esa-sceva/satcom-chunk-collection huggingface dataset:
# A Blockchain Network Communication Architecture Based on Information-Centric Networking
## 7 Discussion  
This paper mainly studied the optimization of broadcast delay and network bandwidth in a blockchain network based on ICN, aiming to address the performance challenges in blockchain networks. However, there are several unresolved issues for future work:  
* While BLOCK-ICN enhances broadcast efficiency, it may also reduce network decentralization, due to the introduction of structured routing and domain gateway nodes in ICN. Thus, more research and experimentation are required to evaluate the performance and scalability of ICN in practical deployments.
* In BLOCK-ICN, ICN nodes typically rely on certification authorities for authentication to mitigate Sybil and Eclipse attacks. As part of our future work, we aim to design a reputation-based evaluation mechanism that utilizes network information to assess node credibility, further reducing the risk of such attacks.
* Regarding the gateway node election algorithm, our current theoretical evaluation considered only simple network topologies. We acknowledge the need for further theoretical validation and experimental analysis in more complex network environments, to comprehensively demonstrate its advantages.
* When BLOCK-ICN is deployed in large-scale networks, the number of multicast groups and members may increase significantly, posing performance challenges for gateway nodes. Since this issue is difficult to fully simulate in a network emulation environment, real-world deployment and testing are necessary for further verification and optimization.
* Although SimBlock provides a controlled environment for evaluating block propagation and communication efficiency, real-world blockchain networks involve dynamic node churn, diverse network topologies, and varying degrees of congestion, which were not fully captured in our simulations. To further validate our approach, future work will incorporate more comprehensive network models or real-world experimental deployments.  
We will continue to delve into the optimization of blockchain networks based on ICN and combine blockchain technology with the underlying ICN network from multiple perspectives, in order to improve the performance of blockchain networks, as well as the security and usability of ICN networks, and prepare for future work.
## 4 Proposed Empirical Models for Weather Factors
### Fog Model  
Utilizing Equations (7) and (8) and particle density functions from [18] for given visibilities to calculate the extinction and backscattering coefficient showed that the empirical models for the extinction coefficient yield reasonable values and even match up with the value to the fourth digit. However, this is not the case for the backscattering coefficient. The direct calculation result in values with the same order of magnitude as the extinction coefficient, whereas the only empirical model for the backscattering coefficient suggests a value 2 orders of magnitude smaller [9]. As a further approach, one can also exploit the observation that Equations (7) and (8) only differ by the efficiency terms \(Q_{\text{ext}}\) and \(Q_{\text{back}}\) and share the same particle density. Thus, the ratio of \(\alpha/\beta\) can be assumed to be invariant and  
Figure 8: Point cloud generation and simulation validation process used in this work.  
can be expressed by the ratio of integrals. Using the particle density function for radiation fog from [18] returns the result below:  
\[\frac{\alpha}{\beta}=\frac{\frac{\pi}{8}\int_{0}^{\infty}D^{2}Q_{\text{ext}}(D)N( D)\,dD}{\frac{\pi}{8}\int_{0}^{\infty}D^{2}Q_{\text{back}}(D)N(D)\,dD}\approx 1.44 \tag{9}\]  
Therefore, an empirical dependency or the backscattering coefficient dependency is proposed as follows:  
\[\beta_{\text{fog}}\approx\frac{\alpha_{\text{fog}}}{1.44} \tag{10}\]  
Important to note that this result is only valid for this kind of particle density function; if a different particle density function is used, the ratio of \(\frac{\alpha}{\beta}\) in Equation (9) will be slightly different, reflecting the fact that the result is highly influenced by the amount of particles with approximately the same diameter size as the photon's wavelength.  
However, a comparative calculation shows that the proposed approach provides a model which is comparable to the computation of the integrals in Equations (7) and (8).
# Performance and Challenges of Service-Oriented Architecture for Wireless Sensor Networks
## 4 The Goals and Challenges of Middleware Architectures for WSNs
### Data Aggregation  
In order to minimize the volume of data for transmission, a sensor network uses data aggregation quality. This ensures that redundant data is not generated in the memory, saving costs through memory usage and energy through processing time. This is a more data-centric approach in comparison to the conventional, address-centric approaches.
