esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Error Correction Codes in Wireless Sensor Network
### Low Density Parity Check Codes  
Low density parity check codes are codes specified by a matrix containing mostly 0's and relatively few 1's. A standard bipartite graph based ensemble which is shown in [21, 22] is used in this paper. The code length is designated by \(n\) and number of constraints by \(m\). Therefore, there are \(n\) variable nodes and \(m\) check nodes. Each variable node corresponds to one bit of the codeword and each check node corresponds to one parity check equation. Edges in the graph connect variable nodes to check nodes and represents the nonzero entries in \(H\) matrix. The term \"low density\" conveys the fact that the fraction of nonzero entries in \(H\) is small, in particular it is linear in the block length \(n\), as compared to \"random\" linear codes for which the expected fraction of ones grows like \(n^{2}\)[23].  
For regular codes, the corresponding \(H\) matrix has \(\delta_{r}\) ones in each row and \(\delta_{c}\) ones in each column. It means that every codeword bit participates in exactly \(\delta_{c}\) parity-check equations and that every such check equation involves exactly \(\delta_{r}\) codeword bits. Low density parity check codes have been constructed mostly using regular random bipartite graphs.  
**Example 1**. Here is an example of a regular parity check matrix with \(\delta_{r}=6\) and \(\delta_{c}=3\).  
\[H=\left[\begin{array}{ccccccccccccc}1&1&1&0&0&1&1&0&0&0&1&0\\ 1&1&1&1&0&0&0&0&0&0&1\\ 0&0&0&0&0&1&1&1&0&1&1\\ 1&0&0&1&0&0&0&1&1&1&0&1\\ 0&1&0&1&1&0&1&1&1&0&0&0\\ 0&0&1&0&1&1&0&0&1&1&1&0\end{array}\right]\]  
The bipartite graph corresponding to this parity check matrix is shown in Figure 3.  
For irregular codes, \(\delta_{r}\) and \(\delta_{c}\) are not fixed for every row and column of the parity check matrix. We consider that the irregular bipartite graph has a maximum variable side degree \(\delta_{r}\) and a maximum check side degree \(\delta_{c}\).  
**Example 2**. The following \(H\) matrix is an example of an irregular parity check matrix with a maximum \(\delta_{r}=6\) and a maximum \(\delta_{c}=3\).  
\[H=\left[\begin{array}{ccccccccccccc}1&0&1&0&0&1&1&0&0&0&1&0\\ 1&1&0&1&1&0&0&0&0&0&0&1\\ 0&0&0&0&0&1&1&1&0&1&1\\ 1&0&0&1&0&0&0&1&1&1&0&1\\ 0&1&0&1&1&0&1&1&1&0&0&0\\ 0&0&1&0&1&1&0&0&0&1&0&0\end{array}\right]\]  
The bipartite graph corresponding to this parity check matrix is shown in Figure 4.  
For this paper, H matrix shown in Figure 5 is used for simulation. This H matrix is a special matrix used in 802.11n standard. WSN is energy constraint in nature and the sensors work as intermediate devices when the data are transferred from a designated area to the data gathering node (DGN). Since  
Figure 3: Bipartite graph corresponding to a regular parity check matrix.  
decoding can be performed in the DGN, energy efficient decoding technique is not a concern for this paper. Encoding is one critical issue considered in the wireless sensor network. In this paper, Richardson encoding scheme is used as a tool for using LDPC code in WSN and is explained in the next subsection.
# Robust Estimators in Geodetic Networks Based on a New Metaheuristic: Independent Vortices Search
## 2 Classical and Robust Approaches
### Least Squares Method  
The adjustment of geodetic networks applying the LS method is given by the Gauss-Markov Model [1]:  
\[\mathbf{Ax}=\mathbf{l}+\mathbf{v}, \tag{1}\]  
where \(\mathbf{A}\) is the design matrix of the partial derivatives, \(\mathbf{x}\) is the parameter correction vector (for point co-ordinates) of the linearized model, \(\mathbf{l}\) is the observations vector minus the calculated data, and \(\mathbf{v}\) is the residuals vector. The calculation of \(\mathbf{A}\) results in a matrix formed by the values 0, 1, and \(-1\), based on the partial derivatives of the observation equations:  
\[\Delta X_{AB} =X_{B}-X_{A}, \tag{2}\] \[\Delta Y_{AB} =Y_{B}-Y_{A},\] \[\Delta Z_{AB} =Z_{B}-Z_{A},\]  
representing the distance between the approximate parameters on each axis.  
