esa-sceva/satcom-chunk-collection huggingface dataset:
# Comparing Monitoring Networks to Assess Urban Heat Islands in Smart Cities
## 3 Choice of System Components and Programming
### Central processing unit or microcontroller
#### 3.2.2 Centralized Processing System for Network Data  
This complex centralized system has the following functions:  
1. Data acquisition and transmission  
LoRaWAN technology was selected as the communication system between the AWSs and the central processing unit. The use of a communication device that includes a LoRA gateway (Figure 3) is necessary for it to function. The components of the device are as follows:  
* Waterproof box for outdoors, the outdoor enclosure model for WisGate Developer D4+ [74]: This box houses the gateway and must have SIM coverage.
* Gateway, RAK WIRELESS 7248C D4H+ model [75]: It consists of a Raspberry Pi processor linked to an RAK2013 Cellular Pi HAT module for connecting to the mobile phone network using a SIM card. It is responsible for receiving and decoding the LoRA packages and sending them to a remote database. It is powered by the electricity grid.
* Omnidirectional LoRa 868 MHz antenna of 8 dBi [76]: This is placed on the upper part of the box and aids in the reception of data packages emitted by the AWSs in the LoRa bandwidth.  
The gateway processor admits a Linux operating system that includes such pre-installed components as the ChirpStack server and connection to the internet via SIM. The ChirpStack server must be configured to fix the bandwidth frequency, the port, and the MQTT service for publishing data. The measurements are published in the MQTT broker installed in the RaspberryPi and specific scripts read them, package them, and send them to the central server using 4G and the HTTP protocol.  
Receiving and storing data  
The data from the gateways are received in the VM 1 virtual machine, situated in the infrastructure of the University of Extremadura in Caceres. Data reception is managed through microservices written in Python (version 3.7.3) and stored in Docker containers. These services put the data in a PostgreSQL database placed in a private network with no access from outside. A system of alerts has been implemented in which a script verifies the last entries in the databases of the AWSs and the gateways, generating, in the case of some device fails, alert messages which are sent via Telegram [77] to a designated user, through a bot and the API of the application. It also makes daily and weekly summaries of the state of the system.  
The static data are organized into related tables that associate each numerical ID of the AWS (from 1 to 25) with its location, latitude, and longitude. The dynamic data include the values for temperature, relative humidity, battery level, Received Signal Strength Indicator, SNR, and the Gateway number of origin with the numerical ID of the AWS.  
The visualization is carried out using Grafana software (version v9.4.3) [78], which allows the data to be represented in a dynamic and simple way that can be personalized in different graphics, lists, tables, or maps. This tool obtains the data in real time directly from the PostgreSQL database.
# A New Method for Haul Road Design in Open-Pit Mines to Support Efficient Truck Haulage Operations
## 3 Methods
### Simplification of Road Layout Using the Douglas-Peucker Algorithm  
The application of line simplification algorithms is needed to convert the zigzag-shaped travel path into a form that is appropriate for actual road design. The representative line simplification algorithms include the Lang simplification algorithm [38], the Reumann-Witkam routine [39], the Opheim simplification algorithm [40], the Visvalingam and Whyatt algorithm [41], and the sleeve-fitting polyline simplification algorithm [42]. In the present study, the layout of the haul road is simplified by applying the Douglas-Peucker algorithm [26], which is regarded as having the least errors and the highest accuracy among the many algorithms [43].  
The Douglas-Peucker algorithm, which is a representative global simplification algorithm that simplifies lines by considering them globally, is implemented in many Geographic Information System (GIS) software packages and cartography software applications [43]. The principle of this algorithm is as follows (see Figure 7). The original line consists of nine vertices in total (see Figure 7a), and \(V\), which is a set of vertices, can be expressed as follows:  
\[V=\ \{v_{0},v_{1},\ v_{2},\ \cdots,\ v_{8}\} \tag{4}\]  
Figure 6: Procedure for generating 2D haulage road layout using the least-cost path analysis (LCPA) technique.  
