esa-sceva/satcom-chunk-collection huggingface dataset:
# Improved Chaff-Based CMIX for Solving Location Privacy Issues in VANETs
## 6 Security Analysis
### Mutual Authentication  
During the mutual authentication, the vehicle \(VID_{j}\) validates \(Sign_{RSU_{i}}(M_{R2V})\) and \(Cert_{RSU_{i}}\) which are received from the broadcast message by \(RSU_{i}\), i.e., \(\mathcal{B}\)=\((M_{R2V},Sign_{RSU_{i}})\)\((M_{R2V}),\)\(Cert_{RSU_{i}})\). Therefore, authentication is provided through signatures and certificates as long as \(LTCA\) is honest. Next, to be able to generate a shared key, \(VID_{j}\) first computes its \(P_{j}=A_{j}^{\hat{r}_{j}}\) and \(P_{i}=A_{j}^{\hat{r}_{j}}\), where \(R_{i}=g_{1}^{\hat{r}_{i}}\) is sent by \(RSU_{i}\). Then, the \(RSU_{i}\) can recover \(P_{j}=A_{j}^{\hat{r}_{j}}\) from \(\{A_{j},P_{j},T_{j}\}\) in the list \(List_{pub}\). Hence, even if \(R_{i}\) and \(A_{j}\) given to other entities, to generate valid \(P_{i}\) and \(P_{j}\) they must know either \(a_{j}\) of \(VID_{j}\) or \(r_{i}\) of \(RSU_{i}\). However, since these private values are only known by \(VID_{j},RSU_{i}\) and certificates are used for authentication, no adversary can compute the shared key due to the underlying Computational Diffie-Hellman (CDH) problem. Therefore, our scheme provides the message confidentially inside the mix-zone.
## 3 Experimental Case Studies

Table 1: Seismic masses for steel (SBF) and timber (TBF) braced frame. Figure 3: Detail of (**a**) hysteretic dampers (HD) devices and (**b**) of dissipative bracing connection of SBF; (**c**) Detail of UFP devices and (**d**) of dissipative bracing connection of TBF. at the same design earthquake as shown in Figure 4. The design response spectrum was defined according to Eurocode [42] for high seismic zone and medium soil class type B, considering a peak ground acceleration of 0.44 g. To ensure consistency with the scale of both experimental models, the recordings were scaled down in time by a factor of 1/\(\surd\) (3/2). A target drift of 0.5% and of 1.25% were assumed in the design of SBF and TBF, respectively. The equivalent elasto-plastic behavior of the SBF is represented in Figure 4a as the combination of a linear elastic element, which characterizes the equivalent bare frame, and an elasto-plastic element which schematizes the equivalent dissipative bracing system [14]. The equivalent flag-shaped behavior of the TBF model is reported in Figure 4b, it consists of a combination of a bilinear elastic element of the post-tensioned timber frame [45] and an elasto-perfectly plastic element of the hysteretic bracing system [44]. As can be seen from Figure 4, experimental case studies are characterized by different stiffness of the bare frame while the equivalent bracing systems obtained from design have almost the same strength and ductility (\(\mu_{d}\) = 5 for the SBF braces and about \(\mu_{d}\) = 4 for the TBF braces). Numerical models of both braced frames were built based on the lumped plasticity approach which combines the use of elastic elements (beams, columns, and braces) with linear or rotational springs representing plastic deformations in the system (hysteretic devices, post-tensioning, and rocking mechanisms) and calibrated against the experimental outcomes. NLDs were modelled using nonlinear Bouc-Wen [46; 47] link elements with hysteretic behavior evaluated by characterization tests. Table 2 reports the main dynamic properties of both braced models in terms of the periods of the first modes of vibration in the direction of loading obtained from experimental (\(T_{exp}\)) and numerical (\(T_{mm}\)) results. As shown in Table 2 a good agreement between the experimental [14; 40] and numerical [14; 48] dynamic characteristics was found for both models. More details about numerical modelling and simulations of SBF and TBF can be found in [PERSON] et al. [14; 24; 48].
