esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Methods
### Terrain Recognition and Stability Analysis
#### 3.3.1 LA-MobileNet Network
Finally, the output results of both classifiers are calculated using cross entropy loss and combined according to certain weights. Figure 6: (**a**) Overall structure of LA-MobileNet; (**b**) Bneck structure with CA; (**c**) the coordinate attention mechanism structure, where \(f_{1}\) and \(f_{2}\) are direction-aware features, and \(f_{3}\) and \(f_{4}\) are attention weights in the vertical and horizontal directions. \[\text{Loss}=\beta\cdot\text{Loss}_{1}-(1-\beta)\cdot\text{ln}(1-\text{Loss}_{2}) \tag{16}\]  
\(\text{Loss}_{1}\) is the backbone network loss, and \(\text{Loss}_{2}\) is the auxiliary classification loss. Through experiments, we found that the best effect is when the weight coefficient \(\beta\) is 0.8. Since there are currently few publicly available terrain datasets, the GTOS-mobile [36] dataset is used for network training. The GTOS-mobile dataset covers 31 types of outdoor ground terrain under different weather and lighting conditions. In order to better apply it to the terrain recognition algorithm, we have reorganized it and retained the 8 common terrain categories in the image dataset, namely soil, pebble, sand, cement, grass, asphalt, brick, and wood_chips. It contains 35,163 training sets and 1713 test sets. Various terrain maps are shown in Figure 7. We named this dataset GTOS-mobile8. During the training process, we use cutmix and mixup methods for data enhancement, and set the training and test image sizes to \(256\times 256\).
## 5 Security Analysis  
The security aspects of the SMP-OTP4 IOT protocol can be rigorously examined and verified by formal analysis using, say, Tamarin Prover [17] or ProVerif [18] (see [19] for an extended review of formal modeling and security analysis of security protocols). However, an accurate description of the formalism required for our protocol lies beyond the scope of this investigation. Instead, we focus on the inherent security properties of the protocol as implemented in Zero-Tce.  
Under the Dolev-Yao threat model [20; 21; 22], we assume a semi-honest adversary that has access to all messages exchanged between parties \(U_{1}\), \(U_{2}\), and \(U_{3}\) and tries to gain some knowledge of the messages being transmitted, without tampering with the transmitted data, or a dishonest adversary, which also actively modifies the messages in transaction.  
In the context of the semi-honest participant scenario, all the information being transmitted consists of random numbers and additions. Observing the communication between \(U_{2}\) and \(U_{1}\) as well as between \(U_{3}\) and \(U_{1}\) in the first round, it is possible to deduce the partial sums \(s_{2}\) and \(s_{3}\). Subsequently, by monitoring communication between \(U_{1}\) and \(U_{2}\) during encryption and decryption, it is possible to deduce \(s_{1}\), and, consequently, the adversary could potentially compute \(K_{1}=s_{1}+s_{2}+s_{3}\), as well as \(K_{2}=s_{2}+s_{3}\). We argue that, since \(K_{1}\) and \(K_{2}\) differ by \(s_{1}\), the eavesdropper would need to know precisely the computations that are being performed privately by each of the involved parties, which is conveniently masked by the use of Zero-C-Ice to achieve decryption with the OTP. On the other hand, in the case of a dishonest attacker, any alteration or substitution of the random numbers being transferred can potentially result in a different key for \(U_{1}\) and \(U_{2}\), thus making it impossible to decrypt the original message, indicating a potential unauthorized interception.  
We examine our protocol along the lines of [8; 23].
