esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction and Motivation  
The most auspicious Next Generation Networks (NGN) are the heterogeneous networks. They are based on the coexistence and interoperability of different types of Radio Access Technologies (RAT), including the current and the forthcoming networks, intended to fit the requirements for various applications in the future. 4G came with the features \"Always Best Connected (ABC)\", \"Anytime Anywhere\" and \"seamless communications\". In [1], the authors assert that a terminal supports the ABC feature means that it is not only always connected, but also connected through the best available network and access technology at all times. The ABC concept includes virtually all types of access technologies; ABC achieve a win-win partnership because it considers user's and operator's benefits. In the mean time, 4G provides a larger coverage area, faster data transfer, low latency and low data transfer cost. The main issue for the 4G heterogeneous networks is Network Selection, _i.e._, a flawless and proficient handover scheme that allows the roaming of mobile devices from one wireless system to another. Therefore, with ABC functionalities, terminals are able to select the best access network to fit various Quality of Service (QoS) requirements; terminals escape a network with high traffic load to avoid congestion and reduce costs by using network selection, this is known as Vertical handover, and vertical handover decision strategies are dressed specifically for this purpose. In generic terms for all wireless standards [2] proposed a handover decision scheme based on multi-criteria decision-making modeling providing a continuous connection in heterogeneous wireless networks, unlike traditional approaches which are mainly concerned with specific scenarios.  
Being the key for resource management in a wireless heterogeneous network, dynamic network selection algorithms intend to provide users with the adequate QoS in terms of attributes and user's preferences. Both are aforethought during the process of network selection ( see Figure 1). Hence, this technology is a hot research topic in the field of wireless communication. Network selection can be programmed by terminal or can depend on measurements of link quality of the network when multiple alternatives are available at the same time. The terminals are equipped more and more by new technologies; every terminal has no less than two network interfaces, such as WLAN, GPRS, HSPA, WIMAX or LIFI [3]. In the optimal cases, the user can move from one wireless network to a different wireless network seamlessly with no deterioration of the connection.  
Figure 1: Network Selection Process.  
The remainder of this paper is organized as follows. Related works about vertical handover decision (VHD) strategies are summarized in Section 2. In Section 3, the authors introduce the performance evaluation model and the new configuration of AHP weights and the handover algorithms are compared. Section 4 presents the simulations and the results discussion. Finally, Section 5 concludes the work and gives some perspectives.
# Study of 5G-NR-MIMO Links in the Presence of an Interferer
## 4 Numerical Results
### General Interferer  
We first simulated the case described in Section 3.1. This is the general case. The received symbols are presented in (13). Figure 6 shows the constellation of the transmitted symbols in blue and the received symbols in orange when sending 1000 bits with 4, 8, 16-PSK modulations with no AWGN and SIR = 10 dB. The received symbols are random and depend on the channel matrix's characteristics, \(H_{TR}\) and \(H_{IR}\). These values are random and independent. Therefore, we obtain random receives symbol results, i.e., the general interferer creates random byproducts in the combining block. This issue poses a difficulty in constructing cost terms for the discovery of the symbols.  
Figure 4: Simulation general block diagram of the signal and interferer using a \(2\times 2\) Alamouti technique.  
Figure 5: Simulation block diagram using the Simulink platform.  
An interesting innovation to examine is the general interferer effect on system performance in terms of Bit Error Rate (BER). The BER performances as a function of SIR are shown in Figure 7. For M = 2, 4, 8, 16-PSK constellation orders, SNR = 15 dB, with \(10^{5}\) transmitted bits, and \(\Delta\)SIR = 0.2 dB.  
Figure 6: A constellation of transmitted symbols (blue) and received symbols in the presence of the general interferer (orange), for (**a**) 4–Phase-Shift Keying (PSK), (**b**) 8–PSK, (**c**) 16–PSK modulations, no Additive White Gaussian Noise (AWGN), and SIR = 10 dB.  
Figure 7: Alamouti 2 \(\times\) 2 with the presence of the general interferer. Bit error rate (BER) as a function of the signal to interference power ratio (SIR), for different modulation orders 2, 4, 8, 16–PSK and SNR = 15 dB.  
As expected, a higher modulation order leads to a lower BER performance because the distance between the transmitted symbols is shorter, as shown in Figure 6. In addition, a higher modulation order increases DG, i.e., the shift of the graphs with respect to a reference curve with DO of 2 [27].
