esa-sceva/satcom-chunk-collection huggingface dataset:
# Ultimate Compressive Strains and Reserves of Bearing Capacity of Short RC Columns with Basalt Fiber
## 4 Results of Testing Short Columns
### Redesign of Typical Columns Taking into Account the Increase in Bearing Capacity  
To determine the effectiveness of the considering the actual stresses in reinforcement bars and fiber-reinforced concrete, the standard prefabricated RC column was recalculated. In the calculation, heavy concrete was replaced by expanded clay concrete of equal strength (variant 1) and using reinforcement with a resistance of 1000 MPa (variant 2). The design of standard precast concrete columns is shown in Figure 10.  
Figure 10: Scheme of a concrete column.  
Figure 9: Dependence of longitudinal strains in the cross section in columns KK-0.6 (1) and KKF-0.6 (2) at a load of 120 kN.  
A typical precast reinforced concrete column has a cross-sectional area of 300 \(\times\) 300 mm, length \(L_{0}\) = 4500 mm. Longitudinal reinforcement consists of four rods with a diameter of 28 mm, class A400 (4028 A400).  
The calculation of the columns (Table 10), carried out considering the proposals, showed that the addition of fiber from coarse basalt fiber makes it possible to reduce the consumption of reinforcement compared to a typical column.  
Considering the replacement of heavy concrete with expanded clay concrete with fiber (option 1), and strain hardening, the consumption of working reinforcement can be reduced by 12.8% (4022 + 2020 A400). When using reinforcement with a resistance of 1000 MPa in a column, its consumption can be reduced by 23.4% (6020 A1000).  
Thus, the correct consideration of the compressive stresses in the reinforcement and concrete of compressed columns made using fiber from coarse basalt fiber gives a significant effect, expressed in a significant reduction in steel consumption and a decrease in the mass of the structure, which in turn reduces transport and installation costs.
# Accelerating Image Classification using Feature Map Similarity in Convolutional Neural Networks
## 4 Experimental Results and Analysis
### Result Based on Cache Location  
Although the precision of the cache varies based on the network model, feature maps generated from the kernel are generally more precise on the final layers that extract features that are more common. However, when the cache is applied to the final layers, the classification time cannot be significantly reduced because the cache is operated after many convolution operations have already been performed. In addition, in GoogLeNet and ResNet, when the cache is located in the final convolution layers, the classification time increases, unlike when using the CNN. In this case, the cache is not available. Experiments show that in most pretrained CNN models, the rate of false positives decreases gradually from the middle layers. As a low number of false positives indicates a high degree of precision, this cache can be used only if the number of false positives is low. The classification time owing to the cache also increases steadily from the first layer; hence, a tradeoff between the classification time and precision can be made. In addition, it may not be desirable to cache all layers or multiple layers. This is because the cache's computation time can exceed the CNN's classification time, as is the case with GoogLeNet and ResNet. In our experiment, the classification time was measured by placing a cache in only one layer; however, in some cases, the classification time required by the cache exceeded the CNN's classification time. Table 5 shows the time spent searching the cache for each layer location. This time was measured when the number of classes written to the cache was 10. If the cache is located in only one layer, the search time of this cache does not take up a large portion of the total classification time. However, the more layers in which the cache is located, the more these values are added; hence, the classification time will increase. In addition, the resource and power consumption can also increase if the classification time is longer. Our proposed cached CNN can temporarily increase the resource and power consumption because it requires operations that extract feature vectors from feature maps and compare them to cached feature vectors. Moreover, if _Cache Misses_ continue to occur, the resource and power consumption will increase significantly. However, if _Cache Hits_ reduce the image classification time, the time required for the device to perform operations is also reduced; hence, the overall resource and power consumption may be reduced. In other words, if caches are executed from many convolution layers and _Cache Misses_ continues to occur, not only does the classification time increase significantly, but also do the resources and power consumption. In this case, a tradeoff between the classification time and precision should be considered.
# Client-Aware Negotiation for Secure and Efficient Data Transmission
## 2 System Description  
The CAN-SEAT strategy is based on HTTPS, which consists of HTTP and SSL/TLS. Its main purpose is to provide authentication to the server and ensure the security and integrity of data transmission. Existing mainstream HTTP servers include Apache, Nginx, IIS, and so on. As an open-source project, the Apache server has always maintained a high market share [20]. The SSL/TLS implementation has OpenSSL, GunSSL, wolfSSL, and so on. As an open-source implementation of SSL/TLS, OpenSSL has become the de facto standard for secure data transmission. The SSL/TLS implementation usually is comprised of two main parts: the SSL protocol library and the cryptographic algorithm library. The SSL protocol library mainly includes various SSL/TLS protocol implementations, and the cryptographic algorithm library contains many kinds of algorithms for data encryption.
