esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Background
### \(SpO_{2}\) Calculation  
To calculate the \(SpO_{2}\) values from the PPG signals, we followed the method in [20]. The ratio \(R_{SpO_{2}}\) was calculated from the ratio of the normalized intensity of the received green light (\(I_{n_{\lambda 1}}\)) to red light (\(I_{n_{\lambda 2}}\)), which is expressed as Equation (24):  
\[R_{SpO_{2}}=\frac{\Delta A_{\lambda 2}}{\Delta A_{\lambda 1}}=\frac{ln(I_{n_{ \lambda 2}})}{ln(I_{n_{\lambda 1}})}. \tag{36}\]  
As light passes through the additional optical path \(\Delta d\) at systole, from Equation (11), it is written as  
\[d_{1}=d_{2}+\Delta d. \tag{37}\]  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Substance**} & \multicolumn{2}{c}{**Absorption Coefficient (cm\({}^{-1}\))**} \\ \cline{2-3}  & \(\mathbf{\lambda 1}\) = 525 nm & \(\mathbf{\lambda 2}\) = 615 nm \\ \hline \(HbA1c\) & 1058.4641 & 396.6405 \\ \hline \(HbO\) & 71.8205 & 2.7126 \\ \hline \(HHb\) & 81.7926 & 17.566 \\ \hline Skin Baseline & 1.0966 & 0.6552 \\ \hline Water & 0.0003927 & 0.0027167 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Absorption coefficients for the whole finger model.  
The normalized intensity of the received light at wavelength \(\lambda\) can be expressed as  
\[I_{n_{\lambda}}=\frac{I}{I_{H_{H_{2}}}}, \tag{38}\]  
where \(I\) represents the light intensity received by the photodetector (PD) and \(I_{H_{2}}\) represents the highest intensity at diastole. The absorbance at wavelength \(\lambda\) can be found using the concentrations of oxyhemoglobin and deoxyhemoglobin as follows:  
\[\Delta A_{\lambda}=(\epsilon_{HbO}(\lambda)\times c_{HbO}+\epsilon_{HHb}( \lambda)\times c_{HHb})\times\Delta d. \tag{39}\]  
Now, replacing \(c_{HbO}\) and \(c_{HHb}\) in Equation (39) using Equation (4), we obtain  
\[\begin{array}{c}\Delta A_{\lambda}=(\epsilon_{HbO}(\lambda)\times SpO_{2}(c_ {HbO}+c_{HHb})+\epsilon_{HHb}(\lambda)(1-SpO_{2})(c_{HbO}+c_{HHb}))\times\Delta d \text{ or }\\ \Delta A_{\lambda}=(\epsilon_{HbO}(\lambda)\times SpO_{2}+\epsilon_{HHb}( \lambda)(1-SpO_{2}))\times(c_{HbO}+c_{HHb})\times\Delta d.\end{array} \tag{40}\]  
Now, (36) can be expressed as  
\[R_{SpO_{2}}=\frac{\Delta A_{\lambda 2}}{\Delta A_{\lambda 1}}=\frac{(\epsilon_{HbO}( \lambda 2)\times SpO_{2}+\epsilon_{HHb}(\lambda 2)(1-SpO_{2}))\times(c_{HbO}+c_{HHb})} {(\epsilon_{HbO}(\lambda 1)\times SpO_{2}+\epsilon_{HHb}(\lambda 1)(1-SpO_{2})) \times(c_{HbO}+c_{HHb})}. \tag{41}\]  
Finally, the oxygen saturation (\(SpO_{2}\)) can be calculated as  
\[SpO_{2}=\frac{\epsilon_{HHb}(\lambda 2)-(\epsilon_{HHb}(\lambda 1)\times R_{SpO _{2}})}{(\epsilon_{HHb}(\lambda 2)-\epsilon_{HbO}(\lambda 2))+\left(\epsilon_{HbO}( \lambda 1)-\epsilon_{HHb}(\lambda 1)\times R_{SpO_{2}}\right)}. \tag{42}\]
# Noncoherent Channel Combining for GNSS Signal Tracking with an Adaptive Antenna Array
## 3 Proposed Method
### Interference Suppression  
This subsection implements interference suppression based on the subspace projection technique.  
