esa-sceva/satcom-chunk-collection huggingface dataset:
# 2D-Unitary ESPRIT Based Multi-Target Joint Range and Velocity Estimation Algorithm for FMCW Radar
###### Abstract  
Millimeter-wave FMCW radar has been widely used in joint range-velocity estimation of multiple targets. However, most existing algorithms are unable to estimate the range-velocity information with high accuracy simultaneously and fail to discriminate the targets with either closely spaced ranges or closely spaced velocities in the 2D range-Doppler spectrum. In order to deal with these problems, this paper proposes a 2D-Unitary ESPRIT-based joint range and velocity estimation algorithm of multiple targets for FMCW radar. Firstly, The 1D-IF signal is constructed into a 2D virtual array signal, the virtual array signals are preprocessed by a 2D-spatial smoothing technique to generate a new matrix signal. Then, according to the 2D-Unitary ESPRIT algorithm, the 2D real-valued information of the target parameters is obtained from this matrix signal, and then a new complex-value matrix is constructed. Finally, the eigenvalue decomposition of this new complex-value matrix is performed, and the range-velocity estimates of multiple targets are, respectively, calculated from the real and imaginary parts of the eigenvalues, and paired automatically. The simulation results illustrate that the proposed algorithm not only provides highly accurate range-velocity estimates but also has high-resolution performance and achieves automatic pairing of the range-velocity estimates in multi-target scenarios, thus effectively improving the multi-target joint range and velocity estimation performance of FMCW radar.  
FMCW radar; spatial smoothing technique; 2D-Unitary ESPRIT algorithm; joint range and velocity estimation
# Feasibility of Non-Remanufactured Waste Bottle Glass as Supplementary Cementitious Material
## 1 Introduction
### Research Significance and Objectives  
The use of glass in concrete production in large quantities needs an adequate characterisation of waste glass properties (chemical and physical) and an understanding of the waste glass effect on final concrete product engineering properties [15; 20; 46].  
Based on the literature survey, cement-based materials incorporating ground waste glass seem promising. However, research is still required to set a guideline for the appropriate use of ground waste glass in cement-based materials. Several glass types may present dissimilar chemical compositions, originating different effects on the final cement-based material product. Therefore, it may be necessary to establish the pre-treatment process before application.  
Theoretically, glass can be recycled entirely, but there are several requirements for remanufacturing. For the first time, this work studied industrial bottle waste glass (WG), which cannot be used to remanufacture new glass as a cement replacement for concrete applications. The first stage of this study consisted of a deep analysis of WG and laboratory treatments to remove (still present) fibre and paper contaminants and improve pozzolanic properties. The second phase studied the mechanical and durability properties of treated wastes in binary blended cement mortars using waste glass as a cement replacement.
###### Abstract  
Detection and classification of acute psychological stress (APS) and physical activity (PA) in daily lives of people with chronic diseases can provide precision medicine for the treatment of chronic conditions such as diabetes. This study investigates the classification of different types of APS and PA, along with their concurrent occurrences, using the same subset of feature maps via physiological variables measured by a wristband device. Random convolutional kernel transformation is used to extract a large number of feature maps from the biosignals measured by a wristband device (blood volume pulse, galvanic skin response, skin temperature, and 3D accelerometer signals). Three different feature selection techniques (principal component analysis, partial least squares-discriminant analysis (PLS-DA), and sequential forward selection) as well as four approaches for addressing imbalanced sizes of classes (upsampling, downsampling, adaptive synthetic sampling (ADASYN), and weighted training) are evaluated for maximizing detection and classification accuracy. A long short-term memory recurrent neural network model is trained to estimate PA (sedentary state, treadmill run, stationary bike) and APS (non-stress, emotional anxiety stress, mental stress) from wristband signals. The balanced accuracy scores for various combinations of data balancing and feature selection techniques range between 96.82% and 99.99%. The combination of PLS-DA for feature selection and ADASYN for data balancing provide the best overall performance. The detection and classification of APS and PA types along with their concurrent occurrences can provide precision medicine approaches for the treatment of diabetes.  
