esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Background
### Synthetic Data Generation with UE4 and NDDS  
For synthetic data generation, UE4 is utilized. This platform is open-source, easily modified, and can provide realistic scenes. It is widely used within several industries, making high-quality 3D content easily accessible [5]. To create gameplay elements within a level, developers can utilize blueprint visual scripting, a powerful and complete game script system made up of code blocks [6]. Furthermore, interaction can also be implemented using the programming language C++.  
Figure 1: The research approach for creating an object detection system is trained on purely synthetic data.  
NDDS is a plugin made for UE4 that implements, among other things, domain randomization and annotation of large datasets. According to [PERSON] et al. [7], the aim of domain randomization is to \"bridge the reality gap\", and to is a tool to combat ineffective synthetic data. It helps create variability in the training process so that the model can interpolate to the real world [8; 9]. The following list elaborates on the quality of the synthetic data:  
* Distance, orientation, and camera rotation regarding the target object.
* Texture and material of the background.
* Texture, material, and position of objects.
* Size, number, and orientation of distractor objects.
* Position, orientation, and brightness of the light source.  
Training images in a static environment will lead to the model learning the specific characteristics of the environment. To better generalize the model for new data, the training environment must be randomized with a slow learning curve to approximate the hidden characteristics of the objects. The following factors were randomized within the synthetic scene utilizing the NDDS feature of domain randomization and will be further described in the Section 4.
## 4 Experimental Results and Analysis
### Comparative Analysis of Experimental Results
#### 4.4.2 Ablation Experiments
CNN-BiLSTM-GRU model: After adding the GRU module to CNN-BiLSTM, the performance of the model is further improved. Loss Value, MAE, RMSE, and other indicators continue to decline, and the decline is larger, indicating that the prediction accuracy is further improved. The significant decrease in the MAPE value also verifies this point. The R-Squared value is further close to 1, indicating that the model fits the data better. At the same time, due to the complementary effect of the GRU module and the BiLSTM module, the model structure is optimized, the training efficiency is improved, and the training time and prediction time are reduced compared with the CNN-BiLSTM model. Therefore, the CNN-BiLSTM-GRU model performs well in terms of accuracy, fitting ability, and efficiency. In summary, it can be seen from the ablation experiment that the combination of different modules has a significant impact on the performance of the model. The CNN provides a basic predictive ability as a basic model; the addition of the BiLSTM module effectively reduces the prediction error and enhances the data interpretation ability. The further addition of the GRU module further improves the model performance, improves the prediction accuracy and fitting ability, and optimizes the training efficiency. Therefore, the CNN-BiLSTM-GRU model shows high reliability and ability in prediction tasks. In order to visually show the differences between the models, Table 2 shows the ablation experiment prediction performance data in detail. Each column corresponds to the use of CNN, BiLSTM, and GRU modules ('\(\surd\)' indicates use, and '\(\times\)' indicates no use) and evaluation index values such as Loss Value, MAE, and RMSE, as well as training time and prediction time. Comparing the three models of CNN, CNN-BiLSTM, and CNN-BiLSTM-GRU, it can be seen that the integrated module has a significant effect on the performance of the model. When the CNN model is used alone, its LOSS value is 0.0163, MAE is 0.0963, RMSE is 0.1265, MAPE is 31.88, R-Squared is 0.7757, training time is 42.97 seconds, and prediction time is 0.0925 seconds. These data reflect the basic level of performance of the CNN model in the prediction task.
