esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Artificial Intelligence (AI) and Machine Learning (ML)
### Artificial Intelligence (AI)  
AI is a wide-ranging domain within the discipline of computer science, focusing on the creation and advancement of computer systems and related technologies that possess the ability to execute tasks that conventionally necessitate human intelligence [17; 18; 19]. The aforementioned tasks encompass a range of cognitive abilities, such as logical thinking, experiential learning, problem solving, comprehension of natural language, perception, and the capacity to interact with and modify the surrounding environment [18; 20].  
Historically, AI emerged as an academic discipline in the 1950s when researchers aimed to develop robots with the ability to think, solve problems, and learn independently [21; 22]. The field remained largely unpopular until the 2000s [21], with repeated cycles of optimism, followed by periods of disappointment and loss of funding, referred to as AI winter [23]. The introduction of data-driven algorithms and ML revolutionized the field by offering the unique dimension of learning from historical information to enhance problem-solving. Funding and interest significantly rose after 2012 when deep learning methodologies exhibited significant learning and predictive capabilities, outperforming perhaps all previous AI techniques [24; 25], and after 2017 with the introduction of the transformer architecture [25; 26]. In the early 2020s, a surge in AI development occurred, primarily led by entities in the US such as big technology firms, universities, and laboratories, resulting in notable progress in the field.  
AI can be categorized into two distinct types: Narrow or Weak AI and General or Strong AI [27]. Weak AI is specifically engineered to carry out a particular activity within set limits (i.e., they are specialized and have a restricted range of use). They do not possess the broad cognitive capabilities seen in human intelligence. Weak AI's primary advantage is its practical utility in several fields, allowing for automation, optimization, and improvement in particular jobs. These systems are commonly utilized in several industries like healthcare, finance, manufacturing, and customer service to optimize processes, increase decision making, and improve user experiences. Weak AI also has significant limitations. These systems have limited comprehension outside of their planned scope, which makes it difficult for them to deal with unforeseen inputs or situations. Concerns about data privacy, prejudice, and ethical implications emerge when these systems handle sensitive information and impact decision-making processes. On the other hand, strong AI seeks to mimic human intelligence in several areas and possesses the capacity to comprehend, acquire knowledge, and apply it across a wide spectrum of tasks, akin to human intelligence [27; 28; 29; 30].
## 2 Materials and Methods
### Proposed Methodology
#### 2.2.3 The Overall Architecture of the Proposed Network Based on YOLOv5  
As a result, the proposed network based on YOLOv5 has been completed with the four enhancements and its architecture is illustrated in Figure 7. It can be clearly seen that the network comprises four main sections, which are the backbone, neck structure based on Bi-FPN, detection head, and cross-layer connection. More specifically, the proposed network for crack detection can be described as follows.  
Figure 5: Neck structure design: (**a**) PANet of YOLOv5s; (**b**) Bi-FPN; (**c**) The neck structure after introducing Bi-FPN and a small-target detection layer.  
Figure 6: Visualization of feature maps: (**a**) Original image; (**b**) The 1 st downsampling feature map; (**c**) The 2 nd downsampling feature map; (**d**) The 3 rd downsampling feature map; (**e**) The 4 th downsampling feature map; (**f**) The 5 th downsampling feature map.  
Figure 7: The proposed network architecture based on YOLOv5.  
Firstly, the backbone has five feature maps for different strides (P1, P2, P3, P4, P5) after 5 down-sampling operations, with Pj representing a stride of 2; the bigger the strides, the smaller the extracted feature maps and the deeper the features. In the original YOLOv5 network, the P2 output is not taken to the subsequent network, while in our network, P2 plays a critical role in providing detailed features and spatial information for detecting small-sized cracks. Secondly, Bi-FPN aggregates the features extracted by the backbone and passes them to the head and optimizes the neck structure by removing two nodes with only one input, which helps to prevent crack information loss from the backbone, making our proposed network lighter and more efficient compared to the original YOLOv5. Then, the detection head generates predictions for crack detection from the updated anchor boxes, where smaller cracks require higher-resolution features and a greater number of anchor boxes. In this network, the added branch has a high resolution of 160 \(\times\) 160 pixels, enabling the detection of cracks as small as 8 \(\times\) 8 pixels, and the number of anchor boxes has been increased from 9 to 12. Lastly, the blue lines in the green part in Figure 7 represent the path of cross-layer connections. Different from the same level cross-layer connections, our proposed network concatenates feature maps from the lower level of the backbone, enabling the 80 \(\times\) 80, 40 \(\times\) 40, and 20 \(\times\) 20 detection branches to also contain more shallow features related to cracks of various scales.  
