esa-sceva/satcom-chunk-collection huggingface dataset:
# Hop-by-Hop Multipath Overlay Routing for Optimizing Network Resource Allocation in WANs
## 4 Performance Evaluation
### Overlay Node Placement Algorithm
Figure 13: Underlay and overlay average path number vs. overlay nodes in Abovenet. Figure 15 shows the value of \(\beta\) under various numbers of overlay nodes in the topologies Germany50 and TA2. In both topologies, \(\beta\) demonstrates a general trend of an initial increase followed by a subsequent decline as the number of overlay nodes grows. Compared with TA2, the objective function of Germany50 attains a lower maximum value, and a smaller number of overlay nodes is sufficient to realize the full potential of multipath overlay transmission. For example, setting the threshold \(\gamma=2\), which represents the minimum marginal gain of the objective function \(F\) required of adding a single overlay node, results in an optimal overlay node count of 25 for the Germany50 topology and 35 for the TA2 topology. Accordingly, similar trends can be observed in Figures 7 and 8, where the improvements in link utilization and path diversity become marginal beyond 25 overlay nodes for the Germany50 topology and beyond 35 nodes for the TA2 topology. In Figures 11 and 12, the growth in the end-to-end path number also plateaus after 25 overlay nodes for Germany50 and 35 for TA2. Figure 20 illustrates the variation in the total throughput over time for different combination methods with request arrival rates of 50 and 80. From both graphs, it can be observed that TAO+ONHS, due to the limited path diversity introduced by TAO node placement, experiences a smaller transmission bottleneck compared with the other two methods, resulting in a longer time to complete all the transmissions. For MOP+HI, the throughput curve drops earlier than that of MOP+ONHS under both request arrival rates of 50 and 80. This is because, under the hashing method, each flow has only a  
Figure 16: The ratio of single-hop path selection to overlay nodes in Germany50. Figure 17: The ratio of single-hop path selection to overlay nodes in in TA2. Figure 18: The ratio of multi-hop path selection to overlay nodes in Germany50. single transmission path. When the utilization of this path reaches its maximum, the flow cannot be diverted to an alternative path. The generated traffic randomly selects two destination nodes, leading to roughly equal amounts of each flow type. When one type of flow completes, the transmission bottleneck is halved (Figure 19(a)), causing the overall transmission to complete more slowly than with MOP+ONHS.
## 2 Materials and Methods
### Model Inputs  
The plant functional type map of the Tianshan North Slope was derived from the land-use map which was extracted from Landsat Enhanced Thematic Mapper (2015) and provided by the Xinjiang and Central Asia scientific data sharing platform [49]. The terrain data including the elevation, aspect, and slope were derived from the 30 m spatial resolution Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) Global Digital Elevation Model Version 2 data set [50]. The soil information such as pH and soil texture was extracted from the data set of soil properties for land surface modeling [51]. The predefined annual CO\({}_{2}\) concentration dynamical data under RCP2.6, RCP4.5, and RCP8.5 was given as input to AEM [39].  
The climate data used to force AEM consist of daily 2 m mean, maximum, and minimum temperature, relative humidity, precipitation, and short wave radiation. The climate data under the RCPs from the global circulation models (GCMs) are too coarse to be used to study the climate change effects at regional or local scales [52; 53; 54; 55]. To solve this problem, the international CORDEX (Coordinated Regional Climate Downscaling Experiment) project provides smaller limited domains for different regions to do the downscaling and provide high-resolution climate information. The climate data we used were extracted from climate runs at 25 km resolution of the regional climate model ALARO-0 driven by the GCM CNMR-CM5 over the Central Asia domain. This dynamical downscaling was conducted at the Royal Meteorological Institute of Belgium.  
In this study, the AEM was forced by the climate data and the corresponding dynamical CO\({}_{2}\) under RCP2.6, RCP4.5, and RCP8.5 from 2006 and 2055. The input data sets are summarized in Table 1.
# Efficient Optimization-Based Trajectory Planning for Truck-Trailer Systems
## 2 Methods
### Proposed Solution
#### 2.3.3 Phase 3: Velocity Profile Planning  
The aim of this phase is to find the missing velocity profile. Solving this problem is possible using information about the total time required for realization of the trajectories, which is one of the outputs of Phase 2. Furthermore, using the trajectories found in Phase 2 as an initial estimate, the actual velocity profile is derived as part of the optimization process in this phase. This velocity profile must satisfy the condition that the total travel distance equal the integral of the velocity over the given time interval. The velocity profile's shape and maximum are both influenced by the \(\tau\) parameter, which determines the rate of change of the velocity signal.  
