esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Design and Analysis of DL-LET
### Stiffness Modeling  
In order to find the equivalent spring constant of DL-LET joints (\(k_{\rm eq,bend}\)), the DL-LET joint can be simplified as a spring system. Since the H18 aluminum foil is soft and its thickness is much smaller than beryllium bronze, when considering the bending stiffness of the DL-LET joint, the H18 aluminum foil is regarded as a bending segment in parallel with the LET joint. Figure 3 shows the linear spring model of the DL-LET joint.  
Figure 1: Schematic of a DL-LET joint.  
Figure 2: Dimension of a DL-LET joint.  
The equivalent stiffness of the DL-LET joint can be derived from the series-parallel connection relationship of the spring, thus \(k_{\text{eq,bend}}\) is,  
\[k_{\text{eq,bend}}=\frac{k_{\text{t1}}k_{\text{b2}}+2k_{\text{t1}}k_{\text{b1}}+2 k_{\text{b1}}k_{\text{b2}}}{2k_{\text{b1}}+k_{\text{t1}}} \tag{1}\]  
In Equation (1), \(k_{\text{b1}}\) and \(k_{\text{b2}}\) represent the equivalent spring stiffness of the bending segments, respectively, and \(k_{\text{t1}}\) represents the equivalent spring stiffness of each torsion segment.  
As for bending segments, the deformation can be regarded as the maximum deflection produced by the free end of the cantilever beam under torque, so each bending segment's equivalent spring constant can be found by,  
\[k_{\text{b1}}=\frac{E_{\text{1}}k_{\text{b1}}}{l_{\text{b1}}} \tag{2}\]  
\[k_{\text{b2}}=\frac{E_{\text{2}}k_{\text{b2}}}{l_{\text{b2}}} \tag{3}\]  
where \(E_{\text{1}}\) and \(E_{\text{2}}\) refer to [PERSON]'s modulus of beryllium bronze and H18 aluminum foil, respectively, and \(l_{\text{b1}}\) and \(l_{\text{b2}}\) represent the moment of inertia.  
As for torsion segments, each spring constant can be determined by,  
\[k_{\text{t1}}=\frac{KG_{\text{1}}}{l_{\text{t1}}} \tag{4}\]  
in which \(K\) is a coefficient related to the cross-sectional geometry of the torsion segments [34]:  
\[K=\frac{1.17w_{t}^{2}+2.191 tw_{t}+1.17t^{2}}{w_{t}^{2}+2.609 tw_{t}+t^{2}}\times \frac{2t^{3}w_{t}^{3}}{7t^{2}+7w_{t}^{2}} \tag{5}\]  
\(G_{\text{1}}\) is the shear modulus,  
\[G_{\text{1}}=\frac{E_{\text{1}}}{2(1+v_{\text{1}})} \tag{6}\]  
and \(v_{\text{1}}\) is the Poisson's ratio. Equation (5) is thickness-to-width ratio independent and suitable for variable cross-section beams and optimization design of torsional elements in compliant mechanisms [24].  
The H18 aluminum foil layer can be seen as a thin flat material, and it plays a major role during the tensile process. With the increase of tensile, the deformation of DL-LET in the \(x\)-direction is minimal to be ignored.
# Analytical Investigation of a Novel System for Combined Dew Point Cooling and Water Recovery
## 2 Materials and Methods
### Mathematical Model of the DPC-WR System  
The DPC-WR system is described by an analytical model based on the convective heat transfer coefficient. Each stage is described by separate models with different initial conditions. All the equations are ordinary differential equations described in an orthogonal coordinate system. The numerical procedure used to solve the differential equations is executed in the MatLab environment with the Ordinary Differential Equations Solver (ode15s). The general assumptions of the model are listed below [20; 21; 22]:  
* Steady-state operation;
* Airflow is modeled as an ideal gas;
* Properties of water and air streams are equal to bulk averaged values;
* Thermal conductivity along channel length through plate and water is negligible;
* Heat losses to the surroundings are neglected;
* Mass transfer of water vapor is driven by partial pressure gradient.
