esa-sceva/satcom-chunk-collection huggingface dataset:
# Practical V2I Secure Communication Schemes for Heterogeneous VANETs
###### Abstract  
Since the roadside infrastructure and vehicles come from different manufacturers, vehicular ad hoc networks (VANETs) now are extremely heterogeneous. It is difficult to communicate securely for heterogeneous facilities in VANETs because secure communication needs to concurrently realize confidentiality, authentication, integrity, and non-repudiation. To meet the above security attributes in one logical step, four bi-directional signcryption schemes are proposed for specific heterogeneous vehicle to infrastructure (V2I) communication in this paper. The first scheme supports batch verification, which allows multiple vehicles registered in a public key infrastructure (PKI) system to transmit messages to a receiver in an identity-based cryptosystem (IBC), both which are the mainstream public key cryptosystems. The second scheme supports a sender in a PKI to securely broadcast a message to multiple vehicles in an IBC. The communication direction of the latter two schemes is opposite to the former two schemes (i.e., from IBC to PKI). All these schemes can be proved to satisfy confidentiality and unforgeability based on the assumptions of decisional and computational Diffie-Hellman problems in the random oracle model. Furthermore, numerical analyses and simulation results demonstrate the computation costs, communication costs, storage, and the aggregate ciphertext length of our schemes are better than the existing ones.  
v 2019; 9, 3131; doi:10.3390/app9153131
## 3 Results and Discussions
### Swelling Behavior of Expansive Soil at Different Depths  
Since the above analyses confirmed the assumption on the migration and aggregation of Ca\({}^{2+}\) and Mg\({}^{2+}\), the next step was to check whether these aggregated cations participate in the ionic exchange with expansive soil and assess the modification effect produced by the latter exchange on expansive soil. After completion of the model test, the surface, middle and bottom expansive soil layers (soil layers at depths of 0-30 mm, 90-120 mm and 180-210 mm, respectively) was removed from the test apparatus and was put into separate containers, then was naturally dried and ground to prepare the powder and cylindrical samples for the free swelling ratio, swelling pressure, and swelling percent tests by the same method in Section 2.3.4. Noteworthy is that the each swelling parameter was determined by the four parallel experiments. The test results obtained are presented in Figure 7.  
It can be observed from Figure 7 that all swelling parameters of the middle and bottom expansive soil layers are lower than their initial values, while these parameters of surface expansive soil layer are slightly higher than its initial values. This phenomenon indicates that the swelling of lower expansive soil layer is inhibited in the saturated process. The most reasonable explanation is that the lower expansive soil layer has been modified due to the cation exchange with the migrating and aggregating cations where the divalent cations (Ca\({}^{2+}\), Mg\({}^{2+}\)) enter into the diffuse double layer (DDL) of clay particles and interlayer of montmorillonite crystals, but the swelling potential of the surface expansive  
Figure 6: The cation concentration of pore solution at three soil layer interfaces after completion of the model test: (a) Mg\({}^{2+}\); (b) Ca\({}^{2+}\).  
Figure 7: The variation of swelling properties of expansive soil after completion of the model test: (a) Free swelling ratio; (b) swelling pressure and swelling percent.  
soil layer is released because of the downward migration of bivalent cations. However, the swelling pressure of the middle and bottom expansive soil layers is over 120 KPa that is still higher than the self-weight of overlying soil layer. This implies that the inhibition effect produced by cation exchange cannot completely balance the swelling pressure. A tentative explanation is that the impact of electrical environment formed by the migrating and aggregating cations counteracts this part of the swelling pressure (described in the next section). But due to the above soil samples soaked in de-ionized water, the electrical environment was destroyed so that the swelling potential of expansive soil cannot be completely inhibited.
