esa-sceva/satcom-chunk-collection huggingface dataset:
# Supporting Immersive Video Streaming via V2X Communication
## 2 Bandwidth Allocation for Diverse Streaming Services in V2X Communication
### Route Planning and Adjustment Module for Vehicles
Whenever the vehicle arrives at the next intersection on the route, correct t the pass time period for each subsequent segment as follows: \[\hat{\mathit{rt}}_{c_{i+1}^{\sigma}}=\hat{\mathit{rt}}_{c_{i}^{\sigma}}+RD_{c_{ i}^{\sigma},c_{i+1}^{\sigma}}\Big{(}\hat{\mathit{rt}}_{c_{i}^{\sigma}}\Big{)}+ID_{c_{i}^ {\sigma},c_{i+1}^{\sigma}}\Big{(}\hat{\mathit{rt}}_{c_{i}^{\sigma}}\Big{)},\;1 \leq i<\mathit{h}_{i}^{\sigma}\] (4) where \(c_{1}^{\sigma}\) represents the current segment of the route for \(\sigma\), \(\hat{\mathit{rt}}_{c_{i}^{\sigma}}\) denotes the time when \(\sigma\) arrives at intersection \(c_{i}^{\sigma}\). \(RD_{c_{i}^{\sigma},c_{i+1}^{\sigma}}\Big{(}\hat{\mathit{rt}}_{c_{i}^{\sigma}} \Big{)}\) stands for the time it takes for \(\sigma\) to pass through the segment connecting \(c_{i}^{\sigma}\) and \(c_{i+1}^{\sigma}\) after arriving at intersection \(c_{i}^{\sigma}\) at time \(\hat{\mathit{rt}}_{c_{i}^{\sigma}}\), while \(ID_{c_{i}^{\sigma},c_{i+1}^{\sigma}}\Big{(}\hat{\mathit{rt}}_{c_{i}^{\sigma}} \Big{)}\) represents the time spent by the vehicle due to traffic control before entering the segment connecting \(c_{i}^{\sigma}\) and \(c_{i+1}^{\sigma}\) after arriving at intersection \(c_{i}^{\sigma}\) at time \(\hat{\mathit{rt}}_{c_{i}^{\sigma}}\). * After transmitting the corrected arrival times at each intersection to the management RSUs of each segment, return to Step 3 to continue execution. * If the vehicle passenger changes the itinerary temporarily, recalculate the shortest driving route using the Dijkstra algorithm as follows: \[\overset{\boldsymbol{\mathsf{A}}^{\sigma}}{\boldsymbol{\mathsf{R}}}=\left( \varepsilon_{1}^{\sigma},\varepsilon_{2}^{\sigma},\cdots,\varepsilon_{k^{ \prime}-1}^{\sigma},\varepsilon_{k^{\prime}}^{\sigma}\right),\] (5) \[rt_{\varepsilon_{i+1}^{\sigma}=}rt_{\varepsilon_{i}^{\sigma}}+RD_{ \varepsilon_{i}^{\sigma},\varepsilon_{i+1}^{\sigma}}\left(rt_{\varepsilon_{i} ^{\sigma}}\right)+1D_{\varepsilon_{i}^{\sigma},\varepsilon_{i+1}^{\sigma}} \left(rt_{\varepsilon_{i}^{\sigma}}\right),\;1\leq i<\hat{h}^{\sigma},\] (6) \[rt_{\varepsilon_{1}^{\sigma}}=CurTime+RD_{\varepsilon_{i}^{\sigma}}\left(rt_{ \varepsilon_{i}^{\sigma}}\right)\frac{rt_{\varepsilon_{i}^{\sigma},\varepsilon _{i+1}^{\sigma}}}{rt_{\varepsilon_{i}^{\sigma},\varepsilon_{i+1}^{\sigma}}}, \;1\leq j<h^{\sigma},\] (7) \[\varepsilon_{1}^{\sigma}=c_{j+1}^{\sigma},\;1\leq j<h^{\sigma},\] (8)  
where \(\varepsilon_{1}^{\sigma}\) and \(\varepsilon_{\hat{h}^{\sigma}}^{\sigma}\) represent the next intersection and the destination of EV \(\sigma\)'s current driving route, respectively, and \(c_{j}^{\sigma}\) is the index value of the previous intersection of the current segment. \(rl_{\varepsilon_{i+1}^{\sigma}}^{\sigma}\) and \(rrl_{\varepsilon_{i+1}^{\sigma}}^{\sigma}\) denote the length of the current segment and the distance from \(\sigma\)'s current position to the next intersection, respectively. Return to Step 2 to continue execution.
