esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experimental Evaluation and Analysis
### Implementation Details  
Our prediction model was run using Python and Pytorch, and the implementation details are as follows.  
1. Spatial interaction range: we set the longitudinal spatial interaction range within \(\pm 100\) m, which is similar to that in Ref [21].
2. Input embedding: we used \(1\times 1\) convolution to increase the number of channels to five, an approach which is beneficial to improve the learning ability of the network.
3. Residual connection: the residual connections were introduced in the interaction feature extraction module and the trajectory prediction module to avoid the problems of overfitting and gradient vanishing.
4. Training process: the VTP-GCN was trained on the NVIDIA GTX3080 GPU. The batch size and the learning rate were set to 128 and 0.01, respectively. We trained the prediction module for 250 epochs using a stochastic gradient descent (SGD) optimizer.
5. Model configuration: to obtain the best prediction accuracy, the number of GCN layers in the interaction feature extraction module and the number of CNN layers in the trajectory prediction module were set to different numbers, so as to select the best model configurations, i.e., two GCN layers and five CNN layers.
## 5 Joint Optimization of Resource Allocation and Computation-Offloading Decisions
### Computation-Offloading Decision
\(Fit(\mathbf{X}_{k})\) is defined as the fitness value at each particle position \(\mathbf{X}_{k}\). According to a previous study [27], a modified version of the BPSO can avoid local optima more effectively than the original version; therefore, we apply a new transfer function instead of that described by the rule in Equation (25), as follows [27]:  
\[Sig(V_{km})=\left|\frac{\pi}{2}arctan(\frac{\pi}{2}V_{km})\right|. \tag{26}\]  
Each candidate solution in the subproblem in Equation (21) (i.e., the offloading decision \(\mathbf{Y}\) and the offloading place \(\mathbf{Z}\) for all the MDs) is constructed according to a single position of each particle and its velocity and describes how the solutions are developed. We established a search space of D-dimension and transferred \(\{\mathbf{Y},\mathbf{Z}\}\) to the position of the particles. We denote \(\mathbf{X}=\{\mathbf{Y},\mathbf{Z}\}\) as the set of computation-offloading decisions and comprising matrices containing the offloading decisions and places. Therefore, the position of the \(k\)th particle is defined by \(\mathbf{X}_{k}\) and the velocity of the \(k\)th particle is denoted by \(V_{k}\). To solve Equation (21), the fitness function for the position can be computed as follows:  
\[Fit=\sum_{m=1}^{M}(\lambda_{m}^{e}E_{m}+\lambda_{m}^{t}T_{m})+P, \tag{27}\]where \(P=\sum_{n=1}^{N}\theta_{n}(\max(0,\sum_{m=1}^{M}y_{mn}-H)\) is the penalty function, which is an integrated form of the inequality constraint function C4 in Equation (21) and \(\theta_{n}\) is the penalty factor. Other constraints are guaranteed during initialization and genetic activities. The penalty function plays an important role in helping particles to fly out of a given area as soon as possible or as close to the feasible area as possible. ```
Randomly generate velocity \(\mathbf{v}_{k}\) and position \(\mathbf{x}_{k}\) for the number of particles \(K\) repeat for each particle \(k\) in \(K\)do  Calculate the fitness value if\(Fit(\mathbf{X}_{k})\) is better than Fit(\(L_{k}\))then  Update the best individual \(L_{k}\) end if if\(Fit(\mathbf{L}_{k})\) is better than Fit(\(\mathbf{G}\))then  Update the global best \(\mathbf{G}\) end if Update velocity variables via: \(\mathbf{V}_{k}=w\mathbf{V}_{k}+c_{1}\tau_{1}(\mathbf{L}_{k}-\mathbf{X}_{k})+c_{2}r_{2}(\mathbf{G}- \mathbf{X}_{k})\) \(Sig(\mathbf{V}_{k})=\frac{1}{1+e^{-\mathbf{V}_{k}}}\)  Generate a series of uniform random number rand in [0, 1]. Update the binary position \(\mathbf{X}_{k}\): \(\mathbf{X}_{k}=(Sig(\mathbf{V}_{k})>rand)\). end for untilMaximum iterations or stopping condition is satisfied;
