esa-sceva/satcom-chunk-collection huggingface dataset:
# Scanning Electron Microscopy Techniques in the Analysis of Gunshot Residues: A Literature Review
## 4 Discussion  
### Gunshot Residue Composition and Formation  
Gunshot residues (GSRs) primarily consist of a complex mix of chemical components resulting from the discharge of a firearm. These components include particles of lead (Pb), barium (Ba), and antimony (Sb), which are commonly found in the primer of ammunition. The chemical composition of GSRs can be further analyzed using advanced techniques such as scanning electron microscopy coupled with energy-dispersive X-ray analysis (SEM-EDX) [49]. This method allows for detailed examination of both the elemental and morphological characteristics of GSR particles. The formation and deposition processes of GSRs are highly dynamic and occur in milliseconds during the firing of a gun. When a firearm is discharged, the primer ignites the gunpowder, generating a high-pressure explosion that expels the bullet from the barrel. This explosion produces a cloud of microscopic particles, which are then deposited on the hands, clothing, and nearby surfaces of the shooter. Several factors influence the distribution of GSR particles, including the type of firearm and ammunition used, environmental conditions, and the shooter's actions immediately following the discharge. For instance, semi-automatic firearms tend to produce a higher quantity of GSRs compared to revolvers due to differences in their firing mechanisms [27]. Additionally, environmental factors such as wind and humidity can affect the dispersion and adhesion of GSR particles.
# The Problem of Stability in Mechanical Systems Using the Example of Mine Hoist Installations
## 1 Introduction
In this article, the authors, who are experienced in the field of hoist installations, have determined the possibility of parametric vibrations in one machine. Their expertise adds significant credibility to the research and the determination of the possibility of parametric vibrations in this machine. Unstable work for the machine leads to most problems with transporting the output. There is also the influence on planning repairs, which makes its work impossible. For different machines, parametric vibrations are checked and analyzed in [16; 17]. In Polish hoist installations, the parameters for the machine systems are legislated by the law, which presents dependence values for parameters like masses, stiffness coefficients, and others [18; 19; 20]. These dependencies are used in the analysis that is presented in the article. When the hoist installations pass by guide shafts, the mining skip interacts with the shaft steelwork, and the irregularities on the shaft guide could generate horizontal vibrations for the mining skip. These irregularities and misalignments influence the changing parameters on the guide shaft used in the model for this machine. These horizontal vibrations for the hoist installation could generate unstable work for the whole system, which is undesirable. Like others, the authors analyzed irregularities and misalignments for the stability of the work of mining hoist installations [2; 3; 4; 5; 6; 7; 8]. The effects of unstable work of a hoist installation could be quick damage to the steel ropes, more irregularities and misalignments of the guide shaft, and damage to the roller guide and guide shaft. Parametric vibrations in this type of installation are undesirable because the system of transporting output could stop and lead to economic problems for the mine. This article presents a comprehensive analysis of the stability problem in hoist installations, building on prior work and extending its findings. The complicated analysis presented in this work is helpful for modeling and designing new hoist installations and modernizing old objects. The analyses presented so far in the cited articles have not dealt with this topic. This is a new look at the vibrations of the mining vessel, which is presented in the conducted analysis. The parameters and models used in this research are akin to those of machines operating in Poland, ensuring a robust and reliable analysis. The practical implications of our findings for professionals in the mining and mechanical engineering industries are significant. Our research can provide information for safety measures and operational planning in hoist installations, thereby contributing to the safety and efficiency of mining operations.
# Prediction of Temperature Distribution for Previous Cement Concrete Pavement with Asphalt Overlay
## 4 Model Verification with Measured Field Data  
### The External Environment of Temperature Field  
#### 4.1.1 Solar Radiation Intensity and Air Temperature  
The selected temperature distributions data were collected on a sunny day from the existing cement concrete pavement with an asphalt overlay in the Foshan section of Highway G321 in Guangdong Province, China. Then, the computed temperature field was compared with the real collected data to ensure the validity of the derived analytical solutions. The pavement structure contains a 0.04m AC-13 asphalt overlay upper layer, a 0.05m AC-20 asphalt overlay lower layer, a 0.26m C35 cement concrete base, a 0.20m C20 cement concrete subbase and a soil subgrade.  
In this paper, the 1 stOpt computer program is developed to fit the measured solar radiation intensities and air temperatures by using a discrete least-squares approximation of the interpolatory trigonometric polynomials to get the relevant parameters \(a_{0}\), \(a_{m}\), \(c_{0}\) and \(c_{m}\) of Equations (5) and (6) (Table 2). The temperatures of the existing cement concrete pavement with an asphalt overlay are computed every half hour (i.e., \(m=24\) in above equations) for 24 h in both summer and winter.
