esa-sceva/satcom-chunk-collection huggingface dataset:
# Channel Model for Estimating Received Power Variations at a Mobile Terminal in a Cellular Network
## 3 Experimental Measurements Conducted in a Cellular Network
### Suburban Environment  
For the study of the suburban area, data collection was conducted in the southeast of Ecuador's capital, specifically in the Rumiloma neighborhood, which belongs to the San Pedro de Taboada parish in the Valle de Los Chillos (Figure 8). It is worth noting that the selected area has a low base station density per km\({}^{2}\). At the end of the data collection process, it was determined that a total of five eNBs participated in handover processes.  
Figure 8: “Rumiloma” neighborhood, suburban area.  
Figure 7: Selected routes for data collection and base stations in the urban area. Each color corresponds to the serving base station associated with the measured data along that route segment.  
This sector encompasses an area with flat terrain, sparse vegetation consisting of trees and crops, and buildings that do not exceed three residential floors. The number of users is moderate, as the area includes training centers, churches, green spaces, and small shopping centers. Additionally, it has vehicular circulation with no heavy traffic congestion.  
The selected routes for data collection in the aforementioned area are presented in Figure 9.  
For the suburban area, the set of routes was selected based on several objective criteria to ensure data representativeness and coverage diversity. These included (i) routes of sufficient length to capture spatial signal variation typical of suburban deployments; (ii) coverage by at least three distinct base stations to enable the analysis of handover and cell selection behavior; (iii) the presence of a variable number of active users, as inferred from traffic observations and time-of-day considerations; (iv) accessibility to the study zone, which was influenced by the presence of private residential areas; and (v) the inclusion of both open areas and low-rise residential zones to reflect typical suburban morphology. Additionally, efforts were made to avoid signal dead zones and ensure continuous 4G coverage along the routes.  
In the suburban area, data were collected for each route as follows: Route 1 with 10,360 samples, Route 2 with 7196 samples, Route 3 with 6241 samples, Route 4 with 10,038 samples, and Route 5 with 9548 samples. As with the urban dataset, inconsistencies such as invalid coordinates, unrealistic power values, and missing attributes were detected and addressed during the data preprocessing stage.  
Using QGIS, five base stations and eleven cells (sectors) were identified as active in the suburban study area. Each measurement point is color-coded according to its associated base station, as shown in Figure 10.  
Figure 9: Selected routes for data collection and base stations in the suburban area.
# A Simulation Study on Pressure Control in Oil Well Drilling Using Gain-Scheduled PID Controllers
## 4 Results
### Specification of Gain Scheduling (GS) Controller  
Offshore drilling, for example, presents a much more complex dynamic compared to other petrochemical processes. The operating range can vary significantly due to factors such as increased drilling depth, which affects the control volume and pressure, variations in fluid density due to pressure increase, and the movement of waves, among others [35]. One of the main challenges in tuning PID controller parameters is that the optimal values for a specific operating condition may not be suitable for others due to inherent process variations. In practice, PID tuning is often adjusted to achieve robust performance across a wide range of operational conditions [33]. However, in certain scenarios, this tuning may amplify system disturbances.  
Figure 5: Transient response of IMC+2 DOF-controlled system at different depths and robustness levels (_Ms_). (**a**) 500 m, (**b**) 1000 m, (**c**) 2000 m, (**d**) 3000 m, (**e**) 4000 m, and (**f**) 5000 m.  
Once the trends of the PID controller were analyzed, the parameters of the intended controller were specified using the Gain Scheduling method. The operations were simulated for each depth, and a similar value of robustness was adopted for the controllers; in this case, the value \(Ms=1.8\) was considered [35].  
Likewise, for each depth, PID hooks were adopted that characterized the best tuning of the two parameters, thus guaranteeing the best performance of the responses, as demonstrated by the IMC+2 GL methodology together with a robustness of \(Ms=1.8\). In detail, the two PID parameters are presented in Table 4.
