esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion and Conclusions
In addition to exploring custom loss functions, another promising research direction is to investigate the integration of state-of-the-art DL techniques into the proposed system. Techniques such as attention mechanisms [45] and diffusion models [46] have shown significant advancements in various domains and have the potential to enhance the mitigation of RF impairments. A more robust approach would involve leveraging cyclostationary or covariance-basedfeatures, which provide valuable information about the underlying signal structure. However, extracting these features traditionally requires significant computational resources. Therefore, a promising research direction is to explore DL methods for estimating these features directly from the received samples. This approach would involve developing specialized architectures and training techniques to efficiently extract informative features and enhance system performance. In our future work, we intend to conduct real-world experiments to validate the system's performance under practical conditions. Furthermore, we aim to explore various use case scenarios and implement necessary modifications to extend the system's applicability to more complex modulation schemes and signals that adhere to specific wireless communication standards. Conceptualization, O.B., A.R.E. and M.K.O. ; methodology, O.B. ; software, O.B. ; validation, O.B., A.R.E. and M.K.O. ; formal analysis, O.B. ; investigation, O.B. ; resources, O.B., A.R.E. and M.K.O. ; data curation, O.B. ; writing--original draft preparation, O.B. ; writing--review and editing, A.R.E. and M.K.O. ; visualization, O.B. ; supervision, A.R.E. and M.K.O. ; project administration, A.R.E. and M.K.O. ; funding acquisition, A.R.E. and M.K.O. All authors have read and agreed to the published version of the manuscript. The research leading to these results received funding from the ECSEL Joint Undertaking in collaboration with the European Union's H2020 Framework Programme (H2020/2014-2020) Grant Agreement-101007321-StorAlge and National Authority TUBITAK with project ID 121N350. The data presented in this study are available on request from the corresponding author. The authors declare no conflict of interest. The following abbreviations are used in this manuscript: \begin{tabular}{l l} DL & Deep Learning; \\ RF & Radio Frequency; \\ CFO & Carrier Frequency Offset; \\ AWGN & Additive White Gaussian Noise; \\ EM & Electromagnetic; \\ MIMO & Multiple-Input Multiple-Output; \\ CR & Cognitive Radio; \\ SNR & Signal-to-Noise Ratio; \\ AI & Artificial Intelligence; \\ NN & Neural Network; \\ FCN & Fully Convolutional Network; \\ RNN & Recurrent Neural Network; \\ CNN & Convolutional Neural Network; \\ QPSK & Quadrature Phase-Shift Keying; \\ MAE & Mean Absolute Error; \\ ROC & Receiver Operating Characteristic; \\ LSTM & Long Short-Term Memory; \\ BiLSTM & Bidirectional Long Short-Term Memory; \\ SA & Self-Attention; \\ SMT & Spectral Minima Tracking. \\ \end{tabular}
## 3 Short-Circuit Current Calculation Considering Power Fluctuation
### Output Power Fluctuation Analysis of Residential Loads  
Different from the total load connected to the high voltage busbar, the decentralized load in the distribution network is usually less than 100 kW, and it has high fluctuation. Since the distributed PV stations exists in a rural area, the corresponding electricity load mainly belongs to residential load. The data from the SGSC project in Australia are selected in this paper [31]. There are thousands of residential load data recorded from 2012 to 2014. Firstly, three hundred households are randomly selected from all records, and then ten households, fifty households, and one hundred and fifty households are randomly selected from the three hundred households separately. As shown in Figure 3, the summary residential load of ten households, fifty households, one hundred and fifty households, and three hundred households in one winter day are analyzed and clustered for output power fluctuation analysis.  
It can be derived from Figure 3 that the fluctuation in loads becomes smaller with the increase in households. The reason is that the volatility of each household load suppresses each other.
# Decentralized Public Transport Management System Based on Blockchain Technology +
## 5 Discussion and Future Perspectives  
### Future Research Directions and Application in Unmanned Transport  
As noted earlier, there is no immediate need for a model of the transport system's operation with vehicles at the enterprise. Further application of the model is possible in a system with unmanned vehicles. The transition to unmanned transport contains numerous technological, legal, and infrastructural challenges. Still, the conceptual advantages of modeling such a system can be obtained already.  
Vehicles travel along pre-set routes. Each vehicle has specific anchor points, as follows:  
Parking place (depot);  
Place of service;  
Stopping points along the route;  
Route from the depot to the beginning of the route;  
Route from the end of the route to the depot;  
Route between the depot and the service station;  
Route between each stop and the service station.  
