esa-sceva/satcom-chunk-collection huggingface dataset:
# Radiation Hardened Read-Stability and Speed Enhanced SRAM for Space Applications
## 3 Simulation Results and Analysis
### Write Access Time  
Write access time (WAT) is important in SRAM design because it determines how quickly data can be written to memory cells. A shorter WAT enables faster updates and modifications, which is essential for applications requiring real-time data processing. Write access time (WAT) is defined as the time interval between when the word line reaches 50% of VDD and when the voltages at the storage nodes Q and QB intersect. Figure 9 shows that using more access transistors typically reduces WAT. However, despite using four pairs of access transistors, DNUCTM has a higher WAT compared to the proposed circuit. This is because the proposed RHSSE-20T allows the S1 node to store a weak '1', enabling faster write times than DNUCTM. Additionally, without any additional paths for write operations, the proposed design uses three pairs of access transistors to directly perform write operations on two storage nodes and four internal nodes, resulting in the lowest WAT. This makes it highly suitable for applications requiring fast write performance. The WAT values of all comparison cells are presented in Figure 9.  
Figure 8: Comparison results of the read access time of the proposed RHSSE-20T with previous works.  
Figure 9: Comparison results of the write access time of the proposed RHSSE-20T with previous works.
## 3 Materials and Methods
### Optimization of Slot Height  
The waveguide slot height is usually chosen as more than two times the substrate height. The effect of a slot height, H\({}_{\text{Slot}}\), less than two times the substrate height, \(2\times\text{h}\) (\(1.5\times\text{h}=0.1905\) mm), and a slot height greater than twice the substrate height (\(0.254/0.381/0.508\) mm) was optimized while keeping the back-short distance constant, i.e., d = 1.9 mm. It was found that when H\({}_{\text{Slot}}\)\(<\) 2 h, a deviation in resonance frequency was observed, whereas when H\({}_{\text{Slot}}\)\(>\) 2 h, in all cases, resonance occurred close to the designed center frequency of 30 GHz, with better performance parameters in terms of RL, IL, and \(-\)10 dB absolute bandwidth, as shown by the family curves of IL and RL in Figure 4 and tabulated  
\begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Back-Short** & **Min.** & **RL** & **Resonance** & \(-\)**10 dB** \\
**Distance** & **IL** & **(dB)** & **Frequency** & **Absolute** \\
**d = \(\lambda_{\text{g}}\)/4 (mm)** & **(dB)** & & **\(\mathbf{f_{r}}\) (GHz)** & **BW (\(\mathbf{f_{l}}\)–\(\mathbf{f_{l}}\))** \\ \hline
3.5 & \(-1.91\) & \(-10.98\) & 22.68 & (21.68–23.19) \\  & \(-1.64\), & \(-20.20\), & 22.05, & (21.21–24.65), \\  & \(-0.75\) & \(-10.68\) & 41.13 & (39.83–41.97) \\  & \(-1.78\), & \(-21.82\), & 22.05, & (21.55–27.55), \\  & \(-0.26\) & \(-42.06\) & 42.13 & (36.19–44.75) \\  & \(-0.36\) & \(-15.26\) & 30 & (21.97–45.0) \\  & \(-0.32\) & \(-18.12\) & 25 & (21.74–45.0) \\
1.9 & \(-0.36\) & \(-16.72\) & 29.75 & (23.52–45.0) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Effect of fish-tail width variations with H\({}_{\text{Slot}}\) = 3 h.  
Figure 3: Effect of back-short distance variations on (**a**) IL and (**b**) RL.  
in Table 3. The optimized minimum value of the slot height was 0.381 mm, for which resonance occurred at a frequency of 29.75 GHz, with a much flatter RL below \(-\)15 dB over a wide bandwidth. It was noticed that with slot height, H\({}_{\text{Slot}}\), greater than or equal to 3 times the substrate height, the return loss value was below \(-\)15 dB over a ultra-wideband. However, as the slot height was increased over 3.5 h, the RL went above \(-\)15 dB, which demonstrated an increase in reflections from the back-short and a reduction in transmission power. It was concluded from the two-parameter variations that variation of the back short distance and slot height affected each other and hence affected the RL, IL, and, therefore, the ultra-wide bandwidth. Therefore, a higher value of back-short distance led to decrease in slot height, and a lower value of back-short distance gives an increase in slot height. The dependency of these two important dimensions necessitates keeping one dimension constant; say the slot height equal to 2 h or 3 h, while changing the back-short distance for lower values of RL over a wider bandwidth.  
