esa-sceva/satcom-chunk-collection huggingface dataset:
# Experimental Validation of a Gyroscope Wave Energy Converter for Autonomous Underwater Vehicles
## 2 Mathematical Model  
GWEC is a device that converts wave energy into electricity by gyroscope effect of the flywheel; its dynamic principle is shown in Figure 1. In order to describe the energy conversion process clearly, two reference systems are introduced, where \(x,y,z\) are the hull reference systems, and \(x_{1}\), \(y_{1}\), \(z_{1}\) are the gyro reference systems. The origins of the two reference systems coincide with the flywheel's center of gravity. In the initial position, the flywheel rotates at a certain speed along the \(z\)-axis direction. Since there is no wave action, \(\delta\) and \(\varepsilon\) are both null. When the hull is subjected to waves with an angular velocity \(\dot{\delta}\) in the \(x\) direction, the flywheel is affected by two angular velocities, \(\dot{\delta}\) and \(\dot{\varphi}\). At this time, the gyroscope effect produces torque in the \(y_{1}\) direction perpendicular to the two angular velocities, and its precession angular velocity is \(\dot{\varepsilon}\). Therefore, power take off (PTO) can extract energy from the torque in the \(y_{1}\) direction, thus realizing the conversion of wave energy to electricity.  
GWEC is less affected by the environment and geographical location because of its energy conversion principle, which is sensitive to external excitation. Unlike solar energy supply, it is not limited to the principle of charging during the day and stoppingat night; it can continuously provide energy for the AUV even under low wave energy underwater. From the technical point of view, the flywheel rotational speed as a controllable parameter can be used to optimize the coupling with the waves, thereby increasing the power generation production. It can be applied not only to AUVs, but also independently to provide energy for island residents or buoy lights in coastal areas.
## 5 Research Progress on Membranes for Redox Flow Batteries
### Membrane Evaluation Methods
#### 5.1.1 Vanadium Ion Permeability Measurements  
The rate of diffusion of vanadium ions across the membrane will determine the coulombic efficiency of the cell and capacity loss with continuous charge discharge cycling. A standard test procedure was developed by [PERSON] and co-workers [60; 61; 65; 66] to eliminate any interference from water transport due to osmotic pressure effects across the membrane. Vanadium ion permeability measurements typically involve the measurement of the diffusion rate of V (IV) ions across the membrane from a 1 M VOSO\({}_{4}\) and 2 M H\({}_{2}\)SO\({}_{4}\) solution on one side, to a blank 1 M MgSO\({}_{4}\) and 2 M H\({}_{2}\)SO\({}_{4}\) solution on the opposite side using UV-Visible spectroscopy. MgSO\({}_{4}\) is used in the blank solution in order to balance the osmotic pressure of the 2 solutions and eliminate the effect of water transport across the membrane [16; 64]. Using Fick's Law of Diffusion and Beer's Law, the rate of V (IV) ion diffusion is determined by the rate of change in the absorbance according to the following derivation [64]:  
\[\ln\left[abs\,B_{0}-2\,abs\,\,A\right]=\ln\left[abs\,B_{0}\right]-\frac{2k_{s} \,At}{V_{\,A}} \tag{2}\]  
where \(abs\)\(B_{0}\) is the initial absorbance of solution B (the 1M V (IV) solution), \(abs\)\(A\) is the absorbance of solution A (the 1M Mg\({}^{2+}\) solution containing no V (IV) ions), \(A\) is the area of the membrane exposed, \(t\) is time and \(V_{\,A}\) is the volume of solution A. The mass transfer coefficient (\(k_{s}\)) of the V (IV) ions across the membranes was calculated from the slope of a plot of \(\ln\) [abs B\({}_{0}-2\) abs A] versus t. The linear plot produced has slope equal to \(-2\)k\({}_{s}\)A/V\({}_{\rm A}\) from which k\({}_{s}\) can be determined. The diffusion coefficient, \(D\)_is_ thus calculated using the following equation:  
\[D=k_{s}\,\mathrm{d}y \tag{3}\]  
where \(y\) is the thickness of the membrane.  
