esa-sceva/satcom-chunk-collection huggingface dataset:
# Lane-Level Traffic Flow Prediction with Heterogeneous Data and Dynamic Graphs
## 3 Experiments and Results
### Experimental Setting  
To demonstrate the superiority of the proposed model, we compared it with seven other algorithms, including parametric methods, non-parametric methods, and deep learning methods:  
* HA (historical average): this method is based on statistics and calculates the average value of traffic flow parameters to derive the prediction results.
* SVM (support vector machines): this is a supervised learning method and classifies data by binary methods. The optimal hyperplane can maximize the distance between multiple categories.
* ARIMA (autoregressive integrated moving average): this is a time series prediction method, composed of an autoregressive block and a moving average block.
* GRU (gate recurrent unit): this model is a variant of LSTM that removes forgetting gates and only consists of update gates and reset gates. It requires fewer parameters and converges more easily.
* T-GCN [26]: this model is a time series neural network with a GRU-GCN structure, where the GCN deals with spatial dependency and the GRU deals with temporal dependency.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Area** & **Sensors** & **Time Range** \\ \hline PeMSD4 & 29 roads of the San Francisco Bay & 307 sensors & 56 days: 1 January 2018—28 February 2018 \\ PeMSD8 & 8 roads of the San Bernardino & 170 sensors & 62 days: 1 July 2016—31 August 2018 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Traffic data details.  
* ST-AFN [27]: this model represents our previous work and mainly has a temporal-spatial attention mechanism construction. The network consists of a speed processing network, a spatial encoder, and a temporal decoder.
* ASTGCN [28]: this model is graph convolution network that divides a temporal sequence into three parts, i.e., recent, daily-periodic and weekly-periodic dependencies. Prediction results are derived by fusing the outputs of the three parts.  
All experiments were run on the same computer, where the OS was Ubuntu 18.04, the memory capacity was 64 GB, the CPU was an Intel Xeon Silver, and the GPU was an NVIDIA Quadro M4000. We used Pytorch 1.6.0 and set a series of hyperparameters as follows: 0.0001 for the learning rate; 128 for the batch size; 64 for the size of the hidden layers and cell states in the GRU; and 12 for the step length for historical data. Moreover, we selected the mean absolute error (MAE), the root mean squared error (RMSE), and the mean absolute percentage error (MAPE) to evaluate the prediction performance.
# A Hybrid Forecasting Model for Self-Similar Traffic in LEO Mega-Constellation Networks
## 5 Simulation Performance and Analysis
### Satellite Network Traffic Forecasting
#### 5.3.1. ARIMA-Based Forecasting for IMFs  
This section follows the ARIMA modelling steps to construct ARIMA(_p, d, q_) for each order of IMF. The optimal parameters of ARIMA(_p, d, q_) for IMFs are determined via an adaptive fixed-order optimization search operator, as shown in Table 5.  
Figure 11. Autocorrelation function of IMF1-IMF10 vs. original data auto\(-\)correlation function.  
Figure 10. Intrinsic mode functions (IMFs) obtained from decomposition of raw traffic.  
In Table 5, \(\phi_{1},\phi_{2},\theta_{1}\) and \(\sigma_{e,p,d,q}^{2}\) are the parameters of the EMD and \(\sigma_{IMF}\) and \(\sigma_{IMF^{\prime}}\) are the standard deviations of the IMFs and fitted IMFs, respectively. The Akaike information criterion (AIC) is calculated as  
\[AIC=T\cdot\ln\Bigl{(}\sigma_{e,p,d,q}^{2}\Bigr{)}+2(p+q+1) \tag{26}\]  
where \(T\) denotes the length of the satellite network traffic. The mean absolute percentage error (MAPE) of the \(i\)-th IMF is calculated as  
\[MAPE_{i}=\frac{1}{T}\sum_{t=1}^{T}\left|\frac{\xi_{i}(t)}{IMF_{i}(t)}\right| \tag{27}\]  
The root mean square error (RMSE) of the \(i\)-th IMF is calculated as  
\[RMSE_{i}=\sqrt{\frac{1}{T}\sum_{t=1}^{T}\xi_{i}(t)^{2}} \tag{28}\]  
where \(T\) denotes the length of the satellite network traffic and \(\xi_{i}(t)\) denotes the \(t\)-th value of the fitted residual of \(IMF_{i}\).  
