esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
Studies have demonstrated that the interaction and adsorption energy between an adsorbate and an activated carbon model can be deeply explored by constructing a monolayer graphene model embedded with functional groups, thereby revealing the molecular adsorption mechanism [18; 19]. Density functional theory (DFT) is a computational method based on the principles of quantum mechanics. By describing and solving the electron density, it can predict the structure, energy and various physical and chemical properties of materials. The development of hybrid DFT, such as B3 LYP [20], brought to the desktop of experimental chemists a tool that allows the calculation of fairly accurate chemical structures and properties in reasonable timeframes [21]. Due to its excellent balance between computational cost and accuracy, this method has been widely used to investigate the atomic-scale adsorption properties of activated carbon [22; 23]. Furthermore, with the introduction of the DFT-D dispersion correction method [24; 25], DFT has become a very suitable method for calculating all kinds of non-covalent interactions. The independent gradient model based on the Hirshfeld partition (IGMH) proposed by [PERSON] et al. [26] is a novel method for the visual analysis of noncovalent interactions. In comparison to the more popular independent gradient model (IGM) often used in recent years [27], the IGMH approach exhibits significantly superior graphical effects and a more rigorous physical background, thus being adopted by numerous scholars [28] since its inception. [PERSON] et al. [29] conducted an IGMH study and found that the hydrogen bonding and \(\pi\)-\(\pi\) interactions can synergize to significantly improve the adsorption by increasing the \(\pi\)-electron density and shortening the distance of aromatic rings; thus, dyes with H-donors show significantly better adsorption capacities. [PERSON] et al. [30] assessed the roles of oxygen-containing functional groups in uranium adsorption by aged polystyrene microplastics through IGMH analysis, revealing that the hydrogen bond formed by oxygen-containing functional groups is major driving force in interactions between uranyl and aged polystyrene microplastics. The primary objective of this study is to investigate the impact of nitrogen-containing functional groups on the adsorption of VOCs by activated carbon using DFT. Specifically, by analyzing electrostatic potential (ESP), adsorption energy, and non-covalent interactions, we have gained a comprehensive understanding of how pyridine and pyrrole functional groups influence the adsorption of VOCs by activated carbon. These findings can provide valuable guidance for designing and synthesizing high-quality activated carbon with enhanced VOC adsorption capabilities.
# Spatial Small Target Detection Method Based on Multi-Scale Feature Fusion Pyramid
## 4 Experimental Results and Discussion
### Comparison with Existing Methods  
In order to prove the effectiveness of the proposed MFFPN algorithm for spatial small target detection, in this study, we conducted comparative experimental analysis with various mainstream feature pyramids on the generated spatial small target dataset, and the comparative feature pyramids included PANet and BiFPN, which have excellent performance in detection ability. We took CSPNet as the backbone algorithm and combined PANet, and BiFPN, and this paper proposes two versions of the MFFPN to construct the target detection algorithm and conduct experiments under the same training environment and parameters, and the specific model evaluation index comparison data are shown in Table 2.  
The experimental results show that the two versions of the MFFPN algorithm proposed in this paper achieve the optimum in precision, recall, and mAP, which is more suitable for the detection task of small spatial targets. This shows the effectiveness of our work in increasing the sensory field and increasing the attention mechanism, which shows that the MFFPN algorithm is more effective and advantageous in performing spatial small target detection.  
Our proposed MFFPN algorithm was used for the detection of small targets in space, which is mainly characterized by the detection of small targets, the presence of weak illumination and interference from the background of the star map, as well as the influence of noise in the process of image acquisition. To address the above problems, we clustered all of the labels using a clustering algorithm and regenerated a new anchor frame to make the current anchor frame more suitable for the current dataset. We also applied a variety of data enhancement methods to generate data with different scales and noise. Smaller datasets can be used, and generating data through data augmentation increases the number of datasets while mitigating overfitting in training.  
