,\hat{e}=0\end{array}\right. \tag{22}\]  
where \(\Delta>0\) is a small constant. Substituting \(\overline{u}_{j}\) into (20), \(\dot{\hat{s}}\) is computed as:  
\[\dot{\hat{s}}_{j}=-\Gamma_{j}R_{maxj}(\frac{k_{1j}}{\varepsilon_{0j}})sign( \hat{s}_{j})-\eta_{3j}\big{|}\hat{s}_{j}\big{|}^{r}sign(\hat{s}_{j})-(\frac{k _{1j}}{\varepsilon_{0j}})\overline{x}_{2j}. \tag{23}\]  
The Lyapunov description of TSMCSPO can be shown as:  
\[\dot{V}\ =\dot{\hat{s}}\hat{s}=\hat{s}(-\Gamma_{j}R_{maxj}(\frac{k_{1j}}{ \varepsilon_{0j}})sign(\hat{s}_{j})-\eta_{3j}\big{|}\hat{s}_{j}\big{|}^{r}sign (\hat{s}_{j})-(\frac{k_{1j}}{\varepsilon_{0j}})\overline{x}_{2j})\ \leq-\eta_{3j}2^{(r+1)/2}V^{(r+1)/2}. \tag{24}\]  
Figure 2: Bode diagram of estimated perturbation to real perturbation.
# Handover-Driven Interference Management for Co-Channel Deployment of Femto- and Macro-Cells
## 3 System Model
### Users' Measurement Reports  
As shown in the handover procedure, existing cellular systems allow a user to acquire the target cell's SI and report the required information within the SI to his serving cell to retrieve the target cell's detailed information to make a better handover decision. Within this framework, a macro BS is assumed to be able to obtain knowledge of the transmit power and DL and UL cell load statuses of femtocells close to its users all from the users' measurement reports. However, since users' measurements consume battery and possibly deteriorate performance (when a single transceiver per user is assumed), a tradeoff exists between the freshness (and, thus, the accuracy) of the measurement and the users' performance.
## 3 Results and Discussion
### Graphical Interpretation of Regression Equations  
The example of mathematical dependencies is illustrated in Figures 12-14.  
Based on the results of experimental studies of the effect of a combination of various types of fibers and volume content on the strength and deformation characteristics of fiber-reinforced concrete, the optimal volume concentrations were determined for each type of fiber combination. When using a combination of steel-basalt fiber, the best strength and deformation characteristics were recorded with the volume content of steel fiber 2% and basalt 2%. When using a combination of steel-polypropylene fiber, the best strength and deformation characteristics were recorded with the volume content of steel fiber 2.5% and polypropylene 1.5%. In the combination of polypropylene-basalt fiber, the best strength and deformation characteristics were recorded with the volume content of polypropylene fiber 1.5% and basalt fiber 2.5%.  
Figure 12: Dependence of the compressive strength of concrete reinforced with a combination of steel and basalt fiber (**a**)—three-dimensional surface; (**b**)—isolines.  
Figure 14: Dependence of the compressive strength of concrete reinforced with a combination of basalt and polypropylene fiber.  
Figure 13: Dependence of the compressive strength of concrete reinforced with a combination of steel and polypropylene fiber (**a**)—three-dimensional surface; (**b**)—isolines.  
After analyzing the obtained experimental data and comparing the strength and deformation characteristics of the control samples with the values of the strength and deformative characteristics of the prototypes of fiber-reinforced concrete, the increments of the investigated characteristics were determined. The increase in compressive strength in fiber-reinforced concrete made using a combination of steel-basalt fiber with a volume concentration of steel fiber of 2% and basalt fiber of 2% was 35%, tensile strength in bending increased by 79%, ultimate deformations during axial compression decreased by 52%, ultimate deformations under axial tension decreased by 39%, and elastic modulus increased by 33%. The increase in compressive strength in fiber-reinforced concrete made using a combination of steel-polypropylene fiber with a volume concentration of steel fiber of 2.5% and polypropylene fiber of 1.5% was 34%, tensile strength in bending increased by 78%, ultimate deformations during axial compression decreased by 54%, ultimate deformations under axial tension decreased by 41%, and the modulus of elasticity increased by 31%. The increase in compressive strength in fiber-reinforced concrete made using a combination of polypropylene-basalt fiber with a volume concentration of polypropylene fiber of 1.5% and basalt fiber of 2.5% was 33%, tensile strength in bending increased by 77%, ultimate deformations during axial compression decreased by 56%, ultimate deformations during axial tension decreased by 42%, and the modulus of elasticity increased by 30%.