## 2 Materials and Methods
### Combination Index (CI) and Interaction Analysis  
The effective concentration causing 50% scavenging activity (EC50) values of each sample was calculated for different antioxidant assays, and the combination index used to determine the antioxidant interactions at different proportions (3:1, 2:1, 1:1, 1:2, and 1:3) was determined employing CompuSyn software (version 1.0). The classification of different interaction (synergism, additive, and antagonism) types was graded based on the combination index method [35] as follows: <0.1--very strong synergism, 0.1-0.3--strong synergism, 0.3-0.7--synergism, 0.7-0.85--moderate synergism, 0.85-0.90--slight synergism, 0.90-1.10--nearly additive, 1.10-1.20--slight antagonism, 1.20-1.45--moderate antagonism, 1.45-3.3--antagonism, 3.3-10--strong antagonism, and >10--very strong antagonism.
# Experimental Investigations on the Effects of Fatigue Crack in Urban Metro Welded Bogie Frame
## 2 Experimental Methods  
### Cracking Site and Fracture Analysis  
A crack was found at the spring sleeve of one metro bogie frame (as shown in Figure 1a) and the service life is less than 200,000 km. The spring sleeve of the frame side beam is welded by upper cover, lower cover and circular plate. The positioning block is welded to the spring sleeve lower cover by four welds, as seen in Figure 1b. The crack initiated from the weld toe of the positioning block and then propagated to the spring sleeve circular plate with maximum 110 mm in length, as shown in Figure 1c,d.  
Figure 1: _Cont._  
The fracture morphology of the cracking zone was examined by scanning electron microscopy (SEM) as shown in Figure 2. It can be clearly observed that some parallel marks are available near the weld toe, which indicates a notable fatigue damage feature during operation. Metallographic specimens were thus prepared at the critical cracking position. Columnar grains were clearly found in the weld and the heat-affected zone, which are composed of lamella ferrite and featherlike widmanstatten structures, as shown in Figure 3. The SEM observation of weld toe shows that the micro crack starts from the surface of the lower plate and propagates internally, and it includes several discontinuous sections and branches appear around the main crack (Figure 4).  
Figure 1: The crack position and the Macro morphology of the crack: (**a**) the crack position found in site; **(b)** the model of bogie frame and the positioning block; (**c**) and (**d**) the macro pictures of crack initiated from the weld toe of positioning block.
## 4 Mathematical Formulation of the Optimization Problem  
Unlike traditional strategies, which always sense wireless radio environments at the terminal end, the energy-efficient transmission strategy proposed in this paper releases the link quality information by broadcasting the LSA in the HAP and satellite ends without measurement at the terminal end. This will potentially reduce the energy cost of the signal processing and improve the energy efficiency at the terminal end, which is very effective for the slow flat fading channels. The mathematical formulation of the optimization problem for the proposed model is given in the following sections.
## 3 Results and Discussion
### Do the Tomato Responses to the Single and Combined Stress Occur at Between- or Within-Plant Levels?
Different letters indicated significant difference among the mean groups (N = 8; \(p\) < 0.05 test of Tukey). The values within each panel indicated the F statistic with the \(p\) values (* 0.05 \(<p\) < 0.01; *** \(p\) < 0.001; NS not significant) derived from one-way ANOVA. Figure 13: Leaf dry weight of three different leaves of tomato plants exposed for 8 days at diverse stresses (abiotic (ABIO); biotic (BIO); combined (COMB)). No stresses (CTR). The box plot indicated the minimum, first quartile, median, third quartile, and maximum value. Different letters indicated significant difference among the mean groups (N = 8; \(p\) < 0.05 test of Tukey). The values within each panel indicated the F statistic with the \(p\) values (*** \(p\) < 0.001; NS not significant) derived from one-way ANOVA. An overall result is that, unlike the CTR and BIO, the ABIO and the COMB treatments significantly modified all the morpho-physiological traits among the three tomato leaves, except the LWC and A (Figures 8-14). Further, by comparing the spatial patterns of plant responses (Figure S5), the physiological responses (A, g\({}_{\text{s}}\), T, and iWUE) to the ABIO and COMB treatments suggested an architectural pattern while the morphological ones pointed out a vascular pattern (Figures 8-14). The abiotic stressors are known to strongly influence the plant photosynthetic traits in relation to the leaf position/age, reflecting an architecture pattern, in order to preserve the highly valuable tissues, such as the young leaf [94]. The vascular pattern of the LFW and LDW in response to the COMB treatment could have been due to the ABA-JA cross-talking signaling pathways [72; 73; 74] which could have occurred between the two vascularly connected leaves (local and orthostic). In this respect, we could hypothesize that the _Tuta_ larvae placed on the local leaf of tomato plants could have triggered, by vascular connection, these hormonal signaling pathways which could have redirected the photosynthetic resources towards the defense compounds rather than leaf growth ones. This kind of hormone cross-talk signaling interaction among the different vascularly connected leaves within the plant has already been observed. For example, abiotic stresses antagonized the immune responses by ABA-SA hormonal interaction in older leaves of Arabidopsis, but such effect was suppressed in the younger leaf through a signaling component of the SA pathway [95].
