esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Methodology
### Predicting Weather Parameters Using Time Series FF-ANN  
Some recent studies have taken advantage of ANN for predicting weather conditions [36; 37; 38]. In this study, a multilayer perceptron (MLP) as an FF-ANN is used for weather parameter prediction. The backpropagation (BP) algorithm is also used for training the FF-ANN [39]. In this study, feedforward expresses the transaction of data from input to output. Feedforward output is determined by passing the input through the neural network. Feedforward networks perform with supervised learning rules, which calculate the best output to minimize the error between inputs and their respective outputs by modifying the network's weights.  
Generally, the time series is a set of data points that are arranged by equal space-time. In fact, in time series prediction, a model is deployed to predict upcoming values based on recently recorded data [40]. In other words, future values are predicted by learning the previous data series. From this perspective, MLP-ANN is introduced to predict time series because this method yields more satisfactory results in some recent studies [41].  
It is important to mention that MLP-ANN demands some memory for temporal information processing. Different methods are established for making this memory in ANN like time delays in this network. The fact is that these delays are used in order to adjust the parameters of ANN during the learning process. A typical modeling of time series with MLP-ANN has been shown in Figure 3. Three layers of this model include the input layer, hidden layer, and output layer. According to Figure 3, single variant inputs, which are depicted through \(X(n-1),\ X(n-2)\ldots X(n-p)\), are the previous data samples, \(p\) is the prediction order, and \(y(n)\) is the output value, which indicates the prediction result.  
In this paper, solar irradiance, temperature, and wind speed predictions are represented based on last year's data that were gathered from a local meteorological center located in Kerman province, Iran. Figure 4 illustrates hourly data by year from 1 January 2007 to 31 December 2007. A three distinct MLP-ANN is considered in order to forecast the solar irradiance, temperature, and wind speed. Two different data of each parameter were used for the training and testing of the MLP-ANN method. In order to have a more suitable prediction for day-ahead optimizations, the time horizon was proposed 24 h ahead at each time series. In addition to the mentioned considerations, input and output data were normalized to [0,1] as follows:  
\[y=y_{min}+\frac{x-x_{min}}{x_{min}-x_{min}}(y_{max}-y_{min}) \tag{19}\]  
Figure 4: _Cont._  
Figure 3: A typical multilayer perceptron artificial neural network (MLP-ANN) model for time series prediction.
# Vision Based Dynamic Thermal Comfort Control Using Fuzzy Logic and Deep Learning
## 2 Related Work
Experiments were performed and demonstrated that the PMV values best matched occupants' responses to a questionnaire when measured metabolic values were used. The proposed method was also shown to improve energy conservation. However, this method requires that a mobile phone device be continuously carried to measure the metabolic equivalent values. Furthermore, a number of common mobile phone usage scenarios could lead to potential problems. In addition, it could be argued that the system is not fully automatic. A different approach was proposed by [28] using a thermal camera for human thermal comfort measurement. PMV was included to quantify the thermal comfort. The proposed system estimated clothing insulation values that were based on thermal measurements. Different variables were considered in the calculation of the clothing insulation value. This included the heat transfer coefficient of the human body, operating temperature, skin surface temperature, and clothing surface temperature. The level of clothing insulation was factored into the PMV calculations. The evaluation results demonstrated a reasonable level of accuracy that could potentially be used for thermal comfort monitoring. A simple temperature difference between the clothing and the skin surface was used for the clothes insulation. The authors also considered a range of poses of the occupant. However, the pose estimates were not directly linked to the metabolic rate calculations in the process of achieving an accurate comfort level. The authors also did not consider different combinations of clothing that could potentially further impact the accuracy of the model. Furthermore, the use of a thermal camera might prove to not be cost-effective for many potential applications. In contrast to existing works, automated action recognition using computer vision is used here to estimate metabolic rates. To the best of the authors' knowledge, a new thermal comfort system is proposed here. It automatically adjusts thermal settings using time adaptive metabolic rates. Uniquely, these are estimated from actions that are recognised from RGB-D camera video sequences. An occupant's recognised actions are used alongside different preferences and comfort levels. A virtue of the included action recognition is the ability to adapt to actions being performed at different speeds. Thus, the system proposed here can be considered to be able to adapt to different age groups, making it highly applicable to assistive living applications. The system is also designed to benefit from the hierarchical approach of the action recognition. Consequently, a new adaptive context-aware environmental comfort management system is proposed here. The performance of the proposed system is demonstrated with the use of pre-recorded action sequences. The proposed system achieves significant improvements in terms of the results of the thermal system.
