esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 OLSR-Based Protocols
### Summary of OLSR-Based Protocols  
Table 2 presents the similarities and differences between standard and modified OLSR protocols. Based on this comparison (Table 2), it can be stated that the original OLSR protocol's limitations consist of a delayed reaction to frequent changes in network topology and do not consider the quality of wireless communications between UAVs. On the other hand, the proactive part of routing protocols gives the route before the routing process. In addition, the enhanced OLSR protocols give superior performance to the original OLSR because they take into account additional metrics, such as ETX (for selecting the MPRs for UAVs) and mobility and load-aware algorithms (for forwarding data packets to the target UAV). However, as UAVs have limited energy, each of the four routing protocols must continue to account for it during the routing process.
## 4 Example Analysis
### Final Scheme Selection  
Gray correlation analysis is used to select the optimal schemes. The evaluation index matrix of the steel pipe pile cofferdam construction scheme is established from the data in Table 2, and then the initialization process is carried out according to Equations (14) and (15) to obtain the initialization matrix \(A^{\prime}\). Further, gray correlation analysis is carried out between the ideal scheme and the to-be-selected schemes, and the gray correlation coefficient is calculated to obtain the gray correlation matrix \(R\). \(A^{\prime}\) and \(R\) are as follows:  
\[A^{\prime}=\begin{bmatrix}1.0000&1.0000&1.0000\\ 1.0000&0.3449&0.0000\\ 0.8333&1.0000&0.1094\\ 0.8333&0.3077&0.8511\\ 0.6667&0.2019&0.9909\\ 0.6667&0.2316&0.8602\\ 0.6667&0.9465&0.1155\\ 0.5000&0.7266&0.8784\\ 0.5000&0.8846&0.1185\\ 0.5000&0.8763&0.2097\\ 0.3333&0.6944&0.8906\\ 0.3333&0.8006&0.8176\\ 0.3333&0.0761&0.9970\\ 0.1667&0.0000&1.0000\\ 0.1667&0.6488&0.9787\\ 0.1667&0.8570&0.8967\\ 0.1667&0.9766&0.1246\\ 0.1667&0.9632&0.2097\\ 0.0000&0.2600&1.0000\\ \end{bmatrix}\quad\begin{bmatrix}1.0000&0.4329&0.3333\\ 0.7500&1.0000&0.3596\\ 0.7500&0.4194&0.7705\\ 0.6000&0.3852&0.9821\\ 0.6000&0.3942&0.7815\\ 0.5000&0.6465&0.8044\\ 0.5000&0.8125&0.3619\\ 0.4286&0.6207&0.8204\\ 0.4286&0.7149&0.7327\\ 0.4286&0.3511&0.9940\\ 0.3750&0.3333&1.0000\\ 0.3750&0.5874&0.9592\\ 0.3750&0.7776&0.8287\\ 0.3750&0.9553&0.3635\\ 0.9315&0.3875\\ 0.3333&0.4032&1.0000\\ \end{bmatrix}\]  
Based on Equation (18) and the gray correlation matrix \(R\), the weights of duration, cost, and quality can be calculated as 0.2817, 0.3477, and 0.3706. Then, from Equation (19), the gray correlation of each scheme is calculated, as shown in Table 4.  
The gray correlation of each scheme in descending order is A2, A15, A4, A14, A7, A3, A11, A10, A6, A12, A18, A5, A13, A17, A16, A9, A8, and A1. From this, it can be concluded that the optimal construction scheme is A2, which has a duration of 69 days, a cost of CNY 7,160,500, and a quality level of 0.9370.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Scheme** & **Gray Correlation** & **Scheme** & **Gray Correlation** \\ \hline A1 & 0.5557 & A2 & 0.6922 \\ A3 & 0.6426 & A4 & 0.6669 \\ A5 & 0.5957 & A6 & 0.6169 \\ A7 & 0.6637 & A8 & 0.5575 \\ A9 & 0.5632 & A10 & 0.6406 \\ A11 & 0.6408 & A12 & 0.6112 \\ A13 & 0.5921 & A14 & 0.6654 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Gray correlations of optimized schemes.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Scheme** & **Duration/Days** & **Cost/(CNY 10k)** & **Quality** \\ \hline A14 & 73 & 724.90 & 0.9656 \\ A15 & 73 & 719.92 & 0.9629 \\ A16 & 73 & 717.06 & 0.9375 \\ A17 & 73 & 717.38 & 0.9403 \\ A18 & 74 & 734.20 & 0.9663 \\ \hline \hline \end{tabular}
\end{table}
Table 3: _Cont._
# Backscatter Communications: Inception of the Battery-Free Era--A Comprehensive Survey
## 5 BackCom: Wireless Communications
### BackCom Modulation Techniques
#### 5.2.4 Differential Modulation in BackCom  
This modulation technique considers the difference between current and previous signal elements (such as amplitude) to generate modulated signals. Differential modulation has two parts: (i) a differential encoder and (ii) a modulator. The differential encoder can be referred to as the feedback system. Figure 22 depicts the differential encoder for the backscatter tag. Multiple \(A(k)\) signals are inserted into the differential encoder to output \(B(k)\) signals. \(B(k)\) signals have the effects of current and previous \(A(k)\) signals at different instants of time. \(B(k)\) signals are further sent to the modulator to obtain modulated signals \(a(n)\). Moreover, [PERSON] et al. used differential modulation to address the signal detection problem of Amb-BackCom [90].  
