esa-sceva/satcom-chunk-collection huggingface dataset:
## 7 Critical Discussion  
This section discusses the taxonomy of detection approaches based on signature (Table 9), anomaly (Table 10), hybrid (Table 12), entropy (Table 14), and filtering tree (Table 16).  
Table 9 shows that [109] is better than others in detecting CC DDoS attacks due to its comprehensiveness in detecting attacks at multiple OSI model layers (network, transport, and application) as opposed to only at the application layer. Moreover, as shown in Table 8, [PERSON] and [PERSON] (2015) also has a high detection rate of intrusion from encrypted traffic, helpful in the detection of insider and known attacks, while others work not [109]. Tables 10 and 11 show that [115], an SVM learning-based flexible detection framework for DDoS attack techniques, is better than the existing techniques and in accurate detection of DDoS attacks under changing environments. Further, it helps hypervisors identify compromised VMs that may try to claim and consume more resources. Most importantly, this approach does not have any major limitations.  
Tables 12 and 13 show that [111] is suitable for detecting only slow scanning attacks. In contrast, the technique of [134] is slightly superior to others in detecting known and unknown attacks in the CC, with high detection rates and low false negatives.  
Table 14 shows that the entropy technique used by [146], i.e., the detection algorithm and confirmation algorithm, has the best attack detection at all OSI layers compared to others. Table 15 shows that the entropy rate is lesser when the class distribution is pure  
\begin{table}
\begin{tabular}{p{42.7 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Refs** & **Approach used** & **Advantages** & **Disadvantages** & **Note** \\ \hline
[148] & (Cloud Service Queing Defender) & effective and efficient in detecting and mitigating most DDoS attack Seelf-learner & More overhead, Low efficiency & the CSQD system detects the malicious request and stores it in its database to prevent similar attacks in the future \\
[156] & Game-theoretic model and analysis & It is very beneficial for a range of Internet clients and services & It generates a list of \({}^{\prime}\)good \({}^{\prime}\) DNS resolvers and platforms and a list of potentially hacked (suspicious) hosts & The model has its own merits and  
in [146] with a minor drawback that the third party handles it, so there might be a few minor security concerns. Alternatively, the technique by [144] performs well with a 99.3% accuracy rate.  
Tables 16 and 17 show that the multilevel thrust filtration (MTF) mechanism in [152] can detect attacks occurring at OSI layer 3 and 7, which are undetectable by others. Furthermore, Table 17 shows that the technique in [152] can be deployed at an attack-prone DC for resource protection and can simultaneously detect four types of attacks.
## 2 Materials and Methods
### Data Analysis  
Statistical analyses were performed using R Statistical Software (v4.2.2; R Core Team 2023) [62] running in RStudio. [63] Analysis models for spatiotemporal and dynamic balance variables (10 models) were mixed-model ANOVA models using lme from the nlme [64] and emmeans packages [65], with a significance level of \(\alpha\) = 0.05. Units of analysis were not Subject but gait cycle sequences with Subject being a factor in the model. Each model had a random effect, limb within subject intercepts included. Limb was a fixed effect but grouping left and right limbs across subjects ignored individual left/right asymmetry, so it was nested within the randomly selected subjects. Variance was not expected to be constant across subjects but was assumed to be constant within subject, so constant variance within subjects was used for the variance structure. Normality was tested using Shapiro-Wilks tests. A few of these failed, but an examination of q-q plots for those variables indicated that the deviation from normality was mild and generally symmetric. ANOVA is known to be robust to normality, especially if the distribution is at least symmetric, so normality was accepted in these cases (see Appendix B for details).  
Three sets of fixed effects were used in the models: two for the spatiotemporal gait parameters and a third for the margin of stability, i.e., dynamic balance variables. The spatiotemporal data were split into two groups. One consisted of stride variables with data taken across the full obstacle-crossing stride. The stride variables included stride length, stride width, and gait velocity. The stride variables were modeled with a single fixed effect: Condition, i.e., CON and PD. Step variables had Step (1 st and 2 nd) added to the fixed effects along with the interaction between Step and Condition. The margin of stability fixed effects comprised Condition and Event (FDS1, MST1, FDS2, MST2, and SDS2), with their interaction. Event comprised five discrete stance phase points, requiring it to be modeled as a repeated measure.  
