esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Phytoremediation of Cd and Zn by Gazania  
#### 3.2.1 Effect of EDDS on the Adsorption of Cd and Zn  
The concentration of Cd and Zn in the shoots or roots of _Gazania_ (mg/kg plant) in all of the pots by adding various concentrations of EDDS after 3 weeks of growth are shown in Figure 8a,b (70% humidity) and Figure S4a,b (50% humidity). The TF of Cd and Zn in _Gazania_ are shown in Figure S5.  
With an increase in EDDS concentration, the uptake of Cd generally tended to increase (Figure 8a); however, a significant increase in the TF was not observed, except when adding 5 mmol kg\({}^{-1}\) of EDDS under 70% relative humidity conditions (Figure S5a). That is to say, it may be difficult to translocate Cd from the roots to shoots by _Gazania_, even when adding EDDS. In the case of Zn, a distinct effect was not observed by adding EDDS compared to the case of no addition.  
Compared to the case of _Taraxacum officinal_ under the condition of no addition of EDDS (i.e., Pot Nos. 1 and 2 in Table 1), a decrease in biomass in _Gazania_ was relatively outstanding when adding Cd and Zn, because a remarkable decrease in biomass was found in Pot No. 2 compared to Pot No. 1, as shown in Table S3 (and in contrast to the  
Figure 8: Effect of the EDDS concentration on the absorption of (**a**) Cd and (**b**) Zn by _Gazania_ (70% humidity). BLK represents the pot for no addition of heavy metals (i.e., Cd and Zn) as well as no addition of EDDS (i.e., Pot No. 1 in Table 1).  
Figure 7: SEM and element mapping image of the (**a**) roots and (**b**) shoots in Taracum officinal. (EDDS at a concentration of 5 mmol kg\({}^{-1}\)).  
biomass of _Taraxacum officinal_ not having greatly changed between Pot Nos. 1 and 2 in Table S2). This suggests that _Gazania_ has a low tolerance against Cd and Zn; however, the plant may be adequately tolerant against EDDS, because the biomass of each shoot or root was not remarkably changed upon the addition of EDDS (judging from the data of Pot Nos. 2 and 3-7 in Table S4). Even if both _Taraxacum officinal_ and _Gazania_ are the same Asteraceae plants, it was found that their tolerance against chelating agents or heavy metals was considerably different.  
Looking at the results from the experiments above, there is not so large selectivity, although EDDS has a higher stability constant with Zn\({}^{2+}\) compare to Cd\({}^{2+}\) (unlike the case of stability constant for EDTA).
## 3 Dataset and Experimental Setup
### Modality Description  
During each recording, the following four physiological signals were captured using the hardware equipment provided by Thought Technology Ltd and the BioGraph Infiniti software:1. _Blood volume pulse (BVP)_: BVP is an estimate of heart rate based on the volume of blood that passes through the tissues in a localized area with each beat (pulse) of the heart. The BVP sensor shines infrared light through the finger and measures the amount of light reflected by the skin. The amount of reflected light varies during each heart beat as more or less blood rushes through the capillaries. The sensor converts the reflected light into an electrical signal that is then sent to the computer to be processed. BVB has been extensively used as an indicator of psychological arousal and is widely used as a method of measuring heart rate [40,41]. The BVP sensor was placed on the index finger. We collect BVP at a rate of 2048 Hz.
2. _Skin conductance_: Skin conductance is collected by applying a low, undetectable and constant voltage to the skin and then measuring how the skin conductance varies. Similar to BVP, skin conductance variations are known to be associated with emotional arousal and changes in the signals produced by the sympathetic nervous system [41,42]. The sensor for these measurements was placed on the middle and ring fingers. Skin conductance signal is captured at 256 Hz.
3. _Skin temperature_: This sensor measures temperature on the skin's surface and captures temperatures between 10 \({}^{\circ}\)C and 45 \({}^{\circ}\)C (50 \({}^{\circ}\)F-115 \({}^{\circ}\)F). The temperature sensor was placed on the pinky finger. Skin temperature is also captured at 256 Hz.
