esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction  
Benign prostatic hyperplasia (BPH) is a common condition afflicting male patients, who may suffer from related lower urinary tract symptoms (LUTS), such as frequency, noturia, and urgency. Several mechanisms have been reported to affect and aggravate LUTS in BPH patients, including age-related impairment of blood flow in pelvic organs and aggravation in bladder dysfunction [1]. If LUTS are medically uncorrectable, surgical treatment should be considered for otherwise healthy male patients, who suffer from moderate-to-severe outlet-obstructive symptoms.  
Transureth resection of the prostate (TURP) is the gold standard for surgical treatment of BPH [2]. However, technological advancements in medical device development have provided many alternative surgical options, including photoselective vaporization of the prostate (PVP), holmium laser enucleation of the prostate (HoLEP), thulium laser enucleation of the prostate (ThuLEP), and UroLift. Furthermore, some studies have shown that there are favorable outcomes for using these new BPH surgical techniques instead of TURP [3; 4; 5; 6].  
In more detail, HoLEP and ThuLEP have demonstrated similar absorbstruction outcomes with a comparable International Prostate Symptom Score (IPSS) improvement at both mid- and long-term follow-up, while similarly improving the overall quality of life (QoL) outcomes [7]. Despite these surgical options, which are still considered as possible alternatives (if the equipment and surgical expertise are available) according to international guidelines, the information regarding the safety profile resulting from the short- and long-term follow-up is in favor of laser-assisted enucleating or vaporesection techniques, making these a possible game changer in case of fragile patients or for those receiving anticoagulant or antiplatelet therapy [8]. Moreover, [PERSON] et al. [9] have recently demonstrated how these new available endoscopic surgical techniques, including PVP, can be valid for an outpatient setting, thus, expanding the potentiality of application and the wide distribution of the techniques when compared to TURP.  
However, these newer modalities, in many cases, may be far more expensive than traditional TURP, and there are few contemporary studies that analyze and compare these costs [10]. Moreover, we have few data regarding the total overall costs incurred from the BPH workup utilized to confirm the need for surgical treatment. Therefore, the aim of our study is to present the annual rates of BPH surgery and to analyze recent trends in the utilization of newer surgical modalities and any changes in medical costs related to these treatments over a 15-year period in the U.S.
## 3 Results
### Morphological and Topography Characterization
In  
Figure 1: FE-SEM image of the cross-section of: (**a**) TiO\({}_{2}\)-nt; (**b**) TiO\({}_{2}\)-nc; (**c**) TiO\({}_{2}\)-ns assembled on TiO\({}_{2}\)-nc; and (**d**) TiO\({}_{2}\)-nb assembled on TiO\({}_{2}\)-nc synthesized by electrochemical anodization. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Nanostructure** & **Outer Diameter (nm)** & **Inner Diameter (nm)** & **Length (nm)** & **S\({}_{4}\) (nm)** \\ \hline TiO\({}_{2}\)-nt & 98 & 65 & 1670 & 20.8 \\ TiO\({}_{2}\)-nc & 84 & 29 & 250 & 3.79 \\ TiO\({}_{2}\)-ns/TiO\({}_{2}\)-nc & 85 & 62/22 & 35 \({}^{\star}\)/362 & 10.6 \\ TiO\({}_{2}\)-nb/TiO\({}_{2}\)-nc & 75 & 55/30 & 40 \({}^{\star}\)/96 & 3.74 \\ \hline \hline \end{tabular} \({}^{\star}\)Thickness.  