```  
**Algorithm 1**The standard binary particle swarm optimization (BPSO) algorithm.
# Blockage Effects in Wind Tunnel Tests for Tall Buildings with Surrounding Buildings
## 3 Blockage Effects of Isolated Model Wind Tunnel Test
### Analytical Model of Drag Coefficient Error Caused by the Blockage Effect  
Based on the above analysis, the relationship between the drag coefficient error caused by the target pressure-measuring model and the blockage ratio can be calculated as follows:  
\[\Delta(R_{t})=kR_{t} \tag{9}\]  
where \(\Delta(R_{t})\) represents the drag coefficient error caused by the blockage effect of the target pressure-measuring model; \(R_{t}\) represents the blockage ratio of the target pressure-measuring model; \(k\) is the influence coefficient of the blockage effect, which is equal to the drag coefficient error caused by the unit blockage ratio. According to the results in Table 3, \(C_{0}=1.37\) and \(k=2.5\) can be obtained by least squares fitting, where \(C_{0}\) is the drag coefficient when the blockage ratio is zero. In fact, this influence coefficient of the blockage effect is exactly the same as the results of [PERSON] [2], whose resulting influence coefficient was also 2.5. Figure 8 shows the fitting result of Equation (9).  
Figure 6: \(C_{D}\) varies with Reynolds number [14; 16; 17; 20; 21].  
Figure 7: \(C_{D}\) varies with blockage ratio [5; 10; 37; 38].  
It should be noted that some studies have indicated that the blockage effect and blockage ratio are not linear, and this phenomenon could also be observed in Figure 8. Even so, when the blockage ratio of the target pressure-measuring model was less than 6%, the relationship between the drag coefficient and the blockage ratio can essentially be linear. Regarding the condition when the blockage ratio of the target pressure model is greater than 6%, this is not likely to occur in reality, otherwise, it will cause unnecessary trouble, especially for grouped models. Therefore, it can meet the accuracy requirements in evaluating the blockage effect according to the linear hypothesis in the common range of blockage ratio experiments.
# Infrared and Visible Image Fusion with a Generative Adversarial Network and a Residual Network
## 4 Experiments and Results
### Model Analyses  
Our fusion method with GAN and ResNet is a DL-based method that is distinct from traditional ones. Most traditional methods like MSD based, SR based, or others, directly make pixel-level operations on source images and somehow can establish the correlations between the source image and fused image easily. However, our DL-based generative model needs to generate the image through weights and bias of the network which are different to train. The structures of G and D may have a great influence on fusion results. In addition, the loss functions of G and D together determine the optimization strategies and what kinds of information will be added to the final image. So the design of loss functions is another troublesome problem.  
In order to solve these difficulties, firstly, we refer to the mature models of [57; 64], and construct the networks of our fusion model with GAN and ResNet. The residual blocks, skip connections, convolution layers, and BN layers work together for feature extraction and combination. The network designed for fusion is other than classification and there is no pooling operation, which may cause the loss of image information. Not only the original residual blocks are introduced, but also the \"big residual blocks\" are designed. They work together to guarantee that more useful features are generated and retained before reducing the dimensionality to a single-channel fused image. Meanwhile, the ADAM optimizer is adopted with proper settings. Furthermore, the loss function of G in our method consists of adversarial loss and the content loss, which help to increase the intensities of infrared image and the texture details of the visible image. The loss function of D which judges the label image and the image generated by G makes further efforts to add information from visible images.
## 2 Development of the Profiler
### Communication and Positioning  
In addition to GSM communication and GPS positioning devices, the external communication module includes a sensor to detect the air-sea interface and a flashing LED beacon to search for the device (Figure 9). The communication module is placed in a small, sealed housing with a transparent cap at the top. In this case, printed circuit boards are installed. An air-sea interface sensor and cable connector are installed in the lower part of the housing.  
Figure 8: Sensor suite of Winchi: (1)—pressure-proof housing with end cap, (2)—CTD probe of logger RBRconcerto C.T.D++, (3)—Seapoint Chlorophyll Fluorometer (SCF), (4)—Seapoint Turbidity Meter, (5)— Aanderaa 4831F dissolved oxygen sensor, and (6)—pressure sensor (this port can be used for a custom sensor). It is assumed that this backbone of core biogeochemical and physical measurement instruments will be supplemented by pH, CO\({}_{2}\), and CH\({}_{4}\) sensors in the future.  
