esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experimental Results
### Implementation for IVR-SDO Streaming Distance Education System  
Each SDO client system has an Intel Core i7-6850K with 32 GB memory and an NVidia GTX 1080 graphics card. The SDO clients used Vive HMDs with Vive controllers. The system was implemented using the Coexistent Reality Software Platform (CRSF) 2.0 [32] and Microsoft Windows 10. A relay server has an Intel Xeon W-2104 CPU with 32-GB memory running Windows server 2016. A SDO image server has an Intel Xeon W-2104 CPU with 32-GB memory running Centos 7. We developed all software programs (SDO client, SDO image server, and relay server) in Microsoft Visual Studio C++.  
Figure 9: SDO image education and participant interactions in a virtual space: (**a**) SDO education using ray-pointing; and (**b**) listening to the explanation of an expert on SDO images through voice conversation.  
Figure 10 shows the network configuration of the proposed system. Every host is connected to KREONET, having a bandwidth of 10 Gbps or higher, using the Mellanox 40G network interface card. The SDO image server unidirectionally transmits 10 types of SDO images through the six channels at 30 fps to the relay server. When the three users access the relay server, the SDO image received through the six channels is merged. Then, the image is rendered in a virtual space. In contrast, the voice, hand, and head pose data are transmitted and received bi-directionally through the relay server. Figure 11 illustrates the educational experience when using the IVR-SDO streaming distance education system. With the help of a pointer, an expert explains the solar phenomena in detail. Students also look at the SDO images and ask questions while listening to the explanation. Remote users are placed in one virtual space; the user's head and hand motions are tracked by the Vive HMD and controller and synchronized with their avatar. By pressing the controller button, the ray exiting the virtual hand is aimed at the desired point.
## 3. Results and Discussion
### Compressive Strength  
Figure 7 shows the compressive strength according to combinations of PCE and VMAs. Overall, the strength exceeds 24 MPa at 28 days of curing duration for all types of mixes. With the addition of PCE (without VMA), the compressive strength was found to be at 13.6, 21.5, and 30.9 MPa after 3, 7, and 28 days of curing duration. Aside from this, PCE with WG showed the highest compressive strength when compared to other samples. In particular, WG provides a 17% higher compressive strength at 28 days, but the other combinations show a decrease of 4-20% in strength compared to the plain sample at 28 days. In particular, the addition of HPMC (HL and HH) shows the greatest decrease in strength for Plain. The reasons for this are that HPMC has the potential to adsorb PCE, and it delays the cement hydration due to its effects on the precipitation of calcium hydroxide and water absorption. With the addition of PCE with two types of VMA, the two mixes show a similar compressive strength at 28 days. Due to the increase in HPMC from 0.3% to 0.6%, HHW shows a 7% strength reduction compared to HLW at 3 days. It was considered that the characteristics of HPMC influenced the setting time and hydration process [44, 45]. With the addition of PCE with three types of VMA, the compressive strength of HLWG and HHWG at 3 days and 28 days are the highest compared to HLWS and HHWS samples. The combination of HL or GG shows a higher compressive strength than the combination of HH or ST. This supposedly reflects each strength characteristic that is confirmed when only one type of VMA is added. In summary, it is shown that the unique characteristics of VMA are exhibited by and affect the compressive strength, according to the combinations of different types of VMAs.  
Figure 6: Yield stress and elapsed ratio of fresh concrete by different combinations of VMAs.  
Figure 7: Compressive strength of hardened concrete by different combinations of VMAs.
# Band Gap Properties in Metamaterial Beam with Spatially Varying Interval Uncertainties
## 3 Results and Discussion
### Band Gap Analysis of Deterministic Metamaterial Beam  
Figure 4 displays the transmission coefficient curve of a deterministic MB. This deterministic MB contains a total of 12 unit cells, and each unit cell consists of four beam elements. The lattice constant \(a\) is 0.07 m. The thickness \(h\) and width \(b\) of the basic beam are equal to 0.0025 m and 0.01 m, respectively. The mass \(m\) and spring stiffness \(k\) are equal to 0.0225 kg and 769.04 N/m, respectively. The Young's modulus \(E\) and density \(\rho\) of aluminum are equal to \(7.76\times 10^{10}\) Pa and 2730 kg/m\({}^{3}\), respectively. As shown in the shaded areas in Figure 4, this metamaterial beam can form a band gap from 29.4452 Hz to 69.9634 Hz.
