esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Discussion
### Optimal Locations  
The budget constraint is defined after proposing the candidate locations and the range of values for the coverage radius, i.e., the definition of the number of facilities to be allocated. No more than five facilities are selected among the ten candidate zones due to the initial interest in comparing the optimal scenario using the proposed MIS with the five facilities of the MP currently operating in Recife.  
The proposed MIS used to perform the optimal allocation for the five facilities along the radius of 1.0 to 5.0 km provides the optimal coverage rate presented in Table 2.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Potential Locations** & **Latitude** & **Longitude** \\ \hline Candidate 1 & \(-7.99214\) & \(-34.92050\) \\ Candidate 2 & \(-8.036942\) & \(-34.88901\) \\ Candidate 3 & \(-8.058401\) & \(-34.90614\) \\ Candidate 4 & \(-8.090015\) & \(-34.89074\) \\ Candidate 5 & \(-8.086555\) & \(-34.93611\) \\ Candidate 6 & \(-8.135032\) & \(-34.91076\) \\ Candidate 7 & \(-8.035501\) & \(-34.91289\) \\ Candidate 8 & \(-8.045003\) & \(-34.94490\) \\ Candidate 9 & \(-8.115055\) & \(-34.90337\) \\ Candidate 10 & \(-8.061584\) & \(-34.88112\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Candidate coordinates.  
Figure 7: Criminal clusters in Recife.  
This information highlights the gains in coverage by increasing the police's operating radius of the allocated facilities. For instance, the operating radii of 2.0, 3.0, and 4.0 km provide acceptable coverages of criminal incidents, that is, 67.7%, 90.1%, and 98.6%, respectively. Figure 8 illustrates the optimal facility allocations based on a coverage radius of 3.0 km.  
It is worth mentioning that the allocation of police facilities in the territory of Recife is plausible given what is designed by the proposed MIS with reference to the specific database. From an efficiency perspective, a service unit aims at improving results while keeping the same use of resources or keeping the same results with fewer resources [10]. In the case of police facilities, this is obtained with a small coverage radius--for instance, operations with three kilometers of coverage, reaching 90.1% of occurrences, which is strategic: fewer police officers can operate more efficiently in communities with the highest crime incidence, improving effectiveness while reducing operating costs.
## 5 Analysis of Coal Gangue Image Processing and Recognition Results
### Comparison of Recognition Before and After Defogging  
To study the defogging effect of coal gangue images under different coal dust concentrations, the images were processed using the defogging algorithm and tested using the trained model in this paper. The test results are shown in Figure 12.  
As shown in Figure 12, due to the influence of dust concentration, the edge contours of coal gangue in the image are blurred, and the features are not clear, causing the trained model to be unable to accurately distinguish between coal and gangue. This issue worsens as the dust concentration increases.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **mAcc/\%** & **mIoU/\%** & **mPA/\%** & **Param/m** & **FLOPs/G** \\ \hline PSPNet & 94.94 & 91.12 & 94.52 & 47.51 & 57.89 \\ PSANet & 95.85 & 93.01 & 95.35 & 25.50 & 34.67 \\ DeepLabV3+ & 96.03 & 96.27 & 95.81 & 41.91 & 35.36 \\ UNet & 96.45 & 95.24 & 95.49 & 31.06 & 37.89 \\ This paper & 97.88 & 97.19 & 96.86 & 28.51 & 26.58 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparative results of different models.  
Figure 11: Comparison of segmentation results from different models.  
Figure 12: _Cont._  
Figure 12: Comparison of coal and gangue recognition before and after defogging under different dust concentration gradients.
## 3 Experimental Section
### Identification of SSR Loci from the Welsh Onion Transcriptome and Primer Design  
Based on the 6206 unigenes obtained by the RNA-Seq approach in a previous study (RNA-Seq data have been uploaded to NCBI under accession number SSR1609126 and SSR1609976) [43], 5983 unigenes were searched for 6486 genic simple sequence repeat (SSR) loci, excluding 280 compound SSRs due to statistical complications, in this study using the MicroSAtellite identification tool (MISA) [44]. The SSR loci containing repeat units of 1-5 nucleotides were identified, and the minimum SSR length criteria were identified as ten iterations for mononucleotide repeats, six iterations for dinucleotide repeats, and five iterations for the other repeat units.  
