esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
For these reasons, most field studies do not directly enable the measurement of basic geotechnical parameters. The results of the CPTU are the following values in relation to the probing depth: qc--cone resistance, fs--sleeve friction, u--pore pressure. None of these parameters, however, directly determine the material parameters of the tested soil, which consequently necessitates the adoption of reliable correlations to determine soil parameters each time. Based on the aforementioned quantities of qc, fs, and u, it is possible to estimate soil parameters; yet, estimating the correlation between the results of static CPTU probing and soil parameters remains both a challenging task and an open problem [9; 13; 14; 15]. This is mainly due to regional factors, i.e., specific geological conditions of the soil foundation within a given region of the country. Determining parameters based on the CPTU is  
Figure 1: Location of the “White Sea” area in Cracow [google maps]. possible using strength parameters estimated from reliable correlations. An example of such a correlation is the assessment of the effective internal friction angle based on parameters obtained from CPTUs [10]. The correlation function (Equation (1)) between the effective internal friction angle \(\varphi^{\prime}\) and the normalised cone resistance Q\({}_{\text{m}}\)[10] is  
\[\varphi^{\prime}[deg]=17.6+11\text{log}(Q_{tn}) \tag{1}\]  
Over the past few years, experimental studies have been conducted aimed at assessing the potential use of various functional relationships in Polish conditions [9; 16; 17; 18]. These studies have also identified the need to modify formulas to achieve a higher value of the Pearson correlation coefficient R\({}^{2}\). New proprietary formulas often rely on the same leading parameters and deviate slightly from the original formulas [16]. Generally, the transformation of function forms for local soils is presented in a simpler form. Due to the high costs and extensive time required for laboratory tests, the scientific literature often lacks information and research on the characteristics of various types of soils [19]. The current Polish standard Eurokod 7 1997 [20] provides assumptions for determining the strength parameters based on the CPTU for natural soils, without considering anthropogenic soils. Therefore, obtaining reliable functional dependencies for anthropogenic soils is only possible after conducting laboratory and field studies. Several construction projects have already been implemented in the area where these anthropogenic soils occur, with more planned for the future. The first investment was the construction of a complex of buildings comprising the John Paul II Center, which was carried out in stages between 2010 and 2013. Another investment in the area was the construction of a footbridge between the Sanctuary of Divine Mercy and the John Paul II Center in 2016. The next investment was a road-tram tunnel section of the Lagiewnicka route, which was completed between 2019 and 2022 (Figure 2).
# Design of a Filtering Power Divider with Simple Symmetric Structure Using Stubs
## 3 Structure of the Proposed WPD  
As mentioned, the short-ended and open-ended stubs, along with the meandered lines and stepped impedance, are considered to design the proposed WPD. The design steps of the proposed circuit are described in the following subsections.
## 4 Results
### Future Work  
The stability of the EG-DGATN's performance and predictive accuracy at different granularities means that it has the potential to be used in multiple scenarios under smart water management systems. For example, 5 to 15 min prediction results can enable sensor-level prediction with real-value rapid anomaly detection for timely detection of leakage incidents in WDNs. Predicting water demand accurately over extended periods, ranging from 60 to 90 minutes, can significantly optimize the operating conditions of water transfer pumping stations, ensuring cost-effective performance. This approach is especially advantageous in cities with diverse water supply sources, such as Zhengzhou City, enhancing the efficiency and economy of water distribution systems. In addition, the EG-DGATN can be coupled with physical models such as EPANET in industrial application scenarios [30], where more comprehensive and multi-dimensional information can be obtained by combining physical simulation data with sensor data, especially in the field of leakage detection where the physical properties of the pipeline itself have a considerable impact. Since the EG-DGATN can effectively regularize the information flow within the model in sensor networks, it can accurately predict head loss in pipelines. This allows for a broader application of the model without relying on intricate hydrodynamic formulas.  
