esa-sceva/satcom-chunk-collection huggingface dataset:
# Synthesis of Star Nanoparticles and Their Applications for Bioactive Compound Encapsulation
## 2 SNP Preparation Method
### Ultrasonic Atomization  
Ultrasonic atomization is a promising way to generate liquid microdroplets through the use of ultrasonic energy. In the droplet formation mechanism, two major hypotheses have been suggested to explain the thin vibrating liquid film disintegration during ultrasonic atomization comprising capillary waves, and cavitation mechanism [65]. Capillary waves consist of an ordered mesh-like structure with the same number of ridges and valleys per unit area [66]. In the common model of ultrasonic atomization, droplets detach from the crests of the stationary capillary waves and in the cavitation mechanism, cavitation bubbles are formed on the vibrating surface due to the application of ultrasonic waves. During the collapse of these cavities, generated hydraulic shocks initiate the segregation of the liquid film and consequently result in the ejection of the droplets [67]. This process is shown in Figure 9.  
Figure 8: Synthesis of SNPs through ultrasound method: (**a**) with acid hydrolysis; (**b**) without acid hydrolysis and SEM micrograph of corn and potato SNPs [64].  
Recently, [PERSON] and [PERSON] developed a bovine serum albumin (BSA) based SNP synthesis method by an ultrasonic atomization procedure using an ultrasonic piezoelectric oscillator. An integrated apparatus was designed for the manufacturing of aerosol solutions, as well as for the drying and collection of the resulting SNPs, and finally, the formation of spherical SNPs with a uniform smooth surface and particle sizes ranging from 200 to 800 nm was resulted [68].
# Coarse-Resolution Satellite Images Overestimate Urbanization Effects on Vegetation Spring Phenology
## 2 Materials  
### Study Area  
The study area is a region covered by a scene of Sentinel-2 (Tile Number: T50 TMK; central coordinate: 40\({}^{\circ}\)09'18.14\" N, 116\({}^{\circ}\)28'11.93\" E) in the north of China (Figure 1). This area covers the city of Beijing, the capital of China, and its surrounding rural area. Beijing is one of the most densely populated cities in the world with a population of 21.71 million. The total size of the study area is 109.8 km\(\times\)109.8 km. The annual average air temperature and precipitation in this study area are approximately 11.5 \({}^{\circ}\)C and 610 mm respectively. This study area is located in the continental monsoon climatic zone with obvious seasonal alternation, and the main vegetation type is temperate deciduous broad-leaved forests. It has both a natural and a highly modernized urban ecosystem and has clear boundaries between the urban and rural areas. Based on a previous study [25], Beijing has significant rural-urban difference in vegetation spring phenology and this study area does not have persistent clouds year-round so that enough cloud-free satellite observations could be obtained to extract the phenology variables.
# Detection of Software Security Weakness Using Cross-Language Source Code Representation (CLaSCoRe)
## 3 The Proposed Architecture for the Cross-Language Source Code Representation
### Multi-Language Tokens Inventory  
The proposed solution is used to train machine learning algorithms for the detection of vulnerable code written in C, C++, and Java and to later detect the same vulnerabilities in C#, so we collect all possible keywords we may find in all these programming languages. We named these keywords tokens, by convention, as they will contribute to the tokenization of source code into the CLaSCoRe format.  
The keywords included in the inventory are the following:  
* the reserved keywords for C++98 and C++11, which include the C keywords;
* the Portable Operating System Interface POSIX.1-2017 identifiers and functions from The Open Group [28];
* Linux or Windows functions which we may find in source code written in C/C++
* the reserved keywords for Java;
* Java predefined classes, methods, variables, operators, constructors, or interfaces [29];
* the reserved and contextual identifiers for C# [30].  
