esa-sceva/satcom-chunk-collection huggingface dataset:
# Analytical Investigation of a Novel System for Combined Dew Point Cooling and Water Recovery
## 3 Results
### Performance of the DPC-WR System under Different Climatic Conditions
The performance indicators SEC and SDWP are calculated for different climatic conditions and presented in Figures 6 and 7, respectively. It can be seen that SEC is lowest in climatic zones where COP and GOR are highest, and SEC is highest where COP and GOR are lowest. The lower the value of SEC, the less energy is required to produce 1 m\({}^{3}\) of water, which means that in climatic zones such as semi-arid, hot desert, and subtropical, the highest DPC-WR system efficiency will be achieved. The same applies to SDWP. The highest SDWP, i.e., the highest water production per unit of air supplied to the system, is obtained in those zones where SEC is lowest, and the least where SEC was highest. One important thing to note is that SDWP is higher than 100 L/day/(kg/s) in all climatic zones. This means that even in subpolar or subarctic climatic zones, where the ambient air temperature is low, it is possible to produce no less than 100 L of water per day (from 1 kg/s of air), with an energy consumption below 2 kWh/m\({}^{3}\). On the other hand, in the most favorable climatic zones, where both COP and GOR are highest, SDWP exceeds 600 L/day/(kg/s), with an energy consumption less than 0.5 kWh/m\({}^{3}\). Figure 4: Performance indicator coefficient of performance (COP) calculated for different climatic zones [25]. As it can be noted, there are climatic zones where the effectiveness of the DPC-WR system will be exceptionally high. These are zones where air temperature is high and humidity ratio is low. Nevertheless, it is also important to notice that even in less favorable climatic zones where either the air temperature is low or humidity ratio is high, combined with the DPC-WR system, the DPC-WR system will be exceptionally high. These are zones where air temperature is high and humidity ratio is low. Nevertheless, it is also important to notice that even in less favorable climatic zones where either the air temperature is low or humidity ratio is high, combined with the DPC-WR system will be exceptionally high. Figure 5: Performance indicator gained output ratio (GOR) calculated for different climatic zones [25]. Figure 6: Performance indicator specific energy consumption (SEC) calculated for different climatic zones [25]. Figure 7: Performance indicator specific daily water production (SDWP) calculated for different climatic zones [25]. cooling and water recovery with DPC-WR is still possible with acceptable effectiveness. This is also confirmed by the EUF performance indicator calculated for different climatic zones (Table 3). The EUF value ranges from 0.76 to 0.96, with an average of 0.90. Such values of EUF prove the high efficiency of utilizing the energy supplied to the DPC-WR system and make it a promising technology that can be applied in the wide range of climatic conditions.
## 2 Experimental
### Characterization  
Fourier transform infrared (FTIR) spectroscopy (NICOLET, iS10, Thermo-Scientific) was used to confirm the structure of the prepared samples. FT-Raman spectra were estimated by a Bruker FT-Raman spectrometer of type RFS 100/S that is attached to a high resolution (better than 0.10 cm\({}^{-1}\)) Bruker-IFS 66/S spectrometer. The morphologies of RFX-Cs composite gels were observed with a FEI Nova(tm) nanoscanning electron microscopy 450 (Nova NanoSEM). CHNS/O, Thermo Scientific FLASH 2000 Organic Elemental Analyzer (Melano, Italy) was used to determine the elemental compositions of the samples. Thermogravimetric analyses (TGA) were carried out using a PerkinElmer Pyris6 TGA analyzer under a flow of N\({}_{2}\) gas in the range of 30 to 800 \({}^{\circ}\)C, with a heating rate of 10 \({}^{\circ}\)C/min. X-ray diffraction (XRD) measurements were conducted by Miniflex II Benchtop XRD apparatus, manufactured by Rigaku Corporation, Japan. The 20 scan data were collected at 0.05\({}^{\circ}\) intervals over the range of 5 to 90\({}^{\circ}\), and at a scan speed of 0.05\({}^{\circ}\)/min. A Micromeritics ASAP2420\({}^{\otimes}\) accelerated surface area and porosimetry analyzer system, with an enhanced micropore capability (utilizing 1-Torr pressure transducer), was used to measure the pore structures of RFX-Cs-0, RFX-Cs-1, RFX-Cs-2, RFX-Cs-3, RFX-Cs-4, and RFX-Cs-5 samples using the adsorption isotherms of N\({}_{2}\) at 77 K. Prior to the adsorption measurements, the samples were regenerated in-situ for 24 h at a temperature of 473 K under vacuum (\(1\times 10^{-4}\) Pa). The pore structure properties were obtained by built-in calculations based on the density functional theory (DFT) [24].  