esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Optimum Irrigation Period of Winter Wheat  
The precipitation-meteorological yield integral regression function was calculated using the winter wheat yield and precipitation data of these typical counties from 1997 to 2017. The effect of \"additional\" precipitation at different growth stages on yield is shown in Figure 4.  
Figure 4a indicates that the precipitation-meteorological yield integrated regression function of winter wheat in subregion I shows a bimodal function. The peak value of the integral regression function appeared in the 40 th to 50 th days and in the 190 th to 200 th days after sowing, which were during the t  
Figure 4: The effect of each additional 1 mm of precipitation on wheat yield.  
these two periods, the yield of winter wheat would increase by 0.6% and 0.8%, respectively. Therefore, irrigation should be conducted during these two periods.  
As in subregion I, the precipitation-meteorological yield integrated regression function in subregion II shows a bimodal function (Figure 4b), and the integrated regression function also reaches its peak value in the 40 th to 50 th days and in the 190 th to 200 th days after sowing. Especially during the jointing stage, a strong evaporation capacity and frequent dry, hot wind, together with severe air and soil drought, resulting in serious water shortage, impeding winter wheat growth. Therefore, to improve the yield of winter wheat, irrigation should be performed during the tillering and jointing stages.  
The amount of precipitation in subregion III is minimal, and the evaporation is large; it is another serious arid area next to subregion I. Therefore, precipitation has a greater impact on winter wheat yield. Moreover, subregion III is a summer maize-winter wheat rotation area, with poorly hydrated soil and water conservation characteristics, resulting in poor water conditions for winter wheat growth during the winter. The final precipitation-meteorological yield integral regression function shows a unimodal function (Figure 4c). The overwintering period is the sensitive period of water demand for winter wheat. From the 100 th to 120 th day after sowing, the yield of winter wheat can be increased by 0.6% for every additional 1 mm of precipitation. Therefore, irrigation should be performed during the winter wheat overwintering period.  
The precipitation in the growth period of winter wheat in subregion IV is large, and the precipitation-meteorological yield integral regression function shows a bimodal function (Figure 4d). The maximum value of the integral regression function appeared in the 70 th to 80 th days after sowing. During this period, wheat enters the overwintering period, and an increase of precipitation is conducive to ensuring safe overwintering. Therefore, while irrigation is key to increasing wheat yield during this period, the temperature should be closely noted during winter irrigation, and it should be completed before freezing.
## 3 Results
### Substituted Benzenes
#### 3.3.8 Acetophenones  
Data for acetophenones are collected in Table 15. Contrary to benzaldehyde, we here needed the GC parameter for a keto Group which we have determined previously [1]. In addition, we needed to introduce the acetyl-benzene interaction parameter to which we attributed the value +8 kJ/mol to arrive at a good fit between the GC model and experimental values, viz. Table 15. The G4 results and available experimental values are just within chemical accuracy. The larger deviation for 2-ethylacetophenone is similar to that observed in similar molecules in the previous subsection, and to be attributed to steric interactions between neighbouring substituents which are generally not accounted for in the GC models. When we disregard these three 2-substituted species, the model values reveal an averaged absolute difference with experimental or, when these are not available, the G4 calculated values of 2.7 kJ/mol.  
The value of the acetyl-benzene parameter can be varied; e.g., when adopting a value of +6 kJ/mol, the averaged absolute deviation drops to 1.9 kJ/mol, but at the same time, the deviation for 2-methylacetophenone increases to 6.0 kJ/mol, beyond chemical accuracy. This can either be attributed to steric effects (as for 2-ethylacetophenone) or considered as acceptable, as the error in the experimental value was given as 2.3 kJ/mol for 2-methyl-acetophenone, and thus the GC model is within chemical accuracy from the  
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**Acetophenones** & **Exp.** & **Model** & **Model – Exp.** & **ABS (Model – Exp.)** & **ABS (G4 – Exp.)** & **G4** \\ \hline acetophenone & \(-\)86.7 & \(-\)82.9 & 3.8 & 3.8 & 4.2 & \(-\)90.9 \\ \hline
2-methylacetophenone & \(-\)115.4 & \(-\)119.2 & \(-\)4.0 & 4.0 & 3.8 & \(-\)111.6 \\ \hline
3-methyl-acetophenone & & \(-\)119.2 & & 1.7 & & \(-\)120.9 \\ \hline
4-methyl-acetophenone & \(-\)121.8 & \(-\)119.2 & 2.6 & 2.6 & 0.2 & \(-\)121.6 \\ \hline
2-ethylacetophenone & & \(-\)139.9 & & 8.9 & & \(-\)130.9 \\ \hline
3-ethyl-acetophenone & & \(-\)139.9 & & 1.6 & & \(-\)141.4 \\ \hline
4-ethyl-acetophenone & & \(-\)139.9 & & 2.1 & & \(-\)141.9 \\ \hline averaged absolute difference & & 3.5 & 2.7 & & \\ \hline averaged absolute difference without 2-substitutions & & 2.7 & & & \\ \hline \end{tabular}
\end{table}
Table 15: Experimental, present GC model, and conformationally averaged G4 model values for the heat of formation for benzoic acids. All values in kJ/mol. All data are from [PERSON] and Verevkin [49] except for the experimental [34] and the G4 result (present work) for acetophenone. In those cases where the experimental value is not available, the G4 value was used in column 5.  