4. _Respiration_: The respiration sensor detects breathing by monitoring the expansion and contraction of the rib cage during inhalation and exhalation. By processing the captured periodic signal important characteristics can be computed such as respiration period, rate and amplitude. The respiration stripe was wrapped around the participant's abdomen and the sensor was placed in the center of the body. Respiration is captured at 256 Hz.  
All sensors can be seen on the top right of Figure 1. Skin conductance, respiration and skin temperature values are padded to match the 2048 Hz sampling rate used for BVP. The total amount of data in terms of time across the different recording segments is shown in Table 1. For each segment, approximately half of the data come from the morning recordings and half from the afternoon.
# Evaluation of AI-Assisted Telemedicine Service Using a Mobile Pet Application
## 4 Results  
### Statistical Hypotheses Testing  
The results of the hypotheses tests were determined by referring to the path coefficient values and t-values for each hypothesis.  
Table 7 presents the results of the analysis. With regard to H1, we concluded that there was no statistically significant relationship between the length of adoption of companion animals and the degree of perception of telemedicine services. In other words, even if the companion animal has been with the user for a long time, there is no direct significance of the user needing a telemedicine service for the companion animal. On the other hand, as shown in H2, depending on the health status of the companion animal, the level of perception of pet telemedicine services is significantly different. That is, when the companion animal is not in good health, the user's perception of the telemedicine service is positive compared to when the companion animal is in good health. In the case of H3, it was statistically verified that the ease of use of mobile app services has a positive effect on the perception of telemedicine services for companion animals and people. In otherwords, those who find it easy to use the latest technology had more positive perceptions about telemedicine using IT technology. H4 is a hypothesis relating to positive-negative evaluation and, in summary, it seeks to determine whether users with positive or negative perceptions about telemedicine for companion animals have the same perceptions about telemedicine for people. The results revealed that users with positive perceptions of pet telemedicine did not maintain positive perceptions about telemedicine for people. Conversely, users with negative perceptions toward telemedicine for animals had a high tendency of maintaining these negative perceptions even about telemedicine for people. This is in line with the results of [PERSON] and [PERSON]'s prospect theory [62]. In other words, dissatisfaction with negative perceptions feels much more ineffective than does satisfaction with positive perceptions of the same intensity. This study shows that the concept transfer of negative recognition was more effective than that of positive recognition.
## 1 Introduction
used the Landsat 7 ETM+ surface reflectance data in 2000 and 2015 and the RF algorithm based on the GEE platform to automatically generate the 30 m resolution cover map of the African continent and analyzed its change characteristics [24]. Based on Landsat 8 images, RF algorithm, and GEE platform, [PERSON] et al. produced the land cover map of Maiella National Park in Italy [25]. Despite all this research, the available studies still lack a comparison of the classification performances and effects of the different classifiers in the same research area. The Qilian Mountains (QLM) are an important ecological security barrier in western China, and have strategic significance for the whole country. A high-resolution and accurate land cover map is a key dataset for ecosystem function monitoring, ecological protection, and restoration in this region. The current high-spatial-resolution global land cover products such as GlobeLand30 [16], FROM-GLC30 [17], and FROM-GLC10 [18] all include land cover maps of the QLM. In addition, China's high-spatial-resolution land cover products, such as China's Multiperiod Land Use Land Cover Remote Sensing Monitoring Dataset (CNLUCC) [26] and SPECLib-Based Land Cover [27], also include land cover maps of this region. On the regional scale, [PERSON] et al. developed the Land Cover Dataset for the QLM Area from 1985 to 2019 (V2.0) based on the GEE platform using Landsat 8 data, with an OA of 92.19% [28]. [PERSON] et al. used MODIS data, based on the GEE and combined with topographic features, to conduct land cover classification research on the QLM [29]. However, the existing land cover products have inconsistencies and uncertainties in the classification results of the QLM, lacking a performance comparison of the different remote sensing classification algorithms in the QLM. Therefore, it is necessary to use different remote sensing classification algorithms to study the QLM, produce more accurate high-resolution land cover products, and assess the performances of the different classification algorithms. According to the research background presented above, and based on the GEE cloud platform, Sentinel-2 remote sensing images were used to produce the land cover classification products for the QLM, with a spatial resolution of 10 m. Specifically, the research content of this study is as follows: (1) to evaluate the performance of the GEE cloud platform in land cover research in the QLM; (2) to assess the performances and accuracies of the different remote sensing classification algorithms in the land cover classification of the QLM; (3) to analyze the impacts of different characteristic variable combinations on the classification results while participating in classification; and (4) to compare and analyze the classification results obtained in this study using existing land cover products.