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \(x\) & **R** & **F** & **W** & **C** & **Cs** & **Acetic Acid** \\ \hline
0 & 18.585\% & 32.676\% & 48.703\% & 0.036\% & 0\% & 0\% \\
1 & 18.528\% & 32.575\% & 47.064\% & 0.036\% & 0.007\% & 1.790\% \\
2 & 18.471\% & 32.475\% & 45.434\% & 0.036\% & 0.015\% & 3.569\% \\
3 & 18.414\% & 32.375\% & 43.815\% & 0.036\% & 0.022\% & 5.338\% \\
4 & 18.358\% & 32.277\% & 42.205\% & 0.035\% & 0.029\% & 7.095\% \\
5 & 18.302\% & 32.178\% & 40.606\% & 0.035\% & 0.037\% & 8.842\% \\ \hline \hline \end{tabular}
\end{table}
Table 1: Composition (wt.%) of starting solutions for the RFX–Cs-”x” gel samples.
## 4 Technology Evaluation Criteria
### Scalability and Number of Users  
Given definition:  
Ability of the system to increase its coverage area and the number of users/devices the system can support.  
Additional commentary:  
Within the changing and expanding environment as well as rise in applications, the extension in space and number of users shall be considered in combination.  
Suggested scale:  
As a scale, we propose the (dynamic) expendability with no or limited additional installations and network capabilities to maintain data rate per device.  
1. Expansion connected to deployment of hardware and/or infrastructure with centralized hub, lowering possible data rate per device and user
2. Expansion connected to deployment of hardware and/or infrastructure with multiple hubs expanding the network
3. Self-expansion with mesh functionality of network by decentralized nodes, rarely lowering data rate per device and user
# Validation of Cable-Driven Experimental Setup to Assess Movements Made with Elbow Joint Assistance
## 2 Methodology  
The primary goal of this research is to test an experimental setup that mimics a cable-driven exosuit for elbow joint actuation with various locations of anchor points relative to the joint. Several studies on the design of cable-driven elbow actuation systems focus on optimizing the cable routing for elbow actuation [35,36]. These studies aim to optimize the anchor point positions around the axis of the forearm and upper arm while keeping the anchor point distance from the elbow joint fixed. [PERSON] et al. studied the human-machine interaction forces by varying cable tension and keeping the distance of the anchor point from the elbow fixed [37]. These studies do not consider the effect of the anchor point position with respect to the elbow joint. Studying the effect of the distance of the anchor point from the elbow joint on a simple mechanism rather than having multiple cables around the axis of the upper arm and forearm highlights the effect of the anchor of the point position explicitly. This allows for an understanding of the importance of the anchor point position from the elbow joint during elbow actuation. The focus is to measure the cable tension (N) and to determine perceived reaction forces (N) at the joint and moments (Nm) at the anchor points for various joint angles and masses. Through the development of an experimental setup, this work aims to address existing gaps in the literature related to anchor point positioning and its influence on the performance of the exosuit. By employing a static model and a practical experiment, this study will test the forces and moments associated with various joint angles. Further, the research also investigated the determination of the optimal anchor point configurations to enhance human comfort, design, safety, and the overall efficiency of the exosuit, making it suitable for rehabilitation applications in the future.
