esa-sceva/satcom-chunk-collection huggingface dataset:
This inhibition would limit the chain length of _trans_-isoprenyl-PPs, whereas some light residues such as G and A might allow the elongation to proceed, producing the longer chain lengths of _trans_-isoprenyl-PPs. Meanwhile, subcellular localizations have been considered important in predicting the biological roles of _trans_-PTs in a compartment-dependent manner. Most intensively in Arabidopsis, various localizations of eleven GGPSs and four SPSs/PPSs into different organelles such as cytosol, endoplasmic reticulum (ER), peroxisome, mitochondria and chloroplasts have supplied meaningful information to elucidate the in planta function of PTs: Eight chloroplast AtGGPSs (AtGGPS2, 6, 7, 8, 9, 10, 11 and 12) for the biosynthesis of GAs, chlorophyll, tocompherols and carotenoids [14; 15], three mitochondrial/ER AtGGPSs (AtGGPS1, 3, 4) for the production of ubiquinone [14], a mitochondrial/chloroplast AtPPS (initially known as AtGPS, [PERSON] et al. [16]) for the generation of ubiquinone and plastoquinone [17] and chloroplast AtSPS1 and 2 for the biosynthesis of plastoquinone [18]. These studies suggest that the functional topology of plant PTs might be a key determining factor for predicting the dynamic network of terpenoid metabolism, together with their timely and spatial expression patterns [19]. Among rice _trans_-PTs, some biochemical functions of seven PTs have been elucidated: OsGPS, OsGGPS1 and rice GGPS recruiting protein (OsGRP) [10], OsPS1 and 2 [20], OsPS3 [21] and OsFPS1 [22]. However, biochemical functions of the remaining rice _trans_-PTs remain undefined. Moreover, no topology-based study has been conducted to offer insight into rice _trans_-PTs overall. Hence, we identified all of the rice _trans_-PT genes from the rice whole genome database, constructed a new phylogenetic tree showing better classification with extended plant _trans_-PT sequence information and predicted biochemical functions by matching sequences with known conserved motifs and the \"three floors\" model. Taken together with expression patterns and subcellular localizations,we predicted a topology-based working model of rice _trans_-PTs as a unique system for terpenoid metabolism in rice plants.
# Analytic Form Fitting in Poor Triangular Meshes
## 4 Results
### Sphere Fitting  
For the sphere fitting, the dihedral angle-based extraction does not produce a segmentation of the input mesh \(M\). Therefore, \(S=M\) and the fitting occurs over \(M\). Figure 23 presents the results for that particular case. However, the parameters obtained by the fitting are accurate (see Table 8).  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & \(A_{p}\) & \(A_{pfit}\) & \(\gamma\) & \(\gamma_{fit}\) & \(\delta\) & \(\delta_{fit}\) & \(\angle(\delta,\delta_{fit})\)**Degrees** \\ \hline Test \#1 & \(\begin{bmatrix}0\\ 0\\ 144\end{bmatrix}\) & \(\begin{bmatrix}0.0010\\ 0.0063\\ 144.1274\end{bmatrix}\) & \(13.87\) & \(13.8852\) & \(\begin{bmatrix}0\\ 0\\ 1\end{bmatrix}\) & \(\begin{bmatrix}0.0000\\ 0.0000\\ 0.9999\end{bmatrix}\) & \(0.0033^{\circ}\) \\ Test \#2 & \(\begin{bmatrix}0\\ 0\\ 72.277\end{bmatrix}\) & \(\begin{bmatrix}-0.1258\\ -3.0241\\ 72.2727\end{bmatrix}\) & \(19\) & \(18.9716\) & \(\begin{bmatrix}0\\ 0\\ 1\end{bmatrix}\) & \(\begin{bmatrix}-4.57\times 10^{-11}\\ -5.96\times 10^{-9}\\ 1\end{bmatrix}\) & \(0.0033^{\circ}\) \\ \hline \hline \end{tabular}
\end{table}
Table 7: Cone fit. Set vs. found parameters. Test 1: \(S\) extraction based on dihedral angles. Test 2: fitting-based \(S\) extraction.  
Figure 21: Test 1. Cone fitting test using dihedral angle-based extraction. Pink: segmentation. Yellow: cone fitting.  
Figure 22: Cone Fit. Fitting-based \(S\) extraction. Pink: extracted \(S\) support subset. Yellow: fitted \(g()\).  
Figure 24 illustrates the results for a fitting-based extraction in a sphere coupled with a cylinder and presenting C\({}^{1}\)-continuity at the cylinder-sphere border.
