esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
With the rapid development of the network, how to effectively reduce the dynamic delay and improve the performance of the network is an important and challenging problem. Specifically, network congestion is one of the key factors that hurt the network performance, so real-time detection of the network congestion is critical for recovering the network failure quickly. Current research in congestion detection mainly faces the problems of occupying extra bandwidth, decreasing the ratio of the effective payload of the packet, increasing the burden of the switches, etc. In this paper, we apply flow watermarking to network congestion detection and propose a fine-grained network congestion detection method based on flow watermarking. We also combine it with the eBPF (extended Berkeley Packet Filter) to improve the performance of congestion detection. Theoretical analysis and experimental results show that the changes in network status can be reflected in real-time and accurately in the watermark decoding information. The network congestion detection based on flow watermarking can detect network status on a small time scale and realize low-overhead and easily deployed congestion detection.  
network congestion detection; network management; network flow watermarking; eBPF
## 3 Simulation Signal Analysis
After joint denoising, the time-frequency analysis of the reconstructed combined harvester test signal was performed using STFT and CWT, as shown in Figure 16.  
Figure 16 displays the time-frequency plot of the combine harvester test signal after joint noise reduction and reconstruction. It can be observed that after joint noise reduction, the overall noise level of the signal is significantly reduced compared to the untreated experimental signal. To more intuitively see the noise reduction effect of various methods, the SNR is calculated to quantify the noise reduction effect, and the calculation results  
Figure 14: A time–frequency diagram of the original signal. (**a**) Original signal STFT; (**b**) Original signal CWT. Figure 16: The time–frequency plot after improved CEEMDAN decomposition with SVD thresholding noise reduction reconstruction; (**a**) STFT; (**b**) CWT. Figure 15: The time–frequency plot after direct SVD threshold denoising; (**a**) STFT; (**b**) CWT. are shown in Table 4. From the comparison of SNR values in Table 4, it can be observed that for the combined harvester test signals, using the improved CEEMDAN denoising method alone and using the SVD denoising method alone result in some improvement in SNR. However, compared with the joint denoising approach of improved CEEMDAN-SVD, the latter shows a more significant improvement in SNR. This indicates that the proposed denoising technique is suitable for processing the vibration signals of combined harvester experiments and exhibits a good denoising performance.
## 5 Conclusions  
In the evolution from the conventional satellites to the satellite-on-a-chip concept it seems to be that two lines of magnetic sensors are developing in parallel depending on the particular application. In the use of magnetic sensors for geomagnetic field mapping, the combination of fluxgates vector sensors and scalar sensors deployed in a boom are the best trade-off between performance and resources consumption. For other applications like magnetic-based ACS-Attitude Control System when the accuracies of pointing requirements are medium-to low, mature off-the-shelf technologies such as AMR-Anisotropic Magnetoresistance and miniaturized commercial fluxgate sensors offer a good solution. For gradient measurements when accurate mapping of a region has to be done, miniaturized sensors like AMR or SDT-Spin-Dependent Tunnel sensors should be used, since they can be configured in arrays offering better spatial mapping. Nevertheless, these miniaturized sensors should be used when high spatial resolution is needed. The use of brand new technologies in the space requires a tested reliability of the technology in the market and the development of a qualification test adapted to the conditions and the life-time of the mission. When technologies are not mature, the cost of the adaptation for space is very high and normally unaffordable by the mission but effort devoted to watch the emerging transducer for a new miniaturized but competitive magnetic sensor technology is very much worthy.  
Regarding the front-end electronics of the sensors it seems to be a convergence of both lines of magnetic sensors to the use of VLSI- Very Large Scale Integration technologies: nowadays FPGAs-Field Programmable Gate Array and ASICs-Applied Specific Integrated Circuit technologies.
## 2 Attitude Control System Model
### Attitude Kinematics  
A satellite's attitude is generally determined by the orientation of its body coordinate system relative to the inertial coordinate system. There are various ways to describe this, with this paper relying on quaternions to describe micro-satellite attitudes. Meanwhile, the satellite attitude kinematics equations do not suffer from singularity issues regarding attitude quaternions. Moreover, the computational process involves only matrix multiplication operations and does not require complex trigonometric calculations. Therefore, this paper employs a satellite attitude kinematics model based on attitude quaternions to model the kinematic characteristics of the satellite.  
