esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Literature Review and Research Objectives  
### Factors Affecting Flood Hazard Assessment  
Based on the literature, flood hazard assessment basically requires the comprehensive consideration of several factors. [PERSON] et al. [14] reported that factors influencing flood hazards can be divided into the following seven groups: hydrological and orographic, meteorological, geomorphologic, cover characteristics, soil properties, infrastructure, and socio-economic. In practice, it is unclear how these factors affect each other and directly impact on flood risk. The existing research on the influential factors leading to floods can be categorized into two approaches, namely, qualitative and quantitative [14,15]. Qualitative evaluation approaches to flood risk mapping mainly rely on expert opinions, including constant sum scale (CSS) [16], entropy theory [17], and optimized additive weighting methodology [18]. On the one hand, quantitative evaluation is mainly based on numerical expressions that can quantify the degree of relationship between all related factors and flood risk. Classic examples of quantitative methods are hydrological and hydrodynamic models (i.e., semi-supervised machine learning, multi-layered coarse grid modeling in 2D urban flood simulations, and GIS-based urban storm-inundation simulation) [19, 20, 21], flood simulation models (i.e., Genetic Algorithm for Rule-Set Production, Quick Unbiased Efficient Statistical Tree (QUEST) [22], metaheuristic approaches [23], and meteorological research and prediction) [24]. Despite the benefits of quantitative research, the foregoing flood model approaches have limited use, especially at the local level. A recent study conducted by [PERSON] et al. [25] highlighted that the quantitative methodology for flood risk analysis mainly relies on a huge amount of high precision data, which is very difficult to gather, limiting their application in urban and local areas to some degree. Due to data scarcity and the uncertainties of quantitative flood forecasting, a semi-quantitative approach (i.e., qualitative and quantitative risk assessment) represents a better alternative based on the available information. A classic example is multi-criteria decision-making (MCDM) (i.e., an analytic hierarchy process (AHP) based on multi-critical indices) [15,26]. Basically, the AHP-pairwise comparison method is employed to determine the weight of the main criteria and sub-criteria. This method helps all decision-makers to explore their opinions and rank the complex criteria [27]. According to a review conducted by De Brito & Evers [28], the number of published papers relating to MCDM applications in flood risk assessment significantly increased from 1995 to 2015 (over 82% since 2009), with the AHP being the most popular. For instance, [PERSON] et al. [29], [PERSON] et al. [30], [PERSON] et al. [31], [PERSON] et al. [32], and [PERSON] et al. [33] applied the AHP method for flood risk assessment and zonation in designated areas (i.e., Thailand, China, and India). [PERSON] et al. [34] also applied the AHP method to assess the technical conditions of hydrotechnical construction.
## 3 Results
### Inference Latency Comparison  
Inference latency is a key performance indicator in edge-device applications where real-time decision-making is paramount. Reducing latency not only improves system responsiveness but also enables more efficient operation on resource-constrained devices, such as those deployed in autonomous vehicles, robotics, and IoT systems.  
For the VGG16 model, the FPGA implementation demonstrated an inference time of 65.20 s, resulting in an 86.3% reduction compared to the 475.42 s observed with the CPU-based implementation. Similarly, VGG19 on FPGA required 84.68 s for inference, achieving an 84.9% reduction relative to the CPU's 559.73 s. These substantial reductions underline the power of FPGA in accelerating DL tasks. This is primarily due to FPGA's inherent ability to parallelize computations and optimize resource utilization, such as leveraging DSPs and BRAMs for efficient computation. In contrast, the CPU's serial execution results in longer inference times, making FPGA a clear advantage in applications demanding low latency.  
Figure 4: Confusion matrix of CIFAR-10 test set images classification by our FPGA-based models: (a) VGG16; (b) VGG19.  
While the observed speed-ups are impressive, they reflect the potential of hardware acceleration for specific DL models (VGG16 and VGG19) and may vary for different architectures or tasks. Comparatively, recent studies [24] also report similar improvements in inference time when deploying CNNs on FPGA, further confirming FPGA's effectiveness as a platform for high-throughput, low-latency applications.  
