esa-sceva/satcom-chunk-collection huggingface dataset:
# IoT Privacy and Security Challenges for Smart Home Environments
## 6 A Suitable Smart Home Architecture for Security
### Gateway Architectures  
An IoT gateway is a relatively resource-rich network processor working on the same LAN with the other IoT endpoints. It can not only be a central management point to deal with the coordination of IoT devices, but it can also improve interconnection and interoperability between smart devices from different manufacturers. In addition, it can act as a bridge to connect the local IoT infrastructure to the cloud. Since the gateway has more computing power and resources, high computation and memory-rich tasks can be offloaded from IoT devices to the gateway. In terms of security, the gateway can centralize user authentication and apply access control to guard against unauthorized access or modification of restricted data. It also acts as a firewall to protect the smart devices and privacy from cyber threats, and to reduce the attack surface.  
In [34], the authors present an integrated access gateway (IAGW) architecture to support various application nodes through standard interfaces for Smart Home environments. The architecture comprises the ubiquitous sensor networks layer, the network layer and the service layer. IAGW includes a security module to implement the authentication, authorization and encryption. One of the benefits of this architecture is that it has a Quality of Service (QoS) module to prioritize traffic and guarantee resources for mission-critical operations.  
A systematic concept called Server-Based Internet-Of-Things Architecture (SBIOTA) [35] is a proposed gateway server to provide an effective, efficient, secure and cooperative integration solution for IoT. This conceptual architecture includes a novel auto-configuration service on the gateway to facilitate the device's deployment and management process so that a device can be plugged into a network and be fully functional on that network with a minimum of manual configuration. Its initial approach is that the authentication and communication between the gateway and devices take place through a separate network port or a short-range antenna physically adjacent to the server. Before connecting the devices to the network, the user needs to place them in physical proximity to the gateway to be authenticated and exchange related information to ensure only legitimate devices are allowed to connect to the network.  
A gateway can implement sophisticated management algorithms on a reasonably powerful processor, and it can operate the critical Smart Home functions. Even in the temporary absence of an Internet connection, it can provide sophisticated firewall and proxy support to IoT devices so that they have minimal exposure to direct network attacks, and it can work with resource-constrained IoT devices without complex middleware. Therefore, this is our preferred Smart Home architecture.
## 3 Results and Discussion
### Fruit Quality Parameter Analysis
#### 3.3.3 The SSC Change of Peaches and Nectarines  
The SSC of nectarines and peaches is largely decreased during the experimental chain. The SSC decline rate of nectarines grown under red net and nectarines from control were 8.15% and 11.06%, respectively. The SSC decline rate for nectarines was more than four times higher than in peaches (2.06% and 2.35%). Regarding the SSC values for nectarines, the decline rate for nectarines grown under the red net was a little smaller than the SSC decline rate of nectarines from control trees. The SSC decline rate of peaches grown under the red net was also a little smaller than the SSC decline rate of peaches from control trees. The quality data results largely reflect the quality change of nectarine and peach fruit in the short chain [37] and suggest that nectarines are more prone to postharvest quality deterioration than peaches. Therefore, postharvest cold chain of nectarines must be monitored and controlled more closely in order to preserve fruit quality.
# Toward QoS Monitoring in IoT Edge Devices Driven Healthcare--A Systematic Literature Review
## 4 Preliminary Concepts
### Fog/Edge Computing  
Due to the numerous drawbacks of conventional centralized computing--such as single point failure, excessive latency, energy consumption, etc.--a more distributed and decentralized paradigm for cloud computing has emerged. Although CC does address  
Figure 9: The front and back ends of a cloud computing system are two separate components. Over an intranet or via the internet, both sides of the connection can communicate with one another [26].  
several problems, such as single points of failure, it is still responsible for concerns with latency and energy use brought on by big uploads to the cloud servers. It became apparent that it would be best to upload only the data that required more processing to the cloud rather than the entire acquisition of data. It would be preferable to carry out operations that require lighter processing locally or very near to the user. Fog and edge computing are concepts that were inspired by this particular idea. This processing idea is shared by fog computing and edge computing. However, they differ technically as indicated by the Venn diagram in Figure 10.  
Both paradigms are crucial in the context of SHAs for providing reliable services to the stakeholders. The application requirements can determine the specific choice. Fog computing, on the other hand, can be used for improved patient data analysis, such as in laboratory reports or prognosis, whilst edge computing can provide quick responses for patients at a distance from hospitals or doctors.
