esa-sceva/satcom-chunk-collection huggingface dataset:
# Mechanical Structure Design and Experimental Study of Gamma-ray Monitor for Small Satellite Payload
## 2 Materials and Methods
### Mechanical Structure Design of the Bracket for the Radiant Cooling Plate  
The main function of the bracket for the radiant cooling plate is to support the novel GRM and heat transfer, which requires sufficient strength and stiffness. Therefore, a frame structure was adopted to ensure its mechanical and thermal performance requirements. The novel GRM was installed with a satellite insulation layer made of FR4, and the thermal insulation pad was added at the four corners of the bracket. In order to better transfer thermal energy, the bracket is closed at the +Y side of the star. The flatness of the closed surface is 0.1 mm/100 mm \(\times\) 100 mm, and the roughness is 3.2 \(\upmu\)m to ensure thermal conductivity. The bracket for the radiant cooling plate was made using magnesium alloy ZK61M and its mass is about 250 g. The structure is shown in Figure 2a, and the whole assembly of the novel GRM is shown in Figure 2b.  
[PERSON], [PERSON] [21] used feedforward ANN and synthetic data to predict the strength of the structure. In order to ensure that the mechanical structure of the novel GRM meets the requirements of the space-borne environment during the process of ground transportation to launch flight, the overload analysis, modal analysis sinusoidal vibration analysis, random vibration analysis and related experimental research were carried out. By combining the data of the sinusoidal characteristic sweep frequency test with the parameter method, the correction of the damping coefficient in the finite element model was modified to reduce the calculation error of the FEM and obtain reliable dynamic response prediction results [22; 23].
## 4 Implementation and Comparison
### Performance Comparison for the Logarithmic Function  
To compare with the performance of a logarithmic function implemented by OML-PLAC and ML-PLAC, the function \(\log_{2}(1+\chi)\) is selected. In addition, we added two comparative experiments with [15; 24], so four sets of parameters are selected for the experiment in total. The results are shown in Table 5.  
In the first set of experiments, iw = 26, qw = 14, kw = 2 and MAE\({}_{\text{def}}\) = 1.8 \(\times\) 10\({}^{-3}\). We get better performance with the same MAE\({}_{\text{def}}\). Using the method in this paper, fewer segments and less area/delay/power consumption are required. In addition, because of the EA approach, we get a smaller MAE. Specifically, the design reduces the number of segments, the area, the delay, the power and MAE value by 3, 11.72%, 8.46%, 22.83% and 4.47%. Compared to the hardware architecture in ML-PLAC, we need fewer comparators and a smaller number of y-intercept selector inputs. As for the critical path, 1 26-bit comparator, 1 5:1 multiplexer and 2 14-bit adders are included.  
In the second set of experiments, iw = 14, qw = 14, kw = 2 and MAE\({}_{\text{def}}\) = 1.63 \(\times\) 10\({}^{-3}\). In this case, the results show that our design reduces 3 segments, 12.44% area, 10.64% delay, 43.23% power and 6.13% MAE, and the critical path includes 1 14-bit comparator, 1 5:1 multiplexer and 2 14-bit adders.  
The optimizations in [15; 24] are specific optimizations of logarithmic function. We control the same values of iw, qw and MAE in the third and fourth experiments. It can be seen from the data in the table that the performance of our proposed method is much higher.  
Figure 7: Definitions of labels in the Tables 5–9.
# A Programmable ROADM System for SDM/WDM Networks
## 5 Conclusions  
In this paper, a programmable SDM/WDM ROADM system is proposed for MCF-based multidimensional optical networks. The ROADM system introduces reconfigurable fibre-core bypass connections to generate different virtual topologies based on network traffic. The design reduced the required number and port count by using spectrum switching devices significantly by comparing with traditional ROADMs. The design takes account of the vast resources provided by the MCFs and provides a practical solution for wavelength switching. Our simulation analysis shows that the proposed SDM/WDM ROADM system provides acceptable performance as the fully flexible ROADM that treats all fibre cores as individual SMFs while saving considerable cost. The SDM/WDM ROADM design also allows dynamic reconfiguration based on network traffic and provides full compatibility with the current SMF-based networks. It is believed that the proposed SDM/WDM ROADM system provides a potential solution for future multidimensional optical networks.  