We have identified a total of 1782 tokens specific to C/C++ programs, 19.775 tokens specific to Java code, and 120 C# tokens. Some of the tokens are specific to more than one programming language. As a result, after removing all duplicates, the total number of tokens present in the source code written in C, C++, Java, or C# languages is 21,464. We selected from the full list of tokens the ones which are present in the NIST datasets for the programming languages in the scope of our research to optimize the speed of processing. We have identified 920 tokens that appear in the NIST dataset at least once.
# Zero-Inflated Patent Data Analysis Using Compound Poisson Models
## 2 Related Works
Data sparsity is one of these problems. The sparseness occurs in the preprocessing of text documents to make the structured data. That is, many elements of the document-word matrix have a value of zero. To solve this problem, [PERSON] et al. (2014) provided a document-clustering method using dimension reduction and support vector clustering [7]. They tried to solve the sparsity problem in the given data by reducing the dimension of the given matrix data. [PERSON] and [PERSON] (2015) used the zero-inflated Poisson and negative binomial models for overcoming the sparseness in patent document data [32]. The proposed method tries to solve the sparsity problem by modeling the zero and non-zero parts of the given data, respectively. However, as the ratio of zeros included in the data increases, the existing methods for solving the sparsity problem show limitations. Therefore, we study one new method to solve the sparsity with zero-inflated problem in text data analysis. There are various methods for zero-inflated data analysis [8; 9; 10; 11; 12; 13; 14]. Among them, the zero-inflated Poisson (ZIP) regression is a popular model for analyzing zero-inflated data [8; 11; 12]. The ZIP model is defined as (1) [9; 11; 34]:  
\[\mathrm{P(X=x)}= \begin{cases}\pi+(1-\pi)e^{-m},\;\mathrm{x}=0\\ (1-\pi)\frac{e^{-m}m^{x}}{x!},\;\mathrm{x}\geq 1\end{cases}  \tag{1}\]  
ZIP separates the probability into two parts: zeros and non-zeros. In (1), the parameters are \(\pi\) and \(m\). \(\pi\) is the proportion of zeros and \(m\) is the parameter of Poisson distribution. That is, \(\pi\) is the probability of \(x=0\) of binomial distribution and \((1-\pi)\) is the probability of \(x\
eq 0\) of Poisson distribution. ZIP is a regression model with two parameters: \(\pi\) and \(m\). The mean of X is defined as (2) [9; 34]:  
\[E(X)=(1-\pi)m \tag{2}\]  
That is, the expectation of X is calculated by multiplying the parameter \(m\) by \((1-\pi)\). Additionally, the variance of X is represented by (3) [9; 34]:  
\[Var(X)=(1-\pi)\Big{(}m+\pi m^{2}\Big{)} \tag{3}\]  
In (2) and (3), the probability \(\pi\) can be a constant or, depending on \(x\) and according to the binary outcome, zero may or may not occur. Using this, we can obtain the maximum likelihood estimations of m and \(\pi\)[9; 34]. In addition, various models such as zero-inflated negative binomial (ZINB) are used for the analysis of zero excess data in diverse fields [11; 12].
# On the Use of Focused Incident Near-Field Beams in Microwave Imaging
## 2 Motivation
### Suppressing Undesired Scattering Effects
Note that there exist situations in which focusing the incident field in one spot does not result in weaker contrast sources elsewhere; however, for situations where Born approximation is valid (\(\mathbf{E}\approx\mathbf{E}^{\text{inc}}\) thus \(\mathbf{w}=\chi\mathbf{E}\approx\chi\mathbf{E}^{\text{inc}}\)), focusing the incident field into a spotlight will weaken the contrast sources at other areas. That is why we have limited the scope of this paper to scenarios in which the total and incident fields share similar distributions. Figure 2: (**a**) represents the scenario in which a focused NF beam is used for irradiation of the OI (central green object); (**b**) represents the scenario in which a non-focused NF beam is used for irradiation. The side black objects are the undesired scatterers. It is also important to note that in some situations, the use of a focused incident field has a disadvantage compared to a non-focused one. In particular, if the transmitter in Figure 2 can vertically move up and down to scan the OI, then at some elevations the focused incident field will not irradiate the OI. On the other hand, the non-focused incident field might still be able to irradiate the OI due to its wider NF beam. This will result in the loss of some useful information for the focused incident field. One remedy for this situation is to steer the antenna toward the OI when we move the antenna vertically up and down to ensure that the focused beam always see the OI. (This is similar to the concept of stripmap and spotlight modes in synthetic aperture radars.)