The vehicle can independently carry out trips along pre-set routes and independently go for repairs and maintenance.  
In this model, not only is the mechanic excluded from the decision-making chain, as in Section 3, but so is the driver. The driver no longer influences the decision to continue working or to go for maintenance or repair. Thanks to distributed registry technology and integration with the compression algorithm, it is possible to create a closed public transport system for a specific geographical region in the future.  
Future research may center on the standardization of the model and the execution of experiments across diverse forms of public transportation.
## Proposed EDOVE Scheme
### Problem Statement  
Consider the underwater network scenario in Figure 3 with sender \(S\), receivers with residual energy \(E_{i}\) within the \(t_{r}^{S}\) of \(S\), and receiver with small hop neighbors. The sender \(S\) either generates or relays the data packet and all the receivers simply receive that packet and that packet has to be relayed through any of these receivers. However, these receivers are at the edge of the void zone and the forwarding algorithm must avoid selecting the relay that fails to further relay the packet due to the void zone. Furthermore, these receivers have different residual energy and the forwarding strategy has to select the receiver with large residual energy to balance network-wise energy consumption. Selection of one receiver among multiple potential ones is done through the holding time of the receiver \(i\), \(T_{h_{i}}\).  
When receiver \(i\) receives a data packet, it computes and starts the \(T_{h_{i}}\). While \(T_{h_{i}}\) is running and \(i\) receives another copy of the same packet, it suppresses the packet transmission. Nonetheless, \(i\) forwards the data packet if it does not receive any more copies of the data packet until the \(T_{h_{i}}\) expires. However, the computation of \(T_{h_{i}}\) must consider the long propagation delay between the receivers and must involve the weighted depth differences, the normalized residual energy of the receiver, and variance of the depth differences of the next hop neighbors of \(i\). So, the duplicate packet broadcast along with the void hole can be avoided. However, the impact of this long holding time may result in the long end-to-end delay.  
Next, we discuss the proposed EDOVE and its holding time computation on how it suppresses the additional data packet and avoids the void area in the underwater network.
## 5 Small Borehole Array Lateral Logging Forward Modeling
### Invasion Impact  
Mud invasion has a significant effect on the numerical response of array lateral logging instruments [13]. Existing literature has already pointed out the impact of mud invasion on logging results, but systematic studies under different invasion conditions are still limited. This research constructed relevant computational models and conducted systematic numerical simulations to further enrich and refine the knowledge in this field. In the simulations, we set the following key parameters: mud resistivity, Rm, at 0.1 \(\Omega\cdot\)m, borehole diameter, d, at 8 inches, invasion zone resistivity under low invasion at Rx0 = 5 \(\Omega\cdot\)m, and formation resistivity, Rt, at 20 \(\Omega\cdot\)m; for high invasion scenarios, Rx0 was taken as 20 \(\Omega\cdot\)m and Rt as 5 \(\Omega\cdot\)m. During the simulation process, we assumed the formation thickness to be infinite. Figure 5 shows the relationship between the invasion zone depth and resistivity changes. As the depth of the invasion zone increased, the impact on resistivity became more pronounced. Moreover, logging modes with shallower investigation depths were more sensitive to mud invasion, thus being more prone to deviations in measuring the original formation resistivity. Notably, the simulation results also revealed different trends in resistivity changes under invasion conditions. Under low- and high-invasion conditions, resistivity exhibited a positive correlation with both invasion zone depth and resistivity [14].  
To thoroughly investigate the resistivity curve responses of different investigation modes under mud invasion conditions, this study constructed various formation models for simulation [4]. The key parameters used in the simulation included mud resistivity, Rm = 0.1 \(\Omega\cdot\)m, and borehole diameter, d = 8 inches. In addition, the invasion ratio Rt/Rx0 was set from 1 to 20 for low-invasion conditions, and from 0 to 1 for high-invasion conditions. The range of the invasion radius, inr, was from 0 to 3 m. Figure 6 displays the analysis of the simulation results. Under low-invasion conditions, the resistivity ratio of RLA4 to RLA1 fluctuated approximately between 1 and 8, depending on the change  
Figure 4: Impact of wellbore shadow.  
in the invasion radius, inr. Of particular interest, the degree of separation in resistivity reached its maximum value when the invasion radius, inr, was within the interval of 0.5 to 0.8 m. Meanwhile, the simulation results under high-invasion conditions also indicated that the resistivity ratio of RLA4 to RLA1 varied roughly between 0.6 and 0.9. Similar to low invasion, when the invasion radius, inr, was between 0.5 to 0.8 m, the degree of separation in resistivity between the deepest and shallowest investigation modes also reached its maximum.
