esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Methodology
### Crop Yield Parameters  
In this study, crop yield predictions were made based on two groups of data: remote sensing data collected by satellite and weather data collected by regional synoptic stations. The remote sensing data included the data from MODIS, which provide information about plant growth during planting, germination, growth, and harvest, and also the soil moisture and CHIRPS data, which offer information about the water used for irrigation in rainfed agriculture. The meteorological data, which were intended to give information about the environmental conditions of the plants, were obtained from ground stations. All collected data are presented in Appendices A and B.
# The Application of Wood Species in Enology: Chemical Wood Composition and Effect on Wine Quality
## 3 Impact of Wood Species on Wine Chemical Composition
Regarding the evolution of bottled red wines with previous wood chips contact, [PERSON] et al. (2010) focused on the study, over a time of 18 months of aging, of several phenolic parameters of bottled Touriga Nacional red wines that had before been in contact with toasted wood chips from two oak species and cherry. Throughout 18 months of aging, the results indicated less reduction in the phenolic compounds and red color of wines which had previous interaction with oak chips, as well as a less developed brown color throughout bottle storage, compared to the wine previously in contact with cherry chips and the wine without contact with wood chips. Furthermore, wine previously in contact with cherry wood chips always presented an evolution similar to the wine without contact with wood chips. Previously, [PERSON] et al. (2014) also reported the evolution of phenolic compounds of a Portuguese red wine aged for 90 days in contact with wood chips from false acacia (_Robinia pseudoacacia_), cherry (_Prunus avium_), and oak species. According to these authors, the diverse wood chip species studied had no clear effect on the evolution of the majority of the red wine phenolic compounds. Nevertheless, from a sensory point of view, the use of diverse wood species induced higher distinction, particularly for aroma descriptors. Wood aging is also applied in some white wines, mostly by fermentation  
A study carried out by [PERSON] et al. [75] indicated differences in the volatile composition between wines aged in contact with oak and false acacia wood barrels. These authors reported an increase in oak lactones, simple volatile phenol, furfural, 5-methylfurfural, eugenol, guaiacol, and _trans_-eugenol during 12 months of aging time. Moreover, [PERSON] et al. [44] studied the influence of aging in an Encruzado white wine in contact with diverse wood chips species (false acacia, cherry, and oak) during 28 days. The results showed that the wine stored in contact with false acacia wood chips revealed a rise in total phenols, non-flavonoid and flavonoid phenolic compounds, and also color intensity. [PERSON] et al. [88,89] studied the volatile composition of young white wine from the _V. vinifera_ grape variety Chardonnay aged in wood barrels of false acacia. After 4 months of barrels aging, it was observed that the quantity of vanillin, syringaldehyde, ferulic acid, and furfural reduced significantly. This reduction is related to the false acacia wood's porosity that induced higher oxidation. There have been studies on the use of different woods in rose winemaking. [PERSON] et al. [37] investigated the influence of the application of cherry and oak wood chips
## 5 Experimental Results
### Rumor Detection Results and Comparisons
#### 5.1.1 Results
\begin{table}
\begin{tabular}{l l l l l l l l l l l} \hline \hline