## 4 Results and Discussion
### The Impact of SLADS on Traffic Efficiency and CO\({}_{2}\) Emissions
#### 4.1.2 The Impact of SLADS on Road Capacity  
Under the real distribution of Chinese driving style, the impact of SLADS on the road capacity on the expressway and the secondary motorway were analyzed by changing the ICV penetration rate from 0 to 100%. In order to find the upper limit, simulation studies for the 60% oversaturated service level and the 80% oversaturated service level were also conducted. For the expressway, as shown in Figure 2a, when the volume input is within the range of the designed service level (the saturated level), SLADS cannot improve the road capacity. But if the volume input exceeds the design service level, SLADS is able to  
Figure 1: The impacts of SLADS on traffic delay on the expressway (**a**) and the secondary motorway (**b**).  
improve the road capacity to some degree, as shown in the red circle in the left picture of Figure 2a. It is indicated in the right picture enlarged from the red circle in the left picture that SLADS can increase it by 256.50 pcu/h on average, which is 4.37% and by a maximum of 283.20 pcu/h, which is 4.81%.  
In terms of the secondary motorway, as depicted in the red circle in the left picture of Figure 2b, SLADS can appropriately improve the road capacity as long as the volume input is higher than the third service level. We found that SLADS is able to raise the road capacity by 117.60 pcu/h, which is 6.02%, as shown in the right picture, enlarged from the red circle in the left picture. The road capacity does not reach the saturated service level even though the ICV penetration rate is 100%. The single-lane structure and the relatively low aggressive ratio style of ICVs may account for this, which will be explained in detail in Section 4.2.2. When the volume input exceeds the saturated service level, SLADS can increase it by 128.40 pcu/h on average, which is 6.58%, and by 140.40 pcu/h at most, which is 7.21%.
# Vision-Based Surface Inspection System for Bearing Rollers Using Convolutional Neural Networks
## 3 Surface Inspection Process
### Defect Classification and Localization
#### 3.3.2 Localization  
If there was a defect in the current position, we calculated the _IoU_ of each anchor box with the ground-truth box, and removed the anchor boxes whose _IoU_ was smaller than the set threshold by non-maximum suppression, leaving the anchor box whose _IoU_ was larger than the set threshold. The boxes left were our predicted boxes. _IoU_ was defined as:  
\[\begin{array}{l}IoU(G_{T},P_{B})=\frac{Area(G_{T}\cap P_{B})}{Area(G_{T}\cup P _{B})}\end{array} \tag{4}\]  
where \(G_{T}\) is the ground-truth box and \(P_{B}\) is the predicted box.  
Each predicted box contained four predicted values, which were the center coordinates \((x,y)\) of the box, and the length and width of the box. Through continuous iteration, the loss was gradually reduced, and the position of the predicted box was constantly approaching the ground-truth box. The loss function was as follows:  
\[\begin{array}{l}L_{loc}(x,t,t^{*})=\frac{1}{N}\underset{i\in Pos}{\sum}x_{ij}^ {p}L_{reg}(t_{i},t_{i}^{*})\end{array} \tag{5}\]  
where \(L_{reg}\) is Smooth L1 loss, \(N\) is the total number of anchor boxes, and \(x_{ij}^{p}=1\) when category \(p\) of \(i\)-th anchor box and category \(p\) of \(j\)-th ground-truth box match, otherwise \(x_{ij}^{p}=0\). \(t_{i}\) is a four-dimensional vector that represents the position of the predicted box. \(t_{i}^{*}\) is a four-dimensional vector that represents the position of the ground-truth box.  
\[\begin{array}{l}t_{x}=(x-x_{a})/w_{a},\ \ \ \ \ t_{y}=(y-y_{a})\ \ /h_{a},\\ t_{w}=\log(w/w_{a}),\ \ \ \ \ t_{h}=(h/h_{a})\ \,\\ t_{x}^{*}=(x^{*}-x_{a})/w_{a},\ \ \ \ \ t_{y}^{*}=(y^{*}-y_{a})\ \ /h_{a},\\ t_{w}^{*}=\log(w^{*}/w_{a}),\ \ \ \ \ t_{h}^{*}=(h^{*}/h_{a})\ \,\end{array} \tag{6}\]  
where \(x\), \(y\), denote the box's center coordinates and \(w\), \(h\), denote its width and height, respectively. Variables \(x\), \(x_{a}\), and \(x^{*}\) are for the predicted box, anchor box, and ground-truth box, respectively (likewise for \(y\), \(w\), and \(h\)).
