esa-sceva/satcom-chunk-collection huggingface dataset:
# Text-Mining-Based Non-Face-to-Face Counseling Data Classification and Management System
## 3 Proposed Scheme  
### System Configuration  
Figure 1 below is the overall configuration diagram of the system proposed in this paper.  
The system proposed in this paper consists of three key components: a client server (user interface), a system server (text mining processing), and storage (for managing and storing counseling data). This system is implemented as a web service. The client server serves as the user interface, accessible via a web browser by clients or counselors. Through this interface, users can input counseling content and view real-time analysis  
Figure 1: System configuration diagram.  
results generated by the system. When a client submits counseling content, the input data are transmitted to the system server for processing. The system server is responsible for performing text mining and analysis, utilizing Python for text data processing. It employs text-mining techniques to analyze the input data and transmits the results back to the client server for display. The storage component manages the storage of clients' counseling records and analysis results, using PostgreSQL to efficiently store and handle the data. This component retains both the counseling records and the analyzed results, which are used for future counseling sessions and treatments. The development environment of the proposed system is as follows: the programming languages used include Python (version 3.12.6), Ajax, ECMAScript 2023, HTML5, and CSS (Cascading Style Sheets), while Django (version 5.0) was employed as the framework. Django offers excellent compatibility with Python-based NLP processing, allowing for the effective application of the TF-IDF and Word Embedding techniques used in this study. Additionally, Django provides a range of functionalities essential for web application development, making it an ideal framework for efficiently managing data flow between the user interface and the system server. PostgreSQL (version 16.4) was selected as the database, an open-source relational database well suited for processing large volumes of data and offering excellent compatibility with the Django framework. Counseling records and classification results are stored in PostgreSQL, which facilitates the efficient management and retrieval of counseling data. The continuous accumulation of counseling records within the database plays a key role in improving the performance of the counseling model. Additionally, PostgreSQL offers stability and high-speed data retrieval performance, ensuring consistent efficiency as the system scales. With the expected need to process increasing volumes of counseling data and handle larger datasets in future studies, PostgreSQL's scalability and robustness make it a highly suitable choice for this system. The server environment for the system was deployed using Amazon Web Services (AWSs).
## 4 Discussion
### Flow Regimes and PM\({}_{2.5}\) Distribution
#### 4.1.3. Perpendicular Wind  
In the symmetrical street canyon, buildings on both sides impede the airflow, creating unfavorable conditions for PM\({}_{2.5}\) dispersion and escape [45; 46]. The flow regime involves a horizontal canyon vortex in the central area of the street and corner eddies at the street ends. The canyon vortex forces air downward from the roof of the building towards the WW, diluting the concentration of particulate matter at the WW, while also transporting particulate matter to the LW [47]. The corner eddies push air inward from both ends of the street, diluting the PM\({}_{2.5}\) concentration on both sides of the street. Therefore, the PM\({}_{2.5}\) concentration at the WW and LW shows a gradually decreasing trend from the center of the street to both ends of the street. The canyon vortex acts as the main source of air exchange in the central part of the canyon, while the combination of the canyon vortex and corner eddies enhances ventilation at the street ends.  
In step-down street canyons, when BHR = 2, the vortex regime inside the canyon aligns with previous research [37], exhibiting a clockwise vortex and causing particles to re-enter the street and increase the concentration at the WW. When the heights of buildings A and B decrease simultaneously and BHR increases to 4, the vortex inside the canyon disappears, and the reverse flow directly transports PM\({}_{2.5}\) to the LW. In the step-up canyon with a BHR of 0.5, a single clockwise vortex appears, transporting PM\({}_{2.5}\) towards the LW. In the case of BHR = 0.25, the small eddy near the LW transports PM\({}_{2.5}\) to the LW, resulting in a higher concentration of PM\({}_{2.5}\) at the LW than at the WW.
