esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Modelling and Methods
### Heat Transfer Analysis, Fire Load, Assumptions, and Conventions
#### 2.2.1 Heat Transfer Fundamentals  
Although the aim of this study is not to review and present the fundamental principles of heat transfer, it is worth briefly presenting the basic mechanisms operating on the finite element analysis model. The analysis commences with the wall samples in thermal balance, with an ambient room temperature applied on either side. At time \(t=0\) sec, an increasing fire load (as described in the following paragraph) is applied on one side (hereon referred to as \"exposed\"), initiating the combined heat transfer mechanism of convection and radiation. The fundamental Equations (2) and (3) govern the convective and radiation heat transfers, respectively [26], from the fire front towards the exposed wall surface:  
\[q_{conv}^{\prime\prime}=h\left(T_{S}-T_{\infty}\right) \tag{2}\]  
\[q_{rad}^{\prime\prime}=\varepsilon\cdot\sigma\left(T_{s}^{4}-T_{sur}^{4}\right) \tag{3}\]  
where \(q_{conv}^{\prime\prime}\) and \(q_{rad}^{\prime\prime}\) are the resulting heat flux due to convection and radiation, respectively, \(h\) is the convection heat transfer coefficient (W/(m\({}^{2}\cdot\)K)), \(T_{s}\) is the surface temperature, \(T_{\infty}\) is the surrounding fluid temperature (for convection), \(T_{sur}\) is the surrounding environment temperature (for radiation), \(\varepsilon\) is the emissivity coefficient, and \(\sigma\) is the Stefan-Boltzmann constant (\(\sigma=5.67\times 10^{-8}\) W/(m\({}^{2}\cdot\)K\({}^{4}\))).  
The heat transfer mechanism of conduction is also activated as heat progressively travels through the solid parts of the wall assembly. Equation (4) is the governing relationship for heat conduction, and it was used to replicate the phenomenon on the finite element analysis model. The clay brick cavities play an integral part in the transfer of heat through the sample [27], and thus attention was paid to modelling them accurately. Heat transfer through radiation between the cavity walls and convection through air movement within the cavities has been considered. The previously described convection and radiation formulas apply:  
\[q_{cond,x}^{\prime\prime}=-\lambda\frac{\partial T}{\partial x} \tag{4}\]  
where \(q_{cond,x}^{\prime\prime}\) is the resulting heat flux due to conduction, \(\lambda\) is the thermal conductivity, and \(\frac{\partial T}{\partial x}\) is the temperature gradient in the direction of the heat's transient movement.  
The final stage of the transient movement of heat through the wall sample is its release to the environment through the non-exposed face of the section. The applicable heat transfer mechanisms are convection, through the surrounding air, and radiation.
## 5 Research Challenges and Future Directions
### Delay  
Another major challenge of the passive wake-up radio is the increased latency. This is as a result of the limited RF energy from the sources, from which the receiver needs to harvest enough energy to power its circuitry. The increased delay in the trigger process and how to reduce it is still a significant issue in the research of passive schemes. The recent works which made an effort to limit the delay are mostly based on ultralow power circuitries such as [124], which are, however, not completely passive. The low latency in this architecture is achieved by making the propagation delay as the main criteria for selecting the comparator circuit elements. The reference voltage source of the comparator is optimized to eliminate the high delay due to long preamble needed in other designs.
###### Abstract  
The gob-side roadway technique is extensively utilized in coal extraction due to its capacity to enhance coal resource recovery efficiency and mitigate mining sequence conflicts. Nevertheless, increasing mining depths lead to progressively intricate stress conditions, posing challenges for maintaining gob-adjacent roadway surrounding rock stability. Taking the belt haulage roadway 1513 (BHR 1513) at Xinyi Coal Mine as an engineering case, this research investigates the application of narrow-pillar gob-side roadway construction under remote working face mining conditions. By integrating field observations, analytical modeling, and computational simulations, the cross-goaf pressure transfer phenomenon and its formation mechanism in narrow-pillar roadways under distant mining operations are systematically examined. Key findings reveal that during the alternating extraction of wide and narrow working faces, the caving angle terminates roof collapse within the narrow working face goaf at the second key stratum (KS2). The subsequent mining of the adjacent wide working face induces stress accumulation in the overlying \"T\"-shaped strata zone, triggering the instability of the inter-working face island pillar. This pillar failure merges the two goafs into an expanded void, initiating sequential fracture, collapse, and rotational displacement across all overlying key strata (KS). Consequently, previously intact KS above the narrow working face goaf undergo fracturing and rotation, amplifying lateral main roof block subsidence toward the goaf. This kinematic process generates substantial deformation in the narrow-pillar gob-side roadway.  
