esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Method: Automated Target Detection  
The identification of lakes and reservoirs that can be monitored by satellite altimetry needs information on the satellite's ground track on the one hand and knowledge of the location and extent of water bodies on the other hand. The latter can be taken from existing data sets such as GLWD or Hydrosheds [24]. These provide constant water body shapes without considering any time-dependent variation due to seasonal or long-term changes. Alternatively, satellite images can be used to derive time-variable land-water or water occurrence masks, as are available from optical Landsat and Sentinel-2 images [25], for example. Those data have already been used to derive inventories of global lakes; e.g., by [PERSON] et al. [26] and [PERSON] et al. [27].  
In this study, the Global Surface Water dataset (GSW) from [PERSON] et al. [21] (see Section 2.3) is used as input data. However, the developed procedure is able to handle any arbitrary water occurrence mask in raster format; e.g., DAHITI occurrence masks [8]. Furthermore, minimal manual interaction is required, and it is therefore suitable for large amounts of data--e.g., the MRB--and can be rerun easily if an updated version of the input water occurrence masks is available. The possibility to set parameters according to particular needs enables the application of the procedure in variable scenarios; e.g., studies of water bodies with permanent water coverage or those that are only seasonally flooded.  
The main idea of this work is to identify connected water areas and to define the water body outlines based on a pre-defined threshold of water probability, which can later be transferred to other probability levels. The assumption is that all areas of one water body have the same height. Thus, the approach is suitable for lakes and reservoirs but not for rivers. Due to the slope of the river, each satellite's overflight defines a new target (i.e., a virtual station) at the crossing between the satellite's ground track and the river. For that reason, the developed approach is only applicable to lakes and reservoirs.  
The developed algorithm consists of two major steps. First, individual water bodies are identified by applying several morphological operations that are widely used in image processing [28]. This is described in more detail in Section 3.1. In the second step (described in Section 3.2), isolated occurrence masks based on the GSW data are processed for each identified water body to analyze their coverage by satellite altimetry data at different water occurrence thresholds. A flowchart illustrating the developed methodology can be found in the Appendix A (Figure A1).
# A Method of Intelligent Driving-Style Recognition Using Natural Driving Data
## 6 Conclusions  
### Main Conclusions  
This paper constructs a complete intelligent method framework for classifying and recognizing the driving style of drivers. Based on driving trajectory data, a large number of feature indicators representing driving styles were selected, and most of the information was retained through dimensionality reduction. The experimental results show that this method can effectively and accurately identify driving styles, with a recognition accuracy rate of over 97%. Through accurate classification and recognition of driving styles, we can not only provide personalized training and guidance for drivers to reduce driving risks but also provide strong support for the development of an auto-drive system and an intelligent transportation system, making them more intelligent, safe, and efficient.  
1. Data Preprocessing and Feature Extraction: Based on the NGSIM dataset, we first categorize the dataset into car-following and lane-changing groups. The Savitzky-Golay filtering algorithm is applied to smooth the original data. Driving-style characteristics for car-following and lane-changing states are then extracted to obtain a high-dimensional driving-style characteristic parameter matrix. Subsequently, correlation analysis and PCA are employed for dimensionality reduction and to optimize these parameters.
2. Clustering and Classification: Using the optimized driving-style characteristic parameters, the K-means algorithm clusters the data for car-following and lane-changing states. The optimal number of clusters is determined to be three based on the Sum of Squared Errors (SSE) and the silhouette coefficient, categorizing driving styles into conservative, moderate, and radical. A comprehensive driving-style classification for drivers is derived based on the principle of minimum distance, validating the robustness of the method.  
Model Development and Evaluation: A driving-style recognition model is established using the Support Vector Machine (SVM), with grid search and cross-validation techniques applied to determine the optimal hyperparameters. The SVM model demonstrates superior performance compared to Random Forest (RF) and K-Nearest Neighbors (KNN) algorithms, achieving recognition accuracies exceeding 97% for both car-following and lane-changing states. In addition, comparing the recognition accuracy of driving-style characteristic parameters before and after dimensionality reduction shows that dimensionality reduction effectively improves recognition accuracy.  
