esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Experimental Program
### Test Setup  
Each specimen was first anchored to a strong floor and reaction wall using prestressed steel bolts to prevent sliding during the test. Details of the loading setup are shown in Figure 7. All specimens were tested under a combination of constant axial and lateral cyclic loads. During the test, a constant target axial load corresponding to an axial compression ratio of 20% was first applied to the top of the specimen using a 30-t hydraulic jack through three steel strands passing through the pre-buried bellows at the center of the pier column. Consequently, the load was transferred to the specimen evenly and stably. A lateral cyclic load was then applied to the side of the top-bearing platform using a 50-t electrohydraulic servo horizontal actuator, and the displacement loading rate was set to 0.1 mm/s. The protocol for the cyclic displacement-control load is shown in Figure 8 and was obtained according to the \"Specification for seismic test of buildings\" code [62; 63; 64; 65]. Additionally, \(\Delta_{y}\) was defined as the yield displacement of a test specimen. One cycle was imposed with 2 mm increments for each loading step. After the specimen entered the yield stage, the lateral load repeated three cycles at the displacement levels of 1\(\Delta_{y}\), 2\(\Delta_{y}\), 3\(\Delta_{y}\), \(\ldots\), and the test was terminated once the lateral bearing capacity of the specimen decreased to 85% of the peak load or the specimen exhibited severe damage. The lateral load and displacement were recorded using a horizontal actuator. Two linear variable displacement transducers (LVDTs) were used to measure the lateral displacements at the top and bottom of the pier column. Several strain gauges were applied to record the strain in the longitudinal rebars and stirrups in the potential plastic hinge area of the pier column. The detailed configuration of the displacement meters and strain gauges is presented in Figure 9.  
Figure 6: Production process for the strengthened specimens. (**a**) Installing steel sleeve and injecting water; (**b**) Laying LCSS and welded steel wire mesh; (**c**) Pouring SSAWC and performing concrete curing; (**d**) Unreinforced areas of the specimens.  
Figure 8: Protocol of the cyclic displacement-control load.  
Figure 7: Layout of the experiment. (**a**) Loading system; (**b**) On-site photographs.  
Figure 9: Layout of LVDTs and strain gauges. (**a**) LVDT distribution on pier columns; (**b**) Strain gauge distribution on steel rebars.
# Estimating the Fractional Vegetation Cover from GLASS Leaf Area Index Product
## 2 Data
### Satellite Data Sets
#### 2.1.1 GLASS LAI Product  
The GLASS LAI product is one of the longest duration LAI products in the world. It has a temporal resolution of 8 days and spans 1981-2014. For the period 1981-1999, AVHIR reflectance data from NASA's Land Long-Term Data Record (LTDR) project [26] were used to retrieve the LAI product, which was provided in a geographic latitude/longitude projection at spatial resolution of 0.05\({}^{\circ}\) (~5 km at the Equator). For the period 2000-2014, the LAI product was derived from MODIS surface reflectance data and provided in a sinusoidal projection at spatial resolution of 1 km [25]. The GLASS LAI product was retrieved using general regression neural networks (GRNNs). Unlike existing neural network methods that use remote sensing data acquired only at a specific time to retrieve LAI, the GRNNs were trained using fused time series LAI values from MODIS and CYCLOPES LAI products and reprocessed time series MODIS/AVHRR reflectance. The reprocessed MODIS/AVHRR reflectance values from an entire year were input to the GRNNs to estimate the one-year LAI profiles. The GLASS LAI product was generated and released by the Center for Global Change Data Processing and Analysis of Beijing Normal University [27]. It is also available from the Global Land Cover Facility [28].
