esa-sceva/satcom-chunk-collection huggingface dataset:
# Stability and Failure of the Edge-Closed Honeycomb Sandwich Panels with Face/Core Debonding
## 4 Progressive Damage Model
### Failure Theory of Honeycomb Sandwich Panels
#### 4.1.3 Honeycomb Core Progressive Failure  
The honeycomb core is equivalent to an orthotropic structure by using the sandwich board theory, and the Besant criterion [38] is adopted as the damage criterion of the honeycomb core. Its expression is as follows:  
\[F_{core}^{2}=\left(\frac{\sigma_{TT}}{X_{TT}}\right)^{n}+\left(\frac{\tau_{LT }}{S_{LT}}\right)^{n}+\left(\frac{\tau_{WT}}{S_{WT}}\right)^{n} \tag{4}\]  
where, \(\sigma_{TT},\sigma_{LT},\sigma_{WT}\) are the normal stress of the honeycomb core in the T direction and the shear stress in the \(L_{T}\) and \(W_{T}\) direction respectively. \(X_{TT}\), \(X_{LT}\), \(X_{WT}\) are the compressive strength and shear strength of the honeycomb core in the corresponding direction respectively. The power index \(n=2\). When the damage variable \(F_{core}=1\), it is considered that the honeycomb core is damaged.  
The direct degradation method can efficiently and accurately simulate the evolution of the honeycomb core at various stages. The stiffness reduction method of the honeycomb core in the User-defined Material Mechanical Behavior (UMAT) subroutine is as follows:  
\[\begin{array}{l}E^{\prime}_{11}=\lambda_{11}E_{11},E^{\prime}_{22}=\lambda_{22} E_{22},E^{\prime}_{33}=\lambda_{33}E_{33}\\ G^{\prime}_{12}=\lambda_{12}G_{12},G^{\prime}_{13}=\lambda_{13}G_{13},G^{ \prime}_{23}=\lambda_{23}G_{23}\end{array} \tag{5}\]  
The superscript in the formula represents the stiffness after reduction.\(\lambda_{ij}(i,j=1,2,3)\) is the reduction factor in each direction, and the factors [39] in different damaged status are given in Table 7.
# Systematic Review of Discrete Event Simulation in Healthcare and Statistics Distributions
## 4 Discussion
### Application Areas
#### 4.2.4 Oncology Services  
Cancer treatment facilities face intricate scheduling demands due to the complexity of chemotherapy and radiation therapy regimens. [PERSON] et al. [40] documented DES applications that enhance chemotherapy scheduling, optimize treatment room usage, and improve staff allocation. Their study in a major cancer center demonstrated a 24% increase in chemotherapy chair utilization while reducing patient waiting times by 17%, highlighting the value of DES in balancing high-demand care with limited resources.
# A Multi-Plant Height Detection Method Based on Ruler-Free Monocular Computer Vision
## 4 Result
### Measurement Results under Dark/Light Conditions  
The illuminance of the measurement environment under dark/light conditions is approximately 42 lx. The measurement results are shown in the Table 4.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Project** & **Plant 1** & **Plant 2** & **Plant 3** & **Plant 4** \\ \hline Manual measurement 1/mm & 255 & 276 & 239 & 199 \\ Manual measurement 2/mm & 254 & 278 & 238 & 200 \\ Manual measurement 3/mm & 252 & 276 & 238 & 199 \\ Benchmark mean/mm & 253.667 & 276.667 & 238.333 & 199.333 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Manual measurement value 1.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Project** & **Plant 1** & **Plant 2** & **Plant 3** & **Plant 4** \\ \hline Manual measurement 1/mm & 244 & 261 & 226 & 182 \\ Manual measurement 2/mm & 244 & 261 & 226 & 183 \\ Manual measurement 3/mm & 243 & 262 & 227 & 183 \\ Benchmark mean/mm & 243.667 & 261.333 & 226.333 & 182.667 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Manual measurement value 2.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Camera and the Plant Distance d/cm** & **Plant 1** & **Plant 2** & **Plant 3** & **Plant 4** & **SSR** \\ \hline \multirow{3}{*}{200} & Algorithmic measurements/mm & 249.149 & 274.910 & 231.102 & 194.472 & \multirow{3}{*}{99.420} \\  & Absolute error/mm & \(-4.518\) & \(-1.757\) & \(-7.232\) & \(-4.861\) & \\  & Absolute proportional error & \(-1.78\) & \(-0.63\) & \(-3.03\) & \(-2.44\) & \\ \hline \multirow{3}{*}{250} & Algorithmic measurements/mm & 250.000 & 279.696 & 235.556 & 198.488 & \multirow{3}{*}{31.055} \\  & Absolute error/mm & \(-3.667\) & 3.030 & \(-2.778\) & \(-0.846\) & \\ \cline{1-1}  & Absolute proportional error & \(-1.45\) & \(1.10\) & \(-1.17\) & \(-0.42\) & \\ \hline \multirow{3}{*}{300} & Algorithmic measurements/mm & 254.492 & 283.683 & 232.291 & 193.827 & \multirow{3}{*}{116.744} \\  & Absolute error/mm & 0.826 & 7.016 & \(-6.043\) & \(-5.506\) & \\ \cline{1-1}  & Absolute proportional error & 0.33 & 2.54 & \(-2.54\) & \(-2.76\) & \\ \hline \hline \end{tabular} Note: Table 1 manual measurement value 1 is used as the reference.  
