esa-sceva/satcom-chunk-collection huggingface dataset:
# A Comprehensive Experimental Emulation For OTFS Waveform RF-Impairments
## 3 Channel Effects Furthermore, RF-Impairments
### Non-Linearity Impairments  
Generally, there are different nonlinearity sources in the RF front-end of communications systems, namely, high power amplifier (HPA) at the transmitter, low-noise amplifier (LNA) at the receiver, mixtures, and analog-to-digital (A/D) and digital-to-analog (D/A) converters [49]. In real-world wireless communication systems, HPAs are the most commonly used component to provide long-distance wireless transmission. According to the nonlinear input-output characteristic of HPAs, the power of the input signal should be amplified within the HPA's linear range to prevent the HPA from being saturated and causing the out-of-band (OOB) that degrades the system performance [49]. The performance of the HPA amplifier is inversely proportional to the PAPR of the transmitted signal. As a result, the PAPR of the transmitted signal should be as minimal as possible [50]. The PAPR of the OTFS system is expressed as follows [20]  
\[PAPR=\frac{N\max_{k,l}|x[k,l]|^{2}}{E\{|\;x[k,l]|^{2}\}}. \tag{14}\]  
Due to the fact that the PAPR is a random variable, the best way to measure and evaluate it is by using the complementary cumulative distribution function (CCDF) where the CCDF is presented as follows [20]  
Figure 7: BER performance comparison between OTFS waveform with \(M=32\), \(N=32\) and OFDM waveform with \(N=1024\) considering different normalized frequency offsets values in 2.4 GHz carrier without using reverberation chamber.  
\[\begin{split}\Pr(PAPR>\gamma_{o})&=1-\Pr(PAPR\leq\gamma_{o}) \\ &\approx 1-\left(1-e^{-\gamma_{o}}\right)^{MN},\end{split} \tag{15}\]  
where \(Pr(.)\) denotes the probability function and \(\gamma_{o}\) represents the threshold level that the PAPR of the transmitted OTFS signal should not exceed.  
Figure 8 compares the CCDF of the PAPR for both OTFS and OFDM with the different values of \(M\) and \(N\) subcarriers. (Note that, for comparison of \(M\)-subcarrier OFDM with OTFS, which have \(MN\) symbols in a frame, we consider the CCDF of the concatenation of \(N\) OFDM symbols.) It is shown that for the same number of sub-carrier where \(M>N\), OTFS provides a better PAPR compared to OFDM which makes OTFS more suitable for systems that have energy consumption constraints such as IoT applications and WSNs. For example, for \(M=256\) and \(N=4\), OTFS has approximately 0.5 dB less PAPR than OFDM at a probability of \(10^{-5}\). Furthermore, it is observed that as \(N\) increases, the PAPR of the OTFS increases, and the performance gap between the OTFS and OFDM almost vanishes. Note that in the case of \(N>M\), it is shown that the PAPR of OTFS is the worst among all, our experimental results agree with the analytical results mentioned in [20].