## 2 Materials and Methods
### Theoretical Calculations Model and Equations
#### 2.6.2 Air Movement  
The air flow is a result of the temperature difference occurred between the start and end of the tube. The air has the property of moving from the points of higher pressure to points of lower pressure. The stack effect is described in Equation (3) [34]. The mass flow equation is described in Equation (4) [34].  
\[\dot{\mathrm{V}}=2\times\mathrm{A}\times\left[\mathrm{g}\times\Delta\mathrm{h }\times\frac{\left(\mathrm{Ti}-\mathrm{To}\right)}{\mathrm{Ti}}\right]^{1/2} \tag{3}\]  
\(\dot{\mathrm{V}}\): air flow (\(\mathrm{m}^{3}\times\mathrm{h}^{-1}\))  
A: opening surface (\(\mathrm{m}^{2}\))  
g: gravity acceleration  
\(\Delta\mathrm{h}\): height difference between inlet and outlet (\(\mathrm{m}\))  
Ti: internal temperature (\(\mathrm{\SIUnitSymbolCelsius}\))  
To: environmental temperature (\(\mathrm{\SIUnitSymbolCelsius}\))  
\[\dot{\mathrm{m}}=\rho\times\mathrm{A_{c}}\times\mathrm{u} \tag{4}\]  
\(\dot{\mathrm{m}}\): air flow (\(\mathrm{kg}/\mathrm{s}\))  
\(\rho\): air density (\(\mathrm{kg}/\mathrm{m}^{3}\))  
\(\mathrm{A_{c}}\): cross-sectional area of drying tube (\(\mathrm{m}^{2}\))  
\(\mathrm{u}\): air velocity (\(\mathrm{m}/\mathrm{s}\))
# Image-Based Malicious Network Traffic Detection Framework: Data-Centric Approach
## 3 Proposed Detection Framework
### NSL-KDD Dataset Description  
To evaluate our framework, we used the NSL-KDD dataset [69], which is widely used for IDS evaluation. This dataset is an enhanced version of the KDD CUP 99 dataset, which was originally developed by the Defense Advanced Research Project Agency (DARPA) for IDS evaluation. Tables 1 and 2 show 41 network traffic features and descriptions of each class, as well as the number of data samples per class in the NSL-KDD dataset [70]. Characteristics of the dataset are provided in a csv file format, consisting of 41 feature columns and 1 class column. The class consists of a normal state and four types of attack: Denial of Service (DoS), Remote-to-Local (R2L), User-to-Root (U2R), and Probe. We grouped data samples of the four attack types into one attack class for the experiments. When the class is divided into two types, normal and attack, the dataset is composed of 77,054 normal samples and 71,463 attack samples, among a total of 148,517 data samples.
## 4 The Proposed Method: SLP-E
### Protocol Execution Phase
#### 4.2.1 Scenario-1
The hop count values used in this phase are taken into account to reduce the likelihood of selecting IN1 which is close to the actual source node. Since in this scenario, a source node is located in the inner circle (threshold circle), we make sure that the intermediate node (IN1) cannot be found inside that circle. * _Phase-4_: In this phase, the packets consider the direction they followed in phase-2. The packets travel in either a clockwise or anti-clockwise direction to reach intermediate node-2 (IN2). IN1 has to relay the packets in either a clockwise or anti-clockwise direction, as was determined in phase-2. That is, if in phase-2, the packets travel in a clockwise direction, then in phase-4 also the packets will travel in a clockwise direction. The goal of this phase is to reduce the likelihood that a packet will enter the sensing range of the actual source node on its way to reach the BS. If, by any chance, the source node is found at the boundary of the inner circle, as the maximum distance a packet can travel in phase-2 is 180\({}^{\circ}\), if this distance was traveled in a clockwise direction, then in this phase, the packet takes a clockwise direction with 90\({}^{\circ}\) as the maximum distance; with these conditions, the packet cannot visit the source node location. We set 90\({}^{\circ}\) as the maximum distance in order to avoid that a packet may travel a distance which is equal to a complete circle, because doing so when a source node is found at that inner circle boundary means that the packet may visit its place, thus the weaker privacy. This is even the reason why the packets must move in the same direction as they did in phase-2, to avoid the possibility of discovering IN2 within the range of the source node's radio or passing through it. Therefore, in this phase, the packet travels for certain hops whose values are selected between 45\({}^{\circ}\) and 90\({}^{\circ}\). Intermediate node-2 (IN2) is the node where this phase ends. * _Phase-5_: Finally, by utilizing the shortest path walk, the packet is delivered to the BS from intermediate node-2.
