esa-sceva/satcom-chunk-collection huggingface dataset:
# Multi-Adjacent Camera-Based Dangerous Driving Trajectory Recognition for Ultra-Long Highways
## 2 Literature Review
### Vehicle Detection and Tracking  
Vehicle detection and tracking is divided into two important parts, target detection and multi-target tracking. Target detection is one of the basic problems in the field of computer vision, and is also the core part of many application scenarios, such as face recognition, pedestrian detection, remote sensing target detection, etc. Deep learning-based object detectors can be divided into four categories: two-stage and one-stage object detection according to the network type, and 2D object detection and 3D object detection according to the data type.  
The two-stage detection consists of two processes: candidate regions and object classification. In the candidate region stage, the object detector selects a region of Interest (ROI) in the input image containing the target object. In the object classification phase, the most probable ROI is selected, other rois are discarded, and the selected object is classified. Several common detectors are R-CNN, Fast R-CNN [10], and Faster R-CNN [11]. In contrast to two-stage detectors, single-stage object detectors create bounding boxes during object detection and perform classification operations on detected objects. Popular single-stage detectors include YOLO [12], SSD [13], and RetinaNet [14].  
For object detection, 2D image data is usually obtained through a 2D object detector. Literature [15] fuses data from radar and camera by learning sensor detection methods. With the booming development of deep learning, researchers are increasingly interested in 3D object detection. Complex-YOLO [16] uses YOLOv2-based Euler Region Proposal Network (E-RPN) to obtain 3D candidate regions.  
Multiple object tracking (MOT) is a computer vision task that aims to correlate detected objects across video frames to obtain the entire motion trajectory. In 2016, [PERSON] et al. [17] proposed the SORT algorithm (Simple Online and Realtime Tracking), which extracts target features in video sequences by combining convolutional neural network and recurrent neural network. Then, the Kalman filter algorithm is used to predict and update the target position, and the Hungarian algorithm is used to realize the data association of the same detection target. Subsequently, [PERSON] et al. [8] introduced deep learning into the SORT algorithm and proposed the DeepSORT algorithm. By learning the deep feature representation, the convolutional neural network was used to extract the target appearance features to replace the hand-designed features used in SORT. With the development of object detection technology, the original two-stage object detector Faster R-CNN is not so outstanding in the tracking effect in practical application scenarios because of its slow detection speed. Therefore, many replacement object detection algorithms have been proposed to improve the DeepSORT algorithm.
## 4 Model Implementation and Validation
### Statistical Interpolation in Sub-Regions
In fact, when the grid resolution changes from \(0.5^{\circ}\) to \(3.5^{\circ}\), the RMSEs increase by a factor of ten. However, the statistical interpolation exhibits a comparatively consistent RMSE trend, fluctuating around \(0.05\). While, at smaller grid sizes, the statistical interpolation yields higher errors than the nearest grid point interpolation. As the grid size exceeds \(2\) degrees, both interpolation methods produce comparable errors. When the grid size exceeds \(3\) degrees, the statistical interpolation proves to be significantly more accurate, yielding much lower errors in comparison to the nearest grid point interpolation. The insensitivity of the statistical interpolation to grid resolution, as demonstrated in Figure 8, expands its practicality and versatility, as it can be effectively employed with both high-resolution and low-resolution grids. In this study, we undertake a comparison of the proposed statistical interpolation method with that of kriging interpolation with various variogram models. In order to establish a baseline for comparison, we obtain the average SIC values within each region through the use of the nearest grid point interpolation method, utilizing the highest resolution reanalysis of SIC data. Subsequently, the average SIC values are determined through statistical interpolation and kriging interpolation, and the root means square errors are calculated and compared against the reference values. The results (in Table 2) reveal that the average SICs obtained through the statistical interpolation method exhibit lower RMSEs in all regions as compared to the kriging interpolation. Figure 8: The errors of different grid sizes and methods. Figure 9 depicts the October to December quarter mean reanalysis SIC (for reference), statistical interpolation, and kriging interpolation as an example. It is evident that the kriging interpolation produces the most pronounced deviation around the year 2010. Conversely, both the statistical and kriging interpolations produce results that are largely comparable, particularly after the year 2017. Hence, the proposed statistical interpolation can serve as an adequate alternative to the kriging interpolation. Furthermore, the statistical interpolation avoids the need for variogram model selection, yielding it more feasible to implement than the kriging interpolation.
