esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction  
In sweet potato transplanting operations, bare seedlings are usually used for transplanting, but the seedlings easily become entangled with each other, increasing the difficulty of seedling separation [1, 2]. Therefore, manual seedling separation combined with mechanical transplanting is currently employed, with an average operational efficiency of 30-40 plants per minute [3, 4, 5, 6]. In order to enhance the automation level of sweet potato transplanters, our research team developed a sweet potato seedling belt transplanter. Sweet potato seedlings are heat-sealed at certain intervals between two layers of low-density polyethylene (LDPE) film belts, which are 120 mm wide and 70 \(\upmu\)m thick, to form a seedling belt; the seedlings are fixed between the two layers of film. The seedling belts are wound into rolls and placed in the seedling belt transplanter for automated transplanting, eliminating manual seedling separation and thereby improving transplanting efficiency [7, 8, 9, 10]. During the transplanting process, the transplanter's separation mechanism peels apart the two layers of film belts, allowing the sweet potato seedlings to be individually separated and ultimately planted into the soil by the planting mechanism. The entire process achieves automated transplanting through seedling belt feeding.  
Heat sealing is a critical step in the production of seedling belts; the heat sealing strength determines the peeling efficiency and stability of the belts. If the strength is too low, it may cause the seedlings to fall off or misalign; if too high, it may lead to tearing or breaking of the seedling belt. Appropriate heat sealing parameters ensure smooth peeling of the belt, thereby guaranteeing normal transplanting of sweet potato seedlings. As a thermoplastic polymer, LDPE film becomes fluid and adhesive when two layers are heated and pressed, and the temperature in the heated area approaches the material's melting point. The intermolecular attraction decreases, and under pressure, the two layers in the contact area fuse together to form a firm seal [11; 12; 13]. The sealing strength is influenced by three factors: temperature, heating time, and pressure. By adjusting these parameters, different heat sealing strengths can be achieved. Therefore, to obtain the optimal heat sealing strength, we designed a heat sealing device and optimized its components to study the effects of temperature, time, and pressure on the sealing strength of the seedling belt, aiming to determine the optimal combination of heat sealing parameters.
# Model Experimental Study on De-Icing Method of Bridge Pylon Beam Based on Electric Heating
## 2 Model Test Design  
### Principal Idea of Tests  
The main idea of this paper is to first obtain the icing characteristics of the beam through icing tests in order to develop targeted de-icing measures. Then, the heating system is installed in the selected de-icing area, and the de-icing tests are subsequently carried out. Finally, the test environment and heating method are changed to compare the de-icing effects under different conditions and to give a recommended de-icing solution. The schematic of the principal idea is shown in Figure 1.  
The detailed technical scheme of this paper is as follows:  
1. The icing characteristics (ice distribution and icing features) of the beam are obtained through pre-tests of the icing. By learning the ice distribution, a suitable de-icing area is selected. Subsequently, the growth rule of the icicles is assessed by analyzing the length, diameter, and tilt angle of the icicles, and finally, the reliability and stability of the test conditions are verified by comparing them with the established icing literature.  
Figure 1: Schematic diagram of the de-icing test idea of the beam.  
2. The heating system is installed at the selected area, and the anti-icing tests are carried out; these tests focus on recording the temperature change and the ice coverage of the heating system surface during the anti-icing process. The test results are analyzed to find the critical anti-icing temperature of the heating system and to give the recommended pre-heating time, a suggested environmental range for the heating system, and an alternative optimized compensation scheme.
3. Passive de-icing tests are carried out to obtain the de-icing time under different ice thicknesses. Subsequently, the passive de-icing of the heating system for the beams is evaluated qualitatively and quantitatively by comparing the relevant de-icing literature. The technical route is shown in Figure 2.
# Design of a Baseband Signal for the 406 MHz Satellite Emergency Radio Transmitter Based on STM32
## 3 Baseband Signal Generation METHOD
### Data Bits Design
#### 3.3.6 Design of Emergency Code  
In the message of bit 107 to bit 112, namely the emergency data from bit 0 to bit 5. The data present in the emergency code are listed in Table 4.  
Table 5 shows the table of emergency codes recommended by the IMO for the type of distress [11].
