esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
More information on the MQ-25 will be provided in Section 2.4. The importance of a UA capable of providing fuel airborne to extend all Carrier Air Wing (CVW) crewed platforms cannot be understated. Furthermore, the vision also outlines that as autonomy and learning enabled systems (LES) mature in capabilities, the USN will continue to evaluate the portions of crewed and uncrewed aircraft to ensure CVW lethality. A3R supports a wide variety of military applications, including surveillance, strategic engagement, large scale, and complex operations. A3R could also support a wide variety of civilian operations UAs currently support to include weather monitoring [10], surveillance for wildfires [11], search and rescue [12], and emergency response and disaster management [13]. For both military and civilian operations, A3R reduces the need for ground-based infrastructure, such as intermediate refueling airfields, and provides additional flexibility for extended flights. A3R can provide significant advantages for large aircraft, as well as for small aircraft, including UAS. Without A3R, small uncrewed aerial vehicles have a limited range due to fuel constraints imposed by their small size and limited load capacity. Use of A3R can provide strategic and tactical advantages both for individual aircraft and for cluster applications [14]. This paper provides a brief history of air-to-air refueling, presents the framework for probe-and-drogue refueling, discusses innovations in automated air-to-air refueling, describes the tasks required to complete air-to-air refueling, and presents a review of the sensor technologies to support A3R for a probe configured UA. The probe-and-drogue method of air-to-air refueling is the focus of this paper and is used by the USN, as well as other NATO combat aircraft.
# Data Reduction in the String Space for Efficient kNN Classification Through Space Partitioning
## 5 Results
The resulting data distribution is graphically shown in Figure 3. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Dataset**} & \multicolumn{2}{c}{**String-Based**} & \multicolumn{2}{c}{**Feature-Based**} \\ \cline{2-5}  & **ALL** & **RHC** & **ALL** & **RHC** \\ \hline NIST & 0.021 & **0.089** & 0.020 & 0.068 \\ \hline MNIST & 0.012 & 0.076 & 0.012 & **0.110** \\ \hline USPS & 0.013 & 0.059 & 0.013 & **0.096** \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Results in term of the _estimated profit per prototype_ metric. All values have been scaled up two orders of magnitude for simplifying the comparison. Bold values represent the best overall profit for each corpus. All results report deviation values lower than 0.01. The results of this statistical test state that, for both string and feature-based spaces, the profit per prototype figures obtained for the case in which no reduction is performed are significantly lower than the results obtained once the RHC method is applied. This fact is somehow graphically confirmed by Figure 3 as the RHC data distribution consistently achieves better profit figures than the non-reduced cases without any overlapping areas between them.
# Cooperative Downlink Listening for Low-Power Long-Range Wide-Area Network
## 4 IoT Query Dissemination Based on CDL
### DL Groupcasting  
Groupcasting, which is a kind of multicasting, is used when data is requested only from the specific types of sensors in the network. For instance, as shown in Figure 9, the application server wants to know the sensors for which the temperature is higher than 30 degrees. In this case, the application server can request a query to the network server and it broadcasts to the nodes the message  
Figure 8: Threshold variation.  
containing the Addr field, in which the temperature sensor bit is set, and the query as shown in Figure 9. As a result of the query, only the sensors which satisfy the condition (>30 \({}^{\circ}\)C) respond to the network server. The network server aggregates data and then the aggregated results might be sent to the application server. This groupcasting capability can reduce entire network traffic as well as excessive data storage in the network server.
# Nighttime Image Dehazing Based on Point Light Sources
## 3 Proposed Method
### Light Source Influence Matrix  
According to the MRP theory, the atmospheric light value is replaced by the illumination of artificial light source. As the distance increases, the attenuation of light decreases. The pixels near the light source are more affected by the light source, while the pixels far from the light source are less affected by the light source.
