esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Effects of F-T Cycles on the Mechanical Properties and Microscopic Parameters
### Effect of F-T Cycles on Particle Morphological Characteristics  
#### 3.2.1 Effect of F-T Cycles on Average Particle Diameter Percentage and Average Particle Diameter  
The internal structure of the soil varied due to water migration and redistribution after F-T cycles, and the particles' reorganization directly caused the particle diameter change. Hence, the particles were divided into four sections according to different particle diameters, including \(<\) 5 \(\upmu\)m, 5-20 \(\upmu\)m, 20-50 \(\upmu\)m, and \(>\)50 \(\upmu\)m. The average particle diameter (D\({}_{\text{P}}\)) percentage in each section was calculated and is shown in Figure 4.  
Figure 4a shows the effect of F-T cycles on the D\({}_{\text{P}}\) percentage of S0. This indicated that the largest proportion of particles was 20-50 \(\upmu\)m, followed by 5-20 \(\upmu\)m and \(>\)50 \(\upmu\)m. The particles \(<\)5 \(\upmu\)m were the least. With the increase of F-T cycles, the particles of 5-20 \(\upmu\)m increased, while 20-50 \(\upmu\)m and \(>\)50 \(\upmu\)m showed a decreasing trend. The reason might be that the F-T cycles caused the large particles of the plain soil to be broken, the small particles to increase, and the particle agglomeration to lower.  
Figure 4b displays that the largest proportion of S2 particles was 20-50 \(\upmu\)m, followed by \(>\)50 \(\upmu\)m and 5-20 \(\upmu\)m. Comparing Figure 4a with Figure 4b, we could conclude that the change of the D\({}_{\text{P}}\) percentage of S2 in each section was exactly contrary to that of S0. The reason was that, with the addition of cement, the small particles adhered to the surface of the skeleton structure dominated by large particles due to the F-T, leading to the rupture and reorganization of soil particles. Hence, the soils still had better agglomeration and high cohesion under F-T cycles.  
To compare and analyze the effect of F-T cycles on D\({}_{\text{P}}\), the D\({}_{\text{P}}\) of S0 and S2 after F-T cycles are listed in Table 3.  
From Table 3, the D\({}_{\text{P}}\) of S0 was the largest when unfrozen-thaured, it decreased sharply after the first F-T cycle and then decreased with the degree of 8.4%-25.87%. The first F-T cycle had a significant influence on unmodified soil. The D\({}_{\text{P}}\) of S2 increased in within the whole change range from 5.5% to 17.7% and was greater than that of S0, indicating that F-T cycles caused S0 particles to shatter  
Figure 4: Average particle diameter percentage under F-T cycles: (**a**) S0 and (**b**) S2.  
and recombine; the particles appeared to be in a \"fragmented\" state. As for S2, the small particles were filled and adhered to the large particles to form larger particles, which was called the state of \"agglomeration\". Therefore, the cohesion of S2 was higher than that of S0 after F-T cycles.
## 8 Conclusions and Future Work  
In this paper, we investigated the novel cell search and frequency tracking approaches for NB-IoT to make economic and green IoT networks. Because of the narrow bandwidth, too much time is taken to conduct the initial cell search. We propose an NPSS detector with FFO pre-estimation and noise elimination. Thanks to the application of the proposed NF, the NPSS detector outperforms previous works. As for cell ID detection in the frequency domain, we adopt a divide-and-conquer method to minimize the number of correlation sequences, which reduces the amount of computation by nearly three-quarters compared with the latest scheme. To make the frequency tracking process more efficient, some preprocessing is done before the residual frequency offset estimation. Furthermore, the adjustable number of correlation pairs helps to make a trade-off between the performance and computation complexity. All of the above proposed schemes can contribute to construct a low complexity and robust green NB-IoT network.  
In the future, we will continuously explore both rapid and low complexity solutions to improve existing methods. The ultra-extended coverage scenario, such as SNR = \(-20\) dB, will also be investigated. Under such a condition, many existing cell search schemes will be invalid, and if not, the execution time will be seriously lengthened, which implies a burden on power consumption. What is worse is that if frequency tracking cannot be completed within the UTG, the uplink will be unable to work properly. In addition to the above, we will investigate the problem of NB-IoT positioning with the assistance of NPRS. Furthermore, we will also focus on constructing a complete NB-IoT downlink communication link. The transmission of all the downlink physical channels will be supported.  
