esa-sceva/satcom-chunk-collection huggingface dataset:
# Securing Relay Networks with Artificial Noise: An Error Performance-Based Approach +
## 2 System Model and Problem Formulation
In addition, we use \(E_{m}\) and \(E_{z}\) to denote the average energy per symbol for the source's signal \(m\) and the AN \(z\) sent by the destination, respectively. Finally, \(E_{m}\) is assumed to be known at the destination for the AN design. Since the destination has perfect knowledge of the instantaneous channel coefficients \(h\) and \(g\), this knowledge can be efficiently used to design the AN symbol \(z\). The relay uses the ideal coherent demodulation with minimum distance detection to recover the source's signal \(m\), and the corresponding SER is the performance metric used in this paper. The objective is to find out the AN symbol \(z\) at the destination in order to maximize the SER performance of the square \(M\)-QAM modulation at the relay, which we address in Section 3.
# Lightweight and Security-Enhanced Key Agreement Protocol Using PUF for IoD Environments
## 6 Proposed Protocol
### Informal Analysis
#### 7.2.6 Ephemeral Secret Leakage Attacks  
In this attack, \(A\) can determine the ephemeral secret values of \(r_{1},r_{2},r_{3},r_{4}\), and \(r_{5}\). However, \(A\) cannot compute the session key because \(A\) does not know the PUF's secret parameter (\(R_{j}\)), which is included in the session key (\(SK=h(h(PID_{D}\parallel K_{D}\parallel R_{j})\parallel h(HID_{M}\parallel K_{ M})\parallel r_{3}\parallel r_{4}\parallel r_{5})\)). Therefore, the proposed protocol is resistant to ephemeral secret leakage attacks.
## 4 Results and Analysis
### Experimental Setup  
For each dataset, we divided the images into training and testing sets, adhering to the 80-20 partitioning scheme. This division enabled us to train our model on a substantial portion of the data while reserving a separate set for rigorous evaluation. Our deep learning model leveraged a carefully crafted architecture, as discussed earlier, involving convolutional layers, feature selection, and fusion mechanisms. The model was trained using the Adam optimizer with specific hyperparameters. We employed standard evaluation metrics, including accuracy, precision, recall, and \(F1\) score, to quantitatively assess the performance of our method. These metrics offer valuable insights into the model's capacity to accurately categorize situations and its overall efficacy. The metrics can be calculated as follows [30]:  
\[Accuracy=\frac{TP+TN}{TP+TN+FP+FN} \tag{5}\]  
where \(TP\) (true positives) is the number of samples correctly classified as positive (belonging to the target class). \(TN\) (true negatives): The number of samples correctly classified as negative (not belonging to the target class). \(FP\) (false positives): The number of samplesincorrectly classified as positive. \(FN\) (false negatives): The number of samples incorrectly classified as negative.  
\[Precision=\frac{TP}{TP+FP} \tag{6}\]  
\[Recall=\frac{TP}{TP+FN} \tag{7}\]  
\[F1-Score=2\times\frac{Precision\times Recall}{Precision+Recall} \tag{8}\]
# Adaptive Attentional Network for Few-Shot Relational Learning of Knowledge Graphs
## 4 Methods
### Neighbor Encoder  
The goal of this module is to obtain implicit features through the entity enhancement encoder to update the feature vector of the entity. The schematic diagram is shown in Figure 4. First, we concatenate the feature vectors \(H_{t_{k}}\) of the triples, and then obtain the high-order domain aggregated information embedding of the source entity through the Transformer encoder between each layer and the hierarchical attention mechanism between different layers. Finally, the entity feature vector with more knowledge is obtained by adding the high-level domain aggregation information to the explicit feature information of the source entity.  
This paper first obtains the high-order domain aggregated feature vectors of tail entities, as shown in Equation (10).  
\[\begin{split} v_{t_{k}}^{\prime}=v_{t_{k}}+H_{t_{k}},\end{split} \tag{10}\]  
In vector weighting, in order to prevent the triple feature vector \(\left(v_{h_{k}},v_{r_{k}},v_{t_{k}}\right)\) from overfitting, the head entity, relation, and tail entity feature vectors are generalized using the dropout [35] method, and the expression \(C_{h_{k},t_{k},t_{k}^{\prime}}\) is shown in Equation (11).  
\[\begin{split} C_{h_{k},r_{k},t_{k}^{\prime}}=W_{c}\left(v_{h_{k}} \oplus v_{h_{k}}\oplus v_{t_{k}}^{\prime}\right)+b_{c}.\end{split} \tag{11}\]  
Among them, \(W_{c}\in R^{d\times 3d}\), \(b_{c}\in R^{d}\) is the parameter that the method in this chapter needs to learn, and \(\oplus\) represents the concatenation of vectors.  