## 3 Results
### Statistical Analysis of AML Precision and Reliability
Due to signal attenuation and limited penetration capability in real-world soils, GPR systems generally struggle to detect non-metallic utilities at depths exceeding 3 m, making them insufficient for applications requiring compliance with South Korean regulatory standards. Additionally, the AML device has demonstrated consistent performance in detecting both metallic and non-metallic utilities across a variety of environmental and installation scenarios, underscoring its robustness and operational reliability.
applied sciences  
The Impact of Coastal Geodynamic Processes on the Distribution of Trace Metal Content in Sandy Beach Sediments, South-Eastern Baltic Sea Coast (Lithuania)  
[PERSON]  
of sand and gravel, mostly due to river and land sediment transport and deposition [4]. However, the ultimate magnitude of the accumulation/erosion of beach sediments depends also on anthropogenic activities [5]. The textural, lithological, and mineralogical composition of beach sediments depend on many factors: the physical and geographical conditions of the location [6; 7; 8], geological framework [9; 10; 11; 12], tectonic settings [13; 14; 15; 16; 17], provenance [18; 19; 20; 21; 22], climate and sea hydrodynamics (waves, tides, and currents) conditions [13; 23; 24; 25; 26; 27], and the anthropogenic activities [27; 28; 29; 30; 31]. Sandy beaches are presently threatened by several forms of environmental degradation, although beach management has traditionally concentrated on geographic hazards [32] and recreational use of coast [33], while trace metals are rarely considered [34]. The geochemical analysis of beach sediments together with lithological and mineralogical characterisation can provide valuable information about the local and regional patterns of sediment transport, distribution, and provenance, as well as the conditions of the coast [35].  
Global studies related to trace elements analysis on beaches focus on assessing the changes in trace element concentrations in the context of the influence of local pollution sources such as mining sites, urban areas, and industrial complexes [28; 29; 36; 37; 38] or tourism [39; 40; 41]. Several studies analysing the migration and distribution of trace elements in coastal environments have been conducted; e.g., the distribution of trace elements concentrations across (from the beach to dunes) [42] and along the coast [43; 44; 45; 46; 47; 48; 38] and vertical migration [49]. All overviewed studies intended to identify the source or how the particular pollution source affects beach sediments, but the coastal processes were not deeply analysed. The concentration of elements can be affected, as it is known in heavy minerals, by changes in the hydrometeorological conditions [23] or by the season [35; 49]. The dominant coastal processes could affect the accumulation of trace elements, which could be of natural origin.  
In Lithuania, the formation and dynamics of recent beach sediments have been analysed using lithological, mineralogical, morphometric, and other methods [12; 27; 50; 51]. The nearshore mineralogical and geochemical composition of the Baltic Sea's Lithuanian territorial waters and distribution has already been well analysed [21; 52; 53]. However, the detailed geochemical composition of beach sediments in relation to active coastal processes has not been previously investigated, except for some local studies [54; 55]. This study aims to assess the temporal change of the trace metal content in sandy beach sediments and the dependence on coastal lithomorphodynamical processes.
# Smart Healthcare Applications over 5G Networks: A Systematic Review
## 3 Results and Discussion
#### 3.1.2 Real-Time Continuous Monitoring
[25] and [PERSON] and [PERSON] [56] highlighted that by integrating technologies such as IoT, big data, AI, and wearable devices, an intelligent health monitoring system can be implemented which would support the collection of physiological parameters such as blood pressure, heart rate, and body temperature. In addition, the collected parameters can be stored in a cloud platform. For remote monitoring of elderly people, [PERSON] et al. [57] analyzed the use of wearable devices for the acquisition of physiological parameters, such as oxygen saturation level, blood pressure, body temperature, rhythm heart rate, and respiratory rate. In addition, a ML system generates reports of the physiological parameters collected from the person, and after a period of time they are sent to the specialist to provide a follow-up and diagnosis.
