esa-sceva/satcom-chunk-collection huggingface dataset:
# Numerical Study on Cooling Performance of a Steam-Cooled Blade Based on Response Surface Method
## 2 Numerical Methods  
### Subsection Physical Model  
Figure 1 gives the experimental schematic diagram of the steam-cooled blade, the mainstream airflow is high temperature and pressure air, its flow rate can reach up to 2.3 kg/s, the temperature can be adjusted in the range of 323-823 K, and the maximum pressure can reach 0.7 MPa. Maximum flow rate of steam stream is 0.5 kg/s, the temperature distribution range is 373-523 K, the maximum pressure is 0.75 MPa. The main experimental equipment includes electric heater, steady flow section, steam generator, steam pipe, steam outlet and mainstream outlet. Therefore, the experimental system for steam-cooled vanes is mainly composed of the mainstream airflow and its heating device, steady flow section, cascade test section, steam airflow, data acquisition system, and exhaust system [22; 23]. The mainstream provides a high-pressure mainstream airflow through the air compressor and electric heater, and the boiler provides the steam airflow. And when the cooling airflow is air, the airflow is provided by a small air compressor and a small electric heater. Figure 1 also shows how the test section is measured. The pressure measurement points and temperature measurement points are evenly arranged before and after the test section to obtain the inlet and outlet pressure and inlet temperature of the test section, and the temperature and pressure measurement points are evenly arranged in the blade mid-section and led out through the grooves on the blade surface for measurement. After calculation, it can be seen that the relative uncertainties of cooling efficiency, temperature non-uniformity and dimensionless temperature are 4.3%, 5.6% and 5.2%, respectively.  
The cascade channel and the experimental blade of the steam-cooled blade are given in Figure 2, where Figure 2a shows the cascade channel and Figure 2b shows the experimental blade and its cooling structure. From Figure 2a, a solid blade without cooling structure is arranged on each side of the test blade in order to ensure the periodicity of flow in the cascade channel. In addition, half of the flow channels are extended on the outer side of the solid blade, forming a periodic cascade with three flow channels. Figure 2b gives the steam-cooled blade and its cooling structure, from the figure it can be seen that the leading edge of the steam-cooled blade to the trailing edge direction, five straight through cooling channels are distributed in an orderly manner, and the hydraulic diameters of the cooling channels are 0.0109 m, 0.0099 m, 0.0093 m, 0.0087 m, 0.0069 m in order. It is worth noting that the steam cooled blade is a straight blade made by stretching the middle section of the first-stage static blade of an F-class gas turbine, with a chord length of 123 mm and a height of 83 mm, made of 321 stainless steel 1 Cr18 Ni9 Ti, which has a thermal conductivity of 16.3-22.2 W-m\({}^{-1}\cdot\)K\({}^{-1}\) and 321 stainless can function properly in the temperature range of 1023-1073 K.
## 2 Materials and Methods
### Anderson-Darling Test  
The Anderson-Darling (AD) test [35; 36], a modification of the Kolmogorov-Smirnov test, is employed to assess which statistical distribution best aligns with the sample data. This test measures the distance between the expected distribution and the empirical cumulative distribution function, focusing on detecting deviations in the distribution's tails. The  
Figure 6: Photographs of B measurements during charging.  
Figure 7: Locations of \(B\) measurements taken outside and inside the bus during charging (O is outside, and I is inside).  
Anderson-Darling test is particularly robust in identifying deviations from normality due to its lower sensitivity to outliers compared to other tests.  
In the AD test, the decision to reject or not reject the null hypothesis is based on a comparison between the test statistic and a critical value, which can be derived analytically or from the asymptotic distribution of the test statistic for each distribution. The test evaluates two hypotheses: assuming the data follow a specific distribution and positing otherwise. The outcome relies on two key metrics: the \(p\)-value and the critical value. A low \(p\)-value (typically below 0.05) or a test statistic that exceeds or equals the critical value suggests that the data do not conform to the specified distribution.  
Previous studies have effectively utilized the Anderson-Darling test to ascertain appropriate statistical distributions for modeling electric field strength in various settings, such as indoor environments and electrically large enclosures. Similarly, in this study, we employ the Anderson-Darling goodness-of-fit test to identify the best-fitting distributions for the measured B and RF-EMF levels. The distributions considered include the normal, generalized extreme value (GEV), lognormal, t-location scale, Weibull, and loglogistic distributions.
