esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract
The city's economic growth is significant, but it faces challenges with air quality and pollution due to high industrial emissions, which impact the environment and public health. It has a hot climate with high humidity in the summer and a mild winter, with limited rainfall throughout the year. Najran is an agricultural city in southwestern Saudi Arabia, with some industrial development [8]. This combination presents challenges and opportunities for pollution control. While agriculture results in lower pollution levels than industry, a balance between agriculture and industry is critical to maintaining air quality as Najran grows. It has a hot climate in summer and a mild winter with rainfall. Future air pollution can be forecasted using time series forecasting methods, which use data provided by satellites and provide essential information in many areas under global observation to understand the effects of climate change, forests, agricultural areas, and the development of large cities and how to deal with them [9]. The Sentinel-5 satellite is the first mission of the Copernicus program to study the atmosphere's composition to combat air pollution. It identifies gases such as O\({}_{3}\), HCHO, CO, NO\({}_{2}\), and SO\({}_{2}\)[10]. Satellite images from Sentinel-5 can effectively study the spatial distribution of pollutants due to their large global views of the Earth's surface. As part of Sentinel-5, the TROPOMI (Tropospheric Monitoring Instrument) was used to provide daily data on air pollution [11]. Many pollutants can be monitored and imaged using the TROPOMI. The pollutant dataset was collected via the Google Earth Engine (GEE) cloud computing platform using JavaScriptScript programming in the Earth Engine code editor. GEE is an online computing platform that processes satellite imagery, geospatial data, and spatial data on a petabyte-scale [12]. It also provides access to software and algorithms for processing satellite data [13]. There are many regulations to control and address the level of air pollution and many efforts to enforce these regulations to improve air quality [14]. Our study aims to contribute to these ongoing efforts through two main objectives:  
1. Descriptive analysis  
By investigating the air pollution trends of three cities with different environmental characteristics, we can comprehensively understand each city's air pollution fluctuation throughout the year. Predictive modeling  
Developing an accurate model to predict the future values of air pollution during a specific period can enhance the feasibility and applicability of monitoring policies in various places. This can provide warning systems for the areas at risk of elevated air pollution levels, which facilitates the tasks of monitoring teams.
## 3 Results and Discussion
### Comparison of Sensing Performance
(**a**) Normalized resistance changes upon sequential exposure to increasing concentrations of NH\({}_{3}\) at different pH values. (**b**) Response and (**c**) recovery times of PPyNPs toward 0.1 ppm of NH\({}_{3}\) at different pH values. (**d**) Normalized resistance changes of pH 1 based-electrode upon sequential exposure to decrease concentrations of NH\({}_{3}\). (**e**) Response and (**f**) recovery times of pH 1 based-electrode toward 0.1 ppm of NH\({}_{3}\), depending on the direction of increase (black) and decrease (green) in concentration of NH\({}_{3}\). The selectivity of the sensor electrodes is also important for practical applications. Figure 10 exhibits the variation in the normalized resistance of the electrode at pH 1 upon exposure to various volatile gases at low concentrations (1 ppm for NH\({}_{3}\) and MeOH; 100 ppm for others). NH\({}_{3}\) yields a signal change that is more than five times larger when compared to those of the other gases, although its concentration is lower than those of other gases. Therefore, NH\({}_{3}\) can be distinguished from other gases based on the extent and direction of the resistance change. Figure 8: (**a**) Change in normalized resistance with different MeOH concentrations. (**b**) Calibration curves of PPyNPs as functions of NH\({}_{3}\) and MeOH concentrations; black for pH 1, red for pH 7, and blue for pH 13. (**c**) Periodic exposure of PPyNPs treated at pH 1 to 10 ppb of NH\({}_{3}\) gas.
## 5 Discussion
### Limitations  
This study focused on the multidimensional user experience analysis of the Chinese battery electric vehicle (BEV) market. Although a comprehensive analytical framework has been established, several limitations remain that may affect the generalizability and applicability of the findings in other contexts.  