## 2 Antenna Design and Analysis
### Single Antenna Configuration
The design parameters of the single-monopole antenna are provided in Table 1.
## 4 Hardware Security
### Physical Unclonable Functions (PUFs)
edge over other PUF types as they are less noise-sensitive and leverage light diffraction complexity, making them stable and difficult to duplicate [225]. Light acts as the challenge input and generates a unique random pattern as the response [226]. Normally optical structures are not compatible with solid-state integration. However, a recently proposed CMOS imager PUF uses photodiode responsivity under uniform ambient light and dark current variations to generate unique identifiers for camera authentication [227].
# ARCFIRE: Experimentation with the Recursive InterNetwork Architecture
## 5 Discussion of Experimental Results
### Quality of Service Experiments  
RINA provides a consistent QoS model from the application to the physical wire: applications that wish to do so can provide quality requirements to the network in a technology-independent way. There is no need to use Deep Packet Inspection (DPI) techniques to identify classes of traffic and infer the quality requirements of the application; hence applications doing end to end encryption can be supported with the appropriate level of quality. Layers (DIFs) provide QoS requirements to lower layers using the same abstract API, which means that there is no need to standardize QoS cube identifiers across DIFs (though the semantics of the QoS parameters passed through the layer API must be standardized).  
QoS cubes provide a useful abstraction to communicate the ranges of quality supported by a DIF to its users, in a way that is decoupled from the mechanisms and policies used internally to enforce the quality expressed by each QoS cube. EFCP traffic marking enables a clear identification of PDUs belonging to different QoS cubes, and enables resource allocation policies (routing, scheduling, congestion control) to act consistently across a DIF [21]. QTAmux scheduling policies have proven a useful means to differentially allocate loss and delay to groups of flows, allowing DIFs to provide consistent levels of quality to the applications using them even at high loads. Moreover, the abstract QoS model provided by the deltaQ [22] theory allows an application to describe statistical bounds in the loss/delay required for its flows in a technology-independent way, which can be implemented in practice. The work started by ARCFIRE will allow future research to understand the join behavior of the QTAMux scheduling policies working in conjunction with ECN-based congestion management policies, giving the DIF the capability to limit the quality degradation experienced by lower urgency classes by decreasing the traffic load offered to the network.
# Measurement of Bridge Vibration by UAVs Combined with CNN and KLT Optical-Flow Method
## 4 Experimental Results and Analysis
### Comparisons between Fixed Camera and UAV  
The comparisons of the measurement results of the fixed camera and UAV processed by the KLT optical-flow method are shown in Figure 17, which demonstrates that the corrected displacement curve measured by UAV and the displacement curve measured by fixed camera have very similar free-vibration characteristics. By calculating the time response assurance criterion (TRAC) between the corrected displacement measured by the UAV and the displacement measured by the fixed camera, the correction effect of CNN can be judged [10]. The higher the TRAC, the higher the degree of consistency, and the better  
Figure 16: PSD measured by UAV: (**a**) results obtained by DIC; (**b**) results obtained by KLT.  
Figure 15: Comparisons of correction effect of CNN: (**a**) results of DIC; (**b**) results of KLT optical-flow method; (**c**) comparisons.  
the correction effect. The TRAC between displacement measured by the UAV and that measured by the fixed camera is 0.996, which shows their high consistency. Combined with the results of Section 4.2, it can be proved that the displacement of bridge model measured by the UAV combined with the CNN and KLT optical-flow method is reliable.