## 4 Experiments and Results
### Architecture Search  
#### 4.2.1 Implementation Detail  
The architecture parameter \(\alpha\) is a criterion. Every parameter used in this experiment was the same as that of DARTS, except for the batch size and epochs. The search space \(O\) is also the same as that of DARTS; in other words, DG-DART uses eight operations: \((none,max\_pool\_3\times 3,avg\_pool\_3\times 3,skip\_connect,sep\_conv\_3\times 3,sep\_conv\_5 \times 5,dil\_conv\_3\times 3\), and \(dil\_conv\_5\times 5\)), which are obtained from the operation search space of NASNet [1]. The batch size was set to 96. Two stages with 25 epochs each in this experiment had a total of 50 epochs, which is the same value as the 50 epochs per 1 training in DARTS. Initially, 16 channels and eight cells composed of six normal cells and two reductions constituted the search network. To train the network weight, the stochastic gradient descent (SGD) optimizer [15] was used, and we set the initial learning rate as 0.025, momentum as 0.9, and weight decay as \(3\times 10^{-4}\) as the parameter values. To train the architecture parameter, the Adam optimizer [16] was used, and we set the initial learning rate to \(3\times 10^{-4}\), momentum in \((0.5,0.999)\), and weight decay to 0.001. The Elkan K-means clustering algorithm [12] was used to cluster operations with an initial iterator of 30, max iterator of 300, and tolerance of \(10^{-4}\). With such clustering parameters, clustering was performed for 1-4 clusters.
# Improved Remote Sensing Image Classification Based on Multi-Scale Feature Fusion
## 5 Discussion
### Effect of Up-Sampling on Accuracy  
The purpose of up-sampling is to encrypt the rough high-level semantic feature graph to generate a feature vector for each pixel. In this study, WFCNN, U-Net, and RefineNet generated feature maps with the same size as compared to the input image through multi-step sampling. However, WFCNN used deconvolution to complete up-sampling, while RefineNet and U-Net used deconvolution to perform up-sampling first and chose linear interpolation for the final up-sampling. The segmentation results for these three models had few mis-segmented pixels within the object, but at the edges, WFCNN's results were significantly better. When RefineNet and U-Net processed camera images, the edges of the object were very fine. We believe that the reason for this phenomenon is that the structure of a remote sensing image is considerably different from that of a camera image, such that bilinear interpolation does not achieve good results when processing the former. Since the resolution of a camera image is generally high, when two objects are adjacent to each other, the pixel changes across the boundary are usually gentle and the difference between adjacent pixels is small, such that up-sampling using bilinear interpolation is more effective. In contrast, the pixel changes in a remote sensing image are usually sharp across the boundary between two objects and the difference between adjacent pixels is large, resulting in a poor bilinear interpolation effect. Unlike RefineNet and U-Net, WFCNN uses deconvolution for up-picking and correction and all parameters are obtained through learning samples, allowing the model to adapt to the unique characteristics of remote sensing images and achieve good results.
# Metasurface-Based Polarimeters
## 4 Conclusions and Outlook  
We have overviewed the rapid development of metasurface-based polarimeters. From the perspectives of physical mechanisms, the concepts of generalized Snell's law, PB-phase, and Stokes parameters were introduced. After that, we reviewed the efforts of metasurface-based polarimeters, including generic polarimeters, spectropolarimeters, OAM spectropolarimeters, and photodetector integrated polarimeters.  
Although much progresses has been achieved in the field of metasurface-based polarimeters, there are still some challenges to overcome in future. To date, most of metasurface-based polarimeters destroy or substantially modify the original wavefronts of an incident beam in the detection process. Therefore, a nondestructive and real-time polarimeter is desired, which leaves the original wavefronts virtually unaffected [99]. Additionally, nanofabrication of the metasurface-based polarimeters mainly relies on high-cost and time-consuming electron beam lithography or FIB milling, which may hinder the potential applications of metasurface optical devices. Nanoimprinting should be a feasible way to the low cost and large area fabrication of metasurfaces [100].  
This work was funded by the European Research Council (the PLAQNAP project, Grant 341054) and the University of Southern Denmark (SDU2020 funding).  
[PERSON] and [PERSON] contributed to writing and finalizing the paper. [PERSON] supervised the project.  
The authors declare no conflict of interest.
# Remote Sensing Technologies for Monitoring Argane Forest Stands: A Comprehensive Review
## 2. _Argania spinosa_ Characteristics  
_Argania spinosa_ (Argane stand) is a species of profound ecological and socio-economic significance, particularly in southwestern Morocco. Comprehending its distinctive characteristics aids in elucidating its ecological role and associated advantages, thereby motivating our commitment to its surveillance via state-of-the-art accessible remote sensing technologies.
