esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
The performance of active vibration control (AVC) systems has been improved by using one or a combination of the following methods: actuation system enhancement, detection system enhancement, and the use of a range of intelligent learning algorithms. Recursive least-squares algorithms (RLS), evolutionary genetic algorithms (GA), general regression neural network (GRNN), and adaptive neural fuzzy inference (ANFIS) are popular machine learning algorithms used to enhance AVC control mechanisms. However, the scope and area of interest for this work are limited to improving the actuation system of AVC.
## 3 Problem Formulation
### Optimization Objective  
We formulate the offloading problem of large-model inference subtasks as a linear programming problem to improve the overall performance in a stable state. The first goal is to minimize latency, i.e., maximize the quality of service, under the premise of successful process task offloading and computing. The second optimization goal is to minimize energy consumption. The specific task offloading model is as follows:  
\[\min \alpha t_{j}+\beta e_{j}\] (15) subject to \[\alpha+\beta=1\qquad\alpha,\beta\in(0,1) \tag{16}\] \[\sum_{i=p}d_{j}^{k}\leq m_{p}\] (17) \[\sum_{p}X_{i,j}^{p}=1\qquad X_{i,j}^{p}\in(0,1)\] (18) \[\sum_{j,p}X_{i,j}^{p}\leq n_{i} \tag{19}\]  
where \(\alpha\) and \(\beta\) are linear weighting coefficients that can control whether the overall optimization goal is to achieve the lowest latency or minimize total energy consumption. By adjusting the values assigned to these coefficients, the optimization process can be tuned to prioritize either low latency or energy efficiency. Given the actual deployment scenario and mathematical reasoning, the deployment strategy is subject to some key constraints. Equation (16) represents the constraint on the weighting coefficients. Equation (17) indicates that the total amount of data required for the task should not exceed the server's storage capacity. Equation (18) imposes constraints on the 0, 1 matrix. Finally, Equation (19) states that the number of offloaded tasks should not exceed the number of cores on the server. Equations (17) and (19) are the constraints related to storage resources.
## 4 Results and Discussion
### Text-Independent  
For text-independent, we split the dataset into two sets as training and test sets. We took 70% of the dataset for training and another 30% for the test set. In this section, we used the top 5, top 10, top 15, top 20, top 25, top 30, top 35, top 40, top 45, top 50, top 55, top 60, top 65, top 70, and all 72 characters to show how the identification accuracy of SVM changes depending on whether features are selected or not over characters. Here, the person can write any combination of the top 5, top 10, etc. characters in order to make it text-independent writer identification. Table 6 shows the identification accuracy of SVM without SFFS (SVM-WO-SFFS) and SVM with SFFS (SVM-W-SFFS). It was observed that SVM-WO-SFFS provided 96.2% identification accuracy for the top 5 characters, whereas SVM-W-SFFS also provided 99.0% identification accuracy. It was also observed that the identification accuracy of SVM-W-SFFS decreased (99.0% to 94.3%) when increasing the number of characters.
# Dynamics of Microbial Community Structure and Metabolites during Mulberry Ripening
## 3 Results and Discussion
### Correlation Analysis  
In order to further clarify the relationship between microbial communities and physicochemical properties and flavour substance species during mulberry ripening, a Spearman (\(1\,\rho\,1>0.5\) and \(p<0.05\))-based co-occurrence network analysis of micro-organisms at the level of WHDS genera was carried out, and the results are shown in Figure 7.  
As can be seen in Figure 7a, 24 micro-organisms and 9 physicochemical properties were significantly correlated. _Nesterenkonia_, a bacterium that has been detected on the leaves of non-organic grapes [68], was the most highly associated micro-organism, with highly significant positive correlations with total acid (\(p<0.01\)), significant negative correlations with the weight of single fruit, juice yield, total phenolics, and total flavonoids, and highly significant negative correlations with soluble solids and total sugar (\(p<0.01\)). pH was the most highly correlated physicochemical property, with significant negative correlations with _Ammoniphilus_, _Bacillus_, _Penaibacillus_, _Chrysobacterium_, and _Unclassified Micrococales_, and significant positive correlations with _Unclassified Proteobacteria_, _Unclassified Erwiniaceae_, _Unclassified Gammaproteobacteria_, _Unclassified Enterobacter_, _Tatumella_, _Enterobacter_, and _Staphylococcus_. Apart from this, total flavonoid content was significantly negatively correlated with _Weissella_. The juice yield, weight of single fruit, total phenolics, and total anthocyanins were significantly and positively correlated with _Unclassified norank  
Figure 7: Correlation network analysis of differential micro-organisms and physiological indicators (**a**) and flavour substance type (**b**).  