# Internet of Food (IoF), Tailor-Made Metal Oxide Gas Sensors to Support Tea Supply Chain
## Appendix A  
In Appendix A, the data are presented that were extracted from the GC-MS-SPME analysis of the volatile fingerprints for each tea that have been analyzed in this work (Tables A1-A11). \begin{table}
\begin{tabular}{c c c} \hline \hline
**RT** & **Compound Name** & **Abundance Mean** \\ \hline
24.495 & 1-Octanol & 905,951.5 \\
24.911 & Cyclohexane, 1-ethenyl-1-methyl-2,4-bis(1-methylethenyl)-, [1S-(1.alpha,2.beta,4.beta.)]- & 122,847.5 \\
25.385 & 1,1-Dimethyl-4-methylencyclohexane & 277,392 \\
25.417 & Cyclohexanone, 2-methyl-5-(1-methylethenyl)-, trans- & 243,765 \\
27.500 & Hotrienol & 80,758 \\
27.795 & Carylecol & 545,775 \\
27.797 & Dispiro[2.1.2.4]undecane, 8-methylene- & 617,343 \\
28.060 & Acetophenone & 82,331 \\
28.301 & Hexanoic acid, 6-bromo- & 217,313 \\
29.001 & 2-Cyclohexen-1-one, 2-methyl-5-(1-methylethyl)-, (S)- & 532,374.5 \\
30.125 & Carveol & 474,827 \\
30.130 & 6-Isopropenyl-3-methoxymethyl-3-methyl-cyclohexene & 465,247 \\
30.242 & 1-Nonanol & 768,727.5 \\
30.972 & cis-p-Mentha-2.8-dien-1-ol & 560,885 \\
30.974 & trans-p-mentha-1(7),8-dien-2-ol & 662,768 \\
31.523 &.alpha.-Terpineol & 1,098,969 \\
32.232 & (-)-Carvone & 3,383,059 \\
32.611 & Undecane, 2-methyl- & 74,437 \\
32.624 & Pentadecane & 155,418 \\
33.721 & 2H-Pyran-3-ol, 6-ethenyltetrahydro-2,2,6-trimethyl- & 203,784 \\
33.982 & Carved & 450,597 \\
34.200 & Ethanone, 1-(3-methylphenyl)- & 150,098 \\
34.340 & 1-Cyclohexene-1-carboxaldehyde, 4-(1-methylethenyl)- & 262,883 \\
34.627 & Decane, 1,1’-oxybis- & 92,683 \\
35.010 & 2H-Pyran-3-ol, 6-ethenyltetrahydro-2,2,6-trimethyl- & 109,855 \\
35.173 & Cyclopropane, octyl- & 181,727 \\
35.176 & Decane, 3-chloro- & 131,903 \\
35.319 & 6-Octen-1-ol, 3,7-dimethyl-, (R)- & 158,561 \\
35.332 & Citronnell & 169,686 \\
35.896 & p-Mentha-1(7),8-dien-2-ol & 152,465 \\
36.474 & 2-Cyclohexen-1-one, 3-methyl-6-(1-methylethenyl)-, (S)- & 98,294 \\
36.694 & 3-hydroxy-2-methyl-5-(propyl-1-en-2-yl)cyclohexanone & 250,595 \\
37.280 & 2-Cyclohexen-1-ol, 2-methyl-5-(1-methylethenyl)-, acetate, (1R-cis)- & 1,127,661 \\
37.837 & Heptanoic acid & 1,468,435.5 \\
38.200 & trans-p-mentha-1(7),8-dien-2-ol & 380,766 \\
38.414 & Benzyl-diseryl phosphate & 162,123.5 \\
38.840 & (2R,4R)-p-Mentha-[1(7),8]-diene, 2- hydroperoxide & 97,386 \\
40.616 & (-)-trans-Mytrramid acetate & 105,666 \\
40.623 & Mytracylatedacet & 146,060 \\
40.857 & Cyclododeca-5,9-dien-1-ol, 2-methyl-, (Z,Z)- & 317,443 \\
40.859 & (2R)-Borrane-10,2-sultam & 219,341 \\
41.850 & Benzaldehyde, 4-methoxy- & 233,353 \\
43.263 & Octanoic acid & 191,580 \\
43.436 & Cyclohexanemethanol, 4-ethenyl-alpha.,alpha.,4-trimethyl-3-(1-methylethenyl)-, & 103,468 \\
44.618 & 2-Pentadecanone, 6,10,14-trimethyl- & 69,726 \\
45.482 & Dodecanoic acid, 3-hydroxy- & 256,424 \\
45.502 & n-Hexadecanoic acid & 335,278 \\
47.527 & 2,6-Ocadiene-1,8-diol, 2,6-dimethyl- & 896,585.5 \\
47.940 & 2(4H)-Benzofuranone, 5,6,7,7,a-tetrahydro-4,4,7a-trimethyl- & 344,852.5 \\
50.351 & Benzophenone & 173,741.5 \\
51.518 & Vanillin & 206,667.5 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: _Cont._
## 2 Principles of Rainfall Field Retrieval by ESLs and HMLs
### Rainfall Field Retrieval by Combined ESLs and HMLs
#### 2.3.1 Rainfall Field Reconstruction by OK Algorithm  
The assumptions for reconstructing the rainfall field using the OK algorithm are as follows: (1) the rain rates at all locations in the area are interrelated; (2) the shorter the distances, the higher the correlation between the rain rates [44]. Therefore, using \(R(x_{i},y_{i})\) from ESLs and HMLs to reconstruct the \(R(x,y)\) of the whole rainfall field can be given by  