experimental value. More data would be required to establish the optimum parameter for the acetyl-benzene parameter.  
Overall, we can conclude that our model performs very satisfactorily in view of the limited availability of experimental data and the agreement with G4 data.
## 3 Experiment Results
### QAM-OFDM Modulation  
In the QAM-OFDM modulation experiment, the length of the CP was 8, and there were 3060 IFFT blocks in a data frame. The number of used subcarriers varied from 53 to 69 for different data rates, and the number of IFFT points was set at 256. Figure 3b presents the BER curves without signal processing (RAW), with RLS (RLS), and with DeepESN (DESN).  
The DESN waveform in Figure 2 demonstrates a better match with the Tx waveform than the RAW waveform. Given the lower mean square error (MSE) for the DESN signals compared to the RAW signals, this suggests that DeepESN processing could also enhance the BER for the OFDM signal. From Figure 3b, it is observed that higher-data-rate signals suffer from a larger power penalty across all speeds and signal processing cases, with the power penalty exceeding 4.6 dB between 100 Mbps and 130 Mbps. Signals processed by DeepESN do not exhibit the abnormal performance observed in the PAM modulation case. The BER curves for the signal without equalization reached the error-free limit at the received power (\(P\)) of \(-20.2\) dBm, \(-19.6\) dBm, \(-18.1\) dBm, and \(-17\) dBm for 100 Mbps, 110 Mbps, 120 Mbps, and 130 Mbps, respectively. For all transmission rates, the signals processed by DeepESN and RLS outperformed the signal without any processing. DeepESN-assisted signals consistently showed better performance compared to the RLS method across all transmission rate cases at the same received power. For DeepESN-assisted signals with different transmission rates, the processed signal demonstrated better performance at the same received power when the transmission rate was lower.  
The power-saving plot for the OFDM signals reveals a trend of power savings decrease for higher-data-rate signals in all equalization methods. Meanwhile, DeepESN signals consistently exhibit the highest power savings at each data rate. The power saving of DeepESN signal declines from 5.6 dBm at 100 Mbps to 3.3 dBm at 130 Mbps, referring to the RAW signal. It is worth noting that the difference of power savings between the RLS signal and DeepESN signal expands from 0.11 dBm at 100 Mbps to 0.65 dBm at 130 Mbps data rate.
## 2 Background and Related Work
We observed that the above approaches omitted the period correlation between these flows. The approach [36] does not focus on the correlation between traffic periods. We proposed the flow classification (FC) technique which finds the correlation of multiple flows' periods to utilize the same links. Based on FC, we proposed the MPFRS-FC algorithm to generate routes based on traffic period characteristics, which is suitable for relatively complex topologies where there are more than two disjoint paths between two end systems that can be transmitted. If during route discovery, flows that are not in the same class must share partial path to reach the destination, then the scheduling step can be terminated early and the newly added flow can be placed in the nonschedulable flow set. This process simplifies the complexity of joint routing and scheduling optimization. Then, the APCRS-FC algorithm is not only appropriate for network topologies with simple or loosely coupled structures but also can further improve the schedulability of the nonschedulable flow set output by the MPFRS-FC algorithm.
# Effective Class-Imbalance Learning Based on SMOTE and Convolutional Neural Networks
## 3 Methodology
### Dataset Preprocessing  
As stated before, the most acute problem in classifying imbalanced data is that classifiers become biased toward the majority class. There are several methods to overcome this issue which are generally called resampling techniques. By adding minority class samples or removing samples from the majority class, resampling turns the data into a more balanced one. In this regard, there are two principal methods: oversampling and undersampling.  
