esa-sceva/satcom-chunk-collection huggingface dataset:
# A Hybrid Brain Storm Optimization Algorithm to Solve the Emergency Relief Routing Model
## 6 Experimental Results  
The simulation experiments are divided into three groups to assess the efficacy of the HBSO algorithm. The performance is tested with different numbers of patients, namely 20, 30, and 50. The three algorithms, namely the BSO algorithm, BSO+LNS algorithm, and HBSO algorithm, are compared. Under identical circumstances, these three algorithms are separately executed 30 times.  
The base data in the simulation experiments are as follows: the maximum loading capacity of the vehicle (C) is 300 kg, the map range is a square with a side length of 100 km, the travel speed of the vehicle (\(v\)) is 30 km/h, the penalty function coefficient for the violation of the capacity constraint (\(\alpha\)) is 10, the penalty function coefficient for the breach of time window constraints (\(\beta\)) is 100, the number of populations (\(NP\)) is 50, and the number of clusters is 5, the probability that a cluster center will be replaced by a random solution (\(p_{clustering}\)) is 0.4, the likelihood of selecting a cluster (\(p_{one}\)) is 0.5, the probability of choosing the cluster center in a cluster (\(p_{oncenter}\)) is 0.3, and the likelihood of selecting the cluster centers in 2 clusters (\(p_{trocenter}\)) is 0.2. The geographic locations of the patients are shown separately in the simulation experiments.  
In this paper, the deprivation cost (\(DC\)) of the patient at the time of obtaining relief is also added to the objective function. \(DC\) is a function of the patient's waiting time, which is a multi-stage continuously increasing function from the time a patient is injured and needs relief to the time he successfully receives relief materials. \(DC\) increases with time. In the simulation experiment, we use the following Equation (26) to compute the \(DC\):  
\[DC_{i}=\begin{cases}8t_{i},&0<t_{i}\leq 15\\ 120+16t_{i},&15<t_{i}\leq 50\\ 680+32t_{i},&t_{i}>50\end{cases} \tag{26}\]  
where \(t_{i}\) represents the waiting time from needing relief to receiving it for the patient \(i\) in minutes, and \(DC_{i}\) is the suffering of patient \(i\) from the time of needing to receiving relief supplies.  
During the simulation, time windows of each patient are subtracted from the left time window of the emergency logistics center to obtain the relative time windows of each patient and the emergency logistics center. Then, the hourly time is converted into minutes. The service time of the emergency logistics center is from 0 min (00:00) to 840 min (14:00), and time windows of each patient are also expressed by the corresponding minutes.
## 3 Methodology
### Evaluation Metrics
#### 3.7.2 Evaluation Metrics for Individual Plant-Level Segmentation  
In the instance segmentation experiments at the individual plant level, model performance is measured using Average Precision (AP). AP is a comprehensive metric that considers performance across various confidence thresholds. It is calculated by sampling multiple Intersection over Union (IoU) thresholds and averaging the precision at these thresholds to assess overall model performance. AP\({}_{50}\) refers to AP calculated at a specific IoU threshold of 0.5, while AP\({}_{25}\) refers to AP calculated at an IoU threshold of 0.25. AP is calculated from the precision-recall (P-R) curve, where precision is defined as follows:  
\[Precision=\frac{TP}{TP+FP} \tag{6}\]  
Recall is defined as:  
\[Recall=\frac{TP}{TP+FN} \tag{7}\]  
AP is obtained by plotting precision and recall at various thresholds to form the \(P(R)\) curve, where \(P(R)\) denotes precision at a given recall \(R\).  
\[AP=\int_{0}^{1}P(R)dR \tag{8}\]  
The calculation methods for AP\({}_{50}\) and AP\({}_{25}\) are similar to those for AP. However, a fixed IoU threshold is used to determine precision and recall, plot the \(P(R)\) curve, and compute the area under the curve.
# Tampered and Computer-Generated Face Images Identification Based on Deep Learning
## 4 Experimental Results
### Performance on PGGAN Dataset  
Previous experiments were conducted on MANFA dataset and proved the TFID ability in identifying manipulated face images and solving the IDP with three extensions of the TFID. This experiment verifies whether the proposed model can effectively detect GAN-generated images from PGGAN dataset [7] similar to what it has achieved on tampered face images.  