First, the research data were primarily sourced from the Chinese Q&A platform \"Zhihu\". While the UGC from Zhihu is relatively authentic and comprehensive, providing deeper insights compared to short-text reviews, the user demographics tend to be younger, more highly educated, and more technologically inclined. These biases limit the representativeness of the data, which may not fully capture the diversity of the broader Chinese BEV user market.  
Second, the sentiment analysis used a domain-adaptive model (e.g., SnowNLP) to enhance adaptability to specific BEV-related terminology. However, as the base training data were mainly derived from general Chinese texts, the model's performance may be limited when analyzing users' experiences from different regions, affecting its cross-regional applicability.  
Third, while the co-occurrence network analysis (CONA) revealed complex associations between various emotional factors, it did not delve into causal relationships or temporal dynamics. Therefore, although key factors, such as interior design, and their links to users' emotions were identified, the nature of these associations, particularly regarding causality, remains unclear.  
In summary, the limitations of this study are largely related to its focus on the Chinese BEV market and the specific constraints of data sources and methodologies. These limitations affect the generalizability of the findings and, to some extent, restrict their applicability to other regions or industries. Future research should aim to expand the diversity of data sources and employ more advanced natural language processing techniques to enhance the external validity and applicability of the conclusions.
# High-Quality Data from Crowdsourcing towards the Creation of a Mexican Anti-Immigrant Speech Corpus
## 3 Methodology
### Stage II: Expert Data and Criteria
#### 3.2.2 Agreement Metrics  
To evaluate the quality and operability of the five criteria, we asked three experts to classify a small dataset of tweets retrieved during the previous stage. The group of experts was integrated with two Ph.D. candidates who specialized in migration issues and one professor who specialized in border studies and migration in the Mexican southern region. Since our goal was to test the limits of the guideline's usefulness to classify potentially problematic and ambiguous tweets, we did not build the dataset randomly. We manually selected a small amount of clearly pro-migrant and neutral tweets, a large amount of anti-immigrant tweets from the sample at hand, and some \"problematic\" examples. The latter were difficult to classify because of a lack of context (irony, sarcasm), a mixture of forms and ideas characteristic of both pro- and anti-immigrant speech, or the group or individual targeted was not clearly identifiable. Lastly, we selected 76 tweets. The experts had to independently classify the dataset using the criteria.  
We then obtained the experts' feedback regarding the criteria and specific tweets where no agreement was reached. As expected, some anti-immigrant tweets were not easily identifiable. We agreed on 73 tweets, three unclassified. Inter-rater agreement was needed to assess the reliability of the classifications made by the aforementioned experts. This was important to ensure that we were working with high-quality control data. We established a baseline rate to evaluate the agreement between expert annotators with a statistical metric named the kappa coefficient value. Its formula is presented in Equation (1).  
\[\kappa=\frac{P_{o}-P_{e}}{1-P_{e}} \tag{1}\]  
In Equation (1), we see the kappa coefficient, where \(P_{o}\) is the observed proportion of agreement between annotators and \(P_{e}\) is the expected proportion of agreement between annotators due to luck.  
As a first result, we obtained a global inter-annotator agreement coefficient of 0.726. Moreover, we calculated the peer-to-peer agreement to have a better idea of the boundaries in the inter-agreement values among experts. The peer-to-peer agreement among the three experts is shown in Figure 3. This result proves an important aspect of anti-immigrant HS data: we cannot expect perfect agreement from individuals, not even experts, during manual annotation. For this reason, we lowered our expectations about the possible coefficient values that non-experts could obtain, which will be described in the following sections.
## 4 Initial Trajectory Generation
### Bernstein Piece-Wise Trajectory Generation  
Bernstein polynomial is a specific form of B-spline, which is similar to the Bezier curve [79; 80]. Bernstein polynomial can be described as follows:  
\[\begin{split}\Gamma_{j}(t)=\lambda_{j}^{0}p_{d}^{0}(t)+\lambda_ {j}^{1}p_{d}^{1}(t)+\cdots+\lambda_{j}^{d}p_{d}^{d}(t)=\Sigma_{i=0}^{d}\lambda _{j}^{i}p_{d}^{i}(t),\\ p_{d}^{i}(t)=\binom{d}{i}\cdot t^{i}\cdot(1-t)^{d-i},\end{split} \tag{34}\]  
where \(d\) is the degree of the polynomial (Figure 4), \(\lambda_{j}^{0},\lambda_{j}^{1},\ldots,\lambda_{j}^{d}\) are the control points of \(j\)th polynomial segment, and \(t\in[0,1]\). Since Bezier is a particular form of the B-spline curve, such curves hold convex hull property. Hence, given a sequence of control points, a constrained convex hull can be defined using the control points that are considered. Both the beginning and end of the curve are determined by the first and the last control points, respectively. Furthermore, the derivative of Bezier is also a Bezier curve.  
