esa-sceva/satcom-chunk-collection huggingface dataset:
# Assessment of MicroPET Image Quality Based on Reconstruction Methods and Post-Filtering
## 5 Conclusions  
Image accuracy of the OSEM2D was higher than those of other reconstruction methods. The overcompensated recovery coefficients filtered by the 2 mm Gaussian filter resembled the values of FBP. The image uniformity of OSEM3D was higher than those of other reconstruction methods. The SOR values of the 3 DRP and FBP reconstruction methods were higher than that of iterative reconstruction methods. The reconstruction time (14 min) of 3 DRP was longer than those of other reconstruction methods.  
In Inveon microPET imaging, iterative reconstruction methods provided better image quality for tumors smaller than 3 mm. For tumors bigger than 3 mm, the FBP, 3 DRP, and OSEM2D reconstruction methods without post-filtering showed better results. The OSEM3D reconstruction method with 1 mm Gaussian post-filtering enhanced the image accuracy and image quality. To conclude, we need to select the appropriate reconstruction method and post-filtering for correct image analysis or interpretation, because post-filtering enhances image uniformity but impairs image accuracy.  
In the myocardial blood flow (MBF) measurement using dynamic PET image, Patlak analysis with recovery coefficients is widely used. Our future work is on validation of Patlak analysis with recovery coefficients and the development of the MBF measurement program using Patlak analysis.  
Conceptualization, B.-i.L.; Data curation, H.-S.K.; Formal analysis, H.-S.K.; Methodology, B.-i.L.; Visualization, J.-S.A.; Writing--Original draft, H.-S.K. and J.-S.A.; Writing--Review & editing, H.-S.K. and J.-S.A. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Basic Science Research Program of the National Research Foundation (NRF) of Korea (NRF-2018R1D1A1B07049322) and by a National Research Foundation of Korea (NRF) grant funded by the Korean government (MSIT) (NRF- 2017M3A9E2056373).  
Not applicable.  
Data Availability Statement: Data is contained within the article.  
This study was supported by a grant from the Basic Science Research Program of the National Research Foundation (NRF) of Korea (NRF-2018R1D1A1B07049322) and by a National Research Foundation of Korea (NRF) grant funded by the Korean government (MSIT) (NRF-2017M3A9E2056373).  
The authors declare no conflict of interest.
## 3 Results and Discussion
### The Influence of the Object's Weight in the OBD Method  
The object-based downscaling (OBD) method effectively mitigates the salt and pepper effect associated with the initial segmentation of land surface temperature (LST) in the Pixel-Based Analysis (PBA) method and resolves the issue of energy non-conservation after downscaling. As illustrated in Figure 7, the red ellipses highlight typical urban surface types. Within these areas, the LST obtained from the PBA downscaling method is closely linked to remote sensing indices (RSIs) with an obvious \"salt and pepper effect\". Following the adjustment of weights for various segmentation objects in regions U1-U6, the spatial distribution characteristics of the downscaled LST using OBD were significantly more aligned with the true temperature values observed by ASTER, and the \"salt and pepper effect\" of PBA disappeared. The average radiance value calculated for the region enclosed by the red ellipse is 176.5 Wm\({}^{-2}\). In contrast, the average radiance calculated for the PBA segmented LST in the same area is 168.5 Wm\({}^{-2}\), while the average for the OBD segmented LST is 174.2 Wm\({}^{-2}\). The green ellipse area indicates a mixed region comprising vegetation and urban areas, where the PBA exhibits a noticeable underestimation of the LST in vegetated regions and an overestimation in urban areas. After recalculating using the OBD method with the weight of V1, the previously underestimated vegetation areas were significantly elevated, while the overestimated urban building areas were mitigated. Nevertheless, compared to the true values from the ASTER, there remained an estimation error of over 2 K in this region. These findings demonstrate that the OBD method effectively addresses the overestimations and underestimations inherent in the PBA method, imposing meaningful constraints on the initially decomposed temperature values through a secondary decomposition process.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Cases**} & \multicolumn{3}{c}{**OBD Method**} & \multicolumn{3}{c}{**PBA Method**} \\ \cline{2-7}  & **ME** & **STD** & **RMSE** & **ME** & **STD** & **RMSE** \\ \hline \multirow{4}{*}{ASTER} & Natural & \(-\)0.96 & 2.94 & 2.12 & 3.13 & 3.34 & 2.48 \\  & terrain & \(-\)1.94 & 2.54 & 3.59 & 3.27 & 2.35 & 4.15 \\  & Urban & surface & \(-\)1.08 & 1.12 & 0.31 & 1.32 & 0
## 3 Results and Discussion
### Llme-SPME Method Optimization
#### 3.1.2 Effect of Extraction Temperature and Time on LLME-SPME  
The extraction temperature and extraction time are usually interacted factors [18]. The full second-order polynomial models are versatile in many systems over a limited factors, and the central composite designs are very useful for obtaining data to fit the full second-order polynomial models [19]. Figure 2 is a central composite design used in our study for two experimental conditions: extraction time and extraction temperature. The model for the response surface is given as Equation (1). The polynomial model is fit to the response values obtained from the central composite design.  