Figure 13: Comparison of frequency tracking performance vs. different numbers of correlation pairs under normal coverage and extended coverage.  
**Author Contributions**: [PERSON] and [PERSON] designed and analysed the algorithms; [PERSON] set up the simulation environment; [PERSON] and [PERSON] completed the algorithms simulation; [PERSON] contributed to the simulation results analysis; [PERSON] wrote the paper; [PERSON] revised the manuscript, and provided the necessary support for the research.  
**Funding:** This research received no external funding.  
**Acknowledgments:** This work is performed at MESIC, a joint lab of USTC and Institute of Microelectronics of Chinese Academy of Sciences (IMECAS). The authors would like to thank the Information Science Laboratory Center of USTC for the software and hardware services.  
**Conflicts of Interest**: The authors declare no conflict of interest.
# An Efficient Task Autonomous Planning Method for Small Satellites
## 2 Question Description and Modeling
### Model Output  
\(x_{j}\) and \(y_{j}\) represent the decision variables in the model, \(j\in J\); where \(x_{j}\) indicates that task \(j\) is selected, \(y_{j}\) indicates that task \(j\) is completed. If task \(j\) is completed, that is, \(x_{j}=1\) and \(y_{j}=1\), otherwise, \(y_{j}=0\). \(ws^{\prime}_{j}\) and \(we^{\prime}_{j}\) represent the start and end time points of task \(j\), respectively.  
W represents the time constrain result. If the \(i\)-th time window can meet the time constraint, \(W_{i}=1\), otherwise, \(W_{i}=0\); \(R\) represents the resource constrain result. If the task of the \(i\)-th time window can meet the resource constraint, \(R_{i}=0\), otherwise, \(R_{i}=0\).  
The objective function is designed to realize the maximization of the total profit of planning tasks. In the model, \(f_{j}(x)\) is the profit of task, the objective function is as follows:  
\[\max\left[\sum_{j=1}^{J}w_{j}f_{j}(x)x_{j}y_{j}\right] \tag{1}\]
## 4 The COASYM-MAC Protocol
### Data Collection
#### 4.4.1 In Case of Symmetric Link  
Upon waking up, the parent node transmits beacons to each of its children. The child, which is waiting to send data, pauses for a random short backoff and performs a Clear Channel Assessment (CCA). If the CCA finds that the channel is available, the child will transmit the data. If the CCA findsthat the channel is unavailable, the child will wait for the parent to send a new beacon. In Figure 5, each of the three children are trying to transmit data and are listening to the channel. The receiver transmits the beacon when it wakes up. Once the children receive the beacon, they each backoff for a random time to prevent collisions. Sender 3 chooses the lowest backoff time and wins control of the channel. It sends its data and stands by for an acknowledgement from the receiver.  
Algorithm 4 presents the pseudo-code that summarize the operation when link is symmetric.  
```
1:if a beacon packet received from \(P_{u}\)then\(\triangleright\) Child \(u\) sending data to Parent \(P_{u}\)
2: Child takes a random backoff and checks the \(CCA\)
3:if win the channel then
4: send data to \(P_{u}\) and go to sleep mode
5:else
6: wait for next beacon
7:endif
8:endif
9:if a data packet received from \(P_{u}\)then\(\triangleright\) receiver \(P_{u}\) received data from child \(u\)