In conclusion, the intelligent driving-style recognition method proposed in this paper, based on natural driving data, effectively identifies driving styles. Future research may focus on finer granularity in driving state classification and the development of more precise and robust recognition algorithms to further improve the accuracy and stability of driving-style identification.
# Automated Multiple Concrete Damage Detection Using Instance Segmentation Deep Learning Model
## 1 Introduction
Although studies have shown the successful application of deep learning techniques to detect damage from images, they have rarely shown the detectability against surrounding objects. This study proposes a concrete damage detection method using an instance segmentation deep learning model called Mask R-CNN (Mask and Region-based Convolutional Neural Network) [33]. The scope of this study is on detecting multiple types of concrete damages and does not include the quantification of detected damages, which has already been proposed in the previous paper by the authors [17]. This method alters conventional visual inspection in the field environment with surrounding objects. Mask R-CNN is a combined deep learning model of Faster R-CNN [31] and a FCN [32]. Faster R-CNN is an object detection model that detects objects in an image by locating the bounding boxes of the objects, and FCN is a type of semantic segmentation model that detects objects in a form of segmentation by classifying every pixel in an image. As a combination of Faster R-CNN and FCN, Mask R-CNN achieved high accuracy in detecting 20 types of objects after training with more than 200,000 images in the COCO dataset [34]. In this study, a pretrained Mask R-CNN is fine-tuned using a concrete image dataset to detect four types of major concrete damage. Beyond the detection of concrete cracks [17], spalling, rebar exposure, and efflorescence are selected as types of damage to be detected by the Mask R-CNN model to alter the conventional visual inspection of concrete structures. This study briefly introduces the overall structure of the Mask R-CNN to present how to localize an object in an image and how to segment the localized object. The next section explains the training procedure of the Mask R-CNN in detail, including the labeling of the training dataset considering typical shapes of four types of concrete damage (cracks, spalling, rebar exposure, and efflorescence) and fine-tuning of the Mask R-CNN parameters. Then, the performance of the trained Mask R-CNN is verified for images taken in the field environment with surrounding objects.
# Analysis and Demonstration of First Cross-Support Interferometry Tracking in China Mars Mission
## 2 Theorem and Methods
### Station Clock Estimation
#### 2.3.3 Analysis  
The algorithm described above has lower computation burden compared to iteration correlation with refreshed delay model (named traditional method). The key computation burden of our algorithm and traditional method are listed in Table 1; N represents FFT point, F is the number of FFT contained in one sub integration time, and M is the number of sub integration time.  
Figure 5: Flow scheme of clock estimation method.  
Computation burden is obviously decreased, since only once N-point FFT is applied in the flow chart of the algorithm in each iteration, and computation quantity of traditional method could be much larger than the fast clock estimation method, usually by a few orders.
# A Chaotic Compressive Sensing Based Data Transmission Method for Sensors within BBNs
###### Abstract  
Body to body networks (BBNs) are a kind of large-scaled sensor network that are composed of several wireless body area networks (WBANs) in the distributed structure, and in recent decades, BBNs have played a key role in medical, aerospace, and military applications. Compared with the traditional WBANs, BBNs have larger scales and longer transmission distances. The sensors within BBNs not only transmit the data they collect, but also forward the data sent by other nodes as relay nodes. Therefore, BBNs have high requirements in energy efficiency, data security, and privacy protection. In this paper, we propose a secure and efficient data transmission method for sensor nodes within BBNs that is based on the perception of chaotic compressive sensing. This method can simultaneously accomplish data compression, encryption, and critical information concealment during the data sampling process and provide various levels of reconstruction qualities according to the authorization level of receivers. Simulation and experimental results demonstrate that the proposed method could realize data compression, encryption, and critical information concealment for images that are transmitted within BBNs. Specifically, the proposed method could enhance the security level of data transmission by breaking the statistical patterns of original data, providing large key space and sensitivity of the initial values, etc.  
compressive sensing; information concealment; chaos theory 2022225909. [[https://doi.org/10.3390/s22155909](https://doi.org/10.3390/s22155909)]([https://doi.org/10.3390/s22155909](https://doi.org/10.3390/s22155909))
## 3 The Monte Scarpino Landfill and the SRS Test-Bed
### The SRS Test Bed  
The planned network will be eventually composed of 18 sensors distributed in 6 different sites enclosed in the landfill's perimeter. This would allow for an estimated coverage of 9.8 km\({}^{2}\), providing the capability to monitor the evolution of the rainfall also on areas adjacent to the landfill. In Figure 3b, a map of the geographical location of the SRS stations and of the satellite connection tracks is shown. At present, only eight sensors are fully functional. Table 1 summarizes the set of sensors adopted for the analysis in the following, with their population of satellites.  