# Adaptive Jamming Mitigation for Clustered Energy-Efficient LoRa-BLE Hybrid Wireless Sensor Networks
## 2 Related Work
\begin{table}
\begin{tabular}{p{85.4 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Reference** & **Energy Consumption Method** & **Wireless Technologies** & **Main Contribution** & **Limitations** \\ \hline [PERSON] et al. (2020) [28] & Adaptive duty cycling & BLE, Wi-Fi on interference & Energy-efficient protocol switching based on interference & High latency during high interference periods \\ \hline [PERSON] et al. (2018) [29] & Sleep scheduling & LoRa, NB-IoT & Optimized sleep-wake scheduling to reduce energy waste & Inefficient in highly dynamic environments \\ \hline [PERSON] et al. (2024) [30] & Machine learning-based power control & 5G, Wi-Fi traffic prediction & Intelligent power adaptation based on traffic prediction & Requires high computational resources \\ \hline [PERSON] et al. (2022) [31] & Energy-aware clustering & LoRa, Zigbee & Cluster-based transmission to minimize redundant messages & Not scalable for dense networks \\ \hline [PERSON] (2010) [32] & Power-aware routing & BLE, Zigbee & Dynamic path selection for energy efficiency & Limited for long-range communication \\ \hline [PERSON] et al. (2023) [33] & Hybrid network switching & BLE, LoRa, 5G LoRa for efficiency & Adaptive switching between BLE and LoRa for efficiency & Increased network overhead \\ \hline [PERSON] et al. (2021) [34] & RF energy harvesting & LoRa, NB-IoT & Utilizes ambient RF energy to extend battery life & Requires additional RF energy sources \\ \hline [PERSON] et al. (2020) [35] & Transmission power control & Zigbee, BLE on network load & Adjusts power levels dynamically based on cause instability in low-power nodes \\ \hline [PERSON] et al. (2020) [36] & Al-based resource allocation & 5G, LoRa & Uses reinforcement learning for energy efficiency & Requires extensive training data \\ \hline [PERSON] et al. (2021) [37] & Energy-efficient MAC protocols & LoRaWAN, Wi-Fi power savings & Reduces idle listening time for better power savings & Increased delay in high-traffic scenarios \\ \hline [PERSON] et al. (2024) [38] & Low-power adaptive communication & BLE, Zigbee, LoRa based on conditions & Implements an adaptive protocol selection based on conditions & Overhead increases with multiple nodes \\ \hline [PERSON] (2022) [39] & Opportunistic data transmission & 5G, Wi-Fi & Reduces power usage by leveraging network conditions & Not optimized for real-time applications \\ \hline [PERSON] et al. (2024) [40] & Al-driven power optimization & LoRa, Zigbee & Predictive analytics for reducing energy drain & Complex deployment in large networks \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Current references on wireless sensor networks and their applications for elderly health monitoring and behavioral change techniques.
# Open Collaborative Platform for Multi-Drones to Support Search and Rescue Operations
## 3 Open Collaborative Platform
### Open Collaborative Platform for Multiple Drones  
Compared to [4], in which Krypto is a stand-alone module, the **Open Collaborative Platform** extends its capability for collaborating with multiple drones that carry the Krypto module. The platform is installed and operated in the control center of the SAR operation. For the experiments, the Open Collaborative Platform is installed and operated under Windows 10 O.S. on a computer with Intel i5-3570 K 3.4 GHz processor with 8G RAM. The platform centralizes path planning with the predefined searching area by the SAR team.  
The platform assigns a searching path to any drone that joined the SAR operations through the registration process which is discussed in the next section. To allow drones to collaborate, the Krypto module is extended as follows. The information collected from the _Sensing Component_ and estimated victim's location by _Initial Location Estimation Algorithm_ are sent to the Open Collaborative Platform.  
