esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Data Description
### Description of Data Simulation and Collection  
Three models are used for the multipath propagation on the Spinent GS8000 GNSS simulator. Rician fading and Rayleigh fading are used to model direct and multipath signals [33; 34]. An urban canyon environment, as shown in Figure 5, is created to test the proposed method. There are four category masks in the simulated environment. Category A is a visibility mask to represent obstructions such as high-rise buildings. Category B represents normal signal arriving at these segments with no reflections (data from Category B is labelled as direct-signal only data). Category C simulates signals with their reflected signals arriving at these segments (data from Category C is labelled as multipath contaminated data).  
Datasets listed in Table 4 were conducted using the Spinent GNSS simulator, using Land Mobile mode. A moving antenna is simulated, and the antenna constantly performs circular movement with a radius of 2 meters and a speed of 1m/s, from a position with International Terrestrial Reference Frame 08 (ITRF08) coordinates of 29\({}^{\circ}\)48\({}^{\prime}\)10.6682\({}^{\prime\prime}\)N, 121\({}^{\circ}\)33\({}^{\prime}\)24.2892\({}^{\prime\prime}\)E, and 1.0m H. In Test 1, Dataset SIMTR is used for training the detector and Dataset SIMT are used for parameter tuning. The tuned and trained detector is then validated with Dataset SIMTE. The proportion of direct-signal only and multipath signals are intentionally balanced (the redundant data are randomly screened out) in the training and test datasets so that the performance of the detector is unbiased for two classes. A Javad Triumph-VS receiver (San Jose, CA, USA) was used to collect simulated signals. The data were then processed by the detector to detect the multipath signals at each epoch, and assess the recall and rate of false detection.
## 2 Related Work  
Regardless of great progress in WSN research and development, limited work has been found in the open literature on WSANs. Some QoS issues in WSNs have been addressed in e.g. [2, 8], but QoS management in WSANs remains an important issue yet to be explored. [PERSON] _et al_[9] suggested a real-time communication framework to support event detection, reporting, and actuator coordination in WSANs. The framework takes into account the heterogeneous characteristics and functionalities of sensors and actuators. [PERSON] _et al_[10] presented a QoS-aware routing protocol with service differentiation for WSANs. [PERSON] _et al_[11] developed a reliable data transmission protocol for lossy and resource-constrained WSANs. [PERSON] _et al_[12] studied the impact of several network parameters on overall network performance via simulations. [PERSON] _et al_[13] presented a power-controlled real-time data transport protocol for energy-efficient and real-time transmission of packets. [PERSON] _et al_[14] deployed a real-world mobile WSAN for animal control in cattle breeding industry. The mobile WSAN is capable of estimating the dynamic states of bulls, and performing real-time actuation on the bulls from location and velocity observations. Trustworthiness issue in WSANs has been discussed in [15]. However, the QoS management issue has not been addressed in any of these works in terms of deadline miss ratio and/or network utilization.  
In our previous work [5], an application-level design methodology was proposed for WSANs in mobile control applications. In [16], a flexible time-triggered sampling scheme was also developed for wireless control systems. However, none of our previous reports have exploited fuzzy logic control based approach.  
Another area closely related to this work is the application of fuzzy logic control to resource management in real-time computing and communication systems. In the literature, the use of control-based methods for resource management is also called feedback scheduling [17-19]. Fuzzy logic control based feedback scheduling methods have been explored in our previous work [20-22] for embedded real-time control systems. In recent years, fuzzy logic control has also been widely applied in network congestion control, e.g., [23]. [PERSON] _et al_[24] proposed an approach to automating parameter tuning in web servers using a fuzzy controller. However, these papers have not explicitly dealt with WSANs. To the best of our knowledge, this paper is the first attempt to apply fuzzy logic control to QoS management in WSANs.
## 2 Materials and Methods
### Delivery Verification  
Calculated plans were dosimetrically verified to assess the deliverability accuracy. Delivered dose distributions were measured using the Octavius-4D phantom and the 1500 2D ion-chamber array (PTW, Freiburg, Germany). The 3D measured dose distributions were compared with the ones obtained with Pinnacle using the \(\gamma\)-index analysis [37].  
Following the tolerance limits suggested by the AAPM TG 218 [30], a dose verification was considered as optimal if the \(\gamma\)-values exceeded 95% and acceptable between 90% and 95%.