\end{table}
Table 3: Measurement results under normal light conditions.
# Efficacy of Fire Protection Techniques on Impact Resistance of Self-Compacting Concrete
## 1 Introduction
Impact resistance is paramount when designing airport runways, landing areas, and military facilities in conflict zones. ACI 544-2R has put forward a cost-effective and straightforward mass drop test as a means to assess the residual impact strength of concrete [19]. This test uses a small-scale concrete disk measuring 150 mm in diameter and 63.50 mm in thickness. And it does not require high-end facilities or measuring equipment, making it highly accurate [20]. Previous studies have extensively examined the behaviour of SCC and other high-performance concretes when subjected to high temperatures. According to Table 1 in the database, it has been noted that an increase in temperature exposure leads to a significant decline in the strength properties of concrete [21; 22; 23; 24; 25; 26]. Despite this, there is considerably less investigation into the impact resistance of sustainable SCC subjected to elevated temperatures. The present study has conducted detailed research on crack patterns and failure analysis of SCC after prolonged temperature exposure, which was not observed in previous studies [27]. Additionally, the present research focused on the temperature resistance of concrete by using passive protection coatings such as EPA and GPP, which have rarely been studied. The purpose of this research is to evaluate SCC's impact resistance during high-temperature exposure. The study will investigate how SCMs affect SCC performance over different temperature ranges. Additionally, the study aims to improve the residual impact load capacity of SCC following high-temperature exposure and analyse the behaviour of CPP post-temperature exposure.
# Comparison of PCR Primers for Analyzing Dentirifying Microorganisms in the Hyporheic Zone
## 2 Materials and Methods
### Comparison of the PCR Primers for Denitrification-Associated Genes  
For the denitrification-associated genes, _narG_, _napA_, _nirS_, _nirK_, _norB_ (_cnorB_ and _qnorB_), and _nosZ_ were selected. For the amplification of each gene, five primer sets for _narG_, two primer sets for _napA_,  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**No.**} & \multirow{2}{*}{**Bacteria**} & \multirow{2}{*}{**Culture Collection \#**} & \multicolumn{3}{c}{**Condition**} \\ \cline{3-5}  & & & **Medium** & **Temp (\({}^{\circ}\)C)** & **Time (days)** \\ \hline
01 & _Achromobacter denitrificans_ & KACC 12986 & NA & 24 & 1 \\
02 & _Aquitalea denitrificans_ & KACC 12729 & R2A & 30 & 4 \\
03 & _Blastobacter denitrificans_ & KACC 14837 & BYTGA & 28 & 3–5 \\
04 & _Burkholderia denitrificans_ & KACC 12733 & R2A & 30 & 2 \\
05 & _Castellaniella denitrificans_ & KACC 13903 & R2A & 30 & 1 \\
06 & _Comamonas denitrificans_ & KACC 13430 & TSA & 28 & 1 \\
07 & _Flavobacterium denitrificans_ & KACC 12500 & R2A & 30 & 2 \\
08 & _Leucobacter denitrificans_ & KACC 14055 & R2A & 30 & 3 \\
09 & _Rloadobacter denitrificans_ & KACC 12251 & NA & 30 & 2 \\
10 & _Roseobacter denitrificans_ & KACC 17256 & R2A & 30 & 2 \\
11 & _Paracoucus denitrificans_ & KACC 13175 & MA & 30 & 1–2 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Information regarding the collected denitrification microorganisms.  