# QoSComm: A Data Flow Allocation Strategy among SDN-Based Data Centers for IoT Big Data Analytics
## 3 QoS Comm: Design and Implementation  
In this section, we present QoSComm, a strategy to allocate QoS-based data flows for BDA running among SDN-based distributed data centers. QoSComm's main objective is to compute, configure, and allocate data flow rules in OF (OpenFlow) switches for QoS network paths across distributed data centers to improve applications' job completion time.  
The QoSComm design consists of two main processes, as illustrated in Figure 2:  
1. Compute QoS network paths according to a constraint-based multi-objective decision algorithm. Gets the optimal paths with the minimal point-to-point delay, which complies with applications requirements considering four network parameters: available bandwidth, delay, packet loss rate, and jitter.
2. Configure QoS optimal paths via SDN controller to OFS (OpenFlow Switch). Communicate to SDN controller to set a data flow rule on each OFS on the network (at the input and output port level). The SDN controller installs the data flow rule on each of the OFS, specified in the communication with the REST API (Representational State Transfer Application Program Interface). The REST API allows the dynamic configuration of data flow rules in the OFS using HTTP requests to a RestFul Web Service provided by the SDN Controller to get, post, put, and delete data.  
We use QoSComm to get and configure the optimal network paths to compute nodes among the SDN-enabled data centers. This process is performed just before the BDA scheduler submits jobs to compute nodes.  
Figure 2: QoSComm general flow forwarding selection process.
## 3 Results and Discussion  
### Changes in Porosity  
It can be seen from Table 2 that the porosity of two sandstones increases with the increase in the number of freeze-thaw cycles. For instance, the porosity of fine sandstone specimen No. 2-20 increases from 1.63% to 3.33% after 120 freeze-thaw cycles, with an increase of 104%; while for the porosity of coarse sandstone specimen No. 1-19, an increase of 82% was observed from 1.92% to 3.49% after 120 cycles.  
The increase in porosity is likely attributed to ice formation in the specimens. On the one hand, when temperature decreases, pore water in rocks can gradually freeze to ice, producing 9% of the volume expansion. On the other hand, under the action of the temperature gradient, the unfrozen water films migrate and segregation ice forms [37; 38; 39; 40; 41].  
Under the action of the above two causes, pores in rocks are bound to increase in forms of in situ expansion or even cracks. When temperature rises, moisture gradually infiltrates into the newly formed micro-cracks or expanded pores, causing even more serious expansion or cracking during freezing in the next cycle. Accordingly, freeze-thaw cycles cause the above function to occur repeatedly, thereby leading to a continuous increase in rock porosity.
