esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
In this case, the final accuracy depends on the skills of the surgeon and their precision in applying adequate mechanical power and fixing/bonding in the correct angle, which plays a key role in achieving a satisfactory final result. The next important aspect, which depends on the skills/methods of the surgeon, is the scanning process, in which the surgeon scanning technique is considered to be more crucial than the influence of various intraoral scanners on the accuracy of the results [42]. To assess the effect of misfit in clinical studies, most authors have attempted to measure the gap between the framework and abutments. The importance of a vertical fit has been emphasized in several studies [43; 44; 45]. However, there is still no standard protocol to assess the fit of dental restorations [46].There are several methods for assessing the fit of screw-retained implant prostheses that have been used by others [47; 48; 49; 50; 51; 52]; however, SST continues to be a simple and popular method to use both clinically and in dental laboratories [16; 26].Moreover, this method is considered to be highly informative for the estimation of workflow accuracy despite its ordinariness. Thus, in the present study, qualitative assessment of the vertical microgap was performed with SST. The findings of the current study can be beneficial in the clinical setting for both surgeons and patients. First, less time is required for the whole process with a reduction in the preparation efforts by the surgeon (total, 9 h; designing, 3 h; milling, 5.5 h; and finishing in patient mouth, 0.5 h) and there is a decrease in the number of patient visits. The provisional prosthesis scanning technique is a relatively new method involving a fully digital workflow. To the best of our knowledge, there have been no published studies on the role of the provisional prosthesis scanning method in improving the accuracy of full-arch implant prosthesis on the dendulous ridge. This limits the comparison of current findings with the results of other studies. Nevertheless, the results of the present study for the provisional prosthesis scanning group lie within the clinically acceptable range, and the findings were derived from a model experiment. Further clinical studies are required to confirm these outcomes in the clinical sphere.
# Secure Quantum Communication Technologies and Systems: From Labs to Markets
## 4 QKD Applications
### Data Center Infrastructure  
Data centers store an unprecedented amount of data, including private sensitive information, government agency data, office data, and any other kind of critical information. Today, around 7500 databases are present worldwide [128]. This is the reason why database interconnection is of utmost importance. In cloud networks, databases are often physically located away from the place where the data are generated, and may adopt a distributed architecture for both efficiency reasons and security purposes. The common distances between data centers are of the order of tens of kilometers, perfectly in the range of DV-QKD or CV-QKD fiber communication. Moreover, such infrastructure may allow dedicated quantum communication infrastructure to be built. These databases probably represent the market segment that will be impacted first by QKD.
###### Abstract  
The ARIA block cipher algorithm is Korean standard, IETF standard (RFC 5794), and part of the TLS/SSL protocol. In this paper, we present the parallel implementation of ARIA block cipher on ARMv8 processors and GPU. The ARMv8 processor is the latest 64-bit ARM architecture and supports ASIMD for parallel implementations. With this feature, 4 and 16 parallel encryption blocks are implemented to optimize the substitution layer of ARIA block cipher using four different Sboxes. Compared to previous works, the performance was improved by 2.76\(\times\) and 8.73\(\times\) at 4-plaintext and 16-plaintext cases, respectively. We also present optimal implementation on GPU architectures. GPUs are highly parallel programmable processors featuring maximum arithmetic and memory bandwidth. Optimal settings of ARIA block cipher implementation on GPU were analyzed using the Nsight Compute profiler provided by Nvidia. We found that using shared memory reduces the execution timing when performing substitution operations with Sbox tables. When using many threads with shared memory instead of global memory, it improves performance by about 1.08\(\sim\)1.43\(\times\). Additionally, techniques using table expansion to minimize bank conflicts have been found to be inefficient when tables cannot be copied by the size of the bank. We measured the performance of ARIA block ciphers implemented with various settings. This represents an optimized GPU implementation of the ARIA block cipher.  
ARIA block cipher; ARMv8; GPU; parallel computation; software implementation; Nsight compute
## 2 Applications of UHT and HTST  
UHT and HTST are methods of thermally preserving liquid products in a flow-through tubular or plate pasteurizers. They are most often accompanied by bottling into pre-sterilized containers [21]. In the literature, different ways of distinguishing UHT and HTST from each other are encountered. For example, [PERSON] et al. [22] stated that the UHT method is heating at temperatures above 135 \({}^{\circ}\)C for 1-2 s, while [PERSON] et al. [21] stated that UHT requires a higher temperature than HTST, above 100 \({}^{\circ}\)C, and a shorter time of up to a few seconds. Based on an analysis of the literature on the topic, it can be concluded that the decisive differentiating factor among the analyzed methods is the process temperature. UHT preservation happens at temperatures above 100 \({}^{\circ}\)C and a time limited to 10 s, while HTST processing takes place at temperatures up to 100 \({}^{\circ}\)C and from few up to several dozen of seconds.  
