esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### MIX-Fixed-W/C
#### 3.2.2 Six ReCiPe2016 Single Score Methodological Options  
The environmental damage caused by the concretes increased in the following order: CBA0 \(<\) CBA25 \(<\) CBA50 \(<\) CBA75 \(<\) CBA100 (Figure 6). It should be noted that this ranking was held for all ReCiPe2016 single score methodological options.  
However, in this case, additional information obtained from the \(p\)-values was available: Judgment was suspended for the difference between concretes CBA0 and CBA25 (\(p=0.0750\)), while the difference between all the other pairs appeared to be positive (\(0.0009\leq p\leq 0.0123\)) (Table 5). Thus, according to the ranking (Figure 6) and \(p\)-value analysis (Table 5) of the LCA results for the five MIX-fixed-W/C concretes, CBA0 and CBA25 were the best concretes, causing the least environmental damage, while CBA50, CBA75, and CBA100 (in ascending order of their environmental damage) were the worst, causing the most damage.  
Figure 6: Environmental damage caused by replacing sand with coal bottom ash (CBA): A—CBA0, B—CBA25, C—CBA50, D—CBA75, and E—CBA100. The life-cycle assessments (LCAs) were evaluated via the six ReCiPe2016 single-score methodological options, i.e., individualist/average (I/A) hierarchist/average (H/A), egalitarian/average (E/A), individualist/individualist (I/I), hierarchist/hierarchist (H/I), and egalitarian/egalitarian (E/E). The concrete mixtures designed with a fixed W/C of 0.52; The FU was normalized by dividing the environmental assessment of 1 m\({}^{3}\) of concrete according to the 28-day concrete compressive strength.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Concrete** & **CBA0** & **CBA25** & **CBA50** & **CBA75** & **CBA100** \\ \hline CBA0 & X & _0.0750_ & **0.0086** & **0.0025** & **0.0009** \\ CBA25 & & X & **0.0123** & **0.0023** & **0.0006** \\ CBA50 & & & X & **0.0116** & **0.0017** \\ CBA75 & & & & X & **0.0030** \\ CBA100 & & & & X \\ \hline \hline \end{tabular} Note: The LCAs were evaluated with the six ReCiPe2016 single score options (I/A, H/A, E/A, I/I, H/H, and E/E). The \(p\)-values were evaluated according to three-valued logic: Bold font—appears to be positive, ordinary font—appears to be negative, and italic font - judgment is suspended.  
\end{table}
Table 5: \(p\)-values (\(p\)) of the paired differences in the life-cycle assessment (LCA) production stage of 1 m\({}^{3}\) of concrete normalized according to the 28-day concrete compressive strength for the five concrete alternatives with different percentages of coal bottom ash (CBA) replacing sand. The concrete mixtures were designed with a fixed W/C of 0.52.
## 3 Challenges
### Dynamic Network Topology  
Unlike WSNs where (sensor) nodes are typically stationary, the actuators in WSANs may be mobile. In fact, node mobility is an intrinsic nature of many applications such as, among others, intelligent transportation, assisted living, urban warfare, planetary exploration, and animal control. During runtime, new sensor/actuator nodes may be added; the state of a node is possibly changed to or from sleeping mode by the employed power management mechanism; some nodes may even die due to exhausted battery energy. All of these factors may potentially cause the network topologies of WSANs to change dynamically.  
Dealing with the inherent dynamics of WSANs requires QoS mechanisms to work in dynamic and even unpredictable environments. In this context, QoS adaptation becomes necessary; that is, WSANs must be adaptive and flexible at runtime with respect to changes in available resources. For example, when an intermediate node dies, the network should still be able to guarantee real-time and reliable communication by exploiting appropriate protocols and algorithms.
