esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
#### 3.1.1 Impact of the Different Extraction Factors on the Extraction Yield Percentage  
Based on the experimental results obtained from the fifteen trials (Table 2), the highest extraction yield percentage of _C. monacantha_ Del. was 13.80%. This yield was obtained in trial number 7 with a solid-to-liquid ratio of 0.5:10 g/mL, an extraction temperature of 40 \({}^{\circ}\)C, and an extraction time of 30 min. In contrast, the lowest percentage of extraction yield was 7.50%, obtained during trial number 2, with a solid-to-liquid ratio of 1.5:10 g/mL, an extraction temperature of 35 \({}^{\circ}\)C, and extraction time of 20 min. According to the variance analysis data (ANOVA) presented in Table 3, the statistical analysis of the extraction yield responses of _C. monacantha_ Del. had high values of the determination coefficient (R\({}^{2}\)) and the adjusted determination coefficient (\(R^{2}_{(adj)}\)), which were 0.9668 and 0.9071, respectively, showing the reliability of this model. They also showed a low variation coefficient (C.V.%) value of 5.84, indicating that the experimental results were highly reliable. In addition, they reported that obtaining a high probability value of lack of fit of 0.8059 is more significant than 0.05 (\(p>0.05\)), demonstrating no significant difference between the experimental results and those predicted by the Box-Behnken Design, thus confirming this model's validity. In addition, based on the data provided in Table 3, the \(p\)-values of each effect revealed that only the linear effects of the three independent variables (\(X_{i}\): solid-to-liquid ratio, \(X_{j}\): extraction temperature, and \(X_{k}\): extraction time) significantly influenced the percentage yield by the UAE method (\(p<0.05\)). On the other hand, this response was not affected by the other effects, whether quadratic or interaction effects, since their probability values were greater than 0.05 (\(p>0.05\)). Positive and negative signs of the estimated regression coefficient of the significant effects presented by the ANOVA analysis (Table 3) revealed that extraction temperature and time positively affected the extraction yield response. In contrast, a negative linear impact of the solid-to-liquid ratio on the extraction yield was demonstrated. Furthermore, these results were expressed in the different three-dimensional response surfaces of extraction yield, as shown in Figure 2. Therefore, after eliminating the
# Reference Broadcast-Based Secure Time Synchronization for Industrial Wireless Sensor Networks
## 2 Related Work
### Threshold Detection  
Normally, the link delay, offset, and relative clock skew between two nodes are stable and bounded in benign environments, and attackers usually broadcast random time information to disrupt the synchronization balance. Accordingly, an attacker can be distinguished by detecting the difference in packet transmission delays, clock offset, or relative clock skews exceeding the statistical threshold or not.  
[PERSON] et al. [19] utilized the threshold detection technique to determine whether the received messages from the node's farther node are valid or not. Each node first calculates the clock offset between its father node and grandfather node. Based on the pretested threshold value of offset, each node determines whether the synchronization reference node is its father node or grandfather node. Furthermore, a spanning tree structure is constructed against fake timestamps for secure time synchronization. [PERSON] et al. [20] divided the synchronization network into several clusters by the adaptive threshold-based K-means clustering algorithm. It should be noted that there is a chief cluster head (CCH) as the standard time source in the clustered network. The predefined threshold about the difference of varying rates of skew (VRS) between each node and the CCH is related to the clock quality. The clock quality varies between clusters, which leads to different synchronization frequencies between clusters, unlike simultaneous synchronization in most algorithms. Subsequently, a threshold-based two-tier fault detection algorithm is developed to overcome malicious attacks during the synchronization process.  
Aimed at secure distributed time synchronization, [PERSON] et al. [15] developed a secure average-consensus-based time synchronization protocol (SATS) against message manipulation attacks. SATS includes two checking processes for the hardware clock and the logical clock. Unlike isolating attack nodes in other safeguard mechanisms, SATS can flexibly utilize attack information to improve convergence speed in logical clock synchronization. Similarly, a secure maximum-consensus-based time synchronization protocol (SMIS) is further proposed in [16]. SATS designs the logical clock-checking process based on the property that the local node can estimate its neighbor's correction parameters. The hardware clock-checking processes in SATS and SMIS determine whether a node is legal or not by comparing whether two consecutive estimated relative hardware skews are equal or not.
