esa-sceva/satcom-chunk-collection huggingface dataset:
# Effect of Nitrogen Atoms in the CNT Structure on the Gas Sensing Properties of PANI/CNT Composite
## 2 Materials and Methods
### Characterization of Samples  
The morphology and structure of CNTs and PANI/CNT composites were studied using SEM (JSM-6610 LV, JEOL, Tokyo, Japan) at the accelerating voltage 20 kV and TEM (JEM-2100, JEOL, Tokyo, Japan) in the bright-field mode at the accelerating voltage 200 kV.  
The electronic structure and chemical state of carbon and nitrogen atoms of CNTs and PANI/CNT composites were analyzed using XPS at the Surface Science Center (Riber, Bezons, France). Photoemission (PE) spectra were excited using a nonmonochromatic X-ray source with the AlK\({}_{\alpha}\) anticathode. The source power during recording PE spectra was 240 W. The depth of analysis by this method was \(\sim\)3 nm. Quantitative elemental analysis was performed using spectra by the method of elemental sensitivity coefficients. Approximation of N 1s and C 1s PE core lines by a set of components was carried out using the CASA XPS 2.3.16 software package [23].  
The NEXAFS analysis of the structural and chemical state of PANI/CNT composites was conducted using monochromatic synchrotron radiation and facilities of the Russian-German beamline at the BESSY II electron storage ring (Helmholtz Center Berlin, Berlin, Germany) [24]. Spectra were measured with the use of the Russian German Photoemission Station (RGL-PES). Spectra were recorded in the leakage current measurement mode. The NEXAFS spectra were normalized to the primary photon current from the gold-covered grid, which was simultaneously recorded. The energy resolution of the monochromator in the range of the C-K absorption edge of carbon was \(\sim\)70 meV. The depth of analysis in these spectrum measurement modes reached \(\sim\)15 nm.  
Conductivity of materials was measured by the standard 4-probe method. The measurement was carried out on a TsIUS13 MP-0.5-001 digital impedance meter (Russia) using the measuring head with four linearly arranged gold-coated tungsten probes. To do this, samples were pressed into tablets 1 cm in diameter and about 1 mm in thickness.  
The work function was determined using Kelvin probe force microscopy (KPFM) implemented on an AFM MFP-3D (Asylum Research, Santa Barbara, CA, USA). HA\(\_\)FM/Au probes with conductive coatings (NT-MDT, Moscow, Russia) were used. The work function of the probe material was calibrated against a freshly cleaved layer of ZYA-grade highly oriented pyrolytic graphite (HOPG, TipsNano, Tallinn, Estonia) with the known work function 4.6 eV.
## 4 Case Study: The Grottalunga Viaduct
### Analysis of the Seismic Response in the Transverse Direction
#### 4.2.3 Single-Run Multimodal (MM) Pushover Analysis in Transverse Direction  
Considering that the second mode in the transverse direction, namely the seventh mode, has a mass participation ratio of 12%, a slightly more reliable estimation of the seismic response is expected with a load pattern consistent with the reaction at the external restraints provided by modal analysis (base of piers and abutments). To this aim, a load pattern in the form \(\mathbf{f}=\mathbf{M}(\phi_{1}+\mathbf{\alpha}\phi_{12})\) is assumed, where \(\mathbf{f}\) is the vector of the seismic force, \(\mathbf{M}\) is the mass matrix of the structure, \(\mathbf{\phi}_{i}\) is the \(i\)-th mode shape. The coefficient of combination \(\alpha\) is evaluated by imposing that the ratio of the transverse reaction at the base of the pier and at the abutment obtained by adopting the force distribution \(\mathbf{f}\) in the elastic phase is equal to its counterpart obtained by linear dynamic modal analyses. The procedure resembles what is prescribed by the seismic code for buildings, where the load pattern is required to be consistent with the story shear obtained by the linear modal analysis.  
