esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Micro actuators have been used to realize the arrival of digestive tract lesions for the local targeted application of drugs in endoscopes. However, there still exists a key safety issue that casts a shadow over the practical and safe implementation of actuators in the human body, namely an overheated environment caused by actuators' operation. Herein, with the aim of solving the temperature rising problem of a piezoelectric micro actuator operating in an endoscopic biopsy channel (OLYMPUS, Tokyo, Japan), a thermal finite element method (FEM) based on COMSOL Multiphysics software is proposed. The temperature distribution and its rising curves are obtained by the FEM method. Both the simulated and experimental maximum temperatures are larger than the safety value (e.g., 42 \({}^{\circ}\)C for human tissues) when the driving voltage of the actuator is 200 V\({}_{\mathrm{PP}}\), which proves that the overheating problem really exists in the actuator. Furthermore, the results show that the calculated temperature rising curves correspond to the experimental results, proving the effectiveness of this FEM method. Therefore, we introduce a temperature control method through optimizing the duty ratio of the actuator. In comparison with a 100% duty ratio operation condition, it is found that a 60% duty ratio with a driving voltage of 200 V\({}_{\mathrm{PP}}\) can more effectively prevent the temperature rising issue in the first 3 min, as revealed by the corresponding temperatures of 44.4 and 41.4 \({}^{\circ}\)C, respectively. When the duty ratio is adjusted to 30% or less, the temperature rise of the actuator can be significantly reduced to only 36.6 \({}^{\circ}\)C, which is close to the initial temperature (36.4 \({}^{\circ}\)C). Meanwhile, the speed of the actuator can be well-maintained at a certain level, demonstrating its great applicability for safe operation in the human body.  
Keywords: piezoelectric micro actuator; endoscopic biopsy channel; thermal analyses; temperature rise control methods; finite element method (FEM) +
Footnote †: journal: Applied Soft. 2019, 9, 4499; doi:10.3390/appr9214499
# Inf-OSRGAN: Optimized Blind Super-Resolution GAN for Infrared Images
## 4 Experiments
### Experiment Setup
#### 4.1.2 Degradation Details  
During the two-stage training process, we employed the degradation process described in Section 3.1, which incorporated a shuffle strategy and gate mechanism into the second-order degradation process of Real-ESRGAN, excluding JPEG compression. The second-order degradation process involves three primary operations: blur, noise, and resize, which can be combined in six different sequences due to the shuffle strategy. The gate mechanism was employed to selectively execute these operations, ensuring a controlled application of degradation processes in the pipeline. For the blur operation, three types of kernels were utilized: Gaussian kernels with a probability of 0.7, generalized Gaussian kernels with a probability of 0.15, and plateau-shaped kernels with a probability of 0.15. The Gaussian kernels had a size range of 7 to 21 and a standard deviation varying from 0.2 to 3 for first-order degradation and 0.2 to 1.5 for second-order degradation. The generalized Gaussian kernels featured a shape parameter \(\beta\) ranging from 0.5 to 4, while the plateau-shaped kernels had a \(\beta\) value between 1 and 2. The noise operation consisted of Gaussian noise and Poisson noise, each with a probability of 0.5. The standard deviation for Gaussian noise ranged from 1 to 30 for the first-order and 1 to 25 for the second-order degradation. Poisson noise scaled from 0.05 to 3 in the first order and 0.05 to 2.5 in the second order. Resizing operations considered area, bilinear, and bicubic interpolation methods, applicable for both upsampling and downsampling, with a scaling range of 0.15 to 1.5. After completing the degradation process in the two stages, all images had to be resized to one-quarter of the size of the high-resolution (HR) images. Subsequently, the images underwent processing through a sinc filter, which was applied in a random manner.
## 3 Distance Distribution  
The traditional V-N connects to the closest device, d (i.e., M, L, N, or V), at a position, \(z\), with a random variable, \(Z_{d}\). For an MBS connected to a V-N, \(Z_{M}\) is the random variable describing distance, and it is assumed that all the interfering equipment is placed outside \(z\). Considering the usual V-N linked to the closest MBS, the cumulative distribution function (cdf) of the \(Z_{M}\) is represented by [34] as  
Figure 2: Interference characterization of a UAV-assisted C-V2X network in 2D space.  