# Decellularized Scaffolds for Skin Repair and Regeneration
## 4 Sterilization and Storage of Decellularized Scaffolds  
Quite a variety of methods have been published for decellularizing tissues, but sterilization procedures are standard. They consist of short immersions in peracetic acid or ethanol solutions [76; 111], gamma [112] or ultraviolet irradiation [81; 101], and ethylene-oxide exposures [82]. Disinfectants such as antibiotic/antimycotic substances [80; 88] or bacteriostatic agents such as sodium azide [87; 97] are also common.  
Regarding storage of acellular matrices, three main methods are commonly applied: immersion in phosphate-buffered saline containing antibiotics or bacteriostatics, lyophilization, and freezing [74; 82; 94]. The two latter methods require specific protocols including exposure of the tissue to preserving agents to protect macromolecules from the extreme pressures and temperatures applied.  
Reviews presenting the different processes of sterilization and storage methods used for natural materials and tissues have been published elsewhere [113; 114].
## 4 Deep Learning-Based MEC System Energy Saving Framework  
### Outline of Controller Operation  
Figure 2 illustrates an overview of the operation of the controller, which is composed of two modules. The first module is responsible for predicting the amount of workload generated from each \(B_{i}\) during a time slot, while the second module determines \(\eta(t)\) by considering the workload distribution across MECSs, which is predicted by the first module, \(Q_{i}(t)\)s, and \(C_{i}s(i\in\mathcal{M})\).  
Figure 2: Operation outline of a controller.  
The problem formulated in (9) involves \(\tilde{\lambda}_{i}(t)\) and requires us to determine \(\eta(t)\) at the beginning of each time slot \(t\). However, the controller does not know \(\tilde{\lambda}_{i}(t)\) at the start of each time slot. To resolve the issue, the controller predicts \(\tilde{\lambda}_{i}(t)\) by using STGCN. We denote the predicted \(\tilde{\lambda}_{i}(t)\) by \(\tilde{\lambda}_{i}(t)\). \(\tilde{\lambda}_{i}(t)\) is determined by the number of users in \(B_{i}\) and the amount of task workloads they request. Additionally, since users may change the base stations to which they connect due to their movement, \(\tilde{\lambda}_{i}(t)\) and \(\tilde{\lambda}_{j}(t)\) (\(\forall i,j\in\mathcal{M}\)) have dependencies across both time and space. Therefore, we employ STGCN in the first module to increase the accuracy of \(\tilde{\lambda}_{i}(t)\) prediction using these spatio-temporal correlations.  
The maximum service delay and the energy consumed during a time slot are influenced by \(\{C_{i}:i\in\mathcal{M}\}\), \(\{\tilde{\lambda}_{i}(t):i\in\mathcal{M}\}\), \(\{\mathcal{O}_{i}(t):i\in\mathcal{M}\}\), and \(\eta(t)\). Since \(\eta(t)\) also affects \(Q_{i}(t+1)\), determining \(\eta(t)\) at the beginning of each time slot is a sequential decision problem. Therefore, to resolve the problem, we design a DQN agent that determines \(\eta(t)\) in order to minimize \(\zeta(t)\) while considering the constraints \(\delta(t)\leq\delta_{th}\). In the following subsections, we provide a detailed description of the operation of each module.
