esa-sceva/satcom-chunk-collection huggingface dataset:
# Energy and Environment-Aware Path Planning in Wireless Sensor Networks with Mobile Sink
## 5 Swarm Based Solution
### Path Planning Problem  
In ACO, ants search for a solution to the path planning issue by randomly moving around the cluster head set at each time interval. Each ant \(k\) begins its tour at each cluster head and constructs connected covers by transitioning between cluster heads in the development graph. Ant \(k\) applies a probabilistic transition rule at each tour construction step to select which cluster head it will visit next. The probability that ant k, currently at cluster head \(u\), will select cluster head \(x\), to visit next is given by:  
\[P_{ux}^{k}(t)=\frac{\left[\tau_{ux}^{1}(t)\right]^{\omega_{1}}\!\!\left[\eta_ {ux}^{1}(t)\right]^{\omega_{2}}\!\!\left[\lambda_{ux}^{1}(t)\right]^{\omega_{3 }}\!\!\left[\beta_{ux}^{1}(t)\right]^{\omega_{4}}\!\!\left[\gamma_{ux}^{1}(t) \right]^{\omega_{5}}}{\sum\limits_{l\in FCH^{t}}\!\left[\tau_{ul}^{1}(t) \right]^{\omega_{1}}\!\!\left[\eta_{ul}^{1}(t)\right]^{\omega_{2}}\!\!\left[ \lambda_{ul}^{1}(t)\right]^{\omega_{3}}\!\!\left[\beta_{ul}^{1}(t)\right]^{ \omega_{4}}\!\!\left[\gamma_{ul}^{1}(t)\right]^{\omega_{5}}} \tag{85}\]  
where \(\tau_{ux}^{1}(t)\) is the pheromone value between cluster head \(u\) and \(x\) at the time \(t\), \(\eta_{ux}^{1}(t)\),\(\lambda_{ux}^{1}(t)\), \(\beta_{ux}^{1}(t)\) and \(\gamma_{ux}^{1}(t)\) are the heuristic information and \(\omega_{1}\),\(\omega_{2}\), \(\omega_{3}\), \(\omega_{4}\), and \(\omega_{5}\) are the weight factors that control the pheromone value and the heuristic information parameters, respectively. A flowchart of the proposed cluster head selection approach is shown in Figure 3.  
Figure 3: The proposed path planning algorithm flowchart.
# One-Part Alkali-Activated Pastes and Mortars Prepared with Metakaolin and Biomass Ash
## 4 Conclusions  
The possibility of manufacturing one-part alkali-activated pastes and mortars using metakaolin as the aluminosilicate precursor and potassium-rich biomass ash obtained by the combustion of wood as the powdered alkaline activator was investigated. In particular, the reaction kinetics of pastes and the mechanical strength of mortars were studied and related to the different K/Al molar ratio and water/solid ratios. Moreover, the possibility of heating biomass ash at 700 \({}^{\circ}\)C to decompose calcium carbonate in order to make it more reactive was investigated.  
The results obtained show that mortar's compressive strength is low and ranges between 1.6 to 3.7 MPa. Moreover, it has been found that:  
* the reactivity of the alkaline precursor (biomass ash) determines the strength development of the one-part alkali-activated material;
* by increasing the K/Al molar ratio, the reaction heat of one-part alkali-activated pastes increases up to a maximum value at K/Al of 2.5;
* the increase in K/Al molar ratio contributes also to increase the mechanical strength of one-part alkali-activated mortars, even though the compressive strength increases more if the w/s ratio decreases;
* the heating of biomass ash causes faster setting and is beneficial to increase the compressive strength of the final one-part alkali-activated mortar.  
This experimentation highlighted that it is possible to manufacture one-part alkali-activated pastes and mortars by activating metakaolin with a powdered alkaline activator such as potassium-rich biomass ash. However, the mechanical properties highly depend on the starting raw materials. To increase the compressive strength of one-part AAMs, it is possible to increase the K/Al molar ratio, decrease the water/solid ratio and even thermally treat the biomass ash.  