distance face; mining; gob-side roadway; coal pillar; stress disturbance; numerical simulation; rock mechanics +
Footnote †: journal: Applied sciences
## 3 Results
### Dough Rheology
tricornutum_ did not significantly affect the dough's stability (\(p\) > 0.05) when compared to control. However, the addition of _C. vulgaris_ led to a slight but statistically significant (\(p\) < 0.05) reduction in stability from 574 s to 496 s. The lower stability observed in the _C. vulgaris_ dough may be attributed to its increased water absorption. This last result is aligned with the study conducted by [PERSON] et al. [12]; however, they reported a more pronounced reduction in stability when _C. vulgaris_ was added at the same level to wheat flour. The pasting curve, specifically characterised by the C2 and C3 parameters, revealed distinct patterns (Table 3). In comparison, samples containing microalgae exhibited a significant (\(p\) < 0.05) decrease in C2 values, which was in contrast to the control (0.44 N.m), with the C. _vulgaris_ sample having the lowest C2 value (0.23 N.m) and the _P. tricornutum_ having the highest C2 value (0.34 N.m) (Table 3). The decrease in C2 suggests a potential destabilisation of protein structures, as the proteins of microalgae formed a discontinuous matrix with the gluten network [45]. The weaker gluten network significantly impacts gas retention during fermentation, leading to reduced bread volume. This result is supported by research from Dhaka and Khatkar [46], which links lower values of C2, dough stability (DS), and development time (DDT) to reduced dough strength and decreased bread volume. The addition of microalgae significantly (\(p\) < 0.05) increased the temperature and time required for starch gelatinisation, as indicated by the C3 parameter values. This phenomenon may be attributed to fibre interference and the microalgae's role in hindering water accessibility within starch granules, thereby rendering them more stable at higher temperatures [23; 47]. The _T. chuii_ sample recorded a torque value of 2.74 N.m, which was higher than that of the control (2.58 N.m), while the _C. vulgaris_ sample yielded a lower value of 2.11 N.m. This indicates that the _C. vulgaris_ biomass could promote impaired starch gelatinisation, possibly due to the ability of _C. vulgaris_ to hold more water, which competes with starch for available water. In a similar trend, _A. platensis_ (Spirulina) increased the C3 temperature and impaired starch gelatinisation in the model gel system [41]. In the case of _P. tricornutum_, there was no significant difference in C3 torque compared to the control. C4 significantly increased (\(p\) < 0.05) for _P. tricornutum_ and _T. chuii_ samples, indicating that these microalgae exhibited high amylase activity. There was a decrease in C5 torque when _C. vulgaris_ was incorporated, whereas _P. tricornutum_, and _T. chuii_ samples did not significantly differ from the control. This suggests that _C. vulgaris_ affects starch gelification, possibly due to interactions between _C. vulgaris_ components and gluten and starch in the dough system. A similar result was found when _Saccharina latissima_ was added to a wheat bread formulation [48].
# A Routing Algorithm Based on Real-Time Information Traffic in Sparse Environment for VANETs
## 4 Routing Algorithm
### Vehicle Utility  
In order to ensure the minimum number of node hops and increase the connectivity of the link, we consider both link utility and effective information traffic when establishing a route. Before the node forwards the packets, current node has received the hello packet sent by the one-hop neighbors which contains the velocity, direction, position and effective information traffic. The computer can use this information to calculate vehicle utility which is used to select relay node.  
