esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Study Area and Data Source
### Data Source  
This experiment utilizes Xiamen City's taxi trajectory and relevant geographic vector data, as shown in Table 1. The geographic vector data for Xiamen City encompasses its road network data, building outlines, and the boundaries of each district ([[http://www.rivermap.cn/](http://www.rivermap.cn/)]([http://www.rivermap.cn/](http://www.rivermap.cn/)) (accessed on 20 October 2022)). The geographic vector data is primarily utilized for data preprocessing and distance calculation in clustering algorithms. The selected dataset spans from 20 May 2020 to 7 June 2020. The data was sourced from the 2020 Digital China Innovation Competition ([[https://data.xm.gov.cn/opendata-competition/index.html#/](https://data.xm.gov.cn/opendata-competition/index.html#/)]([https://data.xm.gov.cn/opendata-competition/index.html#/](https://data.xm.gov.cn/opendata-competition/index.html#/)) (accessed on 18 October 2022)). The spatial dataset range is from 117.908136\({}^{\circ}\) E to 118.337183\({}^{\circ}\) E longitude and 24.425163\({}^{\circ}\) N to 24.817838\({}^{\circ}\) N latitude. During this period, the travel data of Xiamen's residents are densely distributed across the administrative districts. Approximately two million trajectory data points are generated daily, totaling around 1.6 GB, showcasing strong representativeness and research value. The data structure of taxi trajectory points in Xiamen City is illustrated in Table 2. During data collection for each trajectory, issues arise due to the instability of the sampling equipment, leading to problems like missing data points, data redundancy, and erroneous data. Hence, the initial step involves data cleansing, employing the average road interpolation method to fill in missing values between trajectory points due to the instability of sampling equipment. Redundant data and erroneous data points outside the designated research area are removed. Subsequently, longitude and latitude coordinates are adjusted to complete the coordinate transformation  
Figure 1: Study area and spatial distribution map of taxi trajectory data.  
process. After performing data cleansing on the Xiamen taxi trajectory data, it was observed that the cleaning efficiency reached 88.754%.
## 4 Conclusions  
This study aimed to use a MEMS vibrational power generator to detect a bridge's slab damage and pier scour. Eigenvalue analysis of a bridge slab and pier using FEM models, in which slab damage was modelled by a reduced Young's modulus and pier scour was modelled by fixed boundaries of an inclined depth. In the eigenvalue analysis, the change in modal frequency and mode shape caused by the slab damage and pier scour consolidated the proposal of this present study.  
Utilizing a electrostatic vibration-based MEMS-MEH, the change in natural frequency and amplitude could be monitored by the change in the generated power. A power generation index to detect the deteriorated panel in a slab was proposed and its applicability was verified with the numerical models. As for detecting uneven pier scour, a reference-free method was proposed with a MEMS vibrational power generator installed at multiple points of equal height at the cap and monitoring unequal generated powers. When an unequal power generation distribution is found, it may indicate the uneven pier scour.  
The slab and pier models studied herein were simplified and the vibrations used to calculate the MEH generated energy were simply harmonic. The conclusions drawn above could apply only to the bridge types and damage patterns similar to those in this study. More complicated bridge types, models, and vibration patterns would help in generalizing the proposed methods.  
Conceptualization, K.H. and T.S.; formal analysis, K.H. and K.-C.C.; funding acquisition, T.S. and K.H.; investigation, K.H. and H.M.; methodology, K.H. and K.-C.C.; visualization, K.H. and H.M.; validation, K.H. and T.S.; supervision, T.S.; writing--original draft, K.H.; writing--review and editing, K.-C.C. and T.S. All authors have read and agreed to the published version of the manuscript. This work was supported by JSPS KAKENHI Grant Number 20K04641. The authors declare no conflict of interest.
