esa-sceva/satcom-chunk-collection huggingface dataset:
# Simulation Study on the Installation of Helical Anchors in Sandy Soil Using SPH-FEM
## 2 Establishment of SPH-FEM Model
### Computational Model and Material Parameters  
The SPH-FEM coupled computational model is shown in Figure 2. For regions distant from the anchor's penetration area, the FEM is employed for modeling, while the SPH method is used for the penetration zone and the surrounding soil. In the element type setting module of the ABAQUS software, both FEM elements and SPH particles can be defined. In this study, the FEM element type C3D8R is selected, and SPH particles are represented as PC3D elements within ABAQUS. The anchor plate diameter, \(D\), is set to 0.20 m; the anchor plate thickness, \(t\), is set to 0.01 m; the shaft diameter, \(d\), is set to 0.08 m; the pitch of the helix, \(p_{\text{w}}\), is provided in two sizes of 0.06 m and 0.08 m (Figure 2 shows the \(p_{\text{w}}=0.08\) m only); the length of the anchor rod is 1.50 m; and the distance from the center of the anchor plate to the bottom of the shaft is 0.08 m.  
Figure 2: SPH-FEM computational model.  
To avoid boundary effects, the size of the soil is set to 2 m \(\times\) 2 m \(\times\) 2 m. As shown in Figure 2, the whole soil region is divided into two parts: the SPH region and the FEM region. The size of the SPH region is 1 m \(\times\) 1 m \(\times\) 2 m, which is located in the middle of the whole model, and the rest is the FEM region. The contact surface between the SPH region and the FEM region is \"tied\" together using the \"Tie Constraint\" command, preventing any relative movement. A total of 128,000 uniformly distributed SPH particles are generated within the SPH region, while the FEM region is meshed using the C3D8R elements, totaling 6000 elements. The helical anchor is also discretized using the C3D8R elements, with a total of 2013 elements.  
The soil parameters are selected from the literature [13], which are homogeneous dense sand, and the helical anchor is made of steel. The specific material parameters are shown in Table 1. The stiffness of the helical anchor is much greater than that of the soil, and it is approximated as a rigid body in the model. The soil is characterized using the Mohr-Coulomb elastoplastic constitutive model. The contact properties between the helical anchor and the soil are set to general contact, with the normal behavior of the contact surface selected as \"hard contact\" and the tangential behavior as \"penalty\". The friction angle \(\delta\) between the pile and the soil is taken as 3/4 \(\varphi\) (\(\varphi\) is the internal friction angle of the soil) [8], and the coefficient of friction is taken as tan \(\varphi\) = 0.47.
# Time-Sensitive Networking in IEEE 802.11 be: On the Way to Low-Latency WiFi 7
## 5 Use Cases
### Transport  
Transport is experiencing such profound changes that future mobility will certainly be substantiated by automation, sustainability, road/air/sea safety, and energy efficiency. Real-time traffic information is starting to be served on a regular basis to drivers, using for instance city-wide WiFi deployments. Yet the upcoming revolution is being led by autonomous vehicles and automated guided vehicles, which will be able to transport people and goods thanks to their WiFi/5G connections without any human intervention.  
Next-generation vehicle communication and processing systems, such as vehicle-to-everything communication or advanced driver-assistance systems, will assist future transport systems on the basis of TSN and artificial intelligence. Therefore, ensuring very high reliability and low latency in future transport applications will become crucial regardless the employed technology, due to the high relative speeds among end devices, and the continuous dynamism and low predictability of the outdoor environment.
## 4 Results
### Validation of Retrieved SST  
In order to verify that the SSTs derived from COMS/MI were stable even after the period to estimate SST coefficients, we validated the retrieved SSTs based on the in situ SSTs measured by drifting buoys from April 2014 to March 2015. As shown in Figure 11, the errors of retrieved SSTs for whole cases are quite similar to the results for 3 years from 2011 to 2014 in Figure 5. The validation results using the independent matchup points show that the RMSE and biases for retrieved MCSST (Figures 10 and 11) in the study area are, respectively, 0.65 \({}^{\circ}\)C and \(-\)0.05 \({}^{\circ}\)C in daytime and 0.80 \({}^{\circ}\)C and \(-\)0.09 \({}^{\circ}\)C in nighttime. The NLSST (Figures 10 and 11) seems to be more accurate than MCSST, with RMSEs of 0.55 \({}^{\circ}\)C in the daytime and 0.71 \({}^{\circ}\)C in the nighttime.  
