esa-sceva/satcom-chunk-collection huggingface dataset:
# Microstructural Evaluation of Dental Implant Success Using Micro-CT: A Comprehensive Review
## 10 Clinical Implications and Recommendations  
Micro-CT employs two principal scanning techniques, namely ex vivo and in vivo, which are applicable to a range of dental procedures. Ex vivo scans are employed for the analysis of extracted teeth or dental specimens, with the objective of understanding changes in bone density, porosity, and microstructure. The quantitative approach of micro-CT can enhance the precision of research, thereby facilitating evidence-based decision-making among clinicians with respect to treatment strategies and patient management. Micro-CT has a number of applications in the field of dentistry, including dental implantology and endodontics. It is anticipated that micro-CT will make a substantial contribution to enhancing comprehension of dental pathology, optimizing treatment outcomes, and benefiting patients in the field of dentistry [77].  
The use of micro-CT in dentistry is pervasive, including applications such as dental implant placement and evaluation of treatment outcomes. [PERSON] et al. (2020) presented successful techniques for micro-CT analysis of dentoalveolar tissue in rodents, including optimal scanning parameters, a reproducible sample setup, segmentation and measurement of tissue volume, and interpretation of findings. It is imperative that standardized protocols for scanning and analysis related to the alveolar bone, as well as reporting of outcome data obtained through the use of micro-CT, be established in order to ensure the reproducibility and comparability of results across studies [103].  
Computer-guided implant surgery provides the capacity to meticulously plan the positioning of implants in accordance with the intricacies of hard tissue anatomy, soft tissue volume, and the intended location of the prosthesis [104]. Nevertheless, errors and deviations can still occur, which may result in injury to anatomical structures or a mismatch between the prosthesis to be placed and the intended implant. Consequently, there is a continued clinical need for surgeons to utilize a guided implant system. A number of studies have addressed the factors that can result in a discrepancy between the planned implant position and the actual position achieved. Nevertheless, if process errors are avoided and potential deviations are minimized, accurate, stable, and durable results can be achieved. At present, micro-CT represents one of the solutions that can be employed to reduce the occurrence of failures in implant placement. The utilization of this instrument enables the acquisition of precise analytical data pertaining to the quality of bone and the process of osseointegration with dental implants [23]. In guided implant surgery, micro-CT plays an instrumental role in optimal implant planning and placement, thereby enhancing functional and aesthetic outcomes [104]. The further experience of surgeons in the utilization of this system, coupled with an understanding of potential process errors, is therefore necessary for this method to become a commonly employed standard of care [23; 105].
# Integration of Machine Learning-Based Attack Detectors into Defensive Exercises of a 5G Cyber Range
## 3 Integration of Attack Detection Exercices in the SPIDER Architecture
In the context of SPIDER exercises, the collected packets of an attack emulation conducted in the Mouseworld are stored in a pcap file to be used in two different contexts: (i) train ML models that can act as attack detectors in defensive exercises and (ii) reinject the packets into the SPIDER network during the emulation of an attack as part of a defensive exercise. To prepare the Mouseworld Lab, the datasets required for training the ML models, the packets of an attack, are grouped into flow statistics using the same aggregator and in the same way as is in the STA during the execution of the exercise. Before flow statistics can be used in the training of supervised ML models, labels need to be added to each of them (e.g., a \"0\" to normal traffic flows and \"1\" to cryptomining flows). The annotation of flows is performed by the Tagger component in the Mouseworld Lab. Once annotated, the set of labeled flows can be used to train supervised learning models or as ground truth in unsupervised methods. Once trained, the ML models are included in the corresponding STA for the attack and exported to the SPIDER cyber range jointly with the pcap file that contain the packets of the emulated attack. The packets and the ML models corresponding to an attack are stored in the Network Traffic Repository and in the ML Models Repository, respectively, with both repositories acting as links between SPIDER and the Mouseworld Lab.
