esa-sceva/satcom-chunk-collection huggingface dataset:
# Non-Invasive Hydration Monitoring with a Graphene Dual Sweat Sensor
## 3 Results and Discussion
### Practical Application
Error bar was set at 10%. (**c**) Trend showing the changes of sweat sodium concentration across three intervals of exercise (legend: black square—upper arm; green triangle—forearm; blue circle—chest). (**d**) Trend showing the changes of sweat potassium concentration across three intervals of exercise (legend: cyan square—upper arm; purple triangle—forearm; orange circle—chest). the intervals progressed. However, it is noticed that the sweat sodium concentration and the changes across the intervals were on the lower end. Although this participant is physically active, it was reported that fitness level does not have an impact on sweat sodium secretion [11], but the dietary intake of sodium does [29; 30; 31]. This indicates that there might not be an absolute range when one might be considered dehydrated or requires electrolyte replacement based on the sodium concentration; rather, it is subject to the inherent condition of the individual. With the development of this graphene dual sensor that is accurate, easy to use, requiring minimal volume, and able to provide real-time measurements, more studies can be conducted to understand how sodium concentration can be further utilized to understand the hydration status of each unique individual and further optimize their performance. The hydration status, or sodium level, not only offers an indication of how and when the individual should replace water loss, but also provides an indication of whether or not the athlete is acclimatized to the environment [32]. This is especially important for athletes who train or live in cooler climates and travel to regions that are more humid or hot. Acclimatizing to the heat helps them to reach their top performance condition, and this can be tracked through the sweat sodium concentration. With the sensor developed here, the monitoring can be achieved with ease, and instant results can be obtained.
# Physical Layer Latency Management Mechanisms: A Study for Millimeter-Wave Wi-Fi
## 1 Introduction  
The number of connected devices is rising with a 10% compound annual growth rate (CAGR) [1], causing ever higher interference levels in the already saturated sub-6 GHz wireless spectrum. Leveraging multipath signal components and spatial diversity increases communication reliability; however, a large deal of the interference can be avoided by exploiting the 30-300 GHz millimeter-wave (mmWave) spectrum. The 270 GHz wide mmWave spectrum also allows wireless waveforms to occupy larger bandwidths. For example, the IEEE 802.11 ad standard (WiGig), situated around the 60 GHz central frequency, withels 1.76 GHz wide channels [2]. In comparison, the 5 GHz IEEE 802.11 ac (Wi-Fi 5) channel bandwidth is only 160 MHz [3]. Consequently, WiGig achieves data rates surpassing 8 Gbps at its highest modulation and coding scheme (MCS) setting--an enviable feat that makes mmWave Wi-Fi a perfect fit for data-hungry applications such as interactive video streaming.
# Application of Molecularly Imprinted Polymers in the Analysis of Waters and Wastewaters
## 5 Application of MIPs for the Detection of Different Classes of Contaminants
### Heavy Metals
#### 5.1.7 Cadmium  
Cadmium represents a high threat toxic metal, which may lead to lung cancer, kidney, and bone damage as well as hemorrhoids. The WHO indicated that its drinking water limit should not exceed 10 \(\upmu\)g L\({}^{-1}\)[43].  
Poly (N-isopropyl acrylamide) grafted and modified g-C\({}_{3}\)N\({}_{4}\) magnetic Fe\({}_{3}\)O\({}_{4}\) ion-imprinted polymer (IIP) was synthesized for selective interaction with cadmium have been successfully synthesized. The adsorption capacity for Cd\({}^{+2}\) was found to be 2.86 times greater than that for Cu\({}^{+2}\), Pb\({}^{+2}\), and Zn\({}^{+2}\). Pseudo-second-order model and Langmuir isotherm model fit the experimental data well [58].  
Unlike commonly used sorbents for the removal of heavy metals, MIP sorbents were found to be of higher binding capacity, as the mechanism of interaction with the target metal ions not only involves physical adsorption, but also, the recognition cavities imprinted specifically for the target analyte enhances to a great extent the binding efficiency for different ion-imprinted polymers. Additionally, ion-imprinted polymeric beads could be used several times without reducing their adsorption capacity, which indicates the cost-effectiveness, taking into consideration that the knocking out of the analytes can be easily performed using different types of solvents without affecting the physical nature of the prepared polymers, which is one of the main advantages of applying the molecular imprinting approach.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
# A Review of Anomaly Detection in Spacecraft Telemetry Data
###### Abstract  
Telemetry data play a pivotal role in ensuring the success of spacecraft missions and safeguarding the integrity of spacecraft systems. Therefore, the timely detection and subsequent notification of any abnormal events related to the functionality of spacecraft subsystems are crucial to ensure their safe operation. In recent years, several anomaly detection methods have been developed to monitor spacecraft telemetry data and detect anomalies. This manuscript provides a comprehensive literature review of the existing anomaly detection methods for spacecraft telemetry data. It exposes the challenges faced by such systems, highlights the strengths and limitations of each anomaly detection method, and assesses and compares the performance of these approaches in detecting anomalies. Initial results show that GCN and TCN models have achieved promising precision up to 94%. The paper concludes with a series of recommendations and the potential research directions.  
