esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion  
To obtain consistent results, several considerations must be kept in mind. First, the regions should be characterized by obtaining climatic and geographical data to differentiate between areas with higher and lower solar and wind potential. Next, uncertainties need to be estimated by quantifying the variabilities associated with solar and wind resources and those related to the power generation systems, such as the efficiency of solar panels and wind turbines. Then, these uncertainties must be propagated by applying statistical methods and uncertainty propagation models to calculate how uncertainties in solar and wind resources affect total electricity generation. To carry out the uncertainty propagation, as advanced mainly in the introduction and methodological sections, BEPU techniques in advanced energy scenario predictions have been used. In particular, using Wilks' formula and stochastic approaches for the energy generation calculations. Finally, the annual, monthly, and daily electricity generation from solar and wind energy in each region should be characterized, including identifying low-generation periods and comparing the advantages of solar versus wind in each area. Additionally, the complementarity between solar and wind resources should be analyzed to enhance the stability of the generation system.  
Consequently, the section is divided into three subsections. The first subsection describes the resources in each earth's zone. Next, the second one presents the outcomes of applying the methodology to the deterministic base scenario, revealing the results of the 93 simulations. Finally, the third subsection elucidates the principal results and findings obtained by comparing the performance of the analyzed condition.
# Hierarchical Active Tracking Control for UAVs via Deep Reinforcement Learning
## 4 Experiments  
### Training  
#### 4.1.1 Environment Settings  
The V-REP-based simulator is set up for the active tracking task. The observed state of the quadrotor is three sequential raw images \(s_{t}=[\mathbf{O}_{t-2},\mathbf{O}_{t-1},\mathbf{O}_{t}]\) with a resolution of 64 \(\times\) 64. Given the observed state \(s_{t}\), the RL controller outputs the normalized desired speed commands, which consist of the linear velocity along with the head direction \(\bar{V}_{fb}\in[-1,1]\) m/s and the yaw speed \(\bar{V}_{yaw}\in[-1,1]\) rad/s. Then, the normalized desired speed commands are scaled to the final desired speed commands:  
\[\begin{split} V_{fb}&=C_{fb}\bar{V}_{fb}\\ V_{yaw}&=C_{yaw}\bar{V}_{yaw}\end{split} \tag{21}\]  
where the scales \(C_{fb}\) and \(C_{yaw}\) are set to be 0.8 and 0.1, respectively.  
The final desired speed commands are tracked by the PID controller while maintaining the altitude at 2 m. The desired distance between the quadrotor and the target \(\rho_{d}\) is 3 m.  
At the start of each training episode, the quadrotor is 3 m behind the dynamic target (virtual person), and the dynamic target is in the center of the quadrotor's view. The target's end position is set to be 10 m away from the starting position. During the training, the visual, speed, and path randomization augmentation techniques in Section 3.2 are applied. The maximum speed of dynamic target \(V_{target}\) is 0.5 m/s, and the number of sampled control points between the target's start and end positions \(n\) is 8. When one of the following conditions is satisfied, the current training episode is completed, and the next training episode can begin:  
1. The dynamic target is out of the view of the quadrotor.
2. The distance between the target and the quadrotor is out of the interval of [2; 4] m.
3. The target reaches its end position.
## 5 Application Domain
### Safety Inspection  
The Bureau of Labor Statistics reported that one-fifth of worksite fatalities in 2017 occurred on construction worksites [195]. Safety managers perform safety inspections of construction sites on a day-to-day basis to control hazards. Safety inspection involves safety managers performing walkthroughs of the site and visually observing construction operations to enable the early detection of problems [196]. In response, project managers implement precautionary actions to prevent accidents [196]. However, this the project managers from engaging in other productive. Additionally, safety is an important issue that can benefit from more frequent monitoring. One of the leading causes of accidents on construction sites is falls [197]. Implementing site-wide fall protection practices has been found inadequate to prevent falls; instead, continuous monitoring is important for preventing fall accidents [197].  
