esa-sceva/satcom-chunk-collection huggingface dataset:
# An Efficient Resource Allocation Algorithm for Device-To-Device Communications
## 4 Simulation Results
### Complexity and Performance Analysis
#### 4.2.2 Performance Analysis
Figure 7 compares the energy efficiency of the proposed algorithm with the state-of-the-art algorithms under different number of iterations. As can be seen from Figure 7 that the proposed M21 GS algorithm has stable and fixed energy efficiency for any number of iterations, which makes it more robust and stable against interference and has less complex hardware and energy consumption requirement as compared with CG [20], DARA [16] and Random Resource Allocation Algorithm. Moreover, the proposed algorithm has a close performance with the optimal results, which also validates the theoretical analysis. Figure 8 illustrates the system average energy efficiency comparison of the proposed algorithm with CG [20], DARA [16], random resource allocation and optimal algorithm under different number of Cellular User Equipment's (CUEs). It can be seen from Figure 8 that the average energy efficiency of the proposed M21 GS algorithm is better than the other competing alternatives under a different number of CUEs. Also, the proposed algorithm has a much better performance with optimal results, validating the analytical approach. Figure 8: Average Energy Efficiency comparison under different number of CUEs. Figure 7: Comparison of the energy efficiency under different number of iterations. To evaluate the validation of the proposed algorithm further, we have compared the number of iterations required with an increasing number of D2D users under different algorithms in Figure 9. As can be clearly seen from Figure 9, the proposed algorithm requires a smaller number of iterations when the number of D2D users increases as compared with existing reference [20] and reference [16] algorithms. Moreover, the proposed algorithm has closed performance with the optimal scheme, further validating its effectiveness. To make it clearer by evaluating it when the number of D2D users is 150, the proposed scheme requires only 33 iterations to give an optimal performance whereas the schemes in reference [16] and reference [20] require 45 and 40 iterations, respectively. The effectiveness and validation of the proposed algorithm are further described in Figure 10 in which the sum rate is evaluated under D2D transmission power. As can be seen from Figure 10, the sum rate of proposed M21 GS algorithm is
# Biomass and Cellulose Dissolution--The Important Issue in Renewable Materials Treatment
## 3 Dissolution of Biomass and Its Components
### Inorganic Molten Salt Hydrates  
Inorganic molten salt hydrates such as Ca(SCN)\({}_{2}\cdot 3\)H\({}_{2}\)O can dissolve cellulose at 120-140 \({}^{\circ}\)C within 40 min, without any additional pretreatment. Other salts or their mixtures can act differently: From dissolving this polysaccharide (e.g., ZnCl\({}_{2}\cdot\)3-4H\({}_{2}\)O, LiClO\({}_{4}\cdot 3\)H\({}_{2}\)O, Mg(ClO\({}_{4}\))\({}_{2}\)/H\({}_{2}\)O; or FeCl\({}_{3}\cdot 6\)H\({}_{2}\)O, LiClO\({}_{4}\cdot 3\)H\({}_{2}\)O,MgCl\({}_{2}\cdot 6\)H\({}_{2}\)O) or swelling it (e.g., LiCl\({}_{2}\cdot\)5H\({}_{2}\)O, LiClO\({}_{4}\cdot 3\)H\({}_{2}\)O, Mg(ClO\({}_{4}\))\({}_{2}\)/H\({}_{2}\)O; or LiNO\({}_{3}\cdot 3\)H\({}_{2}\)O, LiClO\({}_{4}\cdot 3\)H\({}_{2}\)O, NaClO\({}_{4}\)/H\({}_{2}\)O), and even decomposing it (Mg(ClO\({}_{4}\))\({}_{2}\cdot 6\)H\({}_{2}\)O, ZnCl\({}_{2}\)/MgCl\({}_{2}\)/H\({}_{2}\)O; or MgCl\({}_{2}\cdot 6\)H\({}_{2}\)O) [48; 59]. The ability to dissolve this polymer depends on the level of hydration, e.g., by increasing water content in lithium perchlorate to LiClO\({}_{4}\cdot 4\)H\({}_{2}\)O, cellulose solubility decreases [60]. Iron salts can also be effectively applied in biomass pretreatment, especially upon dissolving hemicelluloses. FeCl\({}_{3}\) and FeSO\({}_{4}\) can dissolve this fraction at a level above 90%. In this process, cellulose is intact, so it could be used as a method for the separation of these fractions [61; 62]. Biomass pretreatment with inorganic salts can also prepare the material for enhanced enzymatic transformations, without delignification. [PERSON] et al. used AlCl\({}_{3}\) with intense mechanical milling of sugarcane bagasse. It resulted in a significant reduction of biomass crystallinity and more efficient further biological hydrolysis of cellulose [63]. Nevertheless, applying inorganic salts has many disadvantages--they are expensive, unstable, toxic, and difficult to regenerate after processing [64].
