esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Spiral Rail Spike Positioning Method
### Improved Canny Algorithm for Image Edge Detection
#### 3.3.2 Gradient Boosting  
The traditional Canny operator only includes gradient information in the \(0^{\circ}\) and \(90^{\circ}\) directions [16], which can lead to imprecise edge localization when the edge gray-level changes are slow. For instance, when there is grease on the surface of a rail spike, the gray-level difference between the spike area and the background area is insignificant, making it easy to lose the edge information of the spike. This paper takes the Sobel operator as a template, integrating the \(90^{\circ}\) and \(135^{\circ}\) directions for gradient enhancement to improve the precision of edge extraction. The four-direction Sobel template is shown in Figure 4.  
The gray value of the pixel point \((x,y)\) is \(f(x,y)\), and the calculation method for the gray values \(g_{0}\), \(g_{45}\), \(g_{90}\), and \(g_{135}\) of the edge detection along the four directions are shown in formula (5):  
\[\left\{\begin{array}{l}g_{0}=\left[f(x+1,y-1)+2f(x+1,y)+f(x+1,y+1)\right.\\ \left.-f(x-1,y-1)-2f(x-1,y)-f(x-1,y+1)\right]\\ g_{45}=\left[f(x+1,y)+2f(x+1,y+1)+f(x,y+1)\right.\\ \left.-f(x-1,y)-2f(x-1,y-1)-f(x,y-1)\right]\\ g_{90}=\left[f(x-1,y+1)+2f(x,y+1)+f(x+1,y+1)\right.\\ \left.-f(x-1,y-1)-2f(x,y-1)-f(x+1,y-1)\right]\\ g_{135}=\left[f(x-1,y)+2f(x-1,y+1)+f(x,y+1)\right.\\ \left.-f(x+1,y)-2f(x+1,y-1)-f(x,y-1)\right]\end{array}\right. \tag{5}\]  
The gradient values in the directions of \(45^{\circ}\) and \(135^{\circ}\) are, respectively, fused to the directions of \(0^{\circ}\) and \(90^{\circ}\) for gradient enhancement. The gradient amplitude of the enhanced image is shown in formula (6), and the gradient direction of the image is shown in formula (7):  
\[G=\sqrt{g{{}^{2}}+g_{45}{{}^{2}}+g_{90}{{}^{2}}+g_{135}^{2}} \tag{6}\]  
\[\theta=\arctan\Biggl{(}\frac{\left(g_{0}+\sqrt{2}/2g_{45}+\sqrt{2}/2g_{135} \right)^{2}}{\left(g_{90}+\sqrt{2}/2g_{45}+\sqrt{2}/2g_{135}\right)^{2}} \Biggr{)} \tag{7}\]  
In the formula, \(G\) represents the magnitude of the pixel gradient, and \(\theta\) represents the direction of the pixel gradient.
## 5 Parametric Modeling of System Characteristics
### Time Delay  
The total time delay (\(T_{V-MIMO}\)) of virtual MIMO system with \(N_{t}\) DSNs, \(N_{r}-1\) DANs and one DGN each with one antenna can be calculated as the sum of transmission delay (\(T_{tr\_MIMO}\)), propagation delay (\(T_{pr\_MIMO}\)) and processing delay (\(T_{pc\_MIMO}\)):  
\[T_{V-MIMO}=T_{tr\_MIMO}+T_{pr\_MIMO}+T_{pc\_MIMO} \tag{18}\]  
\(T_{tr\_MIMO}\) is given by:  
\[T_{tr\_MIMO}=T_{s}\left(\frac{\Sigma_{i=1}^{N_{t}}M_{i}}{b_{n}}+\sum_{j=1}^{N_ {r}-1}\frac{m_{r}M_{s}}{b_{j}^{r}}\right) \tag{19}\]where \(M_{i}\) is the number of bits transmitted by each node \(i\), \(M_{s}=\sum_{i=1}^{N_{t}}\frac{M_{i}}{b_{n}}\)is the total number of symbols received, \(b_{n}\), \(b_{j}^{\tau}\) are the constellation sizes (bits per symbol) used at transmitter side, and receiver side, local communication respectively, \(m_{r}\) represents the number of bits after quantisation of each symbol received at receiver side relay nodes, \(T_{s}\approx\frac{1}{B}\) is the symbol duration, and \(B\) is the transmission bandwidth. \(T_{pr\_MIMO}\) is given by:  
\[T_{pr\_MIMO}=\frac{d^{L}}{c}+\frac{d^{l}}{c} \tag{20}\]  
where \(c\) is the speed of light, and \(d^{L}\) and \(d^{l}\) as defined after Equation (10), and Equation (14), respectively.  
