esa-sceva/satcom-chunk-collection huggingface dataset:
# Interference Alignment for Cognitive Radio Communications and Networks: A Survey
## 3 Interference Alignment in Cognitive Radio: First Paradigm
### Techniques For IA in CR Networks
#### 3.2.1 Propagation Delay IA  
IA can utilize the propagation delay difference among links to achieve theoretical DoFs. The research done in ref. [21] helped to further buttress IA as a general concept by establishing its applications in beamforming solutions and propagation delay based alignment [13; 77]. The work in ref. [77] showed that IA could be achieved in a scenario where desired links constitute even delays and interfering links constitute odd delays. As such, IA is achieved over even time slots at each Rx. Desired signals can then be received without any interference. It was later proved in refs. [22; 77] that even with random user positions, each user could still get half of the cake when propagation delay alignment is used on condition that symbol durations are made small enough or bandwidth made arbitrarily large.  
This technique has been adopted in ref. [78] for two-tier CR networks, where a less opportunistic and more deterministic scheme for the SUs to successfully coexist with the PUs has been defined. This work makes use of an interference draining scheme, which is a specific extension of IA in CR networks. Assuming that IA has been computed on the SU network as expressed as follows:  
\[y_{i}=H_{ii}(.)V_{i(.)}x_{i}+\sum_{j=1,j\
eq i}^{K}H_{ij}(.)V_{j}(.)x_{j}+z_{i} (.), \tag{4}\]  
Figure 2: Multiuser cognitive radio (CR) network model consisting of one primary user (PU) link and multiple secondary users (SUs)—Paradigm 1.  
where \((.)\) indicates the dimension, which could be frequency (f), time (t) or both. The received signals was re-written as:  
\[y(.)=H_{sig}(.)x(.)+H_{int}(.)x(.)+z(.), \tag{5}\]  
where \(H_{sig}=diag[H_{11},.\ldots...H_{KK}]\) is related to the desired signal space and \(H_{11}=H-H_{sig}\) is related to the interference space. Now assuming that the SUs network is aligned internally, then from Equation (4), the desired signal space and interference space can be defined as \(span[H_{sig,j}(.)V_{j}(.)]\) and \(span[H_{int,j}(.)V_{j}(.)]\) respectively. The overlapping SUs interference is then projected to the PU-Rx to maintain a certain threshold and in the given circumstances, \(span[H_{ii}(.)V_{i}(.)]\) is the PU signal space and \(span[H_{ij}(.)V_{j}(.)]\) is the SU interference space. The draining scheme is achieved on a similar condition to the work done in ref. [22], that the interference from the SUs is at different time slots to the PU or spread evenly to every slot at the PU. Differing circumstances can lead to the availability of unused time slots, but this work crucially offers an idle time slot to accept the SUs interference. Therefore, assuming CR network aligns its own internal interference, this work adds another dimension to the SUs other than just matching the PU's transmission space, thus making it more feasible to achieve higher DoF.
# LEACH-MTC: A Network Energy Optimization Algorithm Constraint as Moving Target Prediction
## 1 Introduction
### Paper Organization  
The rest of this paper is organized as follows. In Section 2, the target nonlinear dynamic model and the target state prediction model are described. In Section 3, the new network node dormancy strategy, improved cluster head selection strategy, and network monitoring accuracy criterion are designed; additionally, the LEACH-MTC algorithm based on moving target constraints is proposed. In Section 4, the feasibility and effectiveness of the improved algorithm are simulated and analyzed in terms of saving the number of working nodes and reducing the network energy consumption. The conclusions of this paper are given in the final section.