# Neural Models for Measuring Confidence on Interactive Machine Translation Systems
## 4 Confidence Measures
### Character n-Gram F-Score (Ctr-F)  
The character level n-grams have been a critical part of more complex metrics evaluation like MTeRater[38] and Beer[39]. For this reason, Chr-F [40] calculates the F-score based on character n-grams. This metric does not use any penalty related to the length of the translation. The two unique elements it uses to calculate the F-score are precision and recall. The precision works at the n-gram level and the recall at the character n-gram level. Experimentation has proven that this metric correlates very well with human judgment.  
Although this metric correlates very well with human judgment at the sentence level, we notice some possible problems when calculating the word quality scores. It uses character-level n-grams in its calculation, giving higher scores to longer words. This aspect is not a problem to calculate the intermediate rewards of the metric, but we think it could cause the confidence measure to face some difficulties during the classification process.
# Application of DMLF in Pulse Ranging LiDAR System
## 2 Methods  
### Principle  
The principle diagram of the pulse-ranging LiDAR system based on the DMLF is shown in Figure 1.  
A short pulse is transmitted through the collimating lens 1, and then the pulse is divided into two beams by beam splitter1 (BS1). One beam is focused by a convergent lens 2 and is detected directly by a photodetector (PD), which generates the start signal and sets it as the start time (\(t_{t}\)) of TOF. The other beam is used to illuminate the target. The light reflected from the object surface is divided into two parts by beam splitter2 (BS2), which are received by two APDs through a convergent lens, respectively. The two electrical signals of \(P_{rd1}\) and \(P_{rd2}\), generated by APD1 and APD2, are sent to a subtracter. We will obtain the differential pulse signal \(P_{rd1}\)-\(P_{rd2}\) after the subtraction operation, and send it to the analog-to-digital converter (ADC). There is a PGZCPs between the differential pulse signal and zero axial, so we define the PGZCPs as the stop timing moment (\(t_{t}\)) of TOF. According to the theory of TOF, the calculation formula of range is given by  
\[R=c\times(t_{r}-t_{t})/2 \tag{1}\]  
where \(c\) is the velocity of light.
## 1 Introduction  
With the advent of innovative vehicular technologies, the application of autonomous driving (AD) technologies in public transportation systems is increasingly widespread, and its commercialization is expected to be soon realized on a large scale [1]. AD has already been implemented in fixed areas like underground [2, 3]. Shared Autonomous Vehicles (SAV) [4], especially automated shuttles, have garnered significant attention in this context. In this study, SAV refers to shared ownership of public vehicles with private destinations (see Figure 1), commonly known as robot taxis or automated shuttle buses (ASB) [5]. This type of vehicle demonstrates the capability to autonomously navigate through most driving scenarios without the presence of a service attendant onboard, achieving a Level 4 autonomy rating on a scale ranging from 0 to 5 [6]. Distinct levels of autonomy correspond to varying degrees of human intervention, with levels 1-3 necessitating a human driver, whereas levels 4 and 5 facilitate driverless vehicle operation [7]. Contemporary vehiclesare equipped with various advanced technologies, including optical radar, cameras, touch panels, GPS sensors, and algorithms that analyze the driver's steering behavior. Future vehicles are expected to expand the scope of potential interactions, shifting gradually from driving as the central task to prioritizing leisure and entertainment [8]. The communication interfaces between users and AVs will become more prominent in this evolving landscape. Consequently, examining the transformations in HMIs is crucial [9].  
Users' acceptance of a new technology determines its development and smooth implementation [11; 12; 13]. Therefore, this research focuses on the user acceptance and experience of HMI in AVs [14]. Since most people have no experience with AVs, they are unfamiliar with this technology and cannot imagine what higher levels of automation could be [15]. As a result, people's acceptance of this technology is low [16; 17; 18; 19]. While some studies utilize user acceptance models to statistically analyze user experience data related to AVs, most research evaluates user acceptance by examining respondents' intentions to use and purchase behavior, emphasizing subjective data collection. Moreover, scholars have different definitions of user acceptance of AVs.  
Standardized test protocols are currently lacking for evaluating user acceptance of SAV's HMIs. Agreement has not been reached on the pertinent use cases, assessment criteria, and appropriate experimental design process. Standardizing the evaluation process is essential for advancing HMI development. In response to this need, our study introduces a novel methodological guideline that standardizes the assessment of HMI acceptability in AVs.
