esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Improve Image Colorization Discriminant Network
### Improved Discriminative Network PatchGAN with Added Attention Module  
The GAN network consists of two modules, the generator module and the discriminator module. In this paper, we first improved the generator by adding a multi-scale feature fusion module to the generator U-Net network to improve the feature extraction ability of the generator and improve the coloring effect of the infrared images. In the GAN network, the generator and the discriminator promote each other through confrontation. When the ability of the generator network improves, the discriminator must improve its own discriminatory ability to not be confused by the generator.  
Therefore, in this section, we improved the discriminator by adding an attention module to the discriminator network PatchGAN. When the input passes through, it will first pass through the channel attention and then the spatial attention. Through the attention module, we obtain the channel attention weight, and then multiply the input features with the channel attention weight to get a new feature map, which highlights the features and ignores the unimportant features to complete the filtering of the feature layer in the channel perspective. Then, the spatial attention module is used to filter the important regions on the feature map as well. The attention module allows PatchGAN to focus more on useful features and ignore unnecessary features, and also improves the learning efficiency of the network.  
The structure of PatchGAN is composed of five convolutional layers, using LeakyReLU as the activation function. At the end of the fourth convolution, the attention mechanism module is added, which is composed of two parts, channel attention and spatial attention, in series. The structure of the improved network is shown in Figure 9. The channel attention module goes through a mean pooling and maximum pooling first, which will go through a convolution kernel of 1 \(\times\) 1 and a ReLU activation function, and then a convolution kernel of 1 \(\times\) 1 and a Sigmod activation function. The output is fed to the spatial attention module, which goes through a convolution with a convolution kernel of 7 \(\times\) 7 and a Sigmod activation function, as shown in Figure 10.  
Figure 8: The structural diagram of spatial attention.  
Figure 7: The structure diagram of channel attention.  
Finally, the generator and discriminator are improved simultaneously to reconstruct the network. The improvement of the network consists of two parts, the first part improves the generator by adding a multi-scale feature fusion module to it to improve the semantic understanding of the network, and the second part improves the discriminator by adding an attention mechanism to the discriminator network to make the network more focused on useful features and improve the network recognition ability. Both networks have further improvements in feature extraction ability, so the problems of semantic uncertainty and color leakage for infrared image coloring are improved, and the next experimental validation will be performed on the NIR image dataset.
# Voice Maps as a Tool for Understanding and Dealing with Variability in the Voice
## 3 Voice Maps in Practice
### Time Needed for Collection  
Given that mapping the voice necessarily takes time, it would be useful to estimate how short a measurement time can be, yet still make a voice map that appears smooth to the eye. This will depend on whether the given metric represents some aspect based on individual cycles, or on sequences of several cycles, as for perturbations. A rule-of-thumb can be derived that about 10 observations (phonatory cycles) per cell will suffice for metrics that are defined on individual cycles. For metrics that quantify cycle-to-cycle variability, more cycles may be needed. To be on the safe side, we might adopt a conservative minimum of 20 cycles. Even so, that means that the informant needs to spend a total of only 0.2 s in each cell at \(f_{\text{o}}\) = 100 Hz, and proportionately _less_ at higher \(f_{\text{o}}\).  
We conclude that it is expedient to be as mobile as possible in \(f_{\text{o}}\) and SPL. The bottleneck is not that of acquiring sufficient data points, but rather to get the informant to control their voice such that the relevant part of the voice range is traversed in a short time. In principle, 2-3 min could be enough. Phonating on stationary vowels will only take unnecessary time, without providing more information.
# Research on Wind Field Characteristics Measured by Lidar in a U-Shaped Valley at a Bridge Site
## 4 Results and Discussion
### Wind Speed
#### 4.1.2 Wind Speed Profile  
After analysis, the nondimensional wind speed profiles do not conform to the power or exponential law and are classified into three types, as shown from Figures 10-12, respectively. According to the shape characteristics of the wind profile, Equations (2) and (3) are used for the nondimensional wind profile fitting.  
\[h=au+\overline{h} \tag{2}\]  
where \(u\) is the nondimensional wind speed, \(h\) is the height (m), and \(a\) and \(\overline{h}\) are the parameters (m).  
\[H=h_{*}\pm\eta\sqrt{u-\overline{u}} \tag{3}\]  
where \(H\) and \(h_{*}\) are the height (m), \(u\) and \(\overline{u}\) are the nondimensional wind speed, and \(\eta\) is the coefficient of surface roughness (m).  