## 6 Conclusions  
This work studied ridged waveguide aperture (i.e., open-ended waveguide elements) solutions as array radiating elements for Ka-band downlink (relative bandwidth 13.2%). Both cut-off and full-wave parametrical analyses have been carried out to find the optimal design parameters for each structure. ARC, coupling, scan loss and XPD have been determined for both linear and circular polarizations. The impact of cut-off frequencies on the achievable element bandwidth, the presence of blindness and the effect of the structure symmetry on the radiation pattern have been discussed. The validity of the results has been verified by comparing finite and infinite approaches in both CST and HFSS.  
Good performance has been achieved for both square and triangular lattices with the evanescent quadridge and three/six-ridge hexagonal waveguides, respectively. All elements show limitations in terms of coupling at high scan angles. Therefore, they will be more suitable for single-polarized DRAs operating up to 50\({}^{\circ}\) scan angles, with a consequent slight reduction in radiation efficiency due to the power dissipated on the matched load at the unexcited port. In this configuration, they will still provide a smooth frequency response (without spurious resonances/spikes).  
Future research on this topic includes the design of suitable transitions/discontinuities for better matching across the full scan angle. Moreover, modifications of the radiating aperture to improve both XPD and coupling should be investigated to achieve high-performance dual-polarized DRAs.  
Conceptualization, G.V.; data curation, C.V.-S. and R.M.-R.; formal analysis, C.V.-S. and R.M.-R.; funding acquisition, G.V.; investigation, G.A., O.A.P. and G.V.; methodology, G.A., O.A.P. and G.V.; project administration, G.V.; resources, O.A.P.; software, G.A.; supervision, G.A. and G.V.; validation, G.A., O.A.P. and G.V.; visualization, C.V.-S. and R.M.-R.; writing--original draft preparation, C.V.-S. and R.M.-R.; writing--review and editing, G.A., O.A.P. and G.V. All authors have read and agreed to the published version of the manuscript.  
Funded by the European Union, under ANTERRA 101072363 HORIZON-MSCA-2021-DN-01. Views and opinions expressed are however those of the author(s) only and do not necessarily reflect  
Figure 23: XPD convergence analysis between infinite approaches in CST and HFSS and a finite array simulation in CST.  
those of the European Union. Neither the European Union nor the granting authority can be held responsible for them.  
**Institutional Review Board Statement**: Not applicable.  
**Informed Consent Statement**: Not applicable.  
**Data Availability Statement**: The raw data supporting the conclusions of this article will be made available by the authors on request.  
**Conflicts of Interest**: The authors declare no conflicts of interest.
## 2 Materials and Methods
### Experimental Design and Evaluation Metrics  
The dataset was analyzed with several experiments. Binary classification was performed to demonstrate and analyze the effectiveness of the double-training method over single-training methods. First, single-training methods were trained with ML models to observe how the training strategies affected the players. Then, each single-training method was replaced with a double-training method to show the efficiency of the double-training method due to the progress of the player's abilities. This provided an analysis of how the players' performance improved after training sessions and how effectively single- and double-training methods were distinguished. Table 3 presents the number of training samples, test type, and details of the experiments performed in this study.  
All features were included in the training since removing any feature could cause the misinterpretation of the effects of training strategies on players. However, data normalization (Min-Max normalization) was applied to the whole dataset to scale the data between 0 and 1.  
The experiments were performed using 5-fold cross-validation to train and test all the samples and obtain more reliable results. The samples for each fold were selected randomly with a fixed random seed, and the final results were obtained using the mean scores of all folds. The sum of the confusion matrices of all folds was used to present the overall ability of the models.  
A grid search was performed independently within each cross-validation fold to determine optimal hyperparameters for each model. For every fold, optimal hyperparameters were determined and used for each model. This approach allowed the model to adapt to variations in the training data across folds and avoided data leakage. Therefore, a robust comparison of the models, independent of the data variety, was conducted.  