# A Review of Edge Computing Technology and Its Applications in Power Systems
## 2 Review of Edge Computing Architectures
### Multi-Access Edge Computing (MAEC)  
MAEC, previously known as mobile edge computing (MEC), was defined by the European Telecommunication Standards Institute (ETSI) as applying the concepts of cloud computing on the information technology (IT) based servers at the network edge [16]. MEC can be generally described as a cloud server capable of running at the edge of a mobile network, handling tasks beyond the scope of traditional network infrastructure [16]. While specific designs of MEC vary, the basic idea of MEC can be outlined with the five major characteristics defined by ETSI: on-premises means the edge side is local and may operate separately from the rest of the network while still accessing local resources; proximity, MEC servers are positioned close to the data source, and may have direct access to devices; low latency, due to their proximity to devices, MEC can significantly reduce latency; location awareness, as part of a wireless network, local edge servers can detect the location of connected end devices by leveraging lower-level signaling information; network context information, applications and services can leverage real-time network information to provide context-related services [17].  
In September 2017, the ETSI Industry Specification Group (ISG) officially changed its name from MEC to MAEC. The change reflects that the edge is not only based on mobile networks. The ISG has defined the framework and the reference architecture for MAEC. Figure 2, a simplified version of the MAEC framework from Chapter 5 of [18], illustrates the general entities involved. These entities are categorized into system level, host level, and network level [18]. The network level comprises relative external network entities, such as the 3 rd generation partnership project (3 GPP) network, local network, and external network. The MAEC host level involves the MAEC host and corresponding management entities. The MAEC host can be viewed as the combination of the MAEC platform, MAEC applications, and virtualization infrastructure. The MAEC system level is at the top of this framework, with overall management having an encompassing view of the entire MAEC system.  
The ISG also provided an MAEC reference architecture, which offers more details about the functional entities and their interrelations [19]. Figure 3 illustrates a simplified version of the framework from Chapter 6 of [18]. The MAEC host comprises the MAEC platform and the virtualization infrastructure, responsible for computing, data storage, and network resource management. The MAEC platform encompasses several essential functions necessary for running MAEC applications. The MAEC platform manager and the virtualization infrastructure manager operate at the MAEC host level. They provide management of specific MAEC hosts and their applications. At the MAEC system level, the MAEC orchestrator has comprehensive oversight of the entire MAEC system.  
Figure 3: Multi-access edge system reference architecture.  
Figure 2: Multi-access edge computing framework.
## 2 Methods
### Multi-Attribute Customer Decision-Making System Establish
#### 2.2.2 Sample Customer Clustering Division  
Considering the large number of customers, customers with similar profiles must be clustered in order to reduce the computational effort [43]. The adaptive k-medoids algorithm with a variable number of clusters improves the traditional k-medoids algorithm, but its clustering results are greatly affected by the size of customers and the set self-similarity. For this reason, the BLAP clustering method is chosen in this paper [11]. In this paper, the BLAP clustering method is used, which firstly performs local partition AP clustering on the sample customer set \(U\) through the first layer of AP clustering so as to reduce the influence of customer size on the accuracy and efficiency of clustering. Then, the second layer of AP clustering is performed, and the cluster center set obtained from the first layer of clustering is subjected to adaptive AP clustering [44], which improves the accuracy of the clustering results and reduces the effect of the set customer self-similarity on the clustering results. The details of the BLAP clustering algorithm are referred to in Appendix B.  
Figure 1: Sampling site (a region in the west of Zhejiang, China).  
