esa-sceva/satcom-chunk-collection huggingface dataset:
# Role of Ion-Exchange Resins in Hydrogen Reactions
## 3 Ion-Exchange Resins Application to Hydrogenation Reactions
### One-Pot Multistep Reaction Processes Involving Hydrogenation
As for the use of IERs to produce GVL, [PERSON] and [PERSON] (2014) supported Ru (0.87 wt.%) on DowexTM 50W \(\times\) 2 by contacting it with RuCI\({}_{3}\) and further reduction with NaBH\({}_{4}\), with water being used as the solvent in both metalation and reduction steps. Values of 16.2-99.8% conversions in batch runs and 89-100% conversions in continuous runs (residence time = 62-211 s, T = 70 \({}^{\circ}\)C, P = 4.8-7.0 bar H\({}_{2}\), water) are reported, with selectivity to GVL always above 99%. No significant activity decay was observed after three consecutive runs of recycling the catalyst and no Ru leaching was detected [95]. In another study by [PERSON] and coworkers, metal nanoparticles (Pd, Rh, or Ru) were supported on a perfluorinated, fluorosulfonic acid resin (i.e., Aquivion PFSA), which is claimed to possess an acidity similar to sulfuric acid, a thermal stability well beyond that of conventional IER, and chemical inertness in aggressive environments. Full selectivity at high conversion levels is reported for the conversion of (+)-citronellal to (-)-menthol and levulinic acid to \(\gamma\)-valerolactone (GVL) under mild conditions over Pd-based catalysts [112]. Another prominent reaction system within this field is the synthesis of methyl isobutyl ketone (MIBK) from acetone, involving consecutive steps of condensation, dehydration, and hydrogenation (e.g., [77; 113; 114; 115; 116; 117; 118; 119]). The commercial Pd-doped AmberlystTM CH28 was pinpointed as an effective catalyst to carry out this synthesis. Depending on the operating conditions, reported acetone conversion values span from 25 to \(>\)99%, with selectivity in the range 70-90% [77; 113; 114; 115; 116; 117; 118]. AmberlystTM CH43, a Pd-doped strong acid sytrenic IER, was reported to provide higher selectivity towards MIBK at high temperatures in a continuous 8 h run [118]. Remarkably, AquivionR polymers were also used as Pd supports (0.10-1.04 wt.%) for the synthesis of MIBK, showing conversions in the range 11.0-63.3% and selectivity values of 62.8-96.5% in batch runs (t = 2-22 h, T = 120-180 \({}^{\circ}\)C, P = 10-25 bar H\({}_{2}\)) [119]. Several multistep reactions over metal-doped IER are shown in Scheme 6. Table 7 lists some of the information retrieved from the literature covered. As can be seen, all multistep processes found in the literature combine metal sites with acid sites. Both macroreticular and gel-type resins have been used. Furthermore, the commercial AmberlystTM CH28 needs to be highlighted, since it is a readily available product that has shown good results in different multistep reaction processes. **Scheme 6. ** Multistep reaction processes over metal-doped IERs in the revised literature.
## 2 Materials and Methods
### Data Preprocessing and Normalization  
The data were processed and analyzed using Python(tm) (version 3.10.4), a computational programming language [37]. To handle, visualize, and manipulate the dataset, we made use of the \"pandas\", \"numpy\", \"matplotlib.pyplot\", and \"seaborn\" libraries [38]. With less than 10% null values across the dataset, missing data points were imputed by replacing them with the mean value of each respective column [39]. The target variable, representing the recovery state, was multi-class, and therefore, one-hot encoding was applied to convert categorical class labels into numeric arrays interpretable by ML algorithms [40].  
To address significant discrepancies observed in the numeric scales of the features, normalization was performed using the \"StandardScaler\" function from the \"sklearn.preprocessing\" library [38; 41]. This process scaled the features to a range between \(-1\) and \(1\), enhancing interpretability for algorithms that apply the sigmoid function, defined as \(\sigma(x)=\frac{1}{1+e^{-x}}\), where \"\(x\)\" is the independent variable and \"\(e\)\" represents the Euler's number, \(e=2.71828\)[42]. This normalization ensured the data was suitable for further modeling and analysis.
