esa-sceva/satcom-chunk-collection huggingface dataset:
# Design and Simulation of Electromagnetic Linear Actuators for Jet Dispensers
## 5 Results and Discussion
### Proposed Jet Dispenser Designs  
The present results show that the SA model operates well over a short stroke length and develops a high thrust force with a minimum excitation current of 2.2 A. SA is best suited for the jet dispenser applications when using a return spring. However, for a longer stroke length, the excitation current must be significantly increased in order to compensate for the big drop in \(F_{SA}\). For the MCA and MMA models considered in the case of no spring, the MCA model provides a more suitable solution since it has a low thrust force drop (i.e., 0.7%) and a low excitation current (i.e., 1.8 A) over a long stroke range.  
The results presented in the preceding sections have also confirmed that, given an appropriate design, the SA, MCA and MMA all provide a sufficient thrust force to drive the needle of the considered jet dispenser. In addition, for all three mechanisms, the magnitude of the generated thrust force varies with the relative position between the core and the movable parts of the actuator. For example, for the SA, the thrust force reduces dramatically as the plunger moves further away from the core. Furthermore, since the attraction force between the plunger and the core is strong only when the plunger is close to the core, then irrespective of the current direction in the coil, a return spring is required to push the needle back during the dispensing stage. In the MCA and MMA designs, the largest thrust force is produced when the magnet lies entirely inside the coil windings and the mid-points of the magnets and coils overlap. When the direction of the current flowing through the coil reverses, the direction of the interaction force between the magnet and the coil also reverses. Hence, both actuator mechanisms are capable of producing bi-directional motion and can therefore drive the needle in both the rising stage and the dispensing stage without the need for a return spring. However, a return spring can be incorporated into the mechanism if desired to further actuate the needle in the dispensing stage; thus ensuring that the nozzle gate is fully closed by the needle tip at the end of the dispensing operation such that no leakage occurs. However, this performance advantage is obtained at the expense of a larger thrust force, \(F_{NTFS}\) (i.e., more than twice that required when no return spring is employed).  
Figure 16 shows the basic configurations and dimensions of the three EMA-based jet dispensers developed in the present study. Note that the actuators are based on the modified design variables shown in Table 3, while the dimensions of the other parts, e.g., the coil holder, the flange, the stroke bolt, and the housing, are based on experience. Moreover, the primary dimensions of the jet valve are indicated in Table 1 and the sub-dimensions of the jet valve components are chosen in such a way as to facilitate the assembly process and ensure proper dispensing operation.  
Figure 15: Minimum excitation current for (**a**) SA, (**b**) MCA, and MMA models (with return spring).
# A New Indicator to Assess Public Perception of Air Pollution Based on Complaint Data
## 2 Materials and Methods
### Methods
#### 2.2.2 Kernel Density Analysis and Kriging Interpolation  
After calculating the complaint emotion intensity of air pollution and address matching of complaint data, in this paper, we analyzed the Kernel density of the complaint data and the spatial-temporal distribution of air pollution complaint emotional intensity by the method of Kriging interpolation.  
1. Kernel density analysis of public complaints  
Complaint density can reflect the public's satisfaction with air quality in a specific area. In general, the higher the complaint density, the less satisfied the public. The air pollution complaint density (\(Air_{d}\)) of the complaint data point \(i\) after address matching was obtained by Kernel density analysis.  
\[Air_{d}=\frac{1}{(radius)^{2}}\sum_{i=1}^{n}\left[\frac{3}{\pi}\times\left(1- \left(\frac{dist_{i}}{radius}\right)^{2}\right)^{2}\right] \tag{4}\]  
where \(n\) is the number of complaint points after the outliers are removed, \(dist_{i}\) is the distance between point \(i\) and other points. \(radius\) is the search radius, and the Kernel density method uses the spatial variable of \"Silverman experience rule\" to calculate the default search radius for the input complaints dataset.  
2. Spatial analysis of public complaint emotion based on Kriging interpolation  
The distribution of complaint emotion intensity can also reflect the public's satisfaction with a specific area's air quality. In general, the smaller the air pollution complaint emotional value, the greater the negative emotion, and the more dissatisfied the public is.  