## 3 Preliminaries
### Extended Chebyshev Chaotic Maps  
**Definition 1** (Chebyshev Chaotic Maps).: _Let \(n\) be an integer, and \(x\in[-1,1]\), the Chebyshev polynomial, denoted by \(T_{n}(x)\), maps the interval \([-1,1]\) to itself and is defined as follows:_  
\[T_{n}(x)=\cos(n\times\cos^{-1}(x))\]  
The recurrence formula defining the Chebyshev polynomial is as follows:  
\[T_{n}(x)=2 xT_{n-1}(x)-T_{n-2}(x)\]  
where \(n\geq 2\), \(T_{0}(x)=1\), and \(T_{1}(x)=x\).  
**Definition 2** (Semigroup Property).: _The Chebyshev polynomials are closed under the semigroup operation, which is given as \[T_{a}(T_{b}(x))=\cos\Bigl{(}a\times\cos^{-1}\Bigl{(}\cos\Bigl{(}b\times\cos^{-1}(x )\Bigr{)}\Bigr{)}\Bigr{)}=\cos\Bigl{(}ab\times\cos^{-1}(x)\Bigr{)}=T_{ab}(x)\]  
_where \(a,b\) are integers and \(x\in[-1,1]\)._  
Chebyshev polynomials also satisfy the semigroup property over the entire real line \((-\infty,\infty)\). The extended form of the Chebyshev polynomial is expressed as  
\[T_{n}(x)=2 xT_{n-1}(x)-T_{n-2}(x)\text{ mod }p\]  
where \(x\in(-\infty,\infty)\) and \(p\) is a large prime number.  
**Definition 3** (Chaotic Maps Discrete Logarithm Problem--Cmdlp).: _Given a Chebyshev polynomial and a real number \(x\in[-\infty,\infty]\), the CMDLP states that it is computationally infeasible to determine the value a such that \(y=T_{a}(x)\). In other words, the advantage of a probabilistic polynomial-time (PPT) adversary \(A\) in solving CMDLP within time \(t\) is defined as \(\mathrm{ADV}_{A}^{\mathrm{CMDLP}}(t)=\Pr\Bigl{[}A(x,y)=a:a\in\mathbb{Z}_{p}^{*},y=T_{a}(x)\text{ mod }p\Bigr{]}\), which is negligible. That is, \(ADV_{A}^{\mathrm{CMDLP}}(t)\) of a PPT adversary \(A\) within a time bound \(t\), to solve CMDLP, is negligible. That means \(ADV_{A}^{\mathrm{CMDLP}}(t)\leq\epsilon\), where \(\epsilon\) denotes a negligible function._
## 2 Numerical Modeling
### Governing Equations  
One solves the 3D conservative equations for mass, momentum and energy in their 3D unsteady formulation. The Boussinesq approximation is used to account for buoyancy effects [25; 26]:  
\[\rho\ =\ \rho_{0}(1-\beta((T-T_{0}))). \tag{1}\]  
The continuity equation writes:  
\[\
abla\cdot\overrightarrow{V}\ =\ 0. \tag{2}\]  
The momentum equation reads:  
\[\frac{\partial V}{\partial t}+\overrightarrow{V}.\
abla\overrightarrow{V}\ =\ \frac{1}{\rho}\Big{(}-\
abla P+\mu\
abla^{2} \overrightarrow{V}+\rho\overrightarrow{g}\beta\Big{(}T-T_{ref}\Big{)}\Big{)} +\overset{\rightarrow}{S}. \tag{3}\]  
The energy equation does not include the viscous dissipation, which is negligible in the present case:  
\[\frac{\partial h_{sens}}{\partial t}+\frac{\partial h_{lat}}{\partial t}+ \
abla\Big{(}\overrightarrow{V}h_{sens}\Big{)}\ =\ \
abla\left(\frac{k}{\rho c_{p}}\
abla h_{sens}\right). \tag{4}\]  
The total enthalpy can be obtained by:  
\[h_{tot}\ =\ h_{sens}+h_{lat}, \tag{5}\]  
\begin{table}
\begin{tabular}{l c c} \hline \hline  & \multicolumn{2}{c}{**Pure Water**} \\ \cline{2-3}
**Property** & \multicolumn{2}{c}{**Phase**} \\ \cline{2-3}  & **Liquid** & **Solid** \\ \hline Density (\(\rho\)) [kg/m3] & 999.8 & 917 \\ Dynamic viscosity (\(\mu\)) [kg/(m.s)] & 0.00162 & - \\ Specific heat (cp) []/(kg.K)] & 4180 & 2217 \\ Thermal conductivity (k) [W/(m.K)] & 0.578 & 1.918 \\ Heat of fusion (hsf) []/kg] & 334,000 & \\ Solidification Temperature [K] & 273.15 & \\ Thermal expansion coefficient (\(\beta\)) [K\(-\)1] & \(-6.733353\times 10^{-5}\) & - \\ \hline \hline \end{tabular}
\end{table}
Table 1: Physical and thermal properties of liquid and solid water [9; 24].  
