esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Preliminary
### Scalar Operations and SIMD Operations  
Scalar operations simultaneously process a single data element, executing instructions sequentially, as shown in Figure 5. This approach can be efficient for simple tasks but often leads to significant latency when handling large datasets or complex computations. Conversely, SIMD operations enhance performance by processing multiple data elements simultaneously, with a single instruction. This parallelism greatly accelerates computation, especially in tasks with high data parallelism, by reducing the number of instruction cycles required for large-scale data processing.  
Although individual SIMD instructions, which operate on multiple data elements in parallel, may consume slightly more time than single scalar operations, the aggregate time required for SIMD to process an equivalent dataset is significantly less than that for scalar operations. This efficiency gain arises from the fact that SIMD reduces the total number of instruction cycles required, thereby accelerating the overall computation process. As a result, SIMD operations offer significant speedup and efficiency improvements over traditional scalar operations.  
During Gather and Scatter operations in sparse convolution, point cloud data frequently need to transition between continuity and sparsity to accommodate the demands of matrix multiplication and the propagation between different convolutional layers. Performing these operations with scalar computation can lead to prolonged computation cycles. However, leveraging SIMD techniques to accelerate these operations can significantly reduce the computation cycles while maintaining nearly constant time per operation, thereby achieving substantial speedup.
# Automatic Detection of Airway Invasion from Videofluoroscopy via Deep Learning Technology
## 3 Results
### Performance Evaluation for the Global Localization of the Cervical Spinal Column  
U-Net determines whether each pixel in an image is a positive or negative pixel. In our experiment, the pixels corresponding to the cervical spinal column were defined as positive.  
Table 2 shows the results of detecting the cervical spinal column using U-Net. The performance was evaluated on 157 images that were not used to train the U-Net model, and the values presented in Table 2 were measured at the pixel level. For example, the recall refers to the ratio of the number of pixels predicted to belong to the cervical spinal column by the designed model with respect to the ground truth (pixels labeled as belonging to the cervical spinal column by human annotators). As shown in Table 2, our designed U-Net model detects the cervical spinal column very well, with 75.6% recall, 99.0% accuracy, 88.7% precision, and 94.8% average precision (Figure 8).  
Figure 9A shows an example of the cervical spinal column (red area) detected by U-Net. For the 48\(\sim\)2500 image frames included in one video, the standard deviation of the coordinates of the central point of the cervical spinal column in the 299 \(\times\) 299-sized images is 11 pixels, demonstrating that the localization performance of U-Net for the cervical spinal column is relatively stable. Figure 9B shows an ROI image obtained as described in Section 2.3.1 based on the detected center of the cervical spinal column. In Figure 9B, it can be seen that the ROI is effectively extracted to capture the vestibule and larynx without being affected by head and neck movements during recording.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**Deep Learning Algorithm** & **Accuracy** & **Recall** & **Precision** & **Specificity** & **NPV** & **Average Precision** \\ \hline U-Net & 99.0\% & 75.6\% & 88.7\% & 99.7\% & 99.3\% & 94.8\% \\ \hline \end{tabular}
\end{table}
Table 2: Accuracy of detection of the cervical spinal column by the U-Net deep learning segmentation model.
# Maximum Transmit Power for UE in an LTE Small Cell Uplink
## 3 Network and Propagation Model
### HetNet Deployment with Small Cells near Macro Cell eNodeB
#### 3.3.4 Spectral Efficiency  
To see the impact of the variation in the \(P_{\text{max}}\) limit on the system performance, the spectral efficiency for the cell average and at the specific position of the UE in the cell was computed from Equations (12) and (13) using the received SIR given in Equation (23).  
Figure 3: HetNet scenario for deployment of small cells near the macro cell eNodeB.
# A Systematic Review of Ship Wake Detection Methods in Satellite Imagery
## 3 Results
### Overview of Traditional Methods of Wake Detection
#### 3.2.2 Methods Based on Polarimetric Enhancement  
One interesting recent development came from [PERSON] et al. [49], who explored the application of SAR imagery for detecting ship wakes in the ocean, particularly under challenging conditions such as high sea states. Polarimetric SAR (PolSAR), which captures the polarimetric properties of target backscatter, was tested as a potential solution to enhance ship wake features.  