Despite the advancements made with the EG-DGATN, numerous critical issues still need to be resolved. As the number of sensors in the network increases, the effectiveness of employing EEMD-Granger causality for constructing the graph structure may diminish. Although we have introduced a multi-process optimization strategy to mitigate the overhead associated with graph construction, the time required to construct a single graph has been reduced to 568.79 s from an initial average of 2516.45 s. Consequently, the current iteration of the EG-DGATN is best suited for sensor networks with a relatively small geographic footprint. We aim to explore more efficient methods for extracting information from water supply networks. Moreover, natural language processing could be repurposed to enhance water demand predictions with the burgeoning adoption of artificial intelligence models. For instance, the recently introduced TimeGPT model, which leverages the Transformer architecture, amalgamates the spatiotemporal synchronization approach detailed in this study with large-scale models, representing a promising direction for further research.  
Conceptualization, W.W.; methodology, W.W.; software, Y.K.; validation, Y.K.; formal analysis, Y.K.; investigation, W.W.; resources, W.W.; data curation, W.W.; writing--original draft preparation, Y.K.; writing--review and editing, W.W.; visualization, Y.K.; supervision, W.W.; project administration. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The raw data supporting the conclusions of this article will be made available by the authors upon request.  
The authors declare no conflicts of interest.
## 4 Design Methodology for SOA-Enabled HONoC
### Minimum SOA Gain and Laser Source Power Allocation  
In a conventional mesh-based HONoC, a laser source power is applied considering the insertion loss of the longest path and the receiver sensitivity. However, in the proposed SOA-enabled HONoC, a new analysis of the worst-case insertion loss is required, as the optical signal is amplified whenever it passes through the SOA. If the total SOA gain is larger than the total insertion loss of the overall signal path, the signal power at the destination is greater than that at the source. Therefore, we present the allocation of the laser source power independent of the longest path considering the SOA placement, SOA gain and mesh size.
# Analysis of Sealing Characteristics of Lip Seal Rings for Deep-Sea Separable Pressure Vessels
## 4 Conclusions  
An underwater separable pressure vessel has been designed to integrate a lip seal and a convex seal in its sealing structure. After several years of service, this design has been installed in multiple underwater vehicles and subjected to inspection. The results indicate that the seals are functioning properly and achieving effective sealing performance, with no instances of seal failure detected.  
This paper employs the ANSYS Workbench finite element simulation software to analyze the sealing process of the lip seal, from the initial state to complete compression, and to investigate the sealing groove under the influence of water pressure in both stages. Based on the simulation results, the following conclusions have been drawn:  
(1) Two extremes of contact stress can be observed on the primary sealing surface of the lip seal ring analyzed in this study, resulting in an enlargement of the effective sealing width and a consequent improvement in the sealing performance. The mechanism underlying this phenomenon is attributed to two factors: the extrusion deformation of the seal ring material under the influence of water pressure and the friction occurring on the primary sealing surface;  
(2) The maximum equivalent force and maximum shear stress in the lip seal are both located at the bottom of the lip cavity. As the water pressure increases prior to the complete fitting of the upper and lower spherical shells, the extrusion of the inner cavity of the lip seal intensifies, leading to an increase in both the maximum equivalent force and maximum shear stress. These values reach their peak when the upper and lower spherical shells are fully fitted. Subsequently, as the water pressure continues to rise, it alleviates the extrusion of the inner cavity of the lip seal. After this point, both the maximum equivalent force and maximum shear stress decrease with increasing water pressure, which benefits the enhancement of seal strength and prolongs its service life.  
Moreover, it is imperative to conduct additional research to explore effective ways of utilizing the two contact stress extremes and enhancing the sealing performance of lip seals.  
Conceptualization, X.L.; writing--original draft, X.L.; writing--reviewing and editing, X.L., S.H. and J.Z.; methodology, X.L.; validation, X.L.; formal analysis, X.L.; data curation, X.L.; supervision, S.H. and J.Z. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
All simulation data in this study are represented in the paper.  
The authors thank all reviewers who helped to improve this manuscript.
# Sparse Indoor Camera Positioning with Fiducial Markers
## 4 Experiments
### Discussion on Limitations and Potential Improvements  
In this section, we address the potential limitations of our proposed method and suggest areas for enhancement, particularly with involuntary movements of markers, low-light conditions, outdoor applications, and generalization across various indoor settings.  