Conceptualization, A.M., H.R. and F.T.; methodology A.M. and H.R.; software, H.R.; validation, A.M. and H.R.; formal analysis, A.M.; investigation, A.M.; resources, H.R.; data curation, A.M.; writing--original draft preparation, A.M.; writing--review and editing, A.M., H.R. and F.T.; visualization, A.M.; supervision, H.R. and F.T.; project administration, H.R. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding. The authors wish to thank Universita Politecnica delle Marche for funding the 3-month period abroad during the PhD course of Eng. [PERSON], allowing the development of this research. The APC was waived.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Prediction of Ground Deformation during Pipe-Jacking Considering Multiple Factors
## 5 Conclusions  
This paper presents an approach for calculating the ground deformation. Based on the analysis of jacking process, the effect of grouting pressure on the ground deformation is considered to improve the prediction effectiveness. According to the comparison between calculated results and field observations, the effectiveness of the proposed approach was validated. More specific conclusions are drawn as follows:  
1. The ground deformation caused by bulkhead additive thrust and the friction between jacking machine and the surrounding soils were reanalyzed. The penetration of jacking machine makes surrounding soils and strata in front of the jacking machine under the shearing effect. The shearing disturbance coefficient is employed to evaluate the mitigation effect of shearing behavior on ground deformation.
2. The grouting process can apply pressure on surrounding soils and mitigate the ground movement. [PERSON]'s solution is used to calculate the ground movement in sectional direction caused  
Figure 14: Ground settlements at both sides of pipe-jacking at different depth: (**a**) 4.4 m, (**b**) 7.4 m.  
by slurry grouting. Then the cumulative ground movement along the jacking direction is determined based on the [PERSON]'s solution. The generalized prediction approach is proposed by combining the ground movement caused by bulkhead additive thrust, friction on jacking machine, grouting pressure and ground loss.
3. A field pipe-jacking construction of water-conveying tunnels was used to validate the prediction approach of ground deformation. As the consideration of grouting pressure, the calculated results after the arrival of jacking machine are consistent with the field data. The robustness is better than the other methods.  
This paper represents a result of collaborative teamwork. [PERSON] provided the concept of this manuscript. [PERSON]-[PERSON] offered the related data. [PERSON]-[PERSON], developed the concept and wrote the manuscript. [PERSON] collaborated the writing work and gave significant comments. [PERSON] helped to revise the manuscript and figures. The five authors contributed equally to this work.  
This research was funded by the National Basic Research Program of China (973 Program) grant number 2015 CB057806. This financial support is gratefully acknowledged.  
The authors declare no conflict of interest.
## 3 Results and Discussion
### The Effect of Adsorbent Dosage  
The effect of Fe\({}_{3}\)O\({}_{4}\)/CS/PEI nanocomposite's amount on the adsorption of As(V) and As(III) ions from water was studied in the range of 0.50 g/L to 2.50 g/L as shown in Figure 5a. During the study of adsorbent dosage effect, the other factors which were kept constant included the initial adsorbate concentration, temperature, and pH value that were 10 mg/L, 30 \({}^{\circ}\)C, and 6.7, respectively. According to Figure 5a, the increased amount of adsorbent from 0.50 g/L to 2.0 g/L caused an increase in the removal efficiency of As(III) from 52.0 % to 99.5% while the dosage increase from 0.50 g/L to 1.5 g/L caused an increase in the removal efficiency of As(V) from 95.5% to 99.7%. This relation between adsorbent dosage and the increased removal efficiency was attributed to the vacant adsorption sites that increase by the addition of more adsorbent dosage.  
Figure 4: Nitrogen adsorption desorption isotherm of the synthesized Fe\({}_{3}\)O\({}_{4}\) NPs, Fe\({}_{3}\)O\({}_{4}\)/CS, and Fe\({}_{3}\)O\({}_{4}\)/CS/PEI nanomaterials.  
Figure 5: The adsorbent dosage effect (**a**) and pH value effect (**b**) on the adsorption of As(III) and As(V) ions over the surface of Fe\({}_{3}\)O\({}_{4}\)/CS/PEI nanocomposite.  
As shown in Figure 5a, there was no increase in the removal efficiency of As(III) and As(V) when the dosage of Fe\({}_{3}\)O\({}_{4}\)/CS/PEI nanocomposite was increased from 2.0 g/L to 2.50 g/L and from 1.50 g/L to 2.0 g/L, respectively, meaning that the equilibrium has been reached. So, these values were selected as the optimum adsorbent dosages.