Which should come first--confidentiality, integrity, or availability (CIA triad) [3]--for different applications?  
For instance, ICS systems are increasingly using IoT [4], such as in electricity production, transmission, and distribution [5]. They must reliably transfer data between field devices and control centers. Because they control physical processes, one can assume that the integrity and availability of the data and control commands are more important. On the other hand, confidentiality is more important in systems that carry sensitive information, such as healthcare.  
Figure 1 illustrates the idea of how different applications (e.g., manufacturing, food industries, smart cities, transportation, logistics, energy, and healthcare) have different security requirements in terms of the CIA triad and safety, which can determine the type of IoT communication protocol and encryption features that it will support. This in turn can always be updated based on the latest technologies or modified security requirements.
## 3 Design of Approximate QAM Demodulation Circuits
### Pareto Trade-Off Analysis: Hardware Resources vs. Accuracy  
Figure 7 shows a comparison of all the circuits considering different metric combinations in a Pareto diagram. Specifically, in Figure 7a-c,g, we consider the BER performance versus throughput, power, throughput/power ratio and LUT utilization for a fixed SNR value (8 dB). In these Figures, we do not observe much diversity regarding the circuits that form the Pareto fronts. Specifically, in all four diagrams the Pareto front consist of circuits from ALLR and PLLR algorithms. However, in Figure 7b,g there are also circuits from the AML algorithm that form the Pareto front. However, considering the best trade-off of the examined metrics, the diagrams show that the optimal circuits belong to the PLLR algorithm. Next, in Figure 7d-f,h, we consider the average MRE instead of BER performance, while keeping the other metrics the same. In these test cases, the Pareto front consist of circuits from all algorithms, but the PLLR algorithm has fewer circuits. The most optimal circuits that give the best trade-off of the metrics used are from the AML algorithm; however, one should be cautious when considering the specific algorithm for deployment because it provides the worst BER performance compared to the others for the same SNR values.  
Figure 7: Pareto analysis of the proposed 64-QAM circuits in terms of accuracy and hardware resources. (**a**) BER–throughput. (**b**) BER–power. (**c**) BER–throughput/power. (**d**) MRE–throughput. (**e**) MRE–power. (**f**) MRE–throughput/power. (**g**) BER-LUT. (**h**) MRE-LUT.  
Solely in terms of resources and throughput, our exploration indicates that, regardless of algorithm, fixed-point architectures are the most efficient and truncation is the preferable approximation technique. Taking into account the BER results, the ALLR and PLLR algorithms have better BER performance than AML, with ALLR being slightly better than PLLR. So, we consider the most advantageous QAM demodulator circuit to be the ALLR FXP-T8 implementation.
## 2 Materials and Methods
### Analytical Methods  
The following parameters were analyzed: pH, TS, VS, TCOD, SCOD, VFA, DOC, TOC and TN. All the analyses were performed according to the standard methods described by the APHA-AWWA-WPFC [22].  