The attitude quaternions are denoted by \(\mathbf{q}=\begin{pmatrix}q_{0}\\ \mathbf{q}_{\rm v}\end{pmatrix}=\begin{pmatrix}q_{0},q_{1},q_{2},q_{3}\end{pmatrix}^ {\rm T}\), and the following equation provides the satellite attitude kinematics model:  
\[\left\{\begin{array}{ll}\dot{\mathbf{q}}_{\rm v}=\frac{1}{2}(\mathbf{q}_{\rm v}^{ \rm v}+q_{0}\mathbf{I}_{3})\mathbf{\omega}_{\rm ba}\\ \dot{q}_{0}=-\frac{1}{2}\mathbf{q}_{\rm v}^{\rm T}\mathbf{\omega}_{\rm ba}\end{array}\right. \tag{1}\]  
where \(\mathbf{q}_{\rm v}^{\rm\times}=\begin{bmatrix}0&-q_{3}&q_{2}\\ q_{3}&0&-q_{1}\\ -q_{2}&q_{1}&0\end{bmatrix}\) represents the cross-product skew-symmetric matrix of \(\mathbf{q}_{\rm v}\), and \(\mathbf{\omega}_{\rm ba}\) is the angular velocity of the satellite.  
When the target desired quaternion \(\mathbf{q}_{c}\) and the initial quaternion \(\mathbf{q}_{0}\) are given, the error quaternion \(\mathbf{q}_{e}\) between the two is calculated using the following equation:  
\[\mathbf{q}_{e}=\mathbf{q}_{0}^{*}\otimes\mathbf{q}_{c} \tag{2}\]
# Failure Mechanism and Structural Analysis of Chain Slings with Non-Standard Connections
## 3 Failure Cause Analysis  
### Fracture Surface Analysis  
The fracture surfaces of the two failed chains are shown in Figure 3. Although both chains had identical specifications, their fracture surface morphologies exhibited distinct differences. In the case of Chain A, the fracture surface showed elongation and reduction in cross-sectional area, which indicates that tensile fracture occurred due to the applied load exceeding the yield stress during the lifting operation. In contrast, Chain B exhibited minimal deformation, and the entire fracture surface appeared shiny.  
Figure 3 'a' and 'b' show the fracture surfaces of Chain A. Significant elongation and reduction in cross-sectional area are observed around the fracture surface, indicating that the chain withstood a certain level of tensile load before ultimately failing due to tensile fracture. In contrast, Figure 3 'c' and 'd' present the fracture morphology of Chain B. The fracture surface exhibits a relatively smooth and shiny appearance without noticeable cross-sectional reduction, suggesting a typical shear fracture. Furthermore, the presence of indentation marks near fracture surface 'c' implies that the failure likely occurred abruptly due to compression or impact from an external object.  
Figure 3: Comparative fracture surfaces of Chain A and Chain B: (**a**) Fracture surface of Chain A (**b**) Fracture surface of Chain B. The a, b, c and d indicate the fracture surface of chain A and B.  
Figure 2: Non-standard chain connection using eye bolt and hook.  
As shown in Figure 4, indentation marks were observed near point c of Chain B, suggesting a shear fracture caused by sudden overloading. These fracture surface characteristics imply that the load was not evenly distributed between the two chain slings, likely due to a difference in their effective lengths. Initially, the load was concentrated on Chain A, which sustained the weight for a period before failing. Subsequently, the full load was transferred to Chain B, resulting in a rapid fracture. Moreover, the indentation on Chain B was located on the inner surface of the chain link, suggesting that it was caused by contact with an adjacent link within the chain rather than an external object or force.  
Although no visible abnormalities were observed during a visual inspection of Chain B, Figure 5 reveals that several links of Chain A displayed severe surface damage and deformation. This damage is attributed to the use of a non-standard connection, where the chain was directly wrapped around an eye bolt. This likely induced significant bending stress on the chain, leading to structural degradation.  