The fitted IMFs, i.e., \(IMF_{i}^{\prime}(t)\), are reconstructed to describe the linear feature of the satellite network traffic:  
\[L^{\prime}(t)=\sum_{i=1}^{n}IMF_{i}^{\prime}(t) \tag{29}\]
## Methods
### Tree-Centric Approach  
As an alternative to area-based approaches, we tested whether ACD could be estimated accurately by summing the biomass of individual tree crowns (ITCs; Figure 1). The _itcSegment_ algorithm was used to delineate trees within each of the 27 plots and determine the tree heights and individual crown areas. _itcSegment_ is implemented in R and made freely available on CRAN [38]. It works by finding local maxima in the raster CHM, regarding these maxima as tree tops, then growing crowns around them by local searching of the raster CHM and point cloud. This approach was initially developed for coniferous forests [27; 39; 40; 41] following the concept of [42], and modified in this manuscript to adapt to forests with low variation in height (i.e., oil palm plantations). The tree-centric approach consists of three stages [43]: (1) a Guassian low-pass filter to smooth the canopy height model; (2) an iterative search for local maximum in the CHM, which are assumed to represent the tops of tree crown, using a window size that varied with the height of the CHM; we included a weighting exponent in the _itcSegment_ function to increase the contrast in the CHM and found this greatly improved the accuracy of segmentation; (3) a region-growing step then searches for crowns around each local maximum, constraining the search with pre-determined crown width and depth information. The _itcSegment_ algorithm has been upgraded in R after inclusion of the weighting exponent.  
The next step was to calculate a correction factor to account for the fact that the individual tree heights obtained by the ITC approach overestimated the oil palm tree heights, because large oil palm fronds arch up beyond the meristem at the top of the trunk, which were not accounted for in field measurements. This correction factor was obtained by fitting a nonlinear regression as follows:  
\[\overline{\text{Hc}}=\text{a}\ \overline{\text{H}_{\text{ITC}}}^{\text{b}} \tag{6}\]  
where \(\overline{\text{Hc}}\) is the mean field-estimated height within the 27 0.0625 ha plots and \(\overline{\text{H}_{\text{ITC}}}\) is the mean LiDAR-estimated height within the surrounding 1-ha plots, and a and b are parameters estimated by nonlinear least squares regression. The model was validated using the leave-one-out procedure.  
The next step was to estimate individual biomass, by entering \(\overline{H_{C}}\) values into equation 1. We then summed the AGB of segmented trees, and multiplied by a carbon density of 0.47 to obtain ACD estimates as before. The accuracy of the ITC delineation was assessed by comparing the numbers of delineated trees with the numbers observed in the field plots and ACD predicted from the summed individual biomasses.  
Goodness of fit of models and the tree-centric approach are compared by reporting the normalised RMSE (%), calculated as \(\sqrt{\frac{\sum(y-\hat{y})^{2}}{n}}\ \times\ 100/\overline{y}\), and bias (%) as \(\sum(y-\hat{y})\ \times\ 100/(N\overline{y})\), where y are the field data, \(\overline{y}\) is the mean, \(\hat{y}\) are the model estimates and N is the number of observations [12; 44].
## 6 Clinical and Prognostic Applications  
The COP holds significant promise as a prognostic and clinical marker, offering non-invasive insights into cardiorespiratory efficiency that can be applied across various patient populations [19; 22; 49]. Its submaximal nature makes it particularly advantageous for evaluating individuals who may not tolerate maximal exercise testing, including older adults, patients with HF, and individuals with respiratory limitations [50].  
As a measure that reflects the efficiency of oxygen uptake at low intensity, the COP can provide valuable information about a patient's cardiorespiratory health without imposing the physical demands associated with maximal exertion. For instance, evidence suggests that COP values exceeding 26 significantly influence mortality rates and lead to a worse prognosis in patients with HF due to a pronounced imbalance between ventilatory demand and cardiac function, following a dose-dependent relationship [24]. That is, individuals with a COP > 30 had approximately six times greater risk of mortality (17.1%) than those with a COP < 22 [18]. This characteristic has spurred growing interest in its application as a prognostic marker for assessing and monitoring patients at risk of cardiovascular and respiratory conditions. In populations undergoing rehabilitation, such as post-myocardial infarction patients or those recovering from HF, a COP reduction may demonstrate improvements in response to rehabilitation interventions. Therefore, it is important to emphasize that submaximal exercise testing is less expensive, easier to perform or repeat to observe changes in clinical status or treatment options, and less burdensome for patients compared with maximal exercise testing [51; 52].  
