esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Performance of the PVDF-FS Membranes in the Solar-Assisted Evaporation of Saltwater  
We assembled each membrane in an in-house designed and built thin-film evaporation device and recorded the measurements (Section 2.4). As shown in Figure 5a, all membranes showed a stable flux when solar irradiation was absent, which was consistent over the entire duration of the experiment, indicating negligible fouling. When the experiments were repeated in the presence of solar irradiation (1 sun), the membranes exhibited a significant increase in flux over the first 2 h of the experiment, after which stability was achieved, confirming that the fabricated membranes were capable of utilizing solar energy to drive evaporation. Furthermore, the results (Figure 5b) show that, as compared to the PVDF, PVDF-FS3, PVDF-FS5, and PVDF-FS7 membranes, the PVDF-FS4 membrane had the highest mass flux over the entire duration of the experiment. This could be attributed to its highest hydrophilicity, facilitating more supply of water into the capillaries of the membrane, as well as its larger volume porosity and lower mean flow pore diameter, resulting in the improved capillary pumping effect (as evident from Section 3.1). Higher porosity also facilitates lower mass transfer resistance due to the larger available void volumes for water vapor transport to occur. Furthermore, we suggest that the improvement in performance was not due to any changes in vapor pressure since it was evident that all membranes exhibited similar absorption and heat transfer properties (as evident from Section 3.1). This is also evident from the negligible difference of the temperature rise across the membranes with a range of 5 to 7 \({}^{\circ}\)C between the lowest and highest (Figure 5c). In general, when compared to pristine PVDF, adding fund silica nanoparticles enhanced the membrane performance and characteristics, as shown in Figures 4 and 5b. Moreover, Figure 5c confirms that the change in temperature, which is  
Figure 4: **(a)** Contact angles (**b**) Porosity and Mean flow pore diameter, and (**c**) absorbance measurements for our PVDF-FS membranes. The results show that the PVDF-FS4 membrane resembled the lowest contact angle, the smallest mean pore diameter, and the largest volume porosity, indicating that there was an optimal FS content value to achieve the desired properties. All membranes showed similar absorbance behavior over all of the wavelengths studied.  
primarily due to the accumulation of heat, is negligible. This confirms that the attained evaporation flux is directly related to the effect of the membrane pore properties, i.e., mean pore size and porosity.
# A Critical Review of Flood Risk Management and the Selection of Suitable Measures
## 3 Risk-Based Flood Management
### Assorting Suitable Measures
#### 3.6.4 Interconnected Suitability of Measures  
Flood management measures are strongly connected, and their performance is affected when applied in combination. For example, if the majority of the watershed is barren ground, dredging should not be considered, as surface runoff would carry tons of sediment. Water-logging should not be serious, as there will be a lack of additional capacity due to increased groundwater flow into the system. Dredging is not an option if the river has a serious sedimentation problem. Preferably, the additional river capacity needed to handle damaging floods should be equal to or less than 50% of the current river capacity [58].  
In addition, few measures are alternative and exclusive to each other. For example, [PERSON] et al. [61] evaluated three mitigation steps for flood-prone lowlands with a high population density along the Keelung River in Taiwan, including dykes, a drainage channel, and a storage reservoir. A diversion channel was proven to be more feasible than the other two flood mitigation options because of the high population and economic activity concentration in the floodplain. Where budgets are too poor to support defenses, successful, controlled realignment, or planned destruction will occur, with habitat recreation where necessary [62]. [PERSON] et al. [59] found that for smaller drainage basins, usually less than 100 km\({}^{2}\), the restoration of the river channel morphology and floodplain forest can be the basis of flood risk mitigation.