Our approach assumes that fiducial markers remain stationary throughout the capture process and are removed afterward. Markers do not need to remain in the environment once the calibration is conducted. In environments where there may be slight movements of markers while recording, the accuracy of the system depends on the redundancy of markers and a robust optimization algorithm. However, significant movement would require recalibration or algorithmic adjustments to ensure continued accuracy. Future enhancements could incorporate real-time tracking and adjustment capabilities to better manage such dynamics.  
The design of our system is agnostic to the type of marker and the detection method employed. While traditional fiducial markers, such as Aruco [34], demonstrate high robustness in most conditions, they may struggle in extremely poor lighting. In such cases, considering alternative technologies like DeepAruco++ [40] could be beneficial to ensure high accuracy and robustness.  
While our primary investigations have focused on indoor environments, extending our system to outdoor scenarios could prove beneficial. Outdoor conditions introduce complexities such as changing weather and variable lighting, which could be mitigated by integrating our system with technologies such as satellite imagery or GPS data. A hybrid approach, which combines fiducial markers with natural feature tracking, could provide more reliable solutions for outdoor navigation and mapping, similar to the concept explored in UcoSLAM [41].  
Finally, our method has demonstrated reliable performance across diverse indoor settings in our datasets. However, the efficacy of our system is contingent upon the presence of the markers within the camera images, regardless of variations in room geometry or furniture arrangement. As a consequence, we believe our method can adapt to any indoor layout as long as it is possible to place markers and take pictures of them.
# Influence of Structural Stiffness and Loss Factor on Railroad Vehicle Comfort
## 4 Application to Oversimplified Cases
### Rigid Beam. Influence of Suspension Parameters on Comfort  
Although, as will be shown in the next section, structural behavior strongly influences comfort on a railway vehicle passenger car, it can nonetheless be confidently said that the suspension is the single most important system for comfort. To support this statement, comfort indexes will be determined using the formulation in Section 2 for a rigid car (removing flexible modes or setting a very large cross-section area moment of inertia \(I\) since both methods yield the same results) traveling at 300 km/h on an intermediate quality railroad (\(A_{v}=0.6\)\(\upmu\)m rad in Equation (15)). The distance between axles is 13 m, the mass of the car is 13,370 kg, and the suspension damping factor is 5% (\(\xi_{b}=0.05\), with which \(c=\xi_{b}\sqrt{2\text{ km}}\)). Suspension stiffness will be specified in terms of bounce frequency (\(f_{b}=\sqrt{2\text{ k}/\text{m}}/(2\pi)\)) and allowed to vary from \(f_{b}=0.5\) Hz to \(f_{b}=2\) Hz (see Table 1).  
Figure 5 shows comfort indexes at four locations along the beam versus the bounce frequency \(f_{b}\). The plot clearly shows that comfort improves significantly as the suspension stiffness is reduced. Extremely soft suspensions (\(f_{b}=0.5\) Hz) are so comfortable (\(c_{I}<0.5\)) that the added benefit of geometrical filtering towards the midpoint of the beam is hardly noticeable. On the other end of the range considered (\(f_{b}=2\) Hz), the ride is very uncomfortable near the axles (\(c_{I}>8\)), but the effect of geometrical filtering is significant. Typical bounce frequencies for passenger railway vehicle secondary suspensions are in the vicinity of 1 Hz, which means that, on a track with \(A_{v}=0.6\)\(\upmu\)m rad, at 300 km/h with \(\xi_{b}=0.05\), not every location on the beam can be in the \"very comfortable\" range (\(c_{I}<1.5\), see [11]), not to mention the case of poorer quality tracks.  
The influence of suspension damping is depicted in Figure 6. Here again \(l=13\) m, \(v=300\) km/h, \(A_{v}=0.6\)\(\upmu\)m rad, \(m=13,370\) kg, flexible modes have been removed, and suspension stiffness is now fixed at \(f_{b}=1.3\) Hz, whereas the suspension damping factor \(\xi_{b}\) is allowed to vary from 1 to 10% (see Table 1). Comfort improvements when going from undamped to a damping factor of a few percentage points are quite significant. Nevertheless, marginal benefits go down as \(\xi_{b}\) grows; therefore, there is no point in increasing \(\xi_{b}\) beyond moderate values (6 or 7%, for example).  
