esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Results and Discussion
### Experimental Results and Analysis of Laser Scribing Depth with Laser Incidence Angle
#### 5.3.3 Discussion of Laser Scribing Depth with Laser Incidence Angle
The laser beam incidence angle has a great influence on the laser scribing area. With the change of the inclination angle \(\theta\) of the laser beam, the radiation area of the laser becomes wider (S2 > S1), which is shown in Figure 18.  
With the increase of laser scribing area, the laser power density decreases under the condition of invariable laser power, speed and frequency. It takes a longer time for the single-pulse laser energy to heat the material to the melting temperature, and heat transfer time becomes longer. As a result, the width of the scribing becomes larger with the increase of the heat-affected zone, and the loss of heat transfer energy in the whole process becomes larger. In summary, the above three parameters will lead to a decrease in the slit depth of laser scribing with the increase of incidence angle. Figure 18: The influence of laser angle. (**a**) The influence of laser beam incidence angle on depth and area; (**b**) 3D sketch of laser beam incidence angle on depth and area.
## 2 Results and Discussion
### RNA-Seq Output, Sequence Assembly and Gene Annotation
#### 2.1.3 Functional Annotation  
The 51,865 unigenes were successfully annotated through comparison with the sequences in the major public databases. In total, 39,269 unigenes were annotated to at least one database, which accounted for 75.71% (see Table 3). For Gene Ontology annotation, 29,375 unigenes were mapped to 57 functional groups (see Figure S2), among which, 18,932 were involved in the \"metabolic process\". Of the 12,860 unigenes that were assigned to the COG database, 656 belonged to the cluster \"secondary metabolites biosynthesis, transport and catabolism\" (see Figure S3). The KEGG annotation profiled the biological pathways that are active in _I. asprella_ and 20,752 unigenes were mapped to 128 KEGG pathways. Moreover, 272 unigenes were assigned to five terpenoid-like biosynthesis processes. One hundred and two unigenes (0.49%) mapped to \"terpenoid backbone biosynthesis\", 13 (0.06%) mapped to \"Monoterpenoid biosynthesis\", 77 (0.37%) mapped to \"Diterpenebiosynthesis\", 30 (0.14%) mapped to \"Sesquiterpenoid and triterpen biosynthesis\" and 50 (0.24%) mapped to \"Ubiquinone and other terpenoid-quinone biosynthesis\". Based on the annotation results, the candidate genes related to terpenoid backbone and triterpen synthesis were identified and discussed in detail.  
\begin{table}
\begin{tabular}{c c c} \hline
**Value of FPKM** & **Count** & **Proportion/\%** \\ \hline \(>\)0 & 50,879 & 98.10 \\ \(>\)1 & 46,426 & 89.51 \\ \(>\)10 & 12,650 & 24.39 \\ \(>\)100 & 1475 & 2.84 \\ \(>\)1000 & 102 & 0.20 \\ \hline \end{tabular}
\end{table}
Table 2: FPKM values distribution.
## 2 Materials and Methods
### Division of Impoundment Periods  
According to the construction phase and changes of the water level of the TGD, five impoundment stages were determined (Figure 2): (1) stage 1: pre-impoundment period (October 2000-September 2003), with the highest water level below 70 m; (2) stage 2: the first impoundment period (October 2003-September 2006), with the highest water level peaked at 135 m; (3) stage 3: the second impoundment period (October 2006-September 2008), with the highest water level reached at 156 m; (4) stage 4: the third impoundment period (October 2008-September 2011), with the highest water level from 156 m to 175 m; (5) stage 5: officially operated period (October 2011-September 2020), with water level to 175 m in the dry season and 145 m in the flood season.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Dataset** & **Including Data** & **Time Resolution** & **Time Period** & **Data Source** \\ \hline PML V2 & ET, Ec, Es, Ei & 8-day & 2000–2020 & 97596a75b8858f5 ab34 eed2 bc (accessed on 30 July 2021) \\ MOD16 & ET & 8-day & 2000–2020 & [[https://earthdata.nasa.gov](https://earthdata.nasa.gov)]([https://earthdata.nasa.gov](https://earthdata.nasa.gov)) (accessed on 30 July 2021) \\ Land use data & land use & yearly & 2015 & [[http://www.esa-landcover-ci.org/](http://www.esa-landcover-ci.org/)]([http://www.esa-landcover-ci.org/](http://www.esa-landcover-ci.org/)) (accessed on 30 July 2021) \\ Meteorological data & PT, Ta, WS, RH, SSD & daily & 2000–2020 & the China Meteorological Administration \\ Water level data & water level & daily & 2003–2020 & the Three Gorges Corporation \\ \hline \hline \end{tabular}