## 3 Results and Discussions
### LaFe1-xNix Oxygen Carrier
#### 3.2.1 Characterization of Oxygen Carrier  
LaFe, LaFe0.6 Ni0.4 (represents the LaFe1-xNix), and LaNi were selected to analyze the micro surface morphology and phase composition. Figure 6 shows the typical micro-morphology of LaFe, LaFe1-xNix, and LaNi. The surface of LaFe was regularly covered with small grains of 50-100 nm. Its porous surface was almost closed because of the oxidation of the oxygen carrier. With the addition of Ni (LaFe1-xNix), the surface became more porous, and the grains turned smaller. It favored the gas diffusion into the active center of the oxygen carrier. The LaNi also exhibited a more porous surface covered with disordered nano-crystallines. Overall, these oxygen carriers prepared by the sol-gel method were nano-materials, and the addition of Ni into LaFe helped to improve a porous surface without observable sintering. Figure 7 also shows the EDS analysis of LaFe, LaFe1-xNix, and LaNi. It is clear that all these elements mapping are almost the same for each oxygen carrier sample, indicating uniform dispersion of the main elements. It also means that these oxygen carriers were well prepared without element agglomeration.  
Figure 5: Chemical looping toluene conversion at 900 \({}^{\circ}\)C: (**a**) C1 yield and (**b**) H2 yield.  
Figure 6: SEM images of oxygen carriers: (**a,b**) for LaFe; (**c,d**) for LaFe\({}_{1\text{-}}\)Ni\({}_{\text{x}}\); (**e,f**) for LaNi.  
Figure 7: EDS mapping of main elements of oxygen carriers: (**a**) LaFe, (**b**) LaFe\({}_{1\text{-}}\)Ni\({}_{\text{x}}\) and (**c**) LaNi.  
The Ni addition in LaFe (LaFe\({}_{1\text{-}\text{-}\text{Ni}_{\text{x}}}\)) would change the phase patterns of oxygen carriers and the results were shown in Figure 8. For the LaFe oxygen carrier (x = 0), the perovskite phase of LaFeO\({}_{3}\) was the only observable compound, and it would exhibit a lower full oxidation reactivity during toluene conversion. Therefore, negligible CO\({}_{2}\) was formed when LaFe was used, as is shown in Figure 4. With x increased from 0.2 to 0.8, some LaFe\({}_{0.9}\)Ni\({}_{0.1}\), La\({}_{2}\)O\({}_{3}\), and NiO gradually appeared in addition to LaFeO\({}_{3}\), and La\({}_{4}\)Ni\({}_{3}\)O\({}_{10}\) was formed when x > 0.6. It indicates that excessive Ni addition would initiate complex competitive reactions, depending on Ni amount. Excessive Ni addition cannot be embedded into the perovskite structure of LaFeO\({}_{3}\) but resulted in NiO. For the LaNi (x = 1) oxygen carrier, the mixed oxides of NiO and La\({}_{4}\)Ni\({}_{3}\)O\({}_{10}\) rather than single perovskite were formed, and they would initiate the full oxidation of toluene and lower H\({}_{2}\) yield.
# Hardware Countermeasures Benchmarking against Fault Attacks
## 5 Comparison between Different Protection Solutions
### Fault Coverage-Corrected  
Since each approach has a different fault coverage and considers the protection against different types of DFA, it is necessary to create this factor in order to compare them. The FCC is obtained after assigning a correction factor value to each countermeasure according to the types of covered faults. This aspect is very important since there are countermeasures where the authors consider only one type of fault and do not take into account that there are DFA analyses that use other types of fault and that also allow the recovery of the secret key. Not considering all types of faults, even if some of them are complex to achieve, can lead to a security leak and offer incorrect fault coverage. Due to the different DFAs, we assume eight different types of faults (Table 2). Given that we consider eight different types of faults and considering that all of them are equally important because, depending on the DFA used, they allow recovering the secret key, all of them have been assigned the same weight and the correction factor is 12.5%. The value of each fault coverage corrected is obtained by (1), where FCC is Fault Coverage Corrected, TFC is Type of Fault Considered and FC is Fault Coverage. TFC represents the number of fault types that the countermeasure is able to detect and this value is multiplied by the weight of each fault type, that is, 12.5%. FC represents the percentage of fault coverage offered by the countermeasure considered.  
