esa-sceva/satcom-chunk-collection huggingface dataset:
# Analysis and Improvement of a Two-Stage Centrifugal Compressor Used in an MW-Level Gas Turbine
## 5 Coupling Calculations of the Original and Optimized Designs  
After the low/high-pressure-stage centrifugal compressor was optimized and designed, circular calculations of the MW-level gas turbine were performed under three typical load conditions of 100%, 90%, and 60%. The operating parameters of circular calculation are shown in Table 5. To analyze the actual performance of the low/high-pressure-stage centrifugal compressor, we conducted coupling calculations and analysis of the original and optimized designs under different working conditions for an entire week. The calculated geometric model is shown in Figure 13. Numeca software was used for calculations, and an S-A turbulence model was employed.  
Figure 12: Inlet attack angle distribution of the vaned diffuser.  
Figure 13: Geometric structure.  
Table 6 shows a comparison of the pressure ratio and efficiency of the original and optimized designs of the low/high-pressure-stage compressor under three typical load conditions. The optimized design performed better than the original design. At 100% load, the low-pressure-stage pressure ratio increased by 4.01%, and the efficiency increased by 2.90%. The high-pressure-stage pressure ratio increased by 4.12%, and the efficiency increased by 3.00%. The pressure ratio of the unit increased by 8.4%, and its efficiency increased by 3.70%. At 90% load, the low-pressure-stage pressure ratio increased by 5.22%, and the efficiency increased by 3.30%. The high-pressure-stage pressure ratio increased by 3.41%, and the efficiency increased by 3.10%. The pressure ratio of the unit increased by 9.37%, and its efficiency increased by3.80%. At 60% load, the low-pressure-stage pressure ratio increased by 2.33%, and the efficiency increased by 4.80%. The high-pressure-stage pressure ratio increased by 4.72%, and the efficiency increased by 4.70%. The pressure ratio of the unit increased by 7.70%, and its efficiency increased by 5.40%.  
The specific performance of the low/high-pressure-stage centrifugal compressor under different working conditions was analyzed as follows.
## 4 Simulation Results
### Applications of KC200 GT PV Module
#### 4.2.2 Case 2: DD Model of KC200 GT PV Module  
In this instance, the recommended MGA is used to extract the KC200 GT PV module's DD model features. The seven unknown SD model parameters for various inspirational approaches that produced the greatest results in the experiment are listed in Table 5. The findings demonstrate that, in terms of competitiveness, the suggested MGA performs better than the comparable methods. This indicates that the optimal RMSE value of 5.91445 \(\times\) 10\({}^{-4}\) is achieved by the suggested MGA. Furthermore, the table shows the PV-derived electrical parameters using the established optimization techniques; these include the HTS, GO, and EVO.  
Figure 10(a),b display the measured and simulated I-V and P-V characteristics for the SD model. It is observable that the data produced by the suggested MGA technique closely resemble the data acquired by experimentation, suggesting that the MGA technique was successful in producing power and current at a range of voltage levels. The simulation and measured current absolute errors range from 3.74212 \(\times\) 10\({}^{-7}\) to 3.45977 \(\times\) 10\({}^{-4}\), while the simulation and measured power absolute errors range from 0 to 6.1195 \(\times\) 10\({}^{-1}\), as shown in Table 6.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**MGA** & **Vexp** & **Iexp** & **Isim** & **Pexp** & **Psim** & **Absolut IAE** & **Absolut PAE** \\ \hline
1 & 0 & 8.21 & 8.197656 & 0 & 0 & 0.000152365 & 0 \\ \hline
2 & 4.2 & 8.198 & 8.191457 & 34.4316 & 34.40412 & 4.28089 \(\times\) 10\({}^{-5}\) & 0.027479989 \\ \hline
3 & 8.3 & 8.186 & 8.185388 & 67.9438 & 67.93872 & 3.74212 \(\times\) 10\({}^{-7}\) & 0.005077348 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Absolute errors in currents and powers between the measured and simulated for the DD model of the KC200 GT PV module using MGA.  