Oversampling algorithms generate new samples, duplicated or synthetic, that belong to the minority class. In contrast, the undersampling techniques delete samples that belong to the majority class to afford balance to the dataset [23].  
As a preprocessing step in our methodology, we have utilized various well-known oversampling and undersampling techniques for balancing the dataset. Normalization and split datasets are the next steps in data preprocessing. These are elaborated on in the following.  
Figure 2: The framework overview of our proposed methodology in this study.
## 4 Results and Discussion  
In this section, we provide the numerical results, obtained from the proposed approach, on the two considered datasets. The performance has been evaluated by considering Non-COVID scans as the reference images to evaluate the target histograms. The Non-COVID class has been formed from images related to patients infected by common viral pneumonia (CP) or from healthy subjects (N). The performance comparisons with three state-of-the-art deep architectures are also presented. In the case of CT dataset, the test set, used in experiments, is composed of 500 CT scans belonging to the new coronavirus pneumonia (NCP) and 500 CT scans belonging to the reference class (CP or N). In the case of CXR dataset, instead, the test set, used in experiments, is composed of 100 CXRs belonging to the new coronavirus pneumonia (NCP) and 97 CXRs belonging to the reference class (CP or N). To provide a clear graphical representation of the obtained distances, the test instances have been fed into the proposed algorithm in this order: first, the NCP scans, and then the reference CP or N ones.
## 3 Results and Discussion
### The Permittivity of Conductive Fabrics  
The permittivity' real part (\(\epsilon\)) represents the polarization level of the conductive fabrics in an external electric field. A high permittivity real part indicates the higher polarization ability of the material. Similarly, the permittivity's imaginary part (\(\epsilon\)\") represents the energy loss caused by the rearrangement of the electric dipole moment for the conductive fabrics in an external electric field. A high imaginary part indicates a greater loss capacity against electromagnetic waves. In addition, the loss tangent characterizes the absorption attenuation capability of the materials, and thus a greater loss tangle means an equivalently better absorbing property [33; 34; 35].  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Fabric Type**} & **Thickness** & **Density** & **EMI SE** & **SE/t** & **SSE** & **SSE/t** \\  & **(mm)** & **(g/cm\({}^{3}\))** & **(dB)** & **(dB/mm)** & **(dB cm\({}^{3}\)g\({}^{-1}\))** & **(dB cm\({}^{2}\)g\({}^{-1}\))** \\ \hline PSA-SS-W1\({}^{\prime}\) & 0.41 & 0.56 & 10.64 & 25.95 & 19 & 463.43 \\ PSA-SS-W2\({}^{\prime}\) & 0.41 & 0.57 & 9.38 & 22.87 & 16.46 & 401.33 \\ PSA-SS-1\({}^{\prime}\) & 0.38 & 0.55 & 34.08 & 89.68 & 61.96 & 1623.98 \\ PSA-SS-2\({}^{\prime}\) & 0.38 & 0.56 & 34.42 & 90.57 & 61.46 & 1617.38 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The shielding effectiveness per unit volume (SSE) and SSE/t of four types of fabrics.  
Figure 5: (**a**) The electromagnetic interference shielding effectiveness (EMI SE) of four fabrics without hydrophobic treatment; (**b**) the EMI SE of four fabrics with hydrophobic treatment; (**c**) The average EMI SE value of samples before and after hydrophobic treatment; (**d**) the shielding effectiveness per unit volume (SSE) values of hydrophobic fabrics.  
Figure 6 shows that PSA-SS-1 and PSA-SS-2 which are composed of conductive yarms as both warp and weft yarms have \(\varepsilon^{\prime}\) and \(\varepsilon\"\) values that are higher than those of PSA-SS-W1 and PSA-SS-W2 which have only weft yarms that are composed of conductive yarms. PSA-SS-1 and PSA-SS-2 have a higher storage energy capacity, higher dielectric loss capability, and better EMI SE than PSA-SS-W1 and PSA-SS-W2 because of a higher polarization ability and more loss capacity. Additionally, the shielding mechanisms of EMI SE are absorption, reflection, and multiple reflections. Taking PSA-SS-1 as an example, the \(\varepsilon^{\prime}\) is between 4 and 6 and the \(\varepsilon\"\) is between 0 and 1.2, and the latter is smaller than the former. Due to the low absorbing ability and high polarization, PSA-SS-A thus shields electromagnetic waves primarily by means of reflection.
