esa-sceva/satcom-chunk-collection huggingface dataset:
# Reliability of Social Networks on Activity-on-Node Binary-State with Uncertainty Environments
## 1 Introduction
In addition, regardless of whether we use direct or indirect algorithms, the component is an arc, and the reliability of each component must be known in advance [29; 30; 31; 38]. In real life, it is not practical or reasonable to assume that the reliability of each component is known precisely beforehand [43; 44; 45]. For example, the Mars Rover is a \begin{table}
\begin{tabular}{c c} \hline \(a\) & \(\mathbf{D}\) (\(a\)) \\ \hline \(a_{1}\) & 0.85 \\ \(a_{2}\) & 0.80 \\ \(a_{3}\) & 0.85 \\ \(a_{4}\) & 0.80 \\ \(a_{5}\) & 0.75 \\ \(a_{6}\) & 0.90 \\ \hline \end{tabular}
\end{table} 
Table 1: State distributions in Figure 1.  
Figure 1: Example of an AOA BN with 5 nodes. novel vehicular system, and we cannot know the exact reliability of each component in an environment that is completely different from Earth. Hence, the goal of this study is to overcome the aforementioned obstacle of uncertainty by proposing a new algorithm to deal with uncertain nodes using fuzzy set theory [46; 47] and the binary-addition-tree algorithm (BAT) [11; 29; 48; 49; 50; 51; 52]. It is well known that fuzzy set theory can effectively resolve uncertain and imprecise problems. The BAT, first proposed by [PERSON] [48], can find all feasible and required vectors simply based on binary addition. Its current version, quick BAT [34], is more efficient compared to BDD and all other indirect algorithms in calculating BN reliability. Hence, the proposed algorithm is based on BAT combined with fuzzy set theory to solve the proposed uncertainty-component BN reliability problem owing to its importance and practicality. The paper's contribution is highlighted in the following points:  
1. An activity-on-node (AON) BAT-based algorithm is proposed for computing the reliability of a binary-state network instead of the traditional BN reliability assuming that the network is activity-on-arc (AOA). 2. In real life, it is not practical or reasonable to assume that the reliability of each component is known precisely beforehand. Nevertheless, in the traditional BN, the reliability of each component must be known in advance. Thus, one of this study's contributions is to overcome the aforementioned obstacle of uncertainty by proposing a new algorithm to deal with uncertain nodes using fuzzy set theory and the binary-addition-tree algorithm (BAT). The remainder of this paper is organized as follows. All acronyms, notations, nomenclatures, and assumptions are introduced in Section 2. A review of AOA BAT, AOA path-based layered-search algorithm (PLSA), and some background material on fuzzy set theory is given in Section 3. The proposed BAT-based algorithm is introduced formally in Section 4, including the preprocessing steps of transferring the linguistic variable via many stages to the unreliability, and the proposed AON PLSA to verify the connectivity of each vector obtained from the proposed BAT. The performance of the proposed algorithm in solving the proposed problem is demonstrated as an example. Finally, Section 5 concludes the study.
## 4 Discussion
### HPV DNA + HPV RNA + p16 INKaa Over-Expression  
A comprehensive study of HNSCC, the HPV-AHEAD project, was realized by an international team of researchers, under the guidance of Infections and Cancer Biology Group, International Agency for Research on Cancer, Lyon, France. The algorithm used in this study was a very strict and rigorous one: FFPE HNSCC samples were analyzed using HPV DNA testing, HPV RNA testing, and p16 analysis. Archived HNSCC tissue samples from 772 patients from Belgium, 696 from Italy, 364 from the central region of India, and 189 from North-East Romania were tested using this algorithm. In all the four HPV-AHEAD studies, the highest rate of HPV DNA prevalence was detected for OPSCCs, with similar values (36.4% in Belgium, 40.4% in Italy, 18.9% in India, and 50% in Romania). HPV16 was the most prevalent type in all the samples analyzed by these studies. HPV-driven HNSCC were defined by the presence of both viral DNA and RNA, and the highest prevalence of this double positivity was also found in OPSCC samples [19; 47; 48; 49].
