esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Problem Statement
: _There is a continuous non-negative function \(V(\cdot):\mathbb{R}^{n}\times\mathbb{R}\to\mathbb{R}_{+}\) and a continuous strictly-increasing functions \(\gamma_{i}(\cdot):\mathbb{R}_{+}\to\mathbb{R}_{+}\), \(i=1,2,3\), which satisfy:_  
\[\gamma_{i}(0)=0\qquad i=1,2,3 \tag{9}\]  
\[\lim_{s\to\infty}\gamma_{i}(s)=\infty\qquad i=1,2, \tag{10}\]  
_that for all \((\bar{x},v,t)\in\mathbb{R}^{n}\times\mathbb{R}^{v}\times\mathbb{R}\), such that:_  
\[\gamma_{1}(\|s\|)\leq V(s)\leq\gamma_{2}(\|s\|), \tag{11}\]  
\[\frac{\partial V(s)}{\partial t}+\
abla_{s  
**Definition 1**.: _Uniform tracking boundedness: Consider a linear dynamic system:_  
\[\dot{\chi}(t)=A\chi(t)+B_{1}\omega(t),\chi(0)=\chi_{0}, \tag{13}\]  
_and a reference dynamic system:_  
\[\dot{\chi}_{r}(t)=A\chi_{r}(t),\chi_{r}(0)=\chi_{r0}, \tag{14}\]  
_where \(t{\in}\mathbb{R}\), \(\chi(t)\in\mathbb{R}^{n}\), \(\omega(t)\in\mathbb{R}^{k}\), \(A\in\mathbb{R}^{n\times n}\) and \(B_{1}\in\mathbb{R}^{n\times m}\) are known constant real matrices and \(\chi_{r}(t)\in\mathbb{R}^{n}\). The tracking dynamic system is defined as:_  
\[\dot{\chi}(t)=A\tilde{\chi}(t)+B_{1}\omega(t),\tilde{\chi}(0)=\tilde{\chi}_{0}, \tag{15}\]  
_where \(\tilde{\chi}(t)=\chi(t)-\chi_{r}(t)\in\mathbb{R}^{n}\)._  
_If \(\|\tilde{\chi}_{0}\|\leq r\) and \(\tilde{\chi}(\cdot):[t_{0},t_{1}]\rightarrow\mathbb{R}^{n}\), \(\tilde{\chi}(t_{0})\) is a solution of (15), then:_  
\[\|\tilde{\chi}(t)\|\leq d(r),\forall t\in[t_{0},t_{1}], \tag{16}\]  
_where:_  
\[d(r)=\left\{ \begin{array}{ll}(\gamma_{1}^{-1}\circ\gamma_{2})(R),&\quad if \quad r\leq R\\ (\gamma_{1}^{-1}\circ\gamma_{2})(r),&\quad if\quad r>R\end{array} \right. \tag{17}\]  
_and:_  
\[R=\gamma_{3}^{-1}\Big{(}\frac{\epsilon}{4}\Big{)}. \tag{18}\]  
_Furthermore, the solution has a continuation over \([t_{0},\infty)\)._  
**Definition 2**. : _Uniform ultimate tracking boundedness: Consider the system (15), and \(\tilde{\chi}(\cdot):[t_{0},t_{1}]\rightarrow\mathbb{R}^{n}\) is its solution. If \(\|\tilde{\chi}_{0}\|\leq r\), there exist \(R\) and \(\tilde{d}>(\gamma_{1}^{-1}\circ\gamma_{2})(R)\), such that:_  
\[\|\tilde{\chi}(t)\|\leq\tilde{d},\quad\forall t\geq t_{0}+T(\tilde{d}+r), \tag{19}\]  
_where:_  
\[T(\tilde{d},r)=\left\{ \begin{array}{ll}0,&\quad if\quad r\leq R\\ \frac{\gamma_{2}(r)-\gamma_{1}(\tilde{R})}{\gamma_{3}(\tilde{R})-\frac{ \epsilon}{4}},&\quad if\quad r>R\end{array} \right. \tag{20}\]  
_and:_  
\[\tilde{R}=(\gamma_{2}^{-1}\circ\gamma_{1})(\tilde{d}). \tag{21}\]  
**Remark 1**. : _In most of the previous research, the state regulation, but the tracking control, was studied, and the main performances are about the state, not the dynamic tracking error. Here, we present Definitions 1 and 2 as the performances for a tracking control system._
# Bearing Estimation for Indoor Localization Systems Using Planar Circular Photodiode Arrays
## 4 Measurements and Evaluation
### Reference Measurement  
The LS estimator requires the reference matrix \(R\), which contains the reference intensity characteristics of each channel. Recall that each row of \(R\) corresponds to a channel, while each column corresponds to a bearing. The values of \(R\) can be produced by a measurement, carried out as follows. A beacon was placed in front of the PCPA, at direction of 0\({}^{\circ}\). To minimalize the effects of reflections a black foam was placed on the opposite side of the PCPA (between directions of (\(-90^{\circ}\))-90\({}^{\circ}\), and the beacon was placed in a tube pointing to the measurement device.  