\end{table}
Table 1: Detailed information of datasets used in this study.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Name** & **Longitude** & **Latitude** & **Elevation** & **PT** & **Ta** & **WS** & **SSD** & **VPD** \\  & (°E) & (°N) & **(m a.s.l)** & **(mm)** & **(°C)** & **(m/s)** & **(h)** & **VPD** \\ \hline Badong (BD) & 110.22 & 31.02 & 3340 & 1085.44 & 17.53 & 1.77 & 1602.23 & 0.64 \\ Changshou (CS) & 107.04 & 29.50 & 3776 & 1103.62 & 18.15 & 1.31 & 1126.22 & 0.54 \\ Fengdu (FD) & 107.41 & 29.52 & 2180 & 1035.60 & 18.85 & 1.29 & 1265.67 & 0.64 \\ Fengie (FD) & 109.30 & 31.03 & 6073 & 1038.52 & 18.39 & 1.73 & 1372.31 & 0.69 \\ Jiangjin (JJ) & 106.15 & 29.17 & 2614 & 1009.62 & 18.89 & 1.41 & 1087.69 & 0.41 \\ Shapingba (SPB) & 106.28 & 29.35 & 2591 & 1134.39 & 18.96 & 1.40 & 989.72 & 0.63 \\ Wanzhou (WZ) & 108.24 & 30.46 & 1867 & 1177.85 & 18.87 & 0.93 & 1203.34 & 0.41 \\ Xingshan (XS) & 110.46 & 31.14 & 2755 & 962.15 & 17.23 & 1.11 & 1560.64 & 0.46 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Detailed information of the studied meteorological stations.
## 2 Materials and Methods  
This section outlines the methodology used to conduct a systematic literature review aimed at comprehensively addressing the research questions formulated in Section 1. This review includes studies that apply geospatial techniques across all stages of the HVC. Table 1 provides a summary of the principal geospatial methods identified. Studies focusing on non-renewable hydrogen and/or do not include any of the geospatial techniques listed in Table 1 were excluded from the systematic review. Table 1 classifies geospatial tools by maturity level into three categories: mature, emerging, and experimental. This classification provides an overview of the current status of each technique to support a clearer understanding of the conclusions of the research conducted. A technique is considered mature if it is proven, demonstrated, and widely available for commercial use. Emerging techniques have been validated in controlled environments or at the prototype level but lack standardized application. Finally, experimental techniques are those with no application beyond the laboratory scale. Scopus and Web of Science (WoS) have been used as reference databases. The search strategy consisted of an initial global search that included all terms referring to geospatial techniques, as well as to the different technologies within the HVC. The specific queries were as follows:  
* **Geospatial techniques:** TITLE-ABS-KEY. (\"geospatial\" OR \"geographic information system\" OR \"spatial analysis\" OR \"spatial data\" OR \"spatial modeling\" OR \"geospatial data\" OR \"terrain modeling\" OR \"LiDAR\" OR \"InsAR\" OR \"satellite image\" OR \"geophysics\" OR \"Remote Sensing\" OR \"Earth Observation\" OR \"GIS\" OR \"IoT\" OR \"topographic survey\" OR \"classical surveying\" OR \"land surveying\" OR \"topographic mapping\" OR \"geodetic survey\" OR \"photogrammetry\" OR \"aerial photogrammetry\" OR \"computer vision\" OR \"UAV photogrammetry\" OR \"drone photogrammetry\" OR \"Object detection\" OR \"hyperspectral imaging\" OR \"thermal imaging\" OR \"digital elevation model\" OR \"sensor network\" OR \"geovisualization\" OR \"3D GIS\" OR \"real time monitoring\" OR \"augmented vision\" OR \"virtual reality\" OR \"BIM\" OR \"ground penetrating radar\")