## 3 Results
### Inter-Batch Variability
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Bracket System**} & \multicolumn{2}{c}{**Batch 1**} & \multicolumn{2}{c}{**Batch 2**} & \multicolumn{2}{c}{**Batch 3**} \\ \cline{2-7}  & **N** & **Mean (\(\pm\)SD)** & **N** & **Mean (\(\pm\)SD)** & **N** & **Mean (\(\pm\)SD)** & **\(p\)** \\ \hline SYNTHESIS & 10 & 17.07 (\(\pm\)0.45) & 10 & 17.42 (\(\pm\)0.44) & 10 & 16.94 (\(\pm\)0.19) & 0.026 \({}^{*}\) \\ \hline NU-EDGE & 10 & 15.64 (\(\pm\)1.24) & 10 & 16.73 (\(\pm\)0.44) & 10 & 16.71 (\(\pm\)0.61) & 0.019 \({}^{*}\) \\ \hline DISCOVERY & 10 & 17.00 (\(\pm\)0.28) & 10 & 17.36 (\(\pm\)0.36) & 10 & 17.24 (\(\pm\)0.26) & 0.051 \\ \hline FLI TWIN & 10 & 16.69 (\(\pm\)0.60) & 10 & 17.02 (\(\pm\)0.50) & 10 & 17.75 (\(\pm\)0.55) & 0.001 \({}^{**}\) \\ \hline MINI SPRINT & 10 & 17.09 (\(\pm\)0.28) & 10 & 17.01 (\(\pm\)0.48) & 10 & 17.06 (\(\pm\)0.25) & 0.870 \\ \hline OMNIARCH+ & 10 & 17.24 (\(\pm\)0.35) & 10 & 17.02 (\(\pm\)0.14) & 10 & 17.29 (\(\pm\)0.38) & 0.140 \\ \hline SCAPE & 10 & 17.93 (\(\pm\)0.56) & 10 & 17.66 (\(\pm\)0.89) & 10 & 18.63 (\(\pm\)0.64) & 0.014 \({}^{*}\) \\ \hline MINI-TWIN & 10 & 16.92 (\(\pm\)0.36) & 10 & 17.33 (\(\pm\)0.44) & 10 & 17.24 (\(\pm\)0.35) & 0.062 \\ \hline JAZZ & 10 & 17.24 (\(\pm\)0.87) & 10 & 17.53 (\(\pm\)0.84) & 10 & 17.22 (\(\pm\)1.14) & 0.719 \\ \hline MINI MASTER & 10 & 17.78 (\(\pm\)0.62) & 10 & 17.71 (\(\pm\)0.49) & 10 & 17.95 (\(\pm\)0.64) & 0.632 \\ \hline MINIPREVAIL & 10 & 17.37 (\(\pm\)0.43) & 10 & 17.10 (\(\pm\)0.37) & 10 & 17.16 (\(\pm\)0.35) & 0.271 \\ \hline VICTORY & 10 & 17.05 (\(\pm\)0.22) & 10 & 16.99 (\(\pm\)0.32) & 10 & 16.96 (\(\pm\)0.29) & 0.758 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Actual torque values in different batches for each system (inter-batch variability). Figure 6: Torque values of the different batches (inter-batch variability), displayed as the confidence interval (CI) in degrees. The dashed lines represent the upper and lower tolerance limits of the ISO 27020:2019 norm. Regarding tip values, only three of the twelve systems, FLI TWIN(r), MINI SPRINT(r), and JAZZ(r), presented a statistically significant difference between the three batches (Table 4). The same systems displayed a larger value dispersion. JAZZ(r) was the only system in which one batch had tip values outside the tolerance limits (Figure 7). SYNTHESIS(r), DISCOVERY(r), and MINI-TWIN(r) had low inter-batch variability and lower value dispersion.
# A Novel Traffic Classification Approach by Employing Deep Learning on Software-Defined Networking
## 4 Results and Discussion
### Comparative Assessment of Model Performance  
Figure 11 shows the four models, GRU, BiGRU, LSTM, and BiLSTM, which were trained and evaluated with training and validation datasets. Based on the information provided, the model that generalizes best is the GRU model. This model achieves very high accuracy on the training set (99.07%) and the validation set (99.65%), with minimal difference between the two sets. This minimal difference suggests that the GRU model can generalize effectively to unseen data, indicating a robust ability to learn general patterns in the data. Although the LSTM, BiGRU, and BiLSTM models also show high accuracy and minimal difference between the training and validation sets, the GRU model slightly outperforms them regarding accuracy on the validation set. Therefore, regarding generalization, the GRU model is the most effective of the four models evaluated.  
The F1-score analysis shows different performance levels; the GRU model showed high performance in all classes, with the F1-score ranging from (96.00%) to (100%), and perfect precision in all classes. Although the BiGRU model also showed generally high performance, with the F1-score ranging from (92.00%) to (99.00%), some difficulties in instance recall were observed in certain classes, particularly in classes 2 and 4. On the other hand, both the LSTM and BiLSTM models exhibited variability in performance across classes, with some challenges in precision and recall in certain classes. All GRU and LSTM models showed solid and consistent performance across all classes. In contrast, the BiGRU and BiLSTM models showed some variability and possible areas for improvement, such as recall in some specific classes.  