The MFFPN algorithm extracts the local information of the existing targets in the image through multiple attention mechanisms of joint channel attention mechanism and multi-head attention mechanism to enhance the feature extraction ability for small targets. Meanwhile, the multi-scale object information is obtained by increasing the receptive field through improved spatial pyramid pooling, so that the network obtains different receptive fields and is able to capture information at different scales to improve the feature extraction ability for small targets. We provide two versions to improve the detection performance for small targets by utilizing the added tiny target detection head.
# Kamchatka Berry (_Lonicera caerulea_ L.) Pomace Bioferment as an Innovative Cosmetic Raw Material
## 3 Results  
### Bioferment  
#### 3.1.1 Physico-Chemical Analysis  
The pH, total sugars and water activity were measured in triplicate for Kamchatka berry pomace of the Aurora and Indigo Gem varieties (mixed in a ratio of 1:1), the solid fraction of the bioferment, and for the freeze-dried pomace and the solid fraction of the bioferment. The average results of the measurements together with the standard deviation are given in the table below.  
The data in Table 1 show that the pH changed after the fermentation process: there was a decrease of 0.08 \(\pm\) 0.01. The content of monosaccharides also decreased, which means that the microorganisms inoculated in the bioferment used sugars as a carbon source in the fermentation process, and their low content in the bioferment (0.12 g/100 g \(\pm\) 0.02) shows that 5 days is the optimal fermentation time.  
The activity of the water in the bioferment increased due to the dissolution of the pomace in water before the fermentation process, which translates into better conditions for lactic bacteria and yeast to carry out the fermentation process. High water activity also increases the likelihood of negative effects of external factors on the solid fraction of the bioferment, such as microbial contamination, negative effects of environmental factors such as heat or UV radiation, so this fraction was freeze-dried. The water activity in the freeze-dried fraction was reduced by more than 87% (0.770 \(\pm\) 0.02), resulting in a much higher stability of the formulation.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Sample** & **pH** &
\begin{tabular}{c} **Total Monosaccharides** \\ **(g/100 g)** \\ \end{tabular} & **Water Activity** \\ \hline Pomace & 3.89 \(\pm\) 0.01 & 2.75 \(\pm\) 0.01 & 0.876 \(\pm\) 0.02 \\ Bioferment & 3.81 \(\pm\) 0.03 & 0.12 \(\pm\) 0.01 & 0.883 \(\pm\) 0.01 \\ Freeze-dried pomace & 3.61 \(\pm\) 0.02 & 19.05 \(\pm\) 0.03 & 0.101 \(\pm\) 0.03 \\ Freeze-dried bioferment & 3.58 \(\pm\) 0.02 & 0.84 \(\pm\) 0.02 & 0.113 \(\pm\) 0.01 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Physico-chemical parameters of Kamchatka berry pomace and bioferment solid fraction before and after freeze drying.  
The results collected in the table also show that the freeze-drying process does not lead to a change in the aroma palette, neither in the pomace nor in the solid fraction of the bioferment (Figure 3), which is very beneficial. The colour change in the freeze-dried product is also related to the pH, as it was lower than in the samples not subjected to freeze drying. The process also affected the consistency, which is technologically more suitable for the mass production of cosmetics, as it is homogeneous. This is due to the fact that freeze-dried samples do not contain plant parts such as seeds, peels and stem fragments.
# Heat Loss Coefficient Estimation Applied to Existing Buildings through Machine Learning Models
###### Abstract  
The Heat Loss Coefficient (HLC) characterizes the envelope efficiency of a building under in-use conditions, and it represents one of the main causes of the performance gap between the building design and its real operation. Accurate estimations of the HLC contribute to optimizing the energy consumption of a building. In this context, the application of black-box models in building energy analysis has been consolidated in recent years. The aim of this paper is to estimate the HLC of an existing building through the prediction of building thermal demands using a methodology based on Machine Learning (ML) models. Specifically, three different ML methods are applied to a public library in the northwest of Spain and compared; eXtreme Gradient Boosting (XGBoost), Support Vector Regression (SVR) and Multi-Layer Perceptron (MLP) neural network. Furthermore, the accuracy of the results is measured, on the one hand, using both CV(RMSE) and Normalized Mean Biased Error (NMBE), as advised by AHSRAE, for thermal demand predictions and, on the other, an absolute error for HLC estimations. The main novelty of this paper lies in the estimation of the HLC of a building considering thermal demand predictions reducing the requirement for monitoring. The results show that the most accurate model is capable of estimating the HLC of the building with an absolute error between 4 and 6%.  
