esa-sceva/satcom-chunk-collection huggingface dataset:
# Energy Efficient Routing Algorithm with Mobile Sink Support for Wireless Sensor Networks
###### Abstract  
Recently, wireless sensor network (WSN) has drawn wide attention. It can be viewed as a network with lots of sensors that are autonomously organized and cooperate with each other to collect, process, and transmit data around targets to some remote administrative center. As such, sensors may be deployed in harsh environments where it is impossible for battery replacement. Therefore, energy efficient routing is crucial for applications that introduce WSNs. In this paper, we present an energy efficient routing schema combined with clustering and sink mobility technology. We first divide the whole sensor field into sectors and each sector elects a Cluster Head (CH) by calculating its members' weight. Member nodes calculate energy consumption of different routing paths to choose the optimal scenario. Then CHs are connected into a chain using the greedy algorithm for intercluster communication. Simulation results prove the presented schema outperforms some similar work such as Cluster-Chain Mobile Agent Routing (CCMAR) and Energy-efficient Cluster-based Dynamic Routing Algorithm (ECDRA). Additionally, we explore the influence of different network parameters on the performance of the network and further enhance its performance.  
Wireless sensor network (WSN); clustering; mobile sink; moving trajectory scheduling; energy efficiency +
Footnote †: journal: Sensors
# Simulation Study of Microwave Ablation of Porous Lung Tissue
## 2 Materials and Methods
### Mathematical Models
#### 2.2.2 Blood flow Analysis  
This study investigates blood flow and heat transfer based on the following hypotheses:  
1. Incompressible Navier-Stokes equations for simulating laminar flow in blood vessels [21].
2. Tissues and blood have the same temperature, i.e., local thermal equilibrium [21].
3. Phase changes and chemical reactions occurring within the porous lung tissue are not considered [21].  
[PERSON]'s law is further extended to obtain the Brinkman equation for the study of the blood flow of porous lung tissue. The transient momentum equation is then used. The temperature distribution in porous lung tissue can be obtained by solving the two equations separately [21].  
The controlling equation for blood flow in porous lung tissue is as follows [22]:  
Figure 2: The scheme of the numerical simulation.  
**Continuous equations**  
\[\frac{\partial u}{\partial r}+\frac{\partial w}{\partial z}=0 \tag{4}\]  
**Momentum equation**  
\[\begin{array}{l}\frac{1}{\phi}(\frac{\partial w}{\partial t})+\frac{1}{ \phi^{2}}(u\frac{\partial w}{\partial w}+w\frac{\partial w}{\partial z})\\ =-\frac{1}{\rho_{b}}(\frac{\partial w}{\partial z})+\frac{v}{\phi}(\frac{ \partial^{2}w}{\partial z^{2}}+\frac{\partial^{2}w}{\partial z^{2}})-\frac{ ww}{k}+g\beta(T-T_{\infty})\end{array} \tag{5}\]  
In the equation, \(\mu,w\) represents the blood velocity (m/s); \(\phi\) is the porosity i.e., the ratio of vascular volume to tissue volume; \(p\) is the pressure (Pa); \(v\) is the blood viscosity, \(v\) = 3.78 \(\times\) 10\({}^{-7}\) m\({}^{2}\)/s; \(\beta\) is the coefficient of thermal expansion, \(\beta\) = 1 \(\times\) 10\({}^{-4}\); \(k\) is the permeability (m\({}^{2}\)).  
**Boundary condition**  
1. Application of axisymmetric boundaries for blood flow analysis [21].  
\[\hat{n}\cdot(-pI+(\frac{1}{\phi})\eta(\
abla\cdot\stackrel{{ \rightarrow}}{{u}}+\
abla\cdot\stackrel{{\rightarrow}}{{u}})^{T}))=0 \tag{6}\]
2. The boundary of the blood analysis is considered as an open boundary condition [21].  
\[\hat{n}\cdot(-pI+(\frac{1}{\phi})\eta(\
abla\cdot\stackrel{{ \rightarrow}}{{u}}+\
abla\cdot\stackrel{{\rightarrow}}{{u}})^{T}))=-F_ {0}\cdot\hat{n} \tag{7}\]
## 3 Proposed QoS_mPCD Scheme Procedure
### Step 1: Context-Information Collection  
In this step, context information (CI) is gathered based on three important parameters: user location, user connection priority, and user deployment. This will result in efficiently scheduling users according to public safety priorities. The details of CI collection are as follows.  