# A Systematic Review on Reinforcement Learning for Industrial Combinatorial Optimization Problems
## 3 State Representation
### Entity Features  
Many metrics evaluate job completeness. Examples include the percentage of operations completed, or _completion rate_[35, 41, 94, 136], the number of operations left [96, 113] and the overall job demand [79, 81, 137]. Instead of counting jobs, refs. [41, 132, 133] count the remaining processing time. The working _status_ of a job, if it is waiting, completed, etc., is a common feature [26, 96, 132], either categorical or one-hot-encoded.  
Some problems require monitoring resource or entity locations. In [75], each vehicle is described by its current zone, available seats, pick-up time and destination zone of each passenger. In [165], each of the four flight legs are described in terms of origin and arrival locations and departure and arrival times. For entities, tracking the current entity location [81, 140, 161, 168] can be of interest.
# Self-Healing of Semantically Interoperable Smart and Prescriptive Edge Devices in IoT
## 4 Experimental Results
### Data Set Description for Edge Monitoring Status  
A set of data was collected using measurements from 12 different Rpi devices. The raw data that describe the normal and anomaly events were acquired at regular 10-s intervals. The experiment took place in different working and residential areas. In total, 103,680 data points were collected, including information of CPU temperature (\"C), CPU usage (%), disk usage (%), and virtual memory usage (%). Table 9 presents the necessary statistics for the used variables.  
As the proposed autoencoder (AE) used a gradient descent method as an optimization technique, the input data had to be scaled. A standardization method was used, shifting the distribution of each attribute to have a mean of 0 and a standard deviation of 1.
## 1 Introduction
However, there are data from other authors that present the Sellmeer equations obtained from measurements at other wavelengths or from an NLO conversion [39; 40; 41; 42]. There are several reports on the generation and detection of THz waves in doped and undoped gallium selenide crystals using laser radiation at various wavelengths, including 0.8 um [2; 3; 8; 43], 1 um [9; 10], 2 um [44], and 10 um [45; 46]. However, to the best of our knowledge, crystals have not been studied as NLO generators and detectors of terahertz waves when pumped by laser radiation with wavelengths in the vicinity of 1.5 um. We find only two reports on the electro-optical coefficient measurement of the undoped crystals at a wavelength of about 1 um [47; 48]. Thus, seeing the lack of data, we present the study of the linear and nonlinear optical (electro-optical) properties of sulfur-doped gallium selenide crystals. It is done using a terahertz time-domain spectrometer (THz-TDS) configuration, wherein the crystals serve as electro-optic detectors for THz waves probed by radiation from an Er-fiber laser with a wavelength of 1.55 um. For this purpose, we measure the ordinary refractive index at a wavelength of 1547 nm and in the range of 136-1500 um (2.2-0.2 THz, respectively) of GaSe(1-x)S\({}_{x}\) (GaSe:S) crystals, where \(x\) = 0, 0.03, 0.12, 0.16, and 0.22. The dispersion of the terahertz refractive index is approximated in the form of the Sellmeier equations, and the coherence lengths \(L_{\text{coh}}\) providing efficient NLO interaction between telecom and THz waves were calculated. By comparing THz detection efficiency with a high-quality gallium arsenide (GaAs) crystal, the value of the electro-optical coefficients \(r_{22}\) of GaSe:S samples was estimated. All the results are compared with the previous studies. The optimum level of doping and the potential of employing GaSe:S crystals as nonlinear optical converters for photonic devices operating at telecom wavelengths is discussed.
# Vertex-Weighted Consensus-Based Formation Control with Area Constraints and Collision Avoidance
###### Abstract  
In this paper, a consensus-based formation control strategy is presented, subject to area constraints and collision avoidance. To achieve a desired formation pattern, a control law is proposed that incorporates a vertex-tension function along with signed area constraints. The vertex-tension function provides the capabilities of collision avoidance among agents. Moreover, signed area constraints avoid local minimum stagnation and mitigate ambiguities within the formation shape. Additionally, the proposed approach can be implemented considering a group of differential-drive mobile robots in both centralized and decentralized settings. Simulation and real-world experiments are performed to validate the effectiveness of the proposed approach, where the experimental setup includes a multi-robot system composed of Turtlebot3(r) Waffle Pi robots.  
