esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Seismic Performance Analysis for Different Earthquake Zone Effect/PGA  
The seismicity of a region is one of the significant parameters considered in both the design and evaluation of buildings. The magnitude of an earthquake plays an active role in determining the damage caused by the potential earthquake to structures. This is generally represented by PGA (peak ground acceleration). In this study, four different earthquake zones specified in the previous earthquake zone map of Turkiye were considered. Structural analyses were carried out using PGA values of 0.400 g for the I. degree earthquake zone, 0.300 g for the II. degree zone, 0.200 g for the III. degree zone, and 0.100 g for the IV. degree zone. These parameters, along with local soil classes, were chosen as variables in the analyses, while all other structural features were considered constants. The target displacements from different soil classes and changes in earthquake zones are shown in Table 7.  
As the earthquake hazard of the location of the structure increases, the target displacements expected from the structure also increase. In case the soil properties are good, the target displacements decrease. This shows that structures built on solid ground will have  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Soil Class**} & \multicolumn{2}{c}{**Target**} & \multicolumn{4}{c}{**PGA (g)**} \\ \cline{3-6}  & **Displacements (m)** & **0.100** & **0.200** & **0.300** & **0.400** \\ \hline \multirow{3}{*}{ZA} & DL & 0.0212 & 0  
less displacement under the effect of earthquake. The graphical comparison of these results is shown in Figure 9.
# MonoMR: Synthesizing Pseudo-2.5D Mixed Reality Content from Monocular Videos
## 1 Introduction  
Mixed reality (MR) head-mounted devices (HMDs) are display devices that can overlay virtual content in the real world, and a user can watch it on a free viewpoint. Specifically, compared with 2D media, such as photos and videos, 3D content synthesized from real-world objects has higher immersiveness. Hence, many methods for synthesizing MR content from real-world objects have been proposed [1, 2], and several systems are already commercialized [3, 4]. A common method in creating MR content is to place multiple monocular RGB or depth cameras around an object, synchronously capture the images, and then reconstruct 3D shapes of the object from the captured images. The content synthesized using this method is accurate and impressive and is utilized in various industry fields, such as sports broadcasting and entertainment. However, since most previous systems based on this method require multiple cameras and synchronization devices, configuring the system is complex, and the operating environment is restricted [2, 5, 6]. Thus, these factors make it difficult for end-users to use these systems. In addition, estimating the intrinsic and extrinsic parameters of the cameras from prerecorded videos is a challenging task. Some methods try to estimate the parameters using landmarks in images [7]. However, this limits the types of video that the method can process.  
Nowadays, we can record monocular videos through smartphones and digital cameras, and a large number of various monocular videos, such as sports, entertainment, and daily life, have been uploaded to the Internet. If there is an easy way for end-users to create MR content from these videos, various MR content can be provided without any special equipment. Moreover, the created content can be shared freely, similar to the present video-sharing websites on the Internet. To explore the usability and feasibility of MR content made from monocular content, we propose MonoMR, a system for synthesizing MR content from monocular videos. MonoMR is an end-to-end system (Figure 1) thatuses simple yet effective methods, different from the previous complex systems. In order to create MR content, first, the system detects people from a monocular video through a deep neural network (DNN), then calculates the homography matrix between real-world and image distances using an interactive user interface, and estimates pseudo-3D positions of the detected people. Next, person textures are extracted and placed at the estimated positions. Finally, the MR content is synthesized on these elements. The content synthesized by the proposed system is played on Microsoft HoloLens; the user can freely place the content in the actual world and view it from a free viewpoint. The paper's outline is summarized as follows:  
* We propose MonoMR, a system to synthesize MR content from single or multiple monocular videos.
* We evaluate the quantitative performance of our system.
* We assess the impact of the synthesized content through a user study.
* We develop suitable sample applications using the proposed system.
## 4 Materials and Methods
### Analysis of Quantitative Real-Time PCR (qRT-PCR)  
To evaluate the transcript profiles of floral scent-related genes, the spadices at stage S2 were harvested for RNA extraction. Total RNA was extracted from the above samples using TRIzol Reagent (Invitrogen, Carlsbad, CA, USA) following the manufacturer's protocol. First-strand cDNAs were synthesized from 1.0 ug total RNA using the HiScript II RT-PCR system (Vazyme, Nanjing, China), according to the manufacturer's instructions. Q-PCR reactions (20 microliter (uL) volume containing 1.0 uL cDNA as the template) were performed using the CFX connect Real-Time PCR System (Bio-Rad, Hercules, CA, USA)in standard mode with the KAPA SYBR FAST Universal qRT-PCR Kit (Kapa Biosystems, Wilmington, MA, USA). _Anthurium Actin_ was used as the internal reference gene to quantify the relative expression level of target genes [40]. Primer sequences for qPCR were designed using NCBI Primer-BLAST, and are listed in Supplementary Table S1. The relative expression levels of target genes were calculated by the \(2^{-\Delta\mathrm{ACT}}\) method. Three biological replicates were performed per experiment.