These significant latency improvements have direct implications for real-world use cases, such as autonomous driving, where fast decision-making is crucial for safety, or IoT devices, which require real-time data processing. In such applications, the reduced inference latency offered by our models enhances the practicality of deploying AI solutions in edge devices with limited computational resources.
# BVLOS Unmanned Aircraft Operations in Forest Environments
## 5 Taking a Risk-Based Approach to BVLOS Operations in Forest Environments
### Operational Considerations
#### 5.2.1 BVLOS or EVLOS+?  
One question to ask before performing a BVLOS operation is whether it would be easier to vary typical EVLOS requirements instead. Standard EVLOS requirements involve either the pilot or visual observers having unaided visual line of sight of the aircraft such that they cannot use any tools, such as binoculars, to enhance their vision [20]. However, sometimes regulatory authorities approve operations that vary this requirement and where visual observers use technology such as binoculars, telescopes, night vision goggles, or zoom lenses to observe the aircraft and airspace at a much greater distance than what is achievable using human vision alone. There needs to be some DAA capabilities built into the aircraft in order to justify an EVLOS+ operation because the use of binoculars limits the field of view, and the magnification means that smaller tracts of airspace are being observed at any given time [79]. However, the sophistication of the DAA technology can be significantly less than that for a BVLOS operation because the usual VFR rules of see, detect, and avoid can largely be achieved in the usual manner (i.e., visual scanning) under EVLOS+. It is questionable whether EVLOS+ is safer than a BVLOS operation, however, because it works with existing rules and regulations, and it may be easier to achieve the requisite standards for such an approval. The authors could only find one mention of a similar operation in the literature [80], indicating that this could be an interesting area for future research. Industry experience suggests that this can be more cost-effective than BVLOS (by avoiding the need for highly sophisticated DAA technology) and more practicable than EVLOS (requiring multitudes of visual observers if in a large operating area).
## 3 Test Results and Analyses
### Failure Modes  
Figure 8 illustrates the shear strain contours of the backfill slope determined by PIV in each test. The slip plane can be determined as the zone with the relatively larger shear strains. Test T-1 had a primary slip plane near the slope shoulder. Test T-2 and T-4 had a similar slip plane, which passed from the rear of the top surface to the middle of the side slope. The slip plane of test T-3 was further contacted with the bedrock interface. All the slip planes were close to an arc-like sliding, but the rainfall influenced the sizes of the sliding mass.  
Figure 8: Contour of shear strain in the backfill slope: (**a**) T-1; (**b**) T-2; (**c**) T-3; and (**d**) T-4 (unit: %).  
Figure 7: The moisture content of profile at the height of 25.0.  
Table 3 shows the maximum displacements and shear strains of the slope at failure. The maximum vertical and horizontal displacements of the backfill slope after rainfall were nearly two to three times as large as the test without rainfall, and they increased with the rain intensity and duration. The rainfall caused the backfill to be softened; as a result, relatively large displacements occurred after rainfall.
# Collision Avoidance Behavior Mining Model Considering Encounter Scenarios
## 2 Methodology
### Ship Collision Avoidance Behavior Recognition  
In this section, to address the issue of trajectory fluctuations caused by interference from wind, waves, or currents in AIS data, a recognition algorithm combining the sliding window technique is proposed. The algorithm focuses on identifying the ship's collision avoidance turning points. First, the curvature of the ship's trajectory points is calculated using a sliding window. A fixed window turning factor is then used to identify turning points while filtering out data noise. Next, a variable window is employed to detect the ship's course recovery behavior using a recovering factor, thus determining whether the turn is a genuine collision avoidance maneuver and thereby ensuring the accuracy of the recognition.
# Anomaly Detection in Automotive Industry Using Clustering Methods--A Case Study
## 4 Case Study, Results, and Discussions
### Pre Processing Stages  
Before the first run of the corresponding algorithms, it is necessary to perform a statistical exploration analysis of the data set. We noted some key points that interfere with the overall process quality of machine learning techniques as described below.  