However, despite its potential, we realize that, to date, the COP should not be seen as a marker that will replace CPET (\(V\)O\({}_{2\text{peak}}\)), but rather as a complementary variable, with greater applicability when it comes to clinical monitoring. Finally, longitudinal follow-up studies in different populations and across different levels of clinical severity are needed to identify the effects of different types of cardiovascular rehabilitation on COP behavior.
# A Statistical Analysis of Industrial Penetration and Internet Intensity in Taiwan +
###### Abstract  
This paper is the first to investigate the effect of industrial penetration (geographic concentration of industries) and internet intensity (the proportion of enterprises that uses the internet) for Taiwan manufacturing firms, and analyses whether the relationships are substitutes or complements. The sample observations are based on a unique set of data, namely 153,081 manufacturing plants, and covers 26 two-digit industry categories and 358 geographical townships in Taiwan. The Heckman sample selection model is used to accommodate sample selectivity for unobservable data for firms that use the internet. The empirical results from [PERSON]'s two-stage estimation show that: (1) a higher degree of industrial penetration will not affect the probability that firms will use the internet, but it will affect the total expenditure on internet intensity; (2) for two-digit SIC (Standard Industrial Classification) industries, industrial penetration generally decreases the total expenditure on internet intensity; and, (3) industrial penetration and internet intensity are substitutes.  
Keywords:industrial penetration; internet intensity; sample selection; incidental truncation +
Footnote †: journal: Future Internet
## 2 Materials and Methods
### Preparation of Photoelectrodes  
FTO cleaning was performed by sonicating the slides in Alconox(r) solution for 10 min and in 2-propanol for an additional 10 min. NiO electrodes of type A were prepared by spin coating onto FTO electrodes (10 s at 1000 rpm, 20 s at 2000 rpm) of the NiO paste obtained by ball-milling (2 h) of commercially available NiO nanoparticles (Inframat Advanced Materials) with ethanol (8 mL), glacial acetic acid (0.2 mL), terpineol (3 g), and ethyl cellulose (0.5 g) [19]. NiO electrodes of type B were prepared by doctor blading of a NiO precursor solution prepared by dissolving NiCl\({}_{2}\) (1 g) and the tri-block co-polymer F108 (1 g) in ethanol (6 g) and water (5 g) [21]. For both types A and B, the electrodes were annealed at 550 \({}^{\circ}\)C following a programmed temperature ramping: 25-120 \({}^{\circ}\)C (10 min), 120-450 \({}^{\circ}\)C (22 min), 450 \({}^{\circ}\)C (30 min), 450-550 \({}^{\circ}\)C (10 min), 550 \({}^{\circ}\)C (20 min). Sensitized electrodes were obtained by overnight soaking of the NiO films in 0.5 mM **P1** in acetonitrile solution. Platinum was deposited onto the sensitized electrodes from a precursor solution prepared following a reported procedure [22]. Briefly, a solution of aqueous 0.1 M H\({}_{2}\)PtCl\({}_{6}\) in 0.1 M HCl was prepared; the resulting solution was first neutralized using Na\({}_{2}\)CO\({}_{3}\) and then adjusted to pH 4 with glacial acetic acid. Before the deposition, all NiO electrodes were tested against the photoreduction of 10 mM [Co(NH\({}_{3}\))\({}_{5}\)Cl]Cl\({}_{2}\) as a sacrificial electron acceptor [16; 23]. Only when the maximum photocurrent densities in the order of 250-300 \(\upmu\)A\(\cdot\)cm\({}^{-2}\) were measured, the NiO electrodes, of both types A and B, were considered and thus employed for the Pt deposition (see Figures S1 and S2). Deposition of metallic Pt was achieved through five different methodologies: Method 1 involved illumination with a solar simulator (0.1 W\(\cdot\)cm\({}^{-2}\)) under the application of a \(-\)0.2 V vs. SCE voltage bias. Method 2 involved the sole application of a \(-\)0.2 V vs. SCE voltage bias.  
Method 3 employed 3 consecutive cyclic voltammetry scans from 0.3 to \(-\)0.2 V vs. SCE at a scan rate of 0.1 V\(\cdot\)s\({}^{-1}\) under illumination with a solar simulator (0.1 W\(\cdot\)cm\({}^{-2}\)). Method 4 involved 3 consecutive cyclic voltammetry scans from 0.3 to \(-\)0.2 V vs. SCE at a scan rate of 0.1 V\(\cdot\)s\({}^{-1}\) without illumination. Method 5 employed 3 consecutive cyclic voltammetry scans from 0.3 to \(-\)0.2 V vs. SCE at a scan rate of 0.1 V\(\cdot\)s\({}^{-1}\) and 2 s illumination with a solar simulator (0.1 W\(\cdot\)cm\({}^{-2}\)) during the first return scan from \(-\)0.2 to 0 V vs. SCE.
