esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Test Results and Discussions
### Effect of the Sustained Load  
The last test series, which included the SL, SL_E, and SL_ES coupons, was prepared to investigate the effect of sustained load on the tensile behavior of TRM coupons. The SL coupons refer to the TRM coupons without surface treatment subjected to sustained load. Similarly, the SL_E and SL_ES coupons mean the TRM coupons with epoxy impregnation and the TRM coupons with sand coating after epoxy impregnation, respectively.  
Figure 18 shows the stress-strain relationship of the TRM coupons with different surface treatment methods subjected to sustained load. Similar to the first two test series, the average cracking tensile strengths and the initial stiffness in this series were almost the same (Figure 19 and Table 2). Figure 19b clearly shows the effect of sustained load on the ultimate strain of TRM coupons with the different surface treatment methods. The ultimate strain of the SL_E and SL_ES coupons decreased by 30.78% and 50.59%, respectively, compared to that of the SL coupons. Meanwhile, the ultimate tensile strength in this series showed a fluctuation.  
Figure 20 shows the comparison between TRM coupons with and without sustained load. The SL and Com coupons without surface treatment showed almost equal strength, but 95% deformability difference (Figure 20a). In the three previous test coupons of the Com series, the tensile strength showed a significant difference according to the surface treatment method. However, in the coupons of the SL series under sustained load, such a difference was not observed. This indicates that the improvement of tensile behavior due to surface treatment decreased because of the sustained load.  
Figure 19: Effect of different surface treatments on the tensile behavior of TRM coupons subjected to sustained load: (**a**) cracking and ultimate strength; (**b**) initial stiffness; (**c**) ultimate strain.  
Figure 18: Tensile stress–strain relationship of TRM coupons with different surface treatments subjected to sustained load: (**a**) coupons without surface treatment; (**b**) coupons impregnated by epoxy; (**c**) coupons coated with aluminum oxide powder after epoxy impregnation.
# A Vehicle-Infrastructure Cooperative Perception Network Based on Multi-Scale Dynamic Feature Fusion
## 3 Method
### Multi-Scale Dynamic Feature Fusion
#### 3.3.3 Per-Point Self-Attention Module  
The PPSA module, illustrated in Figure 4, builds upon dynamic feature interactions by performing self-attention at identical spatial locations. This approach effectively captures the relationships between features from various sources at the same spatial coordinates, allowing for comprehensive integration of these features. The input to the PPSA consists of spatial and channel dynamic interaction features, along with the original vehicle and infrastructure features. This holistic input strategy reduces the risk of information loss during MSDI, thereby enhancing the network's robustness and ensuring strong performance in complex environments.  
Initially, the features \(F_{i}^{S}\), \(F_{i}^{C}\), \(F_{i}^{ID}\), and \(F_{i}^{V}\) are concatenated and reshaped into a tensor with dimensions \((H_{i}W_{i})\times 4\times C_{i}\), denoted as \(F_{i}^{Res}\). This reshaping aligns the data with the input requirements of the self-attention mechanism.  
Next, we express the applied self-attention mechanism along the second dimension of \(F_{i}^{Res}\).  
\[F_{i}^{F}=\text{LN}(F_{i}^{Res}+\psi(\frac{(F_{i}^{Res}Y_{i}^{Q})(F_{i}^{Res}Y_{ i}^{K})}{\sqrt{C_{i}}})(F_{i}^{Res}Y_{i}^{V})), \tag{7}\]  
where \(Y_{i}^{Q},Y_{i}^{K}\), and \(Y_{i}^{V}\in\mathbb{R}^{C_{i}\times C_{i}}\) are learnable projection matrices that transform the reshaped features into queries (Q), keys (K), and values (V) through linear transformations. After calculating the scaled dot-product attention scores using the Softmax function, the final fused features are derived by aggregating the value vectors weighted by the attention scores, followed by layer normalization (LN) and a residual connection.