Figure 4: Effect of slot height variations on (**a**) IL and (**b**) RL.
## 5 Dependent Shrink Algorithm and Case Study
### Outline of Shrink Process  
The outline of the dependent shrink process can be briefly described as follows:  
Figure 14: IL-3 signaling pathway [27].  
Step 1: Shrinking of self-loop structure:  
A place randomly selected from a Petri net is stored in a queue after the conversion of the self-loops and the structures of conflict (as shown in Figure 10).  
Step 2: Shrink of conflict structure:  
If a place picked up from the queue has a self-loop or a transition of one-input and one-output (as shown in Figure 9), this place is shrunk.  
Step 3: Changing weight of the input arc:  
If a shrunk Petri net has a place with multiple input transitions, then enqueue and continue to Step 2. This procedure is repeated until the queue becomes empty or the number of places with multiple input transitions equals the length of the queue.  
The variables used in the algorithm are as follows:  
* \(PN_{0}\) is a given signaling pathway Petri net model constituted by \(T_{0},P_{0},\textit{and}\ E_{0}\).
* \(N\) is a variable that stores the Petri net after dependent shrink, constituted by \(T,P\) and \(E\).
* \(Q\) is a queue.
* \(X\) is a set of places initially set as a given place set, \(P_{0}\).
* \(f\) is a flag by which a dependent shrink pattern is determined.
* \(c\) is a counter, which counts the number of places in \(Q\) with multiple input transitions.
# Towards Intelligent Zone-Based Content Pre-Caching Approach in VANET for Congestion Control
## 1 Introduction
### Research Contributions  
The main contribution of the proposed system is to improve road safety and transportation efficiency with wireless communication technologies as follows:  
1. The proposed technique disseminates safety messages using content pre-caching in V2V and V2I scenarios which saves the lives of passengers and drivers;
2. The proposed Geocast communication contributes to a safe information dissemination process within the zone of relevance (ZoR) and without the zone of forwarding (ZoF);
3. The machine learning technology provides early prediction of the content pre-caching with higher accuracy of 99.6% using zone base prediction to overcome further road disasters;
4. We design zone-based V2V and V2I communication using RSU, Interzone, and Intra-zone vehicles, which enhance the content pre-caching availability and expand their coverage to a bigger zone. It is conducive to overcoming the network overhead and improves the cache hit ratio;
5. The proposed VANET content pre-caching technique improves early prediction to reduce the network overhead;
6. We design and propose a pre-caching algorithm using machine learning. The tables and road topology information are recorded in the central manager node, and the proposed technique predicts the vehicle pre-caching early requests with high accuracy, and further content pre-caching is achieved using zone time and delay, which provision future requests in a timely manner to improve the reliability of pre-caching strategy and dynamic adaptability;* The performance of the proposed technique is compared with four previous techniques, and the machine learning algorithm is used to train the model for auto-prediction of content pre-caching, which shows high prediction accuracy. The results are evaluated by comparing the results with zone-based content pre-caching [12] and Geo-SID [13] using NS-2 over cache hit ratio, prediction accuracy, and average delay with time and number of vehicles. The proposed technique performs better than the previous pre-caching schemes in VANETs.  
The rest of the paper is organized as follows. Section 2 presents the related work on content caching and machine learning-based models to improve the caching in VANET. Then, in Section 3, we outline our approach for the research and propose solutions to the relevant problems encountered in Section 1 of the paper. Then, Section 4 presents the proposed method with simulations using a content pre-caching approach. Section 5 concludes our proposed system, highlighting that our technique is straightforward for fault tolerance methodology.