## 2 Materials and Methods
### Flux Data and LUE Calculation  
The eddy covariance (EC) method has greatly advanced the understanding of stand-level carbon fluxes and provides indirect measurements of LUE. EC data has been used in numerous studies for calibrating and validating LUE models at leaf, stand and landscape scales [52, 53, 54, 55].  
Flux data measured by an open path eddy covariance system at 39.6 m and radiation measured at 41.6 m above the ground surface were used in this study [50]. Available meteorological measurements were also made around the same flux tower, mainly including air temperature (Ta), vapor pressure (VP), precipitation, soil moisture (SM) and temperature, _etc._ For long period analysis (_i.e._, month and whole study period), days with total rainfall above 5 mm were excluded owing to inaccuracy of both flux and spectral observations under rainy conditions.  
GPP was calculated from measured net ecosystem productivity (NEP) with daytime ecosystem respiration (R\({}_{\rm e}\)) estimated with an empirical equation fitted using nighttime NEP and temperature measurements, _i.e._,  
\[\rm GPP\,=\,NEP\,+\,R_{e} \tag{1}\]  
APAR was estimated using two methods. The first method calculates APAR with PAR \(\times\) FPAR. FPAR was estimated according to true LAI [56] and the solar zenith angle (\(\theta\)) as [57, 58]:  
\[\rm FPAR\,=\,(1-\rho_{1}(\theta))-(1-\rho_{2}(\theta))\times e^{(-G(\theta) \times\Omega\times LAI/cos\theta)} \tag{2}\]  
where \(\rho_{1}(\theta)\) and \(\rho_{2}(\theta)\) are the PAR reflectivity above and below the canopy and assigned values of 0.05 and 0.06, respectively; \(G(\theta)\) is the projection coefficient of leaves and is set as 0.5 for a spherical (random) distribution of foliage inclination angles [21, 57]; \(\Omega\) is the clumping index determined by the spatial distribution pattern of foliage and is set as 0.5 for this forest. The term \(e^{(-G(\theta)\times\Omega\times LAI/cos\theta)}\) is the gap fraction of the canopy at the zenith angle \(\theta\).  
Half-hourly LUE was calculated as:  
\[\text{LUE}\,=\,\text{GPP}/(\text{PAR}\times\text{FPAR}) \tag{3}\]  
Previous studies indicated that the fractions of diffuse and direct radiation absorbed by canopies differ [59, 60]. To capture this difference, another method for calculating LUE was performed as APAR was calculated using the algorithm in a TL-LUE model developed by [PERSON] _et al._[21, 61]. The specific description of this method was presented in Appendix B.  
Clearness index (CI) is the ratio of the global solar radiation on the surface of the earth (R\({}_{\text{g}}\)) to the extraterrestrial radiation at the top of the atmosphere (R\({}_{0}\)) [62]. R\({}_{0}\) for any day and any moment can be calculated using the solar zenith angle (\(\theta\)) at a given place as:  
\[\text{R}_{0}\,=\,\text{S}_{0}\times(1\,+\,\text{cos}(360\times\text{DOF}/365))/ \text{cos}\theta \tag{4}\]  
where S\({}_{0}\) is the solar constant (1367 W/m\({}^{2}\)); DOY represents day of the year. With observed half-hourly total incident radiation (R\({}_{\text{g}}\)), CI is calculated as R\({}_{\text{g}}\)/R\({}_{0}\).
# Development of the User Requirements for the Canadian WildFireSat Satellite Mission
## 2 Wildfire Management Needs Assessment
### Summary of Wildfire Manager Needs
#### 2.2.4 Detection in Remote Regions  
Wildfire management practices in Canada vary dramatically across the landscape, and generally in relation to population distributions (e.g., [9; 10]). In vast remote areas, wildfire managers do not typically conduct dedicated detection activities due to the decreased likelihood for negative impacts from wildfire, and the higher cost and operational complexity of these patrols (e.g., [60]). Space-based EO is particularly suitable for gathering intelligence in these situations [22]. The value of EO-derived detection of wildfires identified in the survey (Table 2) was highlighted as particularly valuable in these regions in the comments.
