esa-sceva/satcom-chunk-collection huggingface dataset:
# Monitoring and Improving Aircraft Maintenance Processes Using IT Systems
## 4 Data Acquisition Tool--IT System Design
### Functional Characteristics of the Mobile Application  
The mobile application is dedicated to aircraft maintenance technicians. Its main task is to collect data from the service process regarding the time of implementation of individual maintenance tasks. The full functionality of the application has been presented using a UML use case diagram (Figure 4).  
Figure 4: UML use case diagram—application functionality.  
The functionalities described above are organized into three tabs within the application, accessible through a menu typical of the Android system (Figure 5a). This menu is activated by either swiping from the left side of the screen or tapping the icon located in the upper left corner of the action bar. This design provides a user-friendly way to manage the application without interfering with the currently active view.  
The most important application feature in the case of data acquisition is the tab \"Your task/Twoje zadania\". The tab \"Your task\" is launched by default right after starting the application, which contains the most important information from the user's point of view, their identification data, and tasks assigned to their specialty (Figure 5b). The user, after pressing the \"Start\" button, starts the implementation of the maintenance task and, after its completion, presses the \"Stop\" button. After pressing the \"Stop\" button, a question message appears on the device screen for the confirmation of the correctness of the data.  
Once confirmed, the application calculates the duration of the technical task and transmits this information, along with the ID of the user performing this task, to a server database.  
The designed application was successfully verified in a test environment, operating inside its own separate wireless network. Data from the course of CPFI maintenance were collected via the application, which will be used to study the maintainability of the M-346 aircraft. Thus, the usefulness of the application was confirmed.  
Figure 5: An image of the mobile device screen showing: (**a**) application menu; (**b**) “Your Tasks” tab.
# A Delay-Aware and Reliable Data Aggregation for Cyber-Physical Sensing
## 5 Performance Evaluation  
In this section, the simulation results are demonstrated to evaluate the TSR-DARDA scheme. The parameter settings are described firstly, focusing more on the network model applied in simulation experiments. Then, we present the evaluation results for the proposed routing scheme. Finally, some comparisons with existing routing mechanisms are presented.
## 3 Results
### Frost Resistance  
The resistance of concrete to frost (Table 4), i.e., the ability to maintain proper structure and strength with the use of a specified number of freezing and defrosting cycles, was tested according to the standard [40]. The test was carried out in a low-temperature chamber (Figure 13), which allows for cyclic freezing and defrosting of the tested material. The samples were subjected to 25 freezing and defrosting cycles.  
After completion of the test, the samples were inspected in the low-temperature chamber and no visible damage was found. Then, the average weight loss was examined:  
\[\bigtriangleup G=\frac{G_{1}-G_{2}}{G_{1}}\cdot 100\% \tag{3}\]  
where: \(G_{1}\)--average sample weight before freezing (saturated with water); \(G_{2}\)--average sample weight after freezing (saturated with water);  
The average decrease of compressive strength is calculated by  
\[\bigtriangleup R=\frac{R_{1}-R_{2}}{R_{1}}\cdot 100\% \tag{4}\]  
Figure 13: Test of frost resistance (**a**) samples in the chamber, (**b**) low-temperature chamber.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Sample**} & \multicolumn{2}{c}{**Compressive Strength**} & \multicolumn{2}{c}{**Maximum Height of Water Absorption [mm]**} \\  & **F\({}_{m}\) [kN]** & **R\({}_{m}\) [N/mm\({}^{2}\)]** & **At the Edge** & **Average** & **In the Middle** & **Average** \\ \hline SC III & 1437.5 & 63.9 & 47 & & 65 & \\ SC IV & 956.8 & 42.5 & 40 & 39 & 47 & 57 \\ SC V & 930.7 & 41.4 & 30 & & 59 & \\ \hline CR III & 1242.2 & 55.2 & 40 & & 68 & \\ CR IV & 1206.3 & 53.6 & 55 & 44 & 80 & 69 \\ CR V & 1437.7 & 63.9 & 38 & & 60 & \\ \hline BC III & 641.6 & 28.5 & 58 & & 100 & \\ BC IV & 760.8 & 33.8 & 50 & 54 & 60 & 86 \\ BC V & 699.1 & 31.1 & 54 & & 97 & \\ \hline \hline \end{tabular}
\end{table}
Table 3: Results of watertightness tests.  
where: \(R_{1}\)--average compressive strength of unfrozen samples; \(R_{2}\)--average compressive strength of the cubes after the last cycle is thawing.  