# Micro-Raman Analysis of the Pigments on a Crucifix in Calabria
## 3 Results and Discussion
This organometallic complex was discovered at the end of the 18 th century, and its use in painting starts from the 19 th century. Even in the presence of a strong, luminescent background, the characteristic Raman modes are clearly observed, particularly the one at 2152 cm\({}^{-1}\), which is quite a specific marker. The complete list of the pigments found in this study is summarized in Table 2. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Pigment** & **Composition** & **Main Raman Peaks (cm\({}^{-1}\))** & **History: First Use** \\ \hline Gypsum & CaSO\({}_{4}\)\(\bullet\) 2H\({}_{2}\)O & 1138 w; 1007 s; 670 vw; 411 w & All ages \\ Titanium white & TiO\({}_{2}\) & 636 w; 394 vw; 141 s & After 1920 \\ Lithopone & ZnS + BaSO\({}_{4}\) & 984 s; 617 vw; 457 w; 450 w & End of 19 th century \\ Siena earth & Fe\({}_{2}\)O\({}_{3}\) + silicates + MnO\({}_{2}\) & 638 w; 386 w; 271 m; 210 m & All ages \\ Red ochre & Fe\({}_{2}\)O\({}_{3}\) & 603 w; 400 m; 285 w; 217 m & All ages \\ Vermilion & HgS & 340 m; 281 w; 249 vs & All ages \\ Minimum & Pb\({}_{3}\)O\({}_{4}\) & 543 s; 385 w; 114 s & All ages \\ Chrome yellow & PbCrO\({}_{4}\) & 838 s; 356 w & After 1818 \\ Lead yellow & PbO & 138 s & All ages \\ Prussian blue & Fe\({}_{4}\) [Fe(CN)\({}_{6}\)]\({}_{3}\) & 2152 m; 1083 w; 544 m; 270 w & 18 th century \\ Carbon black & Amorphous carbon & 1355 s (br); 1595 s (br) & All ages \\ \hline \hline \end{tabular}
\end{table} 
Table 2: List of all pigments found using micro-Raman spectroscopy analysis with the historic records of use and the principal Raman peaks. Figure 4: Representative Raman spectra collected on the specimen of Figure 2 with modern pigments such as lithopone (**a**) and Prussian blue (**b**). The Raman spectra shown in this picture were baseline-corrected.
# Improving the Strategy of Maintaining Offshore Wind Turbines through Petri Net Modelling
## 4 Dynamic PN Modelling of the O&M of an Offshore Wind Turbine
### Recovery and Maintenance Petri Net (RMPN)
For instance, the offshore distance of the OWT will affect its accessibility and transportation costs. The concept, size, and the location of the OWT (that may affect wind speed and turbulence) in the offshore wind farm will affect its failure rate, thereby influencing its availability and maintenance costs. Moreover, the same type of OWT made by the same supplier may show different reliability and efficiency performances when they are deployed in different offshore wind farms because of the differences in marine and offshore conditions. For these reasons, the model parameters should be updated according to the actual situation of the OWT in practical applications. Figure 7: Recovery and Maintenance Petri Net (RMPN).
## 3 Results
### Development of Gully Erosion Susceptibility Maps  
We calculated gully erosion susceptibility indices for each cell based on the results of the ensemble models. We then constructed gully erosion susceptibility maps for the study area using the Ada-REPTree, Bag-REPTree, REPTree, and RS-REPTree models (Figure 6). Gully erosion susceptibility classes (low, moderate, high, and very high) were created using the natural breaks method. For example, in the case of the Ada-REPTree map, the four susceptibility classes have values of 0.00-0.13, 0.13-0.42, 0.42-0.78, and 0.78-1.00 (Figure 6a). Comparison of the four maps indicates that the REPTree model predicts a larger part of the watershed as having high and very high erosion susceptibilities. More generally, the maps show that most cells of low erosion susceptibility are located on steep slopes in the marginal parts of the watershed. The high and very high susceptibility classes cover the northern and central parts of the watershed where most of the observed gully sites are located.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Models** & **TP** & **FP** & **Precision** & **Recall** & **F-Measure** & **MCC** & **AUC** & **PRSE** \\ \hline REPTree & 0.774 & 0.226 & 0.776 & 0.774 & 0.773 & 0.549 & 0.819 & 0.782 \\ AB-REPTree & 0.768 & 0.232 & 0.77 & 0.768 & 0.767 & 0.537 & 0.844 & 0.838 \\ Bag-REPTree & 0.776 & 0.224 & 0.779 & 0.776 & 0.776 & 0.555 & 0.871 & 0.866 \\ RS-REPTree & 0.806 & 0.194 & 0.809 & 0.806 & 0.805 & 0.615 & 0.874 & 0.865 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Evaluation of gully erosion susceptibility models using machine learning metrics.  