Tables of \(p\)-values were generated for the Step and margin of stability models providing the overall ANOVA fixed effects significance. Since the stride variables had only a single two-level fixed factor, the F-test and post hoc \(t\)-test \(p\)-values were the same, so no ANOVA tables were generated for those variables. Almost all interactions were significant, so interaction analysis (when present in the model) was used for post hoc tables. Post hoc tables for the combined stride and step variables provide specifics for stride variables, the combination of the step variable for each of the first and second steps, and the margin of stability variables. Each line of the table includes group means and standard errors, differences in group means with \(p\)-values, and confidence intervals extracted from each analysis. Note that multiple comparison corrections were not needed for two-level factors.
## 3 Frequency Response Calculation of the CLD Plate Considering Two Kinds of Damping
### Analysis Examples  
A case study was carried out to verify the developed method for solving the frequency response of the CLD plate. In addition, the comparison of the frequency responses obtained by considering only one damping and two kinds of damping was conducted.  
Referring to the literature [5], the shear modulus and loss factor of 3M ISD-112 viscoelastic material with a frequency-dependent characteristic are:  
\[G_{\mathrm{v}}(f)=2.783-1.023/\left(0.394+0.0003736f\right) \tag{19a}\] \[\eta_{\mathrm{v}}(f)=1.683+0.001468/\left(0.005+0.0006134f\right)-0.5274/\left( 0.005+0.0006134f\right)^{0.25} \tag{19b}\]  
where, \(G_{\mathrm{v}}(f)\) is the shear modulus and \(f\) is the excitation frequency with unit Hz. Furthermore, the density and Poisson's ratio of this material are \(\rho_{\mathrm{v}}=789.5\,\mathrm{kg/m^{3}}\) and \(\mu_{\mathrm{v}}=0.49\), respectively.  
It is assumed that the geometric parameters of the composite plate, and the material parameters of the metal substrate and the constraining layer, are the same as those described in Section 5. To introduce two kinds of damping, it is assumed that the first eight modal damping ratios of the plate without damping treatment are known and the specific damping values can be found in Section 5. The composite plate element described in Section 3.3 is adopted to create the finite element model of the CLD plate. The created model consists of 121 nodes and 100 elements, and the picking vibration point is located at \(x=60\) mm, \(y=60\) mm, as shown in Figure 5. The frequency responses of the CLD plate were calculated using the modal superposition method within the range of 600 Hz, by considering only one damping and two kinds of damping, and the calculated results are shown in Figure 6.  
Figure 5: Finite element model of the CLD plate.  
Figure 6: Comparison of the frequency response obtained by considering only material damping and two kinds of damping.  
Figure 6 reveals that the frequency response obtained by considering only material damping is different from that obtained by simultaneously considering two kinds of damping in the resonant region. In the authors' opinions, the results of considering two kinds of damping should be closer to the actual values. The resonance point is generally used as a reference point for the identification of viscoelastic parameters, and thus, the accuracy of the identification results may be influenced when only material damping is considered.
## 4 Results
### Accuracy Analysis on Natural Control Points  
Table 9 presents statistics of the linear RMSE error values on control points detected in pairs of raster images for both scenarios by means of the SURF and FAST detectors in the spherical projection, in the Mercator projection and in orthoimages. In Table 9, only the maximum, minimum, and average values of RMSE for all pair of scans were presented. The accuracy analysis was separately presented for X, Y, and Z components in Table S1.  