Keywords:consensus-based control; differential-drive robots; signed area constraints; collision avoidance; Turtlebot3 Waffle Pi _Article_
## 3 Results and Discussion
### Gas Adsorption and Selectivity
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Sample**} & \multicolumn{2}{c}{**CO\({}_{2}\)**} & \multicolumn{2}{c}{**CH\({}_{4}\)**} & \multicolumn{2}{c}{**N\({}_{2}\)**} & \multicolumn{2}{c}{**H\({}_{2}\)**} & \multicolumn{2}{c}{**O\({}_{2}\)**} \\  & **(\(\mu\)mol g\({}^{-1}\))** & **(\(\mu\)mol g\({}^{-1}\))** & **(\(\mu\)mol g\({}^{-1}\))** & **(\(\mu\)mol g\({}^{-1}\))** & **(\(\mu\)mol g\({}^{-1}\))** \\ \hline HCLPP & 1010 & 310 & 90 & 21 & 12 \\ HCLPP-EDA & 910 & 130 & 33 & 15 & 47 \\ HCLPP-DETA & 820 & 130 & 29 & 12 & 42 \\ HCLPP-DENDRIMER & 1100 & 240 & 63 & 17 & 89 \\ \hline \hline \end{tabular}  
* at temperature 25 \({}^{\circ}\)C and pressure 100 kPa.  
\end{table} 
Table 3: Gas adsorption capacity of pure and functionalized porous HCLPP at 100 kPa and 25 \({}^{\circ}\)C.  
The obtained CO\({}_{2}\) adsorption data of the prepared materials were compared with other hyper-crosslinked porous polystyrene-based polymers studied for CO\({}_{2}\) capture application reported in literature (Table 6), for examples HCLPs prepared via the Friedel-Craft reaction between waste expanded polystyrene and 1,2-dichloroethane as both the solvent and the crosslinking agent [27], similar polymers produced using four cross-linkers in order to enhance the porosity of the polymeric adsorbent [28], or amine-functionalized porous polymer network prepared by conventional radical polymerization. In all cases, the material prepared in our study showed a comparable or slightly lower CO\({}_{2}\) adsorption capacity, but in most cases the higher selectivity. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Sample** & \(\textbf{S}_{\textbf{BET}}\)**, (m\({}^{2}\) g\({}^{-1}\)) & \
\begin{tabular}{c} **CO\({}_{2}\)**, (umol g\({}^{-1}\)) \\ **1 bar, 273 K, 298 K** \\ \end{tabular} &
\begin{tabular}{c} **CO\({}_{2}\)**/**N\({}_{2}\)** \\ **Selectivity** \\ \end{tabular} & **Ref.** \\ \hline HCP-3A & 179 & 932 & 9 & \\ HCP-6A & 299 & 1604 & 22 & \\ HCP-12A & 406 & 1987 & 273 K & 23 & [27] \\ HCP-24A & 548 & 1715 & 20 & \\ HCP-12B & 488 & 1771 & 20 & \\ HCP-12C & 573 & 1846 & 15 & \\ HCP-0 & 425 & 1201 & 17 & \\ HCP-A & 777 & 1473 & 38 & \\ HCP-B & 768 & 1178 & 298 K & 25 & [28] \\ HCP-C & 611 & 1258 & 15 & \\ HCP-D & 673 & 1359 & 15 & \\ P(DVB-VBC)-31 & 744 & 390 & 20 & \\ P(DVB-VBC)-31-EDA & 389 & 1150 & 152 & \\ P(DVB-VBC)-31- & 376 & 1440 & 298 K & 223 & [32] \\ P(DVB-VBC)-11 & 134 & 180 & & - \\ \hline \hline \end{tabular}
\end{table} 
Table 6: CO\({}_{2}\) capture and selectivity adsorption performance of some styrene-based polymers. Figure 5: CO\({}_{2}\) adsorption on the unmodified HCLPP measured at 25 \({}^{\circ}\)C using the laboratory constructed device and commercial apparatus ASAP 2050.
