esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussions
### Result and Discussion of Slump Test  
After the mechanical mixing test, a series of slump tests were carried out in triplicate on each soil sample to evaluate the workability and flowability of the conditioned soils quantitatively. Figure 14 shows the selected pictures of the slump test, and the test results are summarized in Table 3. For conditioned soils with \(w=25\%\) regardless of foam injection ratio, the soil sample was dry, rigid, and stiff, such that they either stood straight with very small drop value (see Figure 14a-c) or collapsed (see Figure 14d) once the cones were lifted. For tested soils with \(w=25\%\) in Table 3, the slump values show that the foamed soil had a small increase in liquidity as _FIR_ increased, although a collapse still occurred after the lifting of the cone. It was hard to obtain a desirable state when \(w=25\%\). Proper slump values of 100-200 mm were obtained when the water content was improved to 40% regardless of foam injection ratio. The mean slump values of \(w=40\%\) &_FIR_ = 0%, \(w=40\%\) &_FIR_ = 10%, \(w=40\%\) &_FIR_ = 20%, and \(w=40\%\) &_FIR_ = 30% were 154.3 mm, 174.7 mm, 179.0 mm, and 182.3 mm, respectively, as shown in Table 3. The slump value increased as the foam injection ratio increased.  
Figure 11: Mixing time for various foamed soils.  
Figure 14e-h shows that the foamed soils with \(w=40\%\) had an increased liquidity and no collapse occurred. It indicates that the flowability of tested soils with \(w=40\%\) was improved dramatically after the improvement of water content, and the flowability increases with increase of _FIR_. Tested soils with \(w=40\%\) regardless of _FIR_ meet the requirement of EPBS tunneling according to the slump value. It also indicates that if the water content is enough, even low _FIR_ can cause it to be too fluid-like [3].  
Figure 12: The measured net power during the mixing tests in various tested soils with \(w=25\%\) and \(40\%\) and: (**a**) _FIR_ = 0%; (**b**) _FIR_ = 10%; (**c**) _FIR_ = 20%; (**d**) _FIR_ = 30%.  
Figure 13: Variation of net power during mixing test: (**a**) Net power value, (**b**) the change rate of net power.
## 3 Results
### Model Estimations and Comparisons
#### 3.3.1 P LSR Model Prediction Results  
The estimation model of three heavy metals was established by the partial least square method. The scatter plot of the measured value and the estimated value is shown in the Figure 8 below. The latent variables (LVs) of the model were determined by leaving-one cross-validation. The LVs of the optimal models of the three metals Cd, Cr, and Pb are 2, 3 and 3. Pb estimation was the best for all PLRG optimal models with FD reflectance, as indicated by the following statistical results: R\({}^{2}\) = 0.853, and RPD = 2.193. Furthermore, the estimated value of Pb is closer to 1:1.
## 2 Power Converter Principle
### Designs of Output Capacitor and Inductor
#### 2.3.3. SBBC Output Capacitor  
In accordance with the study [16], the SBBC output capacitor (Figure 3) can be estimated as follows:  
\[C_{o}=DV_{o}/(2\Delta v_{o}f_{s}R_{o}), \tag{5}\]  
where \(D\) represents the duty cycle from \(D_{bk}\) or \(D_{bt}\). \(\Delta v_{o}\) is the HPLDDC output ripple voltage that can be estimated using \(\langle V_{o(min)}\), and the ripple voltage peak can be defined by a percentage, \(\alpha\). \(R_{o}\) is the output equivalent resistance at the HPLDDC output side.  
In addition, an effective series resistance (ESR) parasitizes on \(C_{o}\) that can be expressed as follows [17]:  
\[ESR\leq\Delta v_{o}/I_{in(peck)}, \tag{6}\]  
where \(I_{in(peak)}\) is the peak value of the HPLDDC input current. According to \(I_{o}/I_{in}=1/D_{bk}\), \(I_{in(peak)}\) can be estimated as \(I_{o}D_{bk}(1+\beta)\).  