\[F_{M}(z)=1-e^{-\pi\lambda_{M}z^{2}}. \tag{9}\]  
The probability density function (pdf) for the MBS connection is represented as  
\[f_{M}(z)=dF_{M}(z)/dz=2\pi\lambda_{M}ze^{(-\pi\lambda_{M}z^{2})}. \tag{10}\]  
For the V-N associated with the LAP (i.e., \(Q\in\{L,N\}\)), the cdf of the LAP's distance is given as [35]  
\[F_{Q}(z)=1-e^{-2\pi\lambda_{U}\int_{h_{U}}^{z}t\mathbf{p}_{Q}\big{(}\sqrt{t^{2} -h_{U}^{2}}\big{)}dt}. \tag{11}\]  
The pdf of the LAP's distance is given as  
\[f_{Q}(z)=2\pi\lambda_{U}t\mathbf{p}_{Q}\bigg{(}\sqrt{t^{2}-h_{U}^{2}}\bigg{)}e ^{-2\pi\lambda_{U}\int_{h_{U}}^{z}t\mathbf{p}_{Q}\big{(}\sqrt{t^{2}-h_{U}^{2}} \big{)}dt}. \tag{12}\]  
For the V-N attached to the closest V-N, the pdf of the V-N's distance may be obtained through the cdf indicated in Appendix A and is represented as  
\[f_{V}(z)=\frac{dF_{V}(z)}{dz}=2\exp\bigg{(}-2\lambda_{V}z+2\pi \lambda_{R}\int_{0}^{z}1-e^{-2\lambda_{V}\sqrt{z^{2}-y^{2}}}dy\bigg{)}\] \[\times\bigg{(}\lambda_{V}+2\pi\lambda_{R}\lambda_{V}\int_{0}^{z} \frac{ze^{-2\lambda_{V}\sqrt{z^{2}-y^{2}}}}{\sqrt{z^{2}-y^{2}}}dy\bigg{)}. \tag{13}\]
## 3 System Model and Problem Formulation
### Problem Formulation
#### 3.2.2 Error Analysis of Orbit Extrapolation Based on Ephemeris  
In the MPDTSC system, satellites' ephemeris data are periodically broadcast to the UE in TLE format. The expected SGP4 model extrapolates the orbit to calculate the satellite's location and velocity, with certain errors being caused by the Earth's gravitational effects, the gravitational effects of other celestial bodies, satellite attitude deviations, and ephemeris  
\begin{table}
\begin{tabular}{c c c} \hline
**Sequence Number** & **Digit Positions** & **Data Meaning** \\ \hline
8 & 53–63 & Number of revolutions per day \\
9 & 64–68 & Number of orbits since launch \\
10 & 69–69 & Checksum \\ \hline \end{tabular}
\end{table}
Table 1: _Cont._  
Figure 4: Parameters of TLE data mapping to the Earth.  
transmission errors [25]. This error can be decomposed into three directional components in the spatial Cartesian coordinate system.
* Radial component R: the direction along the line connecting the satellite and the Earth's center;
* Along-track component A: the tangential component within the orbital plane, perpendicular to the radial direction and pointing in the direction of the satellite's motion;
* Cross-track component C: the component perpendicular to the orbital plane.  
The broadcast ephemeris information received by the UE is denoted as \(eph_{broadcast}\), and is in the form of TLE orbital elements:  
\[\{n,i,e,M,\omega,\Omega\} \tag{5}\]  
Based on the referenced time \(t_{oe}\) and the orbital elements in the TLE ephemeris data, the SGP4 algorithm is used to extrapolate the following three-dimensional coordinate components of the position vector \(r\):  
\[\begin{bmatrix}x(n,i,\cdots,\Omega,t_{oe},t)\\ y(n,i,\cdots,\Omega,t_{oe},t)\\ z(n,i,\cdots,\Omega,t_{oe},t)\end{bmatrix}=\begin{bmatrix}x(P_{t},t_{oe},t)\\ y(P_{t},t_{oe},t)\\ z(P_{t},t_{oe},t)\end{bmatrix} \tag{6}\]  
where \(x(\cdot)\), \(y(\cdot)\), \(z(\cdot)\) are the spatial position calculation functions of the satellite, which are the three-dimensional coordinate components of the position \(r\) calculated using the SGP4 algorithm. \(P_{t}\) denotes the position at time \(t\). The satellite's broadcast ephemeris can be expressed as the sum of the real position and real velocity of the ephemeris \(eph_{0}\) and the ephemeris error \(\Delta eph\):  
\[eph_{broadcast}=eph_{0}+\Delta eph \tag{7}\]  
Unlike previous works that treat the orbit extrapolation error as a single error, we assume that there are multiple types of orbit extrapolation errors with different influencing factors.  
\[\Delta eph(f_{n}(\cdot))=\{\Delta eph(f_{1}(\cdot)),\Delta eph(f_{2}(\cdot)), \cdots,\Delta eph(f_{N}(\cdot))\} \tag{8}\]  
where \(\Delta eph(f_{n}(\cdot))\) denotes a set of orbit extrapolation errors with different influencing factors and \(\Delta eph(f_{N}(\cdot))\) represents the \(n\)-th influencing factor.