## 3 Results and Discussion
### Results from the Integration of the 1 st and 2 nd Models  
This section presents the prediction outcomes from the combined first and second ML models. Figure 20 illustrates the results for a specific test set. By comparing the ground truth (Figure 18) with the model predictions (Figure 20), it is evident that the model performs well in replicating particle spatial, velocity, and temperature distributions upon impact. The ground truth data shows that particles are primarily concentrated near the center of the substrate (\(y=0\) and \(z=0\)), with particle number density gradually decreasing as the distance from the center increases. The ground truth particle velocity in the \(x\)-direction (\(u\)) ranges from 420 to 750 m/s, with an average of 587.89 m/s, while the model predicts a range of 400 to 700 m/s, averaging 585.68 m/s--demonstrating strong consistency in distribution characteristics. In the \(y\)-direction, the ground truth velocity (\(v\)) range is approximately \(-5\) to 25 m/s with an average of 1.09 m/s, and the model predicts an average speed of 1.09 m/s. It should be pointed out that, although the velocity distribution (\(v\)) predicted by the model generally follows the same trend as the ground truth, it ranges from \(-2\) to 12 m/s, which deviates from reality. In the \(z\)-direction, the ground truth speed range is about 0 to 50 m/s with an average of 1.10 m/s, whereas the model only exhibits a range of 0 to 25 m/s with an average of 1.13 m/s. Regarding particle temperature, both the ground truth and the model prediction fall within a range of 550 to 820 K, with average temperatures of 683.29 K and 688.74 K, respectively, showing excellent agreement in temperature distribution.  
Overall, the model excels in predicting the spatial distribution of particles, as well as the normal impact velocity (\(u\)) and temperature of each particle upon impact in complex masked cold spray additive manufacturing. While particle velocities in the \(y\)- and \(z\)-directions are not critical in the cold spray applications, there is still room for improvement in capturing local fluctuations in these directions.
# Investigation of Biochars in Terms of Vitamin E Adsorption Capacity
## 2 Materials and Methods  
### Selection of Biochars  
Biochars were selected based on their authorisation to be used as feed for dairy cows in Germany [41; 42; 43]. To enable direct practical application, e.g., in the feed industry, another aim was to analyse commercially available biochars. Thus, samples of ten biochars authorised for use as feed additives were provided by the respective producers or distributors. The biochars are coded, as the providers do not wish to have a publicly open comparison of the respective adsorption properties. The reason lies in a possible market distortion, as the biochar market in Germany is small. To our knowledge, we received samples of nearly all biochars authorised as a feed additive in 2019. This procedure unavoidably resulted in the disadvantage of not knowing the actual production parameters, such as the pyrolysis temperature, heating rate, or retention time, or the proportions of the biomass. To keep our approach application-oriented, the ten biochars were analysed as received. This means that the particle size distribution and moisture content were neither adjusted nor standardised by sieving or drying. Moreover, biochar should always be administered moist to avoid the formation of dust [39; 42]. Ultimately, our decision to proceed with a strongly application-oriented approach by accepting the demands of the providers compromises reproducibility. Based on the selection of biochars authorised as feed additives in Germany, it is clear that the biochars were made of varying woody biomasses and generated under a range of pyrolysis conditions (Table 1).  
The analysed biochars were produced with an industrial-scale pyrolysis process and, thus, were (semi-)continuously under controlled conditions. One difference between the patented 'PYREG' machine and the kiln is that in the 'PYREG', a screw pyrolyzer and a low-oxygen burner are used, and some heat, generated during the burning of pyrolysis gases, is reused to maintain the process. These pyrolysis gases cannot condense on the biochar, which prevents possible negative effects [44]. In general, pyrolysis processes operate at more than 400 \({}^{\circ}\)C, whereby the retention time and highest pyrolysis temperature vary among producers [44].  
The differentiation between soft- and hardwoods was conducted according to [PERSON] [45]. Hardwoods come from angiosperms, meaning flowering plants, whereas softwoods come from gymnosperms, which are mostly conifers. In the northern hemisphere, most softwoods are needle-leaved and evergreen, such as pine and spruce, while most hardwoods are broad-leaved and deciduous, such as maple, oak, and birch [45].
# Leveraging Digital Twin Technology for Sustainable and Efficient Public Transportation
## 3 Challenges and Limitations of Digital Twins
### Technical Complexities
AVs enhance this trend and have dedicated hardware to compute and gather information on AI systems, allowing them to make decisions based on the road environment. Vehicles will need additional hardware and processes to interact with ITSs, to communicate the road conditions and accident factors or traffic status, which is likely to increase the capital cost of vehicles.
