esa-sceva/satcom-chunk-collection huggingface dataset:
# Audlet Filter Banks: A Versatile Analysis/Synthesis Framework Using Auditory Frequency Scales
## 3 The Proposed Approach
### Invertibility Test  
Overall, the design of an Audlet analysis FB involves a set of seven parameters: the perceptual scale, frequency range \([f_{\min},f_{\max}]\), filter shape \(w\), filter density \(V\) and bandwidth factor \(\beta\), and a target redundancy \(R_{\mathrm{t}}\). To check that a given parameter set results in a stable and invertible system, three methods exist:  
1. An eigenvalue analysis of the linear operator corresponding to analysis with \((H_{k},d_{k})_{k}\) followed by FB synthesis with \((H_{k},d_{k})_{k}\). The frame bounds \(A\) and \(B\) correspond to the smallest (infinimum) and largest (supremum) eigenvalues of the resulting operator, respectively. The largest eigenvalue can be estimated by numerical methods with reasonable efficiency but estimating the smallest eigenvalue directly is highly computationally expensive. In the next section we discuss an alternative method that consists in approximating the inverse operator and estimating its largest eigenvalue, the reciprocal of which is the desired lower frame bound \(A\) (see also Section 5 for an example frame bounds analysis).
2. Computation of \(A\) and \(B\) directly from the overall FB response, i.e., verification that \(0<A\leq\mathcal{H}_{0}(\xi)\leq B<\infty\) for some constants \(A,B\) and almost every \(\xi\in(-1/2,1/2]\).
3. Checking of whether the overall aliasing is dominated by \(\mathcal{H}_{0}\), i.e., if there exist \(0<A_{0}\leq B_{0}<\ \infty\) that satisfy \[A_{0}\leq\mathcal{H}_{0}(\xi)\pm\sum_{j=1}^{D-1}|\mathcal{H}_{j}(\xi)|\leq B_ {0},\] (19) for almost every \(\xi\in[-1/2,1/2]\). This method is a straightforward application of [20] (Proposition 5). The inner term in (19) can be computed or, at least, estimated by direct computation.  
While method 1 above can always be applied, the applicability of methods 2 and 3 depends on \(w\). If \(w\) is compactly supported in the interval \([a,b]\) and \(0<\frac{b-a}{\Gamma_{K-1}}\leq f_{s}\), \(d_{k}\leq\frac{f_{s}}{(b-a)\Gamma_{k}}\ \forall k\in\{1,\ldots,K-1\}\) (i.e., \(c_{bw}\leq(b-a)^{-1}\)), \(d_{0}\leq\frac{f_{s}}{2f_{p,s}}\), and \(d_{K}^{-1}\leq\frac{f_{s}}{f_{s}-2f_{p,s}^{-}}\), then the alias terms \(\mathcal{H}_{j},j\in\{1,\ldots,D-1\}=0\). This setting corresponds to the _painless_ case [53]. This is the only case when method 2 can be applied. If \(w\) has no compact support but is mostly concentrated on \([a,b]\) and decays outside, the alias terms \(\mathcal{H}_{j}\), \(j\in\{1,\ldots,D-1\}\) exist but may be small compared to \(\mathcal{H}_{0}\). In that case, method 3 can be applied.  
In terms of computational costs, method 1 is by far the most demanding of the three. Still, if a certain parameters set is used over a large number of analyses, this one-time investment to determine invertibility easily pays off. However, the user must still be aware of the potential inaccuracies induced by numerical eigenvalue computation.