\[y\ =b_{0}+b_{1}x_{1}+b_{2}x_{2}+b_{3}x_{1}^{2}+b_{4}x_{2}^{2}+b_{5}x_{1}x_{2}+e \tag{1}\]  
for which \(y\) is the response that is the peak area of the TCE; \(b_{0-5}\) are the coefficients for the model; \(x_{1}\) is the extraction time and \(x_{2}\) is the extraction temperature.
# Privacy-Preserving Feature Selection with Fully Homomorphic Encryption
###### Abstract  
For the feature selection problem, we propose an efficient privacy-preserving algorithm. Let \(D\), \(F\), and \(C\) be data, feature, and class sets, respectively, where the feature value \(x(F_{i})\) and the class label \(x(C)\) are given for each \(x\in D\) and \(F_{i}\in F\). For a triple \((D,F,C)\), the feature selection problem is to find a consistent and minimal subset \(F^{\prime}\subseteq F\), where 'consistent' means that, for any \(x,y\in D\), \(x(C)=y(C)\) if \(x(F_{i})=y(F_{i})\) for \(F_{i}\in F^{\prime}\), and'minimal' means that any proper subset of \(F^{\prime}\) is no longer consistent. On distributed datasets, we consider feature selection as a privacy-preserving problem: assume that semi-honest parties A and B have their own personal \(D_{\text{A}}\) and \(D_{\text{B}}\). The goal is to solve the feature selection problem for \(D_{\text{A}}\cup D_{\text{B}}\) without sacrificing their privacy. In this paper, we propose a secure and efficient algorithm based on fully homomorphic encryption, and we implement our algorithm to show its effectiveness for various practical data. The proposed algorithm is the first one that can directly simulate the CWC (Combination of Weakest Components) algorithm on ciphertext, which is one of the best performers for the feature selection problem on the plaintext.  
C 2022, 15, 229. [[https://doi.org/10.3390/a15070229](https://doi.org/10.3390/a15070229)]([https://doi.org/10.3390/a15070229](https://doi.org/10.3390/a15070229))  
1  
CWC algorithm; oblivious sorting; TFHE; IND-CPA
# IoT for Smart Cities: Machine Learning Approaches in Smart Healthcare--A Review
## 4 IOT and Healthcare
### Digital Drugs  
One of the newer innovations in the healthcare industry is what is known as \"smart pills\". Smart pills are taken like normal medication but are equipped with some kind of monitoring technology in addition to the actual medication. They use it to forward information to a sensor worn on the body. These sensors monitor drug levels in the body based on a patient's perceived or diagnosed condition. The data from the portable sensors are then transmitted to a mobile phone app, which means that patients can access data on their vital functions themselves. Doctors can do this if the patient agrees. This is how the treating physicians determine whether a drug is working as intended or possibly causing side effects [1].  
In November 2017, Abilify MyCite launched the first FDA-cleared smart pill that timestamped only when the drug was actually taken. As soon as the pill comes into contact with the patient's gastric acid, it triggers a sensor that marks the time of contact and first forwards this information to the wearable sensor and finally to the mobile phone app. The correct dosage and the prescribed intake are important prerequisites for a successful treatment. Such information is very valuable to medical professionals, and they no longer have to rely on the patient's word alone when treatment plans must be strictly adhered to. If patients fail to do this, the doctor can seek a discussion and clarify the cause directly [25].  