In a different study, [PERSON] and co-workers proposed a method for determining the diffusion coefficients of VO\({}_{2}^{+}\) and VO\({}^{2+}\) ions across cation membranes at high electrolyte concentrations [67]. Using the Nernst-Planck equation, the potential difference across the membrane was calculated. For the three cation membranes that were compared, a linear dependence was observed between the logarithmic value of the selectivity coefficient and the molar fraction of vanadium ions in solution [67].
## 3 Results and Discussion  
### Global Analysis of the Lactobacillus Plantarum wcfs1 Transcriptomic Response to Hydroxyytrosol  
To bring new knowledge into the response of _L. plantarum_ WCFS1 to HXT, a global transcriptomic analysis was carried out using DNA microarrays. The analysis showed 314 DE genes after 10 min exposure to HXT, as compared to control conditions (\(t=0\)). From these genes 90 were upregulated and 224 downregulated. The DE genes can be checked in Supplementary Table S2. These genes were functionally classified according to their Clusters Orthologous Groups (COGs) categories (Figure 1).  
The enriched GO-DIRECT categories and KEGG pathways resulting from the Gene Ontology (GO) analysis carried out with the DAVID tool are shown in Figure 2. This analysis reveals cysteine and methionine metabolism, amino acid transmembrane transporter activity, as well as ribosome and RNA degradation as the most significantly enriched biological processes.  
Figure 1: Hydroxyytrosol-regulated genes from _Lactobacillus plantarum_ WCFS1 classified by functional categories. The number of upregulated and downregulated genes (percentage in parentheses) in the presence of 10 mM hydroxyytrosol is shown for each category.
## 2 Theoretical Analysis
### Determination of Axial Force along the Pile Shaft and Pile-Shaft Model  
The pile-unit shaft resistance is mobilized under vertical loads and will affect the distribution of the axial load along the pile. It is important to estimate the distribution of the pile axial force along the pile shaft to accurately evaluate the pile response. By taking the assumption that the axial force of the pile shaft is varied linearly along the depth, for the pile shaft in the free segment above the ground surface, the axial force \(N_{f}\left(z_{f}\right)\) can be formulated by  
\[N_{f}\left(z_{f}\right)=N_{t}+f_{0}z_{f} \tag{3}\]  
where \(f_{0}=A_{c}\gamma_{c}\) is the coefficient that describes the increase in the axial force of the per-unit-depth pile shaft in the free segment, and where \(A_{c}\) and \(\gamma_{c}\) represent the cross-sectional area and unit weight of the pile.  
For piles below the ground surface, the axial forces \(N_{p}(z_{p})\) and \(N_{a}(z_{a})\), which denote the passive and active segments, respectively, can be evaluated by  
\[N_{p}\left(z_{p}\right)=N_{t}+f_{0}L_{f}+f_{1}z_{p} \tag{4}\]  
\[N_{a}(z_{a})=N_{t}+f_{0}L_{f}+f_{1}\left(L_{p}+z_{a}\right) \tag{5}\]  
where \(f_{1}\) represent the coefficients of the axial force growth for the pile shaft below the ground surface.  
\[f_{1}=A_{c}\gamma_{c}-\frac{\mu\tau}{2} \tag{6}\]  
where \(\mu\) is perimeter of pile shaft; and \(\tau\) represents the ultimate shear friction at the pile-soil interface.  
In view of the influence of shear deformation on the whole deformation for the pile shaft, in the current paper, Timoshenko beam theory [49] is adopted to establish associated differential equations for later analysis for simplicity, as illustrated graphically in Figure 3. In detail, in this theory, the plane section hypothesis is used, viz., the cross-section of the pile shaft remains as a plane after bending, whereas the cross-section is no longer perpendicular to the original neutral axis.  
Hence, according to Timoshenko beam theory, the relationship between the deformation and the internal forces is:  
\[Q=\kappa A_{c}G_{c}\left(\frac{dy}{dz}-\theta\right) \tag{7}\]  
\[M=EI\frac{d\theta}{dz} \tag{8}\]  
Figure 3: Deformation characteristics of Timoshenko beam.  
where \(EI\) is the bending stiffness of the pile shaft; \(\kappa A_{c}G_{c}\) is the equivalent shear stiffness for the pile foundation; \(\kappa\) is the equivalent shear stiffness; and \(G_{c}\) and \(\theta\) denote the shear modulus, cross-section rotation angle the for the pile shaft, respectively.
