esa-sceva/satcom-chunk-collection huggingface dataset:
# The Design and Deployment of an End-To-End IoT Infrastructure for the Natural Environment
## 5 Deployment Experiences of an Environmental IoT
### Simple Network Design  
Our final network design was effectively a simple star network with one long-range hop to the Internet gateway. This was the result of significant field testing of a range of radio technology at the deployment site. Our initial plan was to make use of a multi-hop data collection protocol, as is common in the literature, but field testing indicated that this would not be required. In fact, we observed extremely good levels of point-to-point connectivity across long distances with treelines and other obstacles between nodes. As an anecdotal example of this, one of our nodes was transported in an off-road vehicle to a distant location for range testing; this node remained in contact with our base station for the entire journey despite being inside an enclosed steel vehicle. As a result, we adopted our current simple network design with one node sitting at the top of the hill in the field of study, acting as a relay for all other nodes in the field. The relay then sends its data to a gateway node which sits in the barn at the site. This is, in reality, a very common architecture for IoT deployments when working in the relative constraints of a real deployment at a remote location, where mains-powered sites are very limited. The logic here is that, instead of relying on redundancy, we rely on design heuristics in which the long-range node is sited at a\"safe location\" and is built from more reliable components relative to the end nodes. As long as these properties are true, this gives the long-range node significantly more reliability than each of the end-nodes and so failover support is less of a concern. In our case, the characteristics of the long-range node's location were (1) indoor, entirely sheltered from the environment, and (2) on a reliable mains power, avoiding reliance on batteries or energy harvesting; the node itself was a Raspberry Pi which is a mass-produced VLSI-style device with excellent reliability. These factors compensate for the lack of redundancy at a deployment location containing no other mains-powered locations besides the one that we used. If future deployments were to need a node dispersal over a larger physical radius (more than 2 km apart), a more complex network design may then be required--our most likely solution would be to adopt a design using further relay nodes as cluster-heads in more distant parts of the network, such that only the relays communicated over longer distances.
# Predicting Shot Accuracy in Badminton Using Quiet Eye Metrics and Neural Networks
## 3 Research Gaps Addressed by This Study
### Individual Differences in QE Characteristics  
While general trends in QE have been established in other sports, little is known about how individual differences--such as skill level, playing style, or cognitive traits--affect optimal QE characteristics in badminton. This gap suggests the need for more personalized training approaches based on individual QE strategies [22; 23]. Our study explores these individual variations, contributing to the understanding of how QE strategies can be tailored to each player's unique characteristics for enhanced performance.
## Materials and Methods
### SIF Datasets  
We installed a canopy-level SIF observation system 5 m above the top of the forest canopy. The main component of this system is a spectrometer (HR2000+, OceanOptics, Inc., Dunedin, FL, USA), which has a field of view (FOV) of 25 degrees and a spectral resolution of 0.13 nm (full width at half maximum, FWHM), covering a spectral range of 678.6-775.6 nm. The system is mounted 5 m above the top of canopy, which makes its footprint cover a circle with a diameter of 4 m. The spectrometer is connected with two fiber optics: one collects downwelling solar irradiance and the other measures upwelling radiance from top of canopy. This SIF system measured upwelling and downwelling radiation every 5 min from 07:00 a.m. to 5:00 p.m. for the period from May to September 2014. More details were provided in the previous studies [34; 35].  
We also used data (Level 3, V27) provided by the Global Ozone Monitoring Instrument 2 (GOME-2) [40] in this study. GOME-2 is on board of the MetOp-A/B platforms, which have an equator crossing time of 09:30 a.m. GOME-2 receives radiance from the Earth in the spectral range of 600-800 nm. Signals around 740 nm are used for SIF retrievals by using a principal component analysis approach according to the methodology used in [40], and SIF measurements with a cloud fraction over 50% are discarded. as reported in [41]. SIF measurements are aggregated to monthly means at 0.5\({}^{\circ}\)\(\times\) 0.5\({}^{\circ}\) spatial resolution when at least 5 SIF retrievals within a month are available.
