esa-sceva/satcom-chunk-collection huggingface dataset:
# Dual Histogram Equalization Algorithm Based on Adaptive Image Correction
## 3 Proposed AICHE Transformation
### Adaptive Local Grayscale Correction
#### 3.2.1 Image Over-Enhancement Suppression  
In the equalization process, the gray levels with higher frequencies appear to be over-enhanced, whereas the gray levels with lower frequencies are merged, leading to a loss of image details. Therefore, the AICHE algorithm suppresses the over-enhanced gray levels by setting a threshold T for each of the two sub-histograms. The procedure is as follows.  
1. First, let the input image be \(F\), and obtain the sets \(F_{A}\) and \(F_{B}\) of non-zero cells in the two sub-histograms.  
\[\left\{\begin{aligned} F_{A}=\{F(i)|F(i)\
eq 0\},i\in[MIN,K_{ out}]\\ F_{B}=\{F(i)|F(i)\
eq 0\},i\in[K_{out}+1,MAX]\end{aligned}\right. \tag{13}\]  
where \(i\) is the gray level of the image, and \(F_{A}\) and \(F_{B}\) denote non-zero cells in the two sub-histograms, respectively.  
2. The one-dimensional median filtering of \(F_{A}\) and \(F_{B}\) is performed, and the segmentation thresholds \(T_{A}\) and \(T_{B}\) of the two sub-histograms are calculated as follows. \[\left\{\begin{aligned} T_{A}&=T_{MA}\times\frac{K_{out} -MIN}{MAX-MIN}\\ T_{B}&=T_{MB}\times\frac{MAX-K_{out}}{MAX-MIN}\end{aligned}\right.\] (14) where \(T_{MA}\) and \(T_{MB}\) denote the peaks of the two sub-histograms, respectively. 3. The image \(P_{S}\) is obtained by independently equalizing the two sub-histograms according to Equations (1)-(3), and the equalization equation is as follows. \[P_{S}(i) =\left\{\begin{aligned} MIN+\frac{(K_{out}-MIN)\times \frac{K_{out}^{\text{ext}}}{\text{NA}}}{\frac{i-MIN+1}{NA}},i\leq K_{out}\\ (K_{out}+1)+\frac{[MAX-(K_{out}+1)]\times\frac{K_{out}^{\text{ pred}}}{\text{NA}}}{\frac{i-K_{out}^{\text{ pred}}}{\text{NB}}},i>K_{out}\end{aligned}\right.\] (15) where \(n_{i}\) is the total number of pixels in the image at gray level \(i\), \(P_{S}(i)\) is the histogram after equalization at gray level \(i\), and \(NA\) and \(NB\) are the total numbers of gray levels in region A and region B, respectively. 4. After cropping the balanced histogram according to Equation (16), the image \(P_{T}\) is obtained. \[P_{T}(i)=\left\{\begin{aligned} T_{A},i\leq K_{out}\cap P_{S}(i)& \geq T_{A}\\ T_{B},i>K_{out}\cap P_{S}(i)&\geq T_{B}\\ P_{S}(i)\end{aligned}\right.\] (16) where \(P_{T}(i)\) indicates the cropped histogram with gray level \(i\).