The data transfer speed highly depends on the type of cellular network connection (LTE/3G/GSM) and signal strength. In the best-case scenario, the speed is about 38,400 baud. The limiting factor is the baud rate of the serial connection between the MCU and GSM communication modules.  
In order to operate in real time, Winchi attempts to transfer data each time it reaches the surface. If the communication conditions are not favorable, Winchi puts the current data set transfer request into the queue for later transmission on the next surfacing. As the data files are stored on a SD card, the transfer queue holds only file names and counts of successfully transferred bytes, so its memory footprint is rather small. The most recent data sets are always transferred first. Furthermore, the user can send a remote operation command to transfer a specific file at any time during the mission. Such a command generates a corresponding data transfer request and puts it at the end of the queue. In normal communication conditions, the transfer of one data set takes about 2 to 3 min.
# A Novel Power-Saving Transmission Scheme for Multiple-Component-Carrier Cellular Systems
## 4 Proposed Scheme
### SCC Activation/Deactivation (SAD)  
In order to actually prevent any unnecessary power consumption, the information of the TPE component is utilized by the SAD component to determine when to turn on (or turn off) any necessary (or unnecessary) SCCs. The following pseudo code expresses the details of this component's operation:  
```
//Initialize counting variables \(y_{1}=0\) and \(y_{2}=0\) at the first execution of the proposed scheme//If\((p_{\text{trans}}^{(z-1)}<\hat{p}_{\text{trans\_tot}}(s)<p_{\text{trans}}^{(z)})\) \(y_{1}=0\); \(y_{2}=0\); \(z=z\); \(z=z\); //set that \(p_{\text{trans}}^{(0)}=0\)// ElseIf\((\hat{p}_{\text{trans\_tot}}(s)>p_{\text{trans}}^{(z)})\) \(y_{1}=y_{1}+1\); \(y_{2}=0\); \(z=z\); While\((y_{1}=y_{\text{th}})\)do \(z=z+1\); Else\((\hat{p}_{\text{trans\_tot}}(s)\leq p_{\text{trans}}^{(z-1)})\) \(y_{2}=y_{2}+1\); \(y_{1}=0\); \(z=z\); While\((y_{2}\geq y_{\text{th}})\)do  CC-\(z\) denies new arrivals;  While(all sessions on the CC-\(z\) are completely transmitted) do \(z=z-1\).
```
###### Abstract  
Knowledge of Global Navigation Satellite System (GNSS) antenna phase center variations plays a key role in precise positioning. Proper modeling is achieved by accessing antenna phase center corrections, which are determined in the calibration process. For most receiver antenna types, the International GNSS Service provides such corrections for two GPS and GLONASS carrier signals. In the case of Galileo, access to phase center corrections is difficult; only antennas calibrated in the anechoic chambers have available corrections for Galileo frequencies. Hence, in many of the studies, GPS-dedicated corrections are used for these Galileo frequencies. Differential analysis was conducted in this study to evaluate the impact of such change. In total, 25 stations belonging to the EUREF Permanent Network and equipped with individual calibrated antennas were the subject of this research. The results for both the absolute and relative positioning methods are clear: using GPS L2 corrections for Galileo E5a frequency causes a bias in the estimated height of almost 8 mm. For the horizontal component, a significant difference can be noticed for only one type of antenna.  
Keywords:phase center variations; Galileo frequency; receiver antenna calibrations; coordinates +
Footnote †: journal: Sensors
## 3 Materials and Methods
### Security Metrics for Satellite Networks
#### 3.5.3 Attack Impact Metrics  
Attack impact metrics assess the overall security posture of the system by expanding upon the original TV-HARM methodology. In this study, an adjusted vulnerability score that was tailored to the characteristics of satellite networks was employed to evaluate both the attack success probability and attack impact. Figure 8 depicts the application of these metrics. The attack success probability, \(p_{ci}\), represents the system-wide probability derived from both host and protocol vulnerabilities across all the attack paths. The attack impact is measured by the cumulative system-wide risk, \(r_{ci}\), aggregated across these paths. \(r_{ci}\) is classified into five categories: negligible, minor, moderate, significant, and critical. For example, when \(p_{h}=75\%\), the node is categorized as likely, and if \(r_{h}=5.8\), it is classified as significant. In such cases, the attack impact for the corresponding threat vector is classified as high.
