esa-sceva/satcom-chunk-collection huggingface dataset:
# DualCascadeTSF-MobileNetV2: A Lightweight Violence Behavior Recognition Model
## 6 Conclusions  
To meet the current demand for lightweight and efficient networks in violent behavior recognition tasks, based on the previous research results, this paper designs the DualCascadeTSF-MobileNetV2. The experimental results show that this model demonstrates excellent performance on the three datasets, namely Crowd Violence, Hockey Fights, and RWF-2000. Especially on the Crowd Violence dataset, its accuracy exceeds that of other classic models. In the other two datasets, although the accuracy does not reach the highest level, considering that this model has fewer parameters, a smaller size, and a faster operation speed, these advantages make its deployment on edge devices highly attractive. In practical applications, these performance optimizations can almost make up for the small accuracy gap. Therefore, the DualCascadeTSF-MobileNetV2 model has broad application prospects in future intelligent recognition edge devices.  
Finally, this paper proposes a question worthy of further exploration: the literature [15] has pointed out that cascading more TSM modules within each residual block does not further enhance the model's feature extraction capabilities. The likely reason is that excessive temporal shifting operations make the connections between originally adjacent feature maps sparse. In this paper, we have enhanced the temporal coupling between feature maps by introducing feature fusion of the feature maps before and after each shift. So, if we further cascade more temporal shifting and feature fusion modules within each linear bottleneck layer of this model, will it further improve the model's feature extraction capabilities? We welcome readers to discuss this question with us!  
Conceptualization, Y.L. and Y.C.; methodology, Y.C.; software, Y.C. and S.L. ([PERSON]); validation, Y.C., F.L. and S.L. ([PERSON]); formal analysis, Y.C.; investigation, F.L.; resources, Y.L.; data curation, S.L. ([PERSON]); writing--original draft preparation, Y.C.; writing--review and editing, Y.L.; visualization, S.L. ([PERSON]); supervision, Y.L.; project administration, Y.L.; funding acquisition, Y.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Science and Technology Innovation Project grant number No. ZZKY20222304.  
Figure 12: Comparison of DualCascadeTSF-MobileNetV2 with other classical models in terms of the number of parameters, memory size, and training speed.
# Responses of Laterally Loaded Single Piles Subjected to Various Loading Rates in a Poroelastic Soil
## 4 Parametric Study on Pile Cap Geometry
### Effect of Pile Cap Widths  
The width of the pile cap is also believed to have an impact on the response of the laterally loaded single piles. The lateral load versus lateral displacement relationship was examined for cap widths of 0.6 m, 1.0 m, and 1.8 m, as well as without the pile cap, as shown in Figure 5. The final displacement of the pile cap under various loading conditions is tabulated in Table 4. For all the loading conditions, i.e., the coupled and uncoupled dry soil analyses, the pile cap's resistance rises, which was revealed by the reduction of the pile cap's lateral displacement, as the pile cap width increases. For the cases of without pile cap and cap width of 0.6 m, their lateral displacements at the end of the incremental lateral loading were almost similar. A further increase in the cap width began to show a reduction in the lateral displacement of the pile cap; a discernible reduction is clearly seen when the cap width is increased to 1.8 m, in particular, in the case of the undrained loading (Figure 5a).  
Figure 4: Effect of cap embedment depth on lateral displacement of pile cap: (**a**) undrained (loading rate = 2 kN/s); (**b**) partially drained (loading rate = 2 kN/h); (**c**) drained (loading rate = 2 kN/day); and (**d**) dry soil (loading rate = 2 kN/step).  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Cap** & **2 kN/s** & **2 kN/h** & **2 kN/Day** & **2 kN/Step** \\
**Embedment** & **(Undrained)** & **(Partially-Drained)** & **(Drained)** & **(Dry)** \\
**(m)** & **(m)** & **(m)** & **(m)** & **(m)** \\ \hline
0.0 & 12.0 & 17.0 & 19.0 & 21.1 \\
0.3 & 10.6 & 15.0 & 16.9 & 18.7 \\
0.6 & 10.2 & 14.6 & 16.4 & 17.9 \\
1.0 & 10.1 & 13.8 & 16.0 & 17.5 \\ \hline \end{tabular}
\end{table}
Table 3: Effect of pile cap embedment depth on lateral displacement of pile cap under various loading conditions and in dry soil.
