esa-sceva/satcom-chunk-collection huggingface dataset:
# Impact of Texture Information on Crop Classification with Machine Learning and UAV Images
## Materials and Methods  
### Study Area  
Anbandegi, in the Gangwon Province of Korea, a major highland Kimchi cabbage cultivation area, was selected as the case study area (Figure 1). Summer Kimchi cabbage is usually cultivated in highlands in Korea because high temperature and humidity causes physiological disorders, insect pests, and diseases [35]. The altitude of the study area is about 1000 meters above mean sea level and is relatively higher than the surrounding terrain, which is suitable for highland Kimchi cabbage cultivation [35]. In the study area, cabbage and potatoes are also grown along with highland Kimchi cabbage. The total area of all crop parcels in the study area is 42.5 ha and the average size of each crop parcel is about 0.6 ha. The land-cover type of non-crop areas is mainly forest.
# Sampling Rate Impact on Precise Point Positioning with a Low-Cost GNSS Receiver
## 3 Results
Convergence occurs near 50 min for the horizontal component and near 30 min for the vertical component. This difference in convergence times can be related to the fact that the measurements were carried out on the concrete of a forced-centering monument, which rep  
Figure 14: Low-cost GNSS static mode obtained by processing with CSRS-PPP: (**a**) sampling rate: 0.1, 0.2 and 1 s; (**b**) sampling rate: 5, 15 and 30 s.  
resented greater stability in the vertical component, and in the case of the horizontal it was more affected by the wind currents that hit the antenna. For the case of the PPP-kinematic method, this presented a greater instability in the convergence time, mainly due to the independent solution method presented by the PPP-kinematic method. The convergence time was reached within the first few minutes of measurement and remained for close to an hour, however, the instability of the solution caused the convergence to be lost and it started to restart for small periods of time. Although convergence was achieved in a shorter time it did not remain constant until the end of the measurement period, as was the case with the PPP-static solution. Something new and interesting that should be highlighted in this study is the impact that the measurement interval had in reaching convergence quickly. It is observed in Figure 16 that the highest rate measurement intervals (10, 5 and 1 Hz) are those that had more stability when convergence was reached in static mode. For the case of Figure 17 the same does not occur due to the instability presented by the solutions of the kinematic mode. Of the different measurement intervals, none had a good performance in the 3 components, the 10, 5 and 1 Hz intervals presented good performance in convergence during the first hour but in later times convergence was lost and was reached again only for short periods. Considering the results illustrated in Figures 16 and 17, it is shown that the PPP-static mode had a better performance in converging and staying stable. This is necessary to know when using low-cost GNSS receivers in research areas where the time series represent a physical magnitude of interest, such as displacements, then the static-PPP method must be applied for a real representation of the magnitude. Figure 15: Low-cost GNSS kinematic mode obtained by processing with CSRS-PPP: (**a**) sampling rate: 0.1, 0.2 and 1 s; (**b**) sampling rate: 5, 15 and 30 s.
# Satellite Video Moving Vehicle Detection and Tracking Based on Spatiotemporal Characteristics
## 4 Results
### Evaluation Indicators  
To quantify the accuracy of the target detection algorithm and compare it with previous algorithms, the accuracy evaluation metrics used were the precision (\(P\)), recall (\(r\)), and F1 score (\(F\)), which are commonly used in the target detection field. This was calculated using Equation (11):  
\[\begin{array}{l}P=\frac{TP}{TP+FP}\times 100\%\\ r=\frac{TP}{TP+FN}\times 100\%\\ F=2\times\frac{P\times r}{P+r}\times 100\%\end{array}, \tag{11}\]  
where \(TP\) denotes the number of correct motion target detections, \(FP\) denotes the number of incorrect motion target detections, and \(FN\) denotes the number of undetected motion targets.  