# Emotion Estimation Using Noncontact Environmental Sensing with Machine and Deep Learning Models +
## 4 Results
### Model Comparison  
Figure 18 shows each model's average estimation results and 95% CIs calculated across all subjects. The results of the experiment confirmed a certain level of accuracy in estimating emotional valence, with the highest accuracy achieved by the RF model (RF valence estimation accuracy: mean: 0.870, 95% CI: [0.859, 0.880]). Although the GBDT model was less accurate than the RF model, it demonstrated potential for emotion estimation (GBDT valence estimation accuracy: mean: 0.846, 95% CI: [0.833, 0.859]). The CNN-LSTM model, which was expected to improve accuracy by capturing temporal information, showed a significant drop in accuracy and variability compared to the other two models (CNN-LSTM arousal estimation accuracy: mean: 0.403, 95% CI: [0.360, 0.446], valence estimation accuracy: mean: 0.592, 95% CI: [0.520, 0.664]). Regarding the CIs, no significant differences were observed between the RF model and the GBDT model when averaging the accuracy across all seven subjects, as both models exhibited similar CI sizes. For arousal estimation, accuracy was generally lower across all models compared to valence estimation. The RF model again outperformed the others (Mean: 0.685, 95% CI: [0.669, 0.701]), with GBDT showing slightly lower accuracy (Mean: 0.648, 95% CI: [0.632, 0.665]). These results suggest that environmental data-based emotion estimation is effective for both Valence and Arousal. While the RF model and the GBDT model demonstrated higher accuracy in this experiment, the CNN-LSTM model, which captures temporal patterns, showed lower accuracy and higher variability. This suggests that, despite its potential, the CNN-LSTM model requires further refinement. In the next section, we will analyze the training convergence curves to identify potential areas for improvement.  
Figure 16: Valence result for subject 5.  
Figure 17: Valence result for subject 6.
## 4 Experimental Results
### Analysis Results for False Detection Cases
#### 4.2.2 Similar Forms of False Detections  
Although the model effectively identified entities with forms similar to personal information, it often misidentified them as other named entities; some examples of such cases are listed in Table 14. This constitutes the mainstream of false detection cases, highlighting the importance of establishing clear criteria for proper identification of each named entity as personal information, ensuring consistent and accurate annotation, and considering the context. Similar forms of seven erroneous cases are as follows:  
1. 'name' and 'nickname' are interchangeably used to refer to a person. Because foreign or baptismal names resemble nicknames, confusion may occur when using them.
2. There is a similarity between a nickname and a club/society in that both allow users to freely create words without a fixed format, which may cause false detection.
3. Similar to places that imply participation or visitation, the term 'club/society' can also indicate membership, leading to misinterpretation.
4. Because the club/society and workplace pertain to an individual's affiliation, it is possible to choose the wrong word when filling out the form.
5. The term'self-employed' should be detected as a workplace depending on the context; however, it is sometimes misidentified as a place. Differentiating between places and workplaces in each context is important because insufficient data can lead to false detection.
6. Although the last digits of a resident registration and alien number may have different rules depending on the individual, they are identical in form and usage, leading to cases of mistaken identity. Therefore, conclusions must be drawn based on the context of the preceding and following dialog.
7. Because card and account numbers appear in financial dialogs and are often accompanied by the mention of credit card companies or banks, they can be falsely detected. In this experiment, card numbers were occasionally mistaken for account numbers; however, account numbers were never mistaken for card numbers. Therefore, improving the data reduces the probability of errors.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Division** & **Named Entity** & **Dialog** \\ \hline Original & NName & \textless{}Chilchl-ah:PS\_NICKNAME\textgreater{}, Did you lose your wallet again? \\ Prediction & Nickname & \textless{}Chilchl:PS\_NICKNAME\textgreater{}ah, Did you lose your wallet again? \\ Original & Job title/position & \textless{}Sajang-nim:CV\_POSITION\textgreater{}, my child said he ate free food at your restaurant. \\ Prediction & Job title/position & \textless{}Sajang:CV\_POSITION\textgreater{}nim, my child said he ate free food at your restaurant. \\ \hline \hline \end{tabular}
\end{table}
Table 13: Examples of annotation scope errors.