The recommendations of the standard [40] state that the frost resistance test should be carried out on 12 samples. However, due to the limited amount of aggregate available, it was decided to limit the number of samples to four for each mixture. According to the recommendations of the standard [40], in order to classify concrete to a given frost resistance class (number of cycles), samples after the test must not show visible signs of damage, weight loss must not exceed 5%, and compressive strength loss must not exceed 20% in relation to samples not subjected to cyclic freezing and defrosting. All tested samples met the requirements for minimum F25 frost resistance class.
# Converging Telco-Grade Solutions 5G and beyond to Support Production in Industry 4.0
## 4 5G Functions and Features to Support the Information Flow within Industry 4.0
### Lessons Learned  
MES and ERP systems as well as other coupled services lay in the business and enterprise level of the Industry 4.0 automation stack, therefore they mostly rely upon the software stack itself. However, due to latency and data rate requirements, communication solutions are key factors within this field. The most important driver in this field is real-time decision making and controlling the production plane. These require real-time data gathering and processing, as well as feedback and control mechanisms that are flexible in their communication methods. To enable such a real-time operation, 5G features such as very low latency, high datarate, and network slicing can be utilized in each level of the automation stack. The most important parts of the stack that were discussed before is (i) the SCADA systems, (ii) low-level subsystems such as PLC-s, HMI-s, and (iii) Cloud level, where 5G can be used to ensure that the system meets the requirements for specific applications.
# Coffee-Leaf Diseases and Pests Detection Based on YOLO Models
## 3 Materials and Methods
### YOLO Family
#### 3.5.2 YOLOv9  
The YOLOv9 [16] architecture is based on the advances of YOLOv8, with significant advances in the detection of objects in real time, standing out for its efficiency, precision, and adaptability. This model features innovations that optimize performance and the retention of essential information in deep neural networks.  
One of the main innovations introduced by YOLOv9 is Programmable Gradient Information (PGI). This technique was developed to mitigate the loss of information in the deeper layers of the network, a recurring problem in neural architectures. PGI preserves crucial data throughout the feedforward process, allowing for the generation of reliable gradients that improve model updating. As a result, performance in detection tasks is considerably improved, even in complex scenarios.  
YOLOv9 is available in five variants, including YOLOv9t, YOLOv9s, YOLOv9m, YOLOv9c, and YOLOv9e, offering different configurations to balance speed and accuracy, following the same philosophy as previous YOLO versions. Its main improvements include:  
* The YOLOv9 backbone uses CSPResNeXt50, a new backbone architecture that combines CSP (Cross-Stage Partial) links with ResNeXt blocks. This integration allows for a more efficient representation of features, while reducing the complexity of the model.
* The YOLOv9 neck features a modified PANet (Path Aggregation Network), incorporating DCN (Deformable Convolutional Networks). These networks dynamically adjust their receptive fields according to the geometric variations of the objects, improving the detection of varied shapes and sizes.
* The YOLOv9 head maintains the multi-scale detection approach present in previous versions, but adds new refinement steps. This improves the accuracy of bounding boxes and class predictions, making detection more reliable.
# Impact of Post-Earthquake Seismic Waves on the Terrestrial Environment
## 2 CCSD Long-Term Crustal Movement Observations  
The CCSD project was completed in 2005 by a borehole of 5158 m and achieved fruitful results [3; 4; 5; 6; 7; 8; 9; 10; 11; 12]. In February 2007, the Institute of Geology of the Chinese Academy of Geological Sciences (CAGS) began to use this borehole to establish a long-term crustal movement observation station (referred to \"CCSD-LOS\" below). The CCSD-LOS is located in the eastern part of the Dabie-Sulu ultra-high metamorphic belt and is the first long-term observation experimental base with an observation depth of more than 4000 m in China. The corresponding author of this paper is the scientific consultant and planning master of the CCSD-LOS. Senior engineer [PERSON] is responsible for the installation of the instruments and equipment, and [PERSON], the first author of this paper, is responsible for data processing and management [11; 12; 13; 14; 15].  