The solution of the conventional LS adjustment is given by [9]:  
\[\mathbf{x}=\left(\mathbf{A^{T}WA}\right)^{-1}\mathbf{A^{T}WI}, \tag{3}\]  
where \(\mathbf{{}^{T}}\) represents the matrix transpose and \({}^{-1}\) represents matrix inversion. The quantity \(\mathbf{W}\) is the weight matrix of the observations [1]:  
\[\mathbf{W}=\sigma^{2}\mathbf{\Sigma}^{-1}, \tag{4}\]  
where \(\mathbf{\Sigma}\) is the covariance matrix of the observations and \(\sigma^{2}\) is the a priori variance factor.  
The final solution is given by Equation (5):  
\[\mathbf{\hat{x}}=\mathbf{x_{0}}+\mathbf{x}, \tag{5}\]  
where \(\mathbf{x}\) is the parameters' correction for the initial approximate parameter vector \((\mathbf{x_{0}})\), and \(\mathbf{\hat{x}}\) the vector of adjusted parameters containing the final co-ordinates of the network points.
# A Video-Based Fire Detection Using Deep Learning Models
## 4 Experiments and Results
### Training Faster R-CNN and Its Accuracy  
Faster R-CNN requires still images for training data so we collected them from several data sources. Some fire and smoke images were taken from Youtube video clips. Also, the same data as the previous works were added [6; 24; 25; 26], which include still images and frames taken from video clips. In addition, the Flickr-fire dataset was included in our dataset. Finally, we constructed a dataset of 73,887 still images, consisting of 22,729 flame, 23,914 smoke, and 27,244 non-fire images. The images are divided into 75% for training, 15% for validation, and 10% for test data. For training, the data are augmented by a horizontal flip. Table 1 shows the training parameters for Faster R-CNN.  
The performance of the Faster R-CNN is measured by mAP and is shown in Table 2. The sample results of the flame and smoke detection are shown in Figure 9. There are several false positive detections for clouds, chimney smoke, lighting lamp, steam, etc., which are almost undetectable without considering the temporal characteristics.
## 3 Experimental Setup
### Supervised Classification Method  
To validate our supervised approach, we used several methods: a support vector machine (SVM), a kNN classifier [29], and ensembles with a random forest (RF) classifier.  
To evaluate the performance of the methods in the 15 \(\times\) 15 plus the 4 \(\times\) 4 subgrid with data from one working day, we used the following hyper-parameters for the algorithms, as explained below:  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline
**Cellular** & **Time** & **cc** & **smsin** & **smsout** & **Callin** & **Callout** & **Internet** \\ \hline
5201 & 4 November 2013 16:30 & 380 & 0.0 & 0.0 & 0.0 & 0.0969 & 0.0 \\
5201 & 4 November 2013 16:30 & 39 & 209.2105 & 126.6456 & 195.0901 & 210.6397 & 1983.0141 \\
5201 & 4 November 2013 16:30 & 40 & 0.5123 & 0.0 & 0.0 & 0.0 & 0.0 \\
5201 & 4 November 2013 16:30 & 44 & 0.0 & 0.0 & 0.0 & 0.0 & 0.0327 \\ \hline \end{tabular}
\end{table}
Table 1: Sample of the CDR dataset for the cellid 5201 (Centro Storico).  