# IoT-Driven Resilience Monitoring: Case Study of a Cyber-Physical System
## 3 Theoretical Foundation of Resilience Curve and Its KPIs in This Domain
### Resilience Curve and Selected R-KPIs
#### 3.1.3 Functionality Loss  
Resilience curves are applied across the critical infrastructure literature this shows two kinds of curve: a typical representation with a semi-linear degradation and semi-linear recovery phase, or non-idealized system behavior (Figure 3). Since the complex systems can show the irregular behavior after disturbance because the interdependences between other CIs and the dynamic nature and natural complexity of the system, it is important to have a index to cover the loss during the lifecycle of recovery time. This loss is called Loss of Functionality [67]. Figure 4 shows a graphical illustration of Loss of Functionality.  
Figure 2: Illustration of minimum performance level. The red box highlights the point on the resilience curve where the minimum performance level can be identified.  
The method that is used in this research is adopted from a developed comprehensive framework for assessing the resilience of infrastructure in response to specific events, such as earthquakes [69]. The framework aims to measure the impact of an earthquake by estimating the predicted degradation in the quality of service provided by the infrastructure, denoted as \(Q(t)\). We can break down the process:  
Assuming a disturbance occurs at time \(t_{0}\), the degradation in service quality is assessed from the moment of the shock (\(t_{0}\)) until the service quality (\(Q(t)\)) fully recovers to its pre-disturbance levels at time \(t_{1}\). This period represents the recovery phase.  
The measure of Loss of Functionality, denoted as \(LF\), is determined by considering how much the service quality degrades during the recovery period, regardless of the behavior of the system shown in Figure 3. The calculation can be expressed as follows:  
\[FL=\int_{t_{0}}^{t_{1}}[100-Q(t)]\,dt\]  
Figure 4: Graphical illustration of Loss of Functionality [67].  
Figure 3: (a,b) Graphical illustration of different behaviors after disturbance [68].  
In simpler terms, Loss of Functionality is the difference between the initial service quality at the time of the disturbance (\(t_{0}\)) and the service quality once it has fully recovered (\(t_{1}\)). This provides a quantifiable metric for understanding the impact of the disturbance on the infrastructure's ability to maintain its continuity of service.
## 3 Optimizing In-Orbit SAR Operational Parameters for Altitude Variation
### PRF Optimization  
After defining the swath for the image acquisition, PRF should be optimized considering the altitude variation. Three methods can be used for the PRF optimization. The first method is to select constant PRF neglecting the altitude variation. The second method, which was used for KOMPSAT-5, is to optimize PRF using the ratio between the nominal and predicted altitudes. The third method is a new one proposed in this paper. PRF is optimized using the traveling return time.
# Molecular Hydrogen: The Postharvest Use in Fruits, Vegetables and the Floriculture Industry
## 6 Conclusions and Future Perspectives  
H\({}_{2}\) has been found to have profound effects in plants, this includes the use of H\({}_{2}\) in postharvest processing and packaging of food products. This review has shown that H\({}_{2}\) can improve plant tissue tolerance against different abiotic/biotic stresses, regulate plant growth, increase antioxidant, extend shelf life, and decrease nitrite accumulation during the storage of produce.  
H\({}_{2}\) may even be useful preharvest [61], so further work in this arena seems certainly worthwhile.  
Therefore, if the cost/benefit analysis shows that there are pragmatic advantages to the use of H\({}_{2}\), it should be considered widely in the food industry. As H\({}_{2}\) is being dubbed \"the fuel for 21 st century\" [22], it is likely that the production and transport of H\({}_{2}\) will be more widely adopted, and this would bring the cost down for the use of H\({}_{2}\) in the food industry.  
However, there are a lot of questions which still need to be answered:  
* Which food products would benefit from these treatments?
* How widely can these treatments be adopted by related industries, such as floriculture?