As the formulation and solution to OCP in this phase is similar to Phase 2, below we only mention the differences between these two phases.  
The dynamic model in this phase does not include any additional state variables, and the total optimal time requirement is known from Phase 2. Therefore, the dynamics used here are formed by the first four equations of Equation (2), and as suvh are described by Equation (18).  
\[\dot{x}_{1} =u_{1}\cdot\cos(x_{4}) \tag{18}\] \[\dot{x}_{2} =u_{1}\cdot\sin(x_{4})\] \[\dot{x}_{3} =-\frac{1}{\tau}\cdot x_{3}+\frac{1}{\tau}\cdot u_{2}\] \[\dot{x}_{4} =\frac{u_{1}}{d_{0}}\cdot\tan(x_{3})\]  
The differential defect constraints corresponding to Equation (10) are now related to the dynamics in Equation (18).  
The equality boundary constraints needed for this phase can be formed fro those from Phase 2 according to Equation (13) and extended by constraints related to zero initial and zero final velocity according to Equation (19).  
\[\begin{bmatrix}u_{1_{1}}\\ u_{1_{N}}\end{bmatrix}=\begin{bmatrix}0\\ 0\end{bmatrix} \tag{19}\]  
Therefore, the overall equality boundary constraints for Phase 3 are provided by Equation (20).  
\[\psi=\begin{bmatrix}x_{1_{1}}\\ x_{2_{1}}\\ x_{3_{1}}\\ x_{4_{1}}\\ u_{1_{1}}\\ u_{1_{N}}\end{bmatrix}-\begin{bmatrix}x_{0_{1}}\\ x_{0_{2}}\\ x_{0_{3}}\\ x_{0_{4}}\\ 0\end{bmatrix} \tag{20}\]  
The inequality constraints stay unchanged compared to Phase 2, and are provided by Equation (14).  
The cost function must now also consider the input \(u_{1}\), which represents the truck's velocity. The cost function mainly aims to minimize the rate of control inputs in time for both control inputs. It also penalizes the distance between the truck and the corresponding path point, leading to the cost function provided by Equation (21).  
\[J= 100\cdot\frac{1}{N}\cdot\left(\frac{1}{2}(u_{1_{1}}-u_{1_{2}})^{ 2}+\sum_{n=2}^{N-2}(u_{1_{n}}-u_{1_{n+1}})^{2}+\frac{1}{2}(u_{1_{N-1}}-u_{1_{N }})^{2}\right)+ \tag{21}\] \[+ 10\cdot\frac{1}{N}\cdot\left(\frac{1}{2}(u_{2_{1}}-u_{2_{2}})^{ 2}+\sum_{n=2}^{N-2}(u_{2_{n}}-u_{2_{n+1}})^{2}+\frac{1}{2}(u_{2_{N-1}}-u_{2_{N }})^{2}\right)+\] \[+ \sum_{n=1}^{N}\sqrt{(x_{1_{n}}-C_{x_{n}})^{2}+\left(x_{2_{n}}-C_ {y_{n}}\right)^{2}}\]
## 5 Experiments and Discussion
### Comparative Evaluation and Analysis of Performance
#### 5.3.2 Analysis and Comparison of Model Complexity and Runtime  
To further explore the efficiency of various recommendation models, our experiments were conducted on a dataset aligning with the industrial software component recommendation problem. This enabled comparison of the complexity and model parameter size of FM-based models currently utilized in the field of recommendation. The configuration parameters in the dataset used for comparison were as follows: the DNN had a depth of H = 2, with U = 32 hidden units, utilizing a = 32 attention factors and an embedding size of E = 32, while the CIN had a depth of T = 2 with F = 4 hidden units and the dataset contained N = 104 features. From Table 4, it can be observed that the model complexity of EIAFM is slightly higher than that of other models, while parameter size is the same as that of AFM. This is due to the inputs of the implicit and attention components designed in this paper utilizing feature interactions in common, which greatly reduces model complexity. Thus, EIAFM combines all the above-mentioned advantages without increasing model complexity. In terms of model complexity, EIAFM has lower complexity than AFM and xDeepFM combined. Furthermore, in terms of runtime, although EIAFM takes more time, it is faster than the combined runtimes of basicFM, xDeepFM, and AFM. In summary it can be inferred from the time complexity and runtimes that EIAFM can feasibly be used in industrial software service component recommendation scenarios.