* SVM with a linear kernel (SVM-L);
* SVM with a polynomial kernel (SVM-P);
* SVM with a sigmoid kernel (SVM-S);
* SVM with a radial basis function (rbf) kernel and a one-vs.-one approach (SVM-RO);
* SVM with an rbf kernel and C = 0.1 (SVM-RC);
* kNN with 5 and 10 neighbors and Manhattan (kNN-10M, kNN-5M), Euclidean (kNN-10E, kNN-5E), and complexity-invariant (CI) distances (kNN-10C, kNN-5C);
* RF with 50,100, 200, 250, 500, 750, 1000, and 1500 trees;
* DL with three layers using the cross-entropy loss function.  
To obtain the most suitable method and the mobile traffic metric that yields the most appropriate training set, we conducted a stratified 10-fold cross-validation for each metric of mobile traffic, including incoming SMS, outgoing SMS, incoming calls, outgoing calls, and internet traffic. We used a stratified k-fold in order to preserve the percentage of samples for each class to avoid dealing with imbalance in the class distributions.  
The resulting results will guide us in selecting the optimal method and mobile traffic metric for the entire grid. Subsequently, we will apply the best method to the entire grid and evaluate its effectiveness in predicting home, work, and forest areas across the entire province of Trento.
## 4 Research Methodology
### Performance Metrics  
This study investigates four important performance metrics to compare the reliability and effectiveness of routing protocols in the three simulation scenarios. These performance metrics are: (1) PDR; (2) latency; (3) throughput; and (4) energy consumption.  
PDR displays the proportion between the number of data packets received by the destination and those that are sent by the source. The PDR measures the total number of transmitted packets to the total packet drop for all UAVs in the network during 600 s. Therefore, the PDR analyzes the network's overall routing protocols and not an individual node. Based on the formula below, this metric is calculated as in Equation (6):  
\[PDR=\frac{R_{pkt}}{T_{pkt}} \tag{6}\]  
\(R_{pkt}\) is the number of packets received by the destination UAV.  
\(T_{pkt}\) is the number of packets transmitted by the source UAV.  
The duration of the data packet's transmission from the source UAV to the destination UAV is known as _latency_. Latency is measured by msec. It is noteworthy that a mobility model with minimum latency is required for real-time applications. The following formula in Equation (7) can be used to determine this metric:  
\[Latency=T_{des}-T_{src} \tag{7}\]  
where \(T_{des}\) is the time of reaching the data packet destination UAV; \(T_{src}\) is the time of transmitting the packet from the source UAV.  
Energy consumption is the energy consumed by the UAV during the flying mission. It includes the energy required for transmitting, receiving, forwarding data packets, and computing in a UAV. We set the initial energy for each UAV to 1 J, and then calculated the remaining energy at the end of the simulation. This way, we determined the energy consumption of UAVs employing routing protocols. Energy consumption was measured using Equation (8):  
\[Energy_{con}=Energy_{src}-Energy_{R} \tag{8}\]  
* \(Energy_{R}\) is the remaining energy at the UAV after the end of the mission;
* \(Energy_{src}\) is the source energy set up for UAV;* \(Energy_{con}\) is the energy consumed by the UAV during its mission.  
Throughput was calculated by dividing the total number of packets delivered by the destination UAV during the simulation by the simulation's duration. Throughput is measured by bit/sec and can be expressed with the following equation:  
\[Throughput=\frac{\sum R_{\mathit{pkt}}*Packetsize*8}{T_{sim}} \tag{9}\]  
where \(T_{sim}\) is the simulation time.
## 3 MIMO Antenna Configuration Analysis
### Parallel Placement  
In the first case, both antenna elements are placed parallel to each other with no changes in the radiating patch. The two-port MIMO antenna geometry along with the fabricated prototype is given in Figure 6. The two-port MIMO antenna has an overall size of M\({}_{\text{X1}}\)\(\times\) M\({}_{\text{Y1}}\) = 20.5 mm \(\times\) 12 mm. All of the parameters have the same value and same characteristics of substrate material, as discussed above. The space between two elements is fixed at G\({}_{\text{1}}\) = 4.5 mm to nullify mutual coupling between them. To verify the suggested concept and methodology, the measurement of the prototype is performed and results are compared with predicated results obtained by EM tool HFSSv9 (High Frequency Structure Simulator).  