During the measurement, the PCPA was rotated with the turntable with a resolution of 1\({}^{\circ}\), while the light intensity was averaged from 20 measurements at each angular position. For better visibility, the data corresponding to each channel were normalized. The results of the measurements can be seen for all the 16 channels, and the three selected channels in Figure 8.  
Note, that the resolution of the reference matrix \(R\) can be increased with interpolation, as described in Section 2.3. Additionally, note that the resulting characteristics are relatively \"smooth\" and almost identical (see also Figure 3, showing the same measurements).
## 2 Materials and Methods
### Encapsulation Yield and Efficiency  
The quantification of total and surface phenolic compounds was conducted using the Folin-Ciocalteau method, incorporating specific modifications for this study. An amount of 150 mg of encapsulated freeze-dried sample of phenolic extract was dissolved in 3.0 mL of a solvent mixture composed of ethanol, acetic acid, and water in the volumetric proportions of 50:8:42, respectively. The solution was homogenized by vortexing for one minute and subsequently filtered through 0.45 \(\upmu\)m regenerated cellulose (RC) syringe filters. The total phenolic content (TPC) was then assessed as described in Section 2.5 of the methodology.  
To determine the surface phenolic content (SPC) of the microcapsules, a 300 mg sample of freeze-dried material was subjected to a washing procedure using a 1:1 (\(v/v\)) ethanol-methanol mixture for 5 min. Following this, the microcapsules were filtered through 0.45 \(\upmu\)m regenerated cellulose (RC) syringe filters. The assessment of SPC utilized the methodology previously described for total phenolic content (TPC) quantification. Equations (2) and (3) were used to calculate % encapsulation efficiency and % yield.  
\[\%\text{Encapsulation efficiency}=\frac{\text{TPC}-\text{SPC}}{\text{TPC}} \times 100 \tag{2}\]  
\[\%\text{Yield}=\frac{\text{TPC}\text{experimental}}{\text{TPC}\text{theoretical}} \times 100 \tag{3}\]
## 4 Discussion
#### Limitations and Areas for Improvement  
First, our study had a limited dataset with 15 unique subjects whose data were preprocessed and classified by our traditional machine learning models. Most models would struggle or overfit if trained on very small dataset like ours. Our results can be improved with large amounts of data, which will enable models to learn gait patterns associated with alcohol consumption and intoxication. Viable next steps include implementing data augmentation strategies and exploring deep learning approaches such as few-shot and meta-learning, which are suited to small datasets.  
Second, exploration of more classifiers will improve our work. Even though our study achieved good results with the five selected classifiers, we can improve our study performance by testing a wider range of classifiers, including ensemble techniques.  
Third, working with a real alcohol dataset is necessary to validate our work. Real alcohol intoxication causes widespread physiological and psychological changes, impairing cognition (judgment, decision-making, reaction time) and physical functions (coordination, balance, vision) [85]. In contrast, \"Drunk Busters\" goggles simulate only visual impairments--such as distortion of depth perception and reduced peripheral vision--without replicating alcohol's broader cognitive and physiological changes [85]. Hence, drunk buster goggles may not capture all the effects and variabilities real alcoholic drinks will have on a subject [86]. These factors limit the validity and generalizability of our findings. However, the use of goggles provides a safe and controlled proxy for early investigation and model evaluation. Future work will focus on collecting real intoxication data to validate our findings.  
Fourth, statistical significance testing is needed to validate our model's performance. While our current study provided a comparative analysis of different classifier performances using established metrics, statistical significance testing using methods like ANOVA or the Wilcoxon signed-rank test was not performed. Our future research would include these tests to strengthen comparative claims and support performance differences with statistical rigor.