\[\Gamma_{p}(t)=\left\{\begin{array}{ll}s_{1}\cdot\Sigma_{i=0}^{d}\lambda_{1, p_{d}^{i}}^{i}(\frac{t-t_{0}}{s_{1}})&t_{0}\leq t<t_{1}\\ s_{2}\cdot\Sigma_{i=0}^{d}\lambda_{2,p_{d}^{i}}^{i}(\frac{t-t_{1}}{s_{2}})&t_ {1}\leq t<t_{2}\\ \vdots&\vdots&\\ s_{m}\cdot\Sigma_{i=0}^{d}\lambda_{m_{d},p_{d}^{i}}^{i}(\frac{t-t_{m_{d}-1}}{s _{m_{d}}})&t_{m_{d}-1}\leq t<t_{m_{d}}\end{array}\right., \tag{35}\]  
where \(i,j\) refer to \(i\)th control point in \(j\)th segment, i.e., \(\lambda_{j}^{i},s_{j}\) is a scaling factor of \(j\)th segment for mapping time duration from \([0\,,1]\) to \([t_{j-1},\ t_{j}]\) and \(\mu\in\{x,y,z\}\). Once \(\Gamma_{\mu}(t)\) is obtained, the objective is to minimize the total cost, which can be determined by taking the integral  
Figure 3: Showing the B-spline convex-hull property. Convex hull, which comprises consecutive control points, e.g., \(\mathbf{p}_{i},\mathbf{p}_{i+1},\mathbf{p}_{i+2}\) and \(\mathbf{p}_{i+3}\), always belongs to obstacle-free space if the preceding control points satisfy the inequality (33).  
of square error up to \(k_{r}\) order as given in (15). Such a problem can be formulated as a QP constraint problem. For instance, [PERSON] and [PERSON] [26] proposed a Bernstein-based trajectory optimization approach in which three types of constraints piece-wise trajectory continuity, safety constraints which are based on convex hull property, and dynamical feasibility constraints enforced [26].
# Applications of Satellite Radar Imagery for Hazard Monitoring: Insights from Australia
## 4 Fire Monitoring  
Controlled and uncontrolled fires play a key role in Australian ecology (e.g., [44]) and present a significant hazard to life, impacting 100,000 s km\({}^{2}\) each year. Operational usage of optical EO datasets available at multiple times each day (e.g., MODIS, VIIRS Suomi NPP, AVHRR, and Himawari-8) to produce fire hotspot detection point data (and in some instances, fire-affected area products) have aided fire management activities, with information available on web interfaces hosted by state and federal government (e.g., MyFireWatch myfirewatch.landgate.wa.gov.au and DEA Hotspots hotspots.dea.ga.gov.au--both accessed on 16 March 2021).  
Whilst the potential contribution from current open-access SAR data to remote fire detection is limited by the temporal separation between satellite overpasses (days rather than hours or minutes), mapping of burned areas using SAR has been demonstrated in numerous locations using change-detection in backscatter imagery and InSAR coherence (e.g., Section 2; [44]). Compared to other EO sensors, SAR data is advantageous when fires are still active nearby as it is able to detect changes on the ground through smoke and ash that may inhibit other sensors. Due to the penetration of the SAR signal through vegetation, there is also scope to measure the burning of understorey vegetation not visible to optical sensors. Thus, interest lies in the use of these data to complement other EO data types (mentioned above) for longer-term fire management objectives such as estimates of fuel load and annual burned area mapping to evaluate fuel age and the efficiency of controlled burns [45]. For example, ALOS PALSAR and PALSAR-2 data have been shown to reduce the misclassification of the unburned forest by 9% for eucalypt and 3% foriferous forests (2009 Kinglake fire, Victoria: [46]) and estimate the impacts of prescribed burning of eucalypt forests in Western Australia to within 79% [47]. Consequently, there is scope to further integrate SAR into fire management pracises across Australia.