five primer sets for _nirS_, five primer sets for _nirK_, five primer sets for _norB_ (three sets for _cnorB_ and two sets for _qnorB_), and five primer sets for _nosZ_ were analyzed. For the PCR, 10 ng/\(\upmu\)L of nucleic acid extracted from 11 type strains was used as the template DNA. The PCR mixture had a total volume of 20 \(\upmu\)L and contained AccuPower(r) HotStart PCR PreMix (Bioneer, Seoul, Korea), 1 \(\upmu\)L of template nucleic acid, 2 \(\upmu\)L of primers (1 \(\upmu\)L of forward, 1 \(\upmu\)L of reverse; concentrations were based on the conditions used in the references (Table S1)), and 17 \(\upmu\)L of nucleic-acid-free water. The PCR was performed according to the thermal conditions described in the references listed (Table S1). The amplification of specific bands of each denitrification-associated gene (number of type strains detected) and detection time was compared and analyzed. For each gene, the PCR primer sets that detected the highest number of type strains were selected.
# On the Performance of Underlay Device-to-Device Communications
## 3 System Model
### Power Allocation in D2D Networks
#### 3.2.1 Equal Power Allocation  
Under this scheme, the transmit power of all D2D transmitters is exactly the same and is computed as [6]  
\[P_{D_{k}}^{equ}=\frac{P_{\text{tot}}}{N},k\in\{1,\ldots,N\}, \tag{2}\]  
where \(P_{\text{tot}}\) is the total transmit power of the D2D networks and \(P_{D_{k}}\) is the transmit power of the \(k\)-th transmitter.
# Robust Segmentation of Partial and Imperfect Dental Arches
## 3 Methods
### Model Training  
PTV2 [34] was a point-based transformer model for point cloud representation learning and segmentation. It improved PTV1 [33] with an efficient grouped vector attention and a partition-based pooling strategy. It was experimented on with public datasets such as ScanNet v2, SSDIS, and ModelNet40 and achieved state-of-the-art performance on point cloud classification and semantic segmentation benchmarks. PTV2 was chosen in this work due to its high proven potential in semantic segmentation tasks on 3D data. Automated processing and analysis of intraoral 3D dental scans exhibit a high level of challenge due the high variability and complexity associated with different cases. It is among the objectives of this work to test and report the performance of PTV2 for segmentation of partial and imperfect dental arches. Because the curvature values vary abruptly around each tooth boundary, it has been well-established that adding discrete curvature channels on mesh vertices is expected to improve boundary segmentation accuracy [38; 39].  
Three major segmentation frameworks were trained: a 17-class upper, 17-class lower, and a 3-class model for both upper and lower arches. For each framework, we used PTV2 [34] as the deep learning model and trained with 5-fold cross-validation. Consequently, 15 trained models were obtained. We trained models for lower and upper arches separately because the shape of teeth for the lower arch is different from that of upper. For example, the incisor teeth in upper arches are larger than those in lower arches. The 3-class models were trained with both lower and upper arches, but registered as lower arches. To enhance the registration step, the 3-class models were used to clean the master arches from excessive gingiva, which may have caused registration failure. The proper registration of master arches was particularly important, because the corresponding antagonist arch was registered using the same transformation matrix of its master arch. The 3-class models can also be used to separate teeth from gingiva and localize the preparations in the arches.
## 5 Comparison with Other Approaches  
A qualitative comparison of the proposed algorithm with other approaches discussed earlier is summarized in Table 9. The parameters used for evaluation are (a) type of cost matrix, and (b) ability to address the TSP--the special case of mTSP.  
It can be seen from Table 9, for the evaluation parameter A--the type of cost matrix, the existing approaches do address both the symmetric and the asymmetric nature of the cost matrix. It can be concluded that if a solution could solve an asymmetric mTSP, then it can also be used to solve the symmetric mTSP.  
For the evaluation parameter B--the ability to address TSP, both the exact based solutions and the transformations could also solve the TSP as it is a specific case of mTSP. However, the heuristics-based solutions, in general, and GA-based solutions, in particular, would require a different representation of chromosome for solving the TSP.  
The proposed algorithm provides a feasible solution to the mTSP with either asymmetric or symmetric cost matrix. The proposed algorithm can also be used to transform and solve the Traveling Salesman Problem, which is a specific instance of mTSP. It must be noted that the real utility of the proposed mapping schemes comes into play as constraints are added to mTSP and finding even a feasible solution becomes a complicated task.