# Enhancement of Underwater Images with Retinex Transmission Map and Adaptive Color Correction
## 3 Proposed Underwater Image Enhancement Method
### Preliminary Recovery  
After using the above techniques to estimate the medium transmission map \(t(x)\) and the local backscattering light \(B(x)\), they are substituted into Equation (4) to perform preliminary recovery of scene radiance \(J(x)\).  
However, the recovered \(J(x)\) at this stage may still suffer from low contrast and color shifts, due to unsatisfactory white balance results or unfaithful local backscattered light estimation in some cases. One of such cases is given in Figure 2.
## 2 Finite Element Test Verification
### Test of Replaceable Link
#### 2.1.3 FEM Results  
Figure 6 shows the plastic strain distribution of the specimen from the FEM analysis, which is compared with the test failure mode.  
As can be seen in Figure 6, the replaceable link showed a large shear deformation, the frame beam ends buckled, and tiny deformation occurred out-of-plane due to the restraint. The equivalent plastic strain from the FEM showed that the replaceable link developed full plasticity at the same time, where the beam ends started to develop plasticity, the upper and lower flanges of the beam presented local buckling, and the failure mode of the structure obtained from the FEM was consistent with the test. It is worth noting that the end plates were subjected to relatively large moments and shear forces during the tests, and this led to the deformation of the bolt rod extrusion hole walls; even more, the bending of bolt holes and some of the bolt rods would probably bring more difficulties for the post-earthquake repair of the replaceable link.  
Figure 4: Model meshing.  
Figure 5: Model boundary condition.  
The hysteresis curves from the FEM were compared with the test, as shown in Figure 7; the test and FEM hysteresis curves were generally the same; however, the FEM hysteresis curve was fuller in shape, while the test hysteresis curve descended more obviously. This was mainly due to the tie-bound connections of welding in the FEM model being unable to consider the initial defects in the test. The ultimate loading obtained from the test was 513.25 kN and the ultimate loading obtained from the FEM was 524.76 kN, an error of only 2.2%.  
A comparison of the backbone curves between the test and the FEM is shown in Figure 8. The FEM results were in good agreement with the test results, and both could well reflect the elastic, elastic-to-plastic, and plastic phases of the structure, with an initial stiffness of 80.32 kN/mm from the test and 82.65 kN/mm from the simulation generally being equal with an error of only 2.8%.  
Overall, the steel frame structure with replaceable links showing better energy dissipation and seismic performance can be quickly recovered or repaired using end plates and bolts. The finite element model established in this paper provides an accurate representation of the structural forces and deformation characteristics. However, with the development of the plastic deformation of the replaceable link, some of the bolt holes and bolts also experienced residual deformation, which would affect the replacement of the links after the earthquake.  
Figure 6: Failure modes of replaceable link.  
Figure 7: Comparison of hysteretic curves.
# Application of Internet of Things in a Kitchen Fire Prevention System
## 1 Introduction
### Research Motivation and Objectives
#### 1.2.1 Kitchen Fire Prevention  
1. A sensor is installed above the stove top. When it detects overly high flames or temperature or a gas leak, the automatic shutoff device will be activated to shut off gas supply to the stove.
2. A reporting system then reports the accident to the family members and the community management center via Line, a messenger app, and the main entrance door of the house in question is automatically unlocked to enable the emergency personnel to enter the house to extinguish the kitchen fire.
# Robust Algorithm for Calculating the Alignment of Guide Rolls in Slab Continuous Casting Machines
## 3 Angle Conversion Algorithms
### Optimization-Based Approach
#### 3.3.4 Error Weight Optimization  
To finalize the development of the optimization-based algorithm, the optimal value for the error weight factor \(w_{\text{error}}\) must be determined. This paragraph represents a slight anticipation of the following Section, since some results of the optimization-based algorithm must be analyzed in order to find the optimal error weight. To find the ideal value, the algorithm was tested under different combinations of amplitudes for constant and random angle measurement errors. The amplitudes of both error types range from \(0^{\circ}\) to \(0.1^{\circ}\) in four steps. For each of the resulting 16 cases, the error weight value was varied on a logarithmic scale from \(10^{-7}\) to 1. The error weight on the x-axis is plotted against the mean absolute calculation error of the roll positions. The result is shown in Figure 9.  