# DRAM Retention Behavior with Accelerated Aging in Commercial Chips
## 4 Experimental Results and Analysis
### Retention Error in Fresh DRAM Modules
#### 4.2.2 Dependence on Data Patterns  
We collected retention data for the fixed patterns 0x00 and 0 xFF, as previous work suggests that this testing is sufficient [31]. Consecutive alternate bits of 1 and 0 (checkerboard patterns) increase the coupling noise with a voltage difference between the neighboring bitlines, which may induce the worst-case data pattern [31].  
From the retention error results for 0x00 and 0 xFF, the true cells and anti-cells can be identified. Figure 4a,b present the heatmap of retention errors across the rows and columns in a Micron module for patterns 0 xFF and 0x00. The x-axis represents the 16,384 rows divided into 256 groups and the y-axis represents 1024 columns divided into 128 groups. Aggregated number of errors for 64 consecutive rows and 8 columns is represented by each color-coded block. We observe the number of errors increase from left to right, and there are intermittent gaps after every 512 rows. This can be explained by the true and anti-cell architecture of the module. Some rows show a retention error for the 0 xFF pattern but not for the 0x00 pattern, which are identified as the true cells. Similarly, the rows that induce error only for pattern 0x00 are identified as anti-cells. The rows that induce error for both cases have a mixture of true-cell and anti-cell architecture.
# Improved Control Strategy of MMC-HVDC to Improve Frequency Support of AC System
## 5 Case Study  
The simulation system diagram is shown in Figure 4. The ends of the MMC-HVDC are connected to the offshore wind turbine and onshore AC system. The offshore system is wind turbine generators. They transmit constant power energy to the onshore load system by using MMC-HVDC. The rated capacity of the type-4 offshore wind turbine is 5 MW. The onshore power system includes a synchronous generator and load system. There are two passive load connections on the onshore-AC-system side: \(P_{1}+J\)/\(Q_{1}\) is the fixed passive load, and \(P_{2}+J\)/\(Q_{2}\) is the other passive load caused by switching in or out. As the simulation is performed at different strengths of the AC system, the capacity of the passive load and parameters are detailed in a subsequent subsection. The inertia constant is calculated under the worst case of the allowable range. The 16 s before the simulation in this article is the initialization time of the system, which means that the system is not stable before 16 s. Therefore, the simulations in this  
Figure 4: Single-line view of the simulation system diagram.  
article are all carried out after the 16 s. At 17 s, the switched load was disconnected from the system because of the opening of the switch.  
For example, when the system is subjected to an external disturbance, the frequency fluctuation of the system is controlled within 49.2 Hz. Therefore, the calculated system frequency variation \(\Delta f_{AC}\) is 0.8 Hz. In addition, as the system does not consider the third resonance, the maximum range of the allowable voltage variation is 0.1 p.u. From the above data, the minimum inertia constant required for the MMC-HVDC inertia control is calculated. In this section, the improved inertia control and commonly used \(V_{DC}\) control are compared based on the simulation results. In addition, the simulation also takes into consideration the relationship between the inertia constant and the onshore-AC-system strength.  
In Table 1, for detailed parameters refer to [26; 27]. Through [26], we can know that the detailed parameters reference comes from the design of an MMC-HVDC system, which interconnected the 400-kV systems of France and Spain by 2013. This system has been put into use in practical applications, so the data of this system is reliable. In addition, the [26; 27] have further proved the reliability of the parameters through simulation.
## 5 Application to the Satellite Module Layout Problem
### Results and Analysis
#### 5.2.4 Global Analysis  
The proposed two-stage algorithm combining Genetic Algorithm and CSO-VF provides general better performance than the Oboe-CCEA and Dynamic FS algorithm regarding two aspects:  
* The assignment scheme: The proposed assignment task resolution allows to position the components such that their center of gravity coincides with the center of gravity of the empty module, which contributes to a better global inertia.
* The layout: As highlighted in [124], the CSO-VF outperforms the population-based counterparts like GAs thanks to its ability to solve the constraints with dedicated operators. Indeed, Figure 14 shows that the CSO-VF algorithm systematically positions the heavier components closer to the center of the surfaces, which contributes to minimize the global inertia of each container. On the contrary, it is observed that the Dynamic FS algorithm sometimes positions small and light components around the central bus.  