## 3 Results and Discussion
### Characterization of La-EPRC Particles for Static Adsorption of Phosphorus Removal
#### 3.2.5 Influence of Coexisting Anions on the Effectiveness of Phosphorus Removal  
As depicted in the relationship diagram (Figure 15), the influence of several coexisting anions on the adsorption capacity of La-EPRC particles appears negligible. The presence of Cl\({}^{-}\), SO\({}_{4}^{2-}\), and HCO\({}_{3}^{-}\) has almost no discernible effect on phosphorus adsorption. Among them, Cl\({}^{-}\) exhibited the lowest impact, resulting in a marginal decrease of 1.77% in the adsorption capacity of La-EPRC particles when present at a molar concentration ratio of 2:1 with elemental phosphorus compared to the blank group without other anions, yielding a value of 2.108 mg P/g. Similarly, the presence of SO\({}_{4}\)\({}^{2-}\) and HCO\({}_{3}^{-}\) reduced the adsorption capacity of La-EPRC particles by 4.89% and 5.34%, respectively, compared to the blank group. The most notable impact on the adsorption efficacy of La-EPRC was observed with CO\({}_{3}\)\({}^{2-}\), where the adsorption capacity decreased by 9.34% to 1.945 mg P/g at a molar concentration ratio of CO\({}_{3}\)\({}^{2-}\) to elemental phosphorus of 2:1. This decline is attributed to the lower K\({}_{\rm SP}\) value of La\({}_{2}\)(CO\({}_{3}\))\({}_{3}\) (3.98 \(\times\) 10\({}^{-34}\)) compared to that of LaPO\({}_{4}\) (3.7 \(\times\) 10\({}^{-23}\)), favoring the formation of La\({}_{2}\)(CO\({}_{3}\))\({}_{3}\)[23]. The coexisting anions, particularly CO\({}_{3}\)\({}^{2-}\), exhibited greater favorability for the formation of La\({}_{2}\)(CO\({}_{3}\))\({}_{3}\).  
The reduction in phosphate adsorption by La-EPRC particles in the presence of coexisting anions can be mainly attributed to competition from other oxygenated anions [24]. The results indicate that the impact of competition on adsorption is limited, and La-EPRC demonstrates a selective adsorption capacity for phosphate.  
Figure 15: Effect of coexisting anions on the adsorption capacity of La-EPRC particles.  
+++ ==ERROR: No output for this page==
# Dual-Modality Cross-Interaction-Based Hybrid Full-Frame Video Stabilization
## 2 Background
### Video Stabilization Using IMU Sensors  
Image deblurring using IMU sensors estimates blur function from gyroscope and accelerometer data during shooting [19]. Known blur function allows image improvement through non-blind deconvolution for deblurring. Since the algorithm assumes a constant depth of the scene, there is the limitation of an inaccurate blur estimation due to depth differences in real scenes. Digital video stabilization and rolling-shutter correction using gyroscopes measures camera motion with gyroscopes to perform digital video stabilization and rolling-shutter correction efficiently. Despite its strength under poor lighting and significant foreground motion, it may introduce shaky motion-induced visual artifacts. Deep online video stabilization using IMU sensors synthesizes stabilized images through deep motion estimation using data from IMU sensors. It identifies various motion types with a Deep Neural Network (DNN) classifier and employs Long Short-Term Memory (LSTM) [20] for extracting temporal features, effectively removing shaky artifacts, performing strongly across datasets with less time consumption, although it requires sufficient training data for accurate predictions. Deep online fused video stabilization employs both gyroscope sensor data and image content in an unsupervised learning DNN for video stabilization. The network fuses motion representations combining optical flow with real/virtual camera pose histories, where LSTM cells infer new virtual camera poses for generating warping grids to stabilize video frames. Although numerous studies are underway to enhance stabilization performance using IMU sensors, the issue of cropping still persists in video stabilization.
## 3 Methodology
### Price-Side Model  
Most IO tables are prepared in terms of amount rather than quantity. Assuming the IO table of the unit of quantity, the following basic equation is derived.  