## 2 Materials and Methods
### Experimental Techniques
#### 2.5.1 Thermogravimetric Analyses and Differential Thermogravimetry (TGA/dTG)  
Thermogravimetric analyses were carried out using a TGA92 instrument (Setaram, Caluire, France). Approximately 20 mg of each sample were placed in an alumina crucible and heated at a rate of 10 \({}^{\circ}\)C/min from 20 \({}^{\circ}\)C to 1000 \({}^{\circ}\)C under 100 mL/min flow of inert nitrogen gas. Mass change, differential mass change and heat flow measurements were recorded and analyzed using the built-in software. The differential thermogravimetric curve (dTG) was derived by the TG curve. The first derivative curve was produced for the various samples tested and was used for comparisons instead of the mass loss curve, as it yields sharp distinctive peaks.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Sample** & **PC (\%)** & **LS (\%)** & **FA (\%)** & **\(\upmu\)S (\%)** & **w/b** \\ \hline PC60 LS20 FA20 & 60 & 20 & 20 & 0.0 & 0.3 \\ PC55 LS20 FA20+5\%\(\upmu\)S & 55 & 20 & 20 & 5.0 & 0.3 \\ PC50 LS20 FA20+10\%\(\upmu\)S & 50 & 20 & 20 & 10 & 0.3 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Quaternary \(\upmu\)S enhanced cement paste formulations—Mix Proportions % by total mass of binder.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Sample** & **PC (\%)** & **LS (\%)** & **FA (\%)** & **\(\upmu\)S (\%)** & **w/b** \\ \hline PC43 LS20 FA37 & 43 & 20 & 37 & 0.0 & 0.3 \\ PC43 LS17 FA37+3\% \(\upmu\)S & 43 & 17 & 37 & 3.0 & 0.3 \\ PC43 LS14 FA37+6\% \(\upmu\)S & 43 & 14 & 37 & 6.0 & 0.3 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Quaternary \(\upmu\)S enhanced cement paste formulations—low PC—Mix Proportions % by total mass of binder.  
TGA/dTG was carried out for both constituent materials and cement pastes. With respect to hardened cement pastes, three broad areas, can be distinguished by the TGA; the first one is related to the dehydration of C-S-H ettrigite and monosulfate, between 100 \({}^{\circ}\)C and 200 \({}^{\circ}\)C. It can be postulated that the greater the loss measured the greater the amount of C-S-H and ettrigite was produced by the hydration of the paste.  
The second area of interest is associated with the dehydroxylation of Ca(OH)\({}_{2}\) between 400 \({}^{\circ}\)C and 510 \({}^{\circ}\)C. The amount of Ca(OH)\({}_{2}\) present in the paste at different ages can be computed by the stoichiometric elaboration of the mass loss results within the specific temperature range [25].  
The third area of interest is the decomposition of CaCO\({}_{3}\) known as decarboxylation, occurring between 610 \({}^{\circ}\)C and 810 \({}^{\circ}\)C. The amount of CaCO\({}_{3}\) present in the paste at different ages can be computed by the stoichiometric elaboration of the mass loss results within the specific temperature range. If it is greater than the limestone content of the reference paste, it constitutes an indication of sample carbonation. Particularly for the quantification of Ca(OH)\({}_{2}\), TGA is considered a very accurate technique [26], especially if coupled with XRD [27; 28; 29; 30].
# A Comparative Analysis for Optimizing Machine Learning Model Deployment in IoT Devices
## 3 Methodology
### Measuring Green Carbon Footprint  
The Green Software Foundation's implementation of calculating carbon intensity of software [29] is defined as follows:  
\[SCI=\frac{(E\times I)+M}{R} \tag{2}\]  
where \(SCI\) is the Software Carbon Intensity, \(E\) is the energy consumed, \(I\) is carbon emitted per unit of energy, \(M\) is the embodied emissions of a software system, and \(R\) is the functional unit of the piece of software. This functional unit may be API calls or training runs for an ML model. Carbon emissions per kWh are a location-based parameter and can be found through reports from government agencies [30]. We have considered the number of epochs per training session as the functional unit, and the embodied emissions of the Raspberry Pi as the standard embodied emission of a smartphone.  