For the inter-frame association of moving vehicles, we used the number of correct tracking target identity detections (_IDTP_), number of false tracking target identity detections (_IDFP_), number of missed tracking target detections (_IDFN_), multiple object tracking accuracy (_MOTA_), total number of object identity switches (_IDS_), and identity F1 score (_IDF_1). To obtain accurate precision results, the _IDP_, _IDP_, _IDF_, and _IDS_ were obtained by  
Figure 6: The first frame images of video data.  
manual counting. Among all the evaluation metrics, _MOTA_ and \(IDF_{1}\) are the most important for evaluating multi-objective tracking algorithms, as shown in Equations (12) and (13), respectively:  
\[MOTA=1-\frac{\sum(FN+FP+IDS)}{\sum GT}, \tag{12}\]  
\[IDF_{1}=\frac{2 IDTP}{2 IDTP+IDFP+IDFN}, \tag{13}\]  
where \(GT\) indicates the number of true bounding boxes.
# MSSP: A Blockchain Sharding Protocol Based on Multi-Shard Storage
## 6 Experiment  
### Experimental Implementation  
We implemented a prototype of MSSP using the Go language (v1.13) to evaluate its performance. Additionally, we developed four complete sharding prototypes for comparative analysis. The main difference among these four prototypes lies in their approaches to handling cross-shard transactions. The first one employs the relay mechanism of Monoxide [25], the second utilizes the Broker account mechanism of BrokerChain [22], the third method uses the hierarchical design of Pyramid [34], and the fourth leverages the state splitting mechanism of Estuary [23].  
For our experiments, we deployed a private cloud environment consisting of eight physical machines, each equipped with a 64-core Intel(R) Xeon(R) Gold 5218R CPU @ 2.10 GHz processor, 128 GB of RAM, and 1 TB of SSD storage. Each block was configured to contain up to 2000 transactions with an 8-s generation interval per shard, and the bandwidth for each node was capped at 20 Mbps to simulate realistic network conditions. We varied the number of shards from 4 to 14 to evaluate scalability and performance, using a real-world Ethereum transaction dataset from XBlock [35] and injecting transactions at rates ranging from 1000 to 10,000 transactions per second.  
To assess robustness, we simulated common attack vectors, including Sybil attacks (up to 18% malicious nodes), and double-spending attempts.We assume that Sybil attacks are limited by the cost of identity creation, and double-spending attempts can be detected through priority sorting. Our protocol assumes a majority of honest nodes, reliable message delivery, and reasonable network latency (<100 ms). A potential limitation is the lack of handling for dynamic network topology changes, which may impact performance in highly dynamic environments. We measured system throughput and average transaction latency across different shard configurations to evaluate performance.  
In terms of security, a shard is considered insecure when the number of malicious nodes \(f\) in a shard exceeds \(n/3\). To set the security level of the system and provide an adjustable method to adjust the security, a security parameter \(\lambda\) is introduced to set the probability threshold. The system can be considered sufficiently secure if it meets the following conditions:  
\[\text{SP}[f\geq\lceil n/3\rceil]<2^{-\lambda} \tag{5}\]  
Increasing the value of \(\lambda\) will enhance the security level of the system. We set the security parameter \(\lambda\) to 15, which means that the probability of failure needs to be less than \(2^{-15}\).
# Secure Delivery Method for Preserving Data Integrity of a Video Frame with Sensitive Objects
### Motivation  
The verification of data integrity is crucial for the correct use of digital content, and ensuring the efficiency of the network bandwidth used for data verification is essential [6]. However, many commercial data acquisition and storage systems lack an integrity verification function to detect content modifications such as forgery and deletion. Figure 1 shows the original image and its altered version.  
The purpose of this paper is to preserve and protect sensitive data from unauthorized access throughout their lifecycle while maintaining the ability to access and utilize the original image when necessary. By encoding sensitive objects, the system mitigates risks associated with unauthorized access or misuse while ensuring data integrity for authorized purposes. Annotating sensitive data in images is crucial for privacy protection and compliance with data regulations. In this process, to protect a sensitive object from exposure, such as privacy infringement, a protection technique involving secure transmission of the sensitive object is employed [7].