## 5 IoT-Defined Network/Routing Protocols
### Routing over Low-Power and Lossy Network (RPL)  
An RPL [38] has comparatively less computational complexity and message overhead because it is a distance-vector and source-routing protocol. For the structure of WSN, especially in the use of IoT, the neighborhood must be enquired into for any substantial change in topology or any other restrained parameter. The same characteristics are possessed by distance-vector (DV) protocols. Each node has a vector of connected nodes and their state. This mechanism is used for calculating distances and path costs. DV has relatively less computational complexity because array update is the only process, and it uses distance-vector encounter alteration if any link must be updated. RPL has a self-healing ability, and it has a loop detection and destination-oriented direct acyclic graph (DODAG) repair ability, which is explained in the coming sections. A node calculates the number of hops and the next node to which it transfers its data. The next hop is termed as \"Direction\", whereas the hop count is termed as \"Distance\" in DV. A node can choose between many possible routes based on the priority of constraintson the node. The minimum distance vector is maintained in the case of an isomorphic network. Again, the cost is calculated using different route characteristics for reducing cost and increasing networking efficiency. The dynamic-source routing (DSR) protocol is a path-addressing-based mechanism [31]. Random neighborhood discovery is limited to each node and table information is shared using a data packet that is traversing through the network. The sender may partially embed the route information into the data packet. This mechanism makes the receiver know about all the hops a data packet has traversed before arriving. The mechanism works as a direct acyclic graph; thus, path information, when updated in the packet, takes credit for the reusability of the route. DSR is a destination-oriented protocol. A destination-oriented direct acyclic graph (DODAG) is sometimes used to represent the packet route. The DAG metric is calculated on node limits connectivity based on internal node state (workload, CPU, and memory), energy available, its type (powered or self-charging), and number of hops. The link metric objects are updated to DAG using connectivity characteristics. These characteristics include throughput, latency, link reliability, and link color. Table 5 lists the node and link constraint objects, and their attribute descriptions. Table 5 clearly explains attribute assignment in the node and link object container. Simulation environments, such as Cooja, NS3 and NS2, OMNET, and OPNET, install these containers on nodes to ensure protocol implantation [28].
## 1 Introduction
Wavelet entropy (WE) represents the degree of order/disorder of the vibration signals, hence providing very useful information about the underlying mechanical process associated with them. The level of damage can be derived by quantifying changes in the wavelet entropy of the vibration signals before (_baseline phase_) and after (_inspection phase_) damage. The relative wavelet entropy (RWE) provides a measure of the degree of similarity between two probability distributions. If the relative wavelet energy distributions are almost the same during the baseline and inspection phases, the RWE will be close to zero, and a non-damage state will be assigned to the damper. If the damper is damaged, the distribution of vibration responses during the baseline and inspection phases will be changed, and RWE will be not zero. This idea is the foundation for the formulation of the damage index RWEE. The RWEE index was validated experimentally with dynamic shaking table tests conducted on several Web Plastifying Dampers (WPDs). The results presented here show a very good correlation between the RWEE index and the established mechanical damage index ID. In other words, we demonstrate that the RWEE index can reliably evaluate the level of damage on dampers without the need to know the load-displacement relationship endured by the damper, and thus without resorting to the cumbersome and expensive instrumentation required to obtain the ID.
# Reactance Regulation Using Coils with Perpendicular Magnetic Field in the Tubular Core design
## 4 Results and Discussion
### Resistance and Reactance of Coils  
The resistances of both coils in the equivalent circuit are determined by the net DC conductivity, skin-effect influence, and ferromagnetic losses (eddy current and hysteresis). The resistances of both coils are shown in Figure 7a,b. The DC resistance values of both coils are 1.3 \(\Omega\) and 21.3 \(\Omega\) for internal and external coils, respectively. The DC resistance constitutes a small portion of the equivalent values. The diminished hysteresis losses explain the resistance drops with an increase of the current. The equivalent resistances of both coils may be described by the exponential function like (8). The expressions are shown in Figure 7a,b.  
The experimental studies show that the reactance of both coils is very similar to the impedance since total losses in these coils are relatively small. The external coil reactance is almost equal to the impedance and is 99% of the impedance. The reactance of the internal coil is 91-95% of the impedance.