## 5 Discussion
### Motivational Perceptions
Studies including this variable in gamification evaluations are scarce and inconclusive, suggesting no gender differences in satisfaction with gamification use, consistent with our results. Two studies supporting this are [7], which found no significant differences between men and women in satisfaction, and [1], both in gamification for learning computer programming. A possible justification for these results is that the design and content of educational software, with or without gamification, are equally oriented to meet the needs and expectations of both men and women. Additionally, satisfaction strongly depends on individual factors such as motivation, prior digital competence, and self-efficacy in technology use. Therefore, focusing on the overall learning experience of the individual student rather than generalities about men and women allows the development of educational software that is equally satisfying from a gender perspective. However, in our study, by focusing on the effects of gender conditions, we found that both men and women who used the non-gamified software tool reported greater satisfaction than those who used the gamified version. This is because the task's difficulty (in this case, programming) generated greater compliance from the subjects regarding the results obtained in the non-gamified version. Without elements of competition or external rewards, satisfaction is derived mainly from the ability to complete the task. Thiscontradicts the limited existing evidence. For example, in [70], there was slightly higher, although not statistically significant, satisfaction among men than women in a gamified algorithm learning environment. This could be attributed to social and narrative drivers in the gamified design, which could increase women's satisfaction, while competition could satisfy men more [42]. In summary, there is no conclusive evidence on whether men or women inherently experience greater satisfaction with gamification. As with the previous variables, it depends mainly on the specific instructional design and how it caters to the interests and motivations of each gender. More in-depth studies analyzing the behavior of these variables are needed. In response to RQ2, the study found distinct gender differences in motivation levels when applying gamification. Specifically, males reported significantly higher enjoyment and perceived usefulness levels in the gamified condition than females. Conversely, females reported greater satisfaction in the non-gamified condition, indicating that the gamified environment may not align as effectively with their motivational needs. These findings suggest that while gamification can enhance motivation for males, it may not have the same effect for females, emphasizing the need for a more tailored approach when implementing gamified educational tools.
# Network Tower Sharing Analysis in Greece: A Structure-Conduct-Performance Approach
## 3 Results
### Market Structure
Although mobile data usage in Greece is low, it exhibits the most significant growth. It should be noted that up to 2020, Greece was performing the lowest data usage among the EU countries of reference, but after 2020, it has made significant growth, overtaking countries like Belgium, Germany, and the Netherlands. In parallel, as far as the use of voice is concerned, Greece is the leader, registering the highest value  
Figure 2: Connections in Greece between 2017–2022. among the EU countries of reference, and continues to have very positive prospects for growth in the future. Considering revenues, the total mobile revenue per gigabyte of mobile data is higher than the median of the reference countries; however, Greece performed the third-fastest decrease in the revenue per GB from 2017 to 2022. In 2022, Belgium (3.1 EUR) was the leader regarding revenues per GB, while Germany was second (2.8 EUR), and the Netherlands remained at 2.2 EUR in 1H 2023, while Greece was fourth in ranking with 1.7 EUR in 1H 2023. It should be noted that Greece had the highest revenue per GB in 2019, but since then, Greece has fallen below Belgium, Germany, and the Netherlands, demonstrating a significant decrease. In addition, voice revenue per mobile voice minute reaches the median of the peer group and experiences median erosion between 2017 and 2022. In terms of mobile ARPU, Greece's revenue per mobile subscription, excluding M2M, is, after compensation for the comparative price level, at the median, while Greece is included in the group of countries with a positive CAGR, as far as ARPU is concerned, between 2017 and 2022. As far as value for money is concerned, Greece performs less favorably in mobile data offerings in contrast to most of the EU countries of reference; however, it achieves better of all in voice services. In summary, Greek subscribers tend to pay an ordinary monthly rate to Greek telecom operators, while using comparatively less data and more voice services. Since voice minutes are no longer charged per minute in most modern markets, the focus of value for money is expected to be shifted to mobile data, which is still primarily charged by the gigabyte. Greece's value-for-money position in mobile data ranks among the weakest, on par with countries such as Belgium, Germany, and the Netherlands. However, there is significant potential for improvement, as Greece leads its peer group in the fastest growth rate of data usage. All the above-mentioned results indicate a market where intense competition exists and little space for cooperation seems to be available, in terms of tower infrastructure sharing, especially in regard to cooperating with the incumbent operator.