# The Combined ASTER MODIS Emissivity over Land (CAMEL) Part 2: Uncertainty and Validation
## 2 Data
### Emissivity
#### 2.1.2 Advanced Spaceborne Thermal Emission and Reflection Radiometer Global Emissivity  
Dataset v4  
Housed on NASA's Terra Earth Observing System satellite, ASTER is a cooperative effort between NASA, Japan's Ministry of Economy, Trade and Industry (METI), and Japan Space Systems. The ASTER GED version 4 (v4) product used in this study was produced at JPL and is available at [[https://lpdaac.usgs.gov/dataset_discovery/aster](https://lpdaac.usgs.gov/dataset_discovery/aster)]([https://lpdaac.usgs.gov/dataset_discovery/aster](https://lpdaac.usgs.gov/dataset_discovery/aster)) ([PERSON] et al., 2018). The ASTER GEDv4 is available at a monthly, 0.05\({}^{\circ}\) resolution over 2000-2016. It is based on the ASTER GEDv3 2000-2008, 100 m resolution climatology which uses emissivity derived using the ASTER Temperature Emissivity Separation algorithm ([PERSON] et al., 2018). This climatology is adjusted to vary with the changing land surface (snow cover, vegetation, drought) using a MODIS monthly snow cover (MOD10 CM, 0.05\({}^{\circ}\) resolution) and monthly NDVI (MOD13C2) product. ASTER GEDv4 has 5 bands located in the thermal infrared region at 8.3, 8.6, 9.1, 10.6, and 11.3 microns. These band placements, three of which are in the Restrahen region, give ASTER the ability to characterize quartz doublet features.
## 3 Thermal Buckling Analysis and Optimization
### Thermal Buckling Optimization Model Setup
#### 3.2.1 Size Optimization  
For size optimization, quadratic response surface approximations of critical load factors at Time 1 and Time 2 were adopted. The approximations are functions of design variables \(t_{w1}\), \(t_{w2}\) and \(t_{w3}\). With 3 variables, the quadratic functions would have 10 coefficients, and hence at least 10 function evaluations are required. Latin hypercube sampling (LHS) was used for the design of experiments. Range of variables is listed in Table 4.  
Size optimization can be formulated as:  
\[\left\{\begin{array}{c}\min:-\sum\limits_{i=1}^{2}f_{cri}{(t_{w1},t_{w2},t_{ w3})}^{2}\\ s.t.:\frac{m}{m_{0}}\leq 1\\ t_{\min}\leq t_{wi}\leq t_{\max}\end{array}\right. \tag{15}\]  
The approximated critical load factors at two times are denoted by \(f_{cri}\), \(i=1,\ 2\), \(t_{\min}\) and \(t_{\max}\) are the upper and lower bounds of the design variables, respectively, and \(m_{0}\) is the mass of the initial design depicted in Figure 4. The optimized design is expected to be lighter than the initial design to maintain the structural efficiency of the ITPS.
## 4 Numerical Simulation Considering the Stress Ratio \(R\) Based on the XFEM
### FCP User-Defined Subroutine Considering \(R\) Based on the XFEM  
When the energy release rate of a crack tip satisfies \(G_{th}<\Delta G<G_{pl}\), a fatigue crack begins to grow. \(G_{pl}\) is the upper threshold value of the energy release rate, which is close to the steel fracture toughness \(G_{C}\). \(G_{th}\) is the lower threshold value of the energy release rate. The stress intensity factor \(K_{th}\) of steel Q345 qD is 92 MPa\(\cdot\)mm\({}^{1/2}\)[35]. The energy release rate threshold \(G_{th}\) can be obtained through Equation (3). When the maximum energy release rate of a crack tip is greater than the lower threshold value \(G_{th}\), a fatigue crack propagates along the direction of the maximum principal stress in ABAQUS. Assuming that the \(i\)th element at the crack tip is the crack growth direction, the virtual crack closure technique (VCCT) is used to calculate the energy release rate \(\Delta G_{i}\). \(\Delta N_{i}\) is calculated using Equation (7). The element with the lowest number of cycles is the final fracture element, and the number of cycles increment \(\Delta N\) and crack propagation increment \(\Delta\alpha\) are obtained. The cycle continues until the crack length reaches the final crack length \(\alpha_{f}\). To consider the influence of the stress ratio \(R\), a user-defined subroutine is used in the simulation. A flow chart of FCP considering \(R\) effects based on the XFEM is shown in Figure 8.  
Figure 7: FEM of CT specimen.