\(T_{pc\_MIMO}\) is given by:  
\[T_{pc\_MIMO}=T_{Mod}+T_{Dmod}+T_{Det} \tag{21}\]  
where \(T_{Mod}\),\(T_{Dmod}\) and \(T_{Det}\) are the processing time values for modulator, demodulator, and detection algorithm, respectively. Each block processing time is calculated by dividing the estimated CPU cycles (as mentioned in Section 5.1.2) with TelosB mote processing speed [31].
###### Abstract  
Background: The medial (MG) and lateral gastrocnemius (LG) muscles exhibit differential activation patterns during plantar flexion tasks. However, the influence of range of motion (ROM), exercise type (unilateral vs. bilateral), and limb dominance on muscle activity during heel raise exercises (HREs) remains unclear. Methods: Nineteen physically active adults performed unilateral and bilateral HREs under two ankle ROM conditions: neutral (NROM) and full (FROM). Surface electromyography (EMG) was collected from both legs during each condition and normalized to peak values recorded during overground sprinting. Results: MG activity was significantly higher during the FROM than the NROM, in both the dominant leg (F = 11.55, \(p<\) 0.01, \(\eta^{2}\) = 0.47) and the non-dominant leg (F = 6.63, \(p<\) 0.05, \(\eta^{2}\) = 0.31), and was not affected by exercise type. In contrast, LG activity increased significantly during unilateral versus bilateral HREs, especially in the dominant leg during the FROM (F = 17.47, \(p<\) 0.01, \(\eta^{2}\) = 0.52) and in the non-dominant leg (F = 5.44, \(p<\) 0.05, \(\eta^{2}\) = 0.25). Activation ratios (MG:LG) differed significantly between dominant and non-dominant legs only in the unilateral FROM (\(p\) = 0.03). MG activation during the unilateral FROM was comparable to sprinting values, highlighting its high neuromuscular demand. Conclusion: The MG and LG respond differently to exercise parameters. The MG is primarily influenced by ROM, whereas the LG is sensitive to both exercise type and limb dominance. These findings emphasize the importance of ROM manipulation and unilateral training to target specific gastrocnemius regions. FROM and unilateral execution optimize gastrocnemius activation, with implications for rehabilitation and performance programs targeting calf musculature.  
resistance training; rehabilitation; range of motion; ankle joint; electromyography 2020
## 2 Materials and Methods
### Feature Transfer Learning Strategies  
Consequently, this study utilizes UAVID as the source domain for transfer learning, pre-training the model to recognize path structure features. After this phase, the encoder's weights are frozen, and the model undergoes final training and fine-tuning on the target WIPD dataset. The complete transfer training workflow is depicted in Figure 4.  
Figure 4: The overall schematic diagram of the feature transfer learning process.  
In this study, the model employs a specific feature transfer training approach where, during the initialization phase of IndoorPathNet, the learning rates for some convolutional layers in the first three shallow layers of the pre-trained model are set to zero. This technique effectively preserves the distribution of features, such as textures and shapes, learned from the UAVID source domain data within the low-dimensional feature space. The model then undergoes further training with the WIPD dataset to significantly improve its ability to identify and extract path features in complex indoor environments. A diagram illustrating this process is shown in Figure 5.  
In the fine-tuning phase, this study uses the Adam optimizer for its dynamic learning rate adjustments based on gradient moments, enhancing both the training speed and model stability. The initial learning rates for the encoder's convolution modules are set at \(1\times 10^{-4}\), and \(1\times 10^{-5}\) for the decoder's weights, tailored to the model's specifications. Mixed Precision Training (AMP) is adopted to improve efficiency under memory constraints. All parameters undergo L2 regularization with a 0.1 coefficient. The batch size is set to one to ensure precise data representation, with a maximum of 200 training iterations. To address pixel imbalance in indoor imagery, the model employs a combination of Dice loss [35] and cross-entropy loss [36], effectively optimizing the task of binary path segmentation.