In Figure 9a, the PO curve is shown, characterized by the yielding of the plug-in at both the left and right abutment restrained sides for a displacement of the midspan of the bridge of d\({}_{\text{MM},1}\) = 99 mm and a PGA\({}_{\text{MM},1}\) = 0.129 g. Afterward, the failure of the two plug-in for a displacement d\({}_{\text{MM},2}\) = 115 mm, and a PGA\({}_{\text{MM},2}\) = 0.149 g is reached. This is suddenly followed by the activation of the plastic hinges at the bases of both piers for a  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{2}{c}{**Second Mode**} & \multicolumn{3}{c}{**Seventh Mode**} \\ \hline
**Event** & \begin{tabular}{c} **Displ.** \\ **(mm)** \\ \end{tabular} & \begin{tabular}{c} **PGA** \\ **(g)** \\ \end{tabular} & **Event** & \
\begin{tabular}{c} **Displ.** \\ **(mm)** \\ \end{tabular} &
\begin{tabular}{c} **PGA** \\ **(g)** \\ \end{tabular} \\ \hline PHA LRPB & 142 & 0.167 & YP LRA & 10 & 0.099 \\ \hline YP LRA & 214 & 0.252 & PF RA & 13 & 0.121 \\ \hline PHC LRPB & 236 & 0.277 & PF LA & 17 & 0.157 \\ \hline  & & & ES LRA & 19 & 0.181 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Events and related displacement and PGA for PO with mode 2 and mode 7 load pattern, respectively.  
Figure 8: Pushover curves for modal load pattern in the transverse direction (**a**) second mode; (**b**) seventh mode.  
displacement \(d_{MM,3}\) = 116 mm, and a \(PGA_{MM,2}\) = 0.151 g. The last event is the failure of the aforementioned plastic hinges (attainment of plastic rotation capacity) for a displacement \(d_{MM,c}\) = 190 mm, and a \(PGA_{MM,2}\) = 0.247 g. Comparison of these results with those obtained through the load pattern according to the 2 nd mode shows that the contribution due to mode 7 leads to premature yielding and subsequent failure of the transverse restraints at the abutments for lower values of PGA.
## 4 Numerical Analysis
### Numerical Model Set-Up  
As illustrated in Figure 7, based on the mechanical model, an LS-DYNA model was created with a 4 m \(\times\) 4 m \(\times\) 12 m geometry. The \(x\) and \(y\) axes were positioned horizontally, while the \(z\) axis was positioned vertically, with the coordinate origin situated at the bottom of the blasthole. The \(y\) and \(z\) axes were perpendicular to the blasthole axis, while the \(x\) axis was aligned with it. The model comprised three materials: rock, explosive, and air. A 12 m long blasthole with a 0.048 m diameter was drilled in the center of the rock, and a 0.04 m diameter explosive was loaded into the hole for a charge length of 12 m. The average mesh size of the hexahedral meshes was set to 0.01 m. To eliminate the influences of reflected stress waves, non-reflecting boundaries were established on the model's surfaces. The simulation of the explosive and air was carried out using the ALE algorithm, while the Lagrange algorithm was employed to model the rock material. Finally, both monitoring points A and B were placed 0.4 m from the blasthole's center, with point A positioned horizontally and point B positioned vertically.  
In this study, stress initialization was considered crucial to carry out the simulations accurately. The purpose of stress initialization is to set a pre-defined stress state in the numerical model, which represents the pre-existing in-situ conditions before blasting. In the first step, the NIS distributed along the blasthole axis were applied to the numerical model to simulate the in-situ stress conditions. The calculation results of this stress initialization were output into a dynein file using the keyword \"INTERFACE_SPRINGBACK_LSDYNA. This file captures the stress distribution within the model, ensuring that the subsequent simulations start from a realistic stress state. In the second step, the dynein file was input into the model using the keyword \"INCLUDE, and the dynamic analysis was performed.  
Figure 7: Layouts of the numerical model.  
This analysis involved the inclusion of both explosive and air materials to accurately model the blast process. By initializing the stress state, the simulation can more accurately predict the behavior of the rock mass and the effectiveness of the blast, providing valuable insights into the stress redistribution and potential fracturing patterns post-blast.