## 3 System Architecture
### Detailed System Implementation  
#### 3.4.1 Data-Ingestion Pipeline Architecture  
The data-ingestion system implements a fault-tolerant pipeline pattern based on Apache Kafka's design principles, albeit with a simplified Node.js implementation [14]. The pipeline consists of six sequential stages, each operating independently with retry logic and dead-letter queue handling.  
The API fetcher module implements exponential backoff with jitter for rate limiting, following the pattern described in AWS Architecture Best Practices [6]. It maintains a sliding window of 100 requests per minute to respect Storia API limits while maximizing throughput. Failed requests enter a retry queue with a maximum of three attempts before moving to manual review.  
The data validator employs JSON schema validation (draft-07) to ensure data consistency. Schema definitions specify required fields (price, location, rooms) and validate data types, with 97% of listings passing validation on the first attempt. Invalid listings undergo a transformation phase, attempting to extract required data through regular expressions and fuzzy matching [39].  
The isolation forest anomaly detector processes numerical features including price per square meter, total area, and price-to-average ratios. The implementation uses scikit-learn's  
Figure 5: Recommender first phase.  
isolation forest with 200 estimators and a 0.01 contamination rate, trained on a rolling window of 10,000 recent listings. This approach identifies outliers with 97% precision based on manual verification of 500 flagged listings.
# Mono Camera-Based Optical Vehicular Communication for an Advanced Driver Assistance System
###### Abstract  
This technical paper proposes a new waveform combining the low-rate and high-rate data streams to detect the region-of-interest signal in a high-mobility environment using optical camera communication. The proposed technique augments the bit rate of the low-rate stream; consequently, the link setup time is reduced and the requirement of low frame rate camera is eliminated. Additionally, both the low-rate and high-rate data streams in the proposed bi-level pulse position modulation are decoded with a unique adaptive thresholding mechanism with a high frame rate camera. We also propose a vehicle localization scheme to assist the drivers in maintaining a safe following distance that can significantly reduce the frequency of accidents. Moreover, two neural networks are proposed to detect the light-emitting diodes (LEDs) for localization and communication, and to estimate the road curvature from different rear LED shapes of the forwarding vehicle, respectively. The system is implemented, and its performance is analyzed in Python 3.7. The implementation results show that the proposed system is able to achieve 75% localization accuracy, a 150 bps low-rate stream, and a 600 bps high-rate stream over a range of 25 m with a commercial 30 fps camera.  
visible light communication (VLC); camera; internet of vehicles (IoV); vehicle localization; road curvature; IEEE 802.15.7-2018; vehicle to everything (V2X) communications; neural network (NN) +
Footnote †: journal: Electronics Letters
## 3 Results  
### Description of Developed Software Tool: \"PetriNet editor + PetriNet engine\"  
The role of the Petri net editor is to allow the user to graphically model the Petri net. Our Petri net editor is an extension of PNEditor. PNEditor originally allowed only basic work with places, transitions, and arcs. Support for connection to Arduino and support for time Petri nets were implemented. PetriNet engine is the new module which enriches the application with possibility to generate control code for microcontroller according to the modelled Petri net.  
The following functional requirements were defined:  
* After launching the application, the user is given the opportunity to directly create and model a Petri net, which is a model of a particular selected event system, which the user has chosen to analyse and/or control.
* Petri net modeling must meet at least the basic required mathematical formalisms, ideally this solution concept will be extended by other specific properties of Petri nets, which were the subject of the previous analysis.
* In case the user decides that the created model is final, he/she must be able to clearly associate the individual components of the Petri net with the control modules of the selected microcontroller.
* The application must be able to process the choice of these dependencies and automatically adjust them to avoid possible collisions when defining different actions on one component.
* The necessary functionality of the system must be the possibility of setting of specific values needed for proper communication with the microcontroller.
* The system must be able to communicate with the selected microcontroller based on the selected parameters.
* One of the essential functions is the creation of such an option in the system, by which the user can automatically generate a control logic algorithm based on the specific properties of the selected microcontroller and modelled Petri net.
* It must be possible to upload the generated algorithm (program code) to the microcontroller.
* The system will inform the user through the visualisation of text messages about the current ongoing activity and the result of the system status itself, in order to provide the user with an overview of whether the action was successful, or for what reason and in which step failed.  
The scheme of the proposed and implemented solution can be seen in Figure 3. The application consists of two modules. The first is a tool that can be used to model and simulate a Petri net (PetriNet editor). The second is a module that generate program code based on the mathematical formalisms of the modeled Petri net. This code can be uploaded to the selected microcontroller (Arduino). Then, it can control the real hardware (sensors/actuators) connected to it.  
