esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Experiments
### Accuracy Test and Application Evaluation  
In this section, we evaluated whether the accuracy of our method can meet the requirements of the application. We used the 3D mapping of indoor autonomous mobile robots as the actual application, because a rotating 2D lidar is widely used in the 3D mapping of indoor mobile robots [17; 34; 35; 36], and the 2D lidar Hokuyo UST-10 LX [30] used in our prototype is a product mainly used for indoor. In this application, the main negative effect of the attitude error of the 3D point cloud is to make the robot navigate in the wrong direction, thereby increasing the risk of crashing the wall. The variation curve of the mapping error \(E_{\text{map}}\) with distance \(d\) in 7 modes is shown in Figure 11. The mapping error \(E_{\text{map}}\) is calculated by the following formula  
\[E_{\text{map}}=d\cdot\tan(\varphi_{\text{error}}) \tag{21}\]  
There are 7 graphs in Figure 11, which correspond to the 7 scan modes of the prototype. The horizontal axis of the graph is the distance \(d\), its units are meters. The vertical axis of the graph is the mapping error \(E_{\text{map}}\), its units are millimeters. The maximum range of the horizontal axis \(d\) is 10 m, which is the range of the 2D lidar we used in our prototype. The three curves in each graph are the variation curve of \(E_{\text{map}}\) with distance \(d\) calculated according to the maximum, average and minimum values of angle \(\varphi_{\text{error}}\), respectively. The area between the top curve and the bottom curve represents the variation range of the attitude error of the 3D point cloud in this mode.  
We took \(\pm 50\) mm as a standard, and marked the corresponding \(d\) while the mapping error \(E_{\text{map}}\) of each mode is limited within \(\pm 50\) mm in Figure 11. Even in mode 1, which has the lowest accuracy, the mapping error \(E_{\text{map}}\) can be limited within \(\pm 50\) mm at \(d=2.2996\) m. In mode 7, the mapping error \(E_{\text{map}}\) at the measuring range \(d=10\) m can be limited within \(\pm 50\) mm. For 3D mapping of indoor autonomous mobile robots, a mapping error of \(\pm 50\) mm is acceptable. After all, the accuracy of the 2D lidar Hokuyo UST-10 LX used in our prototype is \(\pm 40\) mm [30]. Moreover, after the robot has approached a target (such  
Figure 11: Variation curve of mapping error with distance in 7 modes.  
as a door), the rotating 2D lidar mounted on it can 3D scan again to refresh the 3D map. According to Formula (21), the mapping error \(E_{\text{map}}\) of this target will be reduced because the distance is closer.  
The above analysis has verified that the accuracy of our method can meet the requirements of 3D mapping of indoor autonomous mobile robots.
## 2 Experimental
### Spectral Data Analysis  
MS raw data were searched against an _A. thaliana_ protein database based on The Arabidopsis Information Resource (TAIR) 10 with the Proteome Discoverer 1.3 using an in-house Mascot server (precursor mass tolerance: 7 ppm; fragment mass tolerance: 0.8 Da; missed cleavages: 2). Carbamidomethyllation of cysteine was set as a static modification. Variable modifications were oxidation (Methionine), acetylation (protein _N_-terminus), deamidation (Asparagine/Glutamine) and phosphorylation (Serine/Threonine). Further data evaluation was carried out with the Scaffold software (Version 3.3, Proteome Software Inc., Portland, OR, USA), Proteome Discoverer 1.3 with phosphoRS 1.0 (Thermo Fisher Scientific) and DanteR [30] for total protein content. Phosphopeptides were identified with the Proteome Discoverer 1.3 software, which includes the phosphoRS 1.0 algorithm (Thermo Fisher Scientific) for phospho-site mapping. A false discovery rate (FDR) was calculated by searching a \"decoy\" database containing all the target database sequences in reverse order. Peptide-spectrum match (PSM) was set at a q-value \(<\)0.05 (_i.e._, a corrected significance threshold employing the Benjamini-Hochberg FDR procedure to control for a family-wise error rate). Protein grouping was enabled. Gene ontology (GO) annotation was achieved with the tool on TAIR [31]. The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium ([[http://proteomecentral.proteomexchange.org](http://proteomecentral.proteomexchange.org)]([http://proteomecentral.proteomexchange.org](http://proteomecentral.proteomexchange.org))) via the PRIDE (PRoteomics IDEntifications) partner repository [32] with the dataset identifier PXD000421.