## 4 Hybrid Algorithm Design
### Handling of Obstacle Blocking Effects  
The NSGAII algorithm currently utilized is limited in its ability to calculate the Euclidean distance solely based on position coordinates for delivery distance processing, without considering the presence of obstacles. To address this limitation, this study proposes the use of a centroid offset distance matrix to assess the existence of obstacles between all positions. The centroid offset distance is defined as the distance between the center of the obstacle material and the line connecting two position points. By analyzing the location points and obstacle coordinate data from the deck map, a matrix of centroid deviation distances between each location point and obstacle is established. Subsequently, by evaluating whether the centroid deviation distance exceeds the coverage range of various obstacles, a matrix of obstacle obstruction coefficients between each location point is generated. By integrating Gaussian process regression prediction in Section 3, the necessary feature data are provided for the subsequent prediction of obstacle impact distances, resulting in the development of a distance matrix tailored to address practical challenges.
###### Abstract  
Background: Recent studies have highlighted the potentialities of a radiobiological parameter, the early regression index (\(\text{ERI}_{\text{TCP}}\)), in the treatment response prediction for rectal cancer patients treated with chemoradiotherapy followed by surgery. The aim of this study is to evaluate the performance of this parameter in predicting pathological complete response (pCR) in the context of low field MR guided radiotherapy (MRgRT) for cervical cancer (CC). Methods: A total of 16 patients affected by CC were enrolled. All patients underwent a MRgRT treatment, with prescription of 50.6 Gy in 22 fractions. A daily MR acquisition was performed at simulation and on each treatment fraction. Gross tumor volume (GTV) was delineated on the MR images acquired at the following biological effective dose (BED) levels: 14, 28, 42, 54 and 62 Gy. The \(\text{ERI}_{\text{TCP}}\) was calculated at the different BED levels and its predictive performance was quantified in terms of receiver operating characteristic (ROC) curve. Results: pCR was observed in 11/16 cases. The highest discriminative power of \(\text{ERI}_{\text{TCP}}\) was reported when a BED value of 28 Gy is reached, obtaining an area under curve (AUC) of 0.84. Conclusion: This study confirmed \(\text{ERI}_{\text{TCP}}\) as a promising response biomarker also for CC, although further studies with larger cohort of patients are recommended.  
MR-guided radiotherapy; predictive models; cervical cancer +
Footnote †: journal: Medical Physics
# A Review of Non-Destructive Damage Detection Methods for Steel Wire Ropes
## 3 Non-Destructive Detection Method for Steel Wire Ropes
### Ultrasound Guided Wave Method  
Ultrasonic guided waves method (UGW) is one of the promising methods for investigation of non-homogeneities for the internal structure of WRs [PERSON] et al. (2019). The measurement schematic diagram is illustrated in Figure 4, which is consisted of the computer, the ultrasonic measurement system, the preamplifier, the transmitter, and the receiver, detailed principles are available for reference [PERSON] et al. (2019). The wave is launched on WRs and propagates along the strand of WRs. Acoustic reflection occurs when the sound wave passes through the damage. After collecting and analyzing the echo at the receiving end, the damage inside and outside the WR can be obtained [PERSON] et al. (2019).  
For purpose of the generation and reception of ultrasonic longitudinal guided waves in seven-wire steel strands in a pitch catch arrangement, [PERSON] et al. [PERSON] et al. (2019) optimized the magnetostrictive transducers configuration for both transmitter and receiver. Consequently, magnetostrictive transducers with an optimized configuration, including permanent magnet distribution and multilayer coil connection, could be efficiently used for the inspection of seven-wire steel strands by using UGW in a pitch catch arrangement. In [PERSON] et al. (2019), [PERSON] et al. studied the transmission characteristics of elastic guided wave in multi-stranded WR. The dispersion curve of spiral WR was obtained by semi-analytical finite element method and signal energy calculation, and the optimum excitation and receiving position of ultrasonic guided wave was calculated. [PERSON] et al. combined UGW, acoustic emission technology and digital image correlation, and fused the features obtained by the three technologies to achieve non-destructive testing [PERSON] et al. (2019). [PERSON] et al. [PERSON] et al. (2019) studied the effect of different frequencies of UGW on the detection rate of steel WR defects. It was found that the higher frequency of guided waves in the steel WR lead to the faster energy attenuation, and the receiving length of elastic waves increased with the frequency. The propagation characteristics of various UGW between strands and  
Figure 4: Non-destructive testing principle of steel wire rope by ultrasonic guided wave method [PERSON] et al. (2019). (a) Detection system schematic diagram; (b) sketch map for waveforms of defect-free and defective signal.  