pH measurements were conducted using a Crison pH meter (Alella, Barcelona, Spain). TS and VS were determined via gravimetric technique, utilizing a self-calibrating balance. TCOD and SCOD were evaluated using the colorimetric method. For the analysis of individual VFAs, such as acetic, propionic, butyric, isobutyric, valeric, isovaleric, isocyanic, caproic and heptanoic acids, as well as the total content of total volatile fatty acids measured in mgAcHequivalent/L, gas chromatography was employed. This analysis employed a gas chromatograph (Shimadzu GC-2010, Kyoto, Japan) equipped with a flame ionization detector (FID) system and a Nukol-packed capillary column (30 m in length, 25 mm inner diameter and 25 \(\upmu\)m film thickness). Hydrogen was employed as the carrier gas at a flow rate of 42.1 mL/min and a pressure of 75.5 kPa, yielding a linear velocity of 45 cm/s and a flow rate of 1.43 mL/min in the column. Nitrogen was used as carrier gas. The FID flame was generated with synthetic air at 400 mL/min and 50 kPa pressure, along with hydrogen at 40 mL/min and 60 kPa. The oven temperature program began at 115 \({}^{\circ}\)C for 0.5 min, followed by a ramp of 30 \({}^{\circ}\)C/min to 150 \({}^{\circ}\)C, then a ramp of 15 \({}^{\circ}\)C/min to 180 \({}^{\circ}\)C, and held at 180 \({}^{\circ}\)C for 4 min. Phenol was added as an internal standard at a perfectly known concentration (500-600 mg/L) [22; 23]. The determination of TOC and TN was carried out using a total organic carbon analyzer (Shimadzu TOC-L CSH/CSN, Kyoto, Japan) in accordance with the APHA-AWWA-WPFC standard methods [22].  
Throughout the BMP tests, daily gas pressure measurements were measured by means of a pressure gauge (XM 5007 I.S., Samson, Frankfurt, Germany) and used to calculate the volume of biogas generated, utilizing the ideal gas formula. The composition of the biogas was analyzed by means of the BIOGAS 5000 Geotech infrared biogas analyzer (LANDTEC North America, Colton, CA, USA), which provided measurements in percentage for CH\({}_{4}\), CO\({}_{2}\) and O\({}_{2}\) composition with an error range of 0.5-1.5% and in ppm for CO, H\({}_{2}\) and H\({}_{2}\)S with a measurement error of 1.5-2%. The equipment is calibrated once a year by the supply company (Fonotest, Madrid, Spain) and periodically verified with a certified chromatographic gas standard.
# Towards Secure Searchable Electronic Health Records Using Consortium Blockchain
## 2 Background and Related Works
### Background  
#### 2.1.1 Electronic Health Records (EHRs)  
Electronic Health Records (EHRs) are the digital format of patient data that is replacing traditional patient data collection which is a part of the rapid advancement of information technology and internet technology. In addition, health information contains highly sensitive private information, which should be shared by individuals, including health specialists, pharmacists, relatives, insurers, and other hospitals.  
[PERSON] et al. [12] were the first to transform paper health records into EHRs. EHR is a popular method for healthcare providers (e.g., hospitals) to store patient data. In addition, Figure 1 illustrates a typical view of EHRs. In such a system, protecting data integrity, anonymity, and confidentiality is certainly a critical issue.  
Figure 1: Overview of Electronic Health Records (EHRs) services.  
On the other hand, EHRs face several privacy and security challenges,which range from malware to Distributed Denial-of-Service (DDoS) attacks that can lead to inefficiency of patient care. Cyber-attacks can lead to major consequences which overtake security breaches and economic damage [13]. These activities illustrated the critical need to safeguard the confidentiality, integrity, availability, protection, and privacy of sharing electronic health records (EHRs) [6].
# 3D Positioning of Drones through Images
###### Abstract  
Drones traditionally rely on satellite signals for positioning and altitude. However, when in a special denial environment, satellite communication is interrupted, and the traditional positioning and height determination methods face challenges. We made a dataset at the height of 80-200 m and proposed a multi-scale input network. The positioning index RDS achieved 76.3 points, and the positioning accuracy within 20 m was 81.7%. This paper proposes a method to judge the height by image alone, without the support of other sensor data. One height judgment can be made per single image. Based on the UAV image-satellite image matching positioning technology, by calculating the actual area represented by the UAV image in real space, combined with the fixed parameters of the optical camera, the actual height of the UAV flight is calculated, which is 80-200 m, and the relative error rate of height is 18.1%.  