## 3 Experimental Results and Discussion
The obtained experimental results were used to define the laser operation regions shown in Figure 5. With a pump power of 1.1 W, CW laser operation is obtained when the gain along the EYDCF is more uniform reaching a level in which the effects of saturable absorption and relaxation oscillations are eliminated [21]. Figure 5: The laser output power for dual-wavelength emission as a function of the pump power (Self Q-switched (SQS) and CW laser operation regions are also indicated). Figure 7 shows experimental results of the SQS laser operation as a function of the pump power. The measurements were obtained in the laser SQS region for dual wavelength laser operation with pump power variations in a range from 322 to 890 mW. The dual-wavelength laser generation was adjusted for the settings of the results obtained in Figure 5. Figure 7a shows the SQS pulses repetition rate and the laser average power as a function of the pump power. In the SQS laser operation range, an increase of the repetition rate and the average power is observed when the pump power is increased. Maximal repetition rate of 71.8 kHz and average power of 249 mW are obtained with the maximum pump power where SQS pulses are observed of 890 mW. With the increase of the pump power, the repetition rate of SQS pulses increases to a limit in which the gain along the EYDCF is more uniform leading to a reduction of the saturable absorption and suppressing SQS operation at high pump. As a result, when a passive fiber with enough length to increase the photon lifetime in the resonator is not added, the full repetition rate of the SQS operation of the laser is mainly determined by the physical length of the EYDCF [21]. SQS laser pulses are observed in a range from 21.7 to 71.63 kHz for a pump range from 322 to 980 mW. Figure 7b shows the estimated pulse energy and the measured pulse duration as a function of the pump power applied. As it can be observed, the obtained pulses exhibit a typical behavior of passive Q-switched pulses. When the applied pump power increases, the pulse energy increases and the pulse duration decreases. With the maximal pump power of 890 mW, the maximal pulse energy of 3.49 \(\upmu\)J and the minimal pulse duration of 1.5 \(\upmu\)s were obtained. Figure 7c shows the estimated peak power of the SQS pulses as a function of the pump power. As it can be observed, the peak power increases as the pump power is increased. It is worth to note that high peak power pulses of around 2.3 W are obtained in the pump power limit between SQS and CW operations of the EYDCF laser. Figure 6: The pulses trains of the SQS laser operation as a function of the pump power for dual-wavelength laser emission.
## 2 Related Work
### UAV Integration into 5G  
In recent years, UAVs support in modern wireless networks has attracted attention from network operators and standardization organizations. The 3 GPP TR 36.777 summarizes the research done on LTE support for UAVs. In particular, it considers several cellular network improvements for efficient service of UAV users, quantifies the impact of UAVs on the network, and evaluates characteristics of UAV-based service in urban and rural environments. Computer simulations of such systems, augmented with measurement data, show that the use of UAVs may lead to increased interference in both uplink and downlink directions. TR 36.777 also suggests methods to eliminate interference. Another issue identified in TR36.777 is related to UAV mobility. The standard defines methods for providing additional information about the deployed ground network that can be used for decision-making during flight.  
Since 3 GPP Release 16, UAV support has been seen as a critical feature of the 5G cellular network infrastructure. In this context, TR 22.829 summarizes the use cases and analyzes UAV functions that may require enhanced support from access networks. It includes video broadcast applications, command and control services, and the use of UAVs as aerial BSs. The latter UAV application is covered in detail in TR 38.811.  
3 GPP is currently continuing research in this area. In particular, some of the tasks are to reduce the negative effects caused by the mobility of UAVs and to adapt to the needs of business, security, and the remote identification of UAVs. Specifically, TR 22.125 defines operational requirements for 3 GPP systems. The 3 GPP is expected to improve UAV integration methods in 5G communication networks in future revisions of TR 23.754 and TR 23.755. Nevertheless, it is already clear that UAVs will soon provide a wide range of services in 5G access networks.
## 4 Experiments
### Comparison with State-of-the-Art Transformer-Based Methods  
This section provides a comparative analysis of two vision transformer (ViT) models, focusing on their performance in detecting and localising anomalies as well as classifying defects found in images of leather surfaces.  
When applying the AnoViT method and reducing the resolution of the MVTec AD leather dataset by a factor of 4.57, i.e., from 1024 \(\times\) 1024 pixels reduced to 224 \(\times\) 224 pixels, not only is the reconstruction error increased due to noise, but the false reconstruction error (not indicating defective region) becomes more prominent. The difference in defect localisation between these resolutions is illustrated in Figure 19.  