## 1 Introduction
It was suggested that a porous and flower-like structure with Fe\({}_{2}\)O\({}_{3}\), FeF\({}_{3}\), Cr\({}_{2}\)O\({}_{3}\), and CrF\({}_{2}\) on the surface of the stainless steel wire gave a high affinity to the hydrophobic PAHs due to cation-\(\pi\) interaction [31]. In this case, how to evaluate the hydrophobic interaction effectively between the sorbent and target compounds in real samples is still a big challenge. In this work, the titanium wires and meshes with different surface wettability were fabricated by a simple hydrothermal digestion and chemical immersion method. The superhydrophilic substrates with flower-like TiO\({}_{2}\) nanoparticles were obtained after being etched by HF and changed to superhydrophobic after being modified using a low-surface-energy material. The micro/nano hierarchical structures and photosensitivity of the formed TiO\({}_{2}\) nanoparticles on the surfaces of the Ti substrates exhibited the switchable wettability. After UV irradiation, the superhydrophobic samples became superhydrophilic. The hydrophilic, superhydrophilic, superhydrophobic, and UV-irradiated superhydrophilic wires were initially selected as the sorbents for online in-tube SPME. The morphologies and element composition of the Ti wires were observed by SEM, EDS, and XPS, and their surface wettability was measured using the Ti mesh by a contact angle goniometer. The extraction tube filled with the prepared Ti wire was connected to the injection valve of HPLC. Six common estrogenic hormones were selected as the target analytes to investigate the extraction efficiency. The online analytical method was established and used for the determination of six estrogens in water samples using the optimal conditions. More importantly, the hydrophobicity interactions between the wire surfaces and the samples were further investigated by the selection of sorbents with different surface wettabilities and the target compounds of hydrophobic PAHs.
# Set-Based Group Search Optimizer for Stochastic Many-Objective Optimal Power Flow
## 5 Conclusions
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Scenario**} & \multicolumn{3}{c}{**HV**} & \multicolumn{3}{c}{\(\mathbf{I_{\text{ave}}(X)}\)} & \multicolumn{3}{c}{**MED**} \\ \cline{3-8}  & & **SetGA** & **SetGSO** & **SetGA** & **SetGSO** & **SetGA** & **SetGSO** \\ \hline \multirow{8}{*}{30\% REs} & Average & 4,411,300 & **6,285,160** & 0.13 & 0.13 & 3090.6 & **2305.8** \\  & Median & 4,435,700 & **6,284,700** & 0.13 & **0.12** & 3084.3 & **2310.3** \\  & STD.Dev & 321,175.1 & **46,912.2** & 0.038 & **0.017** & 109.1 & **37.7** \\  & Worst & 3,965,800 & **6,220,500** & 0.23 & **0.17** & 3248.6 & **2352.6** \\  & Best & 4,673,500 & **6,347,300** & **0.11** & 0.12 & 2980.8 & **2258.0** \\ \hline \multirow{8}{*}{40\% REs} & Average & 4,963,667 & **6,975,260** & 0.35 & **0.31** & 3047.5 & **2378.4** \\  & Median & 4,890,600 & **6,992,700** & 0.34 & **0.30** & 3054.2 & **2380.2** \\ \cline{1-1}  & STD.Dev & 436,916.7 & **16,691.6** & 0.076 & **0.016** & 77.7 & **14.0** \\ \cline{1-1}  & Worst & 4,325,400 & **6,954,200** & 0.47 & **0.34** & 3159.0 & **2395.9** \\ \cline{1-1}  & Best & 5,579,500 & **7,002,500** & **0.25** & 0.29 & 2920.3 & **2354.3** \\ \hline \multirow{8}{*}{50\% REs} & Average & 6,565,667 & **7,058,280** & 0.40 & **0.38** & 1628.9 & **1284.0** \\ \cline{1-1}  & Median & 6,578,500 & **7,069,600** & 0.38 & 0.38 & 1624.2 & **1283.4** \\ \cline{1-1}  & STD.Dev & 298,562.7 & **41,298.0** & 0.031 & **0.024** & 68.98 & **18.08** \\ \cline{1-1}  & Worst & 6,247,500 & **6,979,400** & **0.40** & 0.41 & 1694.0 & **1307.3** \\ \cline{1-1}  & Best & 6,879,500 & **7,114,500** & 0.36 & **0.35** & 1523.5 & **1255.4** \\ \hline \hline \end{tabular} Bold text represents superior values.  
