esa-sceva/satcom-chunk-collection huggingface dataset:
# Efficient and Secure EMR Storage and Sharing Scheme Based on Hyperledger Fabric and IPFS
## 3 Framework Components
### Hyperledger Fabric  
Hyperledger Fabric is an open-source, enterprise-grade, permissioned distributed ledger technology platform developed under the leadership of the Linux Foundation [17]. It features a highly modular architecture that brings innovation, versatility, and optimization to the healthcare industry. As a consortium blockchain application development platform, Hyperledger Fabric allows only authorized organizations and nodes to join designated blockchains for conducting transactions and recording data. Additionally, Hyperledger Fabric does not rely on cryptocurrencies to incentivize mining or drive smart contract execution, thus avoiding the risks or vulnerabilities associated with cryptocurrency use.  
In the Hyperledger Fabric network, smart contracts are referred to as chaincodes. Hyperledger Fabric allows for the creation of different channels as needed, with different chaincodes deployed within each channel. A chaincode is embedded and executed within the nodes, requiring all nodes in the channel to install the chaincode.  
There is isolation between channels in Hyperledger Fabric, meaning that data are not exchanged between different channels, and one channel does not depend on others. Each channel can be understood as an independent instance of Hyperledger Fabric, and all channels are completely separate. This feature helps us separate patient ownership information from medical data.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Literature** & **Limitation** & **Solution in This Study** \\ \hline
[7] & Lack of shared program &
\begin{tabular}{c} Combining blockchain, the \\ IPFS, and proxy re-encryption \\ Using pseudo-identities in the system \\ the system \\ \end{tabular} \\
[11] & No efficient search mechanism & Creating composite keys for \\  & & conditional queries \\
[12; 13; 14; 15; 16] & Requires tokens or gas & Based on the Hyperledger \\  & expenses & Fabric architecture without \\  & & the incentive mechanism \\
[19] & Lack of data integrity & Combining blockchain and \\  & discussion & the IPFS \\
[20] & The source medical data are & Storing medical data \\  & not decentralized storage & ciphertexts using the IPFS \\ \hline \hline \end{tabular}
\end{table}
Table 1: Limitations of related work and solutions in this study.
# LoRaWAN Base Station Improvement for Better Coverage and Capacity
## 1 Introduction
This enables more flexibility for the user, improved capacity, and consequently has a significant indirect impact on energy efficiency since low power end nodes will potentially require fewer repetitions to send their messages thus saving power. Also, by using a dual or multiple concentrator setup, a wide range of customizations for specific applications are possible. For example, it can be beneficial to create sectors with directional antennas and dedicated concentrators to separate and improve reception in certain directions. This simple enhancement of the base station requires little resources, but it can be a very valuable tool in extending and improving the range and capacity of the network alongside the end node improvements, deployment scenario considerations and similar. Within this paper, we present the idea and the development of the LoRAWAN system with an upgraded dual-concentrator base station. To evaluate the system and confirm the improvements, a series of measurements were taken in the city of Zagreb, Croatia using a reference single-concentrator base station and the upgraded one. Both base stations were positioned in the same location and the results confirmed the enhancement in the properties of the overall system. The paper is organized in the following way: in Section 2 a short overview of LoRaWAN is given providing basic terms needed to understand the problem this paper deals with. In Section 3 the novel design of the base station is presented and in Section 4 the results of the measurements and validation are shown.
## 3 Results
### Explaining the Prediction Errors at the Plot Scale
#### 3.2.2 Contributors to Overall Error  
When the bias is eliminated by in situ calibration the overall prediction error (RMSPE) is dominated by the precision error. In Figure 7 the PCA clearly indicates that on PC1 the RMSPE is closely and positively associated with TOC and TN and negatively correlated with \(\rho_{\text{bKop}}\) and Depth. The lower the \(\rho_{\text{bKop}}\), the higher RMSPE, and this is extremely visible in the OFH layers, which obviously have a high TOC and TN content. PC2, explaining still 22% of the variation, is mainly determined by texture fractions Clay, Silt and Sand, but is rather uncorrelated with RMSPE. Conversely, Clay and Silt are associated with greater values for \(\theta_{\text{fc}}\) and \(\theta_{\text{pwp}}\).  