Figure 4: The main structure of the neighbor encoder.  
The neighbor encoder proposed in this paper resembles GCN [22] and makes better on HAF. This paper uses a shared kernel \(\{W_{c},b_{c}\}\) to encode different entity neighbors. The method in this chapter considers the multi-hop propagation of information but is set within the range of length 3 from the source entity. Because the propagation distance of information is too small, the methods will lose a large amount of hidden properties. If the propagation distance is too large, the scope of application of the model will be reduced, and the weight of domain information will also be reduced.
# Dynamic Carrier-Sense Threshold Selection for Improving Spatial Reuse in Dense Wireless LANs
## 4 Performance Evaluation
### Simulation Results
#### 4.2.3 Varying Margin  
For both the model-based scheme and the measurement-based scheme, a margin parameter is used to account for variations in the channel and the possibility of multiple interfering signals. In the previous simulations, we used 6 dB as the default margin. In this simulation, we study the impact of this margin parameter. In the simulation area, we have placed 100 APs in a grid topology and deployed 100 mobile nodes in random locations. We have varied the margin from 0 dB to 10 dB. The result is shown in Figure 7.  
Intuitively, the total throughput of the protocols decreases as the margin becomes larger. Larger margin means the CST is lower, which leads to conservative transmissions. Because of that, the bottom 25% throughput increases when the margin becomes larger. For both the model-based and the measurement-based schemes, the bottom 25% throughput increases with the margin up to some point, and then stays similar afterwards. When the margin is lower than the saturation point, the selected CST causes hidden terminals which leads to packet collisions and unfair channel share. For the simulation environment we use, the proper margin to be used for the model-based scheme and the measurement-based scheme is approximately 5 to 6 dB. This is why we used 6 dB as the default margin for other simulations.  
From the graphs, we can observe that when the margin becomes low, the measurement-based scheme is affected more significantly compared to the model-based scheme. When the margin is below 6 dB, the bottom 25% throughput significantly drops for the measurement-based scheme, whereas the drop rate is slower for the model-based scheme. The same pattern can be observed for the fairness  
Figure 7: Performance of the model-based scheme and the measurement-based scheme varying margin. Number of APs and number of nodes are fixed to 100, and the margin is varied from 0 dB to 10 dB. (**a**) total throughput, (**b**) bottom 25% throughput, (**c**) [PERSON]’s fairness index, (**d**) packet delivery ratio.  
index and the packet delivery ratio. This is because the measurement-based scheme is more aggressive in setting the CST compared to the model-based scheme. The optimal margin value can be different based on the environmental factors such as node density and AP density. Estimating the potential aggregate interference on the receiver and setting the margin value accordingly is an important and challenging issue, and is left as a future work.
# Layer-Wise Query Selection to Eliminate Redundant Queries in DETR
## 3 Methodology: Selected Query DETR (SQ-DETR)
### Model Architecture and Layer-Wise Query Selection
#### 3.1.3 Layer-Wise Selected Query (SQ) Function  
The decoder refines predictions in \(L\) layers. At layer \(l\) (\(0\leq l<L\)), we apply the SQ function \(\mathcal{SQ}(\cdot)\) to prune redundant queries adaptively (Figure 2). Given current queries \(Q_{l}\), a layer-specific head \(\mathrm{MLP}_{\mathrm{class}}^{l}\) yields class probabilities  
\[P_{i}^{l}=\mathrm{softmax}\big{(}\mathrm{MLP}_{\mathrm{class}}^{l}(q_{i}^{l}) \big{)},\qquad S_{i}^{l}=\max_{c}P_{i}^{l}(c), \tag{4}\]  
where \(S_{i}^{l}\) is the foreground confidence of \(q_{i}^{l}\). The top \(k_{l}\) queries with highest \(S_{i}^{l}\) survive:  
\[Q_{l+1}=\Big{\{}q_{s}^{l}\ \mid\ s\in\operatorname*{arg\,max}_{S\subseteq Q_{l},\,|S|=k_{s}\leq S}S_{s}^{l}\Big{\}}. \tag{5}\]  
The ratio \(k_{l}/|Q_{l}|\) can be fixed or adaptively tuned (see Section 4). Discarded queries are dropped permanently, so the decoder's computational load shrinks layer by layer while focusing on increasingly promising object candidates.