## 1 Introduction
For example, [PERSON] [29] proposed a magnetic negative stiffness vibration isolator based on Maxwell normal stress and integrated acceleration feedback to deal with various conditions, which has characteristics of fast response, no mechanical friction, and compactness. Compared with passive isolation, the natural frequency was reduced by 80%. However, active negative stiffness vibration isolators have not been widely used in vibration isolation systems. The reason was that high-precision sensors [30] and linear actuators [31] are required to monitor and counteract the low-frequency micro-amplitude vibration of the payload, leading to high costs and system complexity [27]. Moreover, poor resolution and a dynamic range of commercial inertial sensors at low-frequency also limit the application of the active negative stiffness vibration isolators in vibration isolation systems [30]. In this paper, a novel electromagnetic active-negative stiffness generator (EANSG) is proposed, which uses nano-resolution laser interference sensors to monitor the micro-vibration of an optical platform, and precision electromagnetic actuators integrated with a relative displacement feedback algorithm to counteract the positive stiffness of pneumatic springs. This paper is organized as follows. In Section 2, the principle of the proposed EANSG was presented. In Section 3, the design and realization of EANSG was described including the designs of nano-resolution laser interferometry sensors, electromagnetic actuators, and relative displacement feedback strategy. In Section 4, an experimental setup was established to validate the effectiveness of the proposed method, and the experimental results were analyzed. Conclusions are drawn in Section 5.
## 4 Validation  
### Approach  
Validation of the model is carried out based on real-world maritime traffic data obtained through the research project WINMOS II (Winter Navigation Motorways of the Sea II) [26] covering maritime traffic on the Bothnian Bay in the period 15 January-5 February 2010. The accuracy of the model is assessed by comparing simulated and data-based performance indicators, such as the number of port arrivals, the number of instances of IB assistance, and IB waiting times. To capture the stochastic behavior of the system, a total of 35 individual simulation runs are carried out. All considered maritime traffic data are presented in the Appendix A (see Tables A1 and A2).
## 1 Introduction
Traditionally, business users would submit data requests to the \(\Pi\) department. Technique experts develop the data requirements and return the data results to the business users. An edge-cloud computing platform could help to finish it. The process can take 1-2 weeks at best and up to 1-2 months at worst, severely impacting business decision-making and subsequent strategies. Business data scientists must update data extraction rules based on query results, adding further complexity. This creates a need for an edge-cloud computing platform system where business personnel can directly use data through natural language, as shown in Figure 1. Here, business personnel express data requirements in natural language, and the NL2 SQL engine converts it into SQL to access the database. The resulting data is then returned to the business personnel. This significantly reduces communication costs, and if the data is unsuitable, business users can quickly adjust the input themselves to obtain desired results, thus improving data-driven decision-making efficiency. The NL2 SQL model is the core component of the entire business process and is highly anticipated by users in the financial sector. The financial industry is knowledge-intensive, with business users having more stringent requirements for NL2 SQL accuracy and a lower error tolerance than in other sectors. Traditional NL2 SQL models are largely general-purpose and cross-industry, with few models tailored specifically for the financial industry. In this context, this paper proposes a novel NL2 SQL construction approach for the financial industry, highlighting three key innovations:  
* A financial semantic data model and a knowledge base for the financial industry were developed to enhance the big model's understanding of the sector through database recall and table linking. * An innovative approach was introduced first to convert natural language into Python code and then convert that code into SQL. The model's accuracy was improved through continuous optimization and iteration during this intermediate process. * We tested this innovative method on the BIRD dataset and found that it exceeded most currently popular open-source models. This paper consists of five sections. The Section 1 mainly describes the research background of NL2 SQL; the Section 2 mainly describes the research status of NL2 SQL and mainly describes the main methods used in the research process of this paper; the Section 3 mainly describes the method proposed in this paper and the implementation process; the Section 4 mainly describes the experimental process and comparative experimental results of the method proposed in this paper; the Section 5 summarizes the content of this paper and describes future research.