Figure 5: S-parameter measurements of (**a**) single-element (**b**) MIMO-antenna. (**c**) Far-field measurement setup, (**d**) single element and (**e**) MIMO element.  
Figure 6: Schematic of MIMO antenna for parallel placement of elements.  
The scattering parameters of proposed two-port MIMO antenna operating over 28 GHz is given in Figure 7a. It can be verified from the depicted figure that the antenna offers a wide impedance band of 4 GHz ranging from 25.5-29.5 GHz. The two-port MIMO antenna system provides a return loss \(<\)\(-\)25 dB at resonance frequencies. The suggested design gives an excellent reading of transmission co-efficient. It is clear from the figure that the antenna offers S\({}_{12}\) and S\({}_{21}\) of \(<\)\(-\)35 dB at the operational band. The measured and predicated results did not have any significant differences which makes the suggested antenna a good applicant for future wideband MIMO antennas.  
The AR (Axial Ratio) of the suggested antenna is depicted in Figure 7b. The proposed concept offers LHCP (Left Hand Circularly Polarization) and RHCP (Right Hand Circularly Polarization), as given. The predicated results, with the help of software, are verified by measuring hardware prototype results. It can be seen from the figure that the two-port MIMO antenna system offers a wide axial ratio bandwidth of 2.5 GHz of RHCP ranging from 26.3-28.7 GHz and 2.2 GHz of LHCP ranging from 26.7-28.5 GHz. The measured and simulated results have no major differences which make the suggested antenna a good candidate for future circularly polarized antenna.  
The far-field parameters of the suggested antenna are also examined for further validation. The frequency versus gain plot, along with radiation efficiency, is given in Figure 8. The suggested design gives gain \(>\)7.75 dBi at operational bandwidth of 25.5-29.5 GHz with peak gain of 8.1 dBi at a resonance frequency of 28.5 GHz. Moreover, the radiation efficiency of \(>\)97.5% noted over operating bandwidth with a value of 98% is examined at 28.5 GHz. The predicated and tested result are quite similar, which implies that the proposed antenna is a good candidate for future high gain and high efficiency MIMO antenna devices.
## 3 Proposed TH-CPSK-CEMC System  
In CPSK-CEMC systems, signal shifting, caused by the delay spread characteristics inherent in the MC channel, can lead to errors, even in the absence of external noise. To address this challenge, we introduce the time-hopping concentration-position shift keying (TH-CPSK) modulation technique, which utilizes multiple positions for signal transmission instead of relying on a single position. This method effectively distributes the impact of noise or interference across different positions, thereby reducing the probability of error at any given position. Furthermore, TH-CPSK leverages multiple time slots for signal transmission, diminishing the influence of fading on specific transmission paths. An additional advantage of TH-CPSK is its more efficient utilization of spectrum resources, achieved through time-hopping signal transmission, which optimizes bandwidth use. In particular, the time-hopping mechanism employs multiple positions and thus diminishes interference in a multiuser scenario.  
Figure 2: Block diagram of CPSK-CEMC system with modulation order \(M=4\).  
Figure 3 presents a block diagram of a CEMC system, utilizing TH-CPSK with \(M=2\). As shown in the figure, the transmission of data bit \"1\" is encoded at a concentration of \(Q_{s}/2\) at the second position within each time slot. The signal emanates from the TN and traverses a free-diffusion channel to reach the RN. Upon arrival, the signal, denoted as \(U(r,t)\), can be detected by the RN through the LRBP. However, our analysis primarily examines the efficacy and reliability of the TH-CPSK scheme for multiple access, thus excluding LRBP considerations from this discussion. Instead, we assume a high density of receptors on the RN's surface, ensuring efficient contact between available molecules and receptors [31; 32]. During the decoding phase, a soft decision methodology is employed, aggregating the concentrations at the first and second positions within each time slot. The position exhibiting the higher concentration level is then identified, facilitating the estimated determination of the received symbol position. This process culminates in the decoding of the final output bits at the RN.