In previous work [4], we demonstrated that the Krypto can cover about 4.73 km\({}^{2}\) within 25 min on a single drone with a Space-Filling curve, e.g., Moore curve, Peano curve, etc. With the range of transmission range of 250 m (or 19,6350 m\({}^{2}\) sensing range), the _maximum flight distance_ of Krypto is 10.8 (km) with 20% of power reserved for returning home and extra cost of energy due to crosswind (see Equation (5)). With multiple collaborated drones that carry the Krypto module, our platform can reduce the searching time for SAR teams in a large disaster area. This allows the SAR team to focus only on pinpointing the victim's location and saving lives.
# An Edge-Guided Deep Learning Solar Panel Hotspot Thermal Image Segmentation Algorithm
## 4 Experiment and Result Analysis
### Ablation Experiments  
In order to analyze the impact of each module better and more comprehensively on performance in the segmentation model constructed in this paper and verify the effectiveness of each module structure, ablation experiments are designed and trained. The experimental results are shown in Table 1. Improvement point 1 introduces edge information; improvement point 2 adds an infrared spatial attention module; and improvement point 3 adaptively adjusts the size of the anchor box. The results indicate that the modules and improved methods introduced in this paper can effectively improve the accuracy of the model.  
The multi-scale feature map between the Mask R-CNN model and the improved model is shown in Figure 6b,c. It can be seen from the figure that, through the edge-aware module and the edge-guided feature pyramid structure, edge information is introduced into the network. The edge contour of the hot spot in the feature map is clearer, and the model captures better information about the hot spot boundary at various scales than the original model. The introduction of infrared spatial attention modules can also suppress irrelevant information such as background, and the contrast between hot spots and background in feature maps is significantly improved, proving the effectiveness of introducing edge information and attention mechanisms.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Algorithm** & **Points 1** & **Points 2** & **Points 3** & **[EMAIL_ADDRESS](\%)** \\ \hline Mask R-CNN & \(\times\) & \(\times\) & \(\times\) & 71.64 \\ Improved Point 1 & \(\surd\) & \(\times\) & \(\times\) & 73.75 \\ Improved Point 2 & \(\times\) & \(\surd\) & \(\times\) & 78.90 \\ Improved Point 3 & \(\times\) & \(\times\) & \(\surd\) & 76.60 \\ Improved Point 4 & \(\surd\) & \(\surd\) & \(\surd\) & 82.45 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Statistical results of ablation experiments.
## 2 Materials and Methods
### Model Reduction and Optimization
#### 2.2.3 Real-Time Parameter Adaptation  
The system implements continuous parameter adjustment using a recursive update law based on the prediction error. The adaptation mechanism is defined by the following equations:  
\[\theta(t)=\theta(t-1)+\mathrm{K}(t)[y(t)-\hat{y}(t)] \tag{11}\]  
\[\mathrm{K}(t)=\mathrm{P}(t-1)\phi(t)/[1+\phi^{\mathrm{T}}(t)\mathrm{P}(t-1) \phi(t)] \tag{12}\]  
\[\mathrm{P}(t)=[1-\mathrm{K}(t)\phi^{\mathrm{T}}(t)]\mathrm{P}(t-1) \tag{13}\]  
where  
* \(\theta(t)\) is the parameter vector at time t
* K(t) represents the adaptation gain matrix
* P(t) is the covariance matrix
* \(\phi(t)\) denotes the regression vector
* y(t) and \(\hat{y}(t)\) are the actual and predicted outputs This adaptation mechanism ensures the following:  
1. Rapid convergence to optimal parameters
2. Robust performance under varying conditions
3. Minimal computational overhead
4. Stability of the adaptation process The combination of these three components--model reduction, genetic optimization, and real-time adaptation--creates a robust and efficient system capable of maintaining optimal performance under varying operating conditions.