\end{table} 
Table 1: Summary of some geometric parameters of the four TiO\({}_{2}\) nanostructures. our previous work, we reported that the shape of nanosheets and nanobowls depends on the time of the second anodization step, with a longer time, nanobowls can be obtained while nanosheets are obtained with a shorter anodization time [16]. Figure 3 shows the top view and 3D AFM images of the four TiO\({}_{2}\) nanostructures. The topography of TiO\({}_{2}\)-nt is shown in Figure 3a, here, the different colors, such as black and cream, suggest a difference in the length of the tubes. According to AFM analysis, the outer diameter of these nanotubes was 105 \(\pm\) 8 nm, while the average surface roughness, S\({}_{\text{a}}\), was about 20.8 nm [21]. Figure 3b shows the topography of TiO\({}_{2}\)-nc. As can be observed, the length of the nanocavities is more homogeneous than that of TiO\({}_{2}\)-nt. The AFM analysis showed an outer diameter of 84 nm \(\pm\) 5 nm and a S\({}_{\text{a}}\) of 3.79 nm, considerably less than that of TiO\({}_{2}\)-nt. This lower roughness can be associated with a homogeneous oxidation process on the Ti foil surface. A uniform distribution of TiO\({}_{2}\)-ns/TiO\({}_{2}\)-nc is displayed in Figure 3c where it is possible to visualize the organization of the nanostructure as a flower with six petals and verify its concave shape (on the right side of Figure 3c) as established in the previous SEM analysis. The surface roughness of TiO\({}_{2}\)-ns (10.6 nm) was greater than that of the TiO\({}_{2}\)-nc (3.79 nm) and the nanosheets diameter was 85 nm, both in agreement with the values obtained by SEM. The lowest average surface roughness was obtained on TiO\({}_{2}\)-nb/TiO\({}_{2}\)-nc (3.74 nm) because the nanobowls had better control on diameter size resulting in a significant periodicity as a short-range hexagonal controlled packed array, as shown in Figure 3d. The roughness values of the four structures are included in Table 1.
## 2 Challenges and Routing in 6G Networks
### Routing in 6G Networks  
Factors such as the great increase in the number of smart devices on a global scale, technological developments, the exponential increase in available information, and the expansion in the speed, capacity, and coverage area demanded by users constantly trigger new developments in mobile broadband technologies. The rising count of devices bonded to the internet, including machine-to-machine applications, Internet of Things, energy efficiency in communication devices and equipment, and increasing speed and capacity demand have brought 5G and beyond directly into the agenda [66]. The global mobile sector is focused on 5G technology. In addition, 5G technologies continue to evolve. The global research community has quickly started 6G studies. With the technologies that will come with 6G technology, more intelligent, efficient, environmentally friendly, and autonomous communication systems will be possible. At this point, network routing optimizations are critical to ensure the full efficiency of networks regarding 6G technologies.
## 4 Discussion  
### Selection of Data Sources  
Advancements in high spatial resolution terrain data enable accurate mapping and analysis of intricate terrain, more efficient terrain investigations, and convenient acquisition of detailed geometric feature information [37]. However, terrain data with varying resolutions may impact analysis results [38]. UAV data offer high-resolution images and real-time monitoring with enhanced flexibility. However, UAV have load capacity and flight time limitations, and their efficiency in collecting data for large-scale terrain is relatively low [17; 19]. LiDAR point cloud data can offer high-density and high-precision 3D terrain data, making it suitable for accurate terrain modeling and analysis of intricate terrain. However, it comes with high costs and large data volumes, and demands substantial storage and processing resources. Satellite-borne high-precision terrain data cover extensive areas and are better suited for large-scale terrain characterization.  
In this study, GF-2 data with sub-meter spatial resolution and high positioning accuracy was utilized. Compared to widely utilized remote sensing images like Sentinel-2 and Landsat8, GF-2 exhibits fast data transmission speed and superior image quality, and preserves finer topographic features. The ALOS DEM utilized in this study features a spatial resolution of 12.5 m, representing the highest precision global DEM data available free of charge. In comparison to widely used SRTM3 (90 m) and ASTER (30 m) data, it can offer more detailed topographic information, aiding in the capture of small-scale topographic features and obtaining reliable research results.
## 3 Methodology
### DoA Estimation
To make it applicable to human speech within a certain frequency range between \(i_{low}\) and \(i_{high}\), the response of the MUSIC spectrum can be calculated by summing out \(\omega\) in Equation (6) as,  
\[\overline{\zeta}(\theta,f)=\frac{1}{i_{high}-i_{low}+1}\sum_{i=i_{low}}^{i_{ high}}\zeta(\theta,\omega_{i},f). \tag{7}\]  
At every frame \(f\), the peaks of \(\overline{\zeta}(\theta,f)\) which have larger values than a threshold are detected as the direction of the active sound source. In a cocktail party environment, there are typically several active sound sources present at the same time. To capture clearer speech of the speaker of interest in such an environment, it is natural to get close to that speaker while keeping away from other distracting sound sources, which can be achieved with the motion module in our AV-SSLT.