In this paper, the local air pollution perception \(Air_{s}\) was obtained by Kriging interpolation of complaint emotion results, as shown in Equation (5).  
\[Air_{s}=\sum_{i=1}^{n}\lambda_{i}\times z_{i} \tag{5}\]  
where \(n\) is the number of complaint points, \(\lambda_{i}\) is the weight of complaint point \(i\) in space; in this paper, the spherical function is used as the fitting model to calculate \(\lambda_{i}\); \(z_{i}\) is the emotion intensity of complaint point \(i\).
# A Spatial Fuzzy Co-Location Pattern Mining Method Based on Interval Type-2 Fuzzy Sets
###### Abstract  
The goal of spatial co-location pattern mining is to find subsets of spatial features whose instances are often neighbors in a geographical space. In many practical cases, instances of spatial features contain not only spatial location information but also attribute information. Although there have been several studies that use type-1 fuzzy membership functions to mine spatial fuzzy co-location patterns, there is great uncertainty associated with such membership functions. To address this problem, we propose a spatial fuzzy co-location pattern mining method based on interval type-2 fuzzy sets. First, we collect the interval evaluation values of the interval data of attribute information from experts to form granular data. Next, the original type-1 fuzzy membership function is extended to a granular type-2 fuzzy membership function based on elliptic curves. We use a gradual method to adjust the parameters of the fuzzy membership function so that its footprint of uncertainty satisfies both the connectivity and the given confidence. Based on this granular type-2 fuzzy membership function, we fuzzify the attribute information of instances and define the concepts of fuzzy features and fuzzy co-location patterns. A fuzzy co-location pattern mining algorithm based on spatial cliques is then proposed, termed the FCPM-Clique algorithm. In order to improve the efficiency of the algorithm, we propose two pruning strategies. In addition, we extend two classical spatial pattern mining algorithms, the Join-based algorithm and the Joinless algorithm, to mine fuzzy co-location patterns based on interval type-2 fuzzy sets. Many experiments on synthetic and real-world datasets are conducted, the performance of the three algorithms is compared, and the effectiveness and efficiency of our proposed FCPM-Clique algorithm is demonstrated.  
spatial data mining; fuzzy co-location pattern; interval type-2 fuzzy set; clique +
Footnote †: journal: Applied  
_aciences_  
_Article_  
A Spatial Fuzzy Co-Location Pattern Mining Method Based on Interval Type-2 Fuzzy Sets
## 2 Materials and Methods
### MTT Assay  
For the MTT assay, HepG2 and At-MSCs were seeded in 96-well plates at a density of 1 \(\times\) 10\({}^{4}\) cells/well in 200 ul of cDMEM. After 24 h, cells were treated with 10, 50, 100, 200, 500, or 1000 ug of LLP suspension per well, also maintaining an untreated control (for the effective protein content of each treatment see Section 3.1). After 48 h treatment, 20 ul of MTT (5 mg/mL in saline solution) were added in each well. After 4 h, 100 ul of the solubilization solution (10% sodium dodecyl sulphate in HCl 0.01 M) was added and plates were then maintained at 37 \({}^{\circ}\)C overnight. The formation of tetrazolium salts was evaluated by spectrophotometry at 570 nm, using a reference wavelength of 630 nm. In each experiment, six replicates were prepared for both control and treatment. The experiment was repeated at least five times for each cell line. For At-MSC, seven different cell preparations were analyzed at P3. To evaluate metabolic activity of HepG2 cells in cultures supplemented with LLP alone, cells were seeded in 96-well plates at a density of \(1\times 10^{4}\) cells/well in 200 \(\upmu\)L of serum-free DMEM. After 24 h, cells were treated with 10, 50, 100, 200, 500, or 1000 \(\upmu\)g of LLP suspension per well, maintaining an untreated control. Cells cultured with DMEM containing 10% FBS supplementation were considered as a positive control. MTT assay was performed after 48 h treatment. The experiment was repeated three times. For this set of experiments, LLP solubilization was performed as described in Section 2.2, avoiding the digestion step with type I collagenase.
