esa-sceva/satcom-chunk-collection huggingface dataset:
# Combination of Variable Loads in Structural Design
## 1 Introduction
### Current Theory  
References [1; 2; 6; 7; 8; 9; 10; 11; 12; 13; 14; 15; 16; 17; 18; 19; 20; 21; 22; 23; 24] address the current combination of variable loads in structural design. In this approach, variable loads are combined by adding the leading load with its full value and the accompanying load with a reduced value, multiplied by a combination factor \(\psi\). The calculation procedure for these combination factors employs an independent load combination methodology using a fictitious ideal material, \(V_{\rm M}\) = 0, and a semi-probabilistic reliability model. The current method bases the combination factors solely on a load proportion of 0.5, without considering other load proportions. Discussion is lacking on how combination factors impact the reliability of the overall structural design. The current approach to combining SLS loads involves using mean loads for cases with a single variable load and extreme loads for cases with multiple variable loads. This inconsistency is not addressed. Eurocodes [1] include equations for variable load combination factors \(\psi_{0}\). The general equation is  
\[\psi_{0}=\frac{{{F_{\rm s}}^{-1}}\Big{\{}\Phi\Big{(}0.4\Big{(}{-\Phi^{-1}(\Phi (-0.7\beta))}/{N_{1}}\Big{)}\Big{)}^{N_{1}}\Big{\}}}{{{F_{\rm s}}^{-1}\{\Phi(0. 7\beta)\,^{N_{1}}\Big{\}}}}, \tag{1}\]  
The approximation for large \(N_{1}\) is  
\[\psi_{0}=\frac{{{F_{\rm s}}^{-1}}\Big{[}\text{exp}{-N_{1}}\Big{(}\Phi\Big{(}{ -0.4\Big{(}{-\Phi^{-1}(\Phi(-0.7\beta))}/{N_{1}}\Big{)}\Big{)}}\Big{)}\Big{]}} {{{F_{\rm s}}^{-1}(\Phi(0.7\beta))}}, \tag{2}\]  
The approximation for a normal distribution is  
\[\psi_{0}=\frac{1+(0.28\beta-0.7\text{ln}N_{1})V}{1+0.7\beta V}, \tag{3}\]  
The approximation for the Gumbel distribution is  
\[\psi_{0}=\frac{1-0.78V(0.58+\ln(-\text{ln}\phi(0.28\beta))+\text{ln}N_{1})}{1 -0.78V(0.58+\ln(-\text{ln}\phi(0.7\beta))}, \tag{4}\]  
where F(.) is the probability distribution function of the extreme value of the accompanying action in the reference period \(T\); \(\Phi\)(.) is the standard Normal distribution function; \(T_{1}\) is the greater of the basic periods for the actions to be considered; \(N_{1}\) is the ratio \(T_{1}/T\), approximated to the nearest integer; \(\beta\) is the reliability index; \(V\) is the coefficient of variation of the accompanying action for the reference period. Assuming a Gumbel distribution, an exceedance probability of 0.02, i.e., \(\beta\) = 2.05, \(V_{Q}\) = 0.4, i.e., \(\mu_{\rm Q}\) = 0.491, \(\sigma_{\rm Q}\) = 0.196, \(T\) = 1, \(T_{1}\) = 1, i.e., \(N_{1}\) = 1, Equation (1) provides \(\psi_{0}\) = 0.72; Equation (2), \(\psi_{0}\) = 0.57; Equation (3), \(\psi_{0}\) = 0.78; and Equation (4), \(\psi_{0}\) = 0.72.
## Materials and Methods
### Correlation Analysis
In this case, the prediction equation with the highest \(R^{2}\) was selected to estimate AGB within the entire study area. Studies [9; 11; 33] have demonstrated that PALSAR polarization data actually has a logarithmic relationship with AGB. Therefore, instead of employing linear regression only, the study also attempted to correlate the AGB with the polarizations in a non-linear form. However, this method was applied only on the individual polarizations, i.e., HH, HV, VV, and VH. Similar to simple linear regression, the estimation models used AGB as independent variable to observe the sensitivity of the backscatter to the AGB. The relationship between backscatter and AGB is commonly represented in exponential an function as \(y=a\times e^{(xb)}\), where \(x\) and \(y\) denote image variables and AGB, respectively and \(a\) and \(b\) are the equation coefficients.