# Electroencephalography (EEG) Technology Applications and Available Devices
### Differences between Dry and Wet Devices  
In January of 2019, researchers at the University of California, The Otto von Guericke University of Magdeburg, and The Hebrew University of Jerusalem performed a comparative analysis of the signal quality of dried wireless and wet wire EEG devices, and concluded that the quality of wireless dry devices is significantly comparable with the wired wet. Although some researchers observed that, for those activities that demand body movement like running/walking, wired wet sensors showed better performance [5,6]. This seems to indicate that wet sensors may be more resistant to movement artifacts, although more research needs to be conducted to fully understand which technology can provide more reliable data.
# PowerBridge: Covert Air-Gap Exfiltration/Infiltration via Smart Plug
## 5 Channel Signal Generation
### Infiltration (Control Power On and Off through Smart Plug)  
As laptops are capable of detecting the power supply status, the smart plug switch can serve as a signal to transmit data to the ACPI interface of the laptop and subsequently relay it to an air-gapped computer. To facilitate testing and evaluation, this study presents a simulation system incorporating a modified plug that encodes binary data. The plug comprises MCU chips, WiFi modules, and relay modules. The WiFi module facilitates remote network communication, while the MCU chip receives commands and transmits received exfiltration signals. Additionally, it simultaneously controls the opening and closing of relays for powering on/off purposes. The remote control MCU is responsible for transmitting and receiving binary data based on an encoding scheme.
# Effectiveness of High-Intensity Therapy in Rehabilitation of Parkinson's Disease Patients
## 4 Results
### Heart Rate and Borg Scale Analysis  
The assessment of perceived exertion using the Borg Scale revealed moderate to high exertion levels across therapy groups. The mean Borg RPE scores ranged from 14 to 18, indicating moderate- to high-intensity physical demand. ANOVA results demonstrated significant differences between groups, particularly in Borg1 (F = 8.66, \(p\) < 0.0001) and Borg20 (F = 4.01, \(p\) = 0.0098). The Tukey-HSD test further confirmed that Group 4 experienced significantly higher exertion compared to Groups 2 and 3 (_p_ < 0.0001). Heart rate monitoring showed notable variations across therapy groups. The average heart rate (HR) during exercise ranged between 85.32 bpm and 88.32 bpm, with peak HR values exceeding 100 bpm in some cases. These findings suggest that therapy intensity effectively engaged participants in cardiovascular exertion.
# JIT Production Strategy and Maintenance for Quality Deteriorating Systems
## 3 Problem Statement
### Optimization Problem  
Another aspect of the model development is the description of the optimization problem implied. In this case, it consists of finding the optimal values of the production parameter \(B_{JIT}\), the inventory level \(Z_{p}\), and the critical age for major maintenance \(A_{0}\) which minimize the key performance measure for this type of system. In this case, that is denoted by the expected total incurred cost \(TC\), which is subject to Equations (1)-(7). Furthermore, the total cost includes the inventory/backlog cost, the quality cost, and the maintenance cost. For our discussion, the average inventory-holding and backlog cost per unit of time \(IB(t)\) during the interval \([0,T]\) is defined as:  
\[IB(t)=\frac{1}{T}\cdot\int_{0}^{T}\bigl{(}C^{+}x^{+}(t)+C^{-}x^{-}(t)\bigr{)}dt \tag{11}\]  
with  
\[x^{+}=max(0,x)\]  
\[x^{-}=max(-x,0)\]  
where constants \(C^{+}\) and \(C^{-}\) indicate the inventory and backlog cost, respectively. The average total quality cost per unit of time \(QC(t)\) during \([0,T]\) is defined by the defectives cost \(C_{def}\) as follows:  
\[C(t)=\frac{1}{T}\cdot\left(C_{def}\int_{0}^{T}(\beta(t)\cdot d)dt\right) \tag{12}\]  
For convenience, the average maintenance cost per unit of time \(MC(t)\) during \([0,T]\) includes the minimal repair cost and the major maintenance cost such that:  