# An Asymmetric Encryption-Based Key Distribution Method for Wireless Sensor Networks
## 4 Analysis and Comparison  
Extensive simulations are provided to verify the performance of our scheme, such as memory consumption, communication overhead, connectivity, and recovery capability for node capture. Next, we compare the proposed key management scheme with other schemes from multiple dimensions.  
We evaluate the performance based on NS-2 [39]. In the simulation, we randomly arranged a total of 200 sensors and 20 cluster head nodes with dimensions of 100 m by 100 m. Each sensor moves at a speed of 1-5 m/s. The signal reception range of each sensor is 10 m. The data transmission rate is 32 kbps; the traffic generation uses the CBR model, and the traffic generation interval is 30 s.
## 2 Numerical Methods and Model Setup
### Spray Characterization
#### 2.6.2 Droplet Size Distribution  
For a proper characterization, besides the spray pattern, droplet size distribution is also of great importance as an initial boundary condition. This is because the initial droplet size distribution can influence the spatial distribution of droplet sizes. In other words, large droplets are more likely to maintain their original state of motion due to their large inertia, while small droplets are more likely to be affected by the air flow during momentum transfer.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Value** \\ \hline Number of spray plumes & 3 (evenly distributed) \\ Hole center diameter & 1.9 mm \\ Inclination angle & 9\({}^{\circ}\) \\ Cone angle of a jet & 11\({}^{\circ}\) \\ Mass flow rate & 1.2 g/s \\ Initial velocity & 32 m/s \\ \hline \hline \end{tabular}
\end{table}
Table 2: Spray parameters operated at 0.9 MPa.  
Figure 4: The diagram of spray.  
Due to the spray characteristics of the low-pressure non-air-assisted injector, the droplet size distribution after the primary breakup is usually used as the initial droplet size distribution of the spray (generally at 10-30 mm from the nozzle exit) [24; 25]. In this study, the droplet size distribution (volume basis) at a 30 mm distance from the nozzle exit was measured and fitted with the experimental method and the Rosin-Rammler method, and the fitted droplet size distribution was input as an initial boundary condition. The initial droplet size distribution at 0.9 MPa is shown in Figure 5. For the Rosin-Rammler distribution, the cumulative volume fraction of droplets with a diameter no greater than \(d_{l}\) is given by:  
\[Y_{d_{l}}=1-e^{-(d_{l}/\overline{d_{l}})^{n}} \tag{16}\]  
where \(\overline{d_{l}}\) is the characteristic diameter, and \(n\) corresponds to the spread parameter.
## 3 Results
### Question 6: What Is the Combined Effect of Individual IEQ Aspects on Overall IEQ Satisfaction?
#### 3.6.2 Logistic Regression Analysis  
According to the literature survey, logistic regression analysis is another popular method used to determine the weightings of individual indexes. [PERSON] et al. proposed logistic regression for the IEQ models [14]. As with linear regression analysis, the overall IEQ satisfaction value was calculated based on the occupants' satisfaction with specific IEQ aspects. A typical logistic regression IEQ model that consists of four main IEQ aspects is shown in Equation (20) [14; 49]. Usually, studies that used logistic regression analysis to determine the weights of individual IEQ indexes also preferred to use logistic regression analysis to calculate the unique IEQ indexes.  
\[\theta_{IEQ} =\frac{\frac{\zeta(\beta_{0}+\beta_{Ai}\theta_{Ai}+\beta_{Th} \theta_{Th}+\beta_{Ac}\theta_{Ac}+\beta_{VI}\theta_{Vi})}{1+\zeta(\beta_{0}+ \beta_{Ai}\theta_{Ai}+\beta_{Th}\theta_{Th}+\beta_{Ac}\theta_{Ac}+\beta_{VI} \theta_{Vi})}}{1+\epsilon(\beta_{0}+\beta_{Ai}\theta_{Ai}+\beta_{Th}\theta_{ Th}+\beta_{Ac}\theta_{Ac}+\beta_{VI}\theta_{Vi})} \tag{20}\]
### Advese Impacts of RFI to SAR System  
The presence of RFI is detrimental to global and regional scientific research activity of SAR. Depending on the application, SAR extracts information based on the characteristics of the echo including amplitude, frequency, time delay, polarization, Doppler shift, and phase [5]. RFI can corrupt the desired radar measurements in a number of ways, ranging from raw data collection, image formation, and the subsequent interpretation process.
