esa-sceva/satcom-chunk-collection huggingface dataset:
# Investigating and Modeling of Cooperative Vehicle-to-Vehicle Safety Stopping Distance
## 3 Testbed Overview  
This section will be used to briefly introduce our testbed, experiment methodology, and the field tests conducted to gather our results. This testbed is based on our previous works relating to an empirical DSRC testbed utilizing SDR devices [10], and we conducted empirical evaluations as to the capabilities of the system. Further details on the testbed can be found in this work. Our experiments have shown that the testbed can operate to DSRC standards over many different aspects, such as Broadcast Distance Power, Packet Delivery Ratio, Throughput, Latency, Reliability, and Packet Loss Rate.  
Our testbed was designed with four main considerations: to be capable of operating to DSRC standards in regard to the access and physical layers of the stack, to be open source to allow reproducibility, low cost when compared to commercial DSRC products, and to be portable so tests can be evaluated in the field. To meet all these requirements, various approaches were considered, such as open source modified Wi-Fi cards, traditional laptops, and Software Defined Radios [17; 30; 31]. After considering this, we opted for the use of Ettus Universal Software Radio Peripheral (USRP) B210 software defined radios and Lenovo ThinkPad laptops. We also opted to use an established open source project known as the Wireless Measurement and Experimentation (WiME) project [16; 32; 33; 34].  
The chosen message protocol used to transmit these messages is UDP, to reflect the protocol used in CAM/BSM messaging which transmits vital vehicle information, such as speed, heading, current action, and brake status. This information is vital for safety-related aspects; thus, the information must be reliable and delivered quickly, which UDP/BSM are capable of as no relationship must be formed before information is delivered, such as a broadcast [23].
# Automatic Matching of Multi-Source Satellite Images: A Case Study on ZY-1-02C and ETM+
## 2 Methods
### Finalizing the Matching Process by Lsv
(4) After traversing all the points in TIN, Step 1 was repeated to reconstruct a new TIN by using the remaining points. The process continued until the residual errors of all the points met the requirements.
# On the Impact of the Rules on Autonomous Drive Learning
## 5 Results
### Robustness to Traffic Level  
With the aim of investigating the impact of the traffic level on the behavior observed with the learned policies (in the second learning scenario), we performed several other simulations by varying the number of cars in the road graph. Upon each simulation, we measured the overall distance traveled \(\sum_{i=1}^{n_{\text{car}}}E_{i}\Delta t\) and overall collisions \(\sum_{i=1}^{n_{\text{car}}}C_{i}\). We considered the overall sums, instead of the average, of these indexes in order to investigate the impact of the variable number of cars in the graph: in principle, the larger is this number, the longer is the overall distance that can be potentially traveled, and, likely, the larger is the number of collisions.  
We show the results of this experiment in Figure 6, where each point corresponds to indexes observed in a simulation with a given traffic level \(n_{\text{car}}\): we considered values in \(10,20,\ldots,80\). We repeated the same procedure for both the drivers trained with and without the rules, using the same road graph in which the drivers have been trained. For each level of traffic injected, we simulated \(T\) time steps and we measured the overall distance and overall number of collisions occurred.  
Figure 6: Overall number of collisions in the simulation against the overall traveled distance in the simulation, averaged across simulations with the same \(n_{\text{car}}\). Each dot is drawn from the sum of the values computed on the \(n_{\text{car}}\) vehicles.  
As shown in Figure 6, the two policies (corresponding to learning with and without rules) exhibit very different outcomes as the injected traffic increases. In particular, the policy optimized without rules results in an overall number of collisions that increases, apparently without any bound in these experiments, as the traffic level increases. Conversely, the policy learned with the rules keeps the overall number of collisions much lower also with heavy traffic. Interestingly, the limited increase in collisions is obtained by the policy with the rules at the expense of overall traveled distance, i.e., of traveling capacity of the traffic system.  