# An Analog Circuit Fault Diagnosis Method Based on Circle Model and Extreme Learning Machine
## 2 Feature Engineering Based on Circle Model
### Circle Model-Based Feature Engineering
#### 2.2.1 Circle Model-Based Feature Extraction  
According to the circle model theory, the real and imaginary parts of the fault voltage of a linear circuit can be described by the circle equation on the complex field, and all pass through the fault-free point. Thus, a circle can be defined by three points and only three simulations are needed to obtain the fault features of the analog element in all fault states. If a component suffers from a catastrophic failure (e.g., open circuit and short circuit), the circle model of the element fault can be established based on the output voltages of the element under open circuit and short circuit and the fault-free point that the fault voltages of all element pass through. Suppose the parameter scanning range is \(X/10^{4}\leq\mathrm{p_{X}}\leq 10^{4}\)\(X\) (\(X\) is the parameter value of an element). Taking a resistor for instance, the resistance is 100 M\(\Omega\) in open circuit and 1 \(\Omega\) in short circuit. Here, the fault parameters of each element are scanned within the preset range, revealing the output voltage of each element. Then, the analog voltage phasor \(\dot{U_{o}}\) was decomposed into a real part \(U_{or}\) and an imaginary part \(U_{oj}\). Three voltages \(\{\dot{U_{1}}\), \(\dot{U_{2}}\), \(\dot{U_{0}}\}\) respectively denoting short circuit, open circuit and free fault can be obtained through one parametric sweep simulation. Then the following equations are obtained:  
\[\left\{ \begin{array}{l}(U_{or}-a)^{2}+(U_{oj}-b)^{2}=r^{2}\\ (U_{1r}-a)^{2}+(U_{1j}-b)^{2}=r^{2}\\ (U_{2r}-a)^{2}+(U_{2j}-b)^{2}=r^{2}\end{array} \right. \tag{17}\]  
The output voltage of the test point of the free fault can be expressed as (2.394,3.175) in the complex field. To the fault model, only two more points need to be measured for each component. The simulation results and the parameters of the fault model are shown in Table 1.
## 6 Synergy with Hybrid Communication Systems
### Multi-Band Adaptive Systems  
Flexible transceiver schemes select communication links at microwave, millimeter-wave, and terahertz (THz) bands according to the prevailing conditions and availability of resources. Such systems take the use of each range of frequencies to balance the system's range, data rate, and reliable links [25; 36]. For instance, although THz bands can provide up to UHDR, these are less robust and can be attenuated; hence, the system has to switch to microwave or millimeter-wave bands during unfavorable conditions.  
Table 13 highlights the comparative advantages and limitations of these bands for hybrid systems.  
\begin{table}
\begin{tabular}{p{56.9 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Study** & **Frequency Band** & **Advantages** & **Limitations** & **Applications** \\ \hline
[104] & Microwave & Long-range, robust to weather & Limited data rates & Weather-independent communications \\ \hline
[105] & Millimeter-wave & Higher data rates, moderate range & Susceptible to rain attenuation & Short-range mobile networks \\ \hline
[106] & THz band & Ultra-high data rates, narrow beam & Short range, high attenuation & High-capacity backhaul \\ \hline \hline \end{tabular}
\end{table}
Table 13: Comparison of frequency bands in hybrid systems.  
Figure 7: Synergy with hybrid communication systems.
## 3 Results
### VHIopt Accuracy Evaluation
#### 3.2.3 Verification of Typical Drought Years  
The validation of the drought index using the typical drought year is crucial for ensuring its accuracy and reliability in drought monitoring. For instance, [PERSON] et al. selected 2008 as a representative drought year for evaluating the performance of OSDCI_rev in Central Asia [36]; [PERSON] et al. selected 2001 and 2011 as dry years to evaluate various GPP  
Figure 4: Time series plots of VHIopt, SPEI03, and SMA during the growing season (YRB (**a**), upstream (**b**), midstream (**c**), downstream (**d**)).  
datasets in China [53]. Throughout the study period, the YRB has experienced several droughs. To identify a typical year for monitoring drought evolution, the year with the lowest values of SPEI03 and SMA were selected [33; 78; 79]. As shown in Figure A4, SPEI03 values were lowest in 2001, 2009, and 2011, while SMA values were lowest in 2001, 2011, and 2019. Significantly, 2001 had the lowest values for both SPEI03 and SMA. Therefore, 2001 was chosen as a representative drought year to validate the accuracy of VHIopt in monitoring drought conditions.  