## 4 Results and Discussion
The results indicate that the worst simulated BER in the chosen samples can be observed for channel \(\lambda_{1\text{-}1}\) of \(10^{-12}\), which corresponds to a quality factor (Q) of 6.88, while a remarkable simulated BER of \(10^{-13}\), which corresponds to a quality factor (Q) of 7.03, is noticed for channel \(\lambda_{1\text{-}2}\). An average BER better than \(10^{-12}\) can be observed for the remaining channels. To provide a deeper understanding of Figure 4, it is useful to review some of the eye diagram basics and characteristics. The red curve in Figure 4 represents the ideal received bit pattern only, while the blue pattern represents a normalized pattern for the 2 bits actually received, with the 1 bit  
Figure 4: Eye diagrams for odd and even channels after the retransmitting path (a) \(\lambda_{1\text{-}1}\), (b) \(\lambda_{1\text{-}2}\), (c) \(\lambda_{8\text{-}1}\), and (d) \(\lambda_{8\text{-}2}\). \"eye-opening\" in the center of the display and 1/2 bit to the left and right of the center eye to capture the rise/fall-time transitions [31; 32; 33]. The eye diagram is a useful tool for the qualitative analysis of the signals used in digital transmission. This diagram provides an at-a-glance evaluation of the system performance and can offer insight into the nature of the channel's imperfections. Careful analysis of this visual display can give the user a first-order approximation of the signal-to-noise ratio, clock timing jitter, and skew [31; 32; 33]. Several key values can be extracted directly from Figure 4, such as the One Level, Zero Level, Eye Amplitude, eye height, crossing point, rise time, and fall time. Then, system performance indicators, such as the Eye Crossing Percentage, signal to noise ratio, eye width, Duty Cycle Distortion, BER, and quality factor, can be directly calculated using simple and direct relations based on the key values [31]. Finally, it is worth mentioning that today's manufacturers provide devices that directly display all previous values and system indicators once the system is tested [31; 32].
## 2 Methodology
### A Protocol for the Assessment of Gait of Parkinson's Disease Patients Using Wearable Sensors  
As previously stated, the Smart-Insole Gait Assessment Protocol for PD patients was defined. The protocol focuses on the exploitation of bradykinesia and postural instability, from the cardinal features of PD, and the use of an additional motor symptom that is extensively studied because of its association with disease severity and increased risk of falls, i.e., Freezing of Gait (FoG) [36].  
In the majority of studies, the tests performed for the assessment of gait characteristics of PD patients, are: (1) Free Walking for a predefined duration of time and at a normal pace of walking; (2) the Timed Up and Go test (TUG test), where the participants rise from a sitting position, walk a 3 m distance, turn, walk back and sit on a chair; (3) walking in a corridor with obstacles, and; (4) the Dual-Task test, where the participants walk and at the same time undertake a second process, such as an arithmetic operation, conversation, transferring an object, etc. [37; 38; 39; 40; 41; 42; 43; 44]. For movement disorders experts, the MDS-UPDRS and particularly Part III: Motor Examination is considered the gold standard for clinical assessment.  
Therefore, in developing our research protocol, we have taken into consideration literature findings and guidance of clinical experts and focused on six items of the MDS-UPDRS-Part III: Motor Examination that involve lower extremities, namely 3.9-Arising from chair, 3.10-Gait, 3.11-Freezing of gait, 3.12-Postural Stability, 3.13-Posture, 3.14-Global spontaneity of movement. We also introduced a test for assessing FoG based on the findings of previously published work [45]. As a result, the Smart-Insole Gait Assessment Protocol consists of a set of five different tests, namely, the Walk Straight and Turn test, a modified version of the Timed Up and Go test, the Static Balance test, the Retropulsion test, and finally, the FoG and Dual Tasking test. These are described in detail in Sections 2.2.1-2.2.5. Participants were asked to complete the tests as a continuous set of movements.  
Figure 1: The Moticon Science Sensor Insole (Model Insole 3). (**a**), shows the arrangement of the 16 pressure sensors and the positioning of the 3-axis acceleration sensor. (**b**) shows the positioning of the angular rate sensors. Reproduced with permission from Moticon ReGo AG [35].
## 6 MIMO Antenna Performance Enhancement Techniques  
In recent years in communication systems, the topic of multiinput and multioutput (MIMO) systems has received a considerable attraction, especially the performance in terms of the channel capacity of a communication system which can badly be affected by the mutual coupling of closely correlated interelements of a MIMO system. The capacity gain of a communication system has a significantly deep impact on the overall efficiency of an antenna system. So, a good communication system needs to have good isolation between the interelement or closely collocated elements.  