From another point of view, Figure 6 shows that a traffic system where drivers learned to comply with the rules is subjected to congestion: when the traffic level exceeds a given threshold, introducing more cars in the system does not allow obtaining a longer traveled distance. Congestion is instead not visible (at least not in the range of traffic levels that we experimented with) with policies learned without rules; the resulting system, however, is unsafe. Overall, congestion acts here as a mechanism, induced by rules applied during the learning, for improving the safety of the traffic system.
## 5 Hydrothermal Reactors
### Batch Reactors  
Batch reactors are widely used to produce nanoparticles, mainly because they are often the initial basis of an investigation and are also particularly easy to operate. They normally feature longer reaction times (minutes or even hours) than continuous reactors and have a lower heating rate, since continuous reactors are heated practically instantaneously (the reagents are mixed when the desired temperature is reached). The most commonly used batch reactors for the hydrothermal processing of advanced nanomaterials are as follows [16]: general purpose autoclaves, Morey type and flat plate seal, stirred reactors, cold-cone seal Tuttlee Roy type, TZM autoclaves, piston cylinder apparatus, belt apparatus, opposed anvil, and opposed diamond anvil. Each one of these reactors has its own characteristics. Batch reactors with a volume between 25 and 1000 mL and the possibility of sample extraction are a frequent and interesting choice, since they allow to obtain multiple data for phase diagrams that cast some insight on the development of the process. Small batch reactors (microactors) with a volume between 5 and 10 mL are also very interesting and often used, since they prevent any temperature gradients from appearing inside the reactor and allow the implementation of high heating rates to reach the required conditions in just a few seconds. Thus, the data obtained are closer to those obtained with continuous reactors. Professors [PERSON] and [PERSON] were pioneering supporters of these microreactors and suggested that microreactors with a volume between 5 and 8 mL should be used to avoid slow heating rates and the subsequent temperature gradients [21]. The generation of some of the particles with these reactors, under both subcritical and supercritical conditions, can be seen in Table 4. \begin{table}
\begin{tabular}{c c c} \hline
**Features** & **Batch Reactors** & **Continuous Flow Reactors** \\ \hline Research stage & Initial & Advanced \\ Operability & Easy handing & Difficult handing \\ Reaction time & Minutes and even hours & Seconds or a few minutes \\ Heating time & Long (minutes) & Short to very short (seconds) \\ Particles size & In the order of a few microns & In the order of nanometers \\ Process control & Minor operation range & Major operation range \\ \hline \end{tabular}
\end{table} 
Table 3: Main differences between batch reactors and continuous flow reactors.
# Research on Corrosion Damage Evolution of Aluminum Alloy for Aviation
## 3 Results and Discussion
### Depth of Corrosion Pits  
#### 3.2.1 Measurement Method  
After accelerated corrosion in the laboratory, the surface shape of the test piece was complex and the contour line was uneven. In order to obtain the depth of the corrosion pits, 3D imaging technology can be used to automatically identify the height of the designated part of each pit area, and then obtained by the height difference between the bottom of the pit and the top of the pit. However, because this method cannot accurately lock the height of the bottom of each pit and because of the unevenness of the corrosion area and the overall thinning of the thickness of the specimen, the measurement of the top area of the corrosion pits is often selected too high due to subjectivity.  
The cross-sectional contour of the corrosion area can clearly reflect the detailed characteristics of the corrosion area in the depth direction, and the height difference between the bottom and the top of the corrosion pit can be accurately obtained. Therefore, this paper first used the Rh-8800 3D video microscope (Hirox, Tokyo, Japan) to obtain the three-dimensional morphology of the corrosion specimen at a magnification of 1200 times. We then intersected the section perpendicular to the length of the specimen with the three-dimensional image and then obtained the contour line information of several sections in the x direction of the specimen, as shown in Figure 7.  
Figure 6: 3D topography of surface (\(t=192\) h).  
Figure 7: Obtaining the depth of corrosion pits. (**a**) Section selected for the specimen; (**b**) Three-dimensional morphology of the corrosion specimen; (**c**) Schematic diagram of section contour.  