Unlike the high temperature hot-injection method in a kind of solvent [4] and the supersaturated recrystallization method using two kinds of solvent [15], two key points have to be considered before the synthesis of CsPbX\({}_{3}\) nanocrystals in a kind of solvent at room temperature. On one hand, adequate soluble precursors of Cs, Pb and Br should be included in the solvent. On the other hand, the growth of perovskite crystals owing to the desorption of conventional ligands, such as oleylammonium and oleic acid, should be restricted [32]. Therefore, in this work, we employed Cs oleate, Pb oleate and tetraoctylammonium bromide as precursors, because of their high enough solubility in acrylic monomer. In addition, we introduced APTES as ligands to restrict the crystal growth of CsPbX\({}_{3}\) nanocrystals, due to the strong chemical interaction between the -NH\({}_{2}\) group and CsPbX\({}_{3}\)[33; 34; 35; 36]. APTES, which usually acts as the precursors of SiO\({}_{2}\) coating [37; 38], and also avoids the desorption of ligands, owing to the cross-linking Si-O-Si network around CsPbX\({}_{3}\) by hydrolysis condensation. The synthesis of CsPbBr\({}_{3}\) nanocrystals showed a high PLQY of 87.5%, as well as excellent stability for corresponding film in light, air, moisture and high temperature. The present strategy has the following advantages: (1) direct one-pot synthesis of CsPbX\({}_{3}\) sol at room temperature without complex post-synthesis process, such as purification, centrifuge and redispersion; (2) easy for mass production even fabrication online for film by an ultraviolet (UV) curing technology; (3) uniformly distributed CsPbBr\({}_{3}\) nanocrystals within the polymer matrix, compared to the inevitable aggregation in blending method. Moreover, this strategy can be expanded to the synthesis of red (CsPbI\({}_{3}\)) and blue (CsPbCl\({}_{3}\)) nanocrystals by introducing an appropriate amount of ZnI\({}_{2}\) and HCl, respectively.
# Interclass Interference Suppression in Multi-Class Problems
## 3 Interclass Interference Suppression Methods
### Binary-Classifier-Based Approach  
There is also interclass interference in binary classifiers; that is, the essential features of a certain class are deleted as redundant features. Nevertheless, compared with multi-classifiers, the interclass interference of the binary classifiers is significantly reduced. Therefore, it is possible to solve multi-class problems by constructing binary classifiers to suppress interclass interference. Currently, two popular ways of solving multi-class problems through binary classifiers are one-versus-one (OVO) strategies [35] and one-versus-all (OVA) strategies [40].  
For an M-class problem, the OVA strategy requires M binary classifiers, each of them distinguishes one class from the rest. Taking a three-class classification problem as an example, there are three classes: Class 1, Class 2 and Class 3 in the problem. OVA strategy uses three binary classifiers to solve this multi-class problem. The first binary classifier distinguishes the samples from belonging to class 1 and not belonging to class 1. The second binary classifier distinguishes the samples from belonging to class 2 and not belonging to class 2. The third binary classifier distinguishes the samples from belonging to class 3 and not belonging to class 3. This method is simple and direct in the construction of binary classifiers. However, the samples processed by each of the binary classifiers constructed by OVA are usually class-imbalanced. Unlike the OVA strategy, the OVO strategy builds binary classifiers between any two classes of the original multi-class problem. For an M-class problem, OVO needs to construct M(M-1)/2 binary classifiers, which are usually much larger than the number of classifiers required by OVA.  
Although OVO requires more classifiers, each classifier built by OVO only needs to distinguish any two classes in the original multi-class problem. Thus, each classifier processes fewer instances, and the problems to be learned by each classifier are usually simpler compared with the original multi-class problem and the OVA strategy. In addition, the class-imbalanced problem existing in OVA strategy does not exist in the OVO strategy. Many practical applications have shown that the OVO construction method usually achieves the best performance when dealing with multi-class problems.  
To sum up, although the all-features-based approach and the one-class classifier-based approach can eliminate interclass interference, their classification performances are usually not good. The binary classifier-based approach is a good choice to suppress the interclass inference.
# Identification of NLOS and Multi-Path Conditions in UWB Localization Using Machine Learning Methods
## 3 Related Works
### Conventional NLOS Identification Techniques in UWB
The condition is harsher to solve in MP condition, where the first-path signals are hard to distinguish clearly from the received signal (Figure 2c). Nevertheless, the classification of the three mentioned classes is not straightforward. The complexity of the classification problem increases especially in indoor environments because of several factors such as material characteristics [36], the refractive index of different materials, and so on [1; 3]. Moreover, the phenomenon of the multi-path effects and NLOS depends on the properties of the medium through which the signal travels, the location (dimension of the places and rooms)where the signals are measured, the presence of other objects within the measured environment, the orientation of the UWB antenna, etc. Therefore, ML approaches have been regarded as attractive strategies for solving this complex task in recent years (Section 3.2).