unmanned aerial vehicle (UAV); deep learning; drone height calculation; positioning +
Footnote †: journal: Sensors
# Effect of Radial Height of Helical Static Blade on the Cavitation Performance of Inducer
## 3 Simulation Method
### Cavitation Model  
In the cavitation flow of an inducer, the vapor nuclei form cavities in low-pressure regions, and the cavities collapse in high-pressure regions, with steep density variations in the whole cavitating regions. In this paper, the Schnerr and Sauer cavitation model [27] is used to deal with the evaporation and condensation process:  
\[m^{+}=\frac{\rho_{\mathrm{v}}\rho_{\mathrm{l}}}{\rho}\alpha(1-\alpha)\frac{3} {r_{\mathrm{b}}}\sqrt{\frac{2\left(p_{\mathrm{v}}-p\right)}{\rho_{\mathrm{l}} }}\ \ (p\leq p_{\mathrm{v}}) \tag{6}\]  
\[m^{-}=\frac{\rho_{\mathrm{v}}\rho_{\mathrm{l}}}{\rho}\alpha(1-\alpha)\frac{3} {r_{\mathrm{b}}}\sqrt{\frac{2\left(p-p_{\mathrm{v}}\right)}{\rho_{\mathrm{l}} }}\ \ (p\geq p_{\mathrm{v}}) \tag{7}\]  
Figure 3: Mesh independence check.  
where \(\rho_{\rm v},\rho_{\rm l}\), and \(\rho\) are, respectively, the density of liquid, vapor, and two-phase mixture, \(\alpha\) is the volume fraction of vapor, and \(r_{\rm b}\) is the diameter of bubbles.
# Edge-Guided Hierarchical Network for Building Change Detection in Remote Sensing Images
## 1 Introduction
Although these methods improve the edge accuracy of change detection to some extent, capturing the edge information from complexly distributed grounding objects remains challenging: (1) Buildings often have similar color characteristics to their surrounding regions, making it challenging to capture the edge of changed buildings; (2) Buildings usually have shadows, which can cause the edges of the buildings to be difficult to identify. These factors make it difficult for existing neural network models to discriminate the edges of buildings. To address the above problems, we propose a novel edge-guided hierarchical network (EGHNet) to enhance the accuracy of change detection at the edges of buildings. In our proposed EGHNet, a new edge-extracted module is designed to enhance the ability to capture the edge information of buildings. In the edge-extracted module, we designed a pixel-wise cross-scale feature comparison operator based on the fact that the edge features at large-scale maps are more dense than that in small-scale maps. This operator enables our proposed edge-extracted module to have a more acute ability to capture the information at the edges of remote-sensing objects. Compared to traditional convolution operators, our designed operator can more accurately capture features at the edges. The edge-guided module performs an edge feature compensation process. Specifically, it selectively supplements the edge features onto the multi-scale feature maps under the guidance of the spatial distribution clues captured by the edge-extracted module. The edge-guided module is designed to address the problem of deep convolutional features easily losing detail information, thereby ensuring the model better detects the details of changed buildings. Additionally, our designed network structure is hierarchical, which allows us to better detect macroscopic features and microscopic details of remote sensing scenes, thereby enhancing the accuracy of change detection. The main contributions of this paper are summarized as follows:  
1. An edge-guided hierarchical network that leverages edge information to accurately extract building change information is proposed to utilize edge information to enhance the accuracy of building change detection by focusing on the edges of changed regions;2. An edge-extracted module is designed to combine deep and shallow features for comprehensive edge structure extraction. Additionally, an edge-guided module is developed to fuse edge features with different levels of features, enhancing the network's ability to detect and focus on the edges of changed regions;
3. The proposed method demonstrates superior performance on WHU and LEVIR-CD building change detection datasets, achieving high F1 scores and outperforming recent learning-based change detection methods.
## 5 Simulation and Evaluation Results
### DRTO Algorithm Performance Evaluation  
To verify the superiority of the DRTO algorithm in solving offloading decisions between the satellite-terrestrial cooperative computing architecture, we evaluate the convergence performance, offloading cost of the decision location given by DRTO, and the runtime consumption.