## 1 Introduction
On the other hand, non-Morphometry based feature extraction technique using classical methods such as Dual-Tree Complex Wavelet Transform (DTCWT) [9], wavelet entropy [10], Singular Value Decomposition (SVD) [11], Scaled Chaotic Artificial Bee Colony (SCABC) [12], Downsized Kernel Principal Component Analysis (DKPCA) [13] including machine-learning and deep-learning techniques [14; 15; 16] like Recurrent Neural Network and Semantic Segmentation [17; 18] have been used in recent times in CAD development for AD detection between NC and MCI. Also, major advances in structural and functional neuroimaging studies have resulted in improvements in the early and accurate detection of AD [19; 20; 21; 22; 23; 24]. In this study, we investigated the VBM method using SPM 12 for MRI classification. We had combined the traditional VBM pipeline [3] along with our proposed feature extraction process to support the classification of the normalized feature vectors. Following the VBM execution pipeline, we obtained a \(t\)-map image showing the areas where distinction exists between two groups (CN and AD/MCI), used in the \(t\)-test. The obtained \(t\)-map is used to identify ROIs to generate the features for classification. To support the classifier with a normal distribution of features belonging to each group, we implemented Gaussian smoothing to generate weighted average feature vectors without any feature reduction. Subsequently, the smoothened features are fed into the neural network along with their label and trained based on Bayesian optimization. Once the network is trained, it is tested on other sets of data, and finally, the results are evaluated.
# Frequency-Based Haze and Rain Removal Network (FHRR-Net) with Deep Convolutional Encoder-Decoder
## 3 Experimental Result
### Analysis of Proposed Architecture  
#### 3.6.1 Effectiveness of the Proposed Dilated Convolution and Negative Mapping  
In Section 2.3, FHRR-Net uses dilated convolution with negative mapping for convolution. To prove that the proposed structure is most effective, we conducted a comparative experiment after excluding the dilated convolution and the negative mapping structure, respectively. In Table 4, quantitative measurement results are shown using PSNR and SSIM scores for each structure on the synthesized dataset. Figure 13 also shows a visual comparison of qualitative results for a synthesized dataset.  
Figure 13a shows ground truth of experiments. Figure 13b shows our synthesized test dataset. Figure 13c illustrates a structure learned using a negative mapping structure without using dilated convolution. Figure 13d shows a structure learning using only dilated convolution without using neg mapping structure. The fog is slightly removed as in Figure 13c than in Figure 13e. On the other hand, comparing Figure 13d with Figure 13e shows that the rain streak is less removed without negative mapping. These results implies that removing haze is effected by dilated convolution which gives large receptive field. In addition, it proves that negative mapping gives high-level features through the connection. Table 4 also shows that PSNR and SSIM index are the highest in the proposed structure that uses dilated convention and negative mapping.  
As a result of quantitative and qualitative evaluation, dilated convolution and negative mapping are shown to be effective in removing haze and rain from encoder-decoder networks for image restoration.
# Multi-Party Controlled Semi-Quantum Dialogue Protocol Based on Hyperentangled Bell States
## 3 Security Analysis  
### The Trojan Horse Attack  
Since the proposed MCSQD protocol is bidirectional, it is necessary to consider Trojan horse attacks [31; 32; 33]. In this scenario, [PERSON] generates spy photons and injects them into the transmitted sequences \(S_{A}\) and \(S_{B}\) to perform invisible photon attacks. [PERSON] captures the sequences and then separates and measures the encrypted spy photons to steal information from [PERSON] and [PERSON] while forwarding the remaining legitimate particles to the controller [PERSON]\({}_{\text{n}}\). To counteract such attacks, in Step 2 of the proposed protocol, [PERSON]([PERSON]) has already installed a PNS: 50/50 and a wavelength filter before receiving the sequences \(S_{A}(S_{B})\). The PNS can be used to protect against delayed photon attacks by checking for the presence of multi-photon signals, and the quantum wavelength filter removes illegal photons under invisible photon attacks. This setup effectively resists the Trojan horse attack.
# Simulation Study on Ground Vibration Reduction Measures of the Elevated Subway Line
## 3 Numerical Results and Discussion
### Effect of Installing Vibration Damping Pad on Ground Vibration  
When the vehicle speed is 65 km/h, it can be seen from the calculation results that when using damping fasteners for vehicle running-induced ground vibration control, the ground vibration velocities are 0.08 mm/s and 0.07 mm/s, which does not meet the ground vibration limit requirements. The vibration impact of train running can be reduced by using damping pads. The vibration damping pad arrangement form is as shown in Figure 14. The damping pad has a loss angle of 0.6\({}^{\circ}\), a foundation modulus of 0.019 N/mm\({}^{3}\), and a thickness of 30 mm.  