BacteriaSoluble solids and total sugars were significantly negatively correlated with most micro-organisms.  
As can be seen in Figure 7b, 17 micro-organisms and 5 flavour substance species were significantly correlated. As in Figure 7a, _Nesterenkonia_ was, again, the most highly associated micro-organism, with significant negative correlations with the four flavour substance species and, in particular, a highly significant negative correlation (\(p<0.01\)) with esters. The esters category was the most highly correlated flavour substance type, with significant negative correlations with _Brevibacterium_, _Brachybacterium_, _Geomicrobium_, _Unclassified Paenibacemae_, _Brevundimonas_, and _Unclassified Bacillales_, and significant positive correlations with _Pseudomonas_, _Pantoea_, and _Rosenbergiella_[69]. In addition to this, acids, ketones, and aldehydes were significantly positively correlated with _Unclassified norank Bacteria_, while the alcohols category was significantly negatively correlated with _Anmoniphilus_ and _Unclassified Microccales_ and significantly positively correlated with _Tatumella_, _Unclassified Enterobacterales_, _Unclassified Erviniaceae_, and _Enterobacter_.
## 1 Introduction
Correctly recognizing complete sentences or sentence boundaries from any text format can speed up workflow and reduce the amount of text preprocessing tasks [19]. Correct and complete sentences are one of the prerequisites for both semantic and syntactic analysis of any textual content [20]. As mentioned earlier, a significant part of the text is found in PDF format, especially most of the scientific literature from different journals of different publishers. PDF files differ from traditional plain text files, in which text is structured in a semantic way like words or paragraphs. Instead, it is a layout-based format based on the positions of characters [21]. Therefore, it is important to know which tool is more efficient in extracting text in semantic format from PDF files, more specifically in extracting text in the form of a complete sentence, so that researchers do not have to invest more time to convert the extracted text into the semantic format. Nowadays, NLP researchers need to extract text from PDF files using various programming languages and frameworks. There are many web-based services and PDF-to-text converters. However, they are not suitable for batch processing, such as converting thousands of files at once. This is one of the reasons why NLP researchers use programminglanguages other than web-based conversion services. The Python programming language is one of the preferred languages, as it is ranked first in the PYPL programming language rankings as of 14 November 2021. [8]. Therefore, in this study, different techniques for extracting PDF in text using Python core packages and Python wrapper packages with external services are considered to compare the correct detection of complete sentences/sentence boundaries. The selection of Python PDF libraries or packages is based on the number of downloads in the last 30 days from the top PyPI packages, updated on 1 November 2021 [22; 23]. As far as we know, there is no work to date that compares the performance of different techniques for extracting text from PDF files to recognize complete sentences correctly. The three main contributions of this work are listed below:  
* Recommend a better Python package for extracting text from PDF files;
* Recommend a better NLP framework for sentence boundary detection;
* Determine the most effective technique by comparing the performance of the techniques used in terms of runtime and memory consumption. The remainder of the paper is organized as follows: The Background Study section discusses some related studies, the Methods section describes the methods used in this study, then the Results and Discussion are presented, and the Conclusion section concludes the paper.