Conceptualization, Ruizhi.Yang, Lida.Liu and Shuangyi.Yan; methodology, R.Y. and S.Y.; software, R.Y. and L.L.; validation, R.Y., L.L. and S.Y.; formal analysis, R.Y. and S.Y.; data curation, R.Y., L.L. and S.Y.; writing--original draft preparation, R.Y.; writing--review and editing, R.Y. and S.Y.; project administration, S.Y. and D.S.; funding acquisition, S.Y. and D.S. All authors have read and agreed to the published version of the manuscript.  
The authors acknowledge funding support from the EU H2020 (5G-Clarity, No.: 871428).  
Not applicable.  
The authors declare no conflict of interest.
## 2 Materials and Methods
### Sole Model  
A portable 3D scanner (model Leo, Artec 3D, Luxembourg) was used to capture the sole model, with a maximum scanning frame rate of 50 Hz and a scanning accuracy of 1 mm. Based on the dimensions of the three-point bending fixture, a coupled sole-fixture model was created (Figure 1c). The bending simulation for the sole model was conducted using the Transient Structural module in ANSYS Workbench (version 2019, ANSYS Inc., Canonsburg, PA, USA). To facilitate computational modeling, the material properties of the midsole, outside, and fixture were assumed to be homogeneous and isotropic under the assumption of linear elasticity [28]. To determine the material properties of the midsole, ASTM D638 Type IV [29] standards were followed, and tensile dog-bone samples were cut using a standard cutting knife. Three samples were taken for tensile testing using a miniature in situ mechanical testing system equipped with a non-contact video extensometer (model IBTC-300 SL, CARE, Tianjin, China) with a tensile rate of 5 mm/min. The material properties of the midsole are listed in Table 1, and the corresponding details can be found in the Supplementary Materials (Figure S2). The material of the fixture is structural steel, and the material of the outside was obtained from previous studies [30], as shown in Table 1. Bonded contact was applied between the outside and midsole, while frictional contact (with a coefficient of 0.6 [31]) was applied between the loading head and the shoe sole, as well as between the two support beams and the shoe sole. A tetrahedral patch-conforming method was used for mesh generation, followed by a mesh sensitivity analysis (Table 2), which determined that a mesh size of 4 mm was appropriate for further simulations. The support beams were fixed at the bottom, and vertical downward rates of 7.5 mm/s, 10 mm/s [26], 12.5 mm/s [27], and 15 mm/s [7] were applied to the loading head, with a displacement of 15 mm. The analysis time was set based on the displacement and rate. The simulation  
Figure 1: **(a)** Experimental shoe; **(b)** shoe sole in three-point bending test after removing upper part; **(c)** fixture model, shoe sole model, and coupled model.  
generated force-displacement data for the loading head, from which the linear region was selected to calculate the longitudinal bending stiffness of the sole using Equation (1). The effectiveness of the model was evaluated by comparing the percentage error between the experimentally measured stiffness and the simulated stiffness, with an error of less than 10% considered as an indication of model validity [31].
## 3 Results and Discussion
### Effect of Different Fractures  
In carbonate gas reservoirs, the pore structure of carbonate rock is more heterogeneous, including dissolved vugs, pores and fractures [26]. The existence of fractures has a strong influence on water and gas flow in gas reservoirs [14]. The core samples without different fractures are selected to study the effects of water invasion. The gas production rate is 1000 mL/min and original saturation pressure is 30 MPa, respectively. The experimental results without aquifer and with 3 times, 7 times and infinite aquifer are provided in Figure 9. From the result of Figure 9a, it is seen that the recovery factor of core 6 is higher than that of core 3 when there is no aquifer [21]. This suggests that the existence of fractures increases the discharge area of gas reservoirs when the permeability is close, which provides a prior seepage channel and increases the recovery factor of gas reservoirs [21]. When the aquifer increases from no aquifer to infinity, the recovery factor of core 6 decreases from 88.13% to 29.99%, which is far higher than that of core 3. Figure 9b shows water invasion versus aquifer times for different fractures. The water invasion of core 6 is less than that of core 3. When the core contains fractures, fractures  
Figure 8: Recovery factor versus permeability for different aquifers and production rates. (**a**) Different aquifers; (**b**) different production rates.  
are the main channels of water advance and small water influx can make recovery factor fall sharply. Hence, we should depict and know the location of fractures and aquifer so as to keep away from the connecting zone between fractures and aquifer in the well-spaced development of gas reservoirs.  