\[\left\{\begin{array}{l}R(x,y)=\sum\limits_{i=1}^{N}\lambda_{i}R(x_{i},y_{i}) \\ \sum\limits_{i=1}^{N}\lambda_{i}=1\end{array}\right., \tag{19}\]  
where \(N\) denotes the number of ESLs and HMLs, and \(\lambda_{i}\) is a weight factor indicating the degree of contribution of \(R(x_{i},y_{i})\) to \(R(x,y)\). In addition, the semi-variogram \(\psi\) is defined by the mathematic expectation of the observed rain rates \(R(x_{i},y_{i})\) and the target point rain rates \(R(x,y)\), as given by  
\[\psi(i)=\frac{1}{2}E\Big{[}(R(x,y)-R(x_{i},y_{i}))^{2}\Big{]}. \tag{20}\]  
Additionally, the connection between weight factor \(\lambda\) and the semi-variogram \(\psi\) is given by [45]  
\[\left[\begin{array}{cccc}\psi_{11}&\psi_{12}&\cdots&\psi_{1N}&1\\ \psi_{21}&\psi_{22}&\cdots&\psi_{2N}&1\\ \vdots&\vdots&\vdots&\vdots&\vdots\\ \psi_{N1}&\psi_{N2}&\cdots&\psi_{NN}&1\\ 1&1&1&1&1\end{array}\right]\left[\begin{array}{c}\lambda_{1}\\ \lambda_{2}\\ \vdots\\ \lambda_{N}\\ \delta\end{array}\right]=\left[\begin{array}{c}\psi_{1}\\ \psi_{2}\\ \vdots\\ \psi_{N}\\ 1\end{array}\right], \tag{21}\]  
where \(\delta\) is the Lagrange multiplier. Furthermore, the semi-variogram takes the stable model for which the performance was validated in [45], and it is given by  
\[\psi=A\cdot\Big{(}1-\exp\Big{(}-(d/B)^{C}\Big{)}\Big{)}, \tag{22}\]  
where \(d\) represents the distance between the observed rain rates \(R(x_{i},y_{i})\) and the target point rain rates \(R(x,y)\), and \(A\), \(B\) and \(C\) are coefficients, which can be estimated by fitting the observed values of ESLs and HMLs. Next, after obtaining the weight factor \(\lambda\) on the basis  
Figure 3: Correspondence between the HML signal and rainfall from 25 June to 26 June 2021. The black line is RSL of HML, and the blue line is rain rate.  
of the semi-variogram, we can implement the reconstruction of the rainfall field according to Equation (19).
## 5 Results
### Ablation Study  
This section represents an ablation study that investigates the impact of multiscale deep feature extraction from multiple CNN models on the classification performance of multiple SVM classifiers. In other words, it shows the influence of extracting deep features from multiple layers of three distinct CNN models (MobileNet, EfficientNetB0, and ShuffleNet) and compares the performance of various SVM classifiers fed with these features. Table 2 illustrates the identification accuracy attained by SVM classifiers developed employing deep features obtained from three separate layers of the MobileNet, EfficientNetB0, and ShuffleNet CNN structures, emphasizing the role of every layer in detection performance. In MobileNet, Layer 1 demonstrated superior performance among all SVM classifiers, attaining a peak accuracy of 98.5% with the MGSVM classifier. Layer 2 achieved marginally reduced accuracies, reaching a maximum of 97.2% using LSVM and MGSVM, whereas Layer 3 exhibited a substantial decline in performance, with accuracies plummeting to 73.5-91.4% where 91.4% was reached using LSVM and QSVM, suggesting that features from the deeper layers are less beneficial for classification accuracy. For EfficientNetB0, Layer 2 proved to be the most efficacious, repeatedly attaining the highest accuracies across all SVM variations, with a peak accuracy of 99.4%. In addition, Layer 1 demonstrated strong performance, achieving a maximum accuracy of 98.6%, whereas Layer 3 displayed significantly lower accuracies, fluctuating between 88.5% and 89.3%, indicating a consistent decline in performance for deeper layers.  