Figure 2: Three types of tubes considered in the present paper: (**a**) U-shaped tube, (**b**) coil tube model 1, and (**c**) coil tube model 2.  
where:  
\[h_{sens}\ =\ h_{ref}+\int\limits_{T_{ref}}^{T}c_{p}dT\ =\ h_{ref}+c_{p}\int\limits_{T_{ref}}^{T}dT, \tag{6}\]  
\[h_{lat}\ =\ \lambda\.\text{L} \tag{7}\]  
where h\({}_{lat}\) can be in the range of zero and h\({}_{sf}\), for the solid and liquid phases respectively. \(\lambda\) indicates the liquid fraction. L is the latent heat of fusion. The sensible heat can be written as:  
\[h_{sens}\ =\ h_{tot}-h_{lat}. \tag{8}\]  
The equation for the liquid fraction writes [27]:  
\[\lambda\ =\ \left\{\begin{array}{cccc}&\frac{h_{lat}}{h_{sf}}\ =\ 0\ \ if\ \ T\leq T_{s}\\ &\frac{h_{lat}}{h_{sf}}\ =\ 1\ \ if\ \ T\geq T_{liq}\\ &\frac{h_{lat}}{h_{sf}}\ =\ \frac{T-T_{s}}{T_{liq}-T_{s}}\ \ if\ \ T_{s}<T<T_{liq}\end{array} \right\}. \tag{9}\]  
A temperature difference of 0.1 K is assumed between the liquidus and solidus temperatures. The term \(S\) in Equation (3) is the Darcy's law damping source term, which affects the momentum equation:  
\[\stackrel{{\rightarrow}}{{S}}\ =\ -\frac{(1-\lambda)^{2}}{\lambda^{3}}C _{mush}\stackrel{{\rightarrow}}{{V}}, \tag{10}\]  
where C\({}_{mush}\) is the constant of the mushy zone, which was set to 10\({}^{5}\) kg/(m\({}^{3}\)s) [26].
## 4 Performance Comparison
### Signaling Cost
#### Signaling cost for KITE operation  
Equation (1) describes the signaling cost for the KITE operation, while Table 6 shows the parameter setup.  
\[\begin{split}\text{SC}_{\text{Kite}}=\text{C}_{\text{prodAR}}+ \text{C}_{\text{accessrouter}}+\text{C}_{\text{accessrouter}}+\text{C}_{ \text{prodAR}}+\text{C}_{\text{consumerAR}}+\text{C}_{\text{accessrouter}}+ \text{C}_{\text{prodAR}}\\ =\text{S}_{\text{Interest/data}}\times(2\text{a}+2\text{c})+\text{S}_{ \text{Interest}}\times(2\text{a}+2\text{c})\end{split} \tag{1}\]
## 2 Fluid Model, Plasma Chemistry, and Reactor Configuration
### ICP Reactor Configuration  
The ICP reactor used consists of the vacuum chamber (also called matching box) and the discharge chamber, which are separated by the dielectric window. The discharge chamber is 15 cm in radius and 13 cm in height. The dielectric window and vacuum chamber hold the same radius as the discharge chamber and the heights of them are 1 cm and 3 cm, respectively. A substrate with a radius of 12 cm and a height of 4 cm is seated at the bottom center of discharge chamber. A two-turn coil is installed above the dielectric window, with radial locations of 8 cm and 10 cm, respectively. The coil is square in cross section, with a side length of 0.6 cm. More details on the ICP reactor can be found in Ref. [39] and Figure 1.  