# Chaotic Pulse-Shaping Filter Based on Root-Raised-Cosine Division
## 4 Wireless Communication System Using Chaotic Filter
### Threshold Calculation  
The total impulse response of the pulse-shaping filter, wireless channel and matched filter can be described as:  
\[\begin{split} R(t)&=p(t)*h(t)*g(t)\\ &=\sum_{l=0}^{L-1}\alpha_{l}\frac{\sin\frac{\pi(t-\eta)}{T_{S}}}{ \frac{\pi(t-\eta)}{T_{S}}}\frac{\cos\frac{\beta\pi(t-\eta)}{T_{S}}}{1-\left( \frac{2\beta(t-\eta)}{T_{S}}\right)^{2}}.\end{split} \tag{18}\]  
Thus, the output signal \(y(t)\) is:  
\[\begin{split} y(t)&=s(t)*R(t)+W(t)\\ &=\sum_{m=-\infty}^{\infty}s(m)R(t-m)+W(t),\end{split} \tag{19}\]  
where \(s(m)\in\{+1,-1\}\) represents bipolar information symbols with equal probability. The sampled \(y(t)\) can be calculated by:  
\[\begin{split} y(n)&=\sum_{m=-\infty}^{\infty}s(m)R( n-m)+W(n)\\ &=\underbrace{s(n)R(0)}_{\text{Expected signal}}+\underbrace{ \sum_{m=-\infty}^{m=-\infty}s(m)R(n-m)}_{\text{Intersymbol interference}}+ \underbrace{W(n)}_{\text{Noise}}.\end{split} \tag{20}\]  
In Equation (20), the first term contains the expected symbol \(s(n)\) with \(m=n\). The second term is the ISI from the other symbol \(s(m)\) with \(m\
eq n\). Different from CBWCS with two ISI sources, the method proposed in this paper satisfies Nyquist ISI criteria, so only one ISI source caused by multi-path propagation needs to be considered.  
To eliminate ISI caused by multi-path propagation, we take the second term of Equation (20) as the optimal threshold sequence \(\theta(n)_{opt}\)[21]:  
\[\theta(n)_{opt}=\sum_{\begin{subarray}{c}m\
eq n\\ m=-\infty\end{subarray}}^{\infty}s(m)R(n-m), \tag{21}\]  
where \(R(n-m)\) can be calculated
# A Tiered Control Plane Model for Service Function Chaining Isolation
## 4 Services in the Architecture
### Service Components on the Control Plane
#### 4.2.2 The Tier 1 BGP Service  
A Service Provider establishes a peering with a remote data-center by a contractual Service Agreement. This agreement is established by an abstract level on the orchestration layer and is practically set up as BGP peering between the Service Providers. This article recommends having the BGP peering in a private IP-VPN, but, theoretically, it can be a public Internet connection with extended BGP features. Next, dynamic configuration attributes such as the network controller type, network controller address and data-center to data-center Transport Link are shared over this BGP peering. This information gets distributed to every BGP agent in the network. The second stage of the Tier 1 setup is to establish data-center to data-center Transport Links that are normally set up using VPN connections. A VPN link defines the Transport Link between data-centers, which ensures that the underlying network is transparent to packet forwarding and that does not require intermediate network elements to be able to read SFC headers. The architecture suggests using one VPN gateway to terminate all Transport Links.  
From the SFC routing perspective, the VPN gateway is the next hop for SFC packets going to a remote data-center. The next hop is determined by the VNFs locations of which are distributed by BGP in Tier 2 (Section 4.2.3). For direct peerings, this VPN tunnel is optional. The main purpose of this BGP service is to inform all network controllers about remote Transport Links (Provider A must know that a Transport Link exists between providers B and C). This means that, if no VPN tunnel is needed, the Edge gateway still has to announce that the Transport Link is established in terms of a direct peering such as a direct cable. The Tier 1 agent can verify that the full mesh of Transport Links exists, while it holds a table of the domains (AS numbers) that it is directly or remotely connected. The Tier 1 control agent is responsible for:  
* Sharing network information over BGP,
* Instantiating Transport Links,
* Populating the Tier 1 BGP with Transport Links,
* Serving an Application Programming Interface (API) towards the orchestration layer for end-user services regarding capabilities of encryption and isolation.
# TrustShare: Secure and Trusted Blockchain Framework for Threat Intelligence Sharing
## 3 Materials and Methods
### Hybrid Trust Model  
TrustShare employs a hybrid trust model that combines _direct trust (DT)_ and _indirect trust (IT)_ to evaluate the reliability of participants dynamically.  
**Direct Trust (DT)** is calculated from the history of interactions between participants, capturing metrics such as success rates of past information exchanges and responsiveness. This component reflects the firsthand experience that participant \(i\) has with participant \(j\).  