Before and after the arrangement of damping pads, the ground vibration velocity time domain results and frequency domain results are as shown in Figure 15. From the time domain results, it can be seen that after the arrangement of the vibration damping pads, the amplitude of the vibration velocity is significantly reduced; at this time, the vibration result measures 0.015 mm/s, compared with not installing vibration damping pads, and the ground vibration is effectively controlled to meet the ground vibration limit requirements. Before the installation of damping pads, the vibration peak in the frequency domain at 23 Hz had the value of 0.053 mm/s, and at 47 Hz, it had the vibration speed of 0.038 mm/s. After the installation of damping pads, the vibration speeds of 0-23 Hz and 50-80 Hz are  
Figure 14: Schematic diagram of vibration damping pad arrangement.  
Figure 13: Ground vibration acceleration results under different vibration reduction schemes: (**a**) time domain results; (**b**) frequency domain results.  
effectively suppressed. The installation of vibration damping pads can effectively control the ground vibration generated by train running.  
Figure 16 gives the time domain and frequency domain results of ground vibration acceleration before and after the installation of damping pads. From the figure, we can see that the ground vibration acceleration is effectively controlled from the time domain results after the installation of damping pads. After installing the damping pads, the RMS value of ground acceleration is reduced from 15.63 mm/s to 3.2 mm/s. At 0-80 Hz, the peak value of ground acceleration is effectively controlled after installing the damping pads. Overall, the ground vibration velocity and acceleration can be effectively controlled with the installation of vibration damping pads, which have a good vibration damping effect.
# Spatiotemporal Analysis of Web News Archives for Crime Prediction
## 6 Evaluation  
Different evaluation matrices are used to measure the performance of any algorithm. The performance criteria for evaluations are accuracy, precision, recall, F-measure, ROC curve, root mean square error, absolute error, etc. Accuracy is defined as the ability to predict categorical class labels. This means that it calculated the proportion of correctly predicted instances [17]. Accuracy measurements were done using the formula in Equation (3):  
\[Accuracy=\frac{TP+TN}{TP+TN+FP+FN} \tag{3}\]  
Precision is the measure of closeness of instances with each other and is calculated as per the formula given in Equation (4).  
\[Precision=\frac{TP}{TP+FP} \tag{4}\]  
A recall is the measure of actual positive instances in the dataset that have been correctly classified as positive by the classifier. It is calculated using the formula given in Equation (5).  
\[Recall=\frac{TP}{TP+FN} \tag{5}\]  
F-measure is calculated by taking the weighted harmonic mean of the precision and the recall as in Equation (6).  
\[F-Measure=\frac{2\times Precision\times Recall}{Precision+Recall} \tag{6}\]  
where  
* False Negative (\(FN\)): If the record is positive in the dataset, but the classification outcome is negative;
* True Negative (\(TN\)): If the record is negative in the dataset but the classification outcome is negative;
* False Positive (\(FP\)): If the data record is negative, but the classification outcome is positive;
* True Positive (\(TP\)): If the instance is positive but the classification outcome is positive.