However, despite the highlighted strengths of the proposed algorithm, some limitations can also be identified. First of all, the structure of the algorithm requires that the assignment and layout tasks can be addressed separately, which is not necessarily the case for all multi-container optimal layout problems. Moreover, the upper stage also supposes that the assignment task can be mathematically formulated, which involves problem-specific knowledge in this paper. Finally, in its current formulation, the algorithm is unable to deal with some constraints that involve several surfaces as their layouts are solved in independent optimization processes. For instance, overlapping constraints between components positioned on facing surfaces as well as functional constraints with three-dimensional safety zones could not be handled.  
Figure 13: Best final layouts obtained with the Oboe-CCEA algorithm [20] and the proposed algorithm. The redder the components, the higher their corresponding masses. The functionally incompatible components are highlighted in orange and blue for the heat constraints and orange for the electromagnetic constraint. The redder the components, the heaviest their corresponding masses.  
Figure 14: Best final layouts obtained with the Dynamic FS algorithm [19] and the proposed algorithm. The redder the components, the higher their corresponding masses. The functionally incompatible components are highlighted in orange and blue for the heat constraints and orange for the electromagnetic constraint. The redder the components, the heaviest their corresponding masses.
## 3 Simulation Modeling and Verification
### Creation of a Surface-Fitting Contact Model
#### 3.1.1 Simulation Analysis of the Fitting Contact Between the SG Flexible Tool and the Surface  
When pressure is applied above the SG flexible tool, it causes the SG flexible tool to adhere and contact the curved surface to a certain extent. The degree of adhesion is influenced by changes in the contact angle, applied pressure, and curvature. To accurately analyze the impact of these parameters on the contact area, a simulation analysis was conducted. The simulation results of the contact between the SG flexible tool and marbles with different curvatures are shown in Figure 6. The contact area is leaf-shaped, and its shape varies with changes in curvature and contact angle. The entire contact area consists of the edge arc AHB of the SG flexible tool and the contact arc APB, with the chord height distance PH representing the  
The selection of the contact angle is based on the contact characteristics between the polishing tool and the workpiece surface. During the polishing process, the contact angle determines the contact area and the distribution of contact pressure between the polishing tool and the workpiece surface. Through preliminary experiments and theoretical calculations, it was found that when the contact angle is within a certain range, the contact pressure distribution between the polishing tool and the workpiece surface is relatively uniform, which is conducive to achieving constant-pressure polishing. Therefore, in this study, a contact angle that ensures uniform distribution of contact pressure was selected as the simulation parameter based on extensive preliminary experiments.  
The selection of contact length is based on the balance between polishing efficiency and polishing quality. A shorter contact length may lead to low polishing efficiency, while a longer contact length may increase friction and heat during the polishing process, adversely affecting polishing quality. Through extensive preliminary experiments, it was found that maintaining the contact length within a certain range ensures both polishing efficiency and good polishing quality. Therefore, a contact length within this range was selected as the simulation parameter in this study.  
In the polishing process, the control of contact angle and contact length is primarily achieved through robot motion trajectory planning and polishing tool posture adjustment. By precisely controlling the robot's motion trajectory and the polishing tool's posture, a constant contact angle and contact length can be maintained during the polishing process, thereby achieving constant-pressure polishing.
## 3 Experiments and Results  
### Experimental Setup and Training Parameters  
All experiments were conducted on a Linux-based GPU server running Ubuntu, Python 3.10.0, and PyTorch 2.2.1. The hardware configuration included an NVIDIA GeForce RTX 4090 GPU (24 GB VRAM) with CUDA 12.1 (NVIDIA Corporation, Santa Clara, CA, USA), which provided sufficient computational capacity for large-scale deep learning tasks.  