# A High Gain Modified Quadratic Boost DC-DC Converter with Voltage Stress Half of Output Voltage
## 2 Evolution of the Proposed Modified Quadratic Boost Converter  
### Conventional Quadratic Boost Converter  
The conventional quadratic boost converter is comprised of two inductors, L\({}_{1}\) and L\({}_{2}\), three diodes, D\({}_{1}\), D\({}_{2}\), and D\({}_{3}\), a single switch, and two capacitors, C\({}_{1}\) and C\({}_{0}\). The circuit diagram of the CQBC is shown in Figure 2.  
In a conventional quadratic boost converter, during the first mode of operation, when the switch is on, the inductor L\({}_{1}\) is charged by the source voltage, whereas the inductor L\({}_{2}\) is charged by the capacitor C\({}_{1}\). At this instance, the diode D\({}_{3}\) will be on reverse bias, and the output capacitor, C\({}_{0}\), will transfer the energy to the load. During the second mode, the diode, D\({}_{1}\), will be on reverse bias, and both of the inductors transfer the energy to the load through diodes D\({}_{2}\) and D\({}_{3}\) with a boosted voltage, following the voltage gain formula \(\frac{1}{(1-d)^{2}}\) where the two capacitors store the energy.
# A Programmable ROADM System for SDM/WDM Networks
## 1 Introduction
The bypass core configuration provides direct core-to-core connections from the source node to the destination node without any intermediate wavelength switching, which allows adaptable virtual typology in optical networks for different network traffic. At each node, the total number of fibre cores that require wavelength switching is reduced significantly with the fibre core bypass configuration so that a small-scale traditional WDM ROADM can be setup to provide wavelength switching capability. The proposed SDM/WDM ROADM system minimizes the required scale of WDM-ROADM at each node and reduces the required number of WSSs. The network simulation is carried out to validate the proposed SDM/WDM ROADM system. The simulation results indicate that the proposed SDM/WDM ROADM system provides similar performance as that of a baseline ROADM design, which provides WDM switching in all fibre cores. It is worth mentioning that the baseline SDM/WDM ROADM system is a fully flexible ROADM that treats each core as a single mode fibre. This baseline ROADM system cannot be implemented practically as it requires extremely high-port-count WSSs that is far beyond the current technologies. To evaluate the system cost, we choose the required quantities of WSSs and the total power quantity to reflect the cost. Our proposed SDM/WDM ROADM system requires less number of WSSs with 10-20 % reduction, which depends on the fibre core number and network scale. Most importantly, the port number per WSS in our proposed system is in the practical range. In the simulation network, the maximum port number of WSSs in a fully flexible ROADM scenario is 41 (assuming three degrees in the node, bidirectional transmission in the MCFs and 13 cores in each MCF). Moreover, the maximum total port count of practical WSSs is limited around 30 [10]. In addition, the SDM/WDM ROADM design is fully compatible with the current SMF-based optical networks and allows smooth migration to multidimensional optical networks. With the increase in core number in MCFs, the ratio of the saved hardware can be further increased for better hardware utilization. The remainder of this paper unfolds as follows. The latest development of the SDM ROADM system is reviewed in Section 2. Section 3 presents the design of the programmable SDM/WDM ROADM. Simulation results to validate the proposed ROADM architecture are presented and discussed in Sections 4 and 5 concludes the paper.
# Augmented MIMO: Body-Mounted Antennas for Tiny Wearable Devices
## 2 Principle: How Augmented MIMO Improves Channel Capacity  
Here, we consider an \(N\times N\) Augmented MIMO system, i.e., we assume an equal number \(N\) of transmitter antennas and receiver antennas. Figure 1b is an example of \(N=2\). The channel capacity depends on the signal-to-noise ratio (SNR) at the receiver and the channel correlation. When the channel state information is not shared between the transmitter and receiver, the channel capacity \(C\) (per unit bandwidth) is formulated as [1]  
\[C=\log_{2}\det\left[I_{N}+\frac{\rho}{N}H^{\dagger}H\right]=\sum_{k=1}^{N}\log _{2}\left(1+\frac{\rho}{N}\lambda_{k}\right), \tag{1}\]  
where \(\rho\) denotes the average SNR at each receive antenna, \(H\) represents the channel, \(H^{\dagger}\) is its conjugate transpose, and \(\lambda_{k}\) are the non-negative real eigenvalues of the Wishart matrix \(W\equiv H^{\dagger}H\). \(I_{N}\) is the \(N\times N\) identity matrix. For higher channel correlations, each of the \(\lambda_{k}\) values decreases. When one of the eigenvalues becomes significantly smaller than the others and is practically considered to be zero, the rank of \(W\) decreases, i.e., the number of terms actually summed in (1) decreases.  