# Compacted Area with Effective Links (CAEL) for Data Dissemination in VANETs
###### Abstract  
Vehicular ad hoc network (VANET) is a specialized form of wireless network that is solely intended for collaboration between vehicles. Several studies have shown that standard routing protocols cannot be implemented in VANETs because of their unique characteristics such as their significant count of vehicles on the network and the rapid evolution of the network's design. Because VANET communication links are broken very frequently, it is necessary to address the routing consistency of these highly dynamic networks. The transmission of VANET data may result in a substantial amount of overhead in the routing process; thus, it is vital to address the issue of overhead to enhance the overall network performance. The proposed protocol named compacted area with effective links (CAEL) is designed to focus on decreasing overhead to achieve an enhancement in PDR performance inside the network. The communication between selected nodes that have been judged to be dependable in terms of geographical location and appropriate existing links between vehicles is focused on achieving this goal. With the inclusion of the reliability factor, it is possible to complete the important step of removing extraneous nodes, with the selection of the trustworthy nodes being made based on the link expiration time during the whole routing procedure. When compared to our previously published protocols, i.e., Dynamic Trilateral Enrollment (DyTE) and Reliable Group of Vehicles (RGoV), the results of the simulations demonstrate that CAEL has achieved an overall improvement in the performance of the network.  
ad hoc networks; reliability; link expiration; zone suppression; wireless communication +
Footnote †: journal: Sensors
# Vision-Based Damage Detection Method Using Multi-Scale Local Information Entropy and Data Fusion
## 3 Numerical Investigations of the Methodology
### Noise Immunity Evaluations
#### 3.2.2 Evaluations on Double Damages  
This study considers a steel pinned beam with two damages. The structural geometry and material parameters are identical to those used in the single damage simulation. Two cracks, each characterized by dimensionless parameters \(\alpha_{1}\) = 0.3, \(\beta_{1}\) = 0.2 and \(\alpha_{2}\) = 0.4, \(\beta_{2}\) = 0.8, are introduced into the beam. Similarly, the noise is assigned as WGN and added to the mode shapes to simulate noisy environments at varying SNRs.  
In the simulated scenario with an SNR of 90 dB, Figure 9a-c illustrates the MS-LIE at various scales obtained from the first, second, and third mode shapes, respectively, where the multi-scale space is established on the scale range of 2-10 and a step size of 0.1. The corresponding damage detection results are presented in Figure 9d-f. The presence of multiple damage does not affect the sharp change in the amplitude of the MS-LIE at each damage location, and the MS-LIE heat map of the higher order mode shape is smoother and more robust to noise. Using the proposed data fusion technique, the precise locations of both damages are determined correctly at the peaks of the final damage probability distribution.  
In the simulated scenario with an SNR of 60 dB, the MS-LIE at the scale range of 2-10 is displayed in Figure 10a-c. The corresponding damage detection results obtained from data fusion are shown in Figure 10d-f. The MS-LIE heatmaps indicate that detecting both damages at a single scale does face a significant hurdle due to the high noise level. It is possible to suppress the noise by increasing the scale, which allows the features at the damage location to remain discernible. While the first mode shape is relatively weakly robust to noise, resulting in the omission of one slight damage, the method based on MS-LIE and data fusion can still pinpoint both damages based on the second and third mode shape data.  
Figure 8: Evaluations on single damage with SNR = 60 dB: (**a–c**) the MS-LIE heat map obtained from the 1 st, 2 nd and 3 rd mode shapes, (**d–f**) damage localization results of the 1 st, 2 nd and 3 rd mode shapes.  
Based on the above numerical research, the proposed method based on MS-LIE and data fusion exhibits robustness when detecting slight damage in various noisy environments. Moreover, the method does not rely on direct difference analysis with the original healthy data, and is capable of concurrently revealing single and multiple damages without requiring baseline data as a reference.  
Figure 10: Evaluations on double damages with SNR = 60 dB: (**a**–**c**) the MS-LIE heat map obtained from the 1 st, 2 nd and 3 rd mode shapes, (**d**–**f**) damage localization results of the 1 st, 2 nd and 3 rd mode shapes.  
Figure 9: Evaluations on double damages with SNR = 90 dB: (**a**–**c**) the MS-LIE heat map obtained from the 1 st, 2 nd and 3 rd mode shapes, (**d**–**f**) damage localization results of the 1 st, 2 nd and 3 rd mode shapes.