# An On-Orbit Task-Offloading Strategy Based on Satellite Edge Computing
## 5 Conclusions  
The proposed research work explores the problem of offloading a single dependent task to multiple satellites for collaborative processing in the satellite edge computing scenario. Firstly, a model is proposed in which tasks are offloaded to multiple satellites for collaborative computing without the participation of high-load satellites. Secondly, the crossover and mutation operations of the GA are introduced in this paper to address the drawbacks of the traditional BPSO algorithm. Utilizing the optimized GABPSO algorithm, a lower system cost can be obtained under the scenario. The experiments verified that the optimized algorithm has better performance than other baseline algorithms.  
In practical satellite applications, the size of remote sensing images is huge. Adopting the strategy proposed in this paper can effectively accelerate the on-orbit analysis of images. In addition, in the future satellite-IoT architecture, the analysis of ground monitoring data acquired by satellites, etc., is also applicable to the scenario studied in this work. The strategy proposed in this paper has some reference value for all these applications.
###### Abstract  
In this paper, we target solving the data gathering problem in underwater wireless sensor networks. In many underwater applications, it is not quick to retrieve sensed data, which gives us the opportunity to leverage mobile autonomous underwater vehicles (AUV) as data rules to periodically collect it. For each round of data gathering, the AUV visits part of the sensors, and the communication between AUV and sensor nodes is a novel high-speed magnetic-induction communication system. The rest of the sensors acoustically transmit their sensed data to the AUV-visit sensors. This paper deploys the HAS4 (Heuristic Adaptive Sink Sensor Set Selection) algorithm to select the AUV-visited sensors for the purpose of energy saving, AUV cost reduction and network lifetime prolonging. By comparing HAS4 with two benchmark selection methods, experiment results demonstrate that our algorithm can achieve a better performance.  
Keywords:underwater data gathering; underwater wireless sensor network; AUV +
Footnote †: journal: Sensors
# Personalized Augmented Reality Based Tourism System: Big Data and User Demographic Contexts
## 4 Methodology
### Visualization  
Information visualization is the presentation of the data in a figurative and graphic format. This type of presentation enables the decision makers and the information holders to receive their data analysis visually. This makes it easier for them to understand the difficult concepts and to identify new patterns. In this system, no visualization is done on the proposed items, and these items are displayed only as a list, which is based on the final weight for the user in the final ranking stage. To visualize the information, two methods--the map and the AR--are used to display the information in this system.  
In fact, the AR is an interface that adds the information to the real world. In the AR, the locations will be displayed to the users as the information is added to the environment. In order to display the information in the phone's coordinate system, two conversions are performed. First, the GPS coordinates are converted to the Earth-centered-Earth-fixed coordinates, and the reference ground coordinates are then converted to the navigation coordinates. Finally, the navigation coordinates are converted to the screen coordinates. In addition to the location, information such as the user's distance to the tourist location and the name of the location are added to the existing reality in the form of text. In order to display it, the order of those places that were ranked higher in the previous step will appear as a number on the phone screen. In the map display, the user-specified locations and the location of the tourist places specified in the previous step are first displayed separately on the map. The user's distance to the tourist place and the name of the tourist place are also pinned on the map when the user's location is specified.  
Figure 4: Ranking tourist places using the VIKOR method.
## 3 Results
### Exemplary Results  
The testing database is derived from the original images database, and these are pairs of images that are not part of the training base and are independent of each other (no overlap).  
There are 416 image pairs for network testing. Additionally, the results for the same number of pairs teaching the network are presented. Usually a neural network works better for training images than for testing images. The input H&E image has a resolution of \(256\times 256\) and the reference UV and output images have a resolution of \(242\times 242\).  
Sample training and testing images are shown in Figures 8 and 9, where H&E is RGB input image, UV (main PC) is a preprocessed UV training image and virtual UV is the output image of the convolutional neural network.  
Figure 7: Histogram of slide pair shifts due to mechanical issues.  