After the clustering division is completed, the similarity between the two key metrics of each customer is determined, \(s^{\prime}_{\rho}(U_{i},U_{j})\) and the optimal number of clusters \(c^{\star}\); the similarity is calculated by the following formula:  
\[s^{\prime}_{\rho}(U_{i},U_{j})=\frac{\sum\limits_{y=1}^{7}\left(b_{U_{i,y}}- \overline{b}_{U_{i}}\right)\left(b_{U_{i,y}}-\overline{b}_{U_{j}}\right)}{ \sqrt{\sum\limits_{y=1}^{7}\left(b_{U_{i,y}}-\overline{b}_{U_{i}}\right)^{2}} \sqrt{\sum\limits_{y=1}^{7}\left(b_{U_{i,y}}-\overline{b}_{U_{j}}\right)^{2}}} \quad i\
eq j \tag{1}\]  
where \(b_{U_{i,y}}\) and \(b_{U_{i,y}}\) are the yth image tag of \(U_{i}\) and \(U_{j}\), respectively; and \(\overline{b}_{U_{i}}\) and \(\overline{b}_{U_{j}}\) are the mean value of image tags of \(U_{i}\) and \(U_{j}\), respectively.  
The optimal number of clusters, denoted as \(c^{\star}\), is a crucial parameter in clustering analysis as it determines the level of granularity in the data segmentation. This number is not arbitrarily chosen, but is calculated using a specific methodology. The optimal number of clusters \(c^{\star}\) is calculated as follows, typically involving a combination of statistical measures and algorithmic processes. By determining the optimal number of clusters, researchers can ensure that the resulting segmentation is both meaningful and useful for subsequent analysis. Selecting the right number of clusters is essential for drawing accurate conclusions from the data and avoiding overfitting or underfitting in the clustering process:  
\[c^{\star}=\underset{Q}{\operatorname{argmax}}Z_{Q,av} \tag{2}\]  
where \(Z_{Q,av}\) is the clustering quality index for different pairs of clusters and the cluster number corresponding to the maximum of \(Z_{Q,av}\) is the optimal number of clusters \(c^{\star}\).
# A Coupled Variational System for Image Decomposition along with Edges Detection
## 3 The Proposed Model and Algorithm
### Algorithm  
We now firstly give the detail algorithm for (12), which can be turned into the following two coupled problems:  
For \(\omega,v\) fixed, we solve  
\[\min_{u}\int_{\Omega}h(\omega)|\
abla u|dx+\frac{\beta}{2}\bigg{|}\bigg{|}f-u- v\bigg{|}\bigg{|}_{2}^{2} \tag{14}\]  
For \(u\) fixed, we have  
\[\min_{v}\mu\int_{\Omega}\Big{|}\
abla\Big{(}\Delta^{-1}\Big{)}(v)\Big{|}^{2} dx+\frac{\beta}{2}\bigg{|}\bigg{|}f-u-v\bigg{|}\bigg{|}_{2}^{2} \tag{15}\]  
For (14), using the [PERSON]'s transformation, it becomes  
\[\min_{u}\max_{\mathbf{p}\in B}\langle\
abla u,\mathbf{p}\rangle+\frac{\beta}{ 2}\bigg{|}\bigg{|}f-u-v\bigg{|}\bigg{|}_{2}^{2}, \tag{16}\]  
where \(B=\Big{\{}\mathbf{p}=(p_{1},p_{2})^{T}|\ \|\mathbf{p}\|_{\infty}\leq h(\omega) \Big{\}}\), \(\|\mathbf{p}\|_{\infty}=\sup_{x\in\Omega}\bigl{(}p_{1}^{2}+p_{2}^{2}\bigr{)}^{ \frac{1}{2}}\).  
