esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results
### Results with [PERSON]'s Algorithm  
Figure 12(a) shows the values obtained by the controller when executing the Kruskal algorithm. In this case, it can be seen that the controller must send the information of its default route to each node. For instance, node 0x0004 has its default route equal to 0x002. The controller sends broadcast frames to each node; the value of the default route \(ID_{D}\) and the value of the node to which the \(ID_{S}\) information is directed is in the payload. Figure 12(b) shows the broadcast frames with the values obtained with the Kruskal algorithm sent to each node.  
It is also important to note that the nodes' batteries present similar voltage values (around 2.55 V) in this test, resulting in similar signal levels (between 0x002D and 0x003B). Using [PERSON]'s algorithm, the test generates a route that connects all the wireless network nodes. As a result, the controller will receive frames that follow the new default route. The latter can be verified in Figure 14. Additionally, you can observe a change in the initial default route to a completely different one. This new default route connects all nodes.  
Figure 12: Data sent from nodes to the controller.  
The nodes closest to the controller relay information from the more distant nodes. The reconfiguration of each node's source and destination addresses is identical to what is shown in the application (see Figure 12(a)).  
### Results with [PERSON]'s Algorithm  
Figure 14(a) shows the values obtained by the controller when executing the [PERSON] algorithm. Figure 14(b) shows the broadcast frame that the controller sends to each node with the values obtained from the default route with the [PERSON] algorithm. The frames sent to four nodes are presented in the same way the payload contains the data of the node identifier and the default route.  
Figure 14: Test 1.1: Default route generated by the [PERSON] algorithm with nodes having a similar battery level.  
Figure 13: Execution of the [PERSON] algorithm on the prototype.  
For this test, similar voltage values (approximately 2.55 V) and signal levels between 0x002D to 0x003B obtained for executing the [PERSON] algorithm are used. The test is conducted by requesting the application to generate a new default route using [PERSON]'s algorithm. This algorithm creates a default route where the priority is to connect each node to the controller node. In this test, as seen in Figure 16, the information generated by each node does not go through intermediate nodes to reach the controller. Because this algorithm prioritizes the direct connection of the nodes to the controller, a path similar to a star topology is generated.  
Figure 16: Test 1.2: Default route generated by the [PERSON] algorithm where the nodes have a similar battery level.  
Figure 15: Execution of the Dijkstra algorithm on the prototype.
## 3 Dynamic Network Layout Algorithm
### Node Aging Strategy  
We introduce the concept of \"age\" [16] to further stabilize the network layout and maintain user's mental map, by ensuring that nodes in the network that have remained unchanged for a long time are more stable. With this concept, the update process of dynamic network is considered as an aging process, where the age of nodes in the network is defined as the number of network updates that a node has experienced. Nodes with older ages are less mobile.  
However, age constraints prevent old nodes that have experienced direct changes from reaching a reasonable location. Therefore, we define the age calculation strategy through categorization. Nodes that have never directly encountered changes are kept stable, while nodes that have directly encountered changes are encouraged to move in order to reach more reasonable positions. Let \(a_{i}(t)\) represents the age of node \(i\) at time \(t\), and its update rules are as shown in Equation (6).  
\[a_{i}(t)=\begin{cases}1&N(i)>0\\ a_{i}(t-1)+1&otherwise\end{cases} \tag{6}\]  
Here, \(N_{i}\) is the set of adjacent nodes to node \(i\) that are updated at time \(t\). If a node in the network has directly encountered changes, its age is reset to 1; otherwise its age is simply incremented by 1.
## 1 Introduction
Each pipeline, interface, or facility is regarded as a spatiotemporal object possessing a definite position and shape in space, but also as an object undergoing changes in its state over time. Integrating the concept of spatiotemporal objects means the model can record and track the historical states and future predictions of each object, such as installation time, maintenance history, and expected lifespan, thereby supporting intelligent decision-making [17]. At the same time, deeply integrating semantic information enables the model to not only store the physical and spatial attributes of each object but also understand and process their meanings and functional roles, supporting semantic-based queries and analyses, such as filtering pipelines by usage and analyzing the performance of specific networks. This approach not only meets the basic requirements of spatial data models but also incorporates advanced concepts like spatiotemporal objects, three-dimensional spatial relationships, and semantic information into the parametric modeling of underground pipeline networks. The purpose of this study is to integrate the geometric characteristics, semantic information, and temporal classifications of pipelines into three-dimensional modeling. It involves designing basic and composite parameters to calculate the rotation angles of fittings and other visual parameters based on their interface classifications, thereby laying the foundation for rapid matching of points and lines. By reorganizing pipeline data and establishing a multi-feature fusion underground pipeline model, this research aims to enhance the information carried by underground pipelines and analyze their associations comprehensively. This approach achieves effective representation of underground pipeline scenes, meeting the requirements for efficient management and comprehensive planning of underground pipelines in green urban development [18].
