esa-sceva/satcom-chunk-collection huggingface dataset:
# A Switching Observer for State-of-Charge Estimation of Reconfigurable Supercapacitors
## 4 Experimental Validation
### Experimental Parameters  
(1) Physical Parameters:According to Maxwell's specifications, the BCAP0310 supercapacitor has a rated capacitance of 310 F and a rated voltage of 2.70 V, with an absolute maximum voltage of 2.85 V. It operates between \(-40\)\({}^{\circ}\)C and \(65\)\({}^{\circ}\)C, with our tests conducted at 25 \({}^{\circ}\)C, where the estimated DC life is 10 years, after which capacitance may decrease by up to 20% and ESR may increase by up to 100%.  
To measure the capacitance and ESR, we followed the specified test current and waveform. With a maximum charging current of 10A, the measurements yielded the following results: for the first supercapacitor, \(C_{eq}=298.455\) F, r = 2.031 m\(\Omega\), and \(v_{rated}=2.693\) V; for the second, \(C_{eq}=315.285\) F, r = 1.967 m\(\Omega\), and \(v_{rated}=2.608\) V; and for the third, \(C_{eq}=330.037\) F, r = 1.978 m\(\Omega\), and \(v_{rated}=2.769\) V.  
Figure 5: Hardware platform used for experimental validation.  
(2) System Parameters:For our experiments, we set the observer gain \(L\) to 0.2, a value determined to be suitable based on our theoretical analysis (Lemma 2) and confirmed through experimental observations.The charging current(i) is always set to 2 A.
## 2 Results
### Comparison of Extraction Method (SPME and SBSE/HSSE)  
The efficiency of SPME fibers and SBSE/HSSE bars to extract MVCs from a bacteria culture were tested.  
After a preliminary assay of four SPME fibers on a same bacterial sample, only the polydimethylsiloxane/divinylbenzene (PDMS/DVB) and carboxen/PDMS (CAR/PDMS) fibers were retained due to their wide molecules recovery (91%, data not shown).  
For SBSE/HSSE, we previously observed that the simultaneous use of two different stir bars (PDMS + ethylene glycol/silicone (EGS)) improved the extraction of bacterial metabolites [18].  
Figure 2 represents the distribution of the number of volatile/semi-volatile compounds extracted in _S. aureus_ cultures by SBSE/HSSE or SPME (SPME results combine analyses of both fiber types). The numbers of identified compounds were then compared using a Venn diagram. The name of the compounds of each section of this graphic are indicated.  
The SBSE/HSSE method extracted and identified 17 compounds while the SPME method found only four molecules (Figure 2). Two out of the four molecules identified using fibers were also detected by SBSE/HSSE. A total of 19 different metabolites were identified in _S. aureus_ cultures, 90% of which were extracted via magnetic stir bars highlighting the benefits of this method.  
Our results are in agreement with previous studies showing a better efficiency of stir bars due to the larger volume of phase available on stir bars compared to fibers, thus increasing the extraction capacity of volatile/semi-volatile compounds [17; 28].  
All these results demonstrate the relevance of SBSE/HSSE for the extraction of bacterial metabolites from liquid cultures compared to SPME.
## 3 Smart Feasant Farming System and Data-Mining Process
### Cloud-Computing Platform
#### 3.4.1 IoT Message Hub Service  
In general terms, a cloud messaging service enables a channel for bidirectional communication between IoT devices/mobile applications and the cloud. Beyond the obvious need to create data streams for all the variables that are commonly monitored by a variety of sensors in a smart farming system, bidirectional communication is crucial for a data-mining-based smart farming system to be able to show back to its users the results of the data-mining process that is commonly carried out in the cloud.
## Results
### Data Collections at High Spatial or Stand Level Resolution
#### 3.1.4 Individual Map Products  
Mosaics for eight types of individual map products were made for these variables: CHM, RGB reflectance, the surface temperature from G-LiHT observations, and fluorescence retrievals from _HyPlant_ FLUO (Figure 6A-H). The original high resolution \(\sim\)2 m sampled data obtained from the flight lines (red lines, Figure 1) were aggregated to 14 m\({}^{2}\) spatial resolution in these map mosaics. Mosaics were constructed from data obtained from the parallel N-S flight lines during a single solar noon collection (26 October, centered on midday 13:15 local time, \(\sim\)solar noon) that covered a \(\sim\)45 min time period during which the aircraft was systematically moving from the west side to the east side of the designated campaign subarea of the Parker Tract forest.  
