esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract
Detailed visualisation and coordination between teams reduces miscommunication and promotes a safer working environment [14]. Several studies have highlighted the relevance of BIM in construction safety management, looking at its various applications, such as knowledge-based solutions based on the prevention through design (PtD) approach [15], automated compliance checks that allow for the identification of high-risk areas during the design phase [16], and the verification of compliance with local regulations [17]. In addition, BIM facilitates the detection of site conflicts by identifying the proximity between workers and equipment [18], provides proactive feedback using temporal and spatial data to assess worker safety [19; 20], supports worker training [21], and enables the analysis of worker behaviour by detecting inappropriate use of personal protective equipment (PPE) [22] or integration with behaviour based safety (BBS) [23], helping to raise worker awareness and correct unsafe practices. Regarding the integrated use of BIM with real-time monitoring technologies, the study by [PERSON] et al. [24] shows promising research that provides real-time alerts for unsafe worker behaviour. This research challenge covers not only the individual level, but also focuses on the interactions between workers and the site environment. Another approach is to use BIM in the lifecycle maintenance of the building, where BIM can bring benefits in terms of reduced equipment downtime, reduced costs, and increased customer satisfaction, while improving the exchange of information between stakeholders [25]. Recent evidence suggests that more practical applications of BIM are needed, particularly in regards to safety training and education, using the technology to strengthen the climate of safety and resilience and promoting the development of quantitative risk analysis to support safety management more effectively [24]. The main objective of this article is to analyse the existing technologies in this field of research in order to identify the different types of real-time monitoring technologies related to falls from height, their integration with the BIM methodology, and their contribution to the improvement of occupational safety and health in the construction sector. In light of the above, this article consists of a systematic review carried out using the PRISMA (preferred reporting items for systematic reviews and meta-analyses) review protocol [26; 27] to assess the current state of the field and provide recommendations to guide future research into real-time worker monitoring technologies in the construction industry. This paper is structured as follows: Section 2 outlines the methodology for reviewing the existing literature, Section 3 presents the findings, Section 4 discusses these findings, and the paper closes with concluding thoughts and recommendations for future research.
# Energy Efficiency Perspectives of PMR Networks +
## 5 Technical and Economic Considerations on Energy Efficiency of Next Generation PMR Systems
### Radio Resource Management Strategies for Energy Efficiency
To compare the energy efficiency performance of the advanced TETRA systems and the 4G commercial standard LTE, we consider a volume of traffic 100 times greater than the daily average traffic load of a current TETRA base station. Figure 11  
Figure 10: Cell DTX: power amplifiers can be put in low power state if there is no downlink traffic. displays the performance of the described energy efficiency solutions for TEDS and LTE systems, in terms of daily energy consumption of a single base station. We consider TEDS systems with 50 kHz and 150 kHz bandwidths and assume \(\delta=0.1\) for the case of LTE with cell DTX. We observe that the combination of carrier sleep mode and power control on the BCCH carrier can bring striking energy savings for TETRA network, although the energy efficiency performance of such systems is very far from the performance of the current LTE commercial standard. Despite the high hardware efficiency of LTE system, the introduction of cell DTX helps to further break down the energy consumption of the network by significantly reducing the baseline power consumption of the base stations. Looking at the PMR broadband evolution over the LTE system, such a feature represents a very promising energy saving approach, considering the typical low traffic density of PMR systems, compared to cellular public radiotelephone standards. From an economic perspective, the results presented in Figure 12 show the impact of the power saving features in terms of annual OPEX, considering the scenario of a regional network with 150 base stations and an energy cost of 0.20 Euros per kWh. Adopting advanced energy efficiency strategies in the TETRA network results in significant OPEX savings of up to 70 thousand Euros per year of operation, compared to the current standard technology; this positive trend is also considerable with the introduction of highly efficient cell DTX solutions in LTE networks. Regarding the sustainable development of modern communications systems, the overall network energy saving also guarantees a remarkable reduction in terms of carbon emission of CO\({}_{2}\): considering an average carbon emission of 525 kg of CO\({}_{2}\) per 1000 kWh, the proposed energy efficiency solutions can save more than 1 ton of CO\({}_{2}\) per year in advanced TETRA networks. In the case of LTE systems, the adoption of cell DTX has an equally great impact, by granting more than 80% reduction of annual carbon emission of CO\({}_{2}\), with respect to the standard LTE network configuration. Figure 11: Comparison of daily average energy consumption of TETRA and LTE radio base stations.