## 2 Materials and Methods
### System Definition
The transport distances to the refinery are covered by pipelines based on the European pipeline network [69]. Final transport to the airport is conducted by trucks. The distances for road transport were determined via the Searates website (www.searates.com (accessed on 1 October 2021)). At the airport, storage takes place in tanks. The energy demand for producing the fossil kerosene and the four types of SAFs were obtained from the scientific literature and industry data [36; 66; 67; 68]. The production costs were calculated based on the energy demand during production, the country-specific electricity price, and the scientific literature concerning the cost of SAF production [19; 38; 66; 67; 68]. For analysis of flight operation, a reference flight over a distance of 2000 km with a load of 160 passengers, including luggage, was considered. This corresponded to a flight from Frankfurt am Main, Germany to Moscow, Russia. The flight lasted 185 min, with a take-off and climb time of 45 min and a cruise and landing time of 140 min. The functional unit for the analysis was 100 pkm traveled on a 2000 km medium-haul flight with a load of 160 passengers, including luggage. According to the Lufthansa Group, such a flight consumes 4.12 L of kerosene (specific energy of 39.2 kWh) per 100 pkm [70], representing the fuel consumption within this study. The combustion behavior of the fuels and the resulting emissions were derived from the scientific literature [71; 72]. The impact assessment within this study was based on two life cycle impact assessment methods: one for each considered sustainability dimension. As a common method of environmental assessment, the ReCiPe Midpoint method is used [57], and a life cycle cost-based approach is used for the economic assessment [59]. Seven impact categories were analyzed for the environmental assessment (see Table 2). Based on the objectives of the Flightpath 2050 strategy, these were _climate change_ (CC) and _photochemical oxidant formation_ (POF) as well as, due to health-related impacts, _particulate matter formation_ (PMF) and _human toxicity_ (HT). In addition, _fossil resource depletion_ (FRD) was analyzed, since raw materials such as crude oil and natural gas are of fossil origins and fossil energy carriers are often used to generate electricity. _Agricultural land occupation_ (ALO) and _mineral resource depletion_ (MRD) were analyzed regarding the use of agricultural land to cultivate bio feedstocks and the need for mineral resources to build refinery and synthesis plants. For the economic assessment, the costs associated with the fuel supply chain and flight operation were assessed. These costs are referred to as _life cycle costs_ (LC). Revenues were not considered as they were mainly determined by the airline's pricing strategy and were not directly related to the fuel used. The calculation model for the inventory analysis and impact assessment was implemented in Python using the Brightway2 framework [73].
## 1 Introduction  
There are several thousands of closed landfills in Poland that have not been investigated for an Environmental Risk Assessment or reclamation plans [1]. From a practical point of view, contaminated site investigations are usually based on geological and geotechnical techniques. These methods allow soil and water sampling for analytical tests and permit direct measurements of the contamination level of the study site [2; 3; 4]. However, such methods result in sampling bias, as the material is usually collected locally, from particular boreholes or piezometers. For the spatial assessment of contaminated soil layers within landfill subsoil, direct methods should be accompanied and extended by using geophysical investigations, such as an electrical resistivity method or another similar technique [5; 6; 7; 8; 9].  
The results of soil resistivity tests for contaminated sites generally reveal a close relationship between free electrolytes, oils, and salt solutions [10; 11; 12; 13; 14; 15], which are also the main components of leachate infiltrating from landfills. On the basis of the research results and gained experiences, the method for an assessment of environmental contamination for four independent landfills, was established and then verified. Three of these sites are municipal landfills (one with leakage detected at the base, another without any signs of soil-water environmental contamination, and the other with no containment system installed) and one is a former industrial landfill, located near an environmentally precious area without a sealing liner at the bottom. Two of the study sites are located in the central part of Poland, one is in the east, and the other is in the northern region. All of the sites are provided with a monitoring program, extended to electro resistivity tests, where the capability of using geophysical methods on such sites, was analysed. The proposed investigation helped to decide upon the methodology for testing, but more importantly, allowed the precise detection of a contaminant's leakage location and the final reclamation plans for landfills.  
The geophysical testing of the study sites confirmed that the methodology was appropriate for the geological and environmental surveying purposes. However, it is important to bear in mind that a comprehensive interpretation of the results is only possible when reliable geological and geochemical data are available [7; 11; 16; 17; 18].
# Real-Time Text-to-Cypher Query Generation with Large Language Models for Graph Databases
## 4 Concept
### Objectives of a Solution  
In line with this idea, the goal is to develop an application that takes a modular approach to integrating and querying different databases in natural language, with a particular focus on querying using CQL in Neo4j. On a more granular level, the following objectives of the solution are of particular importance. These are derived from the existing literature and our own assessment. The primary objective of this research is to enhance the natural language interface. The aim is to develop a system that can seamlessly translate natural language queries into CQL without any intermediate substructures. This advancement will allow users to interact with Neo4j graph databases using conversational language, making the process of accessing data both intuitive and user-friendly. In further stages of this approach, this will be a key aspect to make graph database technology more accessible to a wider audience, including those without technical expertise in database querying. Another important goal is to improve the accuracy of these CQL queries. It is important that the queries generated from natural language are correct. If this is not the case at the first attempt, the solution includes mechanisms to correct the query and proceed with optimizing the Cypher queries. In addition, there is a focus on facilitating complex data retrieval. The system will be designed to handle more complex queries, allowing users to access and retrieve data relationships stored in graph databases. Ensuring scalability and performance is also a key consideration. The system should be able to manage different types of datasets and graph schemas while maintaining high performance in data processing and query execution. In addition, the research aims to promote integration and compatibility. The proposed solution will be designed to easily integrate with existing systems and be compatible with various databases and large language models, ensuring its adaptability and long-term utility. Lastly, a user-friendly interface is another crucial aspect of our project. The success of the model depends on its ease of use, underlining the need for an interface that simplifies interactions and enhances the overall user experience.
