esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Methods  
### Patients  
We used data from patients who underwent total and partial nephrectomy at a single institution between 2003 and 2021. Cases of clear cell carcinoma and AML, typically with a fat component of more than 30%, were excluded. Of these, only patients with fat-poor tumors were selected; thus, a total of 499 patients were selected. The CT used on the patient may have differed depending on the year, but it was taken using the same protocol from the same company. (SOMATOM DEFINITION AS+, Siemens, Germany) The mean age of patients was 56.02 \(\pm\) 12.18 years, and the mean size of the tumor on CT was 3.515 \(\pm\) 2.42 cm. Patients with benign tumors, such as oncocytoma and AML, were included, and among malignant tumors, patients with clear cell, chromophore, and papillary type renal cell carcinoma (RCC) were included. Each patient's CT consists of one to four phases, and 1548 sets of CT images were acquired in this manner. The composition of patients is summarized in Table 1. CT configurations were obtained in non-contrast, arterial (20-30 s after contrast injection), portal (60-70 s), and delayed (>180 s) phases. We collected voxel-level segmentation labels for each CT scan, where trained annotators manually delineated the kidneys and tumors in the images, and then a radiologist (with an experience of 11 years) refined the annotations. If the radiologist was adequately confident in the first diagnosis, a second diagnosis was not established. The performance of radiologists was assessed using the first diagnosis (top-1 performance) along with both the first and second diagnoses (top-2 performance).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Training Dataset** & **Test Dataset** & \(p\) **Value** \\ \hline Gender & 396 & 103 & 0.412 \\ Male (\%) & 222 (56.3\%) & 63 (61.2\%) & \\ Female (\%) & 174 (43.7\%) & 40 (38.8\%) & \\ Age (years, range) & 57.0 (22, 83) & 54.0 (27, 79) & 0.708 \\ Cancer size (cm, range) & 3.573 (0.7, 17.5) & 3.291 (1.0, 9.4) & 0.294 \\ Renal tumor & & & 0.9 \\ Benign (\%) & 99 (25\%) & 27 (26.2\%) & \\ Malignant (\%) & 297 (75\%) & 76 (73.8\%) & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Demographic and pathological characteristics of the patients. Training and testing datasets for the classification of malignant kidney cancer.  
We used a paired sample \(t\)-test or a chi-squared test to test the differences in demographic and pathological characteristics of continuous variables (i.e., age and cancer size) and categorical variables (i.e., sex, kidney cancer, and kidney cancer subtype).
# Attosecond Streaking Time Delays: Finite-Range Interpretation and Applications
## 3 Applications
### Resolution of Dynamics  
Since the streaking method accounts for the potential that the photoelectron crosses until the streaking pulse ceases, it is also interesting to study if the streaking time delay is sensitive to dynamical changes in the potential. This would open up the perspective to not only image static potentials, which are related to the conventional Wigner-Smith time delay, but also open the perspective to use the streaking method as an ultrafast imaging method. Because our analysis, c.f. Equation (8), shows that the delay consists of instantaneous field-free time delays weighted by the instantaneous streaking field strength as the photoelectron propagates through the potential, we expect that dynamics of the order of the oscillation period of the streaking field may be resolved. For a typical near-infrared streaking field, this would correspond to a resolution down to a few femtoseconds.  
In order to provide first insights, we have performed a model study using the 1D potential given by Equation (10), where the additional Gaussian potential now oscillated 20 a.u. in each direction about the center at \(x_{0}=850\) a.u. The oscillation period of the potential was varied and for each period we performed a series of ab initio calculations, as described in Section  
Figure 6: Difference between streaking time delays \(\Delta t_{s}\left(b\right)-\Delta t_{s}\left(b=0\right)\) (in attoseconds) in a chirped to those in an unchirped pulse for photoemission from (**a**) \(2p\) shell of neon atom and (**b**) \(3p\) shell of argon atom as function of electron energy and chirp parameter.  
the streaking time delay. The results of these simulations are shown in Figure 7a as a function of the oscillation period of the Gaussian potential. For comparison, the time delay obtained for a static Gaussian potential is also shown. As can be seen, the time delay differs from the static value for a large oscillation period; however, at small periods below 1 fs (for zoom-in, see Figure 7b), the time delay agrees with the static value. This indicates that, while the time delay cannot account for dynamics faster than about one-third of the period of the applied streaking field (here, 800 nm corresponding to a period of about 2.7 fs), the delay is clearly sensitive to dynamics in the few femtosecond regime.