Overall, from the perspective of the network structure, the proposed network matches well with the dataset presented in this study, and is also conducive to enhancing the extraction of crack features, improving the accuracy of crack detection, and strengthening the sensitivity to small-sized cracks. Further experimental tests will be carried out in the upcoming section of this paper to verify the effectiveness of the proposed network architecture.
## 1 Introduction  
Rainfall-induced soil erosion involves three main processes: detachment, transport, and deposition of soil particles by the crush forces of rainfall and surface runoff. During the erosion process, the area of soil erosion can reflect the shaped landscape. The classical estimation models of soil erosion include the USLE model, which is composed of six factors to predict the long-term average annual soil weight [1; 2]; the WEPP model, which is capable of predicting spatial and temporal distributions of soil detachment and deposition on an event or continuous basis at both small (hillslopes, roads, small parcels) and large (watershed) scales [3]; the EUROSEM model, which is a dynamic distributed model, able to simulate sediment transport, erosion and deposition by rill and inter-rill processes in single storms for both individual fields and small catchments [4]; the KINEROS model, which is an event-oriented, physically-based model describing the processes of interception, infiltration, surface runoff and erosion from agricultural and urban watersheds [5]. These models can commonly describe the distributions of soil detachment in large (watershed) scales. By contrast, in our study, a narrow scale of even a single slope is our research object. We aim to investigate the development of the area of soil erosion induced by rainfall in a single slope using an imaging tool. In the practical experiments, the values of rainfall intensity and cumulative rainfall are acquired incrementally with respect to time. The area of soil erosion is achieved by the imaging tool under time domains. Then, in this work, based on incremental learning, we develop an S-curve model to predict the soil erosion area. In detail, we update the input values chronologically to train the parameters of the S-curve model. Five experiments involving different materials and different rainfall intensities are employed to validate the model.
# A Matrix-Based Proactive Data Relay Algorithm for Large Distributed Sensor Networks
## 4 Matrix-Based Data Relay Algorithm
### The Balance  
To make communication decisions for each sensor data in pending queue, sensors balance the benefit of broadcasting with the energy cost as follows.  
\[F= B-\beta E= C\times((\Gamma-D)\bullet(D\times U))-\beta\cdot(Eb+Er\cdot(C \times\overrightarrow{\Lambda}))\times\overrightarrow{\Lambda}^{T} \tag{14}\]  
where \(F\) is a \(1\times|L_{i}|\) matrix and each element in \(F\) indicates the difference between the benefit and the energy cost of transmitting each data. Essentially, this can also be calculated in a single matrix computation.  
In the decision process, for each data in \(pendingQue_{i}\), if \(F_{v_{i},d_{k}}>0\), which represents the benefit of broadcasting this data is bigger than the energy cost, this data is worth to be broadcast at this moment. Otherwise, it may not be broadcast. By ensuring a balance between increasing the quality of receivers and saving energy,  
* the data that has higher confidence (that can make a higher contribution to fuse into an information) has a higher priority to be broadcast, which can avoid the energy consumption on unnecessary data retransmission.
* the data that is more relative to data of neighbors has a high probability to be broadcast. This guarantees that the related data are only transmitted in a small part of the whole network and aggregated toward some node rather than blind coverage.
## 2 The Principle of Structural Reliability Analysis  
The importance of structural reliability lies in ensuring long-term functionality, operational safety, and cost-effective maintenance. Proactively addressing structural reliability significantly reduces risks, prevents early defects or failures, and minimizes the likelihood of catastrophic events throughout a structure's service life. Furthermore, achieving high reliability ensures that structures meet their intended functions while reducing long-term maintenance costs. This approach is particularly critical in fields such as aerospace, civil engineering, and energy systems, where structural failures can result in severe consequences.
# Cybersecurity in Hospitals: An Evaluation Model
## 1 Introduction  
Cybersecurity is a set of processes and technologies created to protect computers, software, and data from cyber-threats [1]. Nowadays, many services and procedures depend mainly on the Internet and computers, such as healthcare services [2]. Even though healthcare organizations have strong policies that mitigate most kinds of risks, they are still vulnerable to cyber-threats. This makes cybersecurity an important aspect for many organizations, especially healthcare organizations [2]. Cybersecurity is a relatively new subject in healthcare [3]. It involves the measures taken to secure and protect the electronic equipment and the information saved digitally. The healthcare sector has been advancing rapidly over the past two decades, increasing the use of more advanced technologies [3]. Currently, all patients have their personal identifiable information (PII) and personal health information (PHI) saved in the servers of the hospitals they visit [4]. Even though this rapid incorporation of technology in healthcare improves their services, it also increases the risk of being vulnerable to cyber-attacks [4]. Nowadays, almost every hospital department deals with the PII and PHI of patients using their electronic health records (EHR) and other software such as e-prescription programs [4]. Other than health information, hospitals also use patients' banking and billing information and share them with insurance companies electronically [4]. In addition, hospitals are full of medical devices connected to the network and are subject to cyber-attacks [5]. There are a variety of medical devices that are connected to the network. Some devices are implanted inside patients, which amplify the effects of cyber-attacks. In the case of this happening, the integrity of the medical devices is compromised, which disrupts the services offered by the hospital [5]. Failing to protect hospitals from all types of cyber-attacks to patients' PIIs, PHIs, banking information, and implanted medical devices can result in significant consequences.  
