esa-sceva/satcom-chunk-collection huggingface dataset:
# Satellite Image Super-Resolution by 2D RRDB and Edge-Enhanced Generative Adversarial Network +
## 2 Related Works
### Perceptual Loss  
The perceptual loss function is usually used to convert images among various styles. The success of the style transfer algorithm lies in the field of image generation. A very important idea has emerged here, that is, the features extracted by the CNN can be used as part of the loss function, and the image generated by a certain layer of pretrained networks can be compared. The feature map obtained from the specific layer of the network makes the semantics in the generated image and the target image more similar. The network is divided into two types: transform network and loss network. The transform network is used to convert the image, and its parameters changed, while the loss network keeps the parameters unchanged. The transformed result map, style map and content map are passed through the loss network to obtain the feature map of each layer and we can use it to calculate the loss.  
General style transfer uses both style loss and content loss. As the name suggests, style loss is used to change the style of the input image, while content loss is used to preserve the content of the image. The difference between the perceptual loss functions of super-resolution and style transfer is that the SR only needs content loss. Recently, SR methods aimed at improving visual quality and added this perceptual loss function. The generated image after adding perceptual loss function is much clearer than the image generated by using only the L1 (Manhattan norm) or L2 (Euclidean norm) loss function. We add the perceptual loss function to our SR method, and find a set of results that are more in line with human vision through different parameter adjustments.  
To summarize, the main contributions and advantages of our proposed approach in this work are:  
1. We incorporate the 2D topology into RRDB for both sub-networks in the generator to obtain extra diagonal connections to achieve better gradient optimizations among different paths to prevent the gradient vanishing and training difficulty problems.
2. We try different edge detection algorithms and choose Canny approach to replace original Laplacian method to get more detailed and clear edge information.
3. The new loss function (e.g., edge perceptual loss) is added into original loss function and different weighting combinations are conducted to obtain the best SR result.
4. Through extensive experiments on four well-known and publicly available satellite image databases, we evaluate all compared SR models with five objective image quality metrics to show the proposed approach is able to generate SR images with better visual quality and more close to the true image.
## 5 Attacks and Countermeasures
### Attacking MP2P  
MP2P networks are affected by both MANET and P2P attacks. Due to the characteristics of MP2P networks, (some of the) attacks may show more severe effects on MP2P networks than on MANETs or P2P networks individually.  
As both systems are susceptible to insider attacks that deny cooperation during data transmission, MP2P networks are affected strongly by this kind of attacks. Especially in layered MP2P networks, denying correct forwarding on network layer would affect the packet delivery ratio and, thus, the lookup process strongly as, due to the multi hop data transmission on application and network layer, a high amount of network layer requests is required to perform a lookup for an object. Due to the high number of required hops, few malicious nodes in the network are sufficient to affect the routing process with a high probability.  
Regarding Sybil attacks, MP2P networks may be attacked on both layers. Due to this, a malicious node will most probably attack the layer with the weakest countermeasure. Therefore, the probability of success for generating multiple identities in an MP2P network is increased compared to a MANET with a more robust overlay or a P2P system with a more robust underlay, respectively.  
Further, MANET attacks can be modified to attack the P2P services. E.g., modified _Blackholes_ may redirect only routes to specific nodes to deny objects managed by them. On the other hand, malicious nodes may misuse P2P mechanisms like the routing table maintenance to increase the traffic and initiate a resource consumption attack on the MANET. Therefore, MANET robustness is required to provide reliable services by the P2P network but a benign behavior on the P2P network is also required as, otherwise, the underlying MANET can be affected.
## 3 Background
### Blocks  
A block's contents can be divided into the header and the payload, which contains a set of transactions. A Merkle tree is often built from the payload's transaction chain, and a Merkle root can be calculated using this Merkle tree. The Merkle root is used to create the block's hash, which is included in the header. The header also includes a hash pointer to the previous block and the date when the block was produced [6]. As the Merkle root becomes the block's hash, changing one transaction within a block is enough to modify the block's hash [54].
## 5 Experimental Results and Analysis
### Performance
#### 5.5.3 Ablation Study
Overall, SBD-Net shows promising results, but further refinements are needed to achieve consistently high accuracy across all behavior categories. In the training process of machine learning and deep learning models, the performance of the loss function is an important indicator of the training effect of the model, as shown in Figure 11. In this paper, we analyze the effect of the optimized ES Loss and the classification loss (cls loss) that comes with YOLOv8 by comparing the changes in the two over 100 training cycles (epochs). The graphs show the trend of the two loss functions, where the red dashed line represents the optimized ES Loss and the blue realization represents  
Figure 9: The graph illustrates the comparison of mAP50-95 over epochs for different methods, including YOLOv8, YOLOv8 with Focal Modulation (FM), YOLOv8 with FM and Dyhead, and our proposed method. The mAP50-95 metric, which indicates the mean Average Precision across various IoU thresholds, is used to evaluate the performance of the models, and it shows that incorporating Focal Modulation and dyhead while optimizing the loss function significantly improve the detection performance of the model over time. the original classification loss of YOLOv8. We can clearly see that the optimized ES Loss shows obvious advantages in both the stability of model training and the final results, and through further optimization and tuning, the ES Loss has the potential to show stronger competitiveness and better classification performance in more practical applications.
