esa-sceva/satcom-chunk-collection huggingface dataset:
# Spikyball Sampling: Exploring Large Networks via an Inhomogeneous Filtered Diffusion
## 4 Theoretical Properties  
In this section, we establish some important properties of the Spikyball family that help to understand the general behavior of these sampling methods. The influence of the parameters on the degree distribution of the collected nodes is made explicit and will be discussed in more details.  
There are three main results in this section. We first prove that the degree distribution of samplings obtained with the Hubball family is independent of the parameter \(\alpha\), for a large number of synthetic networks. Since this family contains the Forest Fire sampling, we prove that this latter sampling, on most random networks, will lead to an equivalent degree distribution as any Hubball defined in the previous section. The reader has to keep in mind that it may not be the case for real networks. This is due to the independence of the degree of a node with respect to the degree of its neighbors in most random networks. The second result goes further in this direction and shows explicitly the dependence of the degree distribution of the sampled graph on the relationship between neighbor nodes. Again, this is for the case of Hubballs. In real networks, the degree of neighbor nodes may be related. For example, high degree nodes may favor connections with other high degree nodes. In that case, it is more probable to find a high degree node when randomly jumping from a high degree node to one of its neighbors. Theorem 2 shows that such relation between the degree of nodes can be revealed indirectly by analysing the difference of degree distribution given by the members of the Hubball family.  
The last result concerns the Coreball family and the influence of its parameter \(\gamma\) on the sampling. For any graph, a large \(\gamma\) will make the Coreball sample more high degree nodes than what the Snowball would do. For negative \(\gamma\), more weakly connected nodes will be sampled.  
For the sake of simplicity, we assume in this section the graphs to be unweighted. In order to establish the first result, we introduce a property of a graph defined as follows:  
**Property 1** (P1).: _The degree \(d_{a}\) of a node \(a\) is independent of the degree of its neighbors. The conditional probability \(p(d_{a}|d_{b})\) of having a node \(a\) with degree \(d_{a}\) if its neighbor \(b\) has degree \(d_{b}\) is:_  
\[p(d_{a}|d_{b})=p(d_{a}). \tag{10}\]  
This property is shared by many random networks, where the creation of edges is independent of the degrees of both the source and target nodes. This is true for Erdos-Renyi (independent of any degree) or for Barabasi-Albert graphs (independent of the degree of the source node). However, this may not hold for real networks. For example, in networks with a strong hierarchy, having a large \(k\)-core, high degree nodes are more connected together than to small degree nodes at the periphery.
# KGNER: Improving Chinese Named Entity Recognition by BERT Infused with the Knowledge Graph
## 3 Model
### Output Layer  
For sequence labeling tasks, there is a strong connection between the labels. For example, compared to a verb, an noun is more likely to follow a adjective. In NER, using a standard BIO annotation [46], I-PER cannot be followed by I-ORG. Therefore, considering the connection between adjacent tags can be of great benefit to NER. As conditional random field (CRF) [47; 48] can make full use of the neighbor tag information when predicting current tag, it can jointly decode the best chain of labels for a given input sentence. Consequently, we consider CRF when modelling a label sequence instead of decoding each label independently.  
Formally, we denote a generic input sequence by \(z=\{z_{1},...,z_{n}\}\). \(z_{i}\) represents the input vector of the \(i\)-th word. We denote a generic sequence of labels for z by \(\mathbf{y}=\{y_{1},\cdots,y_{n}\}\). \(\mathcal{Y}(z)\) represents the set of probable label sequences for \(z\). Of all possible label sequences \(y\) given \(z\), the probabilistic model for sequence CRF defines a family of conditional probability \(p(y\mid\mathbf{z};\mathbf{W},\mathbf{b})\). It takes the following form:  
\[p(\mathbf{y}\mid\mathbf{z};\mathbf{W},\mathbf{b})=\frac{\prod_{i=1}^{n}\psi_{i}(y_{ i-1},y_{i},\mathbf{z})}{\sum_{\mathbf{y}^{\prime}\in\mathcal{Y}(\mathbf{z})}\prod_{i=1}^ {n}\psi_{i}\left(y^{\prime}_{i-1},y^{\prime}_{i},\mathbf{z}\right)} \tag{7}\]  
\(\psi_{i}(y^{\prime},y,\mathbf{z})=\exp\Bigl{(}\mathbf{W}^{T}_{y^{\prime},y^{ \prime}}\mathbf{z}_{i}+\mathbf{b}_{y^{\prime},y}\Bigr{)}\) are potential functions. \(\mathbf{W}^{T}_{y^{\prime},y^{T}}\) denotes the weight vector, and \(\mathbf{b}_{y^{\prime},y}\) denotes bias corresponding to label pair \((y^{\prime},y)\).  