The PCA clearly shows that BRA and HOE are more homogenous sites in terms of soil properties as indicated by smaller ellipses, and independently from the fact that they are texturally very different from each-other, have low overall prediction errors. Conversely, WIJ and GON have high variation in soil properties and thick OFH layers leading to high overall RMSPE especially for the organic layers low in \(\rho_{\text{b}}\).  
When we build a generalized boosted regression tree model to predict RMSPE based on the layer specific soil properties (Tables 1 and 2), the relative influence of the predictors is given in Table 7. Two predictor sets were applied, Set 1 using \(\rho_{\text{bKop}}\) (Table A3), and Set 2 using \(\rho_{\text{b}}\) as listed in Table 1.  
Upon using the first set, bulk density in the sampling rings is clearly the major predictor and has higher influence than depth, \(\theta_{\text{s}}\), clay fraction, and all other soil variables. When replacing \(\rho_{\text{bKop}}\) by \(\rho_{\text{b}}\), relative influence decreases and the saturated soil water content takes over as the major predictor. Since \(\theta_{\text{s}}\) is inversely correlated with \(\rho_{\text{bKop}}\) (Figure 7) this is no surprise. Samples with greater \(\theta_{\text{s}}\) and consequently lower \(\rho_{\text{b}}\) have more macropores, allowing air to enter and influencing the dielectric permittivity and _PA_ output. Two-way interactions between predictors as indicated by [PERSON]'s H-statistic are all below H = 0.11 (between depth and \(\theta_{\text{r}}\)) and therefore negligible.  
Figure 7: Principal component analysis of calibrated functions by layers (dots) and soil properties as vectors, grouped by plot.
## 3 Finite Element (FE) Model
### Procedure for Extraction of \(p\)-Multipliers  
The \(p\)-multipliers were calculated directly from the 3D FE model by dividing the computed average soil resistance of a row within a pile group configuration and within a prescribed depth by that of the single pile model at a selected displacement. The double derivation of the bending moment with respect to depth was used to calculate \(p\). The bending moment profile was first fitted with a seventh order polynomial function before derivation. Figure 2 shows the averaged soil resistance with respect to depth for the single pile and leading row of the \(3\times 3\) free-head group pile. It was observed that the maximum soil resistance occurs within a normalized depth of \(15\) z/\(D\). Similar observations were reported in [PERSON] et al. [29]. Therefore, \(p\) within a depth of \(15\) z/\(D\) was averaged to calculate the \(p\)-multiplier. Static loading was applied at the pile top in the lateral direction. A displacement controlled approach was used in the numerical simulation. It was reported in [PERSON] [14] that the upper range of pile head displacement in the experimental tests listed in Tables 1 and 2 is 50 mm. Hence, to be consistent with the experimental studies, the \(p\)-multipliers and group effect parameters were extracted at a displacement of 50 mm.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Value** \\ \hline Outer diameter (m) & 0.3 \\ Thickness (m) & 0.0095 \\ Moment of inertia (m\({}^{4}\)) & 0.000398 \\ Modulus of elasticity of reference steel pipe pile, E (GPa) & 200 \\ Adjusted modulus of elasticity, E (GPa) & 46 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Properties of steel pile.
## 4 Variable Optical Coupler
### Results of Characterization of the Optical Coupler  
Figure 8 shows the maximum output of the coupler as a function of the micrometer screw and the control voltage. The black curves show the behaviour of the CP-ports and the gray curves that of the TP port. For the sake of clarity, the back-port is not shown in these figures.  
If no control voltage is applied and the micrometer screw is turned, the coupler exhibits a behaviour similar to that described in the literature [17; 52]. The TP port falls while the CP  
Figure 7: Schematic construction of measurement for characterization of tunable coupler.  
port rises. This behaviour is well described by the couple-mode theory. There is another observable effect in Figure 8. There are two local maxima of the CP port or two local minima of the TP port. At this moment, where these effects come from could not be clarified within the scope of this work.  
