esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Sample Treatment and Analysis Methods
### Sample Treatment Methods
Quick, easy, cheap, effective, rugged, and safe (QuECHERS) consists of sample crushing, acetonitrile extraction, water removal by salts (MgSO\({}_{4}\) and others) and purification by ethylenediamine-N-propylilane (PSA) and other adsorbents, is used for simultaneous determination of NPAHs and OPAHs in environmental and biological materials, and foods prior to GC-MS analysis [42; 43]. \begin{table}
\begin{tabular}{l l l l l} \hline \hline \multicolumn{1}{c}{**Compounds**} & \multicolumn{1}{c}{**Sample and Treatment**} & \multicolumn{1}{c}{**Analysis Method**} & \multicolumn{1}{c}{**Recovery and LOD**} & \multicolumn{1}{c}{**Ref. **} \\ \hline  & Urban dust (SRM 1649b). SPE (silica & & & \\  & gel/alumina + & & & \\  & & heare/dichloromethane etc. in & & \\  & & ASE), accelerated by increase in & & \\  & pressure (1 TMP) and temperature & & & \\  & & (200 \({}^{\circ}\)C). & & \\ \hline
7 NPAHs (5-NNap, 2-NNap, 9-NAnt, & Aerosols. LSE with organic solvent & & \\  & 9-NPR, 1-NP, 6-NNap), OPAHs, & \(\rightarrow\) evaporate \(\rightarrow\) redissolve in & & GC-MS. EI. & \\  & Levoglucans. & acetonitrile. & & \\ \hline
27 NPAHs (1-2-NPHs, 2-1-NPHs, 3-1-Methyl-4-NNap, 2-, 3-, 4-NBSps, 1-Methyl-5-Nph, 1-Methyl-6-NNap, 2-methyl-4-NNap, 5-NAce, 2-NEF-2, 3-, 9-NNPs, 2-9-NNPs, 4-NNAg, 6-NNC, 3-NBPs, 7-1-3-NbPs, PAHs, OPAHs. & & [Sediment] LOQs: 51.1-272 \\  & & & \\  & & (1-Methyl-4-NNap, 2-, 3-, 4-NBSps, 1-Methyl-6-NNap, 3-methyl-4-NNap, 5-NAce, 2-NEF-2, 3-, 9-NNPs, 2-9-NNPs, 2-9-NNPs, 3-NNPs, 4-NNPs, 5-NAREs, 3-NNPs, 6-NNBPs, 7-1-3-NbPs), PAHs, OPAHs. & & [Sediment] LOQs: 51.1-272 \\  & & & (2-Methyl-4-NNap, 2-, 3-, 4-NBSps, 1-Methyl-6-NNap, 2-methyl-4-NNap, 5-NAce, 2-NEF-2, 3-, 9-NNPs, 2-9-NNPs, 2-9-NNPs, 3-NNPs, 4-NNPs, 5-NAREs, 6-NNBPs, 7-1-3-NbPs), PAHs, OPAHs. & & [29; 32] \\  & & & (3-Methyl-4-NNap, 2-, 3-, 4-NBSps, 1-Methyl-6-NNap, 2-methyl-4-NNap, 5-
# Empirical Trials on Unmanned Agriculture in Open-Field Farming: Ridge Forming
## 3 Results and Discussion
### Cost Analysis  
Assuming a travel speed of 1.5 km/h for both the conventional method and the developed robot, the time required for ridge forming on 1 ha was 416.66 h. The employment cost for operating agricultural machinery was KRW 15,000/day, with a workable time of 8 h/day. Therefore, the employment cost for ridge forming on 1 ha was KRW 781,240. The total expense of using the conventional method for ridge forming includes the analyzed labor cost and the sum of fixed and variable costs (Table 2). The fixed costs for both methods were assumed to be the same, but the variable costs were higher for the conventional method due to fuel consumption. Additionally, the conventional method incurred employment costs, while the developed robot did not. Consequently, the required cost for ridge forming was 1,764,630 and 896,780 KRW/ha for the conventional method and the developed robot, respectively. Comparing these costs, using the developed robot for ridge forming can reduce expenses by 50.81% per hectare.  
Figure 5: The results of linearity analysis compared with a baseline.