# Occluded Pedestrian Detection Techniques by Deformable Attention-Guided Network (DAGN)
## 4 Experimental Results
### Caltech Pedestrian Dataset
#### 4.2.2 Ablation Experiments on Caltech Pedestrian Dataset  
Ablation experiments were performed on the Caltech heavy occlusion test dataset to demonstrate the effect of each component added in our method. The following items were included: the deformable convolution with attention module (DCAM), the loss function with CIoU and DIoU-NMS, and the preprocessing of ALTM. The baseline method was cascade R-CNN+FPN. The detection performance was improved gradually when these modules were added one-by-one, as shown in Table 2. By applying the DCAM, an improvement of 8.51% in \(MR^{-2}\) was obtained compared to the baseline method. In Figure 6a, the occluded instances on the left side of the image with low contrast are difficult to distinguish from the background. On the right side of the image, the person occluded by the car failed to be detected by using the baseline method. However, these instances were all detected after using the DCAM, which demonstrates that our network can pay more attention to pedestrian regions even with occlusions, while reducing the background inference. Then, the designed loss function with CIoU optimized the prediction localization, which further improved the detection performance by 4.17% in \(MR^{-2}\). By using the CIoU loss for regression, our detector could produce more accurate bounding boxes. DIoU-NMS helped to preserve the target boxes in crowded scenes. Figure 6b shows that box A with a high overlap area over box B was suppressed by the plain NMS, but box A remained with the DIoU-NMS. Thus, the predicted boxes of occluded pedestrians could be retrieved well. After adopting DIoU-NMS, the detection performance was improved by 5.48% in \(MR^{-2}\). Moreover, the preprocessing using ALTM also contributed to an improvement of 3.92% in \(MR^{-2}\), which provided a more robust detection performance under poor illumination.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Cascade** & **DCAM** & **CIoU Loss** & **DIoU-NMS** & **ALTM** & **MR (\%)** \\
**R-CNN+FPN** & **(\(-\)8.51)** & **(\(-\)4.17)** & **(\(-\)5.48)** & **(\(-\)3.92)** & **MR (\%)** \\ \hline \hline - & & & & & \\ \hline - & \(\surd\) & & & & 46.79 \\ \hline - & \(\surd\) & \(\surd\) & & & 42.62 \\ \hline - & \(\surd\) & \(\surd\) & \(\surd\) & & 37.14 \\ \hline - & \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) & 33.22 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Influence of each component of our proposed method on the Caltech heavy occlusion subset.
## 3 Results and Discussion
### Spectroscopic Profile by ATR-FTIR
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{4}{c}{**Spectrum Wavelength cm\({}^{-1}\) 4000-650**} \\ \hline
**Groups** & **Recognition (\%) \({}^{\text{a}}\)** & **Rejection (\%) \({}^{\text{b}}\)** & **Interclass Distance \({}^{\text{c}}\)** & \\ \hline EtOH & 100(8/8) & 100(16/16) & EtOH-MeOH:H2O & 140 \\ MeOH: H2O & 100(8/8) & 93(15/16) & EtOH-H2O & 83 \\ H2O & 100(8/8) & 75(12/16) & MeOH: H2O-H2O & 2 \\ \hline  & \multicolumn{4}{c}{**Spectrum wavelength cm\({}^{-1}\) 3000-2800**} \\ \hline
**Groups** & Recognition (\%) \({}^{\text{a}}\) & Rejection (\%) \({}^{\text{b}}\) & Interclass distance \({}^{\text{c}}\) & \\ \hline EtOH & 100(8/8) & 100(16/16) & EtOH-MeOH:H2O & 23 \\ MeOH: H2O & 100(8/8) & 93(15/16) & EtOH-H2O & 24 \\ H2O & 100(8/8) & 50(8/16) & MeOH: H2O-H2O & 2 \\ \hline  & \multicolumn{4}{c}{**Spectrum wavelength cm\({}^{-1}\) 1750–1524**} \\ \hline
**Groups** & Recognition (\%) \({}^{\text{a}}\) & Rejection (\%) \({}^{\text{b}}\) & Interclass distance \({}^{\text{c}}\) & \\ \hline EtOH & 100(8/8) & 100(16/16) & EtOH-MeOH:H2O & 90 \\ MeOH: H2O & 100(8/8) & 87(14/16) & EtOH-H2O & 670 \\ H2O & 100(8/8) & 100(16/16) & MeOH: H2O-H2O-H2O & 4 \\ \hline  & \multicolumn{4}{c}{**Spectrum