esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Conclusions  
A low-power wireless system is developed on ECG signal, acquired using zeolite-based electrodes, processed on an FR4 substrate-based read-out circuit. It is a compact system that can be used outside hospital settings to transmit ECG signals wirelessly. Using a wireless transceiver, the nRF24L01, which is compatible with any Arduino microcontroller, digitized ECG bits are transmitted and received over a distance of 5 m, with high signal integrity and a low bit error rate of \(\leq\)0.1%. With 250 kbps as the data rate, the transceiver consumes only 11.2 mW. If a CR032 battery is used, the design can run continuously for \(\geq\)70 h. To detect and identify abnormal beats from any new subject's acquired ECG, a subject-wise cross-validated, three-fold CNN model is trained and implemented on an NVIDIA's Jetson, yielding a prediction accuracy of 88%. Thus, an Arduino microcontroller and nRF24L01 transmitter are integrated with the analog front end (the zeolite-based ECG acquisition and COTS-based processing unit) incorporated with a CNN model on a portable NVIDIA Jetson is proposed in this paper.  
[PERSON] performed the wireless transmitter-based experiments, analyzed the data, and compiled the manuscript. [PERSON] contributed to code on the Nvidia board and performed simulation. [PERSON] and [PERSON] contributed to the analysis of the results. [PERSON], [PERSON] and [PERSON] critically revised the manuscript and approved the final version. The authors are accountable for all aspects of the work and ensure that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Reference** & **Dataset** & **Model** & **AUROC** \\ \hline
[29; 30] & EHR &
\begin{tabular}{c} Random Forest, \\ LSTM \\ \end{tabular} & 0.81, 0.88 \\ \hline
[31] & UCI & CNN & 0.86 \\ \hline This work & Physionet(SCA) & CNN & 0.886 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Comparison with other ML works.  
Figure 11: Area under the receiver operating characteristic curve of the CNN model.
## 2 Shadowed Sets  
Fuzzy logic has its beginnings in 1965 [36; 37; 38] and is primarily based on the understanding that our perception of the world cannot only be defined in terms of true or false statements. Instead, it is better to utilize concepts that can have a range of truth values within a set.  
The evolution of fuzzy logic over time led to the development of different types of fuzzy systems. The first was the type-1 fuzzy system (T1-FS), followed by the interval type-2 fuzzy system (IT2-FS), and later the generalized type-2 fuzzy system (GT2-FS). The equations for these different fuzzy systems are presented as Equations (1)-(3), respectively:  
\[\tilde{\mathrm{A}}=\{(\mathrm{x},\mu_{\mathrm{A}}(\mathrm{x}))|\mathrm{x}\in \mathrm{U},[0,1]\} \tag{1}\]  
\[\tilde{\mathrm{A}}=\{(\mathrm{x},\mathrm{u}),\mathrm{u}_{\tilde{\mathrm{A}}}( \mathrm{x},\mathrm{u})|\forall\mathrm{x}\in\mathrm{X},\forall\mathrm{u}\in \mathrm{J}_{\mathrm{x}}\subseteq[0,1]\} \tag{2}\]  
\[\overset{\approx}{\mathrm{A}}=\Big{\{}((\mathrm{x},\mathrm{u}),\mathrm{u}_{ \tilde{\mathrm{A}}}(\mathrm{x}))\Big{|}\forall\mathrm{x}\in\mathrm{X},\forall \mathrm{u}\in\mathrm{J}_{\mathrm{x}}^{\mathrm{u}}\subseteq[0,1]\Big{\}} \tag{3}\]  
The utilization of GT2-FS in real applications entails a high computational cost. However, there are alternative approaches to GT2-FS, such as vertical slices [39] and horizontal slices [40].  
Therefore, this work focuses on the utilization of \(\alpha\)-planes [22], as expressed by Equations (4) and (5) that describe the union of all \(\alpha\)-planes.  
\[\overset{\sim}{\mathrm{A}}_{\alpha}=\{((\mathrm{x},\mathrm{u}),\alpha)|\forall \mathrm{x}\in\mathrm{X},\forall\mathrm{u}\in\mathrm{J}_{\mathrm{x}}\subseteq[ 0,1]\} \tag{4}\]  
\[\overset{\approx}{\mathrm{A}}=\bigcup\overset{\sim}{\mathrm{A}}_{\alpha} \tag{5}\]  
ST2-FS [41] provides a solution to reduce computational costs. This work primarily focuses on modeling GT2-FS using only two \(\alpha\)-planes, based on the theory proposed by [PERSON] for ST2-FS [42; 43; 44].  