## 3 Results
### Summary of Results
#### 3.5.2 Process 2  
The results obtained here are somewhat similar to those obtained with process 1. Again, MPC gives better performance for underdamped systems and systems with larger time delays. On the other hand, the PID controllers give better performance when the time delays are smaller and the process is overdamped, when both TV and IAE are considered. For disturbance rejection, MPC moved relatively less across all values of \(r_{w}\). Hence, both aggressive and conservative MPC controllers moved less than the corresponding PID controllers, which is good for actuators and controller robustness. The IAE values for disturbance rejection show that the MPC had better performance when \(\frac{\theta}{\tau}\) is at least 3. However, there is a noticeable drop in performance from left to right of the table for \(\frac{\theta}{\tau}<3\), i.e., with decreasing damping ratio \(\zeta\). For the setpoint response, the TV values when \(0.01\leq r_{w}\leq 0.1\) indicate that the MPC controller moved more when \(\frac{\theta}{\tau}>1\) for all values of \(\zeta\) or when \(\frac{\theta}{\tau}>1\) and \(\zeta<1\). However, as the controller became more cautious at \(r_{w}=10\), the controller performance boundary moved to around \(\frac{\theta}{\tau}\geq 5\) and \(\zeta\leq 0.8\) making the MPC controller move more towards the left bottom corner of the table. The corresponding IAE values show that the MPC controller had lower IAE values for \(\frac{\theta}{\tau}\geq 1\) for all values of \(\zeta\). For \(\frac{\theta}{\tau}\leq 1\), there is a noticeable increase in IAE values from left to right of the table, i.e., with increasing damping. Looking specifically at the values for \(1\leq r_{w}\leq 10\), there is a noticeable decrease in MPC performance for low values of \(\frac{\theta}{\tau}\) when \(\zeta\) exceeds 2 to 3. Here, the boundary of performance improvement in terms of IAE is seen to remain around \(\frac{\theta}{\tau}\geq 1\) over the range of \(r_{w}\).  
Therefore, as with process 1, MPC tends to give better performance than PID when \(\frac{\theta}{\tau}>1\), irrespective of the damping ratio. But when \(\frac{\theta}{\tau}\leq 1\), the PID controller tends to give a better performance. It is also observed that the relative performance of MPC increases as damping is decreased, i.e., along the values \(1\leq\frac{\theta}{\tau}\leq 1\) as \(\zeta\geq 3\). Therefore, even for lower values of \(\frac{\theta}{\tau}\), MPC is likely to perform better if the system is underdamped as with the first process.
## Materials and Methods
### RAPD Analysis  
The identified _L. plantarum_ and control strains (_O. eeni_ 31-DH and Bactoferm \({}^{\circled@Vegeg}\)-Start 2.0 CN Commercial _L. plantarum_, short for BV-S2) were differentiated by randomly amplified polymorphic DNA (RAPD). The primers M13 (5\({}^{\prime}\)-GAGGGTGGGGCTT-3\({}^{\prime}\)) and M14 (5\({}^{\prime}\)-GAGGGTGGGGCCGTT-3\({}^{\prime}\)) were used to amplify the RAPD. The reaction was performed in a final volume of 25 \(\mu\)L using the following amplification mixture: 5 \(\mu\)L DNA template, 2.5 \(\mu\)L 10\(\times\) TaqMan buffer, 1.5 \(\mu\)L primer, 0.5 \(\mu\)L dNTPs (2.5 mM), 3 \(\mu\)LMg\({}^{2+}\) (25 mM), and 0.3 uL DNA polymerase (5 U/\(\mu\)L) (TransGen Biotech, Beijing, China). Samples underwent an initial 3 min denaturation at 94 \({}^{\circ}\)C, followed by 35 cycles of 1 min at 94 \({}^{\circ}\)C, 1 min at 48 \({}^{\circ}\)C, and 2 min at 72 \({}^{\circ}\)C, with the final extension of 10 min at 72 \({}^{\circ}\)C. PCR products were resolved by electrophoresis in 1.0% (\(v/v\)) agarose gels. The bands of isolated strains were analyzed and compared. Data were reported in a binary format with \"1\" for the presence of a band and \"0\" for its absence. For clustering, fragments were analyzed with NTSYS 2.10e software using the Dice similarity coefficient based on the presence/absence of the bands and clustered by the unweighted pair group method with arithmetic mean (UPGMA).