The authors proposed a methodology that included the simulation of fully polarized SAR imagery of ship wakes (both turbulent and Kelvin types) using a two-scale composite model. A polarimetric whitening filter (PWF) and polarimetric detection optimization filter (PDOF) were applied for feature enhancement, and a pre-processing approach involving logarithmic transformation and z-score normalization was used to mitigate the impact of bright and singular points. They also employed a RT-based method for detecting ship wakes in various polarimetric conditions, demonstrating an average improvement of nearly 50 percent in wake detection performance using PWF and PDOF compared to traditional HH and VV polarizations.  
Their findings suggest that polarimetric enhancement methods, particularly PWF and PDOF, can significantly improve the detection of ship wakes in SAR imagery by reducing the influence of sea clutter and enhancing the visibility of wake features. However, a dark turbulent wake becomes almost invisible after PWF and PDOF, and sometimes single artifact points are created which may cause misdetections. Their study showed that wakes were generally correctly detected in VV polarization, and only partially in the other polarizations.
# A Systematic Mapping of Quality Models for AI Systems, Software and Components
## 5 Analysis and Results
### Data Synthesis
#### 5.2.1 RQ1: What Are the Quality Models for AI Software?  
The following quality models for AI software were identified in the study. [PERSON] et al. [3] proposed a set of quality characteristics for ML software by extending ISO 25010 software products, [PERSON] [69] discussed the quality characteristics of non-functional requirements (NFR) of ML algorithms, [PERSON] et al. [70] proposed quality characteristics of ML software, ref. [71] proposed quality characteristics for Artificial neural-network-based applications, and, lastly, [PERSON] [72] conducted a study on quality evaluation of Deep Neural Networks (DNN) software.  
The above listed quality models for AI software consider the quality characteristics for the AI software level. Table 5 and Table 6 shows quality models and characteristics for AI software, respectively.  
Based on the SMS carried out in this study, few high-quality models for AI software have been proposed. However, only [3] carried out empirical evaluation with limited experts, and the rest of the studies were left out of the empirical evaluation. Furthermore, when compared to traditional systems, research on quality models or standards in AI software receives little attention. The AI software quality models are insufficient in terms of the quality characteristics that they propose. They only proposed a few quality characteristics, leaving out many others. In the literature, there is still a need for comprehensive AI software quality models or standards with empirical evaluations to evaluate the acceptability and applicability of the proposed quality characteristics.  
Figure 4: Quality Characteristics Frequency for AI Systems and Software.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Study** & **Quality Level** & **Evaluation** & **Weakness** \\ \hline
[3] & AI software & Empirical evaluation & The study only consider the quality of ML software at the requirement engineering phase. The empirical evaluation of the proposed quality characteristics was performed by few experts. \\ \hline
[69] & AI software & No evaluation & This study highlighted the need for empirical methods such as surveys or interviews to derive knowledge from ML experts or researchers by asking what are the important qualities for ML software. \\ \hline
[70] & AI software & No evaluation & The study is more focused on quality issues for AI software. It only presents few quality characteristics for AI software such as compatibility, incorruptibility, and completeness. \\ \hline \hline \end{tabular}
\end{table}
Table 5: Existing Quality Models for AI Software.
# GNSS Code Multipath Mitigation by Cascading Measurement Monitoring Techniques
## 5 Combination of Monitoring Techniques for a Reliable Multipath Mitigation
Figure 3: _Cont._  
In comparison, as three and four GNSS frequencies are becoming more common, the first approach is preferred to reduce the number of exclusions and thus preserve measurement geometry. However, with the current GNSS constellation, where a significant number of satellites transmit on only two frequencies, or for users that can only receive one or two frequencies, the second approach offers higher detection performance. This is because the second combined method exploits single-frequency C/N\({}_{0}\)-based detection besides the other differential metrics. As mentioned previously, the drawback is the possibility of excluding (or de-weighting) measurements whose errors can be potentially corrected by a preceding CMC-based error correction. In the following data analysis section, for different test scenarios, both combined methods will be investigated in advance, and one of them will be chosen based on the test scenario and multipath conditions.