# Fuzzy Evaluation and Improvement Decision-Making Model for Machining Operation Performance
## 4 Fuzzy Evaluation and Improvement Decision-Making Model
[12], let \(d_{R}^{+}=\theta_{0R}-V\) and \(d_{T}^{+}=\theta_{0R}-\theta_{0M}\), then  
\[d_{R}^{+}/d_{T}^{+}=\frac{\frac{n}{\frac{\mathrm{G}_{0.005}(n,1)}{\mathrm{G}_{ 0.005}(n,1)}\theta_{0}^{*}-V}}{\frac{n}{\mathrm{G}_{0.005}(n,1)}\theta_{0}^{* }-\frac{n}{\mathrm{G}_{0.5}(n,1)}\theta_{0}^{*}} \tag{27}\]  
(2) \(\theta_{0L}\leq V\leq\theta_{0M}\)  
Subsequently, the diagram of membership functions \(\eta(x)\) with vertical line \(x=V\) for \(\theta_{0L}\leq V\leq\theta_{0M}\) is presented in Figure 3 as follows:  
Let set \(A_{T}^{-}\) be the area from vertical line \(x=\theta_{0L}\) to vertical line \(x=\theta_{0M}\) in the graph of membership function \(\eta(x)\), then  
\[A_{T}^{-}=\{\ (x,\alpha)|\theta_{OPT1}(\alpha)\leq x\leq\theta_{0M},0\leq \alpha\leq 1\} \tag{28}\]  
Similarly, let set \(A_{R}^{-}\) be the area in \(A_{T}^{-}\) but to the right of the vertical line \(x=V\), then  
\[A_{R}^{-}=\{\ (x,\alpha)|\theta_{OPT1}(\alpha)\leq x\leq V,0\leq\alpha\leq a_{1}\} \tag{29}\]  
Figure 3: Membership function \(\eta(x)\) with vertical line \(x=V\) for \(\theta_{0L}\leq V\leq\theta_{0M}\). where \(\alpha=a_{1}\) such that \(\theta_{OPII}(a_{1})=V\). According to [PERSON] et al. [12], let \(d_{R}^{-}=V-\theta_{0L}\) and \(d_{T}^{-}=\theta_{0M}-\theta_{0L}\), then  
\[d_{R}^{-}/d_{T}^{-}=\frac{V-\frac{n}{G_{0.995}(n,1)}\theta_{0}^{*}}{\frac{n}{G_{ 0.5}(n,1)}\theta_{0}^{*}-\frac{n}{G_{0.995}(n,1)}\theta_{0}^{*}} \tag{30}\]  
According to Equations (27) and (30), this paper defines  
\[d_{R}/d_{T}=\left\{ \begin{array}{ll} d_{R}^{-}/d_{T}^{-}= \frac{V-\frac{n}{G_{0.995}(n,1)}\theta_{0}^{*}}{d\frac{n}{G_{0.5}( n,1)}\theta_{0}^{*}-\frac{n}{G_{0.995}(n,1)}\theta_{0}^{*}},\theta_{0L}\leq V <\theta_{0M}\\ d_{R}^{+}/d_{T}^{+}=\frac{\frac{n}{G_{0.005}(n,1)}\theta_{0}^{* }-V}{\frac{n}{G_{0.005}(n,1)}\theta_{0}^{*}-\frac{n}{G_{0.5}(n,1)}\theta_{0}^ {*}},\theta_{0M}\leq V\leq\theta_{0R}\end{array} \right. \tag{31}\]  
As noted above, this fuzzy evaluation method is based on statistical test rules with significance level \(\alpha=0.01\). We let \(\phi<0.25\), where the values of \(\phi\) can be determined based on the past accumulated production data or expert experience [29; 30]. As noted by [PERSON] et al. [31], we may obtain the following rules for the fuzzy evaluation and improvement decision-making:  
1. If \(d_{R}/d_{T}<\phi\) and \(\theta_{0L}\leq V<\theta_{0M}\), then reject \(H_{0}\) and assume that \(\theta_{OPI}<V\). Thus, the machining operation performance must be leveled up. 2. If \(d_{R}/d_{T}<\phi\) and \(\theta_{0M}\leq V\leq\theta_{0R}\), then reject \(H_{0}\) and assume that \(\theta_{OPI}>V\). Thus, the machining operation performance must be maintained. 3. If \(d_{R}/d_{T}\geq\phi\), then do not reject \(H_{0}\) and assume that \(\theta_{OPI}=V\). Thus, the machining operation performance must remain unchanged or consider the transfer of resources.
## 3 Effect of Cryogenic Technologies on Physicochemical and Aroma Compounds of Grape Must and Wine  
### Physicochemical Parameters  
Total soluble solids (TSSs) of must were shown to be the least affected by freezing, as no difference was observed for storage periods less than six months [25; 35; 42; 50; 51]. However, in a study conducted by the authors of [32], different freezing techniques, i.e., involving liquid nitrogen as opposed to ultra-fast mechanical freezing, resulted in significant differences in TSS values. Total acidity (TA) of grape must was lower in cryogenic-treated samples, which validated the earlier findings of [PERSON] et al. [50], [PERSON] et al. [42], and [PERSON] et al. [25]. This was related to the precipitation of potassium (K\({}^{+}\)) salts during the freezing process and the lower solubility of acidic salts during defrosting of the grapes. Additionally, higher pH levels observed in must obtained after freezing and defrosting complemented previous studies [25; 32; 35; 42; 51]. Moreover, the chemical parameter most affected by freeze storage was found to be titratable acidity [35].  
