esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Discrimination of Different Human Skin Tissues Using Vectorial Retardance
#### 3.2.2 Qualitative Assessment by True-Color Vectorial Retardance Imaging
Clinically, the glucose and proteins with circular birefringence are existent in both skin stroma and cervical stroma [62; 95]. Thus, the use of MMIP on thicker skin tissue sections has the potential to yield more significant circular retardance and can be applied for distinguishing MM from BE. In the future, thicker skin tissue sections (e.g., 10 \(\upmu\)m [31], 20 \(\upmu\)m [89], or 28 \(\upmu\)m [90]), ex or in vivo skin tissues would be considered combined with the vectorial retardance imaging technology for distinguishing different skin lesions. In addition, we will continue to employ other imaging techniques to improve birefringence image contrast in the future, including using more valuable polarization parameters, using image fusion methods based on other color space (HSI or HSV), or normalizing colors by non-linear brightness scaling based on RGB color space. Table 4 lists several represented polarimetric imaging techniques combined with polarization staining methods for different applications. In this paper, the MMIP technology was preferred, since the Mueller matrix can describe the full polarization properties of samples, including the full birefringence properties. This is very useful for effectively classifying the normal, BE, and MM. Many studies have reported that the change of birefringence within tissue is tightly related to disease progression, which can be manifested fully as the vectorial retardance measured by MMIP. Additionally, benefiting from the proposed true-color imaging method, the birefringent fiber information with enhanced contrast can be completely visualized in one image. In the future, with the development of advanced technologies to improve the stability and measurement accuracy of the system, the electrically tunable achromatic waveplates, the DoFP polarization technique, or their combination would be considered to replace the rotating waveplates strategy to shorten detection time [96; 97; 98; 99; 100; 101; 102]. A white light source was adopted in this paper to cooperate with the conventional microscope with which the clinicians are familiar. Therefore, it will be convenient for clinicians to perform pathological analysis and realize polarization imaging without additional hardware replacement or manipulation. Through quantitative and qualitative analysis, although the spectral information at different wavelengths will be mixed together [103; 104; 105; 106], the classification accuracy of the three lesions can reach 96.19%, and the orientation and distribution of the fibers can be clearly displayed. Therefore, the broadband light used in this paper has little influence on the classification of the three lesions. In the future, we will further compare the results of studies using different monochromatic light, including different wavelengths from infrared to ultraviolet.
## 4 Discussion

Table 4: _Cont._regarding the recovery of nervous sensitivity of the areas affected by a lesion, confirming the results of the literature regarding the applications of laser-assisted protocols and for the wavelengths tested. Indeed, for a targeted photobiomodulation treatment aimed at recovering sensitivity following peripheral nerve injury, specific wavelengths of light are typically used [57; 58; 59; 60; 61; 62; 63; 64]. Wavelengths within the near-infrared range (600-1100 nm) are commonly employed for this purpose. These wavelengths have a greater ability to penetrate tissues and reach the nerve fibers, allowing for deeper stimulation and interaction with cellular components. Wavelengths around 800-850 nm are often favored due to their optimal tissue penetration and interaction with the mitochondrial enzyme cytochrome c oxidase [57; 58; 59; 60; 61; 62; 63; 64]. This interaction triggers a cascade of cellular responses, including enhanced ATP production, reduced oxidative stress, and the modulation of signaling pathways involved in nerve repair and regeneration. Furthermore, wavelengths in the red-light range (around 630-670 nm) can also contribute to the stimulation of cellular activity and blood flow, aiding in tissue oxygenation and nutrient supply to support nerve healing. By utilizing these specific wavelengths, photobiomodulation can effectively promote nerve tissue repair, axonal growth, and sensory function recovery after peripheral nerve injury. However, it is important to note that treatment parameters, such as power density, duration, and frequency of exposure, need to be carefully optimized based on the severity of the injury and individual patient characteristics for optimal therapeutic outcomes [64; 65; 66; 67; 68; 69; 70; 71].