The use-case diagram of the developed software tool is depicted in Figure 4.  
Figure 4: “PetriNet editor + PetriNet engine”—Use-case diagram.  
Figure 3: “PetriNet editor + PetriNet engine”—Scheme of proposed and implemented solution.
## 3 Results and Discussion
### Optimized GREI-Only and GGI Frameworks with Varying DDL and Resilience Goals
This demonstrates the ability of GI to provide reliable resilience under specific GREI failure scenarios, even when accounting for long-term performance degradation. Furthermore, integrating cost-effective BCs and PP into GGI systems results in modest improvements in Tech-R, enhancing soil water storage and permeability. For instance, the inclusion of BCs and PP in optimized configurations yields a Tech-R increase of 7.1% (Table 2) [49]. These improvements underscore the complementary roles of GI and GREI in augmenting system performance, even within the spatial and hydrological limitations typical of urban environments. All optimized configurations demonstrate exemplary Tech-R performance. Hydraulic reliability--defined as the ability to meet design standards under severe rainfall events (peak flow up to 4.6 m\({}^{3}\)/s)--is a fundamental requirement. Achieving this reliability often necessitates larger-scale GREI designs that increase the capacity and redundancy of drainage networks. For example, Solution 19 achieves a Tech-R of 100%, signifying the complete elimination of flooding--an essential feature for flood-prone urban regions. This result underscores the critical role of expanded drainage capacity in GREI systems to address extreme rainfall scenarios [48]. The optimization strategies also highlight the importance of integrating sufficient quantities of BCs and PP to bolster Tech-R in GGI systems, aligning with recommendations  
\begin{table} \begin{tabular}{c} **Oper-** \\ **R(\%)** \\ \end{tabular}  \\ \hline
0 & 1 & 96.7 & 92.4 & 45.2 & 2 & 65.5 & 80.6 & 66.3 & 3 & 105.8 & 99.9 & 76.8 & 4 & 104.8 & 99.5 & 78.2 \\
20 & 5 & 86.8 & 97.0 & 40.6 & 6 & 61.7 & 93.8 & 65.4 & 7 & 90.9 & 99.9 &  
by [PERSON] et al. [49]. However, the inherent challenges in urban areas, such as constrained soil pore water storage and limited open space, necessitate the adoption of combined strategies and more comprehensive GREI solutions. These findings reinforce the need for a balanced and context--sensitive approach to urban stormwater management, combining innovative GI measures with robust GREI designs to ensure long-term system resilience and cost efficiency.
## 5 Results
### Impact of Combined Basket-Twisted Tape Inserts  
Figure 6 depicts the impact of the basket and combination basket-twisted tape inserts on heat transfer rate. The average \(Nu\) and \(Re\) numbers were noted to be directly related to each other. This might be related as the turbulence intensity goes up with rising \(Re\), which resulted in an increase in convective heat transfer. The use of basket turbulators alone resulted in a considerable increase in heat transmission rate with variation in Re. The average \(Nu\) of the basket turbulators was found to be around 131.8 percent greater than that of the ordinary heat exchanger for constant pitch ratio (PR = 150 mm). The reason for this might be related to the heat exchanger creating a spiral or secondary flow with relatively high turbulent intensity, which leads to the disruption and reintegration of the thermal boundary layer, which further improves the convection heat transfer and momentum process. Using twisted-tape in combination with basket turbulators results in stronger swirl flow around the core region and more mixing occurring within the heat exchanger core and the wall region, which is why the average \(Nu\) is higher than that of basket turbulators alone. Figure 6 shows that when the combined basket-twisted tape is introduced with a rising \(Re\) number and decreasing twist ratio, the average Nusselt number increases. Due to the increased turbulence intensity and longer flow length with a low twist ratio (y/w = 2), the greatest heat transfer rate was attained. The average \(Nu\) of the combined basket-twisted tape inserts was increased by 169.5 percent, 187.7 percent, and 206.5 percent for y/w = 6, 3, and 2, respectively, when compared to the simple heat exchanger. Figure 7 depicts the fluctuation of the \(f\) with the Re number. The employment of basket and coupled basket-twisted tape inserts results in an extra pressure loss when compared to the basic heat exchanger, as predicted. The dissipation of dynamic pressure owing to increased forces generated by swirling flow, as well as the increase in air contact surface area induced by the presence of basket-twisted tape inserts, account for a major portion of the pressure loss. The \(f\) of basket turbulators can be up to three and a half times that of a conventional heat exchanger, while the variation in friction factors induced by differing twist ratios might be up to 5-6.5 times greater.  