The model was trained for 100 epochs using the Adam optimizer with an initial learning rate of 0.01. A cosine annealing schedule without warm-up was adopted to ensure smooth convergence. Momentum and weight decay were set to 0.937 and 0.0001, respectively, to mitigate overfitting. The backbone network remained trainable to support end-to-end learning. Due to GPU memory constraints, the batch size was set to four. To improve training efficiency and reduce memory usage, PyTorch Automatic Mixed Precision (AMP) was enabled, allowing FP16 computation with dynamic loss scaling to maintain numerical stability.
## 6 Conclusions  
This paper presents a method of implementing a 4 \(\times\) 4 correlation matrix to realize the BS correlation characteristics in order to evaluate an uplink channel using bidirectional MIMO-OTA apparatus. The initial phases of the secondary sources were generated by arranging virtual point sources based on Jakes' model according to the BS correlation. An examination of the effectiveness of the proposed method using bidirectional MIMO-OTA apparatus made it clear that the desired BS correlation characteristics can be achieved only by controlling the initial phase of the probe. Hence, the performance of the uplink from a MIMO terminal considering the BS correlation can be evaluated with high accuracy.  
Figure 16: 4 \(\times\) 4 MIMO channel capacity as a function of the incoming wave angle.  
In this paper, only the vertical polarization component was examined. However, the MIMO terminal antenna is expected to have both vertical and horizontal polarization components. It is considered that the BS correlation can be controlled by setting the initial phases of the secondary sources for both the vertical and horizontal polarization components, which will be investigated in future work.  
Furthermore, the practical propagation environment model for the uplink channel is assumed to be a three-dimensional channel model where radio waves propagate over the full-solid angle. In one of my previous studies [36], a methodology for controlling the BS correlation for a 2 \(\times\) 2 MIMO system realized by a three-dimensional bidirectional fading emulator was reported. The results showed that the measured BS correlation of the uplink channel agreed well with the desired values, confirming the effectiveness of the proposed method for a three-dimensional channel model. The accuracy of the 4 \(\times\) 4 correlation matrix will be improved because the flexibility of the virtual point source arrangement increases by placing the virtual point source in three-dimensional space. As a result, the difficulty of realizing all the BS correlations, as shown in Figure 12c, may be resolved.  
The proposed OTA testing method will be applied to the evaluation of communication performance considering the correlation coefficient between the received signals for other wireless sensor networks, such as the Internet of Things (IoT) and emerging Vehicle-to-Everything (V2X) technologies [37; 38; 39]. To ensure the success of upcoming connected car systems, the author is currently developing a 256 \(\times\) 256 MIMO antenna system that utilizes circular array beam steering technology [40]. However, in large-scale MIMO systems, there are some difficulties in terms of realizing OTA measurements. One of the difficulties is that the number of BS correlations is very large compared with the number of BS antennas. Another is that a large number of scatterers are necessary to create the full-rank channel matrix. Therefore, not only the scope of applications of the proposed method for uplink channels, but also OTA testing methods for downlink channels [41] need to be examined, which will be investigated in future work.
## 4 Analysis of Test Variation Parameters
### Duration of Service  
The effects of different durations of service (90 d, 180 d, 270 d) in the tidal environment on the initial rigidity, cracking and peak load, peak deflection, energy dissipation index and ductility index of reinforced sea sand concrete beam specimens are given in Figure 11.  
As seen in Figure 11A, the initial rigidities of the specimens were 136.8 kN\(\cdot\)mm\({}^{-1}\), 131.04 kN\(\cdot\)mm\({}^{-1}\) and 53.35 kN\(\cdot\)mm\({}^{-1}\) with increasing duration of service in the tidal environment. The initial rigidities of the specimens with duration of service of 90 d and 180 d were similar. When the duration of service increased to 270 d, the initial rigidity of the specimens showed a relatively large decrease (59.4%). As can be seen from Table 1, the initial rigidity of the specimens decreased to different degrees (54.2-77.9%) after the duration of service of 270 d in the whole range of specimens. The reason for this may be that after a certain length of service, the presence of Mg\({}^{2+}\) (magnesium ions) in seawater acts on the concrete, decomposing the cement stone in the cement and causing the loosening of the concrete. In turn, the initial rigidity of the specimen decreases, and this effect becomes  
Figure 10: The performance index changes with the replacement rate of sea sand.  
significant with the increase in the service time, so the initial rigidity of the specimen is negatively correlated with the service time.  