**Indirect Trust (IT)** aggregates feedback from other participants regarding participant /'s behavior. This feedback is weighted according to the trustworthiness of the feedback providers and incorporates a decay factor to ensure that recent behavior has greater influence than older assessments.  
The overall trust score \(T_{ij}\) assigned by participant \(i\) to participant \(j\) is computed as a weighted sum of direct and indirect trust:  
\[T_{ij}=\alpha\cdot DT_{ij}+(1-\alpha)\cdot IT_{ij}\]  
where  
* \(T_{ij}\) is the combined trust score.  
* \(\alpha\in[0,1]\) is a tunable parameter balancing the influence of direct and indirect trust.
* \(DT_{ij}\) is the direct trust value based on past interactions.
* \(IT_{ij}\) is the indirect trust value derived from third-party feedback.  
An example calculation is provided in Table 6, illustrating how \(DT_{ij}\) and \(IT_{ij}\) are combined using a chosen \(\alpha\).
## 3 Methodology and Comparative Experimental Setup
### Methodology  
#### 3.1.1 Decomposition Method for Data Pre-Processing  
* Empirical Mode Decomposition;  
Empirical Mode Decomposition, which was proposed by [PERSON] et al. [28], is an adaptive signal time-frequency analysis method that can extract the eigenmode function of the signal and its features. The EMD decomposition method can decompose the original signal into many components and name each component as an eigenmodal function, so it can be applied to the analysis of nonlinear and non-stationary signals [29]. The final decomposition result consists of the Intrinsic Mode Function (IMF) and the residual signal, and the decomposition result is expressed as follows [30]:  
\[s(t)=\sum_{i=1}^{n}imf_{i}(t)+r_{n}(t) \tag{1}\]  
where \(r_{n}(t)\) is the residual signal and \(imf_{i}(t)\) is the IMF component of different frequency components from high to low.  
Each IMF needs to satisfy the following conditions:  
1. The number of zero-crossing points minus the number of extreme points \(\leq\) 1 for all signals.
2. The average value of the upper and lower envelopes is 0 at every point.
* Ensemble Empirical Mode Decomposition;  
Ensemble Empirical Mode Decomposition can take advantage of the good zero-mean property of white noise to better solve the modal confusion problem [31]. By adding white noise of different scales to the original signal several times and then decomposing the processed signal, different IMFs can be obtained. Then, the IMFs of the same order can be averaged and the noise canceled out by using the zero-mean noise property to obtain a new IMF as the final decomposition result [32].  
The detailed process of EEMD decomposition is as follows:  