Figure 4: Modulus of transfer function \(H_{2}\) at four locations on the beam.
# DNN Beamforming for LEO Satellite Communication at Sub-THz Bands
## 2 DNN Beamforming
The output voltage of the DNN beamformer is implemented by a linear activation function and thus can be expressed as  
\[V_{out}^{DNN}=\sum_{m=1}^{M}W_{m}^{l}\delta_{m}^{l}(X) \tag{9}\]  
where \(g_{m}^{l}(X)\) is the output from last hidden layer \(l\), \(W_{m}^{l}\) is the weights associated with these outputs, and \(M\) is the number of neurons in the last hidden layer. In order to learn the parameters of the DNN beamformer, we need to define a loss function that will be minimized by the training algorithm [44]. We define the empirical loss function to train the network as  
Figure 1:0  
where \(V_{out}^{TDD}\) and \(V_{out}^{DNN}\) are the array output voltage with TTD and DNN beamformer respectively, and K is the number of training samples. Our objective is to minimize this loss function by finding the parameters of the DNN beamformer that includes its weights and biases. Figure 1:1 Schematic of the DNN Beamforming with input being fed by the voltages from individual array antenna elements. To summarize, the algorithm takes the input from the output of ADC network of the massive antenna array i.e., \((V_{1},V_{2},\ldots,V_{n})\). Once the input is obtained, it is passed to the neural network with several layers of neurons and the output of neural net \(V_{out}^{DNN}\) is obtained. With the obtained output, we compute the loss (as defined in (10)) with respect to the TTD beamformer \(V_{out}^{TDD}\) and then use backpropagation algorithm to update the weights and biases of the network for all the training samples. Once trained, we deploy the DNN beamformer to predict for many test samples and to test its accuracy in matching the performance close to the TTD beamformer.
## 1 Introduction  
During recent years, the technology of semiconductor optical amplifiers (SOAs) has remarkably been evolving and has matured to the point that it presently is established as a key enabler for the development, implementation, optimization, and overall establishment of photonic circuits, subsystems, and networks (see indicatively [1; 2; 3; 4; 5; 6; 7; 8; 9; 10; 11; 12; 13; 14; 15; 16]). Owing to the remarkable advancements that have been achieved in the field, SOAs are commercially available devices that exhibit several important properties, such as strong nonlinearities, low power consumption, wavelength flexibility, large dynamic range, fast response, broadband and versatile operation, small footprint, and capability for scalable integration in single chips at affordable cost [17]. These attractive characteristics have rendered SOAs core elements for the accomplishment of critical and indispensable tasks at fundamental [18] and system-oriented [19] level. Thus, SOAs have widely been adopted both by the research community and the industrial sector as a principal technological platform for the realization of a diverse range of applications with high performance.
# Rotating Machinery Fault Detection Using Support Vector Machine via Feature Ranking
## 3 Data Collection and Processing
### A Subset of Data for This Study  
Due to the limited power of MATLAB and computer, only a subset of data was extracted for this study. For each mode, a single sequence with approximately the same rotating frequency and a similar setting was chosen: 1 mm horizontal misalignment, 0.51 mm vertical misalignment, 6 g of load imbalance, and 6 g for all bearing faults. The total records or observations for my sampled data set is 2,500,000 as shown in Table 3.  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Mode** & **No. of Sequence** & **Description** \\ \hline Normal & 49 & 49 sequences with a fixed rotation speed within the range from 737 rpm to 3686 rpm with steps of approximately 60 rpm \\ \hline Horizontal Misalignment & 197 & Same 49 sequences from 0.5 mm to 2 mm \\ \hline Vertical Misalignment & 301 & Same 49 sequences from 0.51 mm to 1.9 mm \\ \hline Imbalance & 333 & Same 49 sequences from 6 g to 35 g \\ \hline Underhang bearing—Inner fault & 188 & 42–49 sequence from 0 to 35 \\ \hline Underhang bearing—Outer race & 184 & 37–49 sequence from 0 to 35 \\ \hline Underhang bearing—Ball Fault & 186 & 38–49 sequence from 0 to 35 \\ \hline Overhang bearing—Inner fault & 188 & 41–49 sequence from 0 to 35 \\ \hline Overhang bearing—Outer race & 188 & 41–49 sequence from 0 to 35 \\ \hline Overhang bearing—Ball Fault & 137 & 20–49 sequence from 0 to 35 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Collected data in 10 different machine conditions.  