Primer3 software, version 4.0, was used to design the polymerase chain reaction (PCR) primer in the flanking regions of these SSR loci [45], whose lengths were not less than 50 bp. Excluding mononucleotide and unsatisfactory compound SSRs, a total of 2710 unigenes were used to design primers, and the criteria for screening primer pairs were set to include an optimum primer length of 20 bp, PCR product sizes ranging from 100 to 500 bp, a GC percentage between 40% and 60%, and an annealing temperature ranging from 50 \({}^{\circ}\)C to 62 \({}^{\circ}\)C. Moreover, the secondary structure, such as hairpin, dimmer, false priming, and cross-dimmer, cannot emerge from forward and reverse primers.
## 4 Conclusions  
In the present study, the degradation of mechanical behaviors of sandstone with freezing temperature under repeated freezing and thawing conditions was investigated. Under the same freeze-thaw cycling number (10 times), the effects of freezing temperature (\(-20\), \(-40\), \(-50\), and \(-60\)\({}^{\circ}\)C) on the density, P-wave velocity, static and dynamic uniaxial compressive strength, static elastic modulus, and dynamic energy absorption were discussed. The conclusions obtained are as follows.  
1. The density of sandstone decreases as the freezing temperature decreases from 20 to \(-60\)\({}^{\circ}\)C, and the decreasing extent of density approximately reaches 1.0% at \(-60\)\({}^{\circ}\)C. The P-wave velocity first shows a slightly decreasing trend as the freezing temperature decreases, and then rapidly decreases after \(-20\)\({}^{\circ}\)C.
2. Under the same freeze-thaw cycling number, the static uniaxial compressive strength, static elastic modulus, dynamic strength, and dynamic energy absorption of sandstone all decrease non-linearly as the freezing temperature decreases from 20 to \(-60\)\({}^{\circ}\)C. However, the dynamic mechanical behavior is more sensitive to the freezing temperature during freeze-thawing cycling compared with the static mechanical behavior.
3. The strain rate significantly influences the dynamic mechanical responses of sandstone after low-temperature treatments. The dynamic strength and dynamic energy absorption increase monotonically as the strain rate increases.  
The present study is mainly focused on the macroscopic mechanical behaviors of sandstone. Further research shall be conducted on the micro-scale to show its microscopic mechanisms.  
**Author Contributions:** Conceptualization, J.G. and L.F.; Methodology, J.G. and C.X.; Data analysis, J.G. and C.X.; Writing-original draft preparation, J.G.; Writing-review and editing, L.F., J.G. and Y.X.; Funding acquisition, L.F. All authors have read and agreed to the published version of the manuscript.  
**Funding:** The research is supported by the National Natural Science Foundation of China (NSFC) (No. 12172019 and 41831281) and Beijing Natural Science Foundation (JQ20039).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Molecular and Hormonal Mechanisms Regulating Fleshy Fruit Ripening
## 6 The Identification and Function of Major Regulator TFs
### The Wider Importance of the NAC Gene Family  
In recent years, many lines of evidence have highlighted important roles for a range of NAC TFs in different aspects of plant development, including ripening. There are over 90 _NAC_ genes in tomato and they often form homo- or hetero-dimers. Several, such as _SINAC1_ and _SINAC4_, have been shown to affect ripening, in addition to NAC-NOR, and [PERSON] et al. [271] carried out a systematic search of _NAC_ genes expressed in fruit. Silencing _NOR-like1_ (Solyc07g063420) using VIGS delayed ripening initiation by 14 days. When _NOR-like1_ function was inactivated by CRISPR/Cas9, the tomato fruits produced less ethylene, showed retarded softening and chlorophyll loss and reduced lycopene accumulation. Gene promoter analysis suggested that NOR-like1 targeted genes involved in ethylene biosynthesis (_ACS2_, _ACS4_), colour formation (_GGPPS2_, _SGR1_) and cell wall metabolism (_PG2a_, _PL_, _CEL2_ and _EXP1_), and this was confirmed by electrophoretic mobility shift assays (EMSA), chromatin immunoprecipitation-quantitative PCR (ChIP-qPCR) and dual-luciferase reporter assays [271].  