# In-Band Full-Duplex Relaying for SWIPT-Enabled Cognitive Radio Networks
## 4 Numerical Results
Figure 5 shows the trade-off between SR and PR rates with respect to \(\alpha\) for different numbers of receive/transmit antennas. Considering practicability, we set the number of transmit/receive antennas at the FDR to the powers-of-two values. The energy harvested at the ST and FDR becomes higher as \(\alpha\) increases, leading to a higher SR rate. However, when the value of \(\alpha\) is higher than a certain threshold, the SR rate deteriorates due to the decrease in the information transmission time, \((1-\alpha)\). On the contrary, the PR rate increases with \(\alpha\), since the time of direct information transmission for the PR increases. At the maximum sum-rate point associated with the optimal \(\alpha\), an increase in the number of transmit antennas at the FDR (\(M=4,8,16\)) provides more degrees of freedom, which boosts both the SR and PR rates. On the other hand, the sum rate also increases as the number of receive antennas at FDR (\(N=4,8,16\)) increases. This is primarily attributed to an increase in the SR rate, as FDR would be in the proximity of ST.  
Figure 4: Sum rate of SR and PR versus the transmit power at the primary transmitter (PT) and the number of antennas at the full-duplex relay (FDR). Figure 3: Sum rate of the secondary receiver (SR) and the primary receiver (PR) versus \(P_{\text{p}}\) with \(M=N=4\).
## 3 Hardware Implementation
### Hybrid ECG Classifier
#### 3.4.1 Linear Pre-Classifier (LPC)  
In order to lower the probability of activating the high-performance classifier and address the easy-to-classify segments, based on the morphological observation of the scale-3 approximation coefficients, we propose a method to determine whether the morphology is similar to A/V, as shown in Figure 12, which has a significant difference from normal.  
The criterion involves measuring the difference between the first inflection point on the right side of the R-peak and the terminal point. If this difference surpasses a predefined threshold, we classify the morphology as similar to A/V.  
However, it is too arbitrary to judge based on this one condition alone. In our study, we also incorporate the previous RR interval as an additional feature for judgment. The RR interval is widely recognized as an effective feature for reflecting cardiac arrhythmia [40, 41]. [PERSON] et al. [42] presented that arrhythmia beats such as atrial or ventricular premature beats exhibit a shorter previous RR interval (PRI) compared to the average RR interval (ARI) (PRI \(<\) 0.9 ARI).  
If an ECG segment satisfies both of the aforementioned criteria, we consider it to have an abnormal heartbeat; otherwise, it will activate a high-performance SVM classifier.  
Figure 11: Hardware architecture of the HDWT feature extraction engine.  
Figure 12: The scale-3 approximation coefficients corresponding to QRS morphology.  
Moreover, as the linear pre-classification stage has relatively low accuracy, in order to maintain the overall accuracy, the thresholds used in the pre-classification are set to a very strict level, so that a relatively large number of samples will be sent to the NLSVM stage for further classification.
## 4 Experimental Results and Discussion
### Experimental Results and Analysis  
#### 4.3.1 Experimental Comparison of Optimized Loss Functions  
To verify its contribution to model improvement, the loss function used herein was compared with other common loss functions. Figure 7a shows a comparison of the mAP curves of YOLOv5s with EioU and other loss function models for tears, and Figure 7b shows a comparison of YOLOv5s with \(\alpha\)-IoU for scratches. The detection accuracy and network performance improved with the increasing mAP value. With the same dataset, the improved model after 100 iterations exhibited a higher recall value and higher mAP than the model with the mainstream attention mechanism, representing a 2.9% improvement compared with the YOLOv5s network.
## 2 Data and Methodology
### Data
#### 2.2.3 Remote Sensing Image Data  
Utilizing GEE as a remote sensing big data processing platform enables direct access to Sentinel-2 and Sentinel-1 remote sensing images, circumventing the laborious storage and processing requirements on local computers, thanks to its cloud computing capability [37; 38]. The Sentinel-2 (S2) remote sensing image utilized in this study is sourced from the Sentinel-2 image dataset on the GEE platform with product class 2A. This dataset undergoes ortho-correction and atmospheric apparent reflectivity processing by the GEE official, and the imagery retrieved from the cloud platform corresponds to the \"COPERNICUS/S2_SR_HARMONIZED\" product, representing surface reflectance data. In this investigation, five bands of Sentinel-2 remote sensing satellite data are employed, namely, red, green, blue, near-infrared, and short-wave infrared, with a ground resolution of 20 m for the short-wave infrared band and 10 m for all other bands.