# Analysis of Power Allocation for NOMA-Based D2D Communications Using GADIA
## 3 5G Overview
### Massive Machine Type Communication (mMTC)  
mMTC indicates the communication service for connecting a massive number of devices. This service can foster the fulfilment of the IoT by implementing effective Internet connectivity. Extensive connectivity is expected for numerous applications associated with environmental measurements, traffic monitoring, asset tracking, resource utility management, and the smart grid. These applications may require further communication requirements besides extensive connectivity. For example, many devices are battery-powered and should stand for a long time, e.g., a couple of years, without replacing them. For instance, smart meters might be used in locations with high penetration loss. Therefore, they need prolonged network coverage for their services. Furthermore, a low-cost transceiver is fundamental for recognizing various IoT applications. Some of these characteristics was partially discussed in Long Term Evolution (LTE) releases 13 and 14 by proposing new UE categories, such as UE CAT-0, Cat-M1, and Narrowband Internet of Things (NB-IoT) [PI] [44].
## 3 Experiments
### Data Preprocessing
#### 3.2.4 Dataset Splitting  
To validate the performance of the proposed model, the dataset is usually partitioned into training and test sets. During the model's training, only data from the training set is used to train it, and when testing the model's performance, the model is cross-validated using data from the test set that the model has never seen before. The process of \(k\)-fold cross-validation is one of the widespread cross-validation methods. The original sample is randomly partitioned into \(k\) equal sized subsamples in \(k\)-fold cross-validation, a single subsample from the \(k\) subsamples is retained as test data for evaluating model effectiveness, and the remaining \(k-1\) subsamples are used as training data. The cross-validation process is then repeated \(k\) times, with each of the \(k\) subsamples supplied as test data exactly once. In this paper, \(k\) was set to five.
# Path Planning of Mobile Robots with an Improved Grey Wolf Optimizer and Dynamic Window Approach
## 5 PAGWO-IDWA Algorithm
### Complexity Analysis  
In optimization problems, computational complexity analysis is an important metric for evaluating the performance of an algorithm, and the computational cost of an algorithm is quantified using Big O notation. When applying the proposed PAGWO-IDWA algorithm to the path planning problem, the complexity mainly depends on these key factors: the population size, \(n\), the dimensionality, \(d\), of the path planning problem, the maximum number of iterations, \(t_{m}\), and the evaluation complexity of the objective function, \(c\). The computational complexity of the proposed PAGWO-IDWA is specified as follows:  
\[\begin{array}{ll}O(\text{PAGWO-IDWA})&=O(\text{path planning problem})+O(\text{init.})+O(\text{cost function})\\ &+O(\text{solution update})+O(\text{EPD})\\ &=O(1+nd+t_{m}cn+t_{m}nd+n\log(n)+nd)\\ &\cong O(t_{m}cn+t_{m}nd)\end{array} \tag{29}\]
# Lightweight CoAP-Based Bootstrapping Service for the Internet of Things
## 3 The Bootstrapping Service: CoAP-EAP
### Bootstrapping Security Associations
#### 3.4.2 CoAP Message Protection at Application Level: AUTH Option
It is worth mentioning that while we propose the use of an AUTH Option just as an example to show the protection of CoAP messages at application level, there are additional works, such as [80], that define additional CoAP Options to cope with protection at application level. In parallel, Object Security for CoAP (OSCOAP) has been considered [81] to protect CoAP messages with _CBOR Object Signing and Encryption_ (COSE) [82] objects.
## 4 Results
### RQ1: What AI-Based CDSS Have Been Developed that Incoronates XAI?
They developed a user-centered graphical user interface design, allowing them to optimize the interface for safe interaction with the physician (user experience) as well as for effective integration into the existing clinical workflow. [PERSON] et al. [119] proposed a CDSS for magnetic resonance imaging based Alzheimer's disease or mild cognitive impairment diagnosis which presents a \"regional abnormality map\" to visualize regional abnormalities in the brain space. [PERSON] et al. [127] developed a Deep Neural Network based CDSS for prostate cancer that presents its predictions on the image as visual overlays. Linguistic reasoners [118, 122, 124] and ontology-based CDSS [123, 125] are the second most common class of CDSS using XAI. [PERSON] et al. [122] present a CDSS to rank the cause of death from verbal autopsy. This CDSS provides interpretable outputs by evaluating the most important words. [PERSON] et al. [124] proposed a CDSS based on the Wisconsin diagnostic breast cancer dataset. The authors developed a method to improve the CDSS tractability using human-like reasoning, step-by-step inference, clinical differential diagnosis methodology procedure, explanation capacity, and user-familiar terms to gain user acceptance. [PERSON] et al. [118] presented a framework for human-centered, decision-theory-driven XAI building. Visualization methods, data structures, and atomic elements were used to represent explanations in this CDSS. [PERSON] et al. [125] present a CDSS to diagnose diabetes which mimics the medical expert in both knowledge representation and reasoning process. [PERSON] et al. [123] developed a CDSS for antibiotic treatment. They used a graphical user interface (GUI) to identify the recommended antibiotic, and also to explain why it is recommended and preferred over alternatives. This CDSS used a set visualization technique called rainbow boxes for XAI. We also found a CDSS using physiological signals [117] and a feature-based CDSS that incorporated XAI [120]. [PERSON] et al. [117] describe the implementation of a CDSS to predict sleep quality based on physiological signal trends in deep sleep state. Time-domain features were used to make their system transparent and explainable. [PERSON] et al. [120] developed a CDSS for predicting mortality in critically ill influenza patients using feature importance to quantify the importance of each variable based on SHAP.
