esa-sceva/satcom-chunk-collection huggingface dataset:
# Hydrodynamic Investigation on a Land-Fixed OWC Wave Energy Device under Irregular Waves
## 2 Numerical Model
### Irregular Wave Generation  
In the present study, a Joint North Sea Wave Project (JONSWAP) [27] energy spectrum is chosen as the input wave spectrum. The frequency spectrum \(S(f)\) presents the distribution of the wave energy with frequency \(f\) as follows:  
\[S(f)=\beta_{j}H_{s}f_{p}^{4}f^{-5}\exp\left[-\frac{5}{4}\big{(}f/f_{p}\big{)} ^{-4}\right]\cdot\gamma^{\exp\left[-\big{(}f/f_{p}-1\big{)}^{2}/2\omega^{2} \right]} \tag{9}\]  
\[\beta_{j}=\frac{0.06238}{0.230+0.0336\gamma-0.185\big{(}1.9+\gamma\big{)}^{- 1}}\big{(}1.094-0.01915\ln\gamma\big{)} \tag{10}\]  
where \(H_{s}\) is the significant wave height. \(f_{p}\) is the peak wave frequency. \(\gamma\) is the spectral peak enhancement coefficient which is taken to be 3.3 and \(\delta=0.07\) for \(f\leq f_{p}\) or \(\delta=0.09\) for \(f>f_{p}\).  
It is assumed that the energy of the input spectrum \(S(f)\) is mainly distributed in the frequency range \((f_{L},f_{H})\), where \(f_{L}\) and \(f_{H}\) are the low and high cut-off frequenciesrespectively. The energy spectrum is discretized into a range of equidistant frequency components. The amplitude of each wave component can be defined as follows:  
\[\Delta f_{i}=f_{i+1}-f_{i}=(f_{H}-f)_{L}/M \tag{11}\]  
\[\hat{f_{i}}=(f_{i}+f_{i+1})/2 \tag{12}\]  
\[A_{i}=\sqrt{2S\left(\hat{f_{i}}\right)\Delta f_{i}} \tag{13}\]  
where the number of wave components is represented by \(M\). \(\Delta f_{i}\) is the frequency increment of the \(i\)-th frequency range. \(\hat{f_{i}}\) is the intermediate value of the \(i\)-th frequency range, which is used as the representative frequency of this range, and \(A_{i}\) is the amplitude of \(i\)-th component.
# Low-Cost Active Anomaly Detection with Switching Latency
## Appendix A Proof for Theorems
### Asymptotically Optimality of DMSC Policy  
In this subsection, we focus on Scenario 1 where the parameter \(s\) satisfies \(s=o(B)\) as \(\alpha\to 0\).  
Note that the offset \(\triangle(M,L)\to 0\) holds as \(\alpha\to 0\) in this scenario where \(\lim_{\alpha\to 0}s/B=0\). Then, the two cases of DMSC policy (8) can be expressed as  
\[\begin{split}&\text{Case I}\;\frac{2D(g||f)}{L+1}\geq\frac{D(f||g) }{M-L},\\ &\text{Case II}\;\frac{2D(g||f)}{L+1}<\frac{D(f||g)}{M-L}.\end{split}\] (A19)  
If DMSC policy is in Case I, according to (A2), the asymptotic lower bound on the expected cumulative cost is given by \(c\frac{L(L+1)B}{2D(g||f)}+cs\frac{L(L-1)}{2}\). Then,  
\[\begin{split}&\lim_{\alpha\to 0}\frac{E(C|\text{DMSC})}{C^{*}}\\ \leq&\lim_{\alpha\to 0}\frac{E(C|\text{DMSC})}{C^{*}_{ LB}}\\ \leq&\lim_{\alpha\to 0}\frac{c\frac{L(L+1)}{2}\left(\frac{B}{D(g||f) }+sT_{s}\right)}{c\frac{L(L+1)B}{2D(g||f)}+cs\frac{L(L-1)}{2}}\\ =&\lim_{\alpha\to 0}1+\frac{r_{1}}{\frac{L(L+1)}{2D(g||f) }+\frac{L(L-1)}{2B}}\frac{s}{B},\end{split}\] (A20)  
where \(r_{1}=\frac{L(L+1)T_{s}}{2}-\frac{L(L-1)}{2}\). Since \(\lim_{\alpha\to 0}s/B=0\) and \(r_{1}\) is a constant, then  
\[\lim_{\alpha\to 0}\frac{E(C|\text{DMSC})}{C^{*}}\to 1.\] (A21)  
In Case II, according to (A2), the asymptotic lower bound on the expected cumulative cost is given by \(c\frac{L(M-L)B}{D(f||g)}+csL(M-L-1)\). Following a similar line of arguments as above, we can prove that \(\lim_{\alpha\to 0}\frac{E(C|\text{DMSC})}{C^{*}}\to 1\) holds in Case II. Therefore, the proof of the asymptotic optimality of DMSC policy in Scenario 1 is completed.
