esa-sceva/satcom-chunk-collection huggingface dataset:
# Link Quality Estimation from Burstiness Distribution Metric in Industrial Wireless Sensor Networks
## 4 Evaluation  
By using the Cooja simulator, the Bdist metric was compared with schemes such as ETX and PRR. The key parameters are presented in Table 2.  
\begin{table}
\begin{tabular}{c c} \hline Number of nodes & 10 \\ \hline Number of probes & 1000 \\ \hline Target PRR & 99\% \\ \hline Link quality of the channel (set) & 70–90\% \\ \hline Number of the data packets & 1000 \\ \hline Network area & 100 m \(\times\) 100 m \\ \hline Payload size & 26 bytes \\ \hline Simulation time for each scenario & About 55 min \\ \hline \end{tabular}
\end{table}
Table 2: Summary of the simulation set-up parameters.  
Figure 4: The flowchart of the main procedures in Algorithm 2.  
The link quality of the channel for each link indicates the percentage of successful packets at the receiver, compared with the number of packets from senders. For example, a channel link quality at 70% indicates that the receiver will successfully receive 70 packets if sent 100 packets. The number of probes to measure the link quality and the number of data packets to evaluate the measured link quality metric are both 1000.
# A Very High-Resolution Urban Green Space from the Fusion of Microsatellite, SAR, and MSI Images
## 3 Results
### East Jakarta  
The East Jakarta region is the largest administrative area when compared to the other five regions. With a total area of 185.54 km\({}^{2}\), it comprises 58.27 km\({}^{2}\) of urban green space, which accounts for approximately 31.41% of the total area (Figure 10). This urban green space is primarily composed of mixed areas (29.4 km\({}^{2}\)), followed by shrub-covered areas (15.64 km\({}^{2}\)) in the second place, grass areas (11.46 km\({}^{2}\)) in the third place, and a small portion of forest (1.51 km\({}^{2}\)) and wetland (0.25 km\({}^{2}\)) (Figure 11).  
Figure 9: Urban green space of Central Jakarta. The base map is CartoDB Dark Matter. QGIS version 3.22.8-Bialowieża was used to create the map.  
Despite being the largest area, the East Jakarta region has no dedicated city parks. Instead, it offers small interactive parks, environment parks, and a public building park. However, East Jakarta boasts the highest number of dedicated cemetery locations, totaling 28. The remaining urban green spaces serve as green lane roads and water banks.  
Some parts of mixed areas in East Jakarta fall under the exclusive zone such as Royale Jakarta Golf Course, Suvarna Jakarta Golf Course, and Padang Halim Golf Course. The most famous urban green space that is open to public in the East Jakarta is the Taman Mini Indonesia Indah Complex. It offers visitors the experience of the vibrant Indonesia  
Figure 11: Urban green space of East Jakarta. The base map is CartoDB Dark Matter. QGIS version 3.22.8-Bialowieza was used to create the map.  
Figure 10: The portion of urban green space in East Jakarta.  
culture and breathtaking nature at the park, which showcases these treasures through museums, gardens, and stunning replicas of Indonesia's famous landmarks. Another urban green space is Taman Piknik, a relatively new park located in East Jakarta, which features Tabebuya trees that adorn its main area.
# Mechanical Behavior of a Mine Tailing Stabilized with a Sustainable Binder
## 4 Mechanical Behavior of Mine Tailing after Stabilization
### Results Analysis  
#### 4.3.1 Isotropic Consolidation Test  
The isotropic consolidation curves (ICC) obtained for the mine tailing in the original and stabilized state are shown in Figure 16, according to the system (log (\(\sigma\)), e). It is observed that after the stabilization, the mine tailing presents a much lower evolution of the voids index during the loading, unloading and reloading phases. Similarly to what was observed in the ICC of the original tailing specimen, in the ICC of the stabilized tailing specimen, it was also verified that the tailing is less deformable in the trajectory of unloading and reloading than in the first loading cycle.  
The critical states theory was again applied using the fundaments presented by [PERSON] (2016) [26] from the isotropic consolidation curve obtained for the improved tailing specimen. To determine the isotropic consolidation line and the critical states line (CSL), the ICC was made according to the coordinate system (p', \(\upsilon\)). Figure 17 shows the delimitation of the isotropic compression and expansion lines. The normal compression line (line O'A'C'D') resulted in a value of \(\lambda=0.024\) and N = 1.91, resulting in Equation (18). The isotropic expansion line (line B'A'C') resulted in a k = 0.00038 and \(\upsilon_{k}=1.7526\).  
