esa-sceva/satcom-chunk-collection huggingface dataset:
# Disaster-Resilient Optical Network Survivability: A Comprehensive Survey
###### Abstract  
Network survivability endeavors to ensure the uninterrupted provisioning of services by the network operators in case of a disaster event. Studies and news reports show that network failures caused by physical attacks and natural disasters have significant impacts on the optical networks. Such network failures may lead to a section of a network to cease to function, resulting in non-availability of services and may increase the congestion within the rest of the network. Therefore, fault tolerant and disaster-resilient optical networks have grasped the attention of the research community and have been a critical concern in network studies during the last decade. Several studies on protection and restoration techniques have been conducted to address the network component failures. This study reviews related previous research studies to critically discuss the issues regarding protection, restoration, cascading failures, disaster-based failures, and congestion-aware routing. We have also focused on the problem of simultaneous cascading failures (which may disturb the data traffic within a layer or disrupt the services at upper layers) along with their mitigating techniques, and disaster-aware network survivability. Since traffic floods and network congestion are pertinent problems, they have therefore been discussed in a separate section. In the end, we have highlighted some open issues in the disaster-resilient network survivability for research challenges and discussed them along with their possible solutions.  
network survivability; protection; restoration; disaster-aware routing; congestion-aware routing; optical networks +
Footnote †: journal: _Photomics_
## 2 Predictive Model  
DM tasks can be conducted through prediction and description models [7; 8] (see Figure 2a). In general, the prediction models are constructed through the learning of known data classes, whereas the description models arise from the findings obtained in a dataset [8]. In this regard, predictive DM techniques are the straightforward option to perform the diagnosis of equipment and systems since their different operating conditions can be learned and determined by a prediction model. In this model (see Figure 2b), its learning is carried out by means of the analysis of a data training set (input data with known outputs) and then used to predict the unknown output (class or value) of new input data. According to the nature of data (discrete or continuous), the prediction model can be used to perform either classification or regression functionalities [7] (see Figure 2a). A classification procedure consists of the assignment of an object (unknown class) into one of several predefined classes (predicted class) according to its properties or features. In a different way, a regression procedure involves the modeling of continuous functions to determine new numerical values (predicted values) according to specific inputs. Classification and regression techniques used for fault detection in electric equipment and systems are presented in the following sections.
# Removal of Fluoride and Arsenate from Aqueous Solutions by Aluminum-Modified Guava Seeds
## 3 Results and Discussion  
### Biosorbent Characterization  
Guava seed samples were analyzed by SEM in order to determine their morphology, surface properties, and structures before and after their modification with Al. The presence of a heterogeneous surface with different wrinkled, smooth areas, and singular folds can be seen in Figure 1a,b. In Figure 1c,d, the presence of granules and micropores on AL-GS samples are observed. These sites are important because the pores increase solute diffusion and surface area, and thus the fluoride and arsenate sorption capacities. Moreover, an elemental microanalysis (SEM-EDS) and image mapping were performed on the external surface area of a sample of Al-GSs (Figure 2), where the flat smooth surface and the rough part can be observed. Red-colored areas on Al-GS samples indicate that, independently of the morphological surface characteristics, a certain amount of aluminum was adsorbed on the surface of the biosorbent during the modification process (Figure 2b), with an approximate aluminum concentration of 2.7 g of Al/100 g of Al-GSs. High or low concentrations of aluminum in some of the zones of the biosorbent surface depended on the diversity and heterogeneity of functional groups present in guava seed structure [26]. Arsenic and fluoride were also analyzed by SEM-EDS, and the results showed an approximate punctual concentration of arsenic of 0.54 g of As/100 g of AL-GS (Figure 2c), but fluoride was not detected due to a low concentration of analyzed samples. These results suggest that aluminum-modified parts of Al-GS surface can adsorb As(V) ions, as well as some unmodified parts of the same material.
# Real-Time State of Charge Estimation for Each Cell of Lithium Battery Pack Using Neural Networks
## 3 Proposed Method SOC Estimation
### Multi-Layer Neural Network  
In this paper, to model the relationship between voltage and SOC, an MNN model was constructed, as shown in Figure 3.  
The feed-forward model equation that calculates the output value of the MNN model consists of an input/output relational expression in the hidden and output layer. MNN learning is accomplished by changing the weight using the back-propagation method [27].  