# A Secure Opportunistic Network with Efficient Routing for Enhanced Efficiency and Sustainability
## 7 Performance Evaluation  
The simulator used in the proposed work is an opportunistic network environment (ONE) simulator. Unlike other DTN simulators, which only simulate routing protocols, we combined the simulation mobility modeling and DTN routing. Connectivity between the nodes is based on their location, communication range, and bit rate. The simulations contain different types of groups (cars, pedestrians, etc.), and each one has a set of parameters, such as the message buffer size, radio range, and mobility model.  
Figure 8: Phase 4 functionality.
_Article_  
**Lattice Boltzmann Simulation of Immiscible Two-Phase Displacement in Two-Dimensional**  
**Berea Sandstone**  
[PERSON] 1, [PERSON] 2 and [PERSON] 1,* 2  
Received: 16 July 2018; Accepted: 26 August 2018; Published: 31 August 2018  
**Abstract:** Understanding the dynamic displacement of immiscible fluids in porous media is important for carbon dioxide injection and storage, enhanced oil recovery, and non-aqueous phase liquid contamination of groundwater. However, the process is not well understood at the pore scale. This work therefore focuses on the effects of interfacial tension, wettability, and the viscosity ratio on displacement of one fluid by another immiscible fluid in a two-dimensional (2D) Berea sandstone using the colour gradient lattice Boltzmann model with a modified implementation of the wetting boundary condition. Through invasion of the wetting phase into the porous matrix, it is observed that the viscosity ratio plays an important role in the non-wetting phase recovery. At the viscosity ratio (\(\lambda\)) of unity, the saturation of the wetting fluid is highest, and it linearly increases with time. The displacing fluid saturation reduces drastically when \(\lambda\) increases to 20; however, when \(\lambda\) is beyond 20, the reduction becomes less significant for both imbibition and drainage. The front of the bottom fingers is finally halted at a position near the inlet as the viscosity ratio increases to 10. Increasing the interfacial tension generally results in higher saturation of the wetting fluid. Finally, the contact angle is found to have a limited effect on the efficiency of displacement in the 2D Berea sandstone.  
**Keywords:** lattice Boltzmann method; immiscible displacement; porous media; interfacial tension; contact angle; viscosity ratio
## 2 Related Work
Such studies, including a study reported by [PERSON] et al. [13], grouped the performance metrics in the literature into four main classes and then analyzed the relationships between representative metrics from the groups. However, the study was limited to only investigating the performance metrics categorized in the literature and their relationships among symmetric and continuous Pareto fronts (PFs). The authors suggested further investigation of the relationships of other geometric perspectives in performance metrics, such as asymmetric and discrete PFs, and also highlighted the need for appropriate metrics with hypervolume (HV) use for concave shapes. [PERSON] et al. [14] conducted informal and small review focused on the frequency usage of 54 performance metrics in MOEAs with their advantages and disadvantages from five editions (2005, 2007, 2009, 2011, and 2013) by only main sourcing the published studies of bi-annual evolutionary multi-objective optimization (EMO) community conferences. A recent review study observed by [PERSON] and [PERSON] [34] categorized and analyzed the weaknesses and strengths of 100 state-of-the-art performance metrics with their desirable properties. With the help of that, they concluded that there is no perfect metric to measure the solution sets, since different metrics are appropriate in different situations. Another research direction suggested was to design new performance metrics suitable to the preferences of decision-makers (DMs). [PERSON] et al. [8] reviewed the existing performance metrics by categorizing them into a number of groups based on their functionalities, then showing the advantages and disadvantages of performance metrics. Thus, a comparative study was done that discovered some of the metrics were misleading. Therefore, their point of discussion appeared to be that no single metric alone can quantify the qualities of the solution sets obtained by solvers. [PERSON] and [PERSON] [35] described a taxonomy-based surveying 38 of the existing performance metrics and their definitions along with their advantages and disadvantages. They claim their proposed complementary set of metrics can create meaningful results when used on solution sets obtained by solvers. [PERSON] et al. [36] is another review study of performance metrics recently published, which intended to focus on using 57 metrics grouped into four categories: Cardinality, convergence, distribution, and spread. The research gap reported in this paper is the need for new metrics that can tackle the limitations faced by the HV. While these papers are similarly discussed and focus on discovering the weaknesses of the existing performance metrics in use by the EMO community, no paper tracked how the current research in SBSE practice uses these metrics (i.e., their applications to real-world problems instead of artificial problems). This research will, therefore, discover if enough practitioners are using these metrics and distributions and the types of metrics employed. For further performance metric analysis on their strengths and weaknesses with practical guidance, readers are referred to references [37; 38; 39; 40].