## 2 Materials and Methods  
### Study Area  
This study's area spreads across four Brazilian states, namely, Parana (PR), Sao Paulo (SP), Goias (GO), and Minas Gerais (MG). A total of 1568 fields (Figure 1a) were kindly provided by the ABC Foundation \"[[https://fundacaoabc.org/](https://fundacaoabc.org/)]([https://fundacaoabc.org/](https://fundacaoabc.org/)) (accessed on 15 July 2023)\", with observed soybean sowing and harvest dates during the 2019-2020 growing season. The samples represent soybean farms monitored by the ABC Foundation, where observations were conducted by the landowner and reported to the institution.  
In this study's area (Figure 1a), the soybean growing season is modulated by rainfall. In Parana, soybean sowing usually occurs between the end of September (when the rainy season starts) and mid-November, with a higher concentration between late October and early November [37; 40; 41]. Harvesting is usually performed between mid-January and early April, with the crop cycle in most regions ranging from 120 to 160 days [37; 40; 41]. In Sao Paulo, the sowing dates and the crop cycle duration are quite similar to those performed in Parana, while the harvest comprises the period from February to May (Figure 1b; [42]. In Goias and Minas Gerais, sowing is a little later and can occur until mid-December. The harvest can extend from January to April in the state of Goias and until May in Minas Gerais [42]. Thus, in order to capture the growing season of all regions, our study period was extended from the end of August 2019 to the beginning of May 2020.  
Figure 1: Overview of this study’s area showing the location of the phenology observation fields used for validation and total soybean cropped areas (**a**). Usually, the soybean calendar and length of the season (LOS) for the states of Parana (PR), São Paulo (SP), Goias (GO), and Minas Gerais (MG) study area vary from 120 to 160 days (**b**). Soybean areas downloaded from: [39].
# Improved Retinex-Theory-Based Low-Light Image Enhancement Algorithm
## 3 Proposed Method
### Decom-Net  
The key to the Retinex theory lies in obtaining high-quality illumination and reflection images, which in turn greatly affect the final enhancement results. Therefore, designing a high-quality decomposition network is one of the main focuses of this paper. The design of residual networks aims to solve the problem of gradient explosion or vanishing that may occur when the depth of convolutional models increases. Stacked layers, which include pooling and convolutional techniques, can result in a loss of detail during the processing of images. The proposed network avoids this issue by having a skip-connection structure. This can help minimize the effects of neural networks on the image quality. The decomposition network proposed in this paper is illustrated in Figure 5, where S denotes the input image, R denotes the reflection image, and I denotes the illumination image. A \(1\times 1\) convolutional layer is added as a connecting layer in the decomposition network. During the training process, with the guidance of sharing the reflection component, the illumination and reflection images learn to be decomposed from the paired image dataset containing low-light images and truth images.
# Inferring TLB Configuration with Performance Tools
### Related Work
#### 1.1.1 TLB General Operation  
In computing systems, CPUs must efficiently process data and instructions. The challenge lies in accessing the main memory and transferring the data to the CPU. To improve memory access performance, CPUs utilize a hierarchy of caches, with lower levels consisting of small, fast caches, and higher levels being larger and slower. To further enhance memory access, CPUs employ virtual addresses. Virtual addresses represent locations in an abstract address space, enabling processes to operate as though each has its own isolated memory space, despite sharing physical memory with other processes. Virtual Address (VA) to Physical Address (PA) translations are managed by the MMU. The TLB is a module internal to the MMU that caches address translations. These mappings from VAs to PAs are usually based on the most recently used address pairs. Accessing address translations from TLBs is more efficient than retrieving them from the main memory's page tables, as it avoids the time-consuming process of a page table walk to find the corresponding PA [18,19].  
Figure 1 shows the main modules involve in the address translation process. The data flow begins when a process requests access using a VA. This VA comprises a VPN and an offset. The VPN is then checked against the TLB. If the VPN matches an entry in the TLB (a 'hit'), the VPN is translated to a PPN; the VPN also helps in identifying the specific'set' and 'way' within the TLB where the PPN is located. This PPN corresponds to a page in physical memory, and the offset directs to the precise byte within that page.  
