esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Application to the Shuangjiangkou Hydropower Station
### Inversion Analysis of the In Situ Stress Distribution
#### 3.2.3 Identification of Boundary Conditions  
A reliable nonlinear relationship between boundary conditions and the stress at the measured points is crucial for the accuracy of the inversed in situ stress distribution. However, the traditional method may encounter some issues such as 'over-fitting' or 'under-fitting' due to the uncertainty in the training sample size. The limitations restrict the ability of nonlinear mapping to approximate and subsequently affect the accuracy and reliability of the algorithm results. To address this issue, the PSO optimization method was used to optimize the BP neural network and establish a more accurate and reliable nonlinear model.  
The PSO-BP neural network was trained using the boundary conditions listed in Table 4 as input samples and the corresponding calculated stresses at the measured points as sample outputs. The Shuangjiangkou hydropower station has five measurement points around the left underground caverns, each with six stress values; thus, a 30-16-6-6 topological network was constructed. The mean square error of the network model was set to \(10^{-8}\), which was used to judge the training accuracy of iterations. The PSO-BP network was stopped after 3367 iterations when the error reached the preset value. Subsequently, the actual stresses at each measured point are input into the trained PSO-BP model to search for the optimal combination of boundary conditions. The inversed coefficients obtained by the PSO-BP neutral network are \(k_{\rm g}=1.43\), \(k_{\rm x}=1.26\), \(k_{\rm y}=1.60\), \(\tau_{\rm xz}=1.61\) MPa, \(\tau_{\rm yz}=-2.51\) MPa, and \(\tau_{\rm x}=1.44\) MPa.  
To validate the accuracy of the determined boundary conditions, the obtained coefficients are utilized in the FLAC\({}^{\rm 3D}\) software for additional forward analysis. Table 5 gives the stress results for each measurement point. It shows that the analysis results are generally acceptable for most points, except for the resulted \(\sigma_{\rm z}\) of point 5#, where the analysis error is 20.33%. The average analysis error for the remaining points is 8.09%. Compared with Table 3, the measured \(\sigma_{\rm x}\) and \(\sigma_{\rm y}\) of point 5# are almost equal to those of other points, but the measured \(\sigma_{\rm z}\) of point 5# is 7-15 MPa greater than that of other points, so it may not be accurately measured. As a result, the back-analysis \(\sigma_{\rm z}\) of point 5# does not match very well with the measured data. Figure 6 compares the inversed and tested stresses. The dashed line indicates where the two stresses are equal. It can be observed that there is excellent agreement between the PSO-BP results and measured data, with a correlation coefficient of about 0.95.
## 4 Conclusions and Discussion  
Tropical climates are challenging conditions for thermal imaging. The methodologies followed for thermal imaging in colder countries might not apply to tropical conditions, due to different relative humidity and average temperature levels. Through the surveys conducted at the former British Residency in Hyderabad, we were able to understand the effects of these differences. We noticed that an increase in external temperature caused an overall heating of the building, leading to increased evaporation at the surfaces happens in tropical climates, which is different from the solar insolation and induced heating inside buildings, changing the internal and external temperatures of target structures in countries with colder climates.  
Another aspect to consider, which is not discussed in detail here, is the change of the rate of evaporative flux that is caused due to the porosity of the material. At the Residency, the bricks and lime mortar are very porous materials, and so the results are more visible. This would not be the same with exposed stone masonry structures. Future work on the rubric could be towards including  
Figure 5: This is a graphical representation of the rubric to evaluate optimal and non-optimal conditions for thermal surveys in Hyderabad.  
more parameters that effect thermal properties of the materials and, consequently, their appearance in the thermal images. When developed sufficiently, this study could be further extended to evaluate the difference in thermal properties of local vernacular materials when used in foreign construction typologies to that of vernacular architecture. This would help understand how vernacular construction materials differ in thermal properties when applied in British design, as opposed to vernacular design and architecture. This is since British or Western designs evolved with the use, maintenance, and repair of their building materials, while in this case, and broadly the subcontinent of India, the use of local Indian materials was widespread. This difference comes to light during the restoration of built heritage and necessitates the establishment of guidelines that are context specific.  
Conceptualization, E.R. and M.K.; methodology, E.R.; software, M.K.; validation E.R.; formal analysis, E.R. and M.K.; investigation, M.K.; resources, E.R.; data curation, E.R.; writing--original draft preparation, M.K.; writing--review and editing, E.R.; visualization, M.K.; supervision, E.R.; Both authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors wish to acknowledge the Department of Heritage, Government of Telangana, the Osmania University College for Women, and GN Heritage Matters, the consulting architectural firm for the project of restoration at the Osmania University College for Women. Also, [PERSON] and [PERSON] at the College of Architecture, Texas A&M University for their support.  
