esa-sceva/satcom-chunk-collection huggingface dataset:
# Smart Disaster Response in Vehicular Tunnels: Technologies for Search and Rescue Applications
## 4 Experiments and Discussion
### Localization Experiments  
For efficient search and rescue when an accident involving a tunnel fire occurs, it is important to measure the positions of stopped vehicles, victims, and fire fighters in the tunnel. In an experiment to assess whether the system can accurately measure the position of the fire brigade, a firefighter carrying a UWB terminal repeatedly moved within the anchor's radio range. We assumed that the anchor is already installed in the tunnel, as shown in Figure 8. The measurement results are shown in Figure 18, which shows a distance error of less than 20 cm in most sections. If the UWB infrastructure is successfully constructed, it would be possible to observe in real time how many fire fighters are working in which area and at what time. Thus, this system can efficiently support decisions regarding input personnel as well as the sequence and route of rescue activities according to the situation of the disaster site.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Temperature** & **Humidity** & **Motion** & **Smoke** & **Video** & **Radar** \\ \hline
512 msec & 513 msec & 512 msec & 515 msec & 1821 msec & 533 msec \\ \hline \hline \end{tabular}
\end{table}
Table 4: Transmission and Response Delay.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Temperature** & **Humidity** & **Motion** & **Smoke** & **Video** & **Radar** \\ \hline
98\% & 98\% & 98\% & 98\% & 97\% & 97\% \\ \hline \hline \end{tabular}
\end{table}
Table 3: Data Delivery Ratio.  
Figure 19 shows a comparison of the distance moved by an actual vehicle and the distance measured by the radar system. The radar model was a Smartmicro T-32 model [43], and it was installed in an emergency parking lot in a tunnel. The average distance was calculated by repeatedly driving the vehicle more than 10 times. Of course, due to the nature of radar antenna, it is affected by the presence of surrounding obstacles and physical vibration of the platform. However, when measured under optimal conditions without any obstacles, the distance error is less than 5 m in most sections. In some sections, error of 6 to 7 m occurred. This is because there is more signal distortion and clutter diffusion as the distance increases. Also, some curved or sloping sections of roads and tunnels may cause accuracy degradation. However, considering that the length of a sedan-type car is about 5 m, accuracy of 5 m is very helpful for accident point estimation and situation judgment.  
Figure 19: Accuracy of Radar Localization.  
Figure 18: Accuracy of UWB Localization.
## 2 System Model
It is assumed that the base station of the cell \(m\) allocates the subchannel \(n\) to two users, \(u_{0}\) and \(u_{1}\), where \(\left|\psi_{m,u_{0}}^{n}\right|\geq\left|\psi_{m,u_{1}}^{n}\right|\), so that the user \(u_{0}\) with the higher CNR can eliminate the interference from the user \(u_{1}\) with the lower CNR on the subchannel \(n\), i.e., \(p_{m,u_{1}}^{n}\geq p_{m,u_{0}}^{n}\). Therefore, after SIC, the signal-to-noise ratios (SINRs) of the user \(u_{0}\) and the user \(u_{1}\) on the subchannel \(n\) can be written as  
\[ \begin{split} SINR_{m,u_{0}}^{n}=\frac{\left|H_{m,u_{0}}^{n} \right|^{2}p_{m,u_{0}}^{n}}{\sigma_{n}^{2}+\sum\limits_{c\in M,c\
eq m}\left| H_{c,u_{0}}^{n}\right|^{2}p_{c}^{n}}\\ SINR_{m,u_{1}}^{n}=\frac{\left|H_{m,u_{1}}^{n}\right|^{2}p_{m,u_{1}} ^{n}}{\sigma_{n}^{2}+\sum\limits_{c\in M,c\
eq m}\left|H_{c,u_{1}}^{n}\right|^ {2}p_{c}^{n}+\left|H_{m,u_{1}}^{n}\right|^{2}p_{m,u_{0}}^{n}}\end{split}  \tag{4}\]  
Then, the transmission rate provided by base station \(m\) for the user \(u_{0}\) and the user \(u_{1}\) on the subchannel \(n\) is formulated as follows:  
\[ \begin{split} R_{m,u_{0}}^{n}=\frac{B_{\text{out}}}{N}\log_{2}(1 +SINR_{m,u_{0}}^{n})\\ R_{m,u_{1}}^{n}=\frac{B_{\text{out}}}{N}\log_{2}(1+SINR_{m,u_{1}} ^{n})\end{split}  \tag{5}\]  