Based on the results presented in Table 9, it may be noticed that the orientation accuracy on natural control points is similar for Test site I (3.5-4.0 mm); for Test site II it is considerably better for raster images in the spherical projection, it is about two times better. For Test site III, which is characterised by the poorer texture the lower orientation accuracy was obtained, in the range of 5-6 mm; it is similar for all cartographic projections. In the case of Test site IV, where the scanning resolution equalled to 1.2 mm/10 m and the orientation process has not been successfully completed for particular pairs, it was possible to estimate the orientation accuracy only for the spherical projection for which the orientation error equalled to 9.0 mm for the SURF algorithm and 34.2 mm for the FAST algorithm.
# Application of Improved LightGBM Model in Blood Glucose Prediction
## 3 Experimental Results and Discussion
### Comparative Experiments
#### 3.2.2 Comparison between HY_LightGBM Model and Other Classification Models  
In order to further verify the prediction performance of the HY_LightGBM model, two other models of the same type were selected for comparison, namely the XBGoost model and the CatBoost model. The experiment confirmed that the HY_LightGBM model has a significantly higher prediction accuracy than the other two models of the same type. The experimental results are shown in Table 5 and Figure 12.  
The experiment proved that the LightGBM model optimized by the Bayesian hyper-parameter optimization algorithm used in this paper is significantly superior to the XGBoost model and CatBoost model in prediction accuracy.  
2.3 Comparison of Parameter Tuning among Bayesian Hyper-Parameter Optimization Algorithm, Genetic Algorithm, and Random Searching Algorithm  
This experiment verified the feasibility of the Bayesian hyper-parameter optimization algorithm for parameter optimization by comparing the performance of genetic algorithm, random searching algorithm, and the Bayesian hyper-parameter optimization algorithm in the parameter optimization. The parameters obtained by genetic algorithm were as follows: learning_rate was 0.05, the number of basic learners n_estimators was 400, the minimum row tree that the leaf may have min_data_in_leaf 60, the data ratio used in each iteration bagging_fraction 0.9, and the ratio of randomly selected features in each iteration feature_fraction 0.5.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model Name** & **MSE** & **RMSE** & **R-Square** & **Training Time** \\ \hline HY\_LightGBM & 0.5961 & 0.7721 & 0.2236 & 26.7938 s \\ XGBoost & 0.6284 & 0.7927 & 0.1815 & 6.2837 s \\ CatBoost & 0.6483 & 0.8051 & 0.1556 & 73.3301 s \\ \hline \hline \end{tabular}
\end{table}
Table 5: Performance comparison of the other models.  
Figure 12: Performance comparison chart of HY_LightGBM, XGBoost, and CatBoost.  
The parameters obtained by the random searching algorithm were as follows: learning_rate 0.05, the number of basic learners n_estimators 370, the minimum row tree that the leaf may have min_data_in_leaf 36, the data ratio used in each iteration bagging_fraction 0.9, and the ratio of randomly selected features in each iteration feature_fraction 0.98, as shown in Table 6.  
The prediction results obtained by the LightGBM model optimized by the genetic algorithm and random searching algorithm, that is, the GA_LightGBM and RS_LightGBM model, were compared with those of the LightGBM model optimized by the Bayesian hyper-parameter optimization algorithm, and the comparison results are shown in Table 7 and Figure 13 respectively.  
The experiment demonstrated that the LightGBM model optimized by the Bayesian hyper-parameter optimization algorithm achieves significantly better prediction results than the LightGBM model optimized by the genetic algorithm and random searching algorithm.
# Point Cloud Deep Learning Network Based on Local Domain Multi-Level Feature
## 3 Proposed Method
### Proposed Network Architecture  
The flowchart of the proposed method is described in Figure 1. First, the input point cloud data are locally partitioned using three different scales of k-nearest neighbors (KNN), and a directed graph is constructed for the extraction of features between points.  