The authors declare no conflict of interest.
# Analysis of LTE-M Adjacent Channel Interference in Rail Transit
### Motivation and Contributions  
Existing research focuses on interference between LTE and other communication systems in various frequency bands, with the majority of them examining interference between LTE-FDD, TD-LTE, NR-FDD, TD-SCDMA, and DVB-T system cells caused by base stations. These systems are not the main communication means of urban rail transit system, and their frequency band is not within the range of 1.8 GHz. In addition, compared with the base station, the user terminals are widely distributed, and the location is uncertain, which poses a greater threat to train control. Currently, the analysis approach for a 1.8 GHz LTE-M system subjected to ACI still lacks related deterministic calculation and system-level simulation. Therefore, in this paper, the deterministic model under different interference scenarios for a LTE-M underground system is derived. Moreover, the wireless channel propagation model between different communications objects is presented for LTE-M system; the minimum isolation degree is obtained through interference link calculations. The LTE FDD cellular topology and LTE-M linear topology models, as well as the power control model and throughput calculation process, are all established for the LTE-M ground system. The minimum isolation degree is obtained by changing the simulation parameters. Finally, the anti-interference recommendations for urban rail transit systems are provided as part of the interference reduction strategies.
###### Abstract  
Steam-assisted gravity drainage (SAGD) is widely applied to recover bitumen and heavy oil resources. Reservoir heterogeneity, especially the presence of shale barriers, continues to challenge the performance of SAGD. A novel enhanced oil recovery process, bottom-up assisted pressure drive, is proposed to improve the oil production in the reservoirs with shale barriers. In this work, numerical simulation is applied to investigate the feasibility of a bottom-up assisted pressure drive process. A reservoir model with typical oil sand reservoir properties is developed considering shale barriers. The performance of bottom-up assisted pressure drive and SAGD is compared under the same reservoir conditions, including steam chamber development, oil production rate, cumulative oil production, and the pressure difference between injector and production. The inherent mechanisms associated with the bottom-up assisted pressure drive are also well understood and confirmed. In the bottom-up assisted pressure drive, a flat steam chamber is developed from the bottom of the reservoir in the early stage of the process and grows upward with the injection of steam. The large volume of the steam chamber and the huge contact area between steam and bitumen contribute to a high oil production rate. The peak oil production rate in the bottom-up assisted pressure drive is approximately three times that in the SAGD process. The cumulative oil production in the bottom-up assisted pressure drive is 20% higher than that in the SAGD process. The effect of shale barriers on bottom-up assisted pressure drive is less, indicating one advantage of this novel process over SAGD in oil sands reservoirs with shale barriers. The pressure difference in the bottom-up assisted pressure drive is greater than that in the SAGD process. The pressure drive is another mechanism for improving oil production. The calculated net present value (NPV) in the bottom-up assisted pressure drive process is 27% higher than that in the SAGD process. This is mainly attributed to the high oil production rate in the early stage of the process and high cumulative oil production. The simulation study in this work provides technical support for the future field applications of this novel recovery process.  
thermal recovery; heavy oil or bitumen; steam injection; reservoir simulation +
Footnote †: journal: Applied Sciences
# Design and Implementation of a Multi-Function Hydrophone for Underwater Acoustic Application
## 2 System Overview
### Software Design
#### 2.2.2 Data Uploading  
Since the limitation of MCU for digital signal processing, some emergency data cannot be processed in real time. To address it, such emergency data can be uploaded to a powerful host computer, therefore the results can be measured at once. The diagram of data uploading is shown in Figure 7. When using this function, an additional cable and gateway are required. The gateway can communicate with satellites, or establish local area networks such as WIFI, so researchers in the laboratory or on the ship can receive and analyze the raw acoustic data in time. UDP is a good choice because when it loses connection, it does not require a re-connection, and the transmission will continue. In this mode, the ring buffer is no longer required. When the ping buffer is occupied by DMA, the data from the pong buffer is uploaded to the gateway, and vice versa.  
Figure 5: The flowchart of different threads.  
Figure 6: Data flowchart of “raw data storage” thread.  
This thread is defined as \"mode 2\".