## 5 Performance Evaluation Results and Discussion
### Simulation Model
#### 5.2.2 Mobility Model  
Mobile hosts moved by RWP, and they repeatedly stopped for a random period and moved to a randomly selected destination at a randomly selected velocity (Figure 15). The period for which a mobile host stops was selected to be between 0 s and 5.0 s by a uniform random number. The destination of a mobile host was selected by a uniform random number. The density of the mobile hosts within the movement area varied from location to location, even in this scenario [26; 27; 28]. We used different velocity ranges for the pedestrian and vehicle cases. For the pedestrian case, the host velocity was between 0.1 and 3.0 m/s, as determined by a uniform random number. For the vehicle case, the host velocity was selected to be between 6.0 and 15.0 m/s by a uniform random number.
# Investigation of Laser Polishing of Four Selective Laser Melting Alloy Samples
## 4 Conclusions  
Using selective laser melting technology, laser polishing of four kinds of alloy materials commonly used in metal additive manufacturing was studied using eight different process parameters. The main conclusions can be summarized as follows:  
1. Laser polishing of SLM samples can be carried out by adjusting the laser parameters, without adding any additional lasers. That is to say, laser polishing and printing can be done using the same device. This largely prevents oxidation during the laser-polishing process.
2. Laser polishing of four typical alloys with eight laser strategies was carried out by using the printing laser. By changing the laser power and scanning speed, surfaces treated with different laser energy densities were obtained.
3. Observations of the top surface morphology revealed that the polishing effect was obvious and the top surface roughness was greatly reduced. For the Ti6 Al4V, AlSi10 Mg, 316L and IN718 alloys, the \(S_{\text{a}}\) decreased by 62.3%, 80.0%, 73.2% and 81.9%, respectively.
4. The laser polishing mechanism relies on melting and then re-solidification of metals. When the laser irradiates the surface of the material, the material melts to form a molten pool, with evaporation occurring from the local temperature to boiling point. The surface molten pool and surrounding material undergo sufficient wetting and diffusion, causing the crests and troughs of the initial surface to become smoother.  
[PERSON] designed the experimental scheme, completed the samples preparation and wrote the initial draft of the manuscript. [PERSON] participated in the samples preparation. [PERSON] supervised this work. [PERSON] was involved in manuscript preparation. [PERSON] participated in the experimental scheme. [PERSON] provided guidance for experiments. [PERSON] was involved in the roughness test experiment. [PERSON] participated in the data analysis. [PERSON] checked of accuracy of whole manuscripts. All authors have read and agreed to the published version of the manuscript.  
This work is supported by the National Key Research and Development Program of China (grant no. 2017 YFB1103900, 2018 YFB1105100), the Key Research and Development Program of Guangdong Province (grant no. 2018B090905001), the Natural Science Foundation of Guangdong Province (grant no. 2018A030313044), the Science and Technology Planning Program of Shenzhen (grant no. JCYJ2017081617133384), the Fundamental Research Funds for the Central Universities (grant no. 2042018k60240) and the Hubei Provincial Natural Science Foundation of China (grant no. 2017 CFB657).  
The authors would like to thank Wuhan Huake 3D Technology Co. Ltd. Wuhan, China for providing the technical support.  
The authors declare no conflict of interest.