According to the above method, the depth of corrosion pits at different corrosion times in the pitting phase (\(t\leq 120\) h) was measured. To ensure the reliability of the data, three test pieces were measured for each corrosion cycle and 15 areas were measured for each test piece. We calculated the average of the depth of corrosion pits under each corrosion time. The results are shown in Figure 8.
## 7 Conclusions  
The experimental results clearly demonstrate the high potential of object detection methods for the task of transcription alignment. For the historical Vietnamese dataset, such an alignment could be established without asking humans to manually annotate training images, which is a time-consuming and tedious work hampering the progress for digitizing large quantities of historical manuscripts. At the same time, the proposed method is still learning-based, able to adapt to a variety of page layouts, page backgrounds, and handwriting styles by means of self-training.  
The high structural similarity between the printed font and the handwriting styles observed in the historical Vietnamese dataset is most likely a key factor for the success of the method. In the future, we aim to investigate whether or not similar segmentation-free, annotation-free, and learning-based approaches are also feasible for other scripts and languages, for example in the context of medieval Latin manuscripts. Furthermore, we are interested in applying similar alignment algorithms to historical Vietnamese steles [37], where the stone background and the character engravings show even more variability when compared with manuscripts.  
Finally, a promising line of future research is to attempt an annotation-free transcription using fully convolutional models that are trained without human supervision on scanned manuscript images. This line of research raises the question to what extent human annotations are actually needed for accurate document analysis and recognition, and to what extent printed funds may already be sufficient as a starting point for self-adaptation to different handwriting styles.  
Conceptualization, A.S.-B., M.J., B.W., A.F. and M.B.; methodology, A.S.-B., M.J., B.W., A.F. and M.B.; software, A.S.-B., M.J., B.W. and A.F.; validation, A.S.-B., M.J., B.W. and A.F.; formal analysis, A.S.-B., M.J., B.W. and A.F.; investigation, A.S.-B., M.J., B.W. and A.F.; resources, A.S.-B., M.J., B.W., A.F. and M.B.; data curation, A.S.-B. and A.F.; writing--original draft preparation, A.S.-B., M.J. and A.F.; writing--review and editing, A.S.-B., M.J., B.W., A.F. and M.B.; visualization, A.S.-B., M.J. and A.F.; supervision, A.F. and M.B.; project administration, A.F.; funding acquisition, A.F. All authors have read and agreed to the published version of the manuscript.  
This work has been supported by the Swiss Hasler Foundation (project 20008).  
We would like to thank [PERSON] and [PERSON] from the Vietnamese Nom Preservation Foundation for providing us with the images and transcriptions of the _Tale of Kieu_ and the _Luc Van Tien_. Furthermore, we thank [PERSON], [PERSON], and [PERSON], the authors of [3], for providing us with their ground truth for the test images.  
The authors declare no conflict of interest.
# Replacement of Fault Sensor of Cutter Suction Dredger Mud Pump Based on MCNN Transformer
## 2 Mud Pump Water Sealing System of the Cutter Suction Dredger  
The mud pump studied in this study is a double wall centrifuge pump, including driving system, flushing system, lubrication system, water sealing system, and pressure and air control system. Its composition and structure are shown in Figure 1. The cover plate seals the suction side of the outer pump casing and is connected to the inner pump casing through bolts and nuts. Additionally, the impeller is installed in the inner pump casing, and the mud is transported through the rotation of the impeller. In addition, the impeller is connected to the pump shaft through a multi-thread thread, and the end of the pump shaft is connected to the coupling of the mud pump motor or gearbox, thereby driving the impeller to rotate. The MPWSS consists of an inner pump casing (including impeller and flow channel) and an outer pump casing (including water chamber and shaft seal). The outer pump casing connects the inner pump casing and the bearing body base and seals the inner pump casing from the shaft side. There is a pressure balance chamber between the inner and outer pump casings to ensure that the internal and external pressures are balanced and are the same as the working pressure of the mud pump.  