## 4 Discussion
Moreover, the antioxidant activity changes in the sample with the extract will decrease by 7.85% during one month when compared to the standard sample (LWS) with a decrease of 11.32%. This indicates that the addition of fruit extracts to wafers increased the oxidative stability of the product. The protective effect of the extract is the most evident during the first six months of storage. Many researchers reported the positive effect of a wide spectrum of plants and their extracts added to the confectionary products including fruit extracts on the oxidative stability of fats [15; 16; 17; 18]. [PERSON] and [PERSON] [18] tested the addition of orange peel flour to cookies and confirmed the increase in their antioxidant properties. A similar observation was performed by [PERSON] et al. [17], after the addition of pomegranate extract to cookies. [PERSON] et al. [15] revealed that the ethanol extract from the wafers with the highest addition of the raspberry extract scavenged the DPPH radical almost ten times more effectively than the control extract. However, all those authors did not investigate the antioxidant activity of the lipid fraction of the confectionary product, which is how it was performed in presented study. The decrease in the oxidative stability of confectionary products with time [3; 10; 16] and the protective effect of plant extracts on the lipid fraction has been previously described by other researchers [5; 29; 32]. However, there are no studies on the oxidative stability of wafers treated with commercially available natural extracts. In the food industry, only ingredients of a repeatable quality could be used to ensure food safety to consumers. Since this extract is of natural origin it can be easily accepted by the consumers and their demands for a \"clean label\" can be met. Moreover, in contrast to the previous studies carried out at the laboratory scale, in the presented study, wafers were produced in the factory (macro-scale production), which makes these results more valuable for consumers and also producers since they suit to the real production and storage conditions. The oxidation process of confectionary products is of special interest, since the products are treated with high temperature, often contain a high fat content and their storage time is relatively long (12 months for wafers). As it was indicated by [PERSON] et al. [3] the oxidative stability of the wafers decreased during the 12-month storage period and the oxidation process was the most pronounced during the first 3 months. Others [16] showed that the highest peroxide values of cookies with chokeberry extract were obtained after 9 weeks of storage and thereafter decreased. Moreover, there was no protective effect when comparing the treated-samples (with 0.025% and 0.1% of the extract) to control. Similar observations were made in the presented study, that the tested fruit extract was not able to protect lipids for the entire storage period, i.e., 13 months. However, the period of antioxidant activity of extract was relatively long compared to previous studies, i.e., 10 months.
## 3 Results
### Synthesis of Results
#### 3.4.3 Effect of AI-Assisted Physiotherapy on Mental Health  
Mental health outcomes were assessed in five of the eight included studies. Among these, four studies reported significant improvements in mental health with AI-assisted physiotherapy [21; 30; 32; 33], while one study found no significant differences compared to usual physiotherapy [31]. Of the six studies included in the meta-analysis, three specifically measured mental health status before and after the intervention [30; 31; 33]. Two studies employed the Veteran's Rand 12-Item Health Survey Mental Component Summary (VR-12-MCS) [21; 30], while one used the Fear Avoidance Beliefs Questionnaire (FABQ) [31].  
Pooled effect sizes were calculated using a random-effects model. The results indicated that while AI-assisted physiotherapy demonstrated a slight improvement in mental health status compared to usual physiotherapy, the statistical analysis did not show a significant difference between the two treatments (SMD = \(-\)0.0328, 95% CI: \(-\)0.1972 to 0.1316, \(p\) = 0.6956) (Figure 5a). Heterogeneity analysis revealed no significant heterogeneity between studies (I\({}^{2}\) = 0, \(\tau^{2}\) = 0), indicating consistency in the reported outcomes.  