We employ the maximum conditional likelihood estimation to train CRF. Given a training set \(\{(\mathbf{z}_{i},y_{i})\}\), the logarithm of the likelihood (a.k.a. the log-likelihood) is defined by the following equation:  
Figure 4: Mask matrix.  
\[L(\mathbf{W},\mathbf{b})=\sum_{i}\log p(y\mid\mathbf{z};\mathbf{W},\mathbf{b}) \tag{8}\]  
The parameters maximizing the log-likelihood \(L(\mathbf{W},\mathbf{b})\) will be choosed by maximum likelihood training. Decoding aims to find the label sequence \(y^{*}\) possessing the highest conditional probability:  
\[\boldsymbol{y}^{*}=\underset{y\in\mathcal{Y}(\mathbf{z})}{\operatorname{ argmax}}p(\boldsymbol{y}\mid\mathbf{z};\mathbf{W},\mathbf{b}) \tag{9}\]  
For a sequence CRF model, the Viterbi algorithm is adopted for training and decoding.
## 2 Related Work
In the data retrieval phase, a range query is split into a \(k\)-dimension range of multiple sub-queries. After splitting, each attribute sub-query is assigned with a bit code and tuples of \(k\)-bit codes, referred to as code \"\(B\)\", are produced. The codes \"\(B\)\" are then mapped to range space and data is retrieved from nodes located in the mapped \(R\). Distributed Index for Features (DIFS) [10], performs a data fusion based on data conveyance through the network. The routing in DIFS is designed on top of a quad tree in a manner that balances the communication load across the index, and the range is maintained along the sensor node hierarchy. Unlike traditional binary and quaternary trees, DIFS constructs a multiply rooted hierarchical index, where a non-root node might have multiple parents. Information for a specific range within a particular geographic region is stored in corresponding nodes of that region. A node covering a large area stores a smaller range of values while a node covering a small area stores a wider range of values. [PERSON], _et al._[11] proposed a method called Distributed Index for Multi-dimensional Data (DIM), which includes both a point and a range query in a multidimensional DCS model. In DIM, each sensor is linked as a node in a tree structure where each node represents a range of values. A root node represents the entire range of values and splits into two equal parts for left and right child nodes. This process continues for each non-leaf node until leaf nodes are reached. Table 1 summarizes related work with corresponding features.
## 3 Experimental Results
### Laboratory and Expert Database Installation  
The internal space size of the laboratory used was 8 m (length) \(\times\) 5 m (width) \(\times\) 3.6 m (height), with a camera and vent. There is a glass observation window in a wall. Five sensors: a gas sensor, temperature sensors, an ionization smoke sensor, a flame sensor and a humidity sensor were installed on the ceiling of the laboratory. We use the camera to observe specific combustion conditions and recorded at key points in time.  
We have constructed the corresponding expert database as shown in Table 1. In order to prevent exaggerating certain effects, the data entered into the expert database were normalized to the interval [0, 1]. Moreover, the normalization procedure minimizes the error brought by variations in conditions and precisely represents the probability of fire.