## 3 Multi-Objective Optimization of the Motor Topology
### Multi-Objective Optimization Design  
In order to obtain better torque performance for the proposed model in this paper, the average torque and torque ripple of the proposed model are optimized by changing the rotor topology of the proposed model. Figure 9 shows a parametric diagram of the single-pole topology of the proposed model rotor. Figure 10 shows the flowchart of multi-objective optimization. The optimization objectives of this multi-objective optimization are average torque and torque ripple. The definitions of average torque and torque ripple can be expressed by Equation (8).  
\[\left\{\begin{array}{l}T_{ave}=\dfrac{\sum\limits_{i=1}^{n}T_{i}}{T_{rip}} \\ T_{rip}=\dfrac{T_{max}^{n}-T_{min}}{T_{ave}}\times 100\%\end{array}\right. \tag{8}\]  
where \(T_{ave}\) and \(T_{rip}\) are the average torque and torque ripple, respectively; \(n\) is the number of steps of the motor in one operating period; \(T_{i}\) is the torque at the \(i\) step; and \(T_{max}\) and \(T_{min}\) are the maximum torque and minimum torque of the motor in one operating period, respectively.  
The average torque and torque ripple are taken as the optimization objectives, and the optimization objective function can be expressed by Equation (9).  
\[\left\{\begin{array}{l}O_{1}=max(T_{ave})\\ O_{2}=min(T_{rip})\end{array}\right. \tag{9}\]  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Item** & **Unit** & **Conv. Model** & **Prop. Model** \\ \hline \(T_{PM}\) & N\(\cdot\)m & 6.65 & 8.65 \\ \(T_{PM\text{-}MAX}\) & N\(\cdot\)m & 7.32 & 8.72 \\ \(\xi_{PM}\) & \% & 90.85 & 99.20 \\ \(T_{RE}\) & N\(\cdot\)m & 1.96 & 1.88 \\ \(T_{RE\text{-}MAX}\) & N\(\cdot\)m & 2.42 & 1.89 \\ \(\xi_{RE}\) & \% & 80.99 & 99.47 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of torque characteristics of the conventional and proposed models.  
Figure 9: Parameter diagram of the single-pole topology of the proposed model rotor.  
where \(O_{1}\) and \(O_{2}\) are the average torque optimization objective and torque ripple optimization objective, respectively.  
The constraint function in multi-objective optimization can be represented by Equation (10).  
\[s.t.\left\{\begin{array}{l}20\leq\theta_{k}\leq 25,k{=}1,2,3,4,5,6\\ 0\leq\theta_{7}\leq 90\\ 1\leq\omega\leq 2\\ C_{1}=T_{ave}-10.5\geq 0\\ C_{2}=T_{rip}-0.1\leq 0\end{array}\right. \tag{10}\]  
where \(\theta_{k}\) is the flux barrier angle; \(\theta_{7}\) is the deflection angle of the rotor's single-pole-centered permanent magnet; \(\omega\) is the width of the connected flux barrier; and \(C_{1}\) and \(C_{2}\) are the constraint functions for the average torque and torque ripple.
# Coenzyme Q at the Hinge of Health and Metabolic Diseases
## 4 CoQ Deficiency Syndrome
### Primary CoQ Deficiency  
#### 4.1.1 Characteristics of Primary CoQ Deficiencies  
Primary CoQ deficiencies are autosomal recessive conditions caused by a biallelic mutation in gene-encoding proteins implicated in the CoQ biosynthesis pathway [19]. These diseases in humans are highly heterogeneous and with a broad spectrum of clinical phenotypes. The estimated overall prevalence of this primary form is less than 1:100,000 [99]. Generally, patients manifest a combination of symptoms ranging from isolated affects on organs or tissues, such as the kidney, skeletal muscle, the heart, or the brain, to complex multisystem disorders with different severity levels and age of onset [19; 97; 100]. To date, defects in _PDSSI_, _PDSSI_, _COQ2_, _COQ4_, _COQ5_, _COQ6_, _COQ7_, _COQ8A_, _COQ8B_, and _COQ9_ genes have been described in CoQ deficiency-related diseases. However, there is no relationship between mutation and a specific phenotype. In this regard, mutations in _COQ8A/ADCK3_ cause ataxic cerebellar syndrome, and _COQ8B/ADCK4_ and _COQ6_ genes cause steroid-resistant nephrotic syndrome, while mutations in other _COQ_ genes are more pleiotropic [19]. For this reason, the most ambitious clinical challenge is to be able to establish the correlation between genotype and phenotype to accelerate diagnosis and improve therapies [19].  