First, among the vertices comprising the line, the initial simplified line is formed by interconnecting the first vertex \(v_{0}\) and the last vertex \(v_{8}\) (see Figure 7b). Next, the perpendicular distance between every vertex, the initial simplified line, and every calculated value is compared with the user-defined tolerance (\(\varepsilon\)) (see Figure 7c). If every calculated perpendicular distance is smaller than the tolerance, the vertices excluding the first vertex and the last vertex are deleted, and the initial simplified line becomes the final simplified line. If any perpendicular distance exceeds the tolerance, the vertex (\(v_{3}\)) that has the largest perpendicular distance is set as the new vertex of the initial simplified line (see Figure 7d), and a new simplified line passing the vertex \(v_{3}\) is formed (see Figure 7e). The above-mentioned procedure is repeated for the newly formed simplified line (see Figure 7f). The simplification process finishes when the perpendicular distance calculated at every vertex is smaller than the tolerance (see Figure 7g). The final simplified line becomes the current simplified line and includes four vertices; \(v_{0}\), \(v_{3}\), \(v_{4}\), and \(v_{8}\) (see Figure 7h).
# An Innovative Recompression Scheme for VQ Index Tables
## 2 Related Work
### State Codebook Scheme  
When using the SOC scheme, a desired matched index value may not be found in the search path. [PERSON] et al. proposed a variation, derived from the original SOC, which was introduced by adding on a state codebook [26]. These state codebooks are smaller-sized codebooks derived from the original codebook. The main idea is to expand a smaller-sized codebook from each index number in the search path. These local state codebooks contain more indices which are the closest to the original index. The size of the state codebook depends on the predefined similar index count \(2^{j}\) in SOC and the closest index count \(2^{j}\), where \(i\) and \(j\) are limited bits needed for coding. The expansion amount will be limited to bits to be varied. During local state codebook building, if index values appear in the SOC search path or in previous local state codebooks, the index values are skipped, and the process continues until it finds the next available index value.  
Figure 6 presents an example where a matched index value cannot be found in the SOC search path, but a match is found in one of the local state codebooks derived from the values in the SOC path. The search direction is predefined as 1, and the number of bits for SOC is set to 2. The index value of (3, 3) is 60, and there are no repeated values in its SOC search path. From the indices of (2, 3), (2, 2), (4, 2), and (1, 3), the index values are 55, 54, 52, and 46, respectively. Assuming the expansion \(i\) and \(j\) are limited to 2 bits, the size of the local state codebook becomes \(2^{2}\times 2^{2}=16\). Table 1 displays the closest index values for each search point in the SOC algorithm. If we take 55 as a base to find its closest values, the closest four values will be 53, 54, 56, and 57. Since 54 already appeared in the SOC search path, it is skipped, and 52 is found instead. However, 52 is also in the SOC search path, so it is skipped, and 51 is chosen instead. By expanding 54, 52, and 46 in the same manner, we can finally obtain a complete state codebook without repeated codewords. Once the local state codebook is built, a match is found for the index value 60 in the local state codebook derived from the index value 52 located at (4, 2). If the size of the VQ codebook is 256, each codeword is represented by 8 bits, and the index \"1010\" of the state codebook is used to replace the binary \"00111100\" corresponding to the original VQ index value 60.  
Figure 5: The concept of SMVQ.
## 6 Limitations and Future Studies  
After evaluation and validation, the hybrid model developed in this study demonstrates high prediction accuracy and engineering applicability in rock mass classification. However, several limitations require further improvement:  
1. The dataset used is relatively small and lacks cases of class I. Expanding the dataset size in the future could further enhance the model's applicability to a wider range of engineering scenarios.  
Figure 12: Comparison of model performance on incomplete datasets: (**a**) Fill value is 0; (**b**) Fill value is the average value; (**c**) Fill value is the median value; (**d**) Fill value is the mode.  
2. While ZOA-XGBoost performs well with missing values, its robustness and stability against other types of uncertainty and noisy data have not been fully verified. Future research could incorporate more noise-handling and uncertainty-evaluation methods to improve model reliability.
3. The features used in this study (UCS, RQD, Kv, and W) play important roles in rock mass quality prediction, but other influential factors were not included. Future improvements could involve incorporating additional geological and engineering factors, such as rock mass structural characteristics and stress conditions, to further refine the model.