First, the SINR is measured to determine the cell-edge user from the cell-central user. Calculate the location information according to the user's SINR using  
\[SINR_{u,j}>SINR\_threshold_{u,j}^{location} \tag{10}\]where \(\rm SINR_{u,\ j}\) represents the SINR value of user u connected to BS \(j\)[13].  
Second, collect the number of users who send an emergency connection request to base stations. This helps to differentiate the connection requests belonging to high-priority and low-priority users. Information is collected by classifying users into defined priority levels.  
Finally, information is collected to know the high priority users' connection requests.
# A Web Service for Evaluating the Level of Speech in Korean
## 3 Analysis Modules
### Natural Language Processing Module  
The role of the natural language processing module is data preprocessing and morphological analysis. The sentences read from the transcription file contain various meaningless symbols and  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Evaluation Item** & **Number** \\ \hline Total number of utterances & 57 \\ \hline
**Syntax/Morpheme** & \\ \hline Mean Length of Utterances in morpheme (MLUm) & 14.05 \\ Mean Length of Utterances in words (MLUw) & 5.6 \\ \hline
**Meaning** & \\ \hline Total number of words (TNW) & 801 \\ Number of different words (NDW) & 290 \\ Type-Token Ratio (TTR) & 2.76 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Example of the analysis results from a single transcription file.  
characters. These symbols and characters are removed because they are not used for the evaluation. For example, ', ', ', ', ', indicating admiration or the end of a sentence, are removed.  
When preprocessing is completed, morphological analysis is performed on the sentence. A morpheme is the smallest unit of a word that has a meaning, and if it breaks down, it loses its meaning [19]. The process of segmenting a word into a morpheme and finding a part of speech from an individual morpheme is called morphological analysis [20]. In addition, a system that automatically performs morphological analysis using a computer is called a morphological analyzer [21]. For this paper, KoNLPy was used for Korean speech analysis. KoNLPy is an open source software for Korean language processing with a Python programming language [22]. The morphological analysis package provided by KoNLPy includes Hannanum, Kkma, Komoran, Mecab, and Twitter Class. Among them, this study used Kkma Class that provides a more detailed analysis.  
Table 2 shows an example of analyzing sentences using a morphological analyzer. When a sentence is analyzed, the sentence is divided into morpheme units. Each morpheme acquires a corresponding tag, which is used to evaluate language ability. Kkma used in this study provides nine parts of speech. Korean has nouns, verbs, adjectives, adverbs, determiners, exclamations, josas, and eomies.
## 4 Results
### Results of the Evaluation of Geological Factors
#### 4.1.2 Engineering Geological Conditions
The evaluation results of development suitability based on the characteristics of the rock and soil mass are shown in Figure 7.  
(3) Subgrade Bearing Capacity  
The subgrade bearing capacity is a crucial factor in the development of underground space, directly affecting the stability and safety of underground structures. The stratum of underground space in Nantong City is relatively simple, and the overall subgrade bearing capacity is good. The suitability areas of UUS based on subgrade bearing capacity are shown in Figure 8. In the plain areas with small slopes, the stratum is dominated by a  
Figure 7: Suitability distribution based on the characteristics of rock and soil mass. mixture of silt layers, river alluvium, sandy soil, and clay, with the regional foundation bearing capacity ranging between 60 kPa and 150 kPa. This condition is more pronounced in coastal areas, making them unsuitable for development. In other plain areas, such as Yangkou, Tongzhou, and Lvsigang, the surface powder soil layer can serve as a lightweight building ground, while the soil layer at depths between 6 m and 15 m can be used as a bearing layer for piles after reinforcement treatment. Additionally, since there is no liquefied layer in the Lvsigang area, while different degrees of liquefaction are present in the Yangkou and Tongzhou areas, the Yangkou and Tongzhou areas rank as secondarily suitable for underground space development. In hilly and mountainous areas with steep slopes, such as the Wuhan area in the streets of Wushan Town, Chongchuan District, local exposed bedrock phenomena exist, with the foundation soil bearing capacity ranging between 2000 kPa and 3500 kPa, making them suitable for underground space development.