Therefore, when using multiple chains in lifting operations, it is critical to ensure uniform load distribution by matching the number of chain links connected to the hook. If the lengths or the number of connected links differ, one chain may bear an excessive load, increasing the risk of premature failure.
## 2 Materials and Methods  
### Design of FTS Drive and Test Stand  
The developed drive intended for non-circular turning was designed based on the PiezoMove P-602.8 SL series unit from PI-Ceramic including the E-625.SR dedicated servo amplifier. The drive is a piezoelectric stack equipped with a motion amplifier integrated with flexure guides. The feedback loop included a strain gauge integrated with the stack. The travel range amounted to 1000 \(\upmu\)m with a linearity error of 1.5%, as stated by the manufacturer. The force generated by the drive unit was 100 N in the pushing and 5 N in the pulling direction. The drive unit with the mounted turning tool was attached to the carriage using mounting screws. The carriage could move along the EGR15-T guide rail with two EGH15 CA carriages from Hiwin. The entire assembly was placed in the turret of the TUM-35D conventional turning lathe from Famot Pleszew (currently part of DMG MORI Group) in a manner ensuring that the height of the turret and the piezoelectrically driven tool corresponded to the height of the conventional tool. The kinematic diagram and the overview of the drive are presented in Figure 1.  
The angular position of the lathe spindle was monitored using the MHK-40 incremental encoder from Megatron, the outputs of which were connected to the counter inputs of the Siemens S7-1200 PLC controller. Based on the current angle and machined geometry, the controller calculated and sent the corresponding voltage signal to the servo drive. The FTS position was registered by optoNCDT ILD-1700-10 (NCDT) laser sensor from Micro Epsilon, the input of which was connected to the MGC+ measurement amplifier from HBM. The design of control and measuring systems are presented in Figure 2.  
Figure 1: Overview of the proposed FTS drive: (**a**) kinematic diagram, (**b**) actual drive mounted on the lathe turret: (1) base, (2) piezoelectric drive unit, (3) support frame, (4) guide rail, (5) carriage 1, (6) carriage 2, (7) turning tool holder.  
Figure 2: Design of the control and measuring systems for turning tests.
# EDDA: An Efficient Distributed Data Replication Algorithm in VANETs
## 5 Performance Evaluation
### Performance Metrics  
In the simulation, we vary two parameters including the number of message copies and the number of vehicles in the VANET. The range of these parameters will be elaborated along with the performance analysis.  
The performance is measured using the following criteria: data delivery ratio, transmissions and dissemination delay. The data delivery ratio is the ratio of the number of vehicles that receive the message to the total number of copies. We use the data delivery ratio to evaluate the dissemination performance of the compared algorithms. Transmissions are denoted by the number of stages (see Definition 5), which characterizes the complexity for the system to enter into a balanced status among a set of nodes. As the number of stages can reflect how many transmissions are needed to achieve an \(\epsilon\)-balanced status, it is utilized as a measurement of communication overhead to indicate how many average operations the system would perform when it reaches network consensus. We choose data dissemination delay as a measurement of effectiveness, which indicates the time interval from the beginning of data dissemination to the moment that the network enters into a balanced status.  
Here, it is worth noting that in the compared schemes, to mitigate the simulation complexity, the number of message copies \(n\) represents the maximum message copies that can be spread in the process of data dissemination. In replication-based algorithms, such as epidemic routing, SW [35] and capacity-constrained replication algorithm (CCR) [11], \(n\) is set as the replication limit of the message. As for greedy perimeter stateless routing (GPSR), a single-copy routing algorithm, we use \(n\) to denote the the maximum number of nodes that can receive the message, which increases when the node carries a message meeting a node without the message.