wavelength cm\({}^{-1}\) 1523–1171**} \\ \hline
**Groups** & Recognition (\%) \({}^{\text{a}}\) & Rejection (\%) \({}^{\text{b}}\) & Interclass distance \({}^{\text{c}}\) & \\ \hline EtOH & 100(8/8) & 100(16/16) & EtOH-MeOH:H2O & 18 \\ MeOH: H2O & 100(8/8) & 100(16/16) & EtOH-H2O & 30 \\ H2O & 100(8/8) & 87(14/16) & MeOH: H2O-H2O & 3 \\ \hline  & \multicolumn{4}{c}{**Spectrum wavelength cm\({}^{-1}\) 1170–945**} \\ \hline
**Groups** & Recognition (\%) \({}^{\text{a}}\) & Rejection (\%) \({}^{\text{b}}\) & Interclass distance \({}^{\text{c}}\) & \\ \hline EtOH & 100(8/8) & 100(16/16) & EtOH-MeOH:H2O & 7 \\ MeOH: H2O & 100(8/8) & 93(15/16) & EtOH-H2O & 5 \\ H2O & 100(8/8) & 50(8/16) & MeOH: H2O-H2O & 4 \\ \hline  & \multicolumn{4}{c}{**Spectrum wavelength cm\({}^{-1}\) 944–660**} \\ \hline
**Groups** & Recognition (\%) \({}^{\text{a}}\) & Rejection (\%) \({}^{\text{b}}\) & Interclass distance \({}^{\text{c}}\) & \\ \hline EtOH & 100(8/8) & 100(16/16) & EtOH-MeOH:H2O & 5 \\ MeOH: H2O & 100(8/8) & 81(13/16) & EtOH-H2O & 31 \\ H2O & 100(8/8) & 62(10/16) & MeOH: H2O-H2O & 2 \\ \hline \hline \end{tabular} Notes: \({}^{\text{a}}\) Percentage of recognition in optimal model shall be as close to 100%; \({}^{\text{b}}\) Percentage of rejection in optimal model shall be as close to 100%; \({}^{\text{c}}\) Interclass distances should be as high as possible, minimum 3.  
\end{table} 
Table 5: Interclass distance of ethanol (EtOH), methanol:water (MeOH:H2O) and water (H2O) extracts. Values reported in the Table 6 show that all samples were correctly recognized and in the case of significantly different samples (AN:GO) only one sample could not be correctly classified.
###### Abstract  
Most of the published literature regarding bearing capacity are often focused on linear and associative soils. Concerning the intrinsic strength nonlinearity in dilatancy soils, this study investigates the problem of the seismic bearing capacity in the framework of the kinematic theorem of limit analysis. The conventional linear Mohr-Coulomb criterion is substituted with a nonlinear power law criterion to depict the nonlinearity of the soil strength. The non-associative feature of soil materials is considered by defining a nonlinear dilatancy coefficient. A generalized tangential technique is accordingly introduced to linearize the strength envelope for making the nonlinear criterion tractable in the analysis. A non-symmetrical translational failure mechanism that is comprised of several rigid wedges is used to characterize the failure of the foundation at the limit state. Moreover, the seismic action is considered by the classic pseudo-static method. Based upon the energy equilibrium theory of the upper-bound limit analysis, new analytical solutions are derived from the work-balanced equation with nonlinearity and dilatancy. This rigorous upper-bound solution is formulated as a multivariate optimization problem and is readily addressed by sequential quadratic programming (SQP). To verify the reliability of the new expressions, the present results are compared with already posted solutions and the original pseudo-dynamic solutions. The comparative results show a good agreement with previous works, and the correctness and rationality of the new analytical solutions are validated. The detailed parametric study reveals that, in the non-associative flow soils, the ultimate bearing capacity is significantly decreased with a reduction in the dilatancy coefficient. Particularly in the linear condition, namely m = 1, the larger the internal friction angle is, the more obvious the influence of the non-associative feature on the bearing capacity is.  
seismic bearing capacity; shallow foundations; multi-block mechanism; kinematic theorem +
Footnote †: journal: Applied  
Article  
Prediction of Seismic Bearing Capacity Considering Nonlinearity and Dilatancy by Sequential Quadratic Programming.