Publication bias was assessed using the Egger regression test, which found no significant evidence of bias (intercept = \(-\)0.1133, 95% CI: \(-\)1.0113 to 0.7847, t = \(-\)1.6034, \(p\) = 0.355). This was further confirmed by visual inspection of the funnel plot (Figure 5b), which showed a uniform distribution of scatter points with no clear tendency toward bias. Despite these findings, the limited sample size may preclude the complete exclusion of bias.  
A sensitivity analysis was conducted using a random-effects model to evaluate the robustness of the results (See Figure 5a). The analysis involved sequential exclusion of individual studies to observe their impact on the pooled effect sizes. The results showed no significant changes in the overall findings regardless of which studies were excluded, indicating the robustness of the meta-analysis results (See Figure 5b).
# Paper-Mill Wastes for Bioethanol Production in Relation to Circular Economy Concepts: A Review
## 5 RPS for Energy Applications
### Paper Sludge as a Source of Ethanol  
Several PS valorization routes have already been suggested and explored. Many different products have already been produced from sludge, including: nanocellulose [59], lactic acid [156], cellulase [8], isoprene [157], microbial lipids [158], and building materials supplement [159]. In addition, various WtE routes have also been suggested for a variety of energy products, including: hydrogen [160], bio-gas from anaerobic digestion (AD) [161], butanol through acetone-butanol-ethanol fermentation [162], bio-oil [163], bio-methanol through gasification [164], and bioethanol. These solutions have yet to be found sustainable, resulting in PS being landfilled for the most part. The conversion of sludge to ethanol holds several economic benefits, and even negative costs, since using PS to produce other products reduces landfilling and shipping costs, environmental fees, and eliminates purchasing costs without the need to purchase the original source material. In addition, other by-products from the papermaking process, like kraft pulp and Spent sulfite liquor, have also been suggested for ethanol production, [22]. Table 8 shows different studies on bioethanol production from papermaking process waste by-products.  
A large amount of water is required in the papermaking process for both the reaction media and for use as wash water. Depending on the type of raw material input and the process conditions, there is great variability in PS properties, such as organic content, ash content, and pH [22]. In addition, there is a large variation in the chemical composition of the PS produced in different pulp mills. Previous work showed differences in the chemical composition of 37 PS samples that originated from different mills, depending on the feed material and the upstream processing in papermaking [152].  
Although PS shows great promise, a major barrier in PS conversion to ethanol is its high ash content. The ash, mostly CaCO\({}_{3}\), absorbs enzymes and increases sludge pH levels and suppresses enzymatic activity. Acidic treatments can be used for neutralization to minimize this limitation [172]. Moreover, high ash content limits the total solid load, eventually increasing processing costs. PS also has high water-holding capacity and viscosity, resulting in inefficient mixing and poor mass transfer [22].
# Off-Line Optimization Based Active Control of Torsional Oscillation for Electric Vehicle Drivetrain
## 2 Drivetrain Modelling  
The establishment of drivetrain is based on a rear-wheel-drive electric vehicle with integrated motor-transmission system. A six degrees of freedom model is applied to develop the dynamic equation as shown in Figure 3, it can reveal frequency characteristics, decoupling ratio, transmissibility, etc. of the EV drivetrain more accurately than the conventional two or three degrees of the freedom model [30]. The drivetrain model consists of motor rotor, input shaft and output shaft of transmission (neglecting gear lash), final drive, flexible drive shaft, wheels, and translational mass of vehicle body. The symbols and subscripts of the drivetrain mathematical model are shown in Table 1.  