## 3 Data and Methodology
### Random Forest (RF)  
Random Forest (RF) is a bagging ensemble technique proposed particularly for trees [73]. The base model of an RF is a DT. RF addresses the issue of high variance in DTs by combining the predictions of multiple DTs, with each DT trained on a different subset of the data and a different subset of the features. The resulting combination of trees leads to a more robust and less variable model. Random subsets of the data are generated via replacement, and each subset is trained with the help of a DT. While expanding the trees, RF adds more randomness to the structure [74]. As a result, there is a wide range of diversity, which contributes to a successful model in general. As a result, in an RF the algorithm only considers a random subset of the features when dividing a node. The randomness of the trees can be increased by using additional random thresholds for every feature, instead of looking for the highest suitable thresholds as in a normal DT.
## 7 Conclusions  
In this paper, the tree-cluster-based secure and energy-efficient routing protocol based on the dragonfly algorithm (CTSRD) was proposed for IoT networks in smart agriculture. The proposed method consists of three main steps: weighted trust mechanism (W-Trust), trust-based clustering process (T-Clustering), and dragonfly algorithm-based routing tree (DA-Tree). W-Trust is a lightweight and distributed mechanism that evaluates the trust level of IoT nodes and assigns a penalty/reward coefficient to them. T-Clustering is a trust-based clustering method that chooses nodes based on centrality, energy, neighbor degree, and distance to the BS. When designing DA-Tree, a new fitness function is provided based on the hop count to the base station, residual energy, intra-cluster traffic, and the trust level. Finally, CTSRD was compared with the NS2 simulator, and its results were compared with E-BEENISH and EEMSR. This comparison shows that CTSRD is an energy-efficient method and distributes the energy consumed in the network in a balanced manner. This has reduced the number of dead nodes by 20.92% and 61.57% compared to EEMSR and E-BEENISH, respectively. Furthermore, CTSRD can increase the stored energy by 16.65% and 35.31% compared to EEMSR and E-BEENISH, respectively. CTSRD has improved the packet delivery rate by approximately 8.03% compared to E-BEENISH. However, it has a weaker PDR than EEMSR (approximately 0.4%). In future research directions, we will perform more detailed comparisons to better show the strengths and weaknesses of our scheme. Furthermore, we will focus on the applications of deep learning and other machine learning (ML) techniques for improving communications in IoT in future research directions.  
Conceptualization, M.S.Y. and E.Y.; methodology, J.T., E.Y. and M.H.; validation, A.M.R., M.S.Y., and F.K.; investigation, A.M.R., M.H., and J.T.; resources, A.M.R. and A.H.; writing--original draft preparation, M.S.Y., E.Y., and A.H.; supervision, M.H.; project administration, M.H. and F.K. All authors have read and agreed to the published version of the manuscript. This work is supported by Sejong University Faculty Research Fund. The authors declare no conflict of interest.
# A Study of Multi-Step Sparse Vessel Trajectory Restoration Based on Feature Correlation
## 5 Experiment and Result Analysis
### Analysis of Anomaly Detection and Preliminary Restoration Results  
After the pre-processing of the trajectory data, this study extracted 706 trajectories in the region, covering 140,085 trajectory points. Based on the established data anomalies and missing judgment standard method to identify the anomalies in vessel tracking data, the results are shown in Table 2.  
From Table 2, it can be seen that the proportion of trajectory abnormal points to normal trajectory points in this region is 2.769%, which is mainly manifested in time abnormal points, totaling 3872. It can be seen that the preliminary restoration work of the trajectory is mainly to interpolate the missing time points. Taking a section of the trajectory of one of the vessels as an example, its preliminary restoration results are shown in Figure 8. The figure shows that the research in this study can effectively complement the trajectory restoration points through the cubic spline interpolation method. This restoration method is used in the restoration of trajectory integrity, and restoration results also reflect the motion state of the vessel better.
