esa-sceva/satcom-chunk-collection huggingface dataset:
# Classification of Speed Change and Unstable Flow Segments Using Geohash-Encoded Vehicle Big Data
## 3 Methodology
### Dynamic Segmentation Method  
In this study, the process of spatial segmentation at the study location was executed using the geohash algorithm. The geohash algorithm, a grid-based and string encoding technique, generates a hierarchical grid map by iteratively quartering (2 \(\times\) 2) the target region. Figure 5 illustrates the fundamental components of the geohash algorithm. The black numerals within the figure depict the code assigned to the initial partitioned area, while the blue, green, and red letters signify grid code numbers corresponding to the second, third, and fourth subdivisions, respectively. Employing the geohash algorithm, the study partitioned the geographic expans of the Republic of Korea, encompassing longitudes 126\({}^{\circ}\) to 130\({}^{\circ}\) and latitudes 34\({}^{\circ}\) to 38\({}^{\circ}\).  
Dynamic segmentation, a pivotal facet of this study, orchestrates the hierarchy of segmentation predicated on UF determination. To elaborate, the partitioning process hinges on the interplay between TMS and SMS, dictating the UF categorization for each grid. In essence, grids identified as SF halt further partitioning, while non-SF grids undergo continued subdivision. This dynamic segmentation methodology is comprehensively depicted in Figure 5. The initial step, depicted in Figure 5, entails the division of the grid along both latitude and longitude axes, with sequential codes 0, 1, 2, and 3 allocated. Grid 3, devoid of road passages, is excluded from this partitioning. Similarly, Figure 5 displays a parallel division, with space 1 from Figure 5 now labeled as 10, 11, 12, and 13. To maintain granularity while averting excessive coverage or exceedingly diminutive road lengths, the study instituted a minimum of 5 partitions and a maximum of 12. This selection rationale balances the need to avoid overly extensive regions (fewer than 5 partitions) and the requirement for road segments of adequate length (more than 12 partitions) to furnish  
Figure 4: Establishment of unstable flow boundaries.  
Figure 5: Geohash-based space segmentation method. (**a**) N-th (**b**) N+1 st (**c**) N+2 nd spaces.  
micro-segment speed data for expressways. As earlier outlined, the partitioning process follows the pre-determined UF categories. Notably, space 0 qualifies as SF, while spaces 1 and 2 are identified as UF, as illustrated in Figure 6a. The ensuing space segmentation follows the pattern outlined in Figure 6b. Upon determining space no. 20 as UF, partitioning proceeds accordingly. When the partition count reaches 12, space 203 again meets the criteria for UF classification, as evident in Figure 6c, culminating in the conclusion of further partitioning.
## 2 WSN for Monitoring of Column Shortening in High-Rise Buildings
### Wireless Sensor Network (WSN) System  
#### 2.1.1 Sensor  
Generally, vibrating wire strain gauges (VWSGs) have been widely used to measure axial shortenings of columns and walls during construction of high-rise buildings since VWSGs are not subject to electromagnetic interference (EMI) and have excellent endurance properties [9,10,13]. As shown in Figure 3, a VWSG [18] basically consists of two major components: a vibrating wire whose frequency changes in response to tension or compression and two mounting blocks which are fixed at the ends of the vibrating wire, where the mounting blocks are induced as tension or compression in structures. In Figure 3, a plucking coil excites the vibrating wire and measures resonance frequency of  
Figure 2: Wireless sensor network system for measurement of column shortenings in a high-rise building.  
it. A change in the length of the vibrating wire due to tension or compression leads to variation of the natural frequency of the wire. For a given pretension of the wire during the initial setup, the shortening of a member is calculated from the relationship between the natural frequency of a vibrating wire and the strain of the wire [18, 19].
## 3 Redundant Symmetric Lifting Schemes and the Improved Neighboring Coefficients  
### Improved Neighboring Coefficients Denoising  
[PERSON] and [PERSON] [33] proposed a threshold rule incorporating neighbouring coefficients (NC). [PERSON] [34] introduced this method into multiwavelet denoising, which has achieved a wonderful effect in image denoising and mechanical fault diagnosis. The conventional NC procedure chose a constant size of neighboring window \(l=3\) at each level after wavelet decomposition. However, this method is not accurate enough because the size of neighboring window is invariant but the dependences of coefficients are variant at different levels. [PERSON] presented the improved neighboring coefficients denoising after studying the regularity of wavelet coefficients dependency [28], to resolve the problem of constant neighborhood. The formula of incorporating neighboring coefficients is as Equation (18):  
\[S_{j,k}^{2}=\sum_{n=-N}^{N}d_{j,k+n}^{2}\qquad N=\text{N}_{0}-j \tag{18}\]  
where \(j\) is the level of multiwavelet decomposition, the length of neighboring window is \(2N+1\), \(\text{N}_{0}\) is a constant, it should be selected according to the signal duration of features and the support of wavelet filters.  