Figure 16: Isotropic consolidation curve of the mine tailing before and after stabilization.  
Then, the CSL of the mine tailing after improvement was determined from the stress trajectory of the specimen used to obtain the ICC, in blue, whose initial void ratio was obtained. For the definition of the CSL, it was assumed that this line must be parallel to the NCL. The resulting CSL is represented in Figure 18, with a value of \(\lambda\) = 0.024 and \(\Gamma\) = 1.8705. Once the CSL was determined, the stress trajectory of the remaining specimens was represented, subjected to confining stresses of 50 kPa, 100 kPa and 200 kPa, in green, showing that they do not go beyond the NCL.  
Table 6 presents the resulting parameters of the critical states theory before and after the stabilization of the mine tailings. The obtained parameters revealed notable differences, resulting in significantly lower values after the chemical stabilization by alkaline activation of the tailing. Regarding the NCL obtained for the original and stabilized tailing, the value of \(\lambda\) decreased about 4.2 times (76%), and the value of N decreased about 1.2 times (16%). Comparing the CSL parameters, the values of \(\lambda\) and \(\Gamma\) also reduced by about 76% and 16% after stabilization. Finally, by evaluating the variation of the unloading-reloading line parameters (k-line), a decrease in the value of k of about 26.3 times (96%) was observed. However, a slight increase in the value of \(\upsilon_{\rm K}\), by around 4%, was observed.  
Figure 17: Isotropic compression and expansion curve (stabilized specimen).  
Figure 18: Isotropic consolidation curve with respective NCL and CSL (stabilized MT).
# An Experimental Study on the Influence of Soundscapes on People's Behaviour in an Open Public Space
## 3 Results  
### Effect of Music on Ratio of People Stopping By  
One of the main aims of this research was to investigate whether there is an effect of added music sounds on the number of people stopping by the investigated public area. To address this purpose, two different variables were defined. The fist variable was the Stimulus, consisting of four levels, Classical, Ambient, Jazz music, and Control (No music) condition; while the second variable was defined as the ratio between the number of stoppers by the area and the total number of passers in a given reference time slot; this was defined as a numerical variable, namely the Ratio Stoppers-Passers (RSP). During the experiment, the total exposure time for each Stimulus condition was 180 min. Each of the four overall periods was divided in 18 slots, lasting approximately 10 min (180 min/18 = 10 min), finally obtaining 72 video excerpts (18 \(\times\) 4 stimuli, including the control). Afterwards, the RSP was calculated and the Stimulus variable was defined for each of the 72 recording slots. Table 1 reports the total number of observations of passers-by and stoppers-by for each stimulus condition.  
The 72 RSP values corresponding to the 72 video excerpts were used for the statistical analysis. Since the distributions of the RSP values across the four stimuli were non-normal, a nonparametric test was used. A Kruskal-Wallis H test was run to determine if there were differences in RSP score between the four conditions. Distributions of RSP scores were not similar for all groups, as assessed by visual inspection of a boxplot. The mean rank of RSP scores was not statistically significantly different between groups, \(\chi^{2}(3)=0.24\), \(p=0.971\). Thus, there was no detected effect of the different stimuli on the number of subjects who stopped by the area, nor a significant difference between the RSP values recorded during the music excerpts exposure with respect to the control condition.
## 3 Proposed Method  
### Framework of Constellation Optimization via Neural Network  
Reliable transmission in IM-OFDM systems requires the consideration of PN and PA nonlinearity constraints. Therefore, this subsection integrates the IM-OFDM transmission system with mapping optimization and loss function, presenting a neural network-based constellation optimization system architecture for IM-OFDM.  
In traditional communication systems, constellation design typically follows predefined rules, where constellations such as QAM and PSK are adopted. These constellation designs usually rely on theoretical assumptions of ideal Gaussian channels, thus making them ill-suited to dynamic channel environments and nonlinear PA constraints. In contrast, deep learning can autonomously learn the correlation between channel characteristics and transmission objectives through input-output architectures. More importantly, modeling the constellation mapping process as a trainable neural network layer enables end-to-end optimal mapping training [33]. To be noticed, such a neural network-empowered communication system can learn robust message representations associated with channel impairments during symbol mapping, thereby facilitating the recovery of transmitted messages with minimal error probability.  