# Freeze-Thaw Pre-Treatment of Cassava Tubers to Improve Efficiency of Mechanical Peeling
## 3 Results and Discussion
### Effect of FTP on the Quality of Cassava Tubers
#### 3.3.1 Starch Content of Peeled Cassava Tubers  
The starch content of manually peeled cassava tubers and tubers peeled after FTP are presented in Table 6. It was found that FTP did not significantly affect the starch content of the peeled tubers (\(p<0.05\)). Similar results were also reported by [PERSON] et al. [30], whereas no substantial difference in carbohydrate loss was detected in sweet potato after FTP.  
Freezing can influence textural and gelatinization characteristics of starch [30; 31]. Moreover, it can also cause some alterations in the nutritional properties of the starch [32]. In addition, [PERSON] et al. [33] stated that the freezing period could influence the functional, nutrient, and pasting properties of the product from the frozen cassava tubers. Therefore, it is necessary to further evaluate the quality of cassava tubers peeled by applying FTP in terms of resistance, damage, and gelatinized starch in future research.  
\begin{table}
\begin{tabular}{l c} \hline \hline
**Treatment** & **Total Starch Content (\%, d.b.)** \\ \hline Manually peeled cassava tubers & 70.92  \(\pm\) 3.13 \\ Peeled cassava tubers after FTP using the peeling machine & 70.08  \(\pm\) 2.90 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Comparison of starch content of peeled cassava tubers after FTP using the peeling machine at optimum conditions and manually peeled cassava tubers.
# On the Development of a Neural Network Architecture for Magnetometer-Based UXO Classification
###### Abstract
Building on the foundation of high-fidelity simulated data, this paper addresses the existing gap in comprehensive analyses of neural networks for UXO classification from magnetometer signals. We present a systematic design process for a bespoke Convolutional Neural Network (CNN) architecture and conduct a thorough evaluation of its practical application for underwater UXO detection. Our work analyzes the influence of network depth and width, as well as the impact of various architectural components and meta-features, on classification performance. The proposed CNN is benchmarked against a selection of top-performing classical machine learning models. Finally, we perform a detailed resilience analysis to assess the network's robustness to common signal distortions, including additive noise, linear drift, and time warping. The primary contributions of this work are as follows:  
* We introduce and utilize a novel dataset for model training, featuring simulated data from three parallel magnetometer sensors that includes both UXO and non-UXO objects with remanent magnetization. * We deliver access to this dataset. * We provide a comparative analysis of classical machine learning methods, establishing a robust performance baseline for the UXO classification task. * We present a detailed, systematic design process for a CNN architecture tailored to underwater UXO classification and quantify the performance impact of its key components through extensive ablation studies. * We deliver a comprehensive resilience analysis of the final model, evaluating its robustness against realistic signal distortions. * We derive actionable guidelines for designing field data acquisition protocols, informed by our model's resilience analysis, to mitigate the impact of common signal distortions. The remainder of this article is organized as follows: Section 2 provides an overview of the relevant literature on UXO classification, with a focus on methods applied in underwater environments. Section 3 describes the dataset used in our experiments. Section 4 details the experimental setup, which is divided into four stages: classical model evaluation, neural network development, ablation studies, and resilience analysis. The results of these experiments are presented and discussed in Section 5. Finally, Section 6 provides concluding remarks, summarizes our findings, and outlines potential directions for future research.
## 2 Results and Discussion  
### Development of Defined Medium  
Based on past literature [27; 47; 48; 49; 50; 51; 52] and a series of trials, a defined medium (DM) was developed (Table 1), which supported the growth of _Lev. brevis_ WLP672 (thereafter referred to as LB672). In addition to the other components in the DM, growth of LB672 did not occur in the absence of sodium acetate, as previously reported [48]. Sodium acetate is postulatedto stimulate the growth of several LAB species, owing to it being both a buffering agent and an energy source [47; 48]. Sodium acetate concentrations of 0.1 and 1.2% were the lowest and highest concentrations previously used in nutrient-rich media designed to support the growth of LAB [47; 49]. In the current study, sodium acetate stimulated the growth of LB672 in the DM when glucose was present, but it did not enable growth (function as an energy source) when glucose was absent. LB672 also did not grow in the DM in the absence of peptone (enzymatic protein digest, Bacto peptone), even in the presence of individual amino acid. _Lev. brevis_ strains have previously been reported to lack genes for amino acid biosynthesis [53; 54]. Bacto peptone was used as its composition was defined [55; 56]. Further, LB672 did not grow in the absence of the added vitamins or minerals.  