Figure 10: Overall discrepancies in currents and powers between the measured and simulated for the SD model of the KC200 GT PV module using MGA.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Applied Technique** & **HTS** & **EVO** & **GO** & **MGA** \\ \hline \(I_{\text{P}h}\) (A) & 8.203823597 & 8.20424482 & 8.193643057 & 8.216682898 \\ \hline \(R_{dh}\) (\(\Omega\)) & 92.46395147 & 66.83697866 & 16.37828656 & 6.303160786 \\ \hline \(R_{S}\) (\(\Omega\)) & 0.004562321 & 0.004219395 & 0.004688805 & 0.004829466 \\ \hline \(I_{S1}\) (A) & 5.66339 \(\times\) 10\({}^{-8}\) & 1.82124 \(\times\) 10\({}^{-7}\) & 6.01244 \(\times\) 10\({}^{-8}\) & 8.65143 \(\times\) 10\({}^{-9}\) \\ \hline \(\eta_{1}\) & 1.749082759 & 1.346598774 & 1.832451271 & 1.315186385 \\ \hline \(I_{S2}\) (A) & 8.0133 \(\times\) 10\({}^{-8}\) & 5.75716 \(\times\) 10\({}^{-7}\) & 4.57891 \(\times\) 10\({}^{-8}\) & 2.12348 \(\times\) 10\({}^{-8}\) \\ \hline \(\eta_{2}\) & 1.286121414 & 1.933055486 & 1.248347215 & 1.20450769 \\ \hline RMSE & 0.020515491 & 0.02717656 & 0.009049475 & 0.000591445 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Acquired parameters for the KC200 GT PV module (the DD model) using MGA compared to others.
# Design and Implementation of a Six-Degrees-of-Freedom Underwater Remotely Operated Vehicle
## 2 Mathematical Model
### Kinetic Equations
#### 2.4.3 Damping Matrix  
The ROV's damping matrix in the fluid \(D(v)\in R^{6\times 6}\), which consists of the force and the moment of the first and second-order velocities, can be represented as follows:  
\[D(v)=-\text{diag}\{X_{u},Y_{\dot{v}},Z_{\dot{w}},K_{p},M_{q},N_{\dot{r}}\}- \text{diag}\Big{\{}\,X_{u|u|}\Big{|}u\Big{|},Y_{v|\big{|}}\Big{|}v\Big{|},Z_{w| u|}\Big{|}w\Big{|},K_{p|p|}\Big{|}p\Big{|},M_{q|q|}\Big{|}\Big{|}q\Big{|},N_{r| \big{|}}\Big{|}r\Big{|}\Big{\}} \tag{12}\]
## Appendix A
\begin{split}\int_{0}^{LT}x(t)\bigg{(}s_{l}(t)+y_{-}(t,i)\bigg{)} dt\\ +\underset{(q)}{\text{max}}&\bigg{(}\int_{0}^{LT}x( t)y_{+}(t,q)dt-\frac{\mu}{2}\int_{0}^{LT}[s_{l}(t)+y_{-}(t,i)+y_{+}(t,q)]^{2}dt \bigg{)}\\ \gtrless\int_{0}^{LT}x(t)\bigg{(}s_{r}(t)+y_{-}(t,i)\bigg{)}dt\\ +\underset{(q)}{\text{max}}&\bigg{(}\int_{0}^{LT}x( t)y_{+}(t,q)dt-\frac{\mu}{2}\int_{0}^{LT}[s_{r}(t)+y_{-}(t,i)+y_{+}(t,q)]^{2}dt \bigg{)}.\end{split}  \tag{13}\]When \(M=2\) in (1), \(s_{l}(t)=s_{1}(t)=A_{0}a(t)\cos(2\pi f_{0}t)\), \(s_{r}(t)=s_{2}(t)=-A_{0}a(t)\cos(2\pi f_{0}t)\), and (A6) transforms into  
\[ \begin{split} 2\int_{0}^{LT}x(t)A_{0}a(t)\cos(2\pi f_{0}t)dt\\ \underset{s_{2}(t)}{\overset{s_{1}(t)}{\gtrless}}\underset{(q)} {\max}\bigg{(}\int_{0}^{LT}x(t)y_{+}(t,q)dt-\mu\int_{0}^{LT}s_{2}(t)y_{+}(t,q) dt-\mu\int_{0}^{LT}s_{2}(t)y_{-}(t,q)dt\bigg{)}\\ -\underset{(q)}{\max}\bigg{(}\int_{0}^{LT}x(t)y_{+}(t,q)dt-\mu \int_{0}^{LT}s_{1}(t)y_{+}(t,q)dt-\mu\int_{0}^{LT}s_{1}(t)y_{-}(t,q)dt\bigg{)}. \end{split}  \tag{10}\]  
We can present the resulting equation in terms of baseband quadrature components for \(M=2\) (6)-(8):  
