esa-sceva/satcom-chunk-collection huggingface dataset:
# Implementation of a Topology Independent MAC (TiMAC) Policy on a Low-Cost IoT System
## 5 Experimental Methodology and Results
### Methodology  
As already mentioned, TiMAC is based on TDMA and, in this sense, its implementation is also based on TDMA. Considering the values deriving by the experimental system setup (i.e., \(N=25\) nodes and maximum number of neighbors \(D=4\)), the corresponding values for \(k\) (i.e., \(k=1\)) and \(q\) (i.e., \(q=5\)) are extracted [17]. As a result, first-degree polynomials are going to be used for the slot allocation due to \(k=1\), and the length of the TDMA frame will be set at \(q^{2}=25\), divided into five subframes of \(q=5\) slots each. Then, each node is randomly assigned a first-degree polynomial with coefficients from \(GF(5)\) and, according to the latter polynomial, the particular time slot during which each node is allowed to transmit within each sub-frame is derived. Eventually, every node is allowed to transmit in five slots per frame.  
Eleven different sets of polynomials are considered here, corresponding to the eleven different scenarios developed for the particular system. Consequently, different time-slots were assigned to each node per scenario and the obtained results are the summation of transmissions of 100 frames. Note that increased traffic conditions are assumed for the experimental setup, and therefore each node has always data ready for transmission during every time slot. As for the receivers of transmitted packets, for each node, one of its neighbors is arbitrarily selected as the destination of the transmitted packets. The latter selection of receivers remains unchanged throughout the experiments.  
The first set of polynomials is chosen in order to create a collision-free slot allocation scenario, such that each node transmits packets in different time-slots than that of its neighbors. The rest of the (ten) polynomial sets are created randomly.  
The main parameter under investigation is the _throughput_ value each polynomial set (i.e., scenario) results to. The obtained throughput is derived as the fraction of the average successful transmissions (i.e., total successful transmissions over the number of nodes) over the number of total time-slots i.e.,  
\[\text{throughput}=\frac{\text{total successful transmissions}}{\text{total nodes}\times \text{total time slots}}.\]  
During the period of 100 frames, each node is eligible for transmission for up to 500 time-slots, whereas the number of total time-slots equals 2500. Consequently, the maximum network throughput is given by  
\[\text{capacity}=\frac{500}{2500}=0.2.\]  
Throughput as large as capacity is not expected to be achieved due to various reasons like the channel interference, the transmitter's accuracy, and the receiver's sensitivity, etc., as already mentioned. On the other hand, TiMAC guarantees that at least one successful transmission per node per frame will take place. Consequently, the minimum network throughput is given by\[\text{minimum throughput}=\frac{100}{2500}=0.04.\]
## 3 Low-Strain Integrity Testing
### Applications for Smart Sensors  
With the innovation of products, sensors are also upgraded from simple electromechanical sensors to high-precision digital sensors. These upgraded sensors exhibit substantially improved performance, including enhanced measurement accuracy, stronger anti-interference capability, smarter integration features, and advanced multi-parameter fusion functionality [88]. [PERSON] et al. [41] developed an innovative smart aggregate sensor system utilizing embedded piezoelectric ceramics and active sensing methodology for pile integrity assessment. In this approach, one pre-embedded smart aggregate sensor functions as an actuator to generate stress waves that propagate through the pile structure, while other distributed smart aggregate sensors serve as receivers to detect the wave propagation characteristics. Stress wave propagation is attenuated by defects within the pile, with the degree and characteristics of attenuation serving as indicators for damage identification. Figure 9 illustrates the signals from different defective piles. Ten smart aggregates were placed at different locations and numbered SA1-SA10. The schematic diagram of the pile illustrates the defect types and locations. As shown in Figure 9, the overall signal strength of intact pile specimens exceeds that observed for the four damaged states. Moreover, full  
Figure 8: Principle of acoustic emission [82].  
mud intrusion yields the lowest signal strength. Damage interfaces attenuate stress-wave energy, thereby maximizing the received signal amplitude for intact piles. Time-domain analysis reveals that various defects cause quantitatively different levels of stress wave energy attenuation, thereby validating the effectiveness of this identification methodology.