One of the areas that causes a little more discomfort are so-called \"robotic\" pills. Once ingested, they are able to perform certain functions directly in the patient's body. Companies like Rani Therapeutics are developing pills that have the ability to pass through the body to navigate and perform functions that, for some reason, cannot be performed non-invasively. For example, [PERSON] invented a pill that navigates through the stomach into the small intestine, where it injects without the exposure of the active compound to  
Figure 3: The leading disease types considered in the artificial intelligence literature (PubMed) [3].  
digestive enzymes. When the drug is delivered, the residue dissolves and is digested, an option that is ideal for large, long, chained drug molecules such as proteins, peptides and antibodies.
## 3 Proposed System Model
### Cloud-Based Web Server  
The cloud-based web server is a central repository for all the configuration, and additionally, the number of operations that have been performed. Figure 2 shows the expanded version of the cloud-based web server. There are four main sub-modules: Profile Receiver, Profile Parser, Mission Planning, Client Application.  
Figure 1: Proposed architecture overview.  
The profile receiver receives input data, which contains resources information and the services. The input data is composed of virtual objects (VO) in eXtensive Markup Language (XML) format. A VO has resource name, resource location, resource URI, and properties.The XML format of the configuration of the service is the combination of the resources needed to perform a useful operation. The combination of these operations which are deployed as a unit is called microservice denoted by _uS_ and _uService_ in figure. The interface through which the communication occurs from the mission-planning toolbox is two-fold: DIY to the web server using web sockets and DIY to the IoT server based on Raspberry PI using XML Constrained Application (CoAP) commands. Finally, the IoT server to the web server using HTTP requests and queries the string parameters. The profile parser parses the request and gives it to the mission-planning toolbox. The data passed is in the form of XML, or a query string, and the protocols used are HTTP and CoAP. The data which are passed include resources ID, location, and URI on the IoT server. The interface through which data are shared also has two forms: web sockets for web transfer, and General Purpose Input/Output (GPIO) for IoT server. The data is parsed and presented in a more useful way for visual representation and persisted on a web-based MySQL database. The mission-planning sub-module provides the overall logic for mission planning. The input data for the mission-planning sub-module are sensors and services, and the output data are actuators. The data are represented in the form of XML. The interfaces are web-browsers for the web end and GPIO for the IoT server. Lastly, the client application module which is an abstract view for the end users where end users can plan their missions and leverage the application power to enable their critical missions and plan them accordingly. The data on this layer is visualized using better User Experience (UX) interfaces to provide them with an easy way to interact with the underlying system. Interfaces are well designed using the latest front-end tools such as bootstrap and HTML5/CSS3. The configuration objects which are created as a result of the drag-and-drop operation in the DIY toolbox are represented in XML and passed to the web end which  
Figure 2: Inner view of cloud-based web server.  
in turn represents it in an interactive UX interface for sufficient clarity of the mission to the users. In this layer, the client can also plan their mission by adding the mission statement and the necessary resources needed for the mission.
## 2 Methodology
### Experimental Design  
The experimental setup was designed for single-point (offline) simulations at each of the 105 FLUXNET sites. The atmospheric forcing data used in the experiments are derived from site observations within the FLUXNET2015 dataset. This dataset has a temporal resolution of 30 min and includes the following seven variables: near-surface wind speed, temperature, humidity, air pressure, shortwave radiation flux, longwave radiation flux, and precipitation. The experimental setup involved inputting new atmospheric forcing data every 30 min. To minimize the influence of initial conditions, a 50-year spin-up simulation was first conducted for each site by repeatedly cycling the available years of forcing data. This ensured that key model state variables, such as soil moisture and temperature, reached a quasi-equilibrium state. Following the spin-up, a final simulation was run for the entire observational period of each site to generate the ET and GPP flux data used in our analysis.