# The Use of Construction Waste to Remediate a Thermally Active Spoil Heap
## 3 Results and Discussion
### Mineralogical-Petrographic Characteristics of Tailings  
The mineralogical-petrographic characteristics of tailings are important for assessing their potential impact on the environment. The representation of different types of rocks in the tailings is determined by the layer unit in which coal was mined. The content of combustible substances in the spoil heap material affects both the petrographic composition and the granulometry. During the study of spoil heap material in the Ostrava-Karvina Coalfield (Czech Republic), it was proven that the petrographic composition of the tailings is practically identical within the individual spoil heaps. The main rock types on the spoil heaps are various types of aleuropelites (black and black-gray to gray siltstones to very fine-grained sandstones with root soil content), which come from the immediate vicinity of the coal seams. The decay of siltstone is relatively fast. Fine-grained, medium-grained to coarse-grained sandstones are also represented in the spoil heap material in large and variable quantities. These rocks are somewhat more resistant to weathering. In sandstones especially, the cement of their sand grains is corroded to form sand eluvia. The rate of disintegration of sandstones, therefore, depends on the nature of their cement. Sandstones with carbonate and clay cement are especially less resistant, while silicified sandstones are very stable. The most common type of carbonates is ankerite or a representative of the dolomite-ankerite isomorphic series. As a rock-forming mineral, ankerite occurs relatively rarely in the cement of some sandstones, while its occurrence is more common in seam parting. A significant part of the so-called pelosiderites is made up of ankerite rather than siderite. Calcite is very rare in Ostrava-Karvina Coal Area (OKCA) rocks. Small amounts of clay minerals (of the illite-smectite mixed structure type) may show swelling when in contact with water [36].  
The mining material is characterized primarily by rocks and rock material (clay stones, mudstones, sandstones, etc.). There are also large amounts of silica (SiO\({}_{2}\)) in it, alumina (Al\({}_{2}\)O\({}_{3}\)), iron oxides (Fe\({}_{x}\)O\({}_{y}\)), potassium oxide (K\({}_{2}\)O), carbon (C) and calcium oxide (CaO), sodium oxide (Na\({}_{2}\)O), or titanium oxide (TiO\({}_{2}\)) [37,38]. The mineralogical composition of the tailings sample from the thermally active Hermanice spoil heap is presented in Table 3. SiO\({}_{2}\) is also the most abundant (43%), and the accompanying aluminosilicates are present in smaller amounts.
# Reliability Modeling and Analysis of Multi-Degradation of Momentum Wheel Based on Copula Function
## 2 Key Factors Affecting the Life of the Momentum Wheel  
Analysis of engineering practices and a large number of tests have shown that the main cause of failure in the rail momentum wheel is the failure of its lubrication system and bearing failure. There are three major influencing factors for lubrication system failure: metal wear, cage wear, and lack of lubricant. For bearing, bearing geometry, material selection, environmental factors, etc., are some causes of bearing failure [24]. The failure mode of the bearing and the main failure mode of the momentum wheel are given in [12; 25]. In the case of excessive oil supply to the bearing and excessive lean oil, the shaft temperature will continue to be high, the current telemetering value will increase, and the speed will decrease. When the motor drive current is short-circuited or open-circuited, the current telemetry value fluctuates, the speed drops, and the relationship between the control command and the current value is disordered. After analysis, the shaft temperature, current, speed, and other performance data have complex relationships with the momentum wheel failure modes. Refs. [17; 26] give the results of monitoring the rotational speed, current, and temperature, and found that there is a close relationship between the three. When one of the variables changes, the other two quantities also change. (1) The existing literature has conducted in-depth studies on the relationship between the remaining amount of lubricant and the remaining life of the momentum wheel and (2) there is a clear correspondence between the remaining amount of lubricant and the shaft temperature, current and speed. However, there are no research results on how to describe the relationship between them. In this paper, lubricant residual quantity and current are selected as degradation variables, and momentum life wheel residual life prediction model based on multi-degradation quantity is established.  
As the main indexes describing the performance degradation of the momentum wheel, lubricant residual quantity and current have common impression factors such as the temperature inside the system. It can be further concluded that there is a correlation between different performance degradations, which would cause a huge error in reliability modeling and analysis if it is ignored. To take the correlation into consideration during the analysis process, the Copula function, a professional solution to problems of failure correlation, is introduced in this research. The Copula functions and reliability calculation procedure are illustrated in Section 4 in detail.