Figure 6: _Cont._
# A Novel Intrusion Detection Approach Using Machine Learning Ensemble for IoT Environments
## 4 Experimental Results and Discussion  
### Experimental Setup  
The machine used was a 64-bit Windows 10 Pro operating system with the following specifications: an Intel i7 vPro four-core processor, 3.60 GHz clock speed, and 16 GB main memory. For implementation and performance evaluation of the classifier, the Python (version 3.7.2) environment was used. Additionally, Python library Scikit-learn [35] was used to implement the model of the classifiers, as it highly supports the machine learning algorithms. The PARAM system operating on a 64-bit Ubuntu 14.04 was used for tuning the hyperparameters, and was equipped with an Intel Xeon Gold 6132 twenty-eight core processing CPU unit with a 2.64 GHz clock speed and 16 GB main memory. The IoT application is supported by the Raspberry Pi 3B+ model, which was enabled on an operating system named Raspbian equipped with a 64-bit quad-core ARM processing unit running at a 1.4 GHz clock speed and 1 GB of main memory. It was used to estimate the response time of the classifier. STAC is a web-based application that was used for a statistical test of the classes and the calculation of the performance results [36]:  
\[avg.\;response\;time=\frac{\sum_{i=1}^{n_{test\_sample}}t_{i}}{n_{test\_sample}} \tag{5}\]  
The computation formula of average response time represented in Equation (5), where \(i\) is the instance number; \(t_{i}\) is the total time consumed by the applied classifier to classify the \(i\)th test simple instance into two classes, i.e., attack or normal category; and \(n_{test\_sample}\) represents the overall test instances.
# Distributed Resources Allocation Method for Space-Ground Integrated Mobile Communication System
## 3 System Model
### Network Update Process
#### 3.3.1 Channel State Update  
The channel state monitor block will keep updating every \(t\) minutes, checking the usage rate of each slice.
# UCMAC: A Cooperative MAC Protocol for Underwater Wireless Sensor Networks
## 4 Operation of Proposed Protocol
### Data Transfer Phase  
In Figure 5, the source sends the deferred DATA to the destination, which contains \(\mathbb{N}_{\text{coop}}\). The next section explains the reason for deferment. Overhearing the DATA, the neighbors included in \(\mathbb{N}_{\text{coop}}\) recognize themselves as cooperators and hold the successfully decoded data payload in case a request for retransmission is sent. Those not included in \(\mathbb{N}_{\text{coop}}\) are exempted from the cooperation. The possible reasons for not being included in \(\mathbb{N}_{\text{coop}}\) are:  
* The source fails to receive RTCs.
* The size of \(\mathbb{N}_{\text{coop}}\) reaches the limit, \(N_{\text{coop}}^{\text{max}}\).  
If the payload is successfully decoded, the destination sends ACK back to the source. Otherwise, the destination requests retransmission by continuously sending NACK to the closest available cooperator specified in \(\mathbb{N}_{\text{coop}}\) until the retransmission is successful or \(\mathbb{N}_{\text{coop}}\) is exhausted. Once the retransmission is successful, the destination sends ACK to the source. Overhearing the ACK, the cooperators recognize the completion and discard the stored data.  
As Figure 5 shows, even when cooperators (C\({}_{1}\) in this example) fail at first to overhear DATA from the source, they can recover the failed DATA by overhearing other cooperators' (C\({}_{2}\) in this example) retransmission and therefore can join the retransmission (denoted by bold arrow) with the recovered DATA.  
Figure 4: RTC–RTC and RTC–CTS collision cases: (**a**) without any countermeasures; (**b**) with backoff and prohibition of RTC transmission.  
Figure 5: Enabling cooperation through additional DATA overhearing.
# Effects of Pulsed Pressure Curing on Beef Quality
## 1 Introduction  
Curing involves soaking food products in a curing solution in order to enhance their sensory and organoleptic qualities. During the curing process, water transfers from foods to curing solution, and salt (NaCl) is transferred due to the concentration difference [1]. There are more ions in the curing solution than in the muscle fiber cells, allowing salt ions to diffuse across cell membranes until equilibrium is attained. The myogenic fibers absorb and hold vast amounts of water as a result of ion concentration changes, while osmosis and capillary mechanisms cause swelling to more than double their initial volume [2]. The curing solution samples often take hours to achieve a certain concentration owing to the restricted permeability of salt in cell membranes. As a result, an effective brining technique that minimizes the brining time and increases the product consistency is the need of the hour [3].  