###### Abstract  
This study investigates the effects of low-intensity high-repetition (LI-HR) and high-intensity low-repetition (HI-LR) resistance training (RT) on functional fitness and myokines in older adults. A total of 30 participants (mean age -76 years) were randomized into control (n = 10), LI-HR (n = 10), or HI-LR (n = 10) groups. Participants in LI-HR and HI-LR groups performed elastic band-based RT three times per week for 12 weeks. Pre- and post-intervention assessments included functional fitness (relative grip strength, sit-to-stand, 4 m walk, timed up-and-go (TUG)), ankle muscle strength, lower extremity power, and myokine levels (IL-6, IGF-1, SPARC, BDNF). Both experimental groups showed significant IL-6 reduction (LI-HR: \(p\) = 0.033; HI-LR: \(p\) = 0.015) and IGF-1 increase (LI-HR: \(p\) = 0.003; HI-LR: \(p\) < 0.001). SPARC increased significantly only in the HI-LR group (\(p\) = 0.021). Functional improvements were noted in TUG for both groups, while the 4 m walk improved significantly in the HI-LR group (\(p\) < 0.001). Body fat percentage increased in both LI-HR (\(p\) = 0.003) and HI-LR (\(p\) = 0.047). In conclusion, both LI-HR and HI-LR RT effectively enhance functional fitness and key myokines, with LI-HR emerging as a promising, accessible option for older adults.  
low and high intensity; low and high repetition; resistance training; functional physical fitness; myokines +
Footnote †: journal: Applied Sciences
# Optimizing the Quality of Service of Mobile Broadband Networks for a Dense Urban Environment
## 5 Conclusions  
Based on the findings of this study, it can be concluded that relying solely on a single KPI, such as RSRP, is insufficient for assessing a mobile network operator's quality of service as perceived by end-users. In order to improve mobile broadband performance in the areas under study, a detailed mapping process and potential route and hardware redesign may be required. This can help with issues such as congested networks and insufficient network coverage. According to the probability distribution functions, there was a relatively small chance that the PSO optimization would produce the best results in each of the three areas under consideration. However, the results showed that the PSO successfully optimized the RSSI and RSRP values, significantly reducing the RMSE of \(-59.30\) dBm and \(-58.40\) dBm between the original and optimized values, respectively. Although the improvement in RMSE for the SINR (0 dB) and the RSRQ (\(-5.24\) dB) was not as significant as in the case of RSRP and RSSI, the optimized values can still contribute to better network planning and optimization, leading to improved network performance and quality user experience. The findings also highlight the significance of network reception in influencing user experience. One user's QoS in a location with good reception differs significantly from one with poor reception. Finally, while the values obtained in Oniru VI are consistent, the results indicate a generally worse user experience than in UNILAG and Ikorodu. These findings could provide mobile network operators with valuable information for improving their services and user experience. Future research could explore alternative optimization algorithms and additional network parameters to improve the accuracy and application of the results obtained. Future work will provide correction factors for applying the optimized models in other related dense urban environments.  
The manuscript was written through the contributions of all authors. [PERSON] and [PERSON] were responsible for the conceptualization of the topic; article gathering and sorting were carried out by [PERSON] and F.U.; manuscript writing, and original drafting and formal analysis was carried out by [PERSON], [PERSON], J.I. and [PERSON]; writing of reviews and editing were carried out by [PERSON], [PERSON], [PERSON] and [PERSON]; and [PERSON] led the overall research activity. All authors have read and agreed to the published version of the manuscript.  