# Evaluating the Perceived Quality of Mobile Banking Applications in Croatia: An Empirical Study
## 2 Literature Review
### Service Quality of Mobile Banking Applications  
Service quality denotes the degree to which interaction with a mobile banking application matches customer needs. High service quality of mobile banking applications results in customers' satisfaction which in turn leads to their loyalty [15]. It is, therefore, of great importance that mobile banking applications meet customers' expectations and needs. Service quality has been evaluated with various measuring instruments, the most well-known among them being SERVQUAL [16]. This scale examines the essence of service quality with five diverse dimensions, including tangibles, reliability, responsiveness, assurance, and empathy. [PERSON] and [PERSON] [17] applied SERVQUAL to examine the service quality of mobile banking and discovered that, of the five mentioned dimensions, only assurance does not affect satisfaction. [PERSON] et al. [2] found that privacy and security, customer support, interactivity, efficiency, and content are the key dimensions in evaluating the service quality of mobile banking. A recent study [18] on m-banking quality in which data were gathered from bank clients in Croatia revealed that safety, simplicity, and variability of m-banking services have a significant influence on the perceived quality of m-banking which in turn affects a bank's reputation. As an outcome of their study on m-banking service quality, [PERSON] and [PERSON] [19] concluded that convenience, accuracy, diverse mobile application service features, ease of use, and continuous improvement, are considered the main sources of customer satisfaction. On the other hand, [PERSON] et al. [3] discovered that enjoyment, sociality, and trust are significant antecedents of commitment/satisfaction when the service quality of mobile banking is examined. [PERSON] et al. [20] pointed out that security/privacy, perceived usefulness, perceived ease of use, design, and enjoyment are important predictors of customer satisfaction which, in turn, has a strong impact on the intention to use mobile banking services. Results of a study conducted during the COVID-19 pandemic suggest that service quality and performance of mobile banking applications can be considerably improved if recommendations drawn from vulnerable people's experiences are considered [21].
###### Abstract  
The present study investigated the evolutions of \(b\)-value and fractal dimension of acoustic emission (AE) events during shear rupture of a naturally-created rough fracture in a granite specimen under triaxial compression. Acoustic emission signals were monitored by 16 sensors mounted directly on the surface of the specimen, and AE waveforms were sampled at 16 bits and 25 MHz. Reliable hypocenters were determined using P-wave arrival times picked up from the waveforms. Acoustic emission magnitude was determined from the maximum amplitude monitored by two peak detectors, which have a relative magnitude range of 0 to 2.75. A three-dimensional X-ray computed tomography scan was performed after the test to explore the fracture geometry. Acoustic emission activity was initiated during hydrostatic compression. With increasing differential stress, AEs demonstrated an increasing event rate, a decrease (from approximately 1.8 to 1.6) with a subsequent precursory increase (from 1.6 to 1.8) in fractal dimension, a quick decrease in \(b\)-value (from 1.0 to approximately 0.5), and a quick increase in fractal dimension (from 1.8 to 2.0). The exponentially increasing event rate, gradually decreasing \(b\)-value, and slowly increasing fractal dimension may be an intermediate-term indication of fault reactivation. In contrast, a progressively increasing event rate, a rapid drop in \(b\)-value, and a rapid increase in fractal dimension may facilitate short-term prediction of large events, which reflect the rupture of large patches. Acoustic emission hypocenters were clustered on the entire fracture surface. The present study sheds some light on detecting early signs of fault reactivation by monitoring injection-induced seismicity in areas with faults of different maturity.  
acoustic emission; granite; immature fault; seismic \(b\)-value; fractal dimension +
Footnote †: journal: Applied Soft. 2019, 9, 2498
## 2 Data and Methodology
### Methodological Framework
#### 2.2.2 Analyzing Trends in Net Ecosystem Productivity  
The Mann-Kendall test and Theil-Sen method were employed to analyze the interannual variations and dynamic changes in vegetation NEP from 2000 to 2020. The Mann-Kendall test is a non-parametric statistical method used to detect the presence and direction of trends in time-series data. Its primary objective is to identify monotonic trends in the time series without the need for assumptions about the data distribution. The core idea involves comparing the magnitudes of adjacent datapoints to determine whether the data exhibit an increasing or decreasing trend [34; 35].  