# Secure and Lightweight Cloud-Assisted Video Reporting Protocol over 5G-Enabled Vehicular Networks
## 3 Preliminary
### Attribute-Based Encryption Scheme  
The ABE scheme in [46] is designed for elliptic curve cryptography and is made of the following sub-protocols: Setup, Encryption, Key-Generation, and Decryption algorithms.
## 5 Results and Discussion
### Real-Time Feasibility  
To assess the applicability of the proposed method in embedded or on-board monitoring scenarios such as aircraft or railway systems, we evaluated its real-time feasibility in terms of computational latency and resource demand. The model was deployed on a representative edge computing platform (e.g., NVIDIA Jetson Nano with 4 GB RAM and quad-core ARM CPU), and inference time was measured using preprocessed input signals of fixed length (e.g., 1024 points after FFT).  
The average inference time per sample was approximately 22.6 ms, which meets the real-time processing requirement for most low-frequency hydraulic monitoring tasks. Compared with a baseline 1D CNN, our GNN-based model demonstrated slightly higher latency (by \(\sim\)6 ms) but provided significantly improved accuracy and noise robustness. The memory footprint remained below 250 MB during inference, ensuring compatibility with embedded environments.
# Design, Implementation and Power Analysis of
## 3 Results
### Comparison of PARSLAD Performance in Three Scenarios  
On a normal sunny day a real time deployment was carried out at 12.9898\({}^{\circ}\) N, 80.1434\({}^{\circ}\) E latitude and longitude location on 17\({}^{\rm th}\) August 2018. For the purpose of calculating the efficiency of the PARSLAD device on the power consumption of a 60 Watts incandescent bulb inside a room, the sensor is placed near the window where there is a possibility of penetration of sunrays in ON state from 5:00 pm to 9:00 pm was noted. Examination of sunset timing demonstrates the efficiency of PARSLAD, as the value of daylight changes drastically and changes the LDR values resulting in variations of illumination. Similarly, the existence of intervention is in two time slots one; between 5:00 pm to 6:00 pm and another between 7:00 pm to 8: 00 pm.The observation was carried out for three scenarios simultaneously - base, manually controlled traditional and smart. In the base scenario, the bulb is always in ON state from 5:00 pm to 9:00 pm. The bulb is not controlled automatically or manually and retained in ON state. In the manually controlled scenario, the light switched to OFF condition manually as the obstacle moved away from the place. The smart scenario is one where PARSLAD device assists the light bulb. Detailed description of three scenarios is provided in Table 8. In the same location and under quite similar conditions, the measurement for 0.5 W and 8 W LED bulb was done for the three scenarios as stated before. Table 9 provides the energy spent from 5:00 pm to 9:00 pm for three scenarios with three light bulbs and also shows the annual energy consumption. Annual energy consumption is calculated considering the occurrence of intervention that took place in same manner for 365 days from 5:00 pm to 9:00 pm. In Table 10, the annual cost is calculated for three scenarios in both domestic and commercial use. Graphs as shown in Figures 5-7 for the 60 W incandescent, 0.5 W LED and 8 W light bulb, respectively, for 4 h of observation of the base, manually controlled and smart scenarios. It can be noted that PARSLAD with 60 W incandescent, reduces energy consumption by 58% in contrast to the base scenario and 16% in contrast to the manually controlled scenario, respectively. Figure 5: Graph for energy consumption of a 60 W incandescent light bulb in three scenarios.
## 3 Methodology
### Dependability of the CCS System at the Open Pit Gacko  
On the basis of the obtained ratings for dependability of the system parts, the overall rating of dependability was obtained using the max-min composition and reads as follows (Their distribution is shown in Figure 8 and also the results in Figure 9):  
(0.07125, 0.30333, 0.30333, 0.30333, 0.105).  