## 1 Introduction
[PERSON] et al., however, solved a crystal structure of constitutively active CDC42(Q61L) in complex with the IQGAP2 GRD (GRD2) and proposed that CDC42 binds GRD2 from two different sites in a 4:2 stoichiometry [12; 54]. One is the 'GAPex-mode binding site' (ex stands for 'extra' subdomains consisting of variable N- and C- terminal flanking regions) and the other is the 'RASGAP-mode binding site' very much resembling the RASGAP and CDC42 GAP structures [18; 55] with a conserved core domain (GAPc). Analyzing this structure, [PERSON] et al. proposed that CDC42 IH binding to the GAPex-domain induces GRD2 dimerization and changes the RASGAP site allosterically, which subsequently create another interaction interface for CDC42 binding (leading to a 2:1 stoichiometry of GRD2 and CDC42) [54]. A number of biophysical and biochemical studies have provided valuable insights into the structural and binding properties of the C-terminal domains of IQGAP1 (C794) and IQGAP2 (C795), encompassing the GRD, RGCT and CT domains, with CDC42 [50; 52; 53; 54; 55; 56; 57; 58; 59; 60; 61; 62; 63]. Evidently, all three domains bind with different affinities to CDC42 [5]. However, the mechanistic principles behind these interactions have remained unclear. Moreover, there are conflicting views regarding the assignment of a 'CDC42-specific GBD' for IQGAPs. One model proposes the GRD and its RASGAP-mode binding with the switch regions of CDC42 [12; 54; 56; 58; 64], whereas the other model excludes GRD and marks RGCT, located distal to the GRD, as crucial for high-affinity binding to CDC42 in a GTP-dependent manner [5; 26; 50; 51; 52]. Aiming to shed light on this discrepancy and to understand the molecular basis of CDC42-IQGAP interaction we comprehensively investigated the nature of the GRD interaction with CDC42 in this study and determined the role of the IH of CDC42 in contributing to GRD association. Furthermore, we studied the binding characteristics of C794 regarding the switch region and IH contact sites by mutational analysis, and verified the results in cell-based studies with endogenous IQGAP1. Collectively, our results consolidate and refine the importance of IQGAP RGCT as the true GBD in the recognition of CDC42 and its binding in a GTP-dependent manner. The GRD, although not a central effector domain, is evidently necessary for scaffolding CDC42 and facilitating its recruitment to preexisting cues.
# Unsupervised Learning-Based Spectrum Sensing Algorithm with Defending Adversarial Attacks
## 5 Experimental Results
### Algorithm Performance Analysis
#### 5.2.3 Robustness Comparison of Algorithms to Attack Perturbations  
The false alarm probability is 0.05, and the perturbation values of FGSM and PGD primary user adversarial attack samples are set at 0, 0.06, 0.1, 0.12 and 0.14, respectively, where a perturbation value of zero is equivalent to an absence of attack. The detection probabilities of USDAA and DAED models for FGSM and PGD adversarial attack samples of different perturbation values are shown in Figure 7 and Figure 8, respectively.  
Figure 7: Robustness of FGSM defense model to attack perturbations.  
According to Figures 7 and 8, the following can be asserted:  
1. For the same kind of attack, with the same perturbation values, the detection probability of the USDAA algorithm is higher than that of the DAED algorithm.
2. The robustness of the USDAA algorithm to attack perturbation values is better than that of the DAED algorithm. When the false alarm probability is 0.05 and the \(SNR\) is greater than \(-2\) dB, the detection probability of the USDAA and DAED models can reach 100% for the FGSM and PGD adversarial attack samples with five perturbation values. When the \(SNR\) is lower than \(-6\) dB, the reduction in the detection probability of the USDAA algorithm for two kinds of adversarial attacks is less than 10%; however, the maximum reduction in the detection probability of the DAED algorithm is 29.5%. This is because the USDAA algorithm adopts contrast loss and reconstruction loss to optimize the network in the adversarial pre-training stage and uses KL divergence to constrain the distribution of potential features \(Z_{i}(\mathrm{i}=1,2)\) which makes the decoder reconstruction samples more diverse on the basis of the original input samples. Therefore, the defense effect of the USDAA algorithm is not sensitive to the change in perturbations. However, the distillation used in the DAED algorithm affects the generalization of the model and reduces the robustness of the sensing model to adversarial attack samples with different perturbation values.