Figure 8: Time history of wind speed.  
Figure 9: Flow chart of the raw data selection.  
Figure 11: Wind speed profile of Type-2.  
Figure 12: Wind speed profile of Type-3.  
Figure 10: Wind speed profile of Type-1.  
The first type, Type-1, takes up the highest proportion at 62%, and it is discrete and has no obvious rule. The second type, Type-2, has approximately linear distribution and the nondimensional wind speed in the whole measuring space does not change significantly with height, which is basically the same as the wind speed at the bridge deck. The third type, Type-3, is characterized by a relatively high wind speed at the bridge deck position as the height rises to about 400 m and the nondimensional wind speed decreases by about 20%, presenting an increasing trend. The piecewise exponential function is used for fitting, and the result is shown in the Equation (3).  
\[h_{Upper}=16200.0u-16637.40\]  
\[h_{Average}=18837.21u-18592.30\]  
\[h_{Lower}=23142.86u-21731.15\]  
\[H_{Upper}=357.14\pm 597.61\sqrt{u-0.75}\]  
\[H_{Average}=312.50\pm 559.02\sqrt{u-0.75}\]  
\[H_{Lower}=344.83\pm 587.22\sqrt{u-0.54}\]
## 4 Conclusions  
This study proposed a hybrid ANN-BBO algorithm based on spectral data of 800 to 900 nm and 920 to 980 for non-destructive estimation of two properties, namely starch and TSS. The most important results are:  
1. The most effective wavelengths selected by hybrid artificial neural network-simulated annealing algorithm to estimate TSS correspond to the wavelengths around the peak of 950 nm. In addition, the most effective wavelengths estimate starch corresponds to the wavelengths around the peak of 850 nm.
2. Due to the small amount of standard deviation of the results obtained by hybrid ANN-BBO in 100 iterations, it can be said that the proposed method has high reliability because of close results in different iterations.
3. The TSS were predicted with a regression coefficient above 0.8 in all iterations. Since this feature is related to the ripening stage of fruits, a high-performance portable device can estimate Fuji apple's ripening stage using the most effective wavelengths to manage post-harvest operations better.  
Precision agriculture requires the availability of portable tools that can be used in the field. The results help optimize Fuji apples' harvest time and apply precision agriculture at the orchard, supporting the postharvest operations. One of the requirements of portable devices that can be used in orchards is their portable size and low price. Since effective wavelengths are selected from different ranges, the amount of extracted data from each apple sample was reduced. Thus, a portable device predicting starch and TSS values can be developed. Another advantage of the proposed method was using the biogeography-based optimization evolutionary algorithm, which optimally adjusts the artificial neural network's main parameters and, in practice, ensures its high efficiency in estimating  
\begin{table}
\begin{tabular}{l l c c} \hline \hline
**Researcher** & **Fruit** & **No. of Samples** & \(\mathbf{R^{2}}\) \\ \hline The proposed method (Starch) & Apple & 140 & 0.923 \\ The proposed method (TSS) & Apple & 140 & 0.925 \\ [PERSON] et al. [22] & Passion fruit & 130 & 0.95 \\ [PERSON] et al. [21] & Peach & 40 & 0.80 \\ [PERSON] et al. [38] (in situ) & Orange & 530 & 0.49 \\ [PERSON] et al. [39] & Apple & 160 & 0.865 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparing the performance of the proposed methods on non-destructive estimation of starch content and TSS with other methods.  
starch and TSS values. External validation is needed in order to demonstrate the real potentialities of NIR in field conditions.  
Conceptualization, R.P. and S.S.; methodology, S.S.; validation, T.P., S.S. and S.J.; formal analysis, S.S. and T.P.; investigation, R.P.; writing--original draft preparation, R.P., S.S. and T.P. T.; writing--review and editing, T.P.; visualization, T.P.; supervision, T.P. and S.J. All authors have read and agreed to the published version of the manuscript.  
AGNETICS funded the APC.  
This research was supported by the project NIBAP--Nucleo de Investigacao em Biotecnologia e Agricultura de Precisao FEDER ALG-01-0247-FEDER-037303.  
The authors declare no conflict of interest.