###### Abstract  
An S-band antenna has been designed, developed, measured, space-qualified, and integrated into the INTA ANSER satellite constellation and the future ANSER-AT mission. This antenna will be part of the space-to-ground communication link for the constellation, which consists of one Leader and two Followers. The novel antenna, mounted on the Leader, has been designed and manufactured with materials and processes specifically tested for space. It features dual circular polarization over a wide band without requiring a phase-shifting network, making it very compact and straightforward. Additionally, its gain patterns are highly stable within the desired band, improving its link capacity compared to the UHF monopole alternative used in the previous Leader. Currently, the antenna has been qualified and installed on INTA's Leader-S, set to launch in January 2025, as well as on the future ANSER-AT mission.  
Keywords:constellation; cluster; CubeSat; antenna; MPA; polarization; axial ratio Article
# Enhanced Knowledge Graph Embedding by Jointly Learning Soft Rules and Facts
## 5 Experiment
### Experimental Setup
#### 5.2.2 Evaluation Metrics  
To evaluate the quality of embeddings on link prediction, we used the standard protocol Mean Reciprocal Rank (MRR) and HITS@N (\(n=1,3,10\)). For each triple \((e_{i},r_{k},e_{j})\) in test sets \(\mathcal{O}\), we replaced the head entity \(e_{i}\) with all entities in \(\mathcal{E}\) one by one and calculated their scores. Then, we ranked these scores in descending order and got the rank of the correct entity \(e_{i}\) denoted by \(rank_{e_{i}}\). We performed the same process by replacing the tail entity \(e_{j}\) and obtained another rank denoted by \(rank_{e_{j}}\). Then, the metric MRR could be calculated by:  
\[\text{MRR}=\frac{1}{2|\mathcal{O}|}\sum_{(e_{i},r_{k},e_{j})\in\mathcal{O}}( \frac{1}{rank_{e_{i}}}+\frac{1}{rank_{e_{j}}}). \tag{15}\]  
The metric HITS@N can be calculated by \((\#(rank_{e_{i}}\leq n)+\#(rank_{e_{j}}\leq n))/2|\mathcal{O}|\), which indicates the proportion of the triples whose ranks are not larger than \(n\). Notably, there were two settings, i.e., the \"raw\" setting and the \"filtered\" setting (see [PERSON] et al. (2017)), when calculating these metrics. Our results  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Dataset** & \(|\mathcal{E}|\) & \(|\mathcal{R}|\) & \# **Train/Valid/Test** & **\# Rules** & **\# Grnds** \\ \hline FB15K & 14,951 & 1,345 & 483,142/50,000/59,071 & 457 & 126,423 \\ DB100K & 99,604 & 470 & 597,572/50,000/50,000 & 16 & 15,982 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Statistics of the datasets, where the columns denote the number of entities, relations, training/validation/test triples, extracted rules, and their groundings, respectively.  
\begin{table}
\begin{tabular}{c c c} \hline \hline /location/location/people\_born\_here(\(x\),\(y\)) \(\sim\) /people/person/place\_of\_birth(\(y\),\(x\)) & 1.00 \\ /film//country(\(x\),\(y\)) /location/statistical\_region/grni\_in\_ppp\_dollars/measurement\_unit/dated\_money\_value/currency(\(y\),\(z\)) & 0.96 \\ \(\sim\)/film/estimated\_budget./measurement\_unit/dated\_money\_value/currency(\(x\),\(z\)) & 0.84 \\ /music/record\_label/artist(\(x\),\(y\)) \(\sim\) /music/artist/label(\(y\),\(x\)) & 0.96 \\ \hline sourceMountain(\(x\),\(y\)) \(\sim\) sourcePlace(\(x\),\(y\)) & 0.96 \\ distributing\_label(\(x\),\(y\)) \(\sim\) distributingCompany(\(x\),\(y\)) & 0.91 \\ sisterNewspaper(\(x\),\(y\)) \(\sim\) sisterNewspaper(\(x\),\(x\)) \(\sim\) sisterNewspaper(\(y\),\(z\)) & 0.83 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Examples of rules with confidences (right column) that were extracted from FB15K(top) and DB100K(bottom), respectively.  
are reported in the \"filtered\" setting, where metrics were computed after removing all the other known triples appearing in the training, validation, or test sets from the ranking triples.
