esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### System Validation with Slaked Lime
#### 3.2.1 Fixed-Bed FGD Experiments
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Adsorbent Material** & **Temperature** & **RH\%** & **SO\({}_{2}\) Concentration** & **Label** \\ \hline Portlandite & 75 \({}^{\circ}\)C & 30\% & 600 ppm & PC [75 \({}^{\circ}\)C\_30 RH%] \\ Portlandite & 75 \({}^{\circ}\)C & 45\% & 580 ppm & PC [75 \({}^{\circ}\)C\_45 RH%] \\ Portlandite & 160 \({}^{\circ}\)C & 30\% & 600 ppm & PC [160 \({}^{\circ}\)C\_30 RH%] \\ Portlandite & 75 \({}^{\circ}\)C & 30\% & 600 ppm & PC [75 \({}^{\circ}\)C\_30 RH%] \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Experimental conditions and labels for portlandite. The values in the “Temperature” column refer to the temperature of the adsorbent during the FGD experiments, which correspond to position 5 in Table S1. Figure 3: (**a**) Breakthrough curves obtained from different PC quantities, namely, 0.10 g and 0.14 g. \(C_{0}=600\) ppm SO\({}_{2}\), uptake temperature = 75 \({}^{\circ}\)C, and 30 RH%. (**b**) Breakthrough curves of PC collected in triplicate for repeatability evaluation. \(C_{0}=600\) ppm SO\({}_{2}\), adsorbent quantity = 0.14 g, uptake temperature = 75 \({}^{\circ}\)C, and 30 RH%. (**c**) Breakthrough curves for PC samples at 75 \({}^{\circ}\)C and 160 \({}^{\circ}\)C. \(C_{0}=600\) ppm SO\({}_{2}\), adsorbent quantity = 0.10 g, and 30 RH%. (**d**) Breakthrough curves for PC samples at 30 RH% and 45 RH%. \(C_{0}=600\) ppm and 580 ppm SO\({}_{2}\), respectively; adsorbent quantity = 0.14 g; uptake temperature = 75 \({}^{\circ}\)C.  
The increase in breakthrough sulfur capacity at 45 RH% compared to 30 RH% can be attributed to the formation of liquid-film water molecular layers with extended thicknesses at higher moisture levels. Indeed, such films of water molecules promote SO2 dissolution, enhancing the reaction kinetics at the adsorbent surface. The wetter the surface of the adsorbent, the more SO2 might be dissolved from the flue gas, accelerating the process of desulfurization. The effect of different relative humidities on the desulfurization efficiency of Ca-based adsorbents has been widely discussed in previous studies [30,31]. The more pronounced performance shift caused by increasing the reactor temperature, on the other hand, was explored in greater depth in the current study via Ca(OH)2 characterizations (see Section 3.2.3). The breakthrough sulfur capacity values agree well with those previously reported in the literature, where Ca-based materials were used as adsorbents in dry fixed-bed FGD experiments (see Table S4; [15, 32, 33, 34, 35, 36, 37, 38, 39]). The obtained experimental results (Table 2) validate the experimental apparatus for mild desulfurization tests.
## 2 Experimental Setup  
The schematic diagram of the dual-frequency microchip laser and its relevant measurement method are shown in Figure 1. The composite laser cavity consists of a 1% at doped Nd\({}^{3+}\):YAG gain medium chip with a 0.4 mm thickness and an LTO electro-optic crystal with 1 mm thickness. The two chips both have transverse dimensions of \(5\times 5\) mm\({}^{2}\), and are combined by the optical cement method. The surface of the Nd:YAG crystal is coated with a film that has high reflectivity (R \(>\) 99.7%) at 1064 nm and high transmissivity (T \(>\) 97%) at 808 nm, which serves as the end mirror of the cavity. The surface of LTO is coated with a film of high reflectivity (R = 99%) at 1064 nm, which serves as the output coupling mirror. An 808 nm diode laser coupled with an optical fiber (fiber core diameter is 105 \(\upmu\)m, NA = 0.22) is used as the pump source to match one of the Nd\({}^{3+}\) ion absorption peaks. The pump beam is focused into the Nd:YAG crystal with a spot size of about 100 \(\upmu\)m in diameter. The dual-frequency beam with orthogonal polarizations can be generated by virtue of the birefringence of LTO. A thermoelectric cooler (TEC) is used to control the temperature of the microchip laser. The lateral surface of LTO is coated with gold film, on which two electrodes are glued with conductive adhesive to apply a transverse electric field by a constant voltage source. The dual-frequency beam passes through the polarizer to generate a beat note. A high reflection (HR) mirror for the laser beam can also split pump light. The longitudinal modes could be measured by a Fabry-Perot (F-P) scanning interferometer. And the optical spectrum or beat frequency are measured by an optical spectrum analyzer or a radio frequency (RF) analyzer.