## 4 Discussion
[33] conducted a systematic review and metanalysis to determine if the use of PENS is more effective than TENS for treating musculoskeletal pain. They included nine RCTs in the qualitative analysis and seven in the quantitative analysis and found no difference between the two approaches in terms of pain intensity. Probably, when compared to TENS, PENS could induce a decrease in medication intake. Nevertheless, it is difficult to obtain definitive results due to important differences in protocols, parameters (e.g., frequency of pulsed current), and treatment plans. Other clinical trials were conducted for evaluating the efficacy in different clinical contexts. For example, in patients with myofasical chronic neck pain (n = 40) treated with dry needling (DN) combined with high or low-frequency PENS, [PERSON] et al. [34] found significant improvements in the pain scores in both groups. Another RCT demonstrated that, in the short-term, PENS is more effective than DN in patients with mechanical neck pain [35]. The main limitation of these trials is the small sample size. Keyword analysis is a profitable approach to defining preeminent themes and provides a framework of documents on a research field. The VOSviewer-based knowledge visualization showed an acceptable interconnection between the terms (keywords). This semantic network analysis found eight clusters, indicating a varied distribution of the keywords used and translates into a wide range of topics covered. On the contrary, knowledge-mapping suggested that scientific collaborations between researchers, or institutions, mostly concern the United States, England, and Germany. Furthermore, the co-authorship analysis of authors suggested that collaborations between authors are lacking. Remarkably, only five poorly structured clusters were collected. Since the cooperative networks show limited group characteristics, the authors tend to publish independently. Therefore, the development of research across different countries is unbalanced and partnerships should necessarily be strengthened. The reasons for these gaps need to be analyzed. Multicenter studies, for instance, are very difficult to carry out. Participating centers should be equipped with the same devices; inclusion and exclusion criteria should be adequately established, as well as operational protocols. It presupposes an important economic commitment. [PERSON] et al. [7] completed a multicenter national study in Italy. In their prospective observational investigations, the authors successfully treated patients affected by peripheral neuropathic pain, localized and refractory to pharmacological therapies. The same authors reported that the small sample size (n = 76), the non-randomized design, and the high prevalence of some disease conditions (post-herpetic and occipital neuralgias) affected the reliability of the results.
# Sample-Pair Envelope Diamond Autoencoder Ensemble Algorithm for Chronic Disease Recognition
## 4 Experimental Results and Analysis
### Parameter Analysis
#### 4.5.2 Analysis of Classifier Type  
The impact of different classifiers (extreme learning machines (ELM), SVM, and RF) on the proposed algorithm's performance is experimentally studied. The results are listed in Table 17.  
Table 17 shows that, for five of the eight datasets, the highest accuracy, sensitivity, and specificity are achieved with the SVM. For the AD dataset, classification accuracy obtained using SVM is \(71.11\%\), which is 10 and 3.33% higher than those with RF and ELM, respectively, and the sensitivity is \(71.11\%\), which is 10 and 3.33% higher than those with RF and ELM. In addition, the specificity is \(85.56\%\), which is \(5\%\) and \(1.67\%\) better than those with RF and ELM, and it had better stability, since the SVM is most accurate in most cases. Overall, the SVM outperformed the RF and ELM.
# IoT-Based Cotton Plant Pest Detection and Smart-Response System
## 2 Related Work
### IoT in Agriculture
They deployed IEEE 802.15.4 enabled nodes in Potato and Wheat crops to investigate path loss of 2.4 GHz Radio signals. Initial node deployment experiences disconnectivity and signal attenuation due to the density and height of wheat vegetation. They proposed Non-Dominated Sorting Genetic Algorithm (NSGA-II) based optimized Node Deployment Strategy (NDS) for Wheat and Potato farming. Scientists monitor the significant difference between the NSGA II-NDS and existing NDS Strategy is that to monitor the Path Loss Coefficient for a crop to be monitored before deployment to decrease the possibility of a link dis-connectivity. [PERSON] et al. [39] proposed an IoT-Enabled IEEE 802.15.4 WSN Monitoring Infrastructure-Driven Fuzzy-Logic-Based Crop Pest Prediction. In this proposed architecture, pests and diseases are identified due to various weather factors. The measured parameter includes temperature, relative humidity, and rainfall. The acquired weather information is used as training data by the genetic algorithm (GA) to optimize the rule base of a fuzzy logic-based prediction system. Researchers deployed the low-power consumption IEEE 802.15.4 CC2538-2592 SoC microcontroller to establish a network of sensor nodes in Millet and Rice Crops. The proposed system predicts the occurrence of Rice pests using a fuzzy logic-based mechanism. The predictive results of this proposed network are very good, indicating many diseases and pests. If we compare the use of IoT in agriculture [20; 21; 25; 26; 31; 32; 33; 38; 39], we will see that researchers have conducted great work and tried to improve agricultural operations using various latest technologies. Some researchers are detecting the presence of pests based on weather parameters. Some are directly informed about pests through specific sensors. The only difference between our research and the work of others is that we are proposing a complete system. The proposed system works with the identification of pests until their elimination from crops. Everything will be automated, and the response system will perform its operations automatically. Target is to minimize the use of pesticides and improve the quality of yield.