## 3 The Proposed Method
### Sparse Reconstruction Based on Kronecker Dictionary
2:while\(|\mathscr{I}_{1}|\cdot|\mathscr{I}_{2}|\cdot\cdots\cdot|\mathscr{I}_{N}| \leqslant k_{max}\) and \(\left\|\mathcal{R}\right\|_{F}>\epsilon\), do
3:\(\left[i_{1}^{k},i_{2}^{k},\ldots,i_{N}^{k}\right]\leftarrow\operatorname*{ arg\,max}_{\left[\hat{\mathcal{R}}\times_{1}\mathbf{A}_{1}^{T}\left(:,i_{1} \right)\times_{2}\mathbf{A}_{2}^{T}\left(:,i_{2}\right)\times_{3}\cdots\times_{N }\mathbf{A}_{N}^{T}\left(:,i_{N}\right)\right]}\);
4:\(\mathscr{I}_{n}\leftarrow\mathscr{I}_{n}\cup\left[\hat{\mathcal{R}}_{n}^{k} \right],\;n=1,2,\ldots,N\);
5:\(\mathbf{B}_{n}\leftarrow\mathbf{A}_{n}\left(:,\mathscr{I}_{n}\right)\);
6:\(\mathbf{s}_{nz}\leftarrow\operatorname*{arg\,min}_{\mathbf{u}}\left\|\left(\mathbf{B}_{N} \otimes\mathbf{B}_{N-1}\otimes\cdots\otimes\mathbf{B}_{1}\right)\mathbf{u}-\mathbf{y}\right\|_ {2}^{2}\);
7:\(\mathcal{S}_{nz}\leftarrow\mathbf{s}_{nz}^{n}\);
8:\(\mathcal{R}\leftarrow\mathcal{Y}-\mathcal{S}_{nz}\times_{1}\mathbf{B}_{1}\times_{2 }\mathbf{B}_{2}\times_{3}\cdots\times_{N}\mathbf{B}_{N}\);
9:\(k\gets k+1\);
10:endwhile
11:\(\mathscr{I}\leftarrow\left\{\mathscr{I}_{1},\mathscr{I}_{2},\ldots,\mathscr{I }_{N}\right\}\);
12:\(\hat{\mathcal{S}}\left(\mathscr{I}_{1},\mathscr{I}_{2},\ldots,\mathscr{I}_{N} \right)\leftarrow\mathcal{S}_{nz}\). ```  
**Algorithm 1** Tucker decomposition based SAR imaging algorithm. In iterative process, make sure that the nonzero elements of core tensor are always contained within a small index set. Finally all the nonzero elements and the corresponding index set are obtained and the expected scatter coefficients can be recovered as well. The algorithm flow is represented in Algorithm 1.
## 3 PPK2 Enzymology
### PPK2 Crystal Structures and Catalytic Mechanism
Alanine substitution of _Ft_PPK2 K6, R118, and R178 resulted in weaker polyP binding and dramatically reduced enzyme activity [53]. Similarly, alanine substitution the corresponding residues (K86, K258, and R262) in _Ch_PPK2 also markedly reduced activity. Nucleotide-bound structures of _Ch_PPK2 in complex with AMP, ADP, and G4P, as well as _D. radiodurans_ PPK2 (_Dr_PPK2) in complex with ATP generally corroborated the binding orientations observed for _Ft_PPK2 [46]. Interestingly, two ATP molecules were observed in one active site for _Dr_PPK2 (PDB ID 6 AQE). The first was bound in the typical nucleotide pocket, while the second was bound near the dimer interface [46]. Similarly, an Asn121 Asp mutant of _Mr_PPK2 also contained two ATP, with the adenosine moiety of the second ATP contacting the adjacent protomer (PDB ID 506M) [53]. It is tempting to speculate that this type of secondary nucleotide binding may serve as a regulatory mechanism, perhaps by influencing oligomeric state, which could potentially help explain the substrate inhibition phenomenon observed for other PPK2s [57]. While PPK2s from various species have been observed to form dimers, trimers, tetramers, and octamers in solution [34; 36; 53; 64], it is unclear how oligomerization affects activity or which oligomeric states are predominant in vivo. Based on the described substrate-bound structures and complementary biochemical data, the PPK2 reaction mechanism is proposed to proceed via in-line nucleophilic attack by the nucleotide terminal phosphate oxygen on the terminal phosphorus atom of polyP [53]. The terminal phosphate groups of both substrates are coordinated by a bridging Lewis acidic Mg\({}^{2+}\) (sometimes via a water molecule), which activates the polyP nucleophile. Several conserved positively charged residues located on the lid domain and the bottom of the active site (e.g., R208 and K81 of _Ch_PPK2) serve to stabilize the transferred phosphoryl group of polyP via hydrogen-bonding and charge compensation [46; 53]. It is worth noting that this mechanism is distinct from that of PPK1, in which an autophosphorylated histidine residue mediates transfer of phosphate between the nucleotide and the polyP chain [4; 37].