A summary of MIMO antenna designing is reported in [81]. Most authors have used one or more techniques for designing antennas for mutual coupling reduction in MIMO antennas described in the literature for 5G and wide-band applications, these designing and decoupling techniques can effectively be used and better results could be achieved. The detailed layout is shown in Figure 19. The most prominent techniques of MIMO antenna designing as well as mutual coupling reduction techniques are presented to better explain the idea of mutual reduction or decoupling methods and possibilities discussed in the literature from [29; 30; 73; 74; 81; 82]. In multielement antennas, either in arrays or especially in the case of MIMO antenna design, the collocated antenna elements on a single PCB might affect the performance of each other. To avoid or reduce this effect, researchers have implemented different strategies and techniques for mutual coupling reduction or decoupling of these collocated elements n the case of MIMO antennas, which is the main focus in our study, named as isolation or decoupling strategies and techniques in the literature. Some state-of-the-art techniques are explained in the next section. The decoupling techniques play an excellent role to achieve the optimum performance in case of MIMO antennas. These techniques are a predetermined and unavoidable part in the designing procedure of the MIMO antenna. These techniques are explained below.
# Ground Target Detection and Damage Assessment by Patrol Missiles Based on YOLO-VGGNet
## 1 Introduction
The innovations of this paper are as follows: (1) We propose a real-time detection and timely damage assessment method for mobile ground targets, YOLOVGGNet, which can be performed based on online perception; (2) We cluster and visualize the convolution kernels based on CAM and deconvolution, which not only helps determine whether the targets' components are damaged or not, but also provides a more interpretable perspective for learning-based methods; (3) We build up a simulation system based on Unity and prove the effectiveness of the proposed method by extensive experiments. Experimental results show that the proposed method improves the accuracy of damage level assessment by 16.0% and 8.8% compared with traditional image-change-detection-based methods and the two-CNN learning-based method. The convolutional feature clustering method evaluates the status of the targets' key components with an accuracy of 72%. This paper is organized as follows: Section 2 introduces existing works on damage assessment. Section 3 designs the novel damage level assessment method named YOLO-VGGNet. Section 4 mainly describes damage assessment of the target's components based on convolution feature clustering, and a detailed description of convolutional feature clustering can be found here. The simulation system and experimental result are shown in Section 5. Section 6 concludes the paper.
## 4 Validation Tests
### Datalogging Tests--Information in the Cloud  
Tests were carried out to store strain readings in a Google spreadsheet. Data were saved in a comma-separated-values (CSV) file, and the gathered data from the sensor were stored and displayed in the spreadsheet. The process worked as follows: the script read the last saved row with information; then, it received the package of information and provided the needed format to post counter, timestamp, measurement ID, and the raw value in a separate column. At the end of this action, the script increased its counter by 1 to maintain the correct order in the spreadsheet. An example of a test where the sensor reading was sent and stored in a Google spreadsheet is shown in Figure 19.  
The implemented panel depicted the average strain measured by the smart sensor. During the dashboard-development process, certain tests were carried out to validate the displayed data. Important features of the dashboard and the displayed data during the tests are shown in Figure 20.  
Figure 19: Google spreadsheet with 11 readings (stored in the cloud) coming from the smart strain sensor.  
Figure 18: Experimental setup for energy harvesting: rubber plate and vibrating testing machine at the lab. Figure taken from [39].
# Emotion Recognition from Speech in a Subject-Independent Approach
## 3 Aim of the Article  
Speech recognition using AI in a subject-independent approach remains a significant challenge. Despite many years of technological development, most existing solutionsachieve high accuracy only when tailored to specific users (subject-dependent). This article aims to critically and comprehensively analyze the current capabilities of algorithms to recognize emotions from a speaker's voice in a subject-independent approach. Various approaches used in the voice analysis process were examined, including the latest machine learning and artificial intelligence algorithms applied to identify subtle nuances in tone, pace, volume, and other characteristic voice features. The research utilized two databases, one in Polish and the other in English, recorded in different ways. Our studies focus on selected short speech signals. Special attention was given to how to divide the data so that the results are comparable to a real case of emotion recognition. This means that the test data do not include examples from individuals who participated in the training process (subject-independent). Such an approach aims to ensure that the model can be generalized to new, unknown speech signals. Dividing the data into training and test sets in this way allows for a more accurate assessment of the model's ability to recognize speech in conditions closer to reality. The general research framework presented in the article is shown in Figure 1.  
We expect to achieve worse results than those presented in many articles, but we hope that the obtained results will allow a reliable assessment of the ability to recognize  
Figure 1: Procedures employed in our study on recognizing emotions.  
emotions from short speech fragments. The goal of our research is primarily to understand the limitations of existing methods and identify opportunities for further development in the field of automatic emotion recognition. The results, even if not comparable with the best available models, will provide valuable insights into how emotions manifest in short speech segments and how they can be effectively recognized.