# Camera-Adaptive Foreign Object Detection for Coal Conveyor Belts
## 3 Methodology
### Overview of the Method  
As described in Section 1, the key to camera-adaptive foreign object detection lies in addressing viewpoint variations and leveraging stable context cues on the conveyor belt. We introduce four additional modules Multi-View Data Augmentation (MVDA), Context Feature Perception (CFP), Conveyor Belt Area Loss (CBAL), and Varifocal Loss (VFL) [10]--on top of Deformable DETR [27], as shown in Figure 2.  
The Deformable DETR mainly includes backbone, encoder, decoder, and prediction heads. The backbone processes the raw image \(X\) and generates multi-scale features \(I\) using a ResNet50 architecture. The encoder utilizes the multi-head self-attention mechanism to enhance the feature representation and outputs \(Z_{final}\). The decoder integrates query information to refine the targets' location and category \(Q=\{Q_{box},Q_{target}\}\). The prediction heads are used to predict both location and category.  
These introduced four modules are each integrated at different stages of the pipeline to improve the network's robustness to camera perspective changes, enhance context-based discrimination, focus on belt-region targets, and boost prediction accuracy. Firstly, we apply the MVDA model on image \(X_{i}\), generating an augmented sample set \(D^{\prime}=\{X_{i}^{\prime},B_{i^{\prime}}^{\prime},Y_{i}^{\prime}\}_{i=1}^{n}\) to improve the model robustness to camera view changing. Secondly, all foreign objects should be enclosed by coals (on the conveyor belt). Thus, we add the surrounding features of the potential foreign objects to reject the false detection outside the conveyor belt area by the CFP model. Thirdly, we design a CBAL to enforce the model's focus on the belt area and reduce interference from irrelevant objects outside the conveyor belt area. The labeling work is light as we only need to label the belt area per camera, extra. Finally, we add Varifocal Loss to obtain more accurate results.
## 3 Application of the Proposed Methodology
### Selection of Preliminary Parameters of the Machine  
This is the next step of the proposed methodology (see B on Figure 2). In order to understand the orientation of the individual axes of machine movement, it is advisable to use the nomenclature, see Figure 5.  
The following is a description of the marking of the X-, Y-, Z-, and W axes. The X-axis describes the movement of the gantry on the bed. The Y-axis describes the movement of the headstock on the cross piece. The Z-axis describes the extension of the slide from the headstock and the W-axis describes the movement of the cross-piece on the column. The basic parameters of the machine were selected and are shown in Table 1.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Parameter** & **Unit** & **High Speed Solution** & **High Torque Solution** \\ \hline Power & [kW] & & 119 \\ \hline Spindle speed & [rpm] & 4000 & 3000 \\ \hline Torque & [Nm] & 3000 & 8500 \\ \hline Clearance under spindle & [mm] & 7000 & 7000 \\ \hline Clearance between column & [mm] & 9000 & 9000 \\ \hline X(Gantry)—axis & [mm] & & \(n\times 2000\) \\ \hline Y (Headstock)—axis & [mm] & & 10,000 \\ \hline Z (Ram)—axis & [mm] & & 2000 \\ \hline W (Cross-piece)—axis & [mm] & & 5000 \\ \hline Feed rates & [mm/min] & & 0.5–15,000 \\ \hline Ram extension & [mm] & & 2000 \\ \hline \end{tabular}
\end{table}
Table 1: Basic parameters of the multifunctional portal centre.  
Figure 5: The nomenclature of machine tool axes.  
The parameters were chosen based on selected representative workpieces, technological operations (see Sections 3.1 and 3.3), and with respect to the parameters of existing machines of different manufacturers (Waldrich Siegen (Siegen-Germany), PAMA S.p.A. (Roverato-Italy), SCHIESS (Aschersleben-Germany), TOS KURIM--OS, a.s. (Kurim-Czech Republic), FERMAT CZ s.r.o. (Prague-Czech Republic), etc.).
