esa-sceva/satcom-chunk-collection huggingface dataset:
# An Efficient Framework Model for Optimizing Routing Performance in VANETs
###### Abstract  
Routing in Vehicular Ad hoc Networks (VANET) is a bit complicated because of the nature of the high dynamic mobility. The efficiency of routing protocol is influenced by a number of factors such as network density, bandwidth constraints, traffic load, and mobility patterns resulting in frequency changes in network topology. Therefore, Quality of Service (QoS) is strongly needed to enhance the capability of the routing protocol and improve the overall network performance. In this paper, we introduce a statistical framework model to address the problem of optimizing routing configuration parameters in Vehicle-to-Vehicle (V2V) communication. Our framework solution is based on the utilization of the network resources to further reflect the current state of the network and to balance the trade-off between frequent changes in network topology and the QoS requirements. It consists of three stages: simulation network stage used to execute different urban scenarios, the function stage used as a competitive approach to aggregate the weighted cost of the factors in a single value, and optimization stage used to evaluate the communication cost and to obtain the optimal configuration based on the competitive cost. The simulation results show significant performance improvement in terms of the Packet Delivery Ratio (PDR), Normalized Routing Load (NRL), Packet loss (PL), and End-to-End Delay (E2 ED).  
A Article An Efficient Framework Model for Optimizing Routing Performance in VANETs [PERSON] *, [PERSON] *, [PERSON] and [PERSON]
## 2 Materials and Methods
### Unilateral Hopping Task  
For the unilateral hopping task, a 20 m track (smooth and non-slippery surface), an OPTJOUMP measurement device (Microgate, Bolzano, Italy), and a photocell system (Brower Timing System, Draper, UT, USA) were provided. The task began with a participant positioned behind a clearly marked starting line at the beginning of the 20 m track. On the command \"On your marks\", the participant assumed a position with the test leg pointing forward. After an additional start signal, the participant executed consecutive unilateral horizontal jumps across the 20 m distance. This distance enabled us to record 8-10 hops. We included eight hops in the analysis to keep the number of hops consistent across participants. One repetition of the test was performed before the actual measurement as a warm-up and familiarization. Due to the high load, subjects performed the task only once on each leg. Previous results from a smaller sample showed a high reliability (ICC = 0.94 [0.88-0.98]) in determining the temporal variables [19]. For each consecutive hop, we extracted contact time and flight time. Thereon, the RSI was derived by dividing the flight time by the contact time (referred to as hop-RSI from hereon). This ratio has been extensively used in drop jump tasks to assess vertical reactive strength [1]. Only recently, an interest has been rising in an analogous index for assessing horizontal reactive strength, derived from unilateral hopping for distance [14,17].
# Channel Characterization and SC-FDM Modulation for PLC in High-Voltage Power Lines
## 1 Introduction
#### Objectives and Contributions  
This work's main contributions are the following. First, a PLC-channel model is introduced for the performance evaluation of the SC-FDM scheme in the PLC system, this model taking into account the attenuation caused by the power-line couplings at the terminals and corona noise, as well as by considering the power line as a multiline system [18; 19] by means of the techniques proposed in [2].  
Secondly, it proposes a PLC system capable of operating in bandwidths higher than those previously mentioned. The solution is based on the use of the SC-FDM to counteract the selectivity in the frequency of the PLC channel, apart from adding stages of channel estimation by means of orthogonal bases and the LMS equalization on the receptor, as discussed in [15].  
The resulting system is thus capable of operating at higher frequencies than those found in conventional PLC systems, presenting a similar performance to an ideal lossless channel with white noise.
## 4 Materials and Methods  
### Algal Materials  
_Haematococcus pluvialis_ strain 712 (FACHB-712) obtained from the Freshwater Algae Culture Collection at the Institute of Hydrobiology (FACHB) was cultured in 5000 mL beakers, containing BBM medium without aeration, under a light intensity of 25 umol photons m\({}^{-2}\)s\({}^{-1}\) with a photoperiod of 12 h/12 h light/dark at 22 \({}^{\circ}\)C. When the cell growth reached the logarithmic phase (10\({}^{5}\) cells mL\({}^{-1}\)), the culture was evenly divided into 9 aliquots each of 1000 mL. Samples were divided into 3 groups, including the control group grown under constant white light of 150 umol photons m\({}^{-2}\)s\({}^{-1}\) at 22 \({}^{\circ}\)C (W0 group), the group treated with constant blue light of 150 umol photons m\({}^{-2}\)s\({}^{-1}\) at 22 \({}^{\circ}\)C (B0 group), and the group pre-treated with SA (2.5 mg/L) under constant blue light of 150 umol photons m\({}^{-2}\)s\({}^{-1}\) at 22 \({}^{\circ}\)C (B25 group). The selection of the concentration of SA (2.5 mg/L) was based on the previous study [81] and our pre-experiments, which showed that the production of astaxanthin was increased with reduced concentration of SA. Each group contained three replicates.