\end{table} 
Table 8: Statistical results for the IEEE-118 bus system with three different scenarios. * As the proportion of renewable energy sources progressively increases, the solutions demonstrate relatively stable convergence, albeit with a modest increase in average imprecision. In summary, the SetGSO method outperforms the SetGA approach in terms of the convergence and stability of the obtained frontier solution. Methodology, J.Z. ; Software, M.T. ; Validation, Z.L. ; Formal analysis, Z.L. ; Data curation, M.T. ; Writing--original draft, M.T. ; Writing--review & editing, J.Z. ; Supervision, Q.W. All authors have read and agreed to the published version of the manuscript. This work was supported by the Natural Science Foundation of China (No. 52007066), the Natural Science Foundation of Guangdong Province, China (No. 2021A1515010584), and the Young Elite Scientists Sponsorship Program By CSEE (No.CSEE-YESS-2021014). The authors declare no conflict of interest.
# A Review of Medication Adherence Monitoring Technologies +
## 4 A Review of Medication Adherence Monitoring Systems
### Vision-Based Systems
The literature in this area of visual systems also shows a monitoring system that consists of a digital scale and a camera that was presented in [84]. A digital scale has been used such that it continuously measures and displays the medication bottle weight. The camera has been used to capture and send the scale's readings displayed on the screen to a nearby computer. Upon receiving the images, the computer then runs an image processing algorithm for processing the bottle's weight. From the bottle's weight decrease trend, the system can generate an alarm to remind the patient to take medication. It should be noted that although this work concentrates on vision analysis, it does not include any human subject tracking. It is obvious that such a system does not support mobility due to the fact that it requires the medication bottle to always be placed on the weight scale, and thus provides only a limited view. In addition, it requires the user to have a digital scale, a camera, and a computer. Another visual-based clinical study using Microsoft Kinect (Redmond, WA, USA) as time of flight sensors for quantifying medication adherence among patients with Parkinson's disease [85]. The Microsoft Kinect is used for approximating nodes on the human body while walking and then collecting the 3D locations of a number of skeletal joints. Hence, the velocity and acceleration of each joint was computed such that patient motion is obtained. The data is then classified using different data mining algorithms to discriminate adherent from non-adherent patients. The clinical study included seven Parkinson's disease patients first before taking their prescribed drugs and thereafter taking their prescribed drugs. Accuracies greater than 97% and 78% for an individually customized classification model and a generalized model were respectively achieved. Although vision-based systems will play an important role in AAL environments, the main disadvantages of these approaches are their limitation in use and accuracy. In addition, vision-based approaches may demand several resources, which can be expensive. Another downside, especially with surveillance-video approaches, is the high computational cost associated with system training. Furthermore, as we progress further into the 21 st century, users prefer fully mobile devices [139]. However, in contrast, vision-based approaches do not support mobility. Finally, another limitation is the fact that the user is required to be within the scope of the camera and some parts of the body is not covered (hands and face) such that the system can capture and recognize these parts for detecting medication taking activity.
## 3 Results and Discussion
### Identification of Extracted Compounds by HPLC  
Some phenolic acids and flavonoids were detected and confirmed by HPLC in rhizomes of dragon's blood plant extracts and xoconostle fruits extracts. In the S\({}_{\text{B}}\) extracts, an increase of phenolic compounds was detected with respect to the S\({}_{\text{A}}\) extracts; this result has a behavior similar to that found in the quantification of the total content of phenols by the spectrophotometric method, where the sterilization process increased the content of the phenolic compounds. In the present study, the phenolic acids detected in S\({}_{\text{A}}\) and S\({}_{\text{B}}\) were vanmillic and caffeic acids, while the flavonoids were naringenin, apigenin, and galagin (Tables 4 and 5). [PERSON] et al. [12] analyzed ethanolic-aqueous dragon's blood rhizome extracts by HPLC; in their study, phenolic compounds were not detected due to their low concentrations and the poor purification of the extracts.  