t  
elemetry data; spacecraft; anomaly detection; anomalies
# NASA Global Satellite and Model Data Products and Services for Tropical Meteorology and Climatology
## 2 Satellite Missions, Projects and Data
### Future Satellite Missions
#### 2.5.2 Accp  
Aerosols, clouds, convection and precipitation are closely related and their linkages are still not well understood. The ACCP mission [37] is designed to respond to the Earth System Science themes, science and application questions, and several high priority objectives, highlighted in the National Academies of Sciences, Engineering and Medicine (NASEM) 2017 Decadal Survey [38], \"Thriving on Our Changing Planet: A Decadal Strategy for Earth Observations from Space\". The major themes in the decadal survey include: (a) climate variability and change and (b) weather and air quality. The observables in the ACCP mission may potentially address additional themes such as marine and terrestrial ecosystems, global hydrological cycle, Earth surface and interior. ACCP datasets that are under development will be archived and distributed by GES DISC. The mission is still in the planning stage.
## Materials and Methods
### Quantitative Real-Time PCR (RT-qPCR)  
Ribonucleic acid (RNA) was extracted from the MG-63 cells after osteogenic differentiation with TRIzol reagent (Thermo Fisher Scientific, Waltham, MA, USA), according to manufacturer's instructions. The RNA pellets were resuspended in diethylprocarbonate-treated water, and RNA concentrations were measured with a spectrophotometer (NanoDrop(tm) 2000, Thermo Fisher Scientific). The RNA was reverse-transcribed with a High-Capacity cDNA Reverse Transcription Kit (Applied Biosystems, Foster City, CA, USA) after a DNase treatment with a TURBO DNA-freeTM Kit (Thermo Fisher Scientific). The RNA samples without reverse transcriptase were reverse transcribed as negative controls for DNA contamination in the PCR analyses. Complementary DNA (cDNA) was amplified by real-time PCR using the TaqMan(r) Gene Expression Master Mix (Applied Biosystems, Foster City, CA, USA) and the StepOne machine (Applied Biosystem, Foster City, CA, USA), according to the manufacturer's protocols. The following TaqMan(r)-qPCR assays (Applied Biosystem) were employed: Glyceraldehyde-3-phosphate dehydrogenase, GAPDH (Hs99999905_m1); actin beta, ACTB (Hs99999903_m1); pumilio RNA binding family member 1, PUM1 (Hs00472881_m1); alkaline phosphatase, ALPL (Hs01029144_m1); bone gamma-carboxyglutamate protein, BGLAP (Hs01587814_g1); integrin binding sialoprotein, IBSP (Hs00173720_m1); interleukin 1 alpha, IL-1a (Hs00174092_m1); interleukin 6, IL-6 (Hs00985639_m1). A no-template control was included for each assay. The RT-qPCR reaction was conducted with a 10 min incubation period at 95 \({}^{\circ}\)C, 40 cycles of 95 \({}^{\circ}\)C amplification for 15 s and 60 \({}^{\circ}\)C for 1 min for annealing. GAPDH, ACTB and PUM1 were used as housekeeping genes to normalize the gene expression data of the target genes. The 2\({}^{-\Delta\Delta\text{Ct}}\) method was used to analyze relative changes in gene expression between samples in the form of fold changes.
## 3 Results
### Experimental Scenario Setup  
In this study, we designed annotation text scenarios using a self-learning approach to detect hate-speech in three-ways (very negative hate-speech, negative hate-speech, and non-hate-speech) as shown in Figure 2. The proposed model uses several machine learning methods to divide the dataset (see Table 1). This scenario aims to obtain optimal annotation predictions using minimal data. This scenario has different variations in the number of labeled datasets used and the threshold that identifies the limits of the similarity of the weights of the tested data.  
Significantly, our experimental design diverges from prior studies, such as those represented by Refs. [5,56]. In the experiment detailed in Ref. [5], a common scenario was employed with a training and testing proportion of 80:20. On the other hand, the experiment documented in Ref. [56] sought to enhance the quality of the scenario outlined in Ref. [5] by adjusting the proportions to 20:80:80 for training, labeled datasets, and testing, respectively. In contrast, our proposed research introduces novel scenarios characterized by minimal training data and distinct threshold values, as meticulously detailed in Table 1. This strategic departure is motivated by the quest for a more in-depth understanding of hate speech detection, mainly when limited training data and the model's sensitivity to similarity thresholds are explored.