# Multi-Agent Modeling and Jamming-Aware Routing Protocols for Movable-Jammer-Affected WSNs
## 5 Conclusions  
Wireless Sensor Networks (WSNs) are crucial for mission-critical applications, and their reliability is vital. However, due to energy and cost constraints, WSNs are vulnerable to malicious attacks, which increases the reliability risks. Additionally, in the current research on WSNs being affected by jamming, there is no research on mobile jammers. To fill this gap, we propose a WSN modeling approach based on multi-agent modeling and jamming-aware routing protocol (JRP). The main contributions include: (1) using multi-agent modeling to study the impact of movable jammers on the reliability and performance of WSN for the first time; (2) the proposed routing protocol can guide sensor nodes to choose a jammer-avoiding path, considering the depth of the nodes and the degree of influence by jamming; (3) designing a maintenance mechanism to improve the robustness of the network.  
Simulation results show that the mobility of the jammer has a significant impact on the reliability and performance of WSN, requiring the design of local and global maintenance mechanisms for sensor nodes to maintain the normal operation of WSN. Although the global maintenance mechanism does not show the expected contribution to reducing the information transmission delay of WSN, it can effectively reduce the risk of over-concentration of relay nodes and improve the network robustness of WSN. Furthermore, the simulation experiment confirmed the impact of the number of jammers and the height of their deployment on WSNs. Attackers can significantly weaken the reliability and performance of WSNs by increasing the number of jammers or deploying them in the air.  
This paper only considered the jammer's mobility and did not design scenarios or strategies for the jammer's movement. The jammer mobile deployment strategy of the attacker and the jammer detection strategy of the defender can be discussed based on game theory. In addition, we only considered depth and jamming as the basis for routing decisions. However, due to the energy limitation of sensor nodes in WSN, their residual energy should also play a role in routing decisions, and nodes with heavier relay tasks consume energy faster. Therefore, how to integrate depth, energy, jamming, and other information will be the focus of future research.  
Conceptualization, B.X. and M.L.; methodology, B.X.; programing, B.X.; validation, B.X.; formal analysis, H.Z.; writing--original draft preparation, B.X.; writing--review and editing, M.L. and H.Z.; visualization, B.X. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.
## 3 System Implementation Based on LPA Synchronization Algorithm
### Multi-h CPM Receiver Implementation
#### 3.2.2 Matched Filter (MF) Banks  
The MF banks are detailed in Figure 3. It can be seen that a total of 32 MFs are required for the odd- and even-interval modulation indices using reduced-complexity methods, such as TPT, FPT with \(L^{\prime}=2\), and SSP with 4-value phase state partition. Each MF unit is built referring to Equation (27). The ROM stores the MF coefficients \(e^{-j(\eta_{0}(\hat{\mathbf{x}},\hat{\mathbf{x}})+\phi_{n})}\) for \(\hat{\mathbf{x}}=\{\hat{\mathbf{x}}_{n-1},\hat{\mathbf{x}}_{n}\}\), and the integral is implemented in discrete time by the complex multiplier and the integrate and dump (I and D) filter. Compared with the FIR-based MF, it reduces the usage of complex multipliers and adders and brings higher throughput. The MF outputs are selected according to the trellis with the odd- and even-symbol intervals and are corrected by the tilted phase.
## 3 Dataset Analysis

Table 1: Structure of the urban LoRaWAN dataset. Each row is a LoRaWAN message showing the receiving base station (BS) with RSSI value, message reception time (RX time), LoRa spreading factor, latitude and longitude at time of transmission. Figure 3: Geometrical Medians of Clusters obtained with kNN Technique. is based on the distance between points and does not require knowing the number of groupings in advance. Then, if the distance between two points is less than a custom threshold, for example 150 m, each point is clustered with its nearest neighbors. The function returns basic summary statistics, such as the number of clusters, maximum, minimum and the average cluster sizes, as well as numbers showing the clusters, see Figure 4, the centroid points, see Figure 5, and the geometric median points of each groupings. It also yields two text files that contain the coordinates of all centroid points and geometric median points. For every cluster, the output variables return the \((x,y)\) coordinates of the geometric median point and of the centroid, as well as the \((x,y)\) coordinates of each point that composes the cluster. See an example in Table 2.  