## 5 Computational Experiments
#### A Fuzzy Approach for the Demand and the Vehicle Available Capacity
The computed preference index \(p_{i}\) must be compared with \(p^{*}\) in order to make a decision about the vehicle next destination. If \(p_{i}\geq p^{*}\), the vehicle should visit the next customer directly; otherwise, we estimate that the vehicle available capacity cannot meet the next customer demand. In this case, both preventive (\(c_{prev}\)) and reactive (\(c_{reac}\)) costs are calculated (see Section 3). If \(c_{prev}<c_{reac}\), the vehicle should perform a detour to the depot for a preventive replenishment; otherwise, it should visit the next customer directly and react to its real demand. The lower the threshold level, the greater the inclination to unload the vehicle as much as possible before making a replenishment trip to the depot. In this case, less preventive detours are performed. Hence, the number of times that a reactive round-trip must be carried out increases. Previous tests using modified Akca's instances yielded lower costs when \(p^{*}=0.45\). The following parameters are used by our algorithm to run the experiments: (i) 350 iterations for map perturbations; (ii) 150 iterations for the biased-randomized savings heuristic; (iii) 150 iterations for splitting; (iv) a random value between 0.05 and 0.80 for \(\beta_{1}\), the parameter of the geometric distribution associated with the biased-randomized selection during the allocation map process; (v) a random value between 0.07 and 0.23 for \(\beta_{2}\), the parameter of the geometric distribution associated with the biased-randomized heuristic for routing; (vi) \(n=100\) runs for the initial simulation stage; (vii) \(N=5000\) runs for the intensive simulation stage; and (viii) 100 iterations to estimate the safety stock (\(SS\)), testing only discrete values between 0% and 10%. Our proposed algorithm was coded as a Java application. All experiments were executed on a standard Windows PC with a Core \(i5\) processor and 6 GB RAM. A total of ten different random seeds were used for each instance.
# Analysis and Demonstration of First Cross-Support Interferometry Tracking in China Mars Mission
## 3 Results
### Accuracy Analysis and Comparison
#### 3.3.2 Comparison  
Observables from both agencies are listed in Table 5 and compared within a session.  
Figure 16 shows the histogram of the comparison result.  
The delay difference for the spacecraft scan has a Root Mean Square (RMS) of 0.0624 ns, while quasar scan has RMS of 0.1977 ns. One might hope for better agreement in the delay computations for the quasar scans, since the signal strength for the quasar is rather low, and the flux of 0054 + 161 is about 0.5 Jy, which causes a relatively larger formal error for this source than the spacecraft.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Baseline** & **Delay (ns)** & **Delay Rate (ps/s)** \\ \hline CEB-NNO & 0.0057 & 0.3 \\ CEB-KSH & 0.0059 & 0.3 \\ NNO-KSH & 0.0064 & 0.3 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Random error of observables of all the baselines.  
Figure 16: Histogram of difference statistics.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Source** & **Time Tag** & **ESOC** & **CLTC** & **Difference** \\ \hline Tianwen-1 & 22:52:24.000 & \(-\)0.007204728472941 & \(-\)0.00720472849826374 & 0.0253 \\ \hline
0054 + 161 & 23:02:25.900 & \(-\)0.006395117735975 & \(-\)0.00639511797678744 & 0.2408 \\ \hline Tianwen-1 & 23:11:28.000 & \(-\)0.005083056289743 & \(-\)0.0050830563549182 & 0.0652 \\ \hline Tianwen-1 & 23:16:08.000 & \(-\)0.004550230125038 & \(-\)0.0045502302195971 & 0.0946 \\ \hline
0054 + 161 & 23:26:25.900 & \(-\)0.003660915525043 & \(-\)0.0036609153456465 & \(-\)0.1794 \\ \hline Tianwen-1 & 23:35:28.000 & \(-\)0.002293397934561 & \(-\)0.002293397922336 & \(-\)0.0122 \\ \hline Tianwen-1 & 23:40:08.000 & \(-\)0.001737918208119 & \(-\)0.00173791822062594 & 0.0125 \\ \hline
0054 + 161 & 23:51:29.900 & \(-\)0.00068016381945 & \(-\)0.000680163704212356 & \(-\)0.1152 \\ \hline Tianwen-1 & 23:59:28.000 & 0.000600139424078 & 0.000600139390881393 & 0.0332 \\ \hline Tianwen-1 & +1T 00:04:08.000 & 0.001172134323388 & 0.00117213427254897 & 0.0508 \\ \hline
0054 + 161 & +1T 00:14:25.900 & 0.002128779041363 & 0.00212877927140748 & \(-\)0.2300 \\ \hline Tianwen-1 & +1T 00:23:28.000 & 0.00356557724307 & 0.00356557734894783 & \(-\)0.1059 \\ \hline Tianwen-1 & +1T 00:28:08.000 & 0.004147765127516 & 0.0041477652051242 & \(-\)0.0776 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparison of observables from CLTC and ESOC.