A logarithmic scaling resulting in the following weight data distribution in Figure 5 was applied to avoid a biased response.  
This process was replicated to the remaining dimensions selected with similar results compared to the distribution before and after scaling.  
Another point observed was that the corresponding dimensions presented a high numerical magnitude difference, and this implies a loss of significance to dimension with small magnitudes generating tendentious results. A normalization process known as z-score was applied to avoid this behavior, which is defined by Equation (22):  
\[z_{j}=Z\big{(}x_{j}\big{)}=\frac{x_{j}-\overline{x_{j}}}{\sigma_{j}} \tag{22}\]  
where \(x_{j}\) is the input data, \(\overline{x_{j}}\) and \(\sigma_{j}\) are the samples mean and the standard deviation of the \(j\)-th attribute, respectively. The transformed dimension presents a zero mean and a variance of 1.  
The last preprocessing step used in this work was the PCA (Principal Component Analysis), a method based on the projection of data in a smaller subspace. This process aims at the possibility of reducing the number of dimensions while maintaining its significance in this projected subspace. Dimensional reduction techniques lessen the computational cost in multidimensional problems without harming the result due to preserving the importance of the original dimensions.  
The well-known Iris dataset was used to validate the option for the PCA application. This dataset contains three labeled classes of flowers: Setosa, Versicolour, and Virginia, including 50 samples each, presenting four attributes: sepal length, sepal width, petal length, and petal width. Therefore, we consider the following scenarios:  
1. Four dimensions sepal length, sepal width, petal length, and petal width without PCA application;
2. PCA application using only the first principal components;
3. PCA application using only the first and second principal components.  
Then, we applied the nine clustering algorithms described in Section 3, generating Table 1, which presents the percentage of correct classifications: for each scenario:  
Figure 5: Weight distribution after logarithm scaling.  
Note a slight variation from each of the scenarios chosen, which sustain the application of PCA to the Iris dataset. Indeed, in scenarios (b) and (c), the algorithms often overcame their performances considering the total number of dimensions.  
After this validation, we applied the PCA to the automotive dataset. It was observed that each of the six principal components shows the following respective significances: 0.57, 0.18, 0.13, 0.09, 0.05, and 0. Note that the four first components present 95% of the energy of the signal.
# Design and Evaluation of Flooding-Based Location Service in Vehicular Ad Hoc Networks+
## 4 Performance Analysis
### Node-Location Patterns
#### 4.2.2 Two-Dimensional Scenario  
In this case, our model was a plane with node density \(\lambda\), expressed in vehicles per square meter. The exact model would have to compute number of nodes \(N_{i,j}\) at \(j\) hops from node \(i\), and compute the mean value of \(N_{i,j}\) averaged on the whole spatial configuration. Although this computation did not seem possible with state-of-the-art of stochastic geometry, we proposed to compute an upper bound. We assumed that the largest area in which \(N_{i,j}\) nodes could be found was between the circle of radius \((j-1)R\) and the circle of radius \(jR\) (see Figure 5). This occurred when density of nodes \(\lambda\) was very high. In this case, one could find nodes at \(j-1\) hops exactly at distance \((j-1)R\) from node \(i\). However, this property has still not been rigorously demonstrated, but it was verified in the following simulations. The mean number of nodes at \(j\) hops from \(i\) was thus  
\[E(N_{i,j})\leq\lambda\pi R^{2}(j^{2}-(j-1)^{2})=\lambda\pi R^{2}(2j-1)\quad \forall j>0.\]
## 4 International Runway Skid Resistance Standards and Regulations
### Impact on Runway Length  
During airport and runway construction and design, surface friction is not taken directly into consideration. The required runway length, in this case, mostly depends on the environmental parameters, such as temperature, slope, and elevation. The maintenance of standard or minimum levels of skid resistance is assumed in determining the required runway landing length. To ensure safe landing and aircraft operation, runway ends are usually connected with a stopway and a runway end safety area [67]. To avoid safety problems related to the aircraft overrun and to reduce the runway end safety area, an aircraft arresting system can also be installed. These areas are designed to decelerate an airplane overrunning the runway, by the use of high energy adsorbing materials of specific strength that will reliably and predictably crush under the weight of an aircraft [81]. However, these have many practical issues, such as their expense, reinstatement after use and access to the overrun aircraft to extinguish the fire and rescue the passengers [82; 83].  