Figure 3: Model configuration of MNN.  
Figure 2: Schematic diagram of SOC estimation of each cell of the lithium battery pack.  
In Figure 3, the MNN model uses two types of datasets as input values of the input layer. One of the datasets has only voltage data and 2, 6, and 8 input parameters. The other dataset has voltage and temperature data. This dataset consists of 3 and 7 inputs. There are two hidden layers. In the first hidden layer, the number of nodes is 128, and the rectified linear unit (ReLU) function is employed as the activation function; in the second hidden layer, the number of nodes is 64, and the ReLU function is similarly employed as the activation function. Moreover, there is one output layer. Adaptive Moment Estimation (Adam) was used as the learning method.  
Since the ReLU function does not use exponential calculations in the calculation process, simple calculations can be employed instead. In addition, it changes the negative values to 0 due to its unsaturated form and linearity. Therefore, using this function makes fast convergence possible [28]. The equation of the ReLU function is as follows:  
\[f(x)=\left\{\begin{array}{ll}x,&for\ x>0\\ 0,&otherwise\end{array}\right. \tag{1}\]  
Adam combines Momentum and RMSProp, a kind of learning method. It is a first-order gradient-based optimization algorithm with a stochastic objective function. It can also be easily implemented and exhibits good computational efficiency [29]. The calculation method stores the average of the slopes calculated so far and the average of the squared values of the slopes. [PERSON]'s equation is as follows:  
\[m_{t}=\beta_{1}m_{t}+(1-\beta_{1})\
abla_{0}J(\theta) \tag{2}\]  
\[v_{t}=\beta_{2}v_{t-1}+(1-\beta_{2})(\
abla_{0}J(\theta))^{2} \tag{3}\]  
In the above equation, since mm and v are initialized to 0, a bias close to 0 is expected at the start of learning, and this goes through the process of making them unbiased. The equation is as follows:  
\[\hat{m_{t}}=\frac{m_{t}}{1-\beta_{1}^{t}} \tag{4}\]  
\[\hat{v_{t}}=\frac{v_{t}}{1-\beta_{2}^{t}} \tag{5}\]  
\[\theta=\theta-\frac{\eta}{\sqrt{\hat{v_{t}}+\epsilon}}\hat{m_{t}} \tag{6}\]  
In Equations (2) and (3), m is the first moment vector initialization; v is the second moment vector initialization; t is the time step initialization; and \(\theta\) is the initial parameter vector. The value of \(\beta_{1}\) is 0.9; \(\beta_{2}\) is 0.999; and \(\epsilon\) is \(10^{-8}\)[30].
# Deep Learning in Financial Modeling: Predicting European Put Option Prices with Neural Networks
## 3 Materials and Methods
### Training and Evaluation
#### 3.7.2 Performance Evaluation  
The model's predictive accuracy was assessed using three key metrics: the Mean Squared Error (MSE), the Mean Absolute Error (MAE), and the Coefficient of Determination (R\({}^{2}\)). The MSE provided a measure of the average squared prediction error, where lower values indicate higher accuracy. In contrast, the MAE calculated the absolute differences between predicted and actual prices, offering an intuitive interpretation of prediction deviations. Finally, the R\({}^{2}\) score evaluated the proportion of variance in option prices that the model could explain, with values closer to one indicating stronger predictive power.  
These three metrics collectively provided a comprehensive evaluation of the model's reliability, allowing for a detailed assessment of its strengths and potential weaknesses in capturing pricing dynamics.
# Markov Chain Model-Based Optimal Cluster Heads Selection for Wireless Sensor Networks
## 4 The Proposed Clustering Protocol
### Scheduling Phase (SP)  
Once all the sensor nodes are structured into clusters, each CH creates a schedule for the \(C_{MNs}\) in its cluster. This allows the radio components of each \(C_{MNs}\) to be turned off during all the time slots except its transmit time, thus this action minimizes the energy dissipated by the individual sensors.  
Thus, overall energy consumption in SP is computed as in [11]:  
\[E_{SP}=K\bigg{[}E_{CH-SP}+\bigg{(}\frac{N}{K}-1\bigg{)}E_{N-SP}\bigg{]} \tag{24}\]  
There are two modes of communication like Ready-to-receive and Time Division Multiple Access (TDMA). In the Ready-to-receive mode, the nodes remain active all the time to send its data. Thus consumes a lot of energy resources. While in the second mode, the nodes, after receiving their time slots, turn into sleeping mode and set the right time to awake for communication. In the sleeping mode, the sensor nodes save a lot of energy. We adopted the TDMA mode for our proposed model because it saves the energy resources and also it is designed for communication over shortest path on established links in awake up mode.  