# A Practical Neighbor Discovery Framework for Wireless Sensor Networks
## 5 Methods of Adjusting Duty Cycle
### Latency Constraints  
When the applications have latency constraints, such as fast streaming or real time detection applications, the node has to increase the duty cycle in order to discover the neighbors in boundedtime. However, discovery latency between two neighbors is determined by the chosen algorithm and the duty cycles of both nodes.  
As listed in Table 2, different algorithms lead to different discovery latencies. Take Disco [16] as an example. Given latency constraint \(\hat{L}_{i}(t)\) at time \(t\) for node \(u_{i}\), it can check the recorded information of the discovered neighbors. Suppose one neighbor \(u_{j}\)'s duty cycle is \(\hat{\theta}_{j}\), node \(u_{i}\) has to increase its duty cycle as: \(\hat{\theta}_{i}\geq\frac{4}{\hat{\theta}_{j}\hat{L}_{i}(t)}\). In order to discover all neighbors, node \(u_{i}\) has to check the smallest duty cycle (denote it as \(\hat{\theta}_{m}\)) and it has to increase the duty cycle as \(\hat{\theta}_{i}\geq\frac{4}{\theta_{m}\hat{L}_{i}(t)}\). After satisfying the application requirements, node \(u_{i}\) can then adjust the duty cycle by the remaining energy as described above. In order to reduce communication collisions, the duty cycle has to be larger.  
Combining remaining energy and latency constraints, the duty cycle should be adjusted by both factors; that is, to design a function of adjusting the duty cycle as:  
\[\theta_{i}(t)=f(P_{i}(t),\hat{L}_{i}(t)). \tag{12}\]  
When there is no latency constraint, \(\hat{L}_{i}(t)=+\infty\), PWR and PDR are two representative examples. In Spear, researchers could implement the interface for evaluating more functions which can timely adjust the duty cycle.
## 2 Related Work
### Geographic Routing  
Geographic routing (also called position-based routing or location-based routing) is a long-known data forwarding strategy and has been widely studied [37]. It exploits the geographic information of one-hop neighbors to choose an optimal forwarder at each hop, and thus moves a packet to reach the destination node gradually. To ensure the QoS (Quality of Service) provisioning of geo-routing in WSNs, several routing metrics are introduced for optimizing the forwarder selection [38; 39; 40]. Greedy forwarding in geo-routing may fail when encountering a communications void (i.e., local minimum), and thus an effective approach is required to solve this problem for guaranteeing successful delivery. Existing void handling techniques are systematically surveyed in [41]. The authors classify them into six categories: planar graph-based, geometric, flooding-based, cost-based, heuristic, and hybrid. Therein, the face routing [42] and the perimeter routing in GPSR (Greedy Perimeter Stateless Routing) protocol [43], two very similar approaches, have become standard techniques for void handling. Unfortunately, above traditional geo-routing approaches cannot be directly applied for energy-optimal routing towards dynamic destination regions. Additionally, the problem of dealing with the sink location errors for reliable delivery still remains to be solved.
# Monitoring Vegetation Change and Its Potential Drivers in Inner Mongolia from 2000 to 2019
## 2 Materials and methods
### Methods
#### 2.3.3 Trend Estimation Based on Seasonal Trend Model  
The STM is based on the BFAST and the classical additive decomposition model, which constructs estimation methods for trends and breakpoints [63; 64]. A segmented linear trend and seasonal regression model of NDVI during 2000-2019 to explain the NDVI value \(y\) at moment t can be expressed as:  
\[y_{t}=\alpha_{1}+\alpha_{2}t+\sum_{j=1}^{k}\gamma_{j}\sin\left(\frac{2\pi jt}{f} +\delta_{j}\right)+\varepsilon_{t} \tag{7}\]  
where \(\alpha_{1}\) is the intercept, \(\alpha_{2}\) is the slope of the trend, \(\gamma\) is the amplitude, \(\delta\) is the phase of the \(k\) harmonic term, \(\varepsilon\) is the residual error, and \(f\) is the number of observations per year. The parameters \(\alpha_{1}\) and \(\alpha_{2}\) were estimated using an ordinary least squares regression, wherein the derived time series segments were considered as categorical interaction terms with a trend slope of \(\alpha_{2}\). The significance of the trend in each segment was estimated using a \(t\)-test for the interaction parameter of the regression between the time series segment and \(\alpha_{2}\).  