## 3 Results and Discussion
### Antifungal Activity  
Synthesized gold nanoparticles showed moderate antifungal activity against the test pathogens _Aspergillus flavus_ and _Aspergillus niger_ by comparing it with crude extract (inactive in microgram concentrations while active in milligram concentration). Gold nanoparticles were found to be more active against _Aspergillus niger_. In another study, when the antimicrobial potential of GNPs was determined by mixing gold nanoparticles with antibiotic, enhanced antimicrobial potency of GNPs was observed [50]. In our study, it was found that gold nanoparticles showed moderate activity as compared to the plant extract (Table 4).  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Fungal Strain**} & \multicolumn{3}{c}{**GNPs Zone of Inhibition (mm)**} & \multicolumn{3}{c}{**Plant extract Zone of Inhibition (mm)**} & \multicolumn{3}{c}{**Nystatin**} \\ \cline{2-10}  & **25 \(\upmu\)g** & **50 \(\upmu\)g** & **75 \(\upmu\)g** & **1 mg** & **25 \(\upmu\)g** & **50 \(\upmu\)g** & **75 \(\upmu\)g** & **1 mg** & **5 \(\upmu\)g/mL** \\ \hline _Aspergillus flavus_ & 0 & 0 & 5 & 9 & 0 & 0 & 0 & 4 & 11 \\ _Aspergillus niger_ & 0 & 3 & 5 & 7 & 0 & 0 & 0 & 6 & 17 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Antifungal assay of extract and GNPs.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Pytochemical** & **Result** & **Method** & **References** \\ \hline Alkaloids & + & Mayer’s test & [46] \\ Carbohydrates & + & Molish’s test & [46] \\ Glycos & + & Bortrager test & [46] \\ Proteins and amino acids & + & Biuret test, Ninhydrin test & [47] \\ Flavonoids & + & Alkaline reagent test & [47] \\ Phenolic compounds & + & Lead acetate test & [47] \\ \hline \hline \end{tabular}
\end{table}
Table 2: Phytochemical analysis of _Quercus incana_.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Bacterial** & \multicolumn{3}{c}{**GNPs Zone of Inhibition (mm)**} & \multicolumn{3}{c}{**Plant extract Zone of Inhibition (mm)**} & \multicolumn{3}{c}{**Ciprofloxacinacin**} \\ \cline{2-9}
**Isolate** & **25 \(\upmu\)g** & **50 \(\upmu\)g** & **75 \(\upmu\)g** & **1 mg** & **25 \(\upmu\)g** & **50 \(\upmu\)g** & **75 \(\upmu\)g** & **1 mg** & **5 \(\upmu\)g/mL** \\ \hline _B. subtilis_ & 6 & 8 & 9 & 17 & 0 & 0 & 0 & 8 & 13 \\ _S. aureus_ & 0 & 4 & 6 & 12 & 0 & 0 & 0 & 11 & 22 \\ _S. setubal_ & 3 & 5 & 7 & 15 & 0 & 0 & 0 & 8 & 24 \\ _P. pickettti_ & 0 & 0 & 3 & 9 & 0 & 0 & 0 & 0 & 20 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Antibacterial assay of extract and gold nanoparticles (GNPs).
###### Abstract  
Increased performance with reduced overall cost, and precise design and optimization of launch systems are critical to affordability. In this respect, the use of hybrid motors has increased to ease handling based on motor selection. In the current study, the launch vehicle's performance is enhanced by incorporating a hybrid rocket motor into the last stage and optimized using particle swarm optimization to develop a six-degrees-of-freedom tool. This modification aims to increase payload placement flexibility, facilitate handling, and reduce costs. Thanks to the interactive subsystems within this research, this innovative study more comprehensively considers the launch vehicle trajectory design problem, allowing the simultaneous consideration of the effect of launch vehicle geometry along with other parameters in the system. In this context, the proposed method is applied to the Minotaur-I launch vehicle, and contributions of the detailed design and optimization are presented. Optimization results show that the percentage differences between these models for the original vehicle were observed to be 11.55% in velocity and 8.02% in altitude. However, there were differences of 10.06% and 48.8%, 15.8% and 23.2%, and 19.5% and 78.9% in altitudes and velocities when the center of gravity and moment of inertia changes were neglected, and constant aerodynamic coefficients were assumed, respectively. In all these cases, it was observed that the flight path angle was not close to zero. Moreover, the same mission was achieved by the launch vehicle with the optimized hybrid last stage and the propulsion performance was increased by about 7.64% based on the specific impulse and total impulse-over-weight ratio.  