energy efficiency; heat loss coefficient; machine learning; XGBoost; MLP; SVR 2020, 10, 8968; doi:10.3390/app10248968
# Design, Implementation, and Field Testing
## 3 System Design
### System Design Rationale
#### 3.2.4 Multi-Room Support Design  
According to our original design, each room would install one Kinect sensor (with its controlling computer), and a dedicated smartphone would be placed in each room that communicates only with the program running in the computer in the room. Our expectation was that the smartwatch would automatically bind with the dedicated smartphone in the room that the nursing assistant would go into, and unbind with the smartphone placed in the room where the nursing assistant just leaves. Unfortunately, this design did not work because some of the rooms are fairly close to each other, which is within the Bluetooth operating range. As the result, the smartwatch does not do automatic hand-off from one smartphone to another.  
Hence, we changed the design to the following. Each participating nursing assistant would carry a smartphone in her or his pocket, and wear a smartwatch on her or his wrist. The smartwatch is bound with the smartphone. The smartphone can communicate with the computer program in every room in which FACTS has been deployed. When a nursing assistant registers with the system, the registration request is broadcast to all rooms and the particular system that could see the nursing assistant would process the request and respond.  
This broadcast-based mechanism might appear to be inefficient because it is clearly not scalable. We argue that this is the most desirable design for our use cases. One may suggest that the publish-subscribe pattern could be used for better scalability. Indeed, if we were to broadcast a message to hundreds of destinations, the publish-subscribe approach could be the only viable approach. However, we are targeting nursing homes, which typically have 10-20 rooms that need to have FACTS installed. This is because FACTS is designed to track bedside cares where patients cannot take care of themselves. In a nursing home, only a small fraction of patients are in this category. Hence, we do not need scalability. Furthermore, to enable publish-subscribe, a centralized server would be required. To avoid making this centralized serve a single-point of failure, redundant servers would be needed, which would require sophisticated fault tolerance mechanisms [33; 34; 35; 36; 37; 38]. Such an approach would significantly increase the cost and complexity of the system, which is neither needed nor desirable.  
In fact, broadcast-based protocols are widely used in practice for self-discovery and low-maintenance, such as the Address Resolution Protocol (ARP) and the Dynamic Host Configuration Protocol (DHCP).
## 3 Materials and Methods
### Calculation of Angles and Scales
#### 3.4.1 Calculation of Angles  
The size of the template profile rotation should consider both the angular range and the angular step size of the rotation. Optimizing the angular step size can decrease the number of templates and improve the accuracy of the matching process.  
Let \(D\) represent the distance from the center to the edge of the target cross in the template image. When the template picture is rotated by a certain angle \(\Delta\omega\) measured in radians, the displacement of the edge point should not exceed half a pixel, as specified by the expression presented in Equation (5).  
\[\sin\Delta\omega=\frac{1/2}{D} \tag{5}\]  
When the value of \(D\) is significantly larger than the value of \(\Delta\omega\), the equation can be estimated using Equation (6).  
\[\Delta\omega\approx\frac{1}{2D} \tag{6}\]  
Given that the desired rotation has a time-based angular rang \([\pi,-\pi]\), the estimated number of template angle rotations can be calculated using Equation (7).  
\[N_{\varphi}=\frac{2\pi}{\Delta\omega}\approx 4\pi D \tag{7}\]
## 2 Materials and Methods
### The Index Establishment of DBS Slip Anchoring Performance Assessment Method
#### 2.2.3 Second Index of Slip Anchoring Performance Evaluation: Contact Uniformity Criteria  
The operational environment during horizontal well fracturing exhibits significant complexity, where dynamic shock loads and vibration phenomena may compromise DBS integrity, potentially inducing downward migration or even resulting in seal failure due to slippage. A critical determinant of anchoring reliability lies in the interfacial pressure distribution characteristics between slip elements and casing. Non-uniform contact pressure distribution adversely affects anchoring stability, whereas improved uniformity directly enhances anchoring performance.  