## 5 Experiments
### Dataset Construction  
Currently, there is a lack of publicly available datasets for geometric point clouds. To simulate building structure data and facilitate the training of graph neural networks, we first constructed a graph-structured dataset with building structure characteristics. The geometric building structures are composed of interconnected planes. In the Cartesian coordinate system, a base plane with \(z=0\) is first constructed, consisting of seven randomly generated points with \(z=0\). Since most buildings have horizontal and vertical structures, new planes are added in these directions. Each time, an existing plane is selected probabilistically as a reference plane. If the reference plane is horizontal, two points are randomly selected from this plane to form the base edge of a rectangle, and the height of the rectangle is randomly sampled to construct a vertical plane. If the reference plane is vertical, either the top or bottom edge is chosen as the reference edge \(E_{\text{ref}}\). Within a horizontal plane centered at the midpoint of \(E_{\text{ref}}\) with a radius of 3 m, \(n=10\) points are randomly added, and only those on the side with more points relative to \(E_{\text{ref}}\) are retained. A new horizontal plane is constructed based on the remaining points and the endpoints of \(E_{\text{ref}}\) by computing the convex hull. To form an expanding trend horizontally and vertically, new planes are given a higher sampling probability when selecting reference planes. This process is repeated until a sufficient number of planes are generated. Since all planes are generated based on existing edges, the resulting interconnected structure forms a graph \(G\).  
To construct subgraphs with different overlap rates, we extend new graphs from a base graph that already has a certain number of planes. The common base represents the overlapping part between different new graphs. By applying random rotations, translations, and positional noise to nodes, data under various conditions can be simulated.  
In total, 1000 pairs of graph data were generated for model training and verification, thereby forming a dataset GPCR.
# Channel Code-Book (CCB): Semantic Image-Adaptive Transmission in Satellite-Ground Scenario
## 5 Simulation Results and Analysis  
### Dataset  
We utilize the CIFAR10 dataset [39] to assess the transmission quality of the CCB model and the DIV2K dataset [40] for image visualization training and verification. The CIFAR10 dataset, a well-known resource for universal object recognition in computer vision, consists of 60,000 32 \(\times\) 32 RGB color images across 10 categories. Of these, 50,000 images are allocated to the training set, and 10,000 images to the testing set. The DIV2K dataset comprises 800 high-resolution training images and 100 high-resolution verification images, each characterized by exceptional clarity, making it ideal for training and evaluating super-resolution algorithms.
## 1 Introduction
[15] and it was revealed that by considering the fusion of GPS, BDS, GLONASS, and Galileo, the spatio-temporal resolutions of GNSS reflections were improved substantially. Furthermore, [PERSON] et al. [16] investigated the relationship between the spatial resolution and the temporal one of GNSS-R observations, and analyzed their dependence on key orbit parameters of the LEO satellites. As GNSS constellations were originally designed to meet the requirements for providing positioning, navigation, and timing services, and not for the GNSS-R observations, to exploit navigation signals sent from these GNSS constellations in a GNSS-R mission, the LEO constellation should be carefully designed so that the desired coverage and spatio-temporal resolutions of the reflection observations can be obtained. In most of the previous studies regarding the design of GNSS-R constellations, the spatial and temporal resolutions of the reflection observations obtained by certain fixed LEO constellation configurations was of concern [2; 14]. Among the few studies that aimed to find an optimal LEO configuration for the GNSS-R observations, the variable separation method is usually used [14]. Some evolutionary algorithms such as the particle swarm optimization (PSO) algorithm and the genetic algorithm (GA) have been developed in the fields of computational intelligence and evolutionary computation in recent years. Although it has been testified that these evolutionary algorithms are practical and effective in the optimal designs for the constellations of different scientific missions such as the constellations of the optimal global coverage of geometric dilution of precision (GDOP) [17], and the GNSS radio occultation (RO) constellations [18; 19], they have not been applied in the optimization design of GNSS-R constellations. On the other hand, although the present representative operational GNSS-R mission, CYGNSS, is mainly aimed to study the ocean winds and waves in tropical cyclones, and so the LEO constellation was designed with low inclination orbits, it is expected that in the future, GNSS-R will be used to sense ocean, land, and ice features globally using larger constellations [2]. In this study, the optimal constellations for GNSS-R observations over the globe were developed using an evolutionary algorithm (i.e., the PSO algorithm), and the performance of the optimal constellations were evaluated using the coverages and the spatial and temporal distributions of the reflections. Section 2 describes the principles and the methods used in the optimization process. In Section 3, the optimal constellation configurations are presented and evaluated. Section 4 discusses the dependence of the visit coverages of the optimal constellations on the choice of the size of cells, and some conclusions are reached in Section 5.