## 5 Risk Assessment Method Based on Three-Parameter Interval Grey Number Decision-Making
### Risk Assessment Process  
This paper proposes a power transformer risk assessment method based on three-parameter interval grey number decision-making, and the detailed assessment process is shown in Figure 2.  
1. According to the existing research and relevant guidelines, the selection of equipment fault probability \(P\), equipment importance \(E\), and loss degree \(Loss\) are used as risk indexes in the paper, the transformer risk is divided into five grades (\(N_{1}\)\(\sim\)\(N_{5}\)), and the corresponding risk range of each grade is represented by the normal cloud model, as shown in Table 1.
2. The evaluation results based on [18] and the equipment fault probability \(P\) are calculated by Equations (3)\(\sim\)(6).
3. According to the voltage grades and capacity of the transformer, the equipment importance \(E\) was determined as shown in Table 2.  
Figure 2: Risk assessment process for a power transformer.  
4. The PDF and CDF of random variable \(x_{i}\) and their correlation coefficients are input, and the correlation of random variables is eliminated through the Nataf transformation and Cholesky decomposition.
5. The system cutting load \(P_{Loss}\) is calculated by the probabilistic power flow model based on the three-point estimation method, the loss degree is determined by Formula (13), and the risk value \(R\) is obtained according to Formula (1).
6. According to Formula (22), the origin moments of each order of the output variable \(R\) is obtained, and then the PDF and CDF are obtained by the Cornish-Fisher series expansion--that is, \(A(\otimes)=R=[u_{x_{i}}-\sqrt{3}\sigma_{x_{i}},u_{x_{i}},u_{x_{i}}+\sqrt{3} \sigma_{x_{i}}]\) and \(B(\otimes)=[E_{x}-\sqrt{3}E_{n},E_{x},E_{x}+\sqrt{3}E_{n}]\).
7. The three-parameter interval gray number distance measurement formula is introduced as Formula (24), and the final risk grade of the transformer is judged by measuring the distance \(d(A,B)\) between \(A(\otimes)\) and \(B(\otimes)\).
# Data Conversion Strategies for Effective Aviation Technical Support as a Service
## 3 Results
### Life Cycle Cost Analysis of Centralized and Decentralized Architectures in ATSaaS
Each user (e.g., airline company) operates an independent local system, and scaling requires individual users to upgrade their systems independently. This leads to inefficiencies and redundancy, as each user must perform similar tasks, such as updates, backups, and security patches, separately. Additionally, decentralized systems face significant coordination overhead to synchronize data across independent systems, which becomes increasingly complex as the number of users grows. The cost of managing these connections increases quadratically or exponentially with the number of nodes due to the growing number of interconnections. Unlike centralized systems, decentralized systems do not benefit from economies of scale.
# Estimation of Thermal Radiation in Bed Mattresses
## 5 Discussion  
We confirmed the thermal radiation measurement results through an experiment under conditions of different mattress installation heights and subsequently compared them with the estimated thermal radiation values of ISO 24678-7.  
As shown in Figure 8, near the maximum thermal radiation values, at HB 0 and 215 mm, the calculated values shown in Figure 7b,c, which were assumed by forming the shape of the flame into a cylinder, were closer to the experimental values. HB 515 mm confirmed the necessity of different calculation methods according to the growth of the fire. In addition, as mentioned in the method described in the SFPE Handbook, an experimental design was performed with the direction of the heat flux meter in the horizontal direction, but as shown in Figure 7c, in terms of thermal radiation values, the calculated result assuming the vertical direction was higher than the horizontally assumed calculation result. Therefore, when designing an experiment for fire risk evaluation, setting the heat flux meter's direction to the flame direction and conducting an experiment is considered to be an appropriate method for fire risk assessment.  