UAVs have been used for remote surveillance of construction sites for improving safety and preventing accidents [21; 196; 197]. UAVs move faster than humans and can access hard-to-reach places [196] and perform continuous monitoring. [PERSON] et al. [197] developed computer vision algorithms to detect unguarded openings from video streams transmitted by drones. [PERSON] et al. [21] trained computer vision models for object detection and object tracking for proximity monitoring. They used a drone to survey a construction site and track construction resources (workers, equipment, and material) to prevent struck-by accidents.
## 2 Literature Review and Hypotheses
### Customer's Participation Intention  
[PERSON] et al. (2005) defined the customer's participation intention as \"the intrinsic motivation to interact and cooperate with community customers\" [23]. Customers with high participation intention suggest their interest to help other customers and participate in joint activities [25; 26; 27]. [PERSON] (2000) indicated that the customer's participation behavior in community can be interactive or non-interactive [28]. Non-interactive customers are also called lurkers, they merely browse an online brand community website. Thus, by considering the non-interactive behaviors, [PERSON] et al. (2016) defined the social network-based brand community participation intention as the propensity of customers to read, repost, review and interact with managers and other peer customers [4]. We defined customer's participation intention similar to [4].  
Past studies have reported that community participation is caused by the feeling of enjoyment when customers engage in an activity with total involvement. Two kinds of drivers motivate customers to participate in brand community: Brand community social integration and functional drivers (sharing or acquiring information) [1; 21; 22; 23; 24]. Many previous literatures have investigated the effect of brand community social integration on brand community participation. As we previously mentioned, brand community social integration is comprised of four kinds of customer-centric relationships. Brand community marketers rarely develop the four kinds of customer-centric relationships equally due to energy and money constraints. Thus, studying the individual effects of the four kinds of customer-centric relationships on customer's participation intention and identifying which relationship is important to customer's participation intention, rather than focusing only on the integrated effects of four kinds of customer-centric relationships will be helpful and important. In addition, [PERSON] et al. (2002) indicated that the four kinds of customer-centric relationships in brand community integration are developed interdependently in mutual ways [8]. However, researchers have yet to consider how each relationship affects each other.  
To address these questions, this study develops a model as shown in Figure 1 to examine how information quality and the four kinds of customer-centric relationships influence each other, and how they combine to determine customer's participation intention through the flow. In addition, we will identify the necessary and sufficient conditions for high participation.
## 4 Proposed Solution
### Pre-Processing of the RSS Data
#### 4.4.2 Temporal Smoothing  
In a radio receiver, active analog filters, such as an automatic gain control (AGC) circuit, adjust the input amplification gain depending on the received signal level to protect against large signal interference and attenuate slow changes in the received signal strength caused by shadowing effects [45]. As the objective is to mitigate fast multipath fading effects in the RSS, active analog filters are not suitable, and therefore, we propose a digital exponential moving average (EMA) filter for smoothing rapid variations in the RSS over a given time. The filter implementation is similar to a discrete first order infinite impulse response (IIR) or a single-pole low-pass filter that has a recursive feedback. The filter is characterized by the following equation:  
\[RSS^{i}=\alpha RSS^{i}+(1-\alpha)RSS^{i-1}=RSS^{i-1}+\alpha(RSS^{i}-RSS^{i-1}) \tag{16}\]  
where \(\alpha\) is the smoothness parameter (\(0\leq\alpha\leq 1\)) that yields the filter time constant \(\tau_{f}=\Delta_{T}(\frac{1-\alpha}{\alpha})\) and \(\Delta_{T}\) is the RSS sampling period used in the temporal smoothing.  
The next sub-section discusses how to determine the RSS gradients (which is a key ingredient to the SGA method) after applying spatial and temporal filters for RSS smoothing.