# A Three-Way Acceleration Approach for Interval-Valued Multi-Attribute Decision-Making Problems
## 1 Introduction
### Contribution  
The emergence of the TWD theory offers new opportunities for studying multi-attribute decision-making. Many studies are associated with the TWD theory, such as fuzzy operators, mathematical models with uncertain information, partial order relations based on rough and fuzzy sets, etc. Therefore, using the TWD theory to solve interval-valued multi-attribute decision-making problems is a worthwhile exploration. Different from traditional decision-making methods, this paper combines partition and sorting, that is, by refining the sorting granularity to accelerate the decision-making process. The upper and lower limits of information can be used to express evaluation values by using interval numbers [28], which have gradually become the main trend to express the evaluation of decision-makers [29; 30]. Unlike complex q-Rung Orthopair Fuzzy Sets (cq-ROFSs), the interval number controls the membership of an object within a certain range rather than characterizing its non-membership. In practical complex-decision problems, a complex q-rung orthopair fuzzy framework is an effective method to represent fuzzy information, and q-Rung Orthopair Fuzzy Numbers (q-ROFNs) can be aggregated into a single element by using Maclaurin symmetric mean operators [31]. The contributions of this paper are summarized as follows:  
* We propose a ranking method based on the distance of fuzzy dominant. By determining the importance of the object through the fuzzy dominance degree and the fuzzy dominance distance, we can make full use of the evaluation value to rank accurately by comparing interval numbers.
* A multi-attribute decision-making acceleration method that combines the TWD theory with the fuzzy dominance distance measurement is put forward. We construct multi-attribute acceleration rules based on the TWD to divide the universal set. Taking \"three\" as the basic thinking and starting point for solving complex problems is also an innovation.
* According to the decision rules, the MADM problem is simplified to a selection problem in three. We make decisions on finer information blocks (divisions) and provide an effective method for studying MADM.  
The methods in [22; 32] use intuitionistic fuzzy numbers and fuzzy numbers as attribute values, respectively, and then build loss functions. Compared with traditional MADM methods, we do not directly rank all objects but divide the universal set into three regions. Our method first uses interval numbers as attribute values to construct loss functions. Since a dominance relationship exists among the three regions, we further analyze the decision-making scheme when the needed candidates are determined. Our proposed method is to combine IVMADM and TWD for the first time. Object sets are divided into three regions with a dominance relationship in the fuzzy environment. By contrast, existing MADM methods (TOPSIS, AHP, and VIKOR) directly rank candidates and make the best and worst options.
# Multi-Authority Revocable Access Control Method Based on CP-ABE in NDN
## 3 Design and Implementation of Access Control Method
### Access Control Method Implementation
#### 3.2.2 Content Publishing Process  
Content publishing is the basis of NDN data sharing. Before publishing content, the content is first encrypted by CP-ABE algorithm to obtain ciphertext CT. The content publishing process is shown in Figure 3. The content publishing process needs to execute the following functions:  
1. \(\text{Encrypt}\ (GP,PK_{aid},\{PK_{sk}\}_{aid\in LA},M,(A,\rho))\to CT\)  
The encryption function is executed by the data owner. Input \(GP,PK_{aid}\) and \(\{PK_{sk}\}_{aid\in LA}\), plaintext \(M\) and access structure \(A\), and output ciphertext \(CT\).  
1. \(\text{KeyGen}\ (GP,uid,S_{uid,aid},SK_{aid},\{PK_{sk}\}_{aid\in LA})\rightarrow\{PxK_{ wild,aid},SK_{aid}\})\)  
AA executes the key generation function. Enter \(GP\), \(uid\), a set of consumer user attribute sets \(S_{uid,aid}\), \(SK_{aid}\) and \(\{PK_{sk}\}_{aid\in LA}\), output proxy key \(PxK_{wild,aid}\) and user private key \(SK_{aid}\).  
The encrypted content ciphertext is published to the NDN network as part of the packet data. This packet can be cached by any router and accessed through the NDN infrastructure. This part is usually massive data, so consumers can get the content of interest from the neighbor router that increases distribution efficiency and efficiency of data transmission.