## 4 Truck-UAV Collaborative Delivery Planning Algorithm Based on Improved Evolutionary Algorithm
### Algorithm Design
#### 4.2.2 Two-Stage Population Initialization  
In light of the characteristics of the high-dimensional solution space associated with the air-ground collaborative delivery problem, a two-stage hierarchical initialization strategy is proposed, which comprises random initialization and heuristic initialization methods. These two methods are subsequently combined to form a comprehensive set of initial solutions. The specific steps involved in this process are outlined as follows:  
* Stage 1: Generation of Basic Layers through Random Initialization  
Truck route generation: Randomly assign the number of trucks \(\mathrm{T_{d}}\sim\mathrm{U[1,\,T_{max}]}\) to each distribution center. Use the random walk strategy to generate the initial route \(\mathrm{R_{t}}=\{\mathrm{v_{0}},\mathrm{v_{rand1}},\cdots,\mathrm{v_{randk}}, \mathrm{v_{0}}\}\), where \(\mathrm{v_{0}}\) represents the distribution center, \(\mathrm{v_{randi}}\) represents the route node, and the route length is \(\mathrm{k}\).  
The generation of drone routes can be conceptualized as the allocation of tasks throughout the designated path. The target for each task is determined by selecting the nearest failed node \(\mathrm{v_{traget}}=\arg\min\limits_{\mathrm{v}\in\mathrm{V_{failed}}} \mathrm{d(v_{i},v)}\), where \(\mathrm{d(\cdot)}\) is the Euclidean distance between point \(\mathrm{v_{i}}\) and \(\mathrm{v}\), and \(\mathrm{V_{failed}}\) is the set of failed nodes.  
* Stage 2: Development of the Enhancement Layer (Heuristic Initialization)  
The truck route optimization method is based on the Clarke-Wright savings algorithm [30]. Initially, the savings value \(\mathrm{s_{ij}}=\mathrm{d(v_{0},v_{i})}+\mathrm{d(v_{0},v_{j})}-\mathrm{d(v_{ i},v_{j})}\) is calculated for all pairs of nodes. Subsequently, these values are organized in descending order of \(\mathrm{s_{ij}}\) and the routes are merged sequentially until the capacity constraint is satisfied.  
In the pre-allocation of drone tasks, it is recommended to employ a greedy algorithm to identify the task that offers the maximum marginal benefit. \(\Delta\mathrm{f}=\frac{\mathrm{q(v)}}{\mathrm{d(v_{i},v)}+\mathrm{d(v,v_{i})}}\). Prioritize covering the failed nodes with a high demand and short distance.
# Ontology-Based Knowledge Representation in Robotic Systems: A Survey Oriented toward Applications
### Contribution  
Figure 1 shows the domain and scope of our survey. It illustrates that our review was based on only those ontology-based KR & R systems that have applications in the domain of robotics, while the approaches in the domain of smart environments are beyond the scope of our survey.  
To complete complex tasks in the real world, it is often necessary for the autonomous mobile robots operating in an uncertain environment to have semantic knowledge about their surroundings, tasks, and objects. Therefore, many efforts have been initiated towards the development of semantic knowledge representation in the form of ontologies to help the robotic systems reason about the world to take more robust measurements.  
Researchers have surveyed the knowledge-based systems in particular with the focus on a certain task. For example, Reference [26] reviewed the knowledge base of eight systems with three typical knowledge representation languages for robot task planning and related task planners, while [27] discussed the knowledge base of nine systems in the context of finding a substitute for missing objects. Although, like the previous researchers, we also investigated knowledge representation systems (KRS) for robotics.  
Compared to [28], which concentrated on the field of computer vision with special attention on object perception using hybrid methods, our review was mainly focused on the field of robotic autonomy with a special interest in ontology-based knowledge representation systems that are oriented toward knowledge-based robotic applications.  
However, our current review concentrated on a broader aspect in the context of social robots [3] that perform their tasks in industry, hospitals, and homes using ontology-based knowledge representation systems (KRSs). For this, we discussed ten ontology-based KRS, shown in Table 1, used in industry, hospitals, homes, and public places.  