The CCSD-LOS is located at Donghai County in Jiangsu Province, with latitude of 34.4\({}^{\circ}\) N and longitude of 118.5\({}^{\circ}\) E (Figure 1a). In 2010, the CCSD-LOS installs two sets of high-precision digital seismographs in the borehole at four depths (544 m, 1559 m, 2545 m, and 4190 m) and on the ground as well. Compared with the single groundobservation, deep-well seismic observations provide much better signals. A new 452 m hole was drilled at the CCSD-LOS in 2015. More than a dozen observational instruments, including high-precision measurements of stress-strain components, tilt, pore pressure, displacement, radon concentration, geomagnetic, temperature and others [14]. The station aims to achieve the long-term monitoring of crustal movement in eastern China. Because it is only 24 km apart from the Tanlu fault zone, its observation of overtime records may show the mechanical behavior of the active faults, and the changes of regional human living environment as well. The observation borehole of the CCSD-LOS is shown in Figure 1b, some underground probes are shown Figure 1c, and its supervisory control room is shown in Figure 1d.
# Numerical Investigation of Slurry Fracturing during Shield Tunneling under a Reservoir
## 3 Verification of Numerical Solution
### Blind-Hole Samples Model Setup  
Modeling is based on the blind-hole cylindrical samples involved in [PERSON]'s study. Since the sample is an axisymmetric body, a two-dimensional plane strain model is established by taking its central axis section. The specific dimensions of the model are provided in Figure 5.  
The slurry injection point is preset at the upper right corner of the blind-hole. Since the initial fracture needs to be preset when using the extended finite element to simulate slurry fracturing, a fracture with a length of 0.1 mm is preset. With reference to the angle of the fracture surface of the samples in the samples fracturing test, the preset crack angle is set to 45\({}^{\circ}\). The injection rate of slurry is 1 mL/s, which is consistent with the test. The established sample model is meshed with node elements, and the grid size is 0.5 mm. The mesh type is plane strain pore compression integral units (CPE4 RP). The preset cracks use truss elements, and the mesh size is also 0.5 mm.  
[PERSON] et al. measured the relevant parameters of the sample in detail in the slurry fracturing test of the blind-hole sample. In order to compare the simulation results with the test results, the parameters set by the modeling in this paper are selected according to the parameters of the test samples [17]. The specific mechanical parameters of the samples in the study are provided in Table 1. Among the parameters found in the list, type I and type II fracture energies are calculated by the formula Equations (10) and (11).  
\[G_{I}=\frac{K_{IC}^{2}}{E} \tag{10}\]  
\[G_{II}=\frac{K_{IIC}^{2}}{E} \tag{11}\]  
where \(K_{IC}\) and \(K_{IIC}\) is the fracture toughness of type I and type II, respectively, and \(E\) is the elastic modulus.  
When setting the model displacement boundary conditions, set the bottom as a fixed constraint. When setting the stress boundary conditions of the model, the normal stress perpendicular to the boundary is applied to the inner and outer boundaries of the model, and stress is the confining and axial pressure in the test. The boundary conditions of the model are provided in Figure 6. The initial saturation defaults to 1. The initial void ratio is 0.1. The initial pore pressure is 0 MPa. At the same time, define the amplitude curve and set it to slowly increase the injection speed for the first 10 s to prevent shock fluctuations.  
Figure 5: Model size.  
The numerical simulation process of sample slurry fracturing includes two analysis steps: (i) ground stress balance analysis, which uses geometric nonlinear and asymmetric equations to solve; at this stage, an initial stress field is formed and the initial deformation is zeroed out, and (ii) soil analysis, which simulates the slurry injection process and continues to inject slurry at preset fractures until the fracture penetrates the model.