Further, the output power at the micrometer screw position 134 is also marked in Figure 8. This curve represents the working range of the optical coupler used. At this point it is the best transfer behaviour for this work.  
The shape of the curves varies depending on whether the control voltage increases or decreases. Figure 9 shows the coupling ratio for three different wavelengths. It can be seen that the starting behaviour of the TP and CP of ports has a significant hysteresis. The threshold value of the hysteresis is about 1 V for both outputs. The hysteresis effect is so strong that it can not be neglected in an automated control or regulation. In addition, the coupler cannot be operated with a simple pilot control, since the hysteresis changes depending on the micrometer position, the input power and time. For an accurate setting of the coupling ratio, it is therefore necessary to measure the output power and readjust it in a control loop.  
Figure 8: Representation of the maximum power output in dependence of the position of the micrometer screw and the control voltage. The color-coded line shows the relevant work area for this work.  
Figure 9: Coupling ratio of the transmission output at three wavelengths (1538, 1542 and 1546 nm). This evidence shows that the wavelength-selective coupling is negligible.
# A Review of the Water Desalination Technologies
## 2 The State of the Art
### Secondary Refrigerant Freezing (SRF)  
Secondary Refrigerant Freezing (SRF) is a desalination process based on the liquid solid phase transition [60]. As the ice formed contains a limited quantity of salts, this technique can be used to produce freshwater from seawater. A refrigerant is used to freeze the saline water. The main problem is the removal of the ice produced in the process [28]. A solution proposed by [PERSON] et al. suggests the utilization of low temperatures available for the regasification of LNG (Liquid Natural Gas) to freeze seawater and obtain ice [60].  
Another solution is depicted in Figure 27 [28]. This system is composed of two chambers, a reversing heat pump and solenoid valves. The unit works by alternatively producing ice and freshwater in the tank on the left (L) or in the other one on the right (R).  
Figure 27: A schema of a Secondary Refrigerant Freezing desalination unit.  
In the first case seawater is introduced in tank L. The heat pump is used to transfer heat from this chamber to the other one. As a result of the heat transfer, saline water is converted in a slurry of ice and brine inside tank L, while in tank R ice already formed in a previous step is melted. Stopping the process, brine is drained out by opening the valve in the bottom of the tank L while tank R is refilled with saline water. Reverting the heat pump, inside the tank L ice is melted thanks to the heat supply, producing freshwater. In the meantime, inside the tank R further ice is produced, and the cycle is repeated. This desalination technique is currently under development [28].
# Factors Affecting Trueness of Intraoral Scans: An Update
## 1 Introduction  
The continuous development of computer-aided design and computer-aided manufacturing (CAD-CAM) has led to the creation of several new techniques and methodologies in oral rehabilitation [1; 2; 3; 4; 5; 6; 7; 8]. In consequence, recent studies have aimed at addressing the accuracy of CAD-CAM methods [9; 10] and its impact on the quality of the resulting CAD-CAM prostheses [11; 12]. CAD-CAM accuracy, however, can also be affected by factors associated with the image acquisition procedures [13].  
In comparison to conventional impressions, intraoral scanning (IOS) has been considered more accurate in regard to outcomes of resulting CAD-CAM crowns and short-span fixed partial dentures [13; 14; 15]. A previous systematic review reported mean internal gap values varying between 30 and 154 \(\upmu\)m for IOS and between 42 and 183 \(\upmu\)m for conventional impressions [13]. However, most of the aforementioned studies used different CAM materials and methods. The actual IOS accuracy (i.e., trueness and precision) can also be calculated for IOS devices in comparison to reference industrialized or desktop scanners. A previous study found mean IOS trueness (distance from the reference scan) values ranging between 19 and 26 \(\upmu\)m, while mean IOS precision (distance between two consecutive scans performed with the same device) values ranged between 12 and 21 \(\upmu\)m, approximately [15].  