## 3 Results
### The Results Obtained Through Quantitative Analysis  
Young people who have used or currently use a smartphone, regardless of model or brand, were selected for the questionnaire. The convenient sampling method was chosen due to its accessibility, low cost, and high frequency of use in research of this type. The survey was conducted in December 2024, and we had 456 cases. The questionnaire was applied online to students at Stefan cel Mare University of Suceava and Dunarea de Jose University of Galati. The questionnaire did not demand any identification data. We did not do sampling; we sent the questionnaire to all students from the University of Suceava by email to their professional email box, and for the University of Galati, the questionnaire was posted on some faculty's social media accounts. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Categories (Purchased Actual Phone)** & **Number** & **Percentage** \\ \hline from a physical store & 226 & 49.6\% \\ from an online store & 116 & 25.4\% \\ from the free market (friends, black market) & 32 & 7\% \\ I don’t know (I got it as a gift) & 82 & 18\% \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Structure of the sample by type of acquisition. Figure 7: Sample structure (by age). Table 5 provides insight into consumer behavior in the smartphone purchase process, highlighting brand preferences, purchase patterns, product selection criteria, and the influence of socioeconomic factors on the purchase decision. The first section of the table examines brand preference among respondents. Apple emerges as the most preferred brand, with 37.9% of participants indicating that they own an Apple smartphone. Close behind, Samsung holds a significant market share, with 36.2% of respondents selecting it as their brand of choice. The dominance of these two brands suggests strong consumer loyalty and brand positioning in the high-end smartphone segment. In contrast, Realme, a relatively new competitor in the global market, was selected by only 0.4% of respondents, indicating a minimal penetration rate within this sample group. This distribution of preferences highlights a market inclination toward well-established brands known for their ecosystem integration, reliability, and perceived prestige. The second section explores the channels through which consumers acquire their smartphones. The majority of respondents (64%) reported purchasing their last mobile device from a physical store, indicating a preference for in-person shopping experiences where customers can physically test the device before committing to a purchase. Meanwhile, 18% of respondents indicated that they did not make the purchase themselves but rather received their phone as a gift. This finding suggests that gift purchases constitute a notable portion of the smartphone market, potentially influenced by family decisions, holiday promotions, or brand loyalty within households. When asked about the brand of the next smartphone they intend to purchase, Apple remains the leading choice, with 41.4% of respondents planning to buy an Apple device in
Traditional gait analysis methods encounter several limitations, including sensor dependence, privacy concerns, and data bias. Camera systems are susceptible to lighting conditions and require unobstructed visibility, while wearable sensors may be uncomfortable or impractical for certain users. Utilizing assistive devices such as crutches, canes, and walkers provides a practical approach to acquiring training datasets for machine learning algorithms in osteoporosis management [28]. These devices, designed to support and restore normal locomotion in individuals affected by injuries or pathologies, are easily equipped with sensors to capture gait and posture data [29; 30]. A smart walker, for instance, functions as a minimalist tool in gait classification system development, simultaneously supporting users while acquiring kinetic data [30]. Moreover, while video-based gait analysis raises privacy issues, smart walkers collect data solely from the user's interaction with the device, thus alleviating privacy concerns. Additionally, conventional systems may suffer from data bias due to the limited representation of diverse populations in training data. In contrast, gait data collected through smart walkers, utilized by a broader range of individuals, can contribute to the development of more inclusive and accurate detection models. Smart walkers present numerous advantages, including ease of use, a controlled environment for data collection, and standardized data output. A smart walker functions as a minimalist tool in gait classification system development, simultaneously supporting users while acquiring kinetic data [30]. This technology not only monitors osteoporosis progression and evaluates treatment effectiveness but also reduces fall risks and enhances overall mobility in individuals with osteoporosis [28]. Many users regularly utilize these devices as common assistance tools, especially those who can benefit from gait analysis and require methods needing less user effort for data collecting. The walker functions as a consistent background for gathering data, with minimal interference from external elements such as uneven terrain or garment variations that may impact the sensory information captured by these devices. Furthermore, if the data are collected directly from the walker, they may be pre-processed and standardized, which simplifies analysis and has the potential to enhance the accuracy of machine learning models. This study aimed to develop a machine learning-based model for the detection of osteoporosis using the upper limb force profile of the user obtained with the use of smart walkers. Multiple models analyzed gait data collected by a smart walker to identify patterns that are indicative of osteoporosis.