The experiments were evaluated using accuracy, sensitivity, specificity, and F1-score, which are the primary evaluation metrics for classification tasks.  
We used the sensitivity and specificity metrics to measure the sensing ability of the models for different classes, which were the training types of players. Equations (1) and (2) show the formulae of sensitivity and specificity, respectively:  
\[\text{Sensitivity}=\text{TP}/(\text{TP}+\text{FN}) \tag{1}\]  
\[\text{Specificity}=\text{TN}/(\text{TN}+\text{FP}) \tag{2}\]  
where TP, FN, TN, and FP represent the number of True Positive, False Positive, True Negative, and False Negative samples, respectively.  
We considered accuracy to determine the general classification ability of the models. However, since the data were slightly imbalanced, we used the F1-score to measure the more accurate ability of the models. The formulae of accuracy and F1-score are given in Equations (3) and (4):  
\[\text{Accuracy}=(\text{TN}+\text{TP})/(\text{TP}+\text{TN}+\text{FP}+\text{ FN}) \tag{3}\]  
\[\text{F1-Score}=\text{TP}/(\text{TP}+1/2(\text{FP}+\text{FN})) \tag{4}\]  
Additionally, we computed the 95% confidence intervals of accuracy, sensitivity, and specificity metrics for all experiments.
# Markov-Embedded Affinity Learning with Connectivity Constraints for Subspace Clustering
## 5 Optimization
### Optimization of Variable \(B\)  
Fixing the variables \(Z\) and \(W\), we can obtain the subproblem with respect to \(B\):  
\[\begin{split}\underset{B}{\text{min}}&\gamma Tr(W^{T} (Diag(B1^{*})-B))+\frac{\zeta}{2}\|Z-B\|_{F}^{2},\\ & s.t.\;diag(B)=0,B\geq 0,B=B^{T},\\ & I\preceq W\preceq 0,Tr(W)=k.\end{split} \tag{16}\]  
By calculation, Equation (16) is equivalent to  
\[\begin{split}\underset{B}{\text{min}}&\frac{1}{2}\|B- Z+\frac{\gamma}{\zeta}(diag(W)(1^{*})^{T}-W)\|_{F}^{2},\\ & s.t.\;diag(B)=0,B\geq 0,B=B^{T}.\end{split} \tag{17}\]  
For convenience in calculation, we define \(A\in R^{n\times n}\) and let \(A=Z-\frac{\gamma}{\zeta}(diag(W)(1^{*})^{T}-W)\), and then we substitute \(A\) into Equation (17):  
\[\begin{split}\underset{B}{\text{min}}&\frac{1}{2}\|B -A\|_{F}^{2},\\ & s.t.\;diag(B)=0,B\geq 0,B=B^{T}.\end{split} \tag{18}\]  
The closed-form solution for Equation (18) is [15]  
\[B=[\frac{(\Lambda+\Lambda^{T})}{2}]_{+}, \tag{19}\]  
where \(\Lambda=A-Diag(diag(A))\), \([\cdot]_{+}\) is the non-negative projection of the matrix, and \([L]_{+}=\text{max}(0,L)\).
## 5 Conclusions and Outlook  
This study addresses production planning challenges in the aircraft industry, given order uncertainty, and it proposes a two-step production strategy that incorporates GCA. This strategy helps maintain production stability while significantly shortening order lead times, allowing for rapid modifications from GCA to meet customer delivery requirements after order acceptance. Based on the possible generated scenarios, an SAA GCA optimization model is further proposed, to obtain a robust GCA that can deal with the uncertainty of orders. A comparison of the three production strategies, i.e., MTO, the SAA approach, and an empirical method, is presented, confirming that the proposed SAA GCA optimization approach can save 33.08% of the production time. Moreover, the robustness of the proposed approach was evaluated, and the results show that the GCA obtained by the proposed approach exhibited low variances in the reworking times, being robust for all the order combinations. It should be noted that this study primarily focused on production time minimization for specific aircraft batches, while not fully addressing real-world constraints,  
Figure 6: Distribution of reworking times for the robust solution and scenario optimal solutions in 100 scenarios.  
such as operational costs, workstation loads, or dynamic production cycle adjustments. Further research in these dimensions remains necessary.  