Despite the fact that IOS accuracy has been developing along with the technology, it is still considered challenging to obtain accurate intraoral scans of long-span and completely defendulous arches [16]. Furthermore, updated information on the influence of the different factors affecting IOS accuracy is lacking for the most recent IOS technologies described in the literature.  
Thus, the aim of this systematic review was to identify factors that influence the trueness of intraoral scans from studies in the literature published in the last two years.
# A 28 nm Bulk CMOS Fully Digital BPSK Demodulator for US-Powered IMDs Downlink Communications
## 6 Conclusions  
A new solution for a BPSK demodulator was presented in this paper in order to design a robust, low-area and low-power Downlink for ultrasound (US)-powered IMDs. The implemented system is fully digital and PLL-free, hence reducing area occupation and reducing power consumption. Using a 2 MHz carrier input signal and an implant depth of 1 cm, the data rate was up to 1.33 Mbit/s with a 50% duty cycle, while the minimum average power consumption was cut down to 3.3 uW in the typical corner. Post-layout simulation results were reported using a 28 nm Bulk CMOS technology provided by TSMC. A comparison with the state-of-the-art emphasizes the novelty of this work, this being the first time in which the BPSK demodulation scheme was introduced for the US-data downlink. Furthermore, it presented the lowest energy-per-bit, with the highest bit rate and the lowest area occupation compared to other works that established a data-link exploiting US-waves. The main limitations of the proposed solution were related to voltages and temperature variations; indeed, although the system was simulated with a fixed supply voltage, \(V_{DD}\) = 0.9 V, and in a fixed temperature range, it could be susceptible to both voltage and temperature variations, thus requiring successive calibrations for the current reference used in the CSGRO. In addition, the DCFR could be increased by up to the 100%, as for most of the RF solutions regarding the BPSK demodulator for IMDs.  
From a medical point of view, the proposed design enables miniaturization of the implanted device through both reduced area occupation and power consumption. This latter characteristic, in particular, allows the reduction of the area of the piezoelectric device which constitutes the biggest portion of the overall area of the IMD. Moreover, a higher data rate allows for the transmission of higher data to the IMD for a given value of transmitting frequency. Therefore, for the low invasiveness, the ULP consumption and the quite high bit rate, the proposed system is largely suitable not only for the presented US-powered brain-implantable IC, but also for all the similar electronics systems that could be brought back to the concept of IoMTs.  
Conceptualization: A.D.G. and M.P.; data curation: M.P.; original draft preparation: M.P.; writing, review and editing: all authors; formal analysis: all authors; supervision: A.B. and A.D.G. All authors have read and agreed to the published version of the manuscript.  
This research has been funded by the Brain28 nm Project (Prot.20177 MEZ7T)-Italian Minister of University and Research.  
Not applicable.  
The authors declare no conflict of interest.
## 3 Results and Discussion
### Organization of Knowledge  
The referring hospital is organized according to normal standards for small hospitals in Norway. The stroke patients arrive at the hospital through the emergency unit. There, the intern on duty evaluates the patient. Stroke patients are sent for blood tests and CT scanning. The intern decides if thrombol treatment is relevant for the patient, based on procedures for treatment. If the intern does not consider thrombol treatment as an option, the use of VC is not considered. If necessary, the intern contacts the second on-call specialist to discuss treatment options. If thrombol treatment is considered as an option, the nurses prepare for a VC meeting. If the second call on duty wants to discuss the case, he or she calls up (by telephone) the specialist hospital. They decide if the patient is to have thrombol treatment, if so, they connect via VC.  