The idea of Augmented MIMO is to avoid a \(\lambda_{k}\) reduction due to closely spaced antennas by extending the substantive locations of the MIMO antennas outside the small device itself. Therefore, Augmented MIMO is expected to improve the channel capacity of a wearable MIMO transceiver that faces mutual antenna coupling due to its size limitation. Note that it is not expected to be effective for MIMO systems that originally achieve large eigenvalues.
## 2 Materials and Methods
### XUV Optimization Studies
#### 2.2.3 Chirped Laser Pulses  
The control of the temporal rearrangement of the spectral content of the ultrashort laser pulses, namely the chirp of the laser pulses, has been used as a tool for improving the efficiency of the HHG and their characteristics [38; 39; 40]. Recently, our group showed that by controlling the chirp of intense IR fs laser pulses, the contribution of long and short trajectories in the HHG spectra could be resolved, while the efficiency of their generation, as well as their spectral and divergence characteristics could be accurately controlled [41; 42]. Thus, the investigation of the effects of using positive (instantaneous frequency increasing with time) and negative (instantaneous frequency decreasing with time) chirp pulses was included in the HHG optimization study. The chirp value and corresponding pulse duration of the laser pulse was accurately controlled by varying the distance between the compressor's gratings relative to the position set for delivering FTL laser pulses. Considering only linear chirp, its value was estimated in a shot-to-shot basis via measurements with a femtosecond single-shot-intensity autocorrelator (Amplitude Technologies, Bonsai) located just before the interaction cell.  
In Figure 4, a detailed study of the HHG efficiency as a function of the chirp of the laser pulses and for various Ar gas pressures is presented. As reported in [42], the maximum efficiency for HHG takes place for negatively chirped pulses with a duration around 50 fs and relatively high pressures. Thus, for optimizing the HHG generation at a predetermined central wavelength, we also varied the chirp of the laser pulses to low negative values.  
Figure 3: XUV spectra obtained for the Ar, Kr, and Xe HHG gases. All spectra were obtained at pressures of 70 Torr and identical laser focusing conditions. Dashed lines indicate the wavelengths of interest, corresponding to the two pairs of multilayer mirrors’ reflection narrowband regions.
###### Abstract  
Impaired contractility after myocardial infarction (MI) causes cardiogenic shock. MARK4 activity impairs contractility post-MI by increasing \(\alpha\)-tubulin detyrosination. We assessed the impact of naringenin, a small-molecule MARK4 inhibitor, on contractility post-MI. Naringenin (Nar) was encapsulated in PEG-PCL to augment bioavailability. Wistar rats were randomized to receive either MI + micellized naringenin (0.3 mg/kg) [MI-NarMic], MI + naringenin (0.3 mg/kg) in 1% DMSO [MI-NarDMSO], MI + empty micelle [MI-Mic], MI alone [MI-Untreated], or no MI [Sham]. MI was induced via left anterior descending artery ligation. Invasive hemodynamics with pressure-volume catheterization, cardiomyopathyocyte contractility, and ventricular protein abundance were assessed one day post-MI. A total of 45 rats underwent hemodynamic assessment. MI-NarMic rats demonstrated decreased \(\alpha\)-tubulin detyrosination relative to MI-Untreated rats (\(p<0.05\)). Myocytes isolated from peri-infarct tissue had increased contraction and relaxation velocities in MI-NarMic versus MI-Untreated rats (both \(p<0.0001\)). MI-NarMic rats had higher ejection fractions than MI-Mic and MI-Untreated rats (63 \(\pm\) 3% v. 48 \(\pm\) 5% vs. 39 \(\pm\) 4%, \(p<0.05\)) and similar levels to Sham (61 \(\pm\) 1%, \(p=0.97\)) and MI-NarDMSO (54 \(\pm\) 5%) rats (\(p>0.05\)). MI-Nar rats had greater stroke work and lower end-diastolic pressure and tau than MI-Untreated rats (all \(p<0.05\)). Micellized naringenin is a translatable agent with the potential to rescue hemodynamics post-MI by inhibiting MARK4 and mitigating myocardial \(\alpha\)-tubulin detyrosination.  
naringenin; MARK4; microtubule; intorope; myocardial infarction
## 3 Optimal Service Provisioning Framework
### Customized Mist-Edge-Cloud Framework  
The framework comprises five layers, including Perception, Mist, Edge, Central Cloud, and Cloud Application, working collaboratively for latency-aware and rate-adaptive service provisioning in smart healthcare systems. A depiction of the framework is shown in Figure 2.