Research conducted by [PERSON] et al. [51] and [PERSON] et al. [52] involved the rapid cooling of Bianchello del Metauro grapes by sparging the grapes with liquid CO\({}_{2}\) before crushing and destemming. The white wines produced from these treated grapes were not statistically different from the control wines in terms of alcohol, pH, titratable acidity, and volatile acidity [55; 56]. This was similar to findings observed in earlier research. However, significant differences were observed for malic acid [42; 51]. Previous research showed that the physicochemical parameters of wine produced from fresh and frozen grapes as well as grape juice had significant differences in tartaric acid, but not alcohol content [42; 51].  
Moreover, studies conducted by [PERSON] et al. [25] and [PERSON] et al. [34] found differences in alcohol levels in final wines produced from previously frozen whole grapes and macerated grapes. These differences were attributed to the extraction of compounds, which affected the fermentation process. [PERSON] et al. [25] further found that wines produced from cold-macerated grapes had higher pH and glycerol levels compared to the control and skin-macerated treatments. This is similar to previous findings [42; 51], although it differed from the findings of [PERSON] et al. [36] and other research, which showed that wines produced from grapes subjected to cold maceration did not show significant differences in chemical parameters when compared to the control [32; 35; 38; 51].  
It should, however, be noted that these effects were not necessarily only due to the freezing treatment but resulted from the storage time, duration of skin contact, and grape cultivar [25; 32; 42]. Furthermore, the type of cryomaceration treatment applied as well as type of berries used, i.e., whole berries or macerated grapes or juice, influence must and wine physicochemical parameters as conflicting results were observed between studies [25; 32]. Furthermore, this was not the focus of this review and will not be further discussed.
## 4 Approaches of Optimizing Geolocation Measurements
### Artificial Intelligence (AI)
#### 4.5.3 Machine Learning (ML) and Deep Learning (DL) Approaches  
Machine Learning (ML) and Deep Learning (DL) methods are based on AI networks and GA with representation learning. This learning may be under supervision, semi-supervised or unsupervised [97, 98, 99].  
The authors of [100] presented a position estimate approach based on deep learning algorithms that work directly on TDOA-based locating systems' channel impulse responses. The authors have described the signal and data prepossessing in-depth and demonstrated the effectiveness of the method in a variety of real-world scenarios. While their method matches traditional signal processing methods under line-of-sight conditions, it beats earlier methods under significant multi-path propagation. The addition of a movement model to the DL-based solution should help it perform better. They also proposed a technique for distributing the Convolutional Neural Network (CNN) so that it can be used in Real-Time Locating System (RTLS) designs. They came to the conclusion that they  
Figure 18: Various scenarios of RFI UAV tracking based on TDOA/FDOA/DDR using Iridium and Orbcomm Sat. in VHF-L band. Authors’ own elaboration.  
should reconsider how they currently estimate TOAs and that they could be evaluated using ML or DL methodologies as well. Furthermore, they did not explicitly address the transmitting orientation, despite the fact that this has an impact on the resulting Channel Impulse Response (CIR).  
Table 3 shows the review of a comparison between several approaches to optimizing geolocation and target tracking.
# Method to Measure Light Spot Position in Beacon-Less Inter-Satellite Communication System
## 3 Method of Separating the Two Light Signals
### Modulation of Transmitted and Received Lights  
When QD receives a modulated light, its output photocurrent can be expressed as [16,17]:  
\[I_{ij}=\frac{\eta}{2}a_{ij}^{2}[1+2d_{j}(t)] \tag{5}\]  
where \(a_{ij}\) is the intensity of \(j\)-th laser beam; \(d_{j}(t)\) is the modulation signal. According to Equation (4), after using two cosine signals \(d_{1}(t)=\cos(2\pi\cdot f_{1}t+\varphi_{1})\) and \(d_{2}(t)=\cos(2\pi\cdot f_{2}t+\varphi_{2})\) to modulate transmitted and received lights respectively, we can obtain the corresponding photocurrent signal \(I_{i}\) which can be written as a voltage signal \(s_{i}(t)\):  
\[s_{i}(t)=x(t)+y(t)+n(t)=kA_{i1}\cos(2\pi\cdot f_{1}t+\varphi_{1})+kA_{i2}\cos( 2\pi\cdot f_{2}t+\varphi_{2})+n(t) \tag{6}\]  
where, \(x(t)\) corresponds to the beacon light, \(y(t)\) corresponds to the reflected light, and \(n(t)\) is the noise; \(k\) is the gain between QD and A/D converter; \(A_{ij}=\frac{\eta}{2}\cdot a_{ij}^{2}\) is the signal amplitude corresponding to the power intensity of the \(j\)-th light irradiating in the \(i\)-th quadrant;\(f_{1}\) and \(f_{2}\) are the modulation frequencies.