## 4 Experiments
### Experimental Setup
#### 4.1.2 Evaluation  
We adopt the Macro F1 score as our evaluation metric, consistent with prior stance detection studies. Specifically, we compute the average F1 score for the **favor** and **against** classes only, excluding the neutral class. This evaluation strategy enables fair comparisons with previous studies and better captures the model's effectiveness in distinguishing polarized opinions. The Macro F1 score is calculated by first computing the harmonic mean of precision and recall for each class independently and then averaging the scores for the favor and against categories.  
We conduct two main types of experiments:  
**Supervised Experiment**: In this setting, models are trained and evaluated on the same set of targets. This represents a conventional stance detection scenario where the model has prior exposure to specific targets during both training and testing phases.  
**Zero-Shot Experiment**: In the zero-shot setting, the model is tested on unseen targets, without any prior exposure to them during training. This setting evaluates the model's ability to generalize by inferring stances for previously unseen targets using learned representations and relationships.
# Development and Application of Sub-Cycle Mid-Infrared Source Based on Laser Filamentation
## 4 Carrier-Envelope Phase of the Sub-Cycle MIR Pulses  
After the publication of the sub-cycle MIR pulse generation scheme in 2013, the authors have developed a new waveform characterization scheme, frequency-resolved optical gating capable of carrier-envelope phase determination (FROG-CEP), and succeeded in characterizing the field oscillation of the sub-cycle MIR pulse [25,26].  
In our previous paper published in _Applied Sciences_[7], we have shown that the generated spectrum is highly sensitive to the delay between the fundamental and second harmonic pulses from the Ti:sapphire laser. Later, we also investigated the delay dependence of the phase by using the FROG-CEP [27]. The phase change due to the delay can be explained by the interference between the two parametric processes, \(\omega_{1}+\omega_{1}-\omega_{2}\rightarrow\omega_{0}\) and \(\omega_{2}-\omega_{1}-\omega_{1}\rightarrow\omega_{0}\), which is consistent with the delay dependence of the power spectrum reported in [7]. One example of the phase control by changing the delay between the fundamental and second harmonic pulses is shown in Figure 2.
# A Review of SATis5: Perspectives on Commercial and Defense 5G SATCOM Integration
## 4 Overview of Recent and Existing Commercial SATis5 Testbeds and Projects
### Avanti Communications and ST Engineering iDirect on SATis5  
Avanti Communications and ST Engineering iDirect (AC-STE-iDirect) have played an important role in the EU-Commissioned SaT5G project. AC-STE-iDirect has provided the Avanti Teleport and successfully integrated the Teleport with a 5 GIC remote node and G5 IC central node using a live commercial AVANTI satellite network [16]. For the SaT5G testbed, the Avanti Teleport serves as a 5G-enabled Intelligent Gateway (5G-iGW) connecting the satellite with the 5G testbed at the University of Surrey Innovation Center (5 GIC) remote node and 5 GIC central node. For this 5G-NTN use case, the integrated 5G-NTN includes a remote satellite terminal located at a remote node. This configuration allows for the live connection over satellite to the 5G-iGW located at the 5 GIC central node. The keys for this configuration are: (i) the satellite connection uses the native satellite radio at the physical layer, (ii) the 5G-iGW provides the \"physical network functions\" for ending the native satellite connection using a RAN VNF, also known as \"virtualized satellite RAN\", and (iii) a standard and unmodified commercially available virtualized NGC, also known as \"5G core network\". Figure 11 describes AC-STE-iDirect Teleport serving as a 5G-GW.
# Underwater Excavation Records Using Underwater Acoustic Survey: A Case Study in South Korea
## 4 Application Case: Poor Underwater Visibility (Nakwoldo Site)  
The experiments conducted at the Dangampo site identified the SS as the acoustic device best suited for recording the seafloor topography. However, given that the Dangampo site is characterized by fairly good underwater visibility, it was necessary to test the performance of the SS in a turbid environment with poor underwater visibility. Therefore, we performed SS imaging during UEs at the Nakwoldo and Myeongnyang sites in 2018 and 2019.  
After the discovery and identification of numerous relics, including the Goryeo celadon, in the waters east of Nakwoldo (Yeonggwang County), a UE was conducted from May 4 to November 5, 2018. During the Nakwoldo excavation, the aim was to survey a large seafloor area to determine the relic distribution pattern and discover where relics are concentrated, or the remains of a shipw may be found.