To evaluate the environmental impact, we have introduced an additional Algorithm 1 to calculate the green footprint of training machine learning models, beyond just carbon emissions. The \"Algorithm 1\" considers multiple operational factors, including execution time, memory utilization, power consumption, and CPU temperature. Distinct from existing methodologies, this algorithm is designed to provide a more holistic view of the environmental impact by integrating these diverse metrics, thereby offering a comprehensive assessment of sustainability.  
\begin{table}
\begin{tabular}{p{142.3 pt} p{142.3 pt}} \hline \hline
**Entity** & **Specification** \\ \hline Processor & ARM64 Quad-Core \\ \hline RAM & LPDDR4-3200 8 GB \\ \hline Power consumption & 2.5 A \\ \hline Operating temperature & 0–50 \({}^{\circ}\)C ambient \\ \hline Interfaces & 2 USB jacks, Gigabit Ethernet \\ \hline Storage & 32 GB micro-SD Card \\ \hline Operating System & Raspberry Pi OS \\ \hline \hline \end{tabular}
\end{table}
Table 2: Raspberry Pi 4 Model B specifications.  
Furthermore, this algorithm is structured to enhance the ecological aspects of machine learning applications, making it substantially greener compared to conventional approaches. It encourages the adoption of more sustainable practices in the development and deployment of machine learning models by quantifying the carbon emissions and thereby enabling developers to make informed decisions aimed at reducing the ecological footprint.  
Mathematical Representation  
The green carbon footprint (GCF) is calculated as follows:  
\[GCF=\left(P\times T\times PGCF\right)+\left(M\times MGCF\right)+TC \tag{3}\]  
where \(P\) is the power consumption in Kilowatts (kW); \(T\) is the Execution time in hours; \(PGCF\) is the Power to Carbon Conversion Factor; \(M\) is the Memory utilization in Kilobyte (kB); \(MGCF\) is the Memory to Carbon Conversion Factor; and, finally, \(TC\) is the Temperature Cost, which is a function to calculate the CPU temperature.
## 4 Application of Fiber Optic Cables in Geophysics Exploration
### Microsiseismic of MMF
A convolutional neural network (CNN) algorithm allowed for microseismic event detection, and U-Net modeling was used for both P- and S-wave arrival-time picking. MMF cables were previously deployed with two multiwell DAS datasets acquired during hydraulic fracturing well completions in western Canada. MMF was deployed using a 4 m gauge length, 4 m channel spacing, and a 2000 Hz sampling rate, and data were converted to strain. The developed ML-assisted workflow was highlighted in the event detection and 1D arrival time picking after raw DAS data denoising. The results of applying the CNN algorithm were compared to the conventional short-term average/long-term average (STA/LTA) approach and geophone for microseismic event detection. The CNN detection method demonstrated a decreased false-positive ratio, contributing to a reduction in the time required for quality control. This makes it suitable for effectively handling the substantial volumes of data generated by DAS MMF. The findings indicated that the CNN method exhibited a reduced false-trigger rate and led to an expansion in the size of the microseismic event catalogue. A CNN model can automatically detect microseismic events (hypocenter location) recorded by low-SNR DAS with MMF cable data with excellent model accuracy and superior efficiency. Subsequently,  
Figure 7: Illustration of DAS microseismic detection for hydraulic fracturing measurement. the result of microseismic comparison with a 3C geophone revealed that DAS images have a lower sensitivity in weak events and low frequencies compared to geophones. The innovative aspect of this work lies in the implementation of ML algorithms, specifically the CNN model workflow. This workflow can be retrained as additional data become accessible, enabling the implementation of transfer learning. In other words, it becomes feasible to train a network using DAS data from one well and subsequently apply it to DAS data obtained from other wells. This approach presents an effective way to automatically address the DAS processing workflow through the use of an imaging algorithm. The author indicated that the limitation of this work was that the number event detection results of the 3C geophone was more detectable than that of DAS-CNN. However, the outcomes indicated reasonable counts of microseismic event occurrences observed during each step, indicating the reliability of the CNN algorithm. Future work could focus on developing more automated tools to make use of intricate phase data, including reflections, guided waves, and coda waves. A summary of the relevant literature on microseismic detection and ML can be found in Table 5 below.