\(\bullet\) SBBC Output Capacitor for Buck Mode  
For the buck mode, the notation \(C_{o}\) in Equation (5) can be replaced with \(C_{obk}\). Substitution of \(C_{o}=C_{obk}\), \(V_{o}=V_{o(min)}\), \(R_{o}\)= \(V_{o(min)}/I_{o}\), \(D=D_{bk}=V_{o(min)}/V_{in(max)}\), and \(\Delta v_{o}=\Delta V_{o(min)}\) into Equation (5) obtains the output capacitor of the buck mode operation as follows:  
\[C_{obk}=I_{o}/(2V_{in(max)}\kappa f_{s}). \tag{7}\]  
\(ESR\) in Equation (6) is replaced with \(ESR_{bk}\). Substitution of \(\Delta v_{o}=\alpha V_{o(min)}\) and \(D_{bk}=V_{o(min)}/V_{in(max)}\) into Equation (6) can rewrite the equation as follows:  
\[ESR_{bk}\leq\alpha V_{in(max)}/[I_{o}(1+\beta)]. \tag{8}\]  
\(\bullet\) SBBC Output Capacitor for Boost Mode  
For the boost mode, \(C_{o}\) can be replaced with \(C_{obt}\). Substitution of \(C_{o}=C_{obt}\), \(V_{o}=V_{o(max)}\), \(D=D_{bt}=(V_{o(max)}-V_{o(min)})/V_{o(max)}\), \(\Delta v_{o}=\alpha V_{o(max)}\), and \(R_{o}=V_{o(max)}/I_{o}\) into Equation (5) can obtain the output capacitor of the boost mode as follows:  
\[C_{obt}=I_{o}(V_{o(max)}-V_{in(min)})/(2\alpha V_{o(max)}^{2}f_{s}). \tag{9}\]  
\(ESR\) is replaced with \(ESR_{bt}\). Substitution of \(\Delta v_{o}=\alpha V_{o(min)}\) and \(D_{bt}=(V_{o(max)}-V_{in(min)})/V_{o(max)}\) into (6) can rewrite the equation as follows:  
\[ESR_{bt}\leq\alpha V_{in(min)}/[I_{o}(1+\beta)]. \tag{10}\]
## 3 Overview of the System  
The axial view borehole camera system (AVBCS) was equipped with a wide-angle camera at the front of the probe. The probe was positioned inside a pre-drilled borehole to capture the borehole video. Then, the forward-looking borehole video acquired by AVBCS was used to generate a whole cylindrical borehole-wall panorama using our proposed system with image processing technology.  
The proposed system was composed of four main modules, i.e., locating the drilled hole center, generating of borehole-wall unfolded images, matching of unfolded images and an image filtering module. The center location module was mainly developed to extract the drilled hole center from the front view borehole-wall image so as to resist geometric distortion. The drilled hole center hole was also used as the image unwrapping center for the subsequent step. In the second step, the effective annular region was unfolded into a rectangle, namely the borehole-wall unfolded image. The third step was to achieve the mosaic of the unfolded image sequence by image matching technology based on the proposed PTSAD algorithm. In addition, we introduced an image filtering module to eliminate the invalid frames using three parameters and the valid frames were further stitched into an integral cylindrical borehole-wall panorama. The flowchart of the proposed system is provided in Figure 1.
## 2 Antenna Design Methodology
### Antenna Array Design  
After the miniaturization of the antenna element, the number of possible elements (N) to form the high-gain antenna array was evaluated by considering the limitations of a 10 \(\times\) 10 cm substrate for mounting on a CubeSat. Figure 5 shows some of the evaluated topologies. The array cases use 4, 8, and 16 antennas. More than 16 would be impractical due to the reduced space. Mutual coupling affects the array performance with more than 16 elements. Figure 6 illustrates the behavior of the reflection coefficients versus frequency for the analyzed design cases of the array. Table 3 presents the results of each case in terms of gain. It is demonstrated that the best case is 16 antennas since it considers the feeding network (FN) and obtains a high gain.  
Figure 7 illustrates the final design with the definition of variables for each physical dimension. The elements consider uniform amplitudes and phases with an FN. The CubeSat size restrictions are considered as a base to the size of board L and W, and the constrained  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Parameters** & **A** & **A1** & **A2** & **A3** & **A4** \\ \hline Values (mm) & 7.5 & 2 & 1.67 & 1 & 2.6 \\ Values (\(\lambda\)) & 0.0875 & 0.0233 & 0.0194 & 0.0116 & 0.0303 \\ \hline
**Parameters** & **B** & **B1** & **B2** & **B3** & **B4** \\ \hline Values (mm) & 11.592 & 3.8 & 4.2 & 3.0927 & 3.27 \\ Values (\(\lambda\)) & 0.1352 & 0.0443 & 0.049 & 0.0360 & 0.0381 \\ \hline
**Parameters** & **B5** & **B6** & **B7** & & \\ \hline Values (mm) & 1.32 & 1.32 & 3.8164 & & \\ Values (\(\lambda\)) & 0.0154 & 0.0154 & 0.0445 & & \\ \hline \end{tabular}
\end{table}
Table 2: Dimensions of the proposed miniaturized antenna element.  