## 4 ROI Image Augmentation Algorithm for Strip Steel Defects  
### Category Imbalance Problem for Strip Steel Surface Defects  
The category imbalance problem is mainly reflected in two aspects: positive and negative samples imbalance (the positive and negative samples ratio reached 1:100) and uneven difficulty samples (simple samples leading to loss function). For the classifier, the number of simple samples is very large; those simple samples' cumulative contribution results in a dominant role in the model update. However, these simple samples can be perfectly classified. Therefore, this part of parameter updating cannot improve the model's performance; it will cause the training process to become inefficient instead. An imbalanced sample problem will lead the training model to focus on large numbers of simple samples, while difficult samples with a smaller dataset will be ignored. This kind of model generalization on the testing dataset is poor, it can lead the model to prefer a dataset with more samples or samples with a low identification degree [41].  
For industrial images acquisition, because of the high speed of production lines and many disturbance factors, they are typically few samples and imbalanced datasets with complex backgrounds, low-resolution pixels, and rare qualified images. Traditional image data augmented techniques such as horizontally or vertically flipped, scaled random, random sampling and crop, and various kinds of algorithms add noise data to the original images, which is mainly based on the assumption that images in the ROI region have semantic relevance and geometric correlation. However, various types of industrial image defects do not meet all the above assumptions [42; 43]. Image data augmented techniques based on Generative Adversarial Networks (GANs) use specific areas' prior knowledge and distribution functions to generate virtual samples. These kinds of algorithms are mainly used for relatively few samples datasets with high-resolution, but they are not applied to few samples datasets images with low-resolution on an industrial scale [44]. For the category imbalance problem, starting from the data level approach method, the paper  
Figure 10: Process of strip steel surface rapidly quality screening and defect feature extraction.  
proposed a data augmentation algorithm for uneven images' datasets, to achieve samples balance in the categories again.
# Multiservice-Based Traffic Scheduling for 5G Access Traffic Steering, Switching and Splitting
## 1 Introduction  
To meet diverse user needs for information communication, satisfy the associated bandwidth requirements, and fully utilize the characteristics and advantages of various types of access networks, such as long-term evolution (LTE), new radio (NR), and wireless local-area network (WLAN), heterogeneous network convergence has become popular in fifth-generation (5G) networks [1]. The Access traffic steering, switching, and splitting (ATSSS) feature offers significant advantages for existing mobile networks, and is the principal solution for achieving multi-network convergence. ATSSS is an optional feature that may be supported by user equipment (UE) and 5G Core networks. ATSSS also enables a multi-access protocol data unit (PDU) connectivity service, which can exchange PDUs between the UE and a data network by simultaneously using one third-generation partnership project (3 GPP) access network and one non-3 GPP access network [2]. Among non-3 GPP access networks, WLAN has become the preferred method for convergence with cellular networks in the communications industry owing to advantageous features, such as its high bandwidth, low cost, and flexible deployment [3, 4, 5, 6]. Therefore, technology for the NR and WLAN has attracted significant attention from industries and academia due to its potential to alleviate data traffic congestion in licensed frequency bands using unlicensed frequency bands [3]. The two types of networks (3 GPP and non-3 GPP) exhibit strong complementarity, using unlicensed frequency bands to compensate for insufficient cellular networkbandwidth and relying on the high-security features of cellular networks to complete user-identity-access authentication. The authors of [4] have proposed that data offloading should be seriously considered in 4G LTE networks. To date, several studies on LTE and WLAN aggregation (LWA) have been conducted to explore different interface designs and mechanisms for transport, resource-scheduling, and security [7; 8; 9; 10; 11; 12]. Additionally, NR and WLAN aggregation (NWA) technology has been developed, which enables UE to implement data offloading and aggregation on both WLAN and NR network links within a 5G-specific scenario. However, its technical feasibility and network capacity gain has not yet been explicitly verified.  
For the design, demonstration, and verification of an NWA system, the data splitting and merging mechanism would be the crucial step. During this process, we must consider the performance characteristics of the underlying link between the NR and WLAN, and efficiently utilize the capabilities of the physical layers of multiple networks by reasonably balancing load and allocating power and rate. It is also necessary to reduce delay in data convergence packet aggregation protocol to improve user experience.