## 2 Mobility Models in FANETs
### Path Planned Mobility Models
#### 2.4.3 Paparazzi  
The Paparazzi Model (PPRZM) is a probabilistic path-planned model that replicates Paparazzi UAVs' behavior inside the Paparazzi autopilot flight motion [72]. Further, it is a design based on a state machine that can perform five possible motions: Waypoint, Scan, Stay-At, Eight, and Oval. In the beginning, every UAV selects a starting position, movement type, and speed. After that, UAV selects a random altitude that will be maintained during  
Figure 16: The trajectory of FANETs using FP models.  
Figure 17: The trajectory of FANETs using SRCM models.  
the experiment. PPRZM's varied UAV movement patterns are displayed in Figure 18. This model has been implemented in several FANETs applications, such as software-defined networking FANETs (SDN-FANETs) and a system to predict the UAV information [73]. In addition, this model is used in UAV video dissemination services [74].
# Advancement of the DRPE Encryption Algorithm for Phase CGHs by Random Pixel Shuffling
## 3 From the Synthesis of a 3D Scene to Its Safe Reception: Process Description  
A flowchart of the proposed process is shown in Figure 1. It illustrates the different stages required to securely transmit a scene that has been previously modeled using computer graphics techniques (Figure 1a), stored in a CGH (Figure 1b), encrypted (Figure 1c), decrypted (Figure 1d), and visualized (Figure 1e). Algorithms and procedures are shown in blue, results that can be visualized on an SLM or simulated via a computer are in orange, vectors used to manipulate information during the encryption process are in green, and the key used for encryption or decryption is in red.
# MIMO Over-the-Air Computation for Distributed Estimation
## 3 The Proposed MIMO-AirComp Framework
The computation task of processing \(K\) signals is divided into \((K+1)\) small tasks \(\boldsymbol{\phi}_{1}(\cdot),\ldots,\)\(\boldsymbol{\phi}_{K}(\cdot),\boldsymbol{\psi}(\cdot)\) for the nodes and the FC, respectively. It greatly reduces the computation complexity of the FC, especially when \(K\) is large. While similar system models are presented in [15; 16], the core components of the preprocessing and postprocessing functions are not well defined for practical applications. Our proposed system model decomposes the preprocessing function (the postprocessing function, respectively) into the application-dependent preprocessing function (the application-dependent postprocessing function, respectively) and the source encoder (source decoder, respectively).
###### Abstract  
In direct-current gas-insulated transmission lines (DC GIL), complex heat transfer processes accelerate surface charge accumulation on insulators, causing local electric field distortion and elevating the risk of surface flashover. This study develops a three-dimensional multi-physics coupled mathematical model for \(\pm\)200 kV DC GIL basin-type insulators. The bulk and surface conductivity of insulator materials were experimentally measured under varying temperature and electric field conditions, with fitting equations derived to describe their behavior. The model investigates surface charge accumulation and electric field distribution under DC voltage and polarity-reversal conditions, incorporating multi-field coupling effects. Results show that, at a 3150 A current in a horizontally arranged DC GIL, insulator temperatures reach approximately 62.8 \({}^{\circ}\)C near the conductor and 32 \({}^{\circ}\)C near the enclosure, with the convex surface exhibiting higher temperatures than the concave surface and distinct radial variations. Under DC voltage, surface charge accumulates faster in high-temperature regions, with both charge and electric field distributions stabilizing after approximately 300 h, following significant changes within the first 40 h. Following stabilization, the distribution of surface charge and electric field varies across different radial directions. During polarity reversal, residual surface charges cause electric field distortion, increasing maximum field strength by 13.6% and 47.2% on the convex and concave surfaces, respectively, with greater distortion on the concave surface, as calculated from finite element simulations with a numerical accuracy of \(\pm\)0.5% based on mesh convergence and solver tolerance. These findings offer valuable insights for enhancing DC GIL insulation performance.  
