esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Research Framework of Internet of Emergency Services
### Taxonomy of Concepts and Reference Model
AI algorithms can analyze large amounts of data from various sources to detect patterns and anomalies, predict future events, and provide recommendations for response teams. * Geographic Information Systems (GIS): GIS technologies are used in the IoES to visualize and analyze spatial data. GIS enables emergency responders to understand the geographic context of the emergency and make informed decisions [57]. * Wearable Devices: Wearable devices are a type of IoT device that can be worn by emergency responders to collect data on their physical condition, such as heart rate, temperature, and movement. Wearable devices can also be used to track the location of emergency responders and communicate with the EMC. * Unmanned Aerial Vehicles (UAVs): UAVs, also known as drones, can be used in the IoES to collect data from the air. UAVs equipped with cameras and other sensors can provide real-time video feeds of the emergency scene, detect hazards, and locate victims [58, 59, 47]. * Augmented Reality (AR) and Virtual Reality (VR): AR and VR technologies can be used in the IoES to provide emergency responders with immersive and interactive training experiences. AR and VR can also be used to visualize and analyze data in real-time, enhancing situational awareness [29, 60]. * Blockchain: Blockchain technology can be used in the IoES to provide a secure and transparent record of emergency response activities. Blockchain can also be used to manage the distribution of emergency resources and track their use [61, 62, 63]. * Data Analytics: Data analytics is a critical component of the IoES. Data analytics technologies enable the IoES to analyze and make sense of the large amounts of data generated by the IoT devices. Data analytics can also be used to identify patterns and trends in emergency response data, providing insights for future improvements. In summary, the IoES Reference Model provides a comprehensive view of the components and interactions of a system that uses advanced technologies to enhance the emergency response. The IoES leverages IoT devices, communication networks, cloud computing, AI, GIS, wearables, UAVs, AR/VR, blockchain, and data analytics to improve the emergency response and save lives.
## 1 Introduction
[41] reported a two-stage process consisting of HTC followed by KOH-activation of the resulting hydrochar for the preparation of microporous carbons with high BET surface area under milder operating conditions than those used for direct single-step activation with the same agent. The aim of this work was to study the valorization of grape seeds into carbon materials with the double purpose of obtaining high energy-density solid fuels upon hydrothermal carbonization and low-cost activated carbons by chemical activation of the resulting hydrochar. Temperature and biomass:water ratio have been analysed as operating conditions for HTC and KOH. FeCl\({}_{3}\) and H\({}_{3}\)PO\({}_{4}\) have been tested as activating agents under different conditions. Hydrochars and activated carbons have been characterized by several techniques covering proximate and ultimate analyses, 77 K N\({}_{2}\) adsorption-desorption and CO\({}_{2}\) adsorption isotherms for porous texture, SEM for morphological examination and FTIR and TPD for surface functional groups assessment. Sulfamethoxazole, a sulfonamide antibiotic used to treat urinary infections was selected as the model compound to test the potential of the activated carbons as adsorbents for the removal of water pollutants. That compound is one of the most frequently emerging contaminants found in municipal wastewater, and in many cases is still present in the effluents from sewage treatment plants.
## 2 Materials and Methods
### Influence of Plasma Parameters  
HF were randomly classified into untreated (HF) and plasma-treated (HF\({}_{\text{tr}}\)). The HF were subjected to plasma treatment under atmospheric pressure using a DBD cell (Minismart, PLASMART., Co., Ltd., Daegeon, Republic of Korea). The DBD plasma generator consists of two plane-parallel electrodes, a powered and grounded electrode, and an RF power supply operated at a frequency of 13.56 MHz. Extracted HF were placed on an aluminum tray, positioned above the grounded electrode, which was moved back and forth at a rate of 30 cm s\({}^{-1}\). A schematic of the DBD cell operated under atmospheric pressure is shown in Figure 1. The distance between the powered electrode and sample tray was fixed to maintain a consistent discharge gap of 1 mm.  
