esa-sceva/satcom-chunk-collection huggingface dataset:
# Internet of Vehicles for Sustainable Smart Cities: Opportunities, Issues, and Challenges
## 5 The Layered Architecture of the IoV and Emerging Technologies in Smart Cities  
The IoV's layered architecture introduces a structured data processing and decision-making model from the perception layer to the business layer. Not only does it support efficiency, scalability, and adaptability, which are essential for IoV's integration into the smart city environments and into the higher transportation industry, but it also provides a toolkit for addressing problems existing within the IoV. Additionally, emerging technologies including 5G/6G, D2D communication, SDN, and cyber-physical architectures supplement the IoV. Indeed, even though these technologies play a critical role in enriching the abilities of the IoV in smart city infrastructure, 5G and 6G technologies have better connectivity and data processing features. The IoV architecture consists of multiple layers,including perception, coordination, AI, application, and business layers, each serving distinct functions within the system [70]. This section extends the foundational model by relating it to smart city environments and ITSs. Recent advancements, such as AI-driven coordination, are incorporated along with an exploration of how these layers interact with various levels of connected and autonomous vehicles. The architecture is explained in the context of smart cities highlighting its role in enhancing urban mobility, connectivity, and intelligent transportation services.
###### Abstract  
This study presents a stability analysis of an extended berm reinforced by geotextiles, with a steep slope of 1V:1.1H (vertical: horizontal). Finite element (FE) analyses were carried out to explore the failure mechanism and factor of safety (FOS) of the berm, on which the effect of the strength of geotextiles, leachate level, and anti-slide pile arrangement located at the toe of the berm were considered. It was found that: (1) failure surfaces developed along the interface between the new and the existing berms; (2) the FOS decreased as the leachate increased, and an FOS value of 1.42 could be obtained if the leachate level was controlled at a height of 20 m; (3) the tensile force of geotextiles was far lower than the available strength, which suggested that the geotextile had enough of a safety reserve; and (4) one row of longer piles at the toe of the berm performed better than two rows of shorter piles if the total length of piles was the same. The design and analysis of this project can be used as a reference for landfill expansion. Especially for a site condition with limited space, a geosynthetic-reinforced soil (GRS) berm is a safe, reliable and promising alternative.  
stability analysis; berm; municipal solid waste (MSW) landfill; geotextile; factor of safety 10.390/app10186281 10.  
landfill space. However, the deformation of berms was not considered in their study, in which the berm was regarded as a rigid body and the slip surface needed to be assumed in advance to pass over the back slope of the berm or pass under the bottom of the berm. [PERSON] and [PERSON] [19] performed a finite element numerical simulation to assess the integrity of geogrid-reinforced steep slopes subjected to differential settlement and seismic loading because the finite element method can be used to monitor progressive failure up to and including overall shear failure, without assumption for the locations of slip surfaces. They found that the maximum predicted geogrid strains were indicated to well below the geogrid's allowable strain values; the failure mechanism was not investigated.  
In this study, stability analyses were conducted to provide a better understanding for the behavior of the Xingfeng MSW landfill expansion accompanied by a GRS berm extension. The stability nallyses were performed using strength reduction method combined with FEM to obtain tensile force of geotextile, slip surface and factor of safety required by Chinese MSW landfill design standards (CJJ/176-2012 [20]) to be not less than 1.35 under normal working condition. The influence on the stability of geotextile strength, leachate level, and anti-slide pile arrangement were considered, with the aim to provide a quantitative safety evaluation and guidance for the application of the technique for GRS berm extension.
# Using Improved Brainstorm Optimization Algorithm for Hardware/Software Partitioning
## 4 Hardware/Software Partitioning Based on IBSO Algorithm
### IBSO Algorithm
#### 4.2.1 Improved Clustering Method  
In terms of the clustering method, suppose that there are \(N\) individuals to be clustered into \(M\) clusters. Firstly, setting \(M\) equidistant coordinates in the solution space which can be expressed as \(C=\{c_{1},c_{2},\ldots,c_{i},\ldots,c_{M}\}\), where \(c_{i}\) is an _L_-dimensional vector, and \(L\) is the dimension of the solution. The equidistant coordinates are called cluster points in this paper and \(c_{i}\) is the cluster point of the _i_-th cluster. Then all individuals are sorted according to their fitness values. Finally, clustering the sorted individuals into the cluster whose cluster point is nearest to them. The Manhattan distance is used as the distance between individuals and cluster points. The \(Dis_{j,k}\) between individual \(x_{j}\) and cluster point \(c_{k}\) can be calculated by [33]:  
\[Dis_{j,k}=\sum_{i=1}^{L}|x_{j}(i)-c_{k}(i)|, \tag{5}\]  
where \(x_{j}(i)\) and \(c_{k}(i)\) are the _i_-th dimension of \(x_{j}\) and \(c_{k}\) respectively.  
