esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Observations Focused on Oil and Gas
### Discussion: Correlation of Oil and Gas Deposits with Combed Strike Angles  
The maps with oil and gas deposits (Section 2.4.3) were plotted together with the strike angles \(\theta\) (Figure 15a-d) for the Nile delta, Mediterranean Sea, Suez/Sinai/Red Sea and Western Egypt near Siwa to determine whether and how much the combed \(\theta\) correlate with the locations of known _TD_. They correlate well in all locations that we tested: any larger deposit of _TD_ is marked by the combed \(\theta\). This is a positive finding. Somewhat disquieting is the fact that they correlate on many further places, often close to or being a \"continuation\" of known deposit sites, but the combed strike angles are also on many other places separated from known digging spots.  
This can mean two things: (1) this new method truly navigates to new places with potential _TD_. However, (2) it is good to recall here the axioms from Section 2.2 explaining that the combed \(\theta\) do not automatically mean that the deposits exist there, and correlation does not mean causation. The combed \(\theta\) generally provide information about changed porosity but the reasons for this are diverse and mean various things (for example, groundwater). Figure 12, for example, shows highly combed areas of \(\theta\) (\(l<0.3\)) in regions of Egypt, Israel, Jordan, Saudi Arabia, Lebanon, Cyprus, regions of Turkey and Saudi Arabia, and the Eastern Mediterranean. All these places are candidates with potential _TD_.  
We can conclude that such maps as in Figure 15a-d (possibly with zooms) may navigate the investigator to new possible deposits of _TD_. This suggests the possibility of new discoveries and indicates that researchers should seek to investigate such localities by traditional, much more expensive, methods.  
Figure 14: Magnetic anomalies [nT] according to the model EMAG 2.  
Recall that Figure 14 shows that the regions east of the Nile delta demonstrate a clear absence of significant magnetic anomalies, which indicates that the magnetic sources are fairly deep and that the significant thickness of material in this region is without significant concentration of magnetic materials and/or does not contribute to a larger block of significant magnetic remanence. This is likely due to the pre-existing deep sea floor that was being continuously filled with the sediment eroding from the African continent. In general, the regions of less contrasting magnetic anomalies are consistent with pre-existing impact structures [45; 46; 47].  
Figure 15: **(a,b)** The combed \(\theta\) [deg] for (**a**) \(I<0.3\) and (**b**) \(I<0.9\) for gas (red dots) and oil (green dots) known and already used localities in the Nile delta and the Mediterranean Sea as far north as Zohar. (**c,d**) The combed \(\theta\) [deg], \(I<0.9\), known and already used deposits (triangles show localities in preparation) for Western/Northern Egypt near Siwa (**c**) and for Suez/Sinai (**d**).
# A Complete Key Management Scheme for LoRaWAN v1.1 +
###### Abstract  
Security is one of the major concerns of the Internet of Things (IoT) wireless technologies. LoRaWAN is one of the emerging Low Power Wide Area Networks being developed for IoT applications. The latest LoRaWAN release v.1.1 has provided a security framework that includes data confidentiality protection, data integrity check, device authentication and key management. However, its key management part is only ambiguously defined. In this paper, a complete key management scheme is proposed for LoRaWAN. The scheme addresses key updating, key generation, key backup, and key backward compatibility. The proposed scheme was shown not only to enhance the current LoRaWAN standard, but also to meet the primary design consideration of LoRaWAN, i.e., low power consumption.  
IoT; security; LoRaWAN; LPWAN; key management; key generation +
Footnote †: This paper is an extended version of the conference paper: [PERSON]; [PERSON]; [PERSON] A Fast Session Key Generation Scheme for LoRaWAN. In Proceedings of the 2019 Australian & New Zealand Control Conference (ANZCC), Auckland, New Zealand, 27-29 November 2019.
# Influence of Interference Fit and Temperature on High-Speed Permanent Magnet Motor
## 3 Static Strength Check of Interference Fit
### Measures to Reduce Maximum Stress
#### 3.4.2 Chamfers Size of the Interference Fit Structure  
In conclusion, choosing to add chamfers at the end cover can reduce the maximum principle stress and prevent the problem of strength failure in the structure of the interference fit. According to this design, we make the simulations of the end cover chamfers range from approximately 0.3 to 1.0 mm in order to compare the effect of the size of the chamfers on the stress of the interference fit structure. The exploratory results are shown in Figure 11.  