Equation (6) illustrates the mathematical manifestation of shadowed sets. This formula is constructed from two \(\alpha\)-cuts on a T1-FS, incorporating \(\alpha\) and \(\beta\) values, thereby defining three intervals.  
\[\mathrm{S}_{\mu_{\mathrm{A}}}(\mathrm{x})=\left\{\begin{aligned} & 1,\text{ if }\mu_{\mathrm{A}}(\mathrm{x})\geq \alpha\\ & 0,\text{ if }\mu_{\mathrm{A}}(\mathrm{x})\leq\beta\\ &[0,1],\text{ if }\beta\leq\mu_{\mathrm{A}}(\mathrm{x})\geq \alpha\end{aligned}\right. \tag{6}\]  
Figure 1 illustrates the three regions of a shadowed set. The elevated area (EA) is represented by the blue color, with a membership of 1. The reduced area (RA) is depicted in yellow, with a membership of 0. The gray color represents the shadowed area (SA), which has a membership degree in the range of [0, 1].  
Figure 1: Representation of the shadowed set.  
Taking these regions into consideration, [PERSON] proposed a method for calculating the optimal \(\alpha\) and \(\beta\) values using Equation (7).  
\[\text{EA}_{(\alpha,\beta)}(\mu_{\text{A}})+\text{RA}_{(\alpha,\beta)}(\mu_{ \text{A}})=\text{SA}(\mu_{\text{A}}) \tag{7}\]
## 2 Novel Contributions of the Current Paper  
This research focuses on enhancing the confidentiality, integrity, and scalability of secure authentication systems using PUFs. In a collaborative edge computing environment, where PUFs serve as a security primitive for device authentication and authorization, ensuring data integrity and confidentiality is crucial. In scenarios like load balancing, where Edge Data Centers (EDCs) must authenticate each other, it is essential to consider how CRP data are stored and managed. Since each EDC stores CRP data locally and communicates it across the network, the data becomes vulnerable to security breaches. To address the challenges of secure data communication and integrity, this research proposes the following novel solutions.  
* This research proposes a novel feature-based authentication system that utilizes extracted and vectorized features of PUF data.
* Enhancing data and device integrity by transmitting the vectorized data instead of the raw data using k-mer sequence embeddings.
* Implementing encryption and decryption protocols that use feature vectors as plaintext and convert them to ciphertext using efficient hashing algorithms.
* Utilizing the Elliptic Curve Digital Signature Algorithm (ECDSA) for authentication, eliminating the need for shared secret keys and leveraging asymmetric key cryptography.
* Designing an efficient cryptographic algorithm resistant to man-in-the-middle attacks, ensuring end-to-end data security.
* Communicating both challenges and responses in their vectorized forms to enhance data security and integrity.
## 2 Study Area and Data
### Ground Measurements Data and Analysis  
Field measurement data were obtained from Watershed Allied Telemetry Experimental Research, including the surface soil moisture and surface roughness parameters [51; 52; 53]. The field measurements were taken while the satellite data were acquired. During this period, no precipitation or obvious temperature changes occurred in the experimental area.  
The size of the study area is \(0.36\times 0.36\) km, and there are 49 sample points in this region. The soil samples were collected from the upper 0-5 cm soil layer. Most field measurements of soil moisture were made within \(\pm 2\) h of the ASAR overpasses. The soil texture was analyzed by soil samples.  
The soil moisture content of each sample point was measured by gravimetry. Firstly, the samples were obtained by a cutting-ring method, and the total weight of the soil was obtained using scales and expressed as \(G_{\mathrm{wet}}\). Secondly, the soils were dried, and the weight was expressed as \(G_{\mathrm{dry}}\). Thirdly, the volume of the cutting ring was provided, which was 50 cm\({}^{3}\) and expressed as \(V_{\mathrm{soil}}\). Finally, the measured soil volume water content was calculated by the formula \(m_{\mathrm{\SIUnitSymbolulve}}=(G_{\mathrm{wet}}-G_{\mathrm{dry}})/V_{\mathrm{soil}}\). The soil moisture in the field measurements was 13.5-50.7%.  
