esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Model Analysis  
### System Availability  
Case I: Availability of the system with copula repair.  
Rates of repair are set by the Gumbel-Hougaard copula distribution:  
\[\overset{*}{S_{v}}(s)=\frac{exp[x^{\epsilon}+\{log\xi(x)\}^{\epsilon}]^{\frac{ 1}{2}}}{s+exp[x^{\epsilon}+\{log\xi(x)\}^{\epsilon}]^{\frac{1}{2}}},\quad \overset{*}{S_{\zeta}}(s)=\frac{\zeta}{s+\zeta}\]For \(\sigma_{1}=0.01\), \(\sigma_{2}=0.02\), \(\sigma_{3}=0.03\), \(\sigma_{4}=0.04\), \(\epsilon=1\), \(\zeta=1\), \(x=1\), and \(\zeta(x)=\zeta(y)=1\) in Equation (56) and when applying the inverse Laplace transform using Mathematica, the availability is as follows:  
\[\overset{*}{P}_{up}(t) =0.98531+0.0190956e^{-2.77139t}-0.00422647e^{-1.22903t}-0.000044883e ^{-1.09014t}\] \[\qquad-0.0000623626e^{-1.06847t}-0.0000639626e^{-1.05263t}-4.3467( 10^{-6})e^{-1.02726t}\] \[\qquad-3.67201(10^{-6})e^{-1.0153t}-2.28297(10^{-13})e^{-0.1t}+2.6 9213(10^{-12})e^{-0.08t}\] \[\qquad-4.39599(10^{-12})e^{-0.07t}+2.06294(10^{-12})e^{-0.08t}-2.4 632(10^{-13})e^{-0.05t} \tag{58}\]  
Case II: Availability of the system with general repair.  
Rates of repair are set by two general distributions:  
\[\overset{*}{S}_{v}(s)=\frac{\
u}{s+\
u}\quad,\quad\overset{*}{S}_{\zeta}(s)= \frac{\zeta}{s+\zeta}\]  
Assigning specific numerical values to the rates of failure and repair as in Case I using Equation (56), one can obtain the availability as follows:  
\[\overset{*}{P}_{up}(t) =0.961052+0.0305836e^{-1.27165t}+0.000749247e^{-1.09104t}+0.00140 467e^{-1.0701t}\] \[\qquad+0.001385e^{-1.05447t}+0.000270381e^{-1.02754t}+0.000549342e ^{-1.01579t}\] \[\qquad+0.00401258e^{-1.00535t}-3.68768(10^{-13})e^{-0.1t}+1.6794( 10^{-12})e^{-0.08t}\] \[\qquad-2.69796(10^{-12})e^{-0.07t}+1.28643(10^{-12})e^{-0.06t}-2.8 4601(10^{-13})e^{-0.05t} \tag{59}\]  
Case III: Reduction method  
The overall availability of the system is enhanced By applying a factor \(\rho\), where \(0<\rho<1\), to the failure rates of the individual components of the system, this scaling of the parameter from Case I produces higher levels of system availability compared to the original configuration.  
\[P_{up}(t) =0.991204+0.0103481e^{-2.74674t}-0.00148593e^{-1.13728t}-0.000016 5956e^{-1.05404t}\] \[\qquad-0.0000225276e^{-1.041t}-0.0000244228e^{-1.03143t}-1.08928(1 0^{-6})e^{-1.01633t}\] \[\qquad-1.80207(10^{-6})e^{-1.00916t}+5.1252(10^{-13})e^{-0.06t}-2. 1356(10^{-12})e^{-0.048t}\] \[\qquad+1.75621(10^{-12})e^{-0.042t}-1.28452(10^{-12})e^{-0.036t}+ 1.60821(10^{-13})e^{-0.03t} \tag{60}\]  
By incrementing the time variable \(t\) in Equations (58)-(60) from \(t=0\) to \(t=10\), one can generate Table 2 and the corresponding Figure 3. These results present the evolution of the availability in each of the three distinct scenarios introduced above.  
\begin{table}
\begin{tab
## 5 Results and Discussion  
To evaluate the performance of the EPOA-CHS algorithm, we performed simulations using the MATLAB software. To ensure a fair comparison, we conducted the evaluation under identical experimental conditions, including the SEP, DEEC, Z-SEP, and PSO-ECSM algorithms. By comparing the results obtained from these various algorithms, we can assess the efficacy and superiority of the EPOA-CHS algorithm in terms of energy efficiency and other relevant performance metrics.