## 2 Background  
### Integrated Simulator for Evaluation of C-ITS  
Simulators have been used by researchers in the field of transportation to evaluate vehicle systems. Traffic simulators model large-scale transportation scenarios that are difficult to implement in real environments along with the movement of the vehicles, and thus it is possible to evaluate various scenarios involving transportation systems. Driving simulators model parameters such as the vehicle dynamics, driver model, and ECUs to evaluate the performance of vehicle control systems. Studies on VANETs have recently attracted considerable attention, and thus an evaluation of V2X communication systems using network simulators has been carried out. However, the use of a single simulator does not allow the evaluation of a sophisticated vehicle system when considering both the vehicle control system and the V2X communication system. Therefore, numerous studies have evaluated sophisticated vehicle systems, particularly C-ITS, through the integration of different simulators. There is commercial equipment that provides a simulation environment to develop and evaluate C-ITS considering both the vehicle control system and the V2X communication system; this is a dSPACE solution of closed-loop HIL testing of V2X-based applications [20]. It provides a real-time HIL simulation environment to test V2X applications utilizing HIL simulator and V2X communication devices. However, without the combination of a network simulator, there are limitations to the hierarchical behavior of VANET protocols and VANET algorithm studies such as routing and scheduling algorithms.  
Of the many C-ITSs, we evaluated the cooperative eco-driving systems of CHEVs by developing an integrated simulator. The eco-driving system of the HEV calculates a situation-appropriate speed profile to reduce the acceleration and deceleration as much as possible while driving, which enables the motor and engine to efficiently consume energy through ECU functions such as the optimal power distribution and regenerative braking. To improve the fuel efficiency of HEVs, it is therefore important to design ECU functions properly. Many existing cooperative eco-driving systems have been evaluated using a driving simulator or vehicle simulator, assuming successful V2X communication [21; 22; 23]. However, V2X communication characteristics vary depending on the driving environment and service type, and thus consideration of V2X communication characteristics is essential to evaluate the performance of a cooperative eco-driving system. Herein, a simulation testbed for a CHEV is proposed by integrating a driving simulator with a VANET simulator, which allowed us to design and evaluate a cooperative eco-driving system in an environment similar to that of a real C-ITS.
## 3 Background and Previous Related Work
### Dlt
#### 3.3.5 Cryptographic Key Derivation Functions (KDFs)  
KDFs are used to generate cryptographic keys from user-provided inputs, such as passwords or passphrases. In DLT, KDFs increase the security of keys, making it harder for attackers to guess or brute-force them. Examples of KDFs used in DLT include scrypt, bcrypt, and Argon2.
# A Lidar-Inertial Navigation System for UAVs in GNSS-Denied Environment with Spatial Grid Structures
## 3 Experiment  
### Experimental System Construction  
The hardware used in the navigation system experiment included a surround lidar, a six-axis inertial measurement unit, and an onboard computing unit. The surround lidar was VLP-16, with a maximum detection distance of 100 m and an accuracy of 3 cm. It has a field of view angle of 360\({}^{\circ}\) horizontally and 30\({}^{\circ}\) vertically. During the experiment, the rotational speed was set to 600 rpm, the corresponding horizontal resolution was 0.2\({}^{\circ}\), and the angle between adjacent lines of the 16 scan lines was 2\({}^{\circ}\).  
The proposed navigation system is implemented in C++ language. The software was developed based on the ROS architecture and runs on the Ubuntu18.04 operating system. The computing unit used was Intel NUC8 and the CPU was i5-8259U. The navigation system was mounted on a quadrotor UAV and the experiments were carried out in a GNSS-denied environment with space grid structures. The experimental system equipment is shown in Figure 6.
# Automatic Building Height Estimation: Machine Learning Models for Urban Image Analysis
## 6 Case Study: Extraction of Building Height in Cityscapes
### Discussion  
The image segmentation results are promising, achieving a high performance in the Citiscapes fine val set [26]. This opens the way to further automatisation of the detection of buildings and their parts and features.  