# Unraveling the Biosynthesis of Carvacrol in Different Tissues of _Origanum vulgarae_
## 3 Discussion
### Tissue-Specific Expression Patterns of TPS/CYP and SDR Genes in O. vulgare 'HS'  
The primary drivers of terpene diversification are TPSs (which generate scaffold diversity) and CYPs (which modify and further diversify these scaffolds), paving the way for further downstream modifications [17]. In the present study, we identified 62 full-length TPS genes in _O. vulgare_ 'HS'. Based on VOC contents, we found that the most carvacrol-rich tissue, SEs, presented 17 highly expressed TPS genes, including three \(\gamma\)-terpinene synthase genes which give rise to two different pathways yielding \"cymyl\" and \"sabinyl\" compounds, resulting in diverse chemotypes. \(\gamma\)-terpinene is synthesized by \(\gamma\)-terpinene synthase in the 'cymyl' pathway, which is considered to be the major precursor of carvacrol [27]. Notably, the expression patterns of three \(\gamma\)-terpinene synthase genes in SEs (high expression) and STs (low expression) were exactly consistent with carvacrol contents in these tissues. In both eudicots and monocots, TPS genes were predominantly found in combination with CYP71 clan genes belonging to the CYP71 family [17]. According to these results, CYP71 family genes, especially _CYP71 As_ and _CYP71 Ds_, preferred higher transcripts in carvacrol-rich tissues of _O. vulgare_ 'HS'. Previous research showed that most monoterenes are derived from \(\gamma\)-terpinene via the upregulation of _CYP71D180_ and _CYP71D181_, and revealed significant correlations between the relative expression of _Ovts7_, _Ovts5_, and _CYP71D180_ and carvacrol contents in oregano [19]. In detail, \(\gamma\)-terpinene was firstly oxidized by cytochrome P450 monooxygenases of the CYP71D subfamily to produce unstable cyclohexadienol intermediates. Then, the intermediates were dehydrogenated by a SDR into the corresponding ketones. The subsequent formation of the aromatic compounds occurs via keto-enol tautomerisms [10]. Similarities exist between the carvacrol biosynthesis process and the menthol biosynthesis pathway in _Menthu_\(\times\)_piperta_, in which the CYP71D enzyme catalyzes the allylic hydroxylation of limonene at C-3 to form trans-isopipperitenol. Then, the SDR catalyzes the trans-isopipperitenol into the respective ketone [10; 28; 29]. In the current study, SDR genes also preferred the tissue-specific expression patterns of _O. vulgare_ 'HS'. SDRs are also known to contribute to the production of iridoids and cardenolides, among other terpenoids [30]. These studies demonstrate that biosynthetic routes leading to derivatives of oxygenated monoterpenes are frequently followed by ketone production via dehydrogenation and hydroxylations.
## 5 Results and Discussion
### Stage I  
Figure 5 shows the results from Stage I, in which we separately measured RTT latency and throughput for each of the 24 configurations. The latency and throughput values represent the averages of the measured results aggregated across all packet sizes. The legend indicates which parameter was modified for each baseline configuration. We can observe three distinct groups of data points. The first group is characterized by the highest throughput and the highest latency. The second group presents intermediate values, falling between the maximum measured throughput and the minimum measured latency. The third group exhibits the lowest throughput but, at the same time, the lowest latency.
# Simultaneous Detection of Nine Key Bacterial Respiratory Pathogens Using Luminex xTAG(r) Technology
## 2 Materials and Methods
### PCR Product Identification  
To examine the amplified DNA, the reaction products were resolved on a 2.0% agarose gel stained with GoldView, visualized under UV light, and analyzed using a Gel Doc system (BioRad, Hercules, CA, USA). The target gene fragments for _S. pneumoniae_, _M. catarrhalis, S. aureus_, _S. pyogenes_, _H. influenzae_, _M. pneumoniae_, _Legionella_ spp., _P. aeruginosa_, and _K. pneumoniae_ were 350 bp, 140 bp, 270 bp, 93 bp, 296 bp, 209 bp, 650 bp, 504 bp, and 364 bp, respectively.
## 2 Strategies and Device Designs for Efficient PEC Water Splitting
### Kinetics of Water Redox Reactions
They investigated a strategy to deactivate the surface trapping states between BiVO\({}_{4}\) and the OEC of CoOOH by synthesizing ultrathin CoOOH layers on the BiVO\({}_{4}\) surface. In order to synthesize the ultrathin OECs on the BiVO\({}_{4}\) surface, they used a hydrothermal method for firstly synthesizing the Co(OH)\({}_{2}\) nanosheet. The obtained Co(OH)\({}_{2}\) nanosheets were then dispersed into ethyl alcohol, followed by spin coating onto the prepared BiVO\({}_{4}\) sample. Owing to the hydrothermal process for Co(OH)\({}_{2}\) synthesis, the ultrathin OEC nanosheets were synthesized and the coverage of the multilayered CoOOH on the 3D coral-like BiVO\({}_{4}\) surface were possible to produce. After heating, covalent-bonding between the CoOOH and the BiVO\({}_{4}\) took place, which was crucial for deactivating the surface trapping states and promoting the charge separation. Consequently, the 3D coral-like BiVO\({}_{4}\) with the ultrathin CoOOH OEC showed a larger photovoltage of 0.28 V, and a significant negative shift of onset potential than that of the pristine BiVO\({}_{4}\) sample. The authors claimed that the great reduction in the number of surface trapping states could effectively facilitate the separation efficiency of photocarriers, even at a low-bias potential. Therefore, it could be inferred that more efficient and practical PEC water splitting devices could be realized by designing not only the photoelectrode itself, but also the effective configuration of the OECs on it.