_NAC1_ is involved in tomato ripening, and manipulating its expression affects _ACS2_, _ACS4_, _ACO1_ and ethylene biosynthesis, although there are conflicting reports about whether _NAC1_ delays or accelerates ripening [121; 124]. _NAC4_ and _NAC9_ have also been identified as positive regulators of tomato _ACS2_, _ACS4_ and _ACOI_ expression, and silencing of _NAC4_ and _NAC9_ repressed tomato fruit ripening [122; 123] (see also Section 3.3.1 for the role of NACs in ripening regulation by ABA). NACs also play important roles in development and ripening of many other fruits, including apple, banana, kiwifruit, oil palm and peach [71; 122; 271; 272]. [PERSON] et al. [101] showed that in kiwifruit, _AnaNAC2/3/4_ physically interacts with the promoter of the terpene synthase gene _AaTPS1_, which is important for monoterpene production. _AdNAC6/7_ and _AdNAC2/3_ in kiwifruit were also found to transcriptionally activate _AdACO1_, _AdAC51_, _AdMAN1_ and _AdTPS1_[20]. In peach, the NAC transcription factor BL interacts with NAC1 to amplify its regulatory effect on anthocyanin biosynthesis, and in banana, NAC5 can interact with WRKY1/2 to enhance the induction of resistance genes [273; 274]. MaNACs also can activate the promoters of _ethylene-insensitive_ (_EIL_) genes during banana ripening [275]. These findings support a conserved regulation of genes involved in ethylene biosynthesis and other aspects of ripening by NAC TFs in different fruits, which, in addition, may also involve MYCs and ERFs.
## 3 Numerical Experimental Analysis
### Comparison of Different Strain Reconstruction Algorithms with Experimental Data  
To compare the algorithm proposed here with other, more traditional algorithms used in previous studies, we also used RMSE as a quantitative evaluation index [6; 19]. The RMSE results for experimental and reconstruction strains produced by different swarm intelligence techniques, i.e., the differential evolution algorithm, the quantum-behaved Particle Swarm Optimization algorithm, and the dynamic Particle Swarm Optimization algorithm, can be seen in Figure 8. These RMSE results are also related to different crack lengths during the damage monitoring experience, to further test the stability and accuracy of the reconstruction algorithm. Upon comparison with the traditional algorithms previously mentioned, the proposed GWO method presented the best strain reconstruction performance with the lowest RMSE. In short, the inverse problem in a real fatigue loading test can be effectively solved by use of the GWO method in a strain reconstruction algorithm.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline Crack length (mm) & 5.48 & 5.58 & 5.63 & 5.70 \\ \hline RMSE (dB\({}^{2}\)) & 0.1434 & 0.1511 & 0.1528 & 0.1441 \\ \hline Crack length (mm) & 5.76 & 5.83 & 5.91 & 6.02 \\ \hline RMSE (dB\({}^{2}\)) & 0.1578 & 0.1502 & 0.1653 & 0.1666 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Strain reconstruction accuracy of experimental FBG reflection spectra by GWO algorithm.  
Figure 7: Calculations of non-uniform strains of FBG1 based on the DIC method.
# Remaining Useful Life Prediction of Cutting Tools Using an Inverse Gaussian Process Model
## 3 Example Study  
### Simulation  
For validating the performance of the algorithm of the proposed approach, the following simulation was conducted. The 100 numbered simulated data were generated, under the assumption that the parameters in the inverse Gaussian model were set as \(\alpha\mu=1\), \(\sigma\mu=100\), \(\lambda=2\). The data information, including simulated degradation data, was recorded with the cycle. The degradation trajectory obtained by the simulation is shown in Figure 1. The former 10 data points were regarded as historical data. The parameters \(\alpha\mu\), \(\sigma\mu\), and \(\lambda\) were estimated at different times by the EM algorithm. The simulated data contributed to the performance analysis of the proposed model, without physical meaning. Figures 2-4 show that the estimated values of parameters are approaching the real values, with the accumulation of simulated degradation data.  
Figure 1: Degradation data from simulation.  
Figure 2: Dynamic estimation of parameter \(\alpha\mu\).  
From Figure 5, we can see that, with more and more data available, the PDF of RUL will be narrower, which indicates that the uncertainty of the prediction results becomes smaller, and the corresponding point estimation is closer to the real RUL. In this simulation, the failure threshold was set to [90, 100], and it obeyed uniform probability distribution. Subsequently, the prediction result is drawn in Figure 6.  
Figure 4: Dynamic estimation of parameter \(\sigma\mu\).  
Figure 5: PDF of RUL with simulation data.  
Figure 3: Dynamic estimation of parameter \(\lambda\).