# Blockchain's Grand Promise for the Real Estate Sector: A Systematic Review
## 3 Results
### Blockchain Merits in the Real Estate Sector
#### 3.2.4 Blockchain in the Property Transaction  
Property transaction is the acquisition and conveyance of proprietary rights to real estate or its parts [4]. Property transaction covers actions relating to the buying and selling of real estate, e.g., real estate purchase, real estate sale, exchange of real estate, real estate transactions, and real estate valuation. Property transaction is also known as the conveyance of real property or conveyance of property [4]. The property transaction papers were mainly concepts, followed by reports and examples.  
The primary property transaction issue that blockchain could help solve is that of intermediary-dependent, paper-based, manual, costly, and time-consuming processes. Primarily paper-based processes [106; 114; 132], manual transmission of data between participants, and the required verification of data residing in siloed systems [85; 88; 90; 91; 133; 134; 135; 136] make transactions time-consuming [137; 138; 139; 137; 88; 89; 91; 138; 139] and prone to errors [137; 136; 137; 88; 139; 140; 82; 141; 136] and fraud [82; 139; 140]. The other property transaction market inefficiencies include illiquidity [137; 135; 141; 142; 136; 88; 95; 141; 143; 144; 89; 145; 141; 142], non-transparency [143; 144; 145; 147; 80; 141; 141; 143; 144; 146], information asymmetry [137; 146], personal biases [137; 141], and heterogeneity [145; 88; 91; 91]. The main blockchain pros for property transactions lie in blockchain's ability to reduce the intermediaries' role, simplifying the transaction process and reducing cost and time.  
The general idea is that blockchain could function as a single [141; 139; 147; 25], distributed [147; 134; 148; 149; 15; 15; 160; 134; 138; 139; 140; 151; 15], transparent [15; 15; 15; 160; 161; 162; 163; 164; 165; 166; 167; 168; 169; 170; 171; 172; 173; 174; 175; 176; 177  
transactions could facilitate cross-border operations [94; 147; 157], in line with the EU's foundational goals of free movement of persons and capital, i.e., a \"digital single market\" [94].  
The main challenges of applying blockchain in property transactions include regulatory uncertainty [91], as it would require data standardization [145; 148] and collaboration with many stakeholders [91; 145; 139; 148]. Even if smart contracts could streamline many current tasks and activities, some of the current transaction processes might be too complex to translate into computer code and algorithms [73; 94; 119]. [PERSON] et al. [148] highlight that substantial improvements in real estate transaction processes can only occur through industry-wide collaboration. Changing laws and regulations would also touch on many established players' economic benefits [88], requiring a change in tradition and culture [35]. Thus far, the real estate industry has not been willing to share information and make investments in such technology openly [148], since blockchain technology is complex and requires special skills and competencies [88; 139].
# Automatic Fabric Defect Detection Method Using PRAN-Net
## 3 Experiment and Results
### Detection Results  
The proposed PRAN-Net based on Faster R-CNN in this paper was compared with RetinaNet, Mask R-CNN and Faster R-CNN with Guided Anchoring (GA-Faster R-CNN) on two fabric datasets, and all compared algorithms used ResNet-101-FPN [25] as backbone. The detection results were evaluated by ACC, AR, mAP, IoUs and FPS.
## 5 Hybrid AC/DC Microgrid Control Strategies
### Power-Sharing Control
This method not only improves power-sharing and control but also avoids circulating current due to multiple ICs. When switching from islanding to grid-connected mode, different frequency, phase angle, and amplitude may impact and sometimes even collapse, so there is a need to smoothen the process of switching from island to grid. Reference [92] proposes a design of pre-synchronization control of amplitude and frequency, which is based on droop control and disturbance observation that quickly tracks the sudden change of system current and suppresses the difference of tracking and actual value so as to generate smooth switching effectively. Relevant to the above discussion, Table 2 shows the different control techniques implemented in different research articles. It shows the research community's interest in favor of droop control and that new research is taking place in model predictive control giving promising results. In an islanded mode of operation of a microgrid, to regulate frequency and voltage, control strategies are proposed and are summarized in Table 2. The most commonly used one is droop control, whose main purpose is to stabilize frequency in case of any mismatch between generation and demand. The P-F and Q-V droop methods are usually used to allow flexibility as real power demand increases output frequency reduces. Reactive power also follows the same case, i.e., output voltage magnitude decreases with an increase in reactive power demand. Conventional droop control offers many limitations, e.g., poor power-sharing, no harmonic current sharing in nonlinear loads, and low voltage regulation. Due to these problems, many variants of droop control such as voltage-real power and frequency reactive power droop, angle droop, adaptive droop, and virtual impedance methods are proposed. The virtual impedance approach adjusts the output voltage by adjusting the virtual impedance. Still, in the case of nonlinear loads, this technique offers very high total harmonic distortion, so a high pass filter needs to be used. The virtual inertia approach is also a modified form of droop control. The main purpose of this technique is that in case of maloperations of frequency relay, it avoids unwanted operation of circuit breakers. Figure 6 presents the control strategy and converters used with their controlling variables. Figure 6: Control of AC/DC hybrid microgrid [23].