## 5 Visualized Ground Control Station for Quadcopter Using Mixed Reality Simulation  
A ground control station (GCS) is an essential part of UAV flight, especially in the case of long-time missions. In this study, we developed a visualized GCS for a quadcopter UAV using a MATLAB/Simulink-based control system with our developed MR simulation technique. By using the visualized GCS, we controlled a quadcopter from a remote location, without a remote controller. Figure 10 illustrates the outline of the visualized GCS. In the first phase, we developed an open-loop control system in MATLAB/Simulink to control the quadcopter by a joystick. Note that for sending the control commands from the MATLAB/Simulink control system to the quadcopter, here, we attached a Raspberry Pi (Model 3 B+) single-board computer to the Pixhawk flight controller by a serial connection.  
In the second phase, the MATLAB/Simulink control system sends the control commands (throttle, roll, pitch, and yaw command) to the Raspberry Pi. To do so, we developed a UDP interface in MATLAB/Simulink, which sent the control commands to Raspberry Pi via Wi-Fi. Figure 11 shows the control system with the UDP interface in MATLAB/Simulink. For communication between the Raspberry Pi and Pixhawk, here, we imported DroneKit-Python [35] in Raspberry Pi. DroneKit-Python is an open-source Python program package used to communicate with ArduPilot flight controllers, including Pixhawk. The DroneKit-Python coding in the Raspberry Pi sends the receiving control commands from the MATLAB/Simulink control system to Pixhawk.  
Figure 11: Control system with user datagram protocol (UDP) interface in MATLAB/Simulink.  
Figure 10: Overview of the visualized ground control station.  
In the third phase, we integrated our developed MR simulation technique with the control part for the visualization. The setup of the visualized GCS is shown in Figure 12. Note that the control part worked on a computer (Computer 1) with a joystick, and the visualization part ran on another computer (Computer 2) with the MR simulation setup. Therefore, instead of a remote controller, Computer 1 with the joystick controlled the quadcopter from a remote location using the visualization part on Computer 2.
## 5 Influence of Detection Angles on Detection Distances
### Test Set-Up  
Since the main goal in this study is the same as in the previously mentioned studies, the test set-up is kept fairly similar again. The major difference here is that now, instead of aligning the detection dummies along the road, they have been split into different locations. This is schematically shown in Figure 6. Only dummies on the right side of the road are evaluated in this study. Dummies No. 5 and No. 6 are only used to avoid situations where subjects only look for the dummies on the right side of the road. The positions chosen are 2.65, 5.0, 6.5, and 8.0\({}^{\circ}\) calculated from the detection distances from the previous study. This leads to positions for the dummies at 5.0, 9.6, 12.5, and 15.5 m from the center of the driving  
Figure 5: Required luminous intensity for safe object detection under different velocities.  
line. These detection angles will of course vary according to the detection distance for each subject individually. This is calculated based on each subject's actual detection distance and the location of the detected target and is presented in the next subsection.  
One further difference from the previous tests is that another car was parked at point C. This car was turned off, and all lights were shut off as well. This was done so that the reflection from the lights on the test vehicle coming from the license plate as well as from the rear reflectors was visible, and the test subjects were asked to focus their gaze on these small reflections. This was done to keep the detection angle as consistent as possible. All test runs started after the measured horizontal illuminance on the road was below 0.2 lx.
## 2 UAS Platform Types and Characteristics
### Characteristics and Examples of UAS Categories  
#### 2.2.1 MAV/NAV UAS  
Very small UAS or Micro-UAVs (MAVs) and Nano UAVs (NAVs) are designed as short mission profile surveillance platforms, allowing unobtrusive observation capabilities in confined spaces or hostile environments in military applications. The major MAV development thrusts started in the mid-late 1990s with major development support from the US Department of Defense. Most of these systems developed by industry, such as the Lockheed Sanders \"MicroSTAR\", the AeroVironment \"MicroBat\" and \"Black Widow\", and the Lutronix \"Kolibri\", are designed with small color video system payloads.  
Nano UAVs (NAVs) in development, such as the AeroVironment \"Nano Hummingbird\" are extremely small, low-altitude, short-duration UAS platforms designed to enhance situational awareness in areas where they may go unobserved. The AV Nano Hummingbird is the size of a small bird (e.g., weight of 19 g and wingspan of 16 cm) and can climb and descend vertically, fly in all horizontal directions, is equipped with a small video camera, and has a flight endurance of \(\sim\)8 min (Figure 2).  