Based on the dynamic equations of each part, the EV drivetrain model can be transformed to state space model as shown in Equation (1) and torsional angle, wheel speed and motor rotor speed are selected as the state variables:  
\[\dot{\mathbf{x}}\ =\ \mathbf{A}\mathbf{x}+\mathbf{B}u+\mathbf{H}l \tag{1}\]  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Symbols** & **Indication** & **Subscripts** & **Indication** \\ \hline \(J\) & mass moment of inertia & \(m\) & Motor \\ \(k\) & torsional stiffness & \(t\) & transmission \\ \(d\) & torsional damping & \(p\) & propeller shaft \\ \(i\) & gear ratio & \(f\) & final drive \\ \(\theta\) & torsion angle of drive shaft & \(d\) & drive shaft \\ \(T\) & torque include friction & \(w\) & wheel \\ \(l\) & resisting moment & - & - \\ \(m\) & gross mass of vehicle & - & - \\ \(r\) & wheel radius & - & - \\ \hline \hline \end{tabular}
\end{table}
Table 1: Symbols and subscripts of electric vehicle (EV) drivetrain model cite.  
Figure 3: Six degrees of the freedom model of electric vehicle (EV) drivetrain.  
where \(\mathbf{A,\ B,\ H,\ l}\) denote  
\[\mathbf{A}\ =\ \left(\begin{array}{ccc}0&-1&\frac{1}{t}\\ \frac{k}{l_{2}}&-\frac{d+d_{2}}{l_{2}}&\frac{d}{l_{2}}\\ -\frac{k}{l_{1}}&\frac{d}{l_{1}}&-\frac{d_{1}+\frac{d}{2}}{l_{1}}\end{array} \right)\mathbf{B}\ =\ \left(\begin{array}{c}0\\ 0\\ \frac{1}{l_{1}}\end{array}\right)\mathbf{H}\ =\ \left(\begin{array}{c}0\\ -\frac{1}{l_{2}}\\ 0\end{array}\right)x_{1}\ =\ \frac{\theta_{m}}{l_{1}l_{f}}-\theta_{w},x_{2}\ =\ \dot{\theta}_{w},x_{3}\ =\ \dot{\theta}_{m} \tag{2}\]  
with  
\[i\ =\ i_{t}i_{f},J_{1}\ =\ J_{m}+J_{t}/i_{t}^{2}+J_{f}/i_{t}^{2}i_{f}^{2},\ J_{2}\ =\ J_{w}+mr^{2},\ d_{1}\ =\ d_{t}/i_{t}^{2}+d_{f}/i_{t}^{2}i_{f}^{2},\ d_{2}\ =\ d_{w}+mgc_{r2}r^{2} \tag{3}\]  
In the above equations, \(u\) is the motor torque output, \(c_{r1},c_{r2}\) are coefficients in fitting formula of rolling resistance \(F_{r}\ =\ mg\Big{(}c_{r1}+c_{r2}r\dot{\theta}_{w}\Big{)}\), and from reference [31], they are set as 0.01 and 0.002, respectively. Considering that the process of shifting occurs over a short time period, drag torque \(l\) is regarded as a constant. The EV drivetrain is modeled in AMEsim(r) and these tests are shown in Figure 4.
###### Abstract  
Sign language services are provided so that people with hearing loss are not alienated from socially and politically important information through TV broadcasting. In this paper, we conducted a user survey and evaluation of the current sign language services for deaf or hard-of-hearing (DHH) people, and solutions were proposed for the problems found in the course of the analyses. To this end, a total of five stages of research were conducted. First, the communication problems experienced by DHH individuals and previous studies on their language and information acquisition were investigated. Second, the most typical types of information delivery channels via TV were defined as news, discussions, and weather reports, and by investigating the actual sign language service cases for each type, three visual information delivery elements were identified: sign language interpreters, reference videos, and subtitles. Third, a preference survey, an interview survey, and an eye tracker experiment on the DHH participants were conducted with varying arrangement options of information delivery elements. Fourth, based on the results of the investigations and experiments, the options to be considered when arranging information delivery elements were compiled. The results showed that the sign language interpreter, which is the first element of information delivery, should be presented in a size clearly visible because the visibility of their facial expressions is important. In addition, it is recommended to present the interpreter without a background since DHH participants did not prefer the presence of a background. As for subtitles, which is the third element of information delivery, it was confirmed that the provision of sign language interpretation and subtitles together helped DHH participants to understand the contents more quickly and accurately. Moreover, if there are multiple speakers, individual subtitles for each speaker should be provided so that the viewers can understand who is talking. Reference videos, which are mainly placed on the screen background, the second information delivery element, were considered less important to DHH participants compared to sign language interpreters and subtitles, and it was found that DHH participants preferred reference videos to be visually separated from sign language interpreters. Fifth, based on the overall results of the study, a screen layout design was proposed for each type of information delivery element for DHH people. Contrary to the general conception that there would be no problem in viewing information-delivering TV broadcasts by DHH people simply by placing a sign language interpreter on the screen, the results of this study confirmed that a more delicate screen layout design is necessary for DHH people. It is expected that this study will serve as a helpful guide in providing better sign language services for TV broadcasts that can be conveniently viewed by both DHH and non-disabled people.  