Figure 10: A plot of averaged MCSST errors during the (**a**) daytime and (**b**) nighttime and NLSST errors during the (**c**) daytime and (**d**) nighttime as a function of distance from the coast (km). The error bars indicate the standard deviation of SST errors in each interval.  
Figure 12 shows the spatial distribution of RMSEs and mean biases in 5\({}^{\circ}\)\(\times\) 5\({}^{\circ}\) bins for MCSST and NLSST from COMS/MI with respect to the buoy SST. Most of bins of MCSST errors show relatively small RMSE values, i.e., less than 0.8 \({}^{\circ}\)C, except for the pixels near the boundary of the full-disk region with relatively high SZA (Figure 12a). This trend was also detected in the case of NLSST; however, its amplitudes of the RMSE significantly improved by showing much lower values (<0.6 \({}^{\circ}\)C) except for the boundary region of COMS/MI observation (Figure 12c). The bias errors of MCSSTs ranged from \(-0.5\)\({}^{\circ}\)C to 0.5 \({}^{\circ}\)C, excluding a single bin east of Australia with a bias error of about 1.0 \({}^{\circ}\)C. This large error of about 1.0 \({}^{\circ}\)C is presumably attributed to a significantly small number of matchups, i.e., 29, occupying 1.1% of the maximum number of 2638 at a bin in the Northwest Pacific; therefore, it may be handled as an outlier.  
Figure 11: Comparison between MCSSTs and in situ temperatures during the (**a**) daytime and (**b**) nighttime and between NLSSTs and in situ temperatures during the (**c**) daytime and (**d**) nighttime for the period from April 2014 to March 2015, where the color represents the percentage of the data to the total number of matchup points in a bin of 0.5 \({}^{\circ}\)C \(\times\) 0.5 \({}^{\circ}\)C. Bias, root mean square error (RMSE), scatter index (SI), and correlation coefficient (R) are given in each plot.  
As shown in Figure 12b,d, the spatial distribution of mean biases of NLSST with a standard deviation value of 0.27 \({}^{\circ}\)C was more uniform than those of mean biases of MCSST with a value of 0.32 \({}^{\circ}\)C. This implies that the NLSST algorithm is more effective than the MCSST algorithms for COMS data. As a result of extended validation for both algorithms, it is confirmed that the present algorithms can be operationally used for COMS/MI data.
## 2 Background and Theory
### Preprocessing Methods  
This study incorporates three distinct preprocessing techniques: notch filtering, high-pass filtering, and CAR montage filtering. This section explores the underlying theoretical principles of these methods in detail.
# HydroLab: A Module for the Investigation of Fertigation Strategies in Hydroponics
## 5 Results and Discussion
### Discussion  
In this section, the obtained results are discussed, focusing on the comparison between time-based and radiation-based fertigation strategies, as well as the potential of HydroLab as a module for investigating fertigation strategies in hydroponics.  
The implemented experiment demonstrated that radiation-based fertigation appears to be a more effective and efficient strategy compared to time-based fertigation. This is supported by higher values of the yield and nutrient solution efficiency indicators. While these results are promising, further experiments are necessary before turning this treatment into a _technological recommendation_. Additionally, it is important to monitor more variables, such as the microclimate within the greenhouse, and to compute further indicators, including physiological ones. Physiological indicators can provide insights into the quality of the produced crop. This is crucial because effectiveness is determined by yield as well as the quality of the harvested crop.  
Concerning _HydroLab_, the module facilitated the examination of two categories of fertigation strategies: time-based and climate monitoring. Additionally, the implementation of IoT functionality enabled remote monitoring and control of the module. Therefore, the concept holds promise for investigating various fertigation strategies in hydroponics, although enhancements are needed to generate technological recommendations. These enhancements involve (i) expanding the crop rows to allow for the simultaneous investigation of multiple treatments; (ii) increasing the sample size per treatment by accommodating a greater number of plants per row. However, compared to the state-of-the-art analysis presented in Section 2, the proposed test bench stands out due to its ability to investigate different fertigation strategies and incorporate IoT technologies for improved system monitoring and control.