## 3 Results and Discussions  
### Influence of Anionic Surfactant Type and Content on Oil Removal Efficiency  
The gradation data obtained after separation are detailed in Figure 5a. When the content was 2 wt% compared to other surfactants, SDBS had a better separation effect, and its passing rate of each block was higher than that of the other surfactants. Its gradation curve is closer to that of the extraction. MES and SDS, on the other hand, received poorer treatment, and both present large gaps between the gradation curves and the extracted gradations. Meanwhile, the clear water control group had extremely poor oil removal efficiency; with numerous agglomerates being unopened on its 13.2 mm sieve and a pass rate of less than 80%, the asphalt content was 3.26%. Figure 5b illustrates that SDBS had the best oil removal efficiency, reducing the asphalt content to 2.24% at a content of 2 wt%, which was more than half the reduction. The concentration of SDS must be increased to achieve a corresponding effect, while MES was less effective than the other two. Thus, only the emulsification-separation effect of SDBS on RAP was studied in subsequent experiments.  
Having determined the type of surfactant, the ratio of SDBS emulsion to RAP was varied to investigate the separating effect of different SDBS emulsion ratios on RAP. Figure 5c illustrates that when the ratio of SDBS emulsion to RAP was 1:4, the emulsion was too small, resulting in the eluted asphalt easily re-bonding to the RAP. Clusters were thus re-formed, and the passing rate was the lowest. Figure 5d shows that, as the ratio increased to 1:2, the asphalt content decreased by 44.28% as the solution increased, but the change was not significant as it continued to increase.  
Based on the above results, SDBS had the best elution effect. The optimal doping amount was 2 wt%, and the ratio of SDBS emulsion to RAP was 1:2.
# Post-Quantum Biometric Authentication Based on Homomorphic Encryption and Classic McEliece
## 5 Discussion  
In this work, we proposed a biometric protection scheme based on homomorphic encryption and Classic McEliece as well as its use in a non-device-centric biometric authentication architecture with honest-but-curious servers. Classic McEliece is one of the round 4 candidates of the NIST post-quantum cryptography standardization competition.  
A specific transformation of features is proposed to meet security and Classic McEliece requirements. This precludes decoding-protected biometric features and impostor comparison results, even knowing the private key and even the servers exchanging information. Therefore, in an honest-but-curious adversarial model, the security is maintained and recognition performance is improved in a normal scenario and preserved in a stolen-device scenario. Parameter sets were selected to achieve security levels of 128 and 256 bits for Classic McEliece (mceliece6688128 and mceliece348864 parameter sets, respectively). Irreversibility is ensured by the impossibility of decoding ciphertexts with more errors than those permitted. Further, Classic McEliece-based protected features are random, even though they are generated from the same sample and, thus, revocability and unlinkability are satisfied. In addition, the scheme is robust to FAR and similarity-based attacks.  
A practical realization is illustrated, which was performed by considering a smartphone as the device client and computers as the database, computation and authentication servers. FaceNet embeddings were selected as biometric features. The execution times obtained allow for real-time authentication. A relevant advantage of our proposal is that Classic McEliece generates protected data with sizes much lower than other approaches, while maintaining low computational cost.
## 4 DLRSD-Based Surface Defect Segmentation
### Feature Matrix Construction  
According to [23; 24], for each pixel \(\left\{I_{i}\right\}_{i=1,2,\ldots,N}\) of a surface defect image \(I\), where \(N\) denotes the number of pixels, different types of low-level visual features, including gray-scale, Gabor filters and steerable pyramids, are extracted.  
(1) Gray-scale  
The pixel value of each pixel in defect image \(I\) is extracted for gray-scale feature, which is normalized by subtracting its mean value over the entire image.  
(2) Gabor filters  
Gabor filters responses with eight directions on two different scales are performed on the defect image \(I\), yielding 16 filter responses for each pixel.  
(3) Steerable pyramids  
Steerable pyramid filters with four directions on two different scales are performed on the defect image \(I\), yielding 8 filter responses for each pixel.  