The sum rate and the energy efficiency of this NOMA system can be formulated as follows:  
\[ \begin{split} R_{\text{sum}}=\sum\limits_{m=1}^{M}\sum\limits_{ n=1}^{N}\sum\limits_{u=1}^{K_{m}}R_{m,u}^{n}\\ E_{\text{sum}}=\frac{R_{\text{sum}}}{\sum\limits_{m=1}^{M}\sum \limits_{n=1}^{N}\sum\limits_{u=1}^{K_{m}}p_{m,u}^{n}+p_{0}}\end{split}  \tag{6}\]  
where \(p_{0}\) is the inherent loss of the network base station equipment.
The raw materials that have been studied as precursors for the carbon QD synthesis include a variety of carbon-rich biomass and food waste. Tea leaves, coffee grounds, and fruit peels [7], for example watermelon peels [24], are cheap and easily accessible. Other sources for the synthesis of carbon QDs are sweet potatoes [21; 25], milk [21; 26], fruit juices and garlic [21; 27]. Crayfish shell has garnered research attention recently due to the discovery of the various components it contains, such as chitin and its derivatives, astaxanthin, proteins, and N-Acetyl-d-glucosamine, which can be used in many fields. Alternatively, crayfish shells can be converted to functional materials such calcium-rich biochar and N-doped carbon QDs [10]. Recently, [PERSON] et al. [10] prepared N,S-Carbon QDs from crayfish waste by using L-cysteine as source of S atoms. Simultaneously, [PERSON] et al. [28] prepare N-doped carbon QDs without any chemical addition by using pure crayfish waste powder. In the current study, a simple hydrothermal route was adopted to prepare sulfur and nitrogen co-doped carbon QDs (N/S-Carbon QDs) using waste crayfish shells from a Greek fish processing factory for the first time. Considering that Greece is a country with vast access to marine biomass, the purpose of this article is to raise interest into the industrial and commercial prospects of crayfish shell. The prepared N/S-Carbon QDs were morphologically and structurally characterized by applying various microscopic and spectrometric techniques. The optical and thermal properties of the material were also investigated to evaluate its applicability in food packaging as well as other processes with environmental significance, such as photocatalysis, etc. In this regard, its ecotoxicity was assessed in vitro while its antioxidant capability was also evaluated.
## 2 Experimental Setup
### Previously Validated Research through Numerical Codes  
Limited attempts were made with the numerical codes to model the pipe whip behavior and validate the obtained numerical data with the CEA tests. [PERSON] et al. [2] calculated the elastoplastic models of the CEA tests with the TEDEL numerical code. The dynamic pipe model is a simple pipe geometry with beam formulation modelled with a local linear stiffness at impact. The results found that this local stiffness greatly influences the pipe whip impact when compared to the strain rate, which can be neglected.  
[PERSON] et al. [4] developed three modeling approaches using the Abaqus-EPGEN code with two models created completely with pipe and beam elements throughout the pipe. Two nodal beam and truss elements were modeled for the pipe, and a contact model based on a unidirectional gap element with a compression spring type was created between the pipe and the impacting slab. The impact on the slab is measured by the change in the gap closing of this element during the simulation process. The third model was created using a coupled modeling technique with beam elements modelled along the linear horizontal  
Figure 1: Pipe model for experimental and numerical study.  
section of the pipe and shell elements at the elbow section of the pipe. The impact time, velocity, and forces were solved for the three models under controlled operating pressure and temperature. The ANSI 58.2 standard approach was used as a validatory tool to analyze the model parameters, and the results showed justifiable comparisons and suggested that the strain rate has a significant influence on the stiffness of the pipe when compared to the high-temperature pipe impacts.  