Map products derived from the G-LiHT VNIR spectrometer include the RGB image from the VNIR imaging spectrometer (Figure 6A), and several spectral reflectance indices, including the PRI and the NDVI (Figure 6C,D). Also obtained from G-LiHT were the CHM estimates derived from LiDAR data (Figure 6B), and the adjusted surface temperature, \({}^{\circ}\)C (Figure 6E). Map products from the _HyPlant_ FLUO module included the red-band fluorescence emissions derived at 685 nm (F685), the far-red fluorescence emissions derived at 740 nm (F740), and their red/far-red fluorescence ratio (Fratio), F685/F740 (Figure 6F-H). The spatial patterns for the VNIR and thermal data were relatively smooth across their mosaics, being relatively unaffected by the changing illumination conditions (e.g., solar zenith and azimuth angles) over the \(<\)1 h flight period. The thermal patterns were best for identifying fallow vs. standing vegetation. In contrast, the fluorescence spatial patterns were highly responsive to the changing illumination conditions, such that the merging of flight lines at this resolution scale still retained delineated features related to flight lines. These were due to the within strip scanning views (nadir \(\pm\) 10\({}^{\circ}\)) and the west to east progression of the N-S flight strips while both the SZA and the solar azimuth were changing. This is most evident for the earlier flight lines, which are located on the left side of each fluorescence mosaic (Figure 6F-H), when the solar angles were changing the most rapidly.  
For this dataset, the age class patterns shown in the image (Figure 6A) are retained in the CHM, the PRI, and the NDVI mosaics (Figure 6B-D), and to a lesser extent in the thermal image mosaic (Figure 6E). In this particular mid-day example, the age classes were not well discriminated with fluorescence variables.  
Figure 5: Thermal calibration of the G-LiHT thermal sensor against ground-based measurements.
## 3 Experiments and Results
### Quantitative Analysis  
In order to obtain the optimal number of clusters, \(K\), we looked at the sum of squared errors between the observations and centroids in each cluster by adjusting \(K\) from 2 to 10 (see Figure 9). SSE decreases with each additional cluster, but when considering computational overhead, the curve flattens at \(K\) = 6. Thus, we set the optimal \(K\) at 6, which means that the five behavioral features could be sufficiently classified into six categories.  
As a result of this clustering method, we obtained the labels (i.e., classes or categories). Since this is an unsupervised learning model, and since the elbow method is partly subjective, we need to ensure that the derived labels are well classified.  
Thus, with the chosen \(K\), we performed the decision tree classifier as a supervised model, and used the following parameter options for learning process: a gain ratio for split criterion, max tree depth of 5, and binary tree structure. As a result, Table 3 shows the confusion matrix, with the accuracy remaining at 92.43%, and the average precision, recall, and F1-score all staying at 0.92.  
Thus, with the k-means clustering and decision tree methods, we determined an optimal number of clusters (6), and evaluated the performance of the derived clusters.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Expected Class**} & \multicolumn{6}{c}{**Actual Class**} \\ \cline{2-7}  & **0** & **1** & **2** & **3** & **4** & **5** \\ \hline
**0** & **137** & 1 & 4 & 0 & 1 & 2 \\
**1** & 1 & **212** & 0 & 8 & 1 & 0 \\
**2** & 7 & 0 & **156** & 5 & 2 & 3 \\
**3** & 0 & 7 & 6 & **240** & 1 & 0 \\
**4** & 0 & 3 & 4 & 6 & **75** & 0 \\
**5** & 4 & 0 & 7 & 0 & 2 & **96** \\ \hline \hline \end{tabular}
\end{table}
Table 3: Confusion matrix.  
Figure 9: Sum of squared errors with elbow method for finding optimal \(K\).
## 3 Materials and Methods
### Sensor Nodes Layer  
For smart farming, we considered two main types of IoT sensors to measure various parameters of an agricultural field, which are underground sensors and aboveground sensors. The former is related to sensors measure soil moisture. These sensors can transmit data to the wireless server gateway node for storage and processing. The latter, as Figure 4 shows, uses the HOBO U30 Weather Station Starter Kit, which can accept up to 10 plug-and-play smart sensors to measure temperature/humidity, rain, and solar radiation. The nodes are powered with a rechargeable battery using solar panels. The goal of having a comprehensive sensor is to provide stakeholders in the agricultural field with a full range of data that would help in making a wise decision in managing crops and resources (e.g., water), besides enhancing farm productivity. These wireless sensors can be placed in various locations throughout a farm, even in the harshest environmental conditions.  