## 5 Discussion
### Addressing the Research Questions
Although this enhancement may pose challenges, particularly in terms of usability, potential solutions include the adoption of zero-interaction authentication techniques, facilitated by smart authentication algorithms that leverage devices users already possess. Another important consideration is the mitigation of unauthorized access. This can be achieved by preventing or monitoring the activity of devices already connected to the network. Furthermore, the incorporation of machine learning and AI-driven anomaly detection algorithms is essential to continuously analyze network traffic patterns and quickly detect suspicious behavior indicative of Advanced Persistent Threats (APTs). Additionally, leveraging software-defined networking (SDN) principles enables dynamic segmentation and isolation of network resources, thus hindering attackers' ability to move laterally within the network. In addition, the implementation of zero-trust security models ensures thorough verification for each device and user who attempts to access critical network resources, thus minimizing the attack surface and bolstering overall resilience against APTs. Another crucial consideration pertains to the distrust of back-end networks and the deployment of devices that restrict access to local networks. This might involve the implementation of tunneling techniques such as VPNs to encrypt traffic to trusted nodes, thereby minimizing ISP access. Encrypting traffic ensures that communications cannot be intercepted by ISPs. In addition, the safe implementation of other protocols such as DNS is important to conceal the nature of traffic. This involves utilizing protocols such as DNS over HTTPS (DoH) [91] and DNS over TLS (DoT) [92].
## 2 Mathematical Modeling
### Problem Description  
As previously mentioned, an incompressible, laminar, and transient multiphase flow of resin/air was considered, moving through a two-dimensional plate domain composed of a porous medium, as shown in Figure 1a,b. Empty channels are inserted along the porous domain to facilitate impregnation of the resin throughout the mold. In Figure 1a,b, these channels (I and T-shaped, respectively) are represented by the dark gray region, while the porous region is represented by light gray.  
In both cases, flow is imposed by a pressure difference between the resin inlet (lower surface of the empty channel) and the exit region of multiphase flow (upper surface of the porous medium). Inlet pressure of \(P_{in}=1\times 10^{5}\) Pa is considered, and pressure of \(P_{out}=0\) Pa is imposed in the exit. It is worth mentioning that, in a real LRI process, resin flow is driven by vacuum imposition at the exit of the fibrous reinforcement. Despite this, a pressure difference with the imposition of positive pressure in the resin inlet surface is represented. For computational modeling, it is important to predict the pressure difference. Hence, the imposition made here led to the same results as that achieved when the same pressure difference is imposed with the vacuum in the domain exit. In the remaining domain, bottom, and lateral surfaces, a non-slip and impermeability boundary condition is imposed (\(u=v=0\) m /s). For the resin volume fraction (Equation (5)), \(f=1\) at the inlet section and zero gradients (normal to the surface) on all the other surfaces are imposed. Concerning the physical properties of the resin and air, densities of \(\rho_{res}=916\) kg/m\({}^{3}\) and \(\rho_{air}=1.225\) kg/m\({}^{3}\) and dynamic viscosities of \(\mu_{res}=0.06\) kg/(ms) and \(\mu_{air}=1.7894\times 10^{-5}\) kg/(ms) are adopted. For the porous media, a permeability of \(K=3.0\times 10^{-10}\) m\({}^{2}\) and porosity of \(\varepsilon=0.88\) is imposed.
patient expectations are four of the primary data challenges facing the healthcare industry today. In this paper, to secure such data, we have considered the use of hyper-chaotic maps to encrypt the multimedia data [6; 7; 8]. Thus, the encrypted data will be transferred over the public 6G network, and also the same encrypted data will be stored on a cloud server. Recently, many image encryption techniques have been proposed to provide secure communication of multimedia data [9].  
Figure 1 shows the proposed 6G-enabled secure IoT framework for multimedia applications. Initially, data will be collected at the perception layer by different kinds of IoT sensors. The proposed encryption algorithm is applied to encrypt the sensed data. Thereafter, the encrypted multimedia data are then transmitted over the 6G public networks to the data processing and storage layer for further processing. At the network layer, the different applications and users can only retrieve the extracted data if they have the exact secret key. If the secret key is wrong, even by a single bit, then the proposed model returns completely noisy data without any kind of statistical information about the actual data.  
The main contributions of this paper are as follows:  
1. We develop a hybrid technique to encrypt the confidential data related to e-healthcare.
2. The hyper-chaotic method and zero-watermarking techniques along with the Rivest-Shamir-Adleman (RSA) algorithm are hybridized to encrypt the data.  
Figure 1: Proposed 6G-enabled secure IoT network [10].  
3. The proposed technique is used to secure multimedia data communications over 6G networks. The remaining paper is organized as follows: A literature review is presented in Section 2. Section 3 discusses the proposed model. Section 4 presents the experimental analysis.