# An Evaluation of Orbital Angular Momentum Multiplexing Technology
## 3 Beam Focusing Effect Using Dielectric Lens for OAM Multiplexing
### Experiments of OAM Multiplexing Using Dielectric Lens
Figure 6: Experimental beam diameter results. The beam diameters of the UCA with a lens in modes 1 and 2 were, respectively, 40 and 60 cm smaller than that of a UCA having a 4 cm diameter without a lens. We also found that the beam diameter of the UCA with a lens was in good agreement with the beam diameter of a UCA with a 16 cm diameter. These results enabled us to confirm that the beam divergence can be reduced by using a dielectric lens and that the transmission distance can be correspondingly increased. Experimental results also showed that aUCA with a 4 cm diameter and a lens produced an OAM beam similar to that produced by a UCA with a 16 cm diameter. These results suggested that using a dielectric lens is one of the options that will enable OAM wireless multiplexing systems to effectively address the beam divergence.
# Fabric Defect Detection Method Using SA-Pix2 pix Network and Transfer Learning
## 2 Materials and Methods  
### The SA-Pix2 pix Model  
#### 2.1.1 Network Structure  
The Pix2 pix network [40], comprising a generator and discriminator, features a generator employing a U-Net model [41] that prioritizes direct feature fusion over pooled indexing methods. This design choice avoids memory-intensive full connectivity between downsampling and upsampling, thereby preserving crucial image structural information. Particularly beneficial for non-periodic and complex patterned texture fabrics, this framework efficiently shares essential underlying information between inputs and outputs, expediting image reconstruction.  
To augment the network's ability to extract global features crucial for defect detection within intricate patterned fabrics, this study integrates the image Self-Attention mechanism (Self-Attention) [42] into the generator network structure. Focused on reconstructing the blemished region necessitating less information, the addition of the Self-Attention mechanism selectively incorporates it solely within the input and intermediate layers of the generator's upsampling and downsampling layers. This strategic placement enables the network to associate global features of the patterned fabric image with local features surrounding the blemish, enhancing the network's capacity to comprehend both the broader fabric context and localized defect characteristics. This targeted integration aims to bolster the network's ability to extract comprehensive features crucial for detecting defects within complex patterned fabrics while curbing computational complexity and training time.  
The generator and discriminator components of the architecture employ the \"convolution-batch-activation function\" module for both upsampling and downsampling operations. Figure 1 illustrates the specific structure of the generator network. The discriminator, derived from the Pix2 pix network, is structured as a \"Patch GAN\" and depicted in Figure 2.  
Figure 1: Network structure of generators.  
Figure 2: Network structure of the discriminator.
## 5 Numerical Experiments
Damage based refinement: from left to right, zoom-in images of the damage at the fixed-end of the bar \(X=0\), at \(t=\)88.5 \(\mu\)s, \(t=\)106.1 \(\mu\)s, and \(t=\) 115.9 \(\mu\)s. The damage nucleates at the corners of the bar, and progresses towards the center. **Figure 6**. Energy release rate based refinement, with \(c=1\): from left to right, zoom-in images of the damage at the fixed-end of the bar \(X=0\), at \(t=\)88.5 \(\mu\)s, \(t=\)106.1 \(\mu\)s, and \(t=\) 115.9 \(\mu\)s.  
To serve as a check that the simple damage model we have chosen to use for demonstration purposes is functioning correctly, in Figure 8 we provide a plot of the first principle stress versus the first principle strain at a point which becomes fully damaged (left) and the corresponding time series of the damage variable and Helmholtz free energy density (right). The point we have chosen is the center of the bottom the same number of nodes. In the left cell in Figure 7. Note that both the parametric (in time) stress/strain plot and time series plot of the damage and energy are simply records of the motion of a point which becomes fully damaged, and are not comparable to the usual uniaxial stress strain curves. Figure 6 shows the damage solution of the same problem, but now using the refinement indicators based on the excess energy release rate with the parameter \(c\) in (39) set equal to one. We observe that the damage solution in Figure 6, while not identical to that in Figure 5, is quite similar to it. We attribute the similarity in the damage solutions to the fact that we have set \(c=1\) in (39), and hence the energy release rate refinement indicator is the same as the damage based indicator and produces similar results. To illustrate the effect of adjusting the constant \(c\), in Figure 7 we show the damage solution and mesh refinement for the same problem, but this time obtained using an energy release based refinement  
Figure 8: The first principle stress versus the first principle strain at a point which becomes fully damaged (left) and the corresponding time series of the damage variable and Helmholtz free energy density (right). Dynamic effects can be observed in both data sets. On the right, region I corresponds to the response before damage evolution, region II to the brief period of damage evolution, and region III to the period when the material is fully damaged. Figure 7: Energy release rate based refinement, \(c=0.5\): The same simulation as in Figure 6, but now with the refinement indicator parameter \(c=0.5\). Note the difference in the mesh pattern, specifically the refinement of cells in which damage is not evolving. indicator with the parameter \(c=0.5\). Preemptive mesh refinement is now clearly visible in Figure 7, however, the damage field is still essentially the same as in Figures 5 and 6. Thus, in this circumstance, it appears that either the damage based or energy release rate based schemes would provide adequate performance.
