esa-sceva/satcom-chunk-collection huggingface dataset:
# An Edge-Based Approach to Partitioning and Overlapping Graph Clustering with User-Specified Density
## 3 Preliminary
### Graph and Graph Clustering Type  
A graph is a collection of nodes interconnected by edges, denoted as \(\mathcal{G}(V,E)\), where \(V\) represents the set of nodes and \(E\) represents the set of edges. The type of a graph can be determined based on the attributes of its edges (i.e., direction and weight). Specifically, if edges are assigned weights and exhibit directional properties that indicate the flow of relationships, the graph is classified as a weighted and directed graph. As stated in the Introduction, graph clustering has a wide range of applications. The scope of this work lies in problems that can be represented by undirected unweighted graphs.  
Graph clustering has received much attention recently, offering insights into various aspects of network structures. As stated in the Introduction, graph clustering encompasses three fundamental types, partitioning, overlapping, and hierarchical clustering. Partitioning clustering involves assigning nodes to a disjoint set of clusters where intracluster similarity of each cluster is maximized and intercluster similarity is minimized. While the objectives of maximizing the intracluster similarity and minimizing the intercluster similarity are the same, in overlapping clustering, however, nodes are allowed to belong to multiple clusters. In hierarchical clustering, clusters are formed hierarchically, where partitioning or overlapping clusters may be allowed within a particular layer. Clusters then appear in a tree-like structure [62].  
In simple terms, consider a set of clusters denoted as \(\mathcal{C}=\{c_{1},c_{2},c_{3},\ldots,c_{k}\}\) for a given set of nodes (\(V\)). Here, the cardinality of \(\mathcal{C}\) represents number of clusters. In this study, the number of clusters is indicated by \(k\). Simplified notation is used for clusters in this study, represented as \(c_{i}\), where \(i\) denotes a unique cluster number. The formal mathematical descriptions of clustering types are provided below:  
* Partitioning Clustering: Each node \(v\in V\) is uniquely assigned to a cluster \(c_{i}\). In other words, every node is exclusively a member of one cluster.
* Overlapping Clustering: Each node \(v\in V\) is associated with at least one cluster (\(c_{i}\)) (\(i\in\{c_{1},c_{2},c_{3},\ldots,c_{k}\}\)), indicating that each node may belong to one or more clusters.
* Partitioning Hierarchical Clustering: At each level of the hierarchy, each node (\(v\)) is exclusively assigned to a specific cluster (\(c_{i}\)).
* Overlapping Hierarchical Clustering: At the same level of the hierarchy, a node (\(v\)) can be assigned to one or more clusters (\(c_{i}\)), allowing it to be a member of multiple clusters simultaneously.  
Figure 1 depicts a graphical representation of these three clustering types, highlighting the potential diversity in clustering outcomes within a simple arbitrary graph. It is commonly known that graph clustering is an NP-class problem.
# Research on Computational Method of Supersonic Inlet/Isolator Internal Flow
## 2 RANS Computation Method
### Spatial Discretization  
The core of a CFD solution lies in spatial discretization, which is mainly divided into two steps: reconstruction and flux computation.  
Reconstruction is the process of using the values stored in the cell center to interpolate the specific distribution in the cell. Some schemes need flux/gradient limiters to suppress the numerical oscillations caused by interpolation during reconstruction, which would decrease the spatial accuracy to varying degrees.  
Flux has two contributing parts: viscous flux \(\mathbf{F}_{v}\) and inviscid flux \(\mathbf{F}_{c}\). There are various computational methods for inviscid flux \(\mathbf{F}_{c}\), and most of them are upwind schemes, considering the effect of upstream information on downstream, which are roughly divided into three categories: flux difference splitting (FDS) schemes, flux vector splitting (FVS) schemes, and mixed flux schemes (or AUSM schemes) [19]. Among the computation methods for inviscid flux, AUSM stands out as a scheme that combines the advantages of FDS and FVS while avoiding some of their shortcomings. Therefore, this paper mainly chooses the Roe-FDS scheme [21] and AUSM scheme [22] to get the inviscid flux for simulating the high Mach flow.  
In order to accurately capture the details of the flow field, this paper uses the 3 rd-order MUSCL and the 5 th-order WENO/MP schemes for high-order reconstruction, which are briefly introduced below.