Figure 1: Simulated two-dimensional profiles of (**a**) electron density, (**b**) Ar\({}^{+}\) density, (**c**) O\({}^{-}\) density, and (**d**) plasma potential in Ar/O\({}_{2}\) plasma, by means of fluid model and at the discharge conditions of 300 W, 30 mTorr, and 10% O\({}_{2}\) content.
# Bidirectional Atmospheric Channel Reciprocity-Based Adaptive Power Transmission
## 2 Theoretical Analysis
### Adaptive Power Transfer System
In summary, the adaptive power technique proposed in this paper can effectively mitigate the effects of noise in bidirectional atmospheric channels.
# Detection of Abnormal Symptoms Using Acoustic-Spectrogram-Based Deep Learning
###### Abstract  
Acoustic data inherently contain a variety of information, including indicators of abnormal symptoms. In this study, we propose a method for detecting abnormal symptoms by converting acoustic data into spectrogram representations and applying a deep learning model. Spectrograms effectively capture the temporal and frequency characteristics of acoustic signals. In this work, we extract key features such as spectrograms, Mel-spectrograms, and MFCCs from raw acoustic data and use them as input for training a convolutional neural network. The proposed model is based on a custom ResNet architecture that incorporates Bottleneck Residual Blocks to improve training stability and computational efficiency. The experimental results show that the model trained with Mel-spectrogram data achieved the highest classification accuracy at 97.13%. The models trained with spectrogram and MFCC data achieved 95.22% and 93.78% accuracy, respectively. The superior performance of the Mel-spectrogram model is attributed to its ability to emphasize critical acoustic features through Mel-filter banks, which enhances learning performance. These findings demonstrate the effectiveness of spectrogram-based deep learning models in identifying latent patterns within acoustic data and detecting abnormal symptoms. Future research will focus on applying this approach to a wider range of acoustic domains and environments. The results of this study are expected to contribute to the development of disease surveillance systems by integrating acoustic data analysis with artificial intelligence techniques.  
abnormal symptom; acoustic spectrogram; Mel-spectrogram; MFCC; convolutional neural networks (CNNs) 2025154679  
1
# Advancing TinyML in IoT: A Holistic System-Level Perspective for Resource-Constrained AI
## 3 Key Challenges in AI for Constrained Devices
### Model Generalization vs. Specialization  
Another persistent dilemma is deciding between highly specialized models tailored to a single task and more generalized models that can handle multiple tasks or adapt to new conditions. Specialized models tend to be smaller and more efficient, since they incorporate only the parameters needed for a specific domain [29]. This approach can be ideal for devices that serve well-defined functions, such as a vibration sensor used exclusively for bearing fault detection. However, if the use case evolves--say, the sensor must also detect temperature anomalies--then a specialized model might not transfer well to the new requirement [63].  
On the other hand, more generalized architectures can support multiple tasks but often at the cost of increased parameter counts and complexity, which can be prohibitive for constrained environments [64]. Transfer learning or incremental learning techniques that adapt a pre-trained model to new tasks on-device offer potential workarounds, but they introduce additional inference or fine-tuning overhead that might exceed the device's resource budget [32]. Researchers continue to explore ways to construct models that are flexible yet compact, possibly through dynamic neural networks that can switch off unused modules or layers to meet different objectives [65].
## 4 Measurements and Experimental Results  
### Feature Selection Results  
From the balanced data, the three feature extraction methods each retained the following number of variables: LGR retained 9 variables; C5.0 DT retained 8 variables; and LGR + C5.0 DT retained 11 variables. The variables retained for the three feature extraction methods are listed in Table 3.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Technique** & **Parameter** & **Value/Range** & **Configuration** \\ \hline \multirow{3}{*}{BPNN} & Learning rate & 0.1–1 & 0.9 \\  & Number of hidden units & 1–2 & 2 \\  & Number of learning sample & 9–18 & 18 \\ \hline C5.0 & Minimum number of instances per leaf & & 10 \\ \hline SVM & Kernel & RBF kernel & C:10 \\  & & Gamma:0.1 \\ \hline \end{tabular}
\end{table}
Table 2: Parameter settings in each model.