## 2 Materials and Methods
### Real-Time Cell Analysis of Impedance  
The real-time cell analysis xCELLigence single-plate biosensor (RTCA, ACEA, Agilent) measures cell surface occupancy (i.e., by impedance measurement), expressed as cell index. Cell index depends on cell number, cell size, and cell adhesion force. Cells were grown until the cell index reached around 1 (at least 48 h), i.e., they were in a linear proliferative phase. Then, cells were exposed to fresh culture medium containing different concentrations of GSH (100 \(\upmu\)L/well). The impedance was recorded for each well every 15 min and data were retrieved using RTCA 2.01 (ACEA). Results are represented by mean delta cell indexes (DCIs) \(\pm\) SEM, (n = 6 to 8 replicates), i.e., the cell index at a given time point plus a delta value. The delta value is a constant value for each well and is the difference between the DCI value at time of treatment (t0) and the cell index at the delta time point from t0, as follows: DCItime = Cltime + (DCI 0 - CIDelta time). Data were retrieved on Microsoft Excel for analyses and graphical presentation of results.
## 3 Data Acquisition and Analysis
### MT Inversion  
The MT data were decomposed into two independent modes and the TM mode is defined as Transverse Magnetic mode. The TM mode is more sensitive to resistive structures and is more stable under the 3D effects caused by conductors [42]. There are many algorithms in use for regularized 2D inversion of magnetotelluric data. [PERSON] et al. (1987) find nonlinear least-squares solutions subject to a smoothness constraint (OCCAM method) [43]. [PERSON] (2000) present a new and much more efficient variant on the OCCAM scheme, called as REBOCC [44]. Rapid relaxation inversion (RRI) has been developed which can be very fast, but in practice often fails to converge without significant expert user intervention [45]. [PERSON] (2001) employs a nonlinear conjugate gradients (NLCG) scheme to minimize an objective function that penalizes data residuals and second spatial derivatives of resistivity [46]. Therefore, we inverted the MT data with 2D nonlinear conjugate gradients algorithm. [PERSON] believes that the inversion model should be as simple and smooth as possible, and the roughness should be as small as possible under the condition that the fitting difference meets the requirements, so as to suppress the redundant structure from non-real data [43]. We plotted the RMS curve to determine the roughness regularization parameter (\(\tau\)), which balances the model roughness with root-mean-square misfits (Figure 5). The RMS curve shows that the best roughness regularization parameter is 10. Then, we invert the TM data with the error floor, for TM apparent resistivity and phase are set to be 10% and 1\({}^{\circ}\). All inversions are started from a 100 \(\Omega\)m uniform half space. The number of inversion iterations was 200 and the RMS misfit for the final model is 2.16103 in general. The comparison of observed data and response data is shown in Figure 6. It can be seen that data are generally well fit along the profile. Most stations have RMS <3. However, some stations, such as stations 14, 44, 55, and 105 have larger RMS, indicating poor fitting. The RMS values for individual stations (Figure 7) indicate that the highest misfits occur at the Wulungunan fault zone. The RMS misfit value is low and pseudo sections of observed and calculated data are similar. These results indicate a proper fitting and prove the final model is reliable.  
Figure 4: The phase tensor ellipses of each MT station at different period bands. Phase tensor ellipses are shaded according to skew value. The small value of the skew angle indicates that one of the principal axes of the phase tensor is oriented close to the regional strike direction in 2D and 3D cases.  
