esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Finite Element Modeling for Thermal Analysis
### Thermal Modeling  
The thermal equilibrium equation for 3D heat transfer with isotropic thermal properties is as follows:  
\[\rho c\frac{\partial T}{\partial t}=\frac{\partial}{\partial x}\bigg{(}k\frac{ \partial T}{\partial x}\bigg{)}+\frac{\partial}{\partial y}\bigg{(}k\frac{ \partial T}{\partial y}\bigg{)}+\frac{\partial}{\partial z}\bigg{(}k\frac{ \partial T}{\partial z}\bigg{)}+Q(x,y,z,t) \tag{1}\]  
where \(\rho\) is material density (kg/m\({}^{3}\)), \(c\) is specific heat capacity (J/kgK), \(T\) is temperature (K), \(t\) is interaction time (sec), \(k\) is thermal conductivity (W/mK), and \(Q(x,y,z,t)\) is heat generation per unit volume (W/m\({}^{3}\)).  
The initial and ambient temperature of the powder bed is set to 293 K (20 \({}^{\circ}\)C).  
\[T(x,y,z,t)_{t=0}=293\ (K) \tag{2}\]  
As boundary conditions, the side and bottom surface of the FEM model are fixed at 293 K, and the convection and radiation are considered on the top surface. The convective (\(q_{c}\)) and radiative heat losses (\(q_{r}\)) are as follows, respectively:  
Figure 2: Melt pool size with respect to number of tracks.  
\[q_{c}=-h_{c}\Big{(}T_{surf}-T_{0}\Big{)} \tag{3}\]  
\[q_{r}=-\varepsilon\sigma\Big{(}T_{sur}^{4}-T_{0}^{4}\Big{)} \tag{4}\]  
where \(h_{c}\) is convective heat transfer coefficient (W/m\({}^{2}\) K), \(T_{surf}\) is surface temperature (K), \(T_{0}\) is ambient temperature (K), \(\varepsilon\) is emissivity of the powder bed, and \(\sigma\) is Stefan-Boltzmann constant for radiation.  
The convective heat transfer coefficient (\(h_{c}\)) and emissivity (\(\varepsilon\)) determine the amount of heat release caused by convection and radiation on the powder surface. For sensitivity analysis of thermal analysis parameters, the range of \(h_{c}\) is set from 0, which does not consider convection, to 20 W/m\({}^{2}\) K [12; 22], and the range of \(\varepsilon\) is set from 0, which does not consider radiation, to 0.66, which is the emissivity of polished steel alloy type 316 at 1222 K [23].
# An Efficient SMOTE-Based Deep Learning Model for Voice Pathology Detection
## 3 Results
### Model Evaluation Measures  
Accuracy is one of the most common tools used to measure classifier performance. However, general measurements of classifier performance are inadequate for imbalanced datasets because the results can be biased for one class. Selecting the wrong metric to evaluate the model can lead to the selection of a defective model or incorrect recognition of the expected performance of the model in the worst-case scenario. Therefore, this study uses several evaluation metrics, such as recall, specificity, G, and F1, to determine the performance of the model using an imbalanced class dataset. Recall indicates the accuracy over the minority class. Specificity measures the accuracy over majority class. Additionally, the G and F1 values consider the classification performance achieved for the minority class [24]. In short, these evaluation metrics are generally considered \"unbiased\" because they are unaffected by the numbers of samples obtained from different classes and can be used in the database in which the classes are unbalanced [24]. They are calculated according to Equations (1)-(5). Table 3 contains the values of TP, TN, FP, and FN. In this work, TP refers to actual normal samples predicted as normal samples and is the number of true-positive samples. FN refers to actual normal samples predicted as pathological samples and is the number of false-negative samples. FP refers to the actual pathological samples predicted as normal samples and is the number of false-positive samples. TN  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Main Hyperparameters** & **CNN** & **FNN** \\ \hline Activation function & ReLU & ReLU \\ Kernel size & \((3,3)\) &. \\ Optimizer & SGD + momentum & SGD + momentum \\ Number of epochs & 100 & 100 \\ Loss function & Cross-entropy & Cross-entropy \\ Dropout & 0.3 &. \\ Pooling window & Max pooling (2,2) &. \\ Neurons in the dense layer & 512 &. \\ Learning rate & 0.001 & 0.00001 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The main parameters of each model.  