# Analyzing Key Features of Open Source Software Survivability with Random Forest
## 3 Analyzing the Relationship Between OSS Survivability and Features
### Experimental Setup
#### 3.1.2 Features for OSS Survivability Analysis
Since more forks indicate a higher likelihood of future contributions and modifications, the number of forks was chosen as a feature. LoC represents the size and complexity of a project. Higher LoC values can make the codebase more challenging to understand, requiring greater effort for maintenance. [PERSON] et al. analyzed the impact of the LoC on OSS project maintenance, finding that while a higher LoC indicates larger-scale projects, it also requires more resources for maintenance [16]. Thus, the LoC was selected as a feature to represent project complexity and the associated maintenance costs. Cyclomatic complexity quantifies code complexity based on the number of decision points and branches in the code. [PERSON] et al. explained that higher cyclomatic complexity increases the difficulty of maintenance and can negatively affect project stability [22]. Code with higher complexity is more prone to errors and requires additional time for resolution, which may impact survivability. For this reason, cyclomatic complexity was included as a feature. Project age measures how long a project has been maintained and developed since its creation. [PERSON] et al. investigated the impact of project age on long-term survivability, noting that while older projects signify prolonged maintenance, they may also face declining contributor interest or accumulated technical debt [17]. Older projects can have higher survivability but require consistent maintenance efforts. Therefore, project age was selected as a feature.
# SPEKS: Forward Private SGX-Based Public Key Encryption with Keyword Search
## 3 SPEKS Overview and Definitions
### Overview  
The high level design of our scheme is shown in Figure 1. In the proposed scheme, there are three system entities: data receiver (DR), data sender (DS), SGX-enabled cloud server (CS).  
During the setup phase, a DR initially generates his private key \(SK_{u}\), public key \(PK_{u}\), and symmetric key \(K_{u}\). Using the SGX attestation protocol for enclave authentication, the DR establishes a secure channel with the enclave within the CS (shown in step 1 in Figure 1). After establishing the secure connection, the DR provisions \(SK_{u}\) and \(K_{u}\) into the enclave (step 1). The enclave stores two provisioned keys for future processes. When enclave is unloaded or rebooted, the provisioned keys can be securely stored in the local memory.  
For the \(PEKS\) algorithm, a DS gets search counter of the DR, encrypts data that includes predefined keyword with the search counter, and uploads the encrypted data to the server (see step 2 and 3). Then, the DR generates a search query, encrypts it with symmetric key \(SK_{u}\), and transfers it to enclave within the CS using the \(Trapdoor\) algorithm (step 4). The enclave has provisioned keys required for decryption and the search query from the DR. For the \(Search\) algorithm, the data record is loaded to the enclave (step 4). If the data record size is greater than the EPC, the record are separated into smaller pieces and partial records are loaded multiple times to the enclave. The enclave decrypts the search query using the symmetric key and searches the matching record (step 4). Finally, if there is a matched result, then the enclave returns the result to the DR (step 4).
# Spatial Performance Indicators for Traffic Flow Prediction
## 3 Materials and Methods
### Case Study  
In this case study, we evaluate the proposed spatial KPIs using two approaches. First, clustering scenarios were created based on the traffic prediction results of STSGCN [7] for the PeMSD8 dataset. The result is a graph with RMSE as its node value, with the graph adjacency matrix defining its structure. The PeMSD8 dataset is a subset of the PeMS dataset containing traffic flow data from 170 sensors in San Bernardino, CA, USA. We created various clustering scenarios by swapping the node values of the graph to form hotspots and coldspots. This allowed us to test the sensitivity of the spatial KPIs to various clustering scenarios. Second, we compared the evaluation of the spatial KPIs tested on four traffic flow prediction methods applied to the PeMSD8 dataset. These methods were chosen because of their diverse approaches for incorporating spatial information into traffic prediction. The aim of this approach is to validate the spatial KPIs against real traffic prediction results. By evaluating both synthetic scenarios and real prediction methods, we aimed to demonstrate the capabilities of spatial KPIs in distinguishing spatial patterns that are indistinguishable using standard accuracy metrics.
## 3 Materials and Methods
### Application of LIME for Model Interpretation  
In this subsection, we extended our model interpretability analysis by applying the LIME (local interpretable model-agnostic explanations) algorithm [12] to our dataset. While SHAP provided insights into feature importance at a global level, LIME offers a complementary approach by explaining individual predictions locally. LIME achieves this by approximating complex machine learning models with interpretable models (such as linear models) on small, locally perturbed subsets of the data. By focusing on local explanations, LIME allows us to understand how specific instances are influenced by features in our model, providing a nuanced understanding of predictions that may not be captured by global feature importance measures alone. Similarly to our use of SHAP, we employed LIME to assess the four quadrants of the CMA separately, utilizing all 49 features. Although LIME is designed to provide local explanations for individual instances within the dataset, we aggregated these local explanations to derive insights into the model's overall behavior. For this purpose, we identified the most significant feature for each instance within our testing data using LIME library [12] in Python and subsequently calculated the two most frequently occurring features for each quadrant. This methodology enabled us to delineate the relationship between specific eye-tracking features and their corresponding CMA quadrants.  