The work of [PERSON] is supported by the Nigerian Petroleum Technology Development Fund (PTDF) and the German Academic Exchange Service (DAAD) through the Nigerian-German Postgraduate Program under grant 57473408.  
Not applicable.  
The data that support the findings of this paper are available from the corresponding author upon reasonable request.
## 3 Results
### RSSI Measurement Results  
Using commercial sensor nodes dedicated to smart agriculture, RSSI values were recorded in order to observe the impact of the short grass field under near ground conditions. Results are depicted in plots of Figure 5.  
From Figure 5a it is noticed that the height of the sensors above the ground may have a significant impact not only on the signal strength but also on the signal quality. The achieved results indicate that the higher the position of the sensor above the ground, the stronger the signal strength becomes in accordance to the path loss results previously analyzed. The link quality indicator was estimated from the measured RSSI values using the flowchart given in (1)-(3),  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline  & & \multicolumn{3}{c}{\(\mathbf{F_{1}=868\) MHZ}} & \multicolumn{3}{c}{\(\mathbf{F_{2}=2.4\) GHZ}} & \multicolumn{3}{c}{\(\mathbf{F_{3}=5.8\) GHZ}} \\ \cline{3-10}  & & \(\mathbf{n_{1}}\) & \(\mathbf{n_{2}}\) & \(\mathbf{n_{3}}\) & \(\mathbf{n_{1}}\) & \(\mathbf{n_{2}}\) & \(\mathbf{n_{3}}\) & \#1 & \#2 & \#3 \\ \hline \multirow{2}{*}{**SOIL**} & \(H_{1}\) & 3 & 3.5 & 3.5 & 1.5 & 3.75 & 3.5 & 0.85 & 2.6 & 2.6 \\ \cline{2-10}  & \(H_{2}\) & 1.8 & 2.75 & 2.75 & 0.5 & 1.75 & 2.75 & 2 & 2.25 & 2.6 \\ \hline \multirow{2}{*}{**SHORT GRASS**} & \(H_{1}\) & 3.1 & 2.75 & 3.5 & 1.0 & 3.75 & 3.5 & 1.75 & 2.1 & 2.6 \\ \cline{2-10}  & \(H_{2}\) & 1.75 & 2.75 & 2.75 & 1.25 & 1.75 & 2.75 & 2 & 2.25 & 2.6 \\ \hline \multirow{2}{*}{**TALL GRASS**} & \(H_{1}\) & 3 & 2.75 & 2.75 & 3 & 3.75 & 2.25 & 2.75 & 4.25 & 2.6 \\ \cline{2-10}  & \(H_{2}\) & 2.3 & 2.75 & 2.75 & 1.5 & 2.5 & 3.5 & 2 & 3.5 & 2.6 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Attenuation factors for the path loss model proposed in (5)–(7).  
Figure 5: Difference at \(H_{1}\) and \(H_{2}\) between (**a**) RSSI values; (**b**) radio link quality.  
As shown in Figure 5 the link quality between the nodes improves for \(H_{2}\). At this height, the signal quality remains at 100% until 29 m. However, at \(H_{1}\) the signal quality is decreasing from 5 m. When the receiver node was placed 51 m away from the transmitter the signal quality is over 70% for \(H_{1}\) whilst is under 35% at the same distance for \(H_{2}\).  
The observed results may be interpreted under the sight of a radio link provided of Obstructed-line-of-sight (OLoS). For such cases, a break point \(d_{B}\) occurs defined as the location in which the FFZ contacts the ground, and it is calculated as per (10):  
\[d_{B}\approx\frac{4h_{tx}h_{rx}}{\lambda} \tag{10}\]  
with \(h_{tx}\) and \(h_{rx}\) the transmitter and receiver antenna heights, and \(\lambda\) the wavelength. For a scenario with \(H_{1}\) antenna height, the break point distance corresponds to a distance of around 32 m from the transmitter.
