esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Purpose: The aim of the study is to evaluate the potential of Intravoxel incoherent motion model of diffusion weighted imaging (DWI) and diffusion kurtosis imaging (DKI) in the differentiation of local colorectal cancer recurrence (LCR) from scar/fibrosis tissue in patients that underwent chemo-radiation therapy followed by the total mesorectal excision (TME) for locally advanced rectal cancer (LARC). Methods: Fifty-six patients were retrospectively included for the image analysis. Diffusion and perfusion parameters were extracted by DWI data (apparent diffusion coefficient (ADC), pseudo-diffusion coefficient (Dp), perfusion fraction (fp), and tissue diffusivity (Dt)) and DKI data (mean of diffusion coefficient (MD) and mean of diffusional Kurtosis). Wilcoxon-Mann-Whitney U test, receiver operating characteristic (ROC) analyses, and area under ROC curve (AUC) were used in a univariate statistical analysis. Backward stepwise multivariate logistic regression analysis was also performed. Results: LCR was found in 34 patients and treatment related changes such as scar/fibrosis tissue in 22 patients. At univariate analysis, low performance was reached by the mean value of Kurtosis with and AUC of 0.72 and an accuracy of 75%, respectively. Considering a regression model obtained as weighted sum of the ADC, Kurtosis, fp and Dp mean values, reached an AUC of 0.82 with a sensitivity of 72%, a specificity of 93%, and an accuracy of 81%.  
Conclusions: DWI derived parameters combined with DKI derived metrics in a multivariate model could allow differentiating of local colorectal recurrence from scar/fibrosis tissue after TME of LARC.  
Keywords:rectal cancer; magnetic resonance imaging; recurrence
# Study of Global Navigation Satellite System Receivers' Accuracy for Unmanned Vehicles
## 2 Materials and Methods
### Helix Antenna Design
This changes the characteristic impedance of the wire and creates a quarter-wave transformer, which matches the helix impedance (120-140 Ohm) to one of the connectors (75 Ohm). The simulated results of the return loss are presented in Figure 8 and show acceptable performance for a receive-only antenna. The phase center position estimation for both frequency bands is presented on Figures 9 and 10, showing stable performance at approx. 20 mm from the ground plane. Therefore, the developed helix antenna may successfully serve the purpose of a receive device in the meter positioning range as it covers the GNSS frequency bands L1, L5 and L2. The results show that the maximum horizontal component error of the phase center is about 1.4 mm and the vertical component error is about 2.6 mm for GPS receivers' antennas of the Trimble, Leica and Ashtech GPS receivers [31]. For different types of antennas, such as \"Trimble to Ashtech\", the maximum horizontal component error is about 4.3 mm and the maximum vertical component error is about 33.5 mm. As for \"Trimble to Leica\", the maximum horizontal component error is about 5.2 mm and the maximum vertical component error is about 55 mm. The study of three low-cost GNSS antennas against three high-grade antennas [32] reports noticeable differences in positioning performance with different low-cost antennas. Such an effect may be overcome when using signals from multiple satellite systems. Figure 10: Phase center position estimation from the ground plane @ 1.575 GHz. Figure 9: Phase center position estimation from the ground plane @ 1.2 GHz.
# The Use of Earth Observation Data for Railway Infrastructure Monitoring--A Review
## 2 Railway Infrastructure Monitoring Use Cases and Satellite Data Potential  
The use cases for infrastructure managers (IM) and railway undertakings (RU) reported in the literature as the use cases of EO data applications are described in the following sections of this Section 2, along with the suitable EO data. The goal is to provide a short description of EO technologies as an introduction to detailed examples of the application of EO-based technologies for the considered use cases given in Section 3.
# Wireless Home Energy Management System with Smart Rule-Based Controller
## 3 Experimental Setup and HEM Algorithm
### Hardware
#### 3.1.1 Heater Setup  
Figure 8 shows the setup for monitoring the water heater power consumption and temperatures. The heater was attached to a smart socket and two waterproof temperature sensors were plugged into the inlet and outlet water pipes of the heater.  