## 3 Security and Privacy-Preserving Solutions for IoE
### Quantum-Resilient Security Approaches
#### 3.3.2 Quantum-Resilient Blockchain Protocols: Post-Quantum Digital Signatures  
With the advancement of quantum computing, classical digital signatures such as RSA and ECDSA are becoming vulnerable to attacks. This development threatens the core security layers of blockchain systems [18; 54; 66; 67; 68; 69; 70]. Therefore, quantum-resistant signature algorithms are critically important, especially for blockchain-based healthcare systems and IoE infrastructures [19; 49; 69; 88]. Prominent Post-Quantum Digital Signature methods include:  
* Lattice-Based Signatures (e.g., CRYSTALS-Dilithium): Proposed in the NIST PQC standardization process, these structures are resilient against quantum attacks [33; 52; 122].
* Hash-Based Signatures (e.g., XMSS, SPHINCS+): Based on Merkle trees, these methods provide forward security [123; 124].
* Multivariate Polynomial Signatures (e.g., Rainbow): Considered among alternative structures offering quantum resilience [125].
* Code-Based Signatures: Built on classical error-correcting codes, providing high resistance to quantum attacks [126].  
When these protocols are integrated into the blockchain layer, both data integrity and digital identity authentication are secured against quantum threats. This is particularly significant in medical data storage systems using blockchain, where the protection of sensitive information is paramount [127; 128].
## 5 Implementation
### Defining Environmental Quality Indexes through Cluster Analysis
Thus, the four possible situations were defined into four class IDs, as shown in Table 5. Through the classifying equation for the class IDs, each check-in mark obtained from the extracted data was assigned a class ID. We calculated the eigenvalues of the check-in marks and conducted a principal component analysis (PCA) to verify whether each class ID had been classified effectively. The PCA results are shown in Figure 8a,b. The principal component shows the regular pattern of the dataset. The factors of citizen feelings about the city environment are complicated; therefore, we supposed that the index factors have a relationship with traffic accessibility and urbanization and, thus, classified the check-in marks according to the indexes. A PCA was used to confirm the classification. If the classification does not result in a correct dataset, then the explained variance ratio of the principal component will not be high enough. The PCA results show that the principal components of the dataset (according to the classification) are representative and have a resolution, as shown in \[F_{T}(c)=(Q_{1}+Q_{7})/15 \tag{7}\]0 Figure 8a is a draft of the explained variance ratio of each principal component, where PC1 is 0.36 (36%) and PC2 is 0.19 (19%) for all of the principal components in this dataset. PC1 and PC2 include nearly 55% of all of the components, so we considered them to be sufficiently representative for definition. Figure 8b is a scatter plot of all of the check-in marks of the two principal components (PC1 and PC2) and includes a decision distribution map of the component plot. The symbols from 1 to 4 represent the class IDs that we previously classified, and the decision distribution map clearly divides the class IDs into pairs: 4 and 2; 1 and 3. According to the definition of the class ID (Table 5), ID 4 indicates that the percentage of \(F_{T}(c)\) is stronger than the percentages of \(T(a)\) and \(U(\mathrm{e})\), while ID 2 indicates that the percentage of \(F_{T}(c)\) is stronger than the percentage of \(U(\mathrm{e})\) but weaker than the percentage of \(T(a)\). IDs 4 and 2 were sorted into the near area, which shows that citizens feel that an environment is good when there are lower levels of traffic and construction in an urban location. This inference can also logically explain why IDs 3 and 1 were sorted into the near area. In this experiment, we concluded that people are more likely to feel comfortable in urbanized environment areas with low traffic. High urbanization usually means high traffic accessibility, which means that a high \(T(a)\) results in a high \(U(\text{e})\), and our users usually did not like such locations.
