esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods  
### Device Fabrication  
Figure 1 depicts the schematic structure of the device. The patterning process encompasses the use of traditional photolithography, wet etching, and lift-off techniques. At first, an n+silicon substrate is subjected to a thorough wet etching cleansing procedure to eliminate any present surface oxides and impurities, guaranteeing a perfect base for subsequent additional layers. Subsequently, a 200-nm-thick layer of hafnium oxide (HfO\({}_{2}\)) is applied onto the substrate as the back gate (BG) dielectric layer using radio frequency (RF) sputtering techniques. The sputtering process involves using a flow rate of 30/2 sccm of argon/oxygen gas and a working pressure of 4.7 mTorr. The temperature is set at 250 \({}^{\circ}\)C during this process. The selection of this method is based on its accuracy and capability to achieve uniform coverage on the substrate. The active region is defined by depositing a 50-nm-thick layer of IGZO onto the substrate using RF sputtering methods. This process involves using an argon flow rate of 20 sccm, maintaining a working pressure of 5 mTorr, and conducting the deposition at room temperature. The target contains In\({}_{2}\)O\({}_{3}\), Ga\({}_{2}\)O\({}_{3}\), and ZnO in a mole fraction ratio of 1:1:1. After the semiconductor layer is formed, the source and drain (S/D) contacts of the device are created by directly depositing a 200-nm-thick layer of Au/Ti onto the IGZO layer using electron beam (e-beam) evaporation. The S/D contacts are established, and then the necessary layers for tunneling, charge trapping, and blocking are created using HfZrO films with thicknesses of 5 nm, 20 nm, and 30 nm, respectively. The sputtering conditions are carefully calibrated to deposit these layers. The oxygen partial pressure is set at 3.4 \(\times\) 10\({}^{-4}\) torr, while the argon pressure is maintained at 2 \(\times\) 10\({}^{-3}\) torr. The sputtering temperature is set at 300 \({}^{\circ}\)C to achieve the ideal HfZrO composition. The charge trapping component is created by reducing the oxygen partial pressure to 8 \(\times\) 10\({}^{-5}\) torr, which improves the memory characteristics of the device by  
producing oxygen-deficient oxides. During the last stage of production, a layer of Au/Ti with a thickness of 150 nm is applied using e-beam evaporation to create the floating gate (FG) in an environment with air. The fabricated device is subjected to conventional thermal annealing at a temperature of 400 \({}^{\circ}\)C for a duration of 30 min. This process is carried out to eliminate any defects present in the channel layer and gate insulating film, as well as to enhance the interface state between them. The device possesses a channel length (L) of 20 \(\upmu\)m and a width (W) of 30 \(\upmu\)m.
## 3 Results
### Zooplankton Dynamics  
From April to October 2018, total zooplankton biomass in Lough Feeagh ranged from 7.33 to 151.92 \(\upmu\)g DW\(\cdot\)L\({}^{-1}\) (Figure 6B). From mid-May to just before Storm Hector, total zooplankton biomass started to increase, coinciding with the initial peak in Chl \(a\) at this time. Nevertheless, total crustacean biomass remained below 20 \(\upmu\)g DW\(\cdot\)L\({}^{-1}\), with rotifers dominating zooplankton biomass (Figure 6B).  
In contrast, after Storm Hector at the end of June (intermediate period, 26 th June), all zooplankton groups increased their biomass, achieving maximum values of 151.92 \(\upmu\)g DW\(\cdot\)L\({}^{-1}\). In particular, calanoids (mainly _Eudiaptumus gracilis_) and _Daphnia_ increased their biomass to 46.52 \(\upmu\)g DW\(\cdot\)L\({}^{-1}\) (30.62%) and 18.89 \(\upmu\)g DW\(\cdot\)L\({}^{-1}\) (12.44%), respectively (Figure 6B). Rotifers' total biomass also increased, with a direct effect on _Keratella_ and _Tricochera_ groups (specific rotifers genus not shown). This peak in total zooplankton biomass coincided with a decrease in both phytoplankton and ciliates biovolumes, and Chl \(a\) concentration. (Figure 6).  