## 3 Experimental Results  
### Study Areas and Datasets  
In the experiments, three public datasets from different locations are used in this study--the Coleambally Irrigation Area (CIA) [42], the Lower Gwydir Catchment (LGC) [42], and the Wuhan [43].  
The CIA study site is situated in southern New South Wales, Australia, and includes 17 pairs of cloud-free MODIS-Landsat images, each with a resolution of 6 \(\times\) 1720 \(\times\) 2040 pixels, collected between 2001 and 2002. This area is predominantly covered by rice fields. Although the land cover remained largely consistent during the dataset's collection period, significant phenological variations were observed, making it an ideal dataset for evaluating the capability of BCSR-STF in capturing phenological changes.  
The LGC study site, located in northern New South Wales, comprises 14 pairs of MODIS-Landsat cloud-free images, each measuring 6 \(\times\) 2720 \(\times\) 3200 pixels, acquired from 2004 to 2005. In mid-December 2004, the region experienced a major flood that inundated approximately 44% of the area. This abrupt event caused significant land cover changes, providing an excellent opportunity to evaluate the ability of BCSR-STF in predicting sudden transformations.  
To explore the adaptability of BCSR-STF to different sensors, the Wuhan study site, located in an urban region of Hubei Province, China, is selected. This dataset includes eight pairs of Landsat-Gaofen images from 2015 to 2022, containing rich urban texture features and significant changes. The data size is 4 \(\times\) 1000 \(\times\) 1000.
## 5 Results and Discussion
### Comparison and Discussion with Previous Studies
The specificity for classifying healthy images was 74.19%, while the sensitivity for classifying glaucoma images was 94.29%. Although these results demonstrate high accuracy compared with other studies, the accuracy was somewhat lower than that of [PERSON] [48] and [PERSON] et al. [50]. It is important to consider that those studies applied holdout validation and five-fold cross-validation, respectively. When the hold-out validation results obtained on the ORIGA dataset are compared with previous studies using different models, Swin Transformer had a significantly higher classification performance, with 96.15% accuracy for cropped images. This indicates the robustness of the Swin Transformer model for glaucoma classification. The Swin Transformer model correctly classified glaucoma at a rate of 95.05% (R = 95.05) when applied to the ORIGA dataset. This rate was 100% (S = 100) for healthy images. The results highlight the ability of the Swin Transformer architecture to accurately detect glaucoma cases. In the five-fold cross-validation, the accuracy value obtained for the ORIGA dataset using InceptionV3 was 93.84%. This accuracy value is considerably higher than in previous studies.
# Phase Change Materials and Their Benefits in ETICS
## 3 Results and Discussion  
### PCM Products Tested  
The temperature removal behavior of the filler and thermotropic materials in the climate chamber (see Figures 4 and 5).  
The samples tested with a thermal camera were standard filler Carolith 0.2-0.5 (crushed limestone), PCM Micronal DS 5040 X (M), Nextek (6D), Nextek (18D) and their combinations (6D + 18D) (see Figure 5). The specimens were placed in a climatic chamber at a temperature of 12 \({}^{\circ}\)C and a relative humidity of 50% for 24 h. At the start of the test, the temperature in the climate chamber was lowered to \(-\)12 \({}^{\circ}\)C and the temperature development of the test samples was monitored for 1 h. The ambient temperature dropped to \(-\)8 \({}^{\circ}\)C within 20 min and remained at this level until the end of the measurement. It is clear from Figure 5 that PCM Micronal was close to Carolith in its behavior, as  
Figure 4: Thermal images during filler and PCM testing (from left: crushed limestone Carolith 0.2-0.5, Micronal DS 5040 X, Nextek 6D, Nextek 18D, Nextek 6D + 18D combination).  