## 3 The Use of FTIR Spectra for Binder Durability
### The Effect of an Acidic Attack on the H-O-H and S-O-T Bond Vibrations in FTIR Spectra  
When a cementitious binder is exposed to acetic and sulfuric acids, the intensity and frequency of the H-O-H and Ca(OH)\({}_{2}\) vibration bands may increase [100, 101, 102, 103]. [PERSON] et al. [45, 67, 74, 99, 104] and [PERSON] et al. [27] attributed the range 3500-3640 cm\({}^{-1}\) to OH\({}^{-}\) (Portlandite), while asymmetric stretching (3437-3441 cm\({}^{-1}\)) and bending of H-O-H molecules (1637 cm\({}^{-1}\)) were also reported. Another significant band is the bending vibration of unbound water molecules at 1500-1700 cm\({}^{-1}\), while the band at 1635 cmis for adsorbed water molecules within the silica formation. The stability of hydrogen bonding also depends on the basicity of the electron's donor site, which increases down the group in the periodic table of elements [105]. [PERSON] et al. [45; 74] observed the disappearance of the hydroxyl (OH) band due to an acidic attack at 3500 cm\({}^{-1}\) due to hydroxonium formation and ion exchanges [106; 107; 108; 109].  
Furthermore, [PERSON] et al. [110] observed the bending vibration of hydroxyl due to the formation of hydrogen bonding H-O-H at 1613-1667 cm\({}^{-1}\). [PERSON] et al. [111] observed water within the microstructures of jennite and tobermorite-like C-S-H systems of short-order length chain (Q\({}^{1}\) and Q\({}^{2}\)). It is important to state that the presence of excessive -OH groups diminish the compressive strength of the binder. [PERSON] et al. [100] showed that FTIR spectra (Figure 12) reflected cation detachment from C-S-H or C-(A)-S-H by sulfuric acid attack. Detachment of Ca\({}^{2+}\) from CASH led to an increase in the frequency of vibration of Si-O-T from 1021 cm\({}^{-1}\) to 1097 cm\({}^{-1}\). This caused an increment in the wavenumber of Si-O-T vibration. This also reflects in the bending vibration of Si-O-T at the wavenumber 463 cm\({}^{-1}\) in comparison with an acid-attacked sample at 449 cm\({}^{-1}\) due to decalcification. The same observation was reported by [PERSON] et al. [103], showing that concentration could also influence the shift in the band of Si-O-T.
# Drift Potential Characteristics of a Flat Fan Nozzle: A Numerical and Experimental Study
## 4 Results and Discussion
### Comparison and Analysis of Data and Results
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Wind Speed/m/s** & \multicolumn{2}{c}{**1**} & \multicolumn{2}{c}{**3**} & \multicolumn{2}{c}{**6**} \\ \hline
**Distance/m** & **Numerical** & **Test Value** & **Numerical** & **Test Value** & **Numerical** & **Test Value** \\  & **Simulation** & **Simulation** & **Simulation** & **Simulation** & **Simulation** & **Simulation** \\ \hline
3 & 0.006403 & 0.00064 & 0.009923 & 0.062848 & 0.013863 & 0.0672 \\
4 & 0.004032 & 0.000293 & 0.006228 & 0.030951 & 0.008777 & 0.005733 \\
5 & 0.002681 & 0.00213 & 0.004843 & 0.032853 & 0.006894 & 0.038293 \\
6 & 0.001431 & 0.00016 & 0.003942 & 0.02528 & 0.005624 & 0.03032 \\
7 & 0.000147 & 0.000267 & 0.003108 & 0.02224 & 0.004611 & 0.026267 \\
8 & 1.79 \(\times\) 10\({}^{5}\) & 0.000107 & 0.000191 & 0.01744 & 0.003935 & 0.02424 \\
9 & \(1.76\times\) 10\({}^{5}\) & 0.00008 & 0.000511 & 0.010347 & 0.003367 & 0.024613 \\
10 & \(3.01\times\) 10\({}^{4}\) & \(2.67\times\) 10\({}^{5}\) & 0.000149 & 0.008811 & 0.00282 & 0.0248 \\
11 & \(1.6\times\) 10\({}^{5}\) & 0.000107 & 0.000124 & 0.007061 & 0.02333 & 0.012333 \\
12 & \(1.78\times\) 10\({}^{6}\) & \(5.33\times\) 10\({}^{5}\) & \(9.97\times\) 10\({}^{5}\) & 0.004597 & 0.001436 & 0.019733 \\
13 & \(2.54\times\) 10\({}^{4}\) & \(2.67\times\) 10\({}^{5}\) & \(6.32\times\) 10\({}^{5}\) & 0.004155 & 0.000798 & 0.017867 \\
14 & \(3.74\times\) 10\({}^{6}\) & \(2.67\times\) 10\({}^{5}\) & \(6.34\times\) 10\({}^{5}\) & 0.003291 & 0.003090 & 0.013687 \\
15 & \(1.03\times\) 10\({}^{6}\) & 0 & \(4.3\times\) 10\({}^{5}\) & 0.002347 & 0.000161 & 0.0112 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Simulated and experimental distance drift values at different lateral wind speeds. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Wind Speed/m/s** & \multicolumn{2}{c}{**Nozzle Model/Spray Pressure/(No./MPa)**} \\ \cline{2-4}  & **Lu 120-015, 0.3** & **Lu 120-015, 0.4** & **Lu 120-120, 0.3** \\ \hline
1 & 3.12 & 11 & 3.88 \\
3 & 15.74 & 16.45 & 16.24 \\
6 & 18.63 & 17.87 & 20 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Drift distance for different spraying conditions.