## 4 Discussion  
For this research, a traditional Romanian motif taken from textile heritage was transposed as an ornament on maple wood panels by CNC machining with two different methods: engraving (Engrave) and carving (V-Carve) using the V-Grooving Router Bit angled at 90\({}^{\circ}\). The comparison between the two processing methods included a visual analysis of the appearance of the processed ornament in the two variants, followed by a stereo-microscopy of the areas where fuzzy grains occurred after milling the wood; a tool wear assessment by microscopic investigation of the tool cutting edge after twenty successive millings of ornaments; and a calculation of the tool path and of the mass of the removed wood during the milling process and their correlation with the processing times.  
The visual analysis revealed that the ornament looked totally different when it was engraved or carved by the CNC router. The carved ornament looked more similar the original motif. In addition, the microscopic investigation showed large and numerous wood fuzzy grains on the engraved surfaces, thus bringing another advantage to the V-Carve method.  
The tool wear was highlighted by microscopic analysis of the tool cutting edge, where blunt and rounded edges were compared with their initial state after the ornament had been processed twenty times.  
It is found that the wear of the tool used for engraving is 37.5% for the cutting area (see Figure 5) and 26.7% for the tip area (Figure 6). For the tool used for V-Carve milling, the wear is 41.2% for the cutting area (Figure 7), while for the tip, it was 42.8% (Figure 8). These data show that, in the case of milling with the Engrave method, the wear of the tool was less pronounced than that in the case of V-Carve milling. The explanation is related to the length of the tool path for the two applied methods (approx. 19.2 m when the Engrave method was applied and 49.7 m when the V-Carve method was applied).  
It was also noticed that, when milling with the Engrave method, the wear affects the cutting edge area more, while when milling with the V-Carve method, the wear percentage of the tool tip is higher. This can be explained by the different contact areas between the tools and wood for the two cases, with the difference being the interior surface of the ornament, where the cutting depth varies between 1 mm to 3 mm, and the tip of the tool is involved in the milling process.  
Correlations between tool path lengths calculated for twenty processed ornaments, their corresponding processing times, and masses of the removed wood are presented in Figure 15.  
Figure 15: Comparison between the Engrave and V-Carve methods applied to CNC routing of an ornament on a maple wood surface: (**a**) correlation between the mass of removed wood and tool path length; (**b**) correlation between the tool path length and processing time.
## 4 Conclusions  
In this paper, the carbonate rock matrix dissolution process is comprehensively studied, and its implications on the physical variation in different carbonate reservoirs during CO2 injection and sequestration are simulated and demonstrated. The following conclusions can be drawn from this study.  
Firstly, mineralogy plays an important role in the practice of CO2 sequestration in carbonate reservoirs. Limestone reservoirs exhibit a faster reaction with injected CO2 and residual brine compared to dolostone reservoirs due to their higher reactivity with carbonate. This leads to a slower reservoir pressure buildup in limestone reservoirs due to the more rapid dissolution of pore space.  
Secondly, the type of reservoir, whether pore-dominant or fracture-dominant, significantly influences the dissolution process. Pore-dominant reservoirs exhibit a larger reaction surface area, resulting in faster dissolution and slower pressure buildup compared to fracture-dominant reservoirs.  
Thirdly, carbonate mineral dissolution behavior is also related to pore size. Smaller pore sizes lead to larger reaction surface areas and greater residual brine saturation, resulting in faster pore volume expansion but slower pressure buildup. Conversely, carbonate reservoirs with larger pore sizes exhibit faster pressure buildup and slower pore volume expansion.  
Furthermore, the CO2 injection rate also has an impact on dissolution dynamics. Slower injection rates allow for more time for CO2 to dissolve into residual brine and react with the rock matrix, leading to increased pore volume expansion and reduced pressure buildup compared to faster injection rates.  
Importantly, these effects, i.e., pore space type, pore size, and injection rate, were found to be more pronounced in limestone reservoirs than in dolostone reservoirs, which indicates that the dolostone reservoir is more stable and less affected during the CO2 injection process.  
Lastly, the above-mentioned conclusions hint at the necessity of continually reassessing the characteristics of the carbonate reservoir due to variations induced by injected CO2 and challenge the traditional perspective in the selection of high porosity and permeability zones for CO2 storage in carbonate reservoirs. Also, the idealistic geological model can be used to mechanistically and qualitatively study carbonate rock dissolution during CO2 injection. However, the quantitative application is limited.  
J.Y.: conceptualization, methodology, validation, formal analysis, investigation and writing original draft; Z.C.: conceptualization, methodology, writing review & editing, supervision and funding acquisition; C.J.: Methodology and writing review & editing; X.P.: methodology, investigation and formal analysis. All authors have read and agreed to the published version of the manuscript.  
This work is funded by GeoEnergy program of Natural Resources Canada: 330149.  
Data Availability Statement: Data is available upon request.  