For measuring the effect of the type of plasma gases, the plasma treatment time was 10 min, plasma power 180 W, and the type of gases was argon (Ar), nitrogen (N\({}_{2}\)), and oxygen (O\({}_{2}\)). The Ar gas was used with flow rates of 8 L min\({}^{-1}\), while the O\({}_{2}\) and N\({}_{2}\) gases used were 10 sccm. Samples were treated in triplicate under each condition and stored in foil bags for further analysis. Furthermore, the structural characterization and surface chemical composition of the hemp fibers were thoroughly examined using Fourier-transform infrared (FT-IR) spectroscopy, X-Ray diffraction (XRD) analysis, and scanning electron microscopy (SEM) in conjunction with energy-dispersive X-Ray spectroscopy (EDS).
# Simplified Derivative-Based Carrierless PPM Using VCO and Monostable Multivibrator
## 5 Experiments and Results
### Implementation Methodology
#### 5.1.2 Noise Resistance Analysis  
Building upon the fundamental PWM and PPM synthesis demonstrated in the previous section, this section addresses the critical practical consideration of noise immunity through comparative MATLAB simulation. The analysis specifically examines the robustness of the proposed derivative-based PPM technique when subjected to various noise conditions, providing quantitative assessment of system performance degradation. The implementation code for this section can be found in Code S4 of the Supplementary Materials.
## 4 Experimental Research
### The Parameters' Independent Optimization of MFE
To sum up, the parameter \(n\) is set to 2. Figure 6c exhibits variations of \(M_{FED}\) and \(SD_{FED}\) with parameter \(r\) when \(\tau=1,m=2,n=2\). The \(M_{FED}\) values corresponding to the two imaginary tasks are quite different, while the \(SD_{FED}\) values for the two tasks are both large when parameter \(r\) is relatively small. With the increase of \(r\), the \(M_{FED}\) corresponding to the two imaginary tasks become smaller. In conclusion, the parameter is selected as \(r=0.1 SD\), and \(SD\) is the standard deviation of the original MI-EEG. In summary, when scale factor \(\tau\) is 1, the values of parameters \(m,n\), and \(r\) have a significant influence on the FE of MI-EEG for two imaginary tasks, and this will directly affect the quality of the FE features. Therefore, it is necessary to further optimize the parameters of FE when scale factor \(\tau\) equals 2, 3, and 4. The \(M_{FED}\) and \(SD_{FED}\) values with different parameters \(m,n\), and  
A detailed analysis of Figures 7-9 was performed using the analysis method of Figure 6. It can be seen that the parameters \(m=2,n=2,r=0.1 SD\) are more suitable for the classification of MI-EEG when \(\tau=2,3,\) and \(4\). Note that \(SD\) is the standard deviation of the coarse-grained MI-EEG corresponding to each scale factor \(\tau\), and not the standard deviation of the original MI-EEG. To prove the necessity of parameter optimization, a comparison between IMFE and MFE was carried out on a public dataset and SVM was chosen as a classifier. In the computation of MFE, \(\tau_{\max}=4,n=2,m=2,r=0.1 SD\) were selected, but \(SD\) was different in the two methods. In the IMFE method, \(SD\) was the standard deviation of the coarse-grained MI-EEG corresponding to each scale factor \(\tau\), i.e., \(SD\) was varied with \(\tau\). However, in the MFE method, \(SD\) was the standard deviation of the original MI-EEG on each scale factor \(\tau\) and was constant. The experimental results are listed in Table 2.  
Figure 8: For \(\tau=3\), the mean and standard deviation of FED curves for input variables (**a**) embedding dimension \(m\); (**b**) boundary gradient \(n\); and (**c**) boundary width \(r\). As seen from Table 2, when IMFE is employed to extract the feature of MI-EEG, the average classification rate with 10-fold CV increases by 1.78% from 90.36% to 92.14% compared with MFE, and the experimental results of IMFE show more stability than MFE. This demonstrates that the parameters' independent optimization of MFE is beneficial for enhancing the accuracy and adaptability of the feature extraction method.
# UAV Dynamic Non-Terrestrial Transmission Channel Analysis Based on SSCM-RT Model
## 3 SSCM-RT Channel Model  
In this research, a long-distance 3D hybrid channel model for UAVs is suggested and simulated based on the comparison of several modeling methods. This model combines the spatial statistical channel model (SSCM) and ray tracing (RT).