In Table 1, the name \"Turksat 42E\" refers to a constellation of two satellites (namely, Turksat 3A and Turksat 4A), co-located at the 42\({}^{\circ}\) East orbital position in the Clarke Belt, operated by the Turksat A.S. company (Golbasi, Turkey) [31]. In the same Table, \"Astra 19.2E\" refers to the constellation of Astra communications satellites co-located at the 19.2\({}^{\circ}\) East orbital position [32], owned and operated by SES S.A., based in Betzdorf, Luxembourg. At present such group consists of four satellites, namely, Astra 1 KR, Astra 1L, Astra 1M, and Astra 1N. Both constellations broadcast plenty of channels in the Ku band on strong beams directed towards Europe.  
Figure 3: (**a**) Orthophotograph of the Monte Scarpino landfill [28]. (**b**) Map of the geographical locations of the SRS stations in the Monte Scarpino landfill and of the related satellite connection tracks.  
To provide an example of the measurements provided by the SRS system we show the behavior of the sensors operating at the \"Pala Eolica\" site (Table 1) during a typical event (namely, the one occurred on 4-5 May 2019). In Figure 4, the one-minute rainfall intensity, along with the corresponding signal strength and accumulated rain, are reported. The two panels of Figure 5 show instead the spatial distributions of the accumulated daily rainfall for the same rain event.
## 2 Test Platform  
This Section describes the physical platform used to test mass-market and professional grade GNSS receivers. For both categories, the test approach is the following:  
1. The first iteration of the testing is performed using a clean GNSS signal without the presence of any interference threats. This helps establish the baseline performance of the Receiver Under Test (RUT).
2. Each subsequent test case addresses one threat scenario (one type of interference signal). Thus, the performance of the receiver is recorded sequentially against the different interference signals.
3. Analysis of the recorded results is carried out to show the receiver behavior under conditions of the different interference signals in terms of pre-defined performance metrics. This performance is compared to the baseline under nominal signal conditions.  
The test set-up, which is shown in Figure 1, includes the following equipment:  
1. Spectracom GSG-6 RF GNSS constellation simulator [14],
2. Keysight Vector Signal Generator N5172B [15],
3. RUT, and
4. Laptop for data storage and analysis.  
In addition, generic components such as coaxial cables and signal combiners are used. The clean GNSS signal is generated from a multi-constellation, multi-frequency Spectracom GSG-6 hardware simulator, whereas the threat signature is generated using Keysight's Vector Signal Generator (VSG) [15] through the replay of raw I/Q (In-phase/Quad-phase) sample data. The raw I/Q data are captured in the field from the STRIKE3 monitoring sites and are used as input to the VSG, which then re-creates the detected threat by continuously replaying the data in a loop. Both the GNSS signal simulator and the VSG are controlled via software in order to automate the testing process. The automation script is used to control these devices remotely and to limit human intervention. The script also provides synchronization between the two instruments in order to ensure repeatability of the tests and reliability of the results. A laptop is used to record and analyze the performance of the receiver against the different threat signals. The analysis is performed using a MATLAB-based script that processes the NMEA output messages from the RUT.