recurrent neural network; long short-term memory; feature selection; imbalanced data; activity recognition; acute psychological stress detection; precision medicine; diabetes
# Analysis of Total-Forms of Cyanotoxins Microcystins in Biological Matrices: A Methodological Review
## 4 Quantification of Total MC after Base-Catalyzed Deconjugation  
Conjugated forms of MCs are often present in natural samples, sometimes at much higher concentrations than free MCs [106; 107; 108; 109; 59]. One of the drawbacks of the analysis of total MCs when using the MMPB method is the loss of information on the MC variants. Indeed, the oxidative cleavage at the Adda fragment only preserves the MMPB fragment, common to all MCs and nodularin. It has been observed in the past that the Michael addition of thiols to MCs was reversible [110; 111; 112]. Different studies have then focused on the phenomenon of the deconjugation of MCs. The first studies on the subject focused onMCs that are synthetically conjugated in a laboratory [113; 114; 115] and then on tissues naturally contaminated with cyanotoxins and containing free and bound MCs [59]. Although this deconjugation can occur at neutral pH, studies have shown that deconjugation at basic pH increases the rate and efficiency. Two research teams are interested in deconjugation at basic pH as an alternative to the MMPB method.
## 2 Proposed Architecture  
### Proposed V2V-VLC System  
Figure 1 shows an overall concept of the proposed V2V-VLC system. The vehicle 1 uses two WLED headlamps as a transmitter and also has two front-view cameras (\(C_{L}\) and \(C_{R}\)) to capture the taillights of the front vehicles (2 and 3). The license plate located between the taillights plays a role as an optical receiver. It is composed of a rectangular FC and an avalanche photodetector (APD) located at the edge. Headlamps can be used for both lighting and data transmission purposes. A blue LD module is installed between the two cameras to deliver a high data-rate communication channel by beam steering. It also provides flexibility in reconfiguring the links for robustness against blockage and misalignment. The large coverage area of the FC leads to higher optical gain, which is suitable for both the headlamp and blue-LD-based communication.
# COVID-Related Misinformation Migration to BitChute and Odysee
## 4 BitChute Analysis
### Titles and Descriptions  
We analyzed the titles of the BitChute videos and extracted parts of speech using the spaCy library. The average number of tokens for the BitChute title is 8.2, where 2.1 are nouns, 0.7 verbs, 0.36 numbers, 1.85 pronouns, 0.82 punctuation, and the rest are other parts of speech, such as adjectives, auxiliaries, etc. The nouns dominate, as expected, while verb  
Figure 4: Temporal distribution of BitChute videos.  
Figure 5: Histogram of likes and dislikes for the top 20 disliked BitChute videos.  
usage in BitChute titles is limited. It is noticeable that pronouns are used almost as much as nouns. In general, there is evidence that possessive pronouns (e.g., \"my\"), Wh-Determiners (e.g., \"which\") and Wh-Adverbs (e.g., \"where\") occur more often in clickbait headlines as compared to legitimate news headlines, creating a curiosity gap by asking questions which entice users to click in order to satisfy their need for information [80].  
Additionally, we applied the aforementioned spaCy language detection model on the BitChute titles and similar to the Oxford dataset, we found that English dominates the dataset (\(\approx\)72% of titles). The second most frequent language is German with a percentage of \(\approx\)15%. Spanish is the third language (\(\approx\)2%).  