# Polarimetry for Photonic Integrated Circuits
## 1 Introduction  
The characterization of the polarization properties of photonic integrated circuits (PICs) can be a challenging task. Fiber-based systems introduce unknown rotations in the fibers, while free-space systems are challenging to align and automate. This is why typically, measurements are carried out in terms of extinction ratios between the transverse electric and transverse magnetic components (TE and TM) rather than full polarimetric measurements [1; 2; 3; 4; 5]. Consequently, information on the relative phase between TE and TM gets lost, leaving the birefringence of a device impossible to characterize directly.  
In this work, we present a methodology to fully characterize PICs via the Stokes-Muller formalism using a fiber-based measurement setup. By measuring the Muller matrix of a given PIC, we obtain information on its polarization dependent loss (PDL), birefringence, rotation of the propagating modes as well as depolarization. We demonstrate this type of measurement with two kinds of integrated devices: polarization rotators and electro-absorption modulators (EAMs). We find that our measurement approach allows deep insight into both device types that go beyond the usual characterizations carried out for those devices [6; 7].  
Our polarimetric approach is based on a setup calibration consisting of the following three major steps: in the first two steps we collect measurement data for arbitrary but distinct states of polarization (SOP) at different positions in the fiber link. This is achieved by rotating the linear polarizer (LP), quarter-wave plate (QWP) and half-wave plate (HWP) in a polarization controller. The third step is to fit this data to the mathematical model we use to describe the setup. Once this calibration is performed, we use classical polarimetric techniques to characterize a device under test.  
In the following, we call \(\psi\) and \(\chi\) the angle of the major axis and the ellipticity of the polarization ellipse, respectively. This makes \(2\psi\) and \(2\chi\) the spherical coordinates of a SOP in Stokes space.
# A Comprehensive Literature Review on Hydrogen Tanks: Storage, Safety, and Structural Integrity
## 5 Fundamental Issues
### Safety and Risks
#### 5.3.2 Leakage  
The leakage phenomenon in hydrogen storage tanks is a critical issue due to hydrogen's small molecular size, which allows it to permeate through materials and fittings more quickly than larger molecules. This can lead to potential safety hazards, including fire and explosion risks (Section 5.3.1), as well as efficiency losses in storage systems. Understanding and mitigating hydrogen leakage is essential for safely and effectively deploying hydrogen technologies, particularly in high-pressure environments. [PERSON] et al. [17] conducted a comprehensive study on reactive hydrogen leakage from high-pressure tanks, developing an advanced computational method to analyze fluid-structure interactions. This method integrates particle and Eulerian approaches to simulate crack propagation, hydrogen turbulent diffusion, and combustion phenomena. Their findings indicate that combustion reactions are notably absent near the duct center due to excess hydrogen fuel, which increases scalar dissipation. Instead, combustion reactions occur in distinct upper and lower regions as they progress.  
[PERSON] et al. [19] developed a comprehensive model to simulate the leakage process of high-pressure gas storage systems, including tanks and pipes, which can lead to hazardous jet fires and subsequent disastrous events. They built a leakage process model based on the van der Waals equation of state. This model, combined with the notional nozzle and flame size models, predicts gas state properties, flow parameters, and jet flame height during a leakage event. They propose that adiabatic materials should be designed to prevent heat exchange between the tank and its surroundings during leakage experiments, enhancing the model's accuracy and reliability for better isentropic process approximation. This research provides critical insights into the behavior of hydrogen leakage and combustion, emphasizing the importance of precise modeling for the safety and efficiency of high-pressure hydrogen storage systems. Similar studies have been conducted in the aerospace field [44] but also on FCEVs analysis [161].