hybrid rocket motors; launch vehicles; particle swarm optimization; multidisciplinary optimization Article  
15 April 2024  
Revised: 28 April 2024  
Accepted: 28 April 2024  
Published: 1 May 2024
# Multi-Disciplinary Optimization of UV-C Filter for Air Disinfection
## 2 Filter Modeling and Simulation  
### Combined Action of Air Ventilation and UV Radiation for Pathogen Neutralization  
The effectiveness of germicidal UV depends on the duration a microorganism is exposed to UV, the intensity and wavelength of the UV radiation, and the presence of particles that can protect the microorganisms from UV [17].  
Wavelengths between about 200 nm and 300 nm are strongly absorbed by nucleic acids, causing defects in pyrimidine dimers, which can prevent replication of necessary proteins, resulting in the death or inactivation of the organism, as illustrated in Figure 1[18].  
The effectiveness of UVGI increases when used in conjunction with fans and HVAC ventilation, which raise the exposition time of microorganisms to UV. The repeated circulation of the air ensures multiple passes so that the UV is effective against the highest number of microorganisms and can irradiate resistant microorganisms more than once to inactivate them.  
The UV effectiveness can in fact be estimated by calculating the UV dose that is delivered to the microbial population. The UV dose is calculated as follows:  
\[\text{UV dose [W\cdot s/m^{2}]=UV intensity [W/m^{2}]\times exposure time [s]} \tag{1}\]  
In conclusion, to design an optimal UV-C filter, it is necessary to maximize the air flow rate and the exposure time of the air flow to the UV radiation.
# Deep Learning for Short-Term Load Forecasting--Industrial Consumer Case Study
## 4 Results  
The scope of this work is to identify the best solution for industrial load forecasting. For this reason, several algorithms were analyzed and implemented using the tools mentioned in Section 3 to have a solid comparison standpoint. For the evaluation of the forecast, the metrics applied for the results obtained in the test dataset using the equations in Table 3.  
The forecasting methods used in this article and presented in Table 4, together with the hyperparameters for each ML algorithm. The AR method is implemented as well to showcase the load forecast from a traditional perspective. The standalone AR method for forecasting has the same order as the one used in the hybrid method. Figure 5: The proposed framework for the implementation of deep learning for hourly load curves. \begin{table}
\begin{tabular}{l l} \hline \hline
**Mean Absolute Error** & **Root Mean Square Error** & **Mean Absolute Percentage Error** \\ \hline MAE = \(\frac{\frac{\sum\limits_{i=1}^{n}\left|Y_{i}-\hat{Y}_{i}\right|}}{n}\) & RMSE = \(\sqrt{\frac{\sum\limits_{i=1}^{n}\left(\frac{Y_{i}-\hat{Y}_{i}}{n}\right)^{2}}{n}}\) & MAPE = \(\frac{\frac{\sum\limits_{i=1}^{n}\left[Y_{i}-\hat{Y}_{i}\right]}{n}\star 100}{\frac{ \sum\limits_{i=1}^{n}\left[Y_{i}-\hat{Y}_{i}\right]}{n}}\) \\ \hline \hline \end{tabular} \(n\) = number of observations; \(\hat{Y}_{i}\) = forecasted value; \(Y_{i}\) = actual value.  
\end{table} 
Table 3: Forecast errors metrics. The calculated errors according to Table 3 are presented in Table 5 for each algorithm implemented, highlighting the best results. The results represent the lowest error obtained from multiple implementations for the ML architectures considered in this study. The errors are calculated on a hourly basis and for the entire testing period. The GRU model that uses the feature mentioned in Table 4 as input obtains the best MAPE of 4.82% better than all the other DL algorithms. This approach improved the GRU method by 6.48% and 11.88% compared to the AR method. The LSTM method scored a higher error in comparison with GRU and close to the AR method. The AR result of 5.53% MAPE illustrates that the hourly load curve has a strong repetitive pattern, and the LSTM is overfitting on the training data. The MLP and simple RNN scored higher error than the AR, and the LSTM encoder-decoder has the highest error of 6.28%. In Figure 6, a normal working week (Monday to Friday) is presented to highlight the improvement obtained by using the proposed methodology. It can be observed that using
# An Interactive Method for Detection of Process Activity Executions from IoT Data +
## 2 Background and Motivation
### BPM Meets CPS and IoT  
BPM is considered to be \"the art and science of overseeing how work is performed in an organization to ensure consistent outcomes and to take advantage of improvement opportunities\" [3]. A _business process_ in this context can be seen as a chain of events, activities, and decisions that describe how work should be performed (cf. Figure 1) [3]. In our work, we focus on _activities_ representing units of work performed by actors. Traditionally, these actors can be human actors, organizations, or software systems [3]. As shown in Figure 1, we propose that also components of _Cyber-Physical Systems_ can be active participants involved in the execution of activities.  