## 1 Introduction
Although various research efforts are underway to address these issues, there are limitations to their practical application. High-strength concrete offers advantages such as slimming modular building components to reduce the self-weight and enhancing the durability compared to lightweight aggregate concrete [34; 35; 36; 37]. In particular, ultra-high-performance fiber-reinforced concrete (UHPFRC) boasts compressive strengths of more than 100 MPa and tensile strengths of more than 10 MPa, making it widely utilized in bridge superstructures, substructures, connections, etc. [38; 39; 40]. Research focusing on applying UHPFRC to architectural structures with diverse design forms has garnered increasing amounts of attention. In [41], an optimized method for designing, fabricating, and installing ultra-high-performance concrete (UHPC) panels for use in construction was proposed. In [42], movable and modular wall panels using UHPC were developed through research on lightweight sandwich panels. Additionally, analysis and design techniques for large modular floating structures using UHPC and fiber-reinforced polymers have been proposed [43]. To develop high-performance lightweight concrete with thermal insulation properties, waste glass and UHPC were mixed and tested for their mechanical performance [44]. Currently, 3 Dprinting technology is being actively applied to various structural creations using concrete [45]. By using fiber-reinforced concrete along with 3D printers, compressive strengths of 100 MPa and tensile strengths of 12 MPa were achieved, demonstrating excellent mechanical performance [46,47]. Research on the use of fiber-reinforced high-performance concrete in architectural finishes and structural components is ongoing. However, there is a lack of case studies on developing entire structures using fiber-reinforced high-performance concrete in a modular fashion rather than conventional onsite casting methods. Therefore, in this study, an attempt was made to develop unit box modules using UHPFRC. Performance evaluations were conducted by selecting key variables such as the cross-sectional thickness, reinforcement, fiber content, and presence of an insulation material to develop modular panels constituting the unit box. Safety was assessed through numerical analysis of the developed UHPFRC structural components. Subsequently, the UHPFRC module productivity was verified by producing them at actual size. Additionally, an economic evaluation was conducted by applying the infill method, which involves stacking frames with steel or reinforced concrete onsite and inserting box-shaped non-load-bearing units within them.
## 3 Results
### Relationships between Asphalt Binder Performance and Carbonyl and Sulfoxide Indices
Figure 8: Changes in the FTIR chemical indices of the investigated binders due to RTFOT ageing: (**a**) carbonyl index \(\text{I}_{\text{C}=\text{O}}\), (**b**) sulfoxide index \(\text{I}_{\text{S}=\text{O}}\), (**c**) polybutadiene index \(\text{I}_{\text{PB}}\) and (**d**) polystyrene index \(\text{I}_{\text{PS}}\). Figure 7: Changes in the high-temperature performance of the investigated binders due to RTFOT ageing: (**a**) high-temperature stiffness \(\text{G}^{\star}/\sin(\delta)\), (**b**) non-recoverable creep compliance \(\text{J}_{\text{nr}\,3.2\,\text{R}\text{Pa}}\) and (**c**) percent recovery R in the MSCR test. Regarding the I\({}_{\text{PB}}\) (Figure 8c) and I\({}_{\text{PS}}\) (Figure 8d) indices measured in the 45/80-55 binders, it can be seen that their variability in the presence of the additives was significantly lower compared to the other indices. Nevertheless, the direction of changes in the polybutadiene and polystyrene indices also differed significantly with different contents of the additives. The analysis above shows that the investigated additives did not affect the evolution of the high-temperature performance of the binders in the course of RTFOT ageing, but the opposite was found regarding the FTIR chemical indices. To further evaluate these findings, a correlation analysis was conducted. Tables 10 and 11 present correlation matrices of the performance properties and FTIR chemical indices of 50/70 and 45/80-55 asphalt binders, respectively. It can be firmly stated that, in the conducted experiments, the correlation between the FTIR indices and the high-temperature performance of the binders was weak (r \(\leq\) 0.44), and in none of the cases, the statistical significance of the relationship was confirmed (\(p\) > 0.05). Such an outcome may be attributed to the major effects of the additives on the FTIR indices shown in the previous section and, at the same time, their low impact on the rheological properties of the binders.