The PGGAN dataset is configured similar to [1]. The training dataset contains 3750 pairs of real-GAN-generated face images, and the validation dataset has 1250 pairs of real-GAN-generated face images. The parameters of the models in this section were set similar to previous experiments. The classification results are shown in Table 7.  
Overall, all models show high accuracy of over 89%, precision and recall of over 80%, and AUC values of above 0.87. The results indicate that the models could correctly classify whether an image is real or is generated by GAN. In addition, it is noticeable that ensemble-based TFID-XGB and SE-ResNet-50-XGB models achieve better performance in terms of AUC compared to the original TFID and SE-ResNet-50 models. Among the four models, SE-ResNet-50-XGB has the highest classification accuracy of 93% and an AUC value of 0.953, while the proposed TFID-XGB reaches the accuracy of 91% and an AUC value of 0.914.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **Accuracy** & **Precision** & **Recall** & **AUC** \\ \hline SE-ResNet-50 & 0.94 & 0.83 & 0.87 & 0.894 \\ TFID & 0.89 & 0.82 & 0.85 & 0.874 \\ SE-ResNet-50-XGB & 0.93 & 0.89 & 0.93 & 0.953 \\ TFID-XGB & 0.91 & 0.87 & 0.88 & 0.914 \\ \hline \hline \end{tabular}
\end{table}
Table 7: The AUC values on the GAN dataset proposed by [7].  
Figure 10: The computational complexity of the different models on the MANFA dataset.
## 3 Results
### CoP Data  
A graphical synthesis of the average CoP waveforms for each condition is presented in Figure 3a,b.  
A significant difference can be observed in the areas where the graphs corresponding to each of the conditions appear further apart, since this implies an increase in the displacement of the COP. In the case, for example, of anterior-posterior displacement, an increase in the distance between the conditions in the midstance phase and the pre-swing phase can be observed, showing a significant difference in the statistical analysis in this part. Depending on whether it is in positive or negative numerical ranges, the displacement will be anterior or posterior, or medial or lateral.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Demographic Characteristics** & **Mean and SD** \\ \hline Participants & 24 \\ Age (years) & 34.4 \(\pm\) 8 \\ BMI (kg/m\({}^{2}\)) & 23.8 \(\pm\) 2.7 \\ Size shoe & 40.5 \(\pm\) 2.2 \\ Foot posture index & 7.6 \(\pm\) 1.2 \\ Degrees of dorsiflexion (\({}^{\circ}\)) & 42 \(\pm\) 7.7 \\ \hline \hline \end{tabular} SD (Standard deviation); BMI (Body mass index); kg/m\({}^{2}\) (kilograms per meter squared); \({}^{\circ}\) (Degrees).  
\end{table}
Table 1: Demographic characteristics of all participants.  
Concerning the medio-lateral displacement of the CoP, 1D SPM repeated measure ANOVA analysis revealed significant differences during the loading response and mid-stance phase (\(p<0.001\)) (Table 2). A post hoc \(t\)-test indicated that both the CFO conditions were associated with a more medially oriented CoP during midstance (\(p<0.001\) with AFSE CFO and \(p=0.0036\) with cut-out CFO). This difference can be observed in the graphical synthesis as well (Figure 3).  
Figure 3: Graphical synthesis of the average COP deviation waveform during 100% of the stance phase. (**a**) Mediolateral (M-L) deviation waveform, (**b**) anteroposterior (A-P) deviation waveform.  
With respect to the antero-posterior displacement of the CoP, a 1D SPM repeated measure ANOVA analysis revealed significant differences during the midstance and preswing phase (Table 2).  
The table shows which percentage of the stance phase this difference occurs in.  
A post hoc \(t\)-test indicated that the AFSE CFO was associated with a more anteriorly located CoP during midstance compared to the shed without a CFO condition (\(p<0.001\)).  
The post hoc \(t\)-test further highlighted that the cut-out CFO was associated with a more anterior-located CoP during midstance (\(p<0.001\)) and during the pre-swing phase (\(p=0.046\)) compared to the shed without a CFO condition.  
When considering both the orthotic conditions, post hoc analysis revealed that the AFSE CFO was associated with a more anteriorly located CoP during the loading response and midstance phase (\(p=0.018\)) when compared to the cut-out CFO.