**Key words:** gas-insulated transmission lines; basin-type insulator; surface charge; multi-physics field
## 3 Results
### Taheebo Extract Increased NO Production and Inhibited ROS Generation Induced by LPS  
Macrophages are beneficial for both host defensive mechanisms and inflammation. Many inflammatory disorders, including cancer, create too many macrophages [70]. NO, TNF-\(\alpha\), IL-1, and IL-6 are among the inflammatory mediators secreted by activated macrophages. Therefore, preventing macrophage activation is a key goal for treating inflammatory illnesses. NO is a common cellular mediator of physiological and pathological processes that is mostly produced in inflamed areas [71]. When macrophages are stimulated by LPS, the inducible isoform of nitric oxide synthase (iNOS) produces a considerable amount of NO [72]. Numerous fundamental physiological processes in mammalian cells depend on appropriate NO production [73]. The ROS created by activated neutrophils and macrophages are another significant pathogenic agent that can harm many tissues and organs [74]. Excessive ROS generation can result in protein and nucleic acid oxidation and have harmful effects on cell structures in a variety of pathogenic and infectious situations [75]. A redox imbalance caused by ROS buildup from the endogenous antioxidant response causes oxidative stress [76]. ROS generation may potentially be responsible for the induction of iNOS, as evidenced by previous findings that oxygen radical scavengers could suppress TNF-\(\alpha\) induced iNOS expression [77]. In this study, LPS-stimulated RAW 264.7 cells were exposed to TA and roasted TA samples at concentrations ranging from 15.625 to 1000 \(\upmu\)g/mL, and the amount of NO production was measured. At 500 \(\upmu\)g/mL, the roasted-TA sample produced significantly greater decreases in LPS-induced NO generation (54.07%) than the TA sample (23.56%). In this study, L-NMMA was used as the positive control for preventing NO generation (Figure 8A). RAW 264.7 macrophage cells were exposed to varying doses of the samples in the presence and absence of LPS for 24 h to test how well the samples reduced the ROS production caused by LPS. Figure 8B demonstrates that the LPS-treated groups produced more ROS than the control group. The results show that roasted TA inhibited the LPS-induced inflammatory response better than the TA sample at 500 \(\upmu\)g/mL.  
Figure 8: The effects of TA and roasted TA (**A**) NO production were assessed by 1 \(\upmu\)g/mL LPS-induced RAW 264.7 cells (**B**) generation of intercellular ROS compared to the control. Data presented as \(\pm\) SEM, \({}^{**}p<0.001\) vs. control cell. All treatments were performed three times.
## 3 Results
### Trends in Citations and Publications over Time  
This subsection examines citation trends and the number of publications from 2014 to 2024 across different indexing platforms, including IEEE, Scopus, and Web of Science. Figures 2-4 illustrate these trends, providing insights into the growth and impact of research on ANNs for segmentation tasks over the past decade.  
The abnormal rise in citation trends observed in Figure 2 can be attributed to the influence of several highly cited research papers published in 2018 and 2019. Notably, studies such as [PERSON] et al. (2018) [PERSON] et al. (2018), which introduced advancements in interactive medical image segmentation using deep learning with image-specific fine-tuning, and [PERSON] et al. (2019) [PERSON] et al. (2019), who developed a deep wavelet autoencoder-based approach for brain MRI cancer detection, significantly contributed to the spike in citations.  
Additional works by [PERSON] et al. (2019) [PERSON] et al. (2019) and [PERSON] et al. (2019) [PERSON] et al. (2019) on brain tumor detection using convolutional neural networks and machine learning approaches further fueled this sharp increase, reflecting a surge in interest and acknowledgment of these impactful studies.  
For Figure 3, the abnormal increase in citations can be attributed to the influence of highly cited research papers, particularly the comprehensive survey by [PERSON] et al. (2017) [PERSON] et al. (2017), titled \"A Survey on Deep Learning in Medical Image Analysis\". This foundational work provided an extensive overview of deep learning advancements in medical imaging and became a critical reference for subsequent studies in the field. Its widespread citation underscores the survey's pivotal role in shaping research directions and methodologies, leading to the marked rise in citation counts observed in Scopus.  