Figure 5 illustrates that the VHIopt anomalies reveal a gradual spread of agricultural drought across the YRB from April to July, with the most severe drought conditions occurring in July. Subsequently, the drought conditions began to ease between July and September. Moreover, the spatial distribution pattern of agricultural drought monitored by SMA and SPEI03 is largely consistent with that monitored by VHIopt. In order to further evaluate the enhancement of VHIopt on time series, the changes in the VHIopt for 2001 were compared with those of SPEI03 and SMA. As shown in Figure 6, during the growing season of 2001, the VHIopt initially decreased, reached its lowest value in July, and then increased. This pattern aligns with the results in Figure 5, which indicate that the overall drought conditions in 2001 gradually eased. The time series of VHIopt aligns well with both SMA and SPEI03, demonstrating that VHIopt effectively captures the temporal variations and trends in agricultural drought.  
Figure 5: _Cont._
# Logically-Centralized SDN-Based NDN Strategies for Wireless Mesh Smart-City Networks +
## 3 Proposed SDN-Based System  
In this section, we present the proposed SDN system and the implemented reactive and proactive approaches, which target efficient NDN operation over WMNs. The main objective of both approaches is the selection of the appropriate paths between the NDN producer and consumer. In summary, we consider:  
1. A reactive NDN path selection strategy that aligns NDN paths with the dynamic routing decisions of the WMN protocol. This approach is based on distributed decision-making information (i.e., each node chooses the best route for each destination among its neighbors) which is collected from the SDN Controller to configure the NDN nodes.
2. A proactive NDN path selection strategy based on evaluating available wireless links in terms of RSSI and delay. Specifically, in this approach, we collect historical network monitoring data and classify their performance to select the appropriate NDN path.  
Their detailed description follows next.
## 5 Results and Discussion
### Statistical Modeling  
This section shows the results to when applying Equations (1)-(3) related to the generalized extreme value distribution. Figure 11 shows the different histograms for each class. Please note how the distribution across the frequency of the numerical data is very similar and difficult to differentiate. One solution to this is to use statistical modeling through some probability distribution. Therefore, from the raspberry trays' images, the generalized extreme value distribution was estimated for each class, see the red line in Figure 11. Table 3 shows the three parameters resulting from applying this distribution. For a binary learning system such as healthy raspberry vs. defective raspberry, see Figure 9; all average thresholds can be used for this discrimination, despite the fact that they are very close to each other. With respect to the bounds, the location parameter (\(\mu\)) and the shape (\(\kappa\)) parameter can be interesting predictors to detect and discriminate the different, see Equation (8). While the scale (\(\sigma\)) parameter values are very close between them and thus difficult to differentiate it.  
Let us take the location parameter predictor (\(\mu\)) as an example. \(\mu\) has an average threshold value of 72.69 for a healthy raspberry, which is different with respect to the rest of the average threshold values, 58.22 for albinism disease, 56.25 for fungus rust disease, 77.16  
Figure 11: Histograms and generalized extreme value fitting from the raspberry diseases and defects used in this database. (**a**) Raspberries with albinism disease. (**b**) Raspberries with the fungus rust disease. (**c**) Raspberries with the over-ripeness defect. (**d**) Raspberries with peduncle defect. (**e**) Healthy raspberry.  
for over-ripeness defect, and 64.32 for peduncle defect. This suggests that each disease and each defect of a raspberry with respect to a healthy raspberry can be discriminated by an average threshold. Similarly, it happens with the bounds. A healthy raspberry is between the values \([71.02,74.36]\), which is different too, with respect to the rest bounds values, \([57.07,59.37]\) for albinism disease, \([52.77,59.73]\) for fungus rust disease, \([74.47,79.85]\) for over-ripeness defect and \([61.31,67.32]\) for peduncle defect. Note that all values of the lower and upper bounds are different from each other, except for a small overlap between the upper bounds of the diseases. Similarly, there is an overlap between the other predictors. Applying this analysis of all the predictors together, we suggest that these differences between the lower and upper bounds can achieve good performance on the ML approach. Please note that these predictors are the input for the neural network to classify and detect diseases and defects in raspberries, as shown in Figure 9.