The phenolic compounds quantified in the xoconostle fruits extracts were gallic acid, vanillic acid, and p-coumaric acid, and for the flavonoids, it was only galagina. In this sense, the extract with the highest content of phenols was the xoconostle fruit extract, while the extract with the highest concentration of flavonoid compounds was the dragon's blood rhizome extract. On the other hand, in the extracts of the xoconostle fruit, the variation of the phenolic compounds was lower than in the dragon's blood rhizome extracts; X\({}_{\text{B}}\) extracts presented concentrations of gallic, vanillic, and p-coumaric acid that were similar to that of X\({}_{\text{A}}\) extracts, while the flavonoid content increased in X\({}_{\text{B}}\) extracts (increase the content of galagin) during autoclave-sterilization treatment (Tables 4 and 5).  
Some reports suggest that the presence of compounds such as caffeic, ferulic, and p-coumaric acids inhibit Gram-negative and Gram-positive microorganisms such as _E. coli_, _S. aureus_, and _B. cereus_[46]; the presence of these compounds could maintain the properties of antimicrobial extracts after being subjected to sterilization by autoclaving.
# Constructed Wetlands for the Wastewater Treatment: A Review of Italian Case Studies
## 3 Discussion
[72] recommended a 6-day hydraulic residence time for the acceptable level of treatment of COD, BOD, TN, and TP in horizontal subsurface flow CWs. However, the case studies reviewed here were conducted under different hydraulic retention times contributing to the observed difference in removal efficiencies. Hydraulic loading rate, i.e., the quantity of wastewater supplied to the CWs system, affects the removal potentials. When there is a large increase in hydraulic load, the contact time between wastewater and biofilms is typically reduced, thus influencing the nitrogen and organics removal rates. According to [67], when hydraulic loading was increased from 31 mm/d to 146 mm/d, the mean organics and nitrogen removal efficiency decreased from 84% to 63%, and 84% to 16%, respectively. As there were different hydraulic loading rates utilized in the reviewed Italian cases, the pollutant removal potentials of the CWs have also resulted in various values. The quantity of dissolved oxygen (DO) in the system has an impact on the nutrient removal process. According to [73], NO3 -N was eliminated at a lower DO concentration of 0.5 mg/L, while the removal rate decreased at higher DO concentrations. A total nitrogen (TN) elimination effectiveness of 70% was reported at DO levels of 0.15-0.2 mg/L [73]. In the reviewed works, both horizontal and vertical CWs were utilized, which have different levels of dissolved oxygen that directly control the microbial activities affecting the pollutant removal potentials. The substrate and wastewater feeding mode also affect the pollutant removal efficiency of CWs by enhancing oxygen transfer. In this reviewed work, _Phragmites australis, Typha lufifolia, Arundo donax,_ and _Cyperus interfiloius_ are the most frequently used plant species in the experiments treating various wastewaters. These experiments were conducted at different levels (laboratory, pilot, or large scale) depending on the area/volume of the CWs used. There were more pilot-scale studies than large-scale or laboratory-size studies. Similarly, the types of wastewater treated were also different; Urban/municipal/domestic wastewater being the most frequently treated ones, followed by effluent from wastewater treatment plants, agricultural wastewater, and swine/dairy wastewater. Horizontal subsurface CWs have been utilized more repeatedly than vertical and hybrid CW systems. Moreover, numerous contaminants were eliminated from various wastewaters utilizing CWs. COD (chemical oxygen demand), TN (total nitrogen), TSS (total suspended solids), BOD (biological oxygen demand), and TP (total phosphorus) were the major pollutants removed (based on their frequency) in the reviewed works.