# Security in Wireless Sensor Networks: A Cryptography Performance Analysis at MAC Layer
## 5 Performance Evaluation
### Performance Evaluation: Impersonation Attack
#### 5.5.2. Impersonation Attack with LMAC  
Considering Figure 14, without any mitigation and with the attacker sensor node, the total number of received packets is equal to 496, and the energy consumption is equal to 0.313092 J, considering a packet size of 10 bytes. The number of packets which get lost is 2. Including cryptographic techniques for mitigating the attack, the overall network performance is slightly different but the changes are not so evident as with the BMAC protocol. In particular, in each case (AES, RSA, and ECC) the total number of received packets is 396 with 102 packets lost. So, it is clear that with the LMAC no changes are highlighted, and for this reason the performance analysis will be focused only on the energy consumption.  
Using AES  
* The energy consumption is equal to 0.338924 J and it is greater than the initial setup.
* The energy consumption is equal to 0.801263 J and it is greater than the initial setup and the setup with AES and ECC.  
Using ECC  
* Finally, in this setting, the energy consumption is equal to 0.407812 J, and therefore it is greater than AES and less than RSA.  
RSA is the highest resource consuming technique, among all others, since it produces packets with a size of 128 bytes. Thus, also in this case, it is clear how the energy consumption is strictly related to the packets size.
## 1 Introduction
The balancing speed exhibits direct proportionality to the coefficient [24; 25; 26]. Furthermore, an adaptive SOC coefficient strategy has been developed to accelerate the balancing process [27; 28]. However, CPS-PWM proves unsuitable for high-voltage applications, as the modulation complexity grows substantially with increasing voltage levels. In contrast, artificial intelligence (AI)-based SOC balancing strategies, including model predictive control [29; 30], multi-agent systems [31], fuzzy logic control [32; 33], and reinforcement learning [34; 35], have offered scalable solutions to high-voltage challenges. Distributed control methods, such as multi-agent-based strategies and predictive power model-based strategies, have successfully achieved SOC balancing in HVDC transmission systems [29; 31]. However, these algorithms impose excessive computational demands due to their dependence on iterative optimization and real-time data processing. This poses critical implementation challenges, as industrial-grade embedded systems lack the computational capacity to support such intensive real-time computations without compromising system responsiveness. Additionally, most of these SOC balancing strategies primarily focus on submodules, necessitating controllers with a matching number of bidirectional DC/DC converters as the ESM. However, MMC-SCES is often utilized in high-voltage applications, leading to substantial computational demands and increased system complexity. To overcome the limitations of existing strategies, this paper analyzes the correlation between supercapacitor SOC and submodule capacitor voltage. An enhanced SOC balancing control strategy is proposed to equalize the supercapacitor SOC through phase SOC balancing, arm SOC balancing, and capacitor voltage balancing regulation. Finally, a two-terminal transmission system with MMC-SCES is built in PSCAD/EMTDC to verify the feasibility and effectiveness of the proposed strategy. The paper is organized as follows: Section 2 presents the topology and operating principle of MMC-SCES. Section 3 introduces the balancing principle and the three-level SOC balance control in detail. Section 4 simulates and compares the proposed SOC balancing strategy and the traditional one. Finally, conclusions are drawn in Section 5.