During the operation of the mud pump, mud is transported from the low-pressure area to the high-pressure area through the stirring of the impeller. Nevertheless, the small gap between the impeller and the pump casing can cause mud to flow back towards the center of the impeller. To prevent this, sealing devices are installed on both the pump shaft side and the suction side; the shaft seal is installed at the inlet of the water chamber to prevent water and mud from entering the bearing body.  
However, the transportation of mud by the pump can lead to wear on its flow-passing components. Excessive wear on the outer side of the impeller, lining plate, pump shaft seal and suction seal can result in enlarged clearance, increased backflow, and even leakage from the pump, as depicted in Figure 2. This, in turn, can lead to a significant reduction in the mud pump's performance, necessitating frequent maintenance and parts replacement, increased operating costs, and reduced effective operating time.  
Figure 1: Dredger pump double wall model.  
Hence, the primary objective of the MPWSS is to prevent mud or slurry from entering the pump shaft seal, suction seal, and the gap between the impeller and pump shell lining of the mud pump, thereby reducing the wear of these parts and extending their service life. Furthermore, the main functions of MPWSS are as follows: (1) protecting and lubricating the shaft seal device of the mud pump; (2) protecting and lubricating the suction seal of the mud pump; (3) introducing balance water between the inner and outer pump casings to extend the service life of the inner pump casing of the mud pump.
## 4 Experimental Results and Discussion  
Our system is divided into front-end and back-end from the system architecture. The front-end is mainly responsible for interactive operations and displaying the results of visual exploration. The front-end part mainly uses HTML, CSS and JavaScript programming languages, and also uses some frameworks such as jQuery.js, D3.js and three.js. The back-end is mainly responsible for data processing and storage. Due to the large amount of data in the data set, we use the Hadoop cluster to complete the tasks of data preprocessing, travel feature extraction, cluster analysis of travel groups and passenger travel correlation analysis, and then save the data into the database. Our Hadoop cluster contains 8 nodes that support large file storage, streaming data access, and can also detect and quickly respond to hardware failures. The back-end part mainly uses Java and Python programming languages.  
To verify the effectiveness of our method, we use two real cases to verify the validity of our method. One is the analysis of normal passengers and another is the detection of potential anomalies. We use the Beijing public transport system data from 1 August to 31 August 2015. The data contain more than 100 million pieces of passenger travel information.
# Groundwater Storage Changes in China from Satellite Gravity: An Overview
###### Abstract  
Groundwater plays a critical role in the global water cycle and is the drinking source for almost half of the world's population. However, exact quantification of its storage change remains elusive due primarily to limited ground observations in space and time. The Gravity Recovery and Climate Experiment (GRACE) twin-satellite data have provided global observations of water storage variations at monthly sampling for over a decade and a half, and is enable to estimate changes in groundwater storage (GWS) after removing other water storage components using auxiliary datasets and models. In this paper, we present an overview of GWS changes in three main aquifers within China using GRACE data, and conduct a comprehensive accuracy assessment using in situ ground well observations and hydrological models. GRACE detects a significant GWS depletion rate of 7.2 \(\pm\) 1.1 km\({}^{3}\)/yr in the North China Plain (NCP) during 2002-2014, consistent with ground well observations and model predictions. The Liaohe River Basin (LRB) experienced a pronounced GWS decline during 2005-2009, at a depletion rate of 5.0 \(\pm\) 1.2 km\({}^{3}\)/yr. Since 2010, GRACE-based GWS reveal a slow recovery in the LRB, with excellent agreement with ground well observations. For the whole study period 2002-2014, no significant long-term GWS depletion is found in the LRB nor in the Tarim Basin. A case study in the Inner Tibetan Plateau highlights there still exist large uncertainties in GRACE-based GWS change estimates.  
GRACE; groundwater storage; satellite gravimetry; China; hydrological model; groundwater level; North China Plain 2018 10 674 doi:10.3390/rs10050674
## 1 Introduction  
The use of explosives for rock fragmentation remains the primary method of extraction in surface mining operations for rock raw materials. The application of blasting agents allows for effective resource exploitation; however, it also poses significant environmental and safety risks. One such hazard is flyrock, which presents a danger to mine personnel, equipment, and surrounding infrastructure. Although this risk is critical, it remains difficult to accurately assess.  