The presence of breakpoints can, under certain circumstances, indicate a shift in the mechanism of influence on the time series. Therefore, if breakpoints are not considered, the results of the trend analysis may lead to a misjudgment of the factors that influence vegetation changes in Inner Mongolia [65; 66]. Therefore, in this study, the times at which breakpoints occurred were used as time splitting points, at which the time series was split into two subseries; before and after the breakpoint. Then, the time trends and significance levels of the subseries were monitored separately to obtain accurate conclusions regarding the fluctuations in vegetation trends in Inner Mongolia during 2000-2019.
# Big Data Collection in Large-Scale Wireless Sensor Networks
## 5 Data Transferring in LS-WSNs
### Network Structure-Based Routing Protocols  
Routing protocols are distributed according to network structure, which plays a very important role in the data-routing process. There are three main categories of protocols based on network structure. These protocols are:  
* _Flat routing_. Called 'data centric' as well, all sensor nodes here have the same tasks to perform. This is the first approach used in the routing of data in the WSN. It is based on the collaboration of all the nodes of the network. Data properties are specified by a naming system by attribute (attribute, value) because of the difficulty of assigning a global identifier to each node given their large number. Among their advantages is their simplicity, hence the possibility of establishing communications at no additional cost where each node would only need information from its direct neighbors. The disadvantage is the depletion of the energy resources of the nodes close to the base station because all traffic toward the latter obligatorily passes by them.
* _Hierarchical routing_. This approach is based on the formation of clusters (common areas) [36; 85; 100]. The principle is to route the data collected by each node of the cluster to its head of area, the Cluster Head (CH), which, after processing their common parts, forwards them to the next destination (If the CH cannot directly reach the station, basic information is routed to the next zone leader). The advantage is the reduction of communication and energy costs by minimizing the number of messages circulating on the network, since CHs apply aggregate functions on the data of the cluster, which makes it possible to combine them. The disadvantage concerns the size of the network. In addition, as network size increases, the CH election process becomes critical and greedy in resources.
* _Location-Based Routing_. The identification of the geographic locations of the sensor nodes on the gathering area is of paramount importance for the data-routing mechanisms in the WSN. This location information allows the calculation of sensor positions and the distances between them to build the shortest paths between a source node and its destination. This routing approach  
Figure 17: Taxonomy of routing protocols in WSNs.  
is more energy-efficient because it dispenses sensor nodes from using random or probabilistic methods to search for routes [109; 110]. In addition, the location of the nodes (and consequently of their regions) makes it possible to only broadcast requests to these regions and to avoid their diffusion in broadcast mode (global diffusion to all the nodes), thus significantly reducing the number of transmissions. The disadvantage is the need to equip sensor nodes with a satellite tracking system, such as GPS, which consumes a lot of energy.
# Integrity Monitoring of PPP-RTK Positioning: Part II: LEO Augmentation
## 2 Measurement Geometry under LEO Augmentation
### Hdop
The dashed and dashed-dotted line represents the case that the elevation mask for LEO satellites increases and does not increase after 5 degrees, respectively. can take their role and make up for deficiencies caused by the weakened measurement geometry. In addition, as illustrated by the magenta dashed-dotted line in Figure 6a, it is possible to achieve an HDOP below 2.5 using only the LEO satellites when the elevation mask angle remains 5 degrees (or lower). This is at a similar level to the Galileo-only HDOPs (see the solid green line in Figure 6a). It is worth mentioning that the improvements of the LEO augmentation in the measurement geometry are even more significant in the vertical direction than in the horizontal direction, which would be of interest to, for example, drone and hydrographic applications. When keeping the elevation cut-off angle at 5 degrees for the LEO satellites, the vertical dilution of precision (VDOP) reaches 20% to 40% for the dual- and triple-constellation cases at an elevation mask of 20 degrees (corresponding to the last column of Table 2). The corresponding improvements are around or above 40% for the three single-constellation cases.
# Urban Road Anomaly Monitoring Using Vision-Language Models for Enhanced Safety Management
## 2 Methods
### Performance Evaluation  
This part involved testing the model's accuracy with the remaining 20% of the images, as well as evaluating response times in real-time situations. First, we assessed the accuracy of the road anomalies classification, water depth estimation, and the safety level accuracy to ensure the model's robustness and reliability. This methodological approach, grounded in recent advancements and practices from the latest scientific literature, ensures a rigorous and effective development of a visual language model specifically tailored for real-time road anomalies monitoring applications [9,11,41]. Second, real-time tests were made to evaluate the VLM's response time and robustness.