Figure 4: Miniaturized antenna element.  
spacings are defined as \(\text{LA}\leq 0.5\,\lambda\), \(\text{LC}\leq 0.5\,\lambda\), \(\text{WB}\leq 0.5\,\lambda\), and \(\text{WC}\leq 0.5\,\lambda\). The SMA connector is connected in the center of the array from the other side of the substrate for its feed.  
Figure 5: Antenna array evolution: (**a**) 4-element array, (**b**) 8-element array, (**c**) 16-element array, and (**d**) 16-element array with FN.  
Figure 6: Reflection coefficient comparison of the antenna array evolution.  
The dimensions of the FN are based on the parameters of the board size and the width of the antenna element [33]. Each dimension of the FN was optimized using CST Studio Suite(r) software. The details of the optimization strategy were as follows: the particle swarm optimization algorithm was used with a swarm size of 30, a maximum number of iterations of 15, and a maximum number of solver evaluations of 451. The optimization used the following goals: a reflection coefficient of \(\Gamma\leq-10\) dB (0.4 of weight) at the 3.5 GHz frequency (0.6 of weight). The final design has the same length from the SMA connector to each antenna element. Table 4 lists the numerical values of each parameter for the array model.
## 4 Experimental Evaluation and Results
### Computation Performance  
As the target solution does not require real-time performance, our solution does not require a GPU and is instead CPU-based. The average processing time achieved was 0.494 s per frame on a _Linux Mint 20.2_ computer with a _Intel i5-10500_ CPU and _16 GB DDR4_ RAM, an improvement would be achieved by using hardware-accelerated computing. The processing time per file breakdown can be seen in Figure 20.  
Figure 19: t-SNE plot of 5 workouts (exercises) from the KIMORE dataset.
## 5 Results and Discussion
### Functional Requirements
#### 5.1.1 Identity Management and Data Ownership  
The first theme of functional requirements is identity management and data ownership. It is necessary and critical for patients to own their medical data and to have sufficient information about their health situation and to preserve their privacy [56]. The section below illustrates the codes for this theme.  
* Provide access control  
To protect a patient's privacy, patients should be the owners of their data and have full access and control based on policies regarding, e.g., those who should have access to their data, such as physicians, researchers, and relatives [1; 21; 3; 16; 20]. Permission must be obtained from the patient when anyone tries to view any section of their record. According to Expert 9, \"The patient must provide permission for which type of report the physician must see\".  
* Share data with researchers and receive incentives  
According to a study [16], patients are willing to provide researchers with their data without revealing their identity (i.e., the option to hide); this will contribute to helping researchers obtain medical data more smoothly and quickly without impacting patient privacy. If the patient agrees to provide access to their PHR to researchers or another party, the patient can be incentivized to reveal such data. According to [37], an incentive mechanism is valuable for patients since they can monetize their private data through applications [5; 26; 34; 35].  
* Emergency and relatives' access  
Emergency access to a patient's record is a critical requirement of PHRs when a patient is unconscious [57]. Even in many of the research solutions presented for patient health records using blockchain, this requirement has been overlooked. The medical staff may require knowledge of the patient's medical history, where quick decisions must be made or documented in the PHR. Patients can grant access to their data by enabling an emergency  
Figure 3: PHR stakeholders, adapted from [41].  
physician to access their data for a limited time to assure patient privacy [11; 36; 57]. Expert 6 stated, \"For example, if the patient arrives at Emergency or in a state of unconsciousness... I can see the complete history, as this makes a difference in the physician's decision making\". Another aspect enables relatives to have access to PHRs; this requirement adds value for patients who cannot manage their records effectively, such as the elderly [41]. According to Expert 7, \"The elderly represent a large segment of society because they do not know how to manage their PHR\". Expert 12 states, \"Nevertheless, this should be for a certain period... there should be a method to enable the patient to cancel the authorization of other people and not leave it permanently\".