# Nanomaterial Databases: Data Sources for Promoting Design and Risk Assessment of Nanomaterials
## 1 Introduction
### Composition  
Considering both the mass production of nanomaterials and public health, numerous studies regarding their physicochemical properties, toxic effects, and environmental risks have been performed [33; 34; 35; 36; 37; 38; 39; 40; 41; 42; 43]. To give a few examples, the study performed by [PERSON] et al. showed that the cytotoxic effects of carbon-based nanomaterials (carbon nanofibers, carbon nanoparticles, and carbon nanotubes) were size-dependent, and the hazardous effects were enhanced if the functionalization of these nanomaterials was via acid treatment [44]. In another study, [PERSON] et al. studied the effects of metal oxide nanomaterials on sea urchin development. Their results suggested that sea urchin embryos are severely affected by ZnO nanomaterial treatment, while they are not sensitive to CeO\({}_{2}\) or TiO\({}_{2}\) nanomaterials under the tested conditions [45]. From these studies on nanomaterials, data have been generated, which makes analyzing and designing nanomaterials possible and easier. Thus, how to properly and effectively utilize these data become unavoidable questions in the nanoscience community.  
To take full advantage of these valuable resources, databases that can store and manage the data in a more organized way have been developed to help scientists study and design nanomaterials to meet their specific needs. Various nanomaterial databases are available online. However, there are no guidelines for scientists to select the appropriate databases when performing a specific area of research. Therefore, understanding and comparing the  
Figure 1: Classification of nanomaterials based on composition and dimensionality.  
content, structure, function, advantage, and limitation of these databases become important and necessary for better utilization of them in nanoscience research.  
The primary objective of this review is to provide information for selection of the appropriate databases when conducting certain aspects of nano-research, enabling more convenient and efficient extraction of the nanomaterials-related data. To achieve this objective, eight popular nanomaterial databases including PubVINAS, caNanoLab (cancer Nanotechnology Laboratory), eNanoMapper, NR (Nanomaterial Registry), NBIK (Nanomaterial-Biological Interactions Knowledgebase), NKB (NanoCommons Knowledge Base), NIL (Nanoparticle information library), and Nanowerk were reviewed and systematically compared. According to our comparisons, the NR, eNanoMapper, and PubVINAS databases contain large amounts of data on physicochemical properties of nanomaterials, the caNanoLab and eNanoMapper databases provide biological experiments and relevant protocols, and the caNanoLab database also includes detailed descriptions of experimental designs.
# A Real-Time Safety Helmet Wearing Detection Approach Based on CSYOLOv3
## 4 Experiment
### Experimental Results and Analysis
#### 4.3.2 Real Construction Site Application  
In order to illustrate the practicability, the newly raised detection model is applied to four real-system cases in an actual complex construction site scene, including normal detection, occlusion detection, dense crowd detection and small-scale face detection. The specific detection example effect is shown in Figures 7-10. As can be seen from Figure 7, for normal safety helmet detection, the proposed CSYOLOv3 and YOLOv3 algorithm have all achieved good SHW detection results, safety helmet  
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Network Structure** & **Frames Per Second(FPS)** & **mAP(\%)** \\ \hline YOLOv3 & 19 & 42.5 \\ CSYOLOv3 & 25 & 67.05 \\ tiny-YOLOv3 & 27 & 38 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Statistics of test results for different postures.  
Figure 6: The experimental data curves of YOLOv3 and CSYOLOv3.  
as shown in Figure 9, seven SHW targets have been identified through YOLOv3 in Figure 9a, while eight people with safety helmet are detected by CSYOLOv3 in Figure 9b. In addition, the prediction accuracy of more than half of the detection boxes is more than 95%. As Figure 10 shows, CSYOLOv3 algorithm outperforms original YOLOv3 in the construction site scene of small target detection.  