# Bushfire Disaster Monitoring System Using Low Power Wide Area Networks (LPWAN)
## 2 Peer-To-Peer (P2P) and Ad-Hoc Networks  
In a disaster situation and when the public network is not available due to the impact, P2P networks such as LPWAN, ad hoc and balloon networks can be used to deliver the sensor data. Those networks may be connected to IoT devices, which can forward the information through the internet where available. A P2P network is created when multiple nodes connect and communicate with one another without a server connection such as a client server network. It can be operated at a large scale in which special protocols and applications are used to connect directly with other networks such as LPWAN gateways [16].
## 4 Results and Discussion
### Performance Assessment of Classifiers
In summary, the carried-out experiments demonstrate that Adaptive Random Forest (ARF) has superior performance than the K-Nearest Neighbors (KNN) algorithm regarding Accuracy, Recall, Precision, and F1-score metrics in the context of our study. These findings can be attributed to the specificities of each algorithm. On the one hand, Adaptive Random Forest (ARF) is based on an ensemble approach that combines multiple decision trees to make predictions, which allows for making accurate predictions. Further, ARF's online bagging mechanism allows reducing the impact of poor or irrelevant features on the classification process. This technique makes ARF capable of making accurate predictions by handling imbalance in data and capturing the data patterns more accurately [11]. On the other hand, KNN relies on the calculation of a distance metric and makes predictions based on the majority of the K-Nearest Neighbors [12]. In what follows, we aim to analyze the training and testing time of the ARF and KNN algorithms.
## 2 Time Slotted Channel Hopping (TSCH) Concepts
When a source node wants to send a packet to a destination, a Graph ID will be included in the packet header to enable routing to the destination. At any node in the path, multiple  
Figure 1: TSCH Slot-channel matrix (right) for the network shown on the left. Figure 3: Graph routing sample. Figure 2: Timing of a dedicated TSCH timeslot. next hops could be specified in a mesh graph; path diversity is directly built-in [14]. In Figure 3, for example, an intermediate node 5 may forward a packet identified by Graph 1 to node 12 or 13 and may forward a packet identified by Graph 2 to node 13 or 14. WirelessHART and ISA100.11a standards are designed by using concepts derived from TSMP. In those standards, graph-based routing is used, where the network manager constructs all the graphs/routes in the network, and applies them to the network devices. A collection of communication tables is defined to enable communication and to control communication performance. These tables are configured by the network manager through the system manager module in each device via MAC layer service primitives. These tables and their relationships are shown in Figure 4 and described below:  
* _Superframe table_: This table contains a collection of superframes. Based on the required communication schedule, multiple superframes of different length can be configured for each device by filling in this table. The practical superframe length is defined as \(2^{n}\)sec (\(-2\leq n\leq 9\)) from 250 ms (\(2^{-2}\) sec) to 8 min and 32 sec (\(2^{9}\) sec) [15]. * _Link table_: This table contains a collection of links. This table, together with the superframe table, identifies the communication schedule. Based on the traffic rates, multiple links are scheduled for each device in different periods (by specifying the superframe ID to which the link belongs). Each link is specified by the node address, timeslot, channel offset, link type (Normal, Join, Discovery or Broadcast) and link option (Tx-link, Rx-link, or Shared Tx-link). * _Graph table_: In a graph table, each graph represents the list of potential next-hop neighbors that the data can be forwarded to. This table, in collaboration with the route table located in the upper layer, provides sufficient information for routing the packets. TSCH does not address routing issues, but leaves this to the upper layer. Therefore, the graph table is excluded from D-MSR stack protocol. * _Neighbor table_: Unlike the other communication tables, this table is not filled by the network manager. The neighbor table contains the list of neighbors the device can communicate with. Figure 4: Communication tables stored in each device and their relationship in TSCH and TSMP.