# Design and Analysis of High-Accuracy Telecentric Surface Reconstruction System Based on Line Laser
## 4 Solution Concept  
As the calibration results are achieved, the telecentric surface metrology system can be utilized to measure the region of interest (ROI). For the measurement, put the object on the displacement platform. Then, keep the laser illuminating the ROI, and capture an image every time after the movement of the platform. Thus, a series of measured images are obtained. The measurement procedure mainly consists of three parts, which are laser stripe extraction, intersection measurement, and mosaic of measured points successively. The flowchart of the online measurement procedure is presented in Figure 7.  
Figure 7: The flowchart of the online measurement procedure.
## 4 Result and Discussion
### Information Provided through User Interface  
After Algorithms A1 and A2 were implemented, the system generated the production schedule, as shown in Figure 12. Subsequently, the planner had to confirm the schedule based on the domain experience and the standard operating procedure of the company.  
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Machines** & **Total Number of Orders in Schedule** & **Total Setup Time (min)** \\ \hline M1 without algorithms & 23 & 300 \\ \hline M1 with algorithms & 36 & 210 \\ \hline M2 without algorithms & 19 & 270 \\ \hline M2 with algorithms & 39 & 135 \\ \hline \hline \end{tabular}
\end{table}
Table 6: The performance of schedule for each machine in the two machines’ test.  
Figure 10: The performance of the total number of orders for each machine.  
This is in conjunction with the limitation discovered in the previous study [17], where the methodology to implement a smart production planning and control requires experience and judgement to guarantee that all the relevant contextual variables have been taken into account when evaluating the fit of objectives and priorities with the planning environment variables.  
Figure 12 shows the schedule information, such as production date, JobID, production ID, estimated start time, estimated end time, total in square feet per order, resource ID, time used, setup time, break time, machine break time, and end shift time. Each JobID consists of one or more order items. According to the information shown in the system, users can easily understand the arrangement of the orders, the estimated start time and end time, the total square feet used per order, the machines assigned, the time to produce the order, the setup time, break time, and break down time. This has allowed the planners to have a general view of the production schedule and made it easier for them to perform adjustments. In terms of the computational time, it took from 4 to 6 s for one machine process and from 7 to 9 s for two machines to process. However, the computational time might be affected by the existing network infrastructure.
# Internet of Drones: Routing Algorithms, Techniques and Challenges
## 4 Routing Algorithms
### Genetic Algorithm  
The genetic algorithm is named so because of the generation of new datasets (offspring) obtained from existing ones (parents). It mimics biological evolution by solving optimisation problems based on a natural selection process as it provides new solutions and discards the old ones. This helps to curb the mundane elements of the routing process. GA has four main steps [54]:  
* Elitism
* Crossover
* Mutation
* Elimination  
In the first step \"Elitism\", the best possible paths are set aside as pure solutions. The data routes must be maximally utilised for this step, as the most probable solutions are kept in record. In the next step, a crossover of best data locations is carried out. This is basically a shuffling of routing data possibilities in order to find new ones. The new possibilities or routes are then altered through mutation of 1 bit to help keep up the change in process. Finally, the possibilities least feasible are eliminated from the routing list.  
Drones are programmed to gain assistance from genetic algorithms through a programmable code that is accepted by their GUI. Each BS keeps track of the drone path possibilities with the help of GA. However, GA also has some disadvantages. The choice of all the parameters (fitness function, population size, and mutation and crossover rate) must be carried out very carefully. However, GA still remains one of the most important optimisation techniques. Figure 6 is a flow diagram that shows how routing is done with the help of GA. After creation of the initial population, a fitness assessment is made. If the end condition is reached, this is the choice of the best chromosome. If the end condition is not reached, the chromosome is subjected to crossover with a crossover probability and mutation with a respective mutation probability after which the chromosome reaches the fitness function. If it does not, it is eliminated. These best chromosomes give the paths of drones in a WSN cluster.
## 2 Materials and Methods  
### Study Area  
Xinjiang is a climate-sensitive arid region located deep inland in northwest China. Surrounded by mountains and basins, it is the largest autonomous region in China [25] (Figure 1a). The annual average Temperature Vegetation Dryness Index in the region is 0.75 [26]. The unique topography and climate features of this administrative region are shaped by the Tianshan Mountains (TM) located in its central part (Figure 1b). The region's rivers and lakes mainly originate from glacial melt. It has scarce rainfall and significant temperature differences between hot and cold periods. The administrative region primarily consists of unused land and its ecosystem is fragile, with underlying surfaces such as desert vegetation, desert--asis transition zones--farmland, and grassland/meadow (Figure 1c).  