## 2 Materials and Methods
### Distribution Coefficient
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Radionuclides** & **Zones** & **Log of Kd (cm\({}^{3}\).g\({}^{-1}\))** & **Reference** \\ \hline \multirow{3}{*}{Ag} & Contaminated zone & \multirow{2}{*}{2.07 \(\pm\) 0.65} & [9] \\  & Unsaturated zone & & \\ \cline{2-3}  & Saturated zone & 2.03 \(\pm\) 1.09 a  & [9,16] \\ \hline \multirow{3}{*}{C} & Contaminated zone & \multirow{2}{*}{0.54 \(\pm\) 0.59} & [9] \\  & Unsaturated zone & & \\ \cline{2-3}  & Saturated zone & 0.0 b  & [16] \\ \hline \multirow{3}{*}{Co} & Contaminated zone & \multirow{2}{*}{1.87 \(\pm\) 0.71} & [9] \\  & Unsaturated zone & & \\ \cline{2-3}  & Saturated zone & 2.65 \(\pm\) 0.24 & [9] \\ \hline \multirow{3}{*}{Cs} & Contaminated zone & \multirow{2}{*}{2.52 \(\pm\) 0.99} & [9] \\  & Unsaturated zone & & \\ \cline{2-3}  & Saturated zone & 2.03 \(\pm\) 1.09 & [9] \\ \hline \multirow{3}{*}{Fe} & Contaminated zone & \multirow{2}{*}{1.55 \(\pm\) 0.80} & [9] \\  & Unsaturated zone & & \\ \cline{2-3}  & Saturated zone & 1.55 \(\pm\) 0.80 & [9] \\ \hline \multirow{3}{*}{H} & Contaminated zone & \multirow{2}{*}{non-absorbable} & [9] \\  & Unsaturated zone & & \\ \cline{2-3}  & Saturated zone & non-absorbable & [9] \\ \hline \multirow{3}{*}{Nb} & Contaminated zone & \multirow{2}{*}{2.93 \(\pm\) 0.48} & [9] \\  & Unsaturated zone & & \\ \cline{2-3}  & Saturated zone & 2.41 \(\pm\) 0.57 & [9] \\ \hline \multirow{3}{*}{Ni} & Contaminated zone & \multirow{2}{*}{2.82 \(\pm\) 0.65} & [9] \\  & Unsaturated zone & & \\ \cline{2-3}  & Saturated zone & 1.80 \(\pm\) 0.72 & [9] \\ \hline \multirow{3}{*}{Sb} & Contaminated zone & \multirow{2}{*}{0.87 \(\pm\) 0.55} & [9] \\  & Unsaturated zone & & \\ \cline{2-3}  & Saturated zone & 3.05 \(\pm\) 0.37 & [9] \\ \hline \multirow{3}{*}{Sn} & Contaminated zone & \multirow{2}{*}{3.52 \(\pm\) 0.70} & [9] \\  & Unsaturated zone & & \\ \cline{2-3}  & Saturated zone & 3.20 \(\pm\) 0.94 & [9] \\ \hline \multirow{3}{*}{Sr} & Contaminated zone & \multirow{2}{*}{1.18 \(\pm\) 1.03} & [9] \\  & Unsaturated zone & & \\ \cline{2-3}  & Saturated zone & 0.58 \(\pm\) 0.94 & [9] \\ \hline \multirow{3}{*}{Zr} & Contaminated zone & \multirow{2}{*}{2.78 \(\pm\) 0.50} & [9] \\  & Unsaturated zone & & \\ \cline{2-3}  & Saturated zone & 2.62 \(\pm\) 1.22 & [9] \\ \hline \end{tabular}
\end{table} 
Table 2: Distribution coefficients of various radionuclides.
## 3 Results and Discussion  
### Calcium Carbonate Content  
The acid digestion method was used to measure the content of calcium carbonate in specimens treated by MICP. The content of calcium carbonate generated by each experimental group is shown in Figure 4, and the percentage of calcium carbonate production in the 3-7 mm surficial layer in the total amount of calcium carbonate generated is shown in Figure 5. The results showed that the precipitation of calcium carbonate at the thinner skin layer was effectively improved by the addition of HPMC, and the highest percentage of calcium carbonate to total calcium carbonate in the hard shell reached 78.05%. However, as for the total CaCO\({}_{3}\) content in the samples, the total CaCO\({}_{3}\) mass of the samples with and without HPMC is roughly the same after MICP treatment. This phenomenon illustrated that HPMC did not improve the total content of calcium carbonate, but could enhance the viscosity of reactants and increase the time of liquid infiltration so that more calcium carbonate precipitation was generated at the thinner surficial layer. At the same reaction amount, the highest calcium carbonate produced in the surficial layer with the addition of HPMC for MICP treatment could reach 1.82 times higher than that with only MICP treatment.  