In cases where the VPN does not match any entry in the TLB, a'miss' occurs and the MMU initiates a page table walk to locate the corresponding PA. Once the correct translation is found, the PA is returned to the MMU. Next, the TLB is updated with this new information. The MMU then accesses the data at the PA and relays it back to the CPU. If the MMU's page table walk fails to locate the requested page in Random Access Memory (RAM), a page fault occurs. In response to a page fault, the MMU signals the CPU triggering an interrupt. This interrupt activates the Operating System (OS)'s page fault handler to resolve the fault. The OS may then fetch the needed information from the disk, or it might determine that the page is not allocated.
# Acoustic Parametric Signal Generation for Underwater Communication
## 3 Experimental Set-Up  
The measurements were made in the Centro Tecnologico Naval y del Mar (CTN) in Murcia, Spain, in a lake of tapered shape with a 10 m depth and a diameter of 20 m. Figure 1a,b are pictures of the experimental setup. The distance between the emitter and the receiver was 1 m. An ITC 1032 transducer was used as a receiver with a receiving sensitivity (RVR) of \(-194\) dB re 1 V/\(\upmu\)Pa, without much variation at the resonance frequency region at 33 kHz and below, and thus was quite sensitive to the low frequencies willing to be detected. The Airmar P19 plane transducer was chosen as acoustic transmitter. Figure 1b shows the equipment used in the measurements. The transmitter transducer was driven through the National Instruments 5412 PXI signal generator with a 50 dB gain using the E&I 2100L RF amplifier. Signal reception data taking was done through the PXI National Instruments 5102 card. A sampling frequency \(f_{s}=20\) MHz was used for data acquisition. The position of the transducers was set using a positioning system controlled by brushless motors with axis \(X\) = servo motor BMH1003P32F2A with gearbox, axis \(Z\) = servo motor BMH1003P32F2A axis PAN (flat pan turn) = servo motor BSH0553P01A2A axis tilt (vertical flat turn) = servo motor BSH0553P01A2A.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \(f_{s}\) [kHz] & **Power [W]** & \(TL\)**@10 km [dB]** & \(NL\) [dB] & \(DI\) [dB] & \(SLp\) [dB] & \(SLc\) [dB] & \(SLs\) [dB] & \(SNR\)**@1 kHz [dB]** \\ \hline
40 & 182 & 92 & 33 & 9 & 216 & 225 & 180 & 34 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Results for the beams at a distance of 10 km.
## 7 Discussions and Future Directions
### Securing Flying Base Station  
The base station is part of the telecommunications and wireless network provider infrastructure. Securing the base station's availability and integrity is critical because of the user's reliance on the base station for connectivity and because of our reliance on connectivity and remote access in our everyday lives. The previous research implementing security on the base station, such as a security access gateway for edge-based defense [41] or physical-layer security for confidential communication [42], but these assume stationary terrestrial base station which can afford larger and more processing-powerful computing with stable power supply. Flying base stations provide unique challenges, which we have not seen in the past with the traditional stationary base station. Such challenges include the limited resources in computing and hardware size (which can limit the resource budget for security implementations), battery energy/power availability (e.g., [12; 43; 44]), positioning integrity and reliability (e.g., [12; 45; 46; 47; 48; 49]), and fake/malicious base station (e.g., [50; 51; 52]) in both flying and stationary base station. The security vulnerabilities can arise from the new functionalities or advances in the flying base station (some of which are described in the other subsections in this section). The ongoing 6G research, development, and standardization (before the 6G implementations for practice) can provide opportunities to practice security-by-design to reduce the security costs or enable security objectives, which could have been prohibitive if the security is an afterthought and is added after given the existing protocols/algorithms/designs.
# Supramolecular Materials as Solid-Phase Microextraction Coatings in Environmental Analysis
## 3 Metal-Organic Frameworks
### MOF Features for SPME Extraction  
Since their first application as SPME coatings in 2009 [40], MOFs have demonstrated excellent capability for the extraction of compounds belonging to different chemical classes due to their high surface area, good thermal and chemical stability, controlled pore size, and tunable surface chemistry by selecting metal ions, organic ligands, or synthetic conditions [25; 29; 31]. In particular, adsorption studies have demonstrated that after activation MOFs present surface areas characterized by Brunauer-Emmett-Teller (BET) values up to 7000 m\({}^{2}\)/g [29; 31], higher than commonly applied SPME coatings such as activated carbon (-1500 m\({}^{2}\)/g), zeolites (300-600 m\({}^{2}\)/g), silica-based (300-800 m\({}^{2}\)/g) [41; 42], or commercially available coatings, e.g., DVB (750 m\({}^{2}\)/g) [43]. The proper design of the MOF  
Figure 3: Schematic representation of the different approaches for MOF synthesis. (**a**) Solvothermal method; (**b**) hydrothermal method; (**c**) microwave-assisted synthesis; (**d**) ultrasound assisted method; (**e**) mechanochemical method; (**f**) electrochemical assisted approach. Reprinted with permission from [PERSON] et al. [38].  