# Attosecond Time Delay in Photoionization of Noble-Gas and Halogen Atoms
## 4 Conclusions  
In conclusion, we have calculated partial photoionization cross sections, asymmetry parameter, phase shift, and Wigner time delay for both noble-gas and halogen atoms, finding great similarity between these two groups in the presence of the giant dipole resonance. Our calculated cross sections and asymmetry parameters agree well with other theoretical and experimental values. Wigner time delay presents a flat plateau across the giant resonance regime, due to its slow and monotonic phase change. Near the Cooper minimum, the Wigner time delay forms a negative sink, because the \(l_{0}\to l_{0}-1\) channel with decreasing phase dominates. If the breakdown of spherical symmetry is considered, Wigner time delay should be calculated for different ionic states of halogen atoms. For photoionization from \(s\) subshells, the TDLDA method is not able to reproduce the right asymmetry parameters near the Cooper minimum. Future studies including the initial-state correlations and relativistic effects should give the right asymmetry parameters and different time delays for these cases as well. Our predicted time delays should be useful for future experiments on those atoms and related molecules.  
A.S.L. acknowledges funding from the Max Planck Center for Attosecond Science (MPC-AS).  
[PERSON] performed the calculations; [PERSON]-[PERSON] and [PERSON] analyzed the data and wrote the paper.  
The authors declare no conflict of interest.
## 3 Results
### Image Classification and Accuracy Assessment  
Analyzing the results of the SVM and NN classifiers for April and May 2015 using the four image combinations, the NN results exhibit, in general, a slight improvement compared to those from SVM (Table 7). For these two acquisition dates (late season both for maize and soybean), the addition of the gamma backscattering bands did not increase the overall accuracy. The highest value obtained for the overall accuracy (87.5%) was obtained for the results of an NN classification using just the four spectral bands of the SPOT-5 image for 15 May.  
Figure 8 shows the classification overall accuracies and kappa coefficients when SPOT-5 bands are added cumulatively into the classification process. The accuracy peaked in early June (91% for the overall accuracy and 81% for the kappa coefficient) considering just the first 7 SPOT-5 Take-5 images  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Date** & **DOY** & **Maize** & **Soybean** & **Pasture** & **Bean** \\ \hline
30 April & 120 & 0.9 & 0.7 & 0.9 & 1.0 \\  
eptember & 250 & 0.3 & 0.3 & 0.5 & 0.3 \\
12 September & 255 & 0.3 & 0.4 & 0.5 & 0.3 \\ \hline \hline \end{tabular}
\end{table}
Table 6: \(K_{cb}\) values obtained from the \(K_{cb}\)-NDVI approach.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & & **SPOT** & **SPOT + VV** & **SPOT + VH** & **SPOT + Dual** \\ \cline{2-5}  & **Pair A** & \multicolumn{4}{c}{30 April (SPOT-5) and 19 April (Sentinel-1A)} \\ \hline
**SVM** &
\begin{tabular}{c} OA (\%) \\ KC (\%) \\ \end{tabular} & 78.3 & 78.2 & 78.3 & 78.3 \\
**NN** &
\begin{tabular}{c} OA (\%) \\ KC (\%) \\ \end{tabular} & 57.4 & 57.2 & 57.3 & 57.1 \\ \hline
**Pair B** & \multicolumn{4}{c}{77.9} & 78.3 & 78.7 \\
**SVM** &
\begin{tabular}{c} OA (\%) \\ KC (\%) \\ \end{tabular} & 58.3 & 56.9 & 57.3 & 58.5 \\ \hline
**Pair B** & \multicolumn{4}{c}{15 May (SPOT-5) and 13 May (Sentinel-1A)} \\ \hline
**SVM** &
\begin{tabular}{c} OA (\%) \\ KC (\%) \\ \end{tabular} & 84.5 & 84.4 & 84.5 & 84.4 \\
**68.4** & \multicolumn{4}{c}{68.2} & 68.4 & 68.1 \\ \hline
**NN** &
\begin{tabular}{c} OA (\%) \\ KC (\%) \\ \end{tabular} & **87.5** & 85.7 & 81.6 & 83.1 \\
**73.2** & \multicolumn{4}{c}{70.4} & 64.3 & 66.3 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Overall accuracy (OA) and kappa coefficient (KC) values for the 4 data combinations for 2 acquisition dates.  