## 4 Numerical Results
### Computation Time  
As mentioned before, the equation to be solved is an equation with 200,000 unknowns. The resolution direct is conducted by the preconditioned conjugate gradients method with the tolerance set to \(10^{-12}\). All the times discussed are the CPU time by MATLAB(r). The resolution time is defined as the time to solve Equation (11) without considering the time to generate the matrix \(\mathbf{K}\) and vector \(f\).  
The computation time of direct resolution is 10.07 s, and the computation time of DD (1000 iterations) is 469.78 s, while the computation time of DD-POD is 24.35 s if it stops when the relative error of current reaches 1% (20 iterations), which is much faster than DD.
## 4 Experimental Setup
### Baselines  
To comprehensively evaluate the performance of various KGC approaches, we select a set of representative embedding-based and LLM-based models as baselines across multiple benchmark datasets, with configurations tailored to the characteristics of each dataset. On FB15K-237 and WN18 RR, the comparison includes classical embedding models such as TransE [7], TuckER [43], NBFNet [44], and SimKGC [9], alongside mainstream LLM-based methods including zero-shot and one-shot ChatGPT [45], retrieval-augmented KICGPT [14] with in-context prompting, as well as instruction-tuned models such as DIFT [19]. For the UMLS medical knowledge graph, the CP-KGC [46] framework is included as a key baseline. This method integrates structural encoders like KG-BERT [40] and SimKGC [9] with semantic compression through interactions with ChatGPT (GPT-3.5-Turbo), enabling effective zero-shot triple classification with strong adaptability to biomedical domains. These baselines reflect the prevailing paradigms in KGC research and provide a robust foundation for evaluating model adaptability across different architectures and domains.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Datasets** & **\#Entity** & **\#Relation** & **\#Train** & **\#Valid** & **\#Test** \\ \hline UMLS & 135 & 46 & 5216 & 652 & 661 \\ WN18 RR & 40,943 & 11 & 86,835 & 3034 & 3134 \\ FB15K-237 & 14,541 & 237 & 272,115 & 17,535 & 20,466 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Descriptions and statistics of benchmark datasets.
# Application of Machine Learning Techniques to Predict a Patient's No-Show in the Healthcare Sector
## 2 Materials and Methods
### Data Preprocessing  
#### 2.2.1 Data Collection  
The dataset used in this study consists of data obtained and extracted from the University of Vale do Itajai Center of Specialization in Physical and Intellectual Rehabilitation (CER). The CER is an outpatient care service that performs diagnosis, assessment, guidance, early stimulation and specialized care. It has acted in functional rehabilitation and psychosocial qualification to encourage the autonomy and independence of people with disabilities [21]. Firstly, we collected the relevant information-on the absenteeism problem-in loco at the rehabilitation centre by transcribing 4812 medical records from an electronic spreadsheet of 2017 and 2019. In the initial dataset, each file is composed of the following attributes:  
1. \"Medical record number\": unique identifier of the patient's record;
2. \"Gender\": male or female gender of the patient;
3. \"Appointment Date\": appointment date scheduled;
4. \"Attended\": given whether the patient attended the scheduled appointment or not;
5. \"No-show Reason\": description of the reason why the patient did not attend the scheduled appointment;
6. \"Type of Disability\": the patient's motor or intellectual disability;
7. \"Date of Birth\": the patient's date of birth;
8. \"Date of Entry into the Service\": date of the patient's first appointment at the CER;
9. \"City\": city where the patient resides;
10. \"ICD\": identifier of the patient's disease;
11. \"UBS\": basic health unit that sent the patient to be treated at the CER.  
The dataset contains a target feature, identified by the variable \"Attended\" in which: \"no\" represents a patient that did not attend the medical appointment, and \"yes\" represents a patient that showed up. Unlike a system that performs a task by explicit programming, a ML system learns from data. It means that, over time, if the training process is repeated and conducted on relevant samples, the predictions will be more accurate.