## 4 Network Layer: The Main Building Block of Fixed Networks
### F1G PONs
#### 4.1.7 F5g--2018--Today (Gigaband Era)
In uplink, the wavelengths are in the ranges 1260-1280 nm (or 1268-1282 nm) and 1290-1310 nm (or 1298-1302 nm). _QoS_: The coexistence of 50G TDM PON with the legacy PONs, GPON and XG(S)PON, as well as the 10G EPON is a sine qua non feature. To this end, two methods have been proposed, which differ on the devices included in the OLT [36]: (a) the use of external coexistence elements (CEx) and (b) the use of a multi-PON module (MPM). In the CEx method, the \"OLT\" of 50G TDM PON comprises different OLTs (e.g., the OLT of legacy PONs and the OLT of 50G TDM PON), which are connected to the corresponding ONU/ONT through an external switching device. In the MPM method, the OLT of 50G TDM PON comprises a WDM, in order to distinguish the different wavelengths of the legacy PONs for each direction (Tx or Rx), as well as of the 50G TDM PON. As in all TDM PONs, ONUs/ONTs communicate with the OLT based on the TDMA scheme. To this end, a DBA algorithm or an ONU/ONT activation mechanism is needed. By using the so-called coordinated DBA (Co-DBA) algorithm, the OLT can be informed about the transmission requirements of the ONU/ONT in the uplink and allocate the necessary transmission opportunities to the ONU/ONT in advance so that the data delay in the ONU/ONT is as little as possible. By the ONU/ONT activation mechanism (based on T-CONT), the delay among the transmission opportunities of an ONU/ONT is reduced (each T-CONT is allocated up to 16 times within a period of 125 \(\upmu\)s). The role of the DBA algorithm or ONU/ONT activation mechanism is upgraded, especially when the 50G TDM PON is used as a fronthaul for wireless communications (e.g., 5G mobile fronthaul). Compatibility issues between the 50G TDM PON and the legacy PONs need further investigation [36], given that they will support the exclusive bandwidth requirement of multiple operators or services in the future, possibly through a hard slicing (an important feature for the establishment of rigid pipes for PON downlink scheduling). Moreover, the coexistence of PONs with different transmission rates (10 Gb/s, 25 Gb/s, and 50 Gb/s) is problematic, given that the uplink optical power budget of 50G-PON is relatively tight. Finally, a contradiction between performance and cost always exists.
## 2 Materials and Methods
### Neural Models Development  
Based on the datasets detailed in Table 3, neural models were developed to determine the content of TS and VS in the analysed material. The models were created in the MATLAB computing environment using the Deep Learning Toolbox. As a neural network, the multilayer perceptron (MLP) with ten neurons in the hidden layer and one neuron in the output layer was employed. MLP is one of the most popular feedforward neural network topologies [44]. It has a high degree of flexibility and is widely used to solve a variety of classification and regression problems. Its advantage over more complex networks, such as CNNs, is its much simpler structure. As a result, it is not necessary to have huge amounts of data to train (determine weights and biases) this network. One hidden layer and the number of neurons in this layer were chosen on the basis of the authors' experience and previous preliminary analyses. In the authors' experience, a good starting point for similar regression problems is usually between 5 and 30 neurons in the hidden layer. A sigmoidal function was taken as the activation function in the hidden layer and a linear function in the output layer. The number of inputs to the MLP depended on the number of input parameters in the dataset. The networks calculated a single output in terms of total solids and volatile solids content. The models were trained using a back-propagation algorithm in the form of Bayes regularisation (BR). The mean square error (MSE) was used as the error function. The maximum number of training epochs was set at 1500. The training process was stopped earlier if there was no reduction in the value of the network error function with respect to the validation set within 100 consecutive epochs.