## 2 Materials and Methods
### DOA Calculation
\(S\) and \(S_{1}\) are satellites before and after rotation. \(\overset{\rightharpoonup}{AS}\) and \(\overset{\rightharpoonup}{AS_{1}}\) are the unit vectors of the satellite signal direction before and after rotation. Considering Equation (6), and rewriting Equation (4), we can get the following relationship:  
\[\left\{ \begin{array}{l}\Delta\varphi_{AB}\lambda=cos\alpha_{1}\sin\beta_{1} \Delta x_{AB_{1}}\\ \Delta\varphi_{AC}\lambda=cos\alpha_{1}\sin\beta_{1}\Delta x_{AC_{1}}+cos\alpha _{1}\cos\beta_{1}\Delta y_{AC_{1}}\end{array} \right. \tag{7}\]  
where \(\alpha_{1}\) and \(\beta_{1}\) is the elevation and azimuth angles of the satellite \(S_{1}\). Then \(\alpha_{1}\) and \(\beta_{1}\) can be easily solved by Equation (7):  
\[ \begin{array}{l}\alpha_{1}=\arccos\left(\frac{\Delta\varphi_{AB}\lambda}{ \Delta x_{AB}\sin\varphi_{1}}\right)\\ \beta_{1}=\arctan\left(\frac{\Delta\varphi_{AB}\Delta y_{AC_{1}}}{\Delta\varphi_ {AC}\Delta x_{AB_{1}}-\Delta\varphi_{AB}\Delta x_{AC_{1}}}\right)\end{array}  \tag{8}\]  
\[\Delta X_{AS_{1}}=\left[\cos\alpha_{1}\sin\beta_{1},\cos\alpha_{1}\cos\beta_{1},\sin\alpha_{1}\right]^{T} \tag{9}\]  
By rotating \(\overset{\rightharpoonup}{AS_{1}}\) in reverse, \(\overset{\rightharpoonup}{AS}\) can be obtained. Then further solve \(\alpha\) and \(\beta\). The equation for rotation is as follows:  
\[\Delta X_{AS}={R_{1}}^{-1}(r,p,y)\Delta X_{AS_{1}} \tag{10}\]
## 3 Methodology and Proposed Model
### Data Pre-Processing
#### 3.2.4 Feature Exclusion  
The process of excluding specific features (variables) from a dataset while developing or analyzing a model is known as feature exclusion. By streamlining the model, concentrating on the most pertinent data, and minimizing overfitting, this method is frequently employed to enhance model performance. A quick review of the IBM dataset reveals that several features, such as \"Employee Count\", \"Over18\", and \"Standard Hours\", have identical values for all employees and have therefore been excluded. Furthermore, the \"Employee Number\" feature, as its values do not contribute to the objective of the analysis, has also been omitted.
# Social and Robust Navigation for Indoor Robots Based on Object Semantic Grid and Topological Map
## 6 Experimental Results
### Experimental Setup
#### 6.1.2 Experimental Environment  
The experimental environments include home and office. The home environment includes one bathroom, two bedrooms, one kitchen, and one living room. All doors are 0.74 m wide. Moreover, according to the result of semantic segmentation algorithm, there are 11 objects in the home environment. For the convenience of the experiment, we select five objects as the endpoints of the navigation paths, and they are sink in bathroom, bed in master bedroom, bed in second bedroom, TV in living room, and fridge in kitchen, as shown in Figure 8a. Figure 8c is the object semantic grid and topological map established for the home environment, which contains: object semantic information, object occupied space, object goal space (affordance and nonaffordance space), semantic topological information of room, the room dominant direction, and the coordinates of door endpoints. Figure 8e visualizes the door endpoints in the home map, which is the intermediate product of generating the semantic topology map based on [35].  
Similarly, the office environment includes one meeting room, one laboratory, one corridor, and one elevator hall. The door width in the environment is 0.65-0.96 m. There are 12 objects in the environment, and we select 5 objects as the endpoints of navigation paths, which are notebook in meeting room, desk, and microwave in laboratory, desk in elevator hall, and desk in corridor, as shown in Figure 8b. Figure 8d shows the object semantic grid and topological map of office environment. Figure 8f visualizes the door endpoints in the office map (which is cut and rotate by 90 degrees).