# A Polarization-Based Method for Maritime Image Dehazing
## 3 Experiments and Proposed Method
### Estimation of \(A_{\infty}\)  
The identification of \(A_{\infty}\) plays a crucial role in the dehazing procedure. \(I_{\perp}\) and \(I_{\parallel}\) represent the pictures with the \"brightest\" and \"darkest\" images obtained by rotating a polarizer in front of a camera, respectively. These images depict the maximum and minimum radiance levels affected by the air light. The polarization difference image is calculated as the discrepancy between these two images. \(I_{\perp}\) and \(I_{\parallel}\) can also be computed using Stokes vector operations, as follows:  
\[I_{\perp}=\frac{S_{0}\cdot(1+p)}{2} \tag{9}\]  
\[I_{\parallel}=\frac{S_{0}\cdot(1-p)}{2} \tag{10}\]  
\[\Delta I=I_{\perp}-I_{\parallel} \tag{11}\]  
where \(p\) represents the degree of polarization, directly indicated by the Stokes vector.  
\[p=\frac{\sqrt{S_{1}^{2}+S_{2}^{2}}}{S_{0}} \tag{12}\]  
In the context of the polarization difference image, areas beyond the sky that contain a higher density of object targets display a greater discrepancy in comparison to regions of the sky with fewer object targets. This characteristic can aid in refining the search scope for the celestial area. The variance of an image is an indicator of the level of local detail variation present in the grayscale values of the image. To achieve a precise \(A_{\infty}\) value, a quadrant division method is employed for the polarization difference image. This involves using a variance evaluation function to divide the image into four equal parts, with the segment expressing the lowest variance selected for further quadrant division. From the previous description, it is evident that a smaller variance typically indicates more stable pixel values in that region, suggesting fewer variations. This aligns well with the characteristics of sky regions. Hence, here we opt for the lowest variance. This process is repeated until the segmented regions are smaller than a predetermined threshold area, which has been determined through multiple experiments to be one-eighth of the size of the polarization difference image. Once the final segmented regions are identified in the polarization difference image, the corresponding regions in the \(S_{0}\) image are chosen as potential regions for \(A_{\infty}\). The highest pixel value within each region is then designated as the \(A_{\infty}\) value. This methodology is illustrated in Figure 4, where Figure 4e demonstrates the process of locating \(A_{\infty}\) on the polarization difference image, with the white areas representing potential regions for \(A_{\infty}\). Subsequently, as shown in Figure 4f, the maximum pixel value is extracted from the red region and assigned as the \(A_{\infty}\) value.  
Figure 3: I(0), I(45), I(90), I(135) (from left to right).