The figures illustrate a consistent upward trend in citations and publications from 2014 to 2024 across IEEE, Scopus, and Web of Science. This steady increase highlights the growing relevance and research interest in artificial neural network (ANN) applications for segmentation tasks, with notable growth in recent years. The patterns depicted emphasize the broader academic community's engagement and the significant impact of ANN advancements on segmentation research, reflecting the field's evolving influence and importance in scientific literature.
# Tracking System for a Coal Mine Drilling Robot for Low-Illumination Environments
## 4 Experimental
### Overall Performance on Benchmark Dataset
#### 4.3.2 Performance on UAV123  
The proposed tracker was also evaluated on the UAV123 [4] benchmark dataset, which contains 123 short, challenging videos and 20 long, challenging videos. UAV123 contains twelve challenging attributes, namely (i) Scale Variation, (ii) Aspect Ratio Change, (iii) Low Resolution, (iv) Fast Motion, (v) Full Occlusion, (vi) Partial Occlusion, (vii) Out-of-View, (viii) Background Clutter, (ix) Illumination Variation, (x) Viewpoint Change, (xi) Camera Motion and (xiii) Similar Object.  
Figure 8 shows the overlap success rate of the proposed method and the baseline trackers. Our proposed tracker achieved the second-highest mean overlap success rate of 0.418 and a mean OP of 0.626. The BACF [12] provides the best performance with a slightly higher success rate, whereas the remaining three trackers, i.e., LRCF [17], fast DSST [39], and LCT [18], have to mean overlap success rates of 0.382, 0.375, and 0.334, respectively. The proposed tracker shows considerable performance on UAV123 (Table 2). The LLCT exceeds the performance of the benchmark trackers (fast DSST and LCT) regarding all challenging attributes, and its performance is almost the same as that of the SOTA tracker in the same period.  
Figure 8: Quantitative results: the overlap success rate and distance precision for a threshold of 20 pixels.  
Figure 7: The results of the proposed method and the baseline for different challenging conditions. The AUC score for each tracker is reported in the legend.
# Electrochemical Biosensors Based on Conducting Polymers: A Review
## 3 Electroanalytical Methods
### Potentiometry  
In potentiometric conducting polymer-based biosensors, the potential between a reference electrode and an electrode coated either with a conducting polymer and a biorecognition element is measured using a high impedance voltmeter. The conducting polymer must be sensitive to the products of a reaction involving the analyte bound to the biorecognition element. This modified electrode senses the variation in protons (or other ions) amount since potential and pH are linked by the Nernst equation, leading to a recorded analytical signal which is generally logarithmically correlated with the analyte concentration.  
The most widely studied potentiometric biosensors are enzymatic biosensors that use an enzyme incorporated in the conducting polymer to catalyze a reaction producing protons. For example, in potentiometric urea enzymatic biosensors, urease is immobilized in a polymer and is used to catalyze the conversion of urea to carbon dioxide, ammonia and protons which produce a pH increase detected by the potentiometric biosensor (see Section 4.1).
# Satellite Climate Data Records: Development, Applications, and Societal Benefits
## 4 Societal Benefit  
It is helpful to briefly review the societal benefit of space remote sensing (RS), the foundation of satellite CDRs, in order to discuss the societal benefit of satellite CDRs. Most space RS applications could be inherited and extended to satellite CDR applications, except for real-time applications. Conversely, due to the short period of satellite CDR's history of production and application (comparing to space RS), the societal benefit of satellite CDRs still needs to be explored. Furthermore, potential societal benefits of satellite CDR applications could be revealed by reviewing space RS applications, especially from end-users' point of view.  