10: send ACK to sender
11:endif
```  
**Algorithm 4** Link symmetric.
## 7 Conclusions  
This study presents a comprehensive investigation into the domain of joint human activity recognition and localization, leveraging CSI. Our methodology, based on hybrid deep neural networks, aims to enhance the precision and reliability of identifying specific activities performed by individuals within a given location. We extensively explored various deep neural networks, including 1D-CNN and RNN models such as LSTM, the GRU, the LSTM-GRU, and the LSTM-BIGRU. Despite the valuable insights obtained from real-world experiments conducted at 20 MHz and 40 MHz, inherent limitations in the data necessitated the use of innovative data generation techniques. Specifically, the CTGAN algorithm was employed to generate synthetic data from real CSI data, producing three synthetic datasets that closely resemble labeled real CSI data. The integration of synthetic data with real CSI data facilitated the development of our proposed LSTM-BIGRU model. Before constructing this model, a thorough parameter tuning process was conducted using grid search, focusing on hidden size and the number of layers as the search space. Upon evaluating the model's performance, we found that it performed exceptionally well, particularly at 20 MHz, achieving an accuracy of 99.81% for activity recognition and 98.93% for location estimation. These results demonstrate the model's effectiveness in integrated human activity recognition and localization. However, a noticeable decline in accuracy was observed at 40 MHz, highlighting the correlation between experimental conditions and model performance. Ultimately, our findings not only advance Wi-Fi-based sensing technology but also have significant implications for smart home automation, elderly care monitoring, and real-time security surveillance. By improving accuracy in outdoor settings, this study paves the way for deployable, non-intrusive activity recognition systems in public spaces, emphasizing the significance of experimental configurations and data augmentation strategies in enhancing model accuracy for real-world applications. These results pave the way for future research aimed at refining and expanding the capabilities of hybrid deep neural networks in addressing the complexities of human activity identification and localization. Future studies should focus on improving experimental setups, exploring advanced network architectures, diversifying data collection efforts, and analyzing the impact of different layouts on model performance.  
The work presented here was carried out in collaboration among all authors. Conceptualization, Y.Y.M. and M.A.; formal analysis, Y.Y.M. and Y.B.; methodology, A.M. and Y.Y.M.; supervision, W.A.; visualization, Y.Y.M. and M.A.; writing--original draft, Y.Y.M.; writing--review and editing, Y.B. and A.M.; Fund Acquisition, Y.Y.M. All authors have read and agreed to the published version of the manuscript.  
No funding received for this paper.  
The dataset can be available upon request.  
The authors acknowledge the internet resources provided by the Bahir Dar Institute of Technology and Injbara University.  
The authors reported no potential conflict of interest.
# Prediction of Freeway Traffic Breakdown Using Artificial Neural Networks
## 3 Data Description
### Upsampling  
To construct a model to predict the occurrence of a traffic breakdown in a road segment in the next time interval, binary indicators are assigned to each road segment and each time interval, indicating the occurrence of a traffic breakdown. Specifically, the time intervals during which traffic breakdown occurs are assigned as 1, while the time intervals during which traffic breakdown does not occur are designated as 0. As the purpose of this study is to predict the occurrence of a breakdown, the time intervals within the breakdown period, except for the one with the onset of a breakdown, are not used to construct the model.  
The distribution of indicators in the training subset is imbalanced, as shown in Table 1. This is because only one time interval is labeled as 1 for each traffic breakdown event, while all other time intervals where the breakdown did not occur are labeled as 0. The presence of an imbalance in the distribution of labels can cause a model to prioritize majority labels over minority labels to achieve maximum accuracy. [PERSON] and [PERSON] [25] illustrated that neural networks tend to overfit when a model is used directly in an imbalanced data set. Incorporating feature selection prior to upsampling also improves the robustness of the model. Thus, to avoid overfitting problems, an upsampling mechanism is applied by randomly selecting data from the minority class and replicating it until the number of minority samples is equivalent to that of the majority class [26].
## 3 Numerical Modeling of the Filling Phase
### Rheological Models
#### 3.2.1 Power-Law  
One of the most frequently employed rheological models in the field of injection molding simulation is the Power-Law model, which posits a Power-Law relationship between the shear stress and shear rate of the polymer melt. The Power-Law model can be represented by the following mathematical formulation:  
\[\eta=\eta_{0}\gamma^{n-1} \tag{10}\]  
The parameters that are pertinent to this context are \(\eta_{0}\), which is defined as the Newtonian viscosity or the viscosity at zero shear rate, \(n\) is the Power-Law index, which takes on values between 0 and 1 for polymer melts and \(\dot{\gamma}\) is the shear rate. When the viscosity at zero shear rate is equal to the constant fluid viscosity and the Power-Law index is equal to one, the resulting equation describes a Newtonian fluid [24; 25].
# Noise Control in Air Mechanical Ventilation Systems with Three-Dimensional Metamaterials
## 6 Conclusions  
This paper deals with the acoustic measurements of acoustic filters realized with three-dimensional metamaterial structures. The filters are made with spheres of a diameter equal to 24 mm. The results are reported in terms of insertion loss obtained as the difference between the level measured without and with the filter installed. The goal of this research study is the realization of this acoustic filter with metamaterials to be applied for the sound attenuation produced by the mechanical ventilation systems. These conditions dictate an optimistic approach to the measured results since material properties of the enclosure can potentially create polarized effects inside the limited volume of the housing [39].  