**Dataset** & **NB** & \multicolumn{3}{c}{**RF**} & \multicolumn{3}{c}{**SVM**} & \multicolumn{3}{c}{**CRF**} & \multicolumn{3}{c}{**LSTM-RNN**} \\ \hline  & **Before** & **After** & **Before** & **After** & **Before** & **After** & **Before** & **After** & **Before** & **After** \\ \hline Ferguson & 0.64 & 0.66 & 0.36 & 0.37 & 0.57 & 0.54 & 0.64 & 0.74 & 0.76 & 0.76 \\ Germanwings & 0.65 & 0.63 & 0.76 & 0.21 & 0.51 & 0.53 & 0.62 & 0.64 & 0.65 & 0.54 \\ Crash & 0.65 & 0.72 & 0.48 & 0.62 & 0.58 & 0.56 & 0.70 & 0.69 & 0.61 & 0.54 \\ Ottawa & & & & & & & & & & \\ Shooting & & & & & & & & & & \\ Sydney & 0.71 & 0.69 & 0.49 & 0.57 & 0.51 & 0.49 & 0.71 & 0.72 & 0.66 & 0.60 \\ Siege & & & & & & & & & & \\ Charlie & 0.59 & 0.60 & 0.56 & 0.47 & 0.49 & 0.45 & 0.81 & 0.84 & 0.75 & 0.75 \\ Hebdo & & & & & & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Average precision scores of different classifiers before and after using social-based features associated with each dataset. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Classifier** & **Baseline Non-Rumors Result** & \multicolumn{3}{c}{**Proposed Non- Rumor Result**} \\ \hline  & **Precision** & **Recall** & **F-1** & **Precision** & **Recall** & **F-1** \\ \hline SVM & 0.67 \(\pm\) 0.17 & 0.56 \(\pm\) 0.65 & 0.61 \(\pm\) 0.41 & 0.69 \(\pm\) 0.38 & 0.55 \(\pm\) 0.14 & 0.63 \(\pm\) 0.38 \\ Random Forest & 0.65 \(\pm\) 0.18 & 0.88 \(\pm\) 0.96 & 0.75 \(\pm\) 0.24 & 0.70 \(\pm\) 0.45 & 0.76 \(\pm\) 0.74 & 0.78 \(\pm\) 0.69 \\ Naive Bayes & 0.77 \(\pm\) 0.52 & 0.41 \(\pm\) 0.18 & 0.51 \(\pm\) 0.36 & 0.81 \(\pm\) 0.81 & 0.51 \(\pm\) 0.94 & 0.62 \(\pm\) 0.64 \\ Logistic & & & & & & & \\ Regression & 0.67 \(\pm\) 0.84 & 0.57 \(\pm\) 0.55 & 0.62 \(\pm\) 0.17 & 0.70 \(\pm\) 0.94 & 0.70 \(\pm\) 0.31 & 0.74 \(\pm\) 0.57 \\
**Bi-LSTM-RNN** & \multicolumn{3}{c}{0.82 \(\pm\) 0.82} & 0.77 \(\pm\) 0.65 & 0.79 \(\pm\) 0.87 & 0.87 \(\pm\) 0.18 & 0.88 \(\pm\) 0.64 & 0.87 \(\pm\) 0.24 \\
**(Text)** & & & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparison of non-rumor class result with state-of-the-art baseline. Figure 8: Naive Bayes for rumor and non-rumor detection with proposed features. Figure 9: _Cont_.
# Real-Time Contrail Monitoring and Mitigation Using CubeSat Constellations
## 4 Distributed Systems with CubeSats
### System Design and Technical Specifications
Deploying nanosatellite constellations with hyperspectral sensors across critical aviation regions, like the North Atlantic Corridor, can provide continuous, real-time data to optimize flight routes and reduce contrail formation. Data processing capabilities are another critical aspect of the system design. Each CubeSat would have onboard processing power sufficient to handle the raw data collected by its sensors, performing initial analysis and compression before transmitting the data to ground stations. This approach reduces the amount of data that needs to be transmitted, conserving bandwidth and allowing for more efficient use of the satellite's communication systems. Communication protocols would also be a key consideration, as the CubeSats would need to maintain continuous contact with ground stations to relay their data in real time. This could be achieved through a network of ground stations strategically located around the globe, ensuring that at least one ground station is always within range of the CubeSat constellation. Alternatively, the CubeSats could be equipped with inter-satellite communication links, allowing them to relay data between each other until they can be transmitted to a ground station.
## 3 Results  
### Results of the WB Analysis  
Figure 1 shows the result of a WB analysis focused on ubiquitin conjugates (Ub) and UBB\({}^{+1}\) protein (Ub-\({}^{48}\)UBB\({}^{+1}\)), prepared from cellular lysates. The samples underwent a process involving lysis, centrifugation, TCA protein precipitation, denaturation, SDS-PAGE electrophoresis, transfer to a nitrocellulose membrane, TBST blocking, incubation with primary and secondary antibodies, and chemiluminescent detection.  
The results display a control group (K) with numbers 005 to 006 (on the left side) and an experimental group (S) marked with numbers from 112 to 116. On the right side, bands of synthesized Ub-\({}^{48}\)UBB\({}^{+1}\) dimer are visible. A lack of bands in the UbWT (wild type) section was also observed, indicating antibody binding specificity exclusively to the modified form. The WB analysis included proteins from the peripheral blood of the control group and patients diagnosed with schizophrenia. Detection using anti-Ub\({}^{+1}\) antibodies (40B3) indicates the predominant presence of polymeric forms of Ub\({}_{n}\)-UBB\({}^{+1}\) in both schizophrenia patients (S) and control samples (K).  