## 4 Overview of Trusted AODV
### Framework for 3 VSR  
There are mainly three parts in the 3 VSR framework i.e., standard AODV routing protocol, trust model and secure routing. Using our trust model, the 3 VSR completes the procedure as trust recommendation, trust combination, trust judging, trusted routing behaviors, advance cryptographic routing behaviors and trust updating. From Figure 3, we can see that the relationship and structure between these entities. The general procedure for establishing trust relationships among nodes and for performing routing discovery is described as follows.  
Let us consider the beginning stage when new nodes initiates communication with the network but they are uncertain about each other at beginning. In distributed VANET a node is free to move and join different network, their recent partners will evaluate their trust levels. In most previous work, the new comers are not aware of their forwarding behavior and thus set the trust level to null. This raises the alarm that the node will possibly be excluded from future routing. This approach is not feasible with a highly dynamic network. In our scheme, we did not considered the extreme values for new comers i.e., (honest, dishonest) till the network initialization. However, the uncertain opinion toward new joining node is set as \(u=(0,0,0,1)\), so at least it has a chance to prove its credibility by verifying digital certificate and minimize uncertainty.  
Figure 3: Frame work for the proposed scheme, the standard AODV is applied at second stage with enabling sensing logic-based trust model, while cryptographic technology is considered to take effect before this operation.  
After this initial activity, having some successful or failed, the communication node \(A\) can change its opinion about node \(B\)'s behavior using a trust update algorithm. After establishing bidirectional communicating path nodes can use our secure routing protocol for operation. As trust is asymmetric, mobile nodes uses second hand observation given by its neighbors, and finally combines into a single trust value. Notice that a node can join the existing VANET through many ways and several security algorithms can be used to run this operation. In this framework trust establishment and the route discovery are all treated by node's cooperation without any third or central party.
# Improvement of Buildings' Air Quality and Energy Consumption Using Air Purifying Paints
## 2 Materials and Methods
### Photocatalytic Evaluation in Terms of IAQ Improvement_Real-Scale  
Real-scale applications of the VISIONS photo-paints (organic and inorganic) were implemented in order to estimate their effectiveness to degrade air pollutants. The application took place in the Demo Houses (Figure 6), which are located on the premises of FORTH in Crete, which with the high-end installation and processing equipment comprise a unique European facility. The VISIONS photo-paints were applied on the surface of the interior area in one of the Demo Rooms (ceiling and walls: approx. 40 m\({}^{2}\)), the so-called \"green room\". The other room was painted with conventional paint in order to be considered as a reference: the \"conventional room\". Both rooms were equipped with 8 lamps (Philips 1200 mm, 18 watt, LED Ecofit), which emit in visible spectra, and the light intensity on the walls was measured as 10.1 Btu/ft\({}^{2}\) h up to 11.3 Btu/ft\({}^{2}\) h (Figure 7). The two rooms are separated by a control room where the monitoring equipment coupled with data loggers (NOx, O\({}_{3}\), and BTEX analyzers; temperature; RH%; and light intensity) was placed (Figure 8).  
Figure 4: UV source (AirTechLab (NCSRD Athens, Greece).  
Figure 5: Vis source (AirTechLab/NCSRD Athens, Greece).  
Figure 6: Demo Houses (FORTH).  
The methodological approach to estimate the photocatalytic efficiency of VISIONS photo-paint was as follows:  
First approach  
Both green (VISIONS paint) and conventional rooms were fed with equal amounts of pollutants and were illuminated using the same lamp system. Continuous monitoring of NOx and VOCs (toluene) was applied in both rooms and the pollutant degradation was determined after 1 h.  
Second approach  
The \"green\" (VISIONS paint) room was fed with air pollutants (NO, toluene) without illumination. After stabilizing the pollutant concentration in the room, the elimination of pollutants was calculated after 1 h without illumination. The test was repeated with illumination and the difference in the elimination rates represents the photocatalytic degradation of the pollutants.