The findings for ShuffleNet demonstrate the strong performance of Layer 2, attaining the highest accuracy of 97.2% with MGSVM. Layer 1 exhibited outstanding performance, achieving up to 96.4%, whereas Layer 3 yielded inconsistent and poor outcomes, with accuracies varying from 37.4% to 90.6%, indicating that its deep features are not as efficient as other deep layers for this kind of classification problem. The results demonstrate that intermediate layers (Layer 2) predominantly yield the most useful attributes for breast cancer detection, especially in EfficientNetB0 and ShuffleNet. These findings demonstrate the significance of choosing suitable layers for feature extraction to enhance classification efficacy and reveal the variance in contributions particular to each layer across various CNN designs.
## 2 Materials and Methods
### Tools  
We analyzed the timing information of the spike sequence through an ordinal time-series analysis [22]. Firstly, a pulse sequence with a length of \(N\) is divided into \(N\)-\(D\) vectors of length \(D\), as shown in Equation (5). \(\Delta t(i)\) is the time interval of pulse \(i\), and \(t(i)\) is the time at which pulse \(i\) occurs.  
\[\Delta t(i)=t(i)-t(i-1). \tag{5}\]  
Based on the relative length of \(\Delta t(i)\), each time interval is associated with a word composed of \(D\) symbols. For example, when \(D=2\), two types of words exist. \(\Delta t(i)<\Delta t(i+1)\) gives the word \"01\" and \(\Delta t(i)>\Delta t(i+1)\) gives the word \"10\". Table 2 shows ordinal patterns for \(D=3\). Then, by counting the frequency of occurrence of the different words, their probabilities are computed, as shown in Equation (6),  
\[P_{1}=\frac{n_{1}}{\sum\limits_{j=1}^{6}n_{j}}, \tag{6}\]  
where \(P_{1}\) is the probability of word 012, \(n_{1}\) is the number of the word 012, and the rest of the words are calculated similarly.  
This symbolic transformation has the drawback that it disregards the information about the precise duration of the inter-spike intervals (ISIs), but it has the advantage that it keeps the information about the temporal correlations among them, i.e., correlations in the timing of the optical spikes. Figure 3a shows the words extracted from the output of the drive laser. When the feedback strength increases from 50 ns\({}^{-1}\) to 100 ns\({}^{-1}\), the probabilities of all of the words occur certain degrees of oscillations. In particular, when the feedback strength was within the region from 70 ns\({}^{-1}\) to 80 ns\({}^{-1}\), the probabilities of words \"012\" (blue line) and \"201\" (light blue line) was lower than the average probability (1/6), while the probabilities of other words were higher than 1/6. According to [27], this phenomenon indicates that the laser may has an encoding effect on the feedback strength.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Serial Number** & **Word** & **Relation** & **Quantity** \\ \hline
1 & 012 & \(\Delta t(i)<\Delta t(i+1)<\Delta t(i+2)\) & \(n_{1}\) \\
2 & 021 & \(\Delta t(i)<\Delta t(i+2)<\Delta t(i+1)\) & \(n_{2}\) \\
3 & 102 & \(\Delta t(i+1)<\Delta t(i+2)\) & \(n_{3}\) \\
4 & 120 & \(\Delta t(i+1)<\Delta t(i+2)<\Delta t(i)\) & \(n_{4}\) \\
5 & 201 & \(\Delta t(i+2)<\Delta t(i)<\Delta t(i+1)\) & \(n_{5}\) \\
6 & 210 & \(\Delta t(i+2)<\Delta t(i+1)<\Delta t(i)\) & \(n_{6}\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Ordinal patterns for \(D=3\).  