## 4 Experiment Evaluation
### Experimental Settings
The purpose of setting up drift detectors is to mitigate the impact of concept drift by alerting to drift risks, thus assisting the base classifier in achieving superior classification performance. Secondly, the performance of drift detection algorithms is analyzed using four specific metrics: True Positive (TP), False Positive (FP), False Negative (FN), and Detection Delay (DD). These metrics serve as crucial dimensions for evaluating the performance of concept drift detectors. To quantify these metrics, this study incorporates the concept of an acceptable delay length \(\Delta\)[6]. Here, \(\Delta\) represents the maximum tolerable delay for detecting drift in this experiment. The following will provide detailed explanations for these four metrics, with \(t\) representing the time of drift occurrence and \(T\) indicating the time when the drift detector issues an alert. **True Positive (TP)**--When the drift warning occurs within the time interval \([t,t+\Delta]\), the drift identification is considered effective, and this interval is referred to as the True Positive Effective Interval. The True Positive Rate (TPR), also known as Recall in literature [18], is obtained by dividing the total number of true positives by the set drift quantity. **False Positive (FP)**--When the drift warning occurs outside the True Positive Effective Interval, it is recorded as a false positive, indicating a detection error. The number of false positives can reflect the level of false detection of a drift detector. **False Negative (FN)**: At time \(t\), concept drift indeed occurs, but the drift detector fails to report drift risks within the acceptable interval \([t,t+\Delta]\). In this case, the drift is considered to be ignored by the detector. True Positives can be reflected inversely as False Negatives: the higher the \(TP\), the lower the \(FN\), and \(TP+FN=1\). **Detection Delay (DD):** Defined as the difference between the time \(T\) when the drift warning is issued and the time \(t\) when the drift occurs. The detection delay must be within the acceptable delay length \(\Delta\) to be considered legitimate. Drift warnings that exceed this range will be recorded as false positives (FP).
## 2 Materials and Methods
### Torrefaction Process  
In general, torrefaction converts biomass material into a coal-like solid product with improved fuel characteristics compared to the original form. Torrefaction experiments in the present study were conducted in an electric muffle furnace with 0.42 m diameter, 0.49 m length and 0.66 m height. The furnace consisted of a stainless steel tubular vessel that could fit the sample holder, a combustion chamber and a gas condensing section, as shown schematically in Figure 1.  
The dried, blended samples of sewage sludge and sugarcane bagasse were ground to maintain a homogeneous experimental condition. A prescribed quantity of the samples weighing 20 g each were placed in the sample holder and mounted in the tubular vessel. An inert environment was created by flushing with nitrogen (N\({}_{2}\)) purge gas at a 2 L/min flow rate. The furnace was preheated (at a constant heating rate of 20 \({}^{\circ}\)C/min) to a set temperature prior to placing the tubular vessel containing the sample inside the furnace for the torrefaction process. At the attainment of the desired torrefaction temperature (200 \({}^{\circ}\)C, 250 \({}^{\circ}\)C, 300 \({}^{\circ}\)C and 350 \({}^{\circ}\)C) and residence time (50 min), the experiment was halted, and samples were withdrawn for further analysis. The residence time of 50 min used is within the range applicable to the torrefaction process [2; 11; 23].
## 5 The Molecular Mechanisms Behind the Resistance of Biofilms to Antimicrobial Agents
### Genetic Recombination and Genetic Variation Through Changes in DNA  
The rise of antibiotic resistance in biofilms is intricately linked to genetic recombination and DNA polymorphism within these intricate microbial communities. Biofilms facilitate horizontal gene transfer because of their elevated cell density, genetic competency, and the presence of mobile genetic elements, including plasmids and transposons [129]. Recent studies have shown that biofilms can aid in the transfer of MDR plasmids, emphasizing the possibility of genetic exchange among bacterial populations. A trade-off between horizontal and vertical transmission of plasmid transfer genes has been noted, highlighting the dynamic interplay of evolutionary strategies within biofilms [130]. Genetic adaptation, influenced by mutations and recombination, serves as a fundamental aspect of biofilm resilience, allowing bacterial populations to flourish in challenging environments [131].  