\[FCC=TFC*0.125*FC \tag{1}\]  
For example, the solutions that have been designed and tested considering all types of DFA faults have been assigned a correction factor of 100% and therefore their fault coverage value corresponds to those presented in Table 4. On the other hand, if we consider the countermeasure [37] and Table 4, it can be seen that this countermeasure can protect against two types of fault, type D and E and its percentage of coverage is 99.82%. Therefore, considering (1), the FCC = 2 \(\times\) 0.125 \(\times\) 99.82 = 24.96%. Despite having 99.82% coverage against E and D in Table 4, it does not offer any protection against the other types of faults and therefore its FCC in Table 5 is 24.96%.
# A Novel Method to Measure the Powder Consumption of Dental Air-Polishing Devices
## 4 Discussion
### Clinical Considerations  
The first clinical application that may benefit from these precise flowrate evaluations is the cleaning of sensitive areas like exposed dentine or soft composite. In these cases, even if the powder is quite soft and gentle and the least damaging method is used, overtreatment should be avoided in order to be as minimally invasive as possible. Since the surface-cleaning effect and further overtreatment effects depend on the amount of abrasive material sprayed on the surface [12], it becomes very important to minimize the powder consumption variability as much as possible. Figure 4 shows that Table-top N2, for example, has powder consumption peaks ranging from 1 g/min to 3 g/min over a period of less than 1 s (Figure 4, with a 10 s working time). This instantaneous variability may lead to an instantaneous abrasivity increase by as much as a factor of three. The user would never be able to adapt his or her movements to this sudden abrasivity increase, which would most likely result in overtreatment. Thus, the most reliable device with the most consistent powder consumption is Table-top E1, which was the most minimally invasive device tested in this study. The same considerations apply for the cleaning of implants with powders. The implant surfaces to be cleaned could be damaged by powder consumption peaks, even if those peaks are only of a short duration.  
The second clinical implication of these observations is the powder usage in deep subgingival pockets. Most manufacturer's recommendations are currently set at five seconds, which corresponds to the starting phase. The above-mentioned considerations also have a strong clinical impact. During, for example, the first important powder burst of Table-top M3, the pocket could be overlaid with powder before enough rinsing water is used to remove that powder. This powder layer could then be responsible for less efficient cleaning, reducing the clinical effectiveness of the method. This could also occur through powder overuse with Table-top N2 during the transient phase. Only Table-top E1 showed regular behavior. Nevertheless, with this device, it could be beneficial to increase the application time by 2 s in order to obtain a full 5 s of efficient cleaning. From this observation, it is clear that one device is not equal to another, and that the clinical data obtained with one device (e.g., Table-top E1) cannot be transferred to another device without conducting the type of analysis performed here.
## 5 Discussion  
In environmental management, estimating the vulnerability of an area to future earthquakes is highly necessary and significant for decision-making, land management, and the insurance and reinsurance industries. However, unfortunately, there have not been enough attempts to identify vulnerable areas or develop new methods, software, and technology to map them. In this study, we developed a new GIS-based open source software entitled SEVUCAS that is based on the integration of some popular machine learning and expert opinion techniques, involving the SWARA method and geographic information system (GIS), to assess seismic vulnerability. The created software was tested on the group of schools in Tehran, Iran. Since the determination of seismic vulnerability requires exploiting geophysics, civil engineering, urban development, programming, and GIS, this multidisciplinary knowledge required the cooperation of various experts. We developed this new model based on four groups of criteria, namely geotechnical, structural, socio-economic, and physical distance to needed facilities and away from dangerous facilities. Our findings showed that the highest and lowest effective factors in this study area was peak ground acceleration (PGA) (0.61), and population of employment status density and disaster management centers (0.08 for both), respectively, for the modelling of seismic vulnerability (Table 2). The result of our study is consistent with [PERSON] et al. [9] regarding the seismic vulnerability assessment of school buildings in Tehran city where they indicated that a higher PGA results in a higher probability of liquefaction occurrence is, and hence a higher seismic vulnerability of that area.  