## 5 Securing ITS Applications: A Practical Case Study
### Experimental Results  
Figures 13 and 14 provide an overview of the achieved average number of operations per second for the SN-ENCAP and SN-DECAP service primitives [98]. The results suggest clearly that generic automotive processors (around 1 GHz) are able to achieve up to 162 signature generations (SN-ENCAP) per second (around 6.17 ms per operation) and up to 23 signature verifications (SN-DECAP) per second (around 44.03 ms per operation) using the ETSI's recommended signatureschema (ecdsa_nistp256_with_sha256); whereas the IEEE 1609.2 recommended signature schema (ecdsa_nistp224_with_sha224) is able to achieve a slightly higher SN-DECAP operations rate. The best cryptographic performances were obtained using the ecdsa_nistp192_with_sha256 NIST-recommended schema with 41 SN-DECAP and 281 SN-ENCAP operations per second. However, in ITS safety applications, each vehicle is expected to broadcast at most 10 CAM safety messages per second. Depending on the vehicular network density, each ITS station might thus receive several hundred (or thousand) CAMs per second from surrounding vehicles, whose signatures should be verified prior to their exploitation by the higher layers or ITS applications.  
In order to achieve an important speedup in handling secure V2X communications, all of the cryptographic operations can be delegated to more powerful CPU architectures or dedicated hardware security modules (HSM). For instance, performance gains of up to 50\(\times\) can be achieved using a general-purpose 3-GHz Intel CPU, as shown in Figure 14 and Table 5. In this context, ETSI TS 103 097 was found to be achieving 3831 SN-ENCAP operations per second (around 0.26 ms per operation) and 817 SN-DECAP operations per second (around 1.22 ms per operation). Theoretically, this might enable a vehicle to process the CAM messages received from several tens of neighboring vehicles without a major impact on the end-to-end system's critical-latency (around 100 ms for road safety applications). However, when assuming denser urban environments (e.g., 200/400 vehicles per km\({}^{2}\)[100]) combined with more heterogeneous data traffic loads, new lightweight and adaptive security solutions will need to be designed in order to not exceed the maximum allowable critical latencies of ITS safety-critical applications.  
Figure 13: Cryptographic performances of an ETSI ITS security software implementation (ECDSA) running on an ARMv7 CPU at 1 GHz.
## 6 Conclusions  
This study aims to investigate the ultimate bearing capacity of a novel K-joint consisting of thin-walled SHS members, a U-shape connector and self-drilling screws, and the effect of three patterns of stamping indentation fabricated on the U-shape connector on the ultimate bearing capacity of the proposed K-joint. A total of 12 specimens were tested to failure under monotonic brace axial compressive loading. Failure mode and the ultimate bearing capacity of each specimen have been analyzed. Finally, finite element analyses were carried out to study the effect of three key parameters, including chord stress ratio, chord radius-to-thickness ratio and brace-to-chord thickness ratio, on the ultimate bearing capacity of proposed K-joints using the UTW connector. Based on the testing and FE results in the present study, the conclusion below may be drawn:  
1. Through investigating all K-joint specimens, it can be concluded that joint failure is governed by both the deformation of the U-shape connector and the chord local plastification. Besides, the U-shape connector and the wall of SHS members keep in contact tightly during the testing for all the specimens, and all the screws are sound.
2. Some patterns of stamping indentation fabricated on the U-shape connector may bring an unfavorable effect to the ultimate bearing capacity of the proposed K-joint, such as the stamping pits (UPU connector). For all the specimens investigated, those with the UTW connector, i.e., connectors with strip stamping grooves in the horizontal direction, generally have a higher ultimate bearing capacity and a much smaller connector deformation.
3. Similar to the welded SHS joints, chord axial stresses, including the compression and the tension, can significantly reduce the ultimate bearing capacity of the proposed K-joint using the UTW connector. Moreover, a larger half width-to-thickness ratio of the chord \(\gamma\) will result in a smaller ultimate bearing capacity of the proposed K-joint using the UTW connector.
4. The ultimate bearing capacity of the K-joint using the UTW connector increases with the brace-to-chord wall thickness ratio \(\tau_{1}\) when the chord section is fixed.  
Conceptualization, X.W.; software, X.Y.; investigation, H.Z.; writing--original draft preparation, T.L.; writing--review and editing, X.W.; supervision, Y.Y.; project administration, Y.L. and X.G.; All authors have read and agreed to the published version of the manuscript.  
This study was funded by the National Key Research and Development Program of China (2017 YFC0703702) and Major Technical Innovation Projects of Hubei Province (2018 ACA131).  
Not applicable.  
Figure 27: Effect of \(\tau_{1}\).  