Figure 4: Total calcium carbonate content.  
In addition, it can be seen from Figure 5 that under the same reaction amount, with the increase in MICP treatment times, the percentage of CaCO\({}_{3}\) content in the surface hard shell increases. This trend is consistent with experimental results of [PERSON] et al. (2019). They found the average calcium carbonate content could almost double from 1.9% to 3.7%, as the MICP treatment cycles increase from 3 to 7. The possible reason is that with a rise in the number of MICP treatments, the first cycle MICP treatment could generate CaCO\({}_{3}\) on the surface layer and effectively block the pores of the sample, and more bacteria and cementation solution could be retained in the surface layer for the following treatment cycles.
# Interoperability between Building Information Modelling (BIM) and Building Energy Model (BEM)
## 3 Results and Discussion
### gbXML Import Results
#### 3.1.2 Warehouse Case in Design Builder  
On the other hand, the industrial warehouse gbXML file showed many more problems than the first case study. Some walls and roof construction systems were created as shadow elements, presented apertures in their surfaces, and did not assemble well with each other. Furthermore, the roof was divided into many small parts, some of which were recognized as walls or as windows, while some of them were missing (see Figure 6). Moreover, some walls were generated automatically, although they did not exist in the BIM tool.  
Table 1 summarizes the errors encountered in the gbXML file import into Design Builder. In summary, the issues found were very similar in both models. However, it was noticed that, when the file was bigger and had more data, more problems appeared during model creation in BEM, mainly with respect to the construction of the thermal envelope  
Figure 5: Residential green building extensible markup language (gbXML) file imported in Design Builder.  
Figure 6: Warehouse gbXML file imported in Design Builder.  
(walls, floors, and roof). Moreover, both models showed different dimensions from Revit, which could not be corrected at the importing step. The only way to guarantee that the model has exactly the same external dimensions as in the BIM, would be to not import it as \"building blocks\". Otherwise, all of the model would be created as a building component, without the generation of thermal zones. Therefore, it is better to accept a percentage of dimension error than to have to build all the thermal zones, which were already made in the BIM authoring tool.
# Influence of Interference Fit and Temperature on High-Speed Permanent Magnet Motor
## 4 Results and Discussion
### Thicknesses of the Sheath Wall  
After the chamfers are added to the end cap, the simulation results show that the maximum principal stress area of the interference fit structure changes from the end face of the magnet to the inner wall of the edge of the magnet sheath. Therefore, the influence of the sheath thickness on the interference fit structure can be studied. Based on this conclusion, the interference amount is kept at 60 \(\upmu\)m at the rated speed of 88,000 rpm. Under the premise of no sheath damage failure, different wall thicknesses of the sheath are selected for the simulation results, and these are shown in Figure 17 below. The stress on the sheath is in the minimum range when the sheath wall thicknesses are between 4.5 and 4.75 mm.  
As shown in Figure 18, when the sheath wall thickness is too small, the deformation resistance of the sheath is poor due to the effect of the interference fit, which causes high stress. Nevertheless, as the sheath wall thickness increases, the magnet plasticity is low and not easily deformed, so the total deformation of the sheath increases. While the sheath thicknesses are between 3 and 4.5 mm, the strain has a flat region and gradually increases thereafter. Until the sheath wall thicknesses are higher than 4.5 mm, the stress and strain generated by the sheath gradually increase. This improves the pre-stress of the sheath on the magnet. In the interference fit of the rotor, the priority of magnet protection is higher than that of sheath. However, too much thickness can lead to assembly difficulties and heat dissipation problems. Therefore, the thickness of the sheath should be determined according to the actual situation to achieve the best effect.  
Figure 16: The relationship between the interference amount and the failure temperature of the structure.  
Figure 17: The stress distribution under different thicknesses of sheath wall.