## 5 Conclusions  
The petrographic and geomechanical analysis of the Inzari Formation in the Nizam-pur Basin demonstrated that these rocks possess favorable characteristics for construction purposes. The unconfined compressive strength (UCS) values of the Inzari Formation samples fall within the range of 23 to 94 MPa, which indicates that these rocks are suitable for use in load-bearing structures. This finding is consistent with similar studies on the NikanaiGhar limestone, where UCS values averaged 93.348 MPa, further supporting the potential of these formations for use as building materials. The porosity of the Inzari Formation rocks, ranging between 0.27% and 1.32%, is also comparable to the NikanaiGhar limestone study, which reported porosity values of 0.31% to 0.41%. These low porosity values suggest that the Inzari Formation rocks have a high density and durability, making them suitable for construction in environments where long-term strength and stability are required. Additionally, the specific gravity of the Inzari Formation rocks, ranging from 2.81 to 2.86, aligns with the findings from similar formations, where specific gravity values generally range from 2.71 to 2.81. The higher specific gravity of the Inzari rocks indicates their robustness, further reinforcing their suitability for large-scale construction projects. In comparison to other studies, such as those conducted on the NikanaiGhar limestone and other similar formations, the Inzari Formation exhibits comparable or superior geomechanical properties. These similarities strengthen the argument that the Inzari Formation is a reliable source of construction material, particularly for projects requiring high durability and compressive strength.  
In conclusion, the results of this study affirm that the Inzari Formation rocks have significant potential as a construction material, with properties that are on par with other well-documented formations in the region. Future studies may focus on further testing under different environmental conditions to explore the full range of their practical applications in the construction industry.  
**Author Contributions:** Conceptualization, H.A. and J.X.; validation, M.U.A.; formal analysis, H.A. and M.U.A.; investigation, S.A.; investigation, M.U.A.; writing--original draft preparation, H.A. and J.X.; writing--review and editing, J.X.; supervision, J.X.; All authors have read and agreed to the published version of the manuscript.  
**Funding:** The research work in this paper was supported by the grants from the National Natural Science Foundation of China-Yalong River Joint Fund (No. U1765110) and the Fundamental Research Funds for the Central Universities (22120180312).  
**Data Availability Statement:** The data presented in this study are available on request from the corresponding author.  
**Acknowledgments:** We thank [PERSON] for his contribution to the manuscript.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 3 Experiments
### Evaluation Metrics  
To qualitatively evaluate the performance of the proposed approach, three commonly used evaluation metrics for segmentation applications were adopted, validation loss (Val. loss), validation accuracy (Val. Acc.), and intersection over union (IOU) [61]. To quantify the computational complexity of the proposed approach, two popular resource efficiency evaluation metrics were used, namely the number of parameters to measure the model size and the FLOPs to measure the computational cost. We used the same equation to compute the FLOPs as mentioned in [67].
## 3 Results and Discussion
### Polyphenols Content and Antioxidant Activity  
The fresh apple juice initially contained 32.5 mg GAE/100 mL (Table 1). As a result of the MFP preservation process, the measured TPC increased by 127%. In the apple juice treated by traditional pasteurization, the TPC slightly increased in comparison to the fresh juice, reaching only 38.8 mg GAE/100 mL. The MFP was found to be a more effective method regarding the release of this bioactive compound from higher molecular structures. A similar phenomenon was not observed in the case of TP juice. Throughout the entire storage period, the MFP-treated juice maintained a higher polyphenol concentration compared to the traditionally pasteurized juice and the fresh juice. However, after the 6 th month of storage, a sharp decrease in the TPC was observed in the MFP-treated juice. In contrast, in the traditionally pasteurized juice, the concentration of total polyphenols did not change significantly after 12 months of storage. In a study conducted by [PERSON] et al. (2017) [54], a more favorable effect on the concentration of phenolic compounds in pomelo juice