Data Availability Statement: The data used to support the findings of this study are available from the corresponding author upon request.  
The authors express their great gratitude towards the Structural Test Center of Wuhan University of Technology. The authors also appreciate all assistance from the master students in the research team during the testing and simulation processes.  
The authors declare that they have no conflict of interest.
## 3 Findings and Discussion
### Limitations  
Several limitations are observed in studies examining the effects of RF-EMF exposure on cognitive functions and memory. Some common limitations in the literature are as follows:  
* Small sample size: Several studies have been carried out with a small sample size, which may restrict the findings' generalizability for larger groups.
* Reliance on self-reported data: Some studies included self-reported data on wireless device usage, which may be prone to recall bias and may not adequately reflect real exposure levels.
* Short-term measurements: Some studies only tested memory performance or cognitive functions over a short period of time, which may not reflect the long-term effects of RF-EMF exposure.
* Absence of validated assessment methods: The inconsistent nature of study results may be attributed to the lack of proven methods to accurately detect variations in cognitive performance caused by exposure to RF-EMFs.
* Technical variations: Differences in sample size, study group, design of experiment, exposure setup, and exposure environments may all contribute to the broad variation of findings.  
* Heterogeneity of participants: The majority of investigations featured right-handed subjects, and possible variations in response among various groups of participants might need to be examined thoroughly.
* Absence of specified sensitive tasks: No specific task that is exceptionally sensitive to RF-EMF exposure has been identified.
* Limited research of long-term impacts: Some studies only looked at the short-term effects of RF-EMF exposure; therefore, they are unable to draw any conclusions regarding the long-term effects or the consequences of prolonged exposure on human health.
* Marginal effects: The reported changes in brain function in some research were minor and might have happened by chance, raising doubts regarding their relevance.
* Possible exposure from other sources: The studies did not account for the possibility of exposure to RF-EMF from other sources, such as televisions, remote controls, and wireless networks.
* Variations in the preliminary assessment of exposure: Studies showed variations in the initial assessment of exposure, which could lead to variation in the outcomes.
## 3 Results and Discussion
### Effect of CO\({}_{2}\) Concentration  
CO\({}_{2}\) is the primary source of carbon for microalgal growth. Microalgal biomass consists of approximately 50% carbon of the dry cell weight. All of this carbon is usually received from CO\({}_{2}\)[11]. When CO\({}_{2}\) is dissolved in an aqueous environment, it always remains in equilibrium with H\({}_{2}\)CO\({}_{3}\), CO\({}_{3}\)\({}^{2-}\), and HCO\({}_{3}\)\({}^{-}\) and its concentration is dependent on the temperature and pH of the solutions [27]. Generally, flue gasses released by most industrial facilities contain CO\({}_{2}\) concentrations of around 10-20%, and it has been reported that emissions from cement plants can reach up to 35% CO\({}_{2}\)[28]. Additionally, it has been found that _Chlorella_ species can grow in conditions with a 50% CO\({}_{2}\) concentration [29; 30]. Thus, CO\({}_{2}\) concentrations ranging from 0.03% to 50% were chosen for algal cultivation in this study. The effect of different CO\({}_{2}\) concentrations on microalgae growth is illustrated in Figure 2. The growth curve showed that the microalgae could grow up to 50% CO\({}_{2}\) without any inhibition, and the maximum optical density of 2.91 \(\pm\) 0.27 was obtained from 20% \(v/v\) CO\({}_{2}\) aeration. The effects of different CO\({}_{2}\) concentrations on microalgal biomass and lipid production are shown in Figure 4. The result found that the dry weight of C. _curuabililis_ RSM09 under 10-50% (\(v/v\)) CO\({}_{2}\) concentration was significantly higher than that of 0.03% CO\({}_{2}\) (\(p\) < 0.05). The highest microalgal dry weight was 1.32 \(\pm\) 0.14 g/L with 20% \(v/v\) CO\({}_{2}\) supplementation, while the lowest was obtained under 0.03% \(v/v\) CO\({}_{2}\) supplementation (ambient air) with 0.5 \(\pm\) 0.02 g/L. These results indicate that using CO\({}_{2}\) for microalgae cultivation could promote microalgal growth. Similar results were also found with _Chlorella vulgaris_. The alga grew faster and had a higher growth rate with 15% CO\({}_{2}\) than 0.03% CO\({}_{2}\)[31]. [PERSON] et al. [29] reported that the microalgae _Scenedesmus obliquus_ SJTU-3 and C. _pyrenoidosis_ SJTU-2 were able to grow at 50% (\(v/v\)) CO\({}_{2}\) supplementation and they grew well under the range of 5% to 20% (\(v/v\)) CO\({}_{2}\) supplementation. They also observed that high levels of CO\({}_{2}\), ranging from 30 to 50% (\(v/v\)), were beneficial for the lipid accumulation of microalgae. According to the results of lipid content (Figure 5), the highest lipid content is 21.96 \(\pm\) 0.29% wt. at 20% CO\({}_{2}\) and the lowest lipid content is 12.48 \(\pm\) 0.12% wt. at 50% CO\({}_{2}\).  