## 4 Results and Discussion
### LoRaWAN or LoRaMESH Restricted Network Reach
Of course, for larger distances it is better to use the larger SF due to its range and RSSI that will be much larger, however, for smaller distances the smaller SF settings will always be better options because of the SNR, which are much better and the difference is significant, since the greater the SF the greater its interference in relation to the SNR, as can be seen in the Figure 33b, and can reach a difference of up  
Figure 31: Average RSSI and SNR per LoRaWAN point. (**a**) RSSI. (**b**) SNR. Figure 32: Average RSSI and SNR per LoRaMESH point. (**a**) RSSI. (**b**) SNR. to 10 dBm between the SF. In the LoRaMESH scenario, the results of RSSI and SNR are presented in the Figure 34.  
The RSSI was analyzed in the LoRaMESH protocol for each SF configuration in all four analyzed points, and the results are being represented in the Figure 34a, where it is possible to observe that the closer the node is, the better the RSSI, mainly by SF configuration. The difference of point A pro B is approximately 15 dBm, pro C is 30 dBm and 35 dBm for point D. The results behave as in LoRaWAN, however, the SNR is better for all points and configurations compared to LoRaWAN. It is noticeable that as the distance increases, the SNR will be affected because of the noise, allowing us to conclude that for small distances the smaller SFs are less affected and the RSSI is very close to the smaller configurations, when for larger distances the larger SFs have better RSSI and the SNR has a difference of only 0.5 dBm compared to the smaller SFs, making these the best SF configuration options for each distance, as can be seen in the Figure 34b. Finally, the RSSI and SNR were analyzed by point, by side and by SF configuration as shown in the Figure 35. Where, the Figure 35a,b represent the devices present on the left side, the Figure 35c,d represent the SMs present on the front side of the gateway and the Figure 35e,f represent the SMs on the left side of the gateway. In the figure, it can be observed that the communication on the left side behaves in the same way analyzed in the testbed, that is, as expected. For this side the RSSI varied between \(-95\) and \(-112.5\) dBm and the SRN at 6 and \(-15\) dBm. For the devices in front of the gateway, the RSSI ranged from \(-100\) to \(-114\) dBm while the SNR ranged from 3 to \(-15\) dBm. And for the right side the RSSI ranged from \(-90\) to \(-114\) dBm and the SNR at 6 and \(-15\) dBm. Based on these results, it can be seen that the right side got the best results due to the presence of several open fields and land, as well as the existence of few buildings, the left side was 5 dBm worse than the right side, this because of the commercial buildings while the worst case went forward, due to the presence of several condomiums of buildings and businesses the inference was much higher with a difference of 10 dBm for the best case in terms of RSSI and 3 dBm for the SNR. Figure 34: Average RSSI and SNR per point and SF LoRaMESH. (**a**) RSSI. (**b**) SNR. Figure 33: Average RSSI and SNR per point and SF LoRaWAN. (**a**) RSSI. (**b**) SNR.
## 2 Materials and Methods
### Statistical Analyses  
We applied two-sample \(t\)-tests to test whether the mean strength (dFC-Str) and temporal variability (dFC-SD and ALFF) within each temporal windowed connectivity map present any significant differences between SZ and HC groups. False Discovery Rate (FDR)-corrected activations with \(p\) < 0.05 [76; 77] were conducted, with confounding variables such as age, gender, IQ, and handed-ness (Table 1) as covariates in the GRETNA toolbox [78]. Permutation tests (\(p\) < 0.05) were also used to evaluate the differences between groups in state properties (MDT).  
\begin{table}
\begin{tabular}{l c c c} \hline \hline  & **Schizophrenia (n = 72)** & **Healthy Controls (n = 74)** & \(p\)**-Value** \\ \hline Age (years) & 38.17 \(\pm\) 13.89 & 35.82 \(\pm\) 11.58 & 0.270 1 \\ Sex (Female/Male) & 14/58 & 23/51 & 0.106 2 \\ Handed-ness & & & \\ Right/Left/Both & 60/10/2 & 71/1/2 & 0.106 2 \\ \hline IQ & (n = 68) & (n = 67) & \\ Verbal & 97.88 \(\pm\) 16.73 & 106.79 \(\pm\) 11.16 & \textless{}0.001 1 \\ Performance & 102.68 \(\pm\) 16.64 & 114.03 \(\pm\) 12.32 & \textless{}0.001 1 \\ Sum & 99.59 \(\pm\) 16.86 & 108.33 \(\pm\) 11.83 & \textless{}0.001 1 \\ \hline PANSS (n = 72) & & & \\ Positive scale & 14.96 \(\pm\) 4.83 & & \\ Negative scale & 14.53 \(\pm\) 4.83 & & \\ General & 29.22 \(\pm\) 8.34 & & \\ \hline Education (years) & 12.99 \(\pm\) 1.84 & 13.52 \(\pm\) 1.75 & 0.089 1 \\ Illness duration (years, n = 71) & 16.03 \(\pm\) 12.41 & & \\ \hline \hline \end{tabular}  
* Two-sample \(t\)-test. 2 Chi-square test. Data are shown in mean \(\pm\) SD.  