In order to understand the effects of production rates for different fractures, the core samples are used to simulate water invasion from 500 mL/min to 4000 mL/min. The aquifer size is 7 times and the original saturation pressure is 30 MPa. Figure 10 illustrates recovery factor versus gas production rate for different fractures. From the result of Figure 10, it can be observed that the recovery factor of the cores with dissolved pores decreases with the increase of gas production rate. The recovery factor of the fracture cores increases and then decreases as gas production rate increases, and there exists an optimal gas production rate. That is to say, the production rate of gas reservoirs is equal to the rate which provides gas to fractures from base rocks. Thus, it is very significant to choose a reasonable production rate in the development of fractured gas reservoirs.
## 3 Similarity Matrix Based on the Importance of Shared Nearest Neighbors
### An Improved Adaptive Spectral Clustering Algorithm  
Spectral clustering is a clustering method that is based on graph theory and uses the feature vectors of a data similarity matrix to make the clustering. It can identify a data space of arbitrary shape and converge to the global optimal solution.  
Let us consider a set \(V\) of \(N\) data points, or vertices. We write \(S_{ij}\) for the similarity between the \(i\)-th and the \(j\)-th data point, and \(S=S_{ij}\) for the \(N\times N\) similarity matrix. Let us define the degrees \(D_{ii}\) of vertex \(i\in V\) by Equation (9):  
\[D_{ii}=\sum\
olimits_{j\in V}S_{ij} \tag{9}\]  
Without loss of generality, we assume that all vertices have non-zero degrees. Then, we write \(D=(D_{ij})\) for the \(N\times N\) diagonal matrix.  
One spectral clustering technique, commonly used for image segmentation, is the normalized cuts algorithm or Shi-Malik algorithm introduced by [PERSON] [19]. It partitions points into \(k\) sets, \(\left\{A_{1},A_{2},...,A_{k}\right\},\) based on the eigenvectors \(\
u\) corresponding to the first \(k\) biggest eigenvalues of the symmetric normalized Laplacian defined as, \(L^{norm}:=D^{-1/2}SD^{-1/2}\).  
We introduce the proposed similarity matrix \(SNEW\) to the standard spectral clustering and then get a new adaptive spectral clustering algorithm based on the importance of shared nearest neighbors. The steps of improved adaptive spectral clustering algorithm is described in Table 2:  
**Table2.** Adaptive spectral clustering algorithm based on the importance of shared nearest neighbors.  
**Adaptive spectral clustering algorithm based on the importance of shared nearest neighbors:**  
**Input**: \(n\) data vertexes: \(X=\left\{x_{1},\cdots,x_{n}\right\}\in R^{d}\), clustering number: \(K\):  
**Output**: \(K\) clusters, \(\left\{A_{1},\cdots,A_{n}\right\}\)  
**Step1.** Get the similarity matrix \(SNEW\) according to the calculation steps of the Table1;  
**Step2.** Define \(D\) to be the diagonal matrix, where \(D_{ii}=\sum_{i=1}^{n}SNEW_{ij}\), and compute the Laplacian matrix \(L=D^{-1/2}(SNEW)D^{-1/2}\);  
**Step3.**Compute the first \(K\) largest eigenvalues of the Laplacian matrix and their corresponding eigenvectors \(v_{1},v_{2},...,v_{k}\); construct a matrix \(U=\left\{v_{1},v_{2},...,v_{k}\right\};\)  
**Step4.** Construct the matrix \(Y\) by normalizing each row in \(U\), where \(Y_{ij}=U_{ij}\bigg{/}\sqrt{\left(\sum_{j}u_{ij}^{2}\right)}\);  
**Step5.** Treat each row of \(Y\) as a vertex in space \(R^{k}\) and cluster them into \(K\) clusters via k-means or other clustering algorithms for the ultimate clustering results, \(\left\{A_{1},\cdots,A_{n}\right\}.\)
## 5 Performance Evaluation
### Energy Consumption  
With network coding, the energy consumption in a UWSN increases with the number of sensor nodes as well as the value of \(N\). However, as shown in Figure 9, when \(N\leq 6\), the growth curve of GPNC is clearly lower than that of VBF_NC thanks to the fact that GPNC considers node energy consumption when selecting a path. GPNC avoids using low-energy nodes for forwarding packets in order to assure the transmission reliability and increase the lifetime of the network nodes. In particular, Figure 10 shows the difference of energy consumption between GPNC and VBF_NC when \(N=5\).  