On the other hand, finance in healthcare is limited [4]. When a cyber-attack on a hospital takes place, the hospital might be overwhelmed financially due to fines, loss of services, and lawsuits [3]. Hospitals need to implement cybersecurity measures and reduce the costs of their implementation. To decide which measures are more effective than others,an evaluation model will be created to assist hospitals in making such a decision. The model aims to identify and quantify cyber-attack risks and the measures needed to mitigate them.
## 2 Optical Frequency Comb Self-Referencing  
Strictly speaking, the self-referencing of a frequency comb originally referred to the detection of _f_CEO using nonlinear interferometry (_f_-to-2\(f\) or a similar method) as described in Section 2.1. By extension, self-referencing is often considered as the subsequent step consisting of stabilizing the CEO beat obtained in this way to an RF reference. We consider this situation throughout this paper when talking about comb self-referencing or self-referenced comb. In contrast, we denote as self-referenceable a frequency comb for which _f_CEO has been detected by _f_-to-2\(f\) interferometry but has not yet been stabilized. We review here the principal steps and requirements to achieve comb self-referencing.
## 2 Measurement Principles and Device
### Wavelength Accuracy  
The calibration for the wavelength (or wavenumber) is very important. In general, polystyrene films are widely used as the wavenumber standard for calibrating the wavenumber for Fourier transform infrared spectrometers. The effective light aperture of this film is \(\Phi\)25 mm. The haze value is between 30 and 40, and the weight-average molecular weight is 429,334.  
The molecular formula of polystyrene is shown in the lower right corner of Figure 7. The transmittance of the film is shown in Figure 7. Although there are more than 30 absorption peaks in total, only 13 absorption peaks are stable and that can be used to calibrate the wavelength of the Fourier transform infrared spectrometer.  
The difference between the measured value and the standard value is used as the evaluation value of the wavenumber accuracy of the system.  
As can be seen from Figure 7, the wavenumber error is about 0.5 cm\({}^{-1}\)\(\pm\) 0.25 cm\({}^{-1}\). However, the contribution of wavenumber errors to emissivity is not a very straightforward matter. The contribution coefficient depends on the spectral flatness of the emissivity of the sample. For a sample with a flat spectrum, the error in wavelength is almost negligible.
# Dynamic Vertical Mapping with Crowdsourced Smartphone Sensor Data
## 2 Related Work
On the basis of the barometric formula and using built-in barometric sensors of smartphones as well as information from a local weather station, they are capable of achieving a good discretization between different floor levels. For the current temperature, they consult a local weather station online service. However, this approach is heavily dependent on dense existing infrastructure, while it focuses only on localization and assumes the existence of maps, which describe the location of each sensor. [PERSON] et al. [18] use barometers for medical applications in which a precise altitude estimation of the patient's body is needed. A challenge in this case is the disturbances due to macroscopic flow, such as the influence of ventilation, the opening and closing of doors, or the weather. Calibration between sensors is also needed, in order to compensate for the offset between different sensors. In their research, they created a small sensor network, with sensors attached to the patient body, as well as a reference stationary sensor. They measure a maximum error of 21 cm, but they suggest that a second sensor might reduce the maximum error to 10 cm. However, in our application scenario, we do not focus on such accurate vertical localization; we are looking for an infrastructure-independent approach. [PERSON] et al. [14] argue that the estimation of altitude via GPS is applicable only outdoors, although even there, its error can be 2.5 times the error of the horizontal location. As a result, they suggest barometers for vertical localization. Their main limitation is the lack of reference points, because the only available reference stations are meteorological stations, which are often sparsely located, while they broadcast periodically, usually at 1 h intervals. Therefore, they introduce the concept of ad hoc reference points. They integrate information from multiple points, while they also use forecast models to estimate air pressure on demand. Besides reference meteorological stations, they additionally use other smartphones when the elevation indication is accurate enough. In order to retrieve better accuracy from other phones, first, they take into account all the reference points that are within a specified distance and time period, and then they give higher weights to reference stations that are closer in distance as well as in time. They also assign a different credibility to different reference stations. Hence, a reference station will be more reliable if its location is known and can report better pressure. They score errors of less than 3 m in outdoor walking, 6 m in mountain climbing, and 0.9 m in indoor floor localization. However, an ad hoc reference sensor reading will constantly have the need of being extracted; it is not clear how this can be achieved, particularly without maps that describe those reference locations.