Figure 2: Courtesy AeroVironment, Inc. ([[http://www.avinc.com](http://www.avinc.com)]([http://www.avinc.com](http://www.avinc.com))).  
Development of MAVs and NAVs for the commercial market is in its infancy, and no examples of their uses yet exist. These UAS assets may prove to be effective or inexpensive systems to gather information for law enforcement and may have some utility for wildlife inventories and other remote sensing civilian applications.
# Experimental Evaluation of Unicast and Multicast CoAP Group Communication
## 5 Evaluation
### Performance Evaluation on a Wireless Sensor Testbed
#### 5.2.1 Congestion Control Optimizations
This is also not surprising as with the larger group size, the chance for collisions increases, and the CoAP retransmission mechanism starts to be sometimes insufficient. We will discuss reliability in more detail in the next subsection. As a result of the observations we made in these experiments, we have used an entity delay between requests of 100 ms in all of the following experiments, which is also in line with the results of Equation (2).
# Group Authentication Scheme for Neighbourhood Area Networks (NANs) in Smart Grids
## 4 Security Analysis
### Replay Attack  
Both schemes present in [26; 27] show a vulnerability to replay attack. Our scheme is secure against replay attacks because it uses shared keys for communication, as well as time stamps. Both the communicating parties, based on a shared random secret, generate the shared key. By knowing the shared random secret one cannot derive the shared secret key. Therefore, it will be computationally difficult for an attacker to generate data, which is validated with an appropriate time stamp. Similarly, replaying previously transmitted data will render the data invalid since the time stamps are encrypted along with the data and, when verified at the receiving end, will not match with the expected value of the time stamp recorded on the receiving device [28].  
In the event that an attacker, by some means, is able to decrypt the captured packet and retrieve the contents of the packet, the node identity, the authentication value, and its time stamp will be available to the attacker. Using them, valid data packets can be generated and spurious data can be sent to the NOC. However, this requires the attacker node to estimate the clock ticks of the active node and replay the packets for them to be accepted by the NOC. If the attacker is able to retrieve the value of the clock ticks of the node it has captured packets from, and regenerates the packets with valid time stamps, then the scheme can be broken. This again breaks assumption 12 in Section 3.1. However, such an attack is not termed as a replay attack, since the packets are re-crafted using the time stamp from the clock tick value synchronized with the node and other values from the captured packets.
## 3 Content of 4(5)-Methylimidazole in Food Matrices  
The food industry draws heavily on caramel-colored colorants. Their production is based on the caramelization of sugars and divided into four classes according to their production method [11; 12]. These are:  
* Class I: Plain caramel color-E150 a;
* Class II: Sulfite caramel color-E150 b;
* Class III: Ammonia caramel color-E150 c;
* Class IV: Sulfite ammonia caramel color-E150 d.  
Among these classes, sulfite ammonia caramel accounts for approx. 70% of the worldwide caramel color production [67]. 4(5)-Methylimidazole is found in ammonia caramel and sulfite ammonia caramel color due to their preparation method. In particular, E150 c and E150 d are produced by heating sugars with ammonium compounds under specified conditions [68; 12]. Ammonia caramel color is commonly used in various bakery products, soy sauces, soup flavorings, coffee, soups, vinegar, and beers. It represents 60% of the total use of caramel colorants in Europe and 20-25% in the USA. Sulfite ammonia caramel color is used in soft drinks, pet foods, and soups [6; 67; 69]. Table 1 presents the concentrations of 4(5)MEI in the colorants E150 c and E150 d, as well as in various foods and beverages. Among them, the highest concentration of 4(5)MEI has been measured in sulfite ammonia caramel color. In food and beverages, the highest concentrations of 4(5)MEI were detected in dark beer, soy sauce, and coffee.  