The frequently used techniques in industrial meat processing include pulsed pressure salting, static brining, pulsed discharges technology, high hydrostatic pressure, high hydrodynamic pressure, shock wave treatment, and vacuum tumbling curing [4; 5; 6; 7; 8]. The high hydrostatic pressure accelerates diffusion and osmotic processes, while high hydrodynamic pressure causes intense filtration processes along with diffusion and osmotic processes. Previous studies reported that high hydrostatic pressure is better for tender deboned meat, and high hydrodynamic pressure is superior for meat-on-bone or tough meat [5; 8]. Pulsed pressure curing is a method of curing in which meat is immersed in a constant curing solution and alternately subjected to air pressure, vacuum, and positive pressure. Thismethod could preserve the raw meat appearance and tissue composition while averting softening and distortion. Moreover, vacuum tumbling can enhance the mechanical activity by raising the meat to the highest point of rotation in a cylindrical drum and dropping it to the lower surface by gravity. The other two curing techniques can hasten curing fluid entrance into meat and shorten curing time and increase meat softness and quality compared to static salting [9; 10].  
Previously, various studies reported the impact of vacuum curing techniques on pork and chicken [11; 12]; however, there are fewer studies on the quality of pulsed pressure-cured beef [13]. Therefore, this study was proposed to ascertain the effect of PPC on the quality of beef. The effects of PPC were evaluated by documenting the changes in the curing absorption rate, cooking loss, texture, and color of beef. The superheated-steam-roasted beef was also subjected to sensory evaluation. Scanning electron microscopy was used to examine the microstructure of the beef. This study can deliver theoretical foundations for the advancement of curing technologies in meat processing facilities.
# CQFormer: Cyclic Quasi-Dynamic Transformers for Hourly Origin-Destination Estimation
## 3 Problem Statement  
Our study aims to dynamically estimate the OD matrix within a reference period given road traffic counts for successive sub-periods. Dynamic OD estimation can be formulated as the solution of Equation (1) as below:\[f_{l}(t)=\sum_{o,d,\tau}a_{od\tau}^{lt}d_{od}(\tau) \tag{1}\]  
where the known variable \(f_{l}(t)\) denotes the traffic counts on road \(l\) at time \(t\), and the target variable \(d_{od}(\tau)\) means the number of trips from origin \(o\) to destination \(d\) departing at time \(\tau\). The assignment tensor \(a_{od\tau}^{lt}\) establishes a correlation between \(d_{od}(\tau)\) and \(f_{l}(t)\). However, since both \(d_{od}(\tau)\) and \(a_{od\tau}^{lt}\) are unknown, the OD estimation is severely under-determined. To make the problem solvable, we aggregate OD flows along dimension \(\tau\) and assume the demand stays stable within the specified period \(T\), that is the quasi-dynamic assumption.  
\[d_{od}(T)=\sum_{\tau\in T}d_{od}(\tau) \tag{2}\]  
The period \(T\) can be divided into \(n_{t}\) sub-periods: \(T=T_{1}\cup T_{2}\cup\cdots\cup T_{n_{t}}\), and the traffic counts can be aggregated into different sub-periods as Equation (3).  
\[f_{l}(T_{i})=\sum_{t\in T_{i}}f_{l}(t) \tag{3}\]  
Based on that, we define quasi-dynamic OD matrix \(D_{T}:=[d_{od}(T)]\in\mathbb{R}^{n_{p}\times n_{p}}\) where \(n_{p}\) denotes the number of traffic analysis zones, and traffic count matrix \(F_{T}:=[f_{l}(T_{1}),f_{l}(T_{2}),\)\(f_{l}(T_{3}),\cdots,f_{l}(T_{n_{t}})]\in\mathbb{R}^{n_{l}\times n_{t}}\) where \(n_{l}\) is the number of roads with traffic flow detection. In this article, we intend to estimate \(D_{T}\) through \(F_{T}\).