## 3 Results and Discussion
### Survivability of S. boulardii and LAB in Synthetic Yogurt  
The survivability of _S. boulardii_ as affected by inulin at different concentrations during storage of yogurt at 4 \({}^{\circ}\)C is presented in Figure 3. The viable counts (\(\geq\)8 log CFU/g at day 0 of storage) of the yeast in the yogurt samples S2, S3, S4, and S5 significantly decreased (\(p\leq 0.05\)) throughout the four weeks' storage period. Higher viable counts of the yeast in the yogurt samples the added inulin, than that in the sample without inulin, were observed after 14 days of storage, suggesting that inulin played a role in maintaining the viability of the yeast in yogurt. In the sample (S2) without inulin, the viable count of the yeast decreased from initial \(\geq\)8 to 5.5 log CFU/g at day 28, while it was higher than 6 log CFU/g for S3 (6.22 log CFU/g), S4 (6.41 log CFU/g), and S5 (6.53 log CFU/g) with inulin at the end of the storage, satisfying the minimum requirement of the Food and Agriculture Organization (FAO) and World Health Organization (WHO) guidelines for probiotics [9]. Viability of probiotics was reported to be affected by many factors such as storage time, oxygen content, fluctuation in temperature, low pH, reduced water activity, and high concentration of salutes [40; 41]. Microencapsulation method was applied for protection of _S. boulardii_ to increase its survival, but this technique increased the production cost [23]. The present study employing inulin provided an effective and economic approach in the development of symbiotic yogurt containing the probiotic yeast with enhanced viability.  
The viable counts of LAB in all yogurt samples showed slight decreases during the 28 days of storage. At day 0 the viable counts of LAB for S1, S2, S3, S4, and S5 samples were 8.62, 8.41, 8.35, 8.14, and 8.06 log CFU/g, respectively. Whereas, at day 28 they decreased to 7.43, 7.37, 7.29, 7.17, and 7.10 log CFU/g for S1, S2, S3, S4, and S5, respectively. [PERSON] et al. [23] also showed good viability of LAB when _S. boulardii_ was added to yogurt.
## 1 Introduction
Additionally, they also stated that the humidity ratio significantly influences the dehumidification capacity. [PERSON] et al. [31] found an energy reduction of 26.3% when using the solid desiccant for the mobile air conditioning system. The system was the hybrid system, which was a combination of the solid desiccant and vapor compression cycle. The presence of a desiccant system was able to split up the latent heat load from the sensible heat. Hence, the compressor power consumption reduced because of the increase of the evaporating temperature. The above review shows that the desiccant system is promising for air conditioners; therefore, evaluating their performance would be equally important as understanding the parameter design. Because the current study focused on the desiccant dehumidification system, the latent heat became the primary consideration in performing the performance evaluation. In the adsorption/desorption process, latent heat can be defined as the heat transfer, which occurs during the phase change [32]. Only a few studies provide the effect of the latent heat on the performance of the desiccant system. This reason, therefore, gained our motivation to propose a study on the latent heat effect. Additionally, the consideration of the desorption temperature adjustment was also provided in this study. It is widely known that the increase of desorption temperature would give more dehumidification capacity [30; 33; 34]. However, low desorption temperature [28; 35] is preferable to reduce the power consumption. It is worth considering the low desorption temperature; even so, it is still unclear whether this temperature will contribute to a significant effect on the performance of the system given the energy-saving and moisture removal ability we have discussed. From these issues, our objective is to determine the impact of the desorption air temperature and latent heat on the performance and dehumidification ability of the desiccant dehumidification system. The experiment was conducted by employing low-temperature desorption (lower than 343 K), which also reflected the low-grade energy. A recommendation for such a system was also proposed to obtain an effective and efficient operation.