By increasing the end cover chamfers, the stress in the interference fit region shows a decreasing trend. When choosing the chamfer size, we should consider the size of the stress and the material and processing of the end cover so as to ensure that the maximum principal stress of the interference fit is small, and that it is easy to process and reduce the material loss.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Parameters Structures**} & \multirow{2}{*}{**Total Deformation (mm)**} & \multicolumn{3}{c}{**Strain of Elasticity**} & \multirow{2}{*}{**Principal Stress(MPa)**} \\ \cline{2-2} \cline{5-7}  & & **Min** & **Max** & **Min** & **Max** & **Min** & **Max** \\ \hline (a) The original structure & \(5.59\times 10^{-5}\) & 0.0353 & \(-6.94\times 10^{-5}\) & 0.0059 & \(-86.537\) & 1186.10 \\ \hline (b) The magnet chamfered structure & \(1.94\times 10^{-5}\) & 0.0300 & \(-4.28\times 10^{-5}\) & 0.0020 & \(-26.572\) & 337.55 \\ \hline (c) The end covers chamfered structure & \(3.78\times 10^{-5}\) & 0.0275 & \(-8.32\times 10^{-6}\) & 0.0019 & \(-23.989\) & 308.42 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The comparison of simulation parameters of three structures.  
Figure 10: The comparison of axial and radial stress distribution of three structures. (a) Radial direction; (b) axial direction.
# Combinatorial Optimization Problems and Metaheuristics: Review, Challenges, Design, and Development
## 4 Results: Standardization, Methodology, and Framework for COP and Metaheuristics
### Framework Structure
#### 4.1.2 Search Process Management  
The Search Process Management (SPM) component aims to encapsulate and orchestrate the search process in a standard way. It acts like a \"wrapper\" and \"adapter\", and it is responsible for preparing and automatizing the search process by interpreting all specifications and instantiating all the objects that are necessary to perform a specific search process (e.g., concrete versions of COP and MH). No matter the metaheuristic used, it also  
Figure 6: Basic relationship between metaheuristic and benchmark test.  
will provide the same functioning mechanism, in other words, the same API. Consequently, it will interact directly with the concrete metaheuristics, and it must be transparent for the author of the experiment. He/she only needs to pass some parameters to SPM such as (1) problem (template and instance), (2) metaheuristic (or metaheuristics list), (3) metaheuristics settings, and (4) search settings. The only mutable spec is the metaheuristics settings (3) because they depend on their parameters' definition.  
Moreover, the SPM should provide several searching modes; the most common identified by this study are described as follows. (1) Single-mode is performed by only one metaheuristic. In contrast, (2) multiple-mode is accomplished by a list of metaheuristics. However, they do not share information. (3) The cooperative mode also is realized by several metaheuristics but cooperatively. Therefore, they must be able to share results. (4) The experiment mode is where a configuration manages a set of possible executions and keeps all the data. As a result, an experiment report by comparing all performances tested should be provided (depending on the experiment management component). It is also possible to perform the search in a (5) meta or automatic-mode, where a search is performed in a meta-level, where a metaheuristic orchestrates a target metaheuristic and operates hyper-parametrization tuning automatically.  
Furthermore, the modes that consider more than one algorithm or multi-population search also can activate a parallel processing feature. It enables several processes to run simultaneously.  
The SPM has a configuration component that is responsible for keeping and interpreting a framework configuration. The local environment has a master configuration that defines the default behavior of the framework and its core components. However, it also must permit custom configurations. If a custom configuration is passed as a parameter for a search process instance, it will override the standard behavior. Still, if it is necessary, the master configuration can be modified, directly changing some behaviors used by default.
## Case Study  
The structure of the newly introduced distributed network is shown in Figure 1, and the rated parameters of the traditional microgrid are shown in Table 1. In addition, the nominal parameters of the portable energy system are shown in Table 2. Moreover, the daily thermal demands, electrical demands, wind turbine output, and PV output, which are generated by the Monte Carlo simulation,are displayed in Figure 2. Finally, the emissions generated by different generators and the standard grade of pollutant values are based on previous work [32].  
To optimize the PSO model, the related parameters are defined as follows: the number of particles is 250; the largest number of iterations is 300; the maximum and the minimum speed and positions are \(\pm 1\) and \(\pm 5\), respectively; and the inertia weight c1 and c2 used in this paper are 1.3 and 2.8, respectively. Considering the randomness of the PSO, the results shown in next section are the average values of 20 group simulations.