The inlet and outlet water temperatures were monitored by waterproof sensors, and the temperature readings were acquired and transmitted by the microcontroller every 2 s. The smart socket to which the heater was connected measured power consumption. The heater could be turned on/off through remote commands received by the smart socket.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Appliance** & **Model** & **Power Rating (W)** \\ \hline Refrigerator & Super General 035H & 90 \\ Air conditioner & NIKAI NPAC12512A4 & 1200 \\ Water heater & Florence FWH-50-15A & 1500 \\ LED & V-tac-22w & 22 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Appliance details and specifications.  
Figure 8: Experimental setup of the water heater.
# UWB-Based Real-Time Indoor Positioning Systems: A Comprehensive Review
## 4 Commercial UWB Modules and Chips for IPSs
### Commercial UWB-Based IPSs  
The growing need and demand for indoor positioning systems has led many companies, vendors, and factories to focus on manufacturing and developing commercial systems that suit market requirements. A complete package is developed that includes the hardware components and compatible software enabling the system to display the tasks required to be performed optimally. Table 2 reviews selected providers of indoor positioning systems operating with ultra-wideband technology.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Vendor**} & \multirow{2}{*}{**Chip/Module**} & \multirow{2}{*}{\begin{tabular}{c} **Estimation** \\ **Method** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **LoS** \\ **Accuracy** \\ **(\(\pm\) cm/°)** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Operation** \\ **Range (m)** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Frequency** \\ **Range (m)** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **UWB** \\ **Changes** \\ \end{tabular} } & \multirow{2}{*}{**Application**} & \multirow{2}{*}{**Reference**} \\ \hline Qorvo & DWM1000 & TWR, TDoA & \begin{tabular}{c} 15 in 2D, \\ 30 in 3D \\ 15 in 2D, \\ 30 in 3D \\ 15 in 2D, \\ 30 in 3D \\ 20D \\ \end{tabular} & 300 & 3.5–6.5 & 1–5, 7 & IPS, asset, employer & [42] \\ Qorvo & DW3110, & TWR, TDoA, & \begin{tabular}{c} 15 in 2D, \\ 30 in 3D \\ 15 in 2D, \\ 30 in 3D \\ \end{tabular} & 50 & 6.5, 8 & 5, 9 & IPS, asset, employer & [32; 43] \\ \hline NXP & SR150 & \
\begin{tabular}{c} AoA, TWR, \\ TDoA \\ \end{tabular} & 10/3 & 50 & 6.24–8.24 & 5, 6, 8, 9 & Industrial IoT, RTLS & [35] \\ NXP & SR40 & TWR, TDoA & 10 & 50 & 6.24–8.24 & 5, 6, 8, 9 & IoT battery-based, & [46] \\ \hline Microchip & ATA8350 & TDoA & 15 & - & 6.2–7.8 & 5–8 & IoT battery-based & [47] \\ Microchip & ATA8352 & TDoA & 4 & - & 6.2–8.3 & 5–9 & IoT battery-based & [48] \\ Infineon & 3 DB6830 & TWR & 10 & 120 & 6–8 & 5–9 & Industrial IoT & [39] \\ Spark & SR1010 & TWR & 30 & 100 & 3.1–6 & 1–5 & Asset and individual & [49] \\  & & & & & & tracking & \\ Spark & SR1020 & TWR & 30 & 100 & 6–9.3 & 6–9 & Industrial IoT & [49] \\ STM & MOD1 & TWR & 10 & 600 & 3.25–4.75 & \(1,2,3,4\) &
\begin{tabular}{c} RTLS, asset, \\ employer \\ \end{tabular} & [41] \\ \hline \hline \end{tabular}
\end{table}
Table 1: Commercial UWB modules and chips for IPSs.