The results obtained in the case study are satisfactory for the estimation of the building exposure attribute \"height\", which is defined as the height above ground in terms of the number of storeys (e.g., a building being three storeys high). The height class is usually defined as a range of storeys or floors, such as 1-3, 4-7 and \(>\)8 [4,47]. Thus, the determination of seismic exposure classes can be performed with one-floor-precision building height estimates. The proposed method fulfils this requirement with a maximum error of around one floor.  
These results are comparable with those of other methods, such as those using satellite imagery with absolute errors of around 2-5 m, which usually require SAR data [13] or multi-view imagery [14,15] and are therefore more complicated.  
Street view methods that use machine learning can achieve a lower relative error of 5% but require more data such as building footprints [17,18] or camera heights [19].  
The proposed method works without the need for extra parameters, allowing for the use of images from the Internet or images captured by mobile phones for other purposes as long as the problem associated with an insufficiently diverse dataset can be overcome.
## 3 Beamforming Techniques
### Beamforming with Optimal Technique  
The optimal beamformer is also called a minimum variance distortionless response (MVDR) beamformer. The expression of the constrained processor weight is shown in Equation (1).  
\[W=\frac{R^{-1}\,S_{0}}{S_{0}^{H}\,R^{-1}\,S_{0}} \tag{1}\]
## 2 Modeling and Finite Element Analysis
### Periodic Boundary Conditions and Elastic Property Calculations
\[E_{x}=\frac{\sigma_{x}}{\bar{\epsilon}_{x}}=\frac{F_{x}/A_{x}}{\Delta L_{x}/L_ {x}} \tag{5}\]  
\[v_{xy}=-\frac{\bar{\epsilon}_{y}}{\bar{\epsilon}_{x}}=-\frac{\Delta L_{y}/L_{y }}{\Delta L_{x}/L_{x}} \tag{6}\]  
\[v_{xz}=-\frac{\bar{\epsilon}_{z}}{\bar{\epsilon}_{x}}=-\frac{\Delta L_{z}/L_{ z}}{\Delta L_{x}/L_{x}} \tag{7}\]  
Likewise, to calculate shear modulus \(G_{xy}=G_{yx}\), a pure-shear state is imposed on the RVE by displacing the face \(x=L_{x}\) in the \(y\)-direction by an amount \(\Delta B_{xy}\), and the face \(y=L_{y}\) face in the \(x\)-direction by an amount \(\Delta B_{yx}\). This generates resultant shear forces on these faces (\(F_{xy}\) and \(F_{yx}\), respectively). As before, these deformations and resultants are used in the standard formula for shear modulus (8). Figure 4 demonstrates the deformation in these two cases. \[G_{xy}=\frac{\bar{\tau}_{xy}}{\bar{\tau}_{xy}}=\frac{F_{yx}/A_{y}}{\Delta B_{ xy}/L_{x}+\Delta B_{yx}/L_{y}} \tag{8}\]  
For convenience, the constraints were generated, and requisite analyses were performed using an Abaqus plugin developed by [PERSON] et al. [40]. This results in 12 separate properties for the gyroid, however, there is little difference in the longitudinal moduli (\(E_{x}\), \(Ey\), and \(E_{z}\)), or the shear moduli (\(G_{xy}\), \(G_{xz}\), and \(G_{yz}\)), or [PERSON]'s ratios (\(v_{xy}\), \(v_{yz}\), \(v_{xz}\), \(v_{zx}\), \(v_{yz}\), and \(v_{zy}\)). The gyroid exhibits cubic symmetry, meaning that the elastic behavior is fully defined using three independent constants: E, \(\
u\), and G. Any difference between the different directional properties arises from mesh imperfection. For this reason, the several properties are averaged to one constant each (see (9)-(11)). \[E=E_{x}=E_{y}=E_{z} \tag{9}\]  
\[G=G_{xy}=G_{xz}=G_{yz} \tag{10}\]  
\[\bar{\epsilon}_{xx}=\frac{1}{V}\int_{S}(u_{x}n_{x})dS=\frac{\Delta L_{x}}{L_{ x}} \tag{3}\]0  
Additional discussion on the theory behind periodic boundary conditions is found in [41; 42; 43]. \[\bar{\epsilon}_{xx}=\frac{1}{V}\int_{S}(u_{x}n_{x})dS=\frac{\Delta L_{x}}{L_{ x}} \tag{3}\]1 Examples deformations of the gyroid representative volume element (RVE) (adapted from [40]). (**a**) RVE with applied longitudinal strain; (**b**) RVE with applied shear strain. Figure 5 shows the deformation of a representative gyroid solid model under the different loading modes. It is clear that the stress is not distributed uniformly through the structure, but rather concentrates in certain locations. The bulk elastic properties, however, are calculated from the average stresses and strains of the entire RVE.