## 4 Experiments
### Quantitative Results on RSOD Datasets  
To further substantiate the performance of PSGFE, additional experiments are carried out on the RSOD dataset. We compare our model with classic transfer learning algorithms such as TFA [31], FSCE [21], and CCAFR [51], as well as classic meta-learning algorithms like Meta-RCNN [28] and SAAN [38]. In particular, CCAFR [51] and MSOCL [22] are the two best-performing algorithms on the RSOD dataset. Table 3 shows the precision of several FSOD methods under the mAP50 evaluation criteria. Our results achieve the best performance across all scenarios.  
As shown in Table 3, our results achieve optimal performance. The mAP50 of our method is at least 1.2 times higher than that of the second-best method, with the highest improvement of 3.4 under the 10-shot condition. For a more intuitive comparison, we singled out two suboptimal methods, MSOCL [22] and CCAFR [51] for individual contrast. As illustrated in Figure 3. Compared to CCAFR [51], PSGFE achieves an improvement of more than 9% in all scenarios except the 3-shot setting, with a remarkable improvement of 15.0% in the 5-shot setting. Compared to MSOCL [22], PSGFE also has an increase of more than 4% in all scenarios. In general, when in contrasted with suboptimal algorithms, PSGFE demonstrates significant improvements, particularly in 5-shot and 10-shot settings, where our approach outperforms suboptimal algorithms by at least 4%. This indicates that PSGFE exhibits satisfactory generalization capability, which is also demonstrated to be effective on the RSOD dataset.  
Figure 2: (**a**) Comparison of mAP50 of novel class between PSGFE and MSOCL [22]. ST-FSOD [23] in DIOR datasets. (**b**) Variation in PSGFE relative to MSOCL [22] and ST-FSOD [23] in DIOR datasets.
## 4 Acoustic Testing and Results
### Experimental Setup, Data Acquisition and Processing
The dissipation coefficient is used to evaluate the damping performance of the test object, here the FlexiS liner. For most comparisons, the averaged value \(\Delta_{avg}~{}=~{}\Delta^{+}/2~{}+~{}\Delta^{-}/2\) can be used. For each liner (3 FlexiS liner and one reference liner), measurements were made for 120 dB and 130 dB overall SPL. The Mach number was varied between \(M=0\) (no-flow case), \(M~{}=~{}0.1\), and \(M~{}=~{}0.2\). These values always mark the center-line Mach number. In this paper, only data for the 130 dB excitation is presented. Although the flexible cell walls are not directly accessible inside the assembled liner samples, some tests were made in order to investigate the movement and effect of the flexible structures. Therefore, a specific sample with four cut-outs and TPU film was prepared. This sample represents a single wall segment with cut outs and was installed between the two microphone sections of the DUCT-R instead of the liner damping module. Only no-flow measurements could be made in this configuration, because this specific sample represents a partition wall in the duct. Finally, three microphones were mounted flush with the inside of the back plate of three neighbouring liner cells- aligned along the main flow axis (Figure 3). Thereby, the pressure \(p_{Us}\) upstream, \(p_{Ds}\) downstream and \(p_{Mid}\) at these positions and the phase relation between active cells and inactive cells could be determined.