Figure 8: Exemplary results for training images (no additional contrast enhancement). (**a–d**) four samples from database.
## 3 Different Equalization Algorithms
### Adaptive Median Filtering Algorithm  
The adaptive median filter (AMF) algorithm was proposed by [PERSON] [39] as an effective approach for the smoothing of signals. The median filter [40] is a basic rank selection filter. The principle of the median filter is to replace the occurrence of each pixel or constellation data point of the filtering window with the median of the occurrence of a neighborhood of all of the constellation data points within the window, instead of using the average operation.  
Where \(y(k)\) represents the input constellation value of the distorted constellation at position \(k\). At each position \(k\), the filter window \(c(k)\) of size \(N=2m+1\) (\(m\) is a positive integer); it is described as  
\[c(k)=\{y(k):s=1,...,m,\ m+1,...,\ N\} \tag{13}\]  
The median filter \(u(k)\) is the resultant value at position \(k\) in a filter window which is given by [41]  
\[u(k)=med\{x1(k),\ldots\ xN\ (k)\} \tag{14}\]  
where med is the median operator.
## 5 Performance Evaluation and Analysis  
The results discussed in this section are derived from the deployment of both rule-based and AI-based algorithms in the simulator, as outlined in Section 3. The relevant simulation parameters are detailed in Table 3. To demonstrate the effectiveness of the AI-based approach over the rule-based radio resource scheduling in Cellular V2X environments, three categories of results are presented: (i) measurable parameters at the access layer of the Cellular V2X system; (ii) performance of the GRU layers; and (iii) learning curves of the AI training model.
# Analytical Model to Calculate Radial Forces in Permanent-Magnet Synchronous Machines
## 3 Analytical Model
### Magnetic Flux Density Created by Magnets in a Slotless Machine  
In PMSMs with a rotor of nonconsecutive poles, the magnitude of the flux density created by the magnets in the air gap can be obtained by (21).  
\[\hat{B}_{g}=\frac{B_{r}}{1+\frac{\delta\mu_{rm}}{h_{m}}} \tag{21}\]  
where \(B_{r}\) is the remnant value of the magnetic fields, \(h_{m}\) is the height, g is the length of the air gap, and \(\mu_{rm}\) is the relative permeance of the magnets.  
After the magnitude is computed, the distribution of this flux density can be represented by means of (22).  
\[B_{g}^{m}(t,\theta)=\sum_{n=-\infty}^{+\infty}B_{gn}^{m}e^{-jpn(\varphi+ \Omega_{m}t-\theta)}=\sum_{n=1}^{+\infty}2\cdot B_{gn}^{m}\cos(pn(\varphi+ \Omega_{m}t-\theta)) \tag{22}\]  
where \(B_{gn}^{m}\) are the coefficients of the Fourier series, which are obtained by the following equation:  
\[B_{gn}^{m}=\frac{\hat{B}_{g}}{n\pi}(1-\cos(n\pi))\sin\left(n\cdot p\frac{\beta _{m}}{2}\right)\frac{2}{1+\left(a\cdot n\cdot p\right)^{2}} \tag{23}\]  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Static Eccentricity** & **Dynamic Eccentricity** \\ \hline \(v_{e}\)=\(\pm(n\pm m)\) & \(v_{e}\)=\(\pm(n\pm m)\) \(\pm\left(n\pm m\pm\frac{p}{p}+\frac{k}{p}\right)\) \\ \(\pm\left(n\pm m\pm\frac{p}{p}\right)\) & \(\pm\left(n\pm m\pm\frac{k}{p}\right)\) \\ \hline \hline \end{tabular}
\end{table}
Table 10: Harmonics of the magnetic force in a machine with eccentricity.  
where \(\beta_{m}\) is the magnet pitch and \(a\) is a parameter known as the fringing coefficient. This is employed to adjust the ideal rectangular distribution with the objective of achieving a more realistic waveform, as shown in Figure 3a. In this case, the fringing coefficient has been parameterized as a function of the different dimensions of the machine in order to provide a physical meaning [17; 18; 19].  
\[a=\frac{\sqrt{g\Big{(}g+\frac{h_{m}}{\mu_{m}}\Big{)}}}{D_{ri}+2h_{m}+g} \tag{24}\]  
where \(D_{ri}\) is the inner diameter of the rotor (Figure 3b).  