# The Traffic Adaptive Data Dissemination (TrAD) Protocol for both Urban and Highway Scenarios
## 2 Related Work
### Broadcast Suppression Technique
The typical example is the road topology of the \"Arc de Triomphe\" in Paris, which has twelve roads gathered at a roundabout. The approaches of these two protocols are not flexible enough to recognize the irregular road topology.
# A Novel Edge Platform Streamlining Connectivity between Modern Edge Devices and the Cloud
## 4 Implementation and Evaluation of the Newly Defined Software Architecture
This highlighted the successful execution of our data consolidation strategy, which effectively managed the high volumes of data, ensuring they translated into manageable loads on our cloud infrastructure. The information in Figure 9 showcases the synchronization outcomes for Farm 10001 over a 30-day duration, offering an essential insight into the functionality of our synchronization system. During this period, it became evident that while the system generally maintained a consistent match between new records created at the edge and those recorded in the cloud databases, significant data loss occurred during transmission. Particularly noteworthy were the instances on May 24 th and 23 rd, where the cloud database failed to receive 205 and 203 records, respectively, indicating a notable shortfall in data synchronization. This issue underscores the critical issue of data loss in the transport process from the cloud to the farm. Figure 8: Scalability tests—time to send 5 thousand records from one side to another. Figure 9: Recording sync for 30 days between edge and cloud (Farm 10001). Through several iterations, our project's coding quality reached a high standard, yet we encountered significant reliability challenges in data transfer. The twin problems were the imperfect transmission from edge devices to the MQTT gateway and flaws in the routines writing data into the AWS Aurora cloud database. AWS Aurora's divergence from MySQL 8, especially in its distributed systems architecture for cloud scalability and reliability, contributed to these challenges. Recognizing these issues was pivotal, leading to targeted enhancements in our synchronization solution. Before commencing further trials, we conducted rigorous refinements to strengthen the system against data loss and ensure it could meet the demands of live deployment scenarios. This process underscored the necessity for a novel approach to address the persistent issue of missing records. These challenges led us to a decisive turning point in our project, necessitating a transformative solution for the persistent issue of missing records. As we transition to the next section, we introduce a new approach to address these synchronization shortcomings.
# Check for special characters and punctuation
## 4 Experimental Results and Analysis
### Key Findings  
Three significant findings emerge from our experiments:  
* The enhanced embedding mode doubles capacity without proportional security degradation, representing a significant advancement over previous approaches.
* DeepStego achieves complete undetectability against certain advanced steganalysis methods, particularly deep-learning-based approaches.
* The system maintains text quality even at high embedding rates, with semantic coherence scores remaining above 0.91 across all test conditions.  
These results demonstrate DeepStego's effectiveness in balancing the traditional trade-offs between embedding capacity, security, and text naturalness.
## 4 Review of a V2I Authentication Scheme
### User Registration  
Each \(VU\) sends its real identity \(ID_{v}\) to TA for registration. TA randomly chooses a fresh number \(\beta_{i}\) where \(i\in[1\ldots n]\), \(\beta_{1}\
eq\beta_{2}\
eq\cdots\
eq\beta_{n}\). Then, \(TA\) calculates a group of pseudo identities \(VID_{i}=E_{k}[ID_{v}\|ID_{TA}\|\beta_{i}]\). \(TA\) initializes a counter as \(ctr=N_{0}\), \(N_{0}\in\mathcal{Z}_{p}^{*}\). \(TA\) stores the tuple \(\{h(ID_{v}\|K),ctr\}\) into its database, but does not store them in plaintexts, and transmits \(SC=\{ID_{TA},(VID_{i},\beta_{i}),ctr\}\) to \(VU\). \(VU\) inputs its password \(PW_{v}\) and computes parameters \(PID=N_{1}\oplus h(ID_{v}\|PW_{v})\) and \(AID=h(ID_{v}\|PW_{v}\|N_{1})\). Finally, \(VU\) inserts these parameters into \(SC\), which contains \(\{ID_{TA},PID,AID,(VID_{i},\beta_{i}),ctr\}\). At the beginning of every session, \(VU\) selects the \((VID_{i},\beta_{i})\) pair and updates the set of \((VID_{i},\beta_{i})\) pairs periodically to keep the \(VU\) anonymous.