Figure 21: GFSK: frequency plan for creating BLE-compatible backscatter messages.  
Figure 20: Frequency shift keying (FSK) for BackCom.
# NetAP: Adaptive Polling Technique for Network Packet Processing in Virtualized Environments
## 2 Background and Motivation
### Related Work
#### 2.2.3 Research for Optimizing the Polling Interval  
Many previous studies have tried to overcome the limitations of interrupt-based and polling-based techniques. Some studies [22] suggest changing the period of polling operations by creating and evaluating a simple cyclic transformation equation through modeling and simulation. They focus on general network processing, rather than on I/O processing in virtualized environments. Hence, the results of previous studies [22; 23; 24] are difficult to apply to virtualized environments. Furthermore,in a real system environment, there are other considerations, such as the period of performance monitoring and the change in the packet-processing speed of the VM, which are not included in the previous studies. Therefore, more in-depth research is necessary in order to optimize the polling intervals in virtual environments. This study provides a search method to optimize the polling interval and a detailed analysis of the period of performance monitoring, which is essential for the search method.
## 5 Performance Evaluation
For the AUV-CH case, the mean value of 1500 packets occurs  
Figure 7:2 Data gathering latency _versus_ number of data packets received at the sink. Figure 7:3 Number of data packets gathered at the sink _versus_ round. after the fourth round, while in the case of AUV-PN the mean value of 3100 packets occurs after the seventh round. In comparison with AUV-CH, AUV-PN achieves more data collection at the sink. This is attributed to a higher number of relay nodes, which results in a higher AUV stay time. In Figure 13, the results for protocol overhead are presented with respect to offered traffic at each node for the Domingo routing protocol, AUV-CH, and AUV-PN. The protocol overhead is defined by  
Figure 7:4  
where \(N_{part}\), \(N_{sch}\), and \(N_{data}\) are the number of control packets required for network partitioning, scheduling over MN\(\rightarrow\)PN and PN\(\rightarrow\)AUV links, and the total number of data packets, respectively. \(L_{cont}\) and \(L_{data}\) are the length of control and data packet, respectively. It is observed that the Domingo routing protocol has a larger overhead than AUV-based schemes. One of the main reasons is the effect of the periodic usage of the neighbor discovery technique by the CHs. Maintenance of the route to the sink by CHs is the other main reason for such a high overhead. It is also observed that AUV-PN has a larger overhead than AUV-CH. The additional overhead is a result of the traffic generated during PN selection in NP_Phase. In both cases, the overhead also includes the control packets required for communication between the AUV and CHs for identification of relay nodes in the cluster. For both data-gathering scenarios, the overhead gradually decreases with an increase in the offered traffic. This decrease results from the additional data packets generated at each MN, which reduces the impact of partitioning overhead more evenly. The rate of decrease becomes low because of the additional data packets gathered at each relay node, which contributes to the transmission of additional control packets at relay node\(\rightarrow\)AUV links. The additional relay nodes in AUV-PN result in a higher overhead with respect to AUV-CH, but it is still much lower than the overhead generated by the Domingo routing protocol. Moreover, the additional overhead has a positive effect of uniform energy consumption over the network. Figure 7:5 Protocol overhead.