* Do we need to be concerned about safety: the gas is explosive, and donors leave by-products which may be toxic?
* What is the biochemical action of H\({}_{2}\) in plants? Are there any negative effects being induced?* How deeply into plant tissues do H\({}_{2}\) treatments penetrate? Or are surface effects sufficient for the postharvest effects required?
* How long do the treatments last, and are repeated treatments required?
* Will cost benefit really make these treatments pragmatic?
* If transport moves over to H\({}_{2}\) as fuel, can this be incorporated into the storage of plant materials as the H\({}_{2}\) is being carried anyway?
* Can H\({}_{2}\) treatment be used to decrease the levels of foodborne pathogens on fruit and vegetables?
* Can H\({}_{2}\) be used in conjunction with current industry practices to increase effectiveness and facilitate adoption?  
Having posed all these questions, and considered the evidence, it is clear that H\({}_{2}\) will have uses in the postharvest management of many food products, and it is certainly worth future investigation and considering for wider adoption as a preservative agent.  
[PERSON] wrote the draft of this article. [PERSON] and [PERSON] contributed ideas and edited the manuscript. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest.
# Research on Location Selection Model of 5G Micro Base Station Based on Smart Street Lighting System
## 4 Location Selection Model of 5G Micro Base Station
### Basic Assumption and Location Selection Principle  
#### 4.1.1 Basic Assumption  
The location selection model takes the 5G micro base station as the target location selection device and aims to fully consider the coordination and integration between multiple devices to obtain the optimal number and location of the 5G micro base station, based on the following assumptions:  
* There are road networks and light poles in the location selection area;
* All discrete points in the road network are optional points for the target device location;
* The coverage radius provided by the default operator can meet the capacity and service quality requirements within the coverage area.
## 3 EMF-Aware Routing Framework for WMNs
### EMF-Aware AODV Protocol  
In this section we describe the basic principles of the proposed EMF-aware routing protocol, the so-called EA-AODV, which is theoretically grounded by the algorithm described in Section 3.2. Our implementation is based on the well known AODV protocol, whose operation has been modified to account for the new EMF related metrics. Hereinafter, we introduce the most significant protocol procedures, which are able to disseminate the evolution of the accumulated exposure, as well as the messages format. Other procedures that are not mentioned (i.e., neighbor discovery) are inherited from AODV. The reader may refer to the AODV specification in [7] for a detailed description.  
Before describing the protocol in detail, we highlight some general considerations, with a clear impact on the protocol's specification and implementation. We assume that nodes are able to dynamically adapt their transmission power, according to the link with their neighbors (next hop) and that the protocol is itself aware of such power, leveraging a cross-layer solution. An off-line training procedure was carried out in order to establish the power required to reach the next hop.
###### Abstract  
In the UAV-assisted mobile edge computing (MEC) communication system, the UAV receives the data offloaded by multiple ground user devices as an aerial base station. Among them, due to the limited battery storage of a UAV, energy saving is a key issue in a UAV-assisted MEC system. However, for a low-altitude flying UAV, successful obstacle avoidance is also very necessary. This paper aims to maximize the system energy efficiency (defined as the ratio of the total amount of offloaded data to the energy consumption of the UAV) to meet the maneuverability and three-dimensional obstacle avoidance constraints of a UAV. A joint optimization strategy with maximized energy efficiency for the UAV flight trajectory and user device task offloading rate is proposed. In order to solve this problem, hybrid alternating metaheuristics for energy optimization are given. Due to the non-convexity and fractional structure of the optimization problem, it can be transformed into an equivalent parameter optimization problem using the Dinkelbach method and then divided into two sub-optimization problems that are alternately optimized using metaheuristic algorithms. The experimental results show that the strategy proposed in this paper can enable a UAV to avoid obstacles during flight by detouring or crossing, and the trajectory does not overlap with obstacles, effectively achieving two-dimensional and three-dimensional obstacle avoidance. In addition, compared with related solving methods, the solving method in this paper has significantly higher success than traditional algorithms. In comparison with related optimization strategies, the strategy proposed in this paper can effectively reduce the overall energy consumption of UAV.  
mobile edge computing; UAV communication; task offloading; trajectory optimization; three-dimensional obstacle avoidance