Once the lake restabilised and Chl \(a\) recovered to pre-storm conditions in the surface layer (6 m integrated sample), total zooplankton biomass decreased to 60.35 \(\pm\) 18.11 \(\upmu\)g DW\(\cdot\)L\({}^{-1}\) (July and August, n = 4) (Figure 6B). During that period, zooplankton community biomass was dominated by copepods  
Figure 7: Biovolumes (mm\({}^{3}\cdot\)m\({}^{-3}\)) of different phytoplankton phyla and ciliates recorded at different depths in Lough Feeagh on each specific date (pre-, intermediate and post-storm event). Samples at 6 m (integrated water sample from the surface) correspond to epilimnion, samples at 12 m correspond to metalimnion and samples at 25 m correspond to hypolimnion of Lough Feeagh.  
(\(33.10\pm 10.59\)\(\upmu\)g DW\(\cdot\)L; calanoids, cyclopoids and nauplii). In turn, _Diaphanosoma_ and rotifier biomass remained around averages of \(8.34\pm 4.2\)\(\upmu\)g DW\(\cdot\)L\({}^{-1}\) and \(14.27\pm 8.10\)\(\upmu\)g DW\(\cdot\)L\({}^{-1}\), respectively. On the other hand, _Daplnia_ biomass returned to minimum values of \(3.75\pm 2.43\)\(\upmu\)g DW\(\cdot\)L\({}^{-1}\) (Figure 6B).  
During September, lake stratification ended and Chl \(a\) decreased again to values below \(1\)\(\upmu\)g Chl _a_\(\cdot\)L\({}^{-1}\). In that period (September, n = 3), total zooplankton biomass decreased to 26.52 \(\pm\) 7.5\(\upmu\)g DW\(\cdot\)L\({}^{-1}\), mainly due to a sudden decline in calanoids and cyclopoids. On the other hand, rotifer and _Diaphanosoma_ biomass remained stable with values of \(11.26\pm 4.57\)\(\upmu\)g DW\(\cdot\)L\({}^{-1}\) and \(6.56\pm 1.34\)\(\upmu\)g DW\(\cdot\)L\({}^{-1}\), respectively (Figure 6B).
## 2 Materials and Methods
### Tests and Datasets Creation
#### 2.3.1 Static Datasets  
The preliminary test to study the performance of the static GNSS positioning of the Deeper Smart Sonar Pro+ 2 and CHIRP+ and the G > S > P Sys was performed in Open Sky conditions on the roof of the Polytechnic University of Bari, Taranto, Italy, site of the Geomatics Laboratory in Taranto. To perform the test, the instrumentation was placed on an old vertex of a permanent GNSS network, named FATA which was already used in previous experiments [36]. For greater accuracy, the vertex coordinates were re-acquisition by the professional GNSS receiver Trimble R10. The Deeper Smart Sonars were placed on the observation vertex using a cylindrical vessel approximately 0.25 m high, filled with water (to turn on the sonars and guarantee the recording of GNSS data), with a hole at thetop equal to the diameter of the sonar to allow the sonars to be centred. The G > S > P Sys was positioned by installing the Helical antenna on the point to be surveyed using a stand. Each device conducted 8 h (28,800 s) of static observation and five datasets were produced. Two datasets were built with the real-time planimetric GNSS positioning in SPP, from the Deeper Smart Sonar CHIRP+ and the Deeper Smart Sonar Pro+ 2. One dataset was built with the real-time 3D GNSS positioning in SPP, from the G > S > P Sys. Another dataset was created with post-processed data in PPP 3D static, from the G > S > P Sys, and finally, one dataset post-processed in 3D static positioning was created from the G > S > P Sys. Table 3 shows the composition of the static datasets, \"Deeper GNSS CHIRP+ \" refers to the internal GNSS receiver of the Deeper Smart Sonar CHIRP+ and \"Deeper GNSS Pro+ 2\" refers to the internal GNSS receiver of the Deeper Smart Sonar Pro+ 2.
## 2 Study Area and Data
### Data Availability  
This study is a significant component of the zoning and technical research for groundwater pollution prevention and control in Tangshan City, encompassing two main aspects: the analysis of groundwater dynamics and the evaluation of groundwater vulnerability.  