_\"The intern starts the procedure (...) for a stroke regardless whether it's day or night. Inform the second call and take responsibility for having the blood tests (...) and the CT rapidly. (...) Thrombolytic treatment and CT are considered, and the second call considers the patient. Thrombolytic treatment is not given by the interns themselves. There is always a second call and/or a neurologist involved. We have our procedure. But, if it is a patient where this [thromolytic treatment] per definition is prevented (...) then the second call does not have to be involved\" A: Referring hospital._  
The physician describes how they are organized:  
_\"They [the interns] are the ones who see the patient's condition first, who are in the arrival and who have to make a kind of judgment or decide how serious this is. Should we call someone...\" F: Referring hospital._  
_\"The intern who is on duty calls, as per the procedure, I come in and we wait together to be ready.\" E: Referring hospital._  
As the referring hospital does not have a neurological department, stroke patients are treated in the medical department. In the evening period, the interns on duty need to cover several departments.  
_\"The interns work both acute and on the medical department at the same time during evenings and nights. It means that new interns starting at the surgical department must also handle the medical part. We have stroke patients, medical patients... no neurological department.\" A: Referring hospital._  
Even though the interns are newly arrived the hospital, staying in _i.e._, the surgical department, he or she must handle medical problems related to other departments as well. The interns work both in acute care and the medical department during evenings and nights.
## 2 Materials and Methods
### Study Area Description  
In this study, we focus on urban, peri-urban, and rural regions within the central region of Thailand as depicted in Figure 1. These classifications adhere to national standards and are based on criteria such as administrative divisions, demographic profiles, primary land use, and prevalent occupations as cited in [65; 66]. Therefore, the selected study areasare systematically categorized into urban, peri-urban, and rural types according to these variables. Additionally, these areas are situated within a tropical monsoon climate zone, characterized by three distinct seasons: summer (mid-February to mid-April), rainy (mid-April to mid-October), and winter (mid-October to mid-February). The selected regions are as follows:  
* Urban--Bangkok province is located at latitude 13\({}^{\circ}\)38' N and longitude 100\({}^{\circ}\)54\({}^{\prime}\) E. This area is flat and low-lying, with altitudes ranging from 1.50 m to 2.0 m. It occupies 1600 km\({}^{2}\) and consists of 50 districts with a registered population of 5.52 million, making it the most highly inhabited city in Thailand. The built-up area covers 67.36%, while agricultural land constitutes only 0.16% of the total area. The annual mean air temperature ranges from 28.0 \({}^{\circ}\)C to 30 \({}^{\circ}\)C, with the average maximum air temperature in April ranging from 33.0 \({}^{\circ}\)C to 38.0 \({}^{\circ}\)C, and the average minimum air temperature in January ranging from 16.0 \({}^{\circ}\)C to 25.0 \({}^{\circ}\)C.
* Peri-urban--Pathum Thani province is situated at latitude 14\({}^{\circ}\)01' N and longitude 100\({}^{\circ}\)32' E, with an average altitude of 2.30 m. It covers an area of 1519 km\({}^{2}\) and comprises 7 districts with a registered population of 1.19 million. The annual mean air temperature ranges from 28 \({}^{\circ}\)C to 30 \({}^{\circ}\)C, with the average maximum air temperature in April ranging from 32 \({}^{\circ}\)C to 34 \({}^{\circ}\)C, and the average minimum air temperature in December ranging from 24.0 \({}^{\circ}\)C to 26.0 \({}^{\circ}\)C. This area is characterized by 29.79% of the land serving as city outskirts, accommodating dense dwellings, industrial states, and being devoid of forest areas.
* Rural--Saraburi province is situated at latitude 14\({}^{\circ}\)31\({}^{\prime}\) N and longitude 100\({}^{\circ}\)54\({}^{\prime}\) E, with the landscape ranging from 2.0 m to 10.0 m in elevation, covering an area of 3576 km\({}^{2}\). This region includes 13 districts and has a population of 643,963 registered  
Figure 1: Location of the study area in Thailand and spatial distribution of the meteorological stations in dotted print (**a**), altitude (**b**), and land use (**c**).  
residents. The average annual air temperature is 28.2 \({}^{\circ}\)C, with highs of 31.4 \({}^{\circ}\)C in April and lows of 23.6 \({}^{\circ}\)C in January. Approximately 78% of the land consists of agricultural and forested areas.