## 6 Results
### Energy Mode Consumption
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \begin{tabular}{c} **Energy Mode** \\ **(EFR32 Name)** \\ \end{tabular} & \begin{tabular}{c} **EFR32 BG22** \\ **Manufacturer** \\ **Value** \\ \end{tabular} & \begin{tabular}{c} **EFR32 BG22** \\ **Measured Value** \\ \end{tabular} & \begin{tabular}{c} **ESP32-Equivalent** \\ **Consumption**[74] \\ \end{tabular} & \begin{tabular}{c} **CC2640-** \\ **Equivalent** \\ **Consumption**[75] \\ \end{tabular} & \begin{tabular}{c} **NRF52840-** \\ **Equivalent** \\ **Consumption**[76] \\ \end{tabular} \\ \hline \begin{tabular}{c} EM0 + RX \\ (Active + Receiving Mode) \\ \end{tabular} & 3.6 mA & 5.12 mA & 95 mA & 5.9 mA & **4.6 mA** \\ \hline \begin{tabular}{c} EM0 \\ (Active Mode) \\ \end{tabular} & 1.5 mA & **1.97 mA** & 30 mA & 3 mA & 3.3 mA \\ \hline \begin{tabular}{c} EM1 \\ (Sleep) \\ \end{tabular} & 0.96 mA & 1.16 mA & **0.8 mA** & - & - \\ \hline \begin{tabular}{c} EM2 \\ (Deep Sleep) \\ \end{tabular} & 1.4 \(\mu\)A & **3.39 \(\mu\)A** & - & 0.4 mA & - \\ \hline \
\begin{tabular}{c} EM3 \\ (Stop) \\ \end{tabular} & 1.34 \(\mu\)A & **1.03 \(\mu\)A** & 10 \(\mu\)A & 100 \(\mu\)A & 1.5 \(\mu\)A \\ \hline
\begin{tabular}{c} EM4 \\ (Shutoff) \\ \end{tabular} & 440 nA * & 442 nA * & 5 \(\mu\)A & **100 nA** & 400 nA * \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Measurement results in different EM compared with that of other solutions. energy-efficient, particularly when in sleep mode, although it was the only one that was experimentally tested.
# Modeling the Submergence Depth of Oil Well States and Its Applications
## 3 Results
### Error Analysis  
#### 3.2.1 Number of Iterations and Error Estimation  
The corresponding numerical solutions for \(x\), \(C_{f}\), and \(C_{n}\) in Formulas (5) and (8) are denoted by \(\overline{x}\), \(\overline{C_{f}}\), and \(\overline{C_{n}}\), respectively. For a given error \(0<\varepsilon<1\), \(|\overline{x}-x|<\varepsilon\) is needed. \(M\) denotes the number of iterations, satisfying \(\varepsilon\geq 2^{-M}\). Therefore, the maximum number of iterations of the dichotomy is as follows:  
\[M=1-int\bigg{[}\frac{ln(\varepsilon)}{ln(2)}\bigg{]}. \tag{30}\]  
For the nonlinear-interpolation method, the error in the comprehensive seepage parameter of the full-pumping state model satisfies the following:  
\[\begin{array}{l}\Big{|}\overline{C_{f}}-C_{f}\Big{|}&<\Big{|}\overline{C_{f} }-C_{f}\Big{|}+\frac{|\overline{C_{f}}-C_{f}|^{2}}{2!}+\cdots+\frac{| \overline{C_{f}}-C_{f}|^{n}}{n!}+\cdots\\ &=e^{|\overline{C_{f}}-C_{f}|}-1<\left|e^{-\overline{C_{f}}}-e^{\overline{C_{ f}}}\right|\\ &=|\overline{x}-x|<\varepsilon\end{array}. \tag{31}\]  
Since \(|\overline{C_{n}}-C_{n}|=\Big{|}\overline{C_{f}}-C_{f}\Big{|}\), Formula (31) also holds for the nonfull-pumping state model.  