###### Abstract  
The purpose of this research is to identify and correlate the referred pain evocated by myofascial trigger points (TrPs) pressure pain threshold (PPT) in the sternocleidomastoid muscle using thermal infrared imaging (IR). Facial IR images of 46 volunteers (21 male and 25 female, average age 32 \(\pm\) 6.3) undergoing PPT of five TrPs locations on the sternocleidomastoid belly were recorded. Each PPT lasted 10 s, with an interstimulus interval of 2 min. Sixteen thermal IR images were recorded for each subject: at baseline (10), 2 s before PPT (1), 2 s (12) and 60 s (13) after PPT of each TrPs location. During the interstimulus interval, subjects were asked to draw over a head-neck template displayed on a computer screen the areas of referred pain eventually evoked by the stimulation and the referred pain intensity by means of a Visual Analogue Scale (VAS). The VAS template was then superimposed with the IR records. Two temperature (T) variations were calculated: \(\Delta\)T\({}_{1}\) = T(t2) - T(t1) and \(\Delta\)T\({}_{2}\) = T(t3) - T(t1). Differences in AT range \(\geq\) 0.2 \({}^{\circ}\)C have been considered significant. In 77% of the superimpositions, the referred pain area corresponded to a \(\Delta\)T\({}_{2}\)\(\geq\) 0.2 \({}^{\circ}\)C while only the 59% corresponded to a \(\Delta\)T\({}_{1}\)\(\geq\) 0.2 \({}^{\circ}\)C. In 19% of superimpositions, a \(\Delta\)T\({}_{2}\)\(\geq\) 0.2 \({}^{\circ}\)C did not correspond to a referred pain area indicated by the patient, and this percentage lowers to 4% for \(\Delta\)T\({}_{1}\)\(\geq\) 0.2 \({}^{\circ}\)C. None of the areas that reported a VAS of 0 or 1 showed a \(\Delta\)T\({}_{1}\)\(\geq\) 0.2 \({}^{\circ}\)C or a \(\Delta\)T\({}_{2}\)\(\geq\) 0.2 \({}^{\circ}\)C. Considering the limitations of this pilot study, IR could be used to identify referred pain evocated by TrPs on sternocleidomastoid muscle.  
thermospraphy; myofascial pain syndromes; temporomandibular joint disorders; masticatory muscles; sternocleidomastoid
###### Abstract  
A general approach was developed to fabricate graphene/semiconducting single-wall carbon nanotube (graphene/s-SWCNT) film Schottky junctions on a large scale. The graphene/s-SWCNT film photodiodes array based on the vertically stacked Schottky junction were fabricated. The all-carbon cross-shaped structure consisted of multielement graphene/s-SWCNT Schottky photodiodes and presented a rich collection of electronics and photonics. The as-fabricated carbon-based photodiode presented an ultra-broadband photodetection characteristic with a high responsivity of 1.75 A/W at near-infrared wavelengths and a fast response rise time of 15 \(\upmu\)s. The as-fabricated device clearly showed gate-tunable and wavelength-dependent photoelectric characteristic. Moreover, the corresponding photocurrent excitation spectrum was also demonstrated. In particular, the Si compatible and high throughput fabrication process for the devices made it conducive for large-area multielement optoelectronics devices.  
graphene; semiconducting single-wall carbon nanotube; photodiode; Schottky; broadband
## 5 Discussion of Results and Conclusions  
Due to their ability to track complex visual patterns, powerful and widely used CNN algorithms are employed in the medical image analysis domain to address the problem of CAD diagnosis in nuclear cardiology. In this research study, two different deep learning classification approaches, namely, the RGB-based CNN model and the transfer learning-based CNN models (the benchmark CNN models pre-trained by ImageNet dataset), were adopted to identify perfusion defects through the use of SPECT MPI scans. The first classification approach is based on RGB-CNN algorithms, previously proposed for image classification in nuclear medicine regarding bone scintigraphy. The second approach utilizes transfer learning incorporated in well-known deep learning architectures. The provided dataset, comprising stress and rest images from 224 subjects, is employed to assess the proposed models with respect to their performance. The problem was formulated as a two-class classification problem. For an in-depth assessment of the results, a comparative analysis regarding the classification performance of the proposed model against that of other CNNs reported in the literature is performed (even indirectly) in the examined field of CAD diagnosis in nuclear medicine, using solely SPECT-MPI images. A decent amount of relevant research studies in this scientific field was gathered in Introduction and presented in Table 5, followed by the classification accuracies and evaluation metrics of the respective models. As regards the previous works of [50; 51; 52], where polar map images were used for CAD classification, deep CNNs and graph-based CNNs were employed for normal/abnormal classification. These are not related to this research study and provide classification accuracies up to 91%. Figure 12: Comparison of loss (validation and testing) for all performed CNNs. Figure 13: AUC score values for all performed CNNs.