framework can also play a key role in enhancing selectivity by strengthening the steric complementarity between the host and the guests or by enhancing MOF-analyte interactions via hydrogen bonding, \(\pi\)-\(\pi\), CH-\(\pi\), electrostatic, van der Waals, hydrophobic/hydrophilic interactions, or via the coordination of the free metal sites present in the lattice [25; 44].
# Cooperative Three-View Imaging Optical Coherence Tomography for Intraoperative Vascular Evaluation
## Materials and Methods
### SD-OCT System Configuration  
A schematic of the SDOCT system configuration is shown in Figure 6. The super-luminescent diode (IPSDS1307, InPhenix Technologies Inc., Livermore, CA, USA) laser was used with a central wavelength of 1310 nm and a bandwidth of 100 nm. The emitted light of the SLD source is split into the sample arm and reference arm by a 50/50 fiber coupler (FC1310-70-50 APC, Thorlabs, Newton, NJ, USA). The light of the sample arm propagates along fiber into the collimator and then converts into a 1.5 mm beam input to our novel three-view imaging sample arm as illustrated in Figure 6.  
Figure 5: Mechanical design of the holder in sample arm. (**A**) Photorealistic rendering of the fully assembled kits, 1—lens tube, 2—adjustable ring, 3—scanning head, 4—mirror mounted spacer (**B**) Cross-sectional view of the tube (**C**) Picture of the manufactured holder in system (**D**) Micro stage for vessel placement.  
Figure 6: SDOCT system setup. SLD: super luminescent diode, PC: polarization controller, DAQ: data acquisition card.  
The homebuilt SDOCT system has an axial resolution of 14 \(\upmu\)m and imaging range of 6.7 mm. A GL2048R line scan camera from Sensors Unlimited Inc. (Princeton, NJ, USA) was used in the spectrometer, which has 2018 pixels with a 10-\(\upmu\)m pixel pitch width and 210-\(\upmu\)m pixel height. A-scan rate of the OCT system is 40 kHz and each B-Scan consists of 1000 A-Scan. The sensitivity of the SDOCT system was measured to be 92 dB at a position 0.2 mm away from the focal plane of sample objective and the sensitivity roll-off was 13.6 dB/mm. A numerical dispersion method was implemented into the software to deal with any dispersion mismatch. Each frame contains cross-sectional images of three individual views. The 3D volume scanning data consists of 250 B-Scan frames. The DAQ card generates scanner control waveforms and synchronization signals for data acquisition. A customized software developed based on Qt 5.5, C++ (Microsoft Visual Studio, Redmond, WA, USA) and CUDA 7.5 controls data acquisition, processing and display.
# Shear Capacity of Textile-Reinforced Concrete Slabs without Shear Reinforcement
## 2 Experimental Investigation on Shear Capacity
### Variation of Parameters  
The experimental program focused on the investigation on the variation of two parameters, the shear slenderness \(a/d\) and the effective depth \(d\) (Table 1). By varying the shear slenderness, one could investigate the increase in shear capacity due to concentrated loading near the supports and the formation of direct compression stress fields. The variation of the effective depth allowed for analysis of the size effect. All other parameters with an assumed influence on the shear capacity were kept constant. The geometrical ratio of longitudinal reinforcement \(\rho\) (cross-sectional area of reinforcement divided by effective depth and width) was chosen as \(\sim\)0.24%, aiming at avoiding bending failure. Note that this reinforcement ratio is still typical for TRC plates in this depth range. This is relevant, because over-reinforced cross-sections may show a disproportionate amount of dowel action as shear transfer mechanism. The concrete compressive (and tensile) strength chosen for this study resulted from the idea of matching suitable high-performance materials, i.e., to be able to fully use the high tensile and bond strength of the non-metallic reinforcement. Details are given in Section 2.4. The width of 20 cm was chosen for all specimens considering the maximum specimen height, the maximum grain size, the number of reinforcement elements per layer resulting from the reinforcement grid spacing, and the maximum test load and dimensions of the test machine.