# Continuously Beam-Steered Phased Array Antenna Using GaN Varactors for Millimeter-Wave Applications
## 5 Conclusions  
In this paper, a 41 GHz phased array using GaN HEMT devices for mm wave applications has been presented. This array consists of two antenna patch elements along with a classical phase shifter where two GaN HEMTs are used as varactor diodes to achieve beam steering. The beam of the system can be continuously steered to different directions on the azimuth plane from \(0^{\circ}\) to \(25^{\circ}\) by changing the bias voltage of the varactors from \(-2\) to \(2\) V. Theoretically, this scan range can be easily increased as much as wanted by multiplying the number of the used GaN varactors by a multiple of 2. The compact size, design simplicity, and benefits of GaN technology make this design amenable for implementation in phased array systems, proving to be a suitable candidate for 5G mm-wave communication and beam steering applications.  
GaN device design and measurements, F.M. and M.Z.; GaN data analysis and device modeling, A.H.; conceptualization, A.H., M.H. and O.L.; methodology, A.H., M.H. and O.L.; validation, A.H., M.H. and O.L.; formal analysis, A.H. and M.H.; resources, A.H., M.H., O.L., F.M. and M.Z.; writing--original draft preparation, A.H.; writing--review and editing, A.H. and M.H. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The data are contained within the article.  
The authors declare no conflicts of interest.
## 3 Prediction and Future of EVs in the UK
### Mathematical Formulation of Models
#### 3.3.1 Prophet Model with Regressors  
Prophet is robust to missing data, outliers, and trend shifts. It requires minimal tuning and offers transparent, interpretable components. Its flexibility allows for the integration of known future events and policies, critical in EV adoption forecasting. It is especially suited for time series with clear seasonal patterns and domain-specific drivers.  
The structure of the Prophet model with external regressors is additive and defined as:  
\[y(t)=g(t)+s(t)+h(t)+\sum_{k=1}^{K}\beta_{i}x_{i}(t)+\epsilon_{t} \tag{1}\]  
where:  
* \(y(t)\): is the observed time series (e.g., EV sales);
* \(g(t)\): is the trend component (linear or logistic growth);
* \(s(t)\): is the model's seasonality via Fourier series;
* \(h(t)\): accounts for the holiday effects;
* \(\beta_{i}\): is the coefficient for the regressors;
* \(x_{i}(t)\): are the external regressors (e.g., battery prices, GDP);\(\epsilon_{t}\): is an independent error (assumed to be normally distributed; also called the residual or noise).  
This formulation follows the design proposed by [PERSON] and [PERSON] [25] who developed Prophet to address real-world forecasting challenges in business and policy applications. The model supports automatic detection of structural breaks in the trend (changepoints), and its modular decomposition allows for clear interpretation of underlying drivers.  
The trend function \(g(t)\) for piecewise linear growth is typically written as:  
\[g(t)=\Big{(}k+a(t)^{T}\delta\Big{)}t+\Big{(}m+a(t)^{T}\gamma\Big{)} \tag{2}\]  
where:  
\(a(t)\): is an indicator vector for changepoint activation;  
\(k\): is the initial growth rate (slope);  
\(m\): is the offset (intercept);  
\(\delta\): are the adjustments to the slope at changepoints;  
\(\gamma\): are the adjustments to the offset at changepoints.
# A Reliable Publish-Subscribe Mechanism for Internet of Things-Enabled Smart Greenhouses
## 3 Research Methodology
### Message Standardization
#### 3.2.1 Handling Variations in Network Conditions  
Regarding the number of linked devices, the proposed approach allows for the inclusion, elimination, or substitution of devices and sensors within the greenhouse network using the Registration topic. This feature is very efficient in maintenance situations, guaranteeing that the system stays adaptable and sensitive to operational modifications.  
The architecture is supposed to handle any interruptions in network connection proactively. The Alert topic is utilized for constructing local thresholds and panic messages, such as \"set temp = x\" or \"panic = y\" published before anticipated network fluctuations occur. This proactive strategy allows the system to independently regulate greenhouse climate conditions within defined thresholds, mitigating the impact of network instability on critical operations.  
In summary, this architectural framework facilitates scalability, dependability, and security inside IoT systems. The distinct layers are purposefully built to address certain functions and can be readily expanded or adjusted as required. Moreover, the division of responsibilities among the many levels facilitates the maintenance and updating of the system, therefore guaranteeing its currency and security.