As seen in Figure 11B, the cracking loads of the specimens were 35.9 kN, 35 kN and 30.4 kN in order with the increase in duration of service, showing a small decrease of 2.5% and 13.1%. The peak loads were 171 kN, 172 kN and 161.3 kN in order, which first remained constant and then decreased by 6.2%, indicating that the duration of service (within 270 d) had a small effect on the cracking and peak loads of the specimens.  
As seen in Figure 11C, with the increase in service time, the peak deflection of the specimen is 20 mm, 12.11 mm and 14.67 mm in order, decreasing 39.5% and then increasing 21.1%, with an overall decreasing trend. It can also be observed from Figure 6D that the peak deflection of the specimens with the same reinforcement rate decreased to different degrees for the specimens with duration of service of 180 d and 270 d, indicating that the peak deflection of the specimens was negatively correlated with the service length. When the peak load of the specimen does not change much, its energy dissipation index is similar to the trend of peak deflection, which can be observed in Figure 11D. As seen in Figure 11E, the ductility index of the specimen is significantly negatively correlated with the length of service, with a decrease rate of 34.5% and 10.2%.  
In summary, the increase in duration of service (within 270 d) in a tidal environment causes a significant decrease in the initial rigidity, peak deflection, energy dissipation index and ductility index of the specimens. However, the correlation with the cracking load and peak load of the specimens is small.
# A Novel Protocol Using Captive Portals for FIDO2 Network Authentication +
## 1 Introduction
### Authenticators: Security Keys and Platforms  
FIDO security keys can be used in web authentication thanks to the WebAuthn API, developed by the W3C. These hardware devices are the original idea of having a dedicated hardware known as the authenticator to generate and store WebAuthn credentials. There are some commercial hardware security keys in the market, like the Yubikey and the Solokey, which can generate WebAuthn credentials.  
Apart from hardware authenticators, there are operating systems that already support creating WebAuthn credentials, which are generated by a software known as a platform authenticator. These credentials can be stored in a secure storage at the operating system, or stored in some cloud secure storage. These are known as \"passkeys\". Passkeys are WebAuthn credentials created in compatible smartphones or laptops [7], and stored in a cloud provider to be synchronised among different devices of the user.
# A Distributed Automatic Video Annotation Platform
## 4 Spatiotemporal Based Video Annotation  
Figure 2 illustrates the end-to-end flow of our proposed spatiotemporal-based video annotation platform. The proposed platform is divided into two main parts: spatial video annotation (SVA) and spatiotemporal video annotation (STVA). SVA is responsible for retrieving spatial information from each frame of a video, which is similar to existing video annotation works. In the literature [7; 16] video annotation detects objects from each frame of a video and annotate the object information. However, it ignores the true video information that comes with respect to time. Thus, our works proposed a spatiotemporal-based video annotation to solve this issue. Both parts are further divided into three sub-parts: a feature extractor, a similarity measure, and an annotator.  
We have developed several state-of-the-art algorithms for both spatial and spatiotemporal feature descriptors. We implemented the existing LBP [40], LDTP [41], LTP [42], and CNN model (e.g., VGGNet [43]) on top of Spark in order to support distributed processing. Moreover, we introduced three-channel-based color LDTP. Similarly, we also developed the existing VLBP [44], LBP-TOP [44], and MBP [45] for capturing spatiotemporal features. Furthermore, we also proposed the local directional ternary pattern-three orthogonal planes (LDTP-TOP) and the volume local directional ternary pattern-three orthogonal planes (VLDTP-TOP) for obtaining the spatiotemporal information. Our proposed descriptors show better precision than the existing algorithms. In the subsection, we describe our proposed descriptors.