## 5 Open Issues and Future Directions  
Demand response and market management for unexplored areas is still under study, and applications based on machine learning for energy efficiency and cost analysis may include peer-to-peer energy trade. For example, a real-time billing system can optimize energy pricing based on current and potential energy prices (forecast) and charge the consumer accordingly. The blockchain is viewed as black box in most blockchain solutions. For example, many strategies [122; 135; 136; 135] use smart contracts as the blockchain protocol to grow the architecture. This limits the leverage over the overall architecture and performance of optimizations that cannot be made to the blockchain used in smart contracts. In the future, instead of using the blockchain as black boxes, the blockchain could adopt a problem-specific approach to energy trade.  
There is a need for a network in which all prototypes can operate as a common framework and adapt their behavior to consumers' needs. For example, consumers must be able to sell electricity domestically and globally for large-scale energy storage systems.  
The traditional architectures of energy supply smart meters and every other revolutionary system are not used in blockchain. Many prosumers/consumers are eager to adopt this architecture. Most of the energy sharing frameworks presented to us presume that prosumers and consumers have intelligent devices. This new architecture blends conventional design with a cryptocurrency. Consolidated energy trade by a group of consumers is outperformed by the inefficiency and robustness of individual consumers operating as autonomous firms in terms of renewable energy supplies. In addition, the power source for individual consumers may be insufficient to handle conventional power generators and may be unpredictable due to climatic conditions.
## 4 Results  
### Base Network  
To analyze the transportation network, we employed the urban rail network for the Seoul Metropolitan Area in 2022, which is sourced from the Korean Transport Database (KTDB). KTDB is a data package that officially provides transportation networks, ODs, etc., for transportation demand analysis by the Korean government. We constructed a rail transit network graph by mapping out the nodes and links of urban rail systems, taking into account their distinct types.  
The network graph, constructed using nodes and links categorized by type, encompasses 834 nodes and 1018 links specifically for urban rail systems. In comparison, the rail network, which incorporates interregional railway lines and differs from urban railway systems, spans a more extensive configuration with 1924 nodes and 2058 links. Table 1 presents the configuration of the networks under analysis. Figure 2 shows the urban railway network, which is structured as a vast interconnected system.  
Table 2 presents the results of the network topology analysis for the base network. The given indices represent the values of the initial strategy (0%) in which vertiports have not been introduced, serving as the baseline for scenario-based evaluations of vertiport implementation. The network in the Seoul Metropolitan Area is found to be fragmented, with low connectivity strength and a homogeneous structure, resembling a tree-like random network while exhibiting a distinct community structure.  
The base network's global efficiency, which ranges from 0 to 1, is found to be close to 0, indicating long paths or high fragmentation within the network. The algebraic connectivity value is also close to 0, suggesting weak connectivity and a high risk of disconnection if edges are removed. Degree assortativity is significantly greater than 0, implying that the network exhibits homophilic characteristics. The local clustering coefficient is close to 0, suggesting a tree-like structure and a tendency toward a random network. Additionally, the transitivity index indicates that the overall network does not have a high level of clustering.  
In contrast, the modularity index is close to 0.9, indicating that the overall network has a well-defined community structure.
## 1 Introduction  
In developing and calibrating cryogenic receivers in the terahertz and sub-terahertz frequency range, illumination from an ideal source (blackbody, BB) with exactly known power spectral density is required. The need for a submillimeter-wave photon source with a 10% fractional bandwidth cannot be met by using a room temperature source with attenuation to the required power level, since the process requires the suppression of thermal background radiation and the setting of the radiation temperature with high accuracy. These tasks can be addressed by using a reference absorber mounted inside the cryostat to decrease losses and reflections. The simplest solution to this problem may be to use thin resistive metal film in the input aperture of the receiver or corner reflector with radio absorption coating. A fairly extensive overview of the materials (black surfaces) used in space IR instruments with different purposes is given in [1]. Nowadays, the \"blackest\" material is Vantablack [2], with absorption of 99.965% in the optical range, comprising specially grown arrays of nanotubes. However, the price of such material is rather high, its temperature cannot be electrically modulated, and its operation at temperatures below 1 K is unpredictable. A simple and cheap method of creating a compact cooled BB is to deposit a thin film on a dielectric substrate. For example, a calibrator developed as a submillimeter radiation source made of nichrome thin film on a sapphire substrate is presented in [3, 4]. For bulk sources, a suitable material is silicon or silicon carbide. Such a device comprises a pyramidal moth-eye structure [5]. Ferrite is another option, but both silicon carbide and ferrite are bulky, heavy, and slow to change temperature.