\[ \begin{split} 2\int_{0}^{LT}A_{xc}(t)a(t)dt\underset{s_{2}(t)}{ \gtrless}\delta(i,q),\\ \delta(i,q)=\underset{(q)}{\max}\bigg{(}\int_{0}^{LT}A_{xc}(t) \sum_{(p=1)}^{\left\lceil LR_{n}-1\right\rceil}d_{rq}^{(p)}a(t-p\xi T)dt\\ -\mu\int_{0}^{LT}a(t)\sum_{p=-\left\lceil LR_{n}-1\right\rceil}^{ \left(-1\right)}d_{ri}^{*(p)}a(t-p\xi T)dt\\ -\mu\int_{0}^{LT}a(t)\sum_{p=1}^{\left\lfloor LR_{n}-1\right\rceil }d_{rq}^{(p)}a(t-p\xi T)dt\bigg{)}\\ -\underset{(q)}{\max}\bigg{(}\int_{0}^{LT}A_{xc}(t)\sum_{(p=1) }^{\left\lceil LR_{n}-1\right\rceil}d_{rq}^{(p)}a(t-p\xi T)dt\\ +\mu\int_{0}^{LT}a(t)\sum_{p=-\left\lceil LR_{n}-1\right\rceil}^{ \left(-1\right)}d_{ri}^{*(p)}a(t-p\xi T)dt\\ +\mu\int_{0}^{LT}a(t)\sum_{p=1}^{\left\lceil LR_{n}-1\right\rceil }d_{rq}^{(p)}a(t-p\xi T)dt\bigg{)}.\end{split}  \tag{11}\]  
where \(d_{ri}^{*(p)}\) is the evaluation of the \(p\)-th received symbol.
## 1 Introduction
### Spoken Term Detection Overview  
Spoken term detection aims to find _terms_ within audio archives. This is based on a text-based input, commonly the word/phone transcription of the search term, and hence STD is also called text-based STD.  
Spoken term detection systems typically comprise three different stages: (1) the audio is decoded into word/subword lattices using an automatic speech recognition (ASR) subsystem trained for the target language; (2) a term detection subsystem searches the terms within those word/subword lattices to hypothesise detections; and (3) confidence measures are computed to rank the detections. The STD systems are normally language-dependent and require large amounts of language resources.
# Accuracy Analysis of GNSS Hourly Ultra-Rapid Orbit and Clock Products from SHAO AC of iGMAS
## 3 Assessment and Analysis
### Assessment of 6H and 1H Orbit Products
The BDS-GEO shows the worst accuracy mainly due to the poor distribution of ground stations and limited constellation. In general, we can see the mean RMS of the observation session for 6H orbit was comparative to 1H. However, the accuracy of 6H decreased when the prediction hour session increased. The GPS shows the orbit productions with best accuracy, followed by GLONASS, Galileo, and BDS-MEO/IGSO/GEO. The limited station distribution for some areas for GLONASS/Galileo/BDS, the improper attitude model, and the SRPs model used for Galileo/BDS and the PCC, without better accuracy for BDS, may decrease the accuracy of GLONASS/Galileo/BDS. However, the RMS of the 1H prediction orbit was better than 6H due to the improved strategy and the latest updated observation data being used to generate the productions for GPS/GLONASS/Galileo/BDS. Table 3 shows that the accuracy of 1H GNSS orbits improved about 1%, 31%, 13%, 11%, 23%, 9% for the observation session, and 18%, 43%, 45%, 34%, 53%, 15% for the prediction session of GPS, GLONASS, Galileo, BDS MEO, IGSO, and GEO orbit, relative when compared with reference products from IGS/MGEX, which shows encouraging prospects for real-time or near-real-time studies and applications.