means the actual pathological samples predicted as pathological samples and is the number of true-negative samples [24].  
\[Precision=\frac{TP}{TP+FP} \tag{1}\]  
\[Recall=\frac{TP}{TP+FN} \tag{2}\]  
\[Specificity=\frac{TN}{TN+FP} \tag{3}\]  
\[G=\sqrt{Recall*Specificity} \tag{4}\]  
\[F1=\frac{2*Precision*Recall}{Precision+Recall} \tag{5}\]
# RSSI-Based MAC-Layer Spoofing Detection: Deep Learning Approach +
## 4 Detection Approach: Deep Authentication  
As demonstrated in Figure 1 (and argued in Section 1), given that RSSI time-series values of a wireless IoT device are not i.i.d., one could incorporate dependencies among neighboring RSSI values to build more robust and accurate predictive models for the purpose of device authentication. Deep autoencoders are deep generative neural networks that have demonstrated a strong capability of modeling latent variables in anomaly detection and authentication datasets [16]. LSTM autoencoders [17], in particular, are known for their  
Figure 2: Overview of the threat model: the goal of the receiving node is to use historical (clean) RSSI values from the legitimate sender to learn a robust profile to use in future against identity attacks; while the goal of the adversary is to get past the established profile by taking over \(s\) identity.  
generative modeling capabilities on time-series data. In this section we present our novel technique for authentication of legitimate IoT nodes using RSSI-based anomaly detectors deploying LSTM autoencoders. In addition, expanding on our argument from Section 1 with respect to the _time-series clustering-effect_ of RSSI values in dynamic environments, we also discuss how our novel multi LSTM autoencoder architecture is able to switch between multiple trained LSTM models at runtime. Such a multi-LSTM autoencoder architecture would help with addressing the clustering effect of RSSI time-series.
## 5 CIDF-WSN
### CIDF-WSN Operation  
Before diving deep into the operational details of the CIDF-WSN, we first shed light on the two main data structures of the NDN forwarding daemon--FIB and PIT--and why they need to be tuned for efficient operation in WSNs. Later, we discuss how the CIDF-WSN ensures effective forwarding in the resource-constrained environment where the nodes are usually equipped with a single radio interface.  
The FIB is utilized to forward the Interest packets towards the potential data source that resembles the requested content. As discussed earlier, the FIB table holds the list of outgoing interfaces, and the incoming Interest packets are forwarded towards the potential face by consulting with FIB. The PIT incorporates the information about the Interest that is forwarded towards the upstream (e.g., potential data source) nodes and waiting for the response. Upon receiving an Interest packet, the router first enquires its PIT to ensure whether the same name Interest packet has already been forwarded or not. If the PIT entry already exists, the router adds the face identifier of the incoming Interest packet. Contrarily, the router checks its FIB and selects the face with minimum route cost to forward the Interest packet. Once the forwarding completes, the router inserts the new outgoing face entry in the PIT table (containing the incoming Face address and outgoing Face address  
Figure 2: CIDF-WSN system architecture.  
of the forwarded Interest packet) representing the forwarded Interest packet. Finally, the Data packets arrive on the reverse path guided by the matching PIT entries.  
The aforementioned procedure is utilized for the communication between the devices on the internet where the routers are mostly equipped with an array of interfaces [38]. However, in WSNs the nodes are usually equipped with a single radio interface and the transmissions and receptions of Interest and Data packets are carried out through the same interface. In such networks, the FIB table may contain only a single interface entry, and the selection of optimal interface while having only one candidate interface is meaningless as the nodes have to utilize the same interface to perform Interest and Data transmissions. Therefore, we propose a new neighbor-forwarding information base (NFIB) for single interface equipped WSNs nodes. The NFIB allows the node to select the potential next hop in the presence of a single radio interface. The NFIB maintains a list of virtual faces (potential next-hop node Ids) with their associated cost that assists the node to select the optimal virtual face--representing the next-hop node Id, heading towards the potential Data source. Once the face selection is finalized, the corresponding Interest packet is forwarded to the selected virtual face and the Interest entry is stored in the PIT.  