## 2 System Model
### The General Framework
#### 2.1.1 Beam Direction and Frequency Band Allocation  
To address the challenge of environmental instability, the study employed the MAPPO algorithm to optimize the beam direction and frequency band allocation for V2V vehicles. Specifically, in the \(n\)th time slot, vehicle \(k\) defines the local state of the current time slot based on the sensing information \(\mathbf{SI}_{k,n-1}\) of all target vehicles from the previous time slot, the interference power \(\mathbf{I}_{c}\) received during beam communication, and the interference power \(\mathbf{I}_{r}\) received during echo sensing. Based on the local state, MAPPO is used to overcome environmental instability and optimize the relative change in beam direction \(\mathbf{I}_{k,n}\) and frequency band allocation \(c_{k,n}\) for vehicle \(k\), thereby further reducing interference and enhancing overall sensing-communication performance.
# Around View Monitoring-Based Vacant Parking Space Detection and Analysis
## 3 Algorithm to Detect Vacant Parking Spaces
### Space Occupancy Classification Stage  
Once the parking space detection stage is complete, we need to test the vacancy of each parking space because we cannot park in a space already occupied by another car. Here, we view each parking space as occupying a cell in a grid and determine its vacancy by extracting the features within the parking space.
# Anomaly Detection and Early Warning Model for Latency in Private 5G Networks
## 3 Proposed Methods  
### Framework  
As private 5G networks are the key development direction of operators for business customers, compared with public users, private networks have more demanding requirements for various types of performance. Once faults occur, industrial production will be significantly affected; therefore, timely and efficient troubleshooting is particularly important. This poses a challenge to O&M of private 5G networks. It is needed to quickly diagnose or predict faults based on high-dimensional data of networks and services to reduce the impact of network faults on services.  
Based on this research direction, we investigate the limitations of private 5G networks O&M and find that most business scenarios focus on latency-sensitive AGVs and band-width-sensitive video transmission. According to the feedback of O&M staff, AGVs demonstrating abnormal driving behavior occurred several times. Considering that this is highly correlated with the high latency, the O&M staff deployed a probe to private 5G network parks to obtain monitor data. The result shows that, when the AGVs work abnormally, the latency of private 5G network is higher than the specified threshold.  
Regarding the latency-sensitive scenario in private 5G networks, this study proposes an intelligent O&M framework, as shown in Figure 1, including anomaly detection and early warning models. First, based on the wireless data of private 5G networks, an anomaly detection model (ConvAE-Latency) based on an autoencoder network is proposed to detect whether the latency of network is high by calculating reconstruction error. Moreover, based on the ConvAE-Latency model, the LstmAE-TL model based on LSTM is proposed, which can predict abnormal behavior after 15 min. In addition, transfer learning was used in the training process of LstmAE-TL to solve the problem of low learning speed and difficult convergence because of a limited number of samples.
# Regulation of Leaderless mRNA Translation in Bacteria
###### Abstract  
In bacteria, the translation of genetic information can begin through at least three different mechanisms: canonical or Shine-Dalgarno-led initiation, readthrough or 70S scanning initiation, or leaderless initiation. Here, we discuss the main features and regulation of the last, which is characterized mainly by the ability of 70S ribosomal particles to bind to AUG located at or near the 5\({}^{\prime}\) end of mRNAs to initiate translation. These leaderless mRNAs (lmRNAs) are rare in enterobacteria, such as _Escherichia coli_, but are common in other bacteria, such as _Mycobacterium tuberculosis_ and _Dcinococcus desert_, where they may represent more than 20% and even up to 60% of the genes. Given that lmRNAs are devoid of a 5\({}^{\prime}\) untranslated region and the Shine-Dalgarno sequence located within it, the mechanism of translation regulation must depend on molecular strategies that are different from what has been observed in the Shine-Dalgarno-led translation. Diverse regulatory mechanisms have been proposed, including the processing of ribosomal RNA and changes in the abundance of translation factors, but all of them produce global changes in the initiation of lmRNA translation. Thus, further research will be required to understand how the initiation of the translation of particular lmRNA genes is regulated.  
leaderless mRNA; lmRNA; bacterial translation; expression control; translation regulation 2022
## 3 Measurement Results
### Transmitted Phase Modulated Power Spectrums
Figure 7: Measured frequency spectrums that compare the average output power of an unmodulated carrier tone at 24.125 GHz to PSK and PMCW carrier waves running at an \(F_{Clk}\) of 10 MHz. As the carrier modulation becomes less periodic, the power spreads out over the spectrum.