## 4 Discussion
### HLS-Classification Uncertainty and Its on Impact Simulated Methane Flux
Although NDVI-based classification using HLS data showed to be a good estimate of yearly vegetation cover at the wetland site and improved the estimation of methane flux, there are gaps that need to be addressed in order to further reduce the uncertainty in methane flux estimations. Our NDVI-based classification provides information about the vegetation cover map without accounting for the inundation dynamics at the site. We effectively assumed that the vegetation cover in a given pixel remains the same for the whole year, and that there are no other sources of environmental variability among patches of the same type (e.g., all open-water patches have the same depth). However, these vegetation covers are changing from a diurnal/weekly level due to daily changes in surface water elevation to other slower effects such as saline intrusion and herbivory, which should to be taken into consideration for more accurate estimations of the effects of vegetation cover on surface fluxes [46]. Although our version of the ELM uses observed water surface elevations to force the water dynamics of the wetland, we did not include high-resolution bathymetry information, which could help define the dynamics of intra-annual vegetation cover, and account for additional spatial heterogeneity. In addition, it must be noted that methane estimations in LSMs are subject to many sources of error other than those related to the accurate representation of the within-wetland eco-hydrological patch-types. These limitations include the bulk representation of methane production in LSMs, which is usually  
Figure 8: Results of the different classification methods for year 2018. The upper row shows the results for the whole plot, and the lower row shows the results for the 480 m \(\times\) 480 m square around the tower. The left column corresponds to the classification based on high-resolution WorldView data ((**a**) for whole plot and (**d**) for plot within tower footprint), i.e., expert-supervised classification with no resampling, and the second column corresponds to the classification based on WV data as well but upscaled to the HLS resolution by summing up all high-resolution pixels’ patch-types to determine the majority patch-type of each upscaled, low-resolution pixel (**b**) for whole plot and (**e**) for plot within tower footprint), and the right column shows the results of the HLS classification method ((**c**) for whole plot and (**f**) for plot within tower footprint). set to be a function of upland biogeochemistry while not representing the different pathways for methane production [47; 48]. In addition, the lack of representation of lateral flow between grid-cells, which limits the ability of LSMs in resolving reactive transport, which in turn will result in biases with modeling methane oxidation and inundation dynamics. Many efforts are underway to overcome these limitations and have a better representation of methane dynamics in wetlands [49], and improved patch-type observations will serve these efforts well.
# Digital Health Technologies to Support At-Home Recovery of People with Stroke: A Scoping Review
## 4 Discussion
### Reliability and Clinical Utility  
Most included studies demonstrated that wearable and sensor-based technologies provide high levels of accuracy and reliability, particularly in monitoring mobility and motor function. For instance, SECOSP showed superior performance compared to IMUs [29], SAM reliably tracked stride counts in stroke patients [26], and SULAM enabled low-interference monitoring of daily arm activity [28]. Similarly, pressure mats effectively measured SiSt transitions across different populations [30], while the SyMPATHy cup and Tentaculus System received positive user feedback regarding usability and acceptability [31; 34].  
However, several challenges persist. In some cases, accelerometer feedback enhanced patients' perceived limb function without corresponding objective improvements in motor outcomes, indicating a disconnect between subjective experiences and clinical progress [27]. Moreover, the design of physiological monitoring devices still requires refinement to improve user comfort and facilitate engagement during long-term use [35].  
A recent meta-analysis further supports the potential of home-based digital rehabilitation, demonstrating that it can achieve outcomes comparable to traditional in-clinic services in domains such as upper extremity motor ability, static balance, stroke-related quality of life, and self-reported function [46]. While previous reviews have broadly exploredhome-based stroke care, our review focuses specifically on the monitoring and evaluation functions of digital health technologies, offering a more detailed analysis of these aspects.  
In addition to performance considerations, several studies also highlighted technical and usability challenges that could impact real-world adoption. Issues such as device discomfort, difficulties in handling feedback mechanisms [34], and risks associated with sensor misplacement [32] were frequently reported. Addressing these user-centered concerns is crucial in rehabilitation settings, where comfort, usability, and patient adherence are critical to success.