The INC algorithm is shown in Figure 4. It is obvious that the presented method incorporates more coefficients at a low level while less coefficients at a high level. The threshold of INC is shown in Equation (19):  
\[d_{j,k}=\begin{cases}d_{j,k}(1-\frac{\alpha\lambda_{j}^{2}}{S_{j,k}^{2}}),&S_{ j,k}^{2}\geq\alpha\lambda_{j}^{2}\\ 0,&S_{j,k}^{2}<\alpha\lambda_{j}^{2}\end{cases} \tag{19}\]  
where \(\lambda_{j}=2\text{log}n_{j}\), \(\alpha\) is an adjusting coefficient of the threshold which is determined by the length of the neighbors, \(n\) is the length of measured signals. The threshold became term-by-term soft threshold when \(N=0\) while it became conventional neighboring coefficients when \(N=1\).
## 5 Epigenetic Regulation of ALS
### Studies Using Drosophila Models
#### 5.2.1 Noncoding RNAs
Although the _Drosophila_ genome contains no orthologique of the human _C9 orf72_ gene, _Drosophila_ models for ALS/FTD associated with _C9 orf72_ have been established by expressing the expanded 30 GGGGCC repeats with a CTCGAG interruption (iGGGGC). Expression of iGGGCC repeats in eye imaginal discs induced the rough eye phenotype in adults and that in motor neurons induced locomotive deficits [109]. The following three distinct _Drosophila_ models were established to distinguish between the toxic gain-of-function mechanisms of expanded repeat RNAs and proteins produced by Repeat-associated non-AUG (RAN) translation: (1) model flies expressing expanded pure GGGGCC repeats, (2) model flies expressing the expanded GGGGCC repeat RNA interrupted with a stop codon that only produces the expanded RNAs, (3) model flies expressing non-GGGGC RNAs with alternative codons that only produce translated proteins [24]. By analyzing these fly lines, the expanded GGGGCC repeats were suggested to induce neurotoxicity mediated by the translated proteins and RNA foci may not be a direct cause of neurodegeneration, at least in _Drosophila_ models. Previous studies revealed that overexpression of aubergine, a Piwi-family protein (PIWI) that is responsible for the biogenesis of PIWI-interacting RNA (piRNA), enhanced the phenotype of the neuron-specific _caz_ knockdown flies (Table 1) [110]. Thus, various noncoding RNAs function in the pathogenesis of ALS.
## 5 Simulation of a 40-Channel WDM-PON
### System Margin for ROA\({}_{3}\)  
The proposed WDM-PON system margin is dominated by the state where ROA\({}_{3}\) is functioning as an optical amplifier to backup either ROA\({}_{1}\) or ROA\({}_{2}\) since ROA\({}_{3}\) has an additional IL from the reversible OC. Therefore, the system margin for downstream is calculated by considering the path loss from the output port of the optical amplifier in ROA\({}_{3}\) to the ONU (see Figure 3). After ROA\({}_{3}\) amplifies the signal, the three channels' powers are 2.96 dBm, 7.708 dBm, and 1.335 dBm, respectively. Their powers are attenuated by the total link loss of 16.54 dB, resulting in their received powers of \(-13.58\) dBm, \(-8.832\) dBm, and \(-15.205\) dBm, respectively. The minimum required ROPs for a BER of \(10^{-9}\) at CH1, CH20, and CH40 are \(-21.92\) dBm, \(-22.06\) dBm, and \(-22.19\) dBm, respectively. The detailed system margins for a transmission distance of 50 km of the protection scheme for ROA\({}_{1}\) are listed in Table 5. Under the minimum system margin of 6.985 dB, the CH40 longest transmission distance is equal to 50 km.  