## 7 Conclusions  
In this paper, we proposed a method to analyze the health state of HSR using data-driven approaches based on on-site monitoring data. A novel random-forest-based strategy that combines LMD, time-domain feature extraction, and time-frequency-domain feature extraction for identification of track slabs deformation was proposed. The main conclusions are as follows.  
1. A track-side vibration monitoring method based on fiber optic vibration sensing technology can effectively capture vibration signals that contain the information of train vibration, track slab deformation, noise, and environmental vibration.
2. The preprocessing methods of data interception, denoising, and data segmentation can effectively eliminate the effects of environmental vibration, noise, and time differences on the recognition effects. In addition, the time-domain and time-frequency-domain feature extraction methods based on LMD effectively extract the crucial information for detecting the deformation of track slabs.
3. The proposed intelligent recognition algorithm based on random-forest model can accurately identify the deformation of track slabs. The verification test results showed that the recognition accuracy reached 96.09%.  
This paper provides not only a new method for operational HSR deformation monitoring but also an intelligent identification algorithm for track slab deformation identification. More importantly, the proposed method is applicable to the monitoring and identification of other structural defects. In the future, we will research the random-forest model on the deformation degree of track slabs.  
Conceptualization, [PERSON]; methodology, G.G.; software, G.G.; validation, G.G.; writing--original draft preparation, [PERSON]; writing--review and editing, G.G. and X.C.; project  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Category**} & **Original** & **Optimized** & **Pruned** \\  & **Decision-Tree** & **Decision-Tree** & **Decision-Tree** \\ \hline Class 1 & 93.65 & 94.96 & 94.05 \\ Class 2 & 89.32 & 91.15 & 90.96 \\ Average & 91.49 & 93.06 & 92.51 \\ Error & 6.76 & 5.23 & 6.26 \\ \hline \hline \end{tabular} (Note: Error stands for the cross-validation error).  
\end{table}
Table 4: Classification accuracy (%) and cross-validation error (%).  
administration, B.D.; supervision, B.D. All authors have read and agreed to the published version of the manuscript.  
This research was funded by National Natural Science Foundation of China (51822802, 51778033, U1811463, 71901011, and 51991395), National Key R&D Program of China (2018 YFB2101003), The Science and Technology Major Project of Beijing (Z191100002519012) and Shenzhen Science and Technology Program (KQTD20180412181337494).  
Not applicable.  
The authors thank [PERSON] at the Semiconductor Research Institute of Chinese Academy of Sciences for his provision of optical fiber sensing equipment. The authors would like to thank all the reviewers who participated in the review.  
The authors declare no conflict of interest.
## 2 Description of the Proposed System
### Synchronization System  
The infrared synchronization system consists of a special synchronism beacon (which emits a 1 ms-long pulse every 0.5 s) and of the synchronism photodetectors located both in the receiver and in the transmitters. The infrared transmitter located in the synchronism beacon is the ILH-IW01-85 NL-SC201-WIR200 [22], which has a 150\({}^{\circ}\) radiation angle and a central wavelength response of 850 nm. On the other hand, the synchronism receivers are based on the TSOP7000 infrared detector [23], which is centered at a frequency \(f_{SYN}=455\) kHz. After receiving the carrier from the synchronization beacon, the detector generates a pulse with the same length (i.e., 1 ms).
## 4 Experiment and Its Simulation Environment
### Highway Environment  
This section analyzes operational decision-making frameworks for autonomous navigation, focusing on achieving specific navigational goals through strategic action sequences. Within the controlled expressway environment, these core operational modalities manifest as propulsion adjustments (speed modulation), lateral maneuvers (lane transitions), and velocity maintenance. The simulation environment is shown in Figure 5. The experimental setup adopts a dual-agent system, consisting of a self-driving vehicle and several environment vehicles that mimic human driving behavior. All vehicles operate based on the previously mentioned motion model and environmental configuration, aiming to study the interactive dynamics under standardized highway conditions. The investigation employs a parametric analysis approach, quantifying control responses through measurable kinematic variables including relative velocities, inter-vehicle spacing, and trajectory deviation metrics.  
To enhance the realism and unpredictability of the driving scenario, this paper randomly determines the initial speed and starting position of the environmental vehicle. This practice helps to simulate the actual driving situation more realistically and enhance the adaptability of the autonomous vehicle in complex environments. In addition, in order to better imitate human driving habits, we set the autonomous vehicle to drive as far to the right as possible when there is no need to overtake. These settings are designed to improve the realism of the driving scene.  