In addition, as shown in Figure 4, compared with HB 0 mm and 215 mm, the HRR for HB 515 mm rapidly increased, and the maximum value became large. Due to the nature of mattress fires, if there is a space under the mattress, melted material and some burning parts fall to the floor and burn. The higher the mattress installation height, the larger the flame due to the air flowing under the mattress, and these materials (that accompany the flame) promote the spread of flame in the mattress [20]. Consequently, the HRR and thermal radiation rapidly increase after a certain period of time. Therefore, a suitable method of predicting the flame shape is necessary. For example, if the flame spreads rapidly among solid combustibles, as in HB 515 mm, then the cross-application of the configuration factors according to changes in the flame shape can be used, where thermal radiation is calculated by assuming the flame shape of Figure 7a until a certain time, and subsequently, the configuration factor is determined as in Figure 7b, or the average value of Figure 7a-c Hor. can be applied.  
Furthermore, the prediction formulas described in ISO 24678-7 output values were significantly different from the experimental values. Since the ISO 24678-7 formulas are for estimating thermal radiation from liquid combustibles, to apply them to solid combustibles,  
Figure 10: Comparison of calculated values by [PERSON]’s formula with experimental values: (**a**) HB = 0 mm; (**b**) HB = 215 mm; (**c**) HB = 515 mm.  
the international standard for solid combustibles must be established by calculating and applying the mass loss late, the effective heat of combustion, the heat release rate, and the combustion area through various experiments on solid combustibles.
# Monitoring Changes in the Enhanced Vegetation Index to Inform the Management of Forests
###### Abstract  
In the absence of forest ecosystem time series data, monitoring proxies such as the enhanced vegetation index (EVI) can inform the capacity of forests to provide ecosystem services. We used MODIS-derived EVI at 250 m and 16-day resolution and Breaks for Additive and Seasonal Trend (BFAST) algorithms to monitor forest EVI changes (breaks and trends) in and around the Algonquin Provincial Park (Ontario, Canada) from 2003 to 2022. We found that relatively little change occurred in forest EVI pixels and that most of the change occurred in non-protected forest areas. Only 5.3% (12,348) of forest pixels experienced one or more EVI breaks and 27.8% showed detectable EVI trends. Most breaks were negative (11,969, 75.3%; positive breaks: 3935, 24.7%) with a median magnitude of change of \(-755.5\) (median positive magnitude: 722.6). A peak of negative breaks (2487, 21%) occurred in the year 2013 while no clear peak was seen among positive breaks. Most breaks (negative and positive) and trends occurred in the eastern region of the study area. Boosted regression trees revealed that the most important predictors of the magnitude of change were forest age, summer droughs, and warm winters. These were among the most important variables that explained the magnitude of negative (\(R^{2}=0.639\)) and positive breaks (\(R^{2}=0.352\)). Forest composition and protection status were only marginally important. Future work should focus on assessing spatial clusters of EVI breaks and trends to understand local drivers of forest vegetation health and their potential relation to forest ecosystem services.  
abrupt forest change; gradual forest change; forest disturbance; forest ecosystem services; BFAST 2024 16 2919 10.3390/rs16162919
## 3 Fundamentals of CFD in the Context of Mechanical Processes
### CFD Models for Non-Newtonian and Viscoelastic Food Materials
More advanced models, such as Oldroyd-B and Giesekus formulations, are used in CFD simulations to predict time-dependent viscoelastic flow, particularly under complex flow fields in extrusion dies or shearing zones. These models require the inclusion of additional stress transport equations, making simulations more sensitive to numerical parameters such as mesh quality and time-step size, and significantly increasing computational costs. As a result, viscoelastic models are typically reserved for systems where texture development is critically dependent on elastic properties [37; 38]. The selection of an appropriate rheological model for CFD applications in food engineering must balance several considerations. The accuracy of experimental data--particularly under realistic shear rates and temperature conditions--is essential for model calibration. The nature of the process also dictates model choice: shear-thinning models may be sufficient for high-shear processes such as extrusion, while yield-stress models are necessary for low-shear applications involving pasty or gel-like materials. Viscoelastic models are recommended only when elasticity plays a key role in the process outcome. Numerical stability is another important factor, as viscoplastic and viscoelastic models can cause convergence issues. Additionally, more complex models demand finer meshes, smaller time steps, and longer computational runtimes [39]. A practical example of viscoelastic model validation can be found in the CFD simulation of gluten-rich dough mixing. In this application, experimental data from oscillatory shear rheometry--used to characterize the dough's storage (G\({}^{\prime}\)) and loss (G\({}^{\prime\prime}\)) moduli--served as the basis for calibrating a generalized Maxwell model. The model was then applied in CFD to simulate flow and stress fields in a spiral mixer. Simulated velocity profiles and shear zones were compared with particle tracking velocimetry (PTV) measurements and torque data from pilot-scale trials, demonstrating good agreement and confirming the predictive capabilities of the viscoelastic model under realistic processing conditions [40].
