esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Structural Elucidation
#### 3.2.1 Structural Elucidation of DBP7  
The MS-TOF analysis showed a molecular ion peak at \(m/z\) 249.11 [M + H]\({}^{+}\) corresponding to the compound with the molecular formula C\({}_{14}\)H\({}_{8}\)N\({}_{4}\)O. The \({}^{1}\)H NMR spectrum did not show the presence of an \(n\)-butyl side chain, an A ring, or the proton H-11, just as the \({}^{13}\)C NMR spectrum did not show the corresponding carbons nor the signals of carbons C-7 and C-9 (the B ring). In the \({}^{1}\)H NMR spectrum, only aromatic signals were present, specifically only seven protons at 9.19, 8.73, 8.28, 8.61, 7.98, 7.92, and 8.86 ppm, as well as the signal of an aldehyde proton at 10.30 ppm. The HSQC spectrum allowed for the correlation of the aforementioned signals with the carbons at 127.19, 126.57, 125.41 120.18, 125.09, 130.95, 124.03, and 190.01 ppm. Based on the 1H-1H COSY and the HMBC spectra, the first three signals were attributed to the protons H-13, H-16, and H-17 of 1,2,4-trisubstituted benzene (the C ring). The next four were attributed to the protons H-19, H-20, H-21, and H-22 of 1,2-disubstituted benzene (the D ring), while the last one was attributed to the aldehyde proton H-11. Evidently, the starting product has undergone hydrolysis of the C-7/N-8 and N-8/C-9 bonds [27; 28], along with the partial oxidation of carbon C-11 and the concomitant closure of a new six-term ring. The latter could result from an aromatic nucleophilic substitution reaction on the C-14 carbon of the previously chlorinated aromatic B-ring via the adjacent N-28 nitrogen of the tetrazole ring [29]. The plausible mechanism of DBP7 formation from ribesartan is shown in Figure 3, and its NMR assignments are shown in Table S1.
# Power System Reliability Assessment Considering Coal-Fired Unit Peaking Characteristics
## 3 Multi-Stage Probabilistic Model  
### Multi-State Power Model of Wind Farms  
In PPS, accurately modeling the randomness and time-series characteristics of wind power output is crucial for assessing the reliability and economic performance of power systems. This section establishes a multi-state power model for wind farms, which captures the stochastic nature of wind speed and the nonlinear relationship between wind speed and power output.  
The short-term random variations in wind speed in nature generally follow a Weibull distribution [25], with the probability distribution given by  
\[f(v)=(\frac{k}{c})(\frac{v}{c})^{k-1}\exp[-(\frac{v}{c})^{k}] \tag{5}\]where \(v\) is the wind speed value, \(k\) is the shape parameter, and \(c\) is the scale parameter.  
The power output of wind turbines has a nonlinear relationship with wind speed, expressed as follows:  
\[P(v)=\begin{cases}0,&0\leq v\leq v_{ci}\\ P_{r}\dfrac{(v-v_{ci})}{(v_{r}-v_{ci})},&v_{ci}\leq v\leq v_{r}\\ P_{r},&v_{r}\leq v\leq v_{co}\\ 0,&v\geq v_{co}\end{cases} \tag{6}\]  
where \(v_{ci}\) is the cut-in wind speed, \(v_{r}\) is the rated wind speed, \(v_{co}\) is the cut-out wind speed, and \(P_{r}\) is the rated power. The wind power curve has been linearized between the cut-in wind speed and the rated wind speed in Equation (6).  
By solving Equations (5) and (6) simultaneously, the probability density function of the total wind power at a certain time under a specific scenario can be obtained. To consider the time-series characteristics of wind power output in PPS, the continuous probability distribution of wind power is discretized into multiple states. Each state represents a specific range of power output, and the probability of each state is calculated based on the Weibull distribution and the power curve, as shown in Figure 1. Based on the discretized state model, its UGF model can be established, as shown in Equation (7):  
\[u_{w}^{m}(z,t)=\sum_{j=1}^{k_{w}}p_{w,j}^{m}(t)z^{p_{w,j}^{m}(t)} \tag{7}\]  
where \(u_{w}^{m}(z,t)\) is the UGF model of the wind power output at time \(t\) under scenario m; \(p_{w,j}^{m}(t)\) and \(P_{w,j}^{m}(t)\) are the corresponding state probabilities and state values; and \(k_{w}\) is the number of states.