The results of the acoustic measurements show that the sound attenuation occurs in specific frequency bands, corresponding to the total components of the noise produced by a fan typical of a mechanical ventilation system. The advantage of this proposed acoustic filter consists of its suitability to be used in small spaces, such as those offered by the enclosures of the mechanical systems, in contrast to being 100% sustainable if recycled waste is employed for its creation, such as hard plastic and chipboards. The consequent advantage is the reduced cost of the final product for the manufacturers.  
Extending the application of the three-dimensional lattice to environmental acoustics, attenuation of road traffic noise can be widely incorporated by developing an innovative aesthetic design representing a breathing structure instead of the traditional opaque andcontinuous barrier. In addition, the automotive industry could also benefit from this type of study, as engine noise is one of those issues that car manufacturers tend to keep quiet about. As such, filters inside vehicles' exhaust pipes can help reduce noise.  
Applications of this topic in other scientific fields relate to biomedicine with the control of frequencies within specific cells of the human body so as not to affect adjacent areas.  
Conceptualization, G.I. and A.T.; methodology, G.I. and A.B.; software, G.I.; validation, A.B.; formal analysis, A.B.; investigation, G.I.; resources, A.T.; data curation, A.B. and G.I.; writing--original draft preparation, G.I. and A.T.; writing--review and editing, A.B and A.T.; visualization, A.B.; supervision, G.I.; project administration, A.T.; funding acquisition, A.T. All authors have read and agreed to the published version of the manuscript.  
This work was financially supported by Ministero dell'Istruzione, dell' Universita e della Ricerca (MIUR)--PRIN 2017 Progetto Settone PE8 Codice 2017T88 BH9_001. \"Theoretical modeling and experimental characterization of sustainable porous materials and acoustic metamaterials for noise control.  
Not applicable.  
The authors declare no conflict of interest.
## 1 Introduction
Since a precise cadastral survey requires direct observation of the building's outer wall and a clear description of the special specifications such as the independent column, single column porch, and carrot, oblique or ground view, dense oblique point clouds are becoming gradually more popular in the field of the cadastral survey, in recent years [22, 23, 24]. These point clouds can provide all-round and multi-angle observation of the target building outer walls and special specifications, while bringing huge challenges to current automatic approaches:  
1. The precise and complete fitting of the wall surface remains problematic [5,25]. Because of the limited viewpoints and occlusion, the wall segments are often incomplete, especially in dense built-up areas. Additionally, due to the existence of windows and other building attachments, as well as the interruptions of shadow and vegetation, the wall points by stereo matching may not be strict vertical planes, leading to spurious or incomplete results. 2. Error-proneness of wall segment topology [26]. The intersection of adjacent walls and the searching of closed wall polygons require a clear description of wall-wall topology. Imperfect wall extraction results will result in incorrect connections, which require further interaction for industrial solutions. Furthermore, the regularization of the boundaries also requires a global description of wall topology. In this work, a robust cadastral survey approach is proposed based on dense point clouds. Two attempts are applied to ensure the accuracy and uniqueness of the wall locations: the non-maximum suppression of the projection points on the walls to avoid duplicate extraction results; and, meanwhile, multiple cue weighted RANSAC, considering both point-to-line distance and normal consistency, to reduce the influence of noise and building attachments. As for the wall topology construction, a graph structure is built to store and analyze the connection relationship and parallel/vertical constraints among adjacent wall segments. Minimal closure analysis is used upon the graph structure and,meanwhile, the overall domain direction estimation and boundary optimization adjustment ensure the overall regularization. Finally, the point cloud distribution within the closed polygon can effectively distinguish structures such as houses and attached courtyard walls to obtain the semantic vector scene. In summary, the contribution of the proposed methods is twofold: (1) the non-maximum suppression of the projection points and multiple cue weighted RANSAC to ensure the accuracy and uniqueness of the wall locations, and (2) the analysis of the wall line segment topology graph for the connecting and adjustment of the wall segment. The remainder of the paper is structured as follows: Section 2 presents the proposed methods in detail, followed by the assessment and discussion in Section 3. Section 4 concludes this paper.