# Simulation Study on an ICT-Based Maritime Management and Safety Framework for Movable Bridges
## 5 Methodology
### Bridge Evacuation  
As per the information of the approaching ship detected by the prime buoy at the Alert Belt, the bridge initiates the evacuation process. The bridge evacuation comprises several automated steps to ensure safe traffic flow on the bridge. The bridge is kept stationary until complete clearance of the bridge traffic. The bridge has to be opened before any ship approaches the bridge. For traffic control during bridge evacuation, two separated traffic bollard units are placed as a traffic barrier at the bridge entry and exit lanes of both sides. Furthermore, four independent cameras are suggested for entry and exit lanes at both sides of the bridge. This connection is maintained to detect and count the bridge traffic. For our simulation, these camera points are considered reference points _Ca_, _Cb_, _Cc_, and _Cd_. The bridge is considered completely evacuated when the total number of vehicles remaining on the bridge, \((k-l)+(m-n)\) is zero.  
After evacuation, the bridge launches its moving operation to open and give access for water vehicles to pass safely. Ships and other water transport vehicles pass through these two routes at movable bridge open positions. Different movable bridges such as the Swing and the Bascule bridges have distinct mechanisms to operate. In our simulation environment, a potential maritime management and ship navigation scenario through the Swing Bridge is depicted in Figure 9.  
The figure shows the ship crossing the bridge when the bridge is opened. Here, the bridge vehicles are stopped at the roadway before the bridge, and therefore, the number of on-bridge traffic is measured to zero. The distance between ship and bridge reduces to the lowest and the ship is approaching at a constant velocity with time.  
Figure 9: Automated bridge system and ship navigation at Fort Madison Swing Bridge.
# Implementing AutoML in Educational Data Mining for Prediction Tasks
## 2 Related Work
### Related Work on Predicting Student Dropout
To sum up, various researchers have investigated the problem of student's performance prediction employing a plethora of data mining techniques. The results reveal that there is a strong relationship between students' logged activities in LMSs and their academic achievements. Most of the proposed prediction models achieved notable results (accuracy is more than 80%). However, a variation in the outperformers is observed; i.e., there is no method that can be thought of or shown to be better than others for educational settings (Table 1). Even more, to the best of our knowledge, there is no research that demonstrates the use of advanced machine learning strategies to these settings, such as the autoML. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Paper** & **Prediction Task** & **Metrics** & **Methods Compared** & **Outperformers** \\ \hline \hline \multicolumn{5}{c}{_Related work on predicting student academic performance_} \\ \hline
[21] & Binary Classification & Accuracy, Precision, Recall, Specificity & NB, NNs, DTs & NB \\ \hline
[22] & Binary Classification & Accuracy, Precision, Recall, F-measure & ANN, NB, DTs, RF, Bagging, Boosting & ANN and Boosting \\ \hline
[23] & Binary Classification & Accuracy, Precision, Recall, F-measure, ROC Area & NB, MLPs, SMO, J48, REPTree & MLP \\ \hline
[24] & Multiclass Classification & Accuracy & NB, MLPs, SVM & Deep Neural Network \\ \hline
[25] & Multiclass Classification & Accuracy, Precision, Recall & C4.5, NB, ID3, CART, CHAID & CART \\ \hline
[26] & Binary Classification & F-measure & NNs, J48, SVM, NB & SVM fine-tuned \\ \hline
[27] & Multiclass Classification & Accuracy, Kappa & DTs, NB, NNs, Rule Induction, 1-NN, RF & NB \\ \hline
[28] & Binary Classification & Accuracy, Specificity & De-Tri-Training, Self-Training, Democratic, Tri-Training, Co-Training, RASCO, Rel-RASCO, C4.5 & Tri-Training (C4.5 as base learner) \\ \hline \hline \multicolumn{5}{c}{_Related work on predicting student grade_} \\ \hline
[29] & Regression course grades & & Regression-based methods, Matrix factorization-based methods & Factorization machine \\ \cline
# Numerical Simulation of Rock-Breaking Mechanism by Spherical Tooth Impact in Granite Formation
## 3 Results and Discussion
### Study on the Influencing Factors of Single-Tooth Broken Rock
#### 3.2.3 Influence Range of Rock Stress  
When a spherical tooth impacts rock, the kinetic energy of the tooth is instantly transformed into other forms of energy. The impact stress wave is a primary pathway for this energy release. It not only fractures the rock directly beneath the tooth but also shears the surrounding rock to break it apart. As the distance from the tooth increases, the stress wave within the rock gradually diminishes and eventually dissipates. Therefore,  
Figure 8: The comparison of the stress influence range of 16 mm and 22 mm spherical teeth under different impact energy.  