Conceptualization, X.Z. and K.Q.; investigation, X.Z.; methodology, X.Z. and B.N.; project administration, L.C.; supervision, J.X.; validation, X.Z.; writing--original draft, X.Z. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The data that support the findings of this study are available from the first author [PERSON], upon reasonable request.  
The authors declare no conflicts of interest.  
The following abbreviations are used in this manuscript:  
GCA General-Configuration Aircraft  
SAA Sample Average Approximate  
OEM Original Equipment Manufacturer  
MSN Manufacturing Serial Number  
BOM Bill of Materials  
MTO Make-To-Order  
AMAL Aircraft Moving Assembly Line
## 5 Simulation Results and Discussions  
The performance of the proposed sequential detector is evaluated using the system parameters designed for the LTE-D2D system with 5 MHz bandwidth and extended CP. The conducted simulation results is based on the OFDM simulator implemented using Matlab, which uses the 3 GPP LTE-D2D specifications. The carrier center frequency is 2 GHz, the subcarrier spacing is \(\Delta f=15\) kHz, the sampling time instant is \(T_{s}=1/7.68\) us, the FFT size is \(N=512\), and the GI length is \(N_{g}=128\). For data transmission, QPSK modulation is used. Specifically, the number of guard subcarriers is 212 and the number of occupied subcarriers is 300, leading to 4.5 MHz occupied bandwidth [1]. We adopt the extended pedestrian A (EPA), extended vehicular A (EVA), extended typical urban (ETU), and extended delay spread (EDS) channel models [32]. In the simulations, the channel is generated using [PERSON] model to simulate different fading rates and a different channel realization is generated for each subframe. The channel coefficients are time varying according to the Doppler frequency shift. We consider three Doppler frequencies to represent low, medium, and high speeds of 5 Hz, 70 Hz, and 300 Hz, respectively. Table 1 summarizes the channel profiles used in our experiments. For initial synchronization, we assume the D2D UE may have reference clock uncertainty of \(\pm 10\) ppm, which amounts to putting \(M=3\). For all simulations, the IFO for each iteration is distributed uniformly within \([-M,M]\) and the STO is randomly drawn from \([0,\tau_{max}]\) in samples, where \(\tau_{max}\) is a maximum STO. As for the RCJD scheme, we choose \(T=0.99\) in (13) so that \(N_{b,1}=N_{b,2}=13\) is obtained in (14) [23]. Unless mentioned otherwise, we let \(M=3\), \(N_{b,1}=N_{b,2}=13\), and consider the extended CP type.  
The accuracy of the proposed IFO estimation scheme is analytically measured in term of the detection error probability. As a performance measure, the overall probability of detection failure denoted by \(P_{o1}=\text{Prob}\{(\hat{e},\hat{n})\
eq(\hat{e},i)\}\) and the computational complexity of the presented detection methods are evaluated. As a global performance indicator, the probability that the IFO, SI, and sidelink ID are erroneously detected, denoted by \(P_{o2}=\text{Prob}\{(\hat{e},\hat{n},\hat{g})\
eq(\hat{e},i,j)\}\), is also evaluated.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Model** & **Channel Delay Spread (\(\mu\)s)** & **Doppler Frequency (Hz)** \\ \hline EPA & 0.41 & 5 \\ EVA & 2.51 & 70 \\ ETU & 5.0 & 300 \\ EDS & 28.58 & 5 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Channel profiles.