# Security Protection of 3D Models of Oblique Photography by Digital Watermarking and Data Encryption
## 4 The Proposed Scheme
### Digital Watermarking Algorithm of Vertex Coordinates
#### 4.1.2 Watermark Extraction  
As illustrated in Figure 3, the watermark extraction process follows the same data processing approach as the watermark process. The watermark extraction process can be considered the inverse of the embedding process.  
First, extract the vertex coordinates from the 3D model. The vertex coordinate dataset is denoted as \(P=\{P_{1},\ P_{2},\ P_{3},\ \ldots P_{n}\}\), where \(P_{i}(X_{i},Y_{i},Z_{i})\) represents the coordinates of the vertices, with \(i\in[1,\ N]\), and \(N\) being the total number of vertices. Next, apply the DBSCAN algorithm to perform clustering on the dataset P, dividing it into multiple clusters denoted as \(C=\{C_{1},\ C_{2},\ C_{3},\ \ldots C_{n}\}\). Subsequently, extract feature points from each point cloud cluster and compute the virtual centroids of different clusters. Calculate the distances of feature points to the virtual centroids, resulting in a distance set \(D=\{D_{1},\ D_{2},\ D_{3},\ \ldots D_{i}\}\). Establish a mapping relationship between the distances \(D\) and the extracted watermark \(w^{\prime}\) based on Equation (2). The extraction process is performed as illustrated in Equation (5):  
\[w^{\prime}_{[r,l]}=\left\{\begin{array}{ll}1,\ \ \big{(}[d^{\prime}_{m}*10^{ 6}]\big{)}mod2\ \
eq 0\\ 0,\ \ \big{(}[d^{\prime}_{m}*10^{6}]\big{)}mod2=0\end{array}\right. \tag{5}\]  
From all the clusters \(C=\{C_{1},\ C_{2},\ C_{3},\ \ldots C_{n}\}\), extract \(W^{\prime}=\big{\{}W^{\prime}_{1},\ W^{\prime}_{2},\ W^{\prime}_{3},\ \ldots W^{\prime}_{m}\big{\}}\) for each cluster. In this case, statistical methods are used to select the optimal values. There is a many-to-one relationship between the feature points and watermark positions, meaning that multiple watermark values correspond to the same \(r,l\) value. To extract the watermark information, the proportion of identical values in \(r,l\) is calculated. The most frequent result value is assigned as the watermark value for that row and column, thus completing the watermark information extraction.
# Survey of Smart Parking Systems
## 3 Types of SPS Reported in the Literature
### Electrical Vehicle Parking Systems  
Currently available on the market, electric vehicles (EVs) are becoming increasingly popular, and widespread use is expected in the future [52]. This growth is due to two reasons: the entry into the market of vehicles, both 100% electric and hybrid, and the efforts of some countries to put more low-pollution transport on the streets [53]. In this context, EVPS offer a solution for electric vehicle drivers in terms of parking management and planning. The operation of EVPS is mainly based on one of the major challenges of this type of transport, i.e., the management of the charging needs in the parking spaces [54].  
EVPS are composed of algorithms that allow establishing an optimal route to parking places with charge stations. These routes can be determined by the user's preferences, the vehicle's charge level, the power available from the electricity grid to supply the demand of the circulating EVs, or the price of the electricity charge according to the vehicle's location [55].  
The charging stations that feed the EVs can communicate with the EVPS, to specify which parking lots are available or which will be free. In this way, real-time information can be obtained on the status of the parking spaces, in order to calculate the route of a vehicle [55].  
Generally, EVPS offer drivers a route to the parking lot via a web or mobile app. In addition, they can include information about the status of charge of the vehicle and the expiration of the parking time, which is usually not unlimited.  
The latter refers to the fact that EVPS seek to ensure that charging is carried out efficiently, avoiding overloading the power grid, and limiting the number of parking spaces available, thus maximizing the state of charge of the vehicles and meeting the needs of drivers, but also optimizing the consumption and availability of the power grid [52].  