The presence of genetic variation in biofilms significantly influences antibiotic resistance, the process of biofilm formation, and the potential for pathogenicity. Experimental studies on pneumococcal biofilms indicate significant rates of gene transfer, implying that recombination events frequently occur in these densely populated environments [132]. The close proximity of cells in biofilms increases the chances of genetic exchange, facilitating the persistence and spread of resistance genes over time [133]. This genetic plasticity enhances biofilms' defenses against antibiotics and propels the evolution of increasingly virulent and resistant bacterial strains [134].  
Specific bacterial species, including _A. baumaniii_, utilize biofilms as concentrated areas for the exchange of genetic information. This environment promotes the swift spread and development of resistance genes, exacerbating the medical challenges associated with biofilm-related infections [135]. The intrinsic phenotypic tolerance of biofilms to antibiotics, along with their ability to transfer resistance genes, highlights the significance of biofilms as reservoirs and amplifiers of antibiotic resistance [136].
## 4 Results and Discussions
### Acoustic Performance Using Honeycomb Cores of Different Thickness  
The STL results for sandwich structures with Nomex honeycomb cores of different thickness are shown in Figure 14. As can be seen, increasing the thickness of the core increased the STL of the sandwich structure on the whole.  
In the frequency range 3.6-5.2 kHz, the average STL value increased successively by 2 (C\({}_{2}\) versus C\({}_{1}\)), 4 (C\({}_{3}\) versus C\({}_{2}\)), and 3 dB (C\({}_{4}\) versus C\({}_{3}\)). Thus, the largest increment in STL occured when the thickness of the core was increased from 20 to 30 mm. That is, as the thickness of the honeycomb core was increased, the increment in STL grew at first and then declined. Once the thickness reached 30 mm, however, the benefit gained by increasing the thickness of the honeycomb core further began to weaken (with respect to STL improvement).  
It can be seen that increasing the thickness of the honeycomb core had a stronger effect on STL than increasing the thickness of the face sheet. According to the analysis mentioned earlier, STL is mainly in the stiffness region until 5000 Hz. So, increases in STL are affected by the stiffness of the structure.  
Figure 14: Test results for four structures with honeycomb cores of different thickness.  
Figure 13: Simulated mode shapes for the B\({}_{3}\)-type panel: (**a**) mode at 4917 Hz; (**b**) mode at 6597 Hz; and (**c**) mode at 6634 Hz.  
The stiffness of the structure can be improved by increasing thickness, but [PERSON] et al. [33] found excessive thickness will reduce the stiffness of the honeycomb sandwich panel and optimum middle thickness values maximized the stiffness. Therefore, the benefit gained by increasing the thickness begins to weaken, which can be attributed to the stiffness of structure approaching the maximum. The same phenomenon also appeared in the research conducted by [PERSON] et al. [25].  
Figure 15 shows the shapes of the simulated modes for the C\({}_{2}\)-type panel. This also indicates that the trends observed in the STL curves of the honeycomb sandwich panels were influenced by the structural modes of the panels. Moreover, changing the various structural parameters could change the mode frequencies.
# A Novel Fragmented Approach for Securing Medical Health Records in Multimodal Medical Images
## 4 Results and Discussion
Table 5 shows that the accuracy of diagnosis using the original image is 97.62% and that the accuracy after hiding 100-5000 characters never decreased below the accuracy using the original image. This clearly indicates that the structure of the original image was preserved and will not affect the diagnosis process negatively. Other results of precision, recall, and F1-measure indicate the same result when comparing the value for the original image and the stego image with different character sizes hidden. The proposed system achieves a high level of security for several reasons. The transfer of the stego-image occurs without the need for the original image, enhancing system security by minimizing the impact of the actual image on security measures. The proposed system's security architecture is layered, featuring multiple stages of protection. This means \begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Medical Records** & **Accuracy** & **Precision** & **Recall** & **F1 Measure** \\ \hline Original image & 97.62 & 97.69 & 97.95 & 97.82 \\ Text 1 (100 characters) & 97.61 & 97.71 & 98.31 & 98.01 \\ Text 2 (600 characters) & 97.62 & 97.53 & 97.56 & 97.54 \\ Text 3 (1200 characters) & 97.58 & 97.24 & 97.71 & 97.47 \\ Text 4 (1800 characters) & 97.63 & 97.80 & 97.28 & 97.54 \\ Text 5 (3000 characters) & 97.57 & 97.54 & 97.24 & 97.39 \\ Text 6 (5000 characters) & 97.60 & 97.77 & 97.52 & 97.64 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparison of Classification (ResNet CNN Model) results for Tumor Detection from Brain MR images to check the impact of information hiding. Figure 6: SSIM of processed images after hiding different levels of patient information. that if an external entity compromises the stego-object's original copy, a secondary layer of security is in place. This layer significantly complicates the task for attackers attempting to decrypt the embedded text within the image, making the analysis of the ciphered text nearly impossible. In the proposed system, the complexity of the cipher key directly influences the system's security. This particular security measure is distinct from the first two layers and involves a unique set of security considerations not apparent through conventional techniques. This layer significantly enhances the system's robustness and overall security. In the proposed system, steganography is designed to complement cryptography rather than replace it. Encrypting the message adds an additional layer of protection, decreasing the likelihood of the text being detected. The system incorporates a secret key that must be exchanged between the parties involved in the transmission. This exchange does not necessitate any knowledge of the extraction process or the details of the original cover image. The capacity for embedding information within the cover image is determined by its statistical properties. Regardless of the size of the embedded text, the proposed system achieves rapid extraction speeds without affecting the overall performance.