## 4 Simulation Experiments
### Issue 1: Production and Application Variability  
The first issue aims at quantifying the variability within the module-specific thermal resistances \(R_{\text{th}_{i}}\) assuming \(\sigma_{\varepsilon}^{2}\) is known. The parameters \(\eta_{h}\), \(I_{f}\) and \(V_{f}\) are treated as deterministic input parameters, which refers to type 1 parameters; see Section 3.2.2. The individual thermal resistances \(R_{\text{th}_{i}}\) and the hyper-parameters \(\boldsymbol{\vartheta}_{R_{\text{th}}}\) are treated as unknowns, i.e., \(\boldsymbol{\Theta}_{1}=(R_{\text{th}_{i}},\boldsymbol{\vartheta}_{R_{\text{ th}}})\). Following (4), we assume  
\[y_{i}=\Delta T_{0_{i}}=\mathcal{M}\big{(}R_{\text{th}_{i}},\mathbf{z}_{i}\big{)} +\varepsilon_{i},\qquad\varepsilon_{i}\sim\mathcal{N}\big{(}0,\sigma_{ \varepsilon}^{2}\big{)} \tag{15}\]  
with deterministic parameters \(\mathbf{z}_{i}=(I_{f},V_{f},\eta_{h})\).  
We use a lognormal prior for the thermal resistances, i.e., \(R_{\text{th}_{i}}\sim\mathcal{LN}\big{(}\mu_{R_{\text{th}}},\sigma_{R_{\text {th}}}\big{)}\); see (8) of type 2b parameters in Section 3.2.2. The hyper-parameters \(\boldsymbol{\vartheta}_{R_{\text{th}_{i}}}=(\mu_{R_{\text{th}}},\sigma_{R_{\text {th}}})\) are assigned uniform prior distributions \(\mu_{R_{\text{th}_{i}}}\sim\mathcal{U}[m_{l},m_{u}]\) and \(\sigma_{R_{\text{th}_{i}}}\sim\mathcal{U}[s_{i},s_{u}]\). For the parameters of the prior distributions, we choose \(m_{l}=0\), \(m_{u}=100\), \(s_{l}=0\) and \(s_{u}=10\) to specify weakly informative, flat priors with predefined lower and upper bounds.  
The joint (non-normalized) posterior distribution of \(\mathbf{\Theta}_{1}=(R_{\text{th}_{i}},\mu_{R_{\text{th}}},\sigma_{R_{\text{th}}})\) is given as  
\[p(\mathbf{R}_{\text{th}},\mathbf{\vartheta}_{R_{\text{th}}}|\mathbf{y})\propto\prod_ {i=1}^{n}p(y_{i}|R_{\text{th}_{i}},\mathbf{z}_{i})\cdot\left(\prod_{i=1}^{n}p(R_ {\text{th}_{i}}|\mu_{R_{\text{th}}},\sigma_{R_{\text{th}}})\right)\cdot p(\mu_{ R_{\text{th}}})\cdot p(\sigma_{R_{\text{th}}}). \tag{16}\]  
According to the hierarchical structure of the target distribution, the RW-MH sampler for this default setting consists of two blocks, i.e., the individual thermal resistances \(R_{\text{th},i}\) and the parameters \(\mathbf{\vartheta}_{R_{\text{th}}}=(\mu_{R_{\text{th}}},\sigma_{R_{\text{th}}})\).
# Reconfigurable Multi-Beam Receiver Based on Microwave Photonics Technology
## 2 System Architecture
### Key Parameters Analysis
#### 2.1.2 Number of Splitter Elements  
The splitter is indispensable to the multi-beam receiver and is used to divide the signal into multi-duplication. The multi-stage multi-ports splitter is built like the multi-port optical switch by cascading the basic \(1\times 2\) splitter elements. The number of splitter elements in the conventional structure is \(N\cdot(M-1)\). In the structure DP and EP, they both have \(N\cdot(2M-\log_{2}M-2)\) splitter elements.
# Cybersecurity Requirements for Industrial Machine Control Systems
## 5 Case Study--Examples of Determining SLs of Safety Functions Implemented in Machine Safety Systems
### Example 2--Machine Control System: Category B, PLb; Cybersecurity SL-T 1  
Let us consider a bakery machine for kneading dough. Such a machine performs slow rotations (which implies that a hazard can be avoided (parameter P1)) inside a container with smooth walls and dimensions that prevent the formation of crush and shear zones. Thus, it is assumed that potential injuries may be light (parameter S1), such as abrasion. The user has frequent contact with the machine (dough loading/unloading, cleaning) about 20% of the time during the shift (parameter F2). Taking into account the aforementioned characteristics, PLr = b is required.  