The following stages can be distinguished in both of the preservation techniques: pre-heating, high heating, cooling, and sterile or aseptic packaging. Before these, food products are often homogenized to increase and unify the heat transfer by volume. HTST/UHT systems can work in the direct mode--whereby the product and heating medium are in direct contact with each other. But the most common choice is the use of devices that work in the indirect mode, where the product and heating medium do not come into contact with each other, and heat is transferred via a heat exchanger [23]. In the food industry, UHT and HTST, as continuous flow thermal processes, are mainly applied to milk, soup, honey,-stew, soy milk, wine, yogurt and cream [22]. In fruit and vegetable processing, the UHT and HTST methods are used for juices, nectars and beverages.  
The most common UHT preservation parameter found in the literature is 110 \({}^{\circ}\)C/8.6 s. In the application of the UHT technique for juice preservation, temperatures in a range of 110-138 \({}^{\circ}\)C and durations of 2-8 s are used the most often, following the principle that the higher the temperature, the shorter the time. In the case of the application of the HTST technique for heating juices and beverages, the most common temperature chosen is 90 \({}^{\circ}\)C, although trials are most often conducted with a temperature range of 71.1-100 \({}^{\circ}\)C and times of 2-90 s. Detailed summaries of the temperatures used, depending on the type of technique, as well the type of juice, nectar or beverage, are included in tables in this manuscript.
# From Genomics to AI: Revolutionizing Precision Medicine in Oncology
## 5 Genomics in Prognosis and Early Detection
#### Limitations in Genomics for Prognosis and Early Detection  
In summary, genomics offers transformative contributions to cancer prognosis and early detection through the application of genomic biomarkers and liquid biopsy techniques. Genomic biomarkers like Oncotype DX and PAM50 provide crucial insights into tumor aggressiveness and recurrence risk, enabling personalized treatment intensification or de-escalation [58; 59]. Liquid biopsies, specifically ctd DNA and cfdDNA analysis, allow for minimally invasive, real-time monitoring of genetic alterations, facilitating earlier cancer detection, tracking disease progression, and identifying MRD more effectively than traditional methods [62]. These advancements collectively lead to more precise risk stratification and timely clinical interventions. However, several limitations challenge the widespread application of genomics in prognosis and early detection. While promising, the sensitivity and specificity of liquid biopsies for early cancer detection are not yet universally optimal across all cancer types, potentially leading to false positives or negatives [61]. The clinical utility of MRD detection still requires further validation in large-scale prospective trials to establish definitive actionable thresholds and standardize testing protocols. Furthermore, the high cost of comprehensive genomic profiling and liquid biopsy platforms remains a barrier to equitable access, particularly in low-resource settings. The interpretation of complex genomic data for prognostic purposes requires specialized expertise, and integrating these insights seamlessly into routine clinical workflows can be challenging. Ongoing research is needed to refine these technologies, reduce costs, and develop clearer guidelines for their optimal implementation.
# Building Energy Savings by Developing Complex Smart Windows and Their Controllers
## 4 Discussion  
Additional simulations were conducted (not mentioned in this study). In particular, after comparing the energy consumption of buildings with complex smart window systems based on the orientation of the office building and analyzing the differences, it was found that the highest energy savings occurred when the building faced south, whereas the lowest savings occurred when it faced north. The order of energy savings according to building direction was south, west, east, and north.  
Furthermore, when comparing and simulating the amount of energy saved using different building envelope materials, such as reinforced concrete and brick, the energy-saving rate was found to be the same. However, when the window area differed from that of the wall, energy savings were proportional to the window area. Additionally, deep learning was applied to simulate the energy savings of a complex smart window system, and it was confirmed that a certain amount of energy consumption could be predicted.  
In this study, to reduce cooling and lighting energy simultaneously, a complex smart window was developed by combining SPD glass, which is capable of controlling light transmittance by actively responding to changes in indoor and outdoor environments and outdoor air, with LGG, which can actively control solar reflectance. A dimming LED is also incorporated to maintain the required indoor illuminance level. Therefore, it is necessary to confirm the energy-saving effects associated with the use of this technology in various buildings. Additionally, based on this study, the developed smart window is considered effective in reducing lighting and cooling loads. However, from a long-term perspective, when applying smart skin technology, it is deemed necessary to install real-time sensors for detecting malfunctions in the SPD and LGG window systems and verifying their long-term effects.