# Narrowband Internet of Things (NB-IoT):
## 2 Narrowband-IoT Standard and Releases
### Release 14 Enhancements
#### 2.2.3 New Power Class for Narrowband-IoT User Equipment  
Instead of the two power classes of Release 13 (i.e., 20 dBm and 23 dBm), in Release 14, the maximum allowed device's output power is reduced to 14 dBm. This has led to coverage relaxation of 9 dB that corresponds to 155 dB MCL as compared to 164 dB MCL and hence reduces the drained current. Technically, the use of the new power class facilitates the use of small coin-cell batteries and hence can be suitable for limited-size devices and applications that need a small battery. The compensation of the reduced NB-IoT power is achieved by increasing the NB-IoT transmission time to maintain the same energy per bit as the UE in Release 13 achieves. The newly introduced power class allows the serving base station to acquire the device power class during the establishment of the connection.
# A Novel Deep Learning Network Model for Extracting Lake Water Bodies from Remote Sensing Images
## 3 Results and Discussion
### Ablation Study
Then, the image size is gradually restored by bilinear interpolation and feature fusion is performed by layer-by-layer concatenate with the feature maps output from the first 4 down-sampling layers. Finally, the feature reduction is carried out by four up-sampling layers, and then the water body segmentation image is output by the convolutional layer with a convolutional kernel size of (\(1\times 1\times 2\)).
## 2 Failure Probability Analysis
### Fragility Function  
To calculate the failure probability of the tower-line system under combined ice and wind loads, it is necessary to analyze the fragility of structures. Based on the numerical simulation results, Equation (5) for the engineering demand parameter is determined through regression fitting. Subsequently, by substituting Equation (5) into Equations (6) and (7), the fragility of the tower-line system under combined ice and wind loads can be calculated [22].  
\[\ln(EDP)=a\ln(IM_{1})+b\ln(IM_{2})+c \tag{5}\]  
\[F[EDP\geq LS|IM]=\Phi(\frac{\ln(EDP)-\ln(LS)}{\beta_{EDP|IM}}) \tag{6}\]  
\[\beta_{EDP|IM}=\sqrt{\frac{\sum_{i=1}^{n}[\ln{EDP_{i}}-(a\ln(IM_{1i})+b\ln(IM_{ 2i})+c)]^{2}}{n-2}} \tag{7}\]  
where \(EDP\) is the engineering demand parameter; \(a\), \(b\), and \(c\) are the regression coefficients; \(IM_{1}\) is the ice thickness; \(IM_{2}\) is the wind speed; \(\Phi\) represents standard normal distribution; \(LS\) is the limit performance indexes; \(\beta_{EDP|IM}\) is the lognormal standard deviation of \(EDP\).