###### Abstract  
This study primarily focuses on the analysis of volatile organic compounds using GC-MS, with ICP-MS employed as a complementary method to quantify trace metal content. Headspace GC-MS was conducted to detect alkylphenol ethoxylates (APEOs), formaldehyde, aromatic amines derived from azo dyes, perfluorinated carboxylic acids, chlorophenols (PCPs), tetrachlorophenols (TPCs), and phthalates in textile samples of different origin and composition. Principal component analysis was used to detect patterns in the volatilome according to the origin and the textile composition. In addition, seven metals (Cr, Ni, Cu, Mo, Cd, Hg, and Pb) were quantified in a subset of samples. The study revealed distinct chemical profiles in textiles based on their origin, with GC-MS identifying key volatile organic compounds and ICP-MS quantifying heavy metals in a subset of samples. Principal component analysis highlighted cotton content as a critical factor in differentiating textile profiles. While most samples adhered to regulatory standards, some exceeded thresholds for metals like copper and nickel, underscoring the need for enhanced quality control in manufacturing processes. By integrating advanced analytical methods, this study provides insights into sustainable and safe textile production, offering valuable benchmarks for regulatory compliance and industry best practices. The outcomes contribute to improving product safety, promoting responsible manufacturing, and supporting regulatory bodies in the enforcement of environmental and safety standards, aligning with the growing demand for sustainability in the textile sector.  
GC-MS; ICP-MS; PCA; volatilome; textile; origin; composition Article
# Gear Fault Diagnosis Based on Kurtosis Criterion VMD and SOM Neural Network
## 2 Experiment and the Procedure
### Fault Diagnosis Process Based on Kurtosis Criterion VMD and SOM Neural Network  
Noise will inevitably be introduced in the process of gear vibration signal acquisition, which will cause adverse impact on fault diagnosis. Therefore, this paper proposes a method based on kurtosis criterion VMD and SOM neural network for gear fault diagnosis and identification. Fault diagnosis flow chart is shown in Figure 3:  
The specific steps are as follows:  
Step 1. According to a certain sampling frequency \(f_{s}\), vibration signals of gear under four working conditions of normal gear, gear with tooth wear, gear with tooth crack and gear with tooth break for \(N\) times were collected, with a total of 4\(N\) samples;  
Step 2. VMD decomposition was carried out for each vibration signal, and a total of \(k\) IMFs were decomposed;  
Step 3. Extract the kurtosis value of each IMFs to form the feature vectors;  
Step 4. Normalized feature vectors;  
Step 5. Input normalized vector to SOM neural network for fault diagnosis;  
Step 6. Output fault diagnosis results.  
Figure 2: (**a**) Normal gear, (**b**) gear with tooth wear, (**c**) gear with tooth crack, and (**d**) gear with tooth break.
## 3 Conclusions  
In excimer systems, the direct amplification scheme allows possible use of temporal filters, not only after, but even before the final amplifier(s). In the preferred latter case, however, the larger gain for the noise compared to the short signal pulse rapidly deteriorates the contrast. In this paper, we point out that this is not only the result of the saturation of amplification, but also of the dynamic amplification properties of excimers; of the limited reorientation/relaxation times of the upper B state in _KrF_, together with the contribution of transient absorption of the lower X state in _XeCl_ and _XeF_. For all excimers, both the emission and absorption transition rates are determined, moreover, the dependence of the overall extraction efficiency and contrast coefficient are calculated on the energy density of the signal pulse to be amplified. Based on these results, the applicable maximum short pulse gain-length product of the _KrF_ power amplifier following the temporal filter is found to be limited to \(g_{power}L\sim 1.2\), allowing somewhat more gain (\(G_{power}\approx 3\)) than necessary for the compensation of the typical energy loss of the filter.  