\[MC(t)=\frac{1}{T}\cdot\left(C_{R}\cdot N_{R}(t)+C_{M}\cdot N_{M}(t)\right) \tag{13}\]  
where indexes \(N_{R}(t)\) and \(N_{M}(t)\) refer to the numbers of minimal repairs and major maintenances that were conducted in the interval \([0,T]\), respectively. In summary, the optimization problem is to solve the following stochastic model:  
\[Min\ TC\bigl{(}Z_{p},B_{JIT},A_{0}\bigr{)}=\lim_{t\to\infty}\left(IB(t)+QC(t) +MC(t)\right) \tag{14}\]  
subject to Equations (1)-(7) (dynamics of quality and inventory):  
\[Z_{p},B_{JIT},A_{0}\geq 0\]  
In a sense, the formulated optimization model merely confirms a non-linear and highly stochastic problem. Therefore, classical mathematical programming methods cannot be used to solve the problem (14). This is because it is not straightforward to derive a closed-form solution for the total incurred cost, given the Markov dynamics of the transition rates and the progressive increment of the defectives rate due to deterioration. In this context, a simulation-optimization-based approach is proposed to determine an optimal solution for the objective function and define the respective control parameters.
## 2 Materials and Methods
### Cloning of the ftsZ Gene and Construction of the Recombinant Plasmids  
The _ftsZ_ of _E. coli_ CVCC1515 was amplified by polymerase chain reaction (PCR) with a pair of specific primers (F: 5\({}^{\prime}\)-CATGCCATGGGCATGTTTGAACCAATGGAAC-3\({}^{\prime}\) and R: 5\({}^{\prime}\)-CCGCTCGAGATCAGCTTGCTTACGCAGGA-3\({}^{\prime}\)) with restriction sites of _Nco_ I and _Xho_ I. The _ftsZ_ PCR product was purified by TIANgel Midi Purification Kit and cloned into the pMD-19T vector to produce the pMD-ftsZ plasmid according to the manufacturer's instructions [25].  
The positive pMD-ftsZ plasmids were digested by _Xho_ I and _Nco_ I; the obtained _ftsZ_ DNA fragment was ligated into the pET-28a expression vector digested with the same endonucleases, followed by transformation into _E. coli_ DH5\(\alpha\). The recombinant pET-ftsZ plasmids were identified by DNA sequencing and enzyme digestion methods [26].
# RSCS6D: Keypoint Extraction-Based 6D Pose Estimation
## 4 Experiments
### Limitations  
Rigorous experiments validate that our method excels in real-time applications, responding quickly and maintaining high estimation accuracy across various complex scenarios. This demonstrates the potential of our keypoint-based approach to advance 6D pose estimation and to provide new insights and methods for related research and applications.  
However, the performance of our algorithm is primarily constrained by the accuracy of the front-end semantic segmentation algorithm. Current depth cameras still have limitations in precision and stability, affecting the performance of the semantic segmentation module and, consequently, the pose estimation accuracy of the entire algorithm. Thus, while our algorithm optimizes pose estimation efficiency and accuracy to some extent, its performance is still limited by the front-end semantic segmentation algorithm and the performance of depth camera hardware.
# Robust Symbol and Frequency Synchronization Method for Burst OFDM Systems in UAV Communication
## 3 Improved Burst OFDM Synchronization Method  
In the traditional burst communication system, the AGC guard interval may introduce significant time overhead in a short-frame structure, resulting in low frame efficiency. If the AGC guard interval is eliminated, gain adjustment might occur during the preamble or the useful portion of the OFDM symbol. This can lead to a loss of subcarrier orthogonality, resulting in inter-carrier interference and significant performance degradation of the OFDM system [16]. Meanwhile, both low power consumption and miniaturization are essential for drones, thus necessitating that the hardware design and software processing algorithms of airborne equipment be as simple as possible. To address this, we propose a robust synchronization method that achieves a high frame efficiency with low complexity. The proposed method is analyzed from the perspectives of frame structure design, symbol time synchronization, and carrier frequency synchronization.