## 6 Results and Discussion
### Comparison with Other Approaches  
As discussed in Section 2, studies on implementing OCC systems with event cameras as receivers often rely on computationally expensive algorithms, whether for locating transmitters or demodulating data, while also exhibiting scalability or replicability limitations. In [18], the algorithm for detecting pixels potentially associated with modulated light sources employed a subsampling technique that was linearly dependent on factors such as image size (height, width) and transmitter frequency, leading to scalability challenges. Additionally, the system's performance was influenced by the size of the pixel cluster within the synthetic frame, imposing limitations based on the transmitter dimensions or the distance between the transmitter and receiver. Furthermore, in [19], the detection of LED transmitter pixels involved computationally intensive algorithms such as image morphology and blob tracking in a sliding window, which imposed restrictions on the hardware implementation. Additionally, the demodulation technique relied on numerous heuristic parameters, making it challenging to implement across various scenarios, hindering its applicability. In contrast, our work focused on a different approach: the detection of modulated lights was performed using an automatic image thresholding technique. Furthermore, the demodulator detected event transitions for both synchronization and decoding by using a pulse count-based classification, making this a highly efficient method capable of being implemented on any hardware platform.  
In terms of spectral efficiency, \(D=0.5\) was the best-case configuration.However, as \(M\) increased, the energy efficiency of our scheme tended to equal OOK. Despite the slight penalty on the frequency domain, the advantage achieved in terms of complexity reduction on the receiver side made the adopted family of modulation schemes preferable in most cases. As was aforementioned in Section 1, neuromorphic cameras can provide JC&S (and positioning) capabilities at no additional cost, which could enable their use within the 6G paradigm as a support subsystem for other high-speed technologies such as mmWave, LiFi, and probably the successors of 5G New Radio. In general terms, the signaling, sensing, and positioning-related data streams will require sufficiently small bandwidths so as to consider N-pulse modulation for performing data transmission, with a power budget very similar to OOK and with a dramatically lower complexity at the receiver, which ultimately would impact on the hardware requirements for signal processing and therefore on the power budget of the receiver.
## 7 Conclusions  
The interlaminar stress distributions in a filled-hole laminate with clamp-up load near the hole, especially near the bolt head, were investigated. To evaluate interlaminar stresses under the complex boundary condition efficiently, a calculation strategy using zero-thickness interface element was presented and validated. The interface element was based on a linear elastic traction-separation description. The resulting analysis is conducive to an understanding of the geometry effects and the failure mechanisms of filled-hole compression. Furthermore, it can provide advice for the appropriate design of mechanically fastened joints in a composite structure. According to the numerical simulation for filled-hole compression (FHC) test, the following conclusions were reached.  
1. For the filled-hole laminate with tightened clamp-up load, the tightened bolt will change the interlaminar stress distribution state in the filled-hole laminate. The interlaminar stress concentration occurs both at the hole edge and the laminate interior near the outer edge of the bolt head. This phenomenon is different from the traditional free edge effect of an open-hole laminate. Correspondingly, for the case of non-tightened bolts, the interlaminar stress concentration only occurs near the hole edge.
2. The interlaminar normal and shear stress concentrations in the laminate near the periphery of the bolt head are not distributed in the same circumference location. The former is located under the periphery of the bolt head, whereas the latter occurs near the bolt-head edge away from the hole in the in-plane direction, which is at the outer interface of the filled-hole laminate in the thickness direction.
3. The clamp-up load plays a more important role in the interlaminar stress distribution in the laminate near the periphery of the bolt head. With an increase in the clamp-up load, both the interlaminar normal stress and interlaminar shear stresses rise around the bolt head in a large region. However, the magnitude of interlaminar shear stresses is reduced for most of the region around the hole.  
According to the above conductions, the clamp-up load cannot improve the interlaminar stress distribution of the filled-hole laminate near the bolt-head edge, which may lead to the initiation of delamination in bolt-filled laminates near the periphery of the bolt head. However, the specific location of this failure mode is still to be investigated by numerical and experimental analysis.  
Figure 12: Deformation of filled-hole compression (FHC) testing of composite laminates.  
**Acknowledgments:** The first author is thankful to [PERSON] for English revisions. Additionally, the authors would like to thank the three anonymous reviewers for their valuable comments.  