## 2 FBG Technology  
The technology currently used by the main gas network operators in Italy to detect pipeline strains consists of strain gauge bars with vibrating wire. The vibrating string transducer is essentially composed of a wire tensioned at both ends, able to vibrate at its natural frequency. The vibration frequency varies according to the tension of the wire, which is related to small relative movements between the two ends. However, this method presents some strong limitations, including, for example:  
* Difficulty to communicate via radio in inaccessible areas, thus requiring installation of repeaters;
* Necessity to install control units locally for processing and sending signals for each strain gauge station;
* Electrical signal attenuation over long-distance transmission;
* Sensor damages due to the high magnetic fields generated by pigging tools. FBG technology can overcome issues of conventional electrical sensing. In fact:
* Reflected light, used as a signal carrier through the optical cable, is resilient to harsh environments;
* Complexity of monitoring system architecture is reduced since optical cables may embed several gratings, simultaneously interrogated by a single acquisition unit with multiplexing capability.  
Moreover, this technology ensures by design:
* Minimal attenuation of the optical signal along the measurement chain;
* Immunity to electromagnetic interference.  
The working principle of Bragg grating fiber is based on its ability to reflect wavelengths like a mirror: when the FBG is crossed by the light coming from a broadband source, only a narrow range of wavelength centered on the Bragg wavelength \(\lambda_{B}\) is reflected back (see Figure 1). The remaining light continues to propagate along the optical fiber to the next grating without signal loss [3]. Both strain and temperature variations cause changes in the sensor grating period, thus producing a shift of the characteristic wavelength peak \(\lambda_{B}\). These changes in the back-reflected spectrum are detected by the same acquisition unit that emits the broadband light. In this way, each frequency acquired can be associated with the corresponding FBG and processed in order to obtain strain (see Figure 2) and temperature measurements. Thanks to the operating principle, it is possible to connect FBG sensors in series, possibly on several channels: since FBG is uniquely identified by its characteristic \(\lambda_{B}\), it is then possible to correlate strain and temperature changes to the progressive FBG position along the pipeline.  
Figure 1: Working process of the FBG technology (image source: [[https://scienzapertutti.infn.it/](https://scienzapertutti.infn.it/)]([https://scienzapertutti.infn.it/](https://scienzapertutti.infn.it/))), (accessed on 20 July 2022).
# Home Automation System Based on Intelligent Transducer Enablers
## 3 Plug-and-Play Mechanisms for Transducer Networks  
Home automation systems can benefit from the plug-and-play connection of transducers to the networks' nodes. It allows for simplifying application development, network deployment, service procedures and on-the-fly substitution of the sensors. A transducer can be considered plug-and-play when it becomes fully operational and networked after powering it up. This plug-and-play feature provides significant benefits:  
* Application development, sensor network deployment and services are simplified noticeably.
* Wireless Sensor Networks (WSN) nodes can be upgraded or reassigned dynamically for new tasks by just changing sensors.
* Sensors that become disconnected from a node due to unexpected reasons can be automatically taken out of use.  
It is important to emphasize that, since a common standard has not been established, there is not a straight answer to certain questions on the development of the system, such as how to communicate with the transducer network (wired or wireless), which set of protocols should be used to manage all of the elements, how to perform the system configuration or which are the rules to follow to control the system automatically.