## 2 Materials and Methods
### Computational Docking  
High-throughput _in silico_ Docking Studies: The RelA enzyme (PDB: 5 IQR) was prepared and optimized using Maestro Protein Preparation (Version 11.9.011, MMshare Version 4.5.011, Release 2019-1, Platform Windows-x64, Schrodinger Maestro, New York, NY, USA). The 5 IQR PDB file contained extraneous portions of the ribosome, as the structure was determined as a RelA dimer associated with the ribosome. The ribosome and RNA subunits were removed and RelA was isolated in a separate file. The dockable RelA structure was prepared and minimized using Schrodinger's protein preparation application [41]. This application was utilized to add hydrogens, create missing disulfide bonds, and determine lowest-energy residue orientations. Geometry minimization was carried out using the force field OPLS3e [42]. A docking site was determined using homology studies of bacterial _rel_ genes from several species in combination with the Schrodinger binding site determination tool. Ligands were prepared using Schrodinger LigPrep (Schrodinger Release 2020-1: LigPrep, Schrodinger, LLC, New York, NY, USA, 2020).
# The Use of Radio and Telemedicine by TMAS Centers in
## 4 Discussion
### Regional Context and Digital Divide Issues  
Telemedicine is a rapidly growing field that has the potential to revolutionize healthcare delivery, especially in remote and underserved regions. It is important to consider the regional context and digital divide issues when considering telemedicine for sailing seafarers.  
The availability and accessibility of telemedicine services can vary significantly across different regions. Seafarers may already be able to access telemedicine services in coastal areas or regions with a strong maritime industry, such as major port cities [44]. A dedicated telemedicine center or clinic should be properly equipped to provide remote consultations, diagnoses, and even certain treatments to seafarers at sea in such regions. On the other hand, in less developed or remote coastal regions, the availability of telemedicine services may be limited. Telemedicine may not be feasible in areas lacking reliable internet connectivity or advanced medical equipment [45]. Telemedicine for seafarers would require significant infrastructure development and capacity building.  
The digital divide refers to the gap in access to and utilization of digital technologies between different populations or regions [46]. Seafarers who work on vessels operating in international waters or regions with limited connectivity may face difficulties in accessing telemedicine services due to unreliable or nonexistent internet connections [47]. The digital divide requires collaborative efforts from the government, maritime industry bodies, and telecommunications companies. By improving internet infrastructure and providing training programs for seafarers, telemedicine services can be made more accessible to all. Consequently, while telemedicine can provide valuable medical care to sailing seafarers, regional context and digital divide issues need to be considered. By addressing disparities in internet access and telemedicine infrastructure in a particular region, seafarers can access effective healthcare.
## 4 Methodology  
### Artificial Neural Networks (ANNs)  
ANNs are intelligence tools inspired by the biological neural networks of humans and animals, which can conveniently learn patterns and predict results of a problem in high-dimensional space [81; 82; 83; 84]. They are able to map a set of inputs to a set of outputs in a noisy and complex dataset. Multilayer perceptron (MLP) is a simple and reliable class of feed-forward ANNs. A typical MLP network contains an input layer, one or several hidden layers, and an output layer [85; 86]. The input layer takes the value of inputs and sends them to the available neurons in the hidden layer. Inside each neuron, a weighted sum of inputs is calculated, and this value plus a value of bias is transformed by an activation function, as shown in Figure 5. Finally, the output signal is transferred to the neurons in the next layer.  
The mathematical process can be formulated as below [87]:  
\[y_{j}=f\Big{(}\sum\
olimits_{i=1}^{n}w_{ij}x_{i}+b_{j}\Big{)} \tag{1}\]  
where \(x_{i}\) and \(y_{j}\) are the nodal values in the previous layer \(i\) and the current layer \(j\), respectively. \(n\) is the total number of the nodal values received from the previous layer. \(w_{ij}\) and \(b_{j}\) are also the weights and biases of the network.  
A tangent hyperbolic function has been used in this investigation, as it may lead to more accurate results [88]. This function varies between \(-1\) and \(1\), and is defined as follows:  
\[y_{j}=f(net)=\frac{2}{1+e^{-2.net}}-1 \tag{2}\]  
where \(y_{j}\) is the output signal, and \(f\) is the activation function in the terms of the calculated network value (net).  
Neural networks need to be trained to show efficient performance. Training means that the weights and biases of the network are determined such that the minimal error between targets (actual values) and outputs (network values) occurs [81]. Hence, the training process of neural networks culminates in a minimization problem. Backpropagation (BP) algorithms are commonly used in order to train neural networks [55]. The Levenberg-Marquardt algorithm (LMA) is often the fastest BP algorithm in training [89; 90]; thus, LMA is applied as the BP algorithm in this study.