## 3 Mobile Network Security Evolution and the Evolution of Threat Landscape
### Fifth-Generation Security Architecture
#### 3.3.3 Service-Based Architecture (SBA) Security  
The fifth generation introduces the SBA, which radically departs from the hierarchical architectures seen in earlier generations: (1) Granular security controls: SBA allows for the application of security policies at a much finer granularity, made possible by its modular construction. Each service can be individually secured, which allows for enhanced flexibility in dealing with specific vulnerabilities. (2) Service-to-service communication: In SBA, services communicate through well-defined interfaces, often secured by contemporary security protocols like Transport Layer Security (TLS). This compartmentalization provides a more robust defense against potential attack vectors. (3) Decoupling of functions: The decoupling of network functions in SBA permits easier updates and modifications, thereby enabling quick responses to emerging security threats without affecting the overall network performance [81; 82].
## 5 Results and Discussion  
The proposed EACH-COA technique was evaluated using the MATLAB 2019a simulation framework [35]. Numbers of nodes of 100, 200, and 300 were considered to evaluate the effectiveness of EACH-COA compared with EECHS-ARO, EECHS-ISSADE, and PDU-SLno. The distribution of nodes within the network was randomized. All nodes were initialized with equal energy in the network. A network area of 500 m \(\times\) 500 m was considered for the simulation. The sink node was positioned at 250 m \(\times\) 250 m, i.e., the center of the network. To gauge the effectiveness of EACH-COA, parameters such as the energy consumption of nodes, network throughput, and lifespan were compared with the benchmark studies available in the literature. The simulation parameters and values are listed in Table 2.  
Figure 3: EACH-COA workflow.
# Security Audit of a Blockchain-Based Industrial Application Platform
## 5 Discussion: Application of BISS:4.0 to Real-World Scenarios
### Scenario 3: Careless Maintenance Technician  
* _Intention:_ in this attack scenario, a careless maintenance technician does not correctly execute the maintenance process of machines. Although this attack does not fit into theidentified attack categories, this attack demonstrates the scope of protection offered by BISS:4.0.
* _Attack path and impact_: the maintenance technician performs maintenance tasks and logs them (tasks performed, material consumed, time required to perform the task, etc.) for the purpose of documentation and accounting. As an example, the maintenance task requires the maintenance technician to wear safety glasses and gloves to comply with health and safety regulations. In addition, the maintenance task specifies that a certain type of lubricating oil must be used. The careless maintenance technician fails to wear the required safety equipment, resulting in liability in the event of an accident that could have been prevented by complying with workplace safety regulations. In addition, the careless maintenance technician performs the maintenance task incorrectly by using the wrong type of lubricating oil, which may result in liability in the event of a machine failure.
* _Countermeasure_: the BISS:4.0 platform defines a countermeasure against negligence of wearing the safety equipment by utilizing a safety equipment storage box that is equipped with sensors connected to the blockchain to log the opening and closing of the storage box. While this does not guarantee that the maintenance technician actually wears the security equipment, it increases the likelihood that he or she will do so, as the immutable record of the operation's log on the blockchain obligates him or her to comply. The second countermeasure that the BISS:4.0 platform defines is against the threat of using the wrong type of lubricating oil: the oil canister is equipped with an RFID tag that contains information about the oil and is read by a tablet or smartphone before the oil is put into the machine. The reading of the RFID tag by the tablet or smartphone triggers a check within the blockchain via a smart contract to ensure that the correct lubricating oil grade is used. In the event that the wrong type of oil is used, the maintenance technician is notified of their incorrect choice. While this does not guarantee that the maintenance technician will actually use the correct type of lubricating oil, it, again, increases the likelihood that he or she will do so by committing the technician to perform the maintenance task correctly by immutably recording the log of the operation in the blockchain.