This study establishes a quantitative evaluation framework for slip anchoring performance based on contact uniformity criteria utilizing two key parameters: (1) mean interfacial contact pressure and (2) contact pressure standard deviation.  
The proposed evaluation criteria specify that optimal anchoring requires the maximization of mean contact pressure and the minimization of pressure deviation. Only under the above conditions can spatially uniform contact distribution be achieved.
## 5 Experimental Results and Discussions
### Evaluation Measures
#### 5.2.8 Routing Overhead  
The control overhead mainly refers to the transfer of the control information such as channel length, queue length, to the overall information acquired at each sensor node.
# A Framework to Quantify the Quality of Source Code Obfuscation
## 2 Background
### Source Code Obfuscation
#### 2.1.2 Data Obfuscation  
Data obfuscation conceals how data are stored and processed within the source code. Key techniques include:  
* [noitemsep,topsep=0 pt]
* Data Encoding [47; 48; 49; 50; 41]: transforms strings and values to obscure recognition.
* Instruction Substitution [39; 51; 52]: complicates instruction calculation expressions.
* Mixed Boolean Arithmetic [37; 40; 53; 54]: uses formulas combining Boolean algebra and arithmetic operations.  
These techniques hinder attackers' understanding and modification of data processing mechanisms by reorganizing arrays and objects or employing intricate encoding schemes.
# Secure Utilization of Beacons and UAVs in Emergency Response Systems for Building Fire Hazard
## 2 Related Works and Backgrounds
### Emergency Alert and Response System
Their system consists of two main parts, an emergency broadcast system and an active disaster response system. In the emergency broadcast system, the weather center sends warning messages to embedded boards. Then, the active disaster response system automatically performs specified actions, like cutting off power and gas. During a natural disaster, this system configures embedded boards to construct and maintain a temporary network for sending emergency messages. Using this network, the rescue center can integrate data and enable rescue workers to provide aid where it is needed. Unlike these previous works, our UAVs-EMOR system supports both indoor and outdoor monitoring by incorporating UAVs and automatically identifies the occupant's location by using beacon technology. Therefore, it can provide evacuation information to occupants.
# Dual-Modality Transformer with Time Series Imaging for Robust Epileptic Seizure Prediction
## 6 Limitations and Future Work  
### Current Limitations  
Our study presents several important limitations that warrant acknowledgment and guide future research directions. The primary limitation relates to dataset representation, as our experiments primarily rely on CHB-MIT and Bonn datasets. While these are widely accepted benchmarks, they may not fully capture the diversity of epileptic patterns across different populations, age groups, and clinical conditions. Furthermore, these datasets were collected in controlled clinical environments, potentially limiting our model's generalizability to real-world scenarios with varying noise conditions and recording qualities.  
From a computational perspective, although our model demonstrates improved efficiency compared to existing approaches, the processing of multimodal data in real-time still presents challenges for deployment on resource-constrained devices. The current architecture requires dedicated GPU resources for optimal performance, which may limit its accessibility in some clinical settings. This computational demand becomes particularly relevant when considering widespread deployment in healthcare facilities with varying technological infrastructure.  
The temporal aspects of seizure prediction present another significant limitation. Our current implementation focuses on short-term prediction windows, typically within 30 minbefore seizure onset. Extending the prediction horizon while maintaining high accuracy remains challenging due to the increased variability and complexity of long-term EEG patterns. This limitation affects the model's practical utility in providing earlier warnings to patients and healthcare providers.  
Model interpretability remains a crucial challenge despite our attention mechanism providing some level of insight. The complex nature of our deep learning architecture presents difficulties in providing clear, clinically meaningful explanations for its predictions. This limitation is particularly significant in healthcare applications, where understanding the reasoning behind predictions is essential for clinical decision-making and patient trust.