_Cyber-Physical Systems_ (CPS) are \"integrations of computation with physical processes\" [7]. Computers embedded into physical objects and devices monitor and control processes with bi-directional feedback loops between the physical and digital worlds. Closely related to CPS is the concept of _Internet of Things_ (IoT) [8] representing \"a world-wide network of interconnected objects\" [9]. We will use the term _CPS_ in this paper to address aspects regarding process automation and control. _IoT_ will be used to talk about interaction and data acquisition (_IoT data_) from individual components.  
Being parts of CPS components, _sensors_ and _actuators_ are the basic building blocks for interactions with the physical world. Sensors provide data about physical entities and their surroundings. Actuators are capable of modifying the state of physical entities [10]. Actuators can also provide data about their state or the physical entity they are manipulating. Thus, we regard both, sensors and actuators, as producers of _sensor events_ (cf. Figure 1).  
The focus of this work is on investigating the correlation between these sensor events (_IoT data_) and the execution of process activity executions, and vice versa. Linking IoT data to process executions in CPS promises many benefits [1] including, e.g., the improved monitoring of process activity executions by providing additional context data [11; 12], or the real-time decision making in processes based on sensor data [13]. In this work, we distinguish between the linking of IoT data with processes (_Process-Awareness of IoT Data_) and the linking of process data with IoT (_IoT-Awareness of Process Data_) [14]. Usually, a data set containing one of the two types of data is given as the basis for analysis and the other type needs to be derived or linked.
## 3 Theory
### Clutter Reduction  
In this application, the position of the human body is assumed to remain fixed in bed, except when the body sometimes changes its position. Clutter is therefore assumed to be basically stationary and can be removed using the background subtraction method. For simplicity, we only consider a given range cell. Let \(r(n)\) denote a column of the measured matrix R (4). The background subtraction method consists of subtracting an estimation of the background of \(r(n)\)[54] from the signal:  
\[y(n)=r(n)-b(n-1) \tag{8}\]  
where \(b(n\)-\(1)\) is the background estimated from the average of \(M\) samples. The basic estimator is the moving average method, where the background is estimated as the average of previous \(M\) samples:  
\[b(n-1)=\frac{1}{M}\sum_{j=n-M}^{n-1}r(j)=(1-1/M)b(n-2)+r(n-1)/M \tag{9}\]  
Note that the estimated background can also be expressed in a recursive form. After some manipulations and z transformation, the transfer function can be obtained:  
\[H(z)=Y(z)/R(z)=\frac{1-z^{-1}}{1-\lambda z^{-1}} \tag{10}\]  
where \(\lambda=1-1/M\). The most simple case is to consider \(M=1\), where the background is assumed to be equal to the previous sample.  
An improvement in the basic moving averaging consists in the use of a weighting factor. When the weighting factors follow an exponential law, this is known as exponential moving averaging [36]. This method uses all historical pulses to estimate the background data:  
\[b(n-1)=b(n-2)+(1-\alpha)y(n-1) \tag{11}\]  
where \(0\leq\alpha\leq 1\), controls the averaging. This method emphasizes recent events and gradually attenuates past data. After the same operations, it can be shown that the transfer function for the exponential averaging can be obtained from (10), but using \(\lambda=\alpha\). The exponential averaging can therefore be seen as a particular case of moving averaging, but with a sliding window with a fractional length of 1/(1-\(\alpha\)).  