## 2 Review of Related Works and Technological Trends  
### Review of Related Technologies  
Unlike standalone autonomous driving, truck platooning refers to a synchronized driving mode in which the leading vehicle is manually driven while the following vehicles autonomously operate in a platoon based on driving information (e.g., speed, acceleration, and steering) obtained from the leading vehicle through V2X communication. In this context, the intervehicle distance within the platoon is defined in terms of the time gap, measured in s.  
Truck platooning is generally performed in four stages: (1) general driving, where vehicles travel either before joining or after leaving the platoon; (2) platoon formation, in which the following vehicle requests to join the platoon, and upon approval from the leading vehicle, longitudinal control (pedal release) and lateral control (steering release) are automated sequentially; (3) platooning maintenance, where vehicles in the platoon continue to drive in a synchronized state; and (4) platoon dissolution, where the following vehicle requests to leave the platoon and, upon approval from the leading vehicle, returns to manual longitudinal (pedal operation) and lateral (steering operation) control. In addition to the platoon formation and dissolution phases, emergency response procedures are activated when general vehicles enter the platoon or when emergency braking is required.  
In such situations, the following vehicles automatically adjust their distances and perform synchronized braking when the leading vehicle suddenly applies brakes [8]. Most studies on platooning have been conducted using highways as the spatial background as their simpler road structure, traffic flow, signal systems, intersections, vehicle densities, and lane-changing frequencies make them more technically feasible than urban roads [9].  
The see-through functionality enables the leading vehicle of the platoon to transmit real-time forward-facing traffic footage to the following vehicles. The leading vehicle captures the real-time traffic situation and transmits it to the following vehicles through V2X communication. The following vehicles display the footage through a human-machine interface (HMI), allowing drivers to recognize the traffic situation ahead of the platoon. This functionality could contribute to enhancing the psychological stability of following drivers under distressing conditions such as abrupt braking.
# Integrating ISA and Part-of Domain Knowledge into Process Model Discovery
## 3 Preliminaries
### Representation Formalism (Process Model)  
In SIM, as in many process mining approaches [1], we represent the process model as a direct graph, where nodes represent activities and arcs indicate the precedence between them. For the sake of compactness, in SIM, besides \"_simple_\" nodes, which represent an activity, we also support \"_any-order_\" nodes, labeled by a set of activities separated by \"&\" (e.g., A1&A2&A3) and representing the fact that such activities can be executed in any order (e.g., A1&A2&A3 denotes six different patterns of activities: A1A2A3, A1A3A2, A2A1A3, A2A3A1, A3A1A2, A3A2A1). When multiple arcs exit a node, the node itself represents an XOR splitting point.  
Our running example starts with the log tree in Figure 1, which is the result of the Build-Tree operation on a real-world log, regarding the management of the stroke disease in the acute phase. This log contains 200 traces, expressed as sequences of 15 actions, on average, counting 14 different action types. Each trace describes the activities (tests, exams, and therapies) performed to treat the patient from her/his arrival in the stroke unit to her/his dismissal or transfer to another hospital ward. In the log tree in Figure 1, after the process starts (#), the _blood test_ and _computerized tomography_ (\(BT\) and \(CT\), respectively) are executed in any order, followed by _neurological evaluation_ (_Eval_). From the latter, an XOR split leads to three main paths: on the top, the sequence of activities is composed of _magnetic resonance with contrast_ (_MR DW_), _electrocardiogram_ (_ECG_), _intravenous thrombolysis_ (_r-TPA_), _ECG_, _CT_, _acetylsalicylic acid administration_ (_ASA_), and _rehabilitation_ (_relab_). The middle path is composed of _ASA_, _angiogenic CT_ (_AngioCT_), _intra-arterial thrombolysis_ (_TPA ia_), _ECG_, _CT_, _ASA_, and _rehab_. At the bottom, the third path is composed of _ECG_, _magnetic resonance_ (_MR_), _ASA_, _anti-hypertensive drugs administration_ (_Anti-\(HP\)_), _ECG_, _CT_, _ASA_, and _rehab_. Additional paths stem from the top and middle main routes as XOR splits. A final dummy activity ($) is inserted to reunite all the paths of the log tree to the end of the process.