## 2 Theory  
Error correction for two-port S-parameter measurements using a VNA is usually modeled as shown in Figure 1, where the waves \(a_{\mathrm{ij}}\) and \(b_{\mathrm{ij}}\) at the DUT are mapped to the raw measured waves \(a_{\mathrm{mij}}\) and \(b_{\mathrm{mij}}\) through the error boxes. The subscript \(\mathrm{ij}\) (\(\mathrm{i}\), \(\mathrm{j}=1\), \(\mathrm{2}\)) indicates that the parameter is located at port \(\mathrm{i}\) and the power source is connected to port \(\mathrm{j}\). In this work, the crosstalk and switch termsare assumed to be negligible or have been accounted for. That is, VNA calibrations apply switch corrections independently and ignore (or pre-correct) crosstalk, resulting in the error model shown in Figure 1[24; 25].  
In this work, the error boxes are represented in the form of T-matrices as follows:  
\[\left[\begin{array}{c}a_{\mathrm{lj}}\\ b_{\mathrm{lj}}\end{array}\right]=[T_{\mathrm{10}}]\left[\begin{array}{c}a_{ \mathrm{m1j}}\\ b_{\mathrm{m1j}}\end{array}\right] \tag{1}\]  
\[\left[\begin{array}{c}a_{\mathrm{2j}}\\ b_{\mathrm{2j}}\end{array}\right]=[T_{\mathrm{23}}]\left[\begin{array}{c}a_{ \mathrm{m2j}}\\ b_{\mathrm{m2j}}\end{array}\right]. \tag{2}\]  
The T-matrices of the left and right error boxes, \([T_{\mathrm{10}}]\) and \([T_{\mathrm{23}}]\), respectively, are defined from the VNA side (reference planes 0/3) to the DUT side (reference planes 1/2). The relationships between the waves at the ports of a calibration standard or a DUT are described by  
\[\left[\begin{array}{c}a_{\mathrm{lj}}\\ b_{\mathrm{lj}}\end{array}\right]=\left[\begin{array}{cc}0&1\\ 1&0\end{array}\right][T_{\mathrm{12}}]\left[\begin{array}{c}a_{\mathrm{2j}} \\ b_{\mathrm{2j}}\end{array}\right] \tag{3}\]  
where \([T_{\mathrm{12}}]\) is the T-matrix of the two-port standard or DUT.  
Based on the above error model and the T-matrix definition, the basic theory of the calibration algorithm is explained in detail below.
# Challenges, Threats, Security Issues and New Trends of Underwater Wireless Sensor Networks
## 2 Particularities and Constraints
### Extremely Limited Resources  
Underwater sensor nodes are extremely limited in hardware resources, including energy, computational capability and storage space. Due to higher distances and more complex signal processing at the receivers to compensate for the attenuation of the signal, the power consumed for underwater acoustic communication is much higher than in terrestrial radio communication.  
Figure 1: Underwater wireless sensor network (UWSN) architecture.  
Underwater sensor nodes are deployed in shallow or deep water, where it is inconvenient to charge or replace the nodes' battery. In the case of saving energy consumption to prolong the network lifetime, the computational capability and storage space are constricted by the energy consumption problem. Hence, virtually all existing research and technologies for UWSNs focus on saving energy consumption at the expense of capability and security.
# A Domain-Based, Adaptive, Multi-Scale, Inter-Subject Sleep Stage Classification Network
### Contributions  
Extending on our previous work [22], in this article, to extract more detailed information from a single-lead ECG signal and thereby improve its sleep-staging performance, we developed a domain-based adaptive classification model based on a multi-scale residual network. A branch structure was added to the residual module of the model to construct an extraction module and thus achieve the multi-scale extraction and fusion of spatial features through detailed information gained from a single-lead ECG. To solve the class imbalance, we applied multi-class focal loss to learn hard samples by the factor, and we applied different weights for each of the classes. Meanwhile, the loss of sequence prediction was used as additional supervised information according to the relevance of the state transition in the sleep structure. The main contributions of this work are as follows:1. An end-to-end multi-scale adaptive network is presented to classify sleep stages into four classes in an inter-subject mode based on a single-lead ECG.