The four different amplitudes of the random angle measurement errors are spread across the four diagrams while the different amplitudes of the constant angle measurement errors can be distinguished by color. The best value for the error weight for each of the 16 cases is found at the minimum of each graph.  
The data suggest that the optimal error weight is effectively independent from the amplitude of the constant angle measurement error. This is due to the zero-mean compensation that is applied to the angle measurements, removing most of the constant error component. The amplitude of the random angle measurement error on the other hand strongly influences which value is ideal for the error weight. The best error weight values are marked in the diagram.  
Experiments with SCM revealed that random angle measurement errors typically remain within 0.01\({}^{\circ}\) under real-world conditions. Therefore, the algorithm was optimized for this error range. When random measurement errors reach or exceed 0.1\({}^{\circ}\), the calculation results become too inaccurate for practical application, making optimization for such cases unnecessary.  
Figure 9: Results of the error weight optimization for the optimization-based algorithm. It is effectively independent from the amplitude of the constant measurement error. The optimal error weight for each amplitude of random measurement error is marked.  
Based on this analysis, an error weight of \(w_{\text{error}}=5\cdot 10^{-4}\) was selected for use in subsequent experiments. The chosen weight also ensures acceptable performance even when random measurement errors are lower than 0.01\({}^{\circ}\) (see Figure 9).
## Abstract
This research aimed to evaluate this hypothesis technically and statistically to find evidence for an association between abdominal fat and liver fat content. This current research also supports the research community by providing objective measurements to approve this direct proportional relationship, derived from an investigation of the relationship mentioned above. Two computerized/adaptive methods for abdomen fat segmentation and liver segmentation were proposed. No training data were used to estimate any global parameters or to create models. A hybrid segmentation method was proposed to segment the non-contrast-enhanced CT slices into fat and non-fat tissues. Then, the segmented fat tissues in each CT slice were separated into subcutaneous fat (SF) and visceral fat (VF) by detecting the abdomen wall boundary. Finally, the segmented fat tissues were used to quantify the abdomen fat accumulation for each subject. The whole liver segmentation procedure was based on processing the CT slices one by one. Many image processing techniques and anatomic information were employed together to segment the liver in each CT slice. The slices of the segmented liver in the CT were gathered to form 3D binary liver data. This 3D binary image was refined to produce the final segmented liver for a subject. Then, the diffused fat in the segmented liver was evaluated by measuring the liver attenuation (measured in Hounsfield units (HU)) for the segmented liver for each subject. The lower the average values of the liver intensities, the lower the tissue density, and hence, the greater the fat content. The obtained measurements of the abdomen fat and the liver attenuation for the employed data set were used to draw a general figure for the correlation between these two effective factors of human health. The main contribution of this research is the use of the indexes of abdomen fat and whole liver HU mean values to highlight the relationship between obesity and fatty liver disease. To achieve this goal, methods with high stability, consistency, and accuracy were designed. In addition to the main contribution, other contributions derive from this research. In the fat segmentation method, a modification was made to the FCM clustering technique to increase the ability of the method to detect the abdomen wall. The combination of FCM and Otsu thresholding, on the other hand, helped to achieve a great degree of stability and consistency for the fat segmentation method. Another contribution was made by introducing an approach toward refining the segmented liver using the distance transformation technique. The proposed liver segmentation method successfully limited the effects of problems such as the high inter- and intra-CT gray level, shape, and size of liver variability on the results of liver segmentation.
# Prediction of Total Organic Carbon Content in Shale Based on PCA-PSO-XGBoost
## 2 Materials and Methods
### Machine Learning
#### 2.5.2 Gbdt  
GBDT (Gradient-Boosting Decision Tree) is a gradient drop algorithm based on the CART regression tree and function space, which has the advantages of strong prediction ability and good stability. The basic principle of GBDT includes two core algorithm ideas. One is additive enhancement, and the other is gradient enhancement. The basic idea of additive enhancement is that multiple weak learners can form a stronger learner, while the basic idea of gradient enhancement is that every time a weak learner is selected, the direction of the loss function decreases the fastest, thus accelerating the convergence process [22]. GBDT builds multiple weak learners by optimizing the loss function step by step. Each new weak learner further enhances the model's performance by fitting the residual of the current model and then obtaining the optimal model.  