# Segmentation of Preretinal Space in Optical Coherence Tomography Images Using Deep Neural Networks
## 4 Methods
### Preretinal Space Segmentation
#### 4.1.5 LFUNet  
The LFUNet network architecture is a combination of UNet [35], and FCN [74] with additional dilated convolutions [75]. In this network, the encoder part is the same as in the original UNet and consists of 4 blocks that contain two convolution layers with kernel size \(3\times 3\), and a \(2\times 2\) px max pooling layer with stride 2.  
The decoder part consists of two parallel paths for UNet and FCN. The UNet path utilizes concatenation of up-sampled feature blocks with the corresponding blocks from the encoder part (a procedure also referred to as \"skip-connections\", that allows exploiting high-resolution information). The FCN path performs the addition of up-sampled feature blocks with the matching encoder blocks. The upsampling in both paths is performed with the \(2\times 2\) px up-convolution layer after each convolution block.  
The additional strength of this network introduces the last part, which is a concatenation of final feature maps obtained from both decoder paths. They are subsequently dilated with three separate kernels, and the resulting matrices are again concatenated before final convolution. The output probability map for each pixel belonging to one of the \(C\) classes was obtained with the Softmax function. ReLu was used as all activation functions in the hidden layers.
# An Accurate Probabilistic Model for TVWS Identification
## 4 Proposed Probabilistic Model
### Threshold Considerations  
As found in the literature of TVWS sensing, the sensed signal \(X(f)\) is compared with Threshold \(T\), which is obtained from  
\[T=\overline{\mathrm{W}}(f)+M \tag{3}\]  
where \(\overline{\mathrm{W}}(f)\) is the average noise floor level in dBm, and \(M\) is a margin in dB. Determining a threshold plays a critical role in classifying PU activities as a busy or as an idle channel, i.e., TVWS [13]. Moreover, it is used to determine the duty cycle of the channel which defined as a ratio of the number of sensing samples above the threshold to the total employed sensing samples. Generally, threshold is defined as an average noise floor level plus some margin. Different existing measurement campaigns conducted by other authors, have employed various margin values such as 5 dB in [25], 7 dB in [26], and ITU uses a 10 dB, while the measurement in campaign [27], considered a fixed threshold of \(=\)\(-\)78 dBm. The main reason of considering the margin is samples of noise measurement are not Gaussian distributed, therefore, adding margin excludes a wrong decision of PU existence by not considering noise as a PU signal [25]. In other words, noise samples cannot reach the threshold when adding margin to the average noise floor level, therefore, false alarm will be reduced as only a PU signal can exceed it. However, such thresholds prevent detecting weak PU signals which might result in missed detection, therefore, a margin value should be selected carefully [28; 29].  
In Figure 5 we can observe \(X(f)\), sampled with RFE in panel (b) and with SA in panel (d). Both representations show the previously discussed values \(M\) and \(T\). It is obvious from these figures that the higher the chosen value of \(M\), the higher the occurrence of signal missed-detection. In the specific case of \(T=\)\(-\)78 dBm, all the 32 channels appears to be idle, which is not necessarily true because at least 8 signal peaks can be observed. The purpose of sensing and plotting two signals, i.e., \(W(f)\) and \(X(f)\), is to identify the characteristics of each signal and compare with each other, in order to obtain enough parameters to develop the proposed probabilistic model.  
The above discussion reveals that in order to sense the UHF-TV spectrum for accurate identification of idle channels, it is imperative to define an adaptive \(T\). The value of \(T\) should satisfy the following conditions: First, it should be close enough to \(\overline{\mathrm{W}}(f)\) to avoid missed detections; Second, it should be far enough from \(\overline{\mathrm{W}}(f)\) to avoid false alarms \((fa)\); And finally, it should be adaptive to the possible changes experienced by \(W(f)\) and \(X(f)\), especially when the M-SSS moves from one to the next location.