## 4 Results and Discussion
### Performance Analysis of HDS
#### 4.1.1 Evaporation Temperature  
As illustrated by Figure 4a,c, the \(SMER\) gradually increases and then reduces with the evaporation temperature increasing, and the evaporation temperature corresponding to the optimal \(SMER\) is marked in Figure 4a,c. The optimal \(SMER\) rises up with the increment of moisture content, reaching a maximum of 3.16 kg kW\({}^{-1}\) h\({}^{-1}\). This indicates that HDS has a more efficient moisture load handling capacity as the humidity near the suspension bridge increases. As shown in Figure 4c, when inlet air temperature increases, the optimal \(SMER\) slightly decreases, reaching a minimum of 2.45 kg kW\({}^{-1}\) h\({}^{-1}\). The main reason is that the increment of evaporator sensible heat load as inlet air temperature rises. When the cooling capacity is constant, the dehumidification capacity decreases, which leads to a slight decrease in optimal \(SMER\). Although the high temperature near the suspension bridge is not conducive to the work of the dehumidification plant, the HDS can weaken the negative effects, resulting in only a slight decrease in optimal \(SMER\).  
Above all, HDS can improve the \(D_{c}\) by reducing the evaporation temperature. This fundamentally overcomes the problem of DWDS lacking dehumidification ability and strengthens the protective effects of the main cable in severe cases. Figure 4b,d show the change in \(D_{c}\) and \(W_{tot}\) with the evaporation temperature. In the five simulated inlet air cases, the \(D_{c}\) of the HDS rises up with the decrease in evaporation temperature. Although it is accompanied by an increase in \(W_{tot}\), the improvement of main cable protection reliability due to the increment of \(D_{c}\) is more important.  
Figure 5 shows the optimal cooling capacity in different cases. Although the inlet air cases are different, the optimal cooling capacity is about 3.5 kW. For structurally defined systems, neither low load operation nor overload operation can optimize the \(SMER\). As a result, for HDS designed in this paper, \(SMER\) will be optimal as long as the cooling capacity of the evaporator is controlled to 3.5 kW. This result can be used for the optimal control of HDS in various cases of suspension bridges.
### Related Work
[25] proposed an apparatus for person authentication utilizing air-written signatures. The touchless system employs a three-dimensional sensor to collect and analyze human hand movements. Subsequently, it confirms the individual's identity by using a novel reverse time-ordered shape context. The system can authenticate a person without the need for a mouse, touch panels, or keyboards. SonarSign [26] verifies online handwritten signatures using inaudible acoustic sounds. It uses the smartphone's built-in speakers and microphones to send a custom training sequence and capture the echo for channel impulse response (CIR) estimate. This solution has the benefit of not necessitating specialized equipment, such as a sensor-equipped stylus, tablet, or wristwatch on the dominant hand. In their study, [PERSON] et al. [27] proposed a method for the verification of online cheque signatures. The authors proposed an apparatus for data collection using stylus tablets, graphic tablets, and touchscreen phones. They demonstrated how deep learning algorithms can be leveraged to recognize the various components of a cheque. Moreover, the researchers created DeepSignDB [28], which is an online biometric public database for handwritten signatures. It was generated by combining a popular database with a novel dataset gathered utilizing finger and stylus inputs. Numerous acquisition and writing instruments were employed, including digitizer tablets, tablet computers, and smartphones. The authors also examined two acquisition scenarios, namely office and mobile. An inexpensive hardware prototype PPGSign [29] is an innovative method for authenticating signatures employing photoplethysmography (PPG) sensors integrated into wrist-worn wearable devices. This system validates the signature by exploiting the unique blood flow variations induced by the hand movements.
## 3 Monitoring and Verification
### Performances as Built  
A comparative analysis of numerical simulation and field monitoring test data determined that there are noticeable deformations while the results align closely. To rectify this, a strategy of \"increased density of steel arch supports + augmented initial support strength and stiffness\" is recommended. Based on the feasibility verification of this strategy under the current geological conditions, a preliminary support test will be undertaken in the weaker geological layers. Table 2 contains the specific details of the optimization strategy.  
The optimized support scheme for the monitoring section was simulated using the finite element software FLAC3D.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Steel Arch Type** & **Concrete Type** & **The Spacing of the Steel Arch** \\ \hline Before & H125 & C25, 20 cm & \(1\,\mathrm{m}\) \\ After & H150 & C30, 35 cm & 0.5 m \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of support schemes.  
Figure 10: Axial force distribution diagram of steel arch frame; (**a**) 0.5 m axial force distribution diagram; (**b**) 1.5 m axial force distribution diagram; (**c**) 2.5 m axial force distribution diagram.  