Figure 10: BiLSTM accuracy.  
In the analysis of training time and GPU usage, we observed that all models efficiently used the available hardware resources to accelerate the training process. The GRU and LSTM models showed moderate GPU usage, with utilization percentages of (28.00%) and (33.00%), respectively, indicating adequate resource allocation and appropriate workload for the training task. However, the BiGRU and BiLSTM models demonstrated more significant GPU utilization, with (45.00%) and (48.00%), respectively. While this might suggest a more intensive utilization of hardware resources, it also indicates an efficient allocation of resources to accelerate model training. Overall, all models obtained reasonable training times and efficient GPU utilization; this demonstrates their ability to handle an efficient workload and perform practical training in resource-constrained environments.
## 2 Mobility data
### Testing the Ability to Generate Region-Based Aggregate Geo-Maps from the Unified View  
In this test, we aim at testing the ability of the EMDI system to generate sophisticated region-based aggregate geo-maps (such as choropleth maps) based on conditions and predicates imposed on a unified view representing mobility and pollution data.  
Specifically, the following query, \"retrieve the Top-N neighborhoods in Bologna in Italy in terms of the density of dwellers mobility who are potentially exposed to PM\({}_{10}\) substances on a concentration that exceeds a given threshold'say 24.5 for example\". By using our system, we could easily, with a click of a button, generate such a map, thanks to the integrated view of mobility-pollution data generated previously by our adapted filter-and-refine spatial join method as an integral part of our EMDI system. Figure 14 shows original density in correlation with PM\({}_{10}\) at all levels, whereas Figure 15 shows the resulting map. As can be seen, where PM\({}_{10}\) is less than or equals roughly to 24.5, Figure 16 reveals the density of dwellers exposed to such levels.  
Figure 13: MAPE for geospatial average queries using EMDI versus baseline, geohash precisions 5 and 6.  
Figure 17 shows an example on a predicate applied to pollution PM\({}_{10}\geq 24.5\), where density of commuters is greater than 2k. The figures show that most affected areas are near the airport and near the city center.  
Figure 16: Mobility density in correlation with PM\({}_{10}\) (specifically where PM\({}_{10}\leq 24.5\)).  
Figure 14: Original density in correlation with PM\({}_{10}\) at all levels.  
Figure 15: Mobility density in correlation with PM\({}_{10}\) (specifically where PM\({}_{10}\geq 24.5\)).  
One can then choose to apply spatial-aware distance tools from the information theory such as Earth Mover's Distance (EMD) [49] to quantify the dissimilarities between maps and generate conclusions in terms of the correlation between the density of dwellers and their exposure to PM\({}_{10}\) elevated numbers.
# Design and Study of a New Wave Actuator for a Boat
## 3 Computer Simulation of the Ship Thrusting Performance of the Wave Actuator  
### Simulation Conditions  
Wave characteristics such as height, period, and spectrum vary depending on wind and swell conditions. The simulation is conducted with a significant wave height of 1.25 m, meeting the condition \(H/\lambda<0.1412\), to assess and evaluate the performance of the wave actuator. The simulation using MATLAB R2021 is conducted with input parameters from Table 1. The boat's structure, including the flexible water tank, two types of nozzles and system placement, is presented in Figure 8, and the states of the flexible water tank during up-wave and down-wave phases are described in Figures 9 and 10, respectively. During the up-wave phase, the output nozzle propels the water as the input nozzle closes, and during down-wave phase, the opposite operation occurs. Rising waves press the tank bottom plate such that the water inside of the tank is propelled through the output nozzle, which generates thrust for the boat. During the down-wave phase, the compressed spring is restored such that the water tank expands, and outside water is sucked into the tank through the input nozzle while the output nozzle is closed.  
Figure 8: The wave actuator structure installed on the boat.  
Figure 7: Capture width ratio.  
Equations (4)-(6) are used to calculate the added mass coefficient (\(a\)), damping coefficient (\(b\)), and spring coefficient (\(c\)) of the boat in the heaving direction while in still water. Table 1 presents the boat and tank parameters used in the simulation, including length on waterline (\(L\)), beam (\(B\)), draft (\(d\)), and the weight of boat displacement (\(We\)), which represents the weight of the boat displaced by the waves.