The safety system is implemented with standard components (not necessarily well tried; see the NO limit switch acting as an interlock and the standard PLC--Figure 9). Basic safety principles are met, i.e., the use of a suppressor on the contactor coil. The machine is equipped with a programmable system that can be coupled to a communication network.  
For the circuit implementing the presented interlocking function in Figure 9 with the assumptions described above, protection against cyber-attack is required. The required protection level is assumed to be SL-T 1 (low level).
## 5 Modeling Results and Discussion
### Recommendations  
The usefulness and implications of this research mainly includes two parts:  
(1) the SRP-logit model was proposed to comprehensively accommodate the unobserved heterogeneity and the spatial correlation and to improve the fitting performance of the statistical model for rural SV crashes. This finding promotes the development of the statistical theory in traffic safety analysis and can provide a satisfactory model framework for traffic safety professionals.  
(2) Based on different types of motor vehicles, rural SV crash severity models were established to identify factors that have a significant impact on FS injuries. According to the regression results and previous practical experience, targeted measures can be implemented to remind drivers and improve traffic safety performance in rural areas. The details are shown as follows.  
First, there is a significant correlation between driving behavior and severe collisions. Some risky driving behaviors, such as drunk driving, fatigue, and speeding, can affect the probability and severity of crashes in different ways. Legal measures can effectively reduce risky driving behaviors. Installing electronic police and speed limit signs on rural roadways can effectively remind drivers and improve rural traffic safety. Safety education for drivers is also essential.  
Second, the impact of roadside fixtures on rural traffic safety cannot be ignored. Traffic managers should consider installing a soft protective cover on the surface of roadside infrastructure to absorb collision energy. In addition, streetlights should be installed along rural roads because they can improve traffic safety in rural areas by providing a good view.  
Finally, motorcycles cannot provide adequate protection for drivers. Therefore, more education and encouragement for riders should be implemented to improve their traffic safety awareness. Some legal measures should be formulated to increase the percentage of helmet wearers.
## 5 Architecture Proposal Operation and Fault Tolerance
receives more than one \"CM connect\" messages from the same cluster, first it will choose the closest one, and in case of a draw, the one with highest \(\lambda\) parameter. Then, it will add these neighbors in its CM-CM table and will send them a \"Welcome CM\" message. They will add this entry to its CM-CM table. Finally, both will send \"keepalive MM\" messages periodically to indicate that they are still alive. If any one of them does not receive a \"keepalive MM\" message for a dead time, it will erase this entry from its database, so it will send a new \"CM request\" for this cluster. \"Keepalive MM\" contain the clusterID of the sender and its \(\lambda\) parameter. If the CM does not find any CM of the same type from any neighboring cluster, it will be alone until it receives a \"CM request\" message from the same type of node. Steps explained are shown in Figure 11. The whole procedure explained for a CH and a CM is shown in the flowchart of the Figure 12.  