For the video description, the average number of tokens is 128. It is a typical case for the description to be longer. As a next step, we searched for keywords (deleted, banned, removed) that refer to content take-down to investigate whether the BitChute users informed the viewers that the video was deleted from other platforms. This resulted in only 99 video descriptions with the keyword 'deleted', 330 with the keyword 'banned' and 153 with the keyword'removed', that corresponds to 2.5%, 8.3% and 3.9% of the videos, respectively. Example sentences that appear in video descriptions and contain the keywords mentioned above include: 'Deleted YouTube video of a German Journalist visiting a \"hospital with coronavirus patients\", but finds no one there', 'This video is VERY BIG and it's getting deleted in YouTube. Make sure to download it and spread it around.' and '[PERSON]'s explosive interview with London Real, which sparked controversy after being banned from YouTube and Vimeo.' We calculated the average number of likes for the videos containing the keyword 'deleted' in their description and compared it with the rest. The average number of likes for those containing the keyword is 54, while the average on 10 random subsets of the same number of videos (99) without the keyword in their description was much less, 24.6. Similarly, for the 330 videos containing the keyword 'banned', the average number of likes was found to be 42, while for 10 random subsets, the average number of likes was computed to be 25.5. Different behavior appears for the keyword'removed', where the average number of likes for videos containing the keyword and the same number for 10 random samples was found to be quite close, at 24.5 and 22, respectively.
# Hybrid Renewable Systems for Small Energy Communities: What Is the Best Solution?
## 3 Case Study
### Results and Discussion
#### 3.2.1 Sa1--**Pv + Wind + PSH  
The fundamental configuration of the off-grid system, named SA1, employs the combination of solar and wind energy sources, with the supplementation of a pumped-storage hydropower system. The optimization method, defined as OPT5, which minimizes the required installed capacity while adhering to the 100% grid independence constraint, yielded the power installments for each energy source, which are distributed as 40 kW in the PV system, 120 kW in wind turbine, 20 kW in pump, and 40 kW in turbines, with the total power equal to 220 kW.  
In the absence of an external grid, the peak winter months require the installation of supplementary power sources to meet the increased demand for energy. Accordingly, the power installed yielded by the optimization generates three times the total consumption energy, in case all the energy sources are permanently connected throughout the year. Figure 9 illustrates the energy and water balances throughout the year, with particular emphasis on the excess energy (i.e., wasted energy) generated by the system during periods of low or average demand. The combination of wind and solar energy markedly augments the system's capacity to meet the demand. While the optimized power installations may be deemed excessive for the average consumption load, it is nevertheless imperative to guarantee sufficient capacity during peak months.  
The issue of wasted energy can be addressed by either deactivating primary renewable sources or, ultimately, connecting the microgrid to the national grid for exclusive exportation. Figure 10 presents the daily balance on two distinct days. The consumption profile on 12 February is significantly greater than on 12 November.  
Figure 9: SA1—PV + Wind + PHS yearly balance: (**a**) energy balance, (**b**) water balance, (**c**) energy by sources, (**d**) power data, and (**e**) statistics (between percentile 25 and 75%).  
Figure 10: SA1—PV + Wind + PSH yearly balance: (**a**) daily balance on February 12, (**b**) daily balance on November 12, (**c**) solar and wind generation profile.  
The high energy demand requires more hydropower generation to satisfy the unmet consumption by solar and wind energy sources. Subsequently, February is characterized by the balance presented, wherein the pumped volume is restricted to a few hours, defined in Figure 10a, permanently decreasing the reservoir's stored volume, and hydropower is highly required to fulfill the energy needs. On the contrary, for months of lower demand, such as is presented in Figure 10b, the installed power of solar and wind is enough to completely fulfill the energy load, with its surplus being used to pump significant volumes of water for storage.
## 4 Self-Healing Approaches
### Network Architecture
#### 4.2.4 Virtual Machine  
A virtual machine (VM) is a software emulation of a computer system that can run an operating system and application like a physical computer. In a self-healing system, VM can implement a self-healing mechanism in several ways. For example:  
* Isolation: A VM can isolate applications and services from each other. If one application or service experiences a failure, it can be restarted within the VM without affecting the other applications or services running on the same physical machine;
* Redundancy: Multiple VMs can be deployed to provide redundancy for critical applications or services. If one VM fails, another can take over its workload to ensure continuity of service;
* Rapid provisioning: VMs can quickly be configured to meet changing workload demands. The proposed method enables the network to scale up or down as needed by utilising shared operation and spare nodes in each neural network layer, ensuring performance and availability are maintained;
* Testing and validation: VMs can test and validate self-healing mechanisms before they are deployed in a production environment. Implementing these approaches can help ensure the effectiveness of the tools without causing unintended consequences.  