# A Review of Machine Learning and IoT in Smart Transportation
## 2 Machine Learning and IoT in Smart Transportation
### ML and IoT Applications in Smart Transportation
#### Infrastructure  
The rise of IoT technology has benefited modern transportation in many ways. It has created not only new applications, making transportation smarter, but also novel ways of thinking. Changing the infrastructure of Intelligent Transportation Systems, widely expands the systems' capabilities.  
A new way of communication is proposed in [73]. The authors suggest and simulate a Vehicle to Vehicle (V2V) communication framework based on the IoT principle of M2M communication. In the proposed framework, the cars will be aware of their position using GPS and will exchange information about their speed, movements, location with the cars near them, and, at the same time, uploading the data at a server. In that way, accidents can be avoided by early notifying following cars about a sudden speed change, or traffic congestion information could be distributed to other cars to enable better navigation services.  
In [74], a combination of hardware and software is proposed to support bus fleet monitoring and improve the user's experience. The proposed hardware consists of RFID tags to uniquely identify buses, IR sensors to count passengers entering and leaving the bus, and GPS to track the bus location in real time. All the collected data are collected to a cloud server using a TI CC3200 microcontroller with embedded WiFi module. There is also a TI CC3200 module at every bus stop, connected to an LCD to present the available information to the passengers. The information is also available through a mobile application offered to the users.  
Combining the social network principles with the internet of things for smart transportation application, the Social Internet of Vehicles (SIoV) term arises. The authors in [16] propose a cross layered Vehicular Social Network Protocol (VSNP) to reduce communication congestion in SIOV. The protocol spans to the MAC, Physical, and network layers to speed up the communication. The MAC layer divides circular time slots in a ring type routing. The physical layer consists of Wireless Sensor Network nodes and the network layer supports the routing from the outer rings to a fixed access point. The suggested protocol performs better than an existing protocol (MERLIN) in Matlab conducted simulations.
###### Abstract  
Attenuation of the laser beam in underwater transmission and detection due to absorption and scattering results in a rapid reduction in energy and blurring of the image. By combining the bidirectional reflectivity distribution function (BRDF) with the Monte Carlo (MC) method, a full-link underwater imaging process model was established which comprehensively investigated the influence of water quality, transmission distance and target characteristics on imaging performance. In order to describe the transmission process of the light more accurately, by adding particles with both absorption and scattering functions in the medium, the Mie scattering theory was employed to simulate the real channel. Moreover, while setting the gate width, the pre-calibrated detector response curve was employed to build a corresponding relationship between the image grayscale and the detector collection energy, aiming to simulate the working mode of the detector in the experiment. In various imaging scenarios, the maximum relative errors between the simulated images and experimental results were within 30%, which proved the correctness of the imaging simulation model and the feasibility of the imaging MC (IMC) method to evaluate the quality of whole imaging process.  