# Jumping Locomotion Strategies: From Animals to Bioinspired Robots
## 2 Jumping Animals and Biomimetic Miniature Jumping Robots
### Specialized Jumping Performance: Aquatic Environments
#### 2.3.2 Dolphin-Inspired Jumping Robots  
The entire leaping process is divided into five phases: (i) acceleration phase; (ii) surfacing phase; (iii) take-off phase; (iv) flight phase; (v) diving phase [199]. Dolphins generate large thrust when swimming at almost constant depth in acceleration phase. This is the key reason why dolphins can leap out of water.  
Researchers at the Cornell University studied the strategies that aquatic animals, like tiny crustaceans and huge dolphins, use to leap out of the water. They developed a simple robot that uses catapult mechanism inspired by animals to investigate the important parameters in the \"water entry and exit\" behavior of animals. They concluded that the streamlined body shape is very important because the designed robot takes too much water after leaping out of water [200]. In addition, the click mechanism used by this inelegant robot to leap out of water is rare to see in aquatic animals.  
[PERSON] et al. modeled the leaping of dolphins and found that the main important parameters are the muscle power and the streamline body shape [199]. Based on this, they developed a dolphin-inspired jumping robot, which emphasizes the powerful tail and streamlined body; meanwhile they developed active leaping control methods for a self-propelled, fast-moving dolphin robot [201]. The proposed dolphin robot can leap with a velocity of 2.3 body length per second and it jumps out of water with angle ranging from 35\({}^{\circ}\) to 60\({}^{\circ}\). Meanwhile, this robot can be applied to explore and monitor the environment around the water.  
In addition, studies developed robots that jump out of water with combustion gas source [202; 203; 204; 205].
## 4 What Is NFV?
### Challenges and Solutions
#### 4.4.2 RAN: Challenges and Solutions  
In a RAN, some critical tasks require swift execution. These include Layer 1 processing tasks like bit interleaving, modulation, and encoding. Additionally, the scheduling function, which organizes the order of data packet transmissions and Hybrid Automatic Repeat Request (HARQ)-related baseband processing are also time-critical. There is a need to ensure that the total delay plus jitter of the fronthaul does not exceed 3 milliseconds for HARQ-baseband processing to avoid a loss in air interface throughput [105]. However, virtualizing functions like these could introduce delays, impacting the QoS and user experience [105]. Two acceleration methods, in-line and look-aside, address these challenges. In-line acceleration handles Layer 1 tasks before data reaches the main CPU, freeing it for higher-layer processing. It is efficient and reduces delays. Look-aside acceleration allows the CPU to handle Layer 1 processing while a separate card manages specific functions, providing flexibility to offload tasks [106]. Thus, the in-line accelerator will handle one process at a time, whereas the look-aside accelerator can handle multiple dataflows. On the other hand, the look-aside accelerator will also use more time for transmission between accelerator and CPU, whereas the the in-line option is more scalable due to the separation of accelerator and CPU [107]. Benefits and caveats of the two processing options are examined in [108].  
Figure 12: NFV mechanisms such as prevention, correlation, recovery, resiliency, and detection for improved availability and reliability [104].  
Both in-line and look-aside accelerations have their advantages and disadvantages, and the decision depends on the RAN's requirements and the characteristics of time-sensitive tasks. Some RAN vendors, including Nokia, believe in in-line acceleration, whereas other vendors, including Ericsson, believe in look-aside acceleration [109].
# CoSiWiNeT: A Clock Synchronization Algorithm for Wide Area IIoT Network
## 3 WANs: Time Data Measurement and Analysis
### NTP Measurement in Wide Area Network  
The same WAN was also analyzed by exchanging NTP messages at a variable polling interval decided by the NTP algorithm between two machines placed in two different cities. The server machine was configured as a 'NTP server' and the client machine as a 'NTP client'. The NTP server itself was synchronized to the nearby stratum-2 public server from the NTP pool infrastructure. The 'NTP client' synchronizes to the 'NTP server' by continuously exchanged NTP messages carrying timestamps. The time and frequency offsets resulting from the NTP algorithm were stored in various log files such as loopstats and peerstats. These values were used for comparing NTP performance with other clock synchronization methods.