Figure 5: Evolution of temperature of filler and thermotropic materials placed in climate chamber.  
the wax was no longer activated due to the previous storage of the sample at a temperature of 12 \({}^{\circ}\)C. Micronal acts like a standard filler, without any benefit in terms of heat release, which was assumed due to the wax adjustment exclusively for the interior (thermal comfort in the rooms). On the other hand, Nextek specimens showed the ability to release heat at 12 \({}^{\circ}\)C despite previous storage. Sample 18 D fulfilled its function until the sample temperature was close to 2 \({}^{\circ}\)C, then due to the properties of the wax, the temperature gradually decreases (see Figure 5). It is interesting to activate the wax even after storage, which is below the guaranteed melting temperature point. Sample 6D had a time lag compared to sample 18D, which on the other hand showed the ability to perform PCM function below 2 \({}^{\circ}\)C. The last sample was a 50:50 Nextek 6D and 18D combination. The result of this combination is that it is very efficient and allows you to take advantage of both products and their settings. This combination is then effective to a greater extent and covers a significant proportion of normal outdoor temperatures. The above tests have shown that PCM from Nextek can be used in exterior materials and their thermo-technical properties.  
Differential Scanning Calorimetry (DSC) analysis was used for evaluation of the latent heat thermal energy properties of the PCM (see Figures 6-8 and Table 3).  
The crystallization temperatures (T\({}_{\rm c}\)), melting temperatures (T\({}_{\rm m}\)), crystallization enthalpies (\(\Delta\)H\({}_{\rm c}\)) and melting enthalpies (\(\rm{\Delta H}_{\rm m}\)) obtained from the DSC are reported in the Table 3. With regard to the outdoor use of PCMs, Nextex 6D and Nextex 18D materials were selected for further experimental work.
# GNSS Spoofing Detection and Mitigation Based on Maximum Likelihood Estimation
## 3 Review on MLE-Based Positioning  
Unlike a conventional GNSS receiver, which extracts measurements in several parallel tracking channels independently, the GNSS MLE-based positioning technique provides a way of jointly processing the signals of all in-view satellites. Taking advantage of the gain from the merging of signals, the method is robust against multipath and signal fading conditions [23]. In this section, we will review the MLE-based positioning, which is the foundation of the proposed method in the paper.
## 2 Methodology
### Power Quality Events According to IEEE 1159  
Voltage disturbances in distribution networks are characterized by deviations in magnitude or continuity that impact power quality and operational stability. According to the IEEE 1159-2019 standard [3], these disturbances can arise from various system conditions and are categorized to facilitate monitoring and automated detection.  
This study considers four event types relevant to real-time classification tasks using artificial neural networks (ANNs): normal, voltage sag, voltage swell, and interruption. These categories were selected due to their practical significance in power system operations and are formally defined in Table 1, along with their assigned classification labels used throughout this work.  
\begin{table}
\begin{tabular}{l l l} \hline
**Label** & **Type** & **IEEE 1159 Description** \\ \hline
1 & Interruption & Voltage = 0 for more than 0.5 cycles \\
2 & Normal & Nominal conditions, no disturbance \\
3 & Sag & RMS voltage drops below 90\% for 0.5 cycles to 1 min \\
4 & Swell & RMS voltage exceeds 110\% for 0.5 cycles to 1 min \\ \hline \end{tabular}
\end{table}
Table 1: Classification of power quality events based on IEEE 1159 and ANN labels.  
These event types cover the most critical RMS-based deviations in grid performance. The corresponding labels were embedded into the disturbance generation process to ensure consistency across training, validation, and test datasets. More complex disturbances, such as harmonics, flicker, or transients, are excluded from the current scope and reserved for future studies focused on high-frequency and waveform distortion effects.
## 4 Data and Examples
### Analysis of Multivariate Traffic Parameter Characteristics
#### 4.2.3 Time-Varying Characteristics of Travel Time  
As shown in Figure 3, vehicles traveled between consecutive detection sections and generated multiple travel time sequences. In this example, the time between detection sections 6269 and 6271, the time between detection sections 6271 and 6273, and the time between detection sections 6269 and 6273 are used as examples to display the trip time scatter plot, where the x axis is the travel time and the y axis is the series number of travel time records.  
As shown in Figure 7, the travel times between adjacent detection sections exhibit different variation characteristics. The travel time between two adjacent upstream sections is influenced by the signal control cycle of the intersection and includes red light waiting and queuing times. This characteristic is more obvious in the downstream adjacent sections and follows a layered distribution. The travel time distribution from the starting point detection section to the end point detection section shows a complex increasing and decreasing trend.  