**Lemma 5**: _In the MOCHs, TDMA slot allocation time and message exchange complexity are \(O(1)\) for each node and for the entire network with \((N-O_{CHs})\) nodes is \(O(N-O_{CHs})\). Where \(N\) is number of member nodes and \(O_{CHs}\) is the set of optimal number of CHs._  
Proof.: After cluster formation process, each CH in the network sends a TDMA slot to each member node. So, the message exchange complexity of one node is O(1). Therefore, the message exchange complexity for TDMA slot allocation in MOCHs algorithm is \(O(N-O_{CHs})\). After receiving this TDMA schedule each \(C_{MN}\) sends an acknowledgment message to the CH. This whole procedure is done during a predefined time period. The time complexity of one node is O(1). Thus, the time complexity for TDMA slot allocation in MOCHs algorithm is \(O(N-O_{CHs})\).
## 2 Proposed Operating Reliability Assessment Model
\[X_{k}=\sum_{j=1}^{m-N}c_{Lj} \tag{3}\]  
The probability of capacity \(X_{k}\) reduction can then be obtained from the discrete probability density function \(f(Z)=p(Z_{k\;=\;l\;0\;s})\), where \(p(X_{k})=P(Z_{k})-P(Z_{k\;-\;l})\). When a disturbance occurs, resulting in capacity drop \(X_{k}\), the healthy units carrying spinning reserve will respond to fulfill the capacity mismatch. The response capacity, or the available regulating margin (RM) of the operating system in the response time \(T_{R}\) following the \(k^{\text{th}}\) disturbance is given by (4). \[RM_{k}=\sum_{j=1}^{N}RM_{j} \tag{4}\]  
where \(N\) is the total number of healthy operating units, and RM\({}_{j}\) is the response capacity of unit \(j\) in the response time \(T_{R}\) or the regulating margin of the unit given by (5), where \(\gamma_{j}\) and \(MCR_{j}\) are the ramp rate and maximum continuous rating of unit \(j\). \[RM_{j}=MIN\big{(}\gamma_{j}\times T_{R},\;MCR_{j}-c_{Lj}\big{)} \tag{5}\]  
The response risk associated with capacity drop \(X_{k}\) is the conditional probability given by (6). \[ \begin{array}{ll}RR|Z_{k}&=0\;if\;RM_{k}>X_{k}\\ &=1\;\;\text{otherwise}\end{array}  \tag{6}\]  
The CGRR can then be evaluated using a conditional probability approach as shown in (7) by aggregating the conditional response risk obtained in (6) weighted by the probability \(p(Z_{k})\) for all \(s\) outage states in the discrete distribution \(f(Z)\). \[CGRR=\sum_{k=1}^{s}p(Z_{k})\times RR|Z_{k} \tag{7}\]
## 2 TOPSIS: Relevance and State of the Art  
TOPSIS has been broadly used in multi-attribute decision-making problems because of its ease of calculation, resilience of application compared with other methods, and acceptable results [2]. Similar to TOPSIS in its performance is the VIKOR (Vlse Kriterijumska Optimizacija Kompromisno Resenje) method, which was introduced academically in 1998; however, the VIKOR method has the major disadvantage of requiring a search for a compromise ranking order, such as a compromise between pessimistic and expected solutions [3].  
In contrast, scholars have expressed great satisfaction with the TOPSIS method and have applied it in different fields. Based on TOPSIS, [PERSON] et al. developed multi-objective decision making for a bi-objective decision. In addition, they offered a framework that includes tangible and intangible determinants to construct an integrated multi-objective framework [4]. The study reviewed scholarly interest in TOPSIS and its use in different arenas.  
In an attempt to solve urban planning problems, [PERSON] and [PERSON] applied fuzzy TOPSIS [5]. In concrete manufacturing with siliceous materials, the fuzzy TOPSIS method was used to extend the concrete lifespan and reduce costs, thereby achieving sustainable development in the building sector [6].  