The surface roughness data were measured using needle profilometers. which were 1 m long, and a digital camera. There were 101 needles with a 1-cm sampling interval between needles in the needle profilometers. At each sampling site, two field photographs were taken: along a north-south direction and along an east-west direction. Two roughness profiles were established for each sampling point. Standard deviations of _Hrms_ and correlation length \(l\) were calculated using the mean of the autocorrelation function based on these measurements. The _Hrms_ in the field measurements was 0.68-4.08 cm with an average of 1.4 cm, and the correlation length was 53-69 cm. The soil temperature  
Figure 1: Land cover map of the study site and distribution of sampling point: (**a**) land cover map of the study site; (**b**) distribution of sampling point (_Pii_ represents sampling point number).  
was obtained by a needle thermometer, and it was observed twice at each sampling point. Detailed data are shown in Table 2.
## Appendix A Frequency-Dependent Constitutive Equation for the Dissipative Structure
### Linear Dissipative Constitutive Equation for Modeling Damping Effects  
This section deals with the linear dissipative constitutive equation for modeling damping effects in the part \(\Omega_{\text{damp}}\) of the structure \(\Omega_{S}\). Several models of dissipative constitutive equation (with frequency-dependent coefficients) corresponding to an elastic material are considered for which the mechanical damping is arbitrarily introduced in order to represent damping effects. The first model presented is the constitutive equation for an elastic material with a linear viscous damping term. The second one is a constitutive equation for an elastic material with a parameterized family of damping models depending on frequency. The construction proposed for these two models is such that the causality principle will be verified, and consequently, the fourth-order tensor \(a_{ijkh}(\omega)\) will depend on \(\omega\) although the elastic tensor \(a_{ijkh}^{\text{elas}}\) of the elastic material is independent of \(\omega\).  
_(i) Constitutive equation for an elastic material with a linear viscous damping term._ In this case, the constitutive equation is given by Equation (A1), in which  
\[a_{ijkh}(\mathbf{x},\omega)=a_{ijkh}^{\text{elas}}(\mathbf{x}),b_{ijkh}( \mathbf{x},\omega)=b_{ijkh}(\mathbf{x})\,,\] (A15)in which the tensors \(a^{\rm elas}_{ijkh}({\bf x})\) and \(b_{ijkh}({\bf x})\) are symmetric positive definite and independent of \(\omega\).  
_(ii) Constitutive equation for an elastic material with a parameterized family of damping models depending on frequency_. The constitutive equation is then defined as an elastic material with a parameterized family of damping models depending on frequency and is written as  
\[b_{ijkh}({\bf x},\omega)=\chi(\omega)\,a^{\rm elas}_{ijkh}({\bf x})\,, \tag{101}\]  
in which the tensor \(a^{\rm elas}_{ijkh}({\bf x})\) is symmetric positive definite and where \(\chi(\omega)\) is a positive-valued real function in \(\omega\) which must satisfy the following properties:  
(1) For \(\omega\to+\infty\), function \(\chi(\omega)\) must decrease  
overcome this mathematical difficulty arising in the boundary element method [128; 132; 133; 134; 135; 136]. In this appendix, we present a boundary element method that was initially developed in [137] and detailed in [41]. The formulation of this method is symmetric, and is valid for all real values of the frequency (i.e., without spurious frequencies).
## 2 Methodology
### Principle of the LSTM Algorithm  
The LSTM model, introduced by [PERSON] and [PERSON] in 1997 [PERSON] and [PERSON] (1997), is a type of recurrent neural network (RNN) designed for temporal data with a chain-like structure similar to traditional RNNs. In RNNs, the self-connected hidden layer creates dependencies that require updating the hidden layer state at the current time step based on the previous one. However, RNNs encounter challenges when handling long time series data due to the potential for exponential growth or decay of gradients through repeated multiplications, leading to issues such as gradient vanishing or exploding during training. The LSTM model overcomes this inherent limitation of RNNs by preserving early time series information. While the hidden layer of LSTM retains the self-connection structure, it introduces three types of gate mechanisms (\"forget gate\", \"input gate\", and \"output gate\") to control the flow and update of cell state and hidden layer state information. This effectively addresses problems, like gradient vanishing or exploding, that arise when processing time series data [PERSON] and [PERSON] (1997). The hidden layer structure of LSTM is depicted in Figure 1.  
Figure 1: The LSTM hidden layer structure.  