With the developing FE codes, the versatility of such structural dynamic simulations should be presented from time to time with efficient models without compromising the computational accuracy and predictions. Simplified FE models are necessary for certain applications where the conservative approach is required with negligible changes to the accuracy of the solution. This research finds such model simplifications for the pipe whip tests where the dynamic response of the structural members is studied with a localized solid and beam modeling technique.
## 2 Background and Related Technologies
### Walker-Delta Constellation  
The constellation utilized in this study is based on the Delta-Walker constellation concept initially proposed by [PERSON] [8] and later refined via the Walker-Delta pattern introduced by [PERSON] [9]. This pattern arranges multiple satellites at a shared altitude andinclination while uniformly distributing them across orbital planes, ensuring a consistent phase offset for continuous global or regional coverage.  
In this study, we define a constellation group by its inclination (\(i\)), the number of orbital planes (\(p\)), and the total number of satellites within that group (\(n\)). Additionally, the relative phasing between satellites in different orbital planes is denoted by \(F\). Unlike a traditional single Walker-Delta constellation with a uniform inclination, our approach views the overall satellite constellation as a combination of multiple distinct constellation groups.  
Each group possesses its own specific inclination, along with its associated number of orbital planes and relative phasing. This multi-group strategy not only allows for the deployment of satellite clusters that are optimized for different coverage characteristics or regions of interest but also enhances revisit times and improves regional coverage when compared to a single, uniform-inclination Walker-Delta constellation [10].  
It is important to note that within each individual constellation group, all satellites operate at the same orbital altitude (either LEO or VLEO). This study does not consider a hybrid Walker-Delta constellation in which satellites within the same pattern occupy different altitudes.  
The proposed constellation design, leveraging this multi-group approach with potentially varying inclinations across groups, aims to offer continuous regional coverage and robust data collection while maintaining adaptability to mission-specific objectives. The parameters of each constellation group (inclination, number of planes, number of satellites, and relative phasing) are key variables explored and optimized within this research to achieve the desired performance metrics.  
Figure 1 illustrates key aspects of the Walker-Delta constellation, such as the distribution of planes, inclination angles, and the phase factor (F). In the caption of Figure 1, we further clarify that within each constellation group (satellites sharing a single inclination angle), the phase factor (F) determines the relative longitudinal spacing between satellites in adjacent orbital planes, as per the MATLAB definition of the 'phasing' parameter. The entire constellation comprises multiple groups with possibly different inclinations.  
Figure 1: Walker–Delta constellation.
# Design and Experiment of High-Resolution Multispectral Polarization Imaging System
## 6 Development and Experiment of a High-Resolution Polarization Imaging Instrument
### Static Experiment  
The procedure was as follows: we fixed the polarization camera, infrared camera, and visible camera on the ceiling, and adjusted the aperture and focal length to make the image clear; fixed sand, sand mixture, and soil on a wooden board, divided it into nine areas and made sure that these nine areas were all within the field of view of three cameras; the nine areas were photographed without objects, and then the polarization angle (AOP), linear polarization degree (DOLP), and other images of the background were obtained.  
We used knives, cloth, saws, mineral water bottles, hammers, foam boards, scissors, bricks, and wooden boards, changed the order once for each group, and shot three images of the nine items on three different backgrounds.  
We replaced the target items and used nine items such as meat, bones, saws, stones, slippers, cloth, steel pipes, knives, and hammers, changed the order once for each group, and shot three images of the nine items on three different backgrounds. Bullets, knives, and remote controllers were placed in three target areas, respectively, and images of tiny  
Figure 6: The physical diagram of the system.  
objects under three cameras were taken. Finally, we added a lot of grass to the background shaded, and obtained the image of the target object with shade. It was divided into two situations: natural illumination during the day, and LED lamp illumination at night.  