treated with MFP (705.3 mg GAE/100 L), compared to TP (690.5 mg GAE/100 L), was observed. To confirm that the increase in total polyphenol content in the MFP-preserved apple juices was caused by the release of lower molecular weight phenolic compounds from bigger structures, the content of individual phenolic compounds was determined (Table 2). Primary polyphenonic compounds typically found in apple juice include chlorogenic acid, epicatechin and phloridzin [55]. Our research aligns with these findings. Traditional pasteurization (TP) did not result in statistically significant differences in the content of polyphenolic components in the juice compared to fresh juice (FJ). However, the study showed that microwave pasteurization led to a statistically significant increase in the content of polyphenolic components, namely epicatechin and chlorogenic acid. After 4 months of storage, there was a noticeable degradation of these tested compounds, ultimately resulting in their content falling below the limit of quantification after 8 months of storage. The antioxidant activity of fresh juice was measured at 51.9 uM/100 mL [Table 1] and after MFP treatment, it significantly increased to 176.8 uM/100 mL. Similar to the polyphenol content, the antioxidant activity remained at a higher level throughout the storage period compared to TP juice. This phenomenon can be justified by the presence of various compounds, including simple individual polyphenolic compounds, which was confirmed in Table 2 and other studies [56]. Numerous studies have confirmed a positive correlation between antioxidant activity and the content of polyphenols [57; 58; 59; 60], which was also observed in our study. The calculated Pearson's linear correlation coefficient for these two parameters was 0.840 (\(\alpha\) = 0.05). The increase in polyphenol content and the associated
# A Performance Analysis of Internet of Things Networking Protocols: Evaluating MQTT, CoAP, OPC UA
## 4 Experimental Environment
### Broker Software  
For the brokers, both RabbitMQ and Mosquito have been analysed. The requirements for the selection of the broker software were:  
* To run in embedded devices.
* To have a user-friendly configuration.
* If possible, to provide support for several protocols.  
RabbitMQ [33] is an open-source message broker that supports many communication protocols such as AMQP, STOMP, MQTT, HTTP, WebSockets. It provides good interoperability, being easily extensible, and a simple user interface.  
Mosquito [34] is an MQTT lightweight broker. It is also quite easy to install and use. In contrast to RabbitMQ, MQTT is a broker for MQTT only.  
Although RabbitMQ provides support for MQTT, on our tests the experiments suffered issues when running MQTT with QoS level 2, which leads us to believe that RabbitMQ may have some open issues in the MQTT implementation. Therefore, for MQTT experimentation, we have considered also Mosquito.
## 3 Materials and Methods
### LC-HRMS Analysis of Plastic Additives  
The chromatographic separation to analyse the additives in the extracts of MNPLs was achieved with a C18 analytical column (Purospher\({}^{\text{\textregistered}}\) STAR RP-18 end-capped column (3 \(\upmu\)m, 2.1 \(\times\) 50 mm) from Merck) coupled to HRMS equipped with an ESI source, working in positive and negative ionization conditions by separate injections. The mobile phase consisted of (A) HPLC-water and (B) acetonitrile (in negative mode) or HPLC-water acidified with 0.05% formic acid (in positive conditions). The elution gradient conditions for the LC mobile phase started with 90% eluent A holding for 2 min and decreasing to 10% in 8 min, holding for two more min and rising to initial conditions (90% A) in one min and, finally, the re-equilibration of the system was achieved in 2 min. The flow rate was kept at 0.2 mL/min throughout the total chromatographic run of 15 min. The sample injection volume was set at 10 \(\upmu\)L. Data were acquired in full scan (90-1500 Da) with an FWHM of 70,000 and, in parallel, in data-dependent scan at a resolution of 35,000 FWHM where the 10 most intense ions from the first full scan were further fragmented with an isolation of 1.0 Da and with a collision energy of 30 a.u.  
The whole data were processed using Compound Discoverer 3.1.
## 3 Methodology
#### 3.1.2 Identification of Tools and Techniques  
The designers, translators, and developers of both ontologies and applications are responsible for performing the identification of tools and techniques after defining a domain ontology. In this phase, related and compatible modern tools for ontology construction are selected, and the latest application techniques are identified for knowledge capture.  
