esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Evaluation Metrics and Methodology
#### 2.7.1 Evaluation Metrics  
In a classification of a specific dataset four basic combinations of actual data categories and assigned categories, are:  
* true positives (TP)--correct positive assignments,
* true negatives (TN)--correct negatives,
* false positives (FP)--incorrect positive assignments,
* false negatives (FN)--incorrect negative assignments.  
The accuracy score [41] can be described as a fraction of predictions that ML model got right. In binary classification the accuracy is calculated using the expression:  
\[ACC=\frac{TP+TN}{TP+TN+FP+FN}. \tag{8}\]  
The _AUC_ score [42] computes the area under the receiver operating characteristic (ROC) curve. By computing the area under the roc curve, the curve information is summarized in one number.  
According to [43], the precision is the ability of the classifier not to label as positive a sample that is negative. The precision score is the ratio between TP and the sum of TP and FP which can be written as:  
\[Precision=\frac{TP}{TP+FP} \tag{9}\]  
Recall [43] is the ability of the classifier to find all the positive samples. The recall is the ratio between TP and the sum of TP and FN and can be written as:  
\[Recall=\frac{TP}{TP+FN} \tag{10}\]  
F1-score [44] is the harmonic mean of the precision and recall and can be written as:  
\[F1-Score=\frac{2\cdot precision\cdot recall}{precision+recall}=\frac{2 TP}{2 TP+FP+ FN} \tag{11}\]  
The majority of classification metrics are by default defined for the binary case problems. To expand these metrics to multiclass problems (Datasets), a few additional techniques have to be introduced. The multiclass problems can be broken down into a sequence of binary problems using One-vs-One (OVO) or One-vs-Rest (OVR). In this paper, the OVR was used to obtain a system of symbolic expressions for the detection of patients with HCV and their stage of the disease. Using OVR the multiclass problem is broken down into a series of binary tasks for each class in the target variable. In this case, we have 4 classes (healthy, hepatitis C, fibrosis, and cirrhosis) that are binarized to four tasks using OVR:  
* task 1: healthy versus (hepatitis C, fibrosis, cirrhosis),
* task 2: hepatitis C versus (healthy, fibrosis, cirrhosis),
* task 3: fibrosis versus (healthy, hepatitis C, cirrhosis), and
* task 4: cirrhosis versus (healthy, hepatitis C, fibrosis).  
To evaluate the obtained symbolic expressions in multiclass case (OneVsRestClassifier) the same evaluation metrics were used as in previous case however, the macro averagingmethod was used. The macromethod calculates metrics for each label, and find their unweighted mean.
## 4 Data Quality Control in TasMAN Using Fuzzy Rules
### Sensor Q4 QC Inputs
#### 4.1.4 Node Differences QA/QC  
Node differences QA/QC compares the observations of individual sensors (of the same type) co-located within the same node and identifies any large discrepancies. The output from this process is used to modify the overall data quality assessment at the sensor level. The modification could be either downwards or upwards. For example, if all sensors in the node have recorded a decrease of 2 \({}^{\circ}\)C in water temperature in the past 30 min, then it may indicate an event at this location and we can have more confidence in the data from the individual sensors. However, if only one sensor records this decrease, it is suggestive of a fault with that sensor.  
Where there are only two sensor locations on a node, the percentage difference between the sensor readings could be used as the metric for Node QA/QC. In cases with more than two sensors it would  
Figure 3: Conductivity data, at a depth of one meter at the CSIRO Wharf, showing an example of a plateau followed by a drop in sensor data quality.  
be preferable to compare the sensor reading with the mean of other sensors on the same node. Table 1 shows the characteristics of a membership function for a case where there are two sensors of the same type at the same node location.  
This approach should be modified depending on the system under study. In the results presented below, the water body was well-mixed and data at the two sensors would be expected to be similar, despite their different depths. However, layering typically occurs in estuaries with warmer, less brackish water near the surface and cooler, more brackish water at depth. For these cases, it may be preferable to calculate water density from sensor readings at each depth and use these to check that the measured water column was hydrostatically stable. Contrastingly, a statistical model of the relationship between the two sensors in the stratified water column could be utilized as a quality metric. The model could be developed from training data and the metric could represent the difference between the model prediction and actual different between the two sensors.