The system margin analysis for upstream transmission over 50 km SSMF is summarized in Table 6 for the protection state where ROA\({}_{2}\) fails, as shown in Figure 4. The three channels' powers are all \(-3\) dBm, which are attenuated by the total link loss of 16.54 dB, resulting in their received powers of \(-19.54\) dBm. The minimum required ROPs for a BER of \(10^{-9}\) at CH1, CH20, and CH40 are \(-31.817\) dBm, \(-35.91\) dBm, and \(-35.09\) dBm, respectively, then the three channels' corresponding system margins for the upstream signals are all greater than 12 dB, benefiting from the high gain of ROA\({}_{3}\).  
Figure 19: BER curve of CH1 (190.8 THz).  
Figure 18: Gain flatness of the pre-amplifier in ROA\({}_{3}\).  
Figure 21: BER curve of CH40 (194.7 THz).  
Figure 20: BER curve of CH20 (192.7 THz).
## 4 Design with Time-Varying Input Saturation
By defining the same quadratic Lyapunov function of Theorem 1 with the constraint (30) and the control law (26), the closed-loop stability inequality is obtained for each value of \(\theta\) and \(\phi\) from the condition \(\dot{V}(x)<0\)  
\[A(\theta)P+B\Gamma(\theta,\phi)+PA(\theta)^{T}+\Gamma(\theta,\phi)^{T}B^{T} \prec 0, \tag{34}\]  
where \(\Gamma(\theta,\phi)=K(\theta,\phi)P\). Additionally, (29) is added to (34) through the parameter-dependent term \(2\alpha(\phi)P\) in order to adjust online the closed-loop performance depending on the instantaneous saturation limits. As a consequence, we ensure a guarantee decay rate of \(\dot{V}(x)\) that varies within the interval \([\underline{\alpha},\overline{\alpha}]\), thus obtaining  
\[A(\theta)P+BK(\theta,\phi)+PA(\theta)^{T}+K(\theta,\phi)^{T}B^{T}+2\alpha( \phi)\prec 0, \tag{35}\]  
that can be described by (31) for each vertex \(i=1,\ldots,N\) and \(j=1,\ldots,M\) of \(\Theta\) and \(\Phi\), respectively. Thereupon, let us consider the regions (8) and (18) and the inclusion \(\mathcal{E}\subseteq\mathcal{V}\) described by (19) to obtain (32). Finally, by means of appropiate manipulations and the application of Schur complements, the inclusion \(\mathcal{V}\subseteq\mathcal{U}(\theta,\phi)\) leads to the following inequality  
\[ \begin{bmatrix}Q(\phi)^{-1}&\Gamma(\theta,\phi)\\ \Gamma(\theta,\phi)^{T}&P\end{bmatrix} \succeq 0, \tag{36}\]  
that can be described by (33) for each vertex as mentioned above. Note that the polytopical representation of \(Q(\phi)^{-1}\) described by (28) is valid for \(Q(\phi)\) given by the following  
\[Q(\phi)=\left[ \begin{array}{ccc}\frac{1}{\gamma_{1}(\phi_{1})^{2}}&\cdots&0\\ \vdots&\ddots&\vdots\\ 0&\cdots&\frac{1}{\gamma_{m}(\phi_{m})^{2}}\end{array} \right]. \tag{37}\]  
In this case, the polytopic weights appearing in (28) and (29) are calculated as follows  
\[\eta_{j}(\phi)=\prod_{h=1}^{m}\gamma_{jh}(\phi_{h}), \tag{38}\]  
where  
\[\gamma_{jh}(\phi_{h}) \begin{cases}\phi_{h}&\text{if}\quad mod(j,2^{h})\in\{1, \ldots,2^{h-1}\}\\ (1-\phi_{h})&\text{else}\end{cases} . \tag{39}\]  
Additionally, the vertex coefficients \(\alpha_{j}\) of \(\alpha(\phi)\) can be obtained as follows  
\[\alpha_{j}=\frac{\alpha\mathcal{C}_{j}+\overline{\alpha}(1-\mathcal{C}_{j})}{ m}, \tag{40}\]  
where \(\mathcal{C}_{j}=|\{h\in\{1,\ldots,m\}:mod(j,2^{h})\in\{1,\ldots,2^{h-1}\}\}|\) and \(|\mathcal{A}|\) denotes the cardinality of the set \(\mathcal{A}\). **Remark 4**. : _Note that, in this paper, for illustrative purposes and to maintain the overall formulation simple, we have decided to consider a scheduled guaranteed decay rate as performance criterion but the results could be generalized to other criteria, for example, sector clusters in the complex plane to avoid undesired oscillations [31]._
# A Switching Observer for State-of-Charge Estimation of Reconfigurable Supercapacitors
## 4 Experimental Validation
### Discussion  
Faster Convergence and Accurate Tracking: The proposed observer exhibits the fastest convergence rate, even when initial state errors are present. It maintains accurate SOC tracking throughout the charging process, effectively handling switching events without significant deviations. This is crucial for real-time applications where continuous and accurate SOC estimation is essential.  