When a CM leaves the cluster (because of its mobility, or due to failure or other issues), the CH of its cluster and its neighbor CMs will not receive any \"keepalive\" message from it. After a dead time, the CH will erase this entry from its CM table and its neighbor CMs from other clusters will erase this entry from their CM-CM table. If the CM from the other cluster does not have any other neighbor for this cluster, it will send a \"CM request\" message to that cluster. Figure 13 shows all steps explained. Figure 11: Protocol operation to establish a connection with a CM of another cluster. Figure 12: Flowchart of the architecture operation. Because the CH receives the \(\delta\) parameter from all CMs in its cluster, it knows the best CM to promote in case of a failure or disconnection. The CM with highest \(\delta\) parameter between the nearest ones is called \"backup CH\". The CH sends the CM table and the CH table though \"backup\" messages to the backup CH. The first message has all the tables; the next ones will only be updates. The CH sends \"keepalive H\" messages periodically to its neighbor CHs and to the backup CH. If the CH fails down, the neighboring CHs and the backup CH will know it due to the absence of keepalive messages. If the backup CH does not receive a keepalive message from the CH for a dead time, it will become the CH its cluster. The neighbor CHs of the failed CH will erase that entry from their CH table. That update will be propagated through the CH network using the OLSR routing protocol (although it can be changed by other routing protocol). Because the backup CH has both CM and CH tables of the failed CH, it will become CH and will send a \"H replace\" message to all CMs and CHs in the table to indicate they have to replace the failed CH by the new one, so it establishes, for its first time, a connection with the CMs in its cluster. Figure 14 shows the described procedure.
## 3 Materials and Methods
### Data  
We used 692 focal mechanisms in total to calculate the stress tensor in the study area (35-41\({}^{\circ}\) E and 36-39\({}^{\circ}\) N) from the ISC Bulletin [61; 62] and the Disaster and Emergency Management Authority (AFAD)--Turkish Earthquake Data Center System Regulation (see Table S1 in Supplementary Materials). In addition, we also employed 164 focal mechanisms of seismic events (\(\geq\)3 \(M\)w) in the range of 35-41\({}^{\circ}\) E and 36-39\({}^{\circ}\) N from 21 February 2023 to 12 April 2024 from AFAD. The purpose is to explore what effects existed on subsequent seismic events around Turkiye from the massive earthquakes on February 6 and what interrelationships existed between subsequent earthquakes.  
To calculate the principal stress directions needed for the \(\Delta CFS\) calculations, we input the above 692 focal mechanisms into the STRESSINVERSE (Version 1.1.3) codes for the computation. Using the STRESSINVERSE calculation requires several parameters to be set. The number of random noise realizations for estimating the accuracy was set to 100. The standard deviation of the normal distribution of the errors in the noise estimates of the focal mechanisms was set to 5\({}^{\circ}\). The number of iterations of the stress inversion was set to 6, and the number of initial stress inversions with a random choice of faults was set to 10. The friction values ranged from 0.4 to 1 with a step of 0.05. After calculating the stress tensor of the study area, we chose the commonly used effective friction coefficient of 0.4 to calculate the \(\Delta CFS\)[46]. Previous studies have shown that \(\Delta CFS\) calculations are not sensitive to the effective friction coefficient [63]. We estimate the length and width of the faults based on [PERSON]'s empirical magnitude-area relationship [64]. The fault parameters we used are shown in Table S2 in the Supplementary Materials. We decided to assume that the earthquake originated in the fault's geometric center. We create source faults in Coulomb 3.4 using the \"Create Source Faults from CMT Information\" approach. In this way, we can compute the fault's right-lateral slip and reverse slip by inputting the strike, dip, rake, and \(M\)w information. It is worth noting that only source faults have slips, while receiver faults have no slips. When we want a fault to act as a receiver fault, we need to set its right-lateral slip and reverse slip to 0.
## 4 Proposed Approach
### Haptics/Tactile Internet  
Haptic data transmission demands an ultra-reliable and ultra-responsive connectivity of networks that is mandatory for the successful real-time interaction of actuation and touch. This type of communication is the purpose of Tactile Internet. It will transform many parts of society with exceptional applications drastically and change today's concepts of the Internet from content oriented to a delivery of haptics. Haptic communications will unveil a completely novel Internet through machine-to-machine and human-to-machine interaction, by offering high reliability, low latency, improved security, and expanded network coverage, which comprise the overwhelming conditions for real-time interactive systems. Haptic communication is a rapidly growing technology with applications in several domains ranging from education and entertainment to telerobotics and realistic simulators for planning and training of complex surgical procedures.  