Simultaneously, we utilize EdgeConv (edge convolution) layers to extract and aggregate local features of the point cloud. The regular network is then extended to the irregular structures by using relation convolution to further extract the features of the points. Finally, global features are output through maximum pooling. The point cloud deep learning network proposed in this paper is constructed, as shown in Figure 2. The input of the network is the point set P = {p\({}_{1}\), p\({}_{2}\),..., p\({}_{n}\)} \(\subseteq\) R\({}^{\rm F}\), \(N\) is the number of sampled points and D is the feature dimension of each point of input.  
Figure 1: Flowchart of the proposed method.  
When D = 3, the input points have only three dimensions, which means that the input point cloud data are (x, y, z). The N \(\times\) D dimensional point clouds are input to the spatial transform networks (STN). The spatial transformation matrix learned by this module can align the coordinates of the input point cloud data. The aligned data are manipulated using a layer of the dynamic graph convolutional network module to obtain local neighborhood features, which are mapped to 64, 128 and 1024 dimensions in sequence by Multi-Layer Perceptron (MLP). The local features of the point cloud are extracted and aggregated by using EdgeConv, as shown in Figure 3a. Deeper feature extraction is then performed using a relation-shape convolution network module, as shown in Figure 3b. It utilizes relation-shape convolution to capture local geometric structures by modeling pairwise spatial relations between points. Global features are obtained through max pooling of the local features.  
Point cloud classification is achieved by applying max pooling and an MLP to the global features. Point cloud segmentation is realized according to the combination of global and local features. It concatenates global and local features and outputs the score per point.
# Ensemble Clustering in GPS Velocities: A Case Study of Turkey
## 2 Data and Methodology
### Generation Mechanisms  
Generation is the primary step in clustering ensemble models and its main purpose is to adopt \(n\) different clustering models for generating ensemble members. In theory, any individual clustering algorithm can be used in this step, as long as it is appropriate for certain data. Furthermore, the generated members are expected to be as different from each other as possible. Ensuring a high level of diversity among members denotes that they yield distinct information about the data and this can potentially help improve the performance of the ensemble clustering process [25]. In a particular problem, it is important to apply one or more suitable generation processes to accomplish reasonable quality but also diversity.  
There are several ensemble member generation approaches: different clustering algorithms, different objects representation, different parameter initialization, projection to subspaces, and different subsets of objects. Among these approaches, different clustering algorithms strategy is used for each member with a consideration that different algorithms may generate more diverse members in this study. Several individual clustering algorithms such as \(k\)-means, \(k\)-medoids, Fuzzy \(C\)-means algorithm, self-organizing maps and hierarchical clustering were applied on different data sets to generate ensemble members in the literature [30; 31; 32].
## 4 Conclusions  
The electrophoretic deposition method successfully generated thin zinc oxide films on conductive glass substrates using three different voltage levels, 60 V, 70 V, and 80 V, with deposition times of both 3 and 5 min.  
Spectrophotometry analysis revealed the production of thin films with favorable reflectance in the visible range of the spectra, ranging between 60% and 90%, and band gap values at approximately 3.28 eV, slightly lower than the typical 3.3 eV for ZnO. These films functioned as photoanodes for dye-sensitized solar cells.  
The results showcased enhanced photovoltaic performance for the photoanodes deposited through electrophoretic deposition for 5 min at 70 V and 80 V, resulting in Incident Photocurrent Efficiency (IP-CE) values of 3.8% and 4.48%, respectively. This method demonstrated efficiency in depositing photoconductive material.  
EPD is not a widely used method applied in DSSCs due to challenges associated with achieving a homogeneous film without cracks, and this is essential for improving the parameters of the solar cell. To address these issues, various tests can be conducted, such as reducing the deposition time or minimizing the contact time between the film and the dye. Additionally, the combination of ZnO with other elements that increase the electron injection into the cell can lead to further improvements. This work intended to amplify the use of EPD in renewable energy beyond the typical focus on the bio applications often found in the literature.  