## 3 Results and Discussion
### Thermogravimetric Analysis (TGA)
In the results of this study, as in previous studies, the pozzolan activity was relatively excellent in the C/S molar ratio range of 0.7 to 0.9, and the pozzolan activity was relatively low at the lowest C/S molar ratio of 0.6 and the highest at 0.95. On the other hand, as can be seen in Figure 7b, which shows the experimental results using amorphous silica SF, the amount of C-S-H production was generally not affected by changes in the C/S molar ratio, except for the case where the amount of silica used was the lowest. Although an inflection point appears at the C/S molar ratio of 0.7, the amount of CH production generally decreases with the decrease of the C/S molar ratio. This means that as the amount of silica used increases, the pozzolanic activity increases. From these results, it was confirmed that when amorphous silica was used, the pozzolanic reaction was most active in a mixture with a relatively high amount of silica having a C/S molar ratio of less than 0.7. As described above, it was found that amorphous silica, SF, has a relatively excellent pozzolanic activity compared to quartz, which is crystalline silica, and positive results are obtained even when a larger amount is used. However, in this study, amorphous silica had a very high fineness condition of 200,000 cm\({}^{2}\)/g and quartz had a relatively low fineness condition of 6000 cm\({}^{2}\)/g; thus, there is an experimental limitation that is not equivalent in terms of reaction conditions. Therefore, there is a need to verify the results of this study by reproducing a similar experiment under the same fineness conditions.
# Investigating Supercomputer Performance with Sustainability in the Era of Artificial Intelligence
## 5 Results and Discussion
### Power Consumption
Figure 11: Supercomputer power consumption for 2020: power consumption of the top-performing supercomputers for (**A**) November and (**C**) June; power consumption and energy efficiency rating of the most energy-efficient supercomputers for (**B**) November and (**D**) June. Figure 10: Supercomputer power consumption for 2021: power consumption of the top-performing supercomputers for (**A**) November and (**C**) June; power consumption and energy efficiency rating of the most energy-efficient supercomputers for (**B**) November and (**D**) June. Observation and Implication  
Balancing high computational performance with top EE remains a critical challenge in the pursuit of sustainability. Although advanced supercomputers are enabling major breakthroughs in artificial intelligence and supporting complex simulations across different scientific domains, the substantial power requirements raise serious sustainability concerns. These systems consume significant amounts of energy, often resulting in elevated carbon dioxide emissions, especially when powered by non-renewable energy sources. Top-performing supercomputers typically consume significantly more power than top-EE systems, which are specifically designed to operate with minimal energy usage. Due to the direct correlation between power consumption and carbon dioxide emissions, this suggests that high-performance supercomputers contribute considerably more to the carbon footprint compared to the EE counterparts during the period under study.
## 3 Methods
### Navigation Risk Assessment Model
#### 3.2.1 Criteria for Navigation Risk
Sea fog is the main reason affecting the visibility at sea. Many ship collisions, as well as touching and grounding accidents, are caused by sea fog. Generally, ships will avoid sailing in fog. However, when sea fog arises, it may be difficult for sailing ships to leave the fog area promptly. When sea fog occurs, the sea waves are generally small but limited by the field of vision. It is difficult for the crew to fully observe the surrounding navigation environment, and the probability of accidents increases greatly [40]. Water depth is a factor that must also be considered when planning passage through a channel. Before sailing, the captain should plan the route reasonably according to the ship's draught and abundant water depth. Generally speaking, the nearshore sea is shallow, so when nearshore navigation is affected by other factors, grounding and collisions may easily occur. In addition, the water depth is related to sediment in the sea area, which may cause a change of water depth due to the movement of the sea water. If the relevant properties of the navigation area are not known in advance, accidents can easily occur. In general, the five kinds of navigation environment criteria identified by the accident data statistics have a great impact on ship navigation, and are also the five main environmental factors about which the maritime industry are very concerned. At present, wind, waves, currents, and terrain factors can be supported by the existing product level data, but it is still difficult to obtain sea fog data in large areas. Therefore, this paper uses a remote sensing identification method to obtain the daily sea fog occurrence in Bohai Sea and its surrounding waters over a five-year period, using the method described in Section 3.1.3.