# The Influence of Interlock Loss between Rebar and Concrete on Bond Performance of RC Member
## 3 Experimental Results and Discussion
### Strain Distribution of Main Rebar  
Figure 5 shows the strain distribution of the main rebar along the axial direction in each specimen of the PL series, and Figure 6 shows that in the ST series. In order to compare the specimens between threaded bar and PC rod with different strength, the vertical axis shows the ratio of measured strain to yielding strain of each main rebar. All beam specimens failed before the yielding of the main rebar. In the both PL-100 and ST-100, tensile strain was highest at the center of the beam and decreased closer to the supporting points, maintaining the correlation with the flexure moment distribution. Some unevenness can be observed in PL-100, and it is caused by cracks on strain gauges and cracks along the rebar as shown in Figure 5. In the PL-0 and ST-0 specimens, the strain distribution remained in an arch shape until 100 kN, but the strain along the rebar became almost the same value after 150 kN due to chemical bond loss. This was consistent with the result that PL-0 and ST-0 shared the same failure mode. In the cases of 100% and 0% interlocking, the strain distribution trend was the same regardless of placing stirrups. On the other hand, the axial strain distribution clearly differed between ST-50 and PL-50. In ST-50, the axial strain distribution was a similar arch shape to that in ST-100 until beam failure. On the other hand, in PL-50 the strains at each measured point, except for the supporting points (RS1 and RS7), were almost identical past 150 kN. In the visual inspection after 150 kN, the detachment of the main rebar from the upper concrete was observed. The failure mode and the crack distribution in PL-50 also supported that the bond was lost at 150 kN, whereas in ST-50 sound bond was maintained until failure.
## 3 Profile Fitting and Geometric Modeling
### Parameter Measurement Point Positioning  
After the above series of processing of the \"Measurement unit\" image in Figure 3b, four fitted curves are obtained and placed in the weld coordinate system. To facilitate the derivation of the coordinates of the key measurement points, they are rotated clockwise, and the positions of the relevant measurement points are marked, as shown in Figure 9. The problem of solving the coordinates of all key measurement points can be transformed into the problem of finding special points by analytic functions. The location of the \(x\)-axis is the centerline of the weld, the blue dashed line symmetrical about the \(x\)-axis is the weld groove width line, and the line segment HF and the line segment \(O_{i}O_{i+1}\) length are known quantities.  
Figure 8: Comparison of decision coefficients R-square.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Weld Seam** & **Area** & **Fitted Images** & **R-Square\%** & **Curve Functions** \\ \hline \multirow{4}{*}{\(i\)} & Left fusion line & 99.61 & \(y=-0.003033x^{2}+0.4528x+288.9\) \\  & Right fusion line & 94.03 & \(y=0.002698x^{2}-0.5487x-30.78\) \\  & Left fusion line & 96.42 & \(y=-0.003491x^{2}+0.5551x+296.3\) \\  & Right fusion line & 99.25 & \(y=0.002829x^{2}-0.425x-30.18\) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Least squares fitting results.  
Figure 9: Coordinate positioning diagram of key measurement points.  
Point A and point B are the extreme value of the fusion line, and their coordinates can be obtained by calculating the extreme value of the analytic function. The maximum sidewall fusion width \(B_{w}\) is the sum of the absolute value of the vertical coordinates of points A and B. Point C and point D are the intersection of the fusion line and the \(y\)-axis, so that x = 0 to find its coordinates, and the bottom sidewall fusion width \(B_{l}\) is the sum of the absolute value of the vertical coordinates of points C and D. Point E and point F are the intersection of the extension line of the \(x\)-axis direction of the adjacent fusion line and the weld groove width diagram line, respectively. Setting y = HF, the coordinates of the two points in the first quadrant of their respective coordinate systems can be obtained. The weld height \(h\) is the horizontal coordinate of point F plus the length of the line segment \(O_{i}O_{i+1}\) minus the horizontal coordinate of point E. G point is the median point of the intersection of a pair of fusion line extension lines and the weld center line. Let the value of the corresponding analytic expression be 0, respectively, then the x-coordinate of G point is the mean value of the two x values obtained by solving. Point H is the projection point of point F on the \(x\)-axis, and its x-coordinate is known. The fusion depth \(h_{f}\) is the sum of the absolute values of the abscess coordinates of two points. Because of the iterative relationship between adjacent passes, the height of \(i\) weld is measured in the next \"measurement unit\".