The GNSS signal is much weaker than the noise, while the interference is assumed to be much stronger. Therefore, \(\mathbf{R}_{xx}\) can be separated into auto-correlation matrices of the interference and noise, \(\mathbf{R}_{jj}\) and \(\mathbf{R}_{nn}\), as follows [16]  
\[\mathbf{R}_{xx}\approx\mathbf{R}_{jj}+\mathbf{R}_{nn}=\sum_{q=1}^{Q}p_{q} \mathbf{a}_{q}\mathbf{a}_{q}^{\mathrm{H}}+\sigma_{\mathrm{n}}^{2}\mathbf{I} \tag{8}\]  
where \(p_{q}\) denotes the power of the \(q\)th jammer, and \(\mathbf{I}\) stands for the identity matrix. The eigenvalue decomposition of \(\mathbf{R}_{xx}\) is given by  
\[\mathbf{R}_{xx}=\sum_{n=1}^{N}\lambda_{n}\mathbf{u}_{n}\mathbf{u}_{n}^{ \mathrm{H}} \tag{9}\]  
where \(\lambda_{n}\) and \(\mathbf{u}_{n}\) represent the \(n\)th eigenvalue and the associated orthonormal eigenvector of \(\mathbf{R}_{xx}\), respectively. In the case of strong interference, the eigenvalues can be arranged in nonincreasing order:  
\[\lambda_{1}\geq\lambda_{2}\geq\cdots\geq\lambda_{Q}\gg\lambda_{Q+1}=\cdots= \lambda_{N}=\sigma_{\mathrm{n}}^{2} \tag{10}\]  
Using Equation (10) in Equation (9), \(\mathbf{R}_{xx}\) can be rewritten as  
\[\mathbf{R}_{xx}=\sum_{n=1}^{Q}(\lambda_{n}-\sigma_{\mathrm{n}}^{2})\mathbf{u} _{n}\mathbf{u}_{n}^{\mathrm{H}}+\sigma_{\mathrm{n}}^{2}\mathbf{I} \tag{11}\]  
According to Equations (9) and (10), the inverse of \(\mathbf{R}_{xx}\) is obtained as  
\[\begin{split}\mathbf  
be effectively suppressed by projecting the signal vector into the interference-free subspace. However, the satellite signal can also be attenuated, which is analyzed in the next subsection.
## 4 Results
### Synthetic Experiments and Sensitivity Analysis
#### 4.1.2 Drip Irrigation  
The results of the synthetic drip irrigation experiment are shown in Figure 11 in the Appendix A. The overall behavior of the results is not as conclusive as for the flood irrigation. Yet, for an error <2%, the metrics are good for the three window lengths, while above 4%, no overall behavior is observed, as 10% error yields comparable results to the <2% error. The bias is slightly better for a 3-day revisit time than with 6-day, but both shorter revisit times outperformed the 12-day one. The loss of performance is also clear at 12 days for error above 6% as expected. Indeed, the highest RMSE and bias are obtained for the 12-day window at a 10% error on SSM (RMSE = 28.65 mm and bias = 21.71 mm).  
As for the flooding technique, the 3-day revisit time window performs better than the 6- and 12-day windows time steps. Likewise, very accurate estimation of SSM is needed for a good irrigation timing retrieval, in particular when irrigation events are concomitant with rainfall. This may be explained by the limited information provided by SSM as the upper soil reservoir may dry out quickly all the more in the semi-arid sites considered in this study.