# Comparison Analysis on the Accuracy of Galileo PPP Using Different Frequency Combinations in Europe
## 1 Introduction
However, the receiver antenna phase centers of Galileo constellations are critical for ambiguity resolution when using a different third frequency combination for Galileo PPP [19]. The analysis focused on the convergence time PPP, which is critical for the kinematic PPP solutions. However, the accuracy of static PPP is important for the precise geodetic monitoring applications, which are not sufficiently analyzed. Moreover, the current method for the statistics of PPP convergence can only represent the first convergence period, which cannot indicate the accuracy of PPP afterwards. In this study, we focus on analyzing the performance of dual-frequency to five-frequency Galileo PPP and the factors affecting precision improvement. The methods of undifferenced uncombined multi-frequency PPP models are introduced in Section 2. The used datasets and the processing strategies are introduced in Sections 3.1 and 3.2. In Section 3.3, the performance of Galileo and BDS-3 PPP are first compared to that of GPS. Then, the static and kinematic performance of Galileo multi-frequency PPP is evaluated in Section 3.4. The final section provides the conclusions of the study.
## 2 Method
### Adaptive Weighting Methods Based on Nsvd
#### 2.4.2 Short-Lag Spatial Coherence Weighting Methods Based on Nsvd  
SLSC was proposed as a new imaging technique that utilized spatial coherence between backscattered echoes instead of echo signal amplitudes to form a standalone image [18]. In addition, it can also be used as a weighting factor. In this paper, we use SLSC as a weighting factor to form weighted images.  
Assume that \(s_{NSVD}(i,\theta_{n})\) is the signals filtered by the NSVD filter at sample \(i\) in the \(n\)-th plane-wave. The normalized spatial correlation at lag \(m\) is given by:  
\[\hat{R}(m)=\frac{1}{N-m}\sum_{n=1}^{N-m}\frac{\sum_{i=p_{1}}^{p_{2}}s_{NSVD}(i,\theta_{n})s_{NSVD}(i,\theta_{m+n})}{\sqrt{\sum_{i=p_{1}}^{p_{2}}s_{NSVD}^{ 2}(i,\theta_{n})\sum_{i=p_{1}}^{p_{2}}s_{NSVD}^{2}(i,\theta_{m+n})}}, \tag{16}\]  
where \(p_{2}\) and \(p_{1}\) is normally taken over a length of a wavelength [23]. The value of SLSC is obtained by integrating the first \(M\) lags of the spatial coherence function:  
\[SLSC=\int_{1}^{M}\hat{R}(m)dm\approx\sum_{m=1}^{M}\hat{R}(m). \tag{17}\]  
As the value of \(M\) has an effect on the performance of SLSC, a parameter \(Q=\frac{M}{N}\) is used to determine the selection of \(M\). Generally, SLSC-weighted images provide improved CNR and sSNR when \(Q\) decreases. The final output of SLSC weighted CPWC+NSVD is represented by:  
\[S_{SLSC+NSVD}=SLSC\times S_{CPWC+NSVD}. \tag{18}\]
## 3 Key Technologies
### Industrial Product Safety Consensus Mechanism  
Production and operation units, as quality traceability service providers, can establish nodes and join the network to participate in the management of traceability information after obtaining the approval of the industrial product market supervision and management departments. Once joined, the node is involved in maintaining the ledger of the entire supply chain, and the system requires that any data entry needs to be verified according to the consensus of the nodes in the whole network before it can be recorded, and the key is to solve the distributed consistency problem to ensure the consistency of the information of the nodes in the network. Aiming at the upstream and downstream business relationships in the market circulation of industrial products, the practical Byzantine fault-tolerant consensus algorithm describe in Section 2.2 is chosen, which can effectively deal with abnormal situations that may occur in the distributed network and ensure the consistency and reliability of the data.
## 3 Experimental Results
### Comparative Experimental Results
#### 3.3.2 Quantitative Evaluation  
Table 1 shows the comparison of the shadow compensation results with the target values in the non-shadow area. According to the statistical mean brightness and mean gradient, the smaller \(Q^{\beta+T}\) is, the closer it is to the non-shadow area. It can be seen from the table that the compensation quality of the method proposed in this paper is the minimum value among all methods. However, because this method is based on an overall measure of the global region, it cannot reflect detailed information. In order to more clearly describe the effect of balanced compensation details, the brightness histogram of the shadow area  
Figure 9: The detailed shadow compensation results of windows 1–4 in image #3–5. (a) LCC. (b) HM. (c) GT. (d) CSR. (e) WLC. (f) Ours.  
of the image after compensation is calculated in Figures 6-10. In theory, the histogram of high-quality images presents a smooth normal distribution curve. In comparison, it can be seen that the histograms of the three reference methods all have a certain mutation phenomenon. However, the histograms' distributions are smooth after compensation through the method proposed in this paper. This further shows that the method proposed in this paper improves the proportionality of the effect. Additionally, they are closer to those of the histogram of the non-shadow area under normal lighting conditions.