Further analysis shows that the corresponding values obtained by the best fit method, by (8), are equal to:  
\[d_{1}=\sqrt{\left(0.071-1\right)^{  
The values of normalized reciprocal values \(\beta_{i}\), described in (10), are equal to:  
\[\beta_{1} =\frac{0.69156}{0.69156+0.90211+1+0.91458+0.71468}=0.16376\] \[\beta_{2} =\frac{0.90211}{0.69156+0.90211+1+0.91458+0.71468}=0.21362\] \[\beta_{3} =\frac{1}{0.69156+0.90211+1+0.91458+0.71468}=0.23680\] \[\beta_{4} =\frac{0.91458}{0.69156+0.90211+1+0.91458+0.71468}=0.21657\] \[\beta_{5} =\frac{0.71468}{0.69156+0.90211+1+0.91458+0.71468}=0.16923\]  
Appropriate linguistic evaluation  
\[Z=\frac{5\beta_{1}+4\beta_{2}+3\beta_{3}+2\beta_{4}+1\beta_{5}}{\beta_{1}+ \beta_{2}+\beta_{3}+\beta_{4}+\beta_{5}}=2.9860.\]  
On a scale of 1-5, the mentioned system in operation has a center of gravity of linguistic assessment for the max-min composition of 2.9860. Dependability is 59%.  
Figure 8: Distribution of output values for D dependability for the CCS system using max-min composition.  
Figure 9: Display results for CCS system.  
One of the ways to determine the dispersion that occurs in the output result is the standard deviation, which is calculated according to the formula:  
\[S=\sqrt{\frac{\sum_{i=1}^{N}\left(\beta_{i}-\beta_{mean}\right)^{2}}{N-1}} \tag{13}\]  
where \(N\)-is the number of grades, and \(\beta_{mean}\) is the mean value of values \(\beta_{1}\), \(\beta_{2}\), \(\beta_{3}\), \(\beta_{4}\), and \(\beta_{5}\). In the case of max-min composition, the value of the standard deviation is:  
\[S^{2}=\frac{\left(0.16376-0.19\right)^{2}+\left(0.21362-0.19\right)^{2}+ \left(0.23680-0.19\right)^{2}+\left(0.21657-0.19\right)^{2}+\left(0.16923-0.1 9\right)^{2}}{5-1}\]  
\[S=\sqrt{0.0020}=0.0458\]  
In a similar way, dispersions and \(Z\) (center of gravity of linguistic assessment) values can be obtained for each part of the system separately. The obtained values are shown in the following Table 7.  
If the final \(Z\) score was calculated as a linear combination of \(Z\) scores for each part of the system with coefficients corresponding to the participation of the corresponding dispersion, the following value would be obtained.  
\[Z=\frac{0.0599}{0.0599+0.0278+0.0463}\times 2.9753+\frac{0.0278}{0.01599+0.0 278+0.0463}\times 2.8754\]  
\[+\frac{0.0463}{0.0599+0.0278+0.0463}\times 2.7372=2.8723\]  
On a scale of 1-5, the mentioned system in operation has a center of gravity of linguistic assessment for the max-min composition of 2.8723. The corresponding dependability in this case is 57%.  
The grade obtained in this way is approximate to the value using the max-min composition of the system parts, but unlike it, this one also includes information about the dispersions of the system parts grades.
###### Abstract  
We present packet switching applications based on extended spectral-amplitude-coding (SAC) labels in generalized multi-protocol label switching (GMPLS) networks. The proposed approach combines the advantages of wavelength-division multiplexing (WDM) and optical code-division multiple access (OCDMA). The extended SAC labels preserve the orthogonal property to avoid the effect of multiple access interference (MAI) shown at the decoder. We investigate the node architecture of label generation/recognition based on arrayed waveguide grating (AWG). Combining cyclic-shifted maximal length sequence (MLS) codes with the wavelength routed property of AWG simplifies the node structure. The simulation results show that the proposed labels achieve good performances against receiver noise due to the low average cross-correlation values. Under a given bit-error-rate (BER), the switching efficiency of the extended SAC labels outperforms the previous OCDMA schemes, as the network nodes are capable of processing a large number of labels simultaneously.  