# Phase Centre Corrections of GNSS Antennas and Their Consistency with ATX Catalogues
## 1 Introduction
The study consisted of several analyses. The first was focused on differences of the studied models in the jigs08.atx and jigs14.atx catalogues. The second was the difference in height between the antenna ARP, as calculated by different satellite methods. Additionally, height differences were referenced to the auxiliary stations (KR01 and KR02) and to precise spirit levelling. The third analysis gathered archival material based on several experiments that have been conducted since 2011.
# RISC-Based 10K+ Core Finite Difference Method Accelerator for CFD
## 3 Design and Implementation of Multi-Core Accelerator Architecture
### The FCore Implementation  
#### 3.1.1 Special ISA Designed  
The instruction set architecture (ISA) serves as the interface specification between software and hardware, forming the foundation of information technology ecosystems [35; 36; 37]. Domain-specific ISAs are tailored to application requirements. In this work, targeting CFD and leveraging the characteristics of the FDM, we designed an ISA supporting looping, branch jumps, floating-point operations, and other critical functionalities. For the FCore processor, we developed a 16-bit domain-specific ISA named F-RISC (CFD-Reduced Instruction Set Computer), as depicted in Figure 3. The instruction format follows a three-address structure, comprising a 4-bit opcode and two 6-bit operands (operand_1 and operand_2). Based on operand types, instructions are categorized into three classes.  
Figure 3: 16-bit special instruction set.  
Figure 2: The microarchitecture of the finite difference method accelerator for CFD (FAcc).  
* Register-type (R-type). Both operands sourced from registers. Processor Usage: Operands are retrieved exclusively from the register file. The instruction's opcode triggers ALU computation using two register-addressed values, with results written back to a destination register.
* Immediate-type (I-type). Both operands are immediate values. Processor Usage: Embeds operands directly within the instruction word. During decode, immediate values are sign-/zero-extended and propagated to the ALU or memory interface, bypassing register fetch.
* Hybrid-type (H-type). One operand is sourced from a register, while the other is an immediate value. Processor Usage: Combines one register-sourced operand with an instruction-embedded immediate. The register operand accesses the register file, while the immediate is concurrently expanded and routed to the execution unit.  
The F-RISC instruction set comprises fourteen machine instructions, as detailed in Table 1, including six computational instructions supporting fixed-point addition/subtraction, floating-point addition/subtraction/multiplication, and fixed-point comparison; seven control instructions enabling conditional branching and jump operations for loop implementation; and one data movement instruction for register-to-register or memory-to-register transfers. This streamlined design minimizes instruction length and reduces decoding overhead, aligning with the efficiency requirements of CFD-specific hardware acceleration.
# A Wideband Dielectric Waveguide-Based 160-GHz Radar Target Generator
###### Abstract  
The increasing number of radar sensors in commercial and industrial products leads to a growing demand for system functionality tests. Conventional test procedures require expensive anechoic chambers to provide a defined test environment for radar sensors. In this paper, a compact and low cost dielectric waveguide radar target generator for level probing radars is presented. The radar target generator principle is based on a long dielectric waveguide as a one-target scenery. By manipulating the field distribution of the waveguide, a specific reflection of a radar target is generated. Two realistic scenarios for a tank level probing radar are investigated and suitable targets are designed with full wave simulations. Target distances from 13 cm to at least 9 m are realized with an extruded dielectric waveguide with dielectric losses of 2 dB/m at 160 GHz. Low loss (0.5 dB) and low reflection holders are used to fix the waveguide. Due to the dispersion of the dielectric waveguide, a detailed analysis of its impact on frequency-modulated continuous wave (FMCW) radars is given and compared to free-space propagation. The functionality of the radar target generator is verified with a 160-GHz FMCW radar prototype.  