# Secure Watermarking Schemes and Their Approaches in the IoT Technology: An Overview
## 4 Related Research: Modified Watermarking Approaches in the IoT Applications
### Engryption  
Another different technique that is used as a key management in watermarking methods is proposed in [100], where the researchers produce a lightweight watermarking algorithm, which relies on a dynamic random key for preserving the WSN privacy and data integrity. Two dynamic random key algorithms are developed, which i the global and local algorithms. In the global algorithm, the system initialises the random key j bits through to all WSN nodes, and according to this key, the embedding position is shifted to the right by j bits per round. In the local algorithm, a random key is generated in every WSN. Consequently, a key generator protocol along with the watermark scheme is required. Additionally, the researchers demonstrate that their algorithm is effective due to lower delay, low complexity, and high accuracy. On the other hand, the researchers in [96] combine between the lightweight Elliptic Curve Cryptography (ECC) algorithm and the fragile zero watermarking algorithm rather than the digital signature in the standard ECC. This combination overcomes the standard ECC limitations in the IoT's authentication by reducing the power consumption and memory costs.
###### Abstract  
The use of baffle arrays behind a rigid barrier is an effective method to reduce impact force by dissipating flow energy with filtering boulders from debris flows. To prevent structural damage of the rigid barrier, appropriate configurations of the baffle arrays are needed; however, design guidelines for the geometric construction of baffle arrays have not yet been proposed. One of the major challenges in designing multiple structures (baffles and a rigid barrier, etc.) is to set up a framework for a safe and economical design. The flow-structure interaction and impact force of debris flows on a rigid barrier are affected by the configuration of baffles installed behind the rigid barrier. In this study, to evaluate the flow behavior and the dynamic impact force on the terminal barrier after the flow has passed through the baffle arrays, we conducted a series of small-scale flume tests under various baffle heights and a number of rows. The test results revealed that the installation of baffles reduced the run-up height and the impact force on the terminal rigid barrier. The flow impedance induced by baffles had a significant influence on the dissipation of the kinetic energy of debris flows. Moreover, additional baffle rows produced a greater reduction in the run-up and impact force by decreasing debris flow and boulder mobility.  
debris flow; boulders; impact force; baffle arrays; rigid barrier Article  
Experimental Study on the Impact Dynamics of Cylindrical Baffles with a Rigid Barrier against Baffles with a Rigid Barrier against Debris Flows.
## 4 Experiments
### Experiment Setting  
Among various pre-trained models, BERT-base is a representative model as it has balanced cost and effectiveness. Compared to the original BERT model, Bert4 keras ([[https://github.com/bojone/bert4](https://github.com/bojone/bert4) keras]([https://github.com/bojone/bert4](https://github.com/bojone/bert4) keras), accessed on 10 June 2023) is an integrated framework that is more effective, flexible, and easier to employ and adjust. As a result, we choose BERT-base achieved by Bert4 keras as the default pre-trained model in our experiments. Parameters are trained by an Adam optimizer [42] with a learning rate of \(2\times 10^{-5}\) a loss function of multilabel categorical crossentropy, and a batch size of 16. Experiments are conducted with an AMD R9-5950X CPU and a GeForce RTX 3090. Both our model and the baseline models are trained by the training set in SQ and evaluated by the test set in SQ or WQSP. For comparability, we set the same batch size 16 and the same epoch 10 in each model. Their training time could be considered as the total training time of all the models as there are no trainable parameters in approximate matching.  
Our model contains three components: Token Labeling (TL), Entity Candidate Generation (ECG), and Approximate Matching (AM). In TL, BERT-CRF, EGP, or other NER models could be employed, and then the best-matched subject would be generated via strict matching or our ECG + AM. In our experiments, the following methods are evaluated:  
* BERT-CRF/GP/EGP (Figure 1a): BERT-CRF/GP/EGP, which is achieved by Bert4 keras, is employed to generate \(X\) and the best-matched entity, which is strictly matched to the best-matched subject. In this case, GP and EGP work as flat NER models.
* BERT-CRF + AM (Figure 1b): BERT-CRF is employed to generate \(X\), \(A\), and \(B\) in TL. Then, the best-matched subject is generated by our ECG and AM.  
* INNM-I (Figure 1c): EGP with \(m=3\) is employed to generate \(X\), \(A\), and \(B\) in TL. Then, the best-matched subject is generated by our ECG and AM.
* INNM-II/INNM-III (Figure 1d): BERT-CRF/EGP is employed to generate \(X\) and the best-matched entity. If the entity could be strictly matched to a subject in the KB, it would be considered as the best-matched subject. Otherwise, INNM-I/BERT-CRF + AM is employed to recognize the best-matched subject.