Figure 4 compares the frequency response of moving average filters as a function of the tap-length. It can be shown that the attenuation of clutter decreases with the tap-length, but the cut-off frequency also decreases with the tap-length. A tap-length \(M=30\) (about 4 s of window duration) is a good compromise between clutter attenuation and a cut-off frequency below the frequencies of the breathing signal (0.1-0.7 Hz).  
Figure 3: Signal processing block diagram.
# A Review of Convolutional Neural Network Applied to Fruit Image Processing
## 8 Deep Learning Frameworks and CNN-Based Examples
### CNN-Based Examples for Fruit Classification
#### 8.2.1 Example of Fruit Classification
It should be noted that as the complexity (i.e., depth) of the model increases, overfitting also increases, even by applying data augmentation and adding dropout. This behavior is because of the used dataset is small without sufficient variability. Hence, the results show that the complexity of a CNN model should correspond to the complexity of the classification task and the amount of data available. For this reason, simpler models like the proposed example and AlexNet obtain such good results. Besides, that explains why 66.7% of CNN approaches in the literature for fruit classification are based on own-created models and the AlexNet architecture, as shown in Figure 8a. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**CNN Model**} & \multirow{2}{*}{**Depth**} & \multicolumn{2}{c}{**Training**} & \multicolumn{2}{c}{**Validation**} & \multicolumn{2}{c}{**Testing**} \\ \cline{3-8}  & & **Loss** & **Accuracy** & **Loss** & **Accuracy** & **Accuracy** & **F1-Score** \\ \hline Proposed example & 6 & 0.0167 & 100\% & 0.0165 & 100\% & 95.45\% & 0.96 \\ AlexNet & 8 & 1.2 \(\times 10^{-5}\) & 100\% & 4.9 \(\times 10^{-5}\) & 100\% & 100\% & 1 \\ VGG16 & 16 & 0.2067 & 96.32\% & 0.2070 & 95.94\% & 91.32\% & 0.89 \\ MobileNet & 88 & 0.0799 & 97.45\% & 0.7201 & 73.86\% & 70.02\% & 0.67 \\ InceptionV3 & 159 & 0.5592 & 80.49\% & 1.1755 & 62.82\% & 54.49\% & 0.49 \\ ResNet50 & 168 & 0.1436 & 99.17\% & 0.9102 & 66.69\% & 57.74\% & 0.48 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparison between the proposed example and pre-trained models for fruit classification on Fruit-360 dataset [70]. Figure 13: Confusion matrix for fruit classification results on Fruit-360 dataset [70].
## 4 Discussion

Table 6: Comparison of model size, memory usage, and execution time for various DNNs with convolutional architectures implemented on ARM-based platforms for ECG processing. NA: not available or not applicable; QAT: quantization-aware training. are well-suited for deployment on resource-constrained STM boards with 1-2 MB of flash memory [80,88] and nRF52 SoCs with 0.5-1 MB of flash memory [89,90]. The SRAM usage for DNN models with a raw single-lead ECG input ranges from 9 to 214 kB, as reported in [57,59] and this study. The 214 kB SRAM usage observed in this study is comparable to the 157 kB reported in [59], especially when considering the difference in input sizes (1250 samples in this study vs. 1000 samples in [59]). The SRAM usage in [57] is remarkably low, however, reported for a few specific allocations: 0.8 kB for GRU gates and hidden states, 4 kB for convolution buffers, and two 2 kB arrays for convolution activations. In contrast, the significantly higher SRAM usage of 12-24 MB reported in [7] can be attributed the additional storage of the entire test dataset, which includes approximately 50,000 heartbeats, alongside the model itself. In Table 6, the execution time varies significantly depending on the test platform, with models implemented on Raspberry Pi single-board computers achieving execution times of no more than 1 ms. In contrast, execution times on STM32 boards and nRF52 SoCs range from 298 to 616 ms. The execution times reported in [57,59] and this study (480 ms, 379 ms, and 615 ms, respectively) are comparable, taking into account the differences in input sizes (1000, 640, and 1250 samples, respectively). The presented workflow and experimental results for TF to LiteRT conversion and quantization demonstrate the feasibility of deploying a pre-trained DNN with a fully convolutional architecture on ARM-based microcontrollers. Expanding this approach to support other neural architectures, such as those incorporating recurrent layers (e.g., long short-term memory or gated recurrent units) or others is a potential direction for future investigations.