1. Adding white noise (\(\alpha_{i}(t)\)) to the original signal (\(I(t)\)): \[I_{i}(t)=I(t)+\alpha_{i}(t)\] (2)
2. Adding noise to \(I\), followed by EMD decomposition: \[I_{i}(t)=\sum_{j=1}^{n}imf_{ij}(t)+r_{in}(t)\] (3)
3. Repeat the above two steps by adding white noise to the original signal and performing EMD decomposition.
4. After m times of repeated decomposition, the results are mean-valued. The specific operation is to mean-value the same-order IMFs and trend items, respectively, to obtain the final IMF and trend item. \[imf_{j}(t)=\frac{1}{m}\sum_{i=1}^{m}imf_{ij}(t)\] (4) \[r_{n}(t)=\frac{1}{m}\sum_{i=1}^{m}r_{in}(t)\] (5)Finally, the actual-signal EEMD decomposition results are obtained:  
\[I_{i}(t)=\sum_{j=1}^{n}imf_{j}(t)+r_{n}(t) \tag{6}\]
## 1 Introduction
Specifically, the fatigue evaluation method comprehensively considers the spatial stress states of the evaluation points. Current research papers on transforming multiaxial stress into uniaxial stress are as follows. [PERSON] et al. [19] projected multiaxial stress and evaluated the fatigue strength of electric multiple unit (EMU) wheels based on the principal stress method. [PERSON] et al. [20] established a weld coordinate system according to geometric features and calculated the stress components of nodes under a multiaxial stress state to perform a fatigue life analysis method for a freight car body. These investigators concluded that using the multiaxial stress method to evaluate fatigue strength is beneficial for the lightweight design of a car body. In the above studies, the appropriate evaluation methods were selected mainly according to the specific engineering requirements. While many factors can affect welded joint fatigue, the majority of the current research has only focused on one aspect. Further studies are needed to address the common impacts of multiple factors. The car body is the main bearing structure of a subway vehicle, which is located on a bogie and bears various dynamic and static loads during operation. Fatigue failure is one of the most critical failure modes of car body structures; hence, its fatigue strength analyses are of vital importance to ensure reliable performance. Compared with a carbon steel car body, an aluminium alloy car body offers the following advantages: lightweight body structure, high corrosion resistance, excellent extrusion resistance, and low overall cost. For aluminium alloy materials, the fatigue strength of a welded joint is approximately half that of the base metal, so compared with the welded joint, the base metal is not prone to fatigue failure. As a consequence, the fatigue strength evaluation of an aluminium alloy car body typically focuses on the welded joints [21]. This paper proposes a method that combines the nominal stress method and multiaxial stress method based on multiaxial stress criteria and the fatigue cumulative fatigue damage theory, according to the DVS 1608 standard [22] and EN 1999 standard [23], respectively. This method is accurate and efficient, since it not only considers the material utilization degree in multiple structural directions to demonstrate the comprehensive degree of utilization of the pivotal weld joints, but also shows the estimated fatigue life of the aluminium alloy car body.
# Entropy Weighted TOPSIS Based Cluster Head Selection in Wireless Sensor Networks under Uncertainty
## 1 Introduction
For experiments, we considered a hundred nodes of WSNs where all the nodes were randomly distributed within a \(100\times 100\) m\({}^{2}\) area and utilized the OPNET modeler to assess the MAC layer functionality of 802.15.4 slotted CSMA/CA. Here, we also considered uncertain parameters for the entire network setup. Overall, the work conducted for this study can be summed up as follows: (i) Cluster head selection in the WSN has been conducted using the entropy-weighted TOPSIS approach. (ii) TFNs were utilized to express all the metrics, including initial node energy, the distance between sensor nodes and base stations, data packet size, voltage and transmission energy measurements, and other aspects related to sensor nodes. (iii) We used uniform distribution to create all nodes in the network design. (iv) The NSG2.1 Simulator was employed for the simulation process. (v) For experiment purposes, the Ad hoc On-Demand Distance Vector (AODV) routing protocol [31] was employed. Finally, experimental results were presented and compared graphically. The rest of the work is presented as follows. The fundamental mathematical underpinnings and approaches used to build the entire work and theoretical background are presented in Section 2. Section 3 gives certain assumptions and notations to help with the investigation. The mechanism for forming cluster heads for WSNs has been explained in Section 4. Numerical experiments and discussions have been made in Section 5. Section 6 of this study contains its concluding observations.
## Materials and Methods
### Analytical Methods  
The total organic carbon (TOC) content of the samples was measured with a TOC-5050 analyzer (Shimadzu Corporation, Kyoto, Japan). Samples taken from treated solutions were filtered with 0.20 \(\upmu\)m polytetrafluoroethylene (PTFE) filters before analysis. The UV absorbance of TA aqueous samples was measured using a UV-Visible spectrophotometer by a 1 cm quartz cells at 276 nm. The solution pH was determined with a Micronal pH meter (model B474). TA was analyzed by HPLC using a Nucleosil C18 column (mobile phase, 60% water 40% acetonitrile; flow rate, 0.50 mL min\({}^{-1}\)) with UV detector at 276 nm.  