Then (16) can be solved by the primal-dual method  
\[\begin{array}{l}\mathbf{p}^{k+1}=\mathrm{proj}_{B}\bigl{(}\mathbf{p}^{k}+ \theta\Bigl{(}\
abla u^{k}\Bigr{)}\bigr{)},\\ u^{k+1}=\frac{\frac{u^{k}+\tau\beta\bigl{(}f-v^{k}\bigr{)}+\tau\mathrm{div} \bigl{(}\mathbf{p}^{k+1}\bigr{)}}{1+\tau\beta}}{1+\tau\beta},\end{array} \tag{17}\]  
where \(\theta,\tau\) are the positive parameters.  
Minimizing (15) with respect to \(v\) results in the following Euler-Lagrange equation:  
\[(\mu-\beta\Delta)v^{k+1}=\beta\Delta\Bigl{(}u^{k+1}-f\Bigr{)}, \tag{18}\]  
which can be solved by Gauss-Seidel iteration.  
For (13), the corresponding Euler-Lagrange equation is  
\[(1-\lambda-\lambda\Delta)\omega^{k+1}=(1-\lambda)\
abla u^{k+1} \tag{19}\]  
and we can solve it using the Gauss-Seidel iteration.  
From the above derivation, we now summarize the detail algorithm for the coupled system as Algorithm 1:  
```
Step 1: Compute \(\mathbf{p}^{k+1},u^{k+1}\) by (17), Step 2: Compute \(v^{k+1}\) by (18), Step 3: Compute \(\omega^{k+1}\) by (19), Until: A stopping criterion is satisfied; otherwise set \(k=k+1\) and return to Step 1.
```  
**Algorithm 1** Given the initial value.
# Unmanned Aerial System-Based Data Ferrying over a Sensor Node Station Network in Maize
## 3 Results  
Soil moisture and crop canopy temperature data from stationary sensor node stations were transmitted from the secondary radio (which was linked to the node station datalogger) to the UAS data ferry transceiver radio (which was connected to the UAS data ferry mini laptop). The seven-day data file retrieved from each sensor node station was around 0.3-0.4 megabytes (MB) in size and took approximately one minute to retrieve. Each dataset retrieval was comprised of around 10,000 values (soil water content at three depths, crop canopy, and air temperatures were reported once every 15 min). To accommodate data retrieval and the approximate one minute for the UAS to maneuver from one sensor node location to another, the data retrieval from each sensor node station was scheduled two minutes after the previous sensor node station.  
Three UAS data ferrying experimental trial events were held during the 2020 growing season (September 2, September 16, and September 30). The UAS flights were conducted at two to three UAS heights (31 m, 61 m, and 122 m), with the four sensor node stations installed in the maize. Communication was attempted during the experimental trial events. The maize crop was at the early denting growth stage on the first experimental trial event/flight day (Table 3). However, for the second and third flight days, the maize was 75% past denting reproductive stage (R5.75), and at physiological maturity. A 100% CSR was observed at 31 m above the ground for all attempts (Table 4) but not for the other UAS heights. Therefore, the 31 m height was used in investigating the effect of crop canopy interference on radio communication along different lateral distances (0 m, 38 m, and 76 m) from the stationary sensor node stations during the 2021 growing season.  
height of 31 m) was directly over the node stations (0 m lateral distance) and 38 m laterally away from node stations; the CSR was within 40-60% when the UAS was 76 m laterally away from node stations (at a height of 31 m) (Figure 7). The next four trials were conducted during the late reproductive stages of maize (early to mid-dating, i.e., R5.1-R5.4; Table 3), and the CSR was within 40-80% when the UAS was over the node stations, 60% when the UAS was 38 m laterally away from the stations, and within 20-60% when the UAS was 76 m laterally away from the node stations. For the last three experimental trial events (around the mid-denting reproductive stage and beyond, i.e., R5.5-R5.6; Table 3), the CSR for all three lateral distances was 100% (Figure 7).