## 4 Experiment and Result Analysis
### Implementation Details
#### 4.2.2 Different Types of Batteries  
The g1c38, g2c32, g2c48, g3c2, g5c31 and g7c47 battery data in the Stanford-MIT-Toyota dataset are used as the source domains. Part of the cell data of Cell1 and Cell7 in the Oxford dataset is used as the target domains. And select the Oxford dataset Cell1 and Cell7 battery data except for the target domains as the testing sets. Table 5 provides specific details.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multicolumn{2}{c}{**Group**} & \multicolumn{1}{c}{**Source + Target \(\rightarrow\) Test**} & \multicolumn{1}{c}{**Data Volume Ratio**} \\ \hline \multirow{4}{*}{A} & 1 & g5c31 + g1c38 \(\rightarrow\) g1c38 & 1038:492:123 \\  & 2 & g3c2 + g2c48 \(\rightarrow\) g2c48 & 773:868:217 \\  & 3 & g5c31 + g2c48 \(\rightarrow\) g2c48 & 1038:868:217 \\  & 4 & g2c32 + g7c47 \(\rightarrow\) g7c47 & 988:591:147 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Dataset division and data size ratios in working conditions migration experiment.
# Superpixel Segmentation of Hyperspectral Images Based on Entropy and Mutual Information
## 4 Experiments and Analyses
### Parameters Selection Experiments
#### 4.1.3 Window Size \(m\)  
The window size \(m\) has a great influence on the dimensionality of HSIs based on information measures. In the experiment, three bands (\(n=1\)) were selected and the threshold was set to \(\sigma=0.1\). Using the dataset of Indian Pines, the effects of different window size \(m\) on UE, BR, ASA are shown in Table 2.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \(\sigma\) & **0.05** & **0.1** & **0.15** & **0.2** & **0.3** & **0.4** \\ \hline UE & 0.0203 & 0.0161 & 0.0149 & 0.0185 & 0.0185 & 0.0185 \\ BR & 0.6580 & 0.6132 & 0.6511 & 0.6268 & 0.6268 & 0.6268 \\ ASA & 0.8655 & 0.8408 & 0.8491 & 0.8350 & 0.8350 & 0.8350 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Superpixel segmentation performance under different \(\sigma\) (\(m=11\)) on Indian Pines.  
As shown in Figure 1, in the image of \"Indian pines\", the curvature changed steeply, thus selecting a large window size \(m\) and a moderate \(\sigma\) was more suitable. In addition, from Table 2, the experimental results showed that when \(m=20\), \(\sigma=0.05\), the experimental results of the segmentation were optimal. Under this condition, both BR and ASA were high, even though UE was a little high, the segmentation performance was still considered to be satisfactory. Through the above two parameter selection experiments, the most suitable values for the experimental parameters can be determined in the comparison experiment.
### Object Detection  
The background subtraction (BS) technique is a widely used object detection technique for extracting moving foreground objects from video against a static background. In an autonomous vehicle or robot's vision systems, the choice of BS technique can significantly influence performance due to several factors, such as illumination variations, occlusion, shape deformation, scale variation, and background clutter. According to the model and computational complexity, the BS techniques can be classified into three broad categories: simple BS, statistical BS, and neural networks (NN) based BS [6; 7; 8; 9].  
* The simple BS techniques including the frame differencing [10], average filtering [11; 12], median filtering [13; 14], and histogram over time [15], model the background simply, often by just using the previous frames or an average/median of the recent frames. These are fast and easy to implement but lack adaptability to changes in the background.
* The statistical BS techniques including the mixture of Gaussians (MoG) [16; 17], kernel density estimation [18; 19; 20], support vector models [21; 22], and principal component analysis [23; 24], build a statistical model of the background and classify pixels based on the model. They model the background more robustly using the history of pixels but are prone to the setting parameters and models.
* NN-based BS techniques including the radial basis function NN [25], self-organizing NN [26; 27], the convolutional NN [28; 29], and the generative adversarial networks (GAN) [30; 31], learn the specialized NN architectures that can adapt to changes in the background model to detect foreground over time. These can learn complex representations of background appearance and maintain robust models of multi-modal backgrounds, but they require large training datasets and have expensive computation loads to train and run, which may not be available in a real-time environment.  