Figure 3: Word probability (**a**) and pulse rate (**b**) of the drive laser at different feedback strengths.  
Figure 4: Output of response laser under different pump currents. \(\kappa_{\rm D}=\kappa_{\rm R}=50\) ns\({}^{-1}\). Left column: \(I_{\rm D}=15\) mA. Middle column: \(I_{\rm D}=16\) mA. Right column: \(I_{\rm D}=17\) mA. (**a–c**): \(I_{\rm R}<I_{\rm D}\). (**d–f**): \(I_{\rm R}=I_{\rm D}\). (**g–i**): \(I_{\rm R}>I_{\rm D}\).
## 3 Metrics for Jamming Attack Detection
### Packet Delivery Ratio (PDR)  
Both, [PERSON] _et al._[1] and [PERSON] _et al._[3] define PDR as the ratio of the number of packets successfully sent out by the node (_i.e._, the number of packets for which the node has got the acknowledgement from the destination) to the total number of packets sent out by the node. [PERSON] _et al._[1], however, define two types of PDR: firstly, one to be measured by the transmitter (source), and secondly, one to be measured at the receiver (sink). We, while talking of the PDR, mean only the first one, _i.e._, the one measured at the transmitter-end, and shall discuss the second type, _i.e._, the one to be measured at the receiver-end, separately. The PDR is calculated by keeping counts of the acknowledgements of the successfully delivered packets and the total number of packets sent by the node and then by finding their ratio as a percentage. PDR is a very good metric which is capable of being measured accurately by the node without much of computational overhead, and can indicate the presence of all types of jamming attacks at the physical or data-link/MAC layer. However, the necessary condition is that the network must follow a protocol, like TCP, where the system of acknowledgement of packets exists. We feel that a resource- starved network, like the WSN, cannot afford the luxury of acknowledgements, and hence reject it from our choice.
# Change in Water Quality of the Vistula River During the Emergency Discharge of Untreated Wastewater
## 1 Introduction
In the city of Cuttack, on the other hand, the deterioration of water quality was caused by anthropogenic activity responsible for the inflow of pollutants. Studies conducted in the Mahanadi River Basin have focused their concerns on the impact of aromatic hydrocarbon on human health and ecological conditions [25; 26; 27]. The regulations in force in Poland contained laws and regulations that precisely described definitions of sewage and the conditions that must be met when discharging sewage into water or soil [28]. Poland has 160,000 km of sewage networks, to which 1.33 million m\({}^{3}\) of sewage was discharged. In 2018, we had 3260 municipal wastewater treatment plants in Poland, used by 94.6% of the urban population and 42.9% of the rural population (81% in Poland). In seven countries of the European Union (Luxembourg, the Netherlands, Great Britain, Spain, Germany, Malta, and Austria), the indicator of the population served by sewage treatment plants is at least 95%. The worst situations were in Cyprus (30%), Romania (48%), and Croatia (55%) [29; 30]. The problem accompanying industrial and municipal wastewater treatment is the sewage sludge formed during the treatment processes, which requires appropriate management due to its properties [31]. They were mainly thermally processed and used in agriculture and landfilled, used for land reclamation and plant cultivation. In September 2019 and 2020, there were two colossal failures in sewage infrastructure in the capital of Poland--Warsaw. Both failures involved a broken collector carrying sewage from the left bank of Warsaw to the \"Czajka\" wastewater treatment plant (WWTP). The \"Czajka\" WWTP was launched in 1991, but it was only after 2012 that it began to receive municipal sewage from both the right-bank and left-bank parts of the capital, as well as from neighboring municipalities. The failure of the \"Czajka\" WWTP contributed to the discharge of untreated sewage into the largest Polish river--the Vistula, which belongs to the Baltic Sea catchment area. The failures in 2019 and 2020 have already been described in other papers [32; 33]. [PERSON] et al. [33] showed that in the Vistula River waters downstream of the damaged collector, the concentrations of selected pharmaceuticals increased considerably in comparison with the waters upstream of the collector. The highest concentrations were observed for paracetamol (102.9 \(\mathrm{\SIUnitSymbolMicro\SIUnitSymbolMicro\SI