In their study, Ref. [17] investigated heterogeneous events in which different software stacks run on virtual machines (VMs) and physical machines (PMs) within a cloud environment, each operating at varying levels of primitives and capacity. The primitives tend to correspond to similar heterogeneous QoS for different service instances. Adaptive QoS models concerning each service instance were created to overcome the heterogeneity problem. As such, the service instance on VM (virtual machine) utilises the same computational resources as other service instances running on different VMs. Network virtualisation has been adjudged a novel approach for implementing promising heterogeneous services and building the next-generation network architecture [29]. Network virtualisation through VMs promises better utilisation of resources and increased network service delivery. Network virtualisation's possibilities prompted [29] to propose a novel VN restoration approach called MFP-VNMH, which enhances VN mapping and service restoration. By isolating applications and services, providing redundancy, enabling rapid provisioning, and facilitating testing and validation, VMs can play a critical role in implementing effective self-healing mechanisms.
# Metabolic Energy Consumption during Green Area Management
## 2 Materials and Methods  
### Field Sampling and Employed Equipment  
The research area was located at an altitude of 150 m a.s.l, and the experimental trial was carried out during the springtime. The tests were carried out on a flat area characterised by perennial and small grasses when weather conditions presented an average temperature of 30 \({}^{\circ}\)C at an air velocity of 3 ms\({}^{-1}\) as the benchmark and barometric pressure of 1013.25 hPa, common parameters detectable in the summer period at this latitude. The baseline relative humidity (RH) was set at 50 \(\pm\) 10%. The reason for this choice lies in the fact that humans are sensitive to changes in temperature, even if they are not able to perceive differences in RH between 25 and 60% ([PERSON] et al., 2018). Out of these values, as reported by [PERSON] et al. (2018), the moisture itself cannot evaporate from the skin and increases friction between skin and clothing (RH high) or leads to conditions of dryness on the skin and mucous (RH low). Twelve male workers, classed into three groups of four workers each, according to age (Table 1), were monitored.  
The sampling scheme adopted (Figure 1) was carried out in accordance with the EN ISO 8996:2021 standard, level 4 for experts, to be adopted for light and moderate work. The steady state is reached after a period of 3 min (preliminary period) during which work is performed at low intensity, and the main period (full activity) lasts 10 min, with regular, continuous sampling. An additional 5-min non-work (rest phase) period, during which the operator suspended work and assumed a posture suitable for recovering the energy consumed during the work phase, was also monitored to evaluate the energy recovery process.  
A STHIL brush cutter model FS87R, with a mass of 5.3 kg, motor power of 0.95 kW and a total length of 180 cm, was used during the tests. Three different cutting heads (Figure 2) were tested: (A) a grass-cutting blade for gnarled bushes, young trunks, and low cutting; (B) a shredding blade for tough undergrowth; (C) a brush-cutting blade for thinning and removal of tough grass, plant felt and brushwood. For each worker, an average of three replicates of the sampling scheme were performed for each blade type (totalling nine replicates per worker).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Worker Groups** & **Age** & **Mass** & **Height** \\  & **(Years)** & **(kg)** & **(cm)** \\ \hline I & 20–30 & 70–75 & 160–170 \\ \hline II & 31–40 & 75–80 & 160–175 \\ \hline III & 41–50 & 70–75 & 165–170 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Anthropometric data of workers (range).  
Figure 1: Sampling test scheme adopted to monitor the energy consumed. Three periods can be distinguished: preliminary period of 3 min (in red on the left); the main period of full working of 10 min (in the centre in green); the rest period of non-work with a duration of 5 min (in blue on the right).
## 4 Results
### Model Performance: Sexism and Racism Detection
For racism detection, Logistic Regression delivered notably better results, achieving a precision of 86%, recall of 53%, F1 score of 66%, and ROC-AUC of 0.87 in the single-turn setup. The contextual variant improved precision to 93% but reduced recall to 46%, resulting in a slightly lower F1 score of 61% and an ROC-AUC of 0.89.