The presence of 4(5)MEI has also been determined at a concentration of 2.7 \(\mathrm{\SIUnitSymbolMicro m}\mathrm{L}^{-1}\) in the milk of cows fed with adulterated animal feed. The ammonia-treated feed gives an artificially high nitrogen content that entails a significant amount of proteins, which are a key nutrient in the animal diet [74; 75]. Moreover, the possibility of the formation of 4(5)MEI as a product of the Maillard reaction during the heat treatment of milk has been reported [66]. Furthermore, 4(5)-methylimidazole can be formed by pyrolysis and it has been detected in cigarette smoke [76; 77].  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Matrice** & **Concentration (mg kg\({}^{-1}\))** & **Reference** \\ \hline Ammonia caramel color-E150 c & N.D. 1–463 & [17; 67; 70; 71] \\ Sulfite ammonia caramel color-E150 d & N.D.–1276 & [67; 70; 71; 72] \\ Brown sugar & 0.12–0.16 & [53] \\ Coffee & 0.31–1.24 & [17; 18; 43] \\ Tea & 0.009–0.41 & [30] \\ Black beer & N.D.–28.03 & [17; 30] \\ Soda & 0.30–0.36 & [10] \\ Cola soft drinks & 0.05–0.10 & [25; 53; 73] \\ Whiskey & N.D.–0.14 & [73] \\ Wine & 0.04–0.23 & [30] \\ Soy sauce & 0.03–3.51 & [29; 30; 48; 73] \\ Breakfast cereals & 0.14 & [29] \\ Confectioneries & N.D.–0.78 & [73] \\ Bread & N.D.–0.31 & [25; 30] \\ Biscuits & 0.04–0.19 & [25; 29] \\ Chocolate & 0.03–0.16 & [53] \\ Honey & N.D.–0.09 & [30; 53] \\ \hline \hline \end{tabular} \({}^{\dagger}\) N.D.: Not detected.  
\end{table}
Table 1: Content of 4(5)-methylimidazole in caramel colors and food matrices.
###### Abstract  
This study investigates the optimization of satellite constellations for Low-Power, Wide-Area Network (LPWAN)-based Internet of Things (IoT) communications in Very Low Earth Orbit (VLEO) at 200 km and 300 km altitudes and Low Earth Orbit (LEO) at 600 km using a Genetic Algorithm (GA). Focusing on three LPWAN technologies--LoRa, Narrowband IoT (NB-IoT), and Mioty--we evaluate their performance in terms of revisit time, data transmission volume, and economic efficiency. Results indicate that a 300 km VLEO constellation with LoRa achieves the shortest average revisit time and requires the fewest satellites, offering notable cost benefits. NB-IoT provides the highest data transmission volume. Mioty demonstrates strong scalability but necessitates a larger satellite count. These findings highlight the potential of VLEO satellites, particularly at 300 km, combined with LPWAN solutions for efficient and scalable IoT Non-Terrestrial Network (IoT-NTN) applications. Future work will explore multi-altitude simulations and hybrid LPWAN integration for further optimization.  
VLEO; LEO; LPWAN; LoRa; NB-IoT; Mioty; IoT-NTN Article
###### Abstract
The core of these algorithms lies in decision-making methods, typically categorized into Pareto dominance-based [14], index-based [15], and decomposition-based approaches [16]. However, for large-scale multi-objective optimization problems like SDTSP, traditional MOEAs often perform poorly, and research on large-scale multi-objective optimization algorithms remains limited. Based on the Pareto dominance method, this paper develops a multi-objective SDTSP model to maximize transmission efficiency while minimizing latency. In addition, we enhance the minimum angle selection strategy of the particle swarm optimization (PSO) algorithm. This improvement effectively identifies global and local optimal particles, mitigating the risk of local optima and enhancing the efficiency of solving the SDTSP. To assess the effectiveness of this method, we conducted simulation experiments on tasks of different sizes. The findings show that the proposed method displays robust generalization capabilities and substantially enhances the efficiency of satellite data transmission tasks. The contributions of this manuscript are as follows:  
1. The SDTSP was modeled as an optimization problem with two objectives: minimizing the satellite transmission time and maximizing task gain simultaneously;
2. We proposed a modified MAPSO algorithm to solve the multi-objective SDTS problem. The proposed algorithm was encoded as a discrete optimizer, and the strategies were modified to enhance its searchability. Here is the organization of this paper: Section 2 introduces the satellite data transmission problem and establishes its mathematical model. Section 3 describes our MAPSO based on the problem's key characteristics. Section 4 details the experimental design and evaluates the findings. Finally, Section 5 offers conclusions and suggestions for further research.