\[\begin{split} Q_{i}=\sum\limits_{j=1}^{n}S_{ij}+F_{i}=\sum \limits_{j=1}^{n}d_{ij}Q_{i}+F_{i}\\ X_{i}=P_{i}Q_{i}\\ z_{ij}=P_{i}s_{ij}\\ Y_{i}=P_{i}F_{i}\\ V_{i}=P_{j}^{\mathrm{o}}V_{j}^{\mathrm{p}}\end{split} \tag{9}\]  
\(Q_{i}\) is the total output of the unit of quantity in the sector \(i\), \(F_{i}\) is the final demand in the quantity unit to produce sector \(i\) minus the import of the unit, and \(s_{ij}\) is the input element of the sector \(i\) purchased in the sector \(j\). \(P_{i}\) is the unit price of the total output of the sector \(i\), \(P_{j}^{\mathrm{o}}\) is the unit price of the value-added in sector \(j\), and \(d_{ij}\) is the output coefficient of the unit of supply defined by \(s_{ij}/Q_{j}\). Deriving Equation (9) as an expression for \(P_{j}\) appears as follows.  
\[P_{j}=\sum\limits_{i=1}^{n}P_{i}d_{ij}+P_{j}^{\mathrm{o}}\frac{V_{j}^{\mathrm{ p}}}{Q_{j}}=\sum\limits_{i=1}^{n}P_{i}d_{ij}+P_{i}^{\mathrm{o}}d_{j}^{\mathrm{o}} \tag{10}\]  
As mentioned earlier, because the IO table is prepared in units of amount, it is not possible to know the input coefficient \(d_{ij}\) in real units. However, assuming the price of all outputs is KRW 1, \(d_{ij}=a_{ij}\). In other words, it can be seen that the real unit input coefficient matrix and the amount unit input coefficient matrix match, and the price at this time can be viewed as a normalized price [42]. When Equation (10) is arranged in the form of a matrix, and all prices are normalized to 1, the following equation is derived.  
\[\overline{P}=\big{(}I-A^{\prime}\big{)}^{-1}\hat{A}^{\mathrm{o}}\overline{P^{ \mathrm{o}}} \tag{11}\]  
Therefore, the price-inducing effect of the price change in the value-added sector is \((I-A^{\prime})^{-1}\hat{A}^{\mathrm{o}}\). Therefore, even if the price level is not known, the price-inducing effect can be obtained through the normalized price. This is called Leontief price model. In Equation (11), if the sector of interest (\(K\)) is exogenous and there is no change in value-added input in other sectors, the following equation is derived [50; 51; 52].  
\[\Delta\overline{P_{e}}=\big{(}I-A_{e}^{\prime}\big{)}^{-1}A^{\prime}{}_{Ke} \Delta\overline{P_{K}} \tag{12}\]  
where \(\Delta\overline{P_{e}}\) is the price change rate vector excluding sector \(K\), and \(\Delta\overline{P_{K}}\) is the price change rate of sector \(K\). Furthermore, \(A^{\prime}{}_{Ke}\) means the row vector representing the sector \(K\) in the input coefficient matrix \(A\) excluding the elements of the sector \(K\).
## 3 Methodology  
### Fractional-Order Convolutional Neural Network Architecture  
#### 3.1.1 Caputo Fractional Derivatives  
The Caputo fractional derivative, as defined by Equation (1), is applied to calculate the derivative of the input signal \(f(x)\) with respect to the fractional order \(v\), which can be non-integer. Unlike traditional integer-order derivatives, fractional derivatives accumulate information over the range of integration, providing greater sensitivity to prior inputs. In this context, the Gamma function \(\Gamma(\cdot)\) normalizes the derivative, ensuring the proper scaling of fractional operators.  
\[{}^{c}D_{x}^{v}f(x)=\frac{1}{\Gamma(r-v)}\int_{\xi}^{x}(x-\tau)^{r-v-1}f^{(r)} (\tau)d\tau \tag{1}\]  
where  
* \(v\) is the order of the derivative and can be a non-integer \((0<v<r)\).
* \(r\) is the smallest integer greater than \(\alpha\), so \(r-1<v<r\).
* \(\Gamma(\cdot)\) is the Gamma function, defined by \(\Gamma(z)=\int_{0}^{+\infty}t^{z-1}e^{-t}dt\).
* \(f^{(r)}(\tau)\) denotes the \(r\)-th derivative of \(f(\tau)\).
* \(\xi\) is the lower bound of the integration domain, which can be any value within the domain of \(f(x)\).
* \(\tau\) represents the integration variable, which accumulates the changes of the function \(f(x)\) over the interval \([\xi,x]\).  
We employ the Caputo fractional derivative because it incorporates historical information through fractional-order differentiation while maintaining the same initial conditions as integer-order derivatives [43]. This feature makes it particularly useful for updating gradients in neural networks, providing smoother and more adaptive parameter updates that improve convergence behavior.