# Customer Churn Prediction in B2B Non-Contractual Business Settings Using Invoice Data
## 1 Introduction
### Main Findings  
Our results indicate that robust churn prediction models can be devised by using invoice-level data alone. Using longer spans of historical data tends to lead to better models for top-performing classifiers. Using different churn definitions also yields robust models with the potential to be used as a foundation for creating new retention strategies or as the basis for devising novel segmentation approaches. A multi-slicing approach to dataset creation leads to models potentially capable of delivering more tangible business value compared to those devised by using a single-slicing approach.  
The remainder of this paper is structured as follows: Section 2 provides an overview of relevant literature, Section 3 describes the proposed approach and data used in the experiment, Section 4 presents experimental results, Section 5 provides a discussion on obtained results, and Section 6 contains conclusions, managerial implications, limitations, and future research directions.
## 2 Materials and Methods
### Equal Step-Size Method for Visibility and Relative Humidity Relationship  
Not does \(RH\) affect the source and sink of the aerosol particles but also the size, structure and composition of aerosol particles [1; 46]. The effect of \(RH\) on visibility can be separated as two physical processes [47; 48; 49]: (a) hygroscopic growth of aerosol particles and (b) change in the mass concentration of aerosols and the size distribution of dry particles. Here, an equal step-size statistical method was employed to understand the relation between visibility and \(RH\)[6; 47]. The \(RH\) data was divided into 11 intervals: 0-40%, 40-45%, 45-50%, 50-55%, 55-60%, 60-65%, 65-70%, 70-75%, 75-80%, 80-85%, and 85-90%. The setting of the first interval (0-40%) was based on Equation (2) as the hygroscopic effect being very low or negligible when \(RH\) < 40%. Average visibility, \(RH\)-corrected visibility, and PM\({}_{10}\) were calculated for each interval. The difference between visibility and \(RH\)-corrected visibility with varying \(RH\) partly relate to the relative dominance of the two physical processes.
## 2 Laboratory Data and Field Measurements
### Measured Pile Data  
The reported full-scale tests in this research consist of 30 bidirectional tests in the Southern Nevada Valley, as is shown in Figure 4. Load test data for drilled shafts in caliche were acquired from different locations in Las Vegas to address the variations in the geologic formations of this area. During the tests, constant screenings were performed to measure the displacement of the installed drilled shafts using the strain gauges along the piles' length. The most competent caliche layers at the case study sites  
Figure 3: Measurements of the Standard Penetration Test and California Split Spoon Sampler in the soil containing competent caliche layers.  
Figure 2: Measured unconfined compressive strength (UCS) triaxial test results of the caliche cores.  
were located at 10 to 20 feet depths below the ground level in Southern Nevada. Their thicknesses varied between 5 to 15 feet. The conducted tests based on the pile ID along with the depth and thickness values of the caliche layers in the soil profile are presented in Table 1. Geometries of the drilled shaft and caliche layers, along with the measured bidirectional loads, according to those of the 30 bidirectional Osterberg tests, are also summarized in Table 1. The location of the installed O-cell apparatus within the pile length is reported in this table. The pile diameters' ranges are within 36 to 48 inches and the installation length varies between 62 and 128 feet. In addition, the maximum O-cell load was applied to achieve the ample amount of the pile's tip movement related to the ultimate capacity of the piles. In the design procedure, the required pile tip displacement is selected be equal to 2% of the pile diameter for both upward and downward load application to measure the pile ultimate capacity based on the allowed displacement. The maximum load applications by the O-cell hydraulic jack during the tests were reported in ranges from 1959 to 7358 kips.  