In Figure 1, \(X_{t}\) is the input signal; \(h_{t-1}\) is the output signal at time \(t-1\); \(h_{t}\) is the output value; \(\widetilde{C}_{t}\) is the output signal of the \"input gate\", which also indicates the new memory cell; \(C_{t-1}\) represents the cell state information at time \(t-1\); and \(C_{t}\) represents the final memory cell information of the current layer. \(f_{t}\), \(i_{t}\), and \(o_{t}\) are the control coefficients corresponding to the \"forget gate\", \"input gate\", and \"output gate\" structures, respectively, and \(\sigma\) is the sigmoid activation function.  
The \"forget gate\" determines whether or not to transfer the cell state information from the previous moment to the current moment. The cell state information at moment t\(-1\) is determined based on \(f_{i}\), as illustrated in Figure 1. When \(f_{i}\) is close to 0, it is completely forgotten; conversely, when \(f_{i}\) is close to 1, it is completely retained. The \"input gate\" can control how much new information needs to be added to the cell state through a sigmoid activation function. The \"output gate\" determines the effect of the current cell state on the state and output of the hidden layer, and the output is determined by a sigmoid activation function. The expression of LSTM is as follows:  
\[f_{t} =\sigma[W_{f}(h_{t-1},X_{t})+b_{f}] \tag{13}\] \[i_{t} =\sigma[W_{i}(h_{t-1},X_{t})+b_{i}]\] (14) \[\widetilde{C}_{t} =\tanh\sigma[W_{c}(h_{t-1},X_{t})+b_{c}]\] (15) \[C_{t} =f_{t}C_{t-1}+i_{t}\widetilde{C}_{t}\] (16) \[o_{t} =\sigma[W_{0}(h_{t-1},X_{t})+b_{0}]\] (17) \[h_{t} =o_{t}\times tanhC_{t} \tag{18}\]  
where, W\({}_{f}\), W\({}_{i}\), We, and Wo are weight matrices, b\({}_{f}\), b\({}_{i}\), b\({}_{c}\), and b\({}_{o}\) are biases, and _tanh_ is the hyperbolic tangent activation function.
## 2 Materials and Methods  
### Experimental Plot  
The deployment was carried out at the \"Tomas Ferro\" Experimental Agri-food Station of the Technical University of Cartagena (37\({}^{\circ}\)41\({}^{\prime}\)18.9\({}^{\prime\prime}\) N; 0\({}^{\circ}\)57\({}^{\prime}\)01.7\({}^{\prime\prime}\) W; 32 m above the sea level), located at La Palma (Murcia, Spain) during three growing seasons (2017-2019). The plant material consisted of seventeen-year-old almond trees (_Prunus dulcis_ (Mill.) [PERSON] cv. [PERSON], grafted on Mayor rootstock) and spaced 6 \(\times\) 7 m (238 trees ha\({}^{-1}\)).  
The soil was a silty-clay-loam texture and the bulk density ranged 1.3-1.5 g cm\({}^{-3}\). The irrigation water, from the Tajo-Segura Water Transfer System, has low salinity (Electricalconductivity, EC25\({}_{\text{5}\text{C}}\) of 1.2 dS m\({}^{-1}\)) and was applied with an automatic controller (Agronic 4000, Sistemes Electronics PROGRES, S.A., Lerida, Spain) that performed control of irrigation, fertilization, pH regulation, pumping and filter cleaning with fault detection. The irrigation system was comprised of a double drip line per row with 12 pressure-compensated drippers (PC dipriper 4.0 L h\({}^{-1}\), Netafilm, Tel Aviv, Israel) per tree and spaced 1.0 m.  
Daily agrometeorological data were recorded by a weather station at the experimental orchard owned by the Agricultural Information Network System of Murcia (SIAM--siam. imida.es (accessed on 6 April 2020)). The climate of the area was classified as Mediterranean, with dry and warm summers and mild winters. During the experimental period, most rainfall events occurred between September and May, with the average annual rainfall of 327 mm. The average annual reference evapotranspiration (ET\({}_{0}\)) of 1320 mm with daily average temperatures ranged between 5.5 \({}^{\circ}\)C (January) and 29.5 \({}^{\circ}\)C (August) whereas air temperature rarely drops below 0.0 \({}^{\circ}\)C. Daily average relative humidity was ranged between 36.5% and 86.0%.