# Radio Resource Dimensioning for Low Delay Access in Licensed OFDMA IoT Networks +
## Appendix E Proof of Proposition 7
### Fading Distribution  
Using the distribution of \(A_{f}\) in (37),  
\[\mathbb{E}_{A_{f}}[\mathds{1}_{\{A_{f,k}\leq A_{f}<A_{f,k-1}\}}]=\sum_{p=0}^{ n_{r}-1}\frac{1}{p!}\Big{[}(P_{n}+I)^{p}s_{k}^{p}e^{-(P_{n}+I)s_{k}}-(P_{n}+I)^{p}s _{k-1}^{p}e^{-(P_{n}+I)s_{k-1}}\Big{]}, \tag{11}\]  
By averaging over the noise and the interference level, Proposition 7 is obtained by invoking the following lemma:  
**Lemma A1**.: _Let \(\Xi_{i}(s)=\mathbb{E}\left[s^{i}(P_{n}+I)^{i}e^{-s(P_{n}+I)}\right]\) Then,_  
\[\Xi_{i}(s)=(-1)^{i}\sum_{j=0}^{i}s^{i}\binom{i}{j}\ \frac{d^{i}\mathcal{L}_{P_{n}}(s)}{d \,s^{j}}\ \frac{d^{i-j}\mathcal{L}_{I}(s)}{d\,s^{i-j}}. \tag{12}\]  
Proof.: The expansion of \(\Xi_{i}(s)=\mathbb{E}\left[s^{i}(P_{n}+I)^{i}e^{-s(P_{n}+I)}\right]\) gives  
\[\Xi_{i}(s)=\frac{1}{i!}\sum_{j=0}^{i}s^{i}\binom{i}{j}\ \mathbb{E}\left[\ P_{n}^{j}t^{i-j}e^{-s(P_{n}+I)}\right]. \tag{13}\]  
Due to the independence of \(P_{n}\) and \(I\),  
\[\Xi_{i}(s)=\frac{1}{i!}\sum_{j=0}^{i}s^{i}\binom{i}{j}\mathbb{E}\left[P_{n}^{ j}e^{-sN}\right]\mathbb{E}\left[I^{i-j}e^{-sI}\right]. \tag{14}\]  
Note that, for a given random variable \(X\), we have,  
\[\frac{d^{k}}{d^{k}s}\Big{(}\mathcal{L}_{X}(s)\Big{)} = \frac{d^{k}}{d^{k}s}\left[\int_{\mathbb{R}}e^{-sx}p_{X}(x)dx\right]\] \[= (-1)^{k}\ \mathbb{E}[X^{k}e^{-sX}]\]
## 3 Interference Suppression through Beamforming Techniques
### Deterministic Null Generation
Figure 9: RD maps of CPI 13 showing how most of the interference sources are strongly rejected through deterministic nulling along the estimated DoAs. One of the targets with a clearer trajectory along the processing time (marked with a blue box in Figure 10a) was selected for studying the difference in Signal-to-Noise Ratio (SNR) before and after the application of the proposed technique. The selected target is suspected of being a maritime one, due to their Doppler frequency shift indicating low velocity. SNR has been calculated as the ratio of maximum target power with respect to background level. Figure 11a proves that only in some cases the SNR of a maritime target (the one highlighted with a square in Figure 10a) is greater than it was without the application of this technique. Sub-optimum detection approaches for approximating the envelope detector are usually applied using a fixed detection threshold. Constant False Alarm Rate (CFAR) techniques are used to maintain the \(P_{fa}\) at a constant level despite clutter parameter variations, estimating its unknown parameters from observations acquired by the radar. If some usual detection requirements are imposed, as probability of detection \(P_{D}=80\%\) and probability of false alarm \(P_{fa}=10^{-5}\), a minimum SNR of 17 dB is required to assure these requirements if a CFAR detector is employed considering Swerling I type for targets under AWGN conditions (Figure 11b) [29]. Required SNR is only achieved in a few CPIs in the case of deterministic nulling and not completely assured in classic beamforming, being the target SNR in most of the CPIs just around this border of 17 dB. Figure 11: (**a**) SNR of selected targets in each CPI for classical beamforming and deterministic nulls generation; (**b**) curve of required SNR for a given \(P_{D}\) and \(P_{fa}=10^{-5}\) in case of a CFAR considering Swerling I targets. Figure 10: (**a**) Cumulative of maxima through the 158 CPIs in beam 1 with deterministic null generation; (**b**) array pattern for the same beam and CPI 13 comparing the case of classical beamforming with the Deterministic Nulls (DN) generation one.