Figure 4: Simulator setup to collect data in [PERSON]’s study [10].  
There are eight pieces of information for each sequence: the tachometer data, accelerometer data from the underhang sensor in the x-axis, accelerometer data from the underhang sensor in the y-axis, accelerometer data from the underhang sensor in the z-axis, accelerometer data from the overhang sensor in the x-axis, accelerometer data from the overhang sensor in the y-axis, accelerometer data from the overhang sensor in the z-axis.
## 2 Materials and Methods
### DEG's Processing Using GEO2R  
The GEO2R back-end transforms and analyses GEO data using well-known Bioconductor [35] R tools and then delivers the findings as a table of genes sorted by relevance displayed with GEO Profile visualizations. DEGs between ESCC tissues and normal esophageal tissues were identified via GEO2R online tools with \(|\log\)FC\(|>2\) and adjusted \(p\)-value \(<\) 0.05 with default false discovery rate for multi testing. The top 250 genes were extracted from the whole dataset for further analysis. The DEGs of genes were designated as downregulated ones with log FC \(<\) 0, whereas upregulated ones were designated with log FC \(>\) 0.
# Load Forecasting Based on LVMD-DBFCM Load Curve Clustering and the CNN-IVIA-BLSTM Model
## 4 Case Analysis
### Analysis of Data Processing Results
#### 4.1.2 Analysis of the Clustering Results  
The daily load curve after LVMD-DBFCM clustering is shown in Figure 15.  
Figure 14: Result of LMD decomposition.  
Figure 15: (**a–e**) Daily load curve after LVMD-DBFCM clustering when the number of clusters is determined to be 5.  
The clustering algorithm proposed in this paper is compared with k-means [42], FCM [43], and DBFCM. Their comparison is displayed in Table 3 and Figure 16. From the results, it can be seen that the maximum value of the CHI metric is 1142.509 and the minimum value of the DBI metric is 1.083. The clustering validity of the method proposed in this paper is better than that of the other three methods.
# Analysis of the Near-Ground Wind Field Characteristics during Typhoon Soulik
## 4 Quantitative Analysis of Fluctuating Wind Properties
### Correlation between Turbulence Intensity and Gust Factor  
The following describes the correlation with gust factor and turbulence degree:  
\[G_{u}=1+al_{u}^{b}In\big{(}T/t_{g}\big{)} \tag{16}\]  
where \(T\) denotes the mean duration of wind and \(t_{g}\) denotes the sustained time interval. According to [PERSON] [15], there is a linear correlation between the two variables, and it is recommended that \(a\) be 0.5 and \(b\) be 1.0. [PERSON] [16] enhanced Equation (16) by utilizing observational data and proposed that the value of \(a\) should be 0.62 while the value of \(b\) should be 1.27. [PERSON] et al. [24] examined the properties of high winds near the ground during Typhoon Maemi and employed Equation (16) to establish an association between two variables. The analysis yielded fitting parameters of \(a=0.5\) and \(b=1.15\).  
Figure 12 depicts the association between two variables at heights of 15, 53, and 82 m along with the corresponding empirical curves. As shown, the gust factor increases with the intensity of turbulence and exhibits a linear correlation. When the turbulence intensity is below 0.5, the fitting curve and the empirical curve of [PERSON] and [PERSON] are essentially similar. When the turbulence intensity exceeds 0.5, the fitting curve aligns more closely with [PERSON]'s empirical formula at heights of 15, 53, and 82 m. This suggests that the findings of this study are in greater agreement with [PERSON]'s empirical formula. Furthermore, the collected data are analyzed using Equation (16), and coefficients \(a\) and \(b\) are obtained for heights of 15, 53, and 82 m, as indicated in Table 5.  
Figure 11: Relationship between gust factor, height, and fitting curve.  
Figure 12: Gust factor variation with the intensity of turbulence: (**a**) 15 m, (**b**) 53 m, and (**c**) 82 m.