It is worth mentioning here that the existing FIB Data structure is used to forward the Interest packet towards the face selected from NFIB on AdHoc Interface. The complete procedure of NFIB-based next-hop selection is depicted in Figure 3, whereas the detailed description of NFIB development is provided in Section 5.3.1.
# The Application and Improvement of Deep Neural Networks in Environmental Sound Recognition
## 3 Method Development  
### Data Sets  
In our experiments, we took two kinds of public data sets to evaluate our network structure improvements: ESC50 [30] and ESC10 (Warsaw University of Technology, Warsaw, Poland).  
ESC50 is a collection of environmental sound recordings that contains 50 classes, such as airplanes, car horns, cats, humans laughing, and rain, etc. There are 2000 short clips in total, and each class has 40 files. Each clip is five seconds long, and there is a total length of 2.78 h. It was recommended to test with the official 5-fold setting, as some of the files in the same class are extracted from the same source, using the official fold could avoid some problems.  
ESC10 is a subset of ESC50 that takes out 10 classes from ESC50, while other configurations remain the same. It was beneficial to do a small-scale test in this dataset first.
# Evaluation of Methods Based on CPTu Testing for Prediction of the Bearing Capacity of CFA Piles
## 3 Results
### Analysis of Predicted Pile Bearing Capacity
Mean \(\mu\) and sample standard deviation \(\sigma\) for \(Q_{p}/Q_{m}\) ratio were calculated for each method within R2 criterion. The more suitable method is characterized by the mean closer to 1.0 and the lower sample standard deviation representing the dispersion of data around mean value. Considering this criterion, all the methods overestimate the predicted bearing capacity. The cumulative probability at 50% and 90% (\(P_{50}\) and \(P_{90}\)) were calculated according to cumulative probability curves in criterion R3 (Figure 10b). The more suitable method has \(P_{50}\) value closer to 1.0 and with a smaller range of \(P_{90}\)-\(P_{50}\) at the same time. In criterion R4, ranking of methods was performed using 20% reliability level in log-normal distribution. Higher value signalizes higher efficiency of method. The UniCone method was proved the most reliable from the investigated methods while LCPC and EC 7--2 methods were ranked as second and third, respectively, in terms of efficiency according to ranking index RI (Table 4).
## 5 Proposed Model
### Mobility Movement and Localization Process
#### 5.3.2 Procedure on MA Side  
1. MA will start its journey from a starting point, the starting point can be set in advanced or random.
2. MA has a maximum distance value.
3. The first three movements will be random in any direction.
4. After each movement, the MA will stop and communicate with all nodes in its communications range, providing them with its current position.
5. It will update its routing table, which has the following information: 1. Node IDs 2. Node's status 3. Number of neighbours 4. Neighbours IDs 5. Neighbours' status 6. RSSI value
6. The MA will evaluate all nodes and elect one based on the previous chance table.
7. The next point of the hexagonal will be the shortest point in distance to the elected node.  
Figure 8: The node localization process in the UN.  
MA will travel to that point and provide its current position information.
* MA keeps moving till reaching the maximum distance, \(d_{max}\).  
The flowchart in Figure 9 presents the localization procedure done in the UN's side.
## 2 Prognostic Approach
### Modeling of Linear Stochastic Degenerate Systems  
The linear degradation model is typically used for modelling degradation processes where the degradation rate is approximately a constant [17; 23]. Moreover, the linear model with adaptive updating algorithm used in the literature [17] has a good estimate of the exponential type of data. In this paper, we consider the same linear degradation model based on a Wiener process as follows:  
\[X(t)=\theta t+\sigma B(t) \tag{1}\]  
Figure 1: The flow chart of a linear stochastic degenerate system. EM: expectation maximization; STF: strong tracking filtering; PDF: probability density function; CDF: cumulative distribution function.  
where \(X(t)\) is the degradation detection data at time \(t\). The initial state is shown as \(t=0,X(0)=0\). To be consistent with existing studies, the initial degradation was 0, which can be obtained by translation transformation of the data. And \(\theta\) was the drift parameter, which means the degradation rate of the system, \(\sigma\) is the diffusion parameter. \(B(t)\) denotes the standard Brownian motion, which represents the stochastic dynamics of the degradation process. The degradation detection data is described as \(X_{0:k}=\{x_{0},x_{1},x_{2},\ldots,x_{k}\}\).  