## 2 General Features of ClyA
### Assembled Form  
A common feature of PFTs is their ability to assemble from a soluble, monomeric state into oligomeric, annular membrane complexes [7; 51]. ClyA is one of the very few \(\alpha\)-PFTs for which high-resolution structural information is available for both the soluble monomer and the annular pore complex [14; 16; 52; 53]. ClyA is therefore considered the prototype of a subfamily of \(\alpha\)-PFTs (often referred to as the ClyA family), and its structure has been well-characterized in numerous reports.  
Briefly, the ClyA monomer consists of two domains: a tail domain composed of a bundle of five \(\alpha\)-helices, and a head domain with a small \(\beta\)-hairpin (\(\beta\)-tongue) flanked by two short \(\alpha\)-helices [14; 16]. Molecular analysis has indicated the importance of the \(N\)- and \(C\)-terminal regions, and a partial hydrophobic sequence, in the process of ClyA translocation to the periplasm. In addition, a long \(\alpha\)-helix (\(\alpha\)A1) is crucial for transmembrane channel formation and function [54]. As shown in Figure 1, once ClyA reaches the target membrane, the monomer first undergoes a conformational change to a protomer and then predominantly assembles into an octameric [53] or 13-meric (redox state-independent) [55] pore complex. Alternatively, soluble ClyA monomers can assemble into 400-kDa dodecameric pores after binding to the detergent, \(n\)-dodecylmaltoside [14]. The structural mechanism of ClyA and ClyA-like pore formation has been well described in previous reports and reviews [17; 51; 56; 57; 58].
# A Novel SDWSN-Based Testbed for IoT Smart Applications
## 4 Model Testing and Evaluation
### Distributed Nodes for Ground WSN Use Case: Energy Consumption and Packets Received  
The physical ground network communication performances including the overall network's energy consumption and the percentage of received packets in coordinator nodes based on a range of gateway sparsity \(\sigma\) and communication message rates are calculated and shown in Figures 26 and 27.  
As shown in these two figures, as network density increases, the ground network packet delivery and energy cost for message transmission from leaf nodes (SensorTags) to coordinator nodes (development boards) degrade due to increased interference from other cluster members. Furthermore, as the message rate increases, the percentage of packet delivery in the coordinators' buffers decreases, and the energy consumption of the ground network increases. The reason for this is that as the message rate increases, higher packet intervals of each network component cause the real network components to consume more energy while delivering fewer packets to the coordinator nodes.  