As shown in Figure 2, only the mapper and demapper modules contain trainable parameters, which are shared between the two. As this system is optimized jointly in an end-to-end manner, the training data are not part of a conventional pre-prepared dataset but are dynamically generated during the training process. At the start of each training iteration, a sufficiently long bit stream is randomly generated. In the mapper, the input random bit stream is first utilized to generate constellation-point symbol labels based on the modulation order (for instance, the 16-QAM modulation labels such as '0110', '0010', etc.). Subsequently, these labels are used to select the corresponding points from the constellation generated by trainable weight parameters, completing the constellation mapping. In the demapper, we implement the demapping by calculating the LLR. The LLR estimates the probability of each transmitted bit being 0 or 1 from the received signal and outputs the LLR. After performing a forward propagation through an end-to-end system, we obtain the communication and sensing performance losses. These losses are combined into a joint loss function. Subsequently, the Adam optimization algorithm is employed to update the trainable weight parameters, modifying the mapping relationship of the real and imaginary parts of the constellation diagram, ultimately achieving constellation optimization.
# Optimizing Routing Protocol Design for Long-Range Distributed Multi-Hop Networks
## 6 Simulation Verification  
The experiment involved constructing an LB-OLSR protocol model on a simulation platform and comparing it with the traditional OLSR protocol to validate the effectiveness of the improvements.
# Robustness Analysis of the Collective Nonlinear Dynamics of a Periodic Coupled Pendulums Chain
## 3 Numerical Examples
### Example 2: Three Coupled Pendulums
#### 3.2.2 Stochastic Study
The DM-B2 and TM-B2 attractors of \(\left|A_{1}\right|^{2}\), remain the most robust \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline  & & \multicolumn{4}{c}{**DM**} & \multicolumn{4}{c}{**TM**} \\ \cline{3-10}  & **Dof** & \multicolumn{2}{c}{**LHS**} & \multicolumn{2}{c}{**gPCE**} & \multicolumn{2}{c}{**LHS**} & \multicolumn{2}{c}{**gPCE**} \\ \cline{3-10}  & & **2\%** & **5\%** & **2\%** & **5\%** & **2\%** & **5\%** & **2\%** & **5\%** \\ \hline \multirow{2}{*}{\(\Delta f\) (\%)} & 1 & 172.86 & 272.86 & 162.86 & 262.86 & 165.06 & 287.95 & 161.45 & 275.90 \\  & 2 & 110.47 & 113.95 & 110.47 & 1.95 & 143.18 & 165.91 & 143.18 & 152.27 \\ \hline \multirow{2}{*}{\(\Delta\phi\) (\%)} & 1 & 104.76 & 114.15 & 105.73 & 113.66 & 113.29 & 126.44 & 109.73 & 125.75 \\  & 2 & 100.94 & 103.10 & 100.40 & 1.56 & 100.83 & 97.39 & 100.69 & 96.97 \\ \hline \multirow{2}{*}{\(\frac{\Delta f}{\Delta\phi}\) (\%)} & 1 & 165.88 & 240.00 & 154.12 & 231.76 & 145.61 & 227.19 & 146.49 & 218.42 \\  & 2 & 109.48 & 110.34 & 109.48 & 111.21 & 0.98 & 168.85 & 140.98 & 155.74 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Stochastic amplitude and frequency ranges of the multistability domain, according to the DM and TM, with respect to the deterministic ranges. Figure 16: Evolution of **(a)** the frequency ranges and **(b)** the amplitude ranges of the multistability domain with respect to deterministic ones according to the DM and TM. On the other hand, the gPCE allows reducing the CPU time needed for the LHS method by nearly 95.2% (from 33.6 h down to 1.6 h) and 97.5% (from 489.6 h down to 12 h) when computing the frequency responses and the basins of attraction, respectively.