# Smart Logistics Warehouse Moving-Object Tracking Based on YOLOv5 and DeepSORT
## 4 Logistics Warehouse Target Tracking
### Transfer Learning for DeepSORT  
Trained object-tracking models often need to be implemented in different scenarios and environments in practical applications, which requires the high transferability and generalization of the trained models. To this end, transfer learning could be employed to retrain the model through a new dataset, enhancing the object-tracking performance. Transfer learning [34] is a proposed machine learning technique that facilitates the transfer of trained model knowledge from one task to another related task model. The steps to perform transfer learning on the trained DeepSORT model are as follows:  
* Prepare the dataset: The new dataset needs to be organized, ensuring it is representative and diverse.
* Transfer learning strategy:
* Feature extraction: The majority of the pre-trained DeepSORT model's weights need to be retained while solely adjusting the output layer to suit the new task. It is necessary to freeze the weights of the initial layers to maintain low-level feature extraction capabilities.
* Fine-tuning: In addition to modifying the output layer, fine-tuning some of the lower-level weights to adapt to the new task is considered, allowing certain lower-level weights to undergo slight adjustments.
* Model training: Training the adjusted model using the new dataset.
* Model evaluation: Evaluate the retrained model and compute evaluation metrics to evaluate the model's performance on the new task.
* Refinement and iteration: Further refining the model based on the evaluation results and adjusting hyperparameters or fine-tuning the model network.  
The transfer learning process for DeepSORT is shown in Figure 18.
## 3 Preliminaries
### Review of [PERSON] et al.'s Scheme
#### 3.5.1. Registration Phase  
The user and sensor submit their identities and share secret information with the \(CS\) to be utilized in the authentication phase.  
**Step 1:**: First, \(IoT_{SL_{j}}\) transmits its identity \(SID_{j}\) to the \(CS\) via a secure channel.
**Step 2:**: The \(CS\) computes a shared secret value \(SI_{j}=h(SID_{j}||S_{k})\) with its master key. Then, the \(CS\) securely transmits \(SI_{j}\) to \(IoT_{SL_{j}}\).
**Step 3:**: \(U_{t_{i}}\) chooses its real identity \(ID_{i}\), password \(PW_{i}\), and temporary identity \(UID_{i}\). Next, \(U_{t_{i}}\) calculates the encrypted password \(Encpw_{i}=h(ID_{i}||h(PW_{i}))\). Then, \(U_{t_{i}}\) transmits \(\{ID_{i},Encpw_{i},UID_{i}\}\) to the \(CS\).
**Step 4:**: The \(CS\) computes the user's secret value \(UI_{i}=h(Encpw_{i}||S_{k})\) and \(h(S_{k})\) after receiving the message. The \(CS\) transmits \(\{UI_{i},h(S_{k})\}\) to \(U_{t_{i}}\) via a secure channel and stores \(UI_{i}\) and \(UID_{i}\) in a database.