###### Abstract  
There is accumulating evidence that prenatal exposure to air pollution disturbs fetal growth and development, but little is known about these effects in cold climates or their season-specific or joint effects. Our objective was to assess independent and joint effects of prenatal exposure to specific air pollutants on the risk of low birth weight (LBW). We utilized the 2568 children of the Espoo Cohort Study, born between 1984 and 1990 and living in the City of Espoo. We conducted stratified analyses for births during warm and cold seasons separately. We analyzed the effect estimates using multi-pollutant Poisson regression models with risk ratio (RR) as the measure of effect. The risk of LBW was related to exposure to CO (adjusted RR 1.44, 95% confidence interval [CI]: 1.04-2.00) and exposure to O\({}_{3}\) in the spring-summer season (1.82, 1.11-2.96). There was also evidence of synergistic effects between CO and O\({}_{3}\) (relative risk due to interaction (RERI), all year 1.08, 95% CI: 0.27-4.94, spring-summer 3.97, 2.17-25.85) and between PM\({}_{2.5}\) and O\({}_{3}\) (all year 0.72, -0.07-3.60, spring-summer 2.80, 1.36-19.88). We present new evidence of both independent and joint effects of prenatal exposure to low levels of air pollution in a cold climate on the risk of LBW.  
air pollution; low birth weight; prenatal exposure
## 5 Two-Way Measurement Method in CCFD
### Key Errors of the Two-Way Measurement Method in CCFD  
During the process of inter-satellite two-way measurement, various types of errors exist, including channel time delay, gravitational time delay, the time delay of periodic relativistic effects, the time delay of PCO, and ranging noise. The presence of these errors can significantly impact the precision of inter-satellite measurements, necessitating their calculation and compensation.
## 6 Data Transmission
### Success Rate of Data Transmission
#### 6.3.2 Scenario II: With Retransmission  
When retransmission is considered, by summing all of the successful transmission probabilities in Table 11, the success rate of data transmission in one hop can be written as  
\[S_{re}(i,j)=1-A^{3}-2A^{2}B(1-A)-AB^{2}(1-A)^{2}. \tag{27}\]
## 8 Conclusions  
The hop distance between nodes in wireless multi-hop ad hoc networks have significant impact on the system performance. For this reason, in this paper, we study the hop distance characteristics and their impacts on the end-to-end performances of mmWave multi-hop M2M networks under the blockage effects. We derive the probability distributions of the hop distance with the two routing protocols that have different advantages: FN with the smaller end-to-end delay and NN with the lower transmit energy consumption. In addition, we investigate the blockage-free scenario to establish a baseline to quantify the impact of the blockage effects. Both analysis and simulation indicate that the end-to-end performances of the two routing schemes become more distinct as \(\beta\) decreases or \(\Phi\) decreases. Based on the hop distance analysis, we estimate the end-to-end performances such as outage, hop count, and transmit energy, which is useful to gain insights into system design guidelines instead of time-consuming simulation. For example, when the blockage effect parameter \(\beta\) is given, a pertinent beamwidth and required transmit energy for FN or NN can be estimated to satisfy a target outage probability. Potential extensions of this paper include addressing a wider scenario with different transmission ranges and beamwidths of MTC devices. In addition, because we only consider the transmit energy in this work, we will investigate more comprehensive analysis on energy consumption with more general energy models. Furthermore, as in [55; 56], we will consider a method to maximize the network lifetime by considering the residual energy of battery-powered devices in the network as a long-term plan.  