To investigate the role of amino acid supplementation on the VOCs produced by LB672 during fermentation, the DM was supplemented with amino acids, either separately or in combination (Table 1). Note that amino acid supplementation in the DM resulted in an amino acid concentration 4 to 30 times higher than the amino acid concentration in the DM (from the peptone) [55; 56]. Almost all LAB require glutamic acid (Glu) because of their inability to synthesise its precursor \(\alpha\)-ketoglutate de novo, owing to their lack of a complete TCA cycle [25]. This means aminotransferases, which initiate transamination reactions of amino acid, utilise Glu as the donor substrate of the amino groups and convert them into \(\alpha\)-ketoglutate [36]. To ensure that sufficient Glu was present to enable transamination reactions, Glu was added to all media except the original DM (DM0.1 and DM1.2, Table 1).
# Aromatic Characterization of Grasevina Wines from Slavonia and Podunavlje Sub-Regions
###### Abstract  
This study investigated the chemical aromatic profiles of 60 volatile organic compounds (VCCs), including 21 terpenes, 6 ironisoprenoids, 6 volatile phenols, 4 C-6 alcohols, and 23 esters, in Grasevina wines originating from Slavonia and Podunavlje sub-regions (Croatia). Headspace solid-phase microextraction gas chromatography coupled with mass spectrometric detection (HS-SPME-GCMS) was used to assess 60 VOCs, and a novel HS-SPME-GCMS method for the determination of terpenes and norisoprenoids was developed and validated. Statistical analysis found no significant differences between VOCs present in Grasevina wines and others wines (Chardonnay and Pinot gris); nonetheless, comparison of VOC fingerprints between different wines from the same producer showed that four terpenes (\(\alpha\)-terpinene, \(\gamma\)-terpinene, 1,4-cineol, and 4-terpineol) were present in higher amounts in Grasevina wines when compared to other wines. By exclusively examining VOCs within Grasevina wines through the utilization of a heatmap and hierarchical clustering, a distinct visualization of samples and VOCs emerged, highlighting the vintage effect. Simultaneously, a partial least squares discriminant analysis model was developed with a confidence interval of 95%, demonstrating a noticeable distinction between samples originating from the western and eastern regions. Furthermore, by employing the heatmap using only VOCs from the terpene and norisoprenoidic groups, a clear separation of samples into two groups was observed based on geographical origin; namely, higher concentrations of norisoprenoids and terpenes were observed in the Slavonia sub-region. These results suggest that terpenes and norisoprenoids to a lower extent may be valuable aromatic markers for the regional typicality of Grasevina wines.  
G rasevina-Welschriesling; aromatic profiling; HS-SPME-GCMS; terpenes 2024, 10, 24. [[https://doi.org/10.3390/beverages10020024](https://doi.org/10.3390/beverages10020024)]([https://doi.org/10.3390/beverages10020024](https://doi.org/10.3390/beverages10020024))
## 1 Introduction
Therefore, this paper focuses on path loss prediction in urban environments. Among many path loss models, the COST231 Walfisch-Ikegami model is one of the most widely used models to predict path loss in urban environments [16; 17]. It considers the obstruction of buildings in the propagation path. Specifically, the Walfisch-Ikegami model is a hybrid model combining diffraction down to street level and some empirical correction factors. Inspired by the Walfisch-Ikegami model, this paper proposes a path loss model based on the profile along the direct propagation path in urban environments for 5G cellular communication systems. The essential features of path loss are extracted out of the path profile through a deep neural network; then, the path loss can be predicted precisely according to them. To gain insight into the internal mechanism of the proposed deep neural network, this paper evaluates the relevance of some selected features by changing the input patterns. Subsequently, a linear regression model based on the relevant features is used to quantify the accuracy of the explainability. Figure 1 shows a conceptual graphic of the problem and the proposed solution. The main contributions of this paper lie are as follows:  
* A deep-learning-based path loss model utilizing path profiles is presented for 5G mobile communication systems in urban propagation environments;
* The internal behavior of the proposed deep learning model is explored using an explainable linear model, which considers some selected geometric features of the path profile;
* Simulation results as well as field measurements in a 5G New Radio (NR) network are shown. The rest of this paper is organized as follows. Section 2 reviews some conventional path loss models. Section 3 presents a detailed description of the proposed method. Subsequently, Section 4 explores the prediction performance based on numerical simulations. Section 5 examines the explainability of the proposed model. Section 6 evaluates the prediction performance in a real-world 5G system in urban Taipei City. Finally, Section 7 presents some concluding remarks.
# Development of Predictive Equations for Thermal Conductivity of Compost Bedding
## 3 Results and Discussion
### Thermal Properties of Composts along the Depths Evaluated  
The behavior of the \(k\) as a function of the DC\({}_{0}\) (SC and DC) along the depths evaluated (H1, H2, and H3) is observed in Figures 10 and 11.  