As detailed further below, some high-friction surfaces, such as grooved surfaces and open graded surface mixtures, also allow a braking performance credit to be given to some aircraft [2]. By increasing the wet braking distance required for safe aircraft operations, effectively shortening the length of the runway required, it allows aircrafts to land on runways that would otherwise be considered too short. This is particularly beneficial for airports that were designed and constructed for smaller regional aircraft types but now support B737/A320 sized jet aircraft types.
# Designing a Large-Scale Immersive Visit in Architecture, Engineering, and Construction
## 2 Design Justification and Research Gap  
### From GIS-Based to Professional BIM-Based City Model  
Most of the papers available in the literature about digital twins describe the construction of large-scale 3D models based on GIS data. These models are accurate at a mesoscopic level, the level of the city, which is adequate for urban planning purposes, but still, they are low-LOD models. These are typically 2.5D models consisting of a 2D footprint of a building with its elevation, as shown in Figure 2a, often times with a coarse texture extracted from aerial photogrammetry or from pictures acquired from a ground vehicle. Researchers expose their work designing their own tools or using common GIS software. The emphasis is more on the city scale and number of buildings integrated than on their aesthetics. If one looks at designing an immersive visit, a level of detail much greater is required, that is where BIM Level 4 is required. Figure 2b gives an example of the refinement that can be reached with BIM-based models.  
Figure 2: Level of detail of GIS- and BIM-based 3D models. (**a**) GIS city models are 2.5D models: footprint plus elevation, with textures. (**b**) An immersive visit requires a high level of detail.  
While GIS-based approaches are well documented, there is a lack of information on how a practitioner would proceed if he/she wanted to build a 3D city model from professional BIM files. AEC consultants use commercial tools such as the cloud-based technology BIM 360 [22], which facilitates the visualization of the city and existing infrastructure. Figure 3a illustrates a point of view of our district of interest: in this parcel are represented artwork from several buildings, as well as urban furniture data and landscape data (basically trees). For architectural consideration, BIM 360 is a powerful tool from a functional point of view, but the textures are mainly representative of what one can find in a typical virtual world. Such a digital twin is well adopted for design review, construction support, and stakeholder engagement [23], but lacks realism. It is useful for interaction, but personal imagination is needed to fill in the gaps of the cold textures' impression and improve the immersive experience. Figure 3b illustrates the kind of results that are required for an immersive visit that would allow aesthetic and architectural ambiance study: much work is required in terms of texture replacement and lighting configuration. The game industry is able to deliver such a result using game engines such as Epic Games or Unity technologies [24], but game designers design graphically their own buildings without the need for BIM data, which is more a work of art than an integration of industrial processes.
# A Survey of of Side-Channel Attacks and Mitigation for Processor Interconnects
## 4 Interconnect Contention Side Channel
### CPU Cache Architecture
#### 4.1.1 Non-Uniform Cache Access Architecture  
Large-scale multi-core processors are equipped with a large last-level cache (LLC) to mitigate expensive off-chip accesses. For example, AMD Ryzen (AMD, Santa Clara, CA, USA) features a 256 MB LLC [58], while the Intel Xeon 6900 series has a 108 MB LLC [59]. This LLC is distributed across multiple banks (or tiles, for Intel) interconnected via an NoC to reduce access latency and improve core isolation, known as Non-Uniform Cache Access (NUCA) architecture. The Non-Uniform Cache Access (NUCA) architecture aims to reduce memory access times and improve cache hit rates by increasing cache capacity [58; 60]. However, due to the limited bandwidth and port count of individual physical cache banks, multi-core processor chips employ physically separate banks to share the LLC, which are interconnected with each other through the NoC. Therefore, in the NUCA architecture, cache access latencies to different cache banks exhibit differences [61], as memory operations involve network protocols and message passing within the NoC.