## 6 Evaluation of the Proposed Protocol
### Communication and Computational Complexity Evaluation
\begin{table}
\begin{tabular}{|c|c|c|c|c|c|c|} \hline \hline
**Phase** & **Node’s Role** & **SLEACH** & **SecLEACH** & **MS-LEACH** & **ESPDA** & **The Proposed Protocol** \\ \hline \multirow{3}{*}{Setup} & CH & \(3C(h)\) & \(nc(h)\) & \(6C(h)+C(enc)\) & \multirow{3}{*}{NA} & \(\mathcal{C}(\sigma_{g})+C(\sigma_{v})+(2n+3)C(h)+2C(enc)\) \\ \cline{2-6}  & Ord. Node & \(2C(h)\) & \(C(h)\) & \(5C(h)+C(enc)\) & & \(2C(\sigma_{v})+3C(h)+2C(enc)\) \\ \hline \multirow{3}{*}{Data Aggregation} & CH & & & & 0 & \((2n+n_{s}+3)C(h)\) \\ \cline{2-6}  & \multirow{3}{*}{Ord. Node} & \multirow{3}{*}{NA} & \multirow{3}{*}{NA} & \multirow{3}{*}{NA} & \(\mathcal{C}(h)+\mathcal{C}(enc)\) (*), & \(4C(h)+C(enc)\) (*), \\ \cline{1-1} \cline{4-6}  & & & & & Otherwise & otherwise \\ \cline{1-1} \cline{4-6}  & & & & & **0** & \(3C(h)\) \\ \hline \hline \end{tabular}
\end{table} 
Table 15: Computational cost comparison. that the proposed protocol is the most expensive one in its classes, especially in setup phase. If we refer to the Table 2 on how we fulfill the security requirements of the proposed protocol summarized in Table 1, we can see that the proposed protocol uses cryptographic approaches to meet them. This cryptographic approach is a kind of trade-off between computation cost and security level since the proposed protocol can tackle all listed possible attacks at the sacrifice of computational burden (readers may refer to Tables 3 and 4 in the security evaluation section).
# An Efficient Model-Based Clustering via Joint Multiple Sink Placement for WSNs
## 1 Introduction
In our study, the execution time is a carried-out concern, so a parallel search is needed; (iii) it is scalable in clustering issues because of its distributed nature [41]. In this research, we address the problem of clustering in a constrained environment in which we have a high number of varied sensors and features; (iv) it is robust to the impacts of outlier data [42; 43; 44; 45]; (v) it examines the space issue more deeply than greedy algorithms that use local search. According to [46], ant-based algorithms can find near-optimal solutions. The structure of this work is organized as follows: the second section includes a summary and a review of the most recent works on sink placement and data routing paths. In Section 3, we provide a definition of the problem, the ant clustering algorithm, and the adopted model of energy. In Section 4, we present an appropriate way to formulate the proposed method, which deals with four issues: deployment sensors, applied clustering, multiple sinks placements, and data routing paths. In Section 5, we further discuss the main extracted results. Finally, a thorough conclusion and proposal for future work are presented in Section 6.
## 4 Experimental Test
### Effect of Calibration Spacing  
To study the effect of the transverse calibration spacing \(d\) on the identification of the TP and the GVW, the stress response areas were calculated from the measured data with the \(d\) being 1-8 cm. Then, the stress response area matrix was obtained by interpolating to 1 cm intervals. A two-axle test vehicle with a weight of 96.9 kg was pulled across the model at the transverse position x = \(-\)15.5 cm. The proposed method was used to identify the TP and the GVW. The identified results are shown in Figures 18 and 19.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \multirow{2}{*}{**Cases**} & **Practical TP** & **Identified TP** & **Error of TP** & **Identified GVW** & **Error of GVW** \\  & **(cm)** & **(cm)** & **(cm)** & **(kg)** & **(\%)** \\ \hline
1 & \(-\)15.5 & \(-\)15.5 & 0 & 96.9 & 3.37 \\
2 & \(-\)6.5 & \(-\)5.5 & 1 & 96.9 & 1.2 \\
3 & \(-\)3 & \(-\)2.5 & 0.5 & 96.9 & 3.83 \\
4 & 3.7 & 3.5 & 0.2 & 96.9 & 4.69 \\
5 & \(-\)16.3 & \(-\)16.5 & 0.2 & 106.6 & 2.6 \\
6 & \(-\)16.5 & \(-\)16.5 & 0 & 106.6 & 2.32 \\
7 & 4 & 3.5 & 0.5 & 106.6 & 4.19 \\
8 & 11.7 & 11.5 & 0.2 & 106.6 & 2.41 \\ \hline \end{tabular}
\end{table}
Table 4: Identified results of the TP and the GVW.  