In addition, the number of individuals in each cluster must be same. So when the number of individuals in a cluster reaches \(N/M\), this cluster is saturated. In this case, the individuals which are not clustered should be clustered to other unsaturated clusters whose cluster points are nearest to them. The pseudo-code of the improved clustering method is shown in Algorithm 2.  
```
1 Init_points(); /*Initiating \(M\) equidistant coordinates as the cluster points*/
2 For(\(j=1\) to _N_)
2.1 For(\(k=1\) to _M_)
3 If(Its_cluster_saturated(\(k\))) /*If the \(k\)th cluster is not saturated*/ \(Dis_{j,k}\) = calculate_distance_with_points(); /*Calculating distance \(Dis_{j,k}\) between \(x_{j}\) and \(c_{k}\)*/ If(Its_shortest(\(Dis_{j,k}\))) /*If the distance between individual \(x_{j}\) and cluster point \(c_{k}\) is shorter than that between \(x_{j}\) and other cluster points of unsaturated clusters*/  Cluster_number_of(\(x_{j}\)) = \(k\); /*Clustering individual \(x_{j}\) into cluster k*/ End If End If End for End for
```  
**Algorithm 2**Improved clustering method  
If the individuals in Figure 1a are clustered by the improved clustering method, the clustering result is shown in Figure 3. It can be seen that individuals in the same region are clustered into the same cluster, which will effectively avoid individuals gathering into a local region in the updating process.
## 3 Wireless Sensor Networks
### WSN Applications  
Recent advance in the integration and miniaturization of physical sensors, embedded microcontrollers and radio interfaces on a single chip; wireless networking; and micro-fabrication have provided a new generation of wireless sensor networks suitable for many applications. The features described in the previous section provide a wide range of applications for sensor networks. Wireless sensor networks can be used for military applications, habitat monitoring, machine health monitoring and guidance, traffic pattern monitoring and navigation, plant monitoring in agriculture [29], and infrastructure monitoring. One of the most exciting applications is health monitoring [30; 31]. In this paper we focus on healthcare applications, and mainly on wearable and implantable body sensor networks. A number of physiological sensors that monitor vital signs, environmental sensors, and a location sensor can be integrated into a wearable wireless body area network (WWBAN) [32]. In addition, the concept of micro-sensing and wireless connection of network nodes promises many new application areas. We have categorized the applications into military, environment, health, home and other commercial areas. It is possible to extend this classification with more categories such as space exploration, chemical processing, disaster relief, and so on. We briefly describe these applications and for more details, the reader should refer to [33].
## 1 Introduction
Therefore, it is not surprising that for several years, multisensor systems have been entering the market very dynamically and gaining more and more sympathizers among metrologists. Using a multisensor measuring machine allows one to save time, and thus gives the opportunity to increase production volume without having to invest in new production machines and searching for a suitable place in the organization area. Obviously, when the contact sensor of a multisensor machine is used for measurement of other characteristics, the measurement time will be longer, which does not change the fact that the final balance of the time needed is in favor of the multisensor machine. However, the question is how to compare the results obtained from the aforementioned multisensor machine using an optical sensor with results from a conventional machine with a contact sensor and with results obtained using a height gauge? MSA analysis should help answer this question as well as help determine which system is better for a given characteristic and what strategy to apply for the analyzed cases. In his article, [PERSON] [11] reminds us that measurement system analysis is a mandatory element of organization in the automotive industry. Therefore, before starting the ability tests, criteria and parameters were defined, which were subjected to statistical analysis, and then MSA analysis was completed. The authors state that during these tests, all the devices mentioned in the control plan should be taken into account. Later in the article, it is stated that a lack of supervision and statistical evaluation of measurement equipment may result in false decisions. On the other hand, [PERSON] in [12] states that a measurement system is a process that includes standards, personnel, and methods of obtaining measurements of some quality characteristics. All components and variables of the measurement system that have a significant impact on the selection and acceptance of an appropriate measurement system should be optimized. The primary goal of MSA is to quantify accuracy, precision, and stability, which is directly related to the statistical evaluation of the process. Statistical process control (SPC) methods are one of the important instruments in the quality management strategy [13]. So far, no publication has been found that would describe the differences in measurement results between the CMM with a contact sensor (tactile stylus) and an optical sensor. Questions like those presented above are raised in a majority of companies from the automotive, aviation, and machine industries. This paper tries to give answers to these questions. A methodology for analysis of different system capabilities in relation to the same measuring tasks and guidance on interpreting the results of them are given in this paper.