Figure 4: Growth curve of _C. variabilis_ RSM09 under different CO\({}_{2}\) concentrations. Error bars show \(\pm\) S.D. of three replicates.
# An Information Theoretic Approach to Privacy-Preserving Interpretable and Transferable Learning
## 5 Information Theoretic Measures for Privacy Leakage, Interpretability, and Transferability
### Definitions
: _Transferability (from source domain data representation learning models (i.e., \(\mathcal{P}_{1}^{+sr},\cdots,\mathcal{P}_{C}^{+sr}\)) to the target domain data representation learning models (i.e., \(\mathcal{P}_{1}^{IS},\cdots,\mathcal{P}_{C}^{IS})\)) is measured as the amount of information about source domain feature vector \(\hat{g}_{tg}^{sr}\) leaked by the mapping \(f_{g_{tg}^{sr}\to g_{tg}^{IS}}\) and is defined as_  
\[IL_{f_{g_{tg}^{sr}\to g_{tg}^{IS}}} :=I\Big{(}\hat{g}_{tg}^{sr};f_{g_{tg}^{sr}\to g_{tg}^{IS}}(\hat{g }_{tg}^{sr})\Big{)}-H(\hat{g}_{tg}^{sr}) \tag{114}\] \[=I\Big{(}\hat{g}_{tg}^{sr};\hat{g}_{tg}^{tg}\Big{)}-H(\hat{g}_{tg }^{sr}). \tag{115}\]  
_Here, \(\hat{g}_{tg}^{tg}\) represents the target domain feature vector and \(f_{g_{tg}^{sr}\to g_{tg}^{IS}}:\mathbb{R}^{p_{sr}}\to\mathbb{R}^{p_{sr}}\) is the mapping from source domain feature vector \(\hat{g}_{tg}^{sr}\) to target domain feature vector \(\hat{g}_{tg}^{tg}\)._  
Since the defined measures are in the form of information leakages, Algorithm 8 could be directly applied for practically computing the measures provided the availability of data samples.
## 3 Parametric Investigation
### Varying the TE Noise Source Location and/or Frequency
#### 3.2.1 Characterization of the Installation Effects
In other words, Figure 14 displays two \(\alpha\)-cuts excerpted from Figure 13, each being associated with one of the two extrema in incidence. For the top wall microphone M1\({}^{+}\), when the airfoil incidence is nil (\(\alpha=0^{\circ}\)), the RMS delta oscillates between \(\Delta_{\min}=3.0\) dB and \(\Delta_{\max}=8.0\) dB, cycling over a range of frequencies that is about \(\Delta S_{t}-0.2\). Notably, this delta reaches a minimum (resp. maximum) for source frequencies such that \(St_{min}\sim 0.2\) mod(\(\Delta S_{t}\)) (resp. \(St_{max}\sim 0.3\) mod(\(\Delta S_{t}\))). Differ  
Figure 13: Low to high frequency (\(St=[0.294;2.94]\)) noise radiation from the trailing edge of an airfoil set at low to high incidences (\(\alpha=[0^{\circ};25^{\circ}]\)). Deltas (in dB) between the incident and total RMS pressure fields, as recorded for two pairs of microphones: M1\({}^{\pm}\) (\(x_{M}/c=0.5\)) and M2\({}^{\pm}\) (\(x_{M}/c=2\)) located on top and bottom walls: M1\({}^{+}\) (**a**), M1\({}^{-}\) (**c**), M2\({}^{+}\) (**b**), M2\({}^{-}\) (**d**). ently, when the airfoil incidence is high (\(\alpha=25^{\circ}\)), even though the delta remains of same amplitude, its oscillation frequency shifts towards \(\Delta S_{t}\sim 0.22\) whereas its minimum and maximum peaks move to \(St_{min}\sim 0.22\) mod(\(\Delta S_{t}\)) and \(St_{max}\sim 0.33\) mod(\(\Delta S_{t}\)), respectively. Considering now the bottom wall microphone M1\({}^{-}\); at zero incidence (\(\alpha=0^{\circ}\)), its RMS delta matches that of its top wall counterpart (\(\Delta_{\min}\sim 3.2\) dB, \(\Delta_{\max}\sim 8.2\) dB, \(\Delta S_{t}\sim 0.2\)), which can be explained by obvious symmetry reasons (uninclined airfoil). On the other hand, when set at a high incidence (\(\alpha=25^{\circ}\)), its RMS delta is seen to oscillate less importantly (\(\Delta_{\min}\sim 3.2\) dB, \(\Delta_{\max}=8.0\) dB) and over a shorter cycle of frequencies (\(\Delta S_{t}\sim 0.185\)). Looking then at the downstream pair of microphones M2\({}^{\pm}\); when the airfoil is again free of any incidence (\(\alpha=0^{\circ}\)), they both experience an identical RMS delta, which oscillates slightly more importantly in