Conceptualization, methodology, resources, writing--original draft preparation, visualization, supervision, funding acquisition, S.S.; validation S.S. and I.B.F.; formal analysis, G.A.; investigation, S.S. and R.D.; writing--review and editing, S.S., R.D. and I.B.F.; project administration, R.D. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was supported by the European Social Fund EFOP-3.6.2-16-2017-00005 \"Ultrafast physical processes in atoms, molecules, nanostructures and biological systems\", by the Ministry of Innovation and Technology, Hungary, grant NKFIH-1279-2/2020, and by the National Research, Development and Innovation (NKFIH) K138339 project. This work received funding from the European Union's Horizon2020 research and innovation program under grant agreement number 871124, Laserlab-Europe.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Acknowledgments:** The authors thank [PERSON] for the critical reading of the manuscript.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 2 Design and Optimization
### Optimization of the Multi-Element AVA Design
This antenna is fabricated and the simulated results are validated in the far-field measurement facility. \begin{table}
\begin{tabular}{|c|c|c|c|c|c|c|c|c|c|c|} \hline \hline \multicolumn{1}{|c|}{} & \multicolumn{4}{c|}{**(a) 1– Element**} & \multicolumn{4}{c|}{**(c) 4– Element**} \\ \hline \multirow{2}{*}{**f (GHz)**} & \multirow{2}{*}{**Plane**} & **MLM** & **MLD** & **HPBW** & **SLL** & **f (GHz)** & **Plane** & **MLM** & **MLD** & **HPBW** & **SLL** \\  & & **(dB)** & **(deg)** & **(deg)** & **(dB)** & & & & **(dB)** & **(deg)** & **(deg)** & **(deg)** & **(dB)** \\ \hline \multirow{2}{*}{\(15\)} & E & 1.02 & –31 & 137.3 & –1.1 & \multirow{2}{*}{15} & E & 2.79 & 10 & 48.2 & –11.1 \\ \cline{2-11}  & H & 2.33 & –73 & 153.7 & –2.1 & \multirow{2}{*}{15} & H & 7.17 & 55 & 138.4 & –4.7 \\ \hline \multirow{2}{*}{\(18\)} & E & 1.02 & –139 & 228.6 & 0 & \multirow{2}{*}{18} & E & 5.81 & –6 & 36.9 & –2.1 \\ \cline{2-11}  & H & 3.11 & 94 & 169.7 & –2.1 & \multirow{2}{*}{18} & H & 7.89 & 67 & 125.5 & –2.3 \\ \hline \multirow{2}{*}{\(28\)} & E & 1.19 & –45 & 266 & 0 & \multirow{2}{*}{18} & E & 2.92 & 180 & 24.9 & –1.5 \\ \cline{2-11}  & H & 3.2 & 103 & 120 & –2 & \multirow{2}{*}{28} & H & 7.86 & 35 & 41.7 & –2.6 \\ \hline \multirow{2}{*}{\(38\)} & E & –1.53 & –99 & 201.2 & 0 & \multirow{2}{*}{12} & E & 3.77 & –33 & 85.9 & –0.9 \\ \cline{2-11}  & H & 6.54 & 76 & 61.2 & –5 & \multirow{2}{*}{38} & H & 7.86 & 35 & 41.7 & –2.6 \\ \hline \multicolumn{1}{|c|}{} & \multicolumn{4}{c|}{**(b) 2– Element**} & \multicolumn{4}{c|}{**(d) 8– Element**} \\ \hline \multirow{2}{*}{**f (GHz)**} & \multirow{2}{*}{**Plane**} & **MLM** & **MLD** & **HPBW** & **SLL** & **f (GHz)** & **Plane** & **MLM** & **MLD** & **HDBW** & **SLL** \\  & & **(dB)** & **(deg)** & **(deg)** & **(dB)** & & & **(dB)** & **(deg)** & **(deg)** & **(dB)** \\ \hline \multirow{2}{*}{\(15\)} & E & 1 & –17 & 109.1 & 0 & \multirow{2}{*}{15} & E & 5.4 & 4 & 22 & –7.1 \\ \cline{2-11}  & H & 4.2 & 40 & 156.9 & 0 & \multirow{2}{*}{15} & H & 9.56 & 46 & 155.5 & –4.2 \\ \hline \multirow{2}{*}{\(18\)} & E & 1.62 & –174 & 72.6 & 0 & \multirow{2}{*}{18} & E & 8.24 & 0 & 18.2 & –1.8 \\ \cline{2-11}  & H & 5.1 & 4 & 7 164.3 & 0 & \multirow{2}{*}{18} & H & 10.6 & 58 & 130 & –2.2 \\ \hline \multirow{2}{*}{\(28\)} & E & 3.66 & –7 & 60.3 & –2.2 & \multirow{2}{*}{28} & E & 5.75 & 1 & 13 & –2.6 \\ \cline{2-11}  & H & 6.84 & 61 & 112.9 & –2.5 & \multirow{2}{*}{28} & H & 11.9 & –19 & 26.4 & –2.3 \\ \hline \multirow{2}{*}{\(38\)} & E & 3.55 & –4 & 40.6 & –3 & \multirow{2}{*}{18} & E & 5.11 & 75 & 26.4 & –1.7 \\ \cline{2-11}  & H & 8.13 & 76 & 42.8 & –4.1 & \multirow{2}{*}{38} & H & 9.06 & 74 & 42.7 & –3.8 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Main lobe magnitude (MLM), main lobe direction (MLD), half power beamwidth (HPBW), and side lobe level (SLL) of the proposed antennas AVAs. Figure 7: Eight element AVA with separate ports.
## 3 Results and Discussion
The lower resistivity values less than 20 ohm-m below the water table from comparatively the deeper horizon indicated the sand containing brackish water whereas the same range of values near the surface indicated the silt containing saline water. After drilling, the aquifer must be evaluated in other to determine the aquifer hydraulic properties. These parameters are evaluated with the help of pumping tests that help recommend the capacity of the pump as well as the depth of installation to improve the life and stabilization of the aquifer. The pumping test is conducted to examine the aquifer response, under controlled conditions, to the abstraction of water  
The results of the pumping test are considered accurate and are used to evaluate the hydraulic properties. In this study, the hydraulic conductivity of rock samples collected at different depths from test holes was also available, but these values were lower than the conductivity obtained from the pumping test analysis due to the well-known factors that the samples obtained from different depths were disturbed and their physical properties (porosity, packing, grain orientation) also changed. To map the hydraulic conductivity laterally of the investigated area, the values of hydraulic conductivities evaluated from the pumping test with laboratory analysis were utilized by converting the area into 17 polygons where the pumping test was conducted. The final values of hydraulic conductivities were estimated and are given in Table 3. The estimated hydraulic conductivities that represented the area falls in the range of 4.4 to 85.6 m/day. The highest value is interpreted at the junction of the Indus and Sutlej Rivers. This reflects the way they were deposited of the transported material in nature by rivers. Later, the values of transmissivity have been estimated by calculating the average thickness of the aquifer with the help of VES data for each polygon that ranged from 674 to 8986 m\({}^{2}\)/day. The transmissivity values evaluated for each polygon are shown in Table 4. Afterward, by using the already established relations having the same geological environments, the values of porosity, formation factor, and tortuosity were estimated of the whole investigated area. It is obvious from the results (Table 4) that the hydraulic conductivity increases with the increase in porosity indicating the high potential area. The higher values of the porosity are estimated towards the northern sides of the area where the Indus and Sutlej Rivers interest each other due to the obvious reason those maximum sediments are deposited at these places.