Haptics promises to improve surgical planning by giving doctors access to all the virtual instruments they will need for performing operations. A surgeon may, therefore, feel and touch the patient's bones, muscles, and essential organs. Additionally, contact forces help the surgeon avoid interpenetration of body areas that could be difficult to see. By using haptic feedback to simulate certain surgical operations for teaching purposes, realism is heightened [2]. For educational purposes, simulating specific surgical procedures by implementing haptic feedback will lead to increased realism. A haptic interface provides improved precision and quicker accomplishment of tasks with rarer technical errors, while a visual-haptic interface provides an advanced way to execute force sensitive tasks [15]. Thus, haptic data reinforced by low latency networks will make VR-based headsets that can assist surgeons and doctors in executing partially operations via telepresence available, providing many benefits.  
The transmission of touch and grasp skills will be improved via the Tactile Internet, as illustrated in Figure 2. The utilization of such a technology will empower the physical capabilities of medical professionals to offer a precise diagnosis and treatment remotely. It is anticipated that real-time remote interaction and consultation will be enhanced by the transmission of haptic data that incorporates the sense of touch. Moreover, VR and AR can be used for virtual therapy by delivering an immersive user experience. The aforementioned unique characteristics of e-health are the main incentives that will transform the medical industry in the near future, offering many benefits and capabilities.
# An Initial Machine Learning-Based Victim's Scream Detection Analysis for Burning Sites
## 4 Results and Discussion
### Performance Comparison  
In terms of accuracy, both SVM and LSTM models have almost similar accuracies. SVM outperforms LSTM when it comes to correctly predicted classes as well as correctly predicted scream. Figure 12 shows SVM has a better performance recorded with a 98% correctly predicted scream which is 3% higher than LSTM. Since, our target is to detect victim in burning sites, 3% can make a huge impact on victim search process. This resulted in a smaller number of misclassified samples for SVM compared with LSTM. Additionally, it requires a higher training time than SVM's due to the complexity of the network. It is evident from the previous discussion that SVM is more stable in noisy environment. However, the metrics do provide how the model might perform with an actual microphone attached to the Jetson Nano embedded system. Overall, the SVM is considered due to its higher accurate prediction of scream, performance stability in low SNR and computational efficiency required when running on autonomous embedded systems.  
We further compared our result with previous research works based on scream detection in Table 10. From the comparison, it can be seen that, both of our models have higher scream detection rate than most of the previous works. The scream detection rate is the highest in this referred work [27] which they have achieved by using band-limited spectral entropy as feature extraction. The models performed with 99% accuracy at \(-\)5 dB condition. Our proposed SVM model has achieved 98% accuracy which combines SNR levels varying from \(-\)10 dB to 30 dB. At the \(-\)10 dB condition, the model is able to detect 91% scream accurately. However, the performance of LSTM model degrades drastically when the SNR condition is low. The research work in [14] is focused on fire emergency situations as well, where they have achieved 85.7% overall accuracy. Our result shows the highest accuracy when it comes to scream detection in burning sites.  
Figure 12: Performance Comparison of SVM and LSTM.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**SNR** & **Recall** & **Recall (Scream)** & \
\begin{tabular}{c} **Total** \\ **Misclassified** \\ \end{tabular} &
\begin{tabular}{c} **Scream** \\ **Misclassified** \\ \end{tabular} \\ \hline
30 dB & 0.98 & 1 & 10 & 0 \\
25 dB & 0.99 & 1 & 4 & 0 \\
20 dB & 0.98 & 0.95 & 9 & 5 \\
15 dB & 0.97 & 1 & 17 & 0 \\
10 dB & 0.95 & 0.98 & 25 & 2 \\
5 dB & 0.95 & 0.93 & 27 & 7 \\
0 dB & 0.91 & 0.89 & 44 & 11 \\ \(-\)5 dB & 0.93 & 0.87 & 37 & 13 \\ \(-\)10 dB & 0.86 & 0.66 & 69 & 34 \\ \hline \hline \end{tabular}
\end{table}
Table 9: Effect of different SNR on LSTM model.