Compared to the existing survey papers, the present review was different in the following terms:  
To the best of our knowledge,  
* It facilitated novice researchers and experts to overcome the challenging task of determining and utilizing the most suitable ontology-based semantic knowledge representation system for the intended robotic application (Section 3).
* It provided an analysis of selected KB systems from the domain of robotics, delineated the advantages, summarized the current main research trends, discussed the limitations, and outlined the possible future directions.
* Compared to the earlier surveys, this study tended to be more concerned with the most recent work. Therefore, it provided the readers an important opportunity to advance their understanding of state-of-the-art methods.
## 2 Materials and Methods
### Input Data for Simulations
#### 2.3.4 Thermal Performance  
A white paper from solar tracker manufacturer Soltec [52] provides recommended values for the conductive and convective heat transfer coefficients (U\({}_{0}\)/U\({}_{1}\)) of bifacial modules mounted on their trackers, which are derived from field measurements made in Livermore, USA. In the absence of measured coefficients for the modules at the DTU site, we used the manufacturer-recommended values of 31 and 1.6 W-m\({}^{-3}\)K-m\({}^{-1}\)-s in the simulations here (Table 1). Similar U\({}_{0}\) coefficients were determined in [53] for glass-glass bifacial n-PERT, which provides justification for the use of these values. However, the use of one set of thermal coefficients for all bifacial module types and all projects is likely not ideal  
Figure 4: (**a**) Distribution of albedo measurements made during the one-year period; (**b**) Box and whisker plots of the monthly albedo, the red line connects the mean monthly albedo, the dashed reference line shows the annual average of 21.6%.  
given that the climatic region and module-specific bill of materials are known to influence the value of the U\({}_{0}\)/U\({}_{1}\) coefficients [54]. The thermodynamic behavior of monofacial PV modules has been shown to deviate from that of bifacial modules in side-by-side tests [53; 55] and therefore a U\({}_{0}\) coefficient of 29.5 W-m\({}^{-2}\)-K\({}^{-1}\) was used for these simulations. Finally, as [56] concludes in their review paper on bifacial PV simulation, more experimental validation of bifacial PV thermal coefficients is needed. Due to the uncertainty of these coefficients for the modules at the DTU site, we perform a sensitivity analysis on the results in the discussion section.
# Occluded Pedestrian Detection Techniques by Deformable Attention-Guided Network (DAGN)
## 4 Experimental Results
### EuroCity Persons (ECP) Dataset  
The EuroCity Persons dataset [14] is a recently released large-scale and dense pedestrian dataset in urban traffic scenes, which is more diverse than Caltech and CityPersons. This dataset provides over 238,200 person instances with highly diverse and detailed annotations in over 47,300 images. The image size of the ECP dataset is 1920 \(\times\) 1024 pixels. It records images including day- and night-time, and dry and rainy weather conditions across 31 different cities. For a fair comparison with other methods, we only used the 40,217 day-time images in the experiment. There were 23,229 images for training and 4225 images for validation. Following [14], we compared the results of the test set with 12,059 images.
## 4 Experiments and Results
### RQ3 Which Algorithm Provides the Most Suitable Tracing?  
The first factor to find candidate attack patterns that may arise from vulnerabilities is not to miss associated attacks. The second is the ranking quality. Thus, the most important metric is recall followed by MRR. SBERT had the best recall, as 49 of the 61 CVE-IDs were successfully traced (Figure 11). Similarly, TF-IDF successfully traced 48 CVE-IDs, which is not a significant difference. In MRR, TF-IDF showed the best performance. Therefore,  
Figure 14: Box plot of average precision.  
Figure 15: (**a**) Plotting average precision data; (**b**) heatmap of the results of the Kruskal–Wallis test.  
judging from the two evaluation indices comprehensively, TF-IDF is the most suitable. Below is a detailed analysis of TF-IDF and SBERT.
## 4 System Model Based on Power Splitting and NOMA
### Stage 1  
During this stage, a source node signal \(x_{s}\) with \(P_{s}\) power is transmitted to the \(IoT_{R}\) node for half of the block time \(T\) i.e., \(T/2\) period of time. The \(IoT_{R}\) node divide the received power \(P_{s}\) in the ratio \(\varepsilon P_{s}\):\((1-\varepsilon)P_{s}\). Accordingly here, \(\varepsilon P_{s}\) is for energy harvesting and \((1-\varepsilon)P_{s}\) is for information processing by \(IoT_{R}\) respectively, \(0\leq\varepsilon\leq 1\). The information signal received at \(IoT_{R}\) during this stage is given as:  
\[y_{IoT_{R}}=\sqrt{P_{s}}h_{IoT_{R}}x_{s}+n_{IoT_{R}}, \tag{20}\]  
Figure 4: System model based on power splitting and NOMA.  