###### Abstract  
In recent years, the search for more efficient and environmentally friendly materials to be employed in the next generation of thin film solar cell devices has seen a shift towards hybrid halide perovskites and chalcogenide materials crystallising in the kesterite crystal structure. Prime examples for the latter are Cu\({}_{2}\)ZnSnSn\({}_{4}\), Cu\({}_{2}\)ZnSnSe\({}_{4}\), and their solid solution Cu\({}_{2}\)ZnSn(S\({}_{x}\)Se\({}_{1-x}\))\({}_{4}\), where actual devices already demonstrated power conversion efficiencies of about 13 %. However, in their naturally occurring kesterite crystal structure, the so-called Cu-Zn disorder plays an important role and impacts the structural, electronic, and optical properties. To understand the influence of Cu-Zn disorder, we perform first-principles calculations based on density functional theory combined with _special quasirandom structures_ to accurately model the cation disorder. Since the electronic band gaps and derived optical properties are severely underestimated by (semi)local exchange and correlation functionals, supplementary hybrid functional calculations have been performed. Concerning the latter, we additionally employ a recently devised technique to speed up structural relaxations for hybrid functional calculations. Our calculations show that the Cu-Zn disorder leads to a slight increase in the unit cell volume compared to the conventional kesterite structure showing full cation order, and that the band gap gets reduced by about 0.2 eV, which is in very good agreement with earlier experimental and theoretical findings. Our detailed results on structural, electronic, and optical properties will be discussed with respect to available experimental data, and will provide further insights into the atomistic origin of the disorder-induced band gap lowering in these promising kesterite type materials.  
Cu\({}_{2}\)ZnSnSe\({}_{4}\); CZTSe; chalcogenide; kesterite; Cu-Zn disorder; density functional theory; hybrid functional; special quasirandom structure
# Intelligent Edge-Cloud Framework for Water Quality Monitoring in Water Distribution System
## 5 Results and Discussion
### Numerical Results
#### 5.3.3 Results of Scenario 3  
In this scenario, we applied ensemble learning to enhance the performance of the MLP model. The process of data analysis is divided between the cloud and edge servers. The cloud layer is used to train and develop the ensemble model and the edge layer uses the pertained model sent from the cloud for the detection. More precisely, edge servers are used to apply the voting technique for detection. Figure 28 shows the latency for all areas. In general, it seems that the latency is very high compared to previous scenarios while maintaining the same level between areas (Area-1, Area-2, and Area-3). Throughput and PDR seem to be the same with very little change compared to scenario 2 as shown in Figures 29 and 30. The total average power consumption for the edge servers is presented in Figure 31.
# Internet of Things and Its Applications: A Comprehensive Survey
## 5 IoT Applications
### Healthcare Applications
Concerning the study in [102], the authors performed an analysis regarding the performance of a narrowband IoT (NB-IoT) that requires considerable communication with less throughput for simple sensors and long-life batteries. The main objective of the research work was to analyze and evaluate the latency and throughput of NB-IoT in healthcare services. Another study conducted by [PERSON] et al. [103] assessed the human factors affecting information exchange in the healthcare environment. The study aimed to provide the knowledge required to develop new models of IoT applications in healthcare and focused on gathering and exchanging information among healthcare professionals. The obtained results reported that the major serious factors are the intention of usage, user satisfaction, trust effort, quality of the service, and collaboration environment. [PERSON] et al. [104] suggested a healthcare managing system developed through MySignals following the LoRa wireless network (low-power long-range). In addition to LoRa and MySignals, sensors for the body temperature, oxygen saturation, electrocardiogram, and pulse rate were used in the circuit. The authors evaluated the efficiency and performance of each sensor, as well as the wireless platform devices following physiological and statistical data analysis. The purpose of MySignals consisted of ensuring a possible gathering of information. The purpose of the developed circuit was to transmit the collected information from MySignals to a computer through the LoRa implementation system. The obtained results revealed that MySignals was effective with the electrocardiogram, pulse rate, temperature, and pulse rate sensors. Another study conducted by [PERSON] et al. in [105] presented information regarding the actual necessities and challenges faced in the healthcare department in terms of the use of available healthcare systems. The purpose of the study consisted of examining and evaluating the main factors associated with the use of IoT regarding exchanging health information. Table 3 summarizes the health applications for IoT.