During blasting operations, flyrock can be ejected over considerable distances. As a result of the detonation of explosives within blastholes, rock fragments are displaced, typically causing horizontal movement of the entire blasted rock mass. When the blasting sequence is carefully designed and properly executed, the process should not pose substantial risks. Nevertheless, in addition to the expected displacement, flyrock may be expelled unpredictably, creating safety hazards for personnel, equipment, and structures within its range. Flyrock refers to any rock fragment projected into an unintended area [1].  
The extent of the flyrock zone depends on a number of factors that can be quantitatively determined under controlled conditions through measurements. Identifying and monitoring these parameters enables the implementation of measures to mitigate flyrock hazards. The key contributing factors include [2; 3; 4; 5; 6; 7; 8; 9; 10; 11; 12; 13; 14]:  
* Burden and spacing of blastholes,
* Specific charge (powder factor),
* Inadequately characterized geological structure,
* Blashole deviation,
* Length and quality of stemming,
* Presence of back fractures,
* Initiation sequence,
* Human error.  
In most cases, these factors are interrelated; for instance, blasthole deviation affects burden and spacing, which in turn can locally increase the powder factor. For this reason, the literature offers a range of empirical methods and formulas for estimating the maximum flyrock range, such as the Swedish method [15; 16], the American method (Roth) [17], [PERSON]'s method [18; 19; 20; 21], and those based on explosive energy indices. Traditional empirical formulas and statistical regression models often proved inadequate for accurate flyrock prediction, showing low predictive capacity. In the past decade, a wide range of artificial intelligence (AI) and machine learning (ML) techniques have been applied to this problem, generally achieving much higher accuracy than empirical approaches. This section reviews these techniques, grouped by model type, and discusses their predictive performance, practical field applications, and noted limitations (e.g., data scope and input sensitivity).
## 2 Methodology
### Numerical Model
#### 2.2.3 Solution Procedure  
Figure 2 shows a typical flow domain used in the computations. The computational domain used in the ANSYS computations had a size of 70 m length, 50 m width, and 70 m height. The wall function is adopted for the airflow through the system. The air density is considered to be 1.225 kg/m\({}^{3}\), and the dynamic viscosity is considered to be \(1.81\times 10^{-5}\) kg/(m-s). The mesh is decreased in size, until the difference in the results is within 2%. As shown in Figure 3, the flow domain was discretized with a mesh of tetrahedral elements. In total, 2,153,969 nodes and 9,553,863 elements were used for the simulation in ANSYS CFX. Higher mesh resolution was used near the wall regions with inflation layer applied. The boundary condition is chosen as the following: wall-surface body condition is stationary wall, wall roughness is 0.5, and turbulence intensity is 5%. The convergence criterion is achieved when the variation in the speed between successive iterations is 0.1%. A constant input speed field, representing the free-stream wind, was assigned to the entire frontal plane of the flow domain. The magnitude of the speed was set at 6 m/s. The exit plane was assigned as pressure-outlet boundary condition. The reference pressure was assumed to be the atmospheric pressure throughout the domain.  
Figure 2: Dimensions of computational domain.
## 4 The Proposed Algorithm-TCEB  
From aforementioned energy consumption analysis, we can see that energy dissipated in underwater is quite different from the typical TWSN (energy consumption in TWSN can be found in [7]). Usually, the underwater acoustic communication needs to spend more resources without any energy recharge. Due to the non-uniform distribution of nodes, a node in the critical location (e.g., as the relay node in the communication link) sends the collected information, is prone to burden forwarding too much data. That will lead to network failure caused by premature node energy depletion. To be associated with the links situation, we use Markov model as the channel error model [25] in this paper. In addition, once the network is load imbalance, some nodes need to compete for communication channels queuing to send data, which greatly increases the end-to-end delay. Therefore, a topology control algorithm with energy balance (TCEB) using clustering technology for UWSN is proposed to consider how to improve energy efficiency and load balance to prolong the network lifetime as much as possible.