target generator; dielectric waveguide; millimeter wave radar; dispersion; frequency-modulated continuous wave (FMCW) radar +
Footnote †: journal: Sensors
## 2 Problem Statement
### Availability Metrics of SCOS
When a service has a history of successful integration, a high level of technological maturity, or a well-standardized interface, \(\theta_{i}\) is set to 1, indicating high integration reliability. For newly introduced services, or in cases involving complex interfaces and significant vendor heterogeneity, \(\theta_{i}\) is set to 0.5 to reflect the potential risk of uncertainty. In the model computation, the availability \(A_{i}\) of each service is multiplied by the corresponding \(\theta_{i}\), resulting in the adjusted service availability \(A_{i}\cdot\theta_{i}\). 5. Evaluation of overall system coupling: Components within a system often exhibit complex invocation and data dependency relationships. These dependencies determine the degree of system coupling and directly affect the overall stability and maintainability of the system. Therefore, it is essential to quantitatively assess system coupling within the availability modeling process, using it as a penalty factor for potential risks. A dependency matrix \(D=[d_{ij}]\) of size \(n\times n\) is used to represent the dependency strength of component \(s_{i}\) on component \(s_{j}\). Based on this matrix, the overall system coupling degree \(\mathcal{C}\) can be calculated using Equation (5). \begin{table}
\begin{tabular}{c c} \hline \hline
**Functional Relationship** & **Dependency Strength** \\ \hline No direct dependency, logically unrelated & 0 \\ Control flow dependency (sequential) & 0.5 \\ Control flow dependency with enforced control & 1 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Determination of functional relationships. \[\mathcal{C}=\frac{1}{n(n-1)}{\sum_{i\
eq j}d_{ij}} \tag{5}\]  
where \(n\) denotes the number of components. 6. Composite availability modeling: to accurately reflect the operational reliability of the entire service portfolio, the availability model Equation (6) is established by comprehensively considering the factors mentioned above. \[A_{comb}=\prod_{i=1}^{n}\left(\frac{MTBF_{i}}{MTBF_{i}+MTTR_{i}}\cdot\theta_{ i}\right)\cdot\left(1-\lambda\cdot\mathcal{C}\right)\] (6)  
where \(\lambda\in[0.1,0.3]\) represents the coupling penalty factor, \(\theta_{i}\) denotes the confidence level of service integration, and \(\mathcal{C}\) indicates the overall system coupling degree.
# Visible and Near-Infrared Image Synthesis Using PCA Fusion of Multiscale Layers
## 7 Conclusions  
We proposed the synthesis of VIS and NIR images. A radiance map was created using the Laplacian pyramid, and the soft-mixing method was employed based on PCA. Each pyramid level was implemented on the luminance channel in the CIELAB color space. The Laplacian pyramid was decomposed into its edge and base layers. These decomposed layers were calculated by combining the PCA fusion algorithm with the soft-mixing method. In particular, the base-layer soft-mixing method had a low contrast in the base layer. Therefore, the results were calculated differently from the applied PCA fusion algorithm. The base layer was fused with the NIR base layer to enhance the local contrast. Color compensation was used to preserve the VIS input image. To preserve the VIS image color, the proposed color compensation was adjusted by using the ratio of the luminance channels of the radiance map and the VIS input image. When the proposed color compensation process was completed, the fused image was converted from the CIELAB color space to the RGB color space. The advantages of the resulting image obtained by the proposed method are better local contrast and excellent detail and color representation. The possible real-life applications of the proposed method are to increase the visibility for a number of the camera embedded devices, such as a self-driving car, CCTV, drone and car black-box. In future work, we will attempt to fuse VIS and NIR images under nighttime conditions.  
Conceptualization, S.-H.L.; Methodology, S.-H.L.; Software, D.-M.S.; Supervision, S.-H.L.; Writing--original draft, D.-M.S.; Writing--review & editing, H.-J.K. All authors have read and agreed to the published version of the manuscript.  
This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) and the BK21 four project funded by the Ministry of Education, Korea (NRF-2019R1D1A3A03020225).  
The authors declare that there are no conflicts of interest regarding the publication of this paper.