Figure 11 indicates that the maximum deformation post-optimization is located at the crown, diminishing from 4.08 to 0.16 cm, signifying a decrease of roughly 96%. This reduction is substantial when compared to the original scheme. Hence, the optimized approach yields satisfactory results. In order to ascertain the effectiveness of the support method under similar geological settings, modifications were made to the geological parameters to affirm the suitability of this optimization strategy in more intricate and weaker geological contexts.  
Figure 12 demonstrates that significant displacements occur at both the crown and the bottom of the structure under more complex and weaker geological conditions. The displacements at the crown and bottom decreased from 4.08 and 2.8 to 0.52 and 0.41 cm, respectively, representing approximately 87% and 84% reductions. This support scheme effectively provides safe support, indicating that this method is suitable for application in other engineering projects under similar geological conditions.  
Figure 11: Displacement comparison of an optimized support scheme.  
Figure 12: Displacement comparison of complex weak stratum.
## 4 Discussion
### Possible Data Imprecisions  
How can we explain the finding, at certain sites, of early season bias (GPP simulations > eddy-covariance values)? Time lags may occur between changes in greenness detected by spectral vegetation indices and photosynthetic activity [49]. For temperate deciduous species this decoupling might arise early or late in the growing season. An observed lag in GPP behind LAI during leaf emergence can be attributed to sustained investment in photosynthetic capacity beyond foliation [50]. It is also possible that these deltas arose, in part at least, from fAPAR values inflated by neighbouring vegetation not incorporatedin the flux-tower's footprint. In a study of 21 FLUXNET sites, [PERSON] et al. [51] found that the strength of the correlation between in situ and remotely sensed values of a prevalent vegetation index decreased with decreasing spatial resolution (i.e., correlations were stronger for single than for multi-pixel aggregations). The footprint of the tower (on a given day) may not correspond accurately to the pixel(s) used to generate the remotely sensed forcing variables (here, greenness and temperature) [6]. A review of high-resolution images (GoogleEarth), 1 km\({}^{2}\) centred on the flux-tower, suggested that around 20 of our sites might be classified as 'heterogeneous' (e.g., the sites are in close proximity to agriculture or human habitation, Table A1). A model validation exercise limited to that subset of sites classified as 'homogeneous' did not, however, provide marked improvement over the full dataset (\(r=0.84\) homogeneous sites, Figure A2; \(r=0.83\) all sites, Figure 2).
# The Comparison of Electron Density between CSES In Situ and Ground-Based Observations in China
## 6 Conclusions  
Based on the comparison between the CSES Ne and ground-based observations in China, some conclusions were summarized from the variation trend, correlation and values.  
1. The pattern of the CSES Ne is consistent with that of the ISR Ne at the satellite orbit. The two datasets can be linearly fitted, seemly according to the magnitude, and the correlation coefficient is above 0.88. However, more data are needed to more deeply understand the relationship between the two sets of data.
2. The trend of the CSES Ne is mostly similar to the ionosonde observations no matter whether the data are continuous in time or in a certain month; some correlation coefficients between the two datasets exceeded 0.7. On the China mainland, the correlation coefficient is higher around the peak of EIA compared to other locations.
3. The value of the CSES Ne is low at 80-95% in the daytime, corresponding to the ISR and ionosonde observations in China.  
[PERSON] analyzed the CSES Ne data and some ionosonde observations, and wrote the manuscript. [PERSON] collected and analyzed the ionosonde data at different latitudes. [PERSON] collected and analyzed the ISR data. [PERSON] proposed the topic and conceived the study. All authors have read and agreed to the published version of the manuscript.  
This work is supported by the Specialized Research Funds of the National Key Laboratory of Electromagnetic Environment (202001002), APSCO Earthquake Research Project Phase II, National Basic Scientific Research Program of China (JCKY2021210C614240302), Civil Project (ZH-1-DMYZ-02) and ISSI-Beijing (2019-IT33).  