## 3 Results
### Sea-ice Concentration Evaluation
#### 3.2.3 Merging with Poor-Quality MODIS Sea-Ice Concentration
To demonstrate the benefit of the higher resolution, we compare the open-water extent (area of pixels covered by at least 15% of water) of the datasets. We only consider pixels where all datasets are available, which yields an area of 8191 km\({}^{2}\). The open-water extent of the thin- and thick-ice SIC is 208 km\({}^{2}\) and 1045 km\({}^{2}\), respectively. The open-water extent of the merged SIC (818 km\({}^{2}\)) is close to the thick-ice open-water extent (1045 km\({}^{2}\)), but higher than the thin-ice open-water extent (208 km\({}^{2}\)). The AMSR2 open-water extent (292 km\({}^{2}\)) is close to the thin-ice open-water extent, but lower than the thick-ice open-water extent. The MODIS open-water extent of 4508 km\({}^{2}\) is higher than both the thin- and the thick-ice open-water extent. An overview over all parameters is given in Table 2.  
Figure 10: (**a**) MODIS RGB image of the scene in Figure 9g. (**b**) Ice tie-point of the MODIS SIC. The dashed line marks the 270 K threshold above which no SIC retrieval is performed. (**c**) MODIS SIC as in Figure 9g.
# Towards a Machine Learning Model for Detection of Dementia Using Lifestyle Parameters
### How Are Dementia and Alzheimer's Related? [1; 2]  
Dementia is a broad term encompassing a range of symptoms characterized by cognitive decline, memory impairment, and alterations in thinking and behaviour. It does not represent a distinct illness but rather a collection of manifestations stemming from various underlying factors. dementia can hinder an individual's ability to perform daily tasks and have adverse effects on their emotional and social well-being.  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Early Stage** & **Middle Stage** & **Late Stage** \\ \hline \multirow{2}{*}{Forgetfulness} & Becoming forgetful of recent events and people’s names & Becoming unaware of the time and place \\ \hline \multirow{2}{*}{Losing track of time} & Becoming lost at home & Having difficulty recognizing relatives and friends \\ \hline \multirow{2}{*}{Becoming lost in familiar places} & Having increasing difficulty with communication. & Having an increasing need for assisted self-care \\ \hline \multirow{4}{*}{Losing track of timeplaces} & Needing help with personal care & Having difficulty walking \\ \hline \multirow{4}{*}{Becoming lost in familiar places} & Experiencing behavioural changes that may escalate and include aggression & Experiencing behavioural changes that may escalate and include aggression \\ \cline{1-1} \cline{2-3}  & & \\ \end{tabular}
\end{table}
Table 1: Stages of dementia.  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt}} \hline \hline
**Normal Memory Loss** & **Abnormal Memory Loss** \\ \hline \multirow{2}{*}{Forgetting where you left your cheque book} & Forgetting which bank you use \\ \hline \multirow{2}{*}{Repeating a story to friend or spouse} & Repeating the same story over and over on the same day to the same person \\ \hline \multirow{2}{*}{Forgetting what you had for breakfast} & Forgetting what you had for breakfast 15 minutes ago \\ \hline \multirow{2}{*}{Using calendars and lists to remind you of certain things} & Forgetting to use calendars and lists and not understanding the use of either of these \\ \hline \multirow{2}{*}{Being disorientated for a moment when waking up in strange motel room when travelling} & Getting lost in your own home where you have lived for years \\ \hline \multirow{2}{*}{Sometimes forgetting where you parked the car you have a car} & Forgetting that you drove to the shops or that you have a car \\ \hline \hline \end{tabular}
\end{table}
Table 2: Normal Memory Loss vs. Abnormal Memory Loss [4].  
[PERSON]'s disease, on the other hand, is a specific contributor to dementia. It represents a gradual neurological condition causing memory loss, difficulties in logical reasoning and problem-solving, shifts in language and communication, and ultimately, a decline in overall cognitive functioning.  
Considering that lifestyle factors predominantly correlate with progressive cognitive deterioration, our work is aimed at detecting dementia, primarily characterized by Alzheimer's disease symptoms.