# An NFC on Two-Coil WPT Link for Implantable Biomedical Sensors under Ultra-Weak Coupling
## 2 Dual-Carrier NFC System
### Application Requirements on Coils  
In the intraocular sensor device, the coil sizes are limited by the application requirements and are decided by the doctors from our cooperative hospital. To avoid blocking the eyesight, the in-body coil is designed to surround the periphery of the cornea hidden behind the eyelid. The out-body coil will be embedded inside the glasses frame in front and around the eye that suffers from glaucoma. Table 1 lists the parameters of the coils we use. The coil parameters are optimized for a higher PTE with sufficient power delivered to load (PDL) under the strict constraints on their size in [21].  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & \(\mathbf{L_{\text{o}}}\) & \(\mathbf{L_{\text{i}}}\) \\ \hline Coil Wire Type & Litz wire with 11 strands of 30 AWG & Enameled copper \\ Wire Diameter & 0.8 mm & 0.06 mm \\ Coil Diameter & 40 mm & 20 mm \\ Coil Thickness & 4.5 mm & 0.12 mm \\ Number of Turns & 30 & 35 \\ Coil Inductance & 68.2 \(\mu\)H a  & 65.5 \(\mu\)H a  \\ Equivalent Series Resistance & 8.5 \(\Omega\) a  & 21.2 \(\Omega\) a  \\ Quality Factor & 100.8 a  & 38.8 \({}^{\text{a,b}}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameters of The Coils.  
The separation (\(d\)) between the coils could vary from 10 mm to 40 mm due to the variations of the patient facial forms. Accordingly, the coupling coefficient (\(k\)) varies from 0.1 to 0.01 typically in our sensor system, which is considered as the operating range of our link.
## 2 Materials and Methods
### Research Methods
#### 2.6.1 Interpolation Method  
1. Variance Function  
The variance function is also called the structure function, and one second of it is named as semi-variance. It is a basic tool and a special function of geostatistics. Variance function captures not only the structural variation in regionalized variables but also expresses their persistent variation. Modeling of variance function is a key environment between spatial description and prediction [34]. The variance function models are classified into abutment-valued models, abutment-free models, and pore effect models. In this study, we used the GS+ software to fit the optimal semi-variance model of each parameter.  
\[\gamma(\text{h})=\frac{1}{2N(h)}\sum_{i=1}^{N(h)}[Z(x_{i})-Z(x_{i}+h)]^{2} \tag{3}\]  
where \(\gamma(\text{h})\) is the variance function, \(N(h)\) is the spatial separation distance between two points, \(Z(x_{i})\) and \(Z(x_{i}+h)\) are the regionalized variables, and \(Z\) is the measured values at the spatial locations \(x_{i}\) and \(x_{i}+h\).  
2. Kriging Interpolation  
The Kriging interpolation uses the structural features of sampled data and semi-variance functions of regionalized variables to provide unbiased estimation, and to determine the weighting coefficients at unsampled points according to minimum variance. And it is one of the most widely used interpolation methods. The data of other unmeasured positions are estimated using different sample data distributed in space [35].  
\[Z_{v}^{*}(x_{0})=\sum_{i=1}^{m}\lambda_{i}Z(x_{i}) \tag{4}\]  
Figure 4: Technology roadmap.  
where \(x_{i}\) is the location of any point in the study area, and \(\lambda_{i}\) is the weight factor. The Kriging equations are listed according to the unbiased and optimal conditions, and then the Kriging weight coefficient is obtained by calculating the Kriging equations. \(Z_{v}^{*}(x_{0})\) is the Kriging estimation results, \(Z(x_{i})\) is the sample points with known values, and n is the amount of GEDI footprints.