Figure 4: HOBO U30 Weather Station Starter Kit in Medenine, Tunisia.  
Figure 3: Overview of the agricultural IoT platform.  
Figure 5 shows sensors that are attached to HOBO U30 Weather Station Starter Kit. Figure 5 illustrates an S-THB-M008 temperature/humidity smart sensor, which consists of a monitoring system for recording two environmental parameters. The temperature parameters can be measured within a range between \(-40\) to 75 \({}^{\circ}\)C, while the humidity range is between 0 and 100 %. The stainless-steel sensor tip and durable cable, rated for submersion in water at 50 \({}^{\circ}\)C for up to one year, ensure reliable operation. The length of this model is 2 m. Figure 5 displays the S-RGB-M002 rain gauge, which considers a smart rainfall sensor used to measure up to 5 inches of rain per hour with an accuracy of 0.01 inches and a maximum interval of 4000 tips; also, the sensor contains two-meter cables.  
Figure 5 displays the solar radiation sensor S-LIB-M003, which can effectively measure light levels with a silicon pyranometer sensor. This product offers a measurement range of 0 to 1280 W/m\({}^{2}\) over a spectral range of 300 to 1100 nm. A measurement averaging mode is available. All these smart sensors can transfer all stored data automatically to the recorder without the need for any programming or comprehensive user configuration. Figure 5 shows a soil moisture smart sensor S-SMC-M005, which offers a two-time design for easy installation. In addition, S-SMC-M005 is a smart sensor, allowing users to launch monitoring systems quickly, easily, and affordably. It provides \(\pm\)3% accuracy in typical soil conditions and \(\pm\)2% accuracy with soil-specific calibration. Readings are provided directly in volumetric water content, where this sensor is designed to maintain low sensitivity to salinity and textural effects.
# Vector Signals and Invariant Systems: Re-Tooling Linear Systems Theory
## 8 Conclusions  
Geometric algebra provides a power set of mathematical tools for carrying out coordinate-free signal processing. Unfortunately, it is not obvious how to extend many of the most commonly used tools in signal processing (such as convolution and frequency response) from operations defined on complex-valued (CV) time-series in \(\mathbb{C}\), to operations defined on vector-valued (VV) times-series in \(\mathbb{L}^{n}\). Moreover, a proper holistic extension of the conventional theory could lead to a deeper understanding of multidimensional signal analysis and potentially unlock more efficient computational methods for image and signal processing.  
To that end, we have proposed a set of definitions of convolution, Fourier transform (FT), frequency response, and frequency spectrum for VV signals and linear rotation-invariant time-invariant (LRITI) systems, which closely aligns and generalizes the theory  
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Feature** & **Traditional CV Theory** & **Proposed VV Theory Using TOPs** \\ \hline Intrinsic dimensionality & \(N=2\) only & \(\begin{cases}\frac{N}{2},\ N\ \text{is even},\\ \frac{N+1}{2},\ N\ \text{is odd}.\end{cases}\) \\ Analysis strategy & Channel-wise / component-wise & Plane-wise (2D subspaces) \\ Number of computations & \(N\) scalar complex computations & \(N/2\) (plane-based) \\ Geometric interpretation & No significance as components treated independently & Enable a meaningful geometric interpretation \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparative study of traditional CV theory and proposed VV theory.  
for CV signals and linear time-invariant (LTI) systems. However, we provide a frequency response that is valid in the context of bivector exponential signals; the lack of a matching decomposition algorithm for a general VV signal prevents further utility. This leads to the definition of both a frequency spectrum and frequency response with respect to a set of totally orthogonal planes (TOPs). Finally, the convolution property of the FT for VV signals is derived, establishing a relationship between time-domain and frequency-domain analysis, which we believe aligns as closely as possible with the CV LTI system theory.  
Conceptualization, M.D. and S.S.; methodology, M.D. and S.S.; software, S.S.; validation, M.D. and S.S.; formal analysis, M.D. and S.S.; investigation, M.D.; resources, M.D.; writing--original draft, M.D. and S.S.; writing--review and editing, M.D. and S.S.; visualization, S.S.; supervision, S.S.; project administration, S.S. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
No new data were created or analyzed in this study. Data sharing is not applicable to this article.  