When calculating the utilities of the neighbors, we hope that the node with the highest node utility is the highest \(E(l)\) and the highest \(I(t)\), the vehicle that the utility is the second-highest has the second \(E(l)\) and \(I(t)\), and so on.  
The actual situation is often contrary to the ideal state. Usually, the neighbor nodes perform well in one aspect and mediocre on the other. For example, in Figure 6 (two parameters had normalized), each nodes can be expressed by \(E(l)\) and \(I(t),\{E(l),I(t)\}\).  
Figure 6: Attenuation function.  
As shown in Figure 7, node 1 is the highest of \(E(l)\) but the minimum of \(I(t)\). Therefore, node utilities of the neighbors are required to reflect the impact of two parameters on routing. Actually, we cannot compare the effects of these two parameters quantitatively. We use other methods to solve this problem. The parameter whose variance is large has a greater effect on the routing performance than the parameter whose variance is small. In Table 1, the variance of \(I(t)\) is lower than that of \(E(l)\). Therefore, in the selection process of relaying vehicles, the \(I(t)\) should be more effective than the \(E(l)\) to affect the routing performance.  
Since there are two parameters related to routing performance, in this paper, multi-attribute utility theory (MAUT) [32] is used in our weight calculation method. The weights represent the influence of the parameters on the routing performance. Since parameters with larger variances have greater impact on routing performance than parameters with smaller variances, we will use the variance of the normalized parameters \(E(l)\)and \(I(t)\) as the weight for calculating the utility of the vehicle. The vehicle utility is defined as follows:  
\[U_{i}=E(l)_{i}\times V_{Ei}+I(t)_{i}\times V_{li}. \tag{17}\]  
where \(E(l)_{i}\) is the value of \(E(l)\) for node \(i\), \(V_{Ei}\) is the variance of the \(E(l)\); \(I(t)_{i}\) is the value of \(I(t)\) which is normalized by the maximum value of \(I(t)\) in the neighbor table, and \(V_{li}\) is the variance of \(I(t)_{i}\). Before selecting the next hop node, the sender will calculate the \(U_{i}\) value for all neighbors. The routing process is described thoroughly in the next subsection.
## 7 Approaches to Prevent and Manage Infections Caused by Biofilms
### Therapeutic Strategies
#### 7.1.2 RNAIII-Inhibiting Peptide (RIP) for Biofilm Inhibition  
RNAIII-inhibiting peptide (RIP) has been identified as a potential therapeutic agent for inhibiting biofilm formation, especially in infections associated with _S. aureus_, including _MRSA_[252]. RIP operates by interfering with the QS system, a bacterial communication process that governs the expression of virulence factors and the formation of biofilms. RIP specifically targets the RNAIII activating protein (RAP), thereby inhibiting its phosphorylation of downstream molecules crucial for biofilm formation and the regulation of virulence [253; 254].  
RIP's primary mechanism of action involves disrupting the accessory gene regulator (agr) system, a vital QS pathway in _S. aureus_. The agr system regulates RNAIII expression, a crucial effector molecule that upregulates adhesion proteins and other virulence factors essential for biofilm formation. RIP suppresses the production of these factors by inhibiting RNAIII activation, which impairs bacterial adhesion, biofilm maturation, and virulence. This disruption inhibits biofilm formation and destabilizes biofilms, making bacterial communities susceptible to immune responses and antimicrobial agents [255; 256].  
Research has demonstrated the efficacy of RIP in diminishing biofilm formation in both in vitro and in vivo contexts. In catheter-implanted animal models, [PERSON] et al. (2021) demonstrated that RIP injections significantly reduced biofilm production and decreased infection rates [256]. The findings underscore RIP's potential as a preventive approach for managing medical device-related infections, particularly in the context of biofilm formation, which poses a considerable challenge.