Figure 4: This figure shows the clusters obtained with the function for distance-based clustering of a set of XY coordinates in the coordinate range \(Lat\in[4^{\circ}23^{\prime}00^{\prime\prime},4^{\circ}24^{\prime}00^{\prime \prime}]\)–\(Long\in[51^{\circ}12^{\prime}00^{\prime\prime},51^{\circ}13^{\prime}00^{ \prime\prime}]\), which represents the subarea corresponding to the central area of the city of Antwerp. Figure 5: This figure shows the centroids of clusters obtained in the central Area of Antwerp: (see Figure 4) \(Lat\in[4^{\circ}23^{\prime}00^{\prime\prime},4^{\circ}24^{\prime}00^{\prime \prime}]-Long\in[51^{\circ}12^{\prime}00^{\prime\prime},51^{\circ}13^{\prime}0 0^{\prime\prime}]\). In [15], location experiments were performed in outdoor locations near a building. The Path Loss Factor (PLF) results show that it is possible to achieve good performance even in the presence of errors in the intersensory measurements.
# Implementation of Fault-Tolerant Control for
## 6 Experimental Results
### Experimental Design  
To reduce the high-frequency chattering of fault estimation of \(\widehat{\phi}\) in Equation (10) before it is used in the AFTC scheme, a simple low-pass filter was designed as  
\[\widehat{\phi}_{k}^{\mathit{filled}}=(1-v)\widehat{\phi}_{k-1}^{\mathit{ filled}}+v\widehat{\phi}_{k} \tag{54}\]  
where \(\widehat{\phi}_{k}^{\mathit{filled}}\) and \(\widehat{\phi}_{k}\) are the output and input, respectively, of the low-pass filter at the \(k^{th}\) step. The low-pass filter as seen in the next subsection allows the signal from the fault estimation (FE) to become smoother and is suitably applied to the AFTC schemes. However, the FE also increases the time delay of the feedback to the controller. To ensure that the smoothness and time delay are acceptable, \(v=0.05\) was selected.  
The related parameters of the FE in Equation (10) can be suitably selected as \(\alpha_{1}=2,\alpha_{2}=3,\alpha_{3}=0.3\), and \(\varepsilon=0.01\).  
The parameters of AFTC-SMC (Equation (46)) were suitably selected as \(c=diag(3;3;3),k_{1}=diag(50;110;90)\), and \(k_{2}=diag(50;100;90)\).  
The parameters of the proposed AFTC-SSMC (Equation (25)) were suitably chosen as \(c=diag(3;3;3),\alpha=diag(3;3;3),\)\(\kappa=diag(1.5;1.5;1.5),k_{1}=diag(50;110;90),\)\(k_{2}=diag(50;100;90),k_{3}=diag(30,30,30)\), and the \(sign\) function in the control law in Equation (46) and the AFTC-SSMC was replaced by the saturation function  
\[sat=\left\{\begin{array}{ll}sgn(s)&\quad if\ s>\lambda\\ s/\lambda&\quad if\ s\leq\lambda\end{array}\right.\]  
where \(\lambda=1.6\).
# Trajectory Tracking of Autonomous Vehicle Using Clothoid Curve
## 3 Tracking Control Method Using Clothoid Curve  
### Kinematic Model of Vehicle Lateral Control  
As shown in Figure 2, the relationship between the front wheel angle, wheelbase, and turning curvature of the vehicle can be obtained from the geometric relationship of the [PERSON] steering vehicle, which can be described as  
\[\kappa=\tan(\delta)/L, \tag{2}\]  
where \(\delta\) represents the front wheel angle, \(L\) represents the wheelbase, and \(\kappa\) represents the current turning curvature.  
The change rate of curvature with time can be described as  
\[\frac{dx}{dt}=\frac{dx}{ds}*\frac{ds}{dt}=\kappa^{\prime}*v, \tag{3}\]  
where s represents the distance traveled in a tiny time period. Suppose that the vehicle travels at a constant speed \(v\) within this distance, and \(\kappa^{\prime}\) represents the change rate of curvature with arc length.  