The advancement of space RS observations of the Earth is driven by scientific inquiries and societal needs from the user sectors, such as agriculture, forestry, fishery, energy, health, water, transportation, and tourism. While each user sector has its own requirements, the retrieved meteorological and climatological information and atmosphere, land, and ocean conditions in various spatial and temporalcoverages and resolutions, such as temperature, humidity, and precipitation, and surface condition, are prerequisites of almost every user sector, which will be discussed below.
## 4 Theoretical Results
If the event condition (4) ensures \(\int_{t_{0}}^{t}E^{\mathrm{T}}Eds\leq\theta\rho_{0}^{2}\int_{t_{0}}^{t}X_{c}^{ \mathrm{T}}X_{c}\mathrm{d}\omega\) with \(\theta\in(0,1)\), then \(V(t)-V(0)=\int_{t_{0}}^{t}\dot{V}ds<-(1-\theta)\epsilon_{2}\rho_{0}^{2}\int_{t _{0}}^{t}X_{c}^{\mathrm{T}}X_{c}\mathrm{d}\omega\leq 0\). As a result, if the ellipsoid \(\Gamma(P_{1},P_{2}):=\{(x^{\mathrm{T}},z^{\mathrm{T}})^{\mathrm{T}}\in\mathbb{ R}^{2n}|x^{\mathrm{T}}P_{1}x+z^{\mathrm{T}}P_{2}z\leq 1\}\) satisfies  
\[\Gamma(P_{1},P_{2})\subset\{X_{c}\in\mathbb{R}^{2n}|\,\|HX_{c}\|_{\infty}\leq 1\}, \tag{11}\]  
then the set \(\Gamma(P_{1},P_{2})\) is an invariant set of the closed-loop system (5). Consequently, \(\|HX_{c}\|_{\infty}\leq 1\) holds for all \(t\geq t_{0}\). This means that the closed-loop system is stable. From Lemma 2, a sufficient condition for Label (11) is  
\[\left( \begin{array}{ccc}1&h_{l}^{+}&h_{l}^{-}\\ *&P_{1}&0\\ *&*&P_{2}\end{array} \right)\geq 0,l=1,\ldots,m, \tag{12}\]  
where \(h_{l}^{+}\) is the \(l^{\mathrm{th}}\) row of \(H_{1}\) and \(h_{l}^{-}\) is the \(l^{\mathrm{th}}\) row of \(H_{2}\). Then, we propose the following theorem to obtain the asymptotic stability. **Theorem 1**. : _Consider the closed-loop system (5). Suppose that there exist a group of solutions \(\{P_{1}>0\), \(P_{2}>0\), \(\rho_{0}^{2}>0\), \(h_{l}^{+}\), \(h_{l}^{-}\}\) to the LMIs (10) and (12) for some \(\epsilon_{1},\epsilon_{2}>0\). If the parameters in event condition (4) satisfy the following conditions:_  
1. \(\sum\limits_{s=1}^{p}\left\|\Pi_{u}^{s}\bar{K}\right\|^{2}\sigma_{u}^{s}+\sum \limits_{l=1}^{r}\left\|\Pi_{y}^{t}\mathcal{C}\right\|^{2}\sigma_{y}^{t}< \theta\rho_{0}^{2}\)_,_
2. \(\tau_{u}^{s}\leq\sqrt{\frac{\sigma_{u}^{s}}{L_{1}^{2}+\theta\rho_{0}^{2}L_{2} ^{2}}},s=1,\ldots,p\)_, and,_ \(\tau_{y}^{t}\leq\sqrt{\frac{\sigma_{y}^{t}}{L_{1}^{2}+\theta\rho_{0}^{2}L_{2} ^{2}}},l=1,\ldots,r,\)__  
_where \(\theta\in(0,1),L_{1}=\left\|\bar{A}\right\|+\left\|\bar{B}\right\|\left\|\bar{K }\right\|+\left\|\bar{B}\right\|\left\|H\right\|\), \(L_{2}=\left\|\bar{B}\right\|+\left\|L\right\|\) and the matrices are_  
\[\bar{A}=\left( \begin{array}{cc}A&0\\ 0&A-LC\end{array} \right),\bar{B}=\left( \begin{array}{c}B\\ 0\end{array} \right),L=\left( \begin{array}{c}0\\ L\end{array} \right),\bar{K}=\left( \begin{array}{cc}K&K\end{array} \right), \bar{C}=\left( \begin{array}{cc}C&0\end{array} \right).\]  
_Then, for any initial state in the ellipsoid \(\Gamma(P_{1},P_{2})\), the corresponding closed-loop system (5) is asymptotically stable._  
Proof of Theorem 1.: If it is shown that the parameters in the theorem can guarantee \(\int_{t_{0}}^{t}E^{\mathrm{T}}E\mathrm{d}\omega\leq\theta\rho_{0}^{2}\int_{t_{0 }}^{t}X_{c}^{\mathrm{T}}X_{c}\mathrm{d}\omega\) for \(t\in[t_{0},\infty)\), then the stability can be proved directly according to the preceding analysis. To this end, we first consider the derivative of \(X_{c}\). From Label (6),  