The reason for using linear system equations is not only its universality, but also it is the convenience for calculation. Another important reason is that the life prediction problem can be understood as predicting the future trend of a random curve. According to the Euler method, given a starting point, if we can predict the tangent curve at any point, from the starting point, we can calculate the predictive value step by step, the approximate curve can be obtained. In this paper, the drift parameter \(\theta\) denotes the tangent curve and we use a reasonable method to adjust \(\theta\) in real time. From the Equation (1), each step of the degradation data can be expressed as:  
\[X(t_{k}-t_{k-1})=\theta(t_{k}-t_{k-1})+\sigma B(t_{k}-t_{k-1}) \tag{2}\]  
where \(X(t_{k}-t_{k-1})\sim N\big{(}\theta(t_{k}-t_{k-1}),\sigma^{2}(t_{k}-t_{k-1}) \big{)}\).  
It can also be described in a nonlinear model, which will greatly increase computation time and not improve performance significantly. In addition, we apply Bayesian updating and the EM algorithm to update the system parameters, and the STF is used to improve the robustness of the model parameter mismatch. The above methods can guarantee the accuracy of parameters. The detailed description will be given below.  
Assumption: In the model of \(X(t)=\theta t+\sigma B(t)\), \(\theta\) is assumed to be a random parameter, indicating individual differences. \(\sigma\) is assumed to be the deterministic parameter as a constant.
###### Abstract  
Digital signal processing (DSP) has been widely adopted in sensor systems, communication systems, digital image processing, artificial intelligence, and Internet of Things applications. However, these applications require circuits for complex arithmetic computation. The logarithmic number system is a method to reduce the implementation area and transmission delay for arithmetic computation in DSP. In this study, we propose antilogarithmic converters with efficient error-area-delay products (eADPs) based on the fractional-bit compensation scheme. We propose three mathematical approximations--case 1, case 2, and case 3--to approximate the accurate antilogarithmic curve with different DSP requirements. The maximum percentage errors of conversion for case 1, case 2, and case 3 are 1.9089%, 1.7330%, and 1.2063%, respectively. Case 1, case 2, and case 3 can achieve eADP savings of 15.66%, 80.80%, and 84.61% compared with other methods reported in the literature. The proposed eADP-efficient antilogarithmic converters can achieve lower eADP and digitalized circuit implementation. The hardware implementation utilizes Verilog Hardware Description Language and the digital circuits are created via very-large-scale integration by the Taiwan Semiconductor Manufacturing Company with 0.18 \(\mu\)m CMOS technology. This proposed antilogarithmic converter can be efficiently applied in DSP.  
antilogarithmic converter; fractional-bit compensation; error-area-delay product; very-large-scale integration; digital circuits; digital signal processing Article
## 3 Results
### Results of Economic Evaluation  
With the developed automated sample preparation methods, up to 96 samples (serum, saliva, urine) can be processed in parallel per run and deck. Depending on the cycle time of the method, this results in a maximum number of samples that can be processed per year, which is 72 T samples/year for serum and urine and 192 T samples/year for saliva (see Table 4). Depending on the level of operating and investment costs, the costs per sample are calculated. Costs for automated determination of \(\Delta\)9-THC-D\({}_{3}\) and metabolites from serum are 7.59 \(\upepsilon\)/sample, from urine 6.65 \(\upepsilon\)/sample for urine and 5.93 \(\upepsilon\)/sample for saliva. For comparison purposes, the number of samples per year obtained with manual methods is calculated. Findings indicate that a laboratory technician can prepare more serum or urine samples per year than the automated setup (serum manual/automated: 100.8 T/72 T; urine manual/automated: 96 T/72 T). For Saliva, the Biomek i7 can process more samples/year than a lab technician (192 T/115.2 T). Decreased costs per sample for automation (5.93 \(\upepsilon\)/9.58 \(\upepsilon\)) are a direct result of it. For serum and urine, costs per sample in the automated method (7.59 \(\upepsilon\) and 6.65 \(\upepsilon\)) are less expensive than with the manual method (10.87 \(\upepsilon\) and 11.02 \(\upepsilon\)), despite the lower number of units. This is due to the higher total costs of the manual methods per year (serum m/a: 1.095 T/546 T and urine m/a: 1.058 T/478 T), where the cost driver is the operating costs and in these the high staff costs. The NPV of automation is with 1.533 T for THC from serum, 2.167 T from urine and 8.920 T from saliva significantly higher than with the manual method with 166 T from serum and 1.247 T from saliva. For manual urine sample preparation, the NPV is even negative at \(-\)16 T, which means that a progressive loss is made with the method and this method should not be performed manually. For saliva and serum, the NPV is also comparatively low and close to the level of break-even. The highest automated NPV is reached with the Saliva sample preparation method. In cases of an irrelevant matrix, THC should be determined from saliva rather than from urine or serum.