# A Survey of Visible-Light-Communication-Based Indoor Positioning Systems
## 2 Applications for VLC-Based IPS
### Asset Tracking  
Asset tracking uses LED lights to transmit signals for indoor asset positioning and tracking. Asset tracking for VLC offers several advantages over traditional tracking methods [21]. Similar to scenarios of indoor navigation, it implements the existing LED lighting  
Figure 1: Applications of VLC-based IPS.  
infrastructure, reducing the need for additional hardware investment. This makes it a cost-effective solution for asset tracking in various industries, including warehousing [22], logistics [23], retail [24], healthcare [25], and manufacturing [26].  
One of the key benefits of VLC-based asset tracking is its high accuracy. By using multiple LED lights strategically placed throughout the indoor environment, the position of assets can be determined with great precision. This enables the real-time tracking and monitoring of assets, improving inventory management and reducing the risk of loss or theft. In hospitals, VLP systems can be used to track patients and equipment to ensure the safety and traceability of medical equipment. We carried out tests in a medical laboratory and achieved the positioning and monitoring of medical equipment with a VLP system with a positioning error of less than 1 cm. Another advantage of VLC-based asset tracking is its low interference. VLC operates in the visible light spectrum, which is not crowded with other wireless signals such as Wi-Fi or Bluetooth. This ensures reliable and interference-free communication, resulting in more accurate asset tracking results.  
VLC-based asset tracking systems are also highly scalable [27]. Additional LED lights can be easily installed to expand the coverage area or increase the density of tracking points. This flexibility allows companies to tailor the system to their specific needs and easily scale as their operations grow.
# Structure-Aware Trail Bundling for Large DTI Datasets
## 3 Method
### Structure-Emphasizing Rendering
#### 3.3.4 Creating Outlines  
Our final rendering addition proposed involves the generation of _outlines_ along the boundaries of trail bundles in image space. These outlines are useful to emphasize groups of close and roughly parallel trails, which helps one to disambiguate crossing or overlapping bundles, by e.g., seeing which bundle is in front of another one [13; 60].  
We can easily create outlines by slightly adapting our two-pass rendering method used for peel blending (Section 3.3.2 as follows. In the first pass, used to create the depth buffer \(\mathbf{z}\), we render splats with a slightly larger radius \(r+\epsilon\), where \(r\) is the standard radius given by Equation (9). Then, all pixels that have a \(\mathbf{z}\) value lower than the maximal one (that is, set when clearing the \(Z\) buffer) and that have a color in the framebuffer different from background--that is, pixels that are visited by the first rendering pass but not the second rendering pass--are on an outline of \(\epsilon\) pixels thickness of rendered fibers. In practice, setting \(\epsilon\) to a few pixels gives salient enough, but not too bold (visually disturbing) outlines. Figure 11 illustrates this: In the left set of images, outlines are those pixels that are purple in the \(Z\) buffer (rendered there) and yellow in the framebuffer (not rendered there). Image (b) in this figure shows the added value of outlines: Compared to standard shading, it is now easier to discern how bundles overlap and cross each other. For more clarity, we also show the outlines only in image (c). Separately, note that our outline-creation technique _subsumes_ the DDH technique in [13]: We can create DDH-like images simply by enabling outlines (rendered in black) and using a constant splat profile with white base color (Section 3.3.2).  
Figure 10: Trail set visualized (**a**) no emphasis of planar regions; (**b**) gap-filling in planar regions; (**c**) sparse, and (**d**) dense trail sampling. Images (**a,b**) are colored by planar anisotropy \(c_{p}\). Images (**c,d**) are colored on trail direction. See Section 3.3.3.
# The Environmental Impact of Inland Empty Container Movements Within Two-Depot Systems
## 4. ABM Formulation
### Model Assumptions  
The model is tailored for the repositioning of empty containers in the hinterland in the representative setting of a network of empty container yards, inland freight stations, and customer locations. To simplify the problem formulation, the assumptions should be clearly defined, all of which are commonly found in the literature.  
**Assumption 1:** A closed system, consisting of only four nodes: ports, container yards, inland freight stations, and customer locations. As such, empty containers can only be transported or repositioned between these four nodes, and containers outside the system are not permitted to enter.  
**Assumption 2:** Well-performing containers: containers are assumed to be in good condition, meaning that container maintenance is not addressed within the scope of the problem.  