Conceptualization, M.P.F.G. and F.N.A.F.; methodology, A.F.L.A., F.N.A.F. and V.F.N.; software, V.F.N. and I.H.; validation, M.P.F.G., F.N.A.F. and A.F.L.A.; formal analysis, V.F.N. and A.F.L.A.; investigation, V.F.N.; resources, M.P.F.G. and F.N.A.F.; data curation, V.F.N. and I.H.; writing--original draft preparation, V.F.N.; writing--review and editing, I.H., F.N.A.F. and M.P.F.G.; visualization, I.H.; supervision, F.N.A.F.; project administration, F.N.A.F.; funding acquisition, F.N.A.F. and M.P.F.G. All authors have read and agreed to the published version of the manuscript. This research was funded by FEDER funds through the COMPETE 2020 Program and National Funds through FCT--Portuguese Foundation for Science and Technology under the projects LISBOA-01-0247-FEDER-039985/POCI-01-0247-FEDER-039985, LA/P/0037/2020, UIDP/50025/2020, and UIDB/50025/2020 of the Associate Laboratory Institute of Nanostructures, Nanomodelling and Nanofabrication--i3N.  
Not applicable.  
Data Availability Statement: The data presented in this study are available on request from the corresponding author.  
The authors would like to acknowledge the Brazilian fund Capes, Coordenacao de Aperfeicoamento de Pessoal de Nivel Superior, and the Laboratory of Thin Films and Renewable Energy LAFER.  
The authors declare no conflict of interest.
## 3 Results
### Experimental Group Results
Depending on sex (Table 5), in the comparison before and after the intervention in the EG, the results indicated that there were statistically significant differences between women and men in aerobic endurance (women [F (1, 66) = 37.239, \(p<0.001\), n\({}^{2}\) = 0.361, 95% CI \(-\)46.973, \(-\)23.813]; men [F (1, 66) = 31.539, \(p<0.001\), n\({}^{2}\) = 0.312, 95% CI \(-\)88.302, \(-\)41.983]), lower body strength (women [F (1, 66) = 27.580, \(p<0.001\), n\({}^{2}\) = 0.295, 95% CI \(-\)1.672, \(-\)0.753]; men [F (1, 66) = 6.108, \(p=0.016\), n\({}^{2}\) = 0.085, 95% CI \(-\)2.066, \(-\)0.220]), upper body strength (women [F (1, 66) = 42.687, \(p<0.001\), n\({}^{2}\) = 0.393, 95% CI \(-\)2.257]; men [F (1, 66) = 9.093, \(p=0.004\), n\({}^{2}\) = 0.121, 95% CI \(-\)4.986, \(-\)1.014]), upper body flexibility (women [F (1, 66) = 25.256, \(p<0.001\), n\({}^{2}\) = 0.277, 95% CI \(-\)4.292, \(-\)1.851]; men [F (1, 66) = 7.226, \(p=0.009\), n\({}^{2}\) = 0.099, 95% CI \(-\)5.726, \(-\)0.845]), dynamic balance (women [F (1, 66) = 4.358, \(p=0.041\), n\({}^{2}\) = 0.062, 95% CI \(0.01\), 0.559]; men [F (1, 66) = 6.810, \(p=0.011\), n\({}^{2}\) = 0.094, 95% CI \(0.168, 1.261]), hand pressure strength right (women [F (1, 66) = 22.461, \(p<0.001\), n\({}^{2}\) = 0.254, 95% CI \(-\)5.178, \(-\)2.108]; men [F (1, 66) = 7.263, \(p=0.009\), n\({}^{2}\) = 0.099, 95% CI \(-\)7.212, \(-\)1.074]). In the lower body flexibility and hand pressure strength left variables, statistically significant differences were found in women but not in men (lower body flexibility women [F (1, 66) = 6.984, \(p=0.010\), n\({}^{2}\) = 0.096, 95% CI \(-\)4.013, \(-\)0.559]; men (\(p=0.165\)); hand pressure strength left women [F (1, 66) = 10.096, \(p=0.002\), n\({}^{2}\) = 0.133, 95% CI \(-\)4.536, \(-\)1.035]; men (\(p=0.197\))).