# Terahertz-Wave Absorption Gas Sensing for Dimethyl Sulfoxide
## 1 Introduction
[26] identified DMSO rotational transitions in the 150-660 GHz frequency range using high-resolution (10 kHz) methods carried out at very low pressures (0.2 Torr or 2.6 Pa) and presented a Watson Hamiltonian fit to describe the spectra. [PERSON] et al. [27] extended the work of [PERSON] et al. by adding transitions from low-lying vibrationally-excited states in the 70-700 GHz frequency band. [PERSON] et al. also improved upon the Hamiltonian fit, providing updated rotational constants, including centrifugal distortion terms, for six vibrational ground states. Several technologies have been used in the last several decades to investigate the rotational spectra of polar gas molecules in the THz-wave region. FAst Scan Submillimeter Spectroscopy Technique (FASSST) utilizes quasi-optical radiation sources and high-sensitivity and low time-response detectors, and it is most useful for highly resolved  
Figure 1: Molecular structure of DMSO. spectral characterization (kHz resolution) at extremely low pressures where fast time response is not required [28; 29; 30]. While extremely resolved, FASSST systems are expensive, large, and slow, and, therefore, they are not suitable for remote gas sensing in industrial applications. THz time-domain spectroscopy (THz-TDS) provides greater time-resolution than FASSST and can scan large frequency ranges. However, typical THz-TDS systems do not have the spectral resolution of FASSST systems and may not provide a sufficient resolution for sensitive quantitative gas sensing under some conditions. THz-TDS systems are also generally too large, complex, and expensive to be implemented in remote gas sensing [1; 2; 31]. With advancements made to THz frequency microelectronic sources and detectors, miniature microelectronic THz-wave remote gas sensors are now possible that offer high time-resolution (seconds to milliseconds), offer reasonable spectral resolution (100 kHz to 1 MHz), are robust in industrial processes, and are inexpensive compared to other technologies. The present study presents the development of gas sensing for DMSO carried out using a THz-wave electronic spectrometer.
# Object-Based Time-Constrained Dynamic Time Warping Classification of Crops Using Sentinel-2
###### Abstract  
The increasing volume of remote sensing data with improved spatial and temporal resolutions generates unique opportunities for monitoring and mapping of crops. We compared multiple single-band and multi-band object-based time-constrained Dynamic Time Warping (DTW) classifications for crop mapping based on Sentinel-2 time series of vegetation indices. We tested it on two complex and intensively managed agricultural areas in California and Texas. DTW is a time-flexible method for comparing two temporal patterns by considering their temporal distortions in their alignment. For crop mapping, using time constraints in computing DTW is recommended in order to consider the seasonality of crops. We tested different time constraints in DTW (15, 30, 45, and 60 days) and compared the results with those obtained by using Euclidean distance or a DTW without time constraint. Best classification results were for time delays of both 30 and 45 days in California: 79.5% for single-band DTWs and 85.6% for multi-band DTWs. In Texas, 45 days was best for single-band DTW (89.1%), while 30 days yielded best results for multi-band DTW (87.6%). Using temporal information from five vegetation indices instead of one increased the overall accuracy in California with 6.1%. We discuss the implications of DTW dissimilarity values in understanding the classification errors. Considering the possible sources of errors and their propagation throughout our analysis, we had combined errors of 22.2% and 16.8% for California and 24.6% and 25.4% for Texas study areas. The proposed workflow is the first implementation of DTW in an object-based image analysis (OBIA) environment and represents a promising step towards generating fast, accurate, and ready-to-use agricultural data products.  