## 3 Key Technologies of UAV-Assisted SAGINs
### Deployment of UAVs
#### 3.5.4 Energy-Saving Deployment  
Frequent UAV deployments incur significant costs, making deployment cost reduction a critical aspect of SAGIN energy optimization. UAV deployment involves identifying optimal aerial access point (AAP) locations. For single-UAV deployment, [PERSON] et al. [139] designed a comprehensive optimization model integrating AAP location, channel allocation, and power, proposing a heuristic algorithm based on the maximum weighted independent set to optimize AAP locations. [PERSON] et al. [140] studied UAV broadcast coverage strategies to maximize energy efficiency by optimizing AAP locations through user grouping strategies derived from the traveling salesman problem (TSP). For multi-UAV deployments, [PERSON] et al. [141] proposed an energy-driven optimal deployment strategy, achieving a balance between ground flight distance and flight altitude.
## 3 Results
### Performance with Three Dimensional Effects  
In the proposed technique, the beam passes through a magnetic chicane that may introduce CSR, incoherent synchrotron radiation (ISR) and transverse emittance growth. To study these effects, simulations are carried out by combined use of the codes of ELE  
Figure 10: (**a**) The evolutions of power and bunching factor at 30 THz along the undulator; (**b**) the final spectrum at 30 THz; (**c**) the evolutions of power and bunching factor at 60 THz along the undulator; (**d**) the final spectrum at 60 THz.  
Figure 9: (**a**) The phase space after the dispersion section; (**b**) the current profile after the dispersion section.  
GANT [54] and GENESIS. GENESIS is utilized to perform the simulations in undulators, including the energy modulation and radiation processes, while the density modulation simulation is performed by the ELEGANT, considering the CSR, ISR, and longitudinal space charge effects. The length of each dipole is 1 m, the drift section between the first two dipoles is 3 m and the distance between the middle two dipoles is 0.5 m. As an example, we consider the case of THz radiation at 11.5 THz with a larger R56 of 11 mm, the corresponding bending angle is about 2.8 degree. Figure 11 shows the phase space and current profile at the exit of chicane, where one can find that the density modulation can be well preserved. Similarly, the bunched beam is sent into the undulator to generate intense THz radiation. The peak power and bunching factor evolutions along the undulator and the final spectrum are presented in Figure 11c,d, respectively. The final peak power with three-dimensional effects is lower than that without three-dimensional effects, which is due to the CSR-induced relative energy difference.
# The Influence of Pillar Displacements on Geodetic Measurements
## 1 Introduction
### Linearization  
The relations between the geodetic measurements and other parameters are usually described by non-linear functions. For practical applications, non-linear functions are often (at least locally)  
Figure 3: Polar measurement method.  
approximated by the linear ones. Starting from a Taylor series expansion [11] of a non-linear function  
\[\mathbf{y}=\mathbf{F}(\mathbf{x})=\left[\begin{array}{c}F_{1}(\mathbf{x})\\ \vdots\\ F_{n}(\mathbf{x})\end{array}\right]=\left[\begin{array}{c}F_{1}(x_{1},x_{2}, \ldots x_{u})\\ \vdots\\ F_{n}(x_{1},x_{2},\ldots x_{u})\end{array}\right]\text{about the point }\mathbf{x}_{0}\text{:}\]  
\[\mathbf{y}=\mathbf{F}(\mathbf{x}_{0})+D\mathbf{F}|_{\mathbf{x}=\mathbf{x}_{0}}(\mathbf{x}-\mathbf{x}_{0}) +\frac{1}{2!}(\mathbf{x}-\mathbf{x}_{0})^{T}D^{2}\mathbf{F}|_{\mathbf{x}=\mathbf{x}_{0}}(\mathbf{x} -\mathbf{x}_{0})+\cdots, \tag{6}\]  
where \(D\mathbf{F}=\left[\begin{array}{ccc}\frac{\partial\mathbf{F}_{1}}{\partial \mathbf{x}_{1}}&\cdots&\frac{\partial\mathbf{F}_{1}}{\partial\mathbf{x}_{n}}\\ \vdots&\ddots&\vdots\\ \frac{\partial\mathbf{F}_{n}}{\partial\mathbf{x}_{1}}&\cdots&\frac{\partial \mathbf{F}_{n}}{\partial\mathbf{x}_{n}}\end{array}\right]\) denotes the Jacobian matrix of first-order partial derivatives, \(D^{2}\mathbf{F}\) denotes the Hessian matrix of second order partial derivatives, while in higher-order terms, the higher order partial derivatives are present.  