considering the early stage of impact, where the stress wave has not yet dissipated, we can more accurately determine the boundary of the stress-affected area.  
During the impact process, the stress value in the rock changes with the stress wave. When the rock's stress value reaches its compressive strength, the rock fails. Even if the stress does not reach the compressive strength, the rock can still be affected by the stress wave, leading to a decrease in strength. In this paper, the compressive strength of granite is 120 MPa as the benchmark, and 0.8 times the compressive strength of rock is taken as the stress limit as the standard [29].  
As shown in Figure 9, the curve demonstrates an overall trend of initial increase followed by a gradual plateau. This further confirms that in the drilling process of spherical teeth, higher drilling pressure and impact energy do not necessarily result in better performance. This phenomenon occurs because, beyond a certain impact energy threshold, most energy is not utilized for shear failure but is instead consumed by compressive failure beneath the tooth. Therefore, when determining the optimal drilling parameters, drilling pressure and impact energy must be considered comprehensively, rather than being blindly increased.  
The stress-affected range comparison for 16 mm and 22 mm spherical teeth under impact shows a similar trend: an initial rise followed by a plateau. When the impact energy exceeds a certain threshold, most of the energy is not used for the shear failure induced by stress waves on both sides of the spherical tooth but instead for the compressive failure beneath the tooth. The stress-affected range of the 22 mm spherical tooth is generally larger  
Figure 9: Comparison of stress influence range of 16 mm and 22 mm spherical teeth under different impact energy.  
than that of the 16 mm tooth. For the 22 mm spherical tooth, the curve's inflection point occurs consistently at an impact energy of 30 J, where the stress-affected range reaches its maximum. Therefore, for the 22 mm spherical tooth, the optimal drilling parameters are a drilling pressure of 1.2 kN and an impact energy of 30 J, resulting in a stress-affected range of 43.2 mm.
## 4 Discussion
The oxidative stability obtained herein (10.4 h) is similar to that of biodiesel produced using \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Microorganism** & **Operation Mode** & **Feedstock** & **Biomass (g\(\cdot\) L\({}^{-1}\))** & **Lipids** & **Reference** \\ \hline _Y. lipolytica_ NCYC 2904 & Two-stage batch & 20 g\(\cdot\)L\({}^{-1}\) of glucose + 18 g\(\cdot\)L\({}^{-1}\) & 9.7 & 3.5 & [9] \\  & & of VFAs & & & \\ \hline \multirow{4}{*}{_Y. lipolytica_ MUCL 28849} & 40 g\(\cdot\)L\({}^{-1}\) of glucose + 5 g\(\cdot\)L\({}^{-1}\) & 41 & 16.5 & \multirow{4}{*}{[18]} \\  & Two-stage & of VFAs & & & \\ \cline{1-1} \cline{4-5}
28849 & fed-batch & 40 g\(\cdot\)L\({}^{-1}\) of glycerol + 5 g\(\cdot\)L\({}^{-1}\) & 41 & 14.2 & \\ \cline{1-1} \cline{4-5}  & & of VFAs & & & \\ \hline _Cryptococcus curvatus_ & 40 g\(\cdot\)L\({}^{-1}\) of glucose + 5 g\(\cdot\)L\({}^{-1}\) & 4.6 & 1.06 & [19] \\ MTCC 2698 & Two-stage batch & 15 g\(\cdot\)L\({}^{-1}\) of glucose + 5 g\(\cdot\)L\({}^{-1}\) & 26.5 & 9.6 & [20] \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison of biomass and microbial lipids concentration obtained by different microbial species from VFAs in two-stage batch and two-stage fed-batch cultures. lipids of different _Y. lipolytica_ strains growing in VFAs [8] and detoxified and non-detoxified liquid wheat straw hydrolysate [31]. The kinematic viscosity of biodiesel, related to the ability of flowing, increases at low temperatures and affects the quantity and quality of injection and ignition. On the hand, lower values of kinematic viscosity result in fine particles of biodiesel with high speed and low mass [16]. The value of kinematic viscosity of biodiesel obtained from microbial lipids of _Y. lipolytica_ W29 (this study) was similar to that for _Y. lipolytica_ NCYC 2904 [9], _R. fluviale, R. toruloides,_ and _Lipomyces starkeyi_, and higher than _Y. lipolytica_ strains growing in detoxified and non-detoxified liquid wheat straw hydrolysate [16]. Other estimated properties, such as iodine value and density fall within a narrow range of the EU biodiesel standard EN 14214 and are similar to those of biodiesel produced from common vegetable oils and other oleaginous yeasts [16; 27]. Therefore, the intracellular lipids of _Y. lipolytica_ W29 accumulated from VFAs in two-stage batch cultures have a high potential to be used as feedstock for high-quality biodiesel production. Yet, it is possible to make the process even more economically competitive by increasing the concentration of lipids produced (grams of lipids per liter of medium) if high-cell density cultures were attained (e.g., using a fed-batch mode for biomass production).