According to the clinical symptoms, CoQ deficiency has been classified in five major groups of diseases:  
1. Encephalomyopathy with recurrent myoglobinuria, myopathy, and brain involvement [101];
2. Severe infantile multisystemic disease characterized by encephalopathy and nephropathy [102; 103];
3. Cerebellar ataxia [104; 105];
4. Isolated myopathy [106; 107];
5. Nephropathy [100].  
However, this classification can cause confusion in the difference between primary and secondary CoQ deficiencies, at least until the genetic diagnostic is completed. In fact, this classification needs to be updated due to the emerging number of patients who show overlapping clinical manifestations [108]. However, in the last few years, the use of next-generation sequencing (NGS) technologies, such as whole exome sequencing (WES) and whole genome sequencing (WGS), has helped to progress rapid diagnosis [109]. This means that clinical and biochemical techniques are useful for the rapid identification of CoQ deficiency, but only molecular diagnosis may elucidate the etiology of this syndrome.
## 3 Results
### Chemical Composition
#### 3.6.4 Carotenoid and Chlorophyll Contents and Profiles
\begin{table}
\begin{tabular}{l c c c c} \hline
**Sample** & **\(\alpha\)-Tocopherol** & \(\gamma\)**-Tocopherol** & \(\delta\)**-Tocopherol** & **Total Tocopherols** \\ \hline C-AO & 208.08 \(\pm\) 10.18 a & 74.14 \(\pm\) 1.91 a & 66.19 \(\pm\) 3.68 a & 348.41 \(\pm\) 2.42 a \\ C-VAO & 125.42 \(\pm\) 2.21 b & 34.25 \(\pm\) 0.64 b & 36.74 \(\pm\) 0.24 b,c,d & 196.41 \(\pm\) 2.85 b \\ AL5 & 207.27 \(\pm\) 2.00 a & 39.01 \(\pm\) 0.21 c & 37.88 \(\pm\) 0.78 c,d & 284.17 \(\pm\) 8.68 c \\ AL10 & 306.92 \(\pm\) 1.22 c & 48.50 \(\pm\) 4.72 d & 40.57 \(\pm\) 5.33 d & 395.99 \(\pm\) 14.23 d \\ AL20 & 473.73 \(\pm\) 12.94 d & 53.43 \(\pm\) 1.22 e & 50.42 \(\pm\) 1.81 e & 577.59 \(\pm\) 1.94 e \\ OL5 & 125.79 \(\pm\) 0.65 b & 33.07 \(\pm\) 0.70 b & 37.44 \(\pm\) 0.75 c,d & 196.30 \(\pm\) 3.99 b \\ OL10 & 147.31 \(\pm\) 0.94 e & 36.68 \(\pm\) 1.18 c & 39.97 \(\pm\) 3.87 d & 223.97 \(\pm\) 1.02 f \\ OL20 & 154.05 \(\pm\) 0.59 e & 33.27 \(\pm\) 0.66 b & 38.54 \(\pm\) 1.02 c & 225.86 \(\pm\) 3.79 f \\ OP5 & 132.19 \(\pm\) 2.62 b,f & 33.21 \(\pm\) 0.79 b & 35.27 \(\pm\) 0.74 b,c & 200.66 \(\pm\) 2.87 b \\ OP10 & 134.44 \(\pm\) 2.55 b,f & 31.66 \(\pm\) 0.13 b,f & 34.93 \(\pm\) 0.41 b,c & 201.03 \(\pm\) 1.42 b \\ OP20 & 135.29 \(\pm\) 0.52 f & 29.26 \(\pm\) 1.05 f & 33.23 \(\pm\) 0.1 b & 197.79 \(\pm\) 0.00 b \\ \hline \end{tabular}  
* C-AO = avocado oil; C-VAO = freshly extracted virgin avocado oil; AL-AL10-AL20, OL5-OI10-OL20, and OP5-OP10-OP20 represent C-VAO enriched with avocado leaves, olive leaves, and olive pomacco, respectively, at 5, 10, and 20% (\(\mu\)/kg) employing ultrasound-assisted maceration. ND = not detected. Results expressed as mean value \(\pm\) standard deviation (\(n\) = 3). Different lowercase letters in the same column indicate significant differences (\(p\) < 0.05) within the whole sample population employing Duncan’s test.  