### Organization  
A list of acronyms used throughout the paper is presented in Table 4. Figure 2 illustrates the organization of this manuscript. Section 2 describes the operational model of an IoT-based system. We identify security vulnerabilities in IoT-based systems in Section 3. The analysis of threat model with various security risks and attacks is presented in Section 4. Security requirements of an IoT-based system are presented in Section 5. Current solutions to mitigate IoT attacks are discussed in Section 6. Section 7 enumerates open research problems in the IoT environment. Finally, we conclude in Section 8.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Acronyms** & **Definitions** \\ \hline
6 LoWPAN & IPv6 over Low-Power Wireless Personal Area Networks \\ \hline API & Application Programming Interface \\ \hline BLE & Bluetooth Low Energy \\ \hline CAN & Controlled Area Network \\ \hline CapBAC & Capability-based Access Control \\ \hline CoAP & Constrained Application Protocol \\ \hline DDOS & Denial of Service \\ \hline DH & Diffe Hellman \\ \hline DODAG & Destination Oriented Directed Acyclic Graph \\ \hline DOS & Denial of Service \\ \hline DTLS & Datagram Transport Layer Security \\ \hline ECC & Elliptic Curve Cryptography \\ \hline ECDH & Elliptic Curve Diffie Hellman \\ \hline HIP & Host Identity Protocol \\ \hline IDS & Intrusion Detection System \\ \hline IoV & Internet of Vehicle \\ \hline IPS & Intrusion Prevension System \\ \hline IPSec & Internet Protocol Security \\ \hline LLN & Low Power and Lossy Networks \\ \hline M2M & Machine-to-Machine \\ \hline MTU & Maximum Transmission Unit \\ \hline NFC & Near Field Communication \\ \hline RPL & IPv6 Routing Protocol for LLNs \\ \hline RSU & Road Side Unit \\ \hline SAML & Security Assertion Markup Language \\ \hline TLS & Transport Layer Security \\ \hline V2V & Vehicle-to-Vehicle \\ \hline XACML & eXtensible Access Control Markup Language \\ \hline XSS & Cross Site Scripting \\ \hline \hline \end{tabular}
\end{table}
Table 4: List of acronyms and corresponding definitions.  
Figure 2: The organization and structure of this paper.
## 3 Materials and Methods
### Specimens Fabrication and Raman Analysis  
The proposed approach has been tested on Raman maps acquired from 3D printed polymer-based composites. The graphene oxide (GO; powder, oxidation: 4-10%) was supplied by Sigma-Aldrich (Milan, Italy), while bidistilled water was purchased by Carlo Erba Reagenti (Milan, Italy). Polycaprolactone (PCL) and polylactic acid (PLA) filaments were supplied from 3D4 Makers (Haarlem, The Netherlands) and Formfutura BV (Nijmegen, The Netherlands), respectively. All materials were used as received.  
Specimens were fabricated by preliminary depositing GO on the surface of PCL and PLA filaments. For this aim, GO was suspended in bidistilled water (0.5% w/v) and ultrasonicated for 30 min. Filament samples were then soaked in the resulting suspension, magnetically stirred for 3 h, dried at 40 \({}^{\circ}\)C once recovered, and subsequently loaded in the N2 FFF 3D printer (Raise 3D Inc., Irvine, CA, USA). The geometric models, representative of the composites to be printed, were processed by the ideaMaker software (Raise 3D Inc., Irvine, CA, USA) to be sliced in the Z direction. For the 3D printing process, the temperature of the nozzle (0.4 mm diameter) was set at 160 \({}^{\circ}\)C and that of the build platform at 20 \({}^{\circ}\)C for PCL samples, while the temperature of the nozzle was set at 205 \({}^{\circ}\)C and that of the build platform at 40 \({}^{\circ}\)C for PLA samples. In both cases, they were fabricated by alternatively stacking layers in a regular pattern in the 0\({}^{\circ}\)/90\({}^{\circ}\) directions, the sample size being \(8\times 8\times 3.2\) mm\({}^{3}\).  
Raman characterization was performed to evaluate the GO presence and distribution on the specimen surface using an InVia Raman microscope (Renishaw, UK). The analysis was carried out on GO powder, 3D printed neat samples and 3D printed PCL/PLA-GO samples. Carbon nanofiller distribution on specimen surface was assessed by collecting Raman maps, randomly located. A fixed area of \(250\times 120\) um (step \(5\times 5\) um) was investigated using a laser with a wavelength of 532 nm, 1 s exposure time, and 2 accumulations for each acquired spectrum. Collected spectra were then minimally processed to correct the baseline and remove the possible presence of cosmic rays (WiRE software, Renishaw, UK).
## 4 DUDA Scheme for Interference Avoidance  
### Interference Avoidance Criteria  
The NGEO satellites, being dynamic and positioned at much lower orbital altitudes compared to the GEO satellite in MSS, pose a significant challenge during in line with the GEO satellite. The downlink signal of a NGEO satellite can severely interfere with the GEO UT. Typically, the antenna gain of the NGEO satellite main lobe is approximately tens of dB, significantly degrading the downlink communication quality of the GEO satellite system. Therefore, in-line interference avoidance is imperative for multi-layer satellite systems.  