# Design and Evaluation of a Web- and Mobile-Based Binaural Audio Platform for Cultural Heritage
## 6 Conclusions and Future Work
In fact, even if web-based audio evaluation tools are available [50; 51], none focus specifically on spatial audio topics (e.g., HRTF selection, HRTF adaptation, speech reception threshold and cocktail party effect). Moreover, the hearing loss and hearing aid simulation algorithms available from the 3D TuneIn Toolkit would also allow for hearing impairment specific tests. PlugSonic could be used within a listening test framework that lets researchers easily design different types of online tests (e.g., AB, ABX and MUSHRA). Both the web- and mobile-based apps could be exploited for localisation tests or games within a virtual or physical space. To conclude, it is also worth highlighting how PlugSonic could continuously improve and be extended as a direct result of the constant development of the underlining technologies (WAA, 3D TuneIn Toolkit and ARKit). Cultural institutions have started to adopt social media to promote events and stimulate participation, but cannot rely on a common ground when it comes to communication channels and tools to involve their audience. Being developed within the framework of the PLUGGY social platforms, PlugSonic could help to bridge the gap between general public and cultural institutions in an effort to encourage participation, co-creation and sharing of cultural heritage; especially because PlugSonic does not require the development or installation of software or hardware, and can be used on any device. Anyone could--visiting a museum or a monument--retrieve audio narratives from the social platform servers and experience them straight away.
## 2 Materials and Methods
### Statistical Analysis and Machine Learning Modeling  
An analysis of variance (ANOVA) was conducted to assess significant differences in the LAI values and canopy architectures between the sampled trees, using the SAS(r) 9.4 software (SAS Institute Inc., Cary, NC, USA). A post-hoc analysis with the Tukey studentized method (\(\alpha\) = 0.05) was used. Means and standard deviation (SD) were calculated for each parameter.  
A machine learning model was developed using the Bayesian Regularization training algorithm after testing 17 different artificial neural network (ANN) algorithms (two backpropagation using Jacobian derivatives, 11 backpropagation using gradient derivatives, and four supervised weight/bias training) using a customized and automatic Matlab(r) R2018b developed code (unpublished). Six canopy architecture parameters from the canopy measurements ((i) LAI, (ii) LAI\({}_{e}\), (iii) \(f_{t}\), (iv) \(f_{c}\), (v) \(\phi\), and (vi) \(\Omega\)) were used as inputs to predict six main aroma compounds: (i) Phenethyl acetate, (ii) 2-phenyl-2-butenal, (iii) isoamyl acetate, (iv) tetramethylpyrazine, (v) phenylacetaldehyde, and (vi) 2,3-butanediol. All inputs and target values were normalized from \(-\)1 to 1. To develop the model, a random data division was used with 85% (N = 147) of the samples used for training, 15% (N = 26) for testing with a default derivative function, and a performance algorithm based on mean squared error (MSE). The model (Figure 4) was built with a two-layer feedforward network using a tan-sigmoid function in the hidden layer and a linear transfer function in the output layer. A trimming assessment was performed using 3, 5, 7, and 10 neurons to find the model with the best performance and less chance of overfitting. Statistical results are presented for the correlation coefficient (R) and mean squared error values (MSE).  
Once developed, the machine learning model was run using the canopy architecture data extracted from the aerial imagery as inputs to obtain the six aromas per tree from the three blocks. Geo-location per tree was obtained from the GPS data from block corners and considering the plantation density through a customized code written in Matlab(r) R2019a ([PERSON], unpublished). With this information, two maps of LAI\({}_{e}\) and the aroma profiles were generated in Matlab, with the first using kriging  
Figure 4: Model diagram showing the two-layer feedforward network with six inputs from the canopy architecture, 10 neurons in the hidden layer with a tan-sigmoid function, and a linear transfer function in the output layer. Six targets/outputs from the volatile compounds were used for the model.  
interpolation algorithms and the second using scattered data representing each tree. A graphical representation of the complete methodology used in this study can be found in the supplementary material (Figure S2).