\end{table} 
Table 5: Tocopherol composition of avocado oil samples (mg/kg oil) expressed as \(\alpha\)-Tocopherol equivalents. for chlorophylls [58]. The \(\beta\)-carotene and lutein also provide additional nutritional value to the oils due to their pro-vitamin-A activity [59].
## 1 Introduction  
With the development of science and technology, the drone industry is becoming more and more improved. Drones have been used in a variety of daily tasks, such as agricu'Itrural spraying, multi-angle photography, civil surveys, etc. To ensure that the use of UAVs is within the legal scope, the control of UAVs needs more effective methods. In the past, the detection approaches for UAVs were mainly based on radar and infrared detection technologies. However, radar detection [1] has blind spots, and due to the small size of the UAV itself, radar cannot effectively detect the orientation as well. Moreover, because the UAV is mostly driven by a brushless motor, the heat generation is very small. Therefore, infrared detection (i.e., infrared radiation) cannot be effectively detected. Its detection task is very challenging. The rise of deep learning in the new era has provided a new direction for the UAV field.  
There are many kinds of deep learning target detection algorithms. A two-stage algorithm represented by the Faster-CNN [2, 3] series is superior in detection accuracy. The approaches represented by Yolo [4] one-stage algorithms [5, 6, 7] are far ahead in detection speeds. However, their research on low-altitude small targets such as UAVs is very limited, and related research is very scarce. The lack of datasets further limits the development of -drone identification. Considering there is currently no unified drone dataset, this paper uses the dataset used in the literature [8] and some data collected by ourselves. Existing datasets are the main training object. In the dataset, the background contains a variety of strong and weak lighting, multi-shaped clouds, roads, houses, fields, and other complex scenes.  
To solve the above problem that the UAV is difficult to detect, this paper develops an optimization processing method. The proposed method combines the attention mechanism with Resnext50 of the group convolution network structure to improve the processing efficiency of the network, then determines the type of prior bounding box according to the dataset, calculates the required prior bounding box size through the number of layers of the network, and finally shortens the training time and improves the practicability of detection through migration learning.
# AI-Powered Stroke Diagnosis System: Methodological Framework and Implementation
## 7 Conclusions  
This study presents an AI-powered framework for stroke diagnosis that combines structured clinical data from the eICU Collaborative Research Database with curated imaging data. Traditional machine learning techniques (XGBoost, CatBoost) and modern deep learning approaches (MobileNet-based CNN) were applied to achieve robust overall accuracy (>92%) on structured clinical data. The MobileNet CNN demonstrated a strong ability to distinguish between stroke and no-stroke cases from image data, achieving 96% accuracy with a stroke recall of 91% and stroke precision of 97%. Ensemble methods, particularly the stacking ensemble, improved the overall accuracy, but at the cost of a substantial reduction in stroke recall.  
The research makes several important contributions to the field of AI-driven clinical diagnostics, such as multi-modal data integration, a comprehensive methodological framework, and rigorous theoretical evaluation. However, limitations include data representativeness, image data authenticity, computational constraints, and ensemble calibration. Future work should leverage GPU acceleration to explore more complex architectures and refine ensemble methods to balance the overall accuracy with the detection of minority stroke cases.  