It can be seen from Figure 10a that three SHW targets have been detected based on Original YOLOv3, while a total number of four SHW targets have been detected correctly based on the CSYOLOv3 in Figure 10b. Furthermore, the prediction accuracy of the proposed algorithm has also been improved to a certain extent. In the aspect of real-time performance, the speed of per image processing was calculated based on the above three algorithms for the four SHW situation mentioned above. Experimental data indicates that the speed of CSYOLOv3 is generally faster than that of YOLOv3, and slightly lower than that of tiny-YOLOv3. Specifically, the average processing time of per image based on CSYOLOv3 is 0.04 s while that of YOLOv3 is 0.053 s, which can be seen from Table 2. Among the three methods compared, the real-time performance of tiny-YOLOv3 is best with the processing time of each image only 0.037 s. In conclusion, compared with the original YOLOv3 algorithm, the SHW detection performance of the CSYOLOv3 is significantly improved.
# Digital Twin-Based Clamping Sequence Analysis and Optimization for Improved Geometric Quality
## 1 Introduction
### Influence of Clamping Process in Quality Assurance  
During the assembly process of sheet metals, the clamping step is performed to ensure that the parts are locked in the normal-to-surface direction. There are two parameters associated with the clamping step: firstly, the position of the clamping points and, secondly, the sequence of applying the clamping forces to the assembly. Optimization of the clamping step during the assembly process has also been studied based on these two parameters.  
[PERSON] et al. have considered the impact of the clamping sequence on the design of a fixture. They have introduced an approach for analyzing the clamping sequence on the contact force distribution and machining accuracy of rigid bodies [21]. [PERSON] et al. have considered a computational approach for estimating the buckling effect during welding with respect to the clamping conditions [22]. [PERSON] and [PERSON] have studied clamping and welding sequence optimization to minimize the cycle time and assembly deformation variation. They have considered a genetic algorithm (GA) for this optimization task [23]. Several studies have considered the fixture layout optimization, considering the nominal geometry [24; 25]. The effect of the clamping forces on the working object has been studied, and the minimum clamping forces are identified [26]. In another study, the GA optimization algorithm is considered for optimizing the fixture layout and the clamping sequence [27] on a simple geometry workpiece. Further studies have shown that relying on evolutionary algorithms for joining sequence optimization of non-rigid assemblies is computationally expensive, as the objective function involves the Finite Element Method (FEM) and MC simulations [19]. In another study, [PERSON] and [PERSON] have taken an analytical approach to model the impact of a clamping sequence on a solid workpiece location error [12]. The developed model has shown a 20% prediction error in workpiece location and reaction forces. The influence of the clamping condition of the welding distortion has been studied; however, the sequential process is not taken into consideration [28; 29].  
The previous research has mostly considered applying a GA to optimize the clamping sequence on solid bodies or sheet metal assemblies. The studies are divided into taking the part variation into consideration or solely focusing on identifying clamping sequences on nominal geometries. In this paper, a method has been established to analyze the clamping sequence and optimize the sequences time-efficiently with a digital twin, taking into account individual part deviation.
## 4 Classification of Routing Protocols
### Postural-Movement-Based Routing Protocols
#### 4.4.6 Exploiting Prediction to Enable Secure and Reliable Routing (PSR)  
In [67] the authors present a distributed prediction based secure and reliable routing framework, which can be integrated with any other routing protocol to achieve improvement in its reliability and security performance. A backbone link is a communication link between any two neighbor nodes which are at a constant distance from each other. These backbone links form a shortest path tree at the sink node. PSR selects next-hop node with highest predicted link quality based on the previous link quality measurements. PSR uses lightweight approaches for source and data authentications for secure communication from the three types of data injections attacks: exhaustive source authentication attacks (false authentication requests), exhaustive data authentication attacks (false date packets), and data replay attacks (eavesdropped security materials to introduce fake data packets).  
PSR is categorized into two prediction based sub-algorithms: next hop selection algorithm and data transmission algorithm. In the next hop selection algorithm, each node maintains a matrix to store the link quality measurements for previous pre-defined time slots. The link quality matrix is used to build the prediction model to predict the best link quality. In absence of the prediction model, the data packets will be forwarded using the backbone link based shortest path tree. While in the data transmission algorithm first the source authentication will take place and then the data authentication. The source authentication consists of two steps. In first step, a node will broadcast a source authentication request containing an identity based signature using the signature scheme discussed in [68]. Each of the neighbor nodes will verify the signature and if equality holds then it will reply in the second step, otherwise the authentication process is stopped. The data authentication process will be initiated where a set of secret tokens will be shared among any pair of nodes after they successfully authenticate each other. For every data packet received by the neighbor node is checked for a valid token to authenticate the packet. In the absence of a valid token, the source authentication will start. To avoid any occasional transmission failure, each node initiates the source authenticates for the maximum number of time.