Figure 5: (**a**) Headway scatter for lane 1 at detection point 6269. (**b**) Headway scatter for lane 2 at detection point 6269. (**c**) Headway scatter for lane 3 at detection point 6269.  
Figure 6: (a) Speed scatter for lane 1 at detection location 6269. (b) Speed scatter at detection location 6271. (c) Speed scatter at detection location 6273.  
Figure 7: (**a**) Travel time scatter from 6269 to 6271. (**b**) Travel time scatter from 6271 to 6273. (**c**) Travel time scatter from 6269 to 6273.
# Laser Fabrication of Titanium Alloy-Based Photothermal Responsive Slippery Surface
## 4 Conclusions  
In this work, we prepared a hierarchically structured titanium alloy surface with good anti-reflection property via laser treatment. The reflectivity of the untreated sample is \(-50\%\) between 800 and 200 nm, whereas the reflectance of the structured sample is less than 10% from 300 to 2000 nm. CLSM and SEM images show that the surface of the sample has micro-scale grooves and nanoporous structures, and the hierarchical structures play a vital role in the anti-reflection effect. Furthermore, we also used a simple method to obtain a slippery surface with good hydrophobicity. This method will be beneficial to the preparation of high-absorption metal surface materials or used in the field of microfluidic chips.  
Conceptualization, Y.Z.; investigation, J.Y., H.Z., X.H. and J.M.; validation, Y.Z.; formal analysis, J.Y. and Y.Z.; writing--original draft preparation, J.Y.; writing--review and editing, J.Y. and Y.Z.; supervision, Y.Z.; project administration, Y.Z.; funding acquisition, Y.Z. All authors have read and agreed to the published version of the manuscript.  
Figure 8: (**a**) Schematic diagram of the droplet sliding on the sample surface after laser processing under light irradiation. (**b**) Droplet sliding on the sample surface under light irradiation.  
Figure 7: (**a**) The contact angle of the initial sample and the laser processed sample. (**b**) The surface droplet sliding.  
**Funding:** This research was funded by the National Natural Science Foundation of China (61935008, 61775078).  
**Data Availability Statement:** The data used to support the findings of this study are available from the corresponding author upon request.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## Abbreviations  
IL Interleukin  
IL-1 Ra  
IL-1 Receptor antagonist  
PsA  
Psoriatic Arthritis  
RA  
Rheumatoid Arthritis  
CASPAR  
CIASification criteria for Psoriatic ARthritis  
IL-36R  
IL-36 Receptor  
TLR  
Toll-Like Receptor  
PAMP  
Pathogen-Associated Molecular Pattern  
DAMP  
Damage-Associated Molecular Pattern  
NE  
Neutrophil Elastase  
NET  
Neutrophil Extracellular Trap  
MMP  
Matrix Metalloproteinase  
JNK  
Jun N-terminal Kinase  
IL-1 RAPL1  
IL-1 Receptor Accessory Protein Like 1  
TIR  
Toll/IL-1 Receptor  
MyD88  
Myeloid Differentiation Primary Response Protein 88  
IL-1 RAcP  
IL-1 Receptor Accessory Protein  
IL-1 RL2  
IL-1 Receptor Like 2  
BP  
Binding Protein  
MAPK  
Mitogen-Activated Protein Kinases  
NFrB  
Nuclear Factor-kappa B  
IRAK  
IL-1R-Associated Kinase  
TRAF  
TNF Receptor-associated Factor  
PBMC  
Peripheral Blood Mononuclear Cells  
AP1  
Activator Protein 1  
LPS  
Lipopolysaccharideacharides  
ds  
Doubled-stranded  
CD  
Cluster of differentiation  
MHC  
Major Histocompatibility Complex  
CCL1  
Chemokine (C-C motif) Ligand 1  
CXCL1  
Chemokine (C-X-C motif) Ligand 1  
GM-CSF  
Granulocyte-Macrophage Colony-Stimulating Factor  
Th  
T helper  
VCAM  
Vascular cell adhesion protein  
ICAM  
Intercellular Adhesion Molecule  
DITRA  
Deficiency of the Interleukin-36-Recceptor Antagonist  
CIA  
Collagen-Induced Arthritis  
FLS  
Fibroblast-like Synovicocytes  
ELS  
Ectopic Lymphoid Structure  
STAT  
Signal Transducer and Activator of Transcription  
GPP  
Generalized Pustular Psoriasis  
PPP  
Palmoplantar pustulosisS100A7 S100 calcium-binding protein A7 SCW Streptococcal Cell Wall STIA Serum Transfer-Induced Arthritis CRP C-Reactive Protein DMARD Disease Modifying Anti-Rheumatic Drug