## 6 Discussion  
This study employs the WAW-1000B electro-hydraulic servo universal testing machine loading system to carry out uniaxial compression tests on sandstone samples subjected to different temperatures (25 \({}^{\circ}\)C, 55 \({}^{\circ}\)C, 85 \({}^{\circ}\)C, 95 \({}^{\circ}\)C) and soaking durations (0.5 h, 1 h, 3 h). Additionally, an acoustic emission system is used to study the damage and failure characteristics of sandstone under the combined influence of temperature and water. The findings indicate that at 85 \({}^{\circ}\)C and 95 \({}^{\circ}\)C, the thermal expansion effect of sandstone is more evident, leading to a decrease in the rock's permeability and water absorption capacity. In contrast, at 25 \({}^{\circ}\)C and 55 \({}^{\circ}\)C, the thermal expansion effect of sandstone is relatively less pronounced, which promotes water infiltration and results in a higher moisture content [31; 32; 33; 34; 35]. High moisture content exacerbates the dissolution [36; 37], softening [38; 39; 40], and stress corrosion effects [41; 42] of sandstone mineral particles, causing a decline in their mechanical properties. As the soaking duration increases, sandstone samples soaked for 3 h exhibit the highest moisture content, which intensifies their softening and damage. This results in maximum damage to the sandstone, making the mineral particles within the rock more susceptible to external forces and facilitating the initiation and propagation of cracks.  
The research results indicate that the damage to rocks in high-temperature and water-saturated environments is not solely the result of increased temperature or prolonged immersion time, but rather the result of the interaction between the two factors. This finding further reveals the damage and failure mechanisms of sandstone under the combined effects of high temperature and water, particularly the comprehensive impact of different temperatures on the thermal expansion and moisture migration of sandstone. The experiments found that 55 \({}^{\circ}\)C is the threshold for sandstone damage, indicating that there is a turning point in the thermal expansion effect of sandstone across different temperature ranges. This means that an increase in temperature does not always uniformly affect moisture migration; instead, there are both enhancing and inhibiting effects. This finding provides a new perspective for studying the damage mechanisms of rocks in high-temperature environments and offers theoretical support for establishing more accurate rock damage models. From an engineering perspective, the research results are significant for tunnel construction in high-temperature, water-rich environments. In practical construction, special attention should be paid to the stability of surrounding rock in such temperature ranges. Measures such as optimizing drainage systems, enhancing the impermeability of surrounding rock, and implementing effective reinforcement measures can be employed to mitigate the damage to sandstone caused by high moisture content, thereby reducing the likelihood of accidents.
## 5 Conclusions  
(1) A 0.15% addition of cellulose nanocrystals (CNs) can increase the compressive strength and strain rate of ECCs by 12% and 54.77% and reduce the strain rate loss of ECC samples after 120 days of sulfate corrosion by 24.85%. However, excessive CN dosages may result in poor dispersion. The optimal CN dosage for achieving the desired improvements is 0.15%.  
(2) Scanning electron microscopy (SEM) analysis reveals a reduction in silica fume content and an increase in the formation of ettringite and C-S-H within the CN-ECC sample group, leading to a higher matrix density compared to the control group (without the CN). The CN facilitates greater participation of silica fume in hydration reactions, thereby enhancing the hydration level of the binder system in CN-ECC.  
(3) Molecular dynamics simulations demonstrate that the CN adsorbs onto the surface of C-S-H, forming a protective layer that prevents the infiltration of SO4\({}^{2-}\) and water molecules into the C-S-H structure. Additionally, the CN forms hydrogen bonds with SO4\({}^{2-}\) and water molecules, inhibiting the coordination between C-S-H and these molecules. This interaction restricts the diffusion rate of SO4\({}^{2-}\) and water molecules at the C-S-H interface, thereby mitigating the erosion of hydrated products caused by SO4\({}^{2-}\) and water molecules.  
Conceptualization, L.Y.; Methodology, L.Y. and B.X.; Software, X.X., D.M. and B.X.; Validation, S.N.; Formal analysis, X.M.; Investigation, L.Y., S.N., D.M. and B.X.; Writing--original draft, L.Y.; Supervision, B.X.; Project administration, B.X.; Funding acquisition, B.X. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Project of Natural Science Foundation of Shandong Province (Grant number: ZR2021 QE197) and Qingdao Agricultural University High level Talent Research Fund (Grant number: 1120711).  