**Step 5:**: \(U_{t_{i}}\) stores \(\{Encpw_{i},UID_{i},UI_{i},h(S_{k})\}\) in a mobile device.
## 3 Results
### Synthesis and Resynthesis: The Statements and Their Dimensions, Constructs and Factors
#### Environmental Approach
The company obtain higher-quality data generated from IoT to support decision makers with information from production management on topics such as raw materials, energy consumption, water consumption, water waste, solid waste, by-products [17], the use of packaging [21] and air pollution [19], and the reduced consumption of materials results in less dependency on natural resources [20]. Power consumption can be reduced due to the improved precision of data monitoring via IoT technologies [20; 21]. On the other hand, data centers consume large amounts of energy and resources, impacting the environment negatively, as monitored by IoT technologies [24]. There is an additional environmental liability measurement associated with Industry 4.0 as a consequence of the materials required for electrical devices, which are sometimes scarce and may require intensive extraction and processing efforts, which may negate the environmental advantage of the Industry 4.0 context [18]. Environmental Sustainability and Circular Economy Practices  
Digital technologies are used to develop new ways of coping with waste [19]. IoT technologies improve manufacturing process efficiency regarding the 6R design (\"reduce\", \"reuse\", \"recycle\", \"recover\", \"redesign\" and \"remanufacture\"), in order to save natural resources [18; 20]. Regarding the improvement of the economic and ecological flows of resources, IoT technologies enable collaboration and partnerships among a company's stakeholders with respect to \"closing the loops\" by reusing raw materials, sharing raw materials, reusing waste, sharing waste [21] and allowing the reutilization of materials in a remanufacturing process [20]. Industry 4.0 technologies assist in raw materials purchasing from suppliers when needed (the raw material or semi-finished production material is requested on demand) [24]. The incorporation of different types of sensors allows greater transparency of operations, which adds intelligence to processes to mitigate negative effects on the environment and throughout the supply chain, reducing the losses generated along the entire chain [21]. The IoT supports information and communication technologies such as platforms and applications that are employed within the \"intelligent shop floor\", helping to reduce energy consumption, solid waste, the use of packaging, by-products [21], the use of raw materials, water consumption and water waste [17]. These technologies are employed to integrate the production area with distributors and customers, helping to reduce waste, energy and the use of packaging [21]. The IoT sensors provide real-time monitoring information for better air quality [19]. The utilization of IoT technologies ensures a dynamic interconnection among energy providers, the company and market demand, which leads to better energy management [18]. Continuous monitoring through smart devices increases the visibility and awareness of energy consumption by using real-time problem solving [21].
## 3 Research Design
### Search Query Process and Research Questions  
In this SLR, ScienceDirect, IEEE Xplore, Web of Sciences, Scopus, and ACM databases were included. Later, a query was asked from identified databases for integrating safety and security, based on Bayesian networks or graph theory (also a combination of both). The search query for this SLR is given below:  
(\"security\" AND \"safety\") AND (\"bayesian network\" OR \"graph theory\")  
The SLR is a series of associated arguments in support of the research questions (RQs). The RQs of this SLR is stated as follows:  
1. Why is the integration of security and safety needed?
2. How have BN- and GT-based methodologies been utilized for security and safety studies in CI?
3. What have been the targeted application domains?
4. What solutions have been developed in the identified studies?
5. How is performance validated for developed techniques and algorithmic solutions?
6. What are the advantages and disadvantages of existing studies?
## 1 Introduction
### Comparison to the Previous Work  
Even though the previous work first introduced the concept of a dummy XOR operation with garbage registers and instruction level atomicity (ILA) [10], its performance was still low. This is because the work in [10] utilized the block size of four and the naive Karatsuba technique. Furthermore, the process of the GHASH function was not optimized in the work of [10]. Whereas the basic concept of our current work is similar to previous work, our current proposed method applies several optimization techniques crucial for improving the performance of a binary field multiplication and GHASH function in the GCM of operation. Firstly, we have shown that the number of garbage registers does not affect SPA security by conducting in-depth experiments. By reducing the number of garbage registers from eight to one and applying a multiplier encoding technique, the proposed method could achieve block size eight, which is the well-known maximum block size in the Block-Comb method on 8-bit AVR MCUs. Furthermore, by further optimizing the execution of the Karatsuba technique and the process of the GHASH function, our method could achieve significant performance improvement. As a result, the currently proposed method has achieved performance improvement over 42% compared with the previous best result while providing the same level of security.  
The remainder of our paper is composed as follows. Section 2 introduces characteristics of 8-bit AVR microprocessors, and a multiplication over a binary field. Section 3 describes existing \(BF\) multiplication algorithms on 8-bit AVR MCUs. Section 4 presents the proposed secure and efficient multiplication method over a binary field on 8-bit AVR MCUs. Both the proposed methods are analyzed with respect to performance and security in Section 4. Section 5 describes the proposed GHASH function implementation with the proposed multiplication method on 8-bit AVR MCUs. Section 6 describes a concluding remark with future works.