# An Inference Framework of Markov Logic Network for Link Prediction in Heterogeneous Networks
## 5 Experiments
### Experimental Results
Most significantly, the termination condition (TC) mechanism provides the most substantial accuracy gains, boosting performance by 4-6% when combined with SC across all datasets. The optimal configuration with all components except SRL achieves the highest accuracy. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**MLN** & **SRL** & **VEM** & **GCN** & **SC** & **TC** & **FB15k** & **WN18 RR** & **Aminer** & **Kinship** & **UMLS** \\ \hline ✓ & ✓ & & & & & - & - & 0.68 & 0.76 & 0.79 \\ ✓ & & ✓ & ✓ & & & 0.74 & 0.85 & 0.81 & 0.87 & 0.91 \\ ✓ & & ✓ & ✓ & ✓ & & 0.72 & 0.83 & 0.79 & 0.85 & 0.89 \\ ✓ & & ✓ & ✓ & ✓ & ✓ & 0.78 & 0.88 & 0.83 & 0.89 & 0.94 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Accuracy of LP with different model configurations. Figure 5: Execution time on datasets with less than 10,000 entities. (**a**) TT; (**b**) TT, ToE, and ToM. \begin{table}
\begin{tabular}{l c c c c c c c c c} \hline \hline \multirow{2}{*}{**Methods**} & \multicolumn{3}{c}{**Aminer-10000**} & \multicolumn{3}{c}{**FB15k-237**} & \multicolumn{3}{c}{**Kinship**} \\ \cline{2-10}  & **3** & **4** & **5** & **3** & **4** & **5** & **3** & **4** & **5** \\ \hline ExpressGNN & 70.43 & 76.21 & 80.31 & 35.43 & 38.71 & 40.7 & 86.43 & 87.42 & 91.88 \\ Pgate & 65.12 & 67.31 & 72.45 & 15.12 & 17.31 & 23.7 & 75.12 & 83.21 & 85.33 \\ PlogicNet & 60.44 & 67.12 & 68.3 & 35.44 & 36.12 & 37.7 & 86.44 & **88.31** & 87.12 \\ Ours & **78.32** & **81.44** & **83.32** & **42.32** & **45.44** & **88.32** & **91.44** & **94.87** \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Accuracy of LP with various numbers (3, 4, 5) of logical formulas with statistical analysis. In conclusion, based on the diverse experimental conditions and datasets examined, our method demonstrates the highest average accuracy in multi-relational LP across various datasets and link types, surpassing competitors by over 30% in efficiency.
# Efficient Cluster Tree Topology Operation and Routing for IEEE 802.15.4-Based Smart Grid Networks
## 4 Proposed Scheme
### Proposed Addressing Scheme and Routing Scheme
In addition, even when the parent node is different, the channel information of the corresponding cluster head can be determined. Thereby, the data transmission path can be reduced. This proposed technique has a cluster tree structure. However, it can transmit data directly between cluster heads in the communication range. This simplifies the routing path and reduces the energy and transmission time required for data transmission. Figure 17: An example of the proposed routing protocol.
# P System with Fractional Reduction
## 3 Principles and Algorithms of Fraction Simplification
### The More Phase Derogation Algorithm
#### 3.1.3 Algorithms  
Suppose we want to reduce the fraction \(\frac{x}{y}(0<y<x)\). Based on the principle of Section 3.1.1 and the refinement of Section 3.1.2, the process of fraction simplification by a more phase derogation algorithm is described as follows:  
1. [label=()]
2. Input x,y(0 \(<\) y \(<\) x)
3. Calculate \(\{\mathrm{x_{i}}\},\{\mathrm{y_{i}}\},\{\mathrm{u_{i}}\},\{\mathrm{z_{i}}\}\), where \(\mathrm{i}=2,3,...,\mathrm{t},\mathrm{and}\) x\({}_{1}=\) x,y\({}_{1}=\) y,u\({}_{1}=\) x - y, z\({}_{1}=0\);