cores of WR and their effects on the penetration depth of WR were studied in [57] by [PERSON] et al. In the work of [58], the efficiency of employing the magnetostriction of ferromagnetic materials were studied relying on the UGW method for WRs damage inspection, and the location and severity of damages were approximately identified and characterized using the short-time Fourier transform and wavelet analysis.
## 3 Mountain-Based Test Results
### Comparison of Open-Loop Tracking Inversion Results and Meteorological Observations  
The atmosphere profiles retrieved from observations in OL tracking mode are evaluated with the local observations of temperature, humidity and pressure provided by the Beijing Meteorological Bureau.  
Figure 12: Histogram of the difference of the start/end elevation angle from a rising/setting occultation event between closed-loop and OL. Because the top height of the mountain is about 2 km, the minimum elevation angle value is generally about \(-3^{\circ}\) in OL mode, and the difference is mainly concentrated between \(0.8^{\circ}\) and \(1.2^{\circ}\).  
The atmospheric refractive index of the observation point can be calculated by temperature, relative humidity and pressure data, which is used to verify the accuracy of the inversion of the mountain-based experimental data. The calculation method is as follows [45; 46; 47; 48]:  
Using the temperature data of the analysis field to calculate the corresponding saturated vapor pressure:  
\[E(t)=E_{0}\times 10^{\frac{7.8}{27.3+t}} \tag{14}\]  
In the above formula, \(E(t)\) is the saturated vapor pressure at the corresponding temperature, and \(t\) is the temperature in Celsius. The constant \(E_{0}=6.1078\mu p\). There is the following relationship between actual vapor pressure \(P_{w}\), saturated vapor pressure and relative humidity:  
\[P_{w}(t)=E(t)\times j \tag{15}\]  
Finally, the approximate relationship between refractive index and atmospheric parameters can be used to obtain the atmospheric refractive index:  
\[N=77.6\frac{p}{T}+3.73256\times 10^{5}\frac{P_{W}}{T^{2}} \tag{16}\]  
In the above formula, \(N\) is the refractive index of the atmosphere, \(p\) is the total atmospheric pressure (\(l\)\(p\)\(a\)), \(P_{w}\) is the water vapor pressure (\(l\)\(p\)\(a\)) and \(T\) is the atmospheric temperature (\(K\)).  
For each sounding profile data, find the matching occultation profile (one or more), and interpolate them to the altitude points with a distance of 0.05 km. Calculate the standard deviation of the average refractive index deviation and the absolute refractive index deviation within the height range covered by both the occultation and the sounding profile as the evaluation standard.  
The results are shown in the following Table 2 (partially):  
It can be seen from the results that the BIAS/MSER of the OL tracking method is within 0.5% and 4%, respectively, which meet the accuracy of its applications (\(<\)0.5%, in BIAS; \(<\)5%, in MSER).
## 4 Hardware Circuit Design
### System Configuration and Its Specifications  
Figure 21 shows the system configuration of the proposed circuit applied to a thermoelectric conversion system with two thermoelectric modules at individual inputs. The main power stage is a dual-input high-boost circuit with a digital control kernel based on FPGA to control the system. The FPGA calculates and then sends the duty cycles to the gate drivers to achieve the maximum power tracking. For the voltages and currents to be sampled, analog-to-digital converters (ADCs) are used. The sensed signals are sent to MPPT modules after ADCs via FPGA-embedded series peripheral interfaces (SPIs). Furthermore, the voltage-sensing resistors are used as voltage dividers, the current-sensing resistors are used as current sensors, and the FPGA, named CycloneEP1C3T100, is used as a control kernel, which needs a 20 MHz oscillator and has 2910 logic gates, 104 I/O ports, 59,904 RAM bits, and a phase-lock loop. In addition, the design software is based on the very high-speed hardware description language (VHDL) and Table 4 shows the system specifications.  
Figure 21: System configuration.