This work is supported by the Specialized Research Funds of the National Key Laboratory of Electromagnetic Environment (202001002), APSCO Earthquake Research Project Phase II, National Basic Scientific Research Program of China (JCKY2021210C614240302), Civil Project (ZH-1-DMYZ-02) and ISSI-Beijing (2019-IT33). The authors acknowledge the CSES mission, and the Ne data can be downloaded from the website [[http://www.leos.ac.cn/](http://www.leos.ac.cn/)]([http://www.leos.ac.cn/](http://www.leos.ac.cn/)), accessed on 10 August 2021, after registration. The authors acknowledge the IRI website ([[https://ccmc.gsfc.nasa.gov/modelweb/models/irz0106_vitmo.php](https://ccmc.gsfc.nasa.gov/modelweb/models/irz0106_vitmo.php)]([https://ccmc.gsfc.nasa.gov/modelweb/models/irz0106_vitmo.php](https://ccmc.gsfc.nasa.gov/modelweb/models/irz0106_vitmo.php)), accessed on 1 September 2021) for providing the electron density data. The ionosonde observations from southwest China were provided by the Institute of Earthquake Forecasting, China Earthquake Administration. The ISR data and ionosonde observations at different latitudes were supported by the China Research Institute of Radio Wave Propagation.  
The authors declare no conflict of interest.
# Deep Neural Network Equalization for Optical Short Reach Communication
## 3 Experimental Verification and Discussion  
This section outlines the measurement setup, as used to evaluate the nonlinear compensation performance of the following four NLE architectures on basis of identical offline data: VNLE, MP-VNLE and two types of DNN-NLEs.
# Assessment of Self-Supervised Denoising Methods for
## 2 Materials and Methods
### Evaluation Metrics  
Various objective measures were used to assess the effectiveness of noise reduction. One of the simplest measures available is the signal-to-noise ratio (SNR), which aims to gauge the level of distortion in the waveform coders that reproduce an input waveform. The SNR is determined by the following formula:  
\[SNR=10\log_{10}\frac{\sum_{i=1}^{N}x^{2}(i)}{\sum_{i=1}^{N}\left(x(i)-y(i) \right)^{2}} \tag{9}\]  
where \(N\) is the total number of samples, \(x(i)\) denotes the signal at sample \(i\), and \(y(i)\) represents the noise or interference at sample \(i\).  
Instead of working on the entire signal, the segmental signal-to-noise ratio (SSNR) computes the mean of the SNR values of the small sections (15-20 ms). It is given by  
\[\text{SSNR}=10\log_{10}\frac{\sum_{i=1}^{N}x^{2}(i)}{\sum_{i=1}^{N}\left(x(i) -y(i)\right)^{2}} \tag{10}\]  
where \(N\) is the total number of samples, \(x(i)\) represents the original signal at sample \(i\), and \(y(i)\) denotes the reconstructed or noisy signal at sample \(i\).  
The Perceptual Evaluation of Speech Quality (PESQ) is an objective method for assessing the quality of speech as received by a listener [28]. It evaluates speech quality by calculating the total loudness difference between the processed and clean signals [29]. The PESQ score fluctuates in the range of \(-0.5\) to \(4.5\)[12]. Figure 5 presents a schema of the structure of PESQ.  
Figure 4: Overview of VoiceFixer architecture.  
The Wide-Band Perceptual Evaluation of Speech Quality (PESQ-WB) is an extension of the original PESQ. It assesses the quality of wide-band speech signals based on perceptual evaluation. It covers a wider frequency range, typically 50-7000 Hz, to accommodate modern wideband telephony and VoIP services [28].  
The Narrow-Band Perceptual Evaluation of Speech Quality (PESQ-NB) is a metric used to evaluate the quality of narrow-band speech signals, such as those typically encountered in telephone networks and lower-bandwidth communication channels, mainly operating within the 300-3400 Hz frequency range [28].  
The Short-Time Objective Intelligibility (STOI) metric is used to predict the intelligibility of the processed speech through enhancement or separation algorithms. STOI operates on short-time segments of the speech signal, typically around 400 milliseconds, and provides a score correlating with the intelligibility of the processed speech as perceived by human listeners [30]. Improved speech intelligibility is indicated by a higher STOI rating [12]. Figure 6 presents the structure of STOI.  
We used the \"pypesq\" and \"pystoi\" PyTorch (1.10.0 + CPU on Python 3.9) packages to calculate the evaluation metrics.