On Figure 1, a smear between 25 kDa and 250 kDa corresponds to polychains of Ub\({}_{n}\)-UBB\({}^{+1}\), 15 kDa to Ub\({}_{n}\)-UBB\({}^{+1}\) dimers, indicating the expression of Ub-UBB\({}^{+1}\) conjugates. This analysis, part of a broader research process, includes the synthesis of Ub-\({}^{48}\)UBB\({}^{+1}\) dimers, with bands indicating specific proteins and their modifications, which is significant for understanding biological processes such as protein degradation or DNA repair. Additional labels on the bands, such as 'UBWT', UBB\({}^{+1}\)', 'Ub-\({}^{48}\)UBB\({}^{+1}\)', and 'Ub\({}_{n}\)-UBB\({}^{+1}\)', indicate various forms of Ub, with a scale on the left side showing measurements in kilodaltons (kDa) for assessing protein size.  
Figure 1: WB analysis results: peripheral blood proteins in schizophrenia patients and control group. This analysis compares the control group (K), numbered 005–006, with the schizophrenia patient group (S), numbered 112–116. The molecular weight marker is labeled ‘M’. Notably, Ub-\({}^{48}\)UBB\({}^{+1}\) dimer bands are evident on the right, with an absence in the UbWT (wild type) section. The synthesized Ub-\({}^{48}\)UBB\({}^{+1}\) dimer was also detected, as a positive control for antibodies. The use of anti-Ub\({}^{+1}\) (40B3) antibodies revealed a significant presence of polymeric UBB\({}^{+1-\text{U}}\) in both schizophrenia (S) and control (K) samples. The labels ‘UBB\({}^{+1}\)’ and ‘48 Ub’ denote the sizes of certain ubiquitin-related proteins. ‘UBB\({}^{+1}\)’ indicates a ubiquitin B variant, while ‘48 Ub’ refers to a polyubiquitin chain linked via its K48. ‘Ub\({}_{n}\)-UBB\({}^{+1}\)’ represents a polyubiquitinated UBB\({}^{+1}\) variant (trimer, tetramer, and subsequent oligomeric states), and ‘-Ub-UBB\({}^{+1}\)’ indicates a single ubiquitin addition to UBB\({}^{+1}\) (dimer).
# Applying Deep Learning to Medical Imaging: A Review
## 2 Deep Learning Techniques in Medical Imaging
### Generative Adversarial Networks (GANs)
#### 2.3.2 Architectures and Applications  
Several GAN architectures and variants have been proposed for various tasks, including deep convolutional GAN (DCGAN) [98], Wasserstein GAN (WGAN) [99], and CycleGAN [100]. The basic mechanism of GANs has been shown in Figure 4. GANs have shown promising results in medical imaging applications [101; 102; 103], such as image synthesis, data augmentation, and image-to-image translation [104]. For example, GANs have been used to synthesize realistic medical images, which can be valuable for training other DL models, especially when annotated data is scarce [105]. In this context, GANs have been employed to generate synthetic CT, MRI, and ultrasound images, among others [106]. Another application of GANs in medical imaging is data augmentation, where GANs are used to create additional training samples to improve model performance and generalization [62]. By generating diverse and realistic variations of the available data, GANs can help mitigate issues related to limited datasets in medical imaging contexts [107]. Image-to-image translation is another application where GANs have shown potential in medical imaging. In this task, GANs are employed to transform images from one modality or representation to another, such as converting MRI images to CT images or enhancing image quality [108]. For instance, CycleGAN has been used for cross-modality synthesis between MRI and CT images, enabling the generation of synthetic CT images from MRI scans, which can be helpful in situations where CT scans are unavailable or contraindicated [63].  
Figure 3: A sample illustration of an RNN architecture.