In Figure 10 is shown a detailed late-season map derived from the multitemporal classification of a SPOT-5 Take-5 time series using the NN classifier including images from 30 April to 4 June.
# Performance Analysis of Dual-Hop AF Relaying with Non-Linear/Linear Energy Harvesting
## 4 BEP Analysis  
In this section, the BEP of the considered DH AF relaying system is derived for both non-linear and linear EH models. In order to obtain the BEP expression, the analytical expression of symbol error probability (SEP) is first derived. The overall BEP expression is given for high SNR, assuming the common approximation for Gray mapping [55] as \(P_{b}^{i}\!\!\!\approx\!\!\!P_{s}^{i}/\vartheta\). Here, \(i\in\{\mathrm{L},\mathrm{NL}\}\), \(\mathrm{L}\) and NL represent linear and non-linear EH models, respectively. The conditioned SEP for both linear and non-linear EH models is calculated as [4]  
\[P_{s}^{\mathrm{L},NL}(e|Z,W)=\frac{a\sqrt{b}}{2\sqrt{\pi}}\int_{0}^{\infty} \frac{e^{-b\gamma}}{\sqrt{\gamma}}F_{\gamma}(\gamma|Z,W)\mathrm{d}\gamma= \frac{a\sqrt{b}}{2\sqrt{\pi}}(I_{1}-I_{2}(Z,W)), \tag{11}\]  
where parameters \(a\) and \(b\) denote the modulation coefficients for \(M\)-PSK/QAM [4] and \(F_{\gamma}(\gamma|Z,W)\) is given in (10). In (11),  
\[I_{1}=\int_{0}^{\infty}\frac{e^{-b\gamma}}{\sqrt{\gamma}}\mathrm{d}\gamma= \sqrt{\frac{\pi}{b}} \tag{12}\]  
and  
\[I_{2}(Z,W)=\int_{0}^{\infty}\  
and  
\[P_{\rm s4}=\underbrace{\int_{w=0}^{P_{h}}p_{s}^{L,NL}(e|z,w)f_{\rm W}(w){\rm d}w}_ {A_{2}=A(z)|_{z=P_{h}}}\underbrace{\int_{z=P_{h}}^{\infty}f_{Z}(z){\rm d}z}_{B_{ 2}}. \tag{19}\]  
\(A(w)\), and\(P_{s1}\) in (16) and \(A(z)\) in (19) are calculated in Appendix A, Appendix B, and Appendix C, respectively.  
In (19), \(A_{2}\) is calculated from (A29) as \(A_{2}=A(z)|_{z=P_{th}}\). Furthermore, using ([50], eq. 3.351-2), \(B_{2}\) in (19) is calculated as  
\[B_{2}=\int_{P_{th}}^{\infty}f_{Z}(z){\rm d}z=D\Gamma(m_{z},P_{th}\phi_{z})/ \phi_{z}^{m_{z}}; \tag{20}\]  
where  
\[f_{Z}(z)=Dz^{m_{z}-1}\exp(-z\phi_{z}) \tag{21}\]  
with \(D=\phi_{z}^{m_{z}}/\Gamma(m_{z})\). Finally, \(P_{\rm s4}\) in (19) is calculated by substituting \(A_{2}=A(z)|_{z=P_{th}}\) in (A29) and (20).  
In (18), \(A_{1}\) is calculated from (A2) as \(A_{1}=A(w)|_{w=P_{th}}\). Moreover, using ([50], 3.351-2), \(B_{1}\) in (18) is calculated as  
\[B_{1}=\int_{P_{th}}^{\infty}f_{\rm W}(w){\rm d}w=\Gamma(m_{w},P_{th}\phi_{w}) /\phi_{w}^{m_{w}}, \tag{22}\]  
where  
\[f_{\rm W}(w)=\left.Iw^{m_{w}-1}\exp(-w\phi_{w})\right. \tag{23}\]  
with \(I=\phi_{w}^{m_{w}}/\Gamma(m_{w})\). Moreover, \(P_{s3}\) in (18) is calculated by substituting \(A_{1}=A(w)|_{w=P_{th}}\) in (A2) and (22). Finally, \(P_{s2}\) in (17) is calculated using (22), (20), and replacing \(z=P_{th}\) and \(w=P_{th}\) in (14).