Dynamic backgrounds with movement from objects can be erroneously marked as foreground if the model cannot adapt swiftly enough, leading to noisy segmentation and false detections. Therefore, the simple frame difference-based BS technique with the adaptive threshold (AT) method is appealing in the real-time VOT application due to its simplicity, efficiency, and robustness [32]. In this method, the threshold is adjusted based on the pixel's intensity variations over some time, e.g., the threshold should be smaller for regions with low contrast.
## 4 Analysis of Marginal Spectrum Change of an Active Lamb Wave
### Application Analysis of Marginal Spectrum Change in Plate Structural Health Monitoring
As shown in Figure 7, within each local frequency band, the amplitude of the 10 marginal spectra changes in the same direction had approximately the same amount, indicating that the marginal spectrum could reliably reflect the anomaly of the active Lamb wave. According to the above characteristics, it is suggested to use the comprehensive situation of the marginal spectrum amplitude in multiple continuous frequency bands, such as the amplitude change or amplitude ratio between frequency bands, to diagnose the structural damage of the plate, which can improve the diagnostic accuracy. These changes in the marginal spectrum of signals after damage can be explained by the instantaneous phase changes of signals, and the relationship between them is analyzed in Section 5.
# A Heuristic Algorithm for Locating Line-to-Line Faults in Photovoltaic Systems
## 3 Proposed Method
### Heuristic Algorithm for Line-to-Line Fault Location  
The heuristic algorithm for line-to-line fault location can be divided into two main phases: the fault candidate location marking process, and the fault location determination process. The following sections provide a detailed introduction and case analysis of these two parts.
# PlaIMoS: A Remote Mobile Healthcare Platform to Monitor Cardiovascular and Respiratory Variables
## 4 Results
### Results of the WSN Infrastructure Using the PlaMoS-Routing Protocol  
The PlaMoS-Routing protocol was installed in the 12 PlaMoS communication nodes of this study. For 60 min, the patient used the PlaMoS remote node and moved among the 16 hospital rooms, pausing for 3 min in each. During this period, the PlaMoS remote node generated 6650 packets, of which 6429 were received correctly by the PlaMoS communication node that served as the sink. Only 221 packets failed to be received, indicating a packet loss of 3.32%.  
Figure 18 shows user temperature, heart rate and fall detection data obtained from the PlaMoS platform and professional hospital equipment which was used to validate readings when relevant.
# Erythrocytes and Nanoparticles: New Therapeutic Systems
## 3 NPs and RBC
### RBC and Inorganic NPs  
Various inorganic NPs have been used in anticancer therapy for drug delivery [2; 51]. A coating of RBC membrane can improve the circulation time of these NPs, avoiding immune clearance by reducing macrophages' cellular uptake. Fe\({}_{3}\)O\({}_{4}\) NPs have been proven to possess the good characteristics (e.g., controlled size, low toxicity and biocompatibility) necessary to achieve therapeutic applications [52]. In a recent study [53], researchers coated these NPs with RBC membranes, providing better blood retention, reduced macrophage uptake and less biodistribution into spleen and liver, compared with Fe\({}_{3}\)O\({}_{4}\) NPs and PEG-Fe\({}_{3}\)O\({}_{4}\) NPs. Another important aspect of these NPS regards the pharmacokinetic trend of the second injection of RBC-Fe\({}_{3}\)O\({}_{4}\) NPs (despite PEG-Fe\({}_{3}\)O\({}_{4}\) NPs), which showed a decrease in the Fe content and an accelerated immune clearance.  
Mesoporous silica NPs are another type of inorganic NP which has found application in the anticancer therapy field [54]. To overcome the low efficacy of these NPs due to their aggregation and limited circulation time, researchers have exploited a camouflaged RBC, achieving high loading capacity of doxorubicin and of the near-infrared photosensitizer chlorin e6 loaded therein. [55].  
In recent years ultrasmall nanomaterials (<100 nm)--with application as inorganic nano-antimicrobials--have been prepared utilizing the laser ablation approach [56]. RBC-membranes are used to improve antibiotic delivery systems consisting of gelatin NPs coated with vancomycin antibiotic [57]. The camouflaged RBCs seem to improve the immune evasion, reducing symptoms caused by bacterial infection due to the optimal release of drugs in the targeted site [58].  