Robustness to Parameter Variations: A key advantage of the proposed observer lies in its robustness to parameter uncertainties. Even with capacitance errors as high as 10% or 20%, the observer consistently converges to the true SOC value. This robustness is particularly valuable in practical applications where accurately measuring and maintaining precise supercapacitor parameters can be challenging due to aging and operating conditions.  
Addressing Limitations of Existing Methods: The experiments highlight the limitations of both classical and open-loop observers. Classical observers, while able to converge eventually, suffer from slower convergence speeds and significant deviations in SOC estimation after charging interruptions. This inability to maintain accuracy during dynamic operation restricts their applicability. Open-loop observers, on the other hand, exhibit poor convergence and high sensitivity to both initial state errors and parameter variations, rendering them unsuitable for reliable SOC estimation.
## 4 Experimental Validation
### EQ1. Model Accuracy and Convergence  
First, we evaluate the model accuracies of all competing methods. We train ResNet-50 and VGG-16 on CIFAR-10 (250 epochs) using all competing methods and measure Top-1 test accuracy (%). Table 2 shows the results. The results demonstrate that the models trained by SHAT converge to higher accuracies than those of the baseline methods. In particular, SHAT achieves the higher accuracy up to 5.22% than the existing asynchronous distributed training (i.e., ASP). This result implies that SHAT successfully reduces the difference among the local models of workers in asynchronous distributed training.  
For more in-depth evaluation for model convergence of SHAT, we also measure the model accuracy of SHAT and ASP in terms of training epochs, with varying the number of workers \(n\). Figure 4 shows the results on the training of ResNet-50 and VGG-16 on CIFAR-10, where the \(x\)-axis represents the training epoch and the \(y\)-axis represents the top-1 accuracy. SHAT always outperforms ASP in terms of the epochwise convergence rate, and the gap between SHAT and ASP tends to become larger as the number of workers increases. This is because the update strategy of SHAT carefully considers the scale of distributed training (i.e., \(\log n\) in Equation (4)), and then update the local model of each worker precisely based on them. As a result, SHAT successfully improves the model convergence of asynchronous distributed training, which verifies that SHAT successfully addresses one of the limitations of the existing asynchronous distributed training--i.e., not considering the scale of distributed training--as we claimed.
## 2 Related Work
### Domain Adaptation in Remote Sensing  
In fact, the domain bias phenomenon is very common in the field of remote sensing due to the imaging conditions and sensors, etc. Therefore, domain adaptation methods, which are used to reduce the difference between the target and source domain distributions, have been widely applied in remote sensing fields, such as remote sensing image retrieval [19], scene classification [27], object detection [28], semantic segmentation [18; 29], change detection [30], and so on. For example, [PERSON] et al. [19] used pseudo-label consistency learning to reduce domain bias in retrieval of different optical remote sensing images. [PERSON] et al. [31] employed a correlation subspace dynamic distribution alignment method with convolutional neural networks to reduce the domain difference in remote sensing image scene classification. [PERSON] et al. [32] used a generative adversarial network to align the source and target domains in a land cover classification.  
Recently, many scholars have also used domain adaptation methods for building extraction as well. For instance, [PERSON] et al. [10] utilized a simple Wallis filter method and adversarial learning to reduce the differences between different domains for cross-domain building extraction. [PERSON] et al. [29] proposed a segmentation network based on a domain adaptation transmission attack for cross-domain building extraction. [PERSON] et al. [16] used two adversarial learning-based methods to explore the impact of domain adaptation on building extraction. These domain adaptation methods mainly rely on adversarial learning, self-learning, and differential alignment. They have a substantial improvement in the accuracy in the target domain compared to the methods for closed datasets. This is because these methods migrate the source domain knowledge to the target domain and reduce the effect of domain bias. However, these methods require not only labeled source domain data but also a large amount of unlabeled target domain data. In small-scale scenarios, large amounts of unlabeled data are difficult to obtain. In addition, these methods require a retraining of the model when the target domain or study area is replaced. Unlike these methods, this paper focuses on correlating the knowledge of source and target domains from the perspective of anomaly detection to reduce the domain bias.