# Performance of Fine-Tuning Convolutional Neural Networks for HEp-2 Image Classification
## 3 Materials and Methods
### Statistics  
When it is necessary to discriminate between two classes (which in the case of medical imaging problems often represent healthy/pathological classes), the evaluation of the performance of a diagnostic system is usually expressed through a pair of indices: sensitivity and specificity. In our case, the sensitivity represents the fraction of correctly recognized positive images, true positives (_TP_), on the total number of positive images, obtained by adding the true positives and the false negatives (_FN_), i.e.:  
\[\textit{Sensitivity}=\frac{\textit{TP}}{\textit{TP}+\textit{FN}} \tag{1}\]The specificity of a test is the fraction of recognized negative images, true negatives (_TN_), on the total of negative images, obtained by adding the true negatives and the false positives (_FP_), i.e.:  
\[Specificity=\frac{TN}{TN+FP} \tag{2}\]  
Using the aforementioned indices, various figures of merit recognized in medical imaging are obtained. One is certainly accuracy, which is defined as follows:  
\[Accuracy=\frac{TP+TN}{TP+FN+TN+FP} \tag{3}\]  
An automatic classification system generally returns an output value in the definition range; in this case between zero (image negativity) and one (image positivity). The output value must be compared with a predefined threshold value in order to associate the generic image with the positive/negativity classes. This leads to a variability of performance, in terms of the figures of merit introduced above, based on the threshold value chosen. For this reason, very often in medical statistics the Receiver Operating Characteristic (ROC) curve is used, which allows a performance analysis not dependent on the choice of the threshold value. In fact, when the threshold value changes, different pairs of sensitivity and specificity that will define the specific ROC. Therefore, a further figure of merit for classification systems is the area under the ROC curve, generally indicated with AUC.
Namely, the temperature regimes of viscosity are as follows:  
1. Low temperatures \(T<T_{\delta}\): isostructural Arrhenius-type law viscosity of the glass (\(E=E_{H}\));
2. Intermediate temperatures \(T_{g}<T<T_{A}\): non-Arrhenius-type law formally expressed with an exponent \(E\) with variable activation energy of viscosity \(E=E(T)\);
3. High temperatures \(T>T_{A}\): Arrhenius-type law viscosity (\(E=E_{L}\)).  
The crossover temperature \(T_{A}\) is assumed to be close to the liquidus temperature \(T_{I\!I\!I}\), at which the material is completely melted [5]. It was recently shown [7] that regardless of the type of glass-forming liquid, the crossover temperature is given by the universal Equation (2):  
\[T_{A}=kT_{m}, \tag{2}\]  
where \(T_{m}\) is the melting temperature and \(k=1.1\pm 0.15\) (see for details Figure 3b of reference [7]). In addition, the \(T_{A}\) of certain glass families, such as float and nuclear waste glasses, can be defined using a fixed viscosity value which is independent of composition [8]. Figure 1 also provides the universal viscosity equation [3], which is valid at all temperatures although it requires at least four parameters to be fitted to experimental data-the so-called Douglas-Doremus-Ojovan (DDO) double-exponential model (see Chapter 8 of Ref. [8]).  
This work is devoted to estimating the temperature dependence of the activation energy of viscous flow for several chalcogenide compositions within the temperature range (2): \(T_{g}<T<T_{A}\). Chalcogenide vitreous materials are used in the development of nonvolatile memory techniques both for selectors and storing data in memory cells. Various compositions are used in infrared and fiber optics, sensors and devices utilizing the properties of these glasses. Research of such materials has great prospects in electronics and optics. We have selected chalcogenide glasses as the main objects of the study because for these materials there are experimental data on viscosity available in a wide temperature range. For them, the expansion of the activation energy function \(E(T)\) into a Taylor series is acceptable. The method proposed can be expanded to include other glass-forming compositions.