# Fabrication of a Conductive Pattern on a Photo-Polymerized Structure Using Direct Laser Sintering
## 2 Materials and Methods  
### Experiment Principle  
In order to improve the conductivity of a composite material that has a binder and a metal powder together (such as conductive ink or a metal filament), a process of applying heat is often used [16; 17]. In particular, sintering using a laser is one of the most widely used techniques because it can minimize thermal damage to the entire structure by applying heat to a specific area. In addition, depending on the process conditions of the irradiated laser, only the polymer binder may be removed or a local sintering phenomenon may be made by inducing surface melt bonding between the metal powders. Therefore, in this study, by increasing the laser power used to cure the resin in the PP-type 3D printer and by irradiating it, the polymer binder amongst the printed metal composite structure was removed and the metal powders contained in it were simultaneously sintered to fabricate the conductive pattern.  
For this, as shown in Figure 1, a 3D structure was fabricated using commercial resin and then a high-content composite resin mixed with metal powder and resin was applied and cured on the layer requiring a conductive pattern. In this case, as the composite resin had a significantly lower light transmittance and curability when irradiated with the same energy compared with general commercial resins, separate curing conditions had to be applied. After that, when the cured composite resin structure was irradiated with an increased laser power, the polymer, which mainly consisted of carbon and hydrogen, was decomposed into gas such as CO\({}_{2}\) and H\({}_{2}\)O. At this time, if the laser power was excessive, the metal powder inside was also ablated. Conversely, if the laser power was too low, as only the polymer binder was removed, the remaining metal powder was no longer attached to the base material, but was blown away and removed as well. Accordingly, by properly controlling the laser process conditions, the polymer binder was ablated and, at the same time, local sintering occurred between the metal powders so that it had to be connected to a single metal structure. Thus, this study conducted experiments in which these laser processing parameters were changed to determine their influence on the material removal efficiency and conductivity of the pattern.
## 3 Results and Discussions
### Picking Rate  
In a preliminary analysis, the picking rates, calculated using mixed integer linear programming, offer some insights on the performance of the routing heuristics. The traversal or S-shape routing yields the highest number of items per hour on the average of warehouses and paperless technology, followed by the midpoint policy. Midpoint, however, outperforms traversal in warehouse 1. As expected, the medium size warehouses have a higher picking rate than warehouse 1 and 4. Although warehouse 2 and 3 are bigger than warehouse 1, they have a faster picking device and can  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Cost Factors** & \multicolumn{2}{c}{**Barcodes Handheld**} & **RFID Tags Handheld** & **Voice Picking** \\ \hline \(C_{SL}^{j}\) & & \$1.18 & \$1.39 & \$1.18 \\  & Warehouse 1 & 200 & 200 & 200 \\ \(n_{SL}^{w}\) & Warehouses 2–3 & 400 & 400 & 400 \\  & Warehouse 4 & 800 & 800 & 800 \\ \(h_{SL}\) & & 3520 h & 3520 h & 3520 h \\ \(C_{h,P}\) & & \$20 or \$25 & \$20 or \$25 & \$20 or \$25 \\  & Warehouse 1 & \$167 & \$1244 & \$2000 \\ \(C_{D,P}^{j1}\) & Warehouses 2–3 & \$167 & \$1244 & \$2000 \\  & Warehouse 4 & \$167 & \$1244 & \$2000 \\  & Warehouse 1 & \$466 & \$466 & \$466 \\ \(C_{D,P}^{j2}\) & Warehouses 2–3 & \$1000 & \$1000 & \$1000 \\  & Warehouse 4 & \$2000 & \$2000 & \$2000 \\ \(h_{Dx,P}\) & & 3520 h & 3520 h & 3520 h \\  & Warehouse 1 & \$15,000 & \$15,000 & \$20,000 \\ \(C_{F}^{w}\) & Warehouses 2–3 & \$20,000 & \$20,000 & \$27,000 \\  & Warehouse 4 & \$30,000 & \$30,000 & \$40,000 \\ \(h_{F}\) & & 3520 h & 3520 h & 3520 h \\ \hline \hline \end{tabular}
\end{table}
Table 4: Cost components values.  
handle larger batch sizes. In terms of paperless technology, the voice picking largely outperforms both handheld devices whose performance looks similar (see Table 5).