# Bounded Attitude Control with Active Disturbance Rejection Capabilities for Multirotor UAVs
## 2 Preliminaries  
In the following, \(\|\cdot\|\) denotes the Euclidean norm for vectors and the induced 2-norm for matrices. A scalar continuous function \(\gamma(r)\) defined for \(r\in[0,a[\) is said to belong to class \(\mathcal{K}\) if it is strictly increasing and \(\gamma(0)=0\), and it is said to belong to class \(\mathcal{K}_{\infty}\) if it is defined for all \(r\geq 0\) and \(\gamma(r)\longrightarrow\infty\) as \(r\longrightarrow\infty\). A scalar continuous function \(\beta(r,s)\) defined for \(r\in[0,a[\), \(s\in[0,\infty[\) is said to belong to class \(\mathcal{KL}\) if for each fixed \(s\) it belongs to class \(\mathcal{K}\) and for each fixed \(r\) it is decreasing in \(s\) and \(\beta(r,s)\longrightarrow 0\) as \(s\longrightarrow\infty\). A dynamical system with state \(x\) and input \(w\) is called input-to-state stable (ISS) if there exist a class \(\mathcal{KL}\) function \(\beta\) and a class \(\mathcal{K}\) function \(\gamma\) such that \(\|x(t)\|\leq\beta(\|x(0)\|,t)+\gamma(\left\|w_{0\,a[t]}\right\|_{\infty})\) for all \(t\geq 0\), where \(\|\cdot\|_{\infty}\) denotes the \(\mathcal{L}_{\infty}\)-norm: for a signal \(w\), \(\left\|w_{[0,s]}\right\|_{\infty}=\)sup \({}_{0\leq t\leq s}\|w(t)\|\). For linear systems, ISS is equivalent to global asymptotic stability of the unforced system [54].  
**Definition 1** ([54]).: _A smooth function \(V:\mathbb{R}^{n}\longrightarrow\mathbb{R}_{\geq 0}\) is called an ISS-Lyapunov function for system \(\dot{x}=f(x,u)\) if there exist \(\mathcal{K}_{\infty}\)-functions \(\alpha_{1},\alpha_{2})\), and \(\mathcal{K}\)-functions \(\alpha_{3}\) and \(\chi\), such that_  
\[\alpha_{1}(\|x(t)\|)\leq V(x)\leq\alpha_{2}(\|x(t)\|) \tag{1}\]  
_for all \(x\in\mathbb{R}^{n}\) and_  
\[\|x(t)\|\geq\chi(\left\|u(t)\right\|)\Rightarrow\
abla V(x)\cdot f(x,u)\leq- \alpha_{3}(\|x(t)\|) \tag{2}\]for all \(x\in\mathbb{R}^{n}\)._  
Theorem 1 ([54]): _The following properties are equivalent for any system:_  
1. _It is ISS._
2. _It admits an ISS-Lyapunov function._
3. _It is robustly stable._
## 4 Findings
### Smart Environments
#### 4.3.2 Smart City  
A use case is presented in [116] to address privacy exposure and security threats of cyberinfrastructure in a smart city. This study discusses IoT-based access control first using two primary models; Discretionary Access Control Models (DAC) and Mandatory Access Control Model (MAC). DAC explains how to transmit the rights of the object from one to another; MAC refers to classifying objects in the system and how to regulate access among them. This study compares the difference between implementing this model in traditional and blockchain-based architectures. The process of exchanging data starts between actors (user and organization or two users where one is outside the infrastructure), the data are transmitted to IoT cyberinfrastructure, which is followed by the private cloud and blockchain network. The user needs to encrypt only the part of the data that can be shared. This study is still in the early research phase and must be investigated further. A study on the smart city security model is illustrated in [117], covering the theoretical aspects. The authors started discussing data management and distribution, which were followed by communications, private key management, securing third parties, smart contracts (automation of procedures), and protocols.  
A privacy-preserving innovative framework called PrivySharing [118] is proposed to secure IoT data in a smart city environment. Privacy is preserved as each channel has a finite number of approved organizations and processes a specific type of data (financial, health, energy, etc.). Private data collection and encryption are used to isolate further and secure data within a channel. A private data collection methodology is adopted to ensure the privacy of critical data by sending the data directly to the authorized requesting node (NMSP). A Membership Service Provider (MSP) defines the access rights and which RCAs/CAs are trusted. A different Ch is used for each data type to ensure decentralization, scalability, and privacy. This design provides the concept of \"right to forget\" regarding user data, efficiency in terms of computational requirements and energy consumption, user-defined fine-grained access control, allowing users control over their data while providing an auditable network operation, blockchain access through API, and reward system for data sharing.