# Enhanced Sensitivity of FeGa Thin-Film Coated SAW Current Sensor
###### Abstract  
A surface acoustic wave (SAW) device is proposed for sensing current by employing the patterned FeGa thin film as the sensitive interface. The layered media structure of FeGa/SiO\({}_{2}\)/LiNbO\({}_{3}\) was established to reveal the working principle of the sensors, and an SAW chip patterned by delay-line and operating at 150 MHz was fabricated photolithographically on 128\({}^{\circ}\) YX LiNbO\({}_{3}\) substrate. The FeGa thin film with a larger magnetostrictive coefficient was sputtered onto the acoustic propagation path of the SAW chip to build the sensing device. The prepared device was connected into the differential oscillation loop to construct the current sensor. The FeGa thin film produces magnetostrictive strain and so-called \(\Delta E\) effect at the magnetic field generated by the applied current, which modulates the SAW propagation velocity accordingly. The differential frequency signal was collected to characterize the measurand. Larger sensitivity of 37.9 kHz/A, low hysteresis error of 0.81%, excellent repeatability and stability were achieved in the experiments from the developed sensing device.  
surface acoustic wave; current sensor; FeGa thin film; magnetostrictive strain; \(\Delta E\) effect +
Footnote †: journal: Applied Sciences
# Synchronous Remote Calibration for Electricity Meters: Application and Optimization
## 4 Results and Discussions
### Experimental Scenario Settings  
Below is an overview of the parameter settings for different scale scenarios, including small-scale, medium-scale, and large-scale environments. The settings cover map size, the configuration of detection points and central laboratories, default configurations for servers and clients, and the number of task types. These parameters are designed to simulate environments of varying complexity and requirements, allowing for the testing and evaluation of system performance and adaptability across different scales. The parameters are shown in Table 2.  
* **Small-Scale Parameter Settings:** For the small-scale scenario, the map size is set to 15 \(\times\) 15 units. The coordinates for the detection point and the central laboratories are randomly assigned without repetition. This scenario includes one detection point and two central laboratories. The default configuration consists of four servers and five clients. Additionally, there are eight distinct task types.
* **Medium-Scale Parameter Settings:** In the medium-scale scenario, the map size is expanded to 20 \(\times\) 20 units. As in the small-scale scenario, the coordinates for detection points and central laboratories are randomly and non-repetitively chosen. This scenario features two detection points and two central laboratories, with a default setup of 8 servers and 10 clients. There are six task types in this scenario.
* **Large-Scale Parameter Settings:** For the large-scale scenario, the map size increases to 30 \(\times\) 30 units. The selection of coordinates for detection points and central laboratories remains random and non-repetitive. This scenario includes three detection points and three central laboratories. The default configuration includes 12 servers and 18 clients, managing a total of 10 task types.
# Detecting Deception from Gaze and Speech Using a Multimodal Attention LSTM-Based Framework
## 3 Results
### Results with the Multimodal Fusion  
Table 2 contains the results attained by the systems where the two types of modalities, gaze and speech, are combined. In particular, the corresponding DACC, HACC, ACC and AUC metrics for the different fusion strategies proposed in this work are reported at both, segment and turn level. In terms of ACC and AUC, in the case of the SVM-based models, both combination strategies outperformed the corresponding systems with a single modality, Late Fusion being slightly better than Early Fusion. However, these results were significantly worse than those attained by any of the fused LSTM-based systems.  
Analyzing the behaviour of the multimodal LSTM-based systems, it can be observed that both combination approaches (Late Fusion and AP Fusion) produced better results in terms of ACC and AUC than the LSTM-based systems with a single modality. Moreover, the combination at Attention-Pooling level seemed to be more beneficial than the fusion at decision level. Specifically, the best system (AP Fusion) achieved a relative increase in  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline  & & & \multicolumn{3}{c}{**Segment Level**} & \multicolumn{3}{c}{**Turn Level**} \\ \hline
**Modality** & **System** & **DACC** & **HACC** & **ACC** & **AUC** & **DACC** & **HACC** & **ACC** & **AUC** \\ \hline \multirow{2}{*}{Gaze} & SVM & 62.17 & 48.35 & 55.67 & 55.67 & 65.74 & 51.13 & 58.83 & 57.86 \\  & LSTM & 63.45 & 60.11 & 61.88 & 67.37 & 67.78 & 61.86 & 64.98 & 72.09 \\ \hline \multirow{2}{*}{Speech} & SVM & 60.80 & 59.77 & 60.31 & 59.61 & 53.24 & 63.71 & 58.20 & 60.64 \\  & LSTM & 53.21 & 75.92 & 63.89 & 63.12 & 44.44 & 80.31 & 61.41 & 63.83 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Average deception accuracy (DACC) [%], honesty accuracy (HACC) [%], accuracy (ACC) [%] and Area-Under-the-Curve (AUC) at segment and turn level achieved by the SVM-based reference system and the LSTM-based system with either gaze or speech features as input.  
AUC of 5.18% with respect to Late Fusion, 18.63% with respect to Gaze and 28.01% with respect to Speech at segment level. At turn level, the respective relative increases in AUC were 8.91%, 19.81% and 38.13%.