## 1 Introduction
Although remote sensing technology has made significant progress in recent years, leading to a notable improvement in the spatial resolution of satellite imagery (0.5-10m) such as Sentinel-2, Worldview-3, SPOT, and IKONOS, their limitation in high acquisition cost and time scale present major challenges for monitoring long-term urban changes ([PERSON] et al., 2014). Landsat TM provides a suitable spatial resolution, long time series, and cost-free availability, which makes it an ideal choice for studying long-term trends and changes in landscape, vegetation, and other features ([PERSON] et al., 2014). In the study, Harbin is used as a case study area. Here, we use time-series Landsat satellite imagery from 2004-2020 to investigate the following four aspects under the two seemingly contradicting forces of rapid urbanization development and urban greening strategies: (1) the process and patterns of urban expansion, (2) the evolution of landscape patterns under the rapid urbanization process, (3) the spatiotemporal dynamics and trends of vegetation coverage, and (4) the impacts of urbanization on vegetation coverage. This study will contribute to improving the understanding of urban greening trends and the impact pattern of urbanization on vegetation growth (FVC) changes in China. They will also provide scientific information for identifying the potential environmental effects and appropriate decision-making toward urban sustainable development in the future.
# The Vicious Cycle of Obesity and Low Back Pain: A Comprehensive Review
## 6 The Biochemical Impact of Obesity on the Spine
### Hypoxia and Angiogenesis  
Due to its avascularity, the IVD relies on CEP-mediated diffusion for oxygen and nutrients. In obesity, metabolic stress and inflammation raise oxygen demand while hindering diffusion, causing systemic and local hypoxia [198]. Adipose tissue hypoxia, driven by obesity-related adenine nucleotide translocae 2 (ANT2)-mediated mitochondrial uncoupling, increases oxygen use, activates hypoxia-inducible factor (HIF)-1\(\alpha\), and promotes chronic inflammation [199]. These pathophysiological changes parallel those in the IVD, where obesity-induced CEP sclerosis restricts oxygen diffusion, and adipokine-mediated endothelial dysfunction impairs vascular homeostasis [200]. In response to hypoxia, HIF signalling is activated to promote cellular adaptation; however, prolonged HIF-1\(\alpha\) activation also upregulates VEGF, contributing to pathological angiogenesis [201; 202]. This hypoxia-driven neovascularization is a recognized feature of IDD and may reflect similar mechanisms observed in other degenerative tissues [38; 72]. Obesity contributes to IDD and LBP through a dual mechanism: aggravating hypoxia and promoting pathological vascularization. Elevated systemic and local hypoxia intensifies catabolic activity in the NP, accelerating degeneration [203; 204; 205]. Simultaneously, obesity-driven neovascularization may compromise the IVD's immune-privileged status [40], facilitating nocicpor sensitization, inflammatory cell infiltration into the IVD, and a spread of systemic obesity-related inflammation into the IVD-environment (Figure 1) [28,36]. This interplay between hypoxia, angiogenesis, and chronic inflammation reinforces a cycle of progressive IDD and pain and warrants more careful study.  
In short, obesity profoundly impacts spinal health by inducing systemic and local inflammation, oxidative stress, lipid dysregulation, and metabolic disturbances, all of which accelerate IDD and LBP. Dysregulated adipokines, gut microbiome alterations, and hypoxia-driven angiogenesis create a pro-inflammatory, catabolic microenvironment that disrupts disc homeostasis. Future research should prioritize mechanistic studies and clinical investigations to harness these insights for the development of targeted, personalized interventions of LBP in obese and overweight individuals.
# A Comprehensive Survey on Knowledge-Defined Networking
## 5 Control Plane
### Interfaces and Sub-Planes Connected with the Controller
#### 5.2.2 Southbound Interface
ForCES uses Stream Control Transmission Protocol (SCTP) as the transport layer protocol, which is more reliable and secure than TCP. However, ForCES is less utilized in KDN compared to OpenFlow, as ForCES lacks open-source support. OpFlex is another southbound API with the intention of distributing part of network management back to the data plane in order to improve scalability. Policies are logically centralized and abstracted from the data plane, where they are stored in a repository and communicated to policy elements (data plane elements). It also has an observer repository for storing network events and faults. However, OpFlex lacks network programmability and dynamic network control [293]. Protocol Oblivious Forwarding (POF) makes the forwarding plane oblivious to the protocol by using a generic Flow Instruction Set (FIS), where the forwarding element does not need to know about the packet format [294]. In POF, forwarding elements are under the full control of the controller, which will install keys and table lookups in the switches. Thus, in POF, the packet header field is not required to be inspected in packet forwarding, which speeds up packet forwarding, unlike in the OpenFlow protocol. Another promising protocol for the southbound API is the Path Computation Element (PCE) protocol [295]. In this protocol, the Path Computation Client (PCC) in the data plane requests forwarding paths from the PCE residing in the control plane, which computes paths with the aid of a traffic engineering database. OpenState attempts to extend OpenFlow by extending OpenFlow match-action abstraction to extended finite machines, which is an attempt to decentralize some control back to forwarding plane elements [296]. In OpenState, finite state machines facilitate several stateful tasks inside the switches, where all tasks that involve local state are carried out without explicit control instructions from the controller.