## 5 Numerical Simulation
### Monte-Carlo Studies
In several cases, the CPs can be a bit higher since the CIs are bad with long interval lengths, but with the increasing number of the valid sample, most of the coverage probabilities (CPs) for different CIs are getting closer to the nominal confidence level. For example, the CP of BCI for \(\lambda\) in \(\mathbf{\theta_{2}}\) was higher than 90% under CS 1, but gets closer to 90% (0.8975) due to more valid data under CS 7, with a shorter AL (from 0.4984 to 0.2961). In several situations where the quantity of the units is relatively large, it can be better to have more samples censored at the same time, compared with CSs with less censored samples. In terms of point estimation, for pre-specified valid sample size, the MSEs and ABs of censoring samples at the last five failures are slightly less than those of censoring at the first five failures. In general, both point and interval estimates for parameter \(\lambda\) performed the best, featuring considerably  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c} \hline \hline  & & \multicolumn{6}{c}{\(\mathbf{\theta_{1}}\)} & \multicolumn{6}{c}{\(\mathbf{\theta_{2}}\)} \\ \hline  & & \multicolumn{3}{c}{**ACI**} & \multicolumn{3}{c}{**BCI**} & \multicolumn{3}{c}{**ACI**} & \multicolumn{3}{c}{**BCI**} \\ \hline
**CS** & **EC** & \(\mathbf{\alpha}\) & \(\lambda\) & \(\beta\) & \(\alpha\) & \(\lambda\) & \(\beta\) & \(\alpha\) & \(\lambda\) & \(\beta\) & \(\alpha\) & \(\lambda\) & \(\beta\) \\ \hline
[1] & AL & 0.8303 & 0.4191 & 1.7075 & 0.6306 & 0.3429 & 0.8896 & 1.0574 & 0.5925 & 2.0670 & 0.7581 & 0.4984 & 1.2544 \\  
small MSEs and ABs, whereas the two estimates of parameter \(\beta\) performed the worst, with relatively larger MSEs and longer interval lengths. To be specific, considering the figure of BE for \(\mathbf{\theta_{1}}\), one can find that the MSE and AB for \(\lambda\) under CS 9 are 0.0063 and 0.0661, while for \(\beta\) are 0.0427 and 0.1576, which means more deviation on \(\beta\) than \(\lambda\). Figure 4: Plots of evaluating criteria for \(\mathbf{\theta_{1}}\).
# Remote Low-Cost Differential Isolated Probe for Voltage Measurements
## 2 Hardware Description  
As presented in the introduction, there are different techniques to obtain galvanic isolation for the measurement of high voltages. Since for remote measurement in multipoint systems such as IoT sensor nodes, keeping the cost under control is an important factor, as the price greatly affects the multipoint measurement systems such as IoE systems, a low-cost isolation system is chosen. Figure 1 shows the block diagram of the proposed design.  
To design a low-cost differential remote probe, the main element to be selected is an isolation amplifier (IA), capable of handling high voltage values at its input, without  
Figure 1: Design block diagram. The differential input (left) attenuates the high voltages to be measured before they are sent to the amplifier, which isolates the input from the rest of the probe electronics.  
affecting the electronics connected to its output. In order to achieve a low-cost remote voltage probe, a Texas Instruments (Dallas, TX, USA) (TI) ISO122 (pin compatible with the Texas ISO124 and with similar characteristics) has been selected. This selection is due to its low price, although it can be replaced in the design with an AD215 from Analog Devices, which has similar power ranges and input and output voltage values and an improved bandwidth, but at a quite significant cost increase.  
Both isolation amplifiers present a bandwidth value enough to allow the processing of the harmonics established in the EN 50160 standard for the measurement of the quality of the mains network. This establishes that such a system meter must be able to measure up to voltage harmonic number 25. Thus, for a network frequency of 50 Hz, this corresponds to a maximum harmonic frequency of 1250 Hz.  
An additional important component in building a low-cost differential remote probe is the microcontroller (\(\mu\)C) and its wireless connectivity capabilities. For this purpose, in this work, an Arduino Nano 33 IoT has been used. This \(\mu\)C and its development board include both Bluetooth Low Energy (BLE) and Wi-Fi modules. This processor has been specially designed for IoT and IoE applications.  
As Figure 1 shows, the remote differential probe has two different outputs. The first one (V\({}_{\mu\text{C}}\)-GND) is connected to the microcontroller ADC (analog-to-digital converter). The secondary output (V\({}_{\text{osc}}\)-GND) allows a direct connection to an oscilloscope through a BNC connector for those cases where remote monitoring is not required. In addition, the probe can be energized through two different modes, depending on availability: through the mains network and by means of 18.5 V batteries.  
The design of the different blocks that make up the proposed low-cost differential remote probe are described below.