The cost of implementing this type of SPS is high due to the requirement to implement EV charging plants and their connection to the power grid. Some cities implement the payment of a fee for the charging of vehicles in the parking lots, in order to solve the expense of infrastructure and the provision of electricity. Some EVPS systems are developed to optimize the energy expense according to the cost of this service [56].
# A Virtualization Infrastructure Cost Model for 5G Network Slice Provisioning in a Smart Factory
## 4 Results and Discussion
### Sensitivity Analysis
#### 4.4.2 Cost of Isolation  
The difference between the cost of full isolation and that of no isolation of computing resources between the different slice types is calculated to find the cost of isolation in each deployment scenario. All VNFs are assumed to be dedicated to all slice types in the full-isolation case, while all VNFs are assumed to be common for all slice types in the no isolation case. Thus, the full and no isolation cases imply no and full sharing of infrastructure, respectively. The results are evaluated again with the same input scenario of 10 factories, the given device types, and 6% CAGR (10% for IoT devices) over the time period, as in chapter 4, but with no additional redundancy per slice types for direct comparison between deployment scenarios A, B, and C. The TCOs obtained for the three scenarios with full and no isolation cases are listed in Table 5.  
Figure 11: Sensitivity analysis—TCO savings in scenario A and B over scenario C, by varying the growth of devices and number of factories.  
The cost of isolation for the three scenarios are approximately EUR 0.39, 2.75, and 4.78 M for scenarios A, B, and C, respectively. The cost of isolation is the lowest when all virtualization infrastructure is in the central cloud, while it is the highest when all virtualization infrastructure is in each factory. The high cost of isolation in deployment scenario C also explains the high costs per slice type for each device type. Furthermore, scenario A can offer higher flexibility and scalability for supporting new and increasing number of services. Thus, involved stakeholders can achieve significant cost savings with higher flexibility and scalability through centralization. However, a local virtualization infrastructure can be preferred for reasons such as trust and security, lower end-to-end latencies, and reliability as well as considering the fact that a communication network might not comprise a huge portion of the overall expenses of a large factory's processes.
# An Overview of Infrared Remote Sensing of Volcanic Activity
## 1 Introduction  
Remote sensing is the observation of an object without requiring contact with the object being observed. It is something the human eye does all of the time, and humans have also developed specialist equipment for the purpose, ranging from the simple camera, which detects reflected electromagnetic radiation (in this case, from the visible portion of the spectrum), to complex sensors, which detect electromagnetic radiation in other portions of the spectrum, including in the infrared. In its application to volcanic activity, arguably the chief advantage of infrared remote sensing, as opposed to remote sensing in the visual portion of the spectrum, is that it detects the electromagnetic emissions of hot volcanic surfaces themselves, which are a function of the temperature and emissivity of the surface, as opposed to detecting the electromagnetic radiation, which has simply been reflected from the surface.  
Observations in the infrared region of the spectrum are useful when examining volcanic activity. This is because volcanic activity consists of the transfer of heat from within the Earth to the surface, and observations of this heat often relate directly to the activity of a volcano at a specific time [1]. The activity observed, and expressed in the thermal signature, might be a fresh lava flow or an active lava lake, or may be more subtle, such as a degassing surface or warmed crater-lake. Understanding the activity at a volcanic site using such data is clearly useful in determining a volcano's status and, as such in predicting future activity. Making such observations remotely and at a safe distance is also clearly advantageous.  
This review will firstly explain some of the theoretical concepts and considerations that must be taken into account in the infrared remote sensing of volcanic activity. Many of these considerations will be common to other disciplines, but are here framed from the unique perspective of monitoring volcanic activity. The sensors used for volcanic monitoring, and the history of the discipline, will then be discussed. The prospects, opportunities, and even threats, for the discipline in the coming decades will then be highlighted. It should be noted that while infrared remote sensing may be used for monitoring various aspects of volcanism, from heat to gas and ash emissions, the focus of this review is limited to the monitoring of volcanic heat emissions.