# Fire-Induced Changes in Soil and Implications on Soil Sorption Capacity and Remediation Methods
## 2 Factors Which Determine the Effect of Fire Events on Soil Properties
### Soil Properties  
Soil properties, including moisture content, texture, density, organic matter content, and mineralogy, influence the extent to which fire incidents could affect soil. This influence is related to the role they play in soil thermal conductivity. Soil thermal conductivity refers to the quantity of heat that flows through a unit area in a unit time under a unit temperature gradient [45] and is affected by the degree of soil saturation, dry density, mineral composition, temperature, texture, and time [46; 47]. Coarse-textured, wet, and organic matter-rich soils have higher conductivity than vice versa and provide a conductive platform for wildfires [26; 48; 49; 50; 51]. Wildfires on moist soils would result in higher soil temperatures compared to dry soils [28]. According to [PERSON] et al. [28], the temperature gradient of dry soil is much steeper with depth than what is obtained in wet soil. Sand particles also support high heat conductivity and would likely increase the temperature of soils in the event of a fire more than finer soil particles due to the high thermal conductivity of quartz (7.70 W/m/k), which dominates coarse (sand) particles [52]. Soil packing density (defined as a composite index of bulk density and clay content) also influences the conduction of heat within the soil environment and therefore the depth and extent to which a soil is heated. The higher the packing density of soil, the better its thermal conductivity and the higher the temperature of soil will be raised in the event of a wildfire [53; 54]. Soil minerals vary in their thermal conductivity and temperature threshold depending on their structural properties. Studies by [PERSON] et al. [55] reported thermal conductivities for dolomite, calcite, smectites, muscovite/illite, chlorite and the K-feldspars to be 4.8-5.5 W/m/k, 3.3-3.6 W/m/k, 3.3-6.2 W/m/k, 1.9 W/m/k, 1.9-2.5 W/m/k, and 2.4-2.6 W/m/k respectively. These differences could play a significant role in the extent of changes in soil properties during fire events. Soils dominated by secondary minerals are less likely to experience changes compared to those with primary mineral because of the differences in their thermal conductivities. Densely packed, moist, and coarse-textured soils are also likely to be heated to a greater extent and depth than porous soils and would therefore experience more changes in their properties after a fire event.
## 4 Implementation
### Model Implementation  
The parameters of the hybrid model were obtained during the training phase by trial and error including the number of CNN filters, the number of QRNN hidden units, and the dropout rate. As mentioned in different studies [35], kernel size values of 3 and 5 are the most common, so we used kernel size 3 with both datasets in our experiment. A filter can help in extracting more details from a dataset by increasing the number of filters [51]. Thus, for the first CNN layer, we used 64 filters, and for the other CNN, we used 128 filters. Additionally, we set the value of the batch size for the training at 128 and the value of the number of epochs at 10. The details and the selected parameters of the hybrid DL model are presented in Figure 3.
# An Improved Channel Estimation Technique for IEEE 802.11p Standard in Vehicular Communications
## 7 Conclusions  
This article presents an improved CDP channel estimation scheme for vehicular communications. The vehicular channel is dynamic in terms of frequency and time. An overview of the state-of-the-art vehicular channel estimation schemes is presented briefly in this article. Even though these schemes perform considerably well, however, there are still limitations and challenges. We have discussed these challenges and proposed a scheme which constructs improved pilots from the data and phase tracking symbols through the exploitation of correlation characteristics of vehicular channels. The proposed scheme keeps the structure of IEEE 802.11p physical layer standard intact independent to the nature of the channel. This makes the proposed scheme a suitable candidate for the IEEE 802.11p and different vehicular channel models. The performance evaluation shows that it outperforms the related schemes e.g., CDP scheme, STA scheme, LS scheme, and DFT scheme both in the lower and high SNR regions. In addition to that, the proposed scheme has the little computational complexity as compared to the various V2V channel estimation schemes, e.g., decision-directed scheme, Pseudo pilot scheme, and generalized DPS based scheme.
# Solar-Supplied Satellite Internet Access Point for the Internet of Things in Remote Areas
## 3 Implementation
### Enclosure  
The enclosure encloses the device, which can face various challenges, such as liquid damage from weather. The case size was estimated by using computer-aided design (CAD) software, as shown in Figure 4. This enclosure was used to house the PWM solar controller, the single-board computer, and the DC boost converter. This enclosure could be installed under the solar panel to provide further liquid protection for the device. Waterproof enclosures could be purchased to protect internal electronics. The box was estimated to be 270 mm in length, 150 in width, and 35 mm in height, using acrylonitrile butadiene styrene (ABS) as the material.