Using possibility theory, [PERSON] and [PERSON] [7] applied fuzzy TOPSIS based on fuzzy numbers to challenge multi-attribute decision making. [PERSON] and [PERSON] [8] also used fuzzy TOPSIS and fuzzy cognitive maps to solve complicated decision-making problems. [PERSON] et al. utilized an intuitive fuzzy TOPSIS to assess cutting-edge manufacturing technologies through numerical control of milling machines [9]. [PERSON] et al. [10] developed a hesitant fuzzy linguistic term with TOPSIS to evaluate lean manufacturing and offer optimal alternatives to decision makers.  
[PERSON] et al. proposed a heterogeneous TOPSIS for solving heterogeneous multi-attribute decision-making problems with deviation and fixation attributes. This proposal offered successful use of structural optimization of a high-speed press that considers multi-source uncertainties [11]. A hybrid fuzzy analysis network process based on TOPSIS has been used to select energy plant locations related to solid waste [12]. TOPSIS was improved to assess power quality by correlating indices that were ignored by other traditional methods, such as the analytic hierarchy process (AHP) and entropy weight (EW) [13].
## 3 Simulation Experiments
### Simulation of SVR Short-Time Traffic Flow Prediction Model Based on Associated Roads  
#### 3.3.1 Urban Road Linkage Analysis  
First, an Apriori correlation analysis was performed on the twenty selected roads with 80% support and 90% confidence. Through this analysis, the set of frequent terms for each road, i.e., the associated roads for each road, is obtained. Taking Jiandong Street among the feeder roads as an example, there are six associated roads of Jiandong Street under the selected support and confidence levels, which are Taiyi Road, Youyi Road, Andong Street, Biaoxin Street, Yanta North Road, and Tai'an Street.
## 3 Method
### Mann-Kendall and Sen's Tests  
The Mann-Kendall (MK), commonly known as Kendall's \(\tau\) statistic, is a non-parametric test used for trend analysis. The Mann-Kendall statistical test has been widely used to quantify the significance of trends in hydro-meteorological variables such as precipitation, temperature and streamflow [46; 47; 48; 49]. The Mann-Kendall test uses a statistic value \(Z_{mk}\) to describe the trend, which is described in Mann [50; 51]. Positive values of \(Z_{mk}\) indicate increasing trends while negative \(Z_{mk}\) values show decreasing trends. Testing trends are analyzed at the specific \(\alpha\) significance level. When \(|Z_{mk}|>Z_{1-\frac{\alpha}{2}}\), the null hypothesis will be rejected and a significant trend exists in the time series. \(Z_{1-\frac{\alpha}{2}}\) is obtained from the standard normal distribution. In this study, the significance level \(\alpha=0.05\) is used. At the 5% significance level, the null hypothesis of no trend is rejected if \(|Z_{mk}|>1.96\).  
After the trend direction is derived by the Mann-Kendall test, the magnitude of the trend in time series data can be determined using the non-parametric method known as Sen's estimator. This method assumes a linear trend in the time series. The Sen's slope estimation is described as in Sen [52].
# Implementation and Security Test of Zero-Knowledge Protocols on SSI Blockchain
## 2 Background
### Verifiable Credentials  
We distinguish three essential roles involved in the exchange of verifiable credentials (VCs):  
* Issuer: entity that generates and certifies verifiable credentials;
* Holder: entity that requests a credential and stores it in their digital wallet;
* Verifier: entity that requests a credential to verify information about a user.  
A VC has the same role as the physical credentials we use every day: to prove information about our identity to an agent who does not take our word for it. It may contain information about the identity of the subject of the credential (name, photo, ID), information about the entity that issued the credential, and about the specific attributes that it is attesting to about the subject. A VC makes use of technology to increase its reliability and immutability.
## 2 Related Work
### Auditory Information Processing  
People often use _skimming_, which quickly identifies the gist or general idea of a large volume of contents, and it has been known to be helpful in learning [30, 31, 32]. However, unlike the visual contents, skimming on the audio contents is more challenging because information is processed linearly and sequentially. Combining and extracting relevant information is very limited [33].  
There have been computational tools for efficient information processing. For example, some studies proposed audio skip controls based on the structures of the audio content. Tylfos [34] utilizes the pyramid structure of the document for guiding users from overview to the details. In addition, Digital Accessible Information SYstem (DAISY) [1] uses the structure of the documents like paragraphs, headings, or sections. With DAISY, users can control the current playback position by the level of the document components, as long as the input audio can be formatted. Similarly, Job Access With Speech by Freedom Scientific (JAWS) is a screen reader that reads out the first sentence of each paragraph sequentially to understand the entire text quickly [35,36]. However, according to the study results [35,36], each paragraph's first sentence is not sufficient for understanding the context.  