generalized multi-protocol label switching (GMPLS); spectral-amplitude-coding (SAC); wavelength-division multiplexing (WDM); optical code-division multiple access (OCDMA); arrayed waveguide grating (AWG) +
Footnote †: journal: Applied  
Article
## 2 UAV Communication
### Characterizing the UAV Network
#### 2.1.3 Server or Client?  
Whether the node is in the role of server or client is another distinction. In vehicular networks, the nodes are usually servers and in adhoc networks, the nodes usually act as clients [49]. UAV nodes usually act as servers and perform relaying functions of sensor data and packet forwarding [50]. They are also utilized for provision of data forwarding to other UAV clients. Figure 5 is a depiction of a widely used server-client implementation of UAV network is surveillance mode.  
Figure 4: UAVs serving as aerial adhoc base stations.
# Infrared Multi-Scale Small-Target Detection Algorithm Based on Feature Pyramid Network
## 2 Materials and Methods
### Overall Algorithm Architecture  
Figure 1 illustrates the overall architecture of the proposed infrared multi-scale small-target detection model. In the first step, the image that contains an infrared multi-scale small target is processed using a deep convolutional neural network, and feature maps of 1/2, 1/4, and 1/8 of the original image are obtained. The feature maps from various stages of feature extraction are processed by the PSC module. Then, the feature map generated by the PSC module in each stage is upsampled by a factor of 2 and fused with the feature map obtained in the previous stage with the PSC module to extract the portion that encapsulates information crucial for small-target identification. The fusion is performed with a DAF module. After two fusions, the final fused feature map is used to segment the small target, resulting in the acquisition of the small target in an infrared image.  
In this paper, ResNet18 [30] is chosen as the backbone network for the feature extraction stage. The input infrared small-target image is downsampled to extract detailed and rich small-target features. After multiple operations of downsampling and feature extraction, the deep semantic features of small targets are obtained. The low-level feature map retains detailed information such as the location and specifics of the small target, while the high-level features can capture the semantic information of the small target. The feature map obtained through the fusion of low-level features and high-level features not only preserves the benefits of low-level feature maps but also maintains the advantages of high-level feature maps. The final feature map can detect the position and shape of small targets more accurately, with higher detection accuracy and a lower false-alarm rate.  
Figure 1: The overall model architecture.
# An Attention-Based Method for the Minimum Vertex Cover Problem on Complex Networks
## 2 Basic Concepts and Problem Definition
### Attention
The idea is that of trying to identify, given the previous output state \(s_{t-1}\) (the query), which encoded inputs \(h_{i}\) (the keys) are the best to produce the new output state and, once identified, to compute the relative weights (alignment scores) using the previous output state \(s_{t-1}\) (the query) and the encoded inputs \(h_{i}\) (the values, corresponding to the identified key). According to this framework, the attention function can be described as mapping a query and a set of key-value pairs to an output. The output (the context vector), \(c_{t}=\sum_{i=1}^{T}\alpha_{a_{t},i}v_{k_{i}}\), is computed as a weighted sum of the values \(v_{k_{i}}\), where the weights, \(\alpha_{q,k_{i}}=softmax(e_{q,k_{i}})\), assigned to each value is computed by the alignment score of the query with the corresponding key, \(e_{q,k_{i}}=q\cdot k_{i}\). The attention function can, thus, be computed on a set of queries, keys, and values simultaneously, packed together into matrices \(Q\), \(K\) and \(V\). The matrix of outputs is computed as \[Attention(Q,K,V)=softmax(\frac{QK^{T}}{\sqrt{d_{k}}})V \tag{6}\]  
where \(d_{k}\) is the dimension of queries and keys. This model can also be enhanced with _positional encoding_, i.e., information about the relative or absolute position of the tokens in the sequence, to let the attention scores also focus on the order of the elements in the sequence.