## 5 Experimental Results and Analysis
### Experimental Study on Speed Prediction of Section Vehicles Based on WT-LightGBM
#### 5.2.2 Result Analysis
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **RMSE** & **MAE** & **R\({}^{2}\)** \\ \hline SVR & 9.3619 & 7.423 & 0.6751 \\ KNN & 7.7573 & 5.4283 & 0.7769 \\ GRU & 5.8393 & 4.4038 & 0.7837 \\ RF & 6.9826 & 4.9885 & 0.8215 \\ Xgboost & 6.6867 & 5.2048 & 0.8342 \\ WT-LightGBM & 5.4668 & 3.9739 & 0.8892 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Comparison of LD\({}_{2}\) prediction performance evaluation. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **RMSE** & **MAE** & **R\({}^{2}\)** \\ \hline SVR & 8.2056 & 6.58 & 0.573 \\ KNN & 6.751 & 5.1302 & 0.7109 \\ GRU & 6.3946 & 4.4773 & 0.7244 \\ RF & 5.3202 & 4.0131 & 0.8092 \\ Xgboost & 6.2162 & 4.9936 & 0.7549 \\ WT-LightGBM & 4.8155 & 3.6436 & 0.8529 \\ \hline \hline \end{tabular}
\end{table} 
Table 11: Comparison of LD\({}_{3}\) prediction performance evaluation. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **RMSE** & **MAE** & **R\({}^{2}\)** \\ \hline SVR & 6.1504 & 4.7862 & 0.8561 \\ KNN & 5.1107 & 3.842 & 0.9 \\ GRU & 5.2293 & 4.0131 & 0.8156 \\ RF & 4.3362 & 3.1638 & 0.9276 \\ Xgboost & 4.9316 & 3.9715 & 0.9075 \\ WT-LightGBM & 3.6233 & 2.7256 & 0.95 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Comparison of LD\({}_{1}\) prediction performance evaluation. In the experiment LD\({}_{2}\), the data environment is more complex because there are service areas and noisy data on the road section. Nevertheless, the WT-LightGBM model still performs better than other models, with the R\({}^{2}\) score of 0.8892, and RMSE and MAE of 5.4668 and 3.9739, respectively. This indicates that the WT-LightGBM model has excellent robustness when dealing with noisy data. In the experiment LD\({}_{3}\), it was considered that there is a high traffic volume during holidays, and there is a service area, resulting in high data volatility. In this more complex situation, the WT-LightGBM model still achieved the best results, with the R\({}^{2}\) score of 0.8529, and RMSE and MAE of 4.8155 and 3.6436, respectively. This proves that the WT-LightGBM model can maintain high prediction accuracy when dealing with data with a large amount of volatility. To better observe the experimental effect of the model, this article selects LD\({}_{1}\) and uses the WT-LightGBM model to predict the visualization of the current vehicle traffic speed. The results are shown in Figure 11, where blue represents the actual vehicle speed and yellow represents the predicted vehicle speed. From the figure, it can be seen that the predicted vehicle speed has a similar trend to the actual vehicle speed, indicating that the model can accurately predict vehicle speed.
## 1 Introduction
### Climatology and Microgrid Architectures at the Three Locations
#### 1.3.1. SkyImager at National Renewable Energy Laboratory in Golden, CO  
The site of the first SkyImager deployment was NREL in the Rocky Mountains. Golden's high elevation and mid-latitude interior continent geography results in a cool, dry climate. There are large seasonal and diurnal swings in temperature. At night, temperatures drop quickly and freezing temperatures are possible in some mountain locations year-round. The thin atmosphere allows for greater penetration of solar radiation. As a result of Colorado's distance from major sources of moisture (Pacific Ocean, Gulf of Mexico), precipitation is generally light in lower elevations.  
Eastward-moving storms from the Pacific lose much of their moisture falling as rain or snow on the mountaintops. Eastern slopes receive relatively little rainfall, particularly in mid-winter. The Skylmager enclosure came equipped with a heater/fan that performed well at NREL. Given the  
Figure 5: SkyImager at (**a**) NREL, (**b**) JBSA, (**c**) Tenerife and (**d**) Caleta de Sebo.  
climate, it proved useful in keeping frost off the plastic dome. It adds to the expense and complexity of the technology and may not be required at other locations. Most installations of the security camera enclosure would be facing downward and perhaps under a building overhang. Used facing upward and exposed to the sky, there were issues with water getting inside the enclosure. A simple solution was silicon caulk applied at the base of the dome. In a typical security installation, a green tinted plastic dome is used with the enclosure to protect components from UV radiation. For all-sky imaging a clear plastic dome is a necessity. With any plastic material on a bright sunny day there can be issues with glare caused by the dome, but this was minor. The alternative is a glass dome but that has it own set of problems.  