## 4 Examined Distributed Consensus Gossip-Based Algorithms
### Comparison of Distributed Gossip Consensus Algorithms with Deterministic Ones  
As mentioned above, we focus our attention on the gossip algorithms, where the message receivers are chosen randomly in contrast to the deterministic algorithms. Thus, many parameters of the gossip algorithms vary over the runs--in Figure 6, we provide the evolution of arithmetic mean estimates of the Push-Sum protocol (gossip algorithm) and the Max-Degree weights algorithm (deterministic algorithm) for three independents runs of the algorithms in order to demonstrate differences between these two categories.  
The Max-Degree weights algorithm is s a deterministic distributed consensus algorithm operating in synchronous mode [49]. It requires the exact value of the maximum degree of a graph \(\Delta\) (i.e., the degree of the best-connected agent in MAS) for the proper operation. In this algorithm, this parameter determines the value of the mixing parameter \(\epsilon\), i.e., all the graph edges are set to the value of this parameter. The update rule of this algorithm is defined as follows [49]:  
\[\mathbf{x}(k+1)=\mathbf{W}(k)\mathbf{x}(k) \tag{8}\]  
Here, \(\mathbf{W}\) is the weight matrix of this algorithm, a doubly-stochastic matrix defined as follows [49]:  
\[[W]^{\text{MD}}_{ij}=\begin{cases}\frac{1}{\Delta},&\text{if }e_{ij}\in \mathbf{E}\\ 1-d_{i}\frac{1}{\Delta},&\text{if }i=j\\ 0,&\text{otherwise}\end{cases} \tag{9}\]  
Here, \(d_{i}\) is the degree of \(v_{i}\). The principle of this algorithm can be described as follows: at each time instance, each agent broadcasts its current inner states to all its neighbors as well as receiving the inner states from them. Subsequently, it multiplies all received data and its current inner state with the allocated weights (as shown in (9)). The sum of all these values represents its inner state for the next time instance.  
Figure 6 shows that the evolution of the estimates is the same for each run in the case of the deterministic Max-Degree weights algorithm, while the estimates may differ in the case of the Push-Sum protocol. In both cases, the estimates, however, approach the value of the arithmetic mean (=3 in this case), which is the most important fact.  
In Figure 7, we show the distribution of the convergence rates (a stopping criterion is applied) for 100,000 runs for both algorithms. As seen, the convergence rates are the same for each run in the case of the Max-Degree weights algorithm. However, when the Push-Sum Protocol is applied, the convergence rates may differ from each other for various runs, and the distribution evokes a Gaussian distribution.
## 3 Proof of security of BB84 against collective attacks
### Case of a one-bit key  
We begin with proving the security of a 1-bit key, and then extend our proof to an arbitrary \(m\)-bit length key. This case corresponds to \(m=1\) and the key (when not discarded) is \(\mathbf{x}\cdot v_{r+1}\) where \(\mathbf{x}\) is the string sent by [PERSON] (that is, \(P_{K}\) has only one row, which equals \(v_{r+1}\)). Let \(\boldsymbol{\xi}=\mathbf{x}P_{C}^{T}\) be the \(r\) bit syndrome announced publicly by [PERSON] and let us denote \(\widehat{\rho}_{0}\) and \(\widehat{\rho}_{1}\) [PERSON]'s states corresponding to key 0 and key 1 respectively. Those states are obtained by normalizing the operatorsP  
Footnote ¶: State \(\rho_{\mathbf{x}}^{\mathbf{b}^{\prime}}\) is defined by (9) and (8).  