## 3 Algorithms and Tools
### Word Embedding Models
#### 3.2.2 Siamese Bidirectional Transformer  
BERT is a pre-trained bidirectional transformer that exploits a self-attention mechanism and training on a really large corpus to learn a general representation of words. Its training is often unsuitable for common hardware architectures because it is a very deep neural network composed by a stack of multiple heads encoders followed by multiple decoders. Since BERT is trained on different and general corpus (from Wikipedia contents to the Book corpus), it should not suffer the so-called domain shift that occurs when a pre-trained model for an original domain is used in another target domain. However a fine-tuning is suggested to learn domain-specific words. A well-known issue of the BERT network structure is that no independent sentence embeddings are computed, which makes it difficult to derive directly sentence embeddings from BERT. To bypass this limitation, researchers passed single sentences through BERT and then derive a fixed sized vector by either averaging the outputs (similar to average word embeddings) or using the output of the special CLS token (see the discussion in [35; 36]). For this reason, we used a variant of BERT that enables building a semantic space by fine-tuning a Siamese BERT framework to maximize document similarity that is a similar task used to train Word2 Vec. This framework is called SBERT and it outperforms other BERT-based sentence embeddings techniques [7]. Since it is based on a Siamese pre-trained model we cannot select a vector size but we used the default value equal to 700. SBERT structure is a concatenation of two pre-trained BERTmodels that process the two documents independently followed by an output layer which tries to maximize the Cosine Similarity cost function between the two documents.
# Development of a Path Tracker Based on a 4 WS Vehicle for Low-Speed Automated Driving Systems
## 3 Path-Tracker Design  
Autonomous vehicles require a path-tracking control algorithm to implement the driving strategies derived from the perception and decision-making components of the autonomous driving system [20,21]. The previously mentioned path-tracking control algorithm is also called a path tracker. Path trackers are generally classified into geometry-based, kinematics-based, and dynamics-based algorithms. The geometric path-tracking algorithm is designed to control the lateral movement of the vehicle based on the geometric relationship between the vehicle and the path. In this study, since the focus was on a low-speed environment, which is less affected by vehicle dynamic characteristics, a simpler geometry-based algorithm was used compared to other types of path trackers.