## 2 Related Works
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Reference** & **Datasets** & **Models** & **Metrics** & **Results** \\ \hline
[39] & Kaggle data & CNN with grid search & Accuracy & 89\% \\ \hline
[34] & EyePACS, APTOS & Extreme learning machine & Accuracy & 91.78\% (EyePACS), 97.27\% (APTOS) \\ \hline
[40] & APTOS19, DIARETDB1 & Ensemble model- DenseNet101, ResNext & Accuracy & 96.98\% (binary), 86.08\% (multi) \\ \hline
[15] & IDRiD & VGG16, InceptionV3 & Accuracy & 77.6\% with VGG16 \\ \hline
[35] & APTOS, MESSIDOR & CNN and SVM & Accuracy & 97.25\% (APTOS), 94.8\% (MESSIDOR) \\ \hline
[41] & ODIR, IDRiD & Vision transformer & Accuracy & 82.35\% \\ \hline
[33] & APTOS & AlexNet, ResNet-101 with ACS & Accuracy & 93\% \\ \hline
[17] & EyePACS & Efficientnet-b3 & Quadratic Weighted Kappa & 0.87 \\ \hline
[42] & EyePACS, APTOS & Source-Free Transfer Learning & Accuracy & 91.2\% \\ \hline
[43] & EyePACS (training), APTOS (testing) & ResNet, EfficientNet, and Swin Transformer & Accuracy & 76.35\% \\ \hline
[20] & DDR & DenseNet 121, InceptionV3, ResNet153, VGG16, MobileNet, and InceptionResNet & Accuracy & 100\% \\ \hline
[44] & Kaggle & multi-class feature extraction deep forest & Accuracy & 74\% \\ \hline
[45] & Own dataset & Inception-v4 with ensemble method & AUC & 0.994 \\ \hline
[46] & IDRiD & CNN & Accuracy & 68\% \\ \hline
[31] & DDR, EyePACS, and IDRiD & VGG16 & Accuracy & 85.4\% (DDR), 77.9\% (EyePACS), and and 89.8\% (IDRiD) \\ \hline
[47] & \
\begin{tabular}{c} Messidor-2, EyePACS, and IDARETDB0 \\ \end{tabular} &
\begin{tabular}{c} CNN + SVD + Inception-V3, \\ GoogLeNet, AlexNet, \\ ResNet \\ \end{tabular} & Accuracy & 94.59\% (Messidor-2), 97.92\% (EyePACS), and 97.92\% (DIARETDB0) \\ \hline
[48] &
\begin{tabular}{c} Messidor-2, EyePACS, \\ and DIARETDB0 \\ \end{tabular} & U-net + VGGNet & Accuracy & 93.95\% (Messidor-2), 96.6\% (EyePACS), and 92.25\% (DIARETDB0) \\ \hline
[49] & Kaggle & CNN & Accuracy & 83.5\% \\ \hline
[50] & Own dataset & DenseNet-121 + CNN & Accuracy & 84.47\% \\ \hline
[51] & IDRiD & VGG16 & Accuracy & 93.1\% \\ \hline
[52] & APTOS & ResNet, InceptionV3, InceptionResNet-V2, DenseNet-169, Xception, EfficientNet-B4 & quadratic weighted kappa & 0.824 \\ \hline
[53] & APTOS & AlexNet, Res-Net18, SqueezeNet, GoogleNet, VGG16, and VGG19 & Accuracy & 97.9\% \\ \hline
[54] & EyePACS & Inception-V3 & Accuracy & 90.9\% \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of the related works in DR.