In the investigation of groundwater dynamics, a total of 75 government-monitored wells with long-term observation data were carefully selected to ensure completeness, control, and representativeness. For each district or county level, 1-2 monitoring wells were chosen for analysis, ensuring a uniform distribution across the study area. Specifically, this study focused on analyzing the response of unconfined groundwater, directly recharged by precipitation. Based on these principles, data from 24 monitoring wells were ultimately  
Figure 1: The location of study area and its hydrogeological condition.  
determined. To complement this analysis, three precipitation stations in Zunhua, Tangshan, and Leting were selected to match similar monitoring wells based on their proximity.  
In the evaluation of groundwater vulnerability, data acquisition was conducted through indoor data collection and gathering relevant information about the study area's geology, hydrogeology, and drilling. This included parameters such as groundwater depth, vertical net recharge, aquifer thickness, soil medium, terrain slope, vadose medium type, and permeability coefficient. A total of 1599 boreholes and associated information were gathered from various sources, including survey reports on land pollution from key industries in Hebei Province, China (974 cases), hydrogeology and engineering geology borehole construction and in-situ tests (28 cases), comprehensive geological surveys of major cities and towns (19 cases), the national important geological borehole database service platform (55 cases, [[https://zk.cgsi.cn/](https://zk.cgsi.cn/)]([https://zk.cgsi.cn/](https://zk.cgsi.cn/)) (accessed on 28 August 2023)), groundwater level surveys (164 cases), and geotechnical investigation reports (359 cases). Aquifer permeability data were obtained by combining the aquifer media type with empirical values from the Hydrogeological Manual [34]. All data collected are accurate, providing strong support for the reliability of this study.
# Hollow-Core Optical Fibers for Telecommunications and Data Transmission
## 13 Associated Technologies
### Test Instruments  
In this area, we can expect the following novelties:  
* Testing of loss at new wavelengths including 1800-2050 nm and 1364 nm;
* OTDRs able to detect low levels of Rayleigh scattering (Section 7.4);
* Mandatory PMD testing at the factory to detect distorted fibers.  
The simplest solution to problem (b) is to increase the launched power by adding an optical amplifier like EDFA to laser in the OTDR [135], as HCF exhibits low Brillouin scattering when filled with gas at a normal atmospheric pressure or lower. Unfortunately, this brings in issues of eye safety and possible damage to active devices attached to the fiber under test.  
Cables with expensive HCFs of NANF or similar type are likely to carry important traffic (HST, metro/core DWDM, links between data centers) with enhanced level of maintenance, while the ingress of humidity, e.g., through fiber break or faulty splice (Sections 6.3 and 6.4), is a particular problem. Added loss resulting from entry of water vapor is highest at wavelengths close to 1364 nm (Figure 14); therefore, the deterioration of the cable can be detected by monitoring the loss at 1364 nm. When data transmission takes place at 1550 nm, it will be possible to monitor live HCF by adding WDM multiplexers to the link.  
A new instrument for the maintenance of HCF networks will be a bottle of compressed pure, dry gas like argon or nitrogen, fitted with devices for temporary attachment to a cleaved fiber and the controlled purging of it for several days or even weeks [136]. With purging, the costly replacement of a moisture-penetrated HCF cable will be avoided.
###### Abstract
Thus, addressing limited samples and privacy issues may be necessary when using combined physiological and questionnaire survey data in management science. Specifically, it may be possible to address these issues by developing a secure database with a sufficient sample size that combines physiological and questionnaire survey data. Regarding the limited sample size problem, various data augmentation approaches have been developed [7]. Data augmentation methods based on the synthesized data using deep generative models such as generative adversarial networks (GANs) [8,9] and variational autoencoders (VAEs) [10] have been proposed. Other methods, such as mix-up [11], have also been proposed. In the former data augmentation methods, augmented data are used as synthesized data based on appropriate probability distributions related to actual data. However, regardless of the probability distributions, the latter approach involves trial-and-error based on actual data. Recently, prospective data augmentation approaches, including GAN-based synthesizers and combined approaches of generative models and mix-ups, have been presented [12,13]. Notably, because these data augmentation approaches can augment larger-sized data, they can solve limited sample issues. Moreover, to overcome concerns regarding privacy security, synthetic data-generation methods are presented using this deep generative model [14]. Generating synthetic data has an essential advantage over masking personal details. This technology can create anonymized data that inherit the characteristics of the original data while increasing the data size [14,15]. Personal information may not be revealed even if someone accesses or invades the data. However, the applications of these synthetic data-generation technologies primarily focus on a single modality [14]. It is necessary to address the application of synthetic data-generation technologies for multimodal data, such as combined physiological and asking-based data. This study uses deep generative models to address the data augmentation approach based on synthesized data. Additionally, regardless of these necessaries and effectiveness, few studies have applied the synthetic data-generation approach to multimodal data in management science contexts. Although a few studies were reported in management science study areas, these studies deal with unimodal data, as well as the other studies on synthesizing data [16; 17]. We attempt to validate the data augmentation approach by synthesizing small-combined data consisting of physiological and questionnaire survey data in a management science issue. Concretely, we address the difficulties, such as the limited sample size and privacy problems, by applying deep generative models to the combined multimodality data. In this study, we considered the effectiveness of solving these problems in identifying skilled nurses, an issue in human resource management. Details of this issue are described in the Methods section.