Figure 24: The physical ground network formation for a large-scale network with \(\sigma=2\).  
Figure 26: Energy consumption of physical ground network in terms of message rate and gateway nodes’ spread factor.  
Figure 25: Router capacity based on packet loss.
## 2 Materials and Methods
### Evaluation Methodology  
Our proposed models were subjected to a thorough evaluation process across a variety of carefully crafted simulated scenarios. We began by examining the models' performance in noise-free conditions, focusing on the relationship between tumor radii variations and the resultant F1-Score. To mimic real-world conditions, we introduced noise at various signal-to-noise ratios (SNRs) and assessed the models' robustness against these perturbations. We also evaluated the stability of the models by training them at specific SNRs and testing them under different noise levels, revealing valuable insights into their adaptability. Furthermore, we conducted an in-depth investigation into the models' performance when trained with a wide range of mean tumor radii. Finally, we put the models to the test under stringent conditions involving a broad range of tumor radii and an SNR of 30. This rigorous evaluation process aimed to provide a detailed understanding of the models' capabilities and constraints, thereby presenting a holistic view of their performance across diverse simulated scenarios.
## 5 Discussion & Future Work
### Research Questions Analyses  
To answer RQ1, the present work provided a systematic categorisation and analyses of cybersecurity risks by applying the TARA to the ACS domain. Three main groups were identified: high (risk values of four and five), medium (risk values of two and three) and low (risk value of one). Although the TARA is suitable for threat modelling and analysing risks, it remains limited in assigning an objective risk value with regard to the automation level. In fact, the weight of the automation level depends on the experts' opinion and their expertise. Furthermore, being an asset-based methodology, the automation features are impossible to evaluate as a single asset from the TARA.  
Limited by several real-life pilot restrictions, the pentests that we managed to execute confirm the ease of the necessary setup for an attacker to execute high-risk scenarios. In particular, the affordability of the equipment required as well as the short timespan in which an attacker can perform an attack, have been demonstrated. As far as 3/4/5G jamming is concerned, the most cost-effective solution in terms of time is sweep jamming on the frequencies of the most widely used operators, which means that it is not necessary to find out which operator is used by the manufacturer. However, the black-box penetration tests and vehicle resilience that we observed did not provide any additional insights into whether the vehicle was affected by the intended malicious activities. Therefore, we cannot confirm if the mitigations applied to the vehicle were sufficient; hence, black-box penetration testing is not suitable. To answer RQ2, we believe that the openness of the OEM's ACS ecosystem towards elevating the restriction on internal data access (e.g., logs) is required to both execute physical attacks and cross-check the effectiveness of the conducted wireless pentests.
# Transmission Line Fault-Cause Identification Based on Hierarchical Multiview Feature Selection
## 2 Hierarchical Multiview Feature Selection (HMVFS)
### Optimization  
In order to solve \(l_{2,1}\)-norm minimization and \(F\)-norm minimization problems, the regularization terms \(\|\textbf{W}\|_{2,1}\) and \(\sum\limits_{p=1}^{m}\|\textbf{W}_{v}\|_{F}\) need to be respectively relaxed by \(\textit{Tr}(\textbf{W}^{\textbf{T}}\textbf{CW})\) and \(\textit{Tr}(\textbf{W}^{\textbf{T}}\textbf{DW})\)[32]. The objective function is rewritten as  
\[\begin{array}{l}\min\limits_{\textbf{W},\textbf{M},\textbf{C},\textbf{D}} \|\textbf{XW}-\textbf{Y}-\textbf{B}\otimes\textbf{M}\|_{F}^{2}+\alpha\textit{ Tr}(\textbf{W}^{\textbf{T}}\textbf{CW})+\beta\textit{Tr}(\textbf{W}^{ \textbf{T}}\textbf{DW}),\\ s.t.\ \ C_{ii}=\frac{1}{2\|\textbf{W}_{ii}\|_{2}},D_{jj}=\frac{1}{2\|\textbf{W}_{ z}\|_{F}},\end{array} \tag{8}\]  
where \(\textbf{C}\in\mathbb{R}^{d\times d}\) and \(\textbf{D}\in\mathbb{R}^{d\times d}\) are diagonal matrices and derived from **W**. For \(D_{ii}\), \(\textbf{w}_{i}\) is the row vector of \(\textbf{W}_{v}\).  