## 2 Materials and Methods
### Imputation Methods
#### 2.2.2 ARIMA-Based Kalman Filter (K)  
AutoRegressive Integrated Moving Average (ARIMA) models have been a cornerstone in time series forecasting since their formalization by [PERSON] in 1970 ([PERSON] and [PERSON], 1970). The ARIMA process models time series data as a function of its past values (autoregression), past forecast errors (moving average), and differencing operations to achieve stationarity. ARIMA models are denoted by ARIMA(p,d,q), where p represents the order of the autoregressive part, d the degree of differencing and q the order of the moving average.  
The Kalman filter, introduced by [PERSON] in 1960 ([PERSON], 1960), is a recursive algorithm used to estimate the state of a linear dynamic system. It operates by predicting the state of the system and then updating this prediction using noisy observations. The algorithm comprises two phases a prediction step generating estimates of the current state using the system's model and update step which refines these estimates based on observed data. Mathematically, the Kalman filter works by minimizing the mean squared error of the state estimates. Its robustness to noise and ability to provide real-time updates make it a valuable tool for time series imputation.  
The integration of ARIMA models and Kalman filters has been explored as a solution to the weaknesses of each individual method. While ARIMA models excel at capturingtemporal dependencies and forecasting, the Kalman Filter handles real-time state estimation and noise reduction [40]. By representing ARIMA models in a state-space form, researchers have developed hybrid approaches that offer enhanced performance in imputation tasks [41].  
In our current research, we utilized an ARIMA model as the state space model within the Kalman filter framework. The model employs an autoregressive parameter of 12, reflecting the monthly resolution of the data, a differencing degree of 0, and a moving average parameter of 1. By analyzing the monthly flow data, we observed only a pronounced monthly seasonality and no significant yearly trend requiring differencing or additional seasonal terms. This situation is characteristic for hydrological data series. Consequently, the ARIMA(12,0,1) model--capturing a year's worth of monthly lags and a single moving average term--is sufficient to address the temporal structure without overlapping yearly trends. This method adheres to the principles of ARIMA-based Kalman filtering. The ARIMA state space model captures the underlying temporal dynamics of the data and the Kalman filter recursively updates state estimates, ensuring optimal imputation for missing values. The described imputation method was coded in R [42] using the imputeTS package [43], which is designed to support researchers by providing a range of gap-filling methods. Among these, the _na_kalman_ function facilitates Kalman filter-based imputation. The _na_kalman_ function requires two inputs: the time series with missing values and the specified time series model. For this research, the time series model was defined as ARIMA(12,0,1).
# An Indoor Smart Parking Algorithm Based on Fingerprinting
## 5 Notation  
In this section, some formal definitions are given to define the indoor parking problem. First, let us fix some notation:  
* \(AP\) is the set of access points, with \(|AP|=n\);
* \(S\) is the set of slots in the considered environment, with \(|S|=m\);
* \(A\) is the set of areas of the car park, with \(|A|=k\);
* \(T\) is the set of terminals of the airport, with \(|T|=l\);
* \(rs_{i},\forall i\in AP\) is the received signal strength associated with the \(i\)-th access point in \(AP\);
* \(rs_{j},\forall j\in T\) is the received signal strength associated with the \(i\)-th terminal in \(T\);
* \(av:S\rightarrow\{0,1\}\) is the _available_ function associating to each slot a 0 value if it is taken, or a 1 value if it is available for parking;
* \(area:AP\to A\) is the function associating the belonging area in the car park to each access point;
* \(is:S\cup T\to A\) is the function associating the belonging area in the car park to each slot or terminal, which will be defined in the sequel. Let us move to defining the actual fingerprint definition.  