Figure 8: Throughput with number of nodes.  
Figure 9: Energy consumption with the number of nodes and the value of \(N\).
# Multi-Scale Receptive Fields Convolutional Network for Action Recognition
## 3 Method
### Motivation  
[PERSON] et al. [34] argue that the success of deep learning can be attributed to three key factors: high capacity models, the rapid growth of computing power, and the availability of large-scale datasets. While the significance of models and computing power is undeniable, the importance of datasets cannot be overstated, as they set the performance ceiling for deep learning models. Consequently, a thorough analysis of datasets can aid in designing better models. UCF101 [12] is a widely used dataset in the field of action recognition, consisting of 13,320 video clips spanning 101 action categories, and sourced from YouTube. Upon analyzing the dataset, it is observed that the proportion of moving objects in video frames varies across different samples. In some cases, the moving objects only occupy a small portion of the frame, while in others, they almost fill the entire frame. Figure 1 displays some video samples from UCF101, which highlights that the moving object in the \"Nunchucks\" activity occupies a small proportion of the frame, whereas the moving object in the \"Shaving Beard\" action takes up the entire frame. The proportion of moving objects in other actions also varies. ConvNets typically employ one type of convolution kernel to extract spatial features, resulting in a fixed receptive field. We surmise that using a fixed receptive field to capture moving objects of different sizes can affect the accuracy of action recognition.  
Moreover, it should be noted that the problem of varying object sizes in videos is not unique to the UCF101, HMDB51, or Kinetics-400 datasets. It is a common issue in action recognition datasets. As illustrated in Figure 2, different actions have varying degrees of object-size variation in their video frames. For instance, in the \"Parkour Jump\" and \"Ice Skating\" activities, small moving objects are present. Conversely, larger moving objects are observed in activities such as \"Chewing Gum\" and \"Laughing\". Collecting action datasets with consistent object proportions in all video samples is a challenging task. In fact, even for the same action, the proportion of moving objects in different video samples may vary. This increases the intra-class variation and, therefore, the complexity of the action recognition task.  
\begin{table}
\begin{tabular}{c c} \hline
**Model** & **Parameters** \\ \hline
2D ResNet-50 & 23.71M \\
3D ResNet-50 & 46.39M \\
**P3D ResNet-50** & **31.84M** \\
**multi-scale receptive fields block** & **31.85M** \\ \hline \end{tabular}
\end{table}
Table 1: The comparison of the number of parameters of 2D, 3D and P3D ResNet-50.
# The Linac Coherent Light Source: Recent Developments and Future Plans
## 3 LCLS-II Science Opportunities
### Quantum Materials
For example, recent studies have shown that broadband THz pulses can selectively couple to electronic order, and thereby transiently decouple charge and lattice modes [49]. Such approaches can also trigger phase transitions and create new phases that are inaccessible in thermal equilibrium [50; 51; 52; 53], thus pointing the way toward control of quantum materials. For example, tailored ultrafast vibrational excitation has been shown to drive insulator-to-metal phase transitions in colossal magnetoresistive manganites [54], photo-induced superconductivity has been reported [50], and enhanced superconductivity is claimed to result from transiently-driven nonlinear lattice dynamics in YBCO [55] and K-doped C60 [56]. An unambiguous interpretation and characterization of these novel photo-induced phenomena is still lacking, but time- and momentum-resolved RIXS can provide much clearer insight. For example, time-resolved RIXS at the Cu L-edge can map the evolution of magnetic excitations and phonons in  
Figure 10: (**a**) Illustration of coupling between charge, spin, orbitals, and lattice. (**b**) Collective excitations that can be characterized by RIXS—including excitations within d-orbital manifolds (_d-d_) and charge-transfer excitations (C-T). Higher resolution is essential to reveal collective excitations at energy scales comparable to that of superconducting gap and pseudogap \(\sim\)kBT (<25 meV) (image adapted from ref. [40]). (**c**) Resonant inelastic X-ray scattering (RIXS) probe of collective charge states. time, energy, and momentum to provide a more complete microscopic picture about the transient photo-enhanced superconducting phase. The role of charge order in high-Tc superconductivity remains the subject of ongoing debate, and following the evolution of coexisting charge-stripe order through the transient phase will be incredibly informative. Time-resolved RIXS is applicable to a wide range of problems in quantum materials, such as the recently discovered branch of collective modes near the zone center in Nd\({}_{2x}\)Ce\({}_{x}\)CuO\({}_{4}\) and their putative connection to magnetic fluctuations [57].