_applied_  
_sciences_  
_Article_  
FEM Analysis of the Effect of Polarization on the  
Electromechanical Coupling Factor of Resonators with a  
Wrap-Around Electrode  
[PERSON]  
Faculty of Mechatronics, Informatics and Interdisciplinary Studies, Technical University of Liberec, Studentska 2, 46117 Liberec, Czech Republic; [EMAIL_ADDRESS]  
**Featured Application: The published results show a difference in the size of the electromechanical coupling coefficient in piezoceramic resonators with wrap-around electrodes, compared to resonators with fully electroded bases. The results can be applied in the design of new types of piezoceramic resonators.**  
**Abstract: The efficiency of electromechanical conversion in piezoelectric elements is assessed according to the magnitude of the electromechanical coupling coefficient. For power applications of piezoelectric elements, it is desirable that the efficiency of electrical to mechanical energy conversion be as high as possible. In case of resonators with electrodes incompletely covering their bases, an inhomogeneous electric field is generated, which results in inhomogeneous polarization of the resonator. The resonator will be polarized in some places either in a direction other than the desired one or not polarized at all. The degree of polarization of the resonator is one of the factors affecting the electromechanical coupling coefficient. The aim of this work is to analyze the magnitude of the electromechanical coupling coefficient of resonators with electrodes incompletely covering the bases and to compare the results with the electromechanical coupling coefficient of resonators with fully electroded bases. The physical description is given by the linear piezoelectric equations, the Gaussian equation for the electric field, and by [PERSON]'s law of force. On this basis, a FEM model is developed and used to analyze the electromechanical coupling coefficient. The result of the calculation of the electromechanical coupling coefficient is that for resonators with a wrap-around electrode in studied shape and dimensions there will be a decrease in the electromechanical coupling coefficient compared to a fully electroded resonator of identical dimensions. The presented conclusions are compared with analytically and experimentaly achieved results.**  
**Keywords: electromechanical coupling coefficient; piezoelectric resonator; finite element method; polarization process**
# The Value of Simple Heuristics for Virtualized Network Function Placement
## 3 Placement Heuristics
### Shortest Path Placement  
The presence of interference in wireless multi-hop networks causes a scarcity of BW. To reduce BW consumption, our second heuristic first finds a shortest path between source and destination of the SG request via [PERSON]'s algorithm. NFs then will be placed along the shortest path. The shortest path will be checked for availability of sufficient BW to accommodate this flow, considering both the actual links used and the impact on adjacent links due to interference. Should any link exceed their available BW the request gets rejected. The shortest path placement places the first NF in the first node of the path that has sufficient nodal resources. The next NF of the SG will be placed in the same node as the previous NF if possible, otherwise it will be placed in the next node along the shortest path with sufficient available nodal resource. This process continues until all NFs are placed. If any of the NFs cannot be placed due to a lack of nodes with sufficient nodal resources, the request gets rejected. Figure 2 shows the shortest path placement applied to the same example as the previous section. We can see from the example that this model reduces the BW consumption in comparison to the random placement.