All those 25 features are then stacked vertically to construct a 25-dimension feature vector \(\left\{f_{i}\right\}_{i=1,2,\ldots,N}\in\mathbb{R}^{d}\) for each pixel. Then, in order to improve the efficiency of defect detection and achieve the better structural information about defect image, we conduct superpixel segmentation for image \(I\) by adaptive simple linear iterative clustering (ASLIC) algorithm [30]. Each compact, edge-aware and perceptually homogeneous sub-region \(\left\{R_{j}\right\}_{j=1,2,\ldots,K}\) can be represented by feature vector \(\widehat{\mathbf{f}}_{j}\in\mathbb{R}^{d}\), where \(\widehat{\mathbf{f}}_{j}\) represents the mean feature vector of all pixels that belong to \(R_{j}\), where \(K\) denotes the number of sub-regions. By arranging \(\widehat{\mathbf{f}}_{j}\) into a matrix, the feature matrix \(D=\left(\widehat{\mathbf{f}}_{1},\widehat{\mathbf{f}}_{2},\ \cdots,\widehat{\mathbf{f}}_{K}\right)\in \mathbb{R}^{d\times K}\) of image \(I\) is obtained.  
Figure 2: Diagram of the proposed double low-rank and sparse decomposition (DLRSD)-based segmentation method for surface defect image.
# Investigation of Deformation Inhomogeneity and Low-Cycle Fatigue of a Polycrystalline Material
## 3 Measurements of Deformation Inhomogeneity and Predictions of Fatigue-Life Curve
### Distribution and Inhomodeneity of Strain and Stress in RVE  
Through the numerical simulation tracking cycle one by one for different strain amplitudes, by using the RVE and the crystal plasticity calculation, the distribution transformation of both local stresses \(\sigma_{ij}\) and local strains \(e_{ij}\) in the RVE with the cyclical increase is obtained. The stresses and strains at the grain level in RVE present non-uniformly because the model takes the anisotropic elastoplastic deformation mechanism and random orientation of the grains into account.  
Figure 4 displays the contours and distribution of the longitudinal strain and longitudinal stress at the third (close to cycle beginning) and 840 th (near to the critical point for failure) tensile peak for strain amplitude 0.01003. Some essential phenomena can be observed from Figure 4 that (1) the distribution of the longitudinal stress and strain is presented inhomogeneous, however the inhomogeneity change in extent with cycles for both is very different; (2) the distribution range of strain with the cycles increasing has an increase more than tenfold, but the distribution range of stress varies little (since the change in stress is determined by Hooke's law, whereas the elastic strain change is very small relative to the total strain change); (3) both distributions for longitudinal stress and strain are nearly Gauss alike.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline \multicolumn{2}{c}{**Elastic Constants**} & \multicolumn{8}{c}{**Material Parameters of the Crystal Plasticity Model**} \\ \hline C\({}_{11}\) & C\({}_{12}\) & C\({}_{44}\) & \(\tau_{0}\) & \(\tau_{\mathrm{s}}\) & \(h_{0}\) & \(a\) & \(c\) & \(\lambda\) & \(e_{1}\) & \(e_{2}\) & \(\dot{\gamma}_{0}\) & \(q\) & \(k\) \\ \hline GPa & GPa & GPa & MPa & MPa & MPa & GPa & GPa & s\({}^{-1}\) & & & s\({}^{-1}\) & \\ \hline
230.05 & 153.57 & 81.97 & 289 & 295 & 80 & 59 & 0.428 & 0 & 0 & 0 & \(1\times 10^{-3}\) & 1 & 160 \\ \hline \end{tabular}
\end{table}
Table 1: Elastic constants and crystal plasticity parameters of the GH4169 superalloy at 500 \({}^{\circ}\)C.  
Figure 3: Simulative and experimental steady hysteresis loops.  
Figure 4: Contours and statistical distribution of the longitudinal strain and stress at tension peak for strain amplitude 0.01003: (a,b) for longitudinal strain at 3 rd and 840 th cycle, respectively; (c,d) for longitudinal stress at 3 rd and 840 th cycle, respectively.