###### Abstract  
The power spectral density estimation algorithms, logarithmic frequency axis for power spectral density (LPSD), and the LISA-LPSD algorithm are widely utilized in the implementation of system evaluations for space-based gravitational-wave-detection projects, particularly in the low-frequency band ranging from 0.1 mHz to 1 Hz. However, existing adaptive sine multi-taper algorithms suffer from low resolution and high computational complexity in obtaining the optimal cone number across the entire frequency domain, which has hindered its application in this field. These algorithms often face challenges related to inadequate resolution when dealing with low-frequency signals, as well as the issue of high computational demands. In response to these challenges, this paper introduces an advanced adaptive sine multi-taper algorithm designed to optimize the determination of the cone number. By balancing the relationship between bias and variance, this approach facilitates gradient processing of the cone number specifically tailored for low-frequency signals. Comparative evaluations against the LPSD algorithm, the original adaptive sine multi-taper algorithm, and the LISA-LPSD algorithm reveal that the proposed method demonstrates superior spectral resolution and reduced algorithmic complexity. This improvement offers a more effective solution for the system evaluation of low-frequency gravitational-wave-detection projects.  
gravital wave detection; precision angle measurement; ground simulation system; data spectral analysis +
Footnote †: journal: Applied sciences  
Article
## 2 Materials and Methods
### Outcome Measures  
#### 2.6.1 Self-Reported Emotional Response  
Each participant completed an emotional response audio-based questionnaire, which involved rating their perception of specific sounds using a series of emojis, designed to translate an analogue scale into graphical information (see Figure 1). This was completed 1 week prior to the invention and following completion of the 10-week study. Sounds that prompted the most anxiety as indicated on the emotional response questionnaire were selected as sounds to be used in the application. A total of twenty-two types of sounds were included, based on parental responses in [PERSON] et al's [38] questionnaire on problematic sounds, and eleven \"relaxing\" soundscapes taken from the Eigenscape database [42]. The audio questionnaire was developed in the Unity3D 2018.4.20 game engine software.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Exposure Level** & **Virtual Distance between Player and Stimulus** \\ \hline
1 & 25 m \\
2 & 15 m \\
3 & 5 m \\
4 & 2.5 m \\ \hline \hline \end{tabular}
\end{table}
Table 1: Exposure hierarchy. Each exposure level corresponds to the distance between the participant and the virtual sound source; distance is represented in metres.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Stimuli** & **Participant** \\ \hline Fire Alarm & P01, P02, P03, P09 \\ Children Fighting & P01 \\ Fireworks & P02, P03 \\ Hairdryer & P02, P03, P04, P05 \\ Car Engine & P04 \\ Baby Crying & P05 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Target stimuli and participants interacting with them. Target stimuli represent sounds with a negative self-reported emotional response (\(\geq\)4) recorded at baseline that were subsequently interacted with by the participant.