## 3 Results
### Experiment 1
The trends exhibited by the measured variables in the mixed culture were more similar to _Prorocentrum_ sp. (Figure 5) The Chl-a content ranged from 5.50 \(\mu\)g L\({}^{-1}\) (\(\pm\)0.13) on Day 1 to 58.28 \(\mu\)g L\({}^{-1}\) (\(\pm\)1.07) on Day 16 of the DFB experiment, with a lag phase in the first week during which the values ranged from 5.50 \(\mu\)g L\({}^{-1}\) to 8.89 \(\mu\)g L\({}^{-1}\) (\(\pm\)0.34). The Fv/Fm ratio varied between 0.44 (\(\pm\)0.02) and 0.41 (\(\pm\)0.01) during the first eight days, before decreasing to 0.23 (\(\pm\)0.02) on Day 16. The \(\sigma\)PSII at 440 nm ranged between 11.06 (\(\pm\)0.20) on Day 1 and 11.32 (\(\pm\)0.25) on Day 16, with relatively fluctuating values and a peak of 15.40 (\(\pm\)0.23) on Day 10. The increase in Chl-a was lower in the -B12 treatment than in the DFB treatment, with values ranging from 6.25 \(\mu\)g L\({}^{-1}\) (\(\pm\)0.08) on Day 1 to 43.86 \(\mu\)g L\({}^{-1}\) (\(\pm\)0.98) on Day 16. The Fv/Fm ratio ranged from 0.43 (\(\pm\)0.01) on Day 1 to 0.34 (\(\pm\)0.02) on Day 16, exhibiting relatively high variability during the first week. In contrast to the DFB treatment, \(\sigma\)PSII at 440 nm increased from 10.81 (\(\pm\)0.22) on Day 1 to 14.04 (\(\pm\)0.25) on Day 16, exhibiting considerable fluctuations. The Chl-a content ranged from 5.29 \(\mu\)g L\({}^{-1}\) (\(\pm\)0.06) on Day 1 to 40.27 \(\mu\)g L\({}^{-1}\) (\(\pm\)0.97) on Day 16 in the CTRL treatment, whereas Fv/Fm varied between 0.41 (\(\pm\)0.02) on Day 1 and 0.37 (\(\pm\)0.02) on Day 16, presenting a similar trend to that observed in the DFB treatment, and \(\sigma\)PSII at 440 nm increased from 12.16 (\(\pm\)0.22) on Day 1 to 15.98 (\(\pm\)0.27) on Day 16, peaking at 17.25 (\(\pm\)0.43) on Day 8 (Figure 5). Figure 5: Experiment 1. Changes in Chl-a concentration (\(\mu\)g L\({}^{-1}\)), Fv/Fm, and \(\sigma\)PSII (nm\({}^{2}\)) in the mixed culture of _Phaeocystis antarctica_ and _Prorocentrum_ sp. under different treatments. The Period/Hex ratio was used as a proxy in the HPLC analysis to determine the dominant phytoplankton, i.e., _P. antarctica_ or _Prorocentrum_ sp., in the three treatments (Table 1). This ratio was recorded as 4.25 (\(\pm\)0.01), 4.44 (\(\pm\)0.02), and (\(\pm\)0.02) in the CTRL, -B12, and DFB treatments.
## 4 Experiments and Results
### Quantitative Performance Evaluation of the Framework
#### 4.1.3 Congestion Visualization  
To visualize the density of pedestrian foot traffic over time, the Grand Central Station crowd flow is shown as density heatmaps in 15-min (1125 frames at 1.25 FPS) intervals in Figure 8. Observing Figure 8, we identify a number of significant qualitative observations on the locations where crowds tend to gather based on density heatmaps. In the first 45 min of the video, there appear to be fewer people in the train station, however, in the second half, a greater number of pedestrians can be seen. Regardless of the number of passengers  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Predictor** & **MSE** & **MAE** \\ \hline Baseline 1 (T-GCN) & 0.329 & 0.417 \\ Baseline 2 (A3T-GCN) & 0.320 & 0.403 \\ GCN-GRU & **0.258** & **0.354** \\ \hline \hline \end{tabular}
\end{table}
Table 3: MSE and MAE of crowd volume prediction models.  
Figure 7: A sample sequence of the ground truth and predicted crowd flows at each of the nine egress regions at the Grand Central Station. The crowd volume, normalized to \([-1,\ 1]\) is plotted on the Y-axis.  
in the station, the heatmaps depict a large crowd forming around the ticket booth at almost all times. This indicates the necessity for faster ticket services. In addition, as the crowds become heavier, we observe more people congregating around the information center, possibly seeking assistance or direction guides. Overall, we observe that the train station requires additional services, especially as the number of passengers increases.