This research was supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (Grant No.: NRF-2016R1D1A1B03930060). The contributions of [PERSON] were to create the main ideas and perform simulation, while [PERSON] advised the ideas about the system model and crystallized the mathematical framework and theoretical analysis. The authors declare no conflict of interest. The following abbreviations are used in this manuscript:  
IoT & Internet of things  
M2M & Machine-to-machine  
Machine-type communication (MTC) D2D & Device-to-device  
mmWave & Millimeter-wave  
MWN & Multi-hop wireless networks  
5G & Fifth generation  
RFIC & Radio frequency integrated circuit  
LoS & Line-of-sight  
NLoS & Non-line-of-sight  
PP & Poisson point process  
PMF & Probability mass function  
AODV & Ad hoc on-demand distance vector  
RREQ & Route request  
PDF & Probability density function  
NHPP & Non-homogeneous Poisson process  
CDF & Cumulative distribution function  
FN & Furthest neighbor  
NN & Nearest neighbor  
EE & End-to-end
## 2 Materials and Methods
### Cole-Cole Model  
The Cole-Cole model [28] is widely used to describe the complex relative permittivity of biological tissues, \(\varepsilon_{\mathrm{r}}(\omega)=\varepsilon(\omega)/\varepsilon_{0}\), and its equation is described as follows:  
\[\varepsilon_{\mathrm{r}}(\omega)=\varepsilon_{\infty}+\sum_{n=1}^{N}\frac{ \varepsilon_{\mathrm{sn}}-\varepsilon_{\infty}}{1+(j\omega\tau_{n})^{1- \alpha_{n}}} \tag{3}\]  
in which \(N\) is the number of poles and thence the order of the model, \(\varepsilon_{\infty}=\lim_{\omega\to\infty}\varepsilon_{\mathrm{r}}(\omega)\), is the permittivity at high frequencies, \(\sigma_{s}\) is the static ionic conductivity, and \(\varepsilon_{\mathrm{sn}}\), \(\tau_{n}\), and \(\alpha_{n}\) are the static permittivity, the relaxation time constant, and the distribution parameter of the \(n\)-th addend of the summation, respectively.  
To take into account the conductivity of the material considered, the conductive term is added, obtaining the following expression:  
\[\varepsilon_{\mathrm{r}}(\omega)=\varepsilon_{\infty}+\sum_{n=1}^{N}\frac{ \varepsilon_{\mathrm{sn}}-\varepsilon_{\infty}}{1+(j\omega\tau_{n})^{1- \alpha_{n}}}+\frac{\sigma_{s}}{j\omega\varepsilon_{0}} \tag{4}\]  
where \(\sigma_{s}\) is the static ionic conductivity.  
Such a model incorporates the Debye model [27]. Indeed, the main difference between the Debye and Cole-Cole models is that the latter includes exponent \(1-\alpha\), with \(0\leq\alpha\leq 1\). When the exponent becomes smaller, the relaxation time distribution becomes broader, i.e., the transition between low- and high-frequency values becomes wider, and the peak on imaginary part of the spectrum also becomes wider.  
The complexity of both the structure and composition of biological material is such that the dispersion region of each pole may be broadened by multiple contributions to it. The broadening of the dispersion could be empirically accounted for by using the Cole-Cole model [29]. It is for that reason that the Cole-Cole model is expected to provide more accurate dielectric spectrum curve-fitting.  
Nevertheless, we also consider the Debye model in our study as it is sometimes preferred for its simplicity [4; 5; 7; 8; 9; 10] and easy implementation of computational EM methods, such as finite-difference time-domain FDTD (in the Cole-Cole model, the addition of the parameters \(\alpha\) causes difficulties when transforming to the time domain because the fractional powers of frequency lead to fractional derivatives [11]).
## 2 Materials and Methods
### Parameters of Interest
#### 2.2.2 Thermo-Hydraulic Performance Factor of Ventilated Roof (THPF)  
A common problem in thermo-hydraulic systems is the existence of opposite tendencies between heat transfer and pumping power. As a general trend, it can be said that increasing surface roughness and/or fluid turbulence increases heat transfer, but also increases pressure losses and therefore pumping power. Consequently, a balance must be found between heat transfer improvement and pumping power. This problem has been studied by several authors, but the most relevant research was conducted by the authors in [23], who experimentally studied the relationship between heat transfer and roughness in a pipe; the authors in [24], whose research into heat exchangers compared the performances of rough and smooth tubes; and the author in [25], who defined an efficiency parameter in order to optimize the \"thermo-hydraulic performance of rough surfaces\", which is given by the following expression based on the work in [24]:  
\[THPF=\left(\frac{S_{t}}{S_{st}}\right)^{3}\left(\frac{f}{f_{s}}\right)^{-1} \tag{6}\]  
Currently, this equation is frequently used to calculate an index number that is often called the \"thermo-hydraulic performance factor\" (THPF) [26; 27], \"thermo-hydraulic performance parameter\" [28], or something similar. This index number is widely used in systems based on renewable energies such as the \"solar air heaters\" [27; 28; 29; 30; 31] because the pumping power can be of the same order of magnitude as the heat exchanged.  