## 2 Related Work
### Importance of Simulations for Wireless Sensor Networks
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Criterion** & **TOSSIM** & **NS2** & **OMNeT++/INET** \\ \hline
**Nature of simulator** & Emulator & Simulator & Simulator \\
**Type of the simulator** & Discrete-event & Discrete-event & Discrete-event \\
**License** & BSD 1-license & GNU GPLv2 2 license & Academic Public License. INET models under LGPL 3 or GPL. \\
**User Interface** & GUI 4: through TinyViz. Supported languages: Python, C++, and NesC & GUI: through Nam. Supported languages: C++, and OTcl & GUI: a built-in GUI is available Supported languages: C++, and NED 5 \\
**Supported platforms** & Linux and Windows & Linux, MacOs, and FreeBSD & Windows, Linux and Mac OSX \\
**Heterogeneity** & No & No & Yes \\
**modeling** & Available & Available & Available \\
**Mobility model** & Yes & Yes & Yes \\
**Wireless medium model** & Path loss models: lognormal shadowing & Path loss models: shadowing, 2-ray ground, free space & Path loss models: free-space, log-normal shadowing, Rayleigh fading, 2-ray ground, rician fading, Nagasaki fading \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of different simulators of wireless sensor networks. In [16], a comparison was made between the well-known simulators, including NS2, OPNET, and the OMNeT++ discrete event simulator, which showed that the OMNeT++ discrete event simulator has better performance in terms of large-scale WSN simulation than the other simulators. Because the OMNeT++ discrete event simulator integrates more wireless medium and energetic models, the simulations of a WSN are more realistic than those of other simulators. Additionally, the hierarchical structure of the models makes it one of the best choices for modeling various experiences in real life. The GUI of the OMNeT++ discrete event simulator is based on the Qtenv environment, which supports interactive simulation execution, which we cannot find in other simulators. However, the OMNeT++ discrete event simulator has some limitations: it lacks observation of some metrics, especially for new WSN protocols such as WuRx. The main objective of this study was the integration of those metrics after specific measurements.
# Can Blockchain Strengthen the Energy Internet?
## 2 Energy Sustainability through Heterogeneity
### Future Directions  
The incentive-based benefits obtained through real-time dynamic electricity pricing are receiving attention in the grid integration of DERs beyond small scale capacities at different voltage levels of the grid [28]. This will be facilitated through 5G and beyond 5G technologies, offering ultra-reliable, low-latency communication and edge intelligence supporting remote communication for intermittent connectivity respectively. Blockchain would provide an overlay with distributed storage facility. Precision decision-making, incorporating AI along with reliable communication and data processing through edge devices, as envisaged with 6G, would enable the realization of next-generation electricity networks [29].  
**Lessons:** Decentralized energy trading is a well-established research area with several approaches proposed and real-world scenarios being implemented [26; 27; 30; 31; 32; 33; 34]. Examples include P2P trading of renewable energy [33; 35; 36]. Communication links are expected to be made secure through proposed blockchain integrated architecture, which, however, has room for improvement with novel Smart Grid 2.0-specific security threats to be addressed [37]. Meanwhile, facilitating seamless grid integration of DGs [12] and their interoperability [8; 21] are the challenges with future research prospects considering the existing work, which would require collaborative technological approaches facilitated through blockchain.
## 2 Background
### Feature Extraction Methods used for Object Detection in AVs
#### 2.1.3 Hybrid Approaches  
Several approaches intend to combine multiple modalities of features to provide complementary information and leverage the classification performance. The single modality of the features is not enough to solve some scenarios, such as self-driving, which involves volatile strategies and requires the utmost precision for safety reasons. In these cases, extracting features from different sensors and fusing them might improve obstacle detection effectiveness.  
When it comes to self-driving cars [4], one common approach is to use the data of both cameras and LiDARs. Combining the cameras' detail to achieve reliable object detection with the laser scan reading provided by LiDAR allows us to identify obstacles and place them in the world. As discussed in Section 2.1.2, a 3D LiDAR enables the fusion of a 3D point cloud and a 2D image, combining image detail and object positioning, as shown in Figure 2.  
To validate this, tests were carried out inside a gazebo simulator. The point cloud from a 2D LiDAR (mounted on a 2-wheel robot) was projected onto an image produced by a camera on the same robot. Data were projected from one sensor onto another, thus proving this method to be a low computation and low-cost procedure to accurately detect the distance to objects identified by computer vision algorithms. However, for the robustness needed in ADSs, the 3D LiDAR is still the more reliable option as the 2D LiDAR is bounded to a single line along the x-y plane.  
Figure 2: LiDAR point cloud projection (image under creative commons license).  