As shown in Figure 6, the microgrid at NREL was already well established and the process of deploying the SkyImager went relatively smoothly. Denver International Airport is located some 36 miles from Golden; this distance introduces some error in the Cloud Base Height for the ray-tracing algorithm originally used in the SkyImager. The SEIF building at NREL had the infrastructure necessary for easy installation of both the SkyImager and the Hardkernel Odroid C1 single board computer (SBC) used for load and day-ahead PV forecasts. Information was transferred using a Wi-Fi network on a LAN system. NREL also provided un-interruptible 120 VAC power, ample Ethernet connectivity, and excellent on-site weather and irradiance data. In addition to solar PV arrays, generation included a 500 kW wind power simulator and a 300 kW caterpillar diesel. A 300 kWh battery system provided energy storage and the load was separated into a controllable component (250 kW) and a critical load (250 kW).
# Development and Evaluation of a LiFi-Transceiver Module for TMTC Intra-Satellite Communication +
## 7 Conclusions  
Wireless intra-satellite communication based on LiFi provides reduced harness and more flexible AIT, and it offers advantages over RF-based solutions in terms of EMI and security aspects (spoofing and jamming). In this work, the first developments and evaluations from the SatelLight [7] project were presented. A TIA for a LiFi receiver was simulated and a LiFi-transceiver based on that was implemented for the TMTC communication of small satellites. The transceiver can be highly miniaturized to a size smaller than 9.2 mm \(\times\) 9.6 mm and a maximum power consumption of 38.7 mW. In the future, LiFi can be used in addition to TMTC communication with low bandwidths due to the large and unregulated spectrum of light for various interfaces within the satellite, which was successfully demonstrated in this paper.  
**Author Contributions:** Conceptualization, M.J., B.P. and U.K.; methodology, M.J. and U.K.; software, M.J.; hardware, M.J.; investigation, M.J.; resources, U.K.; writing--original draft preparation, M.J. and U.K.; writing--review and editing, M.J., B.P. and U.K.; project administration, U.K. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work is part of the _SatelLight_ project which is funded and administered by the German Space Agency at DLR, supported by the Federal Ministry for Economic Affairs and Climate Action (FKZ 50 RP2360B).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Data are contained within the article.  
**Conflicts of Interest:** The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of the data; in the writing of the manuscript; or in the decision to publish the results.
## 4 Conclusions  
In this study, experimental results provided a detailed insight into rheological and mechanical behaviors of geopolymer concrete comprising fly ash and FCC residue as aluminosilicate precursors. Key findings from this study are summarized as follows:  
1. With low rheological properties such as yield stress and plastic viscosity, fly ash-based geopolymer concrete showed better workability compared to specimens comprising FCC residue. With the presence of large and angular-shaped FCC residue particles, fresh geopolymer concrete containing both fly ash and FCC residue showed up to 12% and 39% higher yield stress and plastic viscosity compared to that consisting of only fly ash. Therefore, for the incorporation of FCC residue into geopolymer concrete, there was a need for extra alkali liquid to maintain similar workability to fly ash-based geopolymer concrete.
2. The 7-day compressive strength, elastic modulus, and flexural strength of geopolymer concrete investigated in this study varied within 10-26 MPa, 12.3-21.3 GPa, and 0.9-2.8 MPa, with percentages of FCC residue content ranging from 0-100% by mass as replacement of fly ash. In the case of AL:GS ratios of 0.40 and 0.50, mechanical properties (i.e., compressive strength, elastic modulus, and flexural strength) of geopolymer concrete decreased with an increase in FCC residue content. Meanwhile, this trend was opposite with AL:GS ratio of 0.65 where geopolymer performance increased with an increase in FCC residue content. Apart from the mixture with AL:GS = 0.65, with partial replacement of fly ash, FCC residue with large and angular-shaped particles created more voids inside the concrete structure and resulted in mechanical properties reduction.
3. SEM observations showed that the microstructure of geopolymer concrete consisting of FCC residue was more porous than fly ash-based geopolymer concrete, leading to reduced compressive strength.
4. XRD patterns of geopolymer concrete revealed that with the replacement of FCC residue, final products from the geopolymerization of geopolymer concrete containing FCC residue were not much different compared to fly ash-based geopolymer concrete.  
In conclusion, FCC residue containing high contents of alumina and silica can play the same role as fly ash in geopolymer products. From this fact, FCC residue can be partially or fully replaced with fly ash for producing geopolymer concrete.  
Conceptualization, T.A.L. and K.T.N.; funding acquisition, T.A.L.; investigation, K.T.N. and S.H.L.; methodology, T.N.N.; project administration, K.T.N.; supervision, T.A.L. and K.T.N.; visualization, S.H.L.; writing--original draft, K.T.N.; writing--review and editing, T.N.N. and S.H.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Vietnam National University Ho Chi Minh City (VNU-HCM) under grant number B2020-20-01.  
Not applicable.  
The authors declare no conflict of interest.