## 4 Case study
### Topological Optimization Result
By using aluminium, the weight can be dramatically reduced, from 250 g of the original design up to 100 g. If steel is employed, the weight can be reduced to 215 g, thus reaching 14% mass reduction. The starting volume was around 90,000 mm\({}^{3}\), while the final design volume is around 28,200 mm\({}^{3}\) for a steel component and around 40,000 mm\({}^{3}\) for the aluminium version. So, the designer has the chance to choose whether to use steel, which is better in terms of mechanical performance and cost, or aluminium, which guarantees a lower weight but more material consumption is required. The following step of the in-depth analysis has focused on the optimization of the design to evaluate punctual geometric features by means of the proposed indices. The geometric shape has been properly refined to fulfil the DFAM design guidelines and maximise the expected quality of the part. Finally, some printers compatible with the selected materials have been identified. In particular, the ProX 320 is selected as it guarantees the lowest production cost. For the sake of a complete assessment, alternative manufacturing solutions have been evaluated. The complexity of the new shape is hardly obtainable with other processes, for instance, casting, due to the deep recess and inner features. Indeed, the small production batch hardly justifies the investment for a complicated mould. The manufacturing costs of the new solution are still higher than the original one, as confirmed by process simulations carried out in collaboration with a printing service supplier. In fact, almost 3 h are required for the printing process, even taking advantage of the possibility of producing multiple parts at the same time. Indeed, additional machining is required to reach acceptable levels of roughness and tolerances in the functional coupling faces. Such costs could be balanced by an improved lightness of the component and better dynamic properties, which are conferred to the aircraft. However, this trade-off needs to be validated accurately, considering a broader design context extended to the whole airplane performance.
## 2 The Development of Typical Rotating Motion Mechanisms
### 2.1. Gear Drive
#### 2.1.1 Traditional Metal Gears
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Types** & **Advantages** & **Disadvantages** & **Applications** \\ \hline \multirow{4}{*}{\begin{tabular}{c} Spur gear \\ [28; 29; 30] \\ \end{tabular} } & High transmission & High noise level, & Machine tool power \\  & efficiency, simple structure, & significant vibration under & system, multi-stage \\  & & surface sliding. & high load & transmission device \\ \multirow{4}{*}{\begin{tabular}{c} HCR spur gear [32] \\ \end{tabular} } & High load-bearing capacity, & Need to adjust gear & Industrial machinery, \\  & low vibration, and noise & parameters (displacement & heavy-duty transmission \\  & & coefficient) & systems \\ \multirow{4}{*}{\begin{tabular}{c} Helical gear \\ [33; 34; 35] \\ \end{tabular} } & Uniform load distribution, & Generate axial force, & \multirow{4}{*}{\begin{tabular}{c} Reduce, General \\ Industrial Equipment \\ \end{tabular} } \\  & long lifespan, smooth & requiring additional & \\  & transmission & bearing support & \\ \multirow{4}{*}{\begin{tabular}{c} Face gear \\ [36; 37] \\ \end{tabular} } & No/low axial force, & An asymmetric tooth & Robot joints, \\  & variable transmission ratio & surface may cause & non-orthogonal axis \\  & vibration & transmission system \\ \multirow{4}{*}{\begin{tabular}{c} Worm gear \\ [38; 39; 40; 41; 42; 43; 44; 45; 46] \\ \end{tabular} } & High deceleration ratio, & High sliding friction, low & Elevators, car steering \\  & low noise, high stability, & efficiency, and lubrication & systems, and heavy \\  & auto-lock & required & machinery \\ \multirow{4}{*}{\begin{tabular}{c} Straight bevel gear [47] \\ \end{tabular} } & Reliable and & & \\  & straightforward, & High speed noise is loud & Low speed light-load \\  & noncircular spur bevel & and requires axial fixation & transmission \\  & & gears can achieve special & functions. \\ \multirow{4}{*}{\begin{tabular}{c} Hypoid gear \\ [49; 50] \\ \end{tabular} } & The chassis height can be & & \\  & flexibly adjusted, with a & & \\  & high contact rate. & & \\  & Non-uniform variable & & \\ \multirow{4}{*}{\begin{tabular}{c} Non- \\ circular gear \\ [51; 52; 53] \\ \end{tabular} } & Speed, high transmission & & & \\  & accuracy, compact & & & \\ \multirow{4}{*}{\
\begin{tabular}{c} Strong impact resistance \\ and minimal tooth surface \\ deformation, smooth \\ transmission, and \\ low noise \\ \end{tabular} } & Complex processing and & & Printing machines, textile \\  & high cost, uneven stress on & & & \\ \multirow{4}{*}{
\begin{tabular}{c} Printing machines, textile \\ machinery, variable \\ transmission bicycles \\ \end{tabular} } \\  & transmission bicycles & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of transmission performance of different types of traditional metal gears. Figure 12: Model of double cycloid gear pair pre-tensioned gear.