## 3 Discussion
Treatment of these cells with LRM-CFS caused an approximately 1-log decrease in the number of ciprofloxacin-tolerant bacteria, but only when the supernatants were used at the highest concentration (1:4) and towards the non-mucoid strain. In a previous study, we observed that LRM-CFS exerted a marked bactericidal effect on mature biofilms of both PaCF1 and PaCF4 strains [18]. Differently, in this study, a relatively poor activity of LRM-CFS against biofilm-associated persisters was observed. This observation, together with the marked bactericidal effect observed instead against planktonic persisters, suggests that tolerant bacteria in biofilms, generated by antibiotic treatment, may display peculiar features that render them particularly refractory to antimicrobials, including LRM-CFS. Assessment of the cytotoxic effect of LRM-CFS towards a cell line relevant for the lung environment demonstrated good tolerability at concentrations able to target planktonic persisters (1:6, 1:8), while a marked cytotoxic effect was observed at the dilution of 1:4. Although no adverse effect of LRM-CFS was recently reported by us in an in vivo _Gelleria mellonella_ model [18], future strategies aimed at minimizing the cytotoxic potential of LRM-CFS on host cells are warranted. These may include the development of appropriate CFS carriers/formulations, as recently reported by [PERSON] and co-workers [38]. To our knowledge, this study for the first time describes the selection of _P. aeruginosa_ persisters in planktonic and biofilm conditions in ASM using an antibiotic. Only two strains of _P. aeruginosa_ were tested, and this may represent a limit of our study. Nevertheless, the two strains were representative of the two major _P. aeruginosa_ phenotypes (mucoid and non-mucoid) found in CF patients and did not show any major difference in the efficacy of persister formation. Our model may represent a useful method to test new anti-persister agents in CF-relevant conditions. Further studies on anti-persisters activity of LRM-CFS in in vivo models are needed to fully explore their potential to eradicate persistent infections. Nevertheless, the results obtained in this study suggest that LRM-CFS could be potentially administered to CF patients during the antibiotic-free intervals, to decrease the number of _P. aeruginosa_ persisters selected by the recurrent antibiotic treatments. Studies are underway in our laboratory to develop a liquid LMR-CFS formulation for aerosol administration, in view of future evaluations of CFS as inhaled therapy to treat or prevent _P. aeruginosa_ lung infections [39].
## 4 Evaluation
### Sensitivity to Parameter Values
#### 4.3.1 Inhibition  
The top part of Figure 2 shows how changes in the inhibition parameter \(\tau_{I}\) affect the results. An increase of \(\tau_{I}\) increases inhibition and removes activations which are only partially covered by others, while a decrease will allow for more overlapping activations to propagate to higher layers. The plots show that reduced inhibition has a positive effect on occurrence recall, which is expected, as more activations are produced. It is even more interesting that it also positively affects precision of found occurrences, which might be explained by the fact that overlapping activations are successfully merged by the merging algorithm of SymCHMMerge. For the establishment metrics, the effect of changes in inhibition is not so obvious, and apart from extreme values, performance is stable.
# Deep-Reinforcement-Learning-Based Joint Energy Replenishment and Data Collection Scheme for WRSN
## 5 Experimental Results
### Performance Comparison
#### 5.4.4 Average Flight Energy Consumption  
In this section, we analyze the differences in average UAV flight energy consumption among three algorithms. The experimental results are depicted in Figure 10. In Figure (a)a, we observe that the UAV energy consumption in D-JERDG initially increases and then decreases as the number of nodes increases. Before reaching 200 nodes, the larger distances between CH nodes prompt the UAV to adapt its flight speed to minimize the flight time and efficiently collect data, resulting in an increasing energy consumption curve. However, after 200 nodes, as the number and density of CH nodes increase, the distances between them become shorter. Compared with MODDPG and random, the average decline rate of D-JEGDG in terms of average flight energy consumption is about 4.2% and 4%, respectively. When the number of nodes is 350, D-JERDG can decrease the average flight energy consumption by about 10% and 9.8%, respectively. As a result, the UAV adjusts its flight speed by reducing it, which leads to longer flight times but decreases the overall UAV energy consumption. Compared with MODDPG and random, in Figure (b)b, we examine the impact of different flight speeds on average energy consumption under the three algorithms. Overall, there is no significant difference among the algorithms (D-JERDG, MODDPG, and random) in terms of average energy consumption. However, as the flight speed increases, the average energy consumption increases as well. Notably, D-JERDG consistently maintains a lower average UAV flight energy consumption compared with MODDPG and random algorithms under different flight speed conditions.  
Figure 9: Experimental results of UAV throughput.