Figure 17: The variation curve of the \(CI\) index with the transverse position under case 4.  
It can be seen that when the calibration spacing was less than 6 cm, the identified error of the TP was about 1 cm, and that of the GVW was below 7%. When the calibration spacing continued to increase, the fluctuation of identified TP error became larger and the identified GVW error rapidly increased. Thus, the appropriate calibration spacing \(d\) was no more than 6 cm, which is consistent with the numerical simulation, considering that the experimental test was a 1:6 scaled-down model.
###### Abstract
Furthermore, each region of the cell is sectorized into three equivalent sections using directional antennas. A joint resource allocation and power control scheme using FFR technique for underlay D2D communications was presented in [10]. The authors discussed on distance-based resource allocation scheme for a single cell system model and formulate throughput optimization problem. The results showed that their proposed scheme improves the throughput in comparison with the random resource allocation scheme. However, the paper considered the resource reuse scenario for frequency reuse factor (FRF) of 1, which is not spectrally efficient. In [11], outage probability analysis for D2D communications in FRF of 1 network was discussed. In the paper, the author proposed a location-based outage probability analysis of D2D communications for a multi-cell network. However, for a fast growing demand for high data rate services, there is need to further increase FRF. Fairness allocation of cellular resources among the D2D pairs is an important aspect for underlay D2D communications wherein D2D pairs coexist with cellular users. The authors in [12] proposed a novel resource allocation scheme for D2D communications using FFR and soft frequency reuse (SFR) methods to enhance the coverage probability of the network. Also, the power control method was introduced to analyze the trade-off between the power consumption and performance of the system. In this study, the authors assumed the resource reuse factor of inner and outer cell regions for SFR method to be equal to two and one, respectively. Comparing the result of their scheme with that of unity frequency reuse scheme proves the improvement in system capacity. The disadvantage of this scheme is that the inter-cell interference coordination function is not considered. A proportional fairness-based resource allocation and power control scheme was presented in [13]. In the paper, the authors analyze the time-varying feature of the user's channel condition to maximize the throughput and fairness of the system. The remainder of this paper is organized as follows. In Section 2, related works are discussed. Our contribution in this paper is provided in Section 3. Section 4 explains the system model. In Section 5, the proposed scheme is presented. Section 6 provides performance analysis. Finally, Section 7 concludes the paper.
## 2 Radar Jamming Signal Analysis and Modeling
### Noise Modulation Jamming
#### 2.1.1 Noise Amplitude Modulation Jamming  
Noise Amplitude Modulation Jamming (NAM)) is a targeting technique that is both technically simple and popular on the electronic warfare battlefield. It involves modulating Gaussian white noise within the bandwidth of a radar's linear frequency modulated signal. In other words, it utilizes high-powered modulated noise to suppress the radar signal within a specific frequency band, thus affecting the receiver's ability to detect the signal. This method yields effective jamming results and is relatively straightforward to execute. However, due to its narrowband nature, it is unsuitable for conducting broad-spectrum jamming. The mathematical model for NAMJ is expressed as follows:  
\[J(t)=[U_{0}+K_{AM}U_{n}(t)]\text{exp}\big{(}\omega_{j}+\varphi\big{)} \tag{1}\]  
The function expression is a generalized stationary random process, where \(\text{exp}(\cdot)\) denotes the exponential function with the base e, \(U_{n}(t)\) represents zero-mean Gaussian white noise, \(U_{0}\) stands for the carrier voltage, and the modulation coefficient of the noise, \(K_{AM}\), controls the power of \(U_{n}(t)\). The interfering carrier phase \(\omega_{j}\), representing the phase \(\varphi\) of the jamming, is uniformly distributed in the range \([0,2\pi]\) and is independent of \(U_{n}(t)\)  
Figure 1: The Overall Architecture of the Proposed Radar Jamming Recognition Algorithm.