When we neglect the higher-order terms and take only the linear part:  
\[\overline{\mathbf{y}}=\mathbf{F}(\mathbf{x}_{0})+D\mathbf{F}|_{\mathbf{x}=\mathbf{x}_{0}}(\mathbf{x} -\mathbf{x}_{0}), \tag{7}\]  
we get a linear function, which coincides with the original one at \(\mathbf{x}_{0}\) and represents an approximation of the original one \(\mathbf{y}=\mathbf{F}(\mathbf{x})\). Note that the error of approximation is strongly dependent on the distance between \(\mathbf{x}\) and \(\mathbf{x}_{0}\) and that in general such approximation makes sense only in a close neighbourhood to \(\mathbf{x}_{0}\).
## 3 Experiment and Application
### Performance Analysis: Classic Benchmark Functions  
To illustrate the relative success of APO on classic benchmark functions, a series of 23 widely used benchmarks selected from References [62; 63; 64; 65; 66; 67] were introduced in this test. Tables 2 and 3 present the detailed information about the different types of benchmarks. Our APO algorithm was compared with four well-known nature-based algorithms: GWO, WOA, PSO, and DE. \begin{table}
\begin{tabular}{l l l} \hline
**Setting:**_low_, \(up\), \(N\), \(D\), \(ObjFun\), and \(T\) \\  & Initial population according to Equation (1) \\  & Calculating fitness function of population by \(fit(Pop_{i})=ObjFun(Pop_{i})\) \\  & Finding the leading duck \(Pop_{best}\) \\  & \(t=1\) \\  & _while_ (\(t<=T\)) \\  & Calculating the \(Pc\) according to Equation (2)/warming behavior \\  & _for_\(i\) from \(1\) to \(N\)_do_ \\  & _if_\(rand<Pc\) \\  & Updating position according to Equation (3) \\  & _end if_ \\  & Updating parameters \(A\) and \(C\) according to Equations (8) and (9)/moving process \\  & Updating position according to Equation (7) \\  & _if_\(fit(Pop_{i})\_new>fit(Pop_{i})\_old\) /take the minimum as an example \\  & Selecting another particle \(Pop_{rand}\) at random \\  & _if_\(fit(Pop_{i}>fit(Pop_{rand}))\) \\  & Updating position according to Equation (11) \\  & _elseif_\(fit(Pop_{i}=fit(Pop_{rand}))\) \\  & Keeping unchanged \\  & _elseif_\(fit(Pop_{i}<fit(Pop_{rand}))\) \\  & Updating position according to Equation (12) \\  & _end if_ \\  & _end for_ \\  & Finding the leading duck \(Pop_{best}\) \\  & \(t=t+1\) \\  & _end while_ \\  & _return Popest_ \\ \hline \end{tabular}
\end{table} 
Table 1: Pseudo code of the anas platyrhynchos optimizer (APO). \begin{table}
\begin{tabular}{l l l l} \hline
**Test Function** & **D** & **Range** & **Optimum** \\ \hline \(f_{1}(x)=\sum_{i=1}^{n}x_{i}^{2}\) & 30 & [\(-\)100,100] & 0 \\ \hline \(f_{2}(x)=\sum_{i=1}^{n}\lvert x_{i}\rvert+\prod_{i=1}^{n}\lvert x_{i}\rvert\) & 30 & [\(-\)10,10] & 0 \\ \hline \(f_{3}(x)=\sum_{i=1}^{n}\bigl{(}\sum_{i=1}^{i}x_{j}\bigr{)}^{2}\) & 30 & [\(-\)100,100] & 0 \\ \hline \(f_{4}(x)=max_{i}\lvert\lvert x_{i}\rvert,1\leq i\leq n\rvert\) & 30 & [\(-\)100,100] & 0 \\ \hline \(f_{5}(x)=\sum_{i=1}^{n-1}\left[100\bigl{(}x_{i+1}-x_{i}^{2}\bigr{)}^{2}+(x_{i} -1)^{2}\right]\) & 30 & [\(-\)30,30] & 0 \\ \hline \end{tabular}
\end{table} 
Table 2: High-dimensional benchmark functions used in Test \(1\).