# Medium Access Control Protocols for Cognitive Radio Ad Hoc Networks: A Survey
## 1 Introduction
presented in [26] a survey on MAC protocols specifically for CRNs, where they consider both centralized and ad hoc networks at the same time. Similarly, a more recent study by [PERSON] et al. provided in [27] a comprehensive review on MAC protocols for CRN considering both centralized and decentralized networks. As can be observed from existing surveys, most of the concentration is given to spectrum management functionalities, and less attention is given to the MAC protocols, especially for decentralized networks, concluding that there is no comprehensive survey paper on MAC protocols for CRAHN. Thus, this paper anticipates to provide a comprehensive and intensive study by giving a detailed summary of the state-of-the-art in MAC layer protocols with a concentration on CRAHN. Before surveying MAC protocols, we present a brief description of CR fundamentals and explain each cognitive radio function. Later, the MAC protocols are divided into three groups based on their access scheme where the salient features of MAC protocols are presented, and the pros and cons of each protocol are discussed and highlighted. Based on the analytical study of the existing MAC protocols, we have provided a discussion on the open research topics to be investigated more deeply in the future. Moreover, the comparison between protocols is introduced in a table to give the reader a broader view of all of the protocols. Thus, the contributions are the following:  
* It provides a comprehensive survey of the CR spectrum functions of sensing, decision, sharing and mobility together with their connection to MAC protocols. * It reviews the state-of-the-art MAC protocols in CRAHN. The pros and cons of each protocol are discussed, and the performance issues are highlighted. * It highlights open issues and challenges that need to receive more attention in the current literature as the future research direction. The rest of the paper outline is as follows: Section 2 presents the CR fundamental concepts, where the different components of the cognitive cycle are explained further. A detailed review of CRAHN MAC protocols is discussed in Section 3, where the pros and cons of each protocol are highlighted. In Section 4, we introduce a discussion on open challenges that have not been thoroughly explored. Finally, conclusions are given in Section 5, as well as a discussion of open issues and design considerations for future MAC protocols for CRAHN.
## 3 Results
### Results of RTK/RTN Surveys  
The calculated differences of the coordinates between the reference coordinates from the model static GNSS occupation and of RTK/RTN surveys performed using the GNSS receiver from a smart station set are presented below in Figure 5. The number of extreme differences (NoED) of coordinates between the reference coordinates from the model static GNSS occupation and surveys performed using RTK/RTN GNSS are presented below in Figure 5.  
The detailed differences of coordinates (D\({}_{\chi}\), D\({}_{\chi}\), and D\({}_{Z}\)) and the minimal and maximal values of the differences stated in the real-time techniques were considered and are presented in Table 5. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Point** & **Diff.** & \multicolumn{2}{c}{**D\({}_{\chi}\) [m]**} & \multicolumn{2}{c}{**D\({}_{\chi}\) [m]**} & \multicolumn{2}{c}{**D\({}_{Z}\) [m]**} \\ \hline \multirow{2}{*}{10000} & Minimal & 0.002 & SRS & 0.006 & SRS & \(-\)0.004 & MAC \& VRS \\ \cline{2-7}  & Maximal & \(-\)0.012 & MAC & 0.012 & MAC \& VRS & 0.012 & SRS \\ \hline \multirow{2}{*}{18970} & Minimal & 0.004 & SRS & \(-\)0.001 & VRS & \(-\)0.018 & SRS \\ \cline{2-7}  & Maximal & \(-\)0.008 & MAC & \(-\)0.007 & SRS & \(-\)0.037 & VRS \\ \hline \multirow{2}{*}{10010} & Minimal & \(-\)0.005 & SRS & 0.001 & VRS & \(-\)0.014 & SRS \\ \cline{2-7}  & Maximal & \(-\)0.012 & VRS & \(-\)0.004 & SRS & \(-\)0.036 & VRS \\ \hline \multirow{2}{*}{10020} & Minimal & \(-\)0.009 & MAC & 0.000 & SRS & 0.002 & SRS \\ \cline{2-7}  & Maximal & \(-\)0.014 & VRS & 0.002 & MAC & \(-\)0.021 & VRS \\ \hline \multirow{2}{*}{10030} & Minimal & \(-\)0.006 & SRS & 0.000 & MAC & \(-\)0.001 & SRS \\ \cline{2-7}  & Maximal & \(-\)0.018 & VRS & 0.001 & VRS & \(-\)0.027 & VRS \\ \hline \multirow{2}{*}{10040} & Minimal & \(-\)0.003 & MAC & 0.000 & SRS & \(-\)0.008 & SRS \\ \cline{2-7}  & Maximal & \(-\)0.006 & SRS & 0.005 & VRS & \(-\)0.034 & VRS \\ \hline \multirow{2}{*}{18966} & Minimal & \(-\)0.005 & VRS & 0.000 & MAC & \(-\)0.003 & SRS \\ \cline{2-7}  & Maximal & \(-\)0.009 & SRS & 0.002 & VRS & \(-\)0.027 & VRS \\ \hline \multirow{2}{*}{10070} & Minimal & \(-\)0.010 & MAC & 0.000 & MAC & 0.012 & SRS \\ \cline{2-7}  & Maximal & \(-\)0.013 & SRS & \(-\)0.004 & SRS & \(-\)0.034 & VRS \\ \hline \multirow{2}{*}{10080} & Minimal & \(-\)0.004 & MAC & \(-\)0.009 & MAC & \(-\)0.014 & SRS \\ \cline{2-7}  & Maximal & \(-\)0.009 & SRS & \(-\)0.013 & SRS & \(-\)0.038 & VRS \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Differences of coordinates after applying GNSS static occupation and with use of the individual type of corrections in multi-variant measurements.
# Exploiting Concurrent Wake-Up Transmissions Using Beat Frequencies
## 1 Introduction  
Recently, more and more wireless sensor networks [1; 2; 3; 4; 5] have been deployed with low-power wake-up receivers. They listen permanently to the wireless channel, but have a marginal and constant power consumption. Only when a wake-up receiver detects a valid wake-up message does it activate the sensor node. Sensor nodes with wake-up receivers are no longer bound to a fixed duty-cycling, but are able to communicate whenever necessary, for example directly following a sensor read-out or when several sensor readings are accumulated into a large transmission package. Consequently, low-power wake-up receivers can greatly reduce the power consumption of wireless sensor nodes, as investigated for example in [6].  
However, due to the limitations introduced by the low- to ultra-low-power consumption, the sensitivity of wake-up receivers is usually lower than that of state-of-the-art radio transceivers. In the cases of fading channels, it can happen that a wireless sensor node becomes out-of-reach of any other sensor node, for example due to fading simply caused by changes in the environment or in the network topology that increase path losses. In these cases, a sensor node might become temporarily or permanently disconnected from the wireless sensor network, although it is still alive. Figure 1 shows this scenario where Node-3 is out of the wake-up range of either Node-1 or -2 due to an outage of Node-4.  
To improve the reliability and to reduce packet losses, some existing network protocols [7] use concurrent packet transmissions that interfere in a constructive way. The feasibility of concurrent transmissions is demonstrated mainly in the area of IEEE 802.15.4-compliant radios. The IEEE 802.15.4 protocol maps four-bit symbols to 32-bit pseudo-noise sequences that have a length of 0.5 us at 868 MHz. In order to achieve constructive interferences, this imposes a time constraint of 0.5 us, which is not easily accomplished.  
In this article, we present a novel communication protocol to send wake-up messages by purposefully-interfering signals of two or more wireless sensor nodes, which produce the wake-up signal as the beat frequency of superposed slightly out-of-tune carriers. We investigate the method theoretically and demonstrate its feasibility with the help of several experimental setups. The rest of the article is structured as follows. In Section 2, we briefly introduce the principles of concurrent transmissions, as well as wake-up receivers that use on-off keying-modulated wake-up messages. Furthermore, we introduce the main features of Zippy [8], a flooding protocol that is based on concurrently-transmitted wake-up packets. Then, we investigate concurrent wake-up messages theoretically in Section 3 followed by two newly-introduced communication protocols that are based on concurrent wake-up messages in Section 4. In Section 5, we present the experimental results and conclude our article in Section 6.