We commence by analyzing the fundamental criteria of the interference avoidance employed in the DUDA system, which formulates whether or not an NGEO satellite can gain access by the UTs. In other words, if the NGEO satellites fall outside the interference avoidance zone, the satellites possessing the highest SINR will consequently be selected for access to the UTs, and vice versa. When discussing uplink transmission, it is crucial to acknowledge that NGEO UTs have a fixed transmission power and must attain a minimum 35,786 km propagation distance. Given such constraints, uplink interference poses fewer challenges compared to downlink transmission. We postulate that \(\alpha_{U}\) will always be lesser than \(\alpha_{D}\), thereby implying \(\vartheta_{0}^{U}\) will be smaller than \(\vartheta_{0}^{D}\). Consequently, the interference avoidance guidelines for DUDA in relation to NGEO satellites can be summarized as follows  
\[\text{can not be accessed}, \vartheta_{k_{i}}\leq\vartheta_{0}^{U} \tag{32a}\] \[\text{only uplink can be accessed}, \vartheta_{0}^{U}<\vartheta_{k_{i}}\leq\vartheta_{0}^{D}\] (32b) \[\text{Both links can be accessed}, \vartheta_{0}^{U}<\vartheta_{k_{i}}\leq\vartheta_{0}^{D}. \tag{32c}\]  
Based on (32b), the innovative DUDA scheme offers considerable protection to the uplink transmission, thereby providing additional accessible satellites for enhanced performance of the MSS service.
## 2 Mathematical Model
### Constraints  
(1) Uniqueness constraint of well connections.  
Each well is exclusively associated with one station, as shown in Equation (6).  
\[\sum_{j\in J}\delta_{ij}=1\forall i\in I \tag{6}\]  
(2) Plant number constraint.  
There exists a single plant within the pipeline network, as shown in Equation (7).  
\[\sum_{k\in K}\delta_{k}=1 \tag{7}\]  
(3) Well and station affiliation constraint.  
Well \(i\) can be assigned to station \(j\) when station \(j\) exists, as shown in Equation (8).  
\[\delta_{ij}\leq\delta_{j}\ \ \ \forall i\in I,\ \ \ \forall j\in J \tag{8}\](4) Transmission radius constraint.  
The gas pressure may decrease during transportation due to pipe friction. In order to keep the pressure drop within a reasonable range, the distance between the well and the station should be less than a specific value, known as the transmission radius, as shown in Figure 5.  
As such, the transmission radius constraint is calculated as shown in Equation (9).  
\[\delta_{ij}L_{ij}\leq D_{\max}\ \ \forall i\in I,\forall j\in J \tag{9}\]  
where \(D_{\max}\) is the maximal allowable pipeline length from the well to the station, m.  
(5) Station capacity constraint.  
Generally, the gathering radius constraint is not considered when addressing the station capacity constraint, which falls under the capacitated facility location problem (CFLP). In this paper, both the radius constraint and the capacity constraint are considered. When the gathering radius constraint is satisfied but the capacity constraint is not, the pipeline network layout will be redesigned, as shown in Figure 6.  
Thus, the capacity of a station must not exceed that shown in Equation (10).  
\[\delta_{j}q_{j}\leq q_{\max}\ \ \forall j\in J \tag{10}\]  
where \(q_{j}\) is the node gas flow of station \(j\), m\({}^{3}\)/d. \(q_{\max}\) is the capacity of the station, m\({}^{3}\)/d.  
(6) Tree structure constraint.  
The tree connection constraint ensures that the generated topology is the shortest and loop-free, as shown in Equation (11).  
\[\sum_{j\in I}\sum_{j^{\prime}\in J}\delta_{jj}=2\sum_{j\in J}\delta_{j} \tag{11}\]  
Figure 5: Schematic diagram of the transmission radius.  
Figure 6: Pipeline network redesigns in violation of capacity constraints.  
(7) Node flow constraint.  
The node flow constraint ensures that the total gas volume remains constant, as shown in Equation (12).  
\[\sum_{i\in I}q_{i}-\sum_{k\in K}\delta_{k}q_{k}=0 \tag{12}\]  
where \(q_{i}\) is the node gas flow of well \(i\), \(\text{m}^{3}/\text{d}\). \(q_{k}\) is the node flow of plant \(k\), \(\text{m}^{3}/\text{d}\).