## 2 Literature Review
This innovation allowed for more accurate and contextually rich captions, especially in complex scenes, demonstrating the potential of adaptive attention mechanisms in improving the quality of captions. [PERSON] et al. [30] proposed a dense image captioning model in Hindi. Their approach leveraged both computer vision and natural language processing to generate localized captions for multiple image regions, addressing the need for dense captioning in low-resource languages. [PERSON] et al. [31] explored Tamil NLP technologies, outlining the challenges, state of the art, trends, and future scope. Their work provided a comprehensive overview of the advancements and future directions in Tamil language processing, offering valuable insights into the development of NLP technologies for under-resourced languages. [PERSON] and [PERSON] [9] advanced cross-lingual sentiment analysis with their innovative Multi-Stage Deep Learning Architecture (MSDLA), specifically focusing on the Tamil language. Addressing the challenges inherent to low-resource languages, their approach utilized transfer learning from resource-rich languages to overcome the limitations of scarce data and complex linguistic features in Tamil. The model's efficacy was highlighted by its impressive performance on the Tamil Movie Review dataset, where it achieved notable accuracy, precision, recall, and F1-scores. This research not only marks a significant advancement in sentiment analysis for Tamil but also sets a precedent for developing robust models for other low-resource languages, offering vital insights and methodologies for future explorations in the field. Existing research in the domain of image captioning has seen substantial advancements, particularly with the introduction of deep learning architectures and attention mechanisms. However, several gaps remain unaddressed. First, most current models lack the ability to dynamically adapt to varying types of images and contexts, which results in captions that may be accurate but are not necessarily contextually rich or meaningful. Second, while there has been some progress in extending image captioning techniques to low-resource languages, these approaches often do not account for the unique linguistic and contextual complexities such languages present. Finally, the computational efficiency of dynamically adaptive models remains a challenge that has not been thoroughly explored. The proposed DCAT model is designed to bridge these research gaps. By introducing a dynamic context-aware mechanism, it adapts to different types of images and contexts, thereby generating more descriptive and contextually relevant captions. The model is specifically fine-tuned for the Tamil language, addressing the scarcity of research in image captioning for low-resource languages. Furthermore, the proposed DCAT provides a step forward in balancing performance and computational efficiency, opening the door for future optimization strategies.
# Transitioning Broadcast to Cloud
## 2 Processing Chains in Broadcast and Cloud-Based Online Video Systems
### Main Functions and Distribution Flows  
In broadcast, everything is based around processing and delivery of a set of live streams, visible to end users as \"TV channels\". As shown in Figure 1, the selection or scheduling of input feeds that go in each channel is done by master control or playout systems. Such systems also insert graphics (e.g., channel logos or \"bugs\"), slots for ads, captions, metadata, etc.  
After playout, all channels are subsequently encoded and passed to a multiplexer, which combines them in a multi-program transport streams (aka TS or MPEG-2 TS [17]) intended for distribution. In addition to channel's media content, the final multiplex TS also carries program and system information (PSIP [18]), SCTE-35 ad markers [19; 20], and other descriptors required for broadcast distribution [21].  
As further shown in Figure 1, the distribution chain in broadcast systems may have multiple tiers--from main network center to local stations and also multichannel video programming distributors (MVPDs), such as cable or satellite TV companies. At each stage, media streams corresponding to each channel can be extracted, modified (e.g., by adding local content or ads), re-multiplexed into a new set of channels, with new program tables and other metadata inserted, and then again sent down to distribution or next headend.  
In other words, we see that broadcast systems are effectively responsible for both formation of the content, turning it into to a set of channels, and then distribution of content to the end users.  
In contrast, online video platforms are used primarily for distribution. They assume that content is already fully formed. As shown in Figure 2, live inputs are typically turned into live output streams, and pre-recorded media files are typically published as VOD assets. They transcode and repackage inputs into HLS [5], DASH [6], or MSS [7] streaming formats, and then pass them to Content Delivery Networks (CDNs) for propagation and delivery to end user devices (streaming clients). In some cases, OVPs may also be used for live to VOD conversions and VOD content management, but not for formation of live streams.  
Another important difference between online video systems and broadcast is the availability of the feedback chain. In Figure 2 it is shown by contour arrows connecting players and CDNs to the analytics module within OVP. This module collects playback and CDN usage statistics, and turns them into metrics used for ad-monetization/billing, operations-control, and optimization purposes [22; 23].