## 5 Discussion
### Analysis of Hyperparameters and Related Settings  
In Figure 4, it can be observed that as the embedding dimensions in the Relgraph increase from 50 to 200, the model's performance improves accordingly. However, further increasing the dimensions does not significantly enhance the model's performance. Our experimental conclusion is that the optimal embedding dimension is dependent on factors such as the size of the dataset, the number of predicates, and the representation learning method used (TransE, RotatE, etc.).  
As shown in Figure 5, the Relgraph typically achieves rapid convergence, approaching approximate optimal performance at around 1000 epochs, highlighting its relative efficiency. As the number of training epochs increases, the performance of the Relgraph continues to improve slightly.  
The comparative experiment in Figure 6 demonstrates the effectiveness of the rotation angle calculation method (Equation (23)) utilized in RotatE. Compared to using max or min functions, Equation (23) exhibits a performance advantage of approximately 3%.  
Based on the experimental results depicted in Figure 7, it can be concluded that in the Relgraph model, scoring triplets using cosine similarity yields the best performance. Conventionally, representation learning models for KGs utilize the F1 norm to measure vector similarity. The utilization of cosine similarity as the scoring function in Relgraph leads to optimal performance, possibly due to the influence of the relation graph. The predicate representation learned by the model is insensitive to vector magnitude but focuses more on vector angles.
# Suitability of NB-IoT for Indoor Industrial Environment: A Survey and Insights
## 2 Technical Background of NB-IoT
### The Wireless Sensor Networks (WSN) Technology
#### 2.1.2 WSN Network Architecture  
The network architecture of WSN is considered to be of two types: structured and unstructured [43]. A structured WSN network design provides for a well-mannered approach to its planning and distribution of the nodes. With this type of deployment, the network maintenance rate is minimal, which leads to a low management expenditure as well. While in an unstructured network, the nodes' distribution and placements are scattered without proper planning. This type of design features dense and random deployment of nodes. This will lead to uncovered areas of deployment in the field, which may in-turn need monitoring. Since the nodes are not carefully placed, node failure detection and connectivity management become difficult to handle. Sensor nodes are of different types, which are designed or described based on the type of physical quantity they monitor. As shown in Figure 4, sensors such as humidity, temperature, flow, vibration, power sensors, etc., are connected wirelessly to a base station that is capable of aggregating data gathered from the  
Figure 3: A detailed representation of the four units of an IoT node.  
sensors to the network server. The user computer connects with the server to access the data and further process the data, which will be presented in a format that is more understandable by the decision making team. The wireless media used by these nodes enables a multi-hop communication. These communication links could be provided by radio or infrared technologies. Where a radio link is used, WSN often uses the unlicensed frequency band known as the Industrial, Scientific, and Medical (ISM) band [42]. These frequency bands are specified by the International Table of Frequency Allocations, and they also provide huge spectrum allocation and are unregulated but highly prone to interference.
## 2 Measurement Campaign  
The measurements aimed to obtain data describing the propagated signal's average behavior in a specific environment. Data regarding RSSI values and geolocation were collected through devices with LoRaWAN technology, which were used to readjust an empirical model of propagation to minimize the RSSI prediction error and consequently define the positioning of the gateways with greater precision. This section describes measurements, the scenario, equipment, and measurement methodology.