# Centralized vs. Decentralized Cloud Computing in Healthcare
## 7 Results
#### 7.3.2 Data Management  
A centralized model is a patient database that is centrally located and controlled from one server, thus making the healthcare data organization easy. This way, data that could have been scattered across several dashboards can now be accessed by the care health provider in one single place for analysis [57]. This feature is also very useful due to its ability to chain related healthcare organizations and enable the easy sharing of information between them. This system also recaps all patients' details, physical assessments, tests, and scans comprehensively in the analysis and diagnosis helping in facilitating the correct selection of the solution at the right time and with the shortest time [50]. Despite the above benefits of the centralized healthcare systems, they have their demerits. However, the main disadvantage of the approach is the concentration of resources in one location. If the main server is damaged, when there is a failure in the servers' hardware or software or due to any other technicalities, it goes down [84]. Then, hospitals and patient care, which depend on the central IT system, lose their services, and this disrupts patients' treatment, data retrieval, and key processes [85]. However, decentralized models use a data storage technique whereby the data are stored in several nodes, which has more benefits than storing the data in a central place, such as high redundancy and fault tolerance [61]. Every node keeps a part of the data. This makes it possible for the data to be retrievable from the other nodes in case one node in the network fails; hence, this is a more secure system. On the same note, distributed data storage is not without its drawbacks regarding consistency and synchronization in the distributed network. If existing data are updated or some changes are made, then this option may involve consensus mechanisms or synchronization protocols, which can complicate the process or even add a certain amount of time to it [67].
# Prediction of Delayed Surface Subsidence Based on the Improved Knothe-n Model
## 4 Model Validation Results and Analysis  
### Feasibility Verification of the Improved Model  
Using the improved time function model, calculations of the maximum subsidence in the mining area are compared with numerical simulation results. Additionally, the average subsidence of WC1-WC9 is computed and compared with their measured values again, as shown in Tables 3 and 4.  
Comparison of the calculation results of the improved time function model with the numerical simulation results or measured values fully verifies the feasibility and correctness of the improved segmented time function, which can be used for the prediction of delayed surface subsidence.
## 1 Introduction  
Curiosity and desire for knowledge about the universe seems to be something inherent in human beings. Since ancient times, the thrust for learning has gone beyond the outer atmosphere and into space. It was however, not until October 4\({}^{\text{th}}\) 1957, with the placement of the first artificial satellite in Earth orbit that man was able to study space _in situ_.  
Moving into space implies a constant challenge for technology, not only in terms of the technical requirements for particular devices but also because of the need to adapt technology to the extreme and often hostile environment in space. The upscreening of technology for the extreme conditions in space, in combination with the relatively small market constituted by space applications, often results in a huge increase in the cost of each component. Roughly, it can be said that each degree of qualification achieved (considering commercial, military and rad-hardened components for instance) results in an increase in the final price by one order of magnitude.  
In the early stages of space exploration, coinciding with the Cold War, a huge budget was devoted to upscreening and testing of electronic components for space applications. This budget however, suffered a drastic reduction during the nineties. Apart from this, changing political agendas and missions as well as the diversification of the field is making it hard to continuously devote a high budget to all technology options.  
One of the consequences of the end of the Cold War and the divergence of funding away from the space sector was the concept of the modern small satellite. As it was no longer possible to maintain a number of costly traditional missions a new philosophy of mission was needed. The slogan 'Faster, Better, Cheaper' (FBC) was coined by NASA and Aerospace Corporation [1]. This new philosophy consisted in simple and often small satellites developed in a short time and with commercial but highly functional components. This was possible because of the great advances made in semiconductors and integration techniques (VLSI-Very Large Scale Integration) in the last part of the century. The reduction of mission cost was achieved partly by focusing on reduced weight to minimize launch costs and partly by reducing the price of the different components. In contrast, the risk was increased.  