\end{table}
Table 1: Participant demographics.
## 4 Results and Analysis
### Influence of Tunnel Distance  
To exclude the interference of other factors, Figure 6 shows the influence of tunnel spacing distance (\(s/a\)) on the response of the twin tunnel system by taking the results of the vertical incident angle \(\gamma=0^{\circ}\) and the dimensionless frequency \(\eta=1.0\) as an example. Tunnel spacing distance (\(s/a\)) has an important effect on tunnel dynamic response. When the distance between tunnels is small, the displacement amplitude of the twin tunnels is very different from that of a single rigid tunnel, and the deformation distribution of the twin tunnels on the left and right sides is highly symmetrical, which indicates that there is a large interaction between the two tunnels. With the increase of tunnel spacing distance, the interaction between the two tunnels weakens. When \(s/a\) reaches 20, the two tunnels are no longer affected by each other. This indicates that when the dimensionless distance (\(s/a\)) between two tunnels is less than 20, the influence on each other should be considered in the seismic response analysis of tunnels.
# Efficient Implementation of Polymer Microwave Fiber Links Employing Non-Galvanic Coupling Mechanism
## 2 PMF Overview  
This section explores the materials and manufacturing processes used in PMFs, along with their propagation characteristics and electrical performance. Additionally, simulation results are presented to evaluate the PMF's effectiveness in various applications.
# Compositional Analysis and Aroma Evaluation of Feijoa Essential Oils from New Zealand
## 3 Materials and Methods
### Aromatic Profile of Feijoa Essential Oil  
The aromatic volatiles of feijoa essential oil were extracted, detected, and analyzed using headspace-solid phase micro extraction-GC-olfactory-MS (HS-SPME-GC-O-MS). In addition to the GC-MS system, an auto HS-SPME sampler controlled by a CombiPAL auto-sampler software (Cycle Composer Version 1.5.4, CTC Analytics AG, Zwingen Switzerland) and an olfactory port (Phaser OP275, ODP; ATAS/GL Sciences, Tokyo, Japan) connected via a 4-port splitter (SilFlow, Trajan, Australia) were equipped.  
Briefly, 10 \(\upmu\)L of pure essential oil was mixed with 1 mL of glycerol and placed in 20 mL amber glass screw-capped SPME vials (22.3 \(\times\) 46 mm, SUPELCO, PA, USA). Each vial was incubated in a shaking heated cube at 400 rpm for 25 min under 40 \({}^{\circ}\)C. The volatile compounds in feijoa essential oil were further extracted by a Divinylbenzene/Carboxen/Polydimethylsiloxane (DVB/CAR/PDMS) SPME fiber (50/30 \(\upmu\)m film thickness and 1 cm length; Supelco, Bellefonte, PA, USA) for 5 min, and subjected to GC-MS analysis. The GC-MS program and conditions were as described above in Section 3.4. The GC column effluent was split to the MS and Olfactory port in a 1:2 ratio by a 4-port splitter (SilFlow, Trajan, Australia). The splitter was flushed with helium at 3 mL/min. The olfactory port transfer line was set at 220 \({}^{\circ}\)C. To prevent dryness of the nasal mucosa, humidified air was provided to the sniffing port at a rate of 20 mL/min.  
For the detection of aromatic volatiles from feijoa essential oil, four trained panelists, consisting of two females and two males (aged 20-35), carried out the olfactory tests by sniffing through the olfactory port whilst the GC-MS was running. The retention time and the description and intensity of each detected aroma were recorded by the panelists during each test. An initial training using mixed natural aromatic compounds was conducted and the aroma description and intensity was standardized in trial experiments. Five scores, 1 (very weak), 2 (weak), 3 (medium), 4 (strong), and 5 (very strong), were used to evaluate the aroma intensity of each detected aromatic compound. The confirmation of aromatic compounds was made upon consistent detection by more than two panelists. The aroma profile was established after mutual agreement of all panelists. All samples were tested in triplicate.