Figure 18: Vision transformer-based encoder–decoder anomaly detection (AnoViT) fine-tuned results: randomly selected validation image (**top**) and corresponding activation map overlay with localised defective regions of interest (**bottom**). (**a**) Folding mark defect; (**b**) Loose grain defect.  
Owing to more visibly prominent defects found in the MVTec AD dataset, it can be noted that the falsely identified anomalous regions are more pronounced in lower-resolution images (224 \(\times\) 224 pixels) when compared to higher-resolution images (1024 \(\times\) 1024 pixels). The error intensity can, however, be reduced in low-resolution images with further fine-tuning of hyper-parameters for the datasets with dissimilar lighting and surface types.  
A comparison of the ViT-01 model's validation accuracy of 89.86% reached after a training time of 341.01 s and the ViT-02's validation accuracy of 93.89% reached after a training time of 176.27 s conveys a significant improvement in accuracy (4.03%) achieved in nearly half the training time (Table 1). The fact that the ViT-02 model is fully trained in 2 min and 56.27 s on a standard laptop (Intel core i7 with 6 Gb GPU) suggests that the classifier model can be quickly re-trained on a new or modified dataset and deployed in a real-time environment where efficient decision making is essential. Once trained, the model achieved inference of all 60 unseen test images in 4.76 s (79.3 milliseconds per image), making it a viable real-time classification model.  
When comparing the anomaly localisation results produced by the AnoViT and the Dot Product methods on low-resolution images, it can be seen that the latter method did perform acceptably over the majority of the defect types found in the six leather categories; however, the former method localised multiple defects in a single image with more clarity. While the AnoViT method requires a moderate increase in computational resources, the results produced suggest it is a more robust and effective model for the detection and localisation of leather surface defects in low-resolution images.
## 3 Results and Discussion
### Pigment Profile of Hop Extracts Obtained under Different SFE-CO\({}_{2}\) Conditions  
The quantitative composition of the selected supercritical-CO\({}_{2}\) soluble pigments in hop extracts, namely chlorophylls and carotenoids, is reported in Table 4. Generally, the total amount of these pigments was very low compared to the bitter acid content and did not exceed 0.04% of the total extract mass. For example, the extract isolated at 10 MPa had only 20.7 ug/g of carotenoids, while chlorophylls were not detected (the color of this extract was pale yellow). The concentration of pigments in the extracts significantly increased by increasing P and at 37 MPa reached 166.2 and 235.1 ug/g E for chlorophylls and carotenoids, respectively. Consequently, the recovery of carotenoids at 37 MPa was 32, 4.2 and 2.3 times higher than at 10, 12.5 and 15 MPa, respectively. Although humulones are undoubtedly the major contributors to the overall TEACORAC of the extracts, chlorophylls and carotenoids, both of which have a well-documented antioxidant potential in vitro and in biological systems [40], may also influence antioxidant capacity, which was higher for the extracts obtained at 15 and 37 MPa (Table 1).  
Chlorophyll A amounted to 45%, 67% and 88% of the sum of all chlorophylls at 12.5, 15 and 37 MPa, respectively (Table 4). The concentration of this compound significantly (\(\sim\)14-fold) increased from 10.6 ug/g E at 12.5 MPa to 146.1 ug/g E at 37 MPa, thus explaining the shift of SFE-CO\({}_{2}\) extract color from yellow-orange to greenish-brown at elevated P. The chlorophyll B content variations were less pronounced, ranging from 12.8 to 20.5 ug/g across different SFE-CO\({}_{2}\) extracts tested. Higher content of chlorophyll A compared to chlorophyll B (average ratio 7/3) was also characteristic for hydroethanolic extracts recently obtained from _Magnum_, _Maryma_ and _Lubelski_ hop varieties [41]. The presence of chlorophylls in SFE-CO\({}_{2}\) extracts (without quantitative results) isolated from different hop varieties with pure CO\({}_{2}\)[33; 34] and with cosolvent ethanol [42] was previously reported in several articles. To the best of our knowledge, the effects of SFE-CO\({}_{2}\) parameters on the quantitative composition of chlorophylls in the extracts and their recovery rates have not been reported.