According to the expression (23), adjusting the magnet pitch can reduce or even eliminate certain harmonics of the flux density created by the magnets. Thus, it is possible to reduce the magnitude of some force harmonics in order to improve the vibroacoustic performance of the machine.
# Employing Huber and TAP Losses to Improve Inter-SubNet in Speech Enhancement
## 3 Inter-SubNet with New Loss Function  
The primary objective of this study is to revise the training process of the Inter-SubNet architecture by restructuring its loss function. Our approach uses two different loss functions: Huber loss and temporal acoustic parameter (TAP) loss. In simple terms, Huber loss helps address potential convergence and outlier issues when training networks using MSE loss. On the other hand, TAP loss can enhance Inter-SubNet's ability to emulate human sound production and perception.
## 4 Empirical Evaluations Utilizing Open Source Systems
The tool automatically extracts the public methods of the classes of the considered system and the clients of each method, and calculates the client-based similarity between each pair of methods using Definition 4. The input of the tool is the source code of the system and output are the set of public methods of each class and a \(k\times k\) matrix that has the client-based similarity between each pair of public methods in the class, where \(k\) is the number of public methods in the class. The second tool we developed is a Python tool that takes the output of the Java tool and automatically applies Algorithm 1 and Algorithm 2. The output of the Python tool is the set of classes that are suggested to be extracted from the class in question. In addition, the python tool calculates the value of \(CBCS\) for the original class and for each class suggested to be extracted from the original class. The research question of our empirical study investigates whether the cohesion of the extracted classes identified by our approach is higher than the cohesion of the original classes. We believe that the extracted classes will have higher cohesion values than the original classes. Thus, we set up the following null and alternative hypotheses for our research question. * \(\mathbf{H}_{0}\): The difference between the CBCS values of the original classes and the CBCS values of the extracted classes suggested by our approach is 0. * \(\mathbf{H}_{1}\): The original classes have less CBCS values than CBCS values of the extracted classes suggested by our approach.
## 3 Materials and Methods  
### Image Data  
Based on the review of the location selection of CCTVs for image analysis [4], the researcher installed CCTV equipment in the research institute to which the researcher belongs (Goyang-si, Gyeonggi-do, Republic of Korea) to efficiently collect image data. Before installing the CCTV equipment, the researcher received legal consultations on personal information and security issues to minimize the issues that may occur while securing the images. The data acquired from the equipment include the latitude-longitude coordinates of the points where CCTVs were installed, image data, and data related to the date and time of image recording. The acquired data, including image data, were collected through Network Video Record (NVR, XRN-410S v2.44). An NVR server is a special computer system with a software program that records video in digital format on a high-capacity storage device. The CCTV is Hanwha Technwin PNP-9200 RH (Seongnam city, Korea), the focal length is 4.8-96 mm (20\(\times\)) zoom lens, and the frame rate per second can be set up to 30 fps. The image's resolution is 8 megapixels at a maximum of 30 fps, and in this study, it was set to 1920 \(\times\) 1080, considering the resolution of public CCTV images. The CCTV installation point is latitude 37.67102804, longitude 126.7394964, and it was installed at a height of 15 m from the ground with an elevation angle of 0 degrees (horizontal) to simultaneously observe the ground and the atmosphere for fine dust analysis. The viewpoint of CCTV was set to 44.5 degrees north latitude as the azimuth toward the downtown area. The CCTV installation building and camera locations are as shown in Figure 1.  
The image collection period was from 21 April 2021 to 14 February 2022 from 8:00 to 18:00 (from 8:00 to 22:00 for some images), and the image data were utilized in line with the concentration distribution and analysis purpose of PM. The images used for analysis are not all CCTV images but separately stored images of fixed infrastructure (e.g., buildings, facilities, etc.) that is unaffected by seasons and weather conditions and can be continuously analyzed for a long time. Image data processing includes the following processes: First, the recording of images from CCTVs; second, the collection of image data by the NVR; third, data storage on the DB server; fourth, data analysis on the analysis server; and fifth, they are used as learning data on the learning server. Table 1 shows the meta-information about the CCTV image data used for the experiment, and Figure 2 shows the difference between the images of the CCTVs for the experiment according to the deviation of PM.