Additionally, we conducted a comparative analysis of the two XAI models, SHAP and LIME, to evaluate their consistency and robustness in explaining the model's predictions.
# Capacity Analysis of Lattice Reduction Aided Equalizers for Massive MIMO Systems
## 4 Nakagami-m Channel  
The authors considered a correlated Nakagami-m channel. This channel model was selected because of its inherent features of producing different fading environments. The Rayleigh fading model provides a diversity order of one, whereas the Nakagami-m fading model supports a diversity order of m. In particular, it can describe the LOS and NLOS conditions. The channel characteristic can be represented by its probability distribution function (PDF) [42; 43; 44; 45], which, for the Nakagami-m channel, it is defined as  
\[P_{\gamma}(\gamma)=\frac{2}{\Gamma(m)}(\frac{m}{\tilde{\gamma}})^{m}\gamma^{m -1}\exp(\frac{-m\gamma}{\Omega});\gamma\geq 0 \tag{10}\]where \(\gamma\) represent the instantaneous SNR.  
Figures 1 and 2 represent the PDF and the cumulative distribution function (CDF) corresponding to the Nakagami-m channel.
## 2 Materials and Methods
### Image Acquisition and Data Correction  
For each experiment, a dark current image \(\mathrm{I_{DARK,y,\lambda}}\) and a white reference image \(\mathrm{I_{WHTEX,y,\lambda}}\) were recorded to correct the raw intensity images \(\mathrm{I_{RAW_{x},y,\lambda}}\) of the sample. \(\mathrm{I_{DARK,y,\lambda}}\) was obtained by closing the camera shutter and switching off the light source when capturing the HSI image. \(\mathrm{I_{WHTEX,y,\lambda}}\) was acquired with a reflectance standard (Zenith Polymer Target SG3210, SphereOptics GmbH, Germany) placed in the FOV.  
From the raw intensity image \(\mathrm{I_{RAW_{x},y,\lambda}}\), both the reflectance image \(\mathrm{I_{REFL_{x},y,\lambda}}\) (see Equation (2)), as well as the absorbance image \(\mathrm{I_{ABS_{x},y,\lambda}}\) (see Equation (3)), were calculated [19]:  
\[\mathrm{I_{REFL~{}x,y,\lambda}}=\ \frac{\mathrm{I_{RAW~{}x,y,\lambda}~{}-~{}I_{DARK~{}x,y,\lambda}}}{\mathrm{I_{WHTE~{}x,y,\lambda}~{}-~{}I_{DARK~{}x,y,\lambda}}}, \tag{2}\]  
\[\mathrm{I_{ABS~{}x,y,\lambda}}=\ -\mathrm{lgI_{REFL~{}x,y,\lambda}}. \tag{3}\]  
HSI data were collected from the kidneys before and during the drying process.  
Figure 1: Main steps for TWC determination using HSI.
## 4 Conclusions  
In summary, an approach for obtaining uniform LIPSSs on silicon using a pre-deposited Bi-In thin film was proposed in this work. The effects of the Bi-In film were analyzed both theoretically and experimentally. The Bi-In film acted as a buffer layer to homogenize the distribution of thermal energy caused by laser irradiation and avoid damaging the silicon due to excess heat in the formation of the initial LIPSSs. Then, the grating-assisted SP-laser coupling further assisted the formation of LIPSSs. We also investigated the tuning effects of laser parameters (laser fluence and pulse number) on the ripple structures. With the laser fluences varying from 1.02 to 2.55 J/cm\({}^{2}\) at a scanning speed of 20 \(\upmu\)m/s, no obvious change in the ripple period was observed due to the reflection enhancement. Furthermore, the depth of the ripples could be modulated by the laser pulse number. Nevertheless, periodic structures may be distorted by heat accumulation. This approach opens new opportunities for obtaining high-quality periodic structures on easily damaged materials.  
Writing--original draft preparation, Y.C.; writing--review and editing, Y.S., H.T., H.Z., R.H., Y.Z., R.Z., S.W., J.L., and L.C.; supervision, Y.Z. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (61775042, 61427815, 11674062) and the Fudan University-CIOMP Joint Fund (FC2017-003).  
Not applicable.  
Data Availability Statement: Data are contained within the herein article.  
The authors declare no conflict of interest.