Figure 10: Thermal conductivity (\(k\), W m\({}^{-1}\) K\({}^{-1}\)) as a function of static compaction (SC) and depth (H1, H2, and H3) at different moisture contents: (**a**) 30%, (**b**) 45%, (**c**) 60%, and (**d**) interaction between the different conductivity (W m\({}^{-1}\) K\({}^{-1}\)) and the different conductivity (W m\({}^{-1}\) K\({}^{-1}\)).  
Figure 9: Variation in thermal conductivity (W m\({}^{-1}\) K\({}^{-1}\)) and compost particle ranges (mm) for bedding materials. Values followed by different letters are significantly different (\(p<0.05\); Tukey test).  
average values of SC and MC at different depths. Values followed by different letters are significantly different (\(p\) < 0.05; Tukey).  
The values of \(k\) increased with SC due to a reduction in void space and an increase in bulk density (Figure 10). However, statistical analysis revealed that depth alone did not have a significant impact or interaction with another factor. In general, the values of \(k\) reduced along the evaluated depths, presenting higher values at depth H1. This upward trend was contrary to the study by [PERSON] et al. [52], where it was found that depth alone did not have a significant impact or interaction with other evaluated factors (MC and PS) in thermal conductivity values.  
However, when applying DC to the bedding material (Figure 11), it is observed that there is no significant difference in the values of \(k\) along the depths evaluated (Tukey test, \(p\) > 0.05). The variation in DC may have influenced the bulk density of the material along any PVC cylinder. According to [PERSON] et al. [53], the apparent density has a significant effect on the handling and storage of materials and depends on the composition of the material, PS, MC, and applied pressure, among others. The apparent biomass density increases during transportation, handling, and storage, which may be caused by compaction due to normal vibration, buoyancy, or loading [54].
## 4 Experiment and Results
### Area under Curve (AUC) Values of the Used Models  
In Table 4, we depicted our proposed model, where the OCNN model gives the highest AUC score, of 0.99, and both OANN and OLSTM achieved 0.97. Besides, CNN, ANN, and LSTM obtained the AUC score of 0.88, 0.83, and 0.81, respectively.
# Improving Roadside Unit Deployment in Vehicular Networks by Exploiting Genetic Algorithms  
[PERSON]  
1 iNIT Research Group, Computer Science and System Engineering Department, University of Zaragoza, Ciudad Escolar s/n, 44003 Teruel, Spain; mfogue@unizares (M.F.); jsanguesa@unizares (J.A.S.)  
[PERSON]  
1 iNIT Research Group, Computer Science and System Engineering Department, University of Zaragoza, Ciudad Escolar s/n, 44003 Teruel, Spain; mfogue@unizares (M.F.); jsanguesa@unizares (J.A.S.)  
[PERSON]  
1 iNIT Research Group, Computer Science and System Engineering Department, University of Zaragoza, Ciudad Escolar s/n, 44003 Teruel, Spain; mfogue@unizares (M.F.); jsanguesa@unizares (J.A.S.)  
2 IDLab--Faculty of Applied Engineering, University of Antwerp--imec, Groenenborgerlaan 171, 2020 Antwerp, Belgium; [PERSON]### Abstract  
Vehicular networks make use of the Roadside Units (RSUs) to enhance the communication capabilities of the vehicles in order to forward control messages and/or to provide Internet access to vehicles, drivers and passengers. Unfortunately, within vehicular networks, the wireless signal propagation is mostly affected by buildings and other obstacles (e.g., urban fixtures), in particular when considering the IEEE 802.11p standard. Therefore, a crowded RSU deployment may be required to ensure vehicular communications within urban environments. Furthermore, some applications, notably those applications related to safety, require a fast and reliable warning data transmission to the emergency services and traffic authorities. However, communication is not always possible in vehicular environments due to the lack of connectivity even employing multiple hops. To overcome the signal propagation problem and delayed warning notification time issues, an effective, smart, cost-effective and all-purpose RSU deployment policy should be put into place. In this paper, we propose the genetic algorithm for roadside unit deployment (GARSUD) system, which uses a genetic algorithm that is capable of automatically providing an RSU deployment suitable for any given road map layout. Our simulation results show that GARSUD is able to reduce the warning notification time (the time required to inform emergency authorities in traffic danger situations) and to improve vehicular communication capabilities within different density scenarios and complexity layouts.  
genetic algorithms; VANETs; vehicular networks; roadside units; RSU deployment +
Footnote †: journal: Applied sciences