The validity of the collected data was analyzed based on the measured full-scale tests in various scenarios, regarding the location of the caliche layers with respect to the O-cell hydraulic jack in the Osterberg test procedure. To further evaluate the results of the Osterberg test in soil containing caliche deposits, a finite element analysis [43; 44; 45; 46] was conducted using the commercial PLAXIS 2D V8.6 program. The difference between the upward and downward mobilization of the test shafts in Las Vegas, NV was identified following two case study analyses. Moreover, a procedure was recommended to enhance the design accuracy of the drilled shafts in Las Vegas, NV.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Test ID**} & \multirow{2}{*}{**(Caliche Depth \({}^{*}\), Caliche Thickness)**} & \multirow{2}{*}{**Shaft**} & \multirow{2}{*}{**Shaft**} & **O-Cell** & **Top of the** & **Maximum** \\  & & & **Diameter (in)** & & **Length (ft)** & **Depth (ft)** & **Shaft (ft)** & **O-Cell Load** \\ \hline TP-1 & (18, 3), (24, 10), (39, 3), (47, 6) & 48 & 106 & 50 & 20 & 6748 \\ TP-2 & (21, 15), (42, 6), (53, 3), (61
## 2 Beyond 100 Gb/S Era
### Ethernet Standards and Optical Transceiver Applications  
As previously mentioned, owing to innovative applications such as virtual reality, machine-to-machine communication, online gaming, and other time-sensitive services that will be supported by the 5G and beyond network, there has been a growing need to improve the bandwidth in the data center industry. To achieve the system requirements, there has been a notable transition to new categories of fiber connectivity protocols and media. Furthermore, as a data center normally supports heterogeneous applications and services that co-exist at varying speeds and requirements, it is important to understand existing Ethernet standards and related transceiver types. Proper fiber optic infrastructure identification for different network configurations is of utmost importance. In this subsection, we focus on different transceivers (10/25/40/100/400 GbE) and present a clear guide that can facilitate the selection of the appropriate fiber optic infrastructure for different network configurations.
## 3 Method Descriptions
### Data Preprocessing
#### 3.4.3 Data Analysis  
This study transformed the original physiological vibration signal through the MFCC spectrums to display the energy distribution of the physiological vibration signal in the time-frequency domain. This conversion allows the characteristics of various physiological vibration signals to become more prominent and easier to be directly observed by the human eye. Among them, red is the color response when the energy of the time-frequency diagram is higher, and blue is the lower energy. The y-axis of the time spectrum is normalized and displayed, and the frequency interval is divided into 1 Hz to 150 Hz. The example analysis diagram (as comparatively shown in the normal flow in Figure 4a and the tall and peak flow in Figure 4b) shows that the normalized resonance frequency of the measuring cup is around the 50 Hz to 100 Hz. In normal urination, the energy area of the spectrum is continuous and concentrated; on the contrary, in abnormal conditions, as in the case of the tall and peak flow, it indicates that the patient's voiding velocity rises quickly and declines later. Furthermore, no significant resonance in frequency was observed in the decreased, saw-tooth, intermittent, and flattened flow cases as shown in Figure 4c-f, in which multiple impulses can be seen on the time axis according to the RMS signals. However, it is evident based on the time axis that the intervals between and patterns of urination differ among all cases.  
On the other hand, as shown in Figure 4c,e, the patient's initial RMS value is marked by Qmax, and there are small signal impulses. In reality, the saw-tooth (Figure 4d) and intermittent flows (Figure 4e) are presented as multiple and instantaneous occurrences of strained urination, which is a symptom of compensated abdomen forcing due to outlet obstruction or detrusor muscle weakness. However, in comparison with Figure 4d, the intermittent flow in Figure 4e shows more multiple and instantaneous occurrences of strained urination. Therefore, even though both types of flow have similar features, the peak frequency in the same time interval differs throughout the urination process. The flattened flow in Figure 4f suggests that insufficient energy was applied during urination. The resonance energy generated by the measuring cup is low, and the energy sustained is minimal compared to the other cases. Due to the insufficient energy applied for urination, the length of urination is comparatively longer, which reflects the characteristic of this vibration.