# Deep Learning-Based Automatic Safety Helmet Detection System for Construction Safety
## 3 Methodology
### Data Pre-Processing  
#### 3.2.1 Image Enhancement  
The dataset used for this study contains ample low-light images, which are challenging for object detection. To overcome this problem, power-law transformation or gamma correction-based image enhancement was applied to enhance the images [37]. Equation (1) shows how power-law transformation enhanced the visibility of images.  
\[\mathrm{s}=\mathrm{c}*\mathrm{r}^{\gamma} \tag{1}\]  
where \"s\" represents the output pixel value, \"r\" represents the input pixel value, and \"c\" and \"\(\gamma\)\" are the positive constants. A large \(\gamma\) value leads to darker output images; for example, \(\gamma>1\) leads to darker output values against lighter input values. In this scenario, the value of \(\mathrm{c}\) is taken as 1, and to get clear images, the \(\gamma\) value is taken as 0.5. Figure 4 shows a comparison of some images before and after power-law transformation depicting its effectiveness in enhancing low-light images.
# Optimizing Geospatial Data for ML/CV Applications:
## 1 Introduction
### Motivation  
Access to high-quality, comprehensive datasets is crucial for the training and validation of robust ML and CV models [42; 43]. This is particularly important in the mapping and analysis of road networks, which are essential for applications such as navigation systems, urban planning, and the development of autonomous vehicles [44; 45; 43]. While many road datasets exist globally, they often lack the specificity needed for accurately representing the unique characteristics of road infrastructures in different regions [44; 46]. This variation presents a significant challenge for researchers working on localized applications.  
Optimizing geospatial datasets is crucial, as large and unoptimized datasets can slow down training times, increase computational costs, and negatively influence model accuracy and efficiency [47; 48]. For instance, training a neural network on unfiltered geospatial data may require more epochs to converge, leading to increased memory and energy consumption and longer development cycles.  
Despite these challenges, there is a noticeable gap in current solutions for optimizing geospatial data specifically for road network analysis in ML and CV contexts. Existing approaches often lack sufficient automation or cannot effectively remove irrelevant data without compromising essential features.
# Special Issue on Advances in Environmental Applied Physics--2 nd Edition
###### Abstract
Especially for \({}^{137}\)Cs and \({}^{241}\)Am, absorption reached about 50%, highlighting the lichen's utility in radioactive monitoring [23; 24; 25; 26; 27; 28; 29; 30; 31; 32; 33]. The paper \"Ecological and Health Risks from Trace Elements Contamination in Soils at the Rutile Bearing Area of Akoonlaging, Cameroon\" focuses on the analysis of trace elements in soils near a rutile deposit. Using Flame Atomic Absorption Spectrometry (FAAS) on 25 samples, the study found that concentrations followed the order: Fe, Ti, Zr, Mn, Cr, V, Ba, Zn, Nb, Ni, Pb, Ga, Cu, Co, Y, Br, and Sn. Moderate correlations between Fe and several elements point to a shared geogenic source. Enrichment Factors (EFs) revealed notable accumulation of Zr, Nb, and Ti, with localized spikes in Cr and Co. Although the Geo-Accumulation Index (I\({}_{\text{geo}}\)) suggests low overall contamination, the Contamination Factor (CF) indicates significant levels for Cr, Nb, Ti, and Zr. The health risk assessment identified a moderate carcinogenic risk and high non-carcinogenic risks, particularly for children, emphasizing the need for further monitoring and mitigation strategies [34; 35; 36; 37; 38; 39; 40; 41; 42; 43]. The case study titled \"Radon-Specific Activity in Drinking Water and Radiological Health Risk Assessment: A Case Study\" evaluates \({}^{222}\)Rn concentrations in groundwater from the Calabria region, Italy. Measurements were conducted using a PerkinElmer Tri-Carb 4910 TR liquid scintillation counter, and results were compared to the national threshold of 100 Bq/L set by D.Lgs. 28/2016 and EU Directive 2013/51/Euratom. Where values exceeded limits, the annual effective dose from ingestion and inhalation was calculated. The findings provide essential baseline data for assessing regional exposure to radon through drinking water and help inform long-term public health policy [44; 45; 46; 47; 48; 49; 50; 51; 52; 53]. Lastly, the paper \"Systematic Review of Exposure Studies to Radiofrequency Electromagnetic Fields: Spot Measurements and Mixed Methodologies\" synthesizes 86 studies published between 1998 and 2023. It follows PRISMA guidelines, along with PECO criteria and the CASPe assessment tool. Of these, 61 used spot measurement techniques, and 25 used mixed methods. Notably, 43% of spot studies relied on spectrum analyzers, especially the Narda SRM-3006 and SRM-3000, reflecting the increased use of sensors in RF-EMF research. Reported exposure levels varied widely, from 0.0600 \(\upmu\)W/m\({}^{2}\) in Palestine to 200,000 \(\upmu\)W/m\({}^{2}\) in Norway. Across all settings, RF-EMF levels were found to be well below the thresholds recommended by the ICNIRP guidelines, indicating low exposure risks [54; 55; 56; 57; 58; 59; 60; 61; 62; 63; 64; 65; 66; 67; 68; 69; 70; 71; 72]. This research received no external funding. The author declares no conflicts of interest.