# Research Developments of Aerostatic Thrust Bearings: A Review
## 4 Advances in Dynamic Performance
### Structure Improvement
#### 4.2.2 Structural Optimization  
The optimization of the bearing structure is a key step to improving the dynamic performance of ATBs. [PERSON] studied the vortex flow in the bearing clearance numerically with a rounded corner near the outlet of the orifice restrictor [89]. The results showed that vortex flow caused small vibrations in aerostatic guideways. The proposed geometrical arrangements can effectively suppress small vibrations. [PERSON] proposed several novel orifice structures of the aerostatic bearing, including the round hole bearing (RHB), inclined hole bearing (IHB), and arc hole bearing (AHB) [90]. Both theoretical predictions and experimental tests showed that changing the orifice structure can significantly reduce pressure fluctuations and improve the stability of the bearing. Among the above bearings, the AHB had the best performance and vortices were extremely small in the recess. The flow field disturbance structure (FFDS) of the aerostatic thrust bearing was designed to change the microscopic flow field inside the bearing. The influence of the flow field disturbance on the micro-vibration was analyzed by the experiment method. The gas flow characteristics of the local field near the outlet of the orifice were greatly optimized due to the flow field disturbance structure. The pulsating pressure on the bearing surface was significantly reduced. The micro-vibration of the bearing with the flow field disturbance structure was suppressed.  
Vortices are inevitably caused, due to the fixed flow channels from the feed pocket to the bearing outlet for pressurized gas. The back-flow channels connecting the recess and low-pressure region inside the bearing clearance were designed to provide an additional channel for pressurized gas to be exhausted into the atmosphere [91]. The simulation results showed that the back channels can prevent gas from depositing in the recess. The vortex was greatly eliminated. The bearing stiffness and stability of the aerostatic bearings with the back-flow channel increased simultaneously compared to traditional ATBs.
# Influence Analysis of Traveling Wave Effect on Rail Interaction of Long-Span Suspension Bridge
## 6 Conclusions  
In this paper, the large-scale general-purpose finite element software ANSYS is used to study the influence of traveling wave effect on the long-span suspension bridge-rail interaction with the Wufengshan Yangtze River Bridge as the engineering background, and compare the seismic response of the beam-rail system under different apparent wave velocities. The main conclusions are as follows:  
1. The traveling wave effect has a great influence on the dynamic response of the rail system. The traveling wave effect of a low apparent wave speed will increase the relative displacement of the beam and rail, and then increase the rail stress; the main bridge part of the rail stress increase is especially obvious, and with the increase in apparent wave speed, the dynamic response of the rail system tends to be consistent with the dynamic response of the excitation.
2. The effect of traveling wave effect on the longitudinal displacement of the beam end is not negligible. In addition, the effect on the longitudinal displacement of the main bridge end is much larger than that of the approach bridge end.
3. Under high apparent wave speed and consistent excitation, the longitudinal displacement of the main bridge end on the north and south side keeps changing synchronously, under a low apparent wave speed, the longitudinal displacement of the main bridge end on the north and south side will no longer keep changing synchronously.
4. The traveling wave effect only has a large effect on the force of the pier bottom and main tower bottom of N2 and S2 piers with fixed bearings; the force response of the main tower bottom at a low apparent wave speed has a certain oscillation, which tends to stabilize with the increase in apparent wave speed; the bending moment and shear force of 3# main tower bottom at high apparent wave speed and consistent excitation are approximately synchronous changes, and will no longer keep synchronous changes at a low apparent wave speed.  
Conceptualization, X.Y. and C.W.; methodology, X.Y.; software, X.Y.; validation, X.Y., C.W. and H.J.; formal analysis, C.W.; investigation, H.J.; resources, H.J.; data curation, C.W.; writing--original draft preparation, C.W.; writing--review and editing, C.W.; visualization, X.Y.; supervision, H.J.; project administration, X.Y.; funding acquisition, H.J. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
Figure 26: Comparison of time-history curves of internal force at the bottom of 3# main tower under consistent incentives.
## 3 Communication Technologies for Improved Energy Efficiency and Safety in Railways
### Narrow-Band Internet of Things for Distributed Supervision and Control
These data are prepared for transmission by means of quadrature amplitude modulation (QAM) of the radio band carrier signal (see, e.g., [147]) used in LTE communication networks. The conversion of the parallel QAM data output into signals that are unsuitable for transmission is based on inverse fast Fourier transform (IFFT). At the receiver side, the inverse process is performed on the received radio signal, i.e., fast Fourier transform, or FFT is used in conjunction with the QAM demodulator for data extraction, which is subsequently forwarded to the target database, and it is accessible to selected subscribers.