## 4 Discussion
It can be known from Tables 1 and 6 that, compared with the CNN method, the SCNN method not only has a simple network structure but also has fewer network parameters. The SCNN is typically different from a traditional CNN in that it lacks a pooling layer. Generally, the pooling layer has the effect of reducing image dimensions and parameters. However, previous work has shown that high-resolution signals may lose some important information in the pooling layer [49]. In addition, in order to reduce the dimensionality of the image, the size of the convolution kernel in this method has been appropriately adjusted, similar to those described in the literature [42]. To sum up, compared with CNN method, the proposed SCNN method has a better application value.
# Pattern Classification Based on RBF Networks with Self-Constructing Clustering and Hybrid Learning
## 6 Experimental Results
### Experiment 2--Multi-Label Data Sets
Tables 8-10 show the testing EMR, LFM, and HL obtained by different methods, including ML-KNN (K-nearest neighbors classifier for multi-label) [49], MLPC (Multi-layer Perceptron classifier) [64], ML-SVC (Support vector classifier for multi-label), ML-RBF (RBF network for multi-label) [50], and RBF-ISCC, for the data sets in Table 7. The codes of ML-KNN, MLPC, and ML-SVC are downloaded from the scikitlearn library, while the codes of ML-RBF is downloaded from the public domain: [[http://cse.seu.edu.cn/people/zhangml/resources.htm](http://cse.seu.edu.cn/people/zhangml/resources.htm)]([http://cse.seu.edu.cn/people/zhangml/resources.htm](http://cse.seu.edu.cn/people/zhangml/resources.htm)). For these data sets, we set \(\eta=0.1\) in RBF-ISCC, except \(\eta=1.2\) for \"Scene\". The other constants are shown in Table 11. Note that their values are determined in the training phase and are not changed in the testing phase. The average metric values obtained by each method are shown in the bottom line of Tables 8-10. From these tables, we can see that RBF-ISCC performs the best in average EMR and average HL for these data sets. Table 12 ranks different methods based on the average results in Tables 8-10. Clearly, RBF-I
# Computation Offloading and Resource Allocation Based on P-DQN in LEO Satellite Edge Networks
## 4 P-DQN-Based Approach
### P-DQN Training
Therefore, a negative sign is added in (19) to formulate it as a loss function, allowing us to simultaneously maximize the objective while minimize the loss function, as follows:  
\[L_{t}(\mathbf{\theta})=-\sum_{v=1}^{V}Q(\mathbf{s}_{t},v,\mathbf{n}_{v}(\mathbf{s}_{t};\theta );\mathbf{w}_{t})/V. \tag{19}\]  
Next, the data in the experience replay pool is used to obtain the stochastic gradient \(\
abla_{\mathbf{w}}L_{t}(\mathbf{w}_{t})\) and \(\
abla_{\mathbf{\theta}}L_{t}(\mathbf{\theta}_{t})\), and both weights are updated as follows:  
\[\mathbf{w}_{t+1}=\mathbf{w}_{t}-\beta_{t}\
abla_{\mathbf{w}}L_{t}(\mathbf{w}_{t}), \tag{20}\]  
and  
\[\mathbf{\theta}_{t+1}=\mathbf{\theta}_{t}-\zeta_{t}\
abla_{\mathbf{\theta}}L_{t}(\mathbf{ \theta}_{t}), \tag{21}\]  
where \(\beta_{t}\) and \(\zeta_{t}\), respectively, denote the step sizes for updating parameters \(\mathbf{w}\) and \(\mathbf{\theta}\). Till now, the joint computation offloading and resource allocation algorithm for LEO edge networks with P-DQN is listed in Algorithm 2, together with the flowchart in Figure 2. Apparently, the P-DQN is an online and off-policy RL method. ```
0: step sizes \(\{\beta_{t}\}_{t=1}^{T}\) and \(\{\zeta_{t}\}_{t=1}^{T}\), exploration rate \(\varepsilon\) and batch size \(U\). 1: Initialize the simulation environment parameter settings. 2: Initialize the network parameters \(\mathbf{\omega}\) and \(\mathbf{\theta}\), and empty the replay buffer \(D\). 3:for\(t=1,2,...,T\)do