# A Minimal GBT Model for Distortional-Twist Elastic Analysis of Box-Girder Bridges
## 3 A Minimal GBT Model
### Distortional Mode  
The in-plane displacement field is found as that experienced by a planar frame, undergoing displacements at joints, namely (i) prescribed translation of joints, caused by a distortion \(\varphi=1\), with no rotations, (Figure 4a); (ii) unknown rotations of joints of equal amplitude \(\alpha\) with no translations (Figure 4b). After having determined the moments at any of the the four joints, equilibrium is enforced, from which the unknown is evaluated as follows:  
\[\alpha:=-\frac{bt_{2}^{3}-ht_{1}^{3}}{bt_{2}^{3}+ht_{1}^{3}} \tag{16}\]  
referred to below as the _distortional joint rotation_ (which can be positive, nil, or negative according to the geometric parameters. After that, the transverse displacements are computed by integrating the elastic line equation under now-known translations and rotations at the ends (see Appendix B for details). The tangential displacements are instead immediately evaluated from the kinematics illustrated in Figure 4a. By summarizing (Figure 5a,b),  
\[U_{d_{i}}=\begin{cases}-\frac{h}{2}&i=1,3\\ \frac{h}{2}&i=2,4\end{cases}\qquad V_{d_{i}}=\begin{cases}\frac{1}{2}s_{i}(3+ \frac{4s_{i}^{2}(\alpha-1)}{b^{2}}-\alpha)&i=1,3\\ \frac{1}{2}s_{i}\bigg{(}-3+\frac{4s_{i}^{2}(\alpha+1)}{b^{2}}-\alpha\bigg{)}&i= 2,4\end{cases} \tag{17}\]  
to obtain the warping component, the procedure already described for the torsional mode is followed, leading to the following:  
\[W_{d_{i}}=\begin{cases}\Big{(}\frac{h}{2}+\frac{Q_{d}}{t_{i}}\Big{)}s_{i}+C_{i }&i=1,3\\ -\frac{h}{2}+\frac{Q_{d}}{t_{i}}\Big{)}s_{i}+C_{i}&i=2,4\end{cases} \tag{18}\]  
Due to the skew-symmetric nature of \(U_{d_{i}}\), continuity at joints and the zero-average condition require \(C_{i}=Q_{d}=0\). Therefore,  
\[W_{d_{i}}=\begin{cases}\frac{2w_{d}}{b}s_{i}&i=1,3\\ -\frac{2w_{d}}{h}s_{i}&i=2,4\end{cases} \tag{19}\]  
where \(w_{d}:=\frac{bh}{4}\) is the modulus of the warping at joints (Figure 5c).  
It is worth noticing that, differently from torsion, the distortional warping never vanishes. The two warping components are proportional (Figures 3c and 5c), i.e., \(W_{t}(s)=\beta W_{d}(s)\), with  
\[\beta:=\frac{bt_{2}-ht_{1}}{bt_{2}+ht_{1}} \tag{20}\]  
Figure 3: Torsional mode: (**a**) tangential displacements, (**b**) normal displacements, and (**c**) warping.  
defined as the _warping ratio_. The plot of \(\beta\) vs. the aspect ratio \(\frac{b}{h}\), for different thickness ratios \(\frac{t_{1}}{t_{2}}\), is represented in Figure 6. In the range examined, \(\beta\) spans the interval \((-1,1)\), so that the two warpings (for unitary twist and distortion) are of the same order of magnitude, except close to a critical combination of the parameters, for which torsional warping is zero.
# On Finding Optimal (Dynamic) Arborescences
## 5 Experimental Evaluation
### Edmonds' versus Tarjan  
We compared both the Edmonds' and Tarjan algorithms for complete and sparse graphs using generated graph datasets. This comparison is presented in Figure 15. As expected, the Tarjan algorithm was faster, and the experimental running time followed the expected theoretical bound of \(O(m\log n)\). The memory requirements were also lower for the Tarjan algorithm, which grew linearly with the size of the graph, as expected.  
Given these results, we omitted [PERSON] algorithm from the remaining evaluation.  
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Datasets** & \(n=|V|\) & \(m=|E|\) \\ \hline _clostridium.Griffiths_ & 440 & \(193,600\) \\ _Monaxella.Achtman7 GeneMLST_ & 773 & \(597,529\) \\ _Salmonella.Achtman7 GeneMLST_ & 5464 & \(29,855,296\) \\ _Yersinia.McNally_ & 369 & \(136,161\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Phylogenetic datasets. The first three without missing data. The number of vertices \(n\) is the number of genetic profiles in each dataset.  
Figure 15: Comparison between the [PERSON] algorithm and the Tarjan algorithm (three different heap implementation) with respect to complete and sparse graphs. (**a**) Running time for complete graphs. (**b**) Running time for sparse graphs. (**c**) Memory for complete graphs. (**d**) Memory for sparse graphs.
## 3 Results and Reports
### Classification Performances
#### 3.4.2 Optimizing Results Using the Cross Validation Technique
This value was representative of the dataset and would be retained for later usage. A1, A2, A3, A4 and A5 represented the calculated accuracies of each cross validation split. These scores were done using the optimal C and Gamma parameters, which values are represented in Table 10. Accuracies A1, A3 and A5 of the SVM classifier applied to all datasets with optimized parameters (C and Gamma) were therefore equal to 1 (100%). So, our model was as efficient as those XGBoost and RF, which accuracies were also equal to 1.