Figure 5: Model roughness and RMS curve (L-curve) obtained by inversion of different regularization.  
Figure 6: (**a**) Observed apparent resistivity and phase in pseudo section format for the profile. (**b**) Response of the 2D model. The similarities between pseudo sections of observed and calculated data prove the final model is reliable.
## 4 Results and Discussion
### Practical Implementation for Sustainable Management of Wadi Haly Catchment  
This study's conclusions have important ramifications for managing catchments sustainably in Saudi Arabia, especially when it comes to tackling the problem of soil erosion in dry and semi-arid areas like the Wadi Haly catchment. Finding high-risk sub-catchments, such as sub-catchments 1, 7, 11, 12, and 13, provides a targeted starting point for putting specific soil conservation measures into action. For these places, practical solutions like check dam construction, contour farming, and terracing are strongly advised. In erosion-prone areas across the world, these techniques have been shown to be successful in regulating surface runoff, decreasing soil erosion, and improving water infiltration. Furthermore, implementing vegetative cover practices like agroforestry or reforestation can improve soil-water retention, stabilize the soil even more, and lessen the effects of severe episodic rainfall episodes, which are typical of the Wadi Haly catchment.  
This study is built on the foundation of earlier erosion control initiatives in Saudi Arabia. To counteract sedimentation and improve groundwater recharge, for instance, projects utilizing check dams and water harvesting structures have been put into place in areas such as the provinces of Asir and Najran and Wadi Al-Lith basin (e.g., [66,67]). These initiatives showed how engineering-based solutions can effectively reduce erosion in regions with little vegetation and steep slopes. However, a major drawback of many earlier initiatives was the absence of a methodical strategy to rank the regions that needed assistance. This gap is filled by the prioritizing framework created in this study, which combines morphometric indices and geospatial tools in a data-driven technique. This guarantees that erosion prevention strategies are not only successful but also strategically implemented in the areas with the greatest need.  
Additionally, the findings of this study are in line with Saudi Arabia's Vision 2030 goals, especially those pertaining to climate adaption, water resource management, and environmental sustainability. In this study, a reproducible approach is offered that may be applied to more watersheds across the nation by combining sophisticated geospatial analysis with customized prioritization techniques. Combining contemporary technologies like remote sensing and GIS-based monitoring systems with more conventional methods like terracing and gabion structures will help future erosion control initiatives. By facilitating the ongoing evaluation of soil erosion dynamics, these technologies can promote long-term sustainability and adaptive management. To create an integrated framework for watershed  
Figure 9: The prioritization assessment map of soil erosion risk.  
conservation and sustainable land use management throughout Saudi Arabia, cooperation between governmental entities, local populations, and environmental researchers is crucial to success.
## 2 Coding Metamaterials for RCS Reduction
### Coding Metamaterial Parametric Study
#### 2.3.2 Six Lattices  
Figure 7 graphically illustrates the monostatic RCS values and bistatic scattering patterns of the coding metamaterials with six lattices. Meanwhile, the coding sequences, row by row, are tabulated in Table 7. The scattering patterns started to show some changes over the shapes in all three coding sequences. Coding Sequence 2 had the lowest bistatic RCS peak points at \(-19.5\) dBm\({}^{2}\). Meanwhile, Coding Sequences 1 and 3 exhibited maximum reduction values of \(-20.6\) dBm\({}^{2}\) and \(-21.3\) dBm\({}^{2}\), respectively. A comparison of four lattices and six lattices revealed promising outcomes; for example, at C-band, the proposed coding metamaterials exhibited monostatic RCS reduction values between approximately \(-43\) dBm\({}^{2}\) and \(-30\) dBm\({}^{2}\) and between approximately \(-32\) dBm\({}^{2}\) and \(-22\) dBm\({}^{2}\), respectively. This revealed that the increasing number of lattices had a great impact on the monostatic RCS values. Therefore, larger coding metamaterial designs will have better RCS reduction properties compared to smaller designs.  
Figure 6: Monostatic RCS and bistatic scattering patterns (at 8 GHz) of three different coding metamaterials with four lattices.