By substituting (3) with (2), we can obtain  
\[\frac{dx}{dt}=\frac{d(\tan\delta)}{L*dt}=\kappa^{\prime}*v, \tag{4}\]  
Then we can obtain  
\[\kappa^{\prime}=\frac{d(\delta)/dt}{L*v*\cos^{2}(\delta)}, \tag{5}\]where \(d(\delta)/dt\) represents the front wheel steering angular velocity. There is a linear proportional relationship between the angular velocity of the steering wheel and the front wheel, which can be described as  
\[\omega=k*d(\delta)/dt, \tag{6}\]  
where \(k\) represents the transmission ratio coefficient of the steering system, and \(\omega\) represents the angular speed of the steering wheel. Therefore, the relationship between steering angular velocity of the steering wheel and the change rate of the vehicle motion curvature can be expressed as  
\[\kappa^{\prime}=\frac{\omega}{k*L*v*\cos^{2}(\delta)}, \tag{7}\]  
As can be seen from Formula (7), since \(\omega\) has an upper limit constraint by the steering system, it determines that the path curvature of the actual turning cannot change suddenly. Therefore, the curve used to control vehicle driving shall ensure its curvature continuity. Otherwise, tracking error would be inevitably produced between the actual vehicle motion and the reference path.
## 5 Common Systems
### Motion Control  
Motion control is an integral part of any large research facility. This motion ranges from moving several tons with micrometer accuracy (e.g., undulators) to moving samples with nanometer accuracy and almost everything in between. There are several hundred motion axes with diverse applications. In order to minimize the variety of hardware used and thus also the requirements from the Controls group to support all the different platforms, standards were set at an early stage. Although PSI has a motion control system for the SLS, which is controlling over 1000 axes and has proven to be very reliable, special requirements for SwissFEL made it necessary to evaluate a new system to cover all these requirements.  
A wide range of requirements and specifications need to be met. These are among others:  
* Complex coordinated motions,
* Large distances between motion controller and motor,
* Interface to the timing system and
* Low cost per axis.  
In order to cover the widest possible range without having too many different systems, the motions were categorized into 3 different groups:  
1. High performance motion axis
2. Simple motion axis
3. Piezo positioners for sub-um motions  
After an evaluation phase of one year, the DeltaTau PowerBrick IMS motion control system was chosen for the high performance motion axis. Based on the PowerPMAC motion controller, the PowerBrick IMS motion control system is custom built by DeltaTau UK. The PowerBrick is used at several research facilities around the world. The following sections describe the various requirements.
## 8 Concluding Remarks  
In the present study, a novel hybrid machine learning model, namely, MBNBT, was proposed for the spatial prediction of landslides in the Mu Cang Chai District of Yen Bai Province. This model is a combination of two effective machine learning techniques of the MB ensemble and the NBT base classifier. The ORAE technique was used for the selection of landslide affecting factors. Model validation was done using statistical metrics: SEN, SPC, ACC, MAE, RMSE, and AUC. Performance of the proposed model was compared with other popular models, namely, MLPN, SVM, and single NBT. Results indicated that the proposed model, MBNBT, outperformed (AUC = 0.824) the MLPN (AUC = 0.804), SVM (AUC = 0.804), and NBT (AUC = 0.800) models. Thus, the proposed novel model, MBNBT, indicates a great and promising machine learning method for landslide spatial prediction which can also be applicable for other landslide prone areas. In this study, we used a ratio of 70/30 which is a common ratio applied for generating training and testing datasets for modeling of landslide prediction. However, we propose to evaluate the performance of models with different ratios of training and testing datasets in future works for obtaining another best ratio, if any.  
In the present study all the conditioning factors used for modeling had some effect on the prediction results. Thus, all these factors were considered in the analysis. However, for the removal of less important features, sensitivity analysis can be adopted depending on the requirement of the factor of safety of slope. It is also proposed to identify and classify different vegetation in the area which can prevent seepage and also act as anchor in stabilizing the ground mass.  