Target Polarization Image Analysis  
Natural light exposure, contrast map of physical evidence target, as shown in Figure 7:  
When illuminated by LED light at night, the contrast map of the physical evidence target is shown in Figure 8:  
Figure 8: Intensity, polarization, infrared, and fused images under LED light at night. (**a**) intensity image; (**b**) DoLP image; (**c**) Infrared image; (**d**) Intensity Polarization Fusion Image; (**e**) Intensity, polarization, and infrared fusion images.  
Figure 7: Intensity, polarization, infrared, and fusion images under natural light. (**a**) intensity image; (**b**) DoLP image; (**c**) Infrared image; (**d**) Intensity Polarization Fusion Image; (**e**) Intensity, polarization, and infrared fusion images.
## 3 Results
### GPP, Reco, and NEE Fluxes
#### 3.2.1 Gross Primary Production (GPP)
This is particularly evident for the HWL_Si plot, wherein GPP fluxes reached \(-\)1292.32 gCO\({}_{2}\)-C\(\cdot\)m\({}^{-2}\)\(\cdot\)cut\({}^{-1}\) and were two times higher than for the same plot in 2021. When comparing plots with different WTD levels, it can be seen that in the third cut in 2021 and in all cuts in 2022, the HWL plots assimilated more CO\({}_{2}\) than the LWL plots. Therefore, it can be concluded that higher WTD positively affected GPP. In the case of the silicon application, it is apparent that in the area with a higher WTD, the application of the antitranspirant contributed to higher assimilation rates and ecosystem productivity in all cuts except for the third cuts in 2021 and 2022. In the site with a lower WTD, no clear pattern was observed. In 2021, GPP at the plot with lower WTD and silicon application (LWL_Si) was lower by 10% in the first cut and by 19% in the second cut in comparison to the plot without silicon (LWL). In the third cut of 2021, the GPP rates of both LWL plots were similar (\(-\)486.47 gCO\({}_{2}\)-C\(\cdot\)m\({}^{-2}\)\(\cdot\)cut\({}^{-1}\) for LWL and \(-\)488.48 gCO\({}_{2}\)-C\(\cdot\)m\({}^{-2}\)\(\cdot\)cut\({}^{-1}\) for LWL_Si). However, in 2022, a pattern opposite to that of 2021 was observed, wherein the plot with the antitranspirant application (LWL_Si) expressed higher assimilation rates than the LWL one.
# A Comprehensive Evaluation Algorithm of Multi-Point Relay Based on Link-State Awareness for UANETs
## 3 Proposed Algorithm  
Due to the transient characteristics introduced by node mobility in dynamic networks, the network topology undergoes frequent changes. Existing research on dynamic network routing algorithms has paid limited attention to node mobility and network load variations. Additionally, there are a lack of studies that select multiple parameter indicators and utilize multi-attribute decision-making to optimize the MPR mechanism. Therefore, addressing the aforementioned issues, this paper combines the TOPSIS method to establish a multi-attribute comprehensive evaluation model. Leveraging link-state awareness among nodes, a novel MPR selection algorithm named TOPSIS-MPR is proposed. This algorithm emphasizes two key dimensions of link awareness: mobility and load, enabling real-time monitoring of the availability, quality, and data payload of each network link. By integrating multiple parameters and multi-attribute decision-making methods, this research further optimizes the MPR mechanism for dynamic networks.  
The formulas and conclusions derived in the paper are based on the following three hypotheses, namely:  
**Hypothesis 1**.: _Each drone node is equipped with a Global Positioning System (GPS) that can sense motion and analyze link status based on the location and velocity information provided by the module._  
**Hypothesis 2**.: _The effective communication distance of each drone node is the same, and the signal propagation follows a free space propagation loss model. The received signal power is mainly related to the distance between them._  
**Hypothesis 3**.: _There are many data frames in the MAC layer buffer of each drone node, mainly including data frames waiting to be sent, data frames waiting to be forwarded, control frames waiting to be sent, retransmitted data frames, and confirmation frames waiting to be sent. The length of the queue buffer of a node can reflect its load situation._  
Figure 3: The process of selecting MPR set for node 0.