Not applicable.  
The raw data supporting the conclusions of this article will be made available by the authors on request.  
The authors acknowledge Shiyanjia Lab (www.shiyanjia.com, accessed on 25 June 2022) for providing the TEM test.  
The authors declare no conflict of interest.  
Figure 16: MSD results of water molecules and SO4\({}^{2-}\). (**a**) Water molecules; (**b**) SO4\({}^{2-}\).
# Enabling the Orchestration of IoT Slices through Edge and Cloud Microservice Platforms
## 2 Background and Related Work
### End-to-end Coordination of IoT Resources  
There are not many previous studies providing a holistic view of an end-to-end IoT application, mainly as a result of the vast heterogeneity of IoT solutions. This heterogeneity starts from the huge variety of use cases they can address (e.g., from health care to smart cities for instance) and hence the requirements they might cover (e.g., latency, persistence, storage capacity, analytics). In addition, the different technological aspects associated to IoT (e.g., type of devices, connectivity  
Figure 1: Internet of Things (IoT) network slicing example.  
means, IoT platforms) and the variety of standardization bodies trying to address all these aspects (e.g., ETSI, oneM2M [44] or IOTI [45] among others) must also be considered.  
[PERSON] [46], [PERSON] [47] et al succeeded on providing this global view, considering physical resources (IoT devices mainly) and virtual resources allocated at the edge (what they call \"IoT sites\") where IoT gateways are commonly (but not compulsorily) deployed, also inside the network as Virtual Network Functions (VNFs) and finally inside the cloud data centers where IoT cloud services are placed. Thus, authors define the concept of an interoperability hub, so that, regardless of what it is finally used in the implementation, a similar view of the solution is provided to the operator. Hence, the main purpose of this interesting analysis is to harmonize the view of multiple IoT solutions, considering an important variety of real implementations which could be used at the edge, the transport network and the cloud. Still, it does not cover how these functions are deployed and how they can be organized into slices.  
The SINC project [47] also provides an information centric view of a global IoT solution and there is a proposal from [PERSON] et al [48] of how to use software defined networking techniques to connect the functions composing an end-to-end IoT solution. However, they are not covering the coordination of the deployment of the pieces composing an IoT solution into IoT slices. In other papers, it is also possible to appreciate different approaches aiming at coordinating IoT resources to trigger specific actions under a given event (e.g., flooding prediction) [49], but not in relation to having partitions for different purposes.  
Finally, [PERSON] [50], [PERSON] [51] et al suggest an interesting approach about using osmotic computing to facilitate dynamic resource allocation in IoT environments which would be interesting to consider for future enhancements of this IoT end-to-end slicing orchestrator.
## 4 Discussion
### The Necessity of DSS/CMSS and IS Development  
Given that the DSSs for designing, monitoring, controlling, and executing missions are critical for national policy and defense protection planning, it is imperative to focus on the development of DSSs capable of organizing large-scale dataset input and maximizing the reuse of their data cores and programs. This development should consider several factors. Firstly, the cost of the data core production, e.g., the labor cost in hours of specialized personnel in more than one field, the cost of acquiring and developing data and programs, the necessary time for project completion, etc. Additionally, it is essential to consider the current priorities at national and/or international levels, which may dynamically change due to international unexpected incidents, international conditions, or phenomena, or even due to changes in decision makers. Furthermore, technological and operational advancements should be considered in the development planning of DSSs. This includes, among others, the validity of new international, communal, and national directives, regulations, and laws. Moreover, the volume of data is increasing rapidly, thus the relevant databases are continuously expanding in an exponential manner. In light of these factors, significant and crucial considerations include the overall available funding, which encompasses initial development cost, annual cost of updating system elements and services, predictable upgrade costs, etc. Last, but not least, both national and international needs should be considered, alongside the requirements of various authorities that will be supported by these systems. Equally important are the international technical knowledge in the relevant fields and national and international experience in the relevant subjects. It is necessary to initially investigate the relevant needs for development of any kind of DSS (including CMSs and ISs) in order to effectively address these considerations.