**Definition 1** (Fingerprint).: _Given a slot \(s\in S\), let us denote by \(fp_{s}=\langle rs_{1},\ldots,rs_{n}\rangle\) the associated fingerprinting tuple. Equivalently, given a terminal \(t\in T\), let us denote by \(fp_{t}=\langle rs_{1},\ldots,rs_{l}\rangle\) the associated fingerprinting tuple._  
Notice that some issues related to the unreliability of the RSS values could be easily faced by introducing a mechanism similar to the one of DGPS (differential GPS) [25]. Indeed, one can use one or more access points as detectors of variations to be transmitted through the network to improve the quality of the measurements, such as they did in [26] and in [27].  
We denote by \(max(fp_{i})\) the maximum entry of the \(fp_{i}\) tuple, indicating the \(ap\in AP\) with the highest received signal strength for the slot (terminal) \(i\) (namely, the closest access point with respect to \(i\)). Precisely, \(is(i)=area(max(fp_{i}))\).  
To better define the notion of _area_, let us introduce a parameter, \(\theta\), representing the maximum allowed difference between the signal strengths of the fingerprint entries related to the closest access point.  
Figure 1: Example of environment setting: a multi-level car park at the airport.  
**Definition 2** (Parking Area).: _Given \(fp_{i}\) the fingerprint tuple associated to the parking slot \(i\), defined as in Definition 1, and given a threshold \(\theta\), a parking area is made of those parking slots \(i\) and \(j\) such that:_  
\[max(fp_{i})-max(fp_{j})\leq\theta\ \land \tag{1}\] \[max(fp_{i})\text{ and }max(fp_{j})\text{ occupy the same position in the corresponding fingerprints} \tag{2}\]  
_The same holds in the case of terminals in place of slots._
# High-Pressure Effects on Selected Properties of Pea and Soy Protein Isolates
## 3 Results
### Sulfhydryl Groups  
Figure 2 shows the effects of HPP (200-600 MPa/5-15 min) on the free SH of PPI and SPI at different pH values. For PPI, some general trends were observed among the significant effects of the HPP conditions on the content of free SH groups: (1) increasing the holding time of the HPP at 200, 400 and 600 Pa, at the studied pHs (Figure 2A-C), leads to a general decrease in free SH; and (2) the effects of increasing pressure were dependent on the holding time and pH--at pH 6, increasing pressure during 5 min treatments increased the content of free SH, reaching values even higher (\(p<0.05\)) than that for the control sample. No significant changes (\(p>0.05\)) comparatively to the control were observed for a holding time of 10 min, though a decrease in SHs was already observed for a longer pressurization time. At pH 7, and mainly at pH 8, the general observed trend was a decrease in the content of free SH as the pressure increased.  
Figure 1: Relative variation of protein solubility (%) for the high-pressure processed samples, having as reference the solubility measured for non-pressurized samples. Results are shown for PPI ((**A**)—pH 6, (**B**)—pH 7, (**C**)—pH 8) and SPI ((**D**)—pH 6, (**E**)—pH 7, (**F**)—pH 8) samples. Different letters indicate significant differences (\(p<0.05\)) between samples represented in each figure.  
Regarding SPI, overall, all processing conditions lead to a lower content of free SH groups (between 15 and 30%) than those observed for the unprocessed sample for the three pHs analyzed (Figure 2D-F). Increasing pressure or processing times either did not significantly (\(p\) > 0.05) affect the content of SH or decreased it. In general, pressure level and holding time had a lower effect than that observed for the PPI samples.