## 4 Physical and Chemical Treatments to Improve SvGIs Production
### Salt Stress to Enhance SvGIs Production  
Soil salinity poses a major constraint affecting plant growth, energy metabolism, and metabolites production. Different salt concentration induces different stress conditions, which accelerates varying response in plants. An amount of 30 mM NaCl (low salinity) concentration is shown to increase steviol concentration, and it is also suggested that the stevia plant is a moderate NaCl tolerant plant [29]. Several researchers have utilized elicitors to induce defense mechanisms in plants; for instance, NaCl levels of 50 mM and 150 mM along with 0.4 g/L chitosan increased stevioside and Reb A synthesis and produced a stress-tolerant plant [30]. The use of chitosan helps to induce a stress resistance mechanism that helped stevia plants to withstand high-salinity stress. These studies show the significant effect of salt stress on steviol production. However, many such experiments need to be performed to discover the optimum concentration of stress induction that can enhance high SvGIs production in stevia plants.  
Secondary metabolite production by a plant species is influenced by growth conditions and physiology and, to a large extent, by the differential impact of environmental growth conditions on metabolic pathways involved in their biosynthesis. Thus, several efforts have been made to enhance the secondary metabolite in several plants [31]. This resulted in the use of a significant number of elicitors of biotic and abiotic origin in improving metabolite production in _Stevia rebaudiana_ via in vivo and in vitro growth condition manipulation, and increasing evidence from study results has established the role of oxidative stress defense response in secondary metabolite production in _Stevia rebaudiana_ plants.
# Deep Active Learning for Computer Vision Tasks: Methodologies, Applications, and Challenges
## 2 Candidate Selection Strategies in Active Learning
### Committee-Based Selection Strategy
After that, these labeled samples are added to the labeled dataset to continue training the model. Sequentially, we formally define the pool-based active learning method in Algorithm 1. The End conditions include that the performance of the model meets requirements, or the budgets for annotation run out, or the selected samples are hard for annotators to label. As shown in Figure 2, there are the labeled pool \(L=\{(x_{1}^{l},y_{1}^{l}),\cdots,(x_{N_{l}^{l}}^{l},y_{N_{l}}^{l})\}\) and the unlabeled pool \(\mathcal{U}=\{x_{1}^{u},\cdots,x_{N_{u}}^{u}\}\) at the beginning. \(N_{l}\) and \(N_{u}\) are the number of labeled and unlabeled samples, respectively. Then, the data from the labeled pool is fed into the machine learning model for supervised training. After that, the well-trained model is utilized to extract the representation \(\mathcal{R}^{u}=\{(r_{1}^{u}),\cdots,(r_{N_{u}}^{u})\}\) of the unlabeled pool data. Based on \(\mathcal{R}^{u}\), the informativeness is calculated according to the query strategy. The top-K samples \(\mathcal{K}=\{x_{1}^{k},\cdots,x_{N_{k}}^{k}\}\) are selected out to the oracle (human annotators) and obtain the labels \(\mathcal{Y}^{\mathcal{K}}=\{y_{1}^{k},\cdots,y_{N_{k}}^{k}\}\). Finally, the labeled pool \(L\) will be updated to \(L=\{(x_{1}^{l},y_{1}^{l}),\cdots,(x_{N_{l}}^{l},y_{N_{l}}^{l}),(x_{1}^{k},y_{1 }^{l}),\cdots,(x_{N_{l}}^{k},y_{N_{l}}^{k})\}\). With the updated \(L\), the machine learning model will promote the performance in return. Meanwhile, the size of unlabeled pool \(\mathcal{U}\) is reduced to \(N_{u}-N_{k}\). The above loop will be repeated until one of the end conditions is met. Since the selected samples \(\mathcal{K}\) from the unlabeled pool \(\mathcal{U}\) are the most significant ones for training, the size of \(L\) to learn a model can often be much smaller than the size required in classic supervised learning without active learning.