amplitude (\(\Delta_{\min}\sim 2.5\) dB, \(\Delta_{\max}=8.5\) dB) and rapidly in frequencies (\(\Delta S_{t}\sim 0.211\)), compared to that of their M1\({}^{\pm}\) counterpart. Finally, when the airfoil is put at high incidence (\(\alpha=25^{\circ}\)), the top wall microphone M2\({}^{+}\) exhibits a RMS delta that oscillates much less (\(\Delta_{\min}\sim 3.6\) dB, \(\Delta_{\max}=8.0\) dB) albeit more rapidly (\(\Delta S_{t}\sim 0.23\)), compared to its bottom wall counterpart M2\({}^{-}\) (\(\Delta_{\min}\sim-1\) dB, \(\Delta_{\max}=9.75\) dB, \(\Delta S_{t}\sim 0.198\)). The above results illustrate the high variability of the acoustic installation effects, which depend not only on the noise source characteristics (location, frequency), but also on the microphone position. The reason behind this is explored in the next subsection.
## 5 Discussion
### Limitations and Future Work
Role of Mean Teacher: Although we use the Mean Teacher semi-supervised learning framework to improve the model's generalization ability, the model's performance may still be impacted when the features of new encrypted traffic differ significantly from the training data. **Future Work:** In practical applications, continuously updating the training data and incrementally learning new traffic types is an effective way to enhance generalization. We will investigate advanced architectures (e.g., transformer-based models) and regularization techniques (e.g., dropout, mixup) to further enhance the model's ability to handle complex and diverse traffic patterns. Advanced architectures and regularization can improve the model's robustness and adaptability to different traffic types. 4. Real-Time Deployment Strategies:  
**Future outlook:** To address the computational overhead and memory footprint challenges, we will optimize the storage format of the CLSTM-MT framework and reduce its resource requirements using model compression techniques such as pruning, quantization, and knowledge distillation to enable efficient deployment in real-time applications such as edge computing and distributed systems. These strategies will significantly reduce the model's dependence on memory and computing resources at runtime, supporting time-sensitive tasks such as network traffic classification. In addition, we plan to validate the performance of the model on a wider range of real-time scenarios and larger datasets, further improving its practicality and robustness, providing a more efficient solution for encrypted traffic identification, and facilitating practical applications in areas such as network security, traffic management, and the quality of service control.
# Recognition and Location Algorithm for Pallets in Warehouses Using RGB-D Sensor
## 2 Methods
### Template Matching  
The position of the pallet was set to be determined by matching the template and category matrix with the sliding window method [48; 49]. The matching process was a convolution operation. The template matrix matches the category matrix to calculate the matching degree. The matching degree is evaluated by the matching score of grid units, and each unit's score is calculated based on Equation (11). A higher matching score corresponds to a higher probability of being a pallet. If the matching score is higher than MiniScore, the pallet position is determined. MiniScore is determined with a threshold, as shown in Equation (12). Multiple pallets can reach the peak of the matching score matrix, and pallets have no overlap. Thus, a non-maximum suppression algorithm is used to obtain multiple pallet coordination.  
\[S_{ij}=\sum_{m=0,n=0}^{H_{p}L_{t}}T_{mn}M_{(i+m)(j+n)} \tag{11}\]  
\[MiniScore=s_{thred}{}^{1}h_{p}\cdot{}^{1}l_{p} \tag{12}\]  
where \(S_{ij}\) is the matching score of each unit, and \(s_{thred}\) is the threshold of the matching score.