## 4 Local Updates to Handle Mobility of Sensors for Geocasting
### Updating the Convex Hull of MN and Propagating Changes  
If a child node is not in the communication range of MN anymore, MN does not receive ACK packets from this child node. Therefore, the convex hull of this child node should be removed from the convex hull of MN. For a MN to maintain its convex hull, it calculates its new convex hull after it receives new ACK_CHILD messages from its child nodes which are still connected to MN (at times t\({}_{6}\) and t\({}_{16}\) in Figure 5). There are two options for updating MN's convex hull: (i) MN can use a timeout mechanism by utilizing ACK_CHILD packets; if there is no ACK_CHILD for a given interval from a specific child, MN removes the convex hull of this child (similar to timeout mechanism explained in Section 3.3), or (ii) it can wait for a predefined time interval and then calculate its convex hull from the received ACK_CHILD packets. If MN's convex hull is changed [34], MN sends a convex hull update message (_i.e._, HELLO_TO_PARENT) to its parent to inform the parent about its new convex hull, as shown in Figure 5 at time t\({}_{17}\). Any node receiving a convex hull update message recalculates its convex hull and compares this new convex hull with the old one. If the node's convex hull is changed, then it sends an update message (at time t\({}_{18}\) in the figure) to its parent. This forwarding of update messages continues until changes reach to the root of the tree, the sink node.
# An Overview of Indoor Positioning and Mapping Technology Standards
## 2 Related Work
### Standards for Indoor Positioning, Mapping and Navigation  
In this sub-section, we will introduce a series of standards for indoor positioning, and navigation and mapping, including the International Organization for Standardization and the International Electrotechnical Commission (ISO/IEC) Information technology--Real Time Locating Systems (RTLS), ISO Technical Committee (TC) 204 Intelligent Transport System (ITS), IEEE standard for robot Map Data Representation (MDR), ISO TC211 Geographic Information standards, Open Geospatial Consortium (OGC) standards for spatial data encoding and exchanging, and Industrial Foundation Classes (IFC) of buildingSmart.
## 3 Theoretical Formulation
### Kinetic Energy and Potential Energy of the System  
Denote the transverse displacement of the Timoshenko beam as \(w(x,t)\), the bending angle is \(\alpha(x,t)\), and the length is \(L\), the kinetic and the strain energy of the beam are given by  
\[T =\frac{1}{2}\int_{0}^{L}\Big{[}\rho(x)A(x)\dot{w}^{2}+\rho(x)I(x) \dot{\alpha}^{2}\Big{]}\mathrm{d}x \tag{2}\] \[U_{b} =\frac{1}{2}\int_{0}^{L}\Bigg{[}E(x)I(x)\bigg{(}\frac{\partial \alpha}{\partial x}\bigg{)}^{2}+\kappa G(x)A(x)\bigg{(}\frac{\partial w}{ \partial x}-\alpha\bigg{)}^{2}\Bigg{]}\mathrm{d}x \tag{3}\]  
where \(\dot{w}=\partial w/\partial t\), \(\dot{\alpha}=\partial\alpha/\partial t\).  
The reaction of the viscoelastic foundation is assumed to be  
\[p(x)=-k_{f}(x)w(x)-c_{f}(x)w(x) \tag{4}\]  
The elastic potential energy of the foundation is  
\[U_{f}=\frac{1}{2}\int_{0}^{L}k_{f}(x)w^{2}\mathrm{d}x \tag{5}\]  
The Rayleigh dissipation function of the reaction of the viscoelastic foundation is given by  
\[D=\frac{1}{2}\int_{0}^{L}c_{f}(x)\dot{w}^{2}\mathrm{d}x \tag{6}\]  
The work carried out by the non-uniformly distributed load is  
\[W_{q}=\int_{x_{q}(t)}^{x_{q}(t)+\tilde{l}}q(x,t)w(x,t)\mathrm{d}t \tag{7}\]  
where \(\tilde{l}\) is the span of the applied load.  
The Lagrangian function of the system is  
\[\mathcal{L}=T-(U_{b}+U_{f}) \tag{8}\]