2. The multi-class focal loss was combined with the loss of sequence prediction to improve the performance of sleep staging.
3. The proposed method was evaluated on public datasets and achieves state-of-the-art performance in terms of accuracy and Cohen's Kappa for inter-subject sleep staging.  
The rest of this article is organized as follows: Section 2 introduces the method of the multi-scale residual adaptive network proposed in this paper. Section 3 describes the public dataset used in this paper and the results of the experiments. Section 4 discusses the performance of the proposed method in the cross-dataset mode and the comparison results of different losses. Section 5 provides the conclusions of this paper.
# Dynamic Analysis of Fuzzy Systems
## 2 Mamdani Model  
In a general case, the dynamics of the non-linear system are described by the following equation:  
\[x_{n+1}=f(x) \tag{1}\]  
being \(x=[x_{1}...x_{n}]^{T}\) and \(f\) a continuous or discrete non-linear function [35]. The main aim is to build non-linear models capable of reflecting the dynamics of \(f\). System modelling helps to visualise the behaviour and basic structure of processes. Let us suppose that \(X_{l}^{l_{l}}\) are the fuzzy sets for input \(x_{l}\), \(\forall\ i_{l}=\{1,...,r_{l}\},\ \forall\ l=\{1,...,n\}\). \(r_{l}\) is the number of fuzzy sets for \(x_{l}\), and \(\mu_{x_{l}^{(i_{l})}}(x_{l})\) are the corresponding membership functions; see Figure 1.  
Consider the fuzzy sets \(X_{n+1}^{(i_{1}...i_{n})}\) of the output \(x_{n+1}\), \(\forall\ i_{l}=\{1,...,r_{l}\},\ \forall\ l=\{1,...,n\}\), with the corresponding membership functions \(\mu_{x_{l}^{(i_{1}-i_{n})}}(x_{n+1})\), (see Figure 2). Where \(X_{n+1}^{(i_{1}...i_{n})}\) are their centres of gravity, and all their areas \(A_{n+1}^{(i_{1}-i_{n})}\) are the same. Then, each rule \(R^{(i_{1}...i_{n})}\) of the Mamdani model can be defined using the centres of gravity instead of the fuzzy sets for each consequent [36; 37]:  
IF \(x_{1}\) is \(X_{1}^{(i_{1})}\ AND\...\ AND\ x_{n}\) is \(X_{n}^{(i_{n})}\) THEN \(x_{n+1}=X_{n+1}^{(i_{1}-i_{n})}\), (2)  
Therefore, as more sets are used in the measurable variable, the output looks as follows:  
\[x_{n+1}=\frac{\sum_{i_{1}=1}^{r_{1}}...\sum_{i_{n}=1}^{r_{n}}{\rm w}^{(i_{1}- i_{n})}(x)x_{n+1}^{(i_{1}...i_{n})}}{\sum_{i_{1}=1}^{r_{1}}...\sum_{i_{n}=1}^{r_{n}}{ \rm w}^{(i_{1}-i_{n})}(x)} \tag{3}\]  
where  
\[{\rm w}^{(i_{1}-i_{n})}(x)=\prod_{l=1}^{n}\mu_{x_{l}^{(i)}}(x_{l}) \tag{4}\]  
Figure 1: Input membership function \(x_{l}\).  
Figure 2: Output membership function \(x_{n+1}\).
# Multi-Dimensional Sparse-Coded Ambient Backscatter Communication for Massive IoT Networks
## 5 Simulation Results
### Practical Implementation of AmBC
For simplicity of simulations, we consider a single-antenna reader and no forward-error correction (FEC) codes herein, but our model can be extended to the advanced scenarios in straightforward manners to achieve long-range AmBC (e.g., increase the number of FNs at the input of MPA, and implement information exchange between inner sparse code decoder and outer FEC decoder [26]). On the other hand, to achieve high-rate AmBC, the aggregated data rate of tags can be expressed by  
\[\tilde{R}_{b}=\frac{N}{K}R_{\text{s}}\log_{2}M=\lambda R_{\text{s}}\log_{2}M. \tag{47}\]  
The performance of AmBC is numerically evaluated by Monte Carlo simulations, and the system parameters for each simulation are summarized in Figure 9b.