The Theil-Sen trend coefficient (\(\beta\)) is computed using the following calculation [36]:  
\[\beta=Median\bigg{(}\frac{NEP_{j}-NEP_{i}}{j-i}\bigg{)}, \tag{3}\]\(\beta\) represents the gradient of the \(NEP\) regression equation for the corresponding element, and \(NEP_{i}\) and \(NEP_{j}\) denote the time series values of NEP for the i-th and j-th years. When \(\beta\) is greater than zero, it indicates a positive trend in NEP over time, whereas when \(\beta\) is less than zero, it signifies a negative trend in NEP over time.  
If the standardized value Z exceeds 0, the sequence demonstrates an ascending pattern; if it falls below 0, the sequence displays a descending trend. When the absolute magnitude of Z equals or surpasses 1.64, 1.96, and 2.58, it signifies that the time series has successfully undergone significance assessments at the confidence levels of 90%, 95%, and 99%, correspondingly [37]:
## 2 Related Work
Table 1 compares important characteristics of related credit-based incentive approaches with the proposed SocialCode. Except the Barter-based approach, SocialCode is the only one that considers different levels of node selfishness and interest and then classifies all nodes into four types. It also proposes corresponding distributed credit payment processes for these four types of nodes, instead of the centralized process adopted by most approaches. It further avoids the credit overspending problem of RCM and the reluctance to forward packets with FRMAE. Most importantly, SC strongly integrates its credit-based incentive mechanisms with the SLNC-based CCD to stimulate efficient content distribution in VANET, which is a significant advance in this field. On the other hand, MobiCent and RIS focuses on avoiding two kinds of security attacks and investigating how the scope of network information influences node decisions, respectively. Due to space limitation, SocialCode focuses on how to improve the conventional SLNC-based cooperative content distribution protocol and combine the proposed credit-based incentive approach to encourage all classes of vehicular nodes to rise their incentives for sharing content data. Consequently, security and privacy issues of the credit-based system are not discussed in this paper. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **Barter-Based** & **RCM** & **FRAME** & **MobiCent** & **RIS** & **SocialCode** \\ \hline
**Selfishness considered** & Y & Y & Y & Y & Y & Y \\
**Interest considered** & Y & N & N & N & N & Y \\
**Distributed credit control** & N & N & N & N & Y & Y \\
**Credit overspent** & N & Y & N & N & N & N \\
**Nodes may reluctant to** & N & N & Y & N & N & N \\
**forward packets** & & N & N & Y & N & N \\
**Attacks considered** & N & N & N & Y & N & N \\
**Integrating with the** & N & N & N & N & N & Y \\
**SLNC-based CCD approach** & & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Characteristics of related credit-based incentive approaches.
# Design and Implementation of a Multi-Function Hydrophone for Underwater Acoustic Application
## 2 System Overview
### Software Design
#### 2.2.4 Parameters Setting  
In our proposed multi-function hydrophone, some parameters are configurable. To ease operation, we designed a graphical user interface (GUI) to control the configurable parameters as shown in Figure 9. The working mode, the sampling frequency, the file length which is written into SD card, and the gain of pre-amplifier can be configured by drop-down lists. Also, the IP and port are configurable which avoids conflicts in the local area network. In addition, the purpose of the \"set time\" button is to set the RTC with the current time. The two graphs are used to show the signals in the time domain and the frequency domain, respectively, in mode 2.
## 4 Results
### Data Processing
#### 4.2.1 Building Footprint Extraction from Historical Maps
As the resolution of historical map data is usually high, we followed a tile-based approach, cutting the map into equally large parts with a maximum resolution of 500 \(\times\) 500 pixels. This eases the visual comparison to the first approach. The results can be seen in Figure 6.  
Figure 6 shows that the semi-automatic approach outperforms Segment Anything. However, this is only the case after manual segmentation of a large amount of the original map and therefore cannot be seen as a general solution for other historical maps. Without any historical training data, Segment Anything performs quite well in extracting the footprints of the building blocks. However, text and graticule lines provide challenges for the tool and should be removed prior to use. Again, this would require manual work. A similar approach to ArcGIS should be run to regularize the final extracted footprints. This proof of concept shows the potential for automatic footprint extraction from historical maps and its extrusion to 3D building models. However, the provided data show the challenge of the situation and the research potential for finding a holistic solution. Figure 5: Properties of the colored historical map of Jena in 1936 including red building footprints, black text, a blue river, and green spaces. Figure 6: The left image shows example patches of the 1936 map. The middle image shows building footprint extraction using manual labelling and ArcGIS Pro. The right image shows the result retrieved by Segment Anything.