## 3 Results and Discussion
### RI Sensor Employing High \(Q\)-Factor Absorber
From the electric field distributions depicted in Figure 2b, we can find that the electric field is concentrated on the topside surface ends of the dielectric grating strips, which can be easily accessible to the surrounding analytes and makes it very sensitive to the refractive index change that is induced by the change of the analytes concentration. Such a change in refractive index can be measured by detecting the wavelength shift of the resonant peak. Therefore, the presented metasurface absorber has desirable spectral features for refractive index sensing applications in the infrared region. To evaluate the performance of the sensor, the sensitivity (S) and the Figure of merit (FOM) are defined, as following [16]  
\[S =\frac{\Delta\lambda}{\Delta m} \tag{1}\] \[FOM =\frac{S}{FWHM} \tag{2}\]  
\(\Delta\lambda\) is the shift of resonant wavelength due to change \(\Delta\)n of RI in the surrounding. The resonant wavelength has a redshift of 18 nm when the refractive index of the surrounding analytes (n\({}_{\text{s}}\)) changes from 1 to 1.01, as shown in Figure 3e. The corresponding resonant wavelength was further extracted and plotted as a function of the refractive index in Figure 3f. The RI sensitivity and the FOM are estimated to be S = 1800 nm/RIU and FOM = 62 RIU\({}^{-1}\) for the proposed metasurface absorber with Q-factor of 212, where RIU is Refractive Index Unit and RIU\({}^{-1}\) is unit of the FOM, according to Equations (1) and (2). The refractive index sensing performance of this absorber is compared with that of the other kinds of absorber with different structure reported recently, as summarized in Table S1. It can be seen that the sensitivity and FOM of proposed absorber are comparable to or even larger than those of plasmonic sensing devices (such as MIM structure, VSSR Resonator and Hybrid metasurface). When considering that the optic spectrometer has resolution of 1 nm, the detection limit of the sensor is \(5.6\times 10^{-4}\) RIU, which suggests that the proposed refractive index sensor employing high Q-factor absorber offers a promising platform for surface-enhanced infrared sensing applications.
###### Abstract  
This paper demonstrates a wavelength-division-multiplexed passive optical network (WDM-PON) scheme based on novel reconfigurable optical amplifiers (ROAs). The measured switching characteristics of the ROA\({}_{3}\) constructed with a \(2\times 2\) crossbar optical switch and a four-port reversible optical circulator (OC) and a conventional EDFA can meet the requirements of most network management and surveillance. The self-made four-port reversible OC's response time is less than 2 ms, and its insertion losses are about 1 dB or less for all the transmission paths and switching states. An optimal design of ROAs is proposed and evaluated for bidirectional optical amplifier protection, in which ROA\({}_{3}\) has an EDF length of 7.5 m long with a 1480 nm pump laser and possesses a backward or forward pumping configuration with the corresponding pump power of 200 mW or 50 mW. We verified the scheme's feasibility through a simulation of WDM-PON systems with 40 downstream and upstream channels. This scheme enables the intelligent protection switching in practical operation scenarios for high-capacity multi-wavelength networks.  
optical amplifier; reconfigurable optical amplifier; wavelength-division-multiplexed passive optical network; optical amplifier protection +
Footnote †: journal: Applied  
Article
## 4 Deep Learning Methods for HDR Reconstruction
### Diffusion Models for HDR Reconstruction
#### 4.4.1 Diffusion Models for Single-Frame  
[PERSON] et al. [167] proposed an approach to generate HDR images from a single LDR input image. This approach creates a sequence of LDR images, referred to as brackets, which, when merged, produce an HDR image. Initially, pretrained DMs are used to produce multiple exposures from a single LDR image. Next, an exposure consistency term couples the different generated exposures, ensuring that the LDR images remain consistent with one another. Additionally, the approach enables conditioning the HDR content generated in saturated regions using text conditioning and image/histogram guidance combinations, offering more control over image quality and the final tonal distribution of the HDR image.  
Similarly, [PERSON] et al. [11] propose a Denoising Diffusion Probabilistic Model (DDPM) architecture using classifier-free guidance. The proposed architecture uses an autoencoder to produce a latent representation of the LDR input image for conditioning the HDR reconstruction. Furthermore, the authors introduce a hybrid loss function, weighting reconstruction, and perceptual and exposure losses to improve convergence of the proposed architecture.  
On another note, [PERSON] et al. [168] proposed the Diffusion Inverse Tone Mapping (DITMO) approach, similarly aiming to generate HDR images from a single LDR. The proposed method uses a saturation mask to identify saturated areas and a semantic mask to classify objects in the image into different classes. With that information, the method applies a diffusion-based inpainting process that hallucinates details in saturated regions, employing a set of prompts associated with the classes identified by semantic segmentation. Finally, multiple versions of the image are generated at different exposures and merged to form the final HDR image.