## 4 Experiments and Results
### Experiments on Synthetic Datasets
The clustering results of LDP-MST and HCDC are shown in Figure 13 and Figure 14, respectively. It can be seen that LDP-MST and HCDC perform well in dealing with arbitrary shapes and highly overlapping datasets but perform poorly in dealing with uneven datasets such as Pathbased and DS577. HCDC is excellent in dealing with arbitrary shapes and highly overlapping datasets, but performs poorly in dealing with datasets with uneven density such as Pathbased and DS577. Unlike the six comparison algorithms mentioned above, the CPDD-ID algorithm uses two-phase clustering. Local density maxima are detected using kernel density estimation in the partitioning phase, which can effectively detect the density distributions of dense and sparse regions and avoid incorrectly classifying sparse clusters into dense clusters. For example, the Pathbased, Aggregation and Jain datasets provide strong support for the subsequent merging phase by correctly identifying subclusters with different density distributions in the partitioning phase. The merging phase is similar to hierarchical clustering in that the subclusters with maximum similarity are iteratively merged through the interaction degree of shared nearest neighbors among subclusters, starting from structural similarity. The proposed merging strategy shows good performance in dealing with datasets that are highly overlapping and ambiguous to each other's cluster structure and achieves first-place  
Figure 14: (a-j) show the clustering results of the HCDC algorithm on ten synthetic datasets. Figure 13: (a-j) show the clustering results of the LDP-MST algorithm on ten synthetic datasets. results on the Jain, Spiral, Zelink1, Pathbased, and DS577 datasets. Even on datasets with high overlap between clusters such as R15 and S1, it can achieve second-place results. In summary, the CPDD-ID algorithm combines the advantages of both density-based clustering and hierarchical clustering, demonstrating universal performance compared to other algorithms.
# Investigation of Debonding Effect in Internal Replacement Pipe System Under Lateral Loading
## 2 Finite Element Analysis
### Internal Replacement Pipe (IRP) System Modelling and Validation
#### 2.2.2 IRP System Meshing  
Figure 6 illustrates system meshing, an essential step in numerical simulations. Various components of the system, such as the clamp, pin, load head, and blind, have been assigned an element size of 10 mm (0.4 inch) [18]. This size provides a balance between computational efficiency and capturing the essential features of these components. An element size of 5 mm (0.2 inches) has been selected for the host pipe, IRP, and adhesive. Furthermore, an element size of 2 mm (0.08 inch) has been utilized for the crack opening region in the IRP. A bias factor of 5.0 and 2.0 is applied to the IRP and the discontinuity, respectively, with the finer end positioned toward the midspan. This smaller element size ensures a higher level of detail and accuracy in capturing the stress-intensity behavior and response of these critical elements within the system.  
Regarding element thickness, three different thicknesses have been employed for the host pipe and IRP, enabling an appropriate representation of their structural integrity. Given the extremely thin aspect ratio of the adhesive, a single-element thickness has been chosen. As a result of the meticulous meshing process, a total of 181,003 elements have been recorded for the model. The comprehensive meshing scheme employed in this study aims to provide an accurate and detailed representation of the system's behavior and response under various loading conditions.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Properties** & **A36 Steel** & **ALTRA10 IRP** & **Epoxy Adhesive** \\ \hline Density kg/m\({}^{3}\) (lb/inch\({}^{3}\)) & 7850 (0.2836) & 1000 (0.036127) & 1150 (0.041546) \\ Elastic Modulus GPa (ksi) & 200 (29,008) & 3.74 (542) & 2.59 (375) \\ [PERSON]’s ratio & 0.29 & 0.23 & 0.463 \\ \hline \end{tabular}
\end{table}
Table 4: Properties of materials employed in the IRP system.  
Figure 5: Stress and strain curve for ALTRA10 IRP.
# Modeling and NVH Analysis of a Full Engine Dynamic Model with Valve Train System
## 3 Modeling and Analysis
### Multi-Body Dynamic Model
The stiffness of each spring is defined as:  
\[k_{MB}=\frac{F_{\max MB}}{\mu_{0 MB}\cdot r}.A, \tag{11}\]  
where \(F_{\max MB}\) is the maximum main bearing pressure, which can be obtained through the theoretical calculation according to the pressure curve in the cylinder; \(\mu_{0 MB}\) is the radius clearance of the main bearing; \(r\) is the number of all the effective compression springs in the bearing; \(A\) is the correction factor, \(A=1.5\sim 3.0\). The loads of the multi-body dynamic model come from the in-cylinder pressure, the piston slap, and the valve train system. In-cylinder mixed gas combustion produces high pressure periodically. The pressure is imposed to the piston downward and to the bottom of the cylinder head upward. The measured in-cylinder pressure at 2500 rpm under the wide open throttle (WOT) condition is shown in Figure 8. Under this working condition, the engine outputs the maximum torque at a medium rotation speed, which is relatively frequently used when accelerating. The piston reaches the top dead center (TDC) on the compression stroke when the crank angle is at zero degrees. The load of the in-cylinder pressure is transmitted to the crankshaft through the piston-linkage mechanism, as shown in Figure 9. \(F_{P}\) is the downward force of the in-cylinder pressure on the piston. The conrod force \(F_{C}\) transmitting along the conrod is divided into the radial component \(F_{R}\) and the tangential component \(F_{T}\). \[ \begin{split} F_{R}&=F_{C}\cos(\alpha+\beta)=F_{P} \frac{\cos(\alpha+\beta)}{\cos\beta}\\ F_{T}&=F_{C}\sin(\alpha+\beta)=F_{P}\frac{\sin( \alpha+\beta)}{\cos\beta}\end{split}  \tag{12}\]  
where \(\alpha\) is the angle between \(F_{R}\) and \(F_{P}\), and \(\beta\) is the angle between \(F_{C}\) and \(F_{P}\). Due to the existence of the cylinder clearance, the piston has a lateral translation and a rotation around the piston pin during the stroke. The piston lateral force at different height points on the liner TS and ATS is calculated based on the piston dynamic model, then the piston slap excitation to the cylinder liner is obtained. For example, the load of cylinder 1# piston-liner contact force is calculated and shown in Figure 10.  