# Lane following Learning Based on Semantic Segmentation with Chroma Key and Image Superposition
## 3 Materials and Methods
### Solution Design
#### 3.1.2 Working Environment  
A key task is to isolate the road lines from the input images, discarding the other elements. This is a difficult task in real environments. Therefore, we propose the use of a special scenario based on chromas. The materials are selected carefully to allow for the application of the chroma key technique in an HSV color space.  
OpenCV [37] has been used for applying color masks to identify areas of interest in an image based on color similarity. Table 1 includes the boundary values used for masking out each chroma. In OpenCV, the value range for the hue channel is (0, 179), whereas both the saturation and the hue values are in the (0, 255) interval.  
The working environment consists of the following elements, shown in Figure 1:  
* Driving circuit--A track is designed by placing white lines on black cardboard that imitates asphalt. To facilitate the supervised driving and the learning tasks, the track forms a closed loop.
* Target stage--The final learning task is performed in this stage. It serves as the real-world stage.
* Chroma stage--The design of this stage is based on elements with easily separable colors. When the driving circuit is placed inside of this stage, it will be possible to process the captured images to extract either the road or the lines of the road. The isolated road can be combined with the target indoor scenario for training purposes. The following section gives a more detailed view of this process.
# Toward a State-of-the-Art of Fly-Rock Prediction Technology in Open-Pit Mines Using EANNs Model
## 2 Dataset Used  
This study was undertaken at a quarry mine in Vietnam, where the distance from residential was about 450-500 m (Figure 1). Herein, blasting was used as the primary technique for rock fragmentation using 75 mm of the borehole. Although many solutions have been proposed to minimize the distance of fly-rock; however, there are still many potential risks of unsafe activity due to fly-rock.  
In order to investigate the causes, as well as the distance of fly-rock, we collected the basic parameters of 210 blasting events, including explosive charge per delay, powder factor, stemming, spacing, burden, and fly-rock distance, abbreviated as W, PF, ST, S, B, and FR, respectively. These parameters were also used for estimating FR by many previous scientists (e.g., [10; 42; 43; 44]). Subsequently, the experimental datasets were analyzed and visualized.  
To measure the distance of FR in this study, the iGeoTrans app was installed on an iPhone Xs Max, as shown in Figure 2. This app can work on iOS devices, which is a product of Hanoi University of Mining and Geology (HUMG), Hanoi, Vietnam [45]. iGeoTrans used a GPS (Global Positioning System) to mark the positions of blast points and the maximum distance of FR in this study. The remaining parameters (input parameters) were extracted from the blast patterns in blasting design, such as W,
## 3 Methodology
### Indoor Temperature Prediction Algorithms Based on LSTM  
Long Short-Term Memory (LSTM) is an advanced type of recurrent neural network (RNN) designed to effectively capture long-term dependencies in time-series data. It is particularly suitable for modeling continuous time series, such as indoor temperature regulation. Using LSTM, we developed a predictive control algorithm, the details of which are described below. In this LSTM model, the input sequence consists of one year of temperature data, including outdoor and indoor temperatures, collected at each of the 8761 hourly time steps (t = 1 to 8761). The input vector \(x_{t}\) for each time step t is defined by Equation (1) as follows [23; 24; 25; 26].  
\[\text{Input vector }\;x_{t}=[Date_{t},\;T_{outdoor,t},\;T_{indoor,t}] \tag{1}\]  
\(Date_{t}\): encoding representing date/time information.  
\(T_{outdoor,t}\): the outside temperature at time \(t\).  
\(T_{indoor,t}\): the room temperature at time \(t\).  