## 4 Results  
### Whole-Rock Geochemical Characteristics  
The whole-rock geochemical data of 14 samples from the TS-10 skarn profile are displayed in Supplementary Table S1. Among them, the mineral composition of the marble zone is 99% calcite, presenting the highest Ca content (approximately 54 wt%). In the skarn rock body, Ti, Al, Na and Sr exhibit the same evolutionary trend (Figure 7c,e,g,h), displaying enrichment in the inner skarn and depletion in the outer skarn. The changes in Ca, Fe and Mg are more pronounced (Figure 7b,d,f) and are generally enriched in the outer skarn. Ca initially increases gradually in the outer skarn, then decreases in the inner skarn, reaching a minimum in the granite zone (Figure 7b); the trend of Si and Ca changes is generally opposite, with the highest content in granite and the lowest in limestone (Figure 7a). Fe is more enriched, and its content progressively increases in the outer skarn (Figure 7d), while Al is more enriched, and its content remains relatively stable in the inner skarn (Figure 7e). This is consistent with the characteristic that the composition minerals of the outer skarn are mainly andradite and diopside, and the proportion of garnets with higher iron contentincreases from 10% to 88% towards the rock body (Figure 4). In contrast, feldspar, diopside, and epidote are widely developed in the inner skarn, while diopside is not present in the granite. This also results in higher Al content in the alteration zone compared to fresh granodiorite (TS-10-15) (Figure 7e).  
Some trace elements have the same evolutionary trend, such as REE and similar elements. The variations of Dy, Ho, Er, Tm, Yb, Lu, Y and Sc (Figure 8a) are consistent. Transition elements, such as Zn, V and Ni, also show consistent geochemical characteristics (Figure 8b). Overall, from the inner to the outer skarn, there is an obvious decreasing trend in Na, Al, Ti and Sr (Figure 7c,e,g and Figure 8b), indicating that these elements may have been lost during the evolution of the skarn. On the other hand, Fe, Mg, Zn, V and Ni (Figure 7d,f and Figure 8b) show an obvious increasing trend, indicating that these elements may have been enriched during the process of skarnization. At the boundary between the inner and outer skarn, represented by samples TS-10-8 and TS-10-7, Na, Al, Ti and Sr show an obvious decrease, while Fe, Mg, Zn, V and Ni show a significant increase, indicating that the boundary between the two samples is possibly the original contact interface between the rock body and carbonate.  
Figure 7: Geochemical variations of major elements in the TS-10 profile based on whole-rock analysis. (**a–g**) Trend of important major elements, (**h,i**) Trend of important trace element (Sr) and EREE. The labels of different color zones are shown in (**a**), gray-marble, bule-outer skarn, pink-inner skarn, orange-granite.
# Design Method of Primary Structures of a Cost-Effective Cable-Supported Photovoltaic System
## 4 Design Method
### A Case Study  
The basic model shown in Table 2 under a basic wind load in Tianjin (w\({}_{\rm o}\) = 0.5 kN/m\({}^{2}\)) was redesigned using the proposed design procedures. In this case, only the wind load was considered, and according to Equation (1), the design wind load was calculated as w\({}_{\rm K}\) = 0.64 kN/m\({}^{2}\) and 0.76 kN/m\({}^{2}\) (Case 0\({}^{\circ}\) and 180\({}^{\circ}\)). The tensions of cables 1 and 2 were set to be the same as those of the original structure (H\({}_{1,2}\) = 30 kN). The tension of cable 3 was set to H\({}_{3}\) = 18 kN. The triangle bracket height and row spacing were set to h\({}_{1}\) = 1.5 m and 2.9 m, respectively.  
According to the Chinese standards [37], the midspan deflection limit was set as 2% of the sag-to-span ratio in the serviceability limit state. Table 4 lists the allowable internal forces of the cables and triangular brackets of the primary structure, and Table 5 compares the parameters of the original structure and the redesigned structure per MW. The results show that, although the midspan deformation of the original structure can meet the requirements of the serviceability limit state, the design value of the wind load capacity is far less than the requirements under the ultimate capacity state, and the cable design  
Figure 11: Sketch of the overall view of the proposed method.  
parameters of the original structure are unreasonable. The redesigned structure meets the design requirements in both the ultimate and serviceability limit states. The design value of the wind load capacity of the redesigned structure was 1.33 kN/m\({}^{2}\), which was much higher than that of the original structure (0.55 kN/m\({}^{2}\)). The redesigned structure had a similar resistance force in both cases of 0\({}^{\circ}\) and 180\({}^{\circ}\). However, the resistance force of the original structure in Case 0\({}^{\circ}\) was much higher than that in Case 180\({}^{\circ}\). Compared with the original structure, the outer diameter of the triangular brackets and the cross-sectional area of cable 3 in the redesigned structure were reduced by approximately 3%. The cross-sectional areas of cables 1 and 2 increased by approximately 87%. The steel consumption of the cables, triangular brackets, and lateral connectors was reduced by approximately 15.7%. This design method improves both the economy and bearing capacity of CSPS structures.