Alternatively, it has been studied to summarize the audio contents by highlighting essential parts. Summate [9] is a FireFox-based tool that summarizes web pages and presents the summary in an alert box for blind individuals. AcceSS [37] removes the clutter and retains the important sections to give the user a preview of the page. Some studies proposed a skimming method based on audio processing; for example, [PERSON] et al. [10] proposed a method of extracting essential parts of audio content based on voice pitch.  
However, from the perspective of information browsing, the prior studies have some limitations. For information browsing (e.g., studying over an audio lecture), it is important to find the relevant details and link them to a specific context by exploring throughout audio content. As a result that information browsing contains a dynamic process, the existing methods that mainly focus on quickly delivering entire content could be limited. In addition, using the text structure is limited because all the audio contents cannot be easily in a specific format. Furthermore, the summarization of the entire text may not be effective because it cannot preserve the mood and style of the original text [36] and did not itself offer playback controls to respond to the users' dynamic information needs. This work focuses on auditory information browsing and explores the supportive design for it.
## 2 Materials and Methods  
### Sample Preparation and Soil Chemical Analysis  
Soil samples used in this experiment were collected from the area of Daye mine, a typical area of Jianghan Plain in Hubei province, China (114 deg31'-115 deg20'E, 29 deg40'-30 deg40'N). The climate of this area is subtropical with an annual average temperature of 16.9 degC.According to the classification and codes for Chinese soil (National Standard of China, GB/T 17296-2009), the soils in this area are mainly red soil and yellow-brown soil. The Daye area is a production base of crops and rich in mineral resources [31]. The mining has greatly damaged the ecological environment, and the farmland soil near the mining area has been seriously polluted.  
A total of 59 soil samples were collected from different types of cultivated soils near mining areas in Daye. They were taken from the upper soil layer (0-20 cm) in 2018. After removal of the stones and plant roots, then they were sifted through a 200-mesh sieve and then ground into fine particles, approximate particle size after grinding \(\leq\)74 \(\upmu\)m [32]. Each soil sample was then divided into two parts. One part was sent to the laboratory digested with nitric acid/hydrochloric acid/perchloric acid. After that, measured by atomic fluorescence spectrometry (AFS) (AFS-9730, Haiguang, China) (National Standard of China: analysis of total arsenic contents in soils, GB/T 21191-2007, GB/T 22105.2-2008). Instrument limits of detection (LODs, mg/kg) were 0.001 for TAs. The other part was sent to the dark chamber for hyperspectral imaging (HSI) measurement. The highest observed soil TAs content was 16.41 \(\mu\)g/g and lowest was 7.04 \(\mu\)g/g. The averaged TAs content of soils average was 9.65 \(\mu\)g/g. The soil sample concentrations are listed in Table 1.
# STN-Homography: Direct Estimation of Homography Parameters for Image Pairs
## 4 Hierarchical STN-Homography  
### Architecture of Hierarchical STN-Homography  
Similar to the study of [PERSON] et al. [24], we also used a hierarchical model to successively reduce the estimation error of the homography matrix, as depicted in Figure 4. In each module, we used a new STN-Homography model to estimate the \(\overline{H}_{ba\_i}\) between patch_a_i (cropped from image_a_warp_i) and patch_b (cropped from image_b). Accordingly, the estimated \(\overline{H}_{ba\_i}\) will be used with the OpenCV library to warp image_a_warp_i to prepare image_a_warp_i+1 and patch_a_i+1 for the next module. To calculate the final result of the homography matrix between image_a and image_b, all homography matrix estimations of successive modules are multiplied together.  
\[\overline{H}_{ba}=\prod_{i=n,n-1,\ldots,1}\overline{H}_{ba\_i} \tag{3}\]  
Warping with the predicted homography matrix from each module resulted in a visually more similar patch pair of patch_a_i and patch_b (or image pair of image_a_warp_i and image_b). This can be visualized as a geometric morphing process that takes one image and successively makes it to look alike the other, as shown in Figure 4.  
Figure 3: Comparison of mean corner errors of various homography estimation approaches.