# Resource Allocation for Cognitive LEO Satellite Systems: Facilitating IoT Communications
## 7 Conclusions  
In this paper, we have proposed an uplink cognitive LEO SatCom system to enable IoT communication by sharing the spectrum of the legacy satellite system. Specifically, the legacy satellite users and the IoT users are served by the same LEO satellite in the CDMA manner. Considering the randomness of spreading codes, we have analyzed the asymptotic SINRs by using the random matrix theory and obtained the achievable rates for both legacy and IoT systems. We have proposed joint resource allocation schemes for the uplink cognitive LEO SatCom system, which aims to maximize the sum rate of the IoT users, with the consideration to protect the legacy primary satellite users. It has been proved that the sum rate of the IoT users is quasi-concave over legacy satellite user receive power,  
Figure 8: Optimal sum rate of the IoT users vs. the number of the IoT users.  
based on which the optimal receive powers for these two systems are derived. Simulation results have verified our theoretical analysis and validated the effectiveness of our scheme to achieve the optimal sum rate of the IoT users in the cognitive LEO SatCom system.  
Conceptualization, B.C. and Q.Z.; methodology, B.C., Q.Z. and J.G.; software, B.C.; validation, B.C., Q.Z. and W.X.; formal analysis, B.C.; investigation, B.C.; resources, W.X.; data curation, J.G.; writing--original draft preparation, B.C.; writing--review and editing, Q.Z. and J.G.; visualization, W.X.; supervision, Q.Z.; project administration, W.X.; funding acquisition, Q.Z. All authors have read and agreed to the published version of the manuscript.  
This research was funded by National Natural Science Foundation of China under Grant 62201127, China Postdoctoral Science Foundation Grant 2022M720023 and Grant 2022 TQ0055.  
Not applicable.  
Data Availability Statement: Data is unavailable due to privacy.  
The authors declare no conflict of interest.
## Data Acquisition and Model Construction
### Inductor Series Resistance Characterization and Modelling  
The simplest model to represent magnetic component losses is based on the addition of a series resistance with the inductor. Well parametrized, this series resistance is able to represent the losses in the inductor with respect to the current flowing through the device. In this part, we aim to identify a relationship between the series resistance of the inductor and the inductor value itself. If this generic representation is possible at the inductor scale, it will be possible to use it in the inductor component series. Considering the curves in Figure 4, the value of \(R_{DC}\), can easily be derived with respect to \(L\) for any device of the three series, applying linear regression. But it is well k-own that this DC resistance is not representative of the actual resistor of the inductor for high-frequency currents. Skin effects as well as proximity effects are affecting the current distribution in the wire, increasing the apparent resistance. Also, core/iron losses are also generated when AC signals are applied to the magnetic devices. In order to account for it, additional characterization must be performed. The goal of the characterization test bench is to record the power losses into the DUT with respect to realistic and/or representative current waveforms applied to it. To do this, several characterization steps are considered. A first option can be to measure the frequency dependence of the inductor resistance with an impedance analyser. A second option can be to measure the actual losses in the inductor, applying a variable ripple current over a wide frequency range and magnitude, more representative of the actual large signals usually applied to the DUT while implemented in PE converters. The following parts are investigating these two techniques, showing that the first one is not sufficient to describe the characteristics of the tested inductor. It is shown that the most accurate approach to characterize and model the inductor losses is based on a combination of the two methods. Figure 5 presents the characterization and modelling approach used to determine the mathematical model and parameters of the inductor losses. The characteristic factors: \(R_{DC}\), \(R_{lr}\) and \(R_{lr}\) are going to be presented in the following parts.