Besides being small, cheap and with a relatively low weight, thereby contributing significantly to the reduction of mission costs, the commercial components, named COTS - Commercial Off-The Shelf - also had the advantage that delivery time was shortened. This had an important impact on the calendar of the missions as the functionality of the components could now be checked even with the very early models (EM-Engineering Models or even BB-Bread Boards). As a result a whole line of COTS upscreening and validation groups emerged in the working teams of all space agencies. In the following sections this work is focused on magnetic COTS and other small magnetometers, the combination of these sensors with a reduced front-end, and their application for space purposes.
## 3 Particle Shape Classification  
### Classification Method  
The particle shape classification is carried out manually by looking at both top- and side-view images simultaneously since these provide complementary information about the shape. The images provide enough detail so that they can be classified according to the Magono-Lee scheme.  
Figure 1 shows a few examples of successfully classified snow particles. In some cases, classification is relatively simple with one image only, for example, the top view of the stellar crystal of example (a). However, looking at the side view of the same example, the ice particle's shape may be mistaken for a needle type, as it looks similar to example case (b), which is of needle shape. Thus, determining the shape from one image is challenging even when using high-resolution images due to ambiguities arising in the case of unfavorable orientation in that image.  
Cases (c) and (d) are more examples, which, in the side view, appear to have a similar shape. However, from the top-view images, we can distinguish two different shapes: (c) has a dendritic crystal or stellar shape, and (d) is a bullet capped with a dendrite. Similarly, particles in (e) and (f) appear to have similar shapes in the side view, i.e., hexagonal plate shape, however, from the top-view images, we can distinguish two different shapes: (e) is a capped column, and (f) is a hexagonal plate capped on one side with a thin plate. In a few cases, it is difficult to specify the shape despite the availability of top- and side-view images. For instance, we cannot distinguish if (g), (h), or (i) are the same shape, i.e., if they are graupel-like snow or densely rimed ice crystals. For such cases, we determine that they have a graupel shape.  
Figure 1: Shape classification is less ambiguous with dual images. Panels **(a-i)** show nine examples of different particles with side views (_right_) and top views (_left_). Discussion of these examples is in the text.
## 3 DC-DC Converters
### Interleaved Boost Converter  
As mentioned earlier, one of the major disadvantages of the conventional boost converter is operating at a high duty cycle ratio which leads to reducing efficiency, increasing switching losses, and a high voltage drops over the parasitic elements [37]. By proposing the interleaved boost converter, different benefits--such as reduced input-output ripple, higher efficiency, and improved reliability--are realized while using this topology [38]. Furthermore, using the interleaved boost converter would be associated with smaller parasitic elements, which reduces the power losses in the circuit. Here, a two-phase interleaved boost converter, as illustrated in Figure 3, is considered.  
Due to the two-phase structure of this converter, each phase must operate at 180\({}^{\circ}\) according to the phase shift equation. Considering the simultaneous operating of these two phases, the frequency of the whole circuit is doubled which can reduce the voltage ripple. Additionally, the input current ripple decreases by increasing the frequency. During the on state for the first phase, the inductor current increases and it will store energy in the inductor; but during the off state, this stored energy will be delivered to the load through the first phase. By giving the gate pulse to the second phase, a similar process to the first phase will be performed to store and discharge energy in the inductor [39]. Therefore, these two phases alternately feed the load. The voltage conversion ratio is similar to the conventional boost converter, but the duty cycles should be carefully selected so that they do not overlap [40].