The authors declare no conflicts of interest.
# Design Analysis of a Passive Buffer System for Space-Assembled Segmented Mirrors
###### Abstract  
This paper presents a passive buffer system of connection mechanism for space-assembled segmented mirrors. The purpose of assembling telescope in space is to overcome the limits of launch volume and mass to support ever increasing aperture sizes. Using this buffer system, the docking impact between two mirror segments can be alleviated effectively. In this paper, first, a design methodology called \"double-elastic contact model\" is developed. Then, the design details and operation principle of the connection mechanism, are presented to validate the passive buffer system. Finally, by comparing the traditional docking system, the simulated results of dynamic performance show that the proposed buffer system can absorb the impact energy effectively and is considerably suitable for assembling the space optical system.  
passive buffer system; space assembled telescope; double-elastic contact model; dynamic performance +
Footnote †: journal: Applied Sciences
# The Potential of Cannabidiol in the Management of Oral Infections
## 4 Antimicrobial Properties of Cannabidiol  
### In Vitro Evidence  
One of the most studied targets of CBD in oral microbiology is _Streptococcus mutans_, the primary etiological agent in dental caries. _In vitro_ experiments have shown that CBD can significantly reduce the growth and metabolic activity of _S. mutans_, including its ability to form biofilms and produce acid, both of which are critical to cariogenicity [20; 38; 42]. CBD appears to disrupt bacterial membrane integrity and inhibit quorum sensing pathways, thereby reducing bacterial communication and biofilm formation. Other _in vitro_ studies have demonstrated that CBD can reduce established _S. mutans_ biofilms and prevent their formation on hydroxyapatite-coated and enamel-analogous surfaces [20; 21].  
CBD has also exhibited inhibitory effects against several Gram-negative anaerobic bacteria involved in periodontal disease, including _Porphyromonas gingivalis_, _Aggregatibacter actinomycetemomitans_, and _Fusobacterium nucleatum_. These pathogens are noted for their resistance to conventional antibiotics and their role in chronic inflammation and tissue destruction [43; 44].  
_In vitro_ MIC and MBC values for CBD against these species fall within therapeutically relevant ranges, with some studies reporting minimum inhibitory concentrations comparable to chlorheximide [45]. CBD has also been shown to attenuate lipopolysaccharideaccharide (LPS)-induced inflammatory responses in human gingival fibroblasts, suggesting both direct and indirect therapeutic benefits in periodontal disease [46].  
Endodontic infections, particularly those involving _Enterococcus faecalis_, present significant treatment challenges due to biofilm formation and antibiotic resistance. _In vitro_ models have demonstrated that CBD exhibits antimicrobial activity against _E. faecalis_, with the potential to enhance root canal disinfection. CBD's ability to disrupt biofilms within dentinal tubules and reduce viable bacterial counts suggests potential use as an adjunct in root canal irrigants or intracanal medicaments [12; 19; 47].  
CBD has also been shown to inhibit the growth of fungal pathogens, particularly _Candida albicans_, which is commonly associated with denture stomatitis and oral candidiasis. _In vitro_ assays reveal that CBD reduces fungal cell viability, inhibits hyphal transformation, and disrupts biofilm integrity. CBD treatment reduces Candida biofilm adhesion to acrylic surfaces, indicating potential utility in preventing prosthesis-associated infections [24; 48].  
Preliminary studies suggest that CBD may act synergistically with clinical antimicrobials, such as bacitracin or erythromycincin, potentially enhancing their efficacy and overcoming bacterial resistance [49]. Moreover, repeated exposure to sub-inhibitory concentrations of CBD has not been associated with significant microbial resistance, a promising feature for long-term use in oral care products [50].
# Performance of Solar Control Films on Building Glazing: A Literature Review
## 5 Discussion of Existing Studies on the Performance of Solar Control Films
### Building Type, Geometry and Solar Orientation  
The heterogeneity of wall and glazing materials, floor area, room geometry, and building requirements and regulations between existing studies makes it difficult to compare results.  