# LaRaWAN Metering Infrastructure Planning in Smart Cities
## 3 Materials and Methods
### Clustering Techniques  
In machine learning (ML), clustering techniques are a set of algorithms and methods that are used to group similar data points based on their inherent properties or patterns. This specific task of unsupervised learning is particularly useful when dealing with large datasets and can be applied to various domains, including geographical data analysis. The goal of clustering is to identify natural groups or clusters in a dataset without the need for predefined labels or classes. These techniques are widely used in data mining, exploratory data analysis, pattern recognition, and many other fields [37]. They can be used in the process of clustering the end nodes of LoRaWAN networks to find optimal deployment points for packet forwarders (gateways).  
Among clustering algorithms, K-means is one of the most widely used [38]. It aims to partition a dataset into \(K\) clusters, where each cluster is represented by its centroid. K-means groups similar data samples in one group away from dissimilar data samples. Its objective is to minimize the Within-Cluster Sum of Squares (WCSS) and maximize the Between-Cluster Sum of Squares (BCSS). The K-means algorithm has various implementations and conceptual variations. Many implementations and libraries focus on the most common method, known as [PERSON]'s algorithm (Naive K-means). This algorithm follows an iterative approach to find a suboptimal solution and is convenient for an exact, predetermined number of groups (e.g., to maintain a minimum number of gateways--it is a constraint--a business need) and is more suitable for a small number of clusters [39].  
Unlike other clustering methods (e.g., DBSCAN), K-means is computationally efficient, especially for large datasets, because its time complexity is linear with respect to the number of data points. It effectively handles large-scale clustering tasks. Admittedly, DBSCAN determines the optimal number of clusters (although during the design of the network, the number of gateways may already be imposed in advance as an economic factor). However, the result is irregular structures, and the center of gravity of such subgraphs must be determined by additional methods.
# Neural Network Based Quality Control of CYGNSS Wind Retrieval
## 4 Results
### Identifying Dominant Feature Vectors  
In this section the importance of each of the diagnostic variable is assessed using the minimum redundancy maximum relevance algorithm. The algorithm minimizes the redundancy of the feature set and maximizes the set with respect to the training data. Pairwise mutual information of the diagnostic  
Figure 10: Distribution of CYGNSS retrieved winds before and after QC filter.  
Figure 9: Distribution of outliers at different MERRA-2 wind speed bins before and after QC filter.  
variables is used to quantify its redundancy and relevance [18]. Figure 11 shows the score for each of the variable based on its importance in distinguishing outliers from good samples.  
The most dominant feature is the DDMA (NBRCS). This is as expected because the wind retrieval by CYGNSS is directly related to the two observables NBRCS and LES. The other dominant features are pre-dominantly instrument related such as azimuth angle, PRN, star tracker attitude status and satellite roll. This suggests that most of the outliers are caused due to improper instrument calibration.
# Hypoxia Modulates Regenerative Potential of Fetal Stem Cells
## 3 Results
### Effects of Hypoxia on Adipogenic and Osteogenic Differentiation of Fetal Cells
RT-qPCR was used to assess expression of osteogenic marker genes (_BCLAP_, _RUNX2_, _SPP1_, and _SP7_) (**A**), normalized against _GAPDH_ levels as an internal control. Data (_n_ = 4) are represented in bar charts. Different letters indicate a statistically significant difference compared to the group within the same cell type (_p_ < 0.05). Matrix mineralization was evaluated using Alizarin Red S (ARS) staining for calcium deposition (**B**).
## 5 Conclusions and Implications  
### Main Conclusions  
Based on the PM2.5 concentration and industrial development data on Beijing-Tianjin-Hebei region from 2000 to 2017, this paper uses the simultaneous equation model and 3 SLS estimation method to study the interaction between air pollution and industrial development. According to the empirical results, the following conclusions can be drawn.  
First, there is a significant two-way causal relationship between industrial development and air pollution in the Beijing-Tianjin-Hebei region. Compared with the simultaneous equations, traditional single-equation panel data underestimate the impact of air pollution on industrial development.  
Second, the estimation results of the air pollution model show that the proportion of secondary industry and increase the proportion of tertiary industry is conducive to improving air quality.  
In addition, population density, foreign direct investment, and high level development of transportation have brought environmental pressure to the Beijing-Tianjin-Hebei region.  
Third, the estimation results of the industrial development model show that air pollution has a significant positive impact on the development of urban secondary industry and, at the same time, it has a significant negative impact on the development of tertiary industry, which hinders the industrial structure upgrade to a certain extent. Under the same conditions, every increase in PM2.5 concentration will increase the proportion of secondary industry by approximately 0.08% to 0.28%, and decrease the proportion of secondary industry by approximately 0.13% to 0.31%.