## 3 Fundamentals: Workpiece and Moving Head Kinematics
### Axis and Control of the Moving Head
#### 3.1.1 Pan and Tilt  
The movement axes _pan_ and _tilt_ are the only axes that control the actual position of the light spot. For finer control, the _finepan_ and _finetilt_ controls exist, which are further explained in Appendix B.1. The movement ranges are typically in the area of \(pan=[0^{\circ}\ 360^{\circ}]/[0^{\circ}\ 540^{\circ}]\) and \(tilt=[0^{\circ}\ 180^{\circ}]/[0^{\circ}\ 270^{\circ}]\). The moving head is a serial kinematic, where _pan_ describes the rotation around the base, and \(tilt\) describes the rotation of the actual projection head. This movement principle is similar to the definition of a spherical coordinate system and leads to the derivation of the Cartesian control in Section 3.2.
## 4 Discussion
### Time-Varying Analysis  
Through the factor analysis questionnaire results, we determined several major noise events that affect hospital lobbies, and through correlation analysis, we determined the main frequency bands that affect hospital lobbies.  
Lastly, we needed to ascertain the specific noise events in the mid- and high-frequency bands that affect the hall. We therefore conducted time-varying loudness power law and frequency analysis on the audio samples from Hospital C in team \(\alpha\) and Hospital F in team \(\beta\) and then manually listened to the audio samples to determine the specific noise events. The results are shown in Figure 7.  
We can clearly see that the frequency of noise events is mainly concentrated in the 630-1600 Hz range. Combined with the manual listening and reading of audio files, we determined that the noise event that occurs most frequently in the hall and has the greatest impact on the human auditory system is footsteps and conversation sound (MF) and radio broadcast sound (HF). Combining our judgment results with picture analysis, there are several obvious waveform fluctuations in the loudness analysis, and the corresponding  
Figure 7: Hospitals C and F time-varying loudness power law analysis [36] and frequency analysis (conversations and footsteps principally cover the entire audio; thus, they are not marked specifically).  
black mark points (noise events) in the spectrum analysis diagram are consistent. This noise represents the sound of the radio announcement.  
Combining the above results, we can draw the following two conclusions:  
1. To verify the findings of [PERSON] et al. (2017) we uncovered through the above research that the main psychological feeling frequency band of the subjects indoors is concentrated in the MF band in field measurements, followed by the LF band. This result deviates from the results presented by [PERSON] et al.; however, this finding also corroborates the proposed initial assertion that field measurements are likely to yield more accurate experimental results than those simulated in a laboratory setting.
2. Compared with previous studies by [PERSON] et al. and [PERSON] (2019), field measurements, questionnaire surveys, correlation analysis, and factor analysis were used in this study in complex hospital halls. Using these research methods, several main noise sources and main frequency bands (mid-frequency) affecting the hospitals' halls were determined. Moreover, data obtained by analyzing multiple hospitals are more reliable than studying the data of a single hospital.
# Z-OFDM: A New High-Performance Solution for Underwater Acoustic Communication
## 4 Computer Simulation Experiments
### Experimental Results
#### 4.2.1 Fuxian Lake  
As shown in Figure 8a, the BER decreased with increasing Signal Noise Ratio (SNR) of the communication system. However, with a higher factor \(D\), the BER of the communication system decreased more significantly. In other words, a higher factor \(D\) provided higher gain for the communication system. At a BER of \(10^{-3}\), the Z-OFDM system achieved a 3 dB gain compared to the FFT-based OFDM system when the refinement factor \(D=2\). When \(D=3\), the communication system achieved a gain of 5 dB. When \(D=4\), the communication system achieved a gain of 6 dB.  
Figure 9 shows the BER of the Z-OFDM system with varying parameters of the underwater acoustic channel of Fuxian Lake. At the same \(\frac{E_{b}}{N_{b}}\), the BER of OFDM is inconsistent with the varying parameter of the underwater acoustic channel. When the refinement factor \(D=2\), the BER is still variable at the same \(\frac{E_{b}}{N_{b}}\), but the variability of the BER of Z-OFDM becomes weaker compared to FFT-based OFDM. When \(D=4\), the BER decreases, and the variability of the BER becomes weaker. When \(D=6\), the BER decreases even more, and the variability of the BER cannot be observed clearly.