## 4 Empirical Study
### Subjective Evaluations
#### 4.2.2 Experimental Results of Subjective Evaluations  
This subjective evaluation was mainly conducted to determine the overall satisfaction with the proposed idea, including course suggestion and course degree prediction. To this end, we created and implemented an online course decision assistant system that provides course suggestion and course degree prediction services. Next, we announced the system and invited 226 testing students (called testers hereafter) to answer the questions using ratings. The ratings ranged from 1 to 5, where 1 and 2 indicated negative opinions and 3, 4, and 5 indicated positive perceptions. Figures 3-5 depict the rating results for three levels individually.  
* For level 1, Figure 3 shows that, 1. 85.022% of testers agreed that interest in a course is important. 2. 92.982% of testers wanted to know the relationships between courses and personalities before selecting courses. 3. 80.349% of testers did not know what courses they wanted to select. 4. 92.609% of testers had selected courses in which they were not interested.
* For level 2, Figure 4 shows that, 1. 63.717% of testers did not know courses that interested them when selecting courses. 2. 92.035% of testers wanted course suggestions in the existing system. 3. 94.69% of testers wanted to know the possible degrees for future learning. 4.69% of testers agreed that a good system has to be intelligent.
* For level 3, Figure 5 shows that, 1. 96.53% of testers have never used a similar system. 2. 94.273% of testers agreed that the proposed data analysis via mining the relationships between personalities and courses is important. 3. 94.323% of testers agreed that the proposed course suggestions are helpful for choosing courses of potential interest. 4. 96.087% of testers agreed that the proposed course prediction is helpful for choosing future learning. 5. 97.835% of testers agreed that the proposed system is intelligent.  
Figure 3: Ratings for level 1 questions.
## 2 Materials and Methods
### Statistical Analysis  
The sample size was computed based on our previous paper documenting the effectiveness of interventional sialendoscopy and DSIs in 54 patients (36 undergoing interventional sialendoscopy alone, and 18 undergoing sialendoscopy and DSIs) with recurrent sine causa sialadenitis, in terms of the reduction in the number of salivary gland swellings [13]. Based on the published data regarding the mean 6-month post-operative number of salivary gland swelling episodes (about 21 and 11, respectively, in the control and study groups) [13], assuming a variance of 10 in the general population, it was calculated that 37 subjects for each group would lead to an alpha value of 0.05 and a power of 99%.  
The statistical analysis was mainly designed to detect any possible postoperative differences between group A and B in terms of the number and severity (attested by a 0-10 VAS pain scale) of episodes of salivary gland swelling after treatment.  
The results are given as absolute numbers and percentages, or arithmetic mean values \(\pm\) standard deviation. Dichotomous outcomes were analysed using contingency table analysis and the Chi-squared test, and continuous variables were analysed using the Wilcoxon-Mann-Whitney test. Multivariate regression analysis was used to test the effect of possible confounders on the outcome. The data were analysed using STATA 10.0 software (StataCorp, College Station, TX, USA); a \(p\)-value of <0.05 was considered statistically significant. A Bonferroni-adjusted \(p\)-value of 0.0125 was considered for multiple test comparisons.  
Figure 2: Intra-operative view during a right parotid sialendoscopy (main picture) performed in a patient with recurrent sine causa parotitis; in the small picture, the endoscopic finding of diffuse sialodochitis involving the [PERSON]’s duct.
# Trust and Mobility-Based Protocol for Secure Routing in Internet of Things
## 6 Validation of the Proposed Protocol
### Mathematical Model of SMTrust
#### 6.2.2 Monotonicity  
The monotonicity refers to a metric that should ensure the increase in path weight if prefixed by another path [73]. Mathematically, the path weight structure of SMTrust (\(P\), +, \(fn\), \(\leq\)) is (left) monotonic for paths \(x\), \(y\), \(z\) belonging to \(P\), infering that \(fn(x)\leq fn(z+x)\) and \(fn(y)\leq fn(z+y)\).  
Other than these, there are additional properties and rules, as described by [73], that the basic, derived, and composite metric must hold to ensure the routing protocol requirements. For the calculation of the trust index in SMTrust, the defined rules and properties are examined and followed accordingly. The explanation of the rest of the properties for SMTrust is beyond the scope of this paper.