## 3 Partition-Based Clustering
### Elbow Method  
The elbow method [5] is a well-known method which determines the optimal number of clusters in partition-based algorithms, such as the k-prototypes, k-modes and k-means. While the original application of the elbow method is for k-means, it can be extended to accommodate categorical and mixed data types that are prevalent in the k-prototypes and k-modes clustering.  
As far as the k-means clustering is concerned, the elbow method involves plotting the WCSS (calculated using the formula presented in Section 3.1) against the number of clusters (k). As the number of clusters increases, the WCSS tends to decrease, capturing more variance. However, a point is reached where the decrease in WCSS starts to plateau, forming an \"elbow\" point on the plot. The \"elbow\" represents the point of diminishing returns, in terms of clustering performance, beyond which adding more clusters does not provide much additional benefit. Thus, the \"elbow\" point indicates the optimal number of clusters that balances capturing variance and avoiding overfitting and empowers k-means to discover compact and well-separated clusters.  
To apply the elbow method to the k-modes and k-prototypes clustering, adjustments are made to the WCSS calculation to account for categorical and mixed data types. In the k-modes clustering, the WCSS is replaced by the Hamming distance, which quantifies the dissimilarity between categorical attribute values and their corresponding cluster modes by calculating the proportion of attributes that differ between two instances. For k-prototypes, which deals with mixed data, the WCSS incorporates the squared Euclidean and the Hamming distance, for numeric and categorical attributes, respectively.  
Therefore, the elbow method remains consistent regardless of the clustering algorithm used. The method calculates the appropriate clustering measure (WCSS, Hamming or mixed distance), for each candidate number of clusters, and then plots the measure against the number of clusters. The number of clusters corresponding to the \"elbow\" point is considered the optimal one.  
In Figure 1, the \(x\)-axis represents the number of clusters (k), while the y-axis represents the clustering measure (e.g., WCSS). As k increases, the measure decreases, but at \(k=3\), there is a noticeable bend in the plot. This bend represents the \"elbow\" point, which indicates that adding more clusters beyond this point does not provide significant improvements in clustering performance. In other words, increasing the number of clusters does not significantly decrease the clustering measure. In this case, the optimal number of clusters might be chosen as 3.  
Figure 1: Example of an elbow method plot.
## 5 Results and Analytical Discussion
### Evaluating Feature Selection
#### 5.3.2 False Positive Reduction in DDoS Classification  
For all models, feature selection especially affected false positive rates for DDoS attack types. The following DDoS specific classification errors were rather common in basic setups without optimal feature selection:  
**DDoS_HTTP attacks:** KNN variants misclassified about 27% of DDoS_HTTP instances in base configuration; most were incorrectly labeled as XSS attacks. SVM initially misclassified 804 instances (37.9%) of DDoS_HTTP as XSS attacks. Even after feature improvement, KNN variants still showed flaws in DDoS_HTTP classification, although with much lower error rates (23.6-24.8%).  
**DDoS_TCP attacks:** with misclassification rates of approximately 20% for KNN variants and 43% for SVM, these models often confused DDoS_TCP with Port_Scanning attacks in basic configuration. KNN variants still showed the same confusion pattern after feature improvement, but with lower rates (19.5-19.9%). For this attack type, SVM's improvement was negligible, with 41.2% of DDoS_TCP instances still misclassified as Port_Scanning after feature upgrade.  
**DDoS_ICMP attacks:** for DDoS_ICMP detection, base configuration already showed rather high accuracy among the models. With misclassification rates below 1% for most models, feature improvement still managed to further raise detection rates. In particular, SVM showed improvement, lowering misclassifications to 0.4%.  
**DDoS_UDP attacks:** detection of DDoS_UDP attacks was nearly flawless in both base and improved configurations across all models, indicating that this attack type has quite unique and easily recognizable traffic patterns.