# Multi-Scale Deep Feature Fusion with Machine Learning Classifier for Birdsong Classification
## 2 Research Method
### Step 2. Deep Feature Extraction on Multi-Scale Spectrum  
We used a standard ResNet18 model trained on the spectral features. To extract multi-scale deep features, we extended the ResNet18 model. The process of extracting deep features using ResNet18 is illustrated in Figure 5. During the model evaluation phase, the standard ResNet18 architecture was extended by adding global average pooling layers at various stages. The outputs of these layers were concatenated to form a comprehensive multi-scale feature representation. This representation, which includes features at three different scales, was then used as input for machine learning classifiers in downstream tasks.  
The process is as follows:  
Stage 1. Training standard ResNet18.  
Standard ResNet18 was trained using the cross-entropy loss function, defined as follows:  
\[L(y,\hat{y})=-\frac{1}{N}\sum_{i=1}^{N}\sum_{c=1}^{C}w_{c}\cdot y_{ic}\cdot \log(\hat{y}_{ic}) \tag{4}\]  
where \(N\) is the number of samples; \(C\) is the number of classes; \(y_{i,c}\) is the true label of class \(i\) for sample \(c\) (0 or 1), and \(\hat{y}_{ic}\) is the predicted probability of class \(i\) for sample \(c\); and \(w_{c}\) represents the weight assigned to class \(c\).  
Stage 2. Model evaluation phase for extracting multi-scale deep features.  
During the evaluation phase, ResNet18 was utilized to extract feature maps from the final three down-sampling stages of the model, corresponding to resolutions of 28 \(\times\) 28, 14 \(\times\) 14, and 7 \(\times\) 7. Global average pooling was applied to each feature map to reduce its spatial dimensions while retaining its semantic information. This operation generated feature vectors with dimensions of 128, 256 and 512, respectively. The three feature vectors were then concatenated to form a unified multi-scale feature representation with a total dimension of 896. This representation effectively captured both local and global patterns across multiple scales.
## 3 Results and Discussion
### Comparison of Results Obtained by GA and BFWA  
GA is an important swarm intelligent algorithm, and is widely used for feature wavelength selection in hyperspectral images. To further prove that BFWA can be used for wavelength selection, BFWA was compared to a GA algorithm for wavelength selection. Using a trial and error strategy, the optimal model parameters of GA were set as follows: population size was 30, maximum iteration number was 50, crossover probability was 0.6, and mutation probability was 0.01.  
Figure 6 shows the results of the prediction model established by PLSR in selecting different number of wavelengths based on the two methods. Compared to GA, the average correlation coefficient \(R_{p}\) and average \(RMSEP\) values of 10 prediction sets obtained by BFWA were improved at different number of wavelengths.  
Figure 5: Moisture content prediction of soybeans using the PLSR model.  
As swarm intelligent algorithms, the values of initial population in GA or BFWA have an effect on the model performance. A good algorithm should be robust (insensitive) to the value changes of the initial population. That is, the accuracy obtained with different values for the initial population should be stable. In this study, each calibration and prediction set was repeated 10 times by setting different value of initial population. After 10 repeated runs for each prediction set, the mean values and Std of \(RMSE\) and \(R_{P}\) obtained by the PLSR model with 12 selected wavelengths, as shown in Table 2. The BFWA obtained slightly better prediction results (the average of \(R_{p}\) and \(RMSE\) are 0.967% and 5.074%) than the GA method (the average of \(R_{p}\) and \(RMSE\) are 0.962% and 5.439%).  
Figure 6: (**a**) The \(R_{p}\) values obtained by genetic algorithm (GA) and BFWA; (**b**) The \(RMSE\) values obtained by GA and BFWA.  
Although there was only a small improvement in \(RMSEP\) and \(R_{p}\), the statistical test (\(t\)-test) shown that the performance of BFWA was statistically superior to that of GA (\(p<0.05\)). Meanwhile, compared with GA, the Std values of \(R_{P}\) and \(RMSEP\) values were larger than that of BFWA, except for prediction sets 2 and 4, which indicates that the BFWA performed better than the GA in terms of stability.