# Distributed Satellite Relay Cooperative Communication with Optimized Signal Space Dimension
## 2 Materials and Methods
### Signal Model
 \tag{4}\]  
The received signal at the relay can be expressed as  
\[ \begin{array}{ll}\mathbf{y}^{[R]}&=\sum\limits_{i=1}^{K}\mathbf{H}^{[R,i]} \mathbf{x}^{[i]}+\mathbf{n}^{[R]}\\ &=\sum\limits_{i=1}^{K}\mathbf{H}^{[R,i]}\sum\limits_{j=1,j\
eq i}^{K}\mathbf{ v}^{[j,i]}s^{[j,i]}+\mathbf{n}^{[R]}\end{array}  \tag{5}\]  
Therefore, the relay received signal can be further expressed as:  
\[ \begin{array}{ll}\mathbf{y}^{[R]}&=\sum\limits_{i=1}^{K}\mathbf{H}^{[R,i]} \sum\limits_{j=1,j\
eq i}^{K}\mathbf{v}^{[j,i]}s^{[j,i]}+\mathbf{n}^{[R]}\\ &=\left[\mathbf{u}^{[R,1]},\ldots,\mathbf{u}^{[R,\frac{K(K-1)}{2}]}\right] \times\left[\begin{array}{c}s^{[2,1]}+s^{[1,2]}\\ s^{[3,1]}+s^{[1,3]}\\ s^{[4,1]}+s^{[1,4]}\\ \vdots\\ s^{[K,K-1]}+s^{[K-1,K]}\end{array}\right]+\mathbf{n}^{[R]}\\ &=\mathbf{U}^{[R]}\mathbf{s}^{[R]}+\mathbf{n}^{[R]}\end{array}  \tag{6}\]  
In the BC phase, the relay broadcasts the coded message of the physical layer network at the same time \(W^{[R]}_{\pi(1,2)}\), \(W^{[R]}_{\pi(1,3)}\),..., \(W^{[R]}_{\pi(K-1,K)}\) to other destination nodes by encoded flow \(s^{[1,R]}s^{[2,R]}\),..., \(s^{[K-1)/2,R]}\) with beamforming vectors \(\mathbf{v}^{[1,R]}\),\(\mathbf{v}^{[2,R]}\),..., \(\mathbf{v}^{[\frac{K(K-1)}{2},R]}\), which is denoted as  
\[\mathbf{x}^{[R]}=\sum\limits_{i=1}^{K(K-1)/2}\mathbf{v}^{[i,R]}s^{[i,R]} \tag{7}\]  
The receiver uses interference null-space beamforming combined with receive antenna selection (RAS) to suppress undesired interfering signals. Each node utilizes a selection gain to select a portion of the receive antennas for receiving signals. Therefore, the received precoding vector must lie in the intersecting null space of other non-target nodes:  
\[\mathbf{v}^{[1,R]}\in\left\{null\left(\mathbf{\hat{H}}^{[2,R]}\right)\cap null \left(\mathbf{\hat{H}}^{[3,R]}\right)\cap null\left(\mathbf{\hat{H}}^{[4,R] }\right)\cap\ldots\cap null\left(\mathbf{\hat{H}}^{[K,R]}\right)\right\} \tag{8}\]where \(\hat{\mathbf{H}}^{[j,R]}\) is the equivalent transmission channel formed by the antenna selection of the receiving node \(j\), and the matrix size is: \((K-1)\times M\). Beamforming vectors for other nodes can be constructed in the same way. The received signal of node \(j\) is  
\[\hat{\mathbf{y}}^{[j]} =\hat{\mathbf{H}}^{[j,R]}\left[\mathbf{v}_{1}^{[j,R]},\mathbf{v}_ {2}^{[j,R]},\ldots\mathbf{v}_{K(K-1)/2}^{[j,R]}\right]\left[ \begin{array}{c} s^{[1,R]}\\ s^{[2,R]}\\ \vdots\\ s^{[\frac{K(K-1)}{2},R]}\end{array} \right]+\hat{\mathbf{n}}^{[j]} \tag{9}\] \[=\hat{\mathbf{Q}}^{[j,R]}\mathbf{s}^{[R]}+\hat{\mathbf{n}}^{[j]}\]  
where \(\hat{\mathbf{Q}}^{[j,R]}\) represents the equivalent channel of node \(j\) after interference suppression. Each node can use zero-forcing decoding to detect the physical layer network coded signal it expects to receive. The self-information is used to realize information reception that eliminates interference.