Regarding building type, all existing studies were based on the performance of SCFs when installed in commercial (shopping mall) or service (office, hospital, school, hotel) buildings, with the exception of the study conducted by [PERSON] et al. [40] that considered a residential building. The large glazing areas of the facade of commercial and service buildings, which may lead to a higher risk of overheating of the indoor environment, could justify the predominance of this building type. In addition, residential buildings tend to have more restricted occupancy schedules, particularly during the daytime, and lower heating, cooling and artificial lighting energy needs, so the potential benefits of applying SCFs are smaller when compared with commercial and service buildings.  
Single rectangular cell rooms are the dominant type of building geometry scenario that is assessed by existing studies, as opposed to the impact of SCFs on the performance of the whole building [40; 42; 52; 54; 55; 56]. The performance of SCFs installed in open plan spaces [48] is poorly investigated, so future studies should evaluate this research gap, particularly the impact of SCFs on daylight availability due to light scattering in multiple zone spaces that tend to have bright perimeter zones and a sharp light reduction away from the windows. Some experimental studies assessed the performance of SCFs through small scale models [19] and test cells [35; 49].  
The fact that south-oriented glazing systems are usually responsible for higher solar heat gains may justify the high number of case studies with windows facing south in the literature review. When assessing the same glass and film system facing different solar orientations, existing studies conclude that the best performance is achieved with south-oriented glazing, the performance is similar for east- and west- oriented glazing, and the worst performance is obtained for north-oriented glazing [44; 46; 49]. The reduction in solar heat gains through glazing with SCF oriented north penalizes its performance, particularly during the winter season and when considering individual rooms without other glazed surfaces.
# Multi-Object Tracking with Predictive Information Fusion and Adaptive Measurement Noise
## 2 Materials and Methods
### Candidate Boxes Enhanced Filtering (CEF)
#### 2.1.2 Dynamic Threshold  
While updating the scores of detection candidate boxes can improve the handling of foreground occlusion, it still does not effectively address the issue of overlapping objects. When two objects significantly overlap, the classic NMS algorithm often suppresses one of the objects, as the suppression threshold is typically heuristically set based on the entire video, making it insufficient for infrequent overlapping situations. On the other hand, predictive information can indirectly reflect the overlap of objects in the next frame through simple IoU calculations. Therefore, we further utilized predictive information to  
Figure 2: The selection of weight parameters. In Figure (**a**), the x-axis represents the parameter selection, while the y-axis displays the HOTA evaluation metric. A higher HOTA value indicates better performance of the selected parameter, and the figure demonstrates that when \(\lambda=0.7\), the HOTA result reaches its peak, indicating that this is the optimal parameter. Similarly, in Figure (**b**), when \(d=1.2\), the HOTA result reaches its peak, indicating that this is the optimal dilatation coefficient. The best results are highlighted with a red circle.  
dynamically adjust the suppression threshold for each detection box, enhancing our ability to handle occlusion issues.  
For the prediction boxes represented by the set \(P\), we calculated the IoU values between the prediction boxes to reflect their overlapping extent. To simplify the computation, we focused only on the maximum IoU value between each prediction box and all other boxes, resulting in a length \(n\) vector \(O\). \(O\) represents the overlapping extent by each prediction box in the predictive information, as reflected in Equation (3):  
\[O=\{o_{i}|o_{i}=\max_{j\
eq i,1\leq j\leq n}\text{IoU}(p_{i},p_{j}),i=1,2, \ldots,n\} \tag{3}\]  
After obtaining the overlap rates for the tracked object predictions, we used this information to dynamically set thresholds for filtering detection candidate boxes. First, we identified the closest prediction box for each detection box and then assigned the overlap rate represented by the prediction box to the threshold vector. Specifically, if the overlap rate of the detection box \(b_{i}\) exceeded 0.7, we set \(threshold_{i}\) to that overlap rate; otherwise, we assigned it a fixed threshold, and Figure 3 shows that the fixed threshold of \(threshold=0.7\) is the optimal parameter. As a result, we obtained a threshold vector \(threshold\) of length \(m\), and the calculation process can be expressed by Equations (4) and (5):  
\[Index=\{index_{i}\mid index_{i}=\operatorname*{argmax}_{1\leq j\leq n}(\text{ IoU}(b_{i},p_{j})),i=1,2,\ldots,m\} \tag{4}\]  
\[threshold_{i}=\begin{cases}o_{index(i)},&o_{index(i)}>0.7\\ 0.7,&\text{otherwise}\end{cases} \tag{5}\]