# Adversarial Hierarchical-Aware Edge Attention Learning Method for Network Intrusion Detection
###### Abstract  
The rapid development of information technology has made cyberspace security an increasingly critical issue. Network intrusion detection methods are practical approaches to protecting network systems from cyber attacks. However, cyberspace security threats have topological dependencies and fine-grained attack semantics. Existing graph-based approaches either underestimate edge-level features or fail to balance detection accuracy with adversarial robustness. To handle these problems, we propose a novel graph neural network-based method for network intrusion detection called the adversarial hierarchical-aware edge attention learning method (AH-EAT). It leverages the natural graph structure of computer networks to achieve robust, multi-grained intrusion detection. Specifically, AH-EAT includes three main modules: an edge-based graph attention embedding module, a hierarchical multi-grained detection module, and an adversarial training module. In the first module, we apply graph attention networks to aggregate node and edge features according to their importance. This effectively captures the network's key topological information. In the second module, we first perform coarse-grained detection to distinguish malicious flows from benign ones, and then perform fine-grained classification to identify specific attack types. In the third module, we use projected gradient descent to generate adversarial perturbations on network flow features during training, enhancing the model's robustness to evasion attacks. Experimental results on four benchmark intrusion detection datasets show that AH-EAT achieves 90.73% average coarse-grained accuracy and 1.45% ASR on CIC-IDS2018 under adversarial attacks, outperforming state-of-the-art methods in both detection accuracy and robustness.  
graph neural networks; network intrusion detection system; hierarchical intrusion detection; adversarial robustness; edge attention network +
Footnote †: journal: Applied sciences  
Article
# A Review of the Evolution Characteristics and Argillization of Clay Interbeds in Rockslides
## 4 Observation Technique and Analysis Method of Clay Interbed
### Observation Technique of Clay Interbed
#### 4.1.1 Digital Speckle Imaging Technology  
Digital speckle imaging technology is non-contact scanning, which does not damage the sample. Moreover, it is suitable for analyzing the three-dimensional deformation characteristics, stress, and strain analysis of samples under static conditions, and it can automatically visualize the output results for easy observation and recording.  
Through digital speckle imaging technology, the position and rupture angle of the broken surface could be determined in the unconfined compression test, and surface deformation could be observed under the unidirectional stress of clay interbeds. It has been proven that digital scatter imaging technology was of great help in the evolution of the entire process [57].
# An Object Detection Method Based on Feature Uncertainty Domain Adaptation for Autonomous Driving
## 4 Experimental Analysis
### Experimental Results of KITTI to KITTI Rainy  
Table 3 presents the experimental results of KITTI to KITTI rainy. All models were trained on the original KITTI dataset and tested on the constructed KITTI rainy. The proposed FUDA achieved a remarkable mAP improvement of 51.8% compared to the baseline model FRCNN without domain adaptation training and a 4.3% improvement compared to SWDA.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Methods** & **Person** & **Rider** & **Car** & **Truck** & **Bus** & **Train** & **Motor** & **Bicycle** & **mAP** \\ \hline FRCNN [1] & 22.3 & 23.8 & 30.6 & 15.3 & 28.9 & 15.3 & 18.1 & 31.6 & 23.2 \\ DA [25] & 31.5 & 33.1 & 46.2 & 29.8 & 38.5 & 26.2 & 31.7 & 26.1 & 32.9 \\ SWDA [26] & 33.8 & 46.4 & 48.3 & 29.7 & 38.9 & 35.2 & **38.2** & 31.3 & 37.7 \\ FUDA (Ours) & **38.4** & **48.2** & **56.9** & **35.1** & **42.8** & **39.6** & 35.2 & **40.3** & **42.1** \\ \hline Target & 44.2 & 53.8 & 58.3 & 41.7 & 49.6 & 45.7 & 42.4 & 47.2 & 47.9 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Experimental results (%) of Cityscapes to Cityscapes rainy. Bold indicates the highest result.  
Figure 6: The PR curve of Cityscapes to Cityscapes foggy.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Methods**} & \multicolumn{2}{c}{**Car**} & \multicolumn{4}{c}{**Pedestrian**} & \multicolumn{4}{c}{**Cyclist**} \\ \cline{2-10}  & **Easy** & **Mod.** & **Hard** & **Easy** & **Mod.** & **Hard** & **Easy** & **Mod.** & **Hard** & **mAp** \\ \hline FRCNN [1] & 58.9 & 34.7 & 32.0 & 33.5 & 26.3 & 19.6 & 43.2 & 39.4 & 31.8 & 35.5 \\ DA [25] & 62.7 & 55.1 & 40.8 & 39.6 & 25.1 & 24.3 & 56.2 & 50.1 & 48.4 & 44.7 \\ SWDA [26] & 69.1 & 60.7 & 51.9 & **43.1** & **39.6** & 31.3 & 62.4 & 56.0 & 50.8 & 51.7 \\ Ours & **73.4** & **62.8** & **57.3** & 40.3 & 35.3 & **32.7** & **68.7** & **61.3** & **53.2** & **53.9** \\ \hline \hline \end{tabular}
\end{table}
Table 3: The experimental results (%) of KITTI to KITTI rainy. Bold indicates the highest result.