Monte Carlo; Mie scattering; range-gated imaging; lidar +
Footnote †: journal: Medical Imaging and Imaging of Laser  
1
Footnote 1: [[https://www.mdpi.com/journal/applsci](https://www.mdpi.com/journal/applsci)]([https://www.mdpi.com/journal/applsci](https://www.mdpi.com/journal/applsci))
# Error Recovery in the Time-Triggered Paradigm with FTT-CAN
## 6 Results and Discussion
### Assessing the Design Method
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline
**ID** & **Design** & **Simul** & **ID** & **Design** & **Simul** & **ID** & **Design** & **Simul** & **ID** & **Design** & **Simul** \\ \hline
1 & 2 & 2 & 10 & 3 & 2 & 19 & 3 & 3 & 28 & 4 & 4 \\
2 & 2 & 2 & 11 & 3 & 2 & 20 & 4 & 3 & 29 & 4 & 4 \\
3 & 2 & 2 & 12 & 3 & 2 & 21 & 4 & 4 & 30 & 5 & 4 \\
4 & 2 & 2 & 13 & 3 & 3 & 22 & 4 & 3 & 31 & 5 & 4 \\
5 & 2 & 2 & 14 & 3 & 3 & 23 & 4 & 3 & 32 & 5 & 4 \\
6 & 2 & 2 & 15 & 3 & 3 & 24 & 4 & 3 & 33 & 5 & 4 \\
7 & 2 & 2 & 16 & 3 & 3 & 25 & 4 & 3 & 34 & 5 & 4 \\
8 & 2 & 2 & 17 & 3 & 3 & 26 & 4 & 4 & 35 & 5 & 4 \\
9 & 3 & 2 & 18 & 3 & 3 & 27 & 4 & 3 & 36 & 5 & 4 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparing analytical WCRT with that observed in simulation for the _Updated_SAE_ message set with LSW = 55.1% of LEC, considering an Aggressive environment. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Message Set** & **VEIL** & **PSA** & **Updated_SAE** \\ \hline LEC (ms) & 5 & 5 & 2.5 \\ RepLevel & 3-2-2-1 & 3-3-2-1 & 3-3-2-1 \\ Bandwidth utilization (@1 Mbit/s) & 4.4\% & 9.1\% & 27.9\% \\ Error server bandwidth (configuration) & 0.105\% & 0.105\% & 0.108\% \\ LSW/LEC without errors (by design) & 7.1\% & 11.9\% & 37.9\% \\ LSW/LEC with errors+server (by design) & 23.8\% & 28.0\% & 55.1\% \\ LSW/LEC with errors+server (simulation) & 21.1\% & 24.8\% & 48.4\% \\ Pessimism (design over simulation) & 12.8\% & 12.7\% & 13.9\% \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Minimum LSW configuration value by design and simulation. used by the recovery mechanism is not determined by the LSW value, but instead by the average features of the error model and corresponds to the error server bandwidth, which in this case is lower than 0.11% of the available bandwidth (Table 5) e.g., the server for the _Updated_SAE_ message set has \(T_{S}=1/\lambda\) and \(\text{C}_{S}=12\times 3\times\text{C}_{MAX}\).
# Time-Varying Preference Bandits for Robot Behavior Personalization
## 7 Simulation Results
### Performance on Smooth Preference Changes  
To simulate smooth preference changes, we randomly select two parameters, \(\theta_{1}^{*},\theta_{2}^{*}\), within a proper range and linearly interpolate them by dividing the interval into 10 points. \(\theta_{t}^{*}\) is changed every 30 rounds, making a total of nine changes. After reaching \(\theta_{2}^{*}\), 120 additional rounds are executed; hence, 390 rounds are conducted in total.  
Each row in Figure 2 shows the performances of each algorithm in _Driver_, _Tosser_, and _Avoiding_, respectively. For Figure 2c, DPB clearly outperforms the baselines on \(m_{CR}\) since other baselines cannot consider \(m_{CR}\). Lower values of \(m_{CR}\) indicate that the proposed query selection rule effectively balances the trade-off between the user's preference for the presented trajectories and their associated uncertainties, as discussed in Section 4.1. This balance enables the generation of high-quality queries, which are well suited for eliciting meaningful user feedback in real-world scenarios with smooth preference changes. Regarding \(m_{CS}\) in Figure 2a, it can be observed that DPB adapts to smooth parameter changes faster than other algorithms in terms of parameter estimation. We presume that the superior performance of DPB is derived from the effect of discounts on the past data. Finally, the DPB algorithm also outperforms with respect to \(m_{SR}\) as demonstrated in Figure 2b. Thus, in scenarios characterized by smoothly varying preferences, the DPB method demonstrates the capability to generate well-suited queries, facilitating superior estimation of reward parameters and optimal trajectories compared to baseline approaches.  