## 3 Results and Discussion
### Ablation Experiment Results and Discussion
#### 3.1.4 Ablation Experiment Between Different Methods
The rightmost column presents the prediction outcomes of the methodology proposed in this paper, which exhibits the heatmaps and masks predicted by the methodology for the three groups of images in sequence. E1, E2, E3, E4, and E5 present the prediction results of the ablation experiments in sequence. The heatmap and prediction mask in E1 demonstrate that the baseline U-Net++ model encountered difficulties in distinguishing between target and background regions, resulting in the misclassification of non-landslide areas as landslide areas. The Tok-KAN experiment (E2) demonstrated an improvement in accuracy compared with experiment E1, with a reduction in the number of incorrectly identified non-landslide areas as landslide areas. However, as evidenced by the heatmap in E2, the boundary between the target and background regions remained challenging to discern, exhibiting a considerable number of red and blue transition areas. The heatmap of experiment E3 demonstrates that the boundary between the target and the background could be discerned with clarity. However, there persisted the issue of misidentifying certain non-landslide regions as landslide regions. In experiment E4, which incorporated both DLFFSKA and a Tok-KAN (LKN-UKAN), the mask indicates that the model's image segmentation was largely accurate. However, the heatmap persisted in exhibiting an insufficiently clear delineation of the target and background. Further improvements could be made. In experiment E5, the introduction of FTTL resulted in a clear partitioning of the target and background colors, with only a few areas exhibiting recognition errors. The combination of a Tok-KAN with DLFFSKA and the introduction of FTTL resulted in the most accurate heatmap of all the experiments, effectively eliminating background interference and accurately delineating the boundary between the target and the background. This demonstrated excellent performance in the segmentation of landslide images via remote sensing.
# Research on Named Entity Recognition Based on Gated Interaction Mechanisms
## 3 Results and Discussion
### Evaluation Indicators  
In this experiment, the evaluation metrics adopted the three most commonly used and important evaluation indicators for named entity recognition tasks: precision (P), recall (R), and F1-score (F1). These three indicators together form a comprehensive evaluation system, which helps us accurately measure the performance of the entity recognition model. The specific calculation formulas are as follows:
# A Robust Routing Protocol in Cognitive Unmanned Aerial Vehicular Networks
## 2 Related Works
It aims to address challenges like delays, high bandwidth use, scalability, and low network density for improved energy efficiency and reliability. The Novel Optimized Link-State Routing Scheme (OLSR+GPSR) is an innovative advancement in Flying Ad-hoc Networks (FANETs). By integrating optimized link-state routing (OLSR) with greedy perimeter stateless routing (GPSR), it introduces several advantages. It employs a fuzzy system to adjust the hello message broadcast period based on UAV velocity and position prediction error, ensuring that high-speed UAVs have shorter broadcast periods. MPR nodes in OLSR+GPSR are determined using multiple metrics such as neighbor degree, node stability, buffer capacity, and residual energy, leading to more stable and efficient routes. The scheme also reduces routing overhead by eliminating two OLSR phases: TC message dissemination and route calculation. Simulations show that OLSR+GPSR outperforms existing methods like P-OLSR, and OLSR-ETX in terms of delay, packet delivery ratio, throughput, and overhead [21]. The paper [22] presents a novel approach for using UAVs as relays to maximize transmission coverage in challenging terrain. It employs a combination of optimization functions, viewshed analysis, the traveling salesman problem (TSP), and the A* search algorithm for efficient path planning. The optimization function focuses on minimizing the cost while ensuring positive results. Viewshed analysis, conducted at 100 m altitude, identifies visible regions and avoids areas with inadequate coverage by generating binary raster data. The TSP is used to determine the optimal sequence for visiting points of interest, minimizing total distance and energy consumption. The A* search algorithm utilizes irregular terrain elevations to calculate the minimum cost path between points. The results highlight the importance of terrain profiles in signal pathloss calculations and demonstrate the effectiveness of the proposed approach in using drones as signal relays. This method ensures efficient UAV relay planning by optimizing coverage and navigation across diverse and challenging terrains (Figure 1).