The authors declare on conflicts of interests.
## 6 Evaluation/Lessons Learned  
This section evaluates the Gaia-X-Med implementation's applicability to the six eHealth use cases, providing insights into its effectiveness and limitations. The diverse requirements of these use cases offer a comprehensive view of Gaia-X's suitability for healthcare applications.  
Technically, all use cases successfully utilized the Yo-Ga-X framework to establish participants, onboard them to a common federation, and leverage federation services for compliance and contract negotiation. A shared federation approach was adopted to test the Gaia-X service ecosystem's viability. However, cross-use-case service sharing was limited, suggesting that broader and more thematically aligned use cases would better showcase the potential of shared services. The single Trust Anchor implemented posed challenges, as identifying credible certifying institutions remains complex. Often, the participants themselves were the most reliable information sources, diminishing the value of external certification.  
Use cases focusing on business-to-business (B2B) scenarios with well-established organizations demonstrated the framework's strengths in maintaining trust and consent. Conversely, use cases involving individual participants, such as patients, faced technological barriers that hindered full federation integration. This shortcoming highlights the need for conceptual adjustments to Gaia-X's approach, as patients lack organizational affiliations similar to those of employees or customers, complicating their role as participants. We hope to further contribute to these efforts in the future, as many projects, not just from the health domain, are doing.  
While trust and consent were central, other Gaia-X features, such as data exchange, policies, and labels, warrant greater focus. Efforts to unify Gaia-X implementations like XFSC, EDC, and Yo-Ga-X into interoperable federations could enhance cross-domain applicability. Institutional aspects, such as trust anchors and clearinghouses, also require attention, to ensure robust and scalable dataspaces.
## 3 Results and Discussion
### Physicochemical Characterization of the RC/IL Films
In fact, modification in the electrical response of a dry polymeric (cellulose triacetate or CTA) membrane (or thin film) with the content of AliquatCl was already reported [57w], and the effect of the increase in the AliquatCl percentage on both the electrode/film interface and the bulk phase are presented as Supplementary Information (Figure S4). The fit of the IS data points represented in Figure 7a to a parallel association of resistance (R\({}_{\text{f}}\)) and a non-ideal capacitor (Q\({}_{\text{f}}\)) for both RC/IL films (R\({}_{\text{f}}\)Q\({}_{\text{f}}\) equivalent circuit) allowed us to determine the electrical resistance and equivalent capacitance (C\({}^{\text{eq}}\)) values [44], and from these results, the conductivity (\(\sigma_{\text{f}}\)) and dielectric constant (\(\varepsilon_{\text{f}}\)) of each RC/IL film were obtained, taking into account the following well-known expressions (valid for homogeneous conductors and plane-plate capacitors) [45]:  
\[\sigma_{\text{f}}=\Delta\text{x}_{\text{f}}/\text{S}_{\text{f}}.\text{R}_{ \text{f}} \tag{3}\]  
\[\varepsilon_{\text{f}}=\text{C}_{\text{f}}.\Delta\text{x}_{\text{f}}/\text{S }_{\text{f}}.\varepsilon_{\text{o}} \tag{4}\]  
where \(\varepsilon_{\text{o}}\) is the vacuum permittivity, while \(\Delta\text{x}_{\text{f}}\) and \(\text{S}_{\text{f}}\) represent the thickness and area of the films, respectively, and the values obtained for both parameters are: \(\sigma_{\text{f}}\) (RC/BMIMPF\({}_{6}\)) = 4.4 \(\times\) 10\({}^{-6}\) (\(\Omega\).m)\({}^{-1}\) and \(\varepsilon_{\text{f}}\) = 23.8, while \(\sigma_{\text{f}}\) (RC/AliquatCl) = 4.8 \(\times\) 10\({}^{-6}\) (\(\Omega\).m)\({}^{-1}\) and \(\varepsilon_{\text{f}}\) = 28.1, causing a reduction of around 40-45% in the conductivity of the RC-support, but an increase of 10% for the RC/BMIMPF\({}_{6}\) film and 20% for the RC/AliquatCl one in the case of the dielectric constant, in agreement with that already reported [59]; consequently, the modification of the cellulosic support with both ILs increases its insulating character.
# A CMOS-Based Terahertz Reconfigurable Reflectarray with Amplitude Control: Design and Validation
## 3 Element Design
### Amplitude-Control RRA Element
#### 3.2.3 RRA Element Operation Principle and Simulation Results  
The surface current distribution of the element under different switch states is shown in Figure 7, illustrating the working principle of the split-ring RRA element. The horizontally polarized electromagnetic wave is normally an incident for exciting the RRA element. In the ON state of the switch, horizontal surface currents are successfully excited on the split-ring structure. The currents smoothly pass through the switch, which acts as a short circuit, enabling the element to exhibit two \(\lambda_{0}/2\) resonant modes. In the OFF state of the switch, the current in the entire loop decreases significantly. The two current null points on the loop move closer to each other, thereby reducing the resonant length. Since the effective resonant length in the ON state becomes longer than that in the OFF state, the element exhibits a lower resonant frequency in the ON state compared to the OFF state.  