Interestingly, baseline algorithms adopt poorly in _Avoiding_. We believe that this effect is correlated with the abruptness at which the parameters are changed. As the optimal parameters are linearly interpolated, the deviation \(\|\Delta\theta_{t}^{*}\|_{2}\) is consistent with every parameter changes. The average deviation \(\|\Delta\theta_{t}^{*}\|_{2}\) over the seeds is computed as 0.087, 0.094, and 0.184 for _Driver_, _Tosser_, and _Avoiding_, respectively. For _Avoiding_, the optimal parameter changes comparatively drastically, resulting in a bad performance for the baseline algorithms. Furthermore, the limited adaptability in time-varying scenarios for the maximum regret algorithm might stem from its query selection rule over the solution space. This algorithm tends to converge towards local optima due to its greedy selection that restricts the trajectories to be compared, wherein it selects two trajectories exhibiting the highest estimated regret based on parameter obtained from Markov chain Monte Carlo. However, the results of all simulations support that DPB adapts to the smooth preference changes faster than baselines.
## 6 Precise Orbit Determination Methods
### Orbit Prediction Methods for Large-Scale Space Debris  
Space collision will generate a large number of space fragments, posing a significant threat to orbiting space objects and creating substantial computational loads. Therefore, establishing a precise, low-computational-cost model for the evolution of fragment orbit after a space collision is of great significance for assessing the consequences of space collisions and defending against space debris.
## 3 Results  
### Sediment and Pore Water Characteristics  
The physico-chemical properties of sediment and pore water from the five sampling site locations are summarized in Table 1. Sediment pH ranged from 6.7 to 8.1, corresponding to neutral sediments (pH 6.6-7.3) in Av1, Av3 and Tg, slightly alkaline (pH 7.4-7.8) in Av2 and moderately alkaline (pH 7.9-8.4) in Al [58]. ECE values varied between 39-177 dS m\({}^{-1}\), corresponding to saline sediments (ECe > 16 dS m\({}^{-1}\)) [32]. SOM ranged from 2.2-7.22, with the highest values corresponding to Av sites. The concentrations of Na (385-994 mg kg\({}^{-1}\)), Mg (70-355 mg kg\({}^{-1}\)) and K (19-84 mg kg\({}^{-1}\)) were also higher at Av sites than in Tg or Al sediments. The concentration of Ca (84-3016 mg kg\({}^{-1}\)) was the highest in Al sediments. Together, ECE, ESP, SAR and pH of sediments allowed the characterization of sites Av1 and Av3 as saline-sodic soils and Av2, Tg and Al as non-sodic saline soils [32]. Pore water pH ranged from 6.8-7, and ECw values (47-128 dS m\({}^{-1}\)) corresponded to brine [59].
# Tutorial Review on Space Manipulators for Space Debris Mitigation
## 5 Space Manipulator Operations--Evolution from Teleoperation to Autonomy
The implementation of predictors and simulators introduces aspects of autonomy--this is the telerobotic mode. In telerobotic mode, the ground operator uploads designated waypoints which the robot implements locally by autonomously planning and executing a global path while avoiding obstacles, e.g., Sojourner on Mars. The evolution toward autonomy is important for several reasons, but mostly because it requires considerable computational resources onboard. This places a premium on solving manipulator control problems as low down the control hierarchy as is feasible.
## 5 Research and Development Challenges
### Uncertainty-Aware Delivery Service  
In drone-based delivery service, several uncertain issues exist such as weather conditions, varying payload, drone airspeed, and customer demand [34; 48; 80]. Thus, not considering such subtle issues results in increased costs and flight time, which deteriorates the overall delivery performance. Such uncertain conditions could result in drone breakdown, which could affect the next parcel deliveries. In such cases, penalties are significantly higher, which increases the overall cost [81]. Additionally, the presence of no-fly zone, the maximum allowable altitude to fly, nighttime, and maximum transportable load are critical safety issues that must be considered. Most existing studies ignore these uncertain conditions during problem formulation, which remains an open issue. Thus, significant consideration should be given to these factors so that in practical scenarios, the developed approach could handle such situations.