## 3 Design of the Direct Yaw Moment Control System  
As stated earlier, and illustrated in the previous section, there is a need to compensate for the influence of the loading condition on the vehicle dynamics characteristics using chassis control. The control design should also adapt to various velocity conditions. Here, the chassis control system is designed based on the configuration that the lightweight vehicle is equipped with in-wheel motors that can generate the demanded yaw moment. According to the objectification of handling vehicle dynamics in the previous study [7], it is important to improve the transient and the dynamic handling performance but the need to compensate for the change of steady-state cornering characteristics is less important because the driver can adapt to this change without major discomfort. Currently, constant velocity driving maneuvers are considered and it is assumed that the yaw moment demanded by the DYC system is obtained by the distribution of longitudinal tire forces on the rear axle. It is expected that the DYC system is an effective chassis control method to enhance vehicle handling performance even when the tire is not in the linear region [11; 14; 21; 22; 23; 24].  
To improve handling dynamics, the structure of a yaw rate model following DYC, as shown in Figure 4, consists of a feed-forward compensation with respect to the front steering angle and a yaw rate feedback compensation as the following equation.  
\[M_{DYC}(s)=M_{FF}+M_{FB}=G_{M}^{\delta}(s)\delta_{f}(s)+k_{r}r(s). \tag{16}\]
# A New Dynamic Game-Based Pricing Model for Cloud Environment
## 5 Experimental Results: Performance Evaluation of the Proposed Method
### Evaluation of the Proposed Method in Terms of Users' Profit  
Users' profits are defined as the amount of money left over from their budget after their request is met by the system. In order to ensure a more equitable assessment, the mean quantity is presented in Figure 4. Since the number of users who submit their requests to the system can be different based on the supply-and-demand model and the pricing method applied, the average amount of profit per user would be a better comparison criterion. It is calculated based on the following equation:  
\[Profit_{users}=\frac{1}{n}{\sum_{i=1}^{n}Budget_{i}}-\left(\tau_{cp}(VM_{i}^{* },P_{i}^{*}\ )+E_{VM_{i}^{*}}\right) \tag{10}\]  
where \(n\) indicates the total number of requests submitted to the system and fulfilled during the simulation time. The other parameters are described in the previous sections.  
As can be seen, the average amount of profits users gain by the proposed approach, and the method proposed by [16] are the same. However, with this difference based on the supply-and-demand model employed, the total number of requests submitted to the system during the simulation period is 94, 98, and 85 for the proposed method, and  
\begin{table}
\begin{tabular}{c c c} \hline
**Value** & **Definition** & **Request Fields** \\ \hline
63 & Unique identifier for each request & Request ID \\ \hline
8 & Number of processing elements requested by the user & Required PE \\ \hline
2 GB & Storage space requested by the user & Required RAM \\ \hline
1 & CPU-intensive (1) or Memory-intensive (2) & Request type \\ \hline
9 & The time that the request is submitted to the system & Submit time \\ \hline
35 & The time spent by the system executing the task & Execution time \\ (simulation time) & The time spent by the system executing the job & User budget \\ \hline
2505 & Total money that can be spent on executing the job & User budget \\ \hline \end{tabular}
\end{table}
Table 2: A user request example.  
Figure 4: Comparison of the proposed method with [16; 39] from the viewpoint of average profit per user.  
the approaches proposed by [16] and [39], respectively. It should also be noted that 93, 86, and 85 requests from the submitted ones have been fulfilled during the simulation period, respectively.  
The method proposed by [16] has primarily focused on enhancing the users' profitability, which has led to noteworthy advancements in this aspect, as depicted in Figure 4. Their method usually offers the lowest price per time unit for the virtual machines; however, considering the conflicts between virtual machines and heterogeneity in resources by the proposed method results in better mapping of the jobs and thus usually a lower execution time. This is why the final cost for users is reduced in the proposed method and the users experience almost the same profit in the proposed method and the one proposed by [16].