agricultural mapping; DTW; eCognition; land use/land cover mapping; object-based image analysis; satellite image time series 2019 11 1257; doi:10.3390/rs11101257
# Shifting from Conventional to Organic Filter Media in Wastewater Biofiltration Treatment: A Review
## 3 Filter Media Selection on the Efficiency of Biofililtration
### Conventional vs. Organic Filter Media Biofiltration Systems
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multicolumn{5}{c}{**Conventional Filter Media (Synthetic)**} \\ \hline
**Type of Filter Media** & **Operating Condition** & **Removal Efficiency** & **Additional Remark** & **Reference** \\ \hline GAC-sand dual-media & Filtration velocity: & NH\({}_{4}\)-N: 70–74\% & Treatment of effluent from the & \\ biofilter & 8 m/h & Steady removal of & sedimentation tank & [82] \\ \hline GAC (open & EBCT: 18 min & Near-complete removal & \\ superstructure) & Hydraulic Loading & of NH\({}_{4}\)-N in both types & Treatment of raw water & [83] \\ \hline GAC (close & Rate: 1.67 m/h & of GAC & \\ superstructure) & Temperature: 25 \({}^{\circ}\)C & Ammonium: \(>\)97.0\% & \\ \hline  & & Turbidity: 75.0\% & \\ GAC & Filtration velocity: & DOC: 100\% & Treatment of raw stormwater & \\  & 0.12–0.25 m/h & TN: 37\% & Higher heavy metal removal by & [55] \\  & pH: 7.5 & TP: 74\% & GAC compared to anthracite & \\ \hline  & & Flow rate: & \\ Polypropylene & 0.3 L/min & & \\ Airflow rate: & & NH\({}_{4}\)-N: 93.2\% & Treatment of contaminated & \\  & 0.3 L/min & Mn: 79.6\% & drinking water & [84] \\  & HRT: 7.5 h & & \\ \hline GAC & pH: 7.5-8.5 & DOC: 31.2–34.3\% & Start-up period: 6 months & \\  & DO: 6.5, 7.5 mgO\({}_{2}\)/L & AOC: 51.2–60.6\% & Treatment of pre-treated lake & [85] \\ \hline  & & DO: 4.5 mg/L & & \\ Expanded Clay & Temperature: 15–25 \({}^{\circ}\)C & SS: 92.0\% & Treatment of municipal & \\  & Flow rate: & NH\({}_{4}\)-N: 91.0–93.0\% & wastewater & [86] \\  & 1021.6 m\({}^{3}\)/d & & & \\ \hline GAC & Filtration rate: 7–9 m/h & Ammonia: 76.5\% & Start-up period: 38 days & \\  & HRT: 8 min & DON: 54.5\% & Treatment of river water & \\  & & DON: 54.5\% & (Pre-treated with coagulation & [66] \\  & & and sedimentation) & \\ \hline  & C/N ratio: 2.9–3.1 & & \\ Expanded Clay & DO: 7.0–7.2 mg/L & NO\({}_{3}\)-N: 80.0\% & Treatment of synthetic & \\  & Filtration Velocity: & TN: 50.0\% & wastewater & [87] \\  & 1.5–3.9 mg/L & & & \\ \hline  & & Ciprofloxacinacin: 22.0\% & & \\ GAC & ECBT: 50 min & Bezafibrate: 25.0\% & & \\  & Influent flow rate: & Ofloxacin: 30.0\% & & \\  & 48 m\({}^{3}\)/day & Azithromycin: 32.0\% & Treatment of secondary effluent & [53] \\  & & Sulfatethboxazole: & & \\ \hline  & Hydraulic Loading: & & & \\  & 5 dm\({}^{3}\)/m\({}^{2}\) per day & & & \\ Light Expanded Clay & HRT: 4 days & Total Nitrogen: 53.7\% & & \\ Aggregate & Temperature: & Organic Compound: & \\  & 0, 4, 8, 25 \({}^{\circ}\)C & 79.7\% & & \\  & C/N ratio: & & \\  & 0.5, 2.5, 5.0 gC/gN & & \\ \hline  & C/N ratio: 3–5.6 & & & \\  & HRT: 1.8 h, 2.7 h, 3.5 h, & & \\ Polyurethane & 4.2 h & Total nitrogen: & concentration (0.68 mg/L) & \\  & DO: 0.3–0.8 mg/L & 67–96.5\% & Treatment of micro-polluted & [89] \\  & Temperature: 25–28 \({}^{\circ}\)C & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: _Cont.__Appl. Sci._**2021**, _11_, 8650