Though two more variables are introduced, we obtain a convex function, and we can solve the optimization problem iteratively. In each iteration, we update one variable while others are fixed, and all variables can be optimized in order. In view of **C** and **D** derived from **W**, we fix **M** and update **W** at first. The derivative of (8) w.r.t. **W** is calculated as  
\[2\textbf{X}^{\textbf{T}}(\textbf{XW}-\textbf{Y}-\textbf{B}\otimes\textbf{M})+2 \alpha\textbf{CW}+2\beta\textbf{DW}. \tag{9}\]  
Let (9) equal zero, then the updated **W** can be obtained by solving the equation. If there are big-size data or high-dimensional data, the gradient descent method is recommended. Following that, **C** and **D** can be updated.  
When it turns to **M**, the optimization problem can be transformed from (8) to (10).  
\[\begin{array}{l}\min\limits_{\textbf{M}}\|\textbf{Z}-\textbf{B}\otimes \textbf{M}\|_{F}^{2},\\ s.t.\ \textbf{Z}=\textbf{XW}-\textbf{Y}.\end{array} \tag{10}\]  
According to the definition of \(F\)-norm, this problem can be decoupled into \(n\times c\) subproblems [30] and represented as  
\[\min\limits_{M_{ij}}\bigl{(}Z_{ij}-B_{ij}M_{ij}\bigr{)}^{2}. \tag{11}\]  
With \({B_{ij}}^{2}=1\), (11) is equivalent to (12).  
\[\min\limits_{M_{ij}}\bigl{(}Z_{ij}B_{ij}-M_{ij}\bigr{)}^{2}. \tag{12}\]  
With the nonnegative constraint, \(M_{ij}\) is calculated as  
\[M_{ij}=\max(Z_{ij}B_{ij},0). \tag{13}\]  
Accordingly, **M** can be updated as  
\[\textbf{M}=\max(\textbf{Z}\otimes\textbf{B},0). \tag{14}\]  
Up to now, all variables are updated in the iteration and we present the optimization process in Algorithm 1.  
After optimization, we obtain weight matrix **W** learned across all views and then sort all features according to their importance. The importance is measured by the \(l_{2}\)-norm value of each row vector of **W**, \(\|\textbf{w}_{i}\|_{2}(i=1,2,\ldots,d)\). Feature selection can be completed with features ranked in descending order.
## 3 Finite Element Method (FEM)
### Depth to Ground  
It can be observed from Table 4 that the soil compaction coefficients at the centroid and the edge midpoint are the largest in the middle of the pile length, and gradually decrease towards both sides. In this section, based on the results of numerical analysis, the variations in soil compaction coefficients at the centroid and the edge midpoint with the change in the depth to ground are given as shown in Figures 11 and 12.  
The parameter \(m\) was taken as the ratio of the depth from the focused point to ground to the pile length. The mechanical models of the change in the soil compaction coefficient at the centroid and the edge midpoint with the change in the parameter \(m\) were derived as Equations (7) and (8) from the SP\(-\)PSC pile group test and numerical results (as shown in Figures 11 and 12).  
\[\eta_{\rm cc}=-0.14(m-0.46)^{2}+0.79\;(1\geq m\geq 0) \tag{7}\]  
\[\eta_{\rm cm}=-0.36(m-0.46)^{2}+0.84\;(1\geq m\geq 0) \tag{8}\]  
According to Equations (7) and (8), it can be noted that the soil compaction coefficients at the centroid and the edge midpoint reach the maximum values in the depth direction when \(m=0.46\).  
Figure 11: The relationship between the compaction coefficient at the centroid and \(m\).