### Feedback Circuit  
In Figure 1, Ports 1, 2, and 3 are represented as an output of the power amplifier (B), an output of the oscillator, and an output of the feedback circuit (A), respectively. The insertion loss of the feedback circuit \(\mathrm{S_{31}}\) has to be better than \(-15\) dB at 2.45 GHz by considering a small signal gain of 17 dB and \(\mathrm{P_{2 dB}}\) (2 dB gain compressed power) operation of the power amplifier. The output loss of the oscillator is represented as \(\mathrm{S_{21}}\). Moreover, the center frequency of the passband of the imbalanced resonator is depicted as \(f_{\mathrm{o}}\). \(\mathrm{S_{31}}\), \(\mathrm{S_{21}}\), and \(f_{\mathrm{o}}\) are calculated for the schematic diagram of Figure 1 for a variable coupling capacitance \(\mathrm{C_{1}}\) of 0.02 to 0.14 pF with a constant coupling capacitance \(\mathrm{C_{2}}\) of 0.3 pF. The calculated results are shown in Figure 4a. Both the feedback amount \(\mathrm{S_{31}}\) and output loss \(\mathrm{S_{21}}\) increase, as well as the center frequency \(f_{\mathrm{o}}\), decreases with \(\mathrm{C_{1}}\). As the lowest value which can satisfy the Barkhausen Criteria, \(\mathrm{C_{1}}\) of 0.06 pF was chosen. \(\mathrm{S_{31}}\), \(\mathrm{S_{21}}\), and \(f_{\mathrm{o}}\) are calculated again for a variable coupling capacitance \(\mathrm{C_{2}}\) of 0.15 to 0.4 pF with a constant coupling capacitance \(\mathrm{C_{1}}\) of 0.06 pF.  
Figure 3: Simulated power, efficiency, and gain of the power amplifier.  
Figure 2: Schematic diagram of the power amplifier.  
The calculated results are shown in Figure 4b. On the contrary, both the feedback amount S\({}_{31}\) and output loss S\({}_{21}\) decreases, as well as the center frequency \(f_{\infty}\), increases with C\({}_{1}\). As the highest value which can satisfy the Barkhausen Criteria, C\({}_{2}\) of 0.3 pF was chosen. In the references [8; 9], only the transmission lines T\({}_{1}\) and T\({}_{2}\) are used to adjust the insertion phase, which seriously made a phase adjustment more difficult. To address this issue, as shown in Figure 1, the phase shifter was newly employed in the feedback circuit to adjust the insertion phase. When T\({}_{3}\) is 14 mm and C\({}_{3}\) is varied from 0.1 to 1.7 pF, the phase-shifter of Figure 1 can be varied by 70 degrees, allows fine adjustment of feedback loop phase. The length of T\({}_{3}\) is shorter than \(\lambda\)/4 to improve the impedance of the phase shifter.
###### Abstract  
A sensor failure will lead to sensor measurement distortion, and may reduce the reliability of the whole structure analysis. This paper studies the method of monitoring information reconstruction based on the correlation degree. For the faulty sensor, the correlation degree of the normal response of this sensor and the measurements of the other sensors is calculated, which is also called the correlation degree of reconstructed variables and response variables. By comparing the correlation degrees, the response variables, which are needed to establish the correlation model, are determined. The correlation model between the reconstructed variables and the response variables is established by the partial least square method. The value of the correlation degrees between the reconstructed variables and the response variables, the amount of the monitoring data which is used to determine the coefficients of the correlation model, and the number of the response variables are used to discuss the influence factors of the reconstruction error. The stress measurements of structural health monitoring system of Shenzhen Bay Stadium is taken as an example, and the effectiveness of the method is verified and the practicability of the method is illustrated.  
Keywords:structural health monitoring; responses reconstruction; correlation model; stress measurements; Shenzhen Bay Stadium +
Footnote †: journal: Applied Sciences