## 5 Testing Results
### Hop-by-Hop Delay  
The trade-off between the number of hops and the hop-by-hop delay is shown in Figure 5a,b. As the number of hops increases, the hop-by-hop delay also increases. We tested two scenarios. In the first scenario, the total number of hops is 27, and the second scenario uses a maximum of 54 nodes. Based on the results, we compared the hop-to-hop delay of our proposed EDB-CPS and that of the contending models: IA-CPS, BS-MIC, and StreamLAB. We observed that the proposed model shows a lower hop-to-hop delay compared with the contending models. The standard average delay \(D_{a}\) for a single hop can be obtained by  
\[D_{a}=\sum_{i=0}^{n}\frac{S_{ts}}{d_{cy}}\times\frac{S_{ts}}{2}. \tag{9}\]  
The time for the first \(n-1\) slot can be obtained as \(S_{ts}=A_{wt}=1,2,\cdots,n-1\).  
\[D_{a}=(n-1)P(S_{ts})\times\frac{S_{ts}}{2}+P_{t}(S_{ts})\times\frac{S_{ts}}{2}. \tag{10}\]  
When a sensor node sends the number of beacons \(b_{n}\) per duty cycle, the one-hop delay is calculated as  
\[D_{a}=\left[\frac{(n-1)A_{wt2}}{2d_{cy}}+\frac{\left\{(A_{wt})+(1-b_{n})\times 2 d_{cy}\right\}^{2}}{2d_{cy}}\right], \tag{11}\]  
where \(t_{n}=\left(\frac{b_{n}\times d_{cy}}{P+A_{wt}}\right)\).  
Hence, the delay for multiple-hop \(t(D_{a})\) can be obtained as  
\[t(D_{a})=\left\{D_{a}\cdot t_{hops}\right\}, \tag{12}\]  
where \(S_{ts}\) is a short time slot either for listening or sleep, \(d_{cy}\) is a duty cycle time length for the nodes, \(P\) is permeable, \(P_{t}\) is the total number of permeable, \(t_{n}\) is the total number of slots, \(t_{hops}\) is the total number of hops, and \(A_{wt}\) is the sensor wait time.  
Based on the result depicted in Figure 5a, it is observed that the proposed EDB-CPS shows a hop-to-hop delay of 0.05 ms with 27 hops, whereas the contending models BS-MIC, StreamLAB, and IA-CPS show a hop-to-hop delay of 0.609, 0.642, and 0.667 ms, respectively. When the number of hops increases to 54, the hop-to-hop delay increases at a similar rate. Figure 5b shows that the proposed EDB-CPS has a hop-to-hop delay of 0.0781 ms, whereas the contending models achieve a hop-to-hop delay of 0.094, 0.96, and 0.097 ms for StreamLAB, IA-CPS, and BS-MIC, respectively. Thus, the proposed EDB-CPS shows a better performance than the contending models.
## 4 Experimental Setup  
Following experimental testbed is set in the lab environment for capacity analysis of cooperative network using USRP kits.  
In setup 1, as shown in Figure 5, three USRPs and PCs are used for the source destination and relay in the lab environment without any obstacle. USRPs moves to the maximum distance in the lab and relay USRP is placed at three different positions.  
\begin{table}
\begin{tabular}{c c c} \hline
**Protocol Number** & **Time Slot 1** & **Time Slot 2** \\ \hline \multirow{2}{*}{II} & S\(\rightarrow\) R & \multirow{2}{*}{R\(\rightarrow\) D} \\  & S\(\rightarrow\) D & \\ \hline \end{tabular}
\end{table}
Table 1: Transmission Protocol [26].  
In setup 2, as shown in Figure 6, a wooden obstacle of 60 \(\times\) 45 \(\times\) 1.5 cm dimension is placed between the source and the destination. No obstacle is placed in the relay destination path.