# Computer Vision-Based Approach for Reading Analog Multimeter
## 2 Framework and System Setup
### Mapping Relationship of Function Selector  
According to the framework depicted in Figure 3, the sources measured by the A-meter may be the AC voltage, DC voltage, DC current, and resistance. Parts of the system's setup should be made before the algorithms are developed and experiments conducted, such as the mapping functions used in _mapping function for selector arrow_ and _mapping function for pointer_, as indicated in Figure 2, as follows. We assumed that the A-meter image was aligned in the horizontal direction. By observing the function selector sub-image illustrated in Figure 1c, the mapping function for the selector arrow was built with the relationship between \(\theta_{s}\) and \(F(\theta_{s})\). Since there were \(N\) functions that were selectable in this A-meter, each function could be identified within a delta range, \(\Delta=\frac{360}{N}\). Ideally, the \(i\)-th function, say \(F_{i}\), would be indicated with the orientation \(i\times\Delta\in[0^{\circ}\!\sim\!\!360^{\circ})\), \(i=0,1,\ldots,N-1\). However, according to the arrangement of functions in the A-meter, each orientation corresponding a function could be modified within a range, i.e., \([i\times\Delta-\frac{\Delta}{2},i\times\Delta+\frac{\Delta}{2})\). Therefore, the index of selected function to be used in _mapping function for selector arrow_ was expressed as  
\[F(\theta_{s})=\{i|\theta_{s}\in[i\times\Delta-\frac{\Delta}{2},i\times\Delta+ \frac{\Delta}{2})\}. \tag{1}\]  
Because there were 20 selectable functions in the illustration presented in Figure 1c, \(N=20\) and \(\Delta=18^{\circ}\). Thus, based on Equation (1), the wanted mapping table, \(F(\theta_{s})\), could be expressed as listed in Table 1. Note here that the selected functions for \(F(\theta_{s})=0,1,\ldots,15\) correspond to the _linear_ scales on the instrument meter region (\(\mathbf{R}_{IM}\)), whereas those for \(F(\theta_{s})=16,17,18,19\) correspond to the _nonlinear_ ones. In order to facilitate the manipulation of reading value from the pointer, an auxiliary value, \(A(\theta_{s})\), is also provided in Table 1, where \(A(\theta_{s})\) represents the maximum value used in the  
Figure 4: Experimental devices were set up to develop algorithms and verify the capability of reading an A-meter by computer vision. (**a**) A standard resistance box (DC resistance controller ZX99A [20]) was used to output resistance to the probe of the A-meter to build lookup data and measure resistance. (**b**) Measuring the DC current of a 1.5 Vdc battery.  
functions, \(F(\theta_{s})=0,1,\ldots,15\) (_linear cases_) and the multiplier used in the functions, \(F(\theta_{s})=16,17,18,19\) (_nonlinear cases_).
# Characteristics and Origin of Natural Gas in Yongfeng Sub-Sag of Bogda Mountain Front Belt
## 6 Natural Gas Origin
### Process of Oil and Gas Accumulation  
The formation of oil and gas reservoirs in the study area generally went through three stages. The first stage corresponds to the main hydrocarbon generation and expulsion period of the Triassic to Jurassic period. Specifically, the source rocks of the Permian Lucaogou Formation entered the hydrocarbon generation period in the Triassic period. At this time, the maturity \(\rm R_{o}\) of the source rocks could have reached 0.8%. As the strata continued to settle, the source rocks reached their maximum burial depth by the end of the Jurassic period, with a maturity \(\rm R_{o}\) of up to 1.25%. At this time, large-scale hydrocarbon generation and expulsion began and the reservoirs began to form mainly generating oil and moisture. The second stage corresponds to the late Jurassic to Cretaceous period, during which time the Bogda Mountains began to experience large-scale tectonic uplift, leading to the stagnation of hydrocarbon generation in the source rocks. Additionally, the primary oil and gas reservoirs that formed during the first accumulation were destroyed and adjusted, resulting in the formation of secondary oil and gas reservoirs in some areas. The third stage corresponds to the Himalayan period, during which time the Bogda Mountain experienced its last large-scale uplift and formed a deep fault, providing a pathway for surface water carrying microorganisms to infiltrate oil and gas reservoirs. At suitable temperatures (measured in the reservoir at 40 \({}^{\circ}\)C to 50 \({}^{\circ}\)C), crude oil underwent biodegradation to form natural gas, which has continued until today.  