\[\rho_{k}=\sum_{\begin{subarray}{c}\mathbf{x}\in P_{C}^{T}=\boldsymbol{\xi}\\ \mathbf{x}\cdot v_{r+1}=k\end{subarray}}\rho_{\mathbf{x}}^{\mathbf{b}^{\prime}}\]  
and, since \(\mathrm{tr}(\rho_{0})=\mathrm{tr}(\rho_{1})=2^{n-r-1}\), \(\widehat{\rho}_{0}\) and \(\widehat{\rho}_{1}\) are equally likely, and  
\[\widehat{\rho}_{k}=\frac{1}{2^{n-r-1}}\sum_{\begin{subarray}{c}\mathbf{x}\in P _{C}^{T}=\boldsymbol{\xi}\\ \mathbf{x}\cdot v_{r+1}=k\end{subarray}}\rho_{\mathbf{x}}^{\mathbf{b}^{ \prime}}. \tag{32}\]  
Changing the attack to a flat one, does not change \(\rho_{x_{l}}^{b^{\prime}}\), and therefore does not change \(\widehat{\rho}_{k}\). Moreover, since \(|\psi_{x_{l}}^{b^{\prime}_{l}}\rangle\langle\psi_{x_{l}}^{b^{\prime}_{l}}|\) as defined in Equation (26) is a purification of \(\rho_{x_{l}}^{b^{\prime}_{l}}\), it follows that  
\[\widetilde{\rho}_{k}=\frac{1}{2^{n-r-1}}\sum_{\begin{subarray}{c}\mathbf{x} \in P_{C}^{T}=\boldsymbol{\xi}\\ \mathbf{x}\cdot v_{r+1}=k\end{subarray}}|\psi_{\mathbf{x}}^{\mathbf{b}^{ \prime}}\rangle\langle\psi_{\mathbf{x}}^{\mathbf{b}^{\prime}}| \tag{33}\]  
is a lift-up of \(\widehat{\rho}_{k}\). According to lemma 2, \(SD(\widehat{\rho}_{0},\widehat{\rho}_{1})\leq SD(\widetilde{\rho}_{0}, \widetilde{\rho}_{1})\) and \(SD(\widetilde{\rho}_{0},\widetilde{\rho}_{1})\leq\frac{1}{2}\,\mathrm{tr}\,| \widetilde{\rho}_{0}-\widetilde{\rho}_{1}|\) and thus  
\[SD(\widehat{\rho}_{0},\widehat{\rho}_{1})\leq\frac{1}{2}\,\mathrm{tr}\,| \widetilde{\rho}_{0}-\widetilde{\rho}_{1}|. \tag{34}\]
# A Review on LiFi Network Research: Open Issues, Applications and Future Directions
## 4 Applications of the LiFi Network
### Indoor and Outdoor Lighting  
Any light source, such as streetlights, may deliver LiFi hotspots. Indoor and outdoor lights in the office, home, health facilities, business sites, airlines, cars, and streets allow artifacts to be linked to the Internet using attractive VLC features. Therefore, street signage can be used as a hotspot and can be rendered as a proper LiFi application. Moreover, the same networking and sensor technologies may be used for lighting and data management [17; 82; 150; 154].
## 2 Materials and Methods  
### Experimental System  
A SEMOS-LAB experimental system, which was independently developed by our research group, was used for self-potential data collection. The sampling interval of the self-potential data collection was set to 1 ms. The uniaxial compression instrument used for the rock samples was an MTS816 rock mechanics test system (MTS Systems Corporation, Eden Prairie, MN, USA). The uniaxial loading rates were 0.001, 0.002 and 0.005 mm/s and the sampling interval of the mechanical parameters was 1 s. An AE21C system (Shenyang Computer Technology Research and Design Institute, Shenyang, China) was used for the acoustic emission acquisition. In the uniaxial loading process, the self-potential, stress and acoustic emission parameters were synchronously collected and in real-time. The MTS816 rock mechanics test system, SEMOS-LAB experimental system and acoustic emission test system are shown in Figure 1.
## 5 Methodology, Experiments and Results
### Proposed Methodology
#### 5.1.2 Data Preprocessing  
There are two types of system tracking, i.e., general tracking and customized tracking. In general tracking, information is collected from web page history logfile. In customized tracking the information is gathered in a prepared tool. Logs are commonly used in the system management, as logs are often the only data available that record detailed system runtime activities or behaviors in production. The process of creating such records is called data logging. Logs are generated by a wide variety of programmable technologies, including networking devices, operating systems, software, and more. For instance, web servers use log files to record data about website visitors. In general, advanced analytics methods taking modeling into account can play a significant role in extracting patterns from log data. After creating an outgoing data set from an automatic client application, we pre-process our data. For AR we convert the numerical values into categorical values and for RT we convert the categorical values into numerical values.