## 6 Validation in a Semi-Urban Bistatic Radar Scenario
### Array Signal Processing Performance Evaluation
#### 6.2.1 First Array Signal Processing Stage: Target Detection
In both cases, results are provided for beamforming weights calculated for controlling SLL, \(\mathbf{w}_{SLL}\), and for beamforming weights calculated for maximizing the directivity, \(\mathbf{w}_{MD}\). In all cases, the \(P_{D}\) and the ratio of detections per target are higher for the weights calculated for controlling the SLL. Results presented in Tables 10 and 11 require a further explanation because a higher \(P_{D}\) was expected for the controlled car, \(T1\), and the number of detections for \(T1\) and \(T2\) was expected to be higher than for \(T3\) (actually, \(T3\) is significantly further than \(T1\) and \(T2\)). The main reason responsible for these results is shown in Figure 20: a big building made of aluminum (IMMPA) is affecting the propagation of the electromagnetic waves. Although cars can be seen from the passive radar emplacement, the diffraction losses generated by the IMMPA are really high. This effect can be observed in Table 12, where Signal-to-Interference Ratios (SIR) were estimated using the methodology described in [8]. The estimated SIR for targets \(T1\) and \(T2\) is significantly lower than that estimated for target \(T3\), although \(T3\) is further. \begin{table}
\begin{tabular}{c c c} \hline \hline  & \(P_{D}\) & \(P_{FA}\) \\ \hline Single radiating element & 0.04 & \(1.332\times 10^{-5}\) \\
**NULA** (\(\mathbf{w}_{SLL}\)) & **0.61** & \(\mathbf{3.837\times 10^{-5}}\) \\ NULA (\(\mathbf{w}_{MD}\)) & 0.56 & \(3.812\times 10^{-5}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Detection performances evaluated for the controlled car. Figure 20: Targets trajectories in the coverage sector (white). The Polytechnic School of University of Alcalá (EPS, _Escuela Politècnica Superior_) and a big building made of aluminum (IMMPA, _Instituto de Medicina Molecular Principe de Asturias_) are marked in purple.
## 2 Climbing Scaffold Status Monitoring Method
### GNSS/BeiDou Positioning Solution Model with Height Constraint for Climbing Scaffolds
#### 2.1.3 Climbing Scaffold Height Constrained Model  
Climbing frame monitoring falls under short-baseline positioning. Short-term multi-path effects typically refer to those occurring within a very short time span (e.g., milliseconds to seconds) and are particularly pronounced in urban canyon environments or areas with dense high-rise buildings. When monitoring a station located in the middle of a city, short-term multipath effects can cause rapid fluctuations in the signal strength and phase received by the receiver, thereby affecting positioning accuracy. Introducing constraints on the vertical direction during positioning can effectively suppress the impact of short-term multipath effects. Therefore, a height constraint estimation is incorporated into each iteration of the Kalman filter. Due to the structural design of high-rise buildings, the initial stage of external climbing scaffolding ascent does not produce deviations, maintaining a strictly vertical trajectory. However, in the later stages of construction, some high-rise buildings feature a torsional design, causing the ascent path of the climbing scaffolding to deviate by an angle \(\theta\) relative to the horizontal direction. To address this, the following inequality constraint is introduced into each iteration of the Kalman filter to refine height estimation:  
\[\begin{split}\left|U_{k}-U_{k-1}\right|&=\sqrt[2]{ \left(N_{k}-N_{k-1}\right)^{2}+\left(E_{k}-E_{k-1}\right)^{2}}\cdot\tan\theta \\ &\leq\sqrt[2]{\left(N_{k}-N_{k-1}\right)^{2}+\left(E_{k}-E_{k-1} \right)^{2}}\cdot\tan\theta_{th}\end{split}. \tag{19}\]  
In the early stages of monitoring, as the climbing scaffolding ascends almost vertically, a direct constraint on the height threshold can be set. Therefore, the overall filtering height constraint for the climbing scaffolding is formulated as follows:  
\[f(U_{k},U_{k-1},N_{k},N_{k-1},E_{k},E_{k-1})=\left\{\begin{array}{cl}\Delta U \leq\tan(\theta_{th})\cdot\sqrt{\Delta N^{2}+\Delta E^{2}}&\text{ if }later\ stages\\ \Delta U\leq h_{th}&\text{ if }early\ stages\end{array}\right.. \tag{20}\]
## 2 Related Work  
### Application of Wireless Sensor Networks  
A wireless sensor network is composed of a large number of nodes that can sense wireless communication. The nodes in a wireless sensor network collect environmental information from the region of deployment by sensor and deliver data wirelessly to data sinks. Environmental monitoring, forest-fire prevention, and healthcare are examples of traditional WSN applications. In these applications, the wireless sensor networks only monitor, detect and communicate. However, in the last decade, wireless sensor network applications have increasingly been the object of study, and innovative applications have been developed. One promising new application is the Internet of Things (IoT). According to the International Telecommunication Union (ITU), the IoT will enable collaboration and communication between people and things and between things themselves, which was previously unimagined [4]. The system enable the connection of objects equipped with sensors over the Internet, which can subsequently sense one another using an identification protocol and communicate. Most objects on the IoT are embedded with RFID, a sensor or a wireless communication chip. Compared with a WSN, the IoT is \"smarter\" because it can process, analyze and respond to the environmental context, rather than only sense and deliver contextualized real-world information.  