# Atomicity Violation in Multithreaded Applications and Its Detection in Static Code Analysis Process
## 7 Sufficient Condition  
A multithreaded application code is given as a model in which:  
* There are no conflicts causing race condition and deadlock;
* In graph \(G_{P}\)[2], there is a pair of threads \(t_{i}\) and \(t_{j}\) working in the same range of \(u_{b}\);
* In thread \(t_{i}\), there is a pair of operations \((o_{i,\alpha},o_{i,\beta})\in B_{P}\) and these operations are in an agreement relationship which is not completely broken;
* In thread \(t_{j}\), there is operation \(\{o_{j,\gamma}\}\);
* In the set of operations \(\{o_{i,\alpha},o_{i,\beta},o_{j,\gamma}\}\), at least one of them is connected to a resource with a shared use edge (one of the scenarios listed in Table 1 is fulfilled).  
The phenomenon of atomicity violation occurs at the moment when each of the operations of pair \((o_{i,\alpha},o_{i,\beta})\) is present in a different critical section.  
Figure 7 shows the first scenario from Table 1 using a graphical representation of the source code model of a multithreaded application. All characteristics listed in the points above can be found there.  
So, assuming that there are two threads \(t_{i}\) i \(t_{j}\) and set of operations \(\{o_{i,\alpha},o_{i,\beta},o_{j,\gamma}\}\) protected by \(q_{s}\) mutex and using resource \(r_{c}\) in such a way that they fulfil one of the scenarios set out in Table 1, it can be concluded that:  
**Lemma 1**.: _Operations \(o_{i,\alpha}\) and \(o_{i,\beta}\) are connected by one of the agreement relationship, which is not completely broken._  
_If there is a cycle in \(G_{P}\) graph that consists of mutex \(q_{s}\) and only one operation from set \(\{o_{i,\alpha},o_{i,\beta}\}\) then the phenomenon of atomicity violation occurs._  
**Theorem 1**.: _The atomicity is violated if the above lemma is fulfilled._  
Proof.: This is the consequence of Definition 1. If there is a cycle in \(G_{P}\) graph that consist of \(q_{s}\) and only one operation from the set of \(\{o_{i,\alpha},o_{i,\beta}\}\)--that is, the operations \(o_{i,\alpha},o_{i,\beta}\) do not belong to the same critical section. This means that it is possible to execute an \(o_{j,\epsilon}\) operation of another thread (\(t_{\gamma}\)) on the same resource between the \(o_{i,\alpha},o_{i,\beta}\) operations--i.e., order \(o_{i,\alpha}\prec o_{j,\epsilon}\prec o_{i,\beta}\). It is then possible for atomicity violation to occur for the \(o_{i,\alpha},o_{i,\beta}\) operation by execution of the \(o_{j,\epsilon}\) operations.
## 3 Results and Analysis
### Characterization of Modified Nano-SiO\({}_{2}\)
#### 3.1.2 TGA  
Figure 4 shows the TGA of SiO\({}_{2}\) before and after modification. With increased temperature, the weight of the sample gradually decreased, and the mass change with temperature was divided mostly into two stages. In the first stage, with increased temperature from 25 \({}^{\circ}\)C to 120 \({}^{\circ}\)C, the mass of the sample decreased from 100% to 98%, which was primarily caused by the loss in adsorbed water on the sample surface. In the second stage, with increased temperature from 120 \({}^{\circ}\)C to 1000 \({}^{\circ}\)C, the weight of nano-SiO\({}_{2}\) decreased from  
Figure 3: Particle size distribution.  
Figure 2: Infrared spectra of nano-SiO\({}_{2}\) before and after modification.  
98% to 92%, which was due to the weight change caused by the dehydration and polycondensation of the hydroxyl groups on the nano-SiO\({}_{2}\) surface. However, the weight of the modified nano-SiO\({}_{2}\) decreased from 98% to 89% with increased temperature. On the one hand, this was due to the dehydration and polycondensation of the unreacted hydroxyl groups on the surface of the modified nano-SiO\({}_{2}\), the same as with the above-mentioned SiO\({}_{2}\). On the other hand, the weight of the modified KH560 grafted onto the surface of SiO\({}_{2}\) was further reduced due to the fracture of the modified KH560 owing to the excessive temperature. At 1000 \({}^{\circ}\)C, the weight of SiO\({}_{2}\) before and after modification differed by 3%, indicating that 3% of KH560 was grafted onto the surface of SiO\({}_{2}\).