# Deep Convolutional Neural Networks Object Detector for Real-Time Waste Identification
## 1 Introduction
There are some other research works that used different garbage datasets, e.g., in [16], a custom model has been developed and a lightweight neural network based on the AlexNet-SSD model has been used for Garbage Detection, while in [17], a custom dataset has been used for training a multilayer hybrid deep learning model for waste classification and recycling. Most of these methods output only the presence and classification of the object in the picture. In terms of CNN used for object recognition by applying image segmentation, the research in [18] deals with the detecting aluminum profiles within images, using hierarchical representations such as those based on deep learning methods. Taking into account that municipal waste usually contains a clutter of different waste types, it is necessary to identify as many objects as possible in the image. Moreover, for collecting and sorting waste using a mobile robot, object localization in the image is needed. In order to obtain the localization, object detectors such as Single Shot Detectors, regional proposals (Faster R-CNN), or Mask networks (Mask R-CNN) can be used. In this paper, SSD and Faster R-CNN architectures have been chosen for object detection. Compared to Image Classifiers, which assign a class label to an image, Object Detectors are more challenging as they add the localization of the objects in an image by drawing a bounding box around them. Therefore, the TrashNet dataset has been annotated taking into account class labels and object location in the images, in order to train the SSD and Faster R-CNN Object Detectors. In terms of model optimization for robotic applications, the studies reveal different approaches such as developing advanced control systems for the upper and lower limb [19; 20], applying Dezert-Smarandache Theory (DSmT) for decision-making algorithms [21], Extenics control [22], and fuzzy dynamic modelling [23]. The paper is further divided into four sections. In the Methods and Dataset section, the CNN architectures, the transfer learning/fine-tuning methodology, and the dataset used for training the models are presented. In addition, the determination of an appropriate learning rate for training our CNN models is described. Next comes the Results section, where the models are evaluated in terms of generalization, precision, and recall using Pascal VOC metrics. Hyper-parameter setup is detailed here. In the Discussion section, the paper evaluates the models that performed the best in comparison with other state-of-the-art networks. In the final section, the conclusions and future work are presented.
###### Abstract
Statistically significant improvements were observed for processing speed, working memory, executive function and verbal memory, but not for attention or visuo-spatial abilities, concluding that these games are effective in improving cognitive function in such participants [58]. Finally, [PERSON] et al. conducted a systematic review and meta-analysis that evaluated the effectiveness of serious games on symptoms of mental disorder, and despite the small number of the included studies, their findings suggest that serious games may have a positive effect in reducing disorder-related symptoms [59]. The previous reviews and meta-analyses provide valuable information about gamification, serious games and computerized CT, and they shed light on how GEs have been utilized. Although some of them do include a limited number of studies, or studies with no strict methodological frameworks, the results that have been exported seem to be consistent regarding the GEs being used. One big drawback, however, is that the outcomes of the studies were treated in total, despite the fact that there was discrimination between WM, executive functions, attention, etc., but, to our knowledge, there is no systematic review that targets WM specifically. In addition, this is important considering the part that WM plays in overall cognitive status. Furthermore, the included studies in some cases are more than 10 years old, rendering some systematic reviews and meta-analyses outdated, especially considering the increased usage of smartphones, tablets and similar portable devices, which can help computerized interventions to be more easily accessible and can allow interactions between people at any time and place [48]. Thus far, the existing studies on computerized CT have been implemented as pilot studies concerning different samples, and although there are some conclusions regarding which GEs are suitable for each category, the number of studies is limited and cannot lead to safe and solid results. So far, there is a lack of framework for the usage of GEs for treating different groups of people effectively. The aim of the current study was to (a) conduct an updated systematic review of literature (following PRISMA guidelines [60]), which tries to identify which GEs are most frequently used in CCT targeting, mainly in WM performance; and to (b) attempt to categorize GEs based on the audience being applied (children, adolescents, adults, older adults) and provide any useful information for building computerized WM interventions to best serve their specific needs and limitations. In the next sections, we firstly present the methodology that we used for the identification of potential eligible articles that could be used in the systematic review under PRISMA guidelines, then we continued with the results of the search process and the presentation of the data being collected, followed by a discussion on these results and the extraction of any useful conclusions. Lastly, we finish with the research limitations that define this study.