IoT architecture is composed of three layers: sense, network and application. The sense layer is composed of accessible wireless devices with any type of sensor, such as a RFID sensor and wireless sensor nodes. In a wireless sensor network, topology construction is the first step. The nodes deliver the information through neighboring nodes in the network to form an arbitrary network topology. However, an arbitrary network topology is not suitable for all WSN applications. Therefore, a flexible network topology is valuable for a wireless sensor network. A network topology is constructed by topology control according to network information to adjust transmission power with the goal of enhancing service performance and prolonging the lifetime of a network. Topology control is a critical technique in wireless sensor networks and IoT. In sum, an energy-efficient network topology with full reachability is a fundamental requirement in prolonging the lifetime and maintaining the quality of service for WSN and IoT.
# Theis: A Booster for Building Safer Systems Using the Rust Programming Language
## Background  
### Rust Language  
High performance, safety, and productivity are the main features of Rust [16]:  
* High performance: Rust's zero-cost abstraction, high memory efficiency, and no runtime garbage collection make it a high-performance programming language, suitable for the development of OSs and applications.
* Safety: The ownership model ensures Rust's safety by borrowing rules and a rich-type system in safe code. Many vulnerabilities that are difficult to find in C can be found in Rust's compile time, which provides better memory and concurrency safety, providing more manageable and reliable error handling.
* Productivity: Productivity is achieved through well-documented specifications, a user-friendly compiler, clear error messages, and other helpful tools and resources. These features help the developers understand and use the language, leading to increased productivity in the development process.  
Rust systems can be considered as consisting of safe code and unsafe fragments. Safe code is the recommended way to write Rust code, ensuring that the developer does not have to worry about undefined behaviors [22; 23; 24]. Rust's safety mechanism is built around the concepts of ownership and lifetime to prevent memory and concurrency errors. Rust ownership [9] states that \"each value in Rust has an owner; there can only be one owner at a time, and when the owner goes out of the scope, the value will be dropped\". Overall, a safe mechanism can efficiently analyze the use of memory resources during the compilation phase, enabling fine-grained management of memory.  
Unsafe fragments are exactly like safe code with all the rules and semantics, but they do not have the same memory safety guarantees. Unsafe fragments exist because, by nature, static analysis is conservative. When the compiler does not have enough information to guarantee the code's safety, the code may be rejected. Another reason Rust has an unsafe alter ego is that the underlying computer hardware is inherently unsafe [14]. Programmers usually use the keyword \"unsafe\" to bypass compiler checks and perform some operations that may be vulnerable, which provides more flexibility for Rust. However, unsafe fragments are similar to C, suffering from many memory and thread issues. Previous research shows that when checked by the compiler, programs fully written in safe code can completely avoid memory errors [15]; thus, unsafe fragments are the major reason for the reduction in the safety of OSs. Therefore, the issue of Rust safety is focused on the improvement of unsafe fragments. The following subsections analyze the specific reasons and the necessity of using unsafe fragments.