At each time point \(t\), the following gate operations are performed using the hidden state \(h_{t-1}\) and cell state \(C_{t-1}\) from the previous time step. The fundamental equations of the LSTM are defined in Equations (2)-(7):  
\[\text{Input gate }\;i_{t}=\sigma(W_{t}\cdot[h_{t-1},x_{t}]+b_{i}) \tag{2}\] \[\text{Forget gate }\;f_{t}=\sigma\big{(}W_{f}\cdot[h_{t-1},x_{t}]+b_{f} \big{)}\] (3) \[\text{Cell candidate }\;\widetilde{C}_{t}=tanh(W_{c}\cdot[h_{t-1},x_{t}]+b_{c})\] (4) \[\text{Cell state update }\;\;C_{t}=f_{t}\odot C_{t-1}+\big{(}i_{t} \odot\widetilde{C}_{t}\big{)}\] (5) \[\text{Output gate }\;\;o_{t}=\sigma(W_{o}\cdot[h_{t-1},x_{t}]+b_{o})\] (6) \[\text{Hidden state }\;\;h_{t}=o_{t}\odot tanh(C_{t}) \tag{7}\]  
\(\sigma(\cdot)\): sigmoid activation function (output range 0-1).  
\(tanh(\cdot)\): enable hyperbolic tangent (output range \(-1\) to 1).  
\(W_{t}\), \(W_{f}\), \(W_{c}\), \(W_{o}\), and \(b_{i}\), \(b_{f}\), \(b_{c}\), \(b_{o}\): weight and bias parameters of each gate.  
At the output layer, the predicted temperature is derived from the hidden state \(h_{t}\), providing an estimate of the indoor temperature at the subsequent time step \((t+1)\) as described below:  
\[\hat{T}_{indoor,t+1}=W_{t}\cdot h_{t}+b_{h} \tag{8}\]  
The \(\hat{T}_{indoor,t+1}\) prediction output from the LSTM model, generated using one year's worth of outdoor and indoor temperature data, serves as the target control parameter and exhibits characteristics consistent with the existing control framework. This predicted output is subsequently provided as an input to the DQN algorithm, where it is utilized within the reinforcement learning process. The final LSTM prediction is defined by Equation (8).
# Optimizing Session-Aware Recommenders: A Deep Dive into GRU-Based Latent Interaction Integration
## 4 Experimental Results  
### Datasets and Pre-Processing  
A total of three well-known datasets were used in the experiments: Steam [20], MovieLens 1M [21], and Amazon [22]. The Steam dataset comprises video game platform review data, featuring 2,567,538 users, 15,474 items, and 7,793,069 interaction records. The MovieLens 1M dataset, known for its movie rating data, includes information about item categories and encompasses 1,000,209 interaction records. The Amazon e-commerce dataset focuses on clothing, shoes, and jewelry shopping data from 2012 to 2014, containing 402,093 users, 140,116 items, and 770,290 interaction records. With regard to the data preprocessing method, Steam and MovieLens 1M datasets were preprocessed following the method described in [10], while the Amazon dataset followed the method in [17].  
The preprocessing details are as follows: For MovieLens 1M and Steam, the session time division is set to one day (60 \(\times\) 60 \(\times\) 24 s); the maximum session length is capped at 200 for MovieLens 1M and 15 for Steam. The data filtering conditions are consistent across datasets. For each user, there must be at least two sessions, and each session must have a minimum of two interactions. Each interaction (i.e., item ID) should occur at least five times, and the number of user interaction records (i.e., interaction number of items) should be five or more. Additionally, the ratio of training to testing data is set at 8:2.  
For the Amazon dataset, session division is based on interactions occurring at the same time point, with a maximum session length of 20 and a minimum of three sessions. The maximum session lengths for Steam, MovieLens 1M, and Amazon are 15, 200, and 20, respectively. The ratio of training to testing is also 8 to 2. Table 2 lists the characteristics of datasets after pre-processing.