Conceptualization, B.T.P., P.T.N. and L.S.; Methodology, B.T.P., T.T.T., L.S., and I.P.; Software, B.T.P., T.V.P., L.S., A.S., and H.S.; Validation, D.T.B., H.S. and I.P.; Formal Analysis, P.T.N., T.T.T., and B.T.P.; Investigation, T.T.T., T.V.P., and T.B.V.; Resources, E.O., A.A., H.E., and P.T.N.; Data Curation, P.T.N., T.V.P.; T.B.V, T.T., and T.T.T.; Writing--Original Draft Preparation, A.S., B.T.P.; H.S., E.O., A.A., and H.E.; Writing--Review & Editing, I.P. and B.T.P.; Visualization, T.V.P., T.B.V., and T.T.; Supervision, B.T.P. and L.S.; Project Administration, B.T.P. and T.T.T.; Funding Acquisition, L.S.  
This research was supported by the Basic Research Project of the Korea Institute of Geoscience, Mineral Resources (KIGAM) funded by the Minister of Science and ICT.  
The authors are thankful to the Vietnam Institute of Geosciences and Mineral Resources for sharing data.  
The authors declare no conflict of interest.
## 5 The Suppression of Laser Intensity Fluctuation  
### Experimental Principles and Devices  
Figure 5 shows the scheme of the laser intensity fluctuation control system. The part in the green box is the single-atom capturing and observing system. The optical fiber output beam goes through the collimating lens and expended to a near-parallel beam with a beam diameter of 19 mm. Then the parallel beam passing through the focusing lens assembly (NA = 0.29), and become a tightly focused Gaussian beam with a beam wrist of 2.2 \(\upmu\)m. We use the same lens assembly to collect the fluorescence photons of Cs atoms and transfer them into the SPCM through an optical fiber.  
The part in the red box is the laser intensity fluctuation feedback loop. The first-order diffraction light of AOM 1 as the optical tweezer laser, the zero-order diffraction light as the \"energy storehouse\" of the first-order diffraction light. The first-order diffraction beam goes through the PBS, the transmission light as the sampling laser, and collected into the detector (New Focus 2051), and the laser intensity fluctuation reflected as the detector's output voltage. Then the output voltage signal enters the Proportion Integration Differentiation (PID) (Model SIM960, SRS, America) as the reference signal and set feedback-signal-related parameters (e.g., integral time, etc.) of the PID. Next, load the feedback signal on the driving voltage of AOM 1, we can control the diffraction efficiency of AOM 1 precisely. When the optical tweezer laser (the first-order of the diffracted light of AOM 1) intensity decreases (or increases), the output voltage of the sampling detector decreases (or increases) too. The PID output positive feedback (or negative feedback) signal will make the diffraction efficiency of AOM 1 increases (or decreases), which means the power of the first-order diffraction light goes up (or down), and of zero-order diffraction light goes down (or up). Finally, we can achieve the purpose of suppressing the fluctuation of laser intensity.  
Figure 7: Trap frequency and double frequency of the 1064-nm optical tweezer. Transfer efficiency goes an apparent decline when the modulation frequency is resonant or near-resonant to the trap frequency and its double frequency of the optical tweezer and reaches a minimum at resonance.
# Computational Dynamics of Multi-Rigid-Body System in Screw Coordinate
## 2 Kinematics of a Multi-Rigid-Body System  
This section presents the method for establishing the kinematics of a multi-rigid-body system based on screw coordinates. Velocity screw is defined to unify the angular velocity and linear velocity of a joint. With the velocity screw equation, the relative velocity of each joint can be obtained. Then, both displacement and acceleration are calculated by a one-step numerical method. Based on the linear superposition principle, the absolute displacement, velocity, and acceleration of each joint can be derived uniformly in screw coordinates.
# RS-YOLOX: A High-Precision Detector for Object Detection in Satellite Remote Sensing Images
## 2 Related Work  
### Mainstream Object Detection Algorithms  
The current mainstream object detection algorithms can be divided into two categories: two-stage and one-stage.  
As the name suggests, the detection of the two-stage object detection algorithm is divided into two stages: (1) to extract the pre-selected boxes from the input image, (2) to use the convolutional neural network (CNN) to classify and regress. The most famous two-stage object detection algorithm is the R-CNN series [10; 11; 12]. Although the two-stage target detection algorithm can achieve high detection accuracy, the detection speed is slow and it is not suitable for real-time detection.  
The one-stage algorithm directly extracts features from the input image and then calculates the category probability and position coordinates of the targets. Typical one-stage algorithms include SSD [13; 14] and YOLO series [15; 16; 17; 18; 19; 20]. In the past, the detection accuracy of the one-stage algorithm was often lower than that of the two-stage algorithm. However, with the proposal of YOLOv3 [17], the one-stage model surpasses the two-stage model in accuracy, while maintaining high-speed detection.