In this work, different configurations (cases) are studied for the air path in a ventilated slab. Variations in air path can be assimilated to variations in roughness because a change in the way the air travels through the floor will have an effect on heat transfer and pressure losses analogous to a variation in surface roughness. Therefore, the THPF can be used as an analysis parameter. From the general form of the Darcy-Weisbach equation, the equivalent friction factor (Equation (7)) can be determined if all other parameters are known, as in this case, since the pressure drop is an output of the numerical model, and the rest of the variables are inputs defined by the geometry of the chamber (\(L\) and \(D_{h}\)), the propertiesof the air (\(\rho\)), and the air velocity (\(u\)). Therefore, the friction factor can be calculated with Equation (7) for each case, including that of the reference case.  
\[f=\frac{2\Delta PD_{h}}{L\rho u^{2}} \tag{7}\]
###### Abstract  
A decentralized control scheme is developed in this paper based on an improved active disturbance rejection control (IADRC) for output tracking of square Multi-Input-Multi-Output (MIMO) nonlinear systems and compared with the decoupled control scheme. These nonlinear MIMO systems were subjected to exogenous disturbances and composed of high couplings between subsystems, input couplings, and uncertain elements. In the decentralized control scheme, it was assumed that the input couplings and subsystem couplings were both parts of the generalized disturbance. Moreover, the generalized disturbance included other components, such as exogenous disturbances and system uncertainties, and it was estimated within the context of Active Disturbance rejection Control (ADRC) via a novel nonlinear higher order extended state observer (NHOESO) from the measured output and canceled from the input channel in a real-time fashion. Then, based on the designed NHOESO, a separate feedback control law was developed for each subsystem to achieve accurate output tracking for given reference input. With the proposed decentralized control scheme, the square MIMO nonlinear system was converted into approximately separate linear time invariant Single-Input-Single-Output (SISO) subsystems. Numerical simulations in a MATLAB environment showed the effectiveness of the proposed technique, where it was applied on a hypothetical MIMO nonlinear system with strong couplings and vast uncertainties. The proposed decentralized control scheme reduced the total control signal energy by 20.8% as compared to the decoupled control scheme using Conventional ADRC (CADRC), while the reduction was 27.18% using the IADRC.  
MIMO systems; active disturbance rejection control; exogenous disturbance; output tracking; subsystem couplings; extended state observer +
Footnote †: journal: Applied  
_Article_
## 7 Discussion of the Results  
### Verification of the Magnetic Dipole Model  
This experiment falsifies the claim in [8; 19; 20] about thinking beyond projectile propulsion. It is claimed that the mutual induction between the launcher coil and the slug (as a single-turn coil) is the cause of the motion because it induced an opposite current in the projectile, producing an opposite pole that caused an attraction force. In that case, copper and aluminum should be the ideal materials for propulsion because they have a lower resistivity in comparison with iron. But copper and aluminum projectiles move just a few millimeters without reaching the velocity measuring point. This experiment has shown that magnetic dipoles allow the magnetic field to exert an attractive force on the ferromagnetic material as proved theoretically in [13; 18]. FEMM 4.2 software was used to plot Figure 14 to show that domains in ferromagnetic material become magnetized in the direction of the external magnetic field. This magnetization will produce an opposite magnetic pole in the ferromagnetic material to the pole which is nearest to it. Therefore, the developed opposite pole will be attracted to the magnetic field. But mutual induction between the launcher coil and the slug causes energy loss through the projectile's electrical resistance even in the ferromagnetic material.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \multirow{2}{*}{\
\begin{tabular}{c} **Cap.** \\ **Voltage** \\ \end{tabular} } & \multicolumn{2}{c}{**Velocity (m/s)**} & \multicolumn{2}{c}{**Efficiency \(\eta\) (\%)**} & \multirow{2}{*}{
\begin{tabular}{c} **\(\eta\) (\%)** \\ \end{tabular} } \\ \cline{2-2} \cline{4-6}  & **without Iron** & & & & **4 mm Iron** & & **without** & **6.6 mm** & \\  & **Yoke** & & & & **Yoke** & & **Iron Yoke** & \\ \hline