# A Comprehensive Review of Indoor Localization Techniques and Applications in Various Sectors
## 2 Technology of Indoor Localization
### Bluetooth Module  
The HC-05 Bluetooth module (Figure 3) is a widely used device for enabling wireless communication between systems, operating on the 2.4 GHz frequency and supporting Bluetooth 2.0 with Enhanced Data Rate (EDR). This module is designed to provide seamless wireless connections for applications such as healthcare tracking, IoT projects, and RTLSs [52]. It can function in both master mode (initiating connections) and slave mode (accepting connections), giving it flexibility in different communication scenarios. The HC-05 uses a UART (universal asynchronous receiver/transmitter) interface, making it easy to connect with microcontrollers like Arduino and Raspberry Pi.  
The module's key features include an operating voltage range of 3.6 V to 6 V and a communication range of up to 10 m (33 feet), depending on the environment. It supports data transfer rates of up to 2 Mbps, making it suitable for applications requiring low-bandwidth communication, such as transmitting sensor data or control commands. The HC-05 also has several pins for connectivity, including VCC for power, GND for ground, TXD (Transmit), RXD (Receive), and an EN (Enable) pin to control power to the module [50]. In healthcare environments, the HC-05 module is used in BLE-based RTLSs for tracking patients, staff, and medical equipment. Its low power consumption makes it ideal for wearable devices that require prolonged battery life. For example, patient wristbands equipped with HC-05 modules can send real-time location data to hospital systems, helping staff quickly locate patients or equipment such as wheelchairs, defibrillators, and infusion pumps [53].  
The HC-05 also supports IoT integration by enabling wireless communication between medical sensors and monitoring systems. This allows hospitals to collect and analyze real-time data, such as patient vital signs, environmental conditions, and asset locations. In addition, the module can be used for environmental monitoring, connecting sensors to track temperature, humidity, and other conditions critical to patient care and equipment maintenance. The flexibility and ease of use of the HC-05 make it a valuable tool for improving patient safety, staff coordination, and resource management in healthcare and other industries [50].
## 2 Problem Description, the Proposed Approach, and Aircraft Dynamics
### Non-Minimum Phase Problem  
The aircraft of interest in this paper is the Boeing 747 100/200 series, flying at the altitude of 4000 m with 0.567 Mach number corresponding to 185 m/s speed [53]. Non-minimum phase behavior is typically encountered when pitching up an aircraft to increase its altitude. When the elevator is deflected up, a downward force is produced on the tail that pushes it down relative to the center of gravity. Then, the aircraft angle of attack (AOA) is increased due to the lift on the main wing causing the aircraft to climb (pitches upward). However, before the altitude increases, the aircraft goes down initially (undershoot) due to the reduced net lift in the center of mass. A typical step response of such a system is presented in Figure 1. The conventional control laws, such as optimal controllers and classical controllers, cannot work effectively if they are directly applied to the non-minimum phase systems or if the dynamics involve faults due to unstable zeros and internal stability problems. Therefore, designing an effective control system with a standard LQR technique is a challenging issue due to the limitations created by the closed-loop bandwidth, which is narrowed due to the non-minimum phase dynamics.