## 1 Introduction
In an extreme configuration, extra fiber parallel to the transmission line fiber within the same cable can be dedicated to the sole purpose of transporting the optical Raman pumps from the terminal end to the ROPAs. Finally, the transmission performance of an unrepeated ultra-long span system over a transmission distance of 390-km will be evaluated using numerical simulation. The bit error rate (BER) performance improvement of the three-segment hybrid fiber effective core area configuration compared to the conventional single fiber effective core area is examined on various high transmission data rates using a differential quadrature phase-shift keying (DQPSK) modulation format to double the spectrum efficiency, and a noncoherent direct-detection scheme to simplify the receiver design [18]. The availability of silicon (Si) photonics [19], hybrid photonic integration platform [20], and monolithtically integrated InP transmitter that include laser source [21] increase the cost-effectiveness of the combination DQPSK modulation format and noncoherent direct-detection scheme, which is a compact solution compared to complex coherent detection techniques [22] for single span applications. The rest of this paper is structured as follows. In Section 2, we derive the generalized Raman amplification formulas where the fiber effective core area is no longer a constant value but a function of propagation distance by modifying the procedure of the standard Raman amplification formulas [1; 4; 5]. In Section 3, we explain that the reduction of nonlinear phase shift and the improvements of OSNR can be achieved simultaneously by implementing three-segment hybrid fiber core structure. In Section 4, we simulate the transmission performance of the three-segment hybrid structure and compare to the conventional single-segment single fiber core structure on 80 Gb/s and various data rate of DQPSK modulated data signals over a propagation distance of 390 km. Finally, we conclude the article in Section 5.
# HandFormer: A Dynamic Hand Gesture Recognition Method Based on Attention Mechanism
## 5 Discussion  
Real-time gesture recognition plays a key role in many fields, especially in human-computer interaction (HCI) systems, which can bring great convenience to human life. Therefore, exploring real-time and accurate deep learning network models for dynamic gesture recognition has practical significance. In this paper, we proposed a lightweight method based on a spatio-temporal attention mechanism that fuses a CNN with a class of Transformer models. We evaluated our method on the Jester and NVGester datasets, using different evaluation criteria including accuracy, F1 score, sensitivity, specificity value, etc. Since we mainly used the convolution block fused with the attention mechanism to extract the spatio-temporal features of gestures, and comprehensively considered the spatial features and temporal features of gestures globally, we achieved high recognition accuracy on Jester and NVGester datasets with the help of the Transformer architecture. The experimental results show that our method achieved high recognition accuracy while meeting the real-time requirements of interactive systems. We compared our proposed method with other methods, as shown in Table 7. Our proposed method has certain advantages in recognition accuracy compared with other methods, and our network model has fewer parameters, making it a lightweight network model. Therefore, it can be applied to embedded devices with limited computing resources, such as smart cars and smart assembly lines. At the same time, our method uses multimodal data for input, which makes it more versatile. However, our method has a low recognition rate for gesture actions under extreme wild environment, especially in cases where there is multi-hand interaction. This is because the gesture action model is more complex when there is multi-hand interaction, and a larger network needs to be designed.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Method** & **Accuracy (\%)** \\ \hline PoolFormer [5] & 77.50 \\ Remove Spatial Attention & 89.82 \\ Remove Temporal Attention & 91.22 \\ Remove Spatial and Temporal Attention & 82.31 \\ \hline Ours & 96.72 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Ablation results of spatial attention and temporal attention.  
In addition, we are committed to applying the proposed method in specific application scenarios where computer resources are scarce. To improve the robustness of the model, we organized a dataset with different extreme conditions captured by a computer camera under various environments, such as low lighting, excessive lighting, and tilted shooting angles, to train the network model. Furthermore, we used various methods such as affine transformation, projection transformation, skewness, and scaling to augment the datasets and improve the model's adaptability.