The energy harvested at \(IoT_{R}\) in \(T/2\) period of time is given as:  
\[E_{h_{IoT_{R}}}=\frac{\eta\varepsilon P_{s}|h_{IoT_{R}}|^{2}T}{2}, \tag{21}\]  
The signal received at the information receiver of the \(IoT_{R}\) is given as:  
\[\sqrt{(1-\varepsilon)}y_{IoT_{R}}=\sqrt{(1-\varepsilon)P_{s}}h_{IoT_{R}}x_{s}+ n_{IoT_{R}}, \tag{22}\]  
The transmit power of \(IoT_{R}\) i.e., \(P_{IoT_{R}}\) in \(T/2\) block of time is given as:  
\[P_{IoT_{R}}=\frac{E_{h_{IoT_{R}}}}{T/2}=\eta\varepsilon P_{s}|h_{IoT_{R}}|^{2}, \tag{23}\]
# Cloud Gaming Video Coding Optimization Based on Camera Motion-Guided Reference Frame Enhancement
## 2 Related Work  
### Video Compression  
In recent years, various video applications have emerged with the development of network and storage technologies. The diversification and high-definition trend of video applications have put forward higher requirements for video compression. The formulation of international video coding standards has promoted the video compression process. In particular, H.265/HEVC [7] standardized by ITU-T Video Coding Expert Group (VCEG) and ISO/IEC Moving Picture Expert Group (MPEG) is widely used due to its 50% bitrate reduction over its predecessor H.264/AVC [8] while maintaining the same level of video quality. Some researchers proposed algorithms to achieve computational complexity scalability for HEVC encoders [9; 10; 11; 12] and others optimized the loop filters [13; 14; 12], as well as the intra-frame/inter-frame prediction process [15; 16]. Some works use the features of the infrared videos [17], satellite videos [18] and surveillance videos [19; 20] to generate additional reference frames for H.265/HEVC for further bitrate savings. Many works also applied deep learning techniques to video compression, including using deep learning networks to improve the accuracy of sub-pixel motion estimation and motion compensation [21; 22; 23; 24], enhance the bi-prediction performance [25], and improve the quality of reference frames [4; 5; 6], etc. Considering the continuous growth of video services and the development of new industries such as cloud gaming, the demand for bandwidth is increasing exponentially and the exploration of video compression is still urgent.
## 4 Evaluation
### Discussion
#### 4.3.1 Learning Performance  
The results projected in Figure 5 demonstrate the consistent reduction in global loss across training rounds, coupled with an increase in global accuracy. These trends confirm that the model successfully generalized across distributed datasets, enhancing predictive accuracy. The average weight adjustments depicted in Figure 4 further validate the effectiveness of the FL aggregation mechanism, showing convergence toward an optimal global model by leveraging diverse local data contributions.  
To further analyze the role of FL in the experimental results, Figure 6 highlights the contributions of individual client nodes to the global model's training process, demonstrating the diversity of local datasets and training conditions. FL's weighted aggregation mechanism effectively balances these variations, enabling the global model to benefit from heterogeneous data while maintaining privacy. Nodes with higher data quality and larger  
Figure 5: Global loss over rounds (top subfigure), global accuracy over rounds (middle subfigure), and average weight adjustment over rounds (bottom subfigure).  
datasets had a more significant influence on the global model, reflecting the robustness of the FL approach. This decentralized mechanism ensured consistent reduction in global loss and improved accuracy, as shown in Figure 5, while preserving data privacy. FL's ability to aggregate insights from distributed nodes without sharing raw data underscores its suitability for privacy-sensitive IIoT applications, making it a pivotal component in achieving the experimental outcomes.  
Additionally, based on Figure 6, the variations in contributions reflect the diversity of local datasets and training conditions, emphasizing the importance of FL in balancing these differences to create a robust global model. This figure also supports the claim that FL enables collaborative learning while preserving the uniqueness of each node's data, enhancing overall model performance.  
These results demonstrated the framework's ability to generalize across diverse data distributions, as evidenced by the consistent reduction in loss and high accuracy. FL played a pivotal role by enabling collaborative training across nodes while maintaining data locality, which is crucial for privacy-sensitive IIoT environments.