The TOC content prediction model based on GBDT can be expressed as Equation (10).  
\[y_{pre}=F_{0}(x)+\sum_{k=1}^{K}\alpha F_{k}(x) \tag{10}\]where, \(y_{pre}\) is the predicted value of TOC content; \(x\) is the principal component of the log after processing; \(F_{0}(x)\) is the initial model; \(K\) is the number of trees; \(\alpha\) is the learning rate; \(F_{k}(x)\) is the \(k\)th tree.
## 3 Results
The findings among the NSP and HSP groups show that the values of tone and stiffness are higher in the PD muscle and observed lower values of tone, stiffness, and elasticity in the MT muscle in the HSP group, while the highest tone and stiffness values were recorded in the UT, MT, and PM muscles of the NSP group. Interestingly, elasticity values of the UT and PD had no change in both NSP and HSP. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Mechanical** & \multirow{2}{*}{**NSP (\(n\) = 20)**} & \multirow{2}{*}{**HSP (\(n\) = 20)**} & **Calculated** & \multirow{2}{*}{**d (LCL, UCD)**} & \multirow{2}{*}{\(p\)**} \\
**Parameters** & & & & & \\  & & & & & \\ \hline \multicolumn{6}{c}{Tone (Hz)} \\ UT & 18.6 \(\pm\) 4.7 & 17.9 \(\pm\) 9.1 & t (38) = 0.3 & 0.10 (\(-\)0.5 to 0.7) & 0.731 \\ MT & 13.9 \(\pm\) 2.3 & 11.2 \(\pm\) 3.1 & t (38) = 3.0 & 0.96 (0.3 to 1.6) & 0.004 \\ PM & 20.1 \(\pm\) 4.0 & 14.0 \(\pm\) 4.8 & t (38) = 4.3 & 1.38 (0.6 to 2.0) & \textless{}0.001 \\ PD & 16.0 \(\pm\) 2.2 & 22.2 \(\pm\) 5.0 & t (38) = \(-\)4.9 & \(-\)1.57 (\(-\)2.2 to \(-\)0.8) & \textless{}0.001 \\ \multicolumn{6}{c}{Stiffness (N/m)} \\ UT & 350.4 \(\pm\) 72.0 & 278.6 \(\pm\) 69.6 & t (38) = 3.2 & 1.0 (0.3 to 1.6) & 0.003 \\ MT & 282.0 \(\pm\) 62.9 & 204.2 \(\pm\) 9.5 & t (38) = 5.4 & 1.7 (0.9 to 2.4) & \textless{}0.001 \\ PM & 235.9 \(\pm\) 11.7 & 192.5 \(\pm\)19.7 & t (38) = 8.4 & 2.6 (1.8 to 3.5) & \textless{}0.001 \\ PD & 309.9 \(\pm\) 70.7 & 441.7 \(\pm\) 45.9 & t (38) = 6.9 & \(-\)2.2 (\(-\)2.9 to \(-\)1.4) & \textless{}0.001 \\ \multicolumn{6}{c}{Elasticity (log DEC)} \\ UT & 1.2 \(\pm\) 0.3 & 1.2 \(\pm\) 0.3 & t (38) = 0.0 & 0.00 (\(-\)0.6 to 0.6) & 0.993 \\ MT & 1.7 \(\pm\) 0.3 & 2.0 \(\pm\) 0.2 & t (38) = \(-\)3.4 & 1.1 (\(-\)1.7 to \(-\)0.4) & 0.001 \\ PM & 1.1 \(\pm\) 0.6 & 1.6 \(\pm\) 0.1 & t (38) = \(-\)3.1 & 0.9 (\(-\)1.6 to \(-\)0.3) & 0.005 \\ PD & 1.5 \(\pm\) 0.15 & 1.5 \(\pm\) 0.1 & t (38) = \(-\)0.1 & 0.0 (\(-\)0.6 to 0.5) & 0.877 \\ \hline \hline \end{tabular} All values are shown in mean \(\pm\) SD. —–[PERSON]’s effect size; NSP—normal shoulder posture; HSP—hunched shoulder posture; ICI and UCI—lower and upper (95%) confidence interval, respectively; SD—standard deviation; UT—upper trapezius; MT—middle trapezius; PM—pectorialy; PD—posterior deltoid.  
\end{table} 
Table 3: Mechanical parameters of HSP.