# Full Digital Workflow for Aesthetic Rehabilitation of the Upper Tenth: A Case Report
## 2 Materials and Methods  
### Visit I Diagnosis and Treatment Planning  
A 39-year-old male patient in good health, without medical conditions affecting treatment, presented with an unaesthetic smile characterized by excessive gingival display and reduced tooth length due to abrasion (medical history was insignificant). After intraoral examination, the patient was diagnosed with a gummy smile, decreased teeth length and negative smile line, and teeth inclined in the occlusal plane. Additionally, tooth 21 had a discolored resin restoration. Extra- and intra-oral photographs and intra-oral scans (IOS) with CEREC PrimeScan (software 5.2, Dentsply Sirona, Bensheim, Germany) were performed (Figure 1). Both arches and the patient's occlusal relationship were scanned. Intraoral scans and photos enable not only accurate analysis of the smile but also the discussion of the treatment plan with the patient. Additionally, cone-beam computed tomography (CBCT) scans (Axeos; Dentsply Sirona, Fleury-sur-Orne, France) were performed to provide adequate information before gingivectomy.  
The treatment plan included an aesthetic gingivectomy procedure to restore the ideal length of the anterior teeth and final restoration with CAD-CAM veneers to improve the shape and color of anterior teeth and premolars.  
The DSD was performed in the Exocad software (Rijeka 3.1. Darmstadt, Germany). It consisted of the virtual correction of the gingival smile, alignment of the upper occlusal plane, and modifying the size of the upper teeth (first premolars, canines, lateral, and central incisors) with thin ceramic veneers. Next, a digital mock-up was presented to the patient (Figure 2).
# Development and Validation of a Capacitor-Current Circuit Model for Evaporation-Induced Electricity
## 3 Materials and Methods
### Device Fabrication, Data Collection, and Fitting  
The device was constructed using a CA-treated beech wood sample and two polyethylene terephthalate (PET) meshes coated with conductive carbon paste, CH-8. The PET meshes were attached on both sides of the wood piece, serving as electrodes, and secured with rubber bands for stable contact.  
The assembled device was placed in a water filled petri dish under monitored humidity and temperature, which, in general, is 60% RH and 25 \({}^{\circ}\)C. The open-circuit voltage (\(V_{\mathrm{OC}}\)) and short-circuit current (\(I_{\mathrm{SC}}\)) of the working device were recorded by a Keithley2400 multimeter. Capacitor-current model building and data fitting were performed in OriginPro 2021. We prepared four samples using the described fabrication process. Identical measurements were performed on all samples to ensure consistency and repeatability. Among the datasets obtained, the one with the clearest signal and least noise was selected for presentationto illustrate the model fitting process effectively.  
This study was conducted under the following conditions.  
Experimental Setup: The wood-based nanogenerators were fabricated with citric acid-modified microchannels. The assembled devices were placed in a water-filled petri dish under controlled laboratory conditions, with monitored humidity (60% RH) and temperature (25 \({}^{\circ}\)C).  
Modeling Assumptions: The capacitor-current circuit model assumes linearity in the system's behavior, uniform material properties, and idealized circuit components. Nonlinear effects and material heterogeneities are not explicitly accounted for.
# Optimizing Geospatial Data for ML/CV Applications:
## 3 Methods
### Automated Data Retrieval from LP-Portal
#### 3.1.1 Endpoint Identification  
The website loads its services through a series of web requests. The research focused on two key endpoints that were useful for data extraction: 'getInteractiveLegendByLayerId'-- which returns HTML code containing the list of roads; 'getInteractiveLegendInfoByLayerIdAndOptionId' supplied with a road name--which provides detailed information about path coordinates for the road. The script dynamically handled both'sys_ref_06_mapainteraktywna' and'mapainteraktywna_wszystkie_drogi' layer identifiers, depending on the county-specific LP-Portal variant.
## 3 Results and Discussion
### Game Evaluation by Medical Experts  
The process of planning the course of rehabilitation with VR was carried out with the active consultation of medical experts (physiotherapists, neurological physicians, and group therapists). The game was created iteratively, by making corrections and suggestions indicated by them. The final result, as described above, was assessed by the physiotherapists. They tested the application and concluded that it transferred the movement patterns and therapeutic assumptions of conventional rehabilitation to VR. Moreover, they appreciated the interesting environment and the level of immersion, which enhanced the attractiveness of the application. The physiotherapists additionally found the game simple enough, relative to the patients' potential abilities. All these elements and their experience allowed them to conclude that the application satisfactorily fulfills the premise of rehabilitation and therefore is suitable for conducting research with individuals.