## 4 Results and Discussions
### Spatial Pattern Analysis of the Coordination Degree  
#### 4.3.1 Global Spatial Autocorrelation Analysis  
The global Moran's I values of the coordination degree in regard to China's carbon emissions, economic development, and environmental protection between 2009 and 2018 (Table 7) were calculated. Such values were all positive over the years, with the z-test values greater than the test critical value of 2.58, and were statistically significant at the 1% level, with the statistical significance passing the 99% confidence level. It can be seen that the level of China's coordination degree in terms of carbon emissions, economic development, and environmental protection displayed a positive spatial autocorrelation, with certain aggregation patterns found at the provincial level. In other words, China's coordination degree was not randomly distributed. Rather, regions with a high or low degree generally exhibited obvious spatial aggregation patterns. Specifically, provinces with a relatively high level of coordination degree tended to be adjacent to each other, and provinces with a relatively low level of coordination degree tended to be neighbors. Judging from the change trend of the global Moran's I index, the overall index shows a downward trend, suggesting that the degree of spatial autocorrelation kept decreasing. Or, in other words, the degree of spatial aggregation and distribution of provinces with high or low levels of coordination has weakened to a certain extent.
# Improved Low-Depth SHA3 Quantum Circuit for Fault-Tolerant Quantum Computers
## 1 Introduction
### Contributions
#### 1.1.2. Contributions in Terms of Quantum Cost (Resource Trade-Off)  
In a fault-tolerant quantum computer, error detection and error correction are essential to control the errors that accumulate through noise, calculation errors, and incorrect operations. For this operation, additional quantum resources are required. In fault-tolerant circuits, the error increases as the length of the operation increases, so the error can be decreased by reducing the depth. Therefore, in terms of errors, it is more effective to reduce the depth of quantum circuits. The proposed SHA3 reversible quantum circuit showed the results of drastically reducing the depth through a quantum resource trade-off.
# A Few-Shot Learning-Based Material Recognition Scheme Using Smartphones
## 2 Related Work
### Material Recognition Using Vibration  
[PERSON] et al. [19] proposed TouchPass, a system for user identification using smartphone vibrations and accelerometers. TouchPass is an authentication framework designed to prevent smartphone data breaches, enabling user authentication regardless of finger movement when the user touches the smartphone. [PERSON] et al. demonstrated through the mathematical modeling of vibrations that unique physical properties of fingers, such as density and conductivity, can be distinguished. To separate the transient state and steady state of vibrations, they designed vibration signals that oscillate multiple times in short cycles. These signals were preprocessed to distinguish between transient and steady states, and features were extracted using frequency spectrum and cepstrum analysis. The extracted features were then reconstructed using a Siamese Network to ensure behavior-independent user differentiation and trained with a Knowledge Distillation-Based Classifier Training approach. [PERSON] et al. [5] proposed VibroTactor, a system that recognizes objects in contact with a smartphone using smartphone vibrations and microphones. VibroTactor is a cost-effective technology for determining the location of a smartphone (e.g., pocket, bed, desk, etc.). It filters and analyzes acoustic signals generated by smartphone vibrations to determine the device's location. Since it uses built-in microphones on commercial smartphones, VibroTactor enables location recognition at a low cost. Classification is performed using an RBF (Radial Basis Function) Network, achieving an average accuracy of 91% across 12 different locations. [PERSON] et al. [6] proposed VibePhone, a system that identifies objects in contact with a smartphone using smartphone vibrations and accelerometers. It records accelerometer readings when the smartphone vibrates and analyzes vibration patterns based on the material of the object's surface. The system uses a Support Vector Machine (SVM)-based deep learning model to classify materials, improving accuracy through preprocessing and feature extraction techniques. It achieved an average accuracy of 85% across six labels: sofa, plastic table, wooden table, hand, backpack, and pants pocket. For each sample, 1.5 s of vibration data were collected, and training required 120 labeled data points per class.  
Although these studies achieved a high classification accuracy, they share a significant limitation: the requirement for large amounts of training data. For instance, VibePhone required 120 training samples per label, with each sample consisting of 1.5 s of vibration data. Additionally, surfaces not previously trained in the model could not be classified until sufficient data was collected and the model was retrained. These limitations reduce the practical applicability of such systems [20]. Therefore, a new approach is required that reduces the amount of data needed for training while maintaining high performance.