# Inter-Data Center RDMA: Challenges, Status, and Future Directions
## 3 Inter-DC RDMA Requirements and Challenges
The second stage involves dynamic adjustment during flow transmission, where congested flows are rerouted to non-congested paths. These schemes significantly improve intra-DC RDMA performance, especially in scenarios with a high proportion of elephant flows. However, these load-balancing optimization schemes are challenging to deploy in inter-DC scenarios to enhance inter-DC RDMA performance. This is because in DCNs, multiple equivalent paths typically exist between computing nodes (servers), whereas inter-DC transmission is DC oriented, usually with only one dedicated path between DCs. Even if multiple paths are available, the high latency of long-distance transmission results in higher packet arrival time intervals, making out-of-order issues more problematic. Therefore, optimizing inter-DC RDMA performance through current intra-DC multipath-enabled, load-balancing methods is not feasible. As analyzed above, the deployment of RDMA across DCs introduces significant challenges due to long latency and low reliability caused by long-range connection. These challenges affect congestion control, packet loss recovery, load-balancing mechanisms, and network device design. Without appropriate optimizations, RDMA performance can degrade sharply. Numerous experiments have demonstrated that the high latency associated with inter-DC deployments brings a significantly decline in RDMA performance. Additionally, in such scenarios, packet loss exacerbates performance degradation. The worst-case scenario occurs when both high latency and packet loss exist, leading to even poorer performance. Figure 2 illustrates the impact of latency and packet loss on RDMA performance according to [48]. The tests were conducted with 10 G throughput, examining the effects of varying latency, packet loss rates, and their combined impact on the performance of RDMA connections. It is evident that under low throughput like 10 G, the performance degradation caused by increased latency and packet loss can hardly be mitigated by increasing the message size. However, in real-world inter-DC scenarios, where throughput demands are significantly higher and network conditions are far more complex, the performance degradation of RDMA becomes unacceptably severe.
# Comparison Study on Coarseness Index and Maximum Diameter of Rock Fragments by Linear Cutting Tests
## 1 Introduction
They formed an inverse relationship with _SE_, similar to the _CI_. [PERSON], et al. [19] analyzed the rock fragments generated by relieved and unreleased cutting using the _CI_ and Rosin-Ramler distribution. Based on these studies, a high correlation between the rock fragments and efficiency is confirmed. However, among the characteristics of rock fragments, their relationship with _CI_ is concentrated. The _CI_ differs depending on the size and number of sieves used, even for the same specimen, and requires additional calculations. Subsequently, studies on the  
Figure 1: Variation in _SE_ with (**a**) penetration depth and (**b**) spacing (reproduced with permission from [PERSON]. [7]. 2007. [PERSON] and [PERSON]). relationship between the particle size parameters (\(d^{\prime}\), \(d_{50}\), and \(d_{\text{MPS}}\)) have been conducted, but mathematical calculations must be performed, which is disadvantageous. Therefore, this study attempted to determine whether the maximum diameter (\(d_{\text{max}}\)), which is the most intuitive characteristic of rock fragments, could replace the role of \(CI\) and particle size parameters. For this purpose, \(d_{\text{max}}\) and \(CI\) were obtained from rock fragments generated via linear cutting tests under various cutting conditions. By comparing the relationship between \(d_{\text{max}}\) and \(CI\) and that between particle parameters and \(CI\), the similarity between \(d_{\text{max}}\) and particle parameters was confirmed. In addition, the results suggest that \(d_{\text{max}}\) and \(CI\) are similarly affected by cutting conditions and results. In particular, \(d_{\text{max}}\) has an advantage over \(CI\) in terms of the \(SE\).
## 3 Experiment
### Experimental Results
#### 3.4.2 Fitting Effect  
Figure 7 shows the fitting relationship between path loss and frequency. The black dots are sampled data points, the blue curve is all the explored results during training, the red curve is the best 5-10% of the results used for training, the orange curve is the best result of a training epoch, and the black curve is the best result of all epochs. One can see that the data points fall almost evenly on both sides of the black curve, which means that the black curve fits the distribution of the data points very well.  
Figure 8 depicts the relationship between the sampled data points and the curved surface, which is derived from the improved PhySO generated mathematical channel model \(Pl=a\log\left(a^{2}\right)\log\left(a^{2}df\right)+a+b\), \(a=10.00,b=42.44\), where \(a,b\) denote independent variables acting as coefficients in mathematical formulas. All the data points in red fall on the green curved surface, indicating that the improved PhySO finally fits the free space path loss correctly, and the improved algorithm has a good fitting effect on the inter-satellite terahertz channel model.  
Table 3 depicts some typical free space path loss formulas generated by the improved PhySO. For instance, with 400 data points, the algorithm converges to expression \(Pl=b\log\left(b\sqrt{adf}\right)\), \(a=26.17,b=40.00\). Post manual simplification, \(Pl=92.44+20\log(df)\). This formula corresponds to the free space path loss formula, validating the algorithm's precision in deducing the accurate formula for free space path loss from the communication dataset via symbolic regression.  
Figure 8: The surface of the generated model.  
Figure 7: Relationship between path loss and frequency. The black dots are sampled data points.