# Coarsely Quantized Decoding and Construction of Polar Codes Using the Information Bottleneck Method
## 3 Polar Code Construction Using the Information Bottleneck Method
### [PERSON]'s Construction
The error probability of the upgraded channel provides a lower bound on the probability of error of a bit channel. The value of \(|\mathcal{T}|\) is then selected such that the two bounds are very close to each other. When such a value for \(|\mathcal{T}|\) is found, the upgrading merge can be discarded. \(|\mathcal{T}|=256\) or \(512\) was shown to suffice ([23], Figure 2 and Table I). For the binary input AWGN channel \(p(\bar{y}|x)\), the continuous output \(\bar{y}\) is first descritized into a fine resolution with \(|\mathcal{T}_{ch}|\gg|\mathcal{T}|\). However, the bit channels synthesized from this fine resolution quantized channel have their output alphabets reduced to \(|\mathcal{T}|\) using the aforementioned degrading merge procedure. Therefore, the code construction at higher levels \(j>1\) in the code structure for \(N>2\) procedes as explained for channels with discrete output.
## 6 Numerical Simulation Verification of Dewatering Optimization Scheme Based on GMS
### Numerical Simulation Results and Analysis  
Based on the MODFLOW module and SUB subroutine package in GMS, according to the requirements of foundation pit dewatering and settlement control, and comprehensively considering three objectives, the dewatering scheme of running 24 pumping wells and closing the J19 and J22 pumping wells under the third optimal condition is numerically simulated and analyzed to verify its feasibility and accuracy. Therefore, the groundwater level and settlement contour maps in the study area under this dewatering scheme are shown in Figures 12 and 13.  
It can be seen from Figure 12 that the water level at the groundwater level control point in the foundation pit is lower than the elevation 110.3 m of the foundation pit bottom, and the water level in the pit is stable between 108.9 and 109.6 m, which can lower the water level to 1.4 m below the foundation pit bottom plate and meet the construction requirements.  
Figure 12: Contour map of groundwater level in the study area.  
Figure 13: Contour map of settlement in the study area.  
It can be seen from Figure 13 that the settlement in the foundation pit settlement observation points is up to 8.8-9.0 mm, and the average settlement is about 8.9 mm. Compared with the allowable settlement of 15 mm calculated by the layered summation method, the simulation results show that the settlement is far less than the allowable settlement, which can reduce about 41% of the settlement. It can be verified that the optimized dewatering scheme of running 24 pumping wells and closing the J19 and J22 pumping wells under the third optimal working condition will not cause settlement damage to the foundation pit and surrounding buildings, and this can ensure construction safety.  
The numerical simulation results and optimization results under this dewatering scheme are compared and analyzed, and the comparison results are shown in Table 12. The comparison results show that the numerical simulation results can lower the water level in the center of the foundation pit to 1.4 m below the bottom of the foundation pit and meet the requirements of water level drawdown. The observed value of settlement is 8.9 mm, which is less than the allowable value of 15 mm of settlement, which verifies the feasibility of the optimization results.
# Improved Homotopy Perturbation Method for Geometrically Nonlinear Analysis of Space Trusses
## 4 Results and Computational Examples
### Example 3: 120-Member Space Truss  
The third example shown in Figure 6 has 120 elements and 49 nodes. The boundary support condition is pinned to the ground. This example is based on the previous [PERSON] and [PERSON] [29] studies on space trusses. It is noted that the axial stiffness (EA) is considered to be 44486.54 kN, and the concentrated load applied at the crown is equal to 1065 kN with a loading interval of 44.44 kN.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Decrease Percentage** & \multicolumn{2}{c}{**Present Method**} & \multicolumn{2}{c}{**Newton Method**} \\ \hline
**Iteration no.** & **Time (s)** & **Iteration No.** & **Time (s)** & **Iteration No.** & **Time (s)** \\ \hline
29 & 12.89 & 107 & 0.3107 & 151 & 0.3567 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of computational cost and iteration number  
Figure 4: Space truss with 42-members, all dimensions are in mm [28].  
Figure 5: Space truss with 42-members, all dimensions are in mm.  
The variation of the vertical displacement with force P is shown in Figure 7. It is concluded that the PM method precisely predicts the results with less than 10.4% for computational cost and 31% for iteration number, compared to classic Newton-Raphson method, which is summarized in Table 3.