## 3 System Architecture
### General Concept  
The system architecture presented in this study is outlined as follows. Initially, the edge device utilizes an embedded camera module to capture images. In comparison to other published studies, which used standard modules or combined multiple existing modules, our approach features a custom-fabricated prototype. The captured images, which are in grayscale format, are cropped and resized to produce six 16 \(\times\) 24 pixel images, each representing a single digit. These images are then transmitted to a cloud server, where an advanced high-level entity learning model performs inference to classify the images and assign appropriate labels. The resulting labels are transmitted back to the edge device, where they are matched with their corresponding digit images.  
Communication between the edge device and the server side is facilitated through NB-IoT technology. We try to optimize input size for our proposed model, as NB-IoT technology has limitations for the number of bytes that can be transmitted per packet. In our study, each grayscale digit image consists of only 16 \(\times\) 24 pixels, corresponding to 384 bytes. On the other hand, the maximum payload capacity for a single NB-IoT packet is 1600 bytes. Therefore, six-digit images can be transmitted using just two NB-IoT packets. This optimization is essential given the energy constraints characteristic of IoT environments. After the labeling process, the digit images are organized into dedicated labeled folders within the device's memory storage.  
Subsequently, preprocessing is performed on digit images to produce clean, artifact-free black-and-white images. This preprocessing step is iteratively applied until 150 distinct digit combinations are recorded. To facilitate this, we utilize a digit change detection algorithm that enables the identification of new digits in subsequent images compared to previous captures.  
Finally, we designed two experimental test cases to evaluate the system's performance across 10 devices. The first test set consists of two scenarios, where the first scenario contains two successive dataset generations and model training iterations. In the second scenario, FL is used relying on an averaging methodology after each iteration to enhance the average accuracy per device. The second test case represents a scenario in which the system is split in two batches, each containing five devices. Initially, devices from the first batch perform training and FL update. Afterwards, training is performed on the second batch, which is set up using the global model generated by the first batch.
# Using Artificial Neural Network Models to Assess Hurricane Damage through Transfer Learning
## 4 Results and Discussion
### Damage Classification
The locations of four possible predictions are: TN (top left), FP (top right), FN (lower left), and TP (lower right). in Equation (5). As a result, the classifier only obtains a high F1 score if both precision and recall are high. The F1 score result shows that MobileNet obtains a F1 score that is about 9% higher than that obtained by the ResNet-based classifier. In short, both the confusion matrix comparison in Figure 5 and metric comparison in Table 3 show that the flood damage classification models developed through transfer learning are accurate. Furthermore, the classifier using MobileNet as the base model performs better than a transfer learning model developed on the basis of ResNet.
## 2 Methods and Materials
### OCT Scans  
Two OCT scans on macula were performed with Canon OCT-HS100 (version 4.00) on the right eye of the participants without the use of any mydriatic drops. The Canon OCT-HS100 has an A-scan velocity of 70,000 scans/sec with an axial resolution of 3 \(\upmu\)m. A three-dimensional scan protocol composed of 1024 A-scans for each of the horizontally oriented 128 B-scans was used for macular thickness measurements. The scans were centered on fovea and covered an area of 10 \(\times\) 10 mm. The scans were repeated if there was a blink during the measurement or if the signal strength was less than 7. The OCT instrument uses retinal tracking for eye movements during the scanning procedure to minimize artifacts.  
The automated segmentation software of the instrument was used to delineate eight different layers of the retina: Retinal Nerve Fiber Layer (RNFL), Ganglion Cell Layer (GCL), Inner Plexiform Layer (IPL), Inner Nuclear Layer (INL), Outer Plexiform Layer (OPL), Outer Nuclear Layer (ONL), Outer Segment (OS), and Retinal Pigment Epithelium (RPE). No manual adjustment of the segmentation was performed. The thickness of each layer specified above was evaluated in nine sectors based on Early Treatment Diabetic Retinopathy Study (ETDRS) protocol with the central circle, inner ring (Superior, Nasal, Inferior and temporal), and outer ring (Superior, Nasal, Inferior and temporal) with diameters of 1, 3, and 6 mm, respectively. Figure 1 shows an example of individual layer segmentation (panel A) and the schematic representation of the ETDRS sectors (panel B).