4. Calculate \(\{\mathrm{p_{i}}\},\{\mathrm{q_{i}}\}\), where \(\mathrm{i}=\mathrm{t},\mathrm{t}-1,...,1\);
5. Output p\({}_{1},q_{1}\).  
The Algorithm 1 using the more phase derogation technique is as follows:  
```
Input: x,y(0 \(<\) y \(<\) x) Output: p\({}_{1}\), q\({}_{1}\), p\({}_{1}\)/q\({}_{1}\) is the simplest form of x/y fractions. procedure Input: x,y(0 \(<\) y \(<\) x) Output: p\({}_{1}\), q\({}_{1}\), p\({}_{1}\)/q\({}_{1}\) is the simplest form of x/y fractions. procedure
1 \(x_{1}\gets x,y_{1}\gets y\);
2 \(z_{1}\gets 0\);
3 \(u_{1}\gets x-y\);
4 while\(y_{i}!=u_{i}\)
5 if\(u_{i}<y_{i}\)then\(x_{i+1}\gets y_{i}\);
6 \(y_{i+1}\gets u_{i}\);
7 \(z_{i+1}\gets 1\);
8 end if
9 if\(u_{i}>y_{i}\)then\(x_{i+1}\gets u_{i}\);
10 \(y_{i+1}\gets y_{i}\);
11 \(z_{i+1}\gets 0\);
12 end if
13 \(i+\);
14 \(u_{i}\gets x_{i}-y_{i}\);
15 end while
16 \(p_{i}\gets 2;q_{i}\gets 1\);
17 repeat
18 \(i\gets-\);
19 \(p_{i}\gets p_{i+1}+q_{i+1}\);
20 if\(z_{i+1}==1\)then\(q_{i}=p_{i+1}\);
21 end if
22 else\(q_{i}=q_{i+1}\);
23 end else
24 until\(i=1\);
25 end procedure
```  
**Algorithm 1**Algorithm for fraction simplification using more phase derogation.  
In this algorithm, the more phase derogation algorithm is split into two parts: the complexity of the algorithm is \(O(1)\) when \(x\) is twice as long as \(y\). For a general \(\{x_{i}\}\), \(\{y_{i}\}\), the time complexity of the first part of the algorithm is \(O(t)\) if the algorithm performs \(t\) subtraction operations, at which point \(u_{t}=y_{t}\), and the first part of the more phase derogation algorithm ends. It is easy to know that the length of the sequence \(\{z_{i}\}\) is the same as the number of times the first part of the algorithm is executed, so the length of the sequence \(z\) is \(t\). After \(t\) times of execution, the value of the more phase derogation algorithm \(i\) goes to 0, and the second part of the more phase derogation algorithm ends. That is, the time complexity of this algorithm in the second part is \(O(t)\) and its total time complexity is \(O(t)\). That is, in the average case, the time complexity of this algorithm is \(O(t)\). If the difference between \(x\) and \(y\) is large, then the worst time complexity is \(O(max(x,\ y))\).
# A Node Placement Algorithm Utilizing Mobile Nodes in WSN and IoT Networks +
## 3 The MobileCC Framework  
The concept of utilizing mobile nodes in the network for the creation of alternative paths to the sink was initially suggested by [PERSON] et al. in [6]. The authors proposed a mechanism called Mobile Congestion Control (MobileCC), which was used in certain areas of a network that suffer from congestion repeatedly, permanently or for a long duration. The main idea of the mechanism is to create hard alternative disjoint paths only consisting of mobile nodes to resolve congestion problems that occur in the network. The initial position of the mobile nodes is alongside the sink node. They are in sleep mode and are only moved when notified from the sink to help in a congested area. This work shows that it is possible to mitigate the effects of congestion by using mobile nodes and create disjoint paths. However, the actual mobile node placement strategy is not addressed.  
The network consists of randomly deployed static nodes and a set of mobile nodes placed near the sink in sleep mode. The following assumptions are also considered:  
* All nodes, both static and mobile, have the same characteristics, such as computation power, communication capabilities, sensing, and transmission range, with the exception of the mobility characteristic of the mobile nodes.
* A simple MAC protocol, such as CSMA/CA, is employed.
* All nodes are aware of their absolute or relative (to the sink) location.
* The sink is informed by the nodes about their location and communication range.  
The primary objective of this work is to utilize the extra resources (mobiles nodes) efficiently and effectively in order to resolve any problems in the network and, if possible, to improve its performance in terms of delay, energy efficiency and throughput. The problem has two aspects. The first aspect is the placement of the mobile nodes in such a way as to create a disjoint path made up entirely of mobile nodes to the sink, while in the other case, the mobile node creates a local disjoint path that connects with the original routes.  
The framework consists of the following mechanisms (see Figure 1):  
* Problem Detection Mechanism
* Defective Node Selection Mechanism
* Problem Notification Mechanism
* Alternative Path Creation Mechanism Using Mobile Nodes  
* Calculation of Extra Resources  
* Calculation of Optimum Position of Extra Nodes  
* Establishment of Alternative Path  
Concerning the three first mechanisms, a lot of work has already been done so far in the literature. The existing detection mechanisms can be used based on the problem that occurred in the network, such as congestion [18] or failure [19]. Therefore, in this work, we focus on devising mechanisms for creating alternative paths using mobile nodes. We begin with the Node Placement algorithm, presented in the next section.