# Lightweight and Security-Enhanced Key Agreement Protocol Using PUF for IoD Environments
## 6 Proposed Protocol
### Informal Analysis
#### 7.2.4 Session Key Disclosure Attacks  
To compute the session key (\(SK=h(h(PID_{D}\parallel K_{D}\parallel R_{j})\parallel h(HID_{M}\parallel K_{ M})\parallel r_{3}\parallel r_{4}\parallel r_{5})\)), an attacker (\(A\)) must know the random numbers (\(r_{3}\), \(r_{4}\), and \(r_{5}\)). To calculate these random numbers, \(A\) must know the private keys of the drone and mobile device (\(K_{D}\) and \(K_{M}\) respectively). However, \(A\) cannot compute these random numbers because \(K_{M}\) is masked by the user's identity and password and a random number and \(K_{D}\) is masked by the PUF response value (\(R_{j}\)). Thus, the proposed protocol is secure against session key disclosure attacks.
## 6 Conclusions  
1. In order to enhance the precision of rock burst prediction and to guarantee the security of coal mine production, this paper employs a BO-LSTM combined model to forecast the probability of rock bursts. The predicted value and the actual evaluation value of the model after Bayesian optimization exhibit a high degree of correlation. A comparison of the single model GRU and 1 DCNN evaluation indicators reveals that the MAE and MAPE values are the smallest, with a difference of 0.038369 and 0.195484, respectively, in favor of the GRU model and a difference of 0.052916 and 0.310299, respectively, in favor of the 1 DCNN model. Moreover, the relative coefficient R is closest to 1, indicating that the prediction accuracy is higher and the effect is more pronounced.
2. The model presented in this paper was further applied to predict changes in microseismic energy, accurately forecasting microseismic events with energy greater than \(10^{5}\). For high-frequency events within a 10-day period, six accurate predictions were made, and the fitting effect compared to the actual values was excellent.
3. The model in this paper is compared with the monitoring results of the far-near field monitoring and early warning system for rock bursts in the Gengcun Coal Mine. The model makes a more accurate prediction of large energy events above \(10^{5}\), and the MAE (mean square error) reaches 0.026272 in the prediction of rock burst risk. Compared with the model prediction results used in the system, it is closer, but the addition of the Bayesian algorithm for hyperparameter optimization reduces the number of iterations and reduces the calculation cost, indicating that it has practical application value for microseismic event prediction.
4. A new method for predicting rock bursts has been developed by combining microseismic energy change and a neural network model. This approach offers a more comprehensive understanding of the phenomenon than previous methods. However, the early warning index is relatively limited in scope. In the future, a multi-parameter early warning model could be constructed by combining indicators such as ground sound, coal body stress, and support resistance. This would further enhance the accuracy of rock burst prediction.  
Conceptualization, X.F. and S.C.; methodology, S.C.; software, S.C.; validation, X.F., S.C. and T.Z.; formal analysis, S.C.; investigation, X.F.; resources, X.F.; data curation, S.C.; writing--original draft preparation, S.C.; writing--review and editing, X.F. and S.C.; visualization, X.F.; supervision, X.F.; project administration, S.C.; funding acquisition, X.F. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
Informed Consent Statement: Informed consent was obtained from all subjects involved in the study.  
The data presented in this study are available on request from the corresponding author. The data are not publicly available due to privacy concerns.  
The authors declare no conflicts of interest.
# Analysis and Correction of Antenna Pattern Effects in AMAO Spaceborne SAR Images
## 6 Discussion
The results show that the improved method can adequately correct the spatial-variant effect of the antenna pattern, and according to the experimental results, the radiometric error of about 1.69 dB is removed, thus enabling consistent radiometric measurements. Meanwhile, the proposed method has slight benefits on PSLR and ISLR as a side effect. This paper focuses on the analysis and correction of the antenna pattern effect, which is part of the radiometric calibration of AMAO spaceborne SAR. A more detailed discussion on real data has to be deferred to later work.
## 7 Evaluation
### Benchmarks  
The benchmarks we constructed to evaluate our system are demonstrated in Table 2. These benchmarks are composed of six test programs, four programs from RHG 2021 ljb challenge [29] held in China in November 2021, two programs from RHG 2021 challenge [30] held in China in March 2021, four challenges from CTF events and four real-world applications with exposed vulnerabilities. These benchmarks are all 32 bit ELF user-mode applications running under i386 architecture machines. These programs were collected based on the following criteria.  