# LLM-Based Cyberattack Detection Using Network Flow Statistics
## 2 Materials and Methods
### Training the Cyberattack Detection System
#### 2.3.1 Data Preprocessing
Inspired by the data representation strategy that we designed in a previous work [49], where adapted T5 for translation tasks involving multiple input features (such as jokes and puns) against a single model input, we used the vertical bar character (1) as a separator between each feature's string value, following a predefined, consistent order. This process results in a unique character string for each network flow, effectively translating the numerical statistics of a network flow into a structured textual sequence. This abstract language allows the T5 model to process network flow data as if it were a specialized form of text, enabling the fine-tuning process to adapt the model's powerful pattern recognition capabilities from linguistic structures to the complex patterns present in network flow statistics for cyberattack detection. Figure 3 shows an example of the construction of these input strings, given statistics of network flows. The top section displays the characteristics, while the bottom section shows the separator character. The central box presents a preprocessed input entry. Arrows indicate where each feature value is placed, alongside the vertical bar separating them. * Labeling: The labels with the types of attacks contained in the dataset are strings with alphanumeric characters, and, in this way, they are useful if the intention is to identify the type of attack at first sight. However, labels must be encoded to avoid issues in classification. We decided to encode the labels using sequential numbers, starting from 0.
## 4 Experiment and Analysis
### Predicting Rock Surface Strength  
The training results demonstrate that the fine-tuned DNN can classify the spectrograms with high accuracy. In the next step, the outputs of the network were regarded as the features of the spectrograms. Three regression algorithms, including the KNN, the SVM, and the RF, were then tried to fit the relationship between the features and the strengths.
## 2 Nonlinear Mismatches in \(M\)-TIADC  
Figure 1 illustrates a TIADC consisting of \(M\) sub-ADCs and the timing diagram of the sampling clocks for the constituent ADCs. The periods of all of these clocks are \(MT_{s}\); thus, the overall sampling period of the TIADC is \(T_{s}\). The nonlinear characteristics of the \(m\)-th sub-ADC, i.e., \(\text{ADC}_{m}\), are represented by the polynomial \(T_{m}(x)=x+\sum_{l=2}^{L}\alpha_{m,l}x^{l}\), where \(x\) is the band-limited analog input signal \(x(t)\) with bandwidth \(\Omega_{\xi}<2\pi/T_{s}\), and \(\alpha_{m,l},l=2,...,L\) are the polynomial coefficients  
representing the nonlinearity of channel \(m\). As the focus of this paper is the compensation method for the nonlinear behavior of TIADCs, we assume that the linear frequency response mismatches have been calibrated. The quantization process is not considered throughout the theoretical analysis, because its effect can be modeled as adding white noise. Only static (frequency independent) nonlinearities are considered in this paper since static nonlinearities are the dominating part of nonlinear feature in most applications. The nonlinearities are modeled as polynomials with different parameters. This model cannot describe all nonlinearities in ADCs; however, it is sufficient to model several important nonlinearities, such as saturation and dead zone nonlinearities.  
The time-domain output of \(M\)-TIADC with nonlinear mismatches is:  
\[y[n]=x[n]+\sum_{l=2}^{L}\alpha_{n,l}x^{l}[n], \tag{1}\]  
where \(\alpha_{n,l}=\alpha_{n+M,l}\) and \(x[n]=x(nT_{s})\) is the ideally sampled input signal.
# Short-Term Forecasting of Power Production in a Large-Scale Photovoltaic Plant Based on LSTM
## 3 Model Description
### Performance Criteria  
In order to examine the capability of the designed LSTM-models to forecast the power produced by the PV plant, four seasons for each class (spring, summer, autumn, and winter) have been considered. Mean absolute percentage error (MAPE) and root mean square error (RMSE) as shown below will be used to evaluate the accuracy of forecasting and training process [1; 5].  
\[MAPE=\frac{1}{N}\sum_{t=1}^{N}\left|\frac{S_{i}-Y_{i}}{Y_{total}}\right|\times 1 00\% \tag{9}\]  
Figure 8: Photovoltaic system power output under different weather conditions.  
Figure 7: The four seasons’ periods over diurnal variations in irradiance (spring: 12.3 h; summer: 14.7 h; autumn: 12.7 h; winter: 10.2 h).  
\[RMSE=\frac{\sqrt{\frac{1}{n}\sum\limits_{t=1}^{N}{{{(S_{i}-Y_{i})}^{2}}}}}{{Y_{m}}} \times 100\% \tag{10}\]  
where, \(S_{i}\) is the forecasting result at the time point \(i\); \(Y_{i}\) is the actual value at the time point \(i\). \(Y_{total}\) is the PV installation capacity; \(Y_{m}\) is the mean power generation of each season. For the case in this paper, the value of \(Y_{total}\) is 10 MW.