## 3 Transmission Point Selection in Multi-Band Multi-Connectivity Operation
### Transmission Point Selection Algorithm
It is also true that a TP is selected by a user after looking up the TPs disregarding other users' channel status. Thus, the complexity of the transmission point selection depends on the number of allowed connections as well as the total number of users and the total number of TPs, i.e., \(\mathcal{O}(C\times N\times K)\).
## 4 Results and Discussions  
### Comparison of the Results of MGMS Procedures  
Figure 3 presents the seismic demands of wall structure W2 under motion set 6 adopting IGMs selected by MGMS procedures with and without considering the modal response characteristics of the RC shear wall structures, which is denoted as 2-mode and 3-mode, respectively. In Figure 3, the mean demands of the NLRHA with the whole set of IGMs are plotted and marked as \"Mean\". The presented MGMS procedure for RC shear wall structures, considering modal response characteristics of shear wall structures, selected seven IGMs that induced the most significant interaction of the first two modes from the seed IGMs set. The original MGMS procedure that does not take modal response characteristics of the RC shear wall structures into account selected seven IGMs that caused the most significant interaction of the first three modes for the NLRHA of the shear wall structures following the procedure in Reference [30].  
It is found that no matter whether the modal response characteristics of the shear walls were considered, the MGMS procedures can select proper IGMs for the NLRHA of the structure and seismic demands by both MGMS procedures are close to the benchmark demands. It is also noticed that IGMs selected by the MGMS procedure considers the modal response characteristics of the RC shear walls led to a more accurate computation of most of the seismic demands since the seismic demands approximated the benchmark demand better. The more reliable computed seismic demand shows the necessity and rationality of considering the modal response characteristics of shear wall structures in selecting IGMs with the MGMS procedure.
# Recent Advances in the Growth and Characterizations of SILAR-Deposited Thin Films
## 5 Metal Oxide Thin Films
### Tungsten Oxide Films  
Preparation of the tungsten oxide solution (WO\({}_{3}\)) thin films on stainless steel substrates by using NaOH (as an anionic source) and oxotungsten hydrate (WO.H\({}_{2}\)O, as a cationic source)) took place at room temperature [174]. The obtained films were annealed under 673 K for 180 min to remove hydroxide and improve the crystallinity of the obtained thin films. The film thickness increased with the number of deposition cycles (until 100 cycles) due to the growth of nucleation and the coalescence process, reached maximum thickness (0.08 mg/cm\({}^{2}\)), and finally these films were peeled off. The XRD studies showed monoclinic phases with a crystallite size of about 45 nm based on the (200) plane. In the SEM analysis, rod-like morphology with an average width of 1-2 \(\upmu\)m could be detected. In the optical studies, the obtained films had band gaps of 2.5 eV and showed excellent absorbance of light in the visible range. In the cyclic voltammogram, the specific capacitance and interfacial capacitance were 266 F/g (scan rate = 10 mV/s) and 0.0214 F/cm\({}^{2}\), respectively. Lastly, the electrical parameters, including coulombic efficiency (92%), specific energy (55.68 Wh/kg), and specific power (10.12 kW/kg) were reported.
###### Abstract  
The information in the literature on the effect of printing layer thickness on interim 3D-printed crowns is limited. In the present study, the effect of layer thickness on the trueness and margin quality of 3D-printed composite resin crowns was investigated and compared with milled crowns. The crowns were printed in 3 different layer thicknesses (20, 50, and 100 \(\upmu\)m) by using a hybrid resin based on acrylic esters with inorganic microfilters or milled from polymethylmethacrylate (PMMA) discs and digitized with an intraoral scanner (test scans). The compare tool of the 3D analysis software was used to superimpose the test scans and the computer-aided design file by using the manual alignment tool and to virtually separate the surfaces. Deviations at different surfaces on crowns were calculated by using root mean square (RMS). Margin quality of crowns was examined under a stereomicroscope and graded. The data were evaluated with one-way ANOVA and Tukey HSD tests. The layer thickness affected the trueness and margin quality of 3D-printed interim crowns. Milled crowns had higher trueness on intaglio and intaglio occlusal surfaces than 100 \(\upmu\)m-layer thickness crowns. Milled crowns had the highest margin quality, while 20 \(\upmu\)m and 100 \(\upmu\)m layer thickness printed crowns had the lowest. The quality varied depending on the location of the margin.  
3D printing; additive manufacturing; interim crowns; provisional crowns; trueness; accuracy; layer thickness; margin quality +
Footnote †: journal: Applied Sciences