Figure 8: In-cylinder pressure at 2500 rpm under the wide open throttle (WOT) condition.
## 3 Results
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Maturity Groups (13.81)**} & \multicolumn{3}{c}{**Effect of Maturity (13.81)**} \\ \hline
**Variables** & **Early (_n = 37_)** & **On-Time (_n = 135_)** & **\(t\)** & **\(p\)** & \(\eta_{P}^{2}\)** \\ \hline Practice experience (years) & 4.7 \(\pm\) 2.6 & 5.5 \(\pm\) 2.5 & \(-\)1.79 & 0.075 & 0.019 \\ \hline Training load (h/week) & 6.7 \(\pm\) 1.8 & 6.1 \(\pm\) 1.5 & 2.15 & 0.033 & 0.027 \\ \hline CA (years) & 13.9 \(\pm\) 0.3 & 13.7 \(\pm\) 0.5 & 1.64 & 0.103 & 0.016 \\ Maturity offset (years) & 1.36 \(\pm\) 0.40 & 0.22 \(\pm\) 0.70 & 12.75 & \(<\)0.001 & 0.345 \\ APHV (years) & 12.5 \(\pm\) 0.2 & 13.5 \(\pm\) 0.5 & \(-\)17.76 & \(<\)0.001 & 0.451 \\ \hline MORPHOLOGY & & & & & \\ Body Mass (kg) & 70.3 \(\pm\) 7.5 & 56.8 \(\pm\) 8.9 & 8.42 & \(<\)0.001 & 0.294 \\ Stature (cm) & 182.4 \(\pm\) 5.5 & 170.0 \(\pm\) 7.6 & 11.15 & \(<\)0.001 & 0.335 \\ Arm span (cm) & 185.6 \(\pm\) 6.8 & 173.1 \(\pm\) 9.3 & 9.11 & \(<\)0.001 & 0.256 \\ Hand span (cm) & 23.0 \(\pm\) 1.1 & 21.9 \(\pm\) 1.6 & 4.80 & \(<\)0.001 & 0.084 \\ BMI (kg/m\({}^{2}\)) & 21.1 \(\pm\) 1.9 & 19.6 \(\pm\) 2.3 & 3.76 & \(<\)0.001 & 0.077 \\ \%FM & 18.8 \(\pm\) 5.1 & 16.3 \(\pm\) 5.7 & 2.40 & 0.018 & 0.033 \\ FFT (kg) & 56.8 \(\pm\) 3.9 & 47.2 \(\pm\) 6.1 & 11.53 & \(<\)0.001 & 0.324 \\ \hline FITNESS & & & & & \\ V20-m (s) & 3.29 \(\pm\) 0.22 & 3.36 \(\pm\) 0.24 & \(-\)1.55 & 0.123 & 0.014 \\ TT (s) & 10.49 \(\pm\) 0.63 & 10.39 \(\pm\) 0.70 & 0.82 & 0.415 & 0.004 \\ CMJ Height (cm) & 30.8 \(\pm\) 4.7 & 29.7 \(\pm\) 5.2 & 1.15 & 0.250 & 0.008 \\ CMJ Power (w) & 851 \(\pm\) 133 & 684 \(\pm\) 125 & 7.11 & \(<\)0.001 & 0.229 \\ CMJ’S Height (cm) & 36.5 \(\pm\) 4.9 & 35.4 \(\pm\) 5.7 & 0.99 & 0.323 & 0.006 \\ CMJ’S Power (w) & 925 \(\pm\) 136 & 747 \(\pm\) 134 & 7.15 & \(<\)0.001 & 0.231 \\ SUM HG (kgf) & 79.0 \(\pm\) 13.5 & 68.5 \(\pm\) 14.4 & 3.96 & \(<\)0.001 & 0.085 \\ MBT (m) & 5.60 \(\pm\) 0.63 & 4.80 \(\pm\) 0.70 & 6.30 & \(<\)0.001 & 0.189 \\ Seat and reach (cm) & \(-\)2.7 \(\pm\) 8.5 & \(-\)0.8 \(\pm\) 7.6 & \(-\)1.30 & 0.195 & 0.010 \\ \hline GAME PERFORMANCE & & & & & \\ PIR/game & 3.04 \(\pm\) 4.02 & 1.90 \(\pm\) 3.10 & 1.86 & 0.065 & 0.020 \\ PIR/min & 0.16 \(\pm\) 0.30 & 0.11 \(\pm\) 0.22 & 1.10 & 0.274 & 0.007 \\ Points/game & 3.94 \(\pm\) 2.74 & 3.72 \(\pm\) 2.54 & 0.45 & 0.653 & 0.001 \\ Points/min & 0.26 \(\pm\) 0.14 & 0.26 \(\pm\) 0.15 & \(-\)0.05 & 0.962 & 0.000 \\ Rebounds/game & 3.59 \(\pm\) 2.32 & 2.56 \(\pm\) 1.65 & 3.04 & 0.003 & 0.052 \\ Minutes played /game & 14.0 \
# Spectroscopic Approach for the On-Line Monitoring of Welding of Tanker Trucks
## 1 Introduction
The process under analysis in this paper also involves arc-welding of aluminium/ magnesium alloys, in the manufacture of tanker trucks. Contamination via hydrogen plays a major role in this process [18] and a spectroscopic approach is proposed in this work to detect and avoid this issue and other possible defects, along with the acquisition of information about process parameters in addition to welding plasma spectra, such as welding voltage and current, protection gas flow rate, process speed (and position), wire feed speed, ambient