## 2 Materials and Methods
### Transcriptional Profiling of Glucosinolate Candidate Genes  
For RNA isolation, 100 mg of frozen samples (biological replicates \(n=3\)) were finely ground and extracted using the Spectrum Plant Total RNA Kit (Sigma-Aldrich, St. Louis,MO, USA) with on-column treatment with DNase I Amplification Grade (Sigma-Aldrich, St. Louis, MO, USA), according to technical bulletin. Total RNA was eluted in 50 \(\upmu\)L of DEPC-treated water and spectrophotometrically quantified. One thousand ng of total RNA was retrotransed with the High-Capacity RNA to cDNA kit (Thermo Fisher Scientific, Inc., Waltham, MA, USA) according to manufacturer's instructions. The transcriptional levels of target genes were measured by reverse transcription quantitative PCR (RT-qPCR) using a QS3 instrument (Thermo Fisher Scientific, Inc., Waltham, MA, USA) and SYBR Green chemistry. Each reaction contained 2 \(\upmu\)L of a 1:15 dilution of cDNA, 5 \(\upmu\)L of Power Up(r) SYBR master mix (Thermo Fisher Scientific, Inc., Waltham, MA, USA) or PerfeCTa SYBR Green FastMix master mix (Quantabio, Beverly, MA, USA) depending on the target, highly specific primers, and RNA-free water to a final volume of 10 \(\upmu\)L. Primer sequences, master mix and amplicon length for each target are listed in Table S1. Specificity of primer pairs designed for this work was assessed in silico by BLAST +2.15.0 alignment with available _E. sativa_ sequences, with melt curve analysis and Sanger sequencing of PCR products. A standard curve was added in all assays, both for target and reference genes. Standard curves were made of 5 points, prepared from a 1:4 serial dilution starting from a 1:10 dilution of a pool of all the cDNAs. To verify the specificity of the reaction, each assay also included a negative control, and melt curve analysis was performed for all reference and target gene assays. The amplification efficiency of each primer pair was estimated using the slope of the regression line, according to the equation: E = 10\({}^{\circ}\)(\(-\)1/slope) and relative expression was calculated as efficiency-corrected \(\Delta\Delta\)Ct [43]. The _EsGAPDH_ and _EsUBC_ genes were selected as reference genes, and the average Ct of untreated (control) samples was used as a calibrator. Data are presented as expression levels relative to the control.
# Evaluating the Efficacy of Segment Anything Model for
## 3 Methodology
### Segment Anything Model  
In 2023, FAIR Lab which belongs to an American company called Platforms (formerly known as Facebook) introduced its groundbreaking research on the Segment Anything Model (SAM), an image segmentation model gaining quick popularity for its exceptional generalization capabilities and high accuracy [28]. SAM was developed with the aim of addressing the existing limitations in computer vision, particularly in enhancing the robustness of image segmentation. Since the release of SAM, an increasing number of secondary applications and derivative projects rooted in SAM have been deployed across a wide spectrum of tasks. These include image inpainting, image editing, and object detection, among others. Continuous testing and verification have consistently demonstrated SAM's significant impact in various fields [29; 30; 31; 32]. For instance, SAM has found applications in audio and video segmentation, and the enhanced version proposed in that study exhibits robust performance in targeting and segmentation tasks. In civil engineering, a comparative analysis has been conducted between traditional deep learning methods and SAM for concrete defect segmentation, revealing distinctive strengths and limitations for each approach. Similarly, in the domain of remote sensing image segmentation, SAM has been explored for tasks such as semantic segmentation and target detection. The outcomes highlight its robust generalization and zero-sample learning capabilities. Moreover, across diverse fields like multimedia and space exploration, SAM is being investigated for various applications [26; 33; 34; 35; 36]. The ongoing trend of continuously optimizing SAM  
Figure 1: Schematic distribution of the research sites with their geocoordinates.  
models to cater to specific research domains is indicative of its adaptability and potential for widespread utility.  
SAM is currently freely accessible for use, and its source code has been made open access. Utilizing SAM for image segmentation is remarkably straightforward. The model comprises two primary functions: The first method involves the interactive and manual selection of target points or the utilization of various input prompts, such as clicks, selected boxes, and text. This approach leverages a form of approximate supervised learning to achieve precise image segmentation with a minimal amount of sample data, as depicted in Figure 2. The second method is more automated and requires minimal user intervention. In this approach, the model autonomously completes the image segmentation process. The model is processed by the mask decoder [37], delivering results that are illustrated in Figure 3. The segmented portions can be readily exported, and each segmented image is stored within the model, as demonstrated in Figure 4. This object-oriented image segmentation significantly enhances the processing capabilities for ARSI and UGS images. Both segmentation methods are notably efficient, typically requiring only a brief processing time, which may vary depending on computer performance and the size and resolution of the image.