## 3 Results and Discussion
### Analysis of Phase Content Based on Raman Spectra
#### 3.2.1 CA-CaS and MgO-Al\({}_{2}\)O\({}_{3}\) (MA)-CaS Samples  
The Raman spectra shown in Figure 4 are for samples containing CaS and magnesium aluminate (MA) spinel, and are used to explain how a change in a sample's phase content can affect the relative Raman intensity. Figure 4 shows that an increment in the CaS phase fraction in the MA-CaS samples showed a corresponding rise in the Raman shift (band) within a region of 157-162 cm\({}^{-1}\). This increase in intensity within this Raman shift region relates to when the phase content for CaS increased, because the most intense peak for this phase was around 157 cm\({}^{-1}\). Additionally, for the MA-CaS samples, an increase in the MA phase content showed a corresponding increase in the Raman shift in the region of 410-420 cm\({}^{-1}\) and can be attributed to this phase, because the most intense Raman shift located at 412 cm\({}^{-1}\) was a Raman peak feature associated with the MA phase.  
Figure 5 presents the Raman spectra obtained from the binary samples consisting of CA and CaS, where it can be observed that an increase in the CA phase fraction had Raman spectra, illustrating an increase in peak intensity in the region of 520-524 cm\({}^{-1}\). CA's most intense Raman shift (band) was observed at approximately 524 cm\({}^{-1}\), and the Raman peak within the range of 520-524 cm\({}^{-1}\) only increased with a corresponding increase in the phase fraction of CA in the CA-CaS samples. Furthermore, an increase in the CA phase content illustrated a characteristic feature, with the appearance of a peak shoulder in the 545-549 cm\({}^{-1}\) region. Varying the phase content in the CA-CaS samples provided a similar observation to the one made in Figure 4, where an increment in the phase fraction of CaS demonstrated a rising peak intensity in the Raman shift in the region of 157-162 cm\({}^{-1}\).
# Advancing the State of the Fog Computing to Enable 5G Network Technologies
## 7 Convergence of 5G and Fog Computing
### The Needs for Fog and 5G Network Technologies
#### 7.1.4 Storage Modulation  
The functionality of pre-cache technology plays a vital role in fog computing. It fulfills the following requirements and produces low-latency responses. Fog nodes initially anticipate the demand of the users then intuitively select the best content to deliver in geographically dispersed nodes. However, delay in downloading contents from remote sites or data centers can be curtailed substantially by enabling fog applications to exploit storage resources to users' excellent services [48] fully. A study has offered a dedicated caching mechanism that would supply information before users for demanding for it [62]. Therefore, the pre-cache mechanism can be incorporated into fog computing. It can be used in applications use cases like smart vehicle and traffic applications. These systems can proactively anticipate the demands of drivers and stash their devices in edge base stations. It significantly reduces traffic demands, especially at peak moments. Edge devices have constricted storage capacity. Therefore, the storage extension in the fog is proven to be beneficial to improve the strength of fog computing technology. Another study proposed a novel concept of employing personal storage in mobile gadgets, as a method to extend the storage as well as to secure device component [42].