4: Calculate action parameters (computing resources and power): \(\mathbf{n}_{v}(\mathbf{s}_{t},\mathbf{\theta}_{t})\to\mathbf{n}_{v}\). 5: Select the action \(\mathbf{a}_{t}=(v_{t},\mathbf{n}_{v_{t}})\) according to the \(\varepsilon\)-greedy strategy: \[\mathbf{a}_{t}= \begin{cases}(v_{t},\mathbf{n}_{v_{t}})\text{ such that }v_{t}=\arg\max_{v\in\mathcal{V}}Q(\mathbf{s}_{t},v,\mathbf{n}_{v};\mathbf{w}_{t}),& \text{with probability }1-\varepsilon\\ \text{sample an action in }\mathcal{A}\text{ randomly.}&\text{with probability }\varepsilon\end{cases} \]
6: Execute \(\mathbf{a}_{t}\) and store the tuple \((\mathbf{s}_{t},\mathbf{a}_{t},r_{t},\mathbf{s}_{t+1})\) into \(D\). 7: Randomly sample \(U\) tuples \((\mathbf{s}_{u},\mathbf{a}_{u},r_{u},\mathbf{s}_{u+1})\) from \(D\), \(u=1,2,\ldots,U\). 8: Compute the target \(Q\) value: \[y_{u}= \begin{cases}r_{u},&\text{if is\_end is true}\\ r_{u}+\max_{v\in\mathcal{V}}\gamma Q(\mathbf{s}_{u+1},v,\mathbf{n}_{v}(\mathbf{s}_{u+1}, \mathbf{\theta}_{t});\mathbf{w}_{t}).&\text{otherwise}\end{cases} \]
9: Use the experience replay to obtain the stochastic gradients \(\
abla_{w}L_{t}(\mathbf{w}_{t})\) and \(\
abla_{\mathbf{\theta}}L_{t}(\mathbf{\theta}_{t})\). 10: Update the parameters \(\mathbf{w}\) and \(\mathbf{\theta}\) based on (20) and (21). 11:endfor
```  
**Algorithm 2** Joint computation offloading and resource allocation with P-DQN
###### Abstract
Tests are usually conducted on samples of larger dimensions. For example, in Kosarimovahhed and Toufigh [24], samples with dimensions of 150 \(\times\) 150 \(\times\) 180 mm were observed, and in [PERSON] et al. [22], samples had dimensions of 100 \(\times\) 100 \(\times\) 35 mm and 500 \(\times\) 500 \(\times\) 450 mm. However, tests on smaller samples have also been reported [21]. Usually, tests are carried out on samples of a certain age, since rammed earth walls are built inside the formwork and remain inside the formwork for a certain period. In the literature, tests are reported on samples aged 7 days [21], 30 days [23], 60 days [22] and 90 days [24]. When observing the shear strength values, it is necessary to keep in mind the different values at the layer contacts compared to the values within the layer itself. [PERSON] et al. [23] state values of the internal friction angle at the contact in the amount of 90% in relation to the internal friction angle of the layer itself. The recorded value of cohesion in the observed case was even lower, i.e., 80% [23]. In [PERSON] et al. [26], a literature review is presented related to the testing methods of the mechanical properties of unstabilized rammed earth, one of which was applied in this study. In the seismically active area of eastern Croatia (Figure 2, Table 1), there are examples of rammed earth houses that are more than 100 years old. Unfortunately, there are no standards or recommendations in Croatia related to the construction of rammed earth houses nor to their renovation. Moreover, to the best knowledge of the authors, no studies have been carried out on the load-bearing capacity and resistance of such structures for the observed area. Figure 2: The earthquake hazard map of Croatia and bordering countries [27] with markings of the places from which the rammed earth samples observed in this work were taken: Aljmaš (ALJ), Bijelo Brdo (BB), Karanac (KR), Lug (LG) and Zmajevac (ZM). The aim of this research is to determine, based on the results of the shear strength test: (i) the suitable grading curve of mixtures that were formed on the basis of the grading curves of samples from existing traditional rammed earth houses from the area of eastern Croatia; (ii) the influence of lime; (iii) the influence of cereal straw and hemp fibers. A further aim is to determine the change in shear strength for samples of different age. Chapter 2 provides an overview and description of the observed mixtures on which direct shear tests were performed, as well as a description of the test procedure. Chapter 3 presents the results of the performed tests. Chapter 4 provides a discussion of the results, and finally, in Chapter 5, the conclusions are given.