## 2 System Model of MC-MDCSK-MCS
### Transmitter  
A feasible block diagram of the MC-MDCSK-MCS transmitter is given in Figure 2. In Figure 3, the structure of MCS-MDCSK modulation is presented. Bits with the information are split into \(n\) parallel parts, \(d_{1},d_{2},\cdots,d_{n}\), and the length of each information packet is N. Each parallel information packet \(d_{i}(i\in[1,n])\) is transmitted via MCS-MDCSK modulation. The modulated MCS-MDCSK signal \(e_{i}(i\in[1,n])\) for the \(i\)th packet is presented as follows [14]:  
\[e_{i}=W_{r}\otimes\mathbf{x}+\sum_{m=1}^{p}\left(a_{m,i}W_{2m-1}\otimes\mathbf{x }+b_{m,i}W_{2m}\otimes\mathbf{x}\right) \tag{2}\]  
where \(\mathbf{x}[x_{1},\ldots,x_{j},\ldots,x_{N_{c}}]\) is an \(N_{c}\) length fractional-order Chen chaotic signal, which comes from one of the state vectors \(\mathbf{X}\). \(W_{r}\), \(W_{2m-1}\), \(W_{2m}\) are \(R\) length Walsh code sequences generated by a Hadamard matrix [30]. The length of Walsh code sequence must satisfy \(R>2*P+1\) and \(R=2^{\mathrm{u}}\) (u is a natural number). In one symbol duration, \(a_{m,i}\) is the real part of the \(m\)th M-ary constellation symbol \(s_{m,i}\), \(b_{m,i}\) is the imaginary part, and \(s_{m,i}=a_{m,i}+ib_{m,i}\). \(\otimes\) is a Kronecker product operator. P shows how many M-ary constellation symbols are sent in each symbol duration.  
Figure 1: The chaotic behavior of a fractional-order Chen chaos system.  
Figure 2: Block diagram of the MC-MDCSK-MCS transmitter.  
\(e_{i,j}(j\in[1,N_{c}])\) is the \(j\)th element of \(e_{i}(i\in[1,n])\), which undergoes a square-root-raised cosine filtering with the factor of roll-off \(q(0<q\leq 1)\) to shape pulses. The outputs of pulse shaping filter are multiplied by in-phase sinusoidal carriers, in which the center frequencies are \(f_{1},f_{2},\cdots,f_{n}\). Finally, in Figure 2, the summation of these modulated signals is sent by the MC-MDCSK-MCS transmitter. Thus, the transmitted signal is expressed as  
\[p(t)=\sum_{i=1}^{n}\sum_{j=1}^{N_{c}}e_{i,j}\cdot h_{T}(t-jT_{c})\cdot\cos(2\, \pi f_{i}t) \tag{3}\]  
where the chip time is represented by \(T_{c}\). \(h_{T}(t)\) is the impulse response of the pulse shaping filter. \(f_{i}=f_{0}+iF_{s}\) is the center frequency of the \(i\)th subcarrier. \(f_{0}\) is the basic carrier frequency satisfying \(f_{0}\sqrt[]{2}\,1/T_{c}\). \(F_{s}\) is the interval of frequencies among adjacent subcarriers [16].
### Our Contribution  
The main contribution of this research is summarized as follows:  
* An efficient IDS which utilizes minimum redundancy maximum relevance (MRMR) feature selection technique and an enhanced version of long short-term memory (LSTM) based on an artificial recurrent neural network (RNN) is proposed. The proposed IDS system performs well in terms of detecting traffic anomalies with greater accuracy while also reducing the time it takes to perform this task. Minimum redundancy maximum relevance (MRMR) feature selection method is exploited to reduce the dataset dimension and size by selecting only the most essential features. It helps training IDSs more quickly with low computation and high accuracy because it removes irrelevant features to maximize system accuracy.
* The performance of the proposed IDS system was analyzed using two of the most recent datasets based on realistic data: ToN-IoT and InSDN datasets [11; 12].
* The proposed MRMR-SATIDS was applied to a cluster based IoT network which uses an optimized hierarchical routing technique to address the energy consumption issue and conserve devices energy.
* Finally, the performance of the proposed system was compared with and without using an MRMR feature selection technique, then was compared to other ML IDSs.  
The remainder of the paper is structured as follows: Contributions of other researchers to the IDS field are presented in Section 2. Section 3 describes the methods and methodology used in this work. The proposed system is presented in Section 4. Performance analysis of the proposed system and a comparison to existing systems are presented in Section 5. Section 6 concludes the article and presents future work avenues.