This comprehensive analysis shows that the natural gas type of Well Xyd1 is crude oil biodegradation gas, mainly from the Lucaogou Formation source rocks. The source rocks of the Lucaogou Formation in the research area undergo certain thermal evolution to generate crude oil. Later, due to the uplift of the Bogda Mountain, the structure was uplifted, and under suitable geological conditions, biodegradation occurred to form natural gas.
# Novel Methods of AI-Based Gait Analysis in Post-Stroke Patients
## 1 Introduction
### Al-Based Approaches to Gait Analysis
#### 1.4.4 Previous Gait Studies Using Other Methods, Techniques, and Tools  
Other gait tests using methods, techniques, and tools other than those described above can be divided into those using a single method and those combining several methods.  
[PERSON] and [PERSON], in a study belonging to the first group, showed the possibility of discriminating between the vertical ground reaction forces (GRFs) on a treadmill of patients with lower limb fractures and those of a control group using principal component analysis (PCA). Based on their study, they proposed a scale to determine the degree to which a gait deviates from what is normal. The classification accuracy, which depended on the choice of parameters of the PCA algorithm, was 92.2-96.1% in a relatively small study group (13 patients with fractures and 31 healthy subjects) [51]. The above-mentioned study was followed up by [PERSON] et al., who used a group of 51 patients (13 with fractures and 38 healthy controls), combined analytical methods: they used PCA and two different neural networks (a multilayer unidirectional neural network and a self-organizing network). The use of PCA and a multilayer unidirectional neural network gave a better result: an accuracy of 96% [52].  
Examples of studies from the second group include the following:  
* an investigation of the possibility of using an independent component analysis (ICA) together with a fractal dimension analysis in the study of the sEMG of small movements and the recognition of movement patterns (useful for the control of robots and other rehabilitation devices);
* an investigation of the spectrum of analysis possibilities using non-linear methods;
* a study of neuromuscular disorders using a wavelet analysis;
* a number of book studies published in the field of practical biomedical signal analysis which discuss clinical gait analysis.
## 4 Results and Discussion
### Evaluation of the Contamination and Ecotoxicological Risks
#### 4.2.2 Ecotoxicology of Central Western Adriatic Sea Bottom Sediments  
The Numerical Sediment Quality Guidelines (SQGs) developed for marine and estuarine ecosystems as suggested by [PERSON] and [PERSON] (2015) [71], are used here to evaluate the ecotoxicological state of the bottom sediments. According to [PERSON] and [PERSON] SQGs (2015) [71], the following thresholds can be considered (Table 2): the TEL (Threshold Effect Level) that is the concentrations below which sediment-dwelling organism do not exhibit toxic effects; the ERL (effect range low) that is the concentrations below which sediment-dwelling organism rarely exhibit toxic effects; the PEL (Probable effect level) and the ERM (Effect Range Median) that are the concentrations above which toxic effects are detected frequently [82].  
Most of the samples show As values exceeding the TEL; this is the consequence of the background values of As that is rather high in this area and due to the inputs of sediments coming from the North that present high As source catchment areas. Furthermore, two samples (303 and 106 sites) show As values exceeding the PEL. In this case, as previously noted for the contamination indexes, the exceeding of the SQLs thresholds is probably due to anthropic causes due to the discharges into the sea of waste of the fertilizer production of firms that have been operative along the Southern Marche coast during the 20 th century. As regards the Cr, almost all samples exceed the TEL, 11 samples exceed the ERL and 3 samples overcame the PEL. The high contents of Cr that generally exceed the lower levels of some SQGs are ascribed to the composition of the sediments coming from the North that exhibits high Cr levels [14] and are a well-known geochemical signature sediment associated to the Po river in northern areas [83; 84; 85]. As regard the Ni, 34 stations exceed the TEL, 25 the ERL, and 16 the PEL. In this case, as for the Cr, these excesses could be ascribed to the composition of the sediments coming from the North, which naturally show high Ni levels, or due to the sediments affected by anthropogenic input in the North Adriatic. Finally, in the cases of Cu, Pb e Zn no station presents contents higher than the TEL.