**Assumption 3:** Transportation modes: trucks are considered the primary mode of transportation for inland container movement in the model.  
**Assumption 4:** Intermediary property of depots: it is assumed that full containers are discharged at customer locations and subsequently returned to empty yards within the port for short-term storage, where they are kept available for future export demands. If the demand for empty containers is not yet determined, they will be moved to an inland freight station for long-term storage, awaiting customer requests.  
**Assumption 5:** Single transport cost: it is assumed there only exists one transport cost between each pair of demand and supply, that is, deterministic transportation costs between two specific locations.  
**Assumption 6:** Storage costs: it is assumed that the cost of storing containers in the empty yard and freight station is negligible or not a primary factor in environmental analysis. This exclusion allows the model to focus on the structural and environmental impacts of inland container movements without introducing cost variability, which can differ significantly across regions and operators.  
**Assumption 7:** Capacity: the capacity of the empty container yard and inland station depots for storing containers is infinite.  
**Assumption 8:** Type of container: only standard containers, whether 20- or 40-foot containers, are employed for transportation. Special equipment is not considered as it needs to be moved by specific instructions.
## 3 Materials and Methods
### Data Normalization  
Data normalization, which lowers data to a range of 0-1, is one of the most used methods of data processing. Normalization is often employed in the processing of several assessment indexes procedures, to eliminate data unit restrictions and make the comparison and weighting of different indicators easier [28]. The following are some of the benefits of data normalization.  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline
**Dataset** & **Types** & **Explanation** \\ \hline Id & Numerical(Integer) & Identity of the molecular property \\ \hline Molecule\_name & Text(Object) & The name of the molecule where the coupling constant originates \\ \hline Atom\_index\_0 & Numerical (Integer) & The atom indices of the atom-pair creating the coupling \\ \hline Atom\_index\_1 & Numerical (Integer) & The atom indices of the atom-pair creating the coupling \\ \hline Type & Text (Object) & It indicates different types of coupling \\ \hline Scalar\_coupling\_constant & Numerical (Float) & Scalar coupling is the isotropic part (independent on the molecular orientation) of the J coupling \\ \hline \end{tabular}
\end{table}
Table 1: Descriptions of Datasets.  
Figure 1: The Scalar Coupling Constant in the Molecular Property.  
* Data normalization aids in the improvement of data speed, accuracy, and efficiency, as well as the removal of undesired outliers.
* It aids in the reduction of data alteration issues.
* Data normalization reduces duplication, by ensuring that only relevant data is stored in each table.  
The mathematical expression of data normalization is:  
\[x=\frac{x-x_{min}}{x_{max}-x_{min}} \tag{1}\]  
where \(x_{max}\) is the maximum value of the input data and \(x_{min}\) is the minimum value of the input data.
# The Effects of Plasma-Activated Water on Heavy Metals Accumulation in Water Spinach
## 2 Materials and Methods
### Plantation of Water Spinach  
Planting pots (15 cm diameter, 13 cm height) were used for plantation purposes. Clean cultivation soil (control group) and man-made contamination soil with added metals were weighed. A total of 550 g of soil was placed into each plantation pot. For seedling preparation, the treated and untreated seeds were then placed on wet filter paper in a Petri dish for a germination period of 5 days. Six of the best-grown seedlings were selected and moved to each pot for growing. The plasma treatment was applied to the seeds and the water, in combinations as follows: Group 1, no plasma treatment on seeds or irrigation water (NTS + NTW); Group 2, no plasma treatment on seeds, but with the use of PAW as irrigation water (NTS + PAW); Group 3, plasma treatment on seeds, but no plasma treatment was used on irrigation water (PTS + NTW); and Group 4, plasma treatment on seeds, and using PAW as the irrigation water (PTS + PAW). Each group was planted in three pots (triplicate), and each pot contained six seedlings. The procedure was shown in Figure 1. The plantation was carried out for 5 weeks under neutral day conditions (photoperiod: 13-h light between 7:00 a.m. to 8:00 p.m.) with the aid of a T9 fluorescent lamp (6500 K, 18 W). Each pot of water spinach was watered with 150 mL PAW or RO water thrice each week, while the plant height measurement and photoshoot were performed on the 4 th day of each week.  
Figure 1: The procedure of the plantation.