## 3 Experimental Setup and Results
### Results--Energy Consumption and Saving Evaluations
In order to make a conservative calculation, the EER of 3.5 was considered even in the case of partial load. In the psychrometric chart (Figure 11), humid air transformations are highlighted. Points 1 and 4 correspond to the conditions of the inlet and outlet air, indicated in Figure 10. The air transformations 1-2 (cooling) and 3-4 (heating) can be attributed to the heat recovery system. The heat recovery system carries out the first air cooling under the dew point. In these conditions, the electric energy consumption of such a plant can be calculated to be 150 kWh/day. This also represents the energy saving of the combined system (Table 6). \begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameters** & **Expressions and Values** & **Results** \\ \hline Wd = daily DHW consumption [dm\({}^{3}\)] & Wd = Wg * oc * g & Wd = 21,950 dm\({}^{3}\) \\ \hline Wg = daily DHW for each guest [dm\({}^{3}\)/day] & Wg = 140 dm\({}^{3}\)/day & \\ oc = occupancy ratio [\%] & oc = 51.4\% & - \\ g = maximum number of guests & g = 308 & \\ \hline LPGc = fuel consumption [dm\({}^{3}\)] & LPFc = \(\frac{Wd\cdot\varphi\cdot\)At-c}{\eta\cdot\zeta_{\text{LPG}}}\) & LPGc = 164 dm\({}^{3}\) \\ \hline \(\varphi\) = water specific mass [kg/dm\({}^{3}\)] & \(\rho\) = 1 kg/dm\({}^{3}\) & \\ \(\Delta\)t = temperature difference [K] & \(\Delta\)t = (40\(-\)10) K & \\ c = water specific heat [kJ/(kg K)] & c = 4.186 kJ/(kg K) & - \\ \(\eta\)= plant efficiency [\(-\)] & \(\eta\) = 0.7 & \\ CLPG = lower calorific value [kJ/dm\({}^{3}\)] & CLPG = 24,012 kJ/dm\({}^{3}\) & \\ \hline \(\tau\) = time [h] & \(\tau=\frac{1}{3600}\frac{Wd\cdot\rho\cdot\)At-c}{\varphi}\) & \(\tau\) = 6 h \\ \hline \(\varphi\) = heating power [kW] & \(\varphi\) = 120 kW & \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Expressions used for LPG consumption calculations to produce DHW.
# The Machine-to-Everything (M2X) Economy: Business Enactments, Collaborations, and e-Goverance
## 5 Discussion
### Identity  
In order for hardware devices, humans and software agents to conduct digital business transactions, or enact digital collaborations as described in Section 2.1, all these entities require a digital representation of their \"real-world\" identity. To enable secure business collaborations and transaction within the M2X Economy, this digital representation is required to establish and enable trust, reputation mechanisms, perform verifiable and accountable transactions, and establish reliable as well as auditable data provenance [1]. As M2X is a multi-stakeholder ecosystem, the identity management issue applies not only for its users, but also infrastructure providers, OEMs, regulators and service providers. A single central authority for identity management of all these different stakeholders poses the risk of single point of failure. Furthermore, identity silos create privacy concerns and are not interoperable [54].  
As earlier argued in this section, centralized infrastructures are not suitable for facilitating the full potential of the M2X ecosystem. Hence, a centralized identity solution is not an option and a decentralized interoperable identity solution is required. In order to prevent the aforementioned flaws and enable an open interoperable ecosystem, the identity-management solution needs to be self-sovereign and user-centric. Self-sovereign identity puts end-users in charge of decisions about their own privacy and disclosure of their personal information and credentials [54] and not the organizations that traditionally centralize identity. Self-sovereign identity systems that are based on decentralized identifiers (DIDs) [55], utilize distributed ledgers, or blockchains as a distributed storage system that replace centralized and incompatible data silos with a cooperative shared storage resource. The result is a user-controlled identity provision model where users control access and sharing of their data based on a need-to-know-basis using the concepts of DIDs, DID documents, and verifiable claims [1].