\[\dot{X}_{c}\in co\{(\bar{A}+\bar{B}D_{j}^{+}\bar{K}+\bar{B}D_{j}^{-}H)X_{c}+\bar {B}D_{j}^{+}\left( \begin{array}{cc}I&0\end{array} \right)E+\bar{L}\left( \begin{array}{cc}0&I\end{array}
# Road-Side Unit Anomaly Detection
## 5 Conclusions  
In this paper, the detection of failures in operational RSUs by end users is investigated. This issue is critical and crucial for the C-ITS. We have shown that maintenance of road-side units could be achieved in a simple way without any additional investment or any equipment. Vehicles collect RSU observations during their journeys and analyze them using our failure detection mechanism. This mechanism compares the observed behavior to the known proper one, and when an anomaly is detected, any vehicle raises alerts to the road operators in order to inform them about the suspicious RSU. RSU behavior profiles are studied as well and a difference between urban and interurban is drawn and carefully considered in this study.  
We have observed that, in the case of a functioning RSU, there is an acceptable inverse correlation between the range and signal intensity of its transmissions. We have also observed that, in the interurban context with line-of-sight communications, the ID tends to be larger than the OD for a functioning RSU. Both these properties were used for our assessment of RSUs.  
We have so far addressed one particular RSU failure, which is when the RSU radiates only over one direction rather than two directions. But there are other kinds of failures, for instance, the RSU could be completely dysfunctional in both directions. One way for the vehicles to detect that is for them to receive an alarm message about an RSU from a single vehicle, and when the informed vehicles find no RSU on the road (because it recently became faulty), they can conclude that there is an RSU in the designated area that went dysfunctional; thus, they can also report it. Software failures are also a possibility, such a malformed packet from an RSU, in which case we could extend our failure mechanism by calculating the ratio of malformed packets from an RSU; if it exceeds a certain threshold, this would mean that the RSU is experiencing failure.  
In future works, we will assess other variables from the datasets, such as the vehicle's velocity, in order to enhance our failure detection mechanism. We will also build larger RSU behavioral profiles in order to be much more precise during the data analysis step.  
The authors confirm their contribution to the article as follows: study conception and design: M.-L.B., H.F., H.A. and N.-E.E.F.; analysis and interpretation of results: M.-L.B., H.F. and H.A.; initial manuscript preparation: M.-L.B., H.F., H.A. and N.-E.E.F. All authors have reviewed the results and approved the final version of the manuscript.  
This work was done thanks to CINEA funding for Indid project (Agreement N\({}^{\circ}\) INEA/CEF/ TRAN/M2018/1788494).  
The data on which this study is based were collected within the scope of two European Union projects (C-roads and Indid) on more than 300 km of highway networks in Italy, Germany, and France.  
The authors declare no conflict of interest.