temperature and humidity. The novelty of this proposal has various aspects. On the one hand, it will be shown that not only hydrogen but also other elements such as sodium seem to be well correlated with the appearance of porosities. In fact, some results suggest the necessity of distinguishing between continuous porosities over a significative length of a seam and punctual porosities, probably generated by different mechanisms. On the other hand, not only welding trials but results obtained during production are presented in this paper. Welding experiments in which artificial defects are provoked are commonly used to verify the proposed solutions, such as variations in the welding current or gas flow rate [19] or pre-processing of the weld plates to simulate porosities [17]. However, these perturbations are commonly quite unlike those produced in real production scenarios, and the conclusions derived from them cannot always be extrapolated to the relevant target processes. Finally, it will be discussed how the implemented monitoring system might prove useful not only in the detection of possible defects but also in indicating their causes.
# Enhanced Matching Game for Decoupled Uplink Downlink Context-Aware Handover
## 6 Conclusions  
In this paper, we consider uplink performance during the association step for the handover scheme to meet the service requirements. Therefore, in contrast to the conventional handover, it deals with the case where the BSs chosen for the downlink may not be useful for the uplink. Thus, we define new context-aware handover algorithms for the two-tier networks. By introducing a well-designed utility function, our proposition considered the load balancing and data rate for maximizing the sum of the utility function. Furthermore, we have modeled the problem as a many-to-one matching by using the assignment step. In addition, this assignment step will help to confirm the minimum requirement QoS while creating a virtual set of BSs, where those virtual sets of BSs would enable the decoupled handover process. Validation was conducted by a scenario built in NS-3 for a heterogeneous system including LTE and Wi-Fi technologies. Simulation results have shown that the proposed approach yields significant beneficial gains over the traditional techniques.  
Nevertheless, we are conscious that the system can give ideal execution with complete information. However, the signaling cost for the use of complete information algorithms is high. Hence, to address this issue, in our future work we will explore the trade-off between signaling overhead and ideal system execution. Moreover, we can address various intriguing open inquiries and headings; for example, assessing the execution of the proposed scheme in the extended situation with high speed while running diverse application types as well as examining the situation in which nodes are deceiving, which can constitute a captivating subject.  
**Author Contributions:** [PERSON]: Main writing and designing the total system, analyzing and improving the proposed system. [PERSON] and [PERSON]: Total supervision of the paperwork, review, and comments, etc. All authors have read and approved the final manuscript.  
**Conflicts of Interest:** Authors declare no conflict of interest.