# Software-Defined Vehicular Cloud Networks: Architecture, Applications and Virtual Machine Migration
###### Abstract  
Cloud computing supports many unprecedented cloud-based vehicular applications. To improve connectivity and bandwidth through programmable networking architectures, Software-Defined (SD) Vehicular Network (SDVN) is introduced. SDVN architecture enables vehicles to be equipped with SDN OpenFlow switch on which the routing rules are updated from a SDN OpenFlow controller. From SDVN, new vehicular architectures are introduced, for instance SD Vehicular Cloud (SDVC). In SDVC, vehicles are SDN devices that host virtualization technology for enabling deployment of cloud-based vehicular applications. In addition, the migration of Virtual Machines (VM) over SDVC challenges the performance of cloud-based vehicular applications due the highly mobility of vehicles. However, the current literature that discusses VM migration in SDVC is very limited. In this paper, we first analyze the evolution of computation and networking technologies of SDVC with a focus on its architecture within the cloud-based vehicular environment. Then, we discuss the potential cloud-based vehicular applications assisted by the SDVC along with its ability to manage several VM migration scenarios. Lastly, we provide a detailed comparison of existing frameworks in SDVC that integrate the VM migration approach and different emulators or simulators network used to evaluate VM frameworks' use cases.  
cloud-based vehicular network; virtualization; cloud computing; software-defined vehicular cloud network; multi-access edge cloud; vehicular cloud; virtual machine migration +
Footnote †: journal: Sensors
# Averting BER Floor with Iterative Source and Channel Decoding for Layered Steered Space-Time Codes
## 1 Introduction
### Related Work
Moreover, we have deployed multiple antenna arrays (AAs) for boosting the data rate and enhancing the overall system's performance as stated in [34]. STCs are often employed in scenarios having multiple transmit AAs for improving reliability [33]. The V-BLAST architecture along with the proposed turbo detected system has the capability of large spectral efficiency as mentioned in [35]. We have deployed SBC as the error protection scheme, introducing artificial redundancy in the highly compressed H.265 stream for enhancing reliability. The LSSTC system is pretty much capable of averting the BER floor formation due to the employment of low-complexity unity-rate code (URC) decoder. The BER floor is a phenomenon which occurs in error correcting codes in the form of a point beyond which further improvement in BER stops to occur for any value of launched power. This will be observed in the form of a flattened region where the BER curve does not drop down further as before. Generally, error floors of the \(10^{-3}\) order are observed when there are no spreading interleavers [36]. We will be utilizing spreading interleavers in our system. Moreover, the URC operates with an infinite impulse response and uniformly distributes information across the constituent decoders, attaining refined BER after the two iterations.
## 7 Sensitivity Analysis  
In this section, we performed a sensitivity analysis of the suggested approaches by varying the parameters \(p,q\), and \(r\). As a result, the B\({}_{\text{pqr}}\)SF TOPSIS procedure was divided into three situations. First, we set values to the parameters \(p=3\), \(q=4\), and \(r=5\). Second, we swapped the values of \(p\) and \(r\) while keeping the value of \(q\). Finally, we assigned a similar value to each parameter to analyze the performance of the proposed operators. Data from the preceding DM problem were gathered again using the processes outlined in the pqr-TOPSIS technique. Later, the relative closeness degrees for the alternatives were determined, and in all situations, \(\hat{\tau}_{3}\) was found to be the best option and \(\hat{\tau}_{2}\) to be the worst; see Table 9 and Figure 4.  
In the second part of the analysis, we altered the parameters \(p,q\), and \(r\). As a consequence, the operation started with the parameters \(p=3\), \(q=4\), and \(r=5\), in order to investigate the performance of the proposed operator. Data from the aforementioned DM problem were aggregated once more with the B\({}_{\text{pqr}}\)SFWA operator. The score function was then used to calculate the score values for the alternatives, which were ranked by \(\hat{\tau}_{4}\succ\hat{\tau}_{2}\succ\hat{\tau}_{1}\succ\hat{\tau}_{3}\). Fixing \(q=4\) and switching \(p\) and \(r\) values yielded alternatives ranked as \(\hat{\tau}_{4}\succ\hat{\tau}_{2}\succ\hat{\tau}_{3}\succ\hat{\tau}_{1}\) (the rank of the final two options flips). This adjustment occurred when the value of \(q\leq 5\). Table 10 and Figure 5 display the scores for different values of \(p,q\), and \(r\) and show that \(\hat{\tau}_{3}\) is always the best.  
Figure 4: B\({}_{\text{pqr}}\)SF TOPSIS ranking results for different values of p,q,r.  
Figure 3: Ranking scores.