# Model for the Prediction of Rain Attenuation Affecting Free Space Optical Links
## 3 Comparison with Analytical Methods
### Rain Attenuation Models for Terrestrial Radio Links
#### 3.1.3 Moufpourama Model  
Similarly to the ITU-R model, the prediction method proposed by [PERSON] in [13] receives \(R_{0.01}\) as the input to predict \(A\) as:  
\[A(P)=kR_{0.01}^{\alpha}L_{\alpha q}(P,L). \tag{9}\]  
\(L_{eq}\) in (9) is the equivalent path length that is the product between the actual path length \(L\) and a correction factor dependent on the rain rate:  
\[L_{eq}(P,L)=L\exp\biggl{(}-\frac{R(P)}{1-100R(P)}\biggr{)}. \tag{10}\]  
Equation (10) is valid for \(L\leq 7\) km.
# Preliminary Results of an Astri/UWM EGNSS Receiver Antenna Calibration Facility
## 8 Full PCCs Validation
This is valid for LEIAR10/NONE, where the differences in northwestern regions at a low elevation exceed 4 mm, and additionally for the TRM115000.00/NONE antenna as well. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Antenna Type**} & \multicolumn{3}{c}{**Igs14.atx MPC (mm)**} & \multicolumn{3}{c}{**Astri/UWM MPC (mm)**} & \multicolumn{3}{c}{**MPC Differences (mm)**} \\ \cline{2-9}  & **N** & **E** & **U** & **N** & **E** & **U** & **\(\Delta\)N** & **\(\Delta\)E** & **\(\Delta\)U** \\ \hline LEIAR10 NONE & +1.42 & \(-\)0.25 & +88.04 & +1.09 & \(-\)0.43 & +86.55 & 0.33 & 0.18 & 1.49 \\ \hline TRM115000.00 NONE & +0.68 & \(-\)0.12 & +65.19 & +0.72 & +0.47 & +64.10 & 0.04 & 0.59 & 1.09 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: PCO solutions accuracy compared to IGS mean-type (final PCCs model). Figure 9: Differences between two independent calibrations of full PCCs for GPS L1 frequency: (a) LEIAR10/NONE (left) antenna; (b) TRM115000.00/NONE (right) antenna. As in the case of internal accuracy checking, larger differences (reaching 1.5 mm) occur at low zenith angles (from 0\({}^{\circ}\) to 30\({}^{\circ}\)), which is mainly caused by the differences in PCO vertical components. Some significant differences of 3-4 mm can be found close to the horizontal signal reception angles (zenith angles from 80\({}^{\circ}\) to 90\({}^{\circ}\)), possibly caused by the remaining multipath effects and poor antenna receiving performance at low elevations. For the LEIAR10/NONE antenna, such significant differences occur in the northwestern and southeastern hemisphere regions and in the northeastern and southwestern hemisphere regions for the TRM115000.00/NONE antenna.
# A Multipath Data-Scheduling Strategy Based on Path Correlation for Information-Centric Networking
###### Abstract  
Information-Centric Networking (ICN) has revolutionized the manner of content acquisition by shifting the communication mode from host-centric to information-centric. Considering the existing, large amount of IP infrastructure in current networks, the new ICN architecture is proposed to be compatible with existing networks in order to reduce deployment cost. However, due to compatibility with IP networks, ICN data packets must be transmitted through the default path provided by IP routing regulations, which also limits the transmission efficiency and reliability of ICN. In order to address this issue, this paper introduces a multipath transmission method applied in ICN which takes full advantage of the functions and characteristics of ICN and builds multiple end-to-end relay paths by using the ICN routers as relay nodes. We then propose a relay-node-selection algorithm based on path correlation to minimize the impact of overlapping links. Moreover, we comprehensively calculate the path state value by combining the round-trip time and packet loss rate and propose a multipath data-scheduling algorithm based on the path state value. Simulation experiments show that the proposed method can maintain high bandwidth utilization while reducing the number of out-of-order packets.  
ICN; multipath transmission; path correlation; data scheduling +
Footnote †: journal:  
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