###### Abstract  
The global digital elevation model (DEM) is important for various scientific applications. With the recently released TanDEM-X 90-m DEM and AW3D30 version 2.2, the open global or near-global coverage DEM datasets have been further expanded. However, the quality of these DEMs has not yet been fully characterized, especially in the application for regional scale studies. In this study, we assess the quality of five freely available global DEM datasets (SRTM-1 DEM, SRTM-3 DEM, ASTER GDEM2, AW3D30 DEM and TanDEM-X 90-m DEM) and one 30-m resampled TanDEM-X DEM (hereafter called TDX30) over the south-central Chinese province of Hunan. Then, the newly-released high precision ICESat-2 (Ice, Cloud, and land Elevation Satellite-2) altimetry points are introduced to evaluate the accuracy of these DEMs. Results show that the SRTM1 DEM offers the best quality with a Root Mean Square Error (RMSE) of 8.0 m, and ASTER GDEM2 has the worst quality with the RMSE of 10.1 m. We also compared the vertical accuracies of these DEMs with respect to different terrain morphological characteristics (e.g., elevation, slope and aspect) and land cover types. It reveals that the DEM accuracy decreases when the terrain elevation and slope value increase, whereas no relationship was found between DEM error and terrain aspect. Furthermore, the results show that the accuracy increases as the land cover type changes from vegetated to non-vegetated. Overall, the SRTM1 DEM, with high spatial resolution and high vertical accuracy, is currently the most promising dataset among these DEMs and it could, therefore, be utilized for the studies and applications requiring accurate DEMs.  
DEM; SRTM; ASTER GDEM; AWED30 DEM; TanDEM-X DEM; ICESat-2; vertical error; accuracy +
Footnote †: journal:  
Article
## 3 Results
### Relationship Between \(S_{\rm GPP}\) and \(S_{\rm ER}\)  
\(S_{\rm GPP}\) and \(S_{\rm ER}\) exhibited a highly significant positive correlation, \(p<0.001\), \(R^{2}=0.27\) (Figure 5A). The slope of the linear regression (slope = 0.31) indicates that \(S_{\rm ER}\) was more stable compared to \(S_{\rm GPP}\). This suggests that for every one-unit change in \(S_{\rm GPP}\), \(S_{\rm ER}\) changes by 0.31 units.  
Furthermore, we examined the relationship between \(S_{\rm GPP}\) and \(S_{\rm ER}\) across ecosystems. With the exception of mixed forests, all other ecosystems exhibited a significant positive regression relationship between \(S_{\rm GPP}\) and \(S_{\rm ER}\). For example, in cropland, forest, and grassland, \(S_{\rm GPP}\) and \(S_{\rm ER}\) showed significant positive correlations, with \(p\) and \(R^{2}\) of 0.011, 0.32; \(<\)0.001, 0.36; and 0.006, 0.18, respectively (Figure 5B). Broadleaf forest displayed the strongest linear relationship between \(S_{\rm GPP}\) and \(S_{\rm ER}\), with \(p<0.001\), \(R^{2}=0.61\), while no linear relationship was observed in mixed forest. Furthermore, the regression slopes between \(S_{\rm GPP}\) and \(S_{\rm ER}\) were less than 1 for all ecosystems, indicating that \(S_{\rm ER}\) is more stable than \(S_{\rm GPP}\).  
Figure 4: Differences and variability in the sensitivity of gross primary productivity and ecosystem respiration to precipitation (\(S_{\rm GPP}\) and \(S_{\rm ER}\)) among different ecosystems: (**A**) \(S_{\rm GPP}\) differences, (**B**) \(S_{\rm ER}\) differences, (**C**) variability in \(S_{\rm GPP}\), and (**D**) variability in \(S_{\rm ER}\). The letters in panels (**A,B**) indicate significant differences (\(p<0.05\)), while the numbers in panels (**C,D**) represent the coefficient of variation (CV) for each ecosystem.