In this direction, gold (Au) NPs have recently been extensively studied as drug carriers [59; 60]. In a recent study [61], Au NPs with the RBC membrane have shown a prolonged half-life in circulation, a better tumor deposition and, consequently, increased survival in treated mice. The process involved a hypotonic treatment of the RBC, followed by an extrusion through porous membranes, which led to the formation of RBC membrane-derived vesicles that were fused onto the Au NPs [62]. After the fusion process, the resultant RBC-Au NPs showed a core-shell structure with an increase in diameter and a less negative surface charge (Figure 2). Also, in this study, escape from macrophage uptake (after covering NP with RBC membrane) was confirmed, thanks to the presence of specific ligands, such as CD47 and sialic acid, on their surface.  
All of these studies confirm the positive impact in functionalization when RBCs are uses to coat inorganic NPs.
## 3 Possible Interpretations  
Given the ambiguity in its meaning, it is not surprising that there is a diversity of interpretations of vertical resolution. This leads to a wide discrepancy in the quoted values for related specifications, by as much as three orders of magnitude, for instruments that in many cases have similar abilities to detect small variations in surface texture. Table 1 is a sampling of typical published specifications [4]. The range in quantitative values is more a reflection of the divergence in understanding of what is meant by vertical resolution than actual differences in performance. In some cases, the specification corresponds to the computer data representation, as described in the previous paragraph. In other cases, the specification is the standard deviation of the topography RMS (root mean square) or \(S_{q}\) parameter, which, in the limit of a perfectly featureless object surface, is simply the repeatability of the measurement noise level.  
There is a temptation to interpret vertical resolution as the \(z\)-axis equivalent of _lateral_ resolution, which ISO documents define as the smallest distance between two surface features in the \(x\)-\(y\) plane for which the features are clearly distinguishable [6; 9]. For optical instruments, an influence factor for lateral resolution is the diffraction limit, while for stylus tools it is the radius of the stylus tip. Can we apply this concept to surface heights in the \(z\) direction?  
The analogy between lateral and _axial_ resolution has meaning for optical coherence tomography (OCT), where the axial resolution quantifies the ability to separate scattering centers along a common line of sight (that is, within a single axial scan, equivalent to a single pixel). In OCT, this is a fundamental performance parameter that does not improve with averaging, related to spectral bandwidth and focus depth [10]. In contrast to the nanometer values listed in Table 1, axial resolution of less than a micron is considered to be exceptionally good [11]. The comparable specification for surface structures is the minimum transparent film thickness for which there are clearly separable confocal or interferometric height signals [12; 13].  
Vertical resolution usually refers to the ability to measure the difference height of widely separated surface features in the \(x\)-\(y\) plane of an opaque object. In this case, the analogy between lateral and vertical no longer applies. The smallest detectable difference in \(z\) for widely separated points on the two plateaus of a step height specimen is not limited by any fundamental principle other than random noise. Indeed, it can easily reach the picometer range, with sufficient optical power, lateral smoothing and measurement time.
## 5 Critical Aspects of FSS Technology
### Lifespan of FSS Technology  
There is currently a lack of detailed qualitative information regarding the lifespan of FSSs, as most research has focused on their design and electromagnetic properties rather than long-term durability. However, the lifespans of FSSs are expected to vary significantly, depending on the materials used and the environmental conditions to which they are exposed. Metallic FSS structures, for example, may experience degradation due to oxidation or corrosion over time, especially in harsh environments. In contrast, FSSs made from advanced dielectric or composite materials may exhibit longer lifespans, particularly in environments where temperature and mechanical stability are crucial. Future studies are needed to assess the durability of these materials in practical applications, as well as their performance degradation over time.
# Self-Organizing Networks for 5G and Beyond: A View from the Top
## 4 Machine Learning Algorithms for SON  
The application of ML and AI techniques in future wireless networks constitutes a separate and very wide research field, but to address this we must refer briefly to the key algorithms applied in SON systems. In this section, our target is to briefly describe the main machine learning (ML) algorithm taxonomy applied in SON systems. Machine learning (ML) as a subset of artificial intelligence (AI) is a key enabler for SON systems since all related applications are based on intelligent ML algorithms that use real network data (counters and KPIs) as input to make decisions about the action needed for each optimization issue and create executable scripts for the OSS.  
Future research on SON systems for 5G networks includes the development of new ML algorithms that can adapt in a variety of scenarios and provide cognitive behavior and intelligence in past and present network statuses, so that full end-to-end automation through SON can be achieved [30]. Table 2 describes the suggested ML technique and possible algorithm that can be used per SON application.