## 3 Benefits of Biochar Amendment in Green Roofs
### Runoff Retention
In a 27-day experiment (2010), the cumulative outflow volume from a biochar-amended GR was 26 L (14.1 mm) less than the GR without biochar. In addition to the studies presented in Table 2, there are other studies examining biochar to enhance the hydrological performance of GRs. Since other hydraulic parameters were assessed, those studies were not included in Table 2. [PERSON], et al. (2011) used soil columns to compare bare soil with biochar-based soil with different amendment ratios (5%, 10%, and 15% \(w/w\)). The ratio of 5% (\(w/w\)) was found to have the highest water storage. [PERSON], et al. (2011) further evaluated the ability of biochar in runoff reduction. The results indicated that soil amended with 10% (\(w/w\)) biochar was the optimal choice for rainwater management, offering the greatest peak outflow reduction and the longest outflow delay. However, 5% (\(w/w\)) biochar outperformed other amendments in terms of runoff retention and peak outflow delay. [PERSON], [PERSON], [PERSON], [PERSON] and [PERSON] (2010) identified the positive influences of coconut shell biochar on the hydraulic properties of GR substrates in improving both residual and saturated water contents. However, coconut shell fiber was also recommended to be used in conjunction with biochar to reduce the air-entry value for effective stormwater management. [PERSON], et al. (2011) further assessed the runoff retention of biochar-amended soil under both high and low (near-saturated) soil suctions. While a higher retention rate was recorded in soil with biochar due to greater mesopores under high suction, there was no difference between biochar-based soil and bare soil under near-saturated conditions (low suction). [PERSON] and [PERSON] (2011) observed the hydrological response of biochar-amended and non-biochar GRs to natural rainfall. The amendment rate of 20% fine biochar particles was superior to others in terms of accumulated runoff reduction. The average peak flow reduction rates were 55.9%, 60.9%, 63.6%, and 65.9% with the amendment rates of 0%, 10% coarse particles, 20% coarse particles, and 20% fine particles, respectively. Additional positive findings related to stormwater management can be found in studies (2010; 2011; 2012; 2013; 2014; 2015; 2016; 2017).
# Conceptual Framework of an Intelligent Decision Support System for Smart City Disaster Management
## 3 Methodology
### Data Collection and Storage  
Essentially, most structured data are collected by an open API. The data are provided in Comma-Separated Values (CSV) format on a public data portal operated by the Ministry of Administration and Security. Because big data systems use data from various organizations, the range of the collected data is wide. However, only the data considered necessary for the analysis must be used. In the case of image data that require real-time processing, satellite image information can be used to cover a wide area. Along with satellite images, Closed-Circuit Television (CCTV) cameras can collect image data anytime and anywhere, and they can be used to detect a fire early. Most CCTV cameras in South Korea are used for traffic or crime prevention, and their data storage cycle is only 30 days. Thus, their application to machine learning in the disaster field is limited. These elements are listed in Table 1, which describes the types of disaster, data sources, and organizations responsible for answering the questions of decision-makers and enabling them to make rapid and precise decisions.  
Figure 4: System using big data and AI for intelligent decision support.
## 4 Discussion
Different couplants were tested and several proved suitable, including kneadable putty-type pads, hot-melt adhesives (temperature control for the application is noted to be important) and silicone. The humid, alkaline test environment is highly corrosive; hence, waterproof sensors, cables and connections are essential. The fourth response describes several periodic inspection tests on a range of test objects. The effective monitoring duration was up to a few hours each time. For these applications, sensors were standard 150 kHz with integrated preamplifier and, under the test conditions (in any case indoor, one test object even in a clean room), these sensors achieved service lifetimes of between eight and eleven years. The fifth response deals with a process reactor continuously monitored for about one and a half years. The aim was to detect crack initiation and propagation during service. The sensor mount with an adhesive (type Duraseal 1531) that also served as couplant used magnet holders. Due to the surface temperature of the reactor of about 120 \({}^{\circ}\)C, the holding force of the magnet holders was reduced, confirming the effect discussed for the NdFeB magnets above. Sensor tests (AE sensor type ISR15 CA-HAT) were performed once per month with Hsu-Nielsen sources. Data transmission was via internet and TeamViewer software. A reduction in sensor sensitivity was observed (not quantified in the response) and attributed to elevated operating temperatures. Two sensors accidentally removed by someone else had to be replaced and additional sensors were mounted to compensate for a loss in sensitivity. No software updates were installed, and no components changed during monitoring. These examples show different types of problems, from sensor and connection to equipment failure. Both the service environment and possibly misuse or erroneous operation are causes of problems or failure. Sensor coupling and mounting have to be adapted to the test object and the service conditions, and periodic sensor tests are essential. In harsh service environments, signal transmission and data storage may be more easily prone to failure than the AE data acquisition system itself.