Figure 5: Confirmation of friction factor for plain heat exchanger.
# Research on the Jiamusi Area's Shallow Groundwater Recharge Using Remote Sensing and the SWAT Model
## 5 Discussion  
### Applicability of SWAT Model and Remote Sensing Meteorology to Groundwater Recherage Calculation  
At present, the groundwater dynamic balance, rainfall infiltration coefficient, and water balance methods are the three main methods for calculating groundwater recharge [28]. These technologies are well-developed and have been widely used in both practical work and scientific research. For the water balance and rainfall infiltration coefficient methods, the scope of application is wider, and the review process is more straightforward [29]. However, the traditional calculation formula relies on the field observation data. For example, the dynamic groundwater level method requires more field groundwater level observation well data, which requires huge manpower and time to obtain. However, the SWAT model is very effective in studying how people live, how the climate changes, and what surface runoff and groundwater look like [30]. In addition, the data required by this model are easy to obtain, and impact can also be eliminated by adjusting parameters for other environmental impact factors. In addition, the model further subdivides the geographical, meteorological, and hydrological data of each unit on the basis of the division of hydrological units. Local uniformity can be achieved through physical processes such as recharge, evaporation, throttling, and infiltration of water resources in the area. This can effectively solve the problem of insufficient accuracy in water resource calculations caused by hydrogeological differences [31].  
Groundwater level observation wells and other nearly ideal water conservation facilities are the foundation of the dynamic technique of groundwater level [32]. It is expensive and labor-intensive. The remote sensing-based water balance approach offers several advantages over the dynamic groundwater level method, including the ability to integrate RS GIS and a worldwide road surface data assimilation system. In addition, these technical approaches represent current areas of interest for study and development [33]. Data capture is, therefore, quicker and more convenient. Remote sensing compensated for the absence of conventional monitoring stations in the mountainous regions, difficult topography, and significant data inaccuracies in the Jiamusi area. Furthermore, remote sensing has a low temporal precision and a large coverage area. The rainfall infiltration coefficient approach's disregard for the balance method based on remote sensing significantly reduces the impact of these effects on geological water storage and runoff [34]. It significantly increased the  
Figure 10: Groundwater recharge and recoverable amount in the basin (SUP is groundwater recharge amount, EXP is groundwater recoverable amount, Unit: \(10^{8}\) m\({}^{3}\)).  
accuracy of groundwater recharge in Jiamusi, a location with complicated geology and an intricate water infrastructure.
# Fuzzy-Based Multi-Modal Query-Forwarding in Mini-Datacenters
## 2 Related Work
Fuzzy logic has found numerous applications in artificial intelligence (AI) due to its ability to model human reasoning and decision-making processes. Fuzzy intelligence is utilized in data forwarding schemes, as it utilizes more than one parameter to make decisions. [PERSON] et al. [38] developed an IoT-based system for the safe mobility of blind people, as an added gadget to a smart home model. The authors utilized a fuzzy logic controller to develop an obstacle avoidance system and utilized fuzzy-based simulations to verify the developed model. [PERSON] and [PERSON] [39] utilized fuzzy logic for the selection of cluster heads while designing a data forwarding system to push the critical data generated during emergency situations, such as accidents, without data collision. A fuzzy-logic-based hierarchical routing is proposed by [PERSON] et al. [40] to design a better IoT technologies-based healthcare. [PERSON] et al. [41] proposed a fuzzy-logic-based trusted routing protocol for vehicular cloud networks to determine the node candidacy value while identifying a secure path. We have previously explored various aspects of a WSN, with our prior work focusing on data aggregation in periodic data collection environments at the gateways [42; 43]. The primary objective was to aggregate sensor responses at the gateways through task scheduling; the classical scheduling algorithms, such as 'as soon as possible' (ASAP), 'as late as possible' (ALAP), and 'branch-and-bound' (BB), were utilized for effective data aggregation. In subsequent research [44; 45; 46], we developed a fuzzy logic framework to assess the trustworthiness of sensors at the gateways and utilized the resulting trust values as control parameters to facilitate data transmission under uncertain environments [44; 45; 46]. We further developed a fuzzy framework that has enabled sensor nodes to become aware of the equipped resources and the environment conditions for implementing opportunistic transmissions for conserving energy within a wireless sensor network [47]. In this work, we have proposed a mini-datacenter in the form of a WSN for query-driven data collection within an IoT environment, and we have proposed and developed a fuzzy-logic-based multi-model data forwarding technique to dynamically adjust the granularity of transmission to ensure reliability.