# A Unified Vendor-Agnostic Solution for Big Data Stream Processing in a Multi-Cloud Environment
## 1 Introduction
### Research Context
The following three main service models describe how cloud resources are commissioned: IaaS (infrastructure as a service), PaaS (platform as a service), and SaaS (software as a service). The more specialised a model is, the greater the economies of scale since the provider is responsible for commissioning and maintaining more resources at different levels. However, as a service model becomes more specialised, it also becomes less flexible and reliant on specific, if not proprietary, technology. However, a cloud consumer is understood to be vendor locked-in if they perceive better contract opportunities elsewhere in the market but is hindered from breaking an existing agreement with a provider. Vendor lock-in is a major cause of concern for potential cloud adopters [20; 22; 23; 24], and its mitigation is one of the motivating factors for the current research. Recently, significant container-based virtualisation research has been conducted [25; 26; 27; 28]. As containers provide decoupling between applications and the platforms to which they are deployed, they offer a level of mitigation against vendor lock-in. Furthermore, containers can be deployed on a PaaS (instead of IaaS) model because they represent a higher virtualisation level compared to virtual machines [25; 29]. When container and cloud computing technologies are employed together, they afford greater economies of scale and facilitate greater collaboration amongst developers. As an example, images representing applications which are developed as containerised services have been shared in public repositories and libraries [30]. Such images can be deployed on any platform hosted on any infrastructure (cloud or non-cloud-based). Finally, the use of container and container orchestration technology also enables multi-cloud setups, which greatly mitigates the risk of vendor lock-in, giving cloud consumers the ability to compare and combine individual offers from providers to create the most appropriate and preferred configuration [21; 28]. Should changes in prices or service level agreements render it advantageous to do so, resources may be easily and seamlessly transferred between providers.
# A Survey of Routing Protocols in WBAN for Healthcare Applications
## 3 Classification of Routing Protocols for WBAN
### Routing of Other Methods in WBAN
#### 3.6.2 Mobile Sink-Based Routing  
Mobile sink based routing is also a method referred from WSN, but there are only a few studies on WBAN, based on this method, at present. Mobile sink refers to sink nodes in the network that can move randomly, and the direction, speed, and residence time of its movement are pre-set, while the general WBAN stipulates that the location of the sink is fixed. Generally, the nodes in the network center consume too much energy due to the forwarding of information to their neighbors, which is called a hotspot. On the other hand, the nodes on the edge of the network consume too much energy, because of distance, which also leads to premature death and affects the connectivity of the network. The existing methods cannot solve the above problems, very well, but the mobile sink-based method can relatively alleviate the \"energy hole\" caused by hotspot and the \"coverage hole\" caused by edge nodes, and reduce the delay and energy caused by multi-hop. It can greatly promote the further development of WBAN.
## 2 Related Work
### Privacy Protection
Regarding high latency and limited bandwidth issues, existing federated learning methods based on encryption or differential privacy often cannot effectively reduce the communication overhead of model updates. The dynamic nature of Space-Air-Ground Integrated Networks presents a major challenge. Traditional privacy protection methods typically assume a static network topology, but in practical applications, dynamic topology may cause uncertainty in the joining and exiting of nodes during the federated learning process, thereby affecting the stability and reliability of privacy protection mechanisms. Therefore, how to provide stable and reliable privacy protection under dynamic network topologies, ensuring that the federated learning process is not disturbed by topological changes, is an urgent problem to be solved. In Space-Air-Ground Integrated Networks, significant heterogeneity exists in data generated by different devices (such as satellites, UAVs, ground base stations, etc. ), which can lead to slower model convergence speeds in federated learning, or even degraded model performance. Additionally, differences in computational resources between devices also pose challenges to the effectiveness of federated learning. Given these challenges, this paper will focus on proposing a novel framework that can effectively implement privacy-preserving federated learning in Space-Air-Ground Integrated Network environments.