# A Survey on Congestion Control for RPL-Based Wireless Sensor Networks
## 4 Congestion Control Schemes for RPL
### Schemes Exploiting Path Diversity
#### Ca-Rpl  
[PERSON] et al. propose CA-RPL which is a multipath routing scheme for congestion avoidance. CA-RPL is focused on performance improvement in emergency scenarios where monitoring information about sudden events needs to be quickly and reliably transmitted to the sink [40]. To this end, the authors propose a new metric,'minimized delay metric' (or DELAY_ROOT) which is defined as the cumulative sum of the hop-by-hop forwarding delays along the path from a node to the DODAG root. The minimized delay metric is calculated based on the ContikiMAC duty cycling mechanism. To compute the weight of a path, each node uses a composite routing metric of ETX, its rank, the number of received packets in a time period, and DELAY_ROOT. After ranking parents according to their weights, a node selects two parent candidates which have the highest weights. Packets are distributed over the parents in proportion to the path weight.  
The authors evaluated performance via Cooja simulation with the ContikiMAC duty cycling protocol. CA-RPL outperformed the basic RPL in terms of packet reception number (PRN) of the root node per unit time, overall throughput of the network, packet loss rate, and average latency.  
However, since the amount of metric information to be carried in a DIO message is not small, control overhead needs to be analyzed. In particular, as [PERSON] et al. point out, adding a lot of additional fields to the DIO message increases a risk of L2 fragmentation [42].
# Endoscopic Optical Imaging Technologies and Devices for Medical Purposes: State of the Art
## 2 Medical Imaging Applications
### Optical Coherence Tomography
The axial FOV or depth of field in the possible image zone is given by:  
\[FOV_{z}=0.9\frac{n\lambda}{NA^{2}}, \tag{4}\]  
with \(n\) being the index of refraction in the medium [9; 10]. OCT has gone through enormous development since its first appearance in the early 1990s. These improvements comprise the higher imaging resolution (axial resolution in the range of 1-15 \(\upmu\)m, lateral resolution in 2-25 \(\upmu\)m range), acquisition rate, deep tissue penetration (up to 3 mm), and methods of light transmitted to the target area [2; 10]. OCT is largely used in ophthalmology to image the retina [12; 13]. The cross-sectional OCT image allows the identification of different structure layers in the eye, which can be used as a biomarker in diagnosis. [PERSON] et al. imaged the thickness of the retinal layer using an SD-OCT device and correlated the thickness to the mini-mental state [12]. [PERSON] et al. determined that patients with Alzheimer's disease (AD) have a thickness reduction of the peripapillary retinal nerve fiber layer (pRNFL) and the internal macular layer. An OCT image of the human retina is shown in Figure 2 along with the nine sector Early Treatment Diabetic Retinopathy Study (ETDRS) grid of the macular area with the average thickness values [14]. Recent development in the optical field utilizes and facilitates miniaturization of the OCT scanners at microscopic ranges enabling in vivo imaging of the blood vessels and hollow cavities in the body, such as the colon, esophagus, urinary tracts, and lungs [8; 15; 16]. Various instruments used to transmit and scan illuminating light on the target tissue sample and collect the backscattered light are discussed later in the paper.
## 3 Progress in Atmospheric Density Inversion for Low Earth Orbit Satellites  
With the development and application of precision orbiting of LEO satellites and onboard high-precision accelerometers, the inversion method is constantly improved to enhance the accuracy of the inversion results based on these high-precision density probe data. This section mainly introduces the optimization of the inversion method based on high-precision detection data.
# A Simple and Robust Equalization Algorithm for Variable Modulation Systems
## 4 Equalization Algorithm Simulation and Hardware Implementation
### Performance Analysis of Different Algorithm Implementations  
The proposed VM-DDLMS algorithm was implemented on the hardware platform XCKU040-FFVA1156-2-E, and the development environment was Vivado2017.4. The DDLMS algorithm cannot converge when the decision error is large; thus, it was deployed jointly with other blind equalization algorithms. The DDLMS algorithm needs to make a symbol decision based on the different modulation methods, which is highly complex for the VM system. The performance of the CMA, CMMA, and VM-DDLMS algorithms was mainly compared in this study. As shown in Table 2, the resource consumption and operating frequency of the parallel 11 th-order CMA, MCMA, and VM-DDLMS algorithms were compared, in which the resource consumption of the parallel 11 th-order CMA, MCMA, and VM-DDLMS algorithms was almost the same, and the operating frequency of the VM-DDLMS algorithm was as high as 333.33 MHz, which improved the operating frequency by 26.66% compared with CMA and MCMA. The VM-DDLMS algorithm was more stable in the VM system without adjusting the correction parameters and switching the symbol decision mode in real time according to the modulation, and the algorithm also converged when the decision error was large; it was also simple to implement.