In conclusion, this research illustrates the potential of an AI-powered, multi-modal approach for early stroke diagnosis, providing a comprehensive diagnostic tool that could significantly enhance clinical decision making. The results underscore the promise of combining traditional machine learning with deep learning techniques, while also highlighting the need for continued refinement and validation.  
Conceptualization, M.N., A.R. and A.V.; Methodology, Y.M.; Validation, M.N., A.V. and Y.M.; Investigation, M.N.; Writing--original draft, M.N.; Writing--review and editing, A.R., D.B. and Y.M.; Supervision, A.R. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the European Commission under the EU Recovery and Resilience Facility through the project \"Implementation of Consolidation and Governance Changes at Riga Technical University, Liepaja University, Rezekne Academy of Technologies, Latvian Academy of Culture, and Latvian Maritime Academy towards Excellence in Higher Education, Science, and Innovation\" (Project No. 5.2.1.1.1.0/2/24/I/CFLA/003) and the Academic Career PhD (Doctoral) Grant (Grant No. 1016).  
Data Availability Statement: The datasets presented in this article are not readily available because they are part of an ongoing PhD dissertation study. Requests to access the datasets should be directed to [PERSON].  
**Acknowledgments:** During the preparation of this manuscript, the authors used SciSpace for the purposes of grammar and style improvements, as well as the literature analysis to compare articles based on specific criteria. The authors have reviewed and edited the output and take full responsibility for the content of this publication.  
**Conflicts of Interest:** The authors declare no conflicts of interest.
# Identification and Isolation Techniques for Plant Growth Inhibitors in Rice
## 5 Isolation and Identification Techniques for PGIs in Rice  
### Preparation of Rice Samples  
The phytochemical composition of rice is different throughout its life cycle. It is possibly influenced by both endogenous (varieties and growing stages) and exogenous (nutrition, climate conditions, and natural enemies) factors [116]. On the other hand, processing and preserving conditions are determinants that likely affect the phytochemicals during the process of analyzing rice samples [79; 117]. Therefore, sample preparation and preservation are essential steps prior to the isolation and purification of the bioactive compounds of rice. The selection of the pre-extraction method not only ensures the purity of rice samples from other contaminants, but also probably establishes the target groups which can be obtained.  
Figure 5: The structure of alleolopathy-secondary metabolites in rice.  
Rice samples including roots, leaves, husks, brans, and grains can be used in either a combinatory form or a separatory form. After collection, the rice samples should be cleaned and sterilized to eliminate all dirt and other biotic factors such as bacteria and fungi. In the next step, rice samples are dried to reduce the moisture, which is the main cause of the spoilage and hydrolysis of samples by microorganisms. There are various drying methods, among which the use of sunlight and ovens is the most common approach in isolating PGIs from rice. The optimal condition for the drying process is normally recorded at 40 \({}^{\circ}\)C within 7-10 days, which can preserve the integrity of most plant growth inhibitor (PGI) compounds [42; 69; 78; 118; 119]. Afterward, samples can be ground into powder or cut into small sizes in order to obtain homogeneous materials, which can be easily extracted using an organic solvent in a shorter time. This physical processing helps to partly break down the cutin layer as well as the cell wall, which facilitates a quick release of phytochemicals into the extraction solvent. Subsequently, dried samples can be extracted immediately or kept in a fridge until a later experiment. The storage conditions should consist of a low temperature (\(-4\)\({}^{\circ}\)C) and darkness, which help to avoid the natural decomposition of some photosensitive compounds such as phenolics [70; 120; 121].
## 2 Service-oriented and Self-managed Optical Networks  
Assured QoS in IP/optical networks should allow network customers to create, modify and release services in a dynamic manner and according to pre-established service level agreements (SLA). To achieve this, two main architectures have been recently proposed: service-oriented optical networks and self-managed networks.