# Spectrogram Classification Using Dissimilarity Space
## 2 Proposed Approach  
The proposed method for spectrogram classification using dissimilarity space is based on several steps which are schematized in Figure 1. This figure is followed by the pseudo-code for each step (Algorithms 1 and 2). In order to define a similarity space, it is necessary to select a distance measure and a set of prototypes in the training phase. The distance measure \(d(x,y)\) is learned by means of a SNN trained to maximize the similarity between couples of spectrograms in the same class, while minimizing the similarity for couples in different classes. The set of prototypes \(P=p_{1},...p_{k}\) are obtained as the \(k\) centroids of the clusters generated by a supervised clustering procedure. The final step represents each training sample \(x\) in the dissimilarity space by a feature vector \(f\in\Re^{k}\), where each component \(f_{i}\) is the distance between \(x\) and the prototype \(p_{i}\): \(f_{i}=d(x,p_{i})\). These feature vectors are used to train a SVM for the final classification task. In the testing phase, each unlabeled spectrogram is first represented in the dissimilarity space by calculating its distance to all the prototypes, then the resulting feature vector is classified by SVM.  
```
1:Training images (\(imgsTrain\)), training labels (\(labelTrain\)), the number of training iterations (\(trainIterations\)), batch size (\(trainBatchSize\)), number of centroids (\(k\)), and the clustering technique (\(type\)).
2: Trained SNN (\(tSNN\)), set of centroids (\(C\)), and trained SVM (\(svm\)).
3:\(tSNN\leftarrow\textsc{trainSiamese}(imgsTrain,labelTrain,trainIterations,trainBatchSize)\)
4:\(P\leftarrow\textsc{clustering}(imgsTrain,labelTrain,k,type)\)
5:\(F\leftarrow\textsc{getDissSpaceProjection}(imgsTrain,P,tSNN)\)
6:\(tSVM\leftarrow\textsc{trainSVM}(labelTrain,F)\)
```  
**Algorithm 1** Training phase  
```
1:Test images (\(imgsTest\)), trained SNN (\(tSNN\)), Set of centroids (\(C\)), Trained SVM (\(tSVM\)).
2:Actual test labels (\(labelTest\)).
3:\(F\leftarrow\textsc{getDissSpaceProjection}(imgsTest,P,tSNN)\)
4:\(labelTest\leftarrow\textsc{predictSVM}(F,tSVM)\)
```  
**Algorithm 2** Testing phase  
Each of the main functions used in the pseudo-code are described below.
# Trellis Tone Modulation Multiple-Access for Peer Discovery in D2D Networks
## 4 Multi-User Detection of TTMMA via Message-Passing
### Message-Passing Algorithm
Candidate codewords can be obtained by concatenating the trellis graphs for all \(k\), \(1\leq k\leq N-1\), if a connected path exists and can be separated then the tone-path may yield a valid codeword. The verification can be performed by using embedded CRC codes. In the proposed message-passing decoding, the computational complexity (in total number of additions) can be given by  
\[(N-1)\times 2I_{max}\times M\times\left(\sum_{i=1}^{d}\!\left( \begin{array}{c}d \\ i\end{array} \right)\! (i-1)+2^{d}\right) \tag{12}\]  
where \(I_{\max}\) denotes the maximum number of iterations. In (12), the sum \(\sum_{i=1}^{d}\!\left( \begin{array}{c}d\\ i\end{array} \right)\! (i-1)\) is the number of additions for generating \(C_{\mathbf{p}}=\mathbf{u}_{q}\mathbf{p}^{\mathsf{T}}\) in each node and \(2^{d}\) represents the number of comparisons in \(\left\|\left\{\mathbf{y}_{k+1}\right\}_{q}-C_{\mathbf{p}}\right\|^{2}\leq(w_{ \mathrm{H}}(\mathbf{p})+1)\delta\) for all \(\mathbf{p}\). The complexity of the comparison is equivalent to that of the addition. Note that the complexity in (12) is an upper bound, which can be reduced by optimization. The message-passing is performed between PostNs and PreNs only for \(I_{\max}\) iterations (e.g., \(I_{\max}=3\)), which keeps the complexity of the demodulation scheme sufficiently low. Sometimes it is not straightforward to distinguish tone-paths from the concatenated trellis graphs simply. That problem is addressed in the next subsection.