Prior to initiating the driving task, 60 environmental vehicles are randomly positioned on a four-lane highway ahead of the autonomous vehicle. Each simulation cycle, termed a round, commences with the autonomous vehicle's activation and concludes upon either its successful completion of the journey within a 30 s timeframe or collision with surrounding vehicles. Vehicle configurations follow standardized parameters: all units measure 5 m in length and 2 m in width. Environmental vehicles are assigned randomized initial speeds between 20 and 30 m\(\cdot\)s\({}^{-1}\), while the autonomous vehicle starts at 25 m\(\cdot\)s\({}^{-1}\). Operating at a 10 Hz simulation frequency [24], rounds are automatically reset upon meeting termination criteria, ensuring iterative scenario testing under controlled temporal constraints. For the specific settings of vehicle parameters, see Table 2.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Numeric** \\ \hline Number of lanes & 4 \\ Vehicle length/m & 5 \\ Vehicle width/m & 2 \\ Initial speed of environmental & 20–30 \\ vehicle/(m\(\cdot\)s\({}^{-1}\)) & 25 \\ Initial speed of autonomous & 10 \\ vehicle/(m\(\cdot\)s\({}^{-1}\)) & 30 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Vehicle parameter settings.  
Figure 5: Decision-making environment for driving on a four-lane highway.
## 4 Discussion
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**OLR** & \begin{tabular}{c} **Lignosulfonic** \\ **Acid** \\ \end{tabular} & \
\begin{tabular}{c} **Aromatic** \\ **Compounds** \\ \end{tabular} &
\begin{tabular}{c} **Lignin** \\ **Compounds** \\ \end{tabular} \\ \hline \multicolumn{5}{c}{_P. radiata_ leachate} \\ \hline Planted CW & - & - & - & - \\
12 & 0.001 & 0.005 & 0.005 & 1.083 \\
13 & 0.002 & 0.006 & 0.005 & 1.280 \\
19 & 0.002 & 0.006 & 0.005 & 1.175 \\ \hline Unplanted CW & - & - & - & - \\
12 & 0.001 & 0.004 & 0.004 & 1.148 \\
13 & 0.001 & 0.005 & 0.005 & 1.162 \\
19 & 0.002 & 0.006 & 0.005 & 1.163 \\ \hline \multicolumn{5}{c}{_E. globulus_ leachate} \\ \hline Planted CW & - & - & - & - \\
14 & 0.007 & 0.019 & 0.015 & 1.252 \\
26 & 0.008 & 0.020 & 0.016 & 1.212 \\
40 & 0.007 & 0.019 & 0.015 & 1.258 \\ \hline Unplanted CW & - & - & - & - \\
14 & 0.006 & 0.010 & 0.012 & 1.203 \\
26 & 0.007 & 0.018 & 0,014 & 1.248 \\
40 & 0.014 & 0.017 & 0,017 & 1.206 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Variations of the ratio of aromatic compounds to COD for the effluents of planted and unplanted CW units treating _P. radiata_ and _E. globulus_ leachates (average values are shown).
# Enhancing the Privacy of Network Services through Trusted Computing
## 3 A Trusted and Backward-Compatible Addressing Service
### Security Analysis  
The DNS protocol did not fully consider network security issues at the beginning of the design., while our proposed DoTT ensures the credibility of the results with the RA and trusted execution features provided by TEE [44]. Although SGX limits the length of the verification report, we can still ensure that the verification results are generated in a trusted environment through hash values. The privacy between DoTT and public root servers and iterate servers is protected by the same TLS. Note that DoTT only plays the role of DNS resolver. Its connection with the NS is an ordinary TLS connection. Although we can refactor the NS to run it in an SGX enclave, SGX only exists in desktop CPUs at present, while NS generally runs in servers equipped with high-end chips, which generally do not yet support SGX features. The IAS and MREnclave ensure that DoTT establishes a trusted TLS connection according to the expected behavior and performs the specified DNS processing without leaking DNS query messages and metadata to external applications. The DNS client already holds a certificate from IAS as well as a trusted list during the deployment process. Therefore, a malicious user cannot tamper with the generator of the evidence and forge the verifier of the evidence at the same time. If the authentication stepin the last section fails, a valid TLS connection cannot be established, and the following DNS query will fail. Finally, we use the disk as a cache to enhance system performance. The contents of the cache are also stored as ciphertext and can only be decrypted by a properly loaded DoTT enclave. Therefore, there is no risk of compromise as well.