60 v & 4.7 & 5.19 & 5.49 & 1.509 & 2.06 & 36.5 \\ \hline
80 v & 8.75 & 8.81 & 9.56 & 2.942 & 3.512 & 19.37 \\ \hline
100 v & 11.59 & 11.7 & 12.04 & 3.3 & 3.566 & 8 \\ \hline
120 v & 13.18 & 13.46 & 13.61 & 2.967 & 3.164 & 6.6
## 1 _Introduction_
The models have the following assumptions: the dominant component of the dark current is the thermal excitation current, the proportion of the continuous tunneling current to the dark current can be neglected, and both the light and electric field distributions are ideally homogeneous. All three models were experimentally verified to be reasonable. There were some problems in the application of the analytic model, such as errors in calculating the dark current of the same focal-plane device in different states. There are numerous parameters in the equation for calculating the dark current in the [PERSON] model, including the electric field strength, electron drift velocity, and thermal excitation energy, which are difficult to measure accurately at the same time. Dark current is the main parameter governing the performance of a QWIP detection system. It is important to monitor the dark current and its generated response in real-time. After integrating the whole satellite-borne on-board IR detection system until the on-orbit operation, the focal-plane dark current value is difficult to measure accurately in real-time. Both Landsat 8 TRIS and this research team set up dark pixels at the QWIP, and the response of the dark pixels can be used as a reference for the overall dark current level of the device [18; 19]. A recurrent neural network was used to obtain the relationship between the dark pixels and the imaging pixels, which was then corrected for the target data. The above method cannot be applied to QWIP detection systems without dark pixels. Using semiconductor simulation software such as Sentaurus TCAD, the QWIP structure characteristics determined that the dark current simulation was difficult to conduct below the temperature of liquid nitrogen. The method of dark current noise suppression with a readout circuit is highly accurate, but the presence of this circuit increases the overall detector size and power consumption, complicates the fabrication process, and is not validated at a low cooling temperature or in large surface array detectors [20]. Multipleradiometric calibrations in a short period can reduce the effect of dark current noise, but this compresses the continuous imaging time, especially in hyperspectral detection, and misses part of the focused spatial dimensional image information [21]. In this study, the signal full-link response model of the satellite-borne LWIR QWIP detection system is established, and the QWIP dark current model is elaborated as the theoretical basis of the method. A method of measuring dark current based on focal-plane temperature data and a correction method of dark current temporal noise are introduced. The structure of the detection system and the design details closely related to this method are described in the experimental section, experimental verification is carried out and results are obtained, and the experimental steps and conclusions are analyzed.
# The Effect of Pitch Surface on Match Running Performance in Women's Soccer
###### Abstract  
This study aimed to identify differences in match running performance (MRP) on artificial turf (AT) and natural grass (NG) among female football players. The players' MRPs (AT; 96 observations, NG; 80 observations) were obtained from all matches (\(n=22\)) of the First Slovenian women's football league in the season 2023/24 using a global positioning system. Data were categorized into four subsets according to the players' tactical roles: central defensive player (CD), wide defensive player (FB), midfield player (CM), and offensive player (OF). The variables included total distance (TD), high-intensity running (HIR), high-intensity accelerations (HIA), and decelerations (HID). Results indicated that (i) CDs (Cohen's d (d) = 0.93) and CMs (d = 1.07) covered significantly greater TD on AT compared to NG, with (ii) no significant differences in TD among FBs and OFs. Additionally, (iii) similar HIR, HIA, and HID values were found for players on all playing positions, irrespective of the pitch surface. These findings suggest that overall match intensity remains consistent between surfaces, but that AT may impose a higher physical demand regarding match volume for CDs and CMs. Therefore, the physical condition of these players should be a major consideration when playing on AT.  
women's soccer; artificial turf; natural grass; physical performance Article