Figure 1: **(a)** Research area and its main mountain ranges. **(b)** Stations and elevation in the research area; the elevation data were derived from GDEMV2 30 M resolution data on the geospatial data cloud, and their acquisition website is [[http://www.gscloud.cn/](http://www.gscloud.cn/)]([http://www.gscloud.cn/](http://www.gscloud.cn/)) (accessed on 11 December 2022). **(c)** The land-use classification map of the study area; the data (30 M) were from the data registration and publishing system of the Chinese Academy of Sciences, accessed at [[https://www.webmap.cn/](https://www.webmap.cn/)]([https://www.webmap.cn/](https://www.webmap.cn/)) (accessed on 11 December 2022).  
Owing to the unique terrain, landforms, climate characteristics, hydrological conditions, and ecological system of Xinjiang, the seasonal fluctuations of GPP in the region are significant. It has relatively low attribute values and unique spatiotemporal patterns [19; 20], especially in the Gurbantunggut Desert, where short-lived vegetation exists [27]. Analyzing the spatiotemporal characteristics and driving factors of GPP in the region is of great significance for evaluating the carbon sequestration capacity of ecosystems under drought stress, implementing energy-saving and emission-reduction policies, and promoting sustainable development. Therefore, this study took the local arid areas in Xinjiang as the study area (bounded by the Altai Mountains in the north, the southern slope of the TM in the south, the border of Xinjiang in the west and east, and the central part comprising the Gurbantunggut Desert with short-lived vegetation), coupling the comprehensively improved SIF and MLR models with other elements of the desert vegetation, grassland, and farmland ecosystems in the region. The aim is to address the imperfections of GPP inversion based on SIF and MLR models in the region and ultimately reveal the spatiotemporal characteristics and driving factors of GPP.
## 5 Conclusions  
In this paper, we developed an analytical framework which describes the relationship between the transmission range and the energy efficiency as well as the relationship between the transmission range and the connectivity in an UWSN scenario. We illustrated that the selection of the transmission range needs to consider the tradeoff between the energy efficiency and the connectivity. Meeting a certain level of network connectivity incurs either cost for additional node deployment or energy due to operational deviation from the optimal transmission range. Our analytical framework provides a means for network designers to plan the deployment of an UWSN. We further illustrated that employing multiple sinks helps to meet the connectivity requirement in a more cost-effective way.  
Although we consider the underwater environment in this paper, the developed analytical framework is general and can be applied to other random network scenarios. In the future, we shall extend our investigation by considering a medium access control (MAC) protocol such as [20, 21] in the computation. This allows us to capture the effect of transmission interference (e.g., due to transmission collisions) and energy overhead (e.g., due to retransmissions) in our formulations.
## 5 Results and Discussion
### Feature Selection Results
#### 5.1.3 Meta-Heuristic Feature Selection Results  
The utilized genetic algorithm's objective function balances model accuracy and reduces the number of selected features. Specifically, the algorithm seeks to maximize \(R^{2}\) while penalizing the fraction of features used. The resulting score reflects both the predictive power of the model and the efficiency of the feature set, thereby promoting solutions that are both accurate and concise. Figure 9 presents the convergence of the score over multiple iterations (fitness calls), illustrating how the population of candidate solutions evolves toward higher-scoring configurations. For clearer visualization, the rolling maximum scores obtained in each iteration, shown in Figure 9, are computed over a sliding window equal to the number of agents (30). As the genetic algorithm proceeds, it steadily refines the population's feature subsets, leading to an optimal or near-optimal blend of strong predictive performance and minimized feature usage.  
Table 1 presents the top five agents produced by the genetic algorithm and their corresponding sets of selected features and average \(R^{2}\) scores. Although the performances of these agents are extremely close, there is a noticeable overlap in the chosen features; specifically, apparent temperature and diffuse radiation appear consistently. This observation suggests that both parameters dominate in predicting PV output, making them prime candidates for inclusion in a streamlined yet effective feature set. Methods like genetic algorithms can converge rapidly within fixed time constraints by tuning factors such as iteration count, population size, and the duration spent training each model. However, they can be susceptible to getting stuck in local optima, which prevents them from finding  
Figure 8: The correlation matrix of the selected features using the hierarchical clustering method.  
the optimal combination of features. Additionally, interpreting the final feature subsets can be challenging, especially when balancing multiple objectives (e.g., maximizing \(R^{2}\) while minimizing the number of features).