# A Robust Method Based on Deep Learning for Compressive Spectrum Sensing
## 3 The Proposed CSS Method
### Beista-Net Architecture
The output of the feature enhancement module is  
\[\mathbf{X}^{(k)}=\tilde{g}^{(k)}\Big{(}\mathbf{A}\odot\tilde{g}^{(k)}\Big{(} \mathbf{Z}^{(k)}\Big{)}\Big{)}, \tag{9}\]  
where \(\mathbf{A}\) is the coefficient matrix generated by CA, \(g^{(k)}(\cdot)\) is the function for extracting BSF, \(\tilde{g}^{(k)}(\cdot)\) is the reverse process of \(g^{(k)}(\cdot)\). The BSF of wideband spectrum signals is analogous to texture features in images, which enables the extraction of the BSF using convolutional kernels. The BSF exhibits a one-dimensional distribution, which allows for the efficient use of one-dimensional convolutional kernels for its extraction. Moreover, in practical applications, blocks may vary in scale, and employing multiple one-dimensional convolutional kernels of different sizes can more accurately extract features. The size of the convolutional kernels can be flexibly adjusted to extract BSF at different scales. Additionally, the CA in this work is a lightweight attention mechanism that only involves one-dimensional convolution, resulting in significantly reduced computational complexity compared to self-attention mechanisms. Note that when using the \(\mathbf{X}^{(k)}\) module, we first convert the two-dimensional wideband spectrum signal into a one-dimensional form. This allows for better extraction of the one-dimensional BSF.
# Review of Indoor Positioning: Radio Wave Technology
## 2 Parameter Based Positioning
### RSRP and RSRQ  
The RSRP and RSRQ parameters are physical layer data from the 4G cellular system that are used to reasonably forecast the user position [91]. The RSRP computation is based on RSSI. It calculates mean obtainable strength by cell-specific reference signals [92]. Thus, it can afford greater signal strength information associated with various positions contrasting normal RSSI. The PHY layer RSRP reduces local disturbances in the surroundings. In the office location, the RSSI estimates from 4G towers produce a better forecast than RSSI signals from 2G towers, due to the existence of small cells.  
The RSRQ parameter that delivers the value of received signals within the object device is developed from the RSSI and RSRP value. RSRQ is influenced by adjacent station interference and thermal noise and thus, when only RSRQ is utilized, achieves less precision than RSRP estimates. On the other hand, the accuracy of the RSRQ-based system is better than that by the RSRP signals when RSRQ values are used together with RSRP values [91].
###### Abstract  
In Holoscopic imaging, a single aperture is used to acquire full-colour spatial images like a fly's eye by gently altering angles between nearby lenses with a micro-lens array. Due to its simple data collection and visualisation methods, which provide robust and scalable spatial information, and its motion parallax, binocular disparity, and convergence, this technique may be able to overcome traditional 2D imaging issues like depth, scalability, and multi-perspective problems. A novel disparity-map-generating method uses angular information from a single Holoscopic image's micro-images, or Elemental Images (EIs), to create a scene's disparity map. Not much research has used EIs instead of Viewpoint Images (VPIs) for disparity estimation. This study investigates whether angular perspective data may replace spatial orthographic data. Using noise reduction and contrast enhancement, EIs with a low resolution and lack of texture are pre-processed to calculate the disparity. The Semi-Global Block Matching (SGBM) technique is used to calculate the disparity between EI pixels. A multi-resolution approach overcomes EIs' resolution constraints, and a content-aware analysis dynamically modifies the SGBM window size settings to generate disparities across different texture and complexity levels. A background mask and nearby EIs with accurate backgrounds detect and rectify EIs with erroneous backgrounds. Our method generates disparity maps that outperform two state-of-the-art deep learning algorithms and VPIs in real images.  
hologc; elemental images; viewpoint images; micro-lenses; disparity; SGBM Article