deaf or hard-of-hearing (DHH) people; TV sign language service; usability test; layout design
# Anomaly Detection in Blockchain: A Systematic Review of Trends, Challenges, and Future Directions
## 4 Discussion
### Drivers and Implications of Research Trends  
The evolution of anomaly detection research in blockchain ecosystems reflects a dynamic interplay of technological advancements, security needs, and strategic policy initiatives. Bibliometric cluster analysis reveals a clear progression from basic rule-based methods to advanced machine learning, graph neural networks, and privacy-preserving federated learning systems. One of the key enablers of this transition is the exponential growth of blockchain data. Traditional systems have proven inadequate for the real-time analysis of high-volume transactions, prompting a shift toward scalable, data-driven approaches. Machine learning models, particularly those employing graph neural networks, have demonstrated high accuracy in capturing complex transactional patterns and interdependencies.  
Simultaneously, the nature of blockchain threats has evolved. Between 2020 and 2024, the landscape shifted from isolated technical exploits to multi-vector attacks involving social engineering, cross-chain vulnerabilities, and oracle manipulation.  
The development of anomaly detection solutions has also been influenced by national priorities. China, USA, and India lead in research output, driven by strategic investments and regulatory frameworks [135; 136]. China's centralized investment strategy contrasts with the USA's decentralized, regulation-led approach. In Europe, the MiCA regulation [137] fosters security innovation by emphasizing compliance.  
Cluster transitions indicate a movement from unsupervised learning (cluster #0) and basic cryptocurrency security (cluster #1) toward lightweight, federated learning architectures (cluster #2), reflecting several key trendsseveral emerging trends in the field. First, there is growing emphasis on methods that support the proactive detection of complex anomalies, particularly those capable of capturing relational structures within blockchain networks. Techniques based on graph neural networks have demonstrated notable effectiveness in this regard. Second, privacy-preserving collaboration is becoming increasingly important. Approaches such as federated learning enable anomaly detection across multiple organizations without requiring direct data sharing, thereby aligning with modern data protection regulations. Third, the demand for explainable artificial intelligence is rising, driven by the need for transparency and regulatory compliance. Techniques that improve the interpretability of detection outcomes--such as SHAP [138] and LIME [139]--are being progressively adopted to enhance trust in automated systems used by auditors, developers, and regulators.
# User Comment-Guided Cross-Modal Attention for Interpretable Multimodal Fake News Detection
## 3 Methodology
### Problem Statement  
In this paper, our focus is on determining the authenticity of news articles circulating on social media platforms. Given a multimodal news article \(P\) composed of text \(T\), images \(I=\{I_{1},I_{2},\ldots,I_{m}\}\), and associated user comments \(C=\{C_{1},C_{2},\ldots,C_{n}\}\), our goal is to predict the authenticity label \(y\in\{0,1\}\), where \(y=0\) indicates real news and \(y=1\) indicates fake news.  
The challenge lies in effectively modeling the complex relationships between these heterogeneous modalities while filtering noise and irrelevant information that may confound the detection process. Our approach must consider the following:  
* **Multimodal Consistency**: Analyzing whether textual claims are supported by visual evidence.
* **Social Validation**: Leveraging user comments to identify credibility signals and fact-checking information.
* **Temporal Dynamics**: Considering the timing and sequence of content publication and user responses.
* **Noise Resilience**: Filtering irrelevant content, spam comments, and manipulative elements.