## 3 Analysis of Monitoring Results
### Stress Analysis of Supporting Structure at Different Temperatures
#### 3.2.4 Stress Analysis of Rigid Panels at Different Temperatures  
The rigid panels are an important part of the support system and can resist the pressure of the soil and water. If a rigid panel is damaged due to excessive deformation, the soil behind it is likely to pour into the foundation pit, causing delays in the construction period and casualties. The stress on the middle row of rigid panel 1 and rigid panel 2 was selected for analysis, and the monitoring points were represented by G1-G7 and H1-H7, respectively, as shown in Figure 16. The stress curves of the rigid panels versus the temperature are delineated and shown in Figure 17.  
Figure 17a shows that the stress on rigid panel 1 increased with the temperature rise. When the temperature rose from 10 to 25 \({}^{\circ}\)C, the stress on points G1 and G7 enlarged by 0.33 and 7.19 MPa, respectively, corresponding to an increment of 5% and 78%, respectively. The variation in the stress on the bottom of rigid panel 1 was significantly larger than that in the stress on the top of rigid panel 1 because the rigid panel and the retaining piles formed a supporting system, and the displacement of the retaining piles also gave rise to the displacement of the rigid panel; thus, the top of the rigid panel released a large amount of stress. The bottom of the rigid panel was constrained by the soil and waist beam 2, resulting in a large amount of stress on the panel.  
Figure 17b is basically similar to Figure 17a. After raising the temperature to 15 \({}^{\circ}\)C, the stress on points H1 and H7 increased by 0.68 and 9.34 MPa, respectively, corresponding to an increment of 11% and 82%, respectively. The increment in the stress on rigid panel 2 was slightly larger than that in the stress on rigid panel 1. The analysis confirmed that the  
Figure 16: The layout of the strain gauges installed on the rigid panels.  
Figure 17: Stress-temperature relationship curve of the (**a**) rigid panel 1, (**b**) rigid panel 2.  
deformation of rigid panel 2 was larger than that of rigid panel 1 because it was closer to the middle of the foundation pit, which led the stress on rigid panel 2 to increase more rapidly.
## 4 Experiments and Analysis
### Comparative Analysis of Throughput  
A comparison of throughput (Transactions Per Second, TPS) of the two methods is conducted under the simple task scenarios, where the rate of the classic token bucket  
Figure 11: Average latency for dynamic traffic scenarios.  
Figure 12: The 90 th percentile latency for dynamic traffic scenarios.  
bucket algorithm at its optimal fill rate. When the concurrency level exceeds 100, the average throughput of the proposed method is higher than that of the classic token bucket algorithm. At a concurrency level of 155, the adaptive token bucket algorithm shows an average throughput that is 6 TPS higher than that of the classic token bucket algorithm. This indicates that, in complex task scenarios, the proposed method can automatically adjust the token fill rate, and its processing efficiency even surpasses that of the classic token bucket algorithm with the optimal rate.  
A comparison of the average throughput of the two methods is also conducted under the mixed task scenarios, where the rate of the classic token bucket algorithm is set to 150 tokens/s. From Figure 16, the following observations can be made: (1) as the task concurrency increases, the average throughput of both methods exhibits a fluctuating trend, but after the concurrency exceeds 40, the values become progressively stable; (2) when the concurrency is less than 100, the performance of the proposed method is close to that of the classic token bucket algorithm. When the concurrency exceeds 100, the average throughput of the proposed method is consistently higher than that of the classic token bucket algorithm. At a concurrency level of 130, the adaptive token bucket algorithm shows an average throughput that is 8 TPS higher than that of the classic token bucket algorithm. This indicates that, in mixed task scenarios, the proposed method can automatically adjust the token fill rate, and its processing efficiency outperforms the classic token bucket algorithm under high-concurrency conditions.  
Having validated the performance of the proposed adaptive task traffic-shaping algorithm in terms of average latency under different system load levels, this study next explored its performance in terms of throughput using the same methodology. From Figure 17, the following observations can be made: (1) as the time steps increase, both the  
Figure 16: Average throughput for mixed task scenarios.  
Figure 15: Average throughput for complex task scenarios.  
classic token bucket algorithm and the proposed adaptive token bucket algorithm exhibit a fluctuating trend in average throughput, but the values remain stable; (2) in the vast majority of experiments, the average throughput of the proposed adaptive token bucket algorithm is 5.57% higher than that of the classic token bucket algorithm. This indicates that the proposed algorithm performs exceptionally well under dynamic traffic conditions.