## 4 Discussion  
The present study's aim was to develop a special test to diagnose the specific fitness of athletes in the stand-up fighting plane, in terms of the widely understood special fitness and exercise performance of practitioners of Brazilian juu-jitsu (BJJ). We made sure that the diagnosis, on the one hand, did not require complex apparatus and, on the other hand, that the tests could be performed under training room conditions. In order to effectively  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Parameter** & \(\overline{\mathbf{x}}\) & **SD** & **Min** & **Max** & **Q1** & **Q3** & **CV\%** & \(p\)** & **d** \\ \hline SBJJFT-TZ I-first date (R1) & 5.78 & 1.55 & 3 & 8 & 4.50 & 7.00 & 26.87 & 0.006 \({}^{*}\) & 0.39 \\ \hline SBJJFT-TZ I-first date (R2) & 5.26 & 1.10 & 3 & 7 & 5.00 & 6.00 & 20.82 & \\ \hline SBJJFT-TZ I-second date (R1) & 5.85 & 1.46 & 3 & 8 & 5.00 & 7.00 & 24.95 & \\ \hline SBJJFT-TZ II-second date (R2) & 5.33 & 1.21 & 3 & 7 & 4.00 & 6.00 & 22.67 & \\ \hline SBJJFT-TZ I-first date (SR) & 11.04 & 2.53 & 6 & 15 & 9.50 & 13.00 & 22.96 & \\ \hline SBJJFT-TZ II-second date (SR) & 11.19 & 2.59 & 6 & 15 & 9.50 & 13.00 & 23.13 & \\ \hline SBJJFT-TZ I-first date (Final HR) & 183.26 & 6.68 & 173 & 195 & 177.50 & 187.50 & 3.64 & \\ \hline SBJJFT-TZ II-second date (Final HR) & 183.56 & 7.56 & 172 & 198 & 178.00 & 189.00 & 4.12 & \\ \hline SBJJFT-TZ I-first date (HR 1 min) & 156.63 & 11.35 & 139 & 173 & 145.50 & 169.50 & 7.25 & \\ \hline SBJJFT-TZ II-second date (HR 1 min) & 157.04 & 10.33 & 140 & 173 & 146.50 & 167.50 & 6.58 & \\ \hline SBJJFT-TZ I-first date (SHR) & 339.89 & 17.24 & 313 & 363 & 323.00 & 357.00 & 5.07 & \\ \hline SBJJFT-TZ II-second date (SHR) & 340.59 & 17.28 & 312 & 363 & 323.50 & 359.00 & 5.07 & \\ \hline SBJJFT-TZ I-first date (Index) & 33.11 & 10.68 & 20.87 & 59.00 & 24.89 & 37.85 & 32.25 & \\ \hline SBJJFT-TZ II-second date (Index) & 32.75 & 10.39 & 18.35 & 60.50 & 24.89 & 37.90 & 31.74 & \\ \hline \hline \end{tabular} \({}^{*}\)Arithmetic mean, SD—standard deviation, min—minimum value, max—maximum value, Q1—bottom quartile, Q3—upper quartile, CV%—coefficient of variation, Student’s \(t\)-test, \(p\)—level of significance of variation, [PERSON]’s d—variation effect size, SBJFT-TZ I—value of parameters from the first date of the study; SBJJFT-TZ (SR)—total throws, (Final HR)—heart rate immediately after the test, (HR 1 min.)—heart rate 1 min after the test, (SHR)—sum of heart rate measurements; (Index)—special fitness index.  
\end{table}
Table 3: Statistical characteristics of the SBJJFT-TZ parameters from two measurement dates.  
use tests to evaluate athletes, they must be valid and reliable [33]. Therefore, the further discussion of results is divided into validity and reliability sections.