## 3 Results and Discussion
Thereafter, the forward voltages remained more stable during operation between 48 and 3500 h, whether silicone was encapsulated or not. To separate the influence of the encapsulated silicone, two possible mechanisms have been suggested as being responsible for the degradation processes occurring in UVB-LED chips [30; 31; 32; 33]. A possible scenario is that the number of non-radiative centers increased at the onset of operation due to an increased junction temperature, causing more rapid decay at the early stage; thereafter, a slow proliferation rate was gradually achieved, after saturating the number of non-radiative centers, through the other mechanism in response to thermal stress inside the UVB-LED chip. In other words, from our investigation of the degradation of LOPs during long-term operation, we suggest that, when encapsulating silicone in the cavities of the LFs when packaging UVB-LEDs, the hermetic cover of the proposed package structure should be removed to avoid cracks forming in the encapsulated silicone. Moreover, excluding any influences of degradation processes in the UVB-LED chips, we suggest that the main mechanism responsible for the different rates of degradation of the LOPs in these proposed UVB-LED packaging structures involves the encapsulated silicone undergoing slight destruction in response to thermal stress and UVB irradiation without cracks appearing in the encapsulant. If cracks did appear in the encapsulated silicone, we attribute the slow rate of degradation to the silicone being further deteriorated by the UVB irradiation during the aging period. Figure 5: EL spectral characteristics of samples (**a**) S1, (**b**) S2, (**c**) S3, and (**d**) S4 measured at 100 mA before and after 3500 h of aging.
# Towards a Hybrid Approach to Context Reasoning for Underwater Robots
## 5 The Proof of Concept: A Case Study on Oil Spill Detection
### Context Reasoning in the Case Study
#### 5.3.4 Discussion  
Oil spill detection is one of the use cases that are defined in the SWARMs project. It implies different reasoning problems (e.g., inference under uncertainties) and thus is a good benchmark scenario to testify the proposed hybrid reasoning mechanism, including ontological, rule-based, and MEBN reasoning. The three different reasoning methods are shown to address different reasoning problems. The use of the MEBN reasoning to reason under uncertainties in underwater robots is explored first. Simulation results using the Protege (along with the Pellet reasoner) and UnBBayes GUI tools show that the three context reasoning methods employed can deal with partial reasoning problems in the scenario and can compensate for each other's weakness. Therefore, the effectiveness of using these three context reasoning methods in underwater robots is verified theoretically. The simulated proof of concept shows that it is a promising approach to encase these three context reasoning methods in the context reasoner component. In further implementation, the Pellet reasoner API can be employed to provide ontological and rule-based reasoning, while the UnBBayes Java API can provide functionalities for the MEBN reasoning. As the MEBN makes reasoning based on probabilistic ontologies and ensures compatibility with OWL, it can be foreseen that the integration of ontological, rule-based, and MEBN reasoning in the context reasoner could be realistic without inconsistencies. However, the combination of these three context reasoning methods should be rigorously proven by a full implementation of the context reasoner, which will be done in the near future. In a nutshell, in this case study, the focus was to provide a theoretical foundation for applying ontological, rule-based, and MEBN reasoning in the context reasoner component to tackle underwater  
Figure 11: SSBN generated for the scenario with incomplete findings.  
robot reasoning. However, the efficiency of the proposed hybrid context reasoning should also be studied in the future. In particular, the reasoning time of each context reasoning method should be recorded under different conditions. For instance, how the number of rules inserted into the ontology model would affect the reasoning time in rule-based reasoning is worth investigating.
## 4 Experimental Evaluation
### Results and Analyses
#### 4.2.3 Effectiveness Evaluation  
In this section, we analyze the effectiveness (i.e., accuracy) of embedding methods in computing the similarity of nodes and compare it with those of similarity measures with each dataset as follows. As explained in Sections 4.2.1 and 4.2.2, to compare the effectiveness of similarity measures with embedding methods for each dataset in this section, _we consider their accuracies on their best iterations and best values of_\(d\) represented in Tables 2 and 3, receptively.  
Figure 5: Accuracy of embedding methods with TREC and Wikipedia datasets for different values of \(d\).  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline  & **ATP** & **BoostNE** & **DeepWalk** & **DWNS** & **graphGAN** & **Line** & **NERD** & **NetMF** & **node2 vec** \\ \hline BlogCatalog & 512 & 256 & 128 & 512 & 64 & 512 & 128 & 256 & 128 \\ Cora & 512 & 512 & 512 & 512 & - & 512 & 128 & 128 & 64 \\ DBLP & 512 & 512 & 128 & 256 & 64 & 128 & 128 & 512 & 128 \\ TREC & 512 & 128 & 64 & 64 & - & 256 & 256 & 512 & 128 \\ Wikipedia & 512 & 128 & 64 & 256 & 64 & 64 & 512 & 64 & 64 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Best values of \(d\) for all embedding methods.