The simulation results shown in Figure 8 validate this operational principle. It can be observed that the resonance frequency of the element changes with the switching state: when the switch is in the ON state, the resonance frequency is 271 GHz; when the switch is in the OFF-state, the resonance frequency increases to 290 GHz. The amplitude-control RRA element exhibits an amplitude response of \(-4.3\) dB in the ON state and \(-17.0\) dB in the OFF state at 290 GHz.
# YOLO-DFAN: Effective High-Altitude Safety Belt Detection Network
## 3 Proposed Method
### YOLO-Dfan  
In total, YOLOv4-tiny consists of three parts: CSPDarkNet-tiny, FPN, and the detection head. As the backbone network, CSPDarkNet53-tiny is responsible for performing feature extraction. CSPDarkNet53-tiny consists of 15 convolutional layers, composed of two deep residual convolutional (CBL) units and three ResBlock residual modules, as depicted in Figure 1. The CBL units consist of a convolutional layer, a batch normalization layer, and an activation function--the leaky rectified linear unit. The working mechanism of the ResBlocks is to combine four CBL units with residual nesting, and then the maximum pooling process is performed. Through these processes, they solve the network degradation problem while increasing the network depth, alleviating the gradient dispersion, and making the data transfer smoother. The FPN is the feature fusion module used to construct a multiscale feature pyramid, and the YOLO head finally integrates and predicts the features extracted from the whole network.  
As a simplified version of YOLOv4, YOLOv4-tiny is a lightweight model with its parameters reduced to 6 million. Thus, this model can improve the speed with a slight loss of accuracy and meet high-altitude safety belt detection time requirements. However, given that the feature extraction of YOLOv4-tiny is insufficiently rich and fine, and that the scale-matching range is insufficiently diverse, a YOLO-DFAN network based on the original network is proposed in this paper.  
An improved algorithm of YOLOv4-tiny is YOLO-DFAN, as illustrated in Figure 2. After the backbone network outputs a 13 \(\times\) 13 feature layer, ASPPNet can be introduced, ensuring a denser capture of contextual information in multiple proportions to improve the features' receptive field, giving the network-acquired features a more refined representation capability, and enriching the extracted contextual features. By adding the DFAN module after the second output of the backbone and the output of ASPPNet, the model effectively adjusts the extracted deep and shallow information to obtain more effective feature information, respectively. Then, the two parts of the features are input into the multipath PANet fused with DFAN. The features on both paths can be aggregated by performing attention-containing upsampling and downsampling. Moreover, the feature utilization of deep and shallow information can be enhanced, improving the network's ability to represent the features of interest for multiscale and small objects. Finally, the two parts of the features after multipath aggregation are input into the detection head for feature integration and detection.  
Figure 1: YOLOv4-tiny network structure.
## 2 System Description  
We consider a VLC-based chain network among vehicles driving within a tunnel, as illustrated in Figure 1. In this scenario, each vehicle is assumed to exchange its driving dynamics data (e.g., target speed and acceleration) with neighboring vehicles ahead and behind. According to the findings in [25], incorporating target speed and acceleration data in cooperative adaptive cruise control significantly improves traffic stability and response times. Moreover, in this work, the wheel angle data are employed for adaptive optical beam alignment, which will be explained in the next section, between consecutive vehicles. The primary objective of this data exchange is to enhance road safety by mitigating the risks of chain collisions and hard braking in tunnels, where limited visibility and the lack of GPS signals result in various problems, such as a lack of real-time traffic updates and incorrect next-turn guidance. For data exchange between vehicles within the tunnel, we adopted V2V communication with the VLC system, where each vehicle utilizes its existing LED tail-lights for data transmission and photo-diodes (PDs) installed at the front of the vehicle for data reception. Additionally, each vehicle is equipped with a microcontroller unit (MCU) for processing the exchanged data and controlling the servo-motor, enabling adaptive optical beam alignment for optimized communication.  
Figure 1: Example of the proposed VLC-based chain network involving multiple vehicles within a tunnel.
## 3 Mathematical Formulation
### Geometry Definition  
Given a specific 3D array aperture, the first step is to calculate positions \((x,y,z)^{n}\) where \(n=1,\ldots,N\) represents the element index and \(N\) is the number of elements in the aperture. Additionally, since we are not dealing with isotropic sources, the element pattern must be rotated according to its position. To achieve this, rotation angles \((\theta_{r},\varphi_{r})^{n}\), respectively, in elevation and azimuth, are defined. A sketch of the coordinate system is represented in Figure 3.