# A Novel Algorithm to Optimize the Energy Consumption Using IoT and Based on Ant Colony Algorithm
## 4 The Proposed Method
### Data Transfer  
When the clusters are formed, and the cluster heads of each cluster are selected, normal nodes send the sensed data to the relevant clusters. The cluster heads send the data packet to the base station after applying the community functions or data combination. In order to calculate the energy, first, the energy of each node is measured, and then the consumed energy to transmit k data bits to the distance d is calculated as Equations (7) and (8):  
\[E_{Tx}(k,d)=E_{Tx}(k)+E_{Tx\ amp}(k,d) \tag{7}\]  
\[E_{Tx}(k,d)=\left\{\begin{array}{l}kE_{elec}(k,d)+k{\it{refisd}}^{2}\quad \mbox{if}\quad d<d_{\it{crossor}}\\ kE_{elec}(k,d)+k{\it{\epsilon}}_{\rm{two-ray-amp}}d^{4}\quad\quad\quad\quad else \end{array}\right. \tag{8}\]  
Energy consumption to receive k data bits is calculated as Equation (9):  
\[E_{Rx}(k,d)=E_{Rx\_dec}(k)=kE_{elec} \tag{9}\]  
In the above relationships, \(E_{dec}\) is the electronic transmit/receive energy, k, message size in terms of number of bits, d distance between receiver and transmitter, \(E_{tx\_amp}\)amplification energy, \(\varepsilon_{fris}\) amplification factor, \(dc_{nasser}\), threshold distance whose transmit factor changes in it.  
The following conditions are then considered for routing:  
1. The most optimal path is selected as the shortest path to transmit a message between two nodes;
2. Routing is conducted in a progressive way to reach the goal;
3. The distance of the nodes is measured to select the path with the source node;
4. The distance of the desired nodes to the destination is also measured.  
With the above conditions applied, a neighbor node condition is selected with all the above conditions. For optimization with the ant colony algorithm, these paths are entered in this algorithm. Then, a matrix is generally formed for each path. When this matrix is formed, the most optimal path is selected using the ant colony algorithm. For the nodes' energy to be close together and the network not to put too much pressure on individual nodes, all nodes are used for routing during execution, and the energy variance is low.
# Overall Design of Satellite Networks for Internet Services with QoS Support
## 2 Proposed Design of Satellite Networks  
To design and evaluate the whole satellite network guaranteeing the QoS, it is necessary that the design of link and network layers in the satellite network precedes [1; 13; 11; 23]. In this section, we first address our customized design of the link and the network layers in the satellite network based on the DVB-RCS system, which is a widely-used satellite system [11; 12; 25; 26; 27; 28; 29; 30; 31; 32; 33; 34; 35]. We also suggest the QoS architecture for the satellite network and discuss the other considerations to provide the QoS support in the satellite network. In this paper, we consider the satellite with on-board processing. We make the following assumptions regarding the proposed design:  
1. Based on the DVB-RCS system, the satellite network in the paper consists of a GEO satellite, a hub terminal with a network control center (NCC) and a network management center (NMC) and user terminals (UTs), as shown in Figure 1[11; 12; 25; 26; 27]. The terrestrial network between UTs is not considered.
2. In the link layer, the resource allocation scheme is demand assigned multiple access (DAMA) with the MF-TDMA technology based on the DVB-RCS system [11; 12; 25; 27].
3. In the satellite, the uplink data channel (UDC) is switched to the downlink data channel (DDC). Thus, the mesh connection is provided to satellite terminals, as shown in Figure 1.
4. For the network control, there are a forward control channel (FCC), a return control channel (RCC), and a log-on channel (LOC). The FCC is used to broadcast control messages from the hub terminal with the NCC and the NMS to UTs. The RCC and the LOC are used to transmit control messages from UTs to the hub terminal with the NCC and the NMS. For the synchronization of the MF-TDMA in all UTs and hub terminals, a ranging channel (RC) is used to correct time and frequency synchronization.  
The detailed design of the link and the network layers is explained in the following sections.