# A Transformer-Based Channel Estimation Method for OTFS Systems
## 4 Performance Evaluation
### Computational Complexity
The 32-bit system and NVIDIA V100 with 14.13 TFLOPs, manufactured by the American company NVIDIA Corporation headquartered in Santa Clara, California, United States, are used as examples. As observed from the table, the number of parameters required by the Transformer estimation model is fewer than that of the LSTM and RNN. The higher parameter requirement in RNN and LSTM can be attributed to the inclusion of self-connecting feedback loops. These loops necessitate processing information from previous time steps before computing the hidden layer state for the next time step. Consequently, the historical hidden layer state information needs to be preserved until all time step inputs are processed, resulting in a substantial memory overhead. On the other hand, the Transformer estimation model eliminates the need for such sequential dependencies, leading to a smaller parameter count. The Transformer estimation model enables parallel processing of the input time series by using a multi-head attention mechanism, which greatly reduces the number of parameters in the model, and thus takes up only a small amount of memory space. A comparison of the FLOPs of the different models shows that the Transformer estimation model has the highest FLOPs. The higher FLOPs in the Transformer estimation model can be attributed to its extensive use of matrix transformations. These transformations involve a larger number of parameters and consequently result in longer computational time. However, this increased computational complexity allows the Transformer estimation model to effectively capture complex patterns and dependencies in the data, leading to superior performance in various tasks. Despite the additional computational requirements, the benefits of the Transformer estimation model make it a compelling choice.
## 3 Results
### Experiment  
#### 3.2.1 Experiment Conditions  
An experiment was conducted to confirm the effect of the angle change of the surface asperity according to the pad temperature on the material removal rate. The experiment was carried out under two conditions as shown in Table 1.  
First, a series of CMP runs, each 1 min long, were carried out continuously without any conditioning process, using fumed silica as the slurry. The purpose of the runs conducted under this first condition was to examine the reduction in material removal rate due to the continuous CMP process performed without conditioning, from the viewpoint of the change in the surface asperity angle rather than from the viewpoint of simple surface roughness. In addition, as can be seen in the schematic diagram of these experimental runs in Figure 6a, an attempt was made to check whether the differences in polishing and angle patterns arise with changes in pad temperature, which were artificially induced by using heated slurry under the same process conditions.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**CMP Parameters** & **Conditions [Experiment 1]** & **Conditions [Experiment 2]** \\ \hline Polisher & Poli-500 [GnP Technology] & Poli-500 [GnP Technology] \\ Pad (Top/Bottom) & IC/Suba & IC/Suba \\ Wafer [inch] & 8 (SiO\({}_{2}\)) & 8 (SiO\({}_{2}\)) \\ Pressure (Head/Retainer) [psi] & 2/3 & 2/3 \\ Velocity (Head/Platen) [rpm] & 93/87 & 93/87 \\ Pad temperature [\({}^{\circ}\)C] & 42, 55, 69 (Polishing) & 26, 42, 58, 69 (Conditioning) \\ Polishing time [min] & 1 min per 1 cycle & 1 min \\ Conditioning & Ex-situ & Ex-situ \\ \hline \hline \end{tabular}
\end{table}
Table 1: Experiment conditions.  
Figure 5: (**a**) Micro-CT images (cross section and top view) and data on the surface asperity angles according to pad temperature and (**b**) the surface asperities’ characteristics (perimeter, number of contacts) according to pad temperature.  
Second, for conditioning of the glazed pad, runs were conducted to assess the asperity shape recovery and the removal rate after the conditioning process according to variations in temperature. Conditioning was performed at four temperatures. Details of this second set of runs are given in Table 1 and Figure 6b.
## 3 System Design
### System Architecture  
Figure 3 illustrates the proposed CEA system architecture. To interact with this system, each user must install the decentralized application named CEA DApp, IPFS private network, and Ethereum private network named CEA Network. The users consist of archivists and BPK. One account is set as an administrator, and it is responsible for registering and monitoring other users. Each user has two identities. First, there is the peer ID for interacting with the IPFS private network. Second, there is the Ethereum address for communicating with CEA Network.  
To maintain the confidentiality of archives, both the IPFS and CEA Network are set as private networks. The files will only be distributed between archivists and BPK. The hash of files will also be stored and retrieved only by them. The user must install Metamask [14] to run CEA DApp on traditional browsers, such as Google Chrome or Firefox. In addition, Metamask has to import the  
Figure 3: Proposed CEA system’s architecture.  
Ethereum addresses, which are generated on the CEA Network. The CEA DApp has a smart contract to store the hash in CEA Network and retrieve the hash of files from the CEA Network.
## 5 Experimental Results and Discussion  
The experiment consisted of two parts. First, the standard text data sets marked from UCI were clustered to illustrate the validity of the SSC algorithm in high-dimensional sparse text vector space classification. Then, based on the SSC algorithm, cluster experiments were performed on the interests of actual Microblog users.