UV/S\({}_{2}\)O\({}_{8}\)\({}^{2-}\) and UV/H\({}_{2}\)O\({}_{2}\)/Fe\({}^{2+}\) Processes  
The photochemical experiments were carried out in the photo-reactor (pyrex) of 2 L capacity equipped with a 125-W Heraeus Noblelight (TNN 15/32) mercury vapor lamp, a magnetic stirrer, and a thermometer. The lamp was located in a quartz sleeve at the center of the reactor in an axial position and emitting at 254 nm. 1 L of TA aqueous solution was added into the reactor in each experiment. The solution pH was adjusted to the desired values by addition of sodium hydroxide or sulfuric acid. Then, K\({}_{2}\)S\({}_{2}\)O\({}_{8}\) (UV/persulfate) or FeSO\({}_{4}\).7H\({}_{2}\)O (photo-Fenton) were directly added to the photoreactor at the beginning of each experiment. After switching on the lamp, a precise amount of hydrogen peroxide (30%) was immediately added to 1 L of TA aqueous solution (for photo-Fenton process). Allthe experiments were performed in duplicate. Samples of 10 mL were withdrawn at predetermined time intervals and immediately quenched with Na\({}_{2}\)SO\({}_{3}\). The samples were filtered through 0.20 \(\upmu\)m polytetrafluoroethylene (PTFE) filters and then tested to determine the pH, TOC, and absorbance at wavelengths of 276 nm in duplicate.
# Performance Evaluation Method for Intelligent Computing Components for Space Applications
## 4 Benchmark Execution and Evaluation
### Typical Application Benchmark Experimental Results  
Based on the two typical benchmarks introduced in Section 3.4 of this paper, experiments were conducted on three platforms, including implementations based on the CUDA framework on GPU processors, the OpenMP framework on CPU processors, and statistics on the throughput and power consumption of each platform during benchmark testing.
## 3 Results
### Cytoskeleton Modifications of HaCaT Exposed to Simulated Microgravity
#### 3.2.1 F-Actin and Vinculin Organization after 24 h of Simulated Microgravity  
As we described in a previous paper [18], cells cultured in 1g for 24 h showed F-actin distributed in peripheral cortical bundles, while in simulated microgravity exposed cells, a relevant increase of lamellipodia, filopodia, and ruffles were frequently observed. Overall, those findings suggest that cells exposed to weightlessness acquire a motile/invasive phenotype.  
To obtain further morphological insights, we performed confocal analysis of the cell to recognize how vinculin colocalizes and correlates with actin filament in shaping FAs, which plays a pivotal role in modulating cell adhesion to the substrate and, henceforth, cell migration. The colocalization analysis and fluorescence intensity of vinculin and actin (SUM (I)) at the free front of the cell membrane, and in an internal compartment at the level of cell-cell contact was performed as described in the Materials and Methods section. As shown in Figure 4A, in control condition, vinculin is predominantly distributed nearby the cell-cell contacts. Colocalization and correlation analyses showed a few or no colocalization of vinculin with actin at the leading edge of the cell culture (Figure 4C) and no significant positive relationship. (r = 0.174 \(p>\) 0.05). In addition, in cell contact, there is no significant relationship between F-actin and vinculin (r = 0.29 \(p>\) 0.05), although these two proteins are tightly associated (at a few points, the cellular contacts colocalize), as indicated in the green and red picks in the figure (Figure 4E). After 24 h of exposure to weightlessness, vinculin was mostly displaced at the cell periphery, and it showed an increased density in the newly formed ruffles and filopodia (Figure 4B). Colocalization and correlation analyses demonstrate that vinculin colocalizes with actin filament at the level of membrane protrusions (Figure 4D), and the correlation coefficient revealed a positive relationship between these two markers (r = 0.97; \(p<\) 0.001), indicating the presence of FA. By contrast, both colocalization (Figure 4F) and the association between F-actin and vinculin decrease at the site of cell adhesion contacts, and there is no significant positive relationship (r = 0.29 \(p>\) 0.05) between the two markers. These results confirm the formation of new FAs necessary to the acquisition of the motile/invasive phenotype by the cells exposed to simulated microgravity.