# Solving Multi-Document Summarization as an Orienteering Problem
## 2 Related Work
### Optimization-Based Approaches  
Several summarization studies propose to solve the text summarization as an optimization problem. The selection of summary sentences has been reduced to different optimization problems. The summary sentences are selected according to one of the following approaches: (1) the greedy selection approach, in which the best textual units are selected one item at a time, and (2) the global optimal selection approach that searches for the best summary rather than the best sentences. The first approach rarely produces the best summaries [45] and, thus, most of the summarization studies are based on the second one. In the literature, several objectives have been studied and optimized using different optimization methods. [PERSON] and [PERSON] [46] described some of these objectives, including text coherence, which is the relatedness of summary contents (e.g., sentences) and significance, which is how relevant the summary content is to the documents to be summarized and to the user's needs (e.g., user query). Nevertheless, in all these formulations, searching for the optimal summary is an NP-hard problem [47], and it is therefore essential to approximate the solution to MDS. Meta-heuristics can be used to find approximate solutions to NP-hard problems, such as GA [48] and a population-based method [49]. For example, [PERSON] and [PERSON] [50] proposed a linear programming-based global optimization method to extract summary sentences.  
Promising results have been produced from the global optimal selection approach. For example, the evaluation results of summaries produced by [PERSON] and [PERSON]'s summarization framework [47] are not far from the results of the best methods in various DUC competitions. In addition, all the state-of-the-art methods on corpora from DUC 2004 through DUC 2007 in both generic and query-driven summarization were outperformed by the proposed solution of [PERSON] and [PERSON] [51], a monotone, non-decreasing submodular function for summarizing documents. Finally, in addition to the fact that obtaining approximate solutions are much faster than obtaining the exact one, some studies have shown that the results of both solutions are comparable [52]. Nevertheless, these experiments have been conducted on a limited size problem.
## Materials and Methods  
### Material  
Raw montmorillonite (Mt) with a specific surface area and mean grain size of 49.23 m\({}^{2}\)/g and 12.55 um, respectively, was obtained from Adamas Reagents. Sodium montmorillonite (Na-Mt) was prepared by treatment of Mt with a 1.0 M sodium chloride solution. The cation exchange capacity (CEC) of Na-Mt was 100 mmol/100 g, which was calculated using the ammonium chloride-50% ethanol method [23]. Titanium(IV) isopropoxide (98%), (3-aminopropyl)triethoxysilane (APTES, 98%), ethanol, and sodium chloride (NaCl) were supplied by Aladdin Chemistry (Shanghai, China). Polyoxyethylene (80) sorbitan monooleate (T Tween 80, C\({}_{24}\)H\({}_{44}\)O\({}_{6}\)) was provided by Xilong Chemical Co., Ltd. (Beijing, China) Hydrochloric acid (HCI) was purchased from Beijing Xingqing Red Fine Chemical Technology Co., Ltd. (Beijing, China). Deionized water was used throughout the experiment. All chemicals were directly used without further treatment.
## 5 Simulation
### Varying Nodes Density in Network
#### 5.3.2 Average Packet Loss  
As shown in Figure 19 below shows that WEAMR performs much better as compared to the AOMDV and AODV. As the number of nodes is increasing the packet loss average in the compared protocols is decreasing. This is because of the fact that with the increase in density of the network there will be more congestion and interference hence chances of packet loss will be higher [29]. On the other  
Figure 18: Average end to end delay with increasing number of nodes.  
hand, we notice that packet success ratio for the hotline assisted routing remains almost unchanged regardless of the network density. The reason behind this is the permanent wired link between any given two gateways.  
Therefore it will be fair to conclude that communication between the source and the sink is independent of the density of the network in the case of hotline assisted routing, so WEAMR also has lower routing overhead and routing path discovery requests when the node density is changing.