## 2 Related Work
### Reliable and Energy Efficient Routing  
The main idea in [10] is of a forwarding scheme for reliable and energy-efficient data delivery in a cluster-based sensor network. [11] presents a delay aware reliable transport (DART) protocol. Authors focus on timely event detection with a focus on congestion and energy efficiency. Reference [12] discusses the tradeoffs of enhancing the reliability of WSN in a ZigBee network. In [13], the concept of multiple gateways for efficient routing and data delivery within the 6 lowPAN is proposed. Quite recently an effort has been made to enhance the reliability of WSN using emergency routing paths [14]. Authors in [14] presents an AODV based routing protocol that uses a multipath algorithm to increase the reliability in a sensor network. However, their routing protocol fails to provide reliable packet delivery for a network having high degree of failed or sleeping nodes. Authors in [15] try to improve the reliability and availability of WSNs by proposing a data forwarding algorithm. However, they just focus on low duty cycle WSNs and they have not presented any good comparison of their approach with other existing reliable routing approaches for WSNs.
# Urban PM2.5 Concentration Prediction via Attention-Based CNN-LSTM
## 4 Results and Discussion
### Model Comparison
The results show that the proposed AC-LSTM model can effectively learn the spatiotemporal correlation of air pollutants, and it is suitable for predicting urban PM\({}_{2.5}\) concentration in the future. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Models** & **1 h** & **2 h** & **3 h** & **4 h–6 h** & **7 h–12 h** & **13 h–24 h** \\ \hline SVR & 7.72 & 12.37 & 15.6 & 22.4 & 26.79 & 30.2 \\ \hline RFR & 7.9 & 12.59 & 16.02 & 21.74 & 25.77 & 28.86 \\ \hline MLP & 7.82 & 12.27 & 15.71 & 23.02 & 27.2 & 30.43 \\ \hline Simple RNN & 8.91 & 8.9 & 8.88 & 9.39 & 9.75 & 9.94 \\ \hline LSTM & 8.37 & 8.38 & 8.7 & 8.49 & 8.98 & 9.03 \\ \hline CNN–LSTM & 7.79 & 7.97 & 8.05 & 8.38 & 8.79 & 8.92 \\ \hline AC-LSTM & 7.68 & 7.97 & 7.98 & 8.38 & 8.51 & 8.98 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: The performances of the different models in terms of mean absolute error (MAE). SVR—support vector regression; RFR—random forest regression; MLP—multilayer perceptron. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Models** & **1 h** & **2 h** & **3 h** & **4 h–6 h** & **7 h–12 h** & **13 h–24 h** \\ \hline SVR & 13.46 & 20.92 & 26.14 & 35.48 & 41.59 & 49.09 \\ \hline RFR & 13.57 & 20.99 & 26.25 & 33.06 & 38.47 & 43.46 \\ \hline MLP & 13.7 & 20.73 & 26.15 & 36.01 & 42.68 & 48.07 \\ \hline Simple RNN & 14.1 & 14.24 & 14.62 & 15.19 & 15.38 & 15.15 \\ \hline LSTM & 13.91 & 13.97 & 14.32 & 14.58 & 14.99 & 15.11 \\ \hline CNN–LSTM & 13.25 & 13.73 & 13.84 & 14.43 & 14.53 & 15.02 \\ \hline AC-LSTM & 13.06 & 13.23 & 13.64 & 14.41 & 14.45 & 14.83 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: The performances of the different models in terms of root-mean-square error (RMSE). However, our study has several limitations. Emissions have a significant impact on air quality. Since emission data are difficult to obtain, the data collected in this paper do not include emissions from factories and vehicles in the area. This does affect the prediction accuracy of our model. Moreover, when a sudden pollution accident occurs, the PM\({}_{2.5}\) concentration changes suddenly. Whether the proposed model can predict it well still needs to be demonstrated.