## 6 VLC-IoT: Existing and Proposed Approaches
### VLC-IoT: Existing and Possible Network Topologies
#### 6.4.2 Proposed Network Technology  
Existing IoT communication technologies e.g., LoRaWAN, SigFox, NB-IoT, WiFi, BLE, NFC, and ZigBee are able to function well in the star topology [61]. By extension, VLC-IoT systems can be deployed in a star topology due to their simplicity in applying routing algorithms which reduces network complexity [60]. In real VLC scenarios or testbeds, which deploys existing infrastructures, an LED fitted to a ceiling can act as a gateway communicating with several IoT devices in a star topology. Thus, several LED light sources acting as a separate gateway can communicate with hundreds of non-interfering small cell networks with a high probability of LOS link and high signal-to-noise ratio [60].
# Towards Resolving Challenges Associated with Climate Change Modelling in Africa
## 5 Discussion
### Overcoming the Challenges  
Many practices can be employed to overcome the challenges detailed above. The implementation and increase in the number of open access data repositories (for instance, PANGAEA [74], NOAA [75], FAO [76], WorldClim [77], etc.) may be an underlying mechanism responsible for the upward trend in locally authored publication numbers. Particularly noteworthy would be the online availability and the open data policy of such archives as that associated with Landsat imagery [78] coupled with the later improvement in internet bandwidth in Africa, which may have been a fundamental cause of the exponential increase in the number of downloads of Landsat imagery from 2013 onwards [79].  
However, the dissemination of the scientific repositories within the community would accelerate the adoption of and expand contributions to their inventories. The CORDEX-Africa initiative [45], hosted by the University of Cape in South Africa, is central to African-based modelling endeavors, hosting training workshops since 2011 and encouraging inter- and intra-regional research objectives. The development of the Coupled Model Intercomparison Project (CMIP) and subsequent phases provides an additional avenue for data acquisition through a standardized organization, with the curation and dissemination of model outputs for similar simulations [80]. Advances have been made with the innovation of open-source software, online platforms, and virtual training courses. The advent of open-access software such as that provided by Python, R and its associated packages, and Google Earth Engine [81], coupled with online tutorials to make coding easier to learn, read, and debug, has lifted the financial constraints that restrict access to licensed software--a hurdle many researchers experience in developing countries. To take GEE as an example, the cloud-based platform hosts a wide range of geospatial datasets which are updated almost daily [82]. Therefore, a primary goal for African countries to become more self-reliant in their research would be to enhance capacity-building, both human and infrastructural, in disciplines that can contribute to addressing climate change through modelling.
## 5 Experiments
### Phase Unwrapping  
To enable the network to learn the mapping from low-resolution amplitude images to high-resolution unwrapped phase image, we set the ground truth phase range for the Stage 2 to \(-2\pi\) to \(2\pi\) and generate an augmented dataset to train the network. The reconstruction capability of SFPM for thicker samples with phase ranges exceeding \(2\pi\) was validated using a simulated dataset. The reconstruction results are presented in Figure 7. As shown in Figure 7, SFPM demonstrates a certain degree of reconstruction capability for phase data exceeding \(2\pi\).  
Figure 5: The visual comparison of the different methods reconstructs results in a real erythrocytes dataset.  
Figure 6: The visual comparison of the SFPT two-stage reconstruction results in real dataset.  
To further validate the model's performance on in a real experimental environment, we captured images of bullfrog erythrocytes, approximately 16-25 \(\upmu\)m thick [40], and the phase range was calculated to be approximately \(2\pi\) to \(4\pi\) in water. We compared the reconstructed phase of traditional FPM with the reconstructed phase obtained by SFPT trained on different phase range datasets; the reconstruction results are shown in Figure 8:  
Figure 8c shows the inference results of the network trained with an augmented dataset generated using phase values distributed from \(-\pi\) to \(\pi\), while Figure 8d shows the inference results of the network trained with an augmented dataset generated using phase values distributed from \(-2\pi\) to \(2\pi\). Comparing the traditional FPM algorithm and unwrapping algorithms, it is evident that the dataset trained with the \(-\pi\) to \(\pi\) phase distribution encounters phase saturation problems when reconstructing phases beyond the \(2\pi\) period.  
In contrast, the reconstruction results in Figure 8d exhibit distinct cell nuclei that resemble the unwrapped phase output of the FPM algorithm, indicating that adjusting the distribution of training data enables the network to accurately reconstruct the authentic phase. This method allows the network to precisely restore the unwrapped phase without incurring additional costs.