**Author Contributions:** [PERSON] conducted the research and wrote the paper; [PERSON] and [PERSON] supervised the research and helped in the preparation of manuscript; [PERSON] and [PERSON] contributed data analysis work and revision of this paper.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Blockchain Based Delay and Energy Harvest Aware Healthcare Monitoring System in WBAN Environment
## 4 System Model
### Patient Block Agent
#### 4.5.3 Channel Selector  
Upon receiving the critical data, an idle channel is selected to transmit the data without delay, whereas periodical data are forwarded via the best channel. It is determined by the multi-objective-based channel selection scheme from the multiple inputs: RSS, \(Re_{ss}\), SNR \(s/n_{r}\), channel capacity \(C_{ct}\) and radio power \(R_{pr}\).  
\[RSSI=t_{p}+a_{g}-p_{l} \tag{13}\]  
\(RSSI\) is a significant parameter that measures the total received signal with respect to the noise. \(RSSI\) is computed using \(t_{p}\) and \(a_{g}\) which are transmit power and antenna gain, respectively, and \(p_{l}\) is the path loss.  
The best channel is chosen for forwarding the periodic packets. \(Re_{ss}\) represents the sum of the average of the total power obtained from the specific antenna. The higher the signal value is, the higher the RSS. The SNR estimates the value of accurate signals based on the noise present in them.  
The signal-to-noise ratio measures the ratio of the valid signal with respect to the noise present in it. The parameter \(s/n_{r}\) is computed as follows,  
\[\frac{s}{n_{r}}=10\log_{10}\frac{P_{sg}}{P_{Ns}} \tag{14}\]  
The terms \(P_{sg}\) and \(P_{Ns}\) denote the amount of signal and noise present in a particular signal. Higher values of \(s/n_{r}\) imply the presence of more signal than noise. Furthermore, the channel capacity of the signal is computed mathematically using [PERSON]'s theorem given below,  
\[C_{ct}=b_{w}*\log_{2}(1+s/n_{r}) \tag{15}\]  
Where \(b_{w}\) is the bandwidth, which is identified as the amount of data transfer rate that exists in the channel. On estimates, the three significant parameters of a channel and the best channel are selected. The radio power consumption is different for each packet, and hence, the dynamic value of the radio power is computed in the running state. The determined channel ensures successful data transmission without any delay or loss. With the use of channels, packets are encrypted at the PBA and forwarded to the corresponding sink node.  
Finally, the data reach the blockchain according to the data traffic type. If the sensed packet is critical, it is first sent to the healthcare unit and then stored into a cloud server, whereas the periodical data are stored in the cloud server and sent to the healthcare unit.
## 4 Materials and Methods
### Feature Selection (Step 3)  
The features contribute a lot in building a predictive model which has an excessive impact on the overall performance. Thus, it is important to intelligently select the optimal features that contribute to a high prediction accuracy, before training the model, to create a more comprehensive model having a low variance while training.  
We used XGBoost to measure the significance of each feature using the characteristics of the classification and regression tree (CART). To lessen the cost of the segmented tree, the features having the highest weight are selected for segmentation until the maximum depth is reached. The gradient lifting algorithm is used continuously to lessen the loss of the previously generated decision tree, which diminishes the objective function and guarantees the trustworthiness of the final decision. The objective function helps to avoid an over-fitting [1].  
We used XGBoost to select important features based on the importance given to each attribute. This technique helps to select the optimal features having a high dependency on the output.  
* **Display Dataset Before and After Performing Experiment** The PROMISE dataset has 20 features before the experiment and after applying XGBoost, we were left with 15 features. XGBoost selected the LOC, NPM, RFC, CBO, WMC, LCOM, AMC, CA, CAM, MFA, AVG_CC, CE, LCOM3, MAX_CC, and CBM as the optimal features based on their importance, as shown in Figure 8. LOC has a higher f score, which is measured as an average of its importance in all the subtrees.
## 3 Materials and Methods
### ML Classification Algorithms
#### 3.3.3 Decision Trees  
DT is a well-known and commonly used classifier in classification problems [22; 25]. The algorithm can handle complex datasets efficiently [26] and does not rely on data distribution assumptions [27]. A tree's accuracy is based on its complexity [25]. DT is valued for its flexibility, efficiency with large datasets, and ability to handle diverse types of data without making strong assumptions about their distribution.  
The DT algorithm predicts target beam by learning simple decision rules that it infers from the data features. The tree works as a piecewise constant approximation, and it can handle multi-class output problems. The DT classifier predicts the class of sample with the highest probability. In the event of multiple classes with the same and highest probabilities, the class with the lowest index among the classes is predicted.