## 2 Experiments
### SOA Formation Study  
As the quantification of SOAs formed in reaction (R1) requires the knowledge of the loss of T2M2B by reaction with Cl atoms, the experiments were performed simultaneously in the 16-L and 264-L chambers, which were connected by a Teflon tube. Thus, the FMPS spectrometer (TSI Incorporated, model 3091, Shoreview (MN), USA) sampled from the 264-L chamber and its outlet flow was brought to the 16-L cell. With this configuration, the gas mixture was continuously flowing through the two chambers, and data of the particles in the 5.6-560 nm range inside the chambers could be recorded by the FMPS every 1 s (although they were averaged for 1 min), and gaseous T2M2B could be monitored at the same time with the FTIR spectrometer every 2 min. The total timescale of the experiment was 80 min. In the first 10 min, the Cl\({}_{2}\)/T2M2B/air mixture was not irradiated (i.e., no Cl reaction was taking place) to monitor the dark losses of gaseous T2M2B or its reaction with Cl\({}_{2}\). After that, the lamps were turned on and the Cl reaction started, monitoring T2M2B loss and particles during 60 min. To evaluate the loss of the formed SOAs by wall loss or through the filters of the FMPS, the lamps were switched off for 20 min more. At each reaction time, the concentration of T2M2B and the SOA mass were corrected by accounting for their loss as described in a previous research. [20] The SOA yield was determined as the ratio between the aerosol mass formed, M\({}_{\text{SOA}}\), and the loss of reactant due to the Cl reaction, \(\Delta\)[T2M2B]:  
\[\text{Y}_{\text{SOA}}=\frac{\text{M}_{\text{SOA}}}{\Delta\text{[T2M2B]}}. \tag{3}\]
# Long-Term Structural State Trend Forecasting Based on an FFT-Informer Model
## 2 Long-Term Structural State Trend Forecasting Based on FFT-Informer Model  
### Overview  
In this article, a long-term structural state forecasting method combined Fast Fourier transform (FFT) with an improved Informer model is proposed, as shown in Figure 1. The model mainly includes two parts: feature extraction by FFT and LSTF by the improved Informer model. Firstly, structural state data are processed into equally sized data blocks through sliding windows, which are not overlapping, and feature vectors (amplitude and phase) of each window are extracted by the FFT method. Then, using the improved Informer model, the future features are predicted in a one-forward operation. What is more, the forecasted feature vectors can be transformed back into time series data with a trigonometric function to obtain predicted long-term structural state in feature analysis.  
Figure 1: Framework of the FFT–Informer model.
## 8 Discussion  
### Impact of Blockchain Technology on Performance Matrices  
The use of blockchain technology in agriculture will have a major impact on key performance metrics, including supply chain throughput, latency, cost, scalability, security, and trust.  
* _Throughput:_ By improving the effectiveness of transaction processing, blockchain technology, with its decentralized and distributed ledger design, may favorably improve throughput. Consensus mechanisms and the removal of middlemen facilitate quicker and more efficient data transfers, which raises the agricultural supply chain's total throughput.
* _Latency:_ By offering an open and unchangeable record of data, the use of blockchain technology may lower transaction latency. Smart contracts, a crucial component of blockchain technology, allow for the automatic and immediate execution of predetermined actions, reducing processing times and enhancing system responsiveness.
* _Cost:_ The agricultural supply chain might see cost optimization thanks to blockchain technology. Operational expenses may be greatly decreased by doing away with the need for middlemen, automating record-keeping, and increasing automation thanks to smart contracts. This may result in agricultural practices that are more economical and resource-efficient.  
* _Scalability:_ The decentralized structure of blockchain enables better scalability in the agricultural industry. The need for supply chain solutions that are transparent and traceable is growing, and blockchain technology can readily expand to handle higher transaction volumes without sacrificing performance. The technology's capacity to scale guarantees that it can be adjusted to the ever-changing needs of the agricultural sector.
* _Security:_ Cryptographic encryption and consensus procedures, two of blockchain's built-in security features, make the agricultural supply chain more secure. Data integrity is guaranteed by the blockchain's immutability, and the decentralized structure lessens the possibility of a single point of failure. The agricultural ecosystem is more resilient to fraud, manipulation, and unauthorized access thanks to this increased protection.
* _Trust:_ The improvement of trust is perhaps one of blockchain's most important effects on agriculture. A product's validity and provenance may be confirmed by stakeholders across the supply chain using transparent unchangeable ledgers. In the end, this openness benefits both producers and consumers by fostering confidence among participants and resolving issues with food safety, authenticity, and ethical sourcing.