* The benchmark programs should cover different types of vulnerabilities. As we can see in Table 2, there are five types of memory-corruption vulnerabilities, separately, integer overflow, format string, use after free, heap overflow and stack overflow, covered in this collection. These are all memory-corruption vulnerabilities commonly seen in binary programs.
* The benchmark programs should run in environments where ASLR is deployed, and leaking an address is a must in the vulnerability-exploitation process. As for the latter, there are two types of address leakage, separately, explicit leakage and forge-needed leakage. In the former cases, the target program would intentionally leak an address to the output channel, while, in the latter cases, when there exists no such direct information leakage, the analysis engine has to forge an address leakage event utilizing the vulnerabilities in the target program so as to carry out the complete exploitability evaluation process.
* The real-world applications are mainly from user-mode applications with public vulnerabilities. These programs take input either from the command line arguments,the environment variables or the standard input channel, sending output directly through the standard output channel.  
We conducted experiments trying to answer the following two research questions, with some results published at [[https://github.com/helios-ops/ExpGen.git](https://github.com/helios-ops/ExpGen.git)]([https://github.com/helios-ops/ExpGen.git](https://github.com/helios-ops/ExpGen.git)).  
* RQ1: Can ExpGen generates partial exploits that can leak a sensitive address locating in the address space layout of the target programs more efficiently and effectively than current test-case generation methods?
* RQ2: Can ExpGen generates an exploitation session able to exploit the target programs in environments where ASLR is deployed, when crafting an address leakage is a must in exploitation?
# Preprocessing for Unintended Conducted Emissions Classification with ResNet
## 3 Results
### FFT Window Type  
Another experiment performed was a variation of the window type. The differences in the frequency domain of the windows used in this investigation were discussed in Section 2.3.5 and are shown in Figure 2.  
Figure 6 shows classification results as a function of training epochs for five different FFT window types. Knowing the attributes of the tested window types and the performances recorded in Figure 6, provide additional understanding of features of the data set are most important to the ResNet classifier.  
Figure 5: Classification accuracy vs. training epoch for various FFT window lengths with 15\(\times\) noise added.  
Resolving features of a particular collection that are close in frequency necessitates a smoothing window with a maximally narrow main lobe in the frequency characteristic. The authors saw the rectangular window perform poorly, as shown in Figure 6. However, they also saw that the other extreme in the trade space (Kaiser \(\beta=16\)) was approximately equally poor in performance. The optimal window type for this window family of shapes would be somewhere between Kaiser \(\beta=8\) and Kaiser \(\beta=12\) but closer to the \(\beta=8\) case because of its 6.6% better performance. The 95% confidence intervals for the percentages given in Figure 6 are given in Table 3.  
The ability to resolve spectral components (either to isolate a feature from noise or separate between two features of two devices) appears to be valuable within some level but must be balanced with the need to reject noise far from the features of interest and prevent the spectral leakage that occurs in the finite-length FFT calculation. It also must be balanced with the possible need to preserve the associated spectral amplitude accuracy. The window function application provides a certain amount of processing gain for certain features. Although the window-type experimental results follow an understandable pattern, they do not point to a specific window that would be optimal but instead to certain window  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Window** & **Classification Accuracy \%** & **Confidence Interval \%** \\ \hline Kaiser \(\beta=16\) & 58.8 & \(\pm 1.67\) \\ Kaiser \(\beta=12\) & 61.3 & \(\pm 1.65\) \\ Kaiser \(\beta=8\) & 67.9 & \(\pm 1.57\) \\ Kaiser \(\beta=4\) & 57.3 & \(\pm 1.67\) \\ Rectangular & 56.5 & \(\pm 1.67\) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Listing of Key Window Function Characteristics.  
Figure 6: Classification accuracy vs. training epoch for various FFT window types with 10\(\times\) noise added.  
characteristics that are more favorable and then inherently to the types of features being favored by the deep ResNet classifier.