Both Formula (22) for the full-pumping state model and Formula (24) for the nonfull-pumping state model satisfy the following:  
\[\Big{|}\overline{h}_{E}-h_{E}\Big{|}\leq\beta\cdot\varepsilon, \tag{32}\]  
where \(\beta=\Big{(}1+\frac{p}{C_{f}\cdot S\cdot h_{p}}\Big{)}\frac{|h_{0}-h_{i}|t, \overline{x}_{i}}{\big{(}1-\overline{x}_{i}\big{)}^{2}}+O(\varepsilon)\), \((1\leq i\leq 2)\). It can be seen in Formula (31) that \(0<\overline{x}<1\) holds when \(\varepsilon\) is sufficiently small. Therefore, the larger the value of the sampling time point is, the smaller the \(\beta\) value.  
For the least squares curve-fitting method, the numerical error on the parameter \(C_{f}\) is the same as that for nonlinear interpolation, and the numerical error on the parameter \(h_{E}\) can be obtained from Formulas (27)-(29):  
\[\Big{|}\overline{h}_{E}-h_{E}\Big{|}\leq\Bigg{(}1+\frac{v}{C_{f}\cdot S\cdot h _{p}}\Bigg{)}\Big{|}\overline{H}_{E}-H_{E}\big{|}=\overline{\beta}\cdot\varepsilon. \tag{33}\]  
Here, \(\overline{\beta}=O\big{(}t_{1}\overline{x}^{t_{1}}\big{)}\); that is, the parameter \(\overline{\beta}\) is of the same order as \(t_{1}\overline{x}^{t_{1}}\).  
In fact, it can be seen in Formulas (16) and (26) that when \(N=2\), the solutions obtained through nonlinear interpolation and least squares curve-fitting are the same; that is, the former is a special case of the latter.  
The following takes the pumping process of the oil well in the full-pumping state as an example to verify and analyze the sampling data characteristics and error trends of the nonlinear-interpolation method and the least squares curve-fitting method.
# Fault Detection in Wireless Sensor Networks through the Random Forest Classifier
## 3 Faults in WSNs
### Spike Fault  
When the rate of change of measured time series with forecasted time series is more than the expected changing trend, spikes are created, which is categorized as spike fault. It can be modeled [3] in the way presented in Equation (5).  
\[\frac{|f(t)-f^{p}(t)|}{t}>\lambda \tag{5}\]where \(f(t)\) is the original data, \(f^{p}(t)\) is the predicted time series at time \(t\), while \(\lambda\) represents the normal changing trend in sensed data. The spike fault was also discussed in [8].
## 3 Results
### Effects of HR2302-30E and C3G on Anti-DNP-IgE-Induced Degranulation of RBL-2H3 Cells  
To evaluate the effect of HR2302-30E and C3G on the degranulation of RBL-2H3 cells, the release of \(\beta\)-hexosaminidase and histamine was measured. In activated mast cells, pre-formed \(\beta\)-hexosaminidase and histamine are released into the granules during degranulation [59]. \(\beta\)-hexosaminidase and histamine levels are widely used as markers of mast cell degranulation, which plays an important role in allergic responses [60,61]. To evaluate the inhibitory effect on degranulation, an early marker of allergic response, IgE-primed RBL-2H3 cells were stimulated with 250 ng/mL DNP-HSA and treated with HR2302-30E (1000 and 2000 \(\upmu\)g/mL) and C3G (5 and 10 \(\upmu\)g/mL). The release of \(\beta\)-hexosaminidase and histamine was measured to assess the degranulation of RBL-2H3 mast cells. As shown in Figure 4, HR2302-30E and C3G significantly decreased \(\beta\)-hexosaminidase (Figure 4a) and histamine release (Figure 4b) in activated mast cells. PP2, an Src family kinase inhibitor, was used as a positive control. These results suggest that HR2302-30E and C3G inhibit the activation-induced degranulation of mast cells.