It is essential to point out that using a LiDAR sensor is not the only way to obtain point clouds. Using a stereo camera and performing SIFT-like algorithms, point clouds can also be generated for autonomous driving applications. The only issue is the frame rate, which is not as good as when using a LiDAR swift. Moreover, this alternative would require image pre-processing to acquire the point clouds properly. Nonetheless, this alternative is much less expensive, and even some GPU-based architectures of this kind have shown a very competitive point cloud generation compared to LiDAR sensor-based architectures [15].
# Static Wind Reliability Analysis of Long-Span Highway Cable-Stayed Bridge in Service
## 4 Case Analysis of Static Wind Instability and Parameter Influence of Xiangshan Harbor Bridge
### Influence of Static Three-Component Force Coefficients on Static Wind Stability  
Due to the randomness of the wind field and the fluid-structure interaction during the use of the bridge, the magnitude of the static three-component force coefficient will also fluctuate accordingly. These coefficients obtained by boundary layer wind tunnel test or numerical simulation also are random. It is very important for the static wind load calculation, and the static wind load is closely related to the static wind instability of the bridge. Therefore, it is necessary to discuss the sensitivity of the static three-component force coefficient to the static wind instability of the bridge. In this research, the static wind stability of the Xiangshan Harbor Bridge is respectively explored when only the moment coefficient is expanded to 1.2 times, and other parameters remain unchanged, or only the lift coefficient is expanded to 1.2 times, or only the drag coefficient is expanded to 1.2 times, to analyze the displacement response of the bridge and obtain the critical instability wind speed. The comparison of the maximum displacement of the main beam under different cases is shown in Figure 14. The critical instability wind speed of Xiangshan Harbor Bridge under different cases is listed in Table 2 at the initial wind attack angle of 0\({}^{\circ}\).  
Figure 14: Maximum displacement curve of main beam with wind speed under different three-component force coefficients; **(a)** Lateral displacement; **(b)** Vertical displacement; **(c)** Torsion angle.  
Figure 13: Critical aerostatic instability wind speed with the steel wire breakage rate.  
From the above results, it can be concluded that the change in the moment coefficient has the greatest influence on the static wind stability, and the change in the lift coefficient has a great influence on the vertical displacement of the bridge but it has little effect on the aerostatic instability wind speed.
## 2 System Model  
We consider an uplink wireless communication using MUSA in a single-cell system with one base station (BS), one relay, and K users. For the MUSA scheme, the K users are multiplexed in code domain and are equipped with Nr antennas and the BS is equipped with \(Nt\) antennas, while the relay is equipped with \(Nrr\) receive antennas and \(Ntr\) transmit antennas. We took into consideration the DF protocol to be implemented at the relay. We assume that all channels are independently and identically distributed Rayleigh fading. The block diagram of the system is depicted in Figure 1. The binary input data sequence is LDPC encoded, then modulated using BPSK or QPSK techniques, converted from series to parallel by the S/P block, applied to an Inverse Fast Fourier Transform (IFFT) block, then converted back to series by the P/S block; finally, the cyclic prefix is added and the resulting signal is transmitted through the channel. At the receiver the inverse operations are performed: the cyclic prefix is removed, the data is converted from series to parallel by the S/P block and applied to a Fast Fourier Transform (FFT) block, then converted back to series by the P/S block, demodulated and LDPC decoded using the sum product algorithm (SPA) algorithm.
# Concept Drift Detection Based on Deep Neural Networks and Autoencoders
###### Abstract  
In domains such as fraud detection, healthcare, and industrial equipment maintenance, streaming data often exhibit characteristics such as continuous generation, high real-time processing requirements, and complex distributions, making it susceptible to concept drift. Traditional shallow models, with their limited representational capacity, struggle to fully capture the latent conceptual knowledge inherent in the dynamic and evolving nature of streaming data. To address this challenge, we propose a concept drift detection method based on deep neural networks combined with autoencoders (Concept Drift Detection Based on Deep Neural Network Combined with Autoencoder, DNN+AE-DD). In the DNN+AE-DD, a deep neural network is first employed as the base model for pretraining, and the hidden layer parameters of the model are transferred to a network with an identical structure for stream data processing, where certain hidden layers are frozen. Subsequently, the hidden layer outputs from both the pretraining and stream data processing phases are collected and used as training and testing data to initialize and predict using an autoencoder model. Concept drift is then detected by combining the reconstruction error of the autoencoder with the 3\(\sigma\) principle. Experimental results on both real-world and synthetic datasets demonstrate that, compared to traditional shallow concept drift detection methods, this approach effectively and accurately detects anomalies in streaming data, confirming the proposed model's high sensitivity to concept drift.  
c onteprint; deep neural network; autoencoder; concept drift detection Article