## 2 Materials and Methods
### Hardware Development and Layer Description
#### 2.2.4 Layer 4: Host Platform  
This layer performs data collection, processing, calibration, conversion, and transmission. We selected a \(nrf\)51822-based microcontroller (\(BTL3H3\), iProtoki, Oulu, Finland). The circular platform (33 mm in diameter) is enriched with accelerometer, gyroscope, and magnetometer for motion tracking, air temperature, pressure, and humidity sensors, as well as internal memory. The sensors and memory components utilize the IIC bus with different sampling rates (Figure 4). We also apply the ambient physical parameters (air pressure, air temperature, and air humidity) for gas sensor calibration.
## 3 Experimental Results and Analyses
### Representative Stress-Strain Curves  
To further explore the effect of the different frequencies on the properties of the rocks, the stress-strain curves were plotted, as shown in Figure 6.  
Figure 5: Typical loading paths for the argillaceous shale samples under the different frequencies: (**a**) 0.5 Hz, (**b**) 0.2 Hz, (**c**) 0.1 Hz, and (**d**) 0.05 Hz.  
Figure 6: Typical stress–strain curves for the argillaceous shale samples under the different frequencies: (**a**) 0.5 Hz, (**b**) 0.2 Hz, (**c**) 0.1 Hz, and (**d**) 0.05 Hz.  
Based on the full stress-strain curve diagrams, under the fatigue loading, a sharp leaf-shaped blank area existed between the loading and unloading curves. When the loading stage curve was above the unloading stage curve, the stress and strain were not synchronized. The morphology of the hysteresis curve varied with the loading time and then changed from sparse to dense before the samples were destroyed. The sparse form indicated a large plastic deformation owing to the increase in the stress amplitude, after which the formed cracks steadily closed. Subsequently, the hysteresis curve became dense, and the rock fatigue damage caused by each additional unloading cycle was significantly reduced. However, the final loading stage was different from the previous stage. The hysteresis curve was very sparse because the sample was in the destruction stage, which accelerated the deformation rate, resulting in a large deformation over a small number of cycles. This demonstrated that the variations in the internal structure of the rock could be reflected in the morphological characteristics of the hysteresis curve.
## 2 Testing Spacecrafts and Data Collection  
### Testing Spacecrafts  
In this study, the GRACE-FO and CentiSpace-1 satellites were selected as testing spacecrafts. The GRACE-FO [17] mission is one joint project between the National Aeronautics and Space Administration (NASA) and the German Research Centre for Geosciences (GFZ), providing continuity for the GRACE data set. Similarly to GRACE (GRACE-A and GRACE-B), the GRACE-FO consists of two satellites, GRACE-C and GRACE-D, and they are equipped with scientific instruments, such as GPS receivers and star cameras [29]. Since these two satellites are almost identical, and given the lack of data on GRACE-D in our testing period, we only select GRACE-C (abbreviated as GRCC) as one of our research objects.  
The CentiSpace-1 (abbreviated as CS01) [30] is the first technology experiment satellite with an LEO satellite navigation enhancement system, developed by Beijing Future Navigation Technology Co. Ltd. The CS01 satellite is equipped with a GNSS receiver and laser inter-satellite crosslink instrument. The primary mission of CS01 is to explore the capability of the LEO-based augmentation system. This enhancement system will consist of more than 120 LEO satellites and deliver high accuracy and low-cost service [31].