A literature review showed that although several methods and techniques have been focused on seismic vulnerability, such as multi-criteria decision-making analysis [18; 113] and multivariate statistical analysis [114], few studies have been conducted to develop an optimization algorithm as part of seismic vulnerability GIS-based software. The advantage of this software is the application of the C# programming language using ArcGIS Engine functions to provide enhanced visualization as well as user-friendly and automatic software for the seismic vulnerability assessment of buildings. Indeed, the RBF and TLBO algorithms enhanced the performance of the SWARA weights to optimize the weights of the classes of each conditioning factor (Figure 5). In other words, these algorithms increased the performance of SWARA weights by decreasing the bias and noise between the dataset and the over-fitting problem.
# Force-Based Self-Organizing MANET/FANET with a UAV Swarm
## 2 Mobile Ad Hoc Networks (MANETs)
### Target MANET-Applications and -Scenarios
#### 2.3.3 Contested Environments  
The previous two scenarios covered the case where infrastructure was wiped out or never existed. In both cases, information can be very patchy, but what is available is considered trustworthy. For the third scenario, we want to consider the case where the available infrastructure or information cannot always be trusted [47]. In the simplest case this could be due to wide-spread sensor malfunctions due to some natural phenomenon (e.g., a solar storm). However, the literature increasingly considers the case of intentional and malevolent interference. This can come, e.g., in the form of a directed attack on the Global Positioning System (GPS) infrastructure or as GPS-spoofing.  
Regarding drone-based MANETs, this is of high relevance given the massive deployment of drones in, e.g., the conflict in Ukraine where countermeasures are known to massively impair localization and remote control of drones.
# A Multitask Learning Framework for Abuse Detection and Emotion Classification
## 1 Introduction
### Motivations and Contributions  
Our main motivation is to build a multitask framework that integrates emotion classification and abuse detection to improve the results of abuse detection through the auxiliary task. Different from existing multitask frameworks for emotion classification and abuse detection [1], our approach starts with an automatic emotion label generation because many datasets for abuse detection do not provide user emotion labels and manual labeling is too time-consuming and costly. Therefore, we use the most advanced pretrained model for sentiment analysis [15] to derive the emotion probability distribution for the text to be detected to generate the predictive data needed for MTL. Then, we use BERT as the encoder of the model to get the dynamic encoding of the text. Multihead self-attention ensures that the model can better capture context information. For two different tasks of abuse detection emotion classification, considering the different emphases of the two tasks, we use TextCNN [16] and BiLSTM [17] as decoders, respectively. In order to make better use of the benefits of emotion classification, we propose a cross-attention (CA) interaction mechanism similar to self-attention, further improving the effect of abuse detection.  
Overall, our main contributions are as follows:  
* We propose a multitask framework combining emotion classification and abuse detection to construct auxiliary tasks of emotion classification. We use a pretrained sentiment analysis model to derive emotion labels, thus avoiding a lot of manual labeling.
* We propose a decoding structure containing cross-attention to further enhance the positive effect of the auxiliary task on the primary task through the cross-attentional mechanism.
* Our label utilization approach is also easy to incorporate into other frameworks and take advantage of multitasking to improve the original model performance.
* We conduct a large number of experiments and show the superiority of our method compared with several most advanced algorithms.  
The rest of the paper is organized as follows. Section 2 introduces some of the most relevant work to this paper, including multitasking learning and abuse detection. Section 3 introduces our proposed framework in detail. Section 4 illustrates the experimental process and results, the performance and key parameters of the model are further discussed. Finally, in Section 5, we draw conclusions and present future work.