## 3 Methodology
### ANN Model Training  
Utilizing insufficient in situ Chla concentration data and unsuitable initialization for the unknown parameters in ANN model training may lead to model overfitting and make the loss function difficult to converge. [PERSON] et al. [34] proposed a two-stage training approach consisting of pretraining and main-training, which is shown to be able to deal with the aforementioned problems. The first stage provides a better initialization for the unknown parameters before the main stage by pretraining the model with the simulated labelled data \(\left\{\left(\textbf{K}_{i},s\_chla_{i}\right)\right\}_{i=1}^{n}\). Here, the estimation error is large and backpropagating the error could make the extraction of the spatial feature not optimum. Moreover, the convergence of the loss function may not reach its global minimum due to the utilization of the simulated data. However, this allows the model to have suitable initial values of the unknown parameters before the main training stage. Then, the pretrained model is refined with the in situ labelled data \(\left\{\left(\textbf{P}_{i},t\_chla_{i}\right)\right\}_{i=1}^{m}\). This procedure is also known as transfer learning.  
In this study, the two-stage training was adopted and the main stage part was improved by the implementation of fine-tuning, another kind of transfer learning technique. Moreover, data augmentation and rebalancing were also proposed and performed before  
Figure 3: Network structure of WaterNet.  
the training in the improved main stage. The aim was to have more in situ labelled data with balanced amounts of samples in Chla concentration distribution space. Details regarding the data augmentation and rebalancing and the proposed transfer learning approach are explained below.
## 5 Signal Acquisition Approaches
### Inductive Sensors  
These sensors detect changes in the electromagnetic field caused by brain electrical activity, capturing comprehensive brainwave patterns. They are typically used in research settings [100]. Operating on the principle of electromagnetic field detection, these sensors are adept at capturing specific types of brainwave patterns. While their non-contact, non-invasive nature is a significant advantage, there are limitations to the kinds of signals they can detect. They are most beneficial in studies that require non-contact methods of brain activity monitoring.
## 4 Real-Time Tissue Recognition and Margin Assessment  
The ability to identify tumor boundaries with high precision during oncologic surgery is essential for achieving negative surgical margins (NSMs) while minimizing collateral tissue damage. This challenge is particularly prominent in procedures, such as RARP and RAPN, where the trade-off between oncologic control and the preservation of neurovascular or renal parenchymal structures is delicate. A range of real-time intraoperative technologies has emerged to address this need, including fluorescence imaging, optical spectroscopy, digital histopathology, and machine learning assisted systems.
## 4 Test Results  
Real-time LEO satellite clocks determined based on near-real-time clock determination and ultra-short-term clock prediction are tested and analyzed in this section. Firstly, near-real-time LEO satellite clocks are assessed. Based on the clock predictions tested for different periods, the real-time clocks are then analyzed with their total error budget. GPS L1/L2 observations with a sampling interval of 30 s from Sentinel-3B on DOY 227-229, 2018 are used for the processing.
## 3 Proposed Watermarking Framework
### Structure of Watermarking Network to Be Trained
#### 3.2.2 Pre-Processing Network for WM  
The WM pre-processing network is configured to gradually increase the resolution to match the host image pre-processing network's resolution. This is to increase the WM invisibility, as explained before. It has been confirmed by our experiments that the case maintaining the resolution of the host image in WM embedding has high WM invisibility than the case reducing the resolution to that of the WM and increasing the resolution to output the watermarked image. This network includes four network blocks: the first three consist of the CL, batch normalization (BN), activation function (AF), an average pooling (AP), but the last block consists of CL and AP. All CLs have a 0.5 stride for up-sampling. The corresponding number of filters is 512, 256, 128, and 1, respectively. AF is the rectified linear unit (ReLU), and AP is a 2 \(\times\) 2 filter with a stride of 1. AP is used because WM is a binary data that the values are discrete, but the host image data is real and continuous; it is necessary to smoothen the WM data with the APs to combine with the host image data retaining the continuous characteristics. It also has been confirmed with our experiments. The WM pre-processing network output is multiplied by the strength scaling factor to control the invisibility and robustness of the WM.