The tools used for browsing, inspecting, coding and editing ontologies are called Ontology Construction Tools (OCTs) or Ontology Development Editors (ODEs). Some examples are SWOOP, IsaViz, WebProtege and Apollo [46]. WebProtege can also be used for the validation and verification of domain ontologies [47]. Some popular examples of ontology representation languages are the Knowledge Interchange Format (KIF), Agent Markup Language (AML) of DARPA, Web Ontology Language (OWL) and Resource Description Framework (RDF) [31; 48].
## 8 Use Cases and Applications
### Healthcare  
The use of drones in public spaces faces global restrictions due to airspace and privacy regulations. While these constraints partially impact the development of healthcare-related drones, certain organizations, such as WeRobotics, persist in implementing solutions in this field worldwide. Notably, researchers at the Johns Hopkins University School of Medicine have discovered that transporting lab tests using drones did not compromise the accuracy of their results.  
Drones exhibit the capability to transport biological samples, thus proving particularly valuable in scenarios where road transport is inefficient. This capability becomes especially indispensable during natural disasters. Drones excel in delivering medical supplies to communities where infrastructure and transportation are often limited, thereby significantly reducing the time required for supply delivery [172]. Addressing this challenge, Aidronix aims to create an unmanned system dedicated to providing medical assistance in rural communities. Their plan involves establishing distribution centers from which drones will transport medical supplies. An emerging concept known as \"lab-on-a-drone\" envisions drones as mobile laboratories providing diagnostic tools for disease diagnosis and treatment.  
Unmanned aerial vehicles (UAVs) can also play a crucial role in controlling infectious diseases, particularly those caused by pathogens such as viruses or bacteria. Drones equipped with sensors and artificial intelligence can gather data on environmental factors like water or vegetation. The use of intelligent drones may gradually replace satellites employed for monitoring and ecological research [173].  
Expanding beyond aerial solutions, autonomous ground vehicles add another dimension to healthcare logistics. These vehicles, leveraging technologies like machine learning and computer vision, offer efficient transport solutions [174]. Autonomous ground vehicles are well suited for scenarios where drones may face limitations, thereby providing a versatile tool for precision healthcare logistics.  
Ensuring the safety of both medical drones and ground vehicles is paramount. Measures include preventing collisions with other objects to avoid property damage, personal injury, or harm to transmitted samples or instruments. Designated safe areas for takeoff and landing, along with weatherproof and rainproof features, are essential considerations. However, achieving these safety features may involve tradeoffs between payload capacity, performance, speed, and maximum the range of the drones and ground vehicles [175].
## 6 Algorithms and Frameworks in Federated Learning
### Model Aggregation Methods
#### 6.3.4 Personalized Aggregation  
Recent advances have led to methods for personalized model aggregation, where the global model is fine-tuned or adjusted based on individual clients' characteristics. This approach acknowledges the heterogeneity of the data and user behaviors across clients.  
Advantages:  
* Better model performance for individual clients.  
* Addresses the diverse nature of federated data sources. Challenges:
* Can lead to overfitting if not properly regulated.
* More complex than traditional aggregation techniques.
# Data Hiding and Authentication Scheme for Medical Images Using Double POB
###### Abstract  
To protect the security of medical images and to improve the embedding ability of data in encrypted medical images, this paper proposes a permutation ordered binary (POB) number system-based hiding and authentication scheme for medical images, which includes three parts: image preprocessing, double hiding, and information extraction and lossless recovery. In the image preprocessing and double hiding phase, firstly, the region of significance (ROS) of the original medical image is segmented into a region of interest (ROI) and a region of non-interest (RONI). Then, the bit plane of the ROI andRONI are separated and cross-reorganization to obtain two new Share images. After the two new Share images are compressed, the images are encrypted to generate two encrypted shares. Finally, the embedding of secret data and attaching of authentication bits in each of these two encrypted shares was performed using the POB algorithm. In the information extraction and lossless recovery phase, the POBN algorithm is first used to extract the authentication bits to realize image tamper detection; then, the embedded secret message is extracted, and the original medical image is recovered. The method proposed in this research performs better in data embedding and lossless recovery, as demonstrated by experiments.  
data hiding; medical images; POB number system; lossless recovery +
Footnote †: journal: Medical Image Processing  
1