## 4 Experiment Results  
In this section, in order to test the performance of VDSR in channel estimation, BER and MSE are used as reference indicators to compare with traditional channel estimation methods of OCDM and OFDM systems. The frame structure of OFDM and OCDM are  
Figure 8: Overall process of using VDSR method for UWA channel estimation.  
Figure 7: Convert complex channel matrix to two-channel real matrix.  
shown in Figure 9, the purpose of a preamble sequence in signal transmission is to facilitate synchronization and timing at the receiver, and \(T_{g}\) is the time interval between the preamble sequence and the data.
## 2 Literature Review
### System Theoretic Process Analysis (STPA)  
STPA [12], proposed by [PERSON] in 2011, is a hazard analysis technique that has gained popularity among researchers and practitioners in engineering the safety of complex systems in various domains. It is developed based on STAMP (Systems-Theoretic Accidents Model and Process)--a model that considers the safety of a complex system as a control problem rather than a component failure or reliability issue. It aims to identify hazardous control scenarios that might lead to unwanted losses, and then accordingly define the constraints/requirements to avoid/mitigate such scenarios. Inadequate controls can arise because of human error, software error, inappropriate interaction between system components, etc. Detailed reasons why traditional safety engineering approaches are unfit for addressing the safety of complex systems can be found in [12]. However, STPA has not explicitly considered the security aspects of the system.  
Basically, the flow of STPA can be summarized as follows: (1) Identify the high-level (systemic) accidents/losses that are to be prevented; (2) Identify the potential hazards that would lead to the identified accidents/losses; (3) Define the safety constraints with regards to identified hazards; (4) Draw the control structure diagram showing the components of the system of interest as well as their interfaces and boundaries; (5) Identify all the control actions involved in the control structure diagram; (6) Identify the hazards that may arise when these control actions are inadequate--i.e., not provided, incorrectly provided, untimely provided, and stopped/applied too soon/long; (7) Translate the hazardous control actions into safety constraints; (8) Identify how the hazardous control actions would occur based on the causal factors shown in Figure 1; and (9) Translate the identified causal scenarios into safety constraints.  
Figure 1: STPA’s generic control loop diagram annotated with relevant causal factors resulting in hazardous/insecure control actions [12; 13; 14]; note that the underlined texts are security related guide-words proposed by [PERSON] et al. [15] to help deducing intentional/malicious scenarios.
## 4 Conclusions  
In this study, a set of simulations was conducted using the LES method to evaluate the transport phenomena of fire-induced smoke flow in a 21-story stairwell model. The influences of the fire location and HRR on the flow field and temperature distribution were analyzed, respectively.  
The results can be presented as follows:  
(1) With an increase in the fire HRR, there is an increase in the velocity of the smoke flow, followed by more pronounced wall attachment effects. It is mainly because the fire strength determines the buoyancy of smoke flow. Higher fire HRRs result in larger temperature differences, and an increase in height also results in larger temperature differences. The temperature difference in the near-fire area is greater, whereas an increase in the height leads to a lower increase in temperature. The vertical distribution of the temperature is in accordance with natural logarithms.  
(2) The heights of openings and fire locations have a great influence on the turbulent mixing movement. Changes in the height of the fire location leads to changes in the direction of air flow through the side openings. With an increase in the height of the fire location, different amounts of ambient air flowing into the stairwell have a significant influence on the combustion process and smoke flow. Moreover, most previous studies were conducted in stairwells with openings set at a fixed height. Moreover, a relationship among \(N_{outlet}\), \(N_{inlet}\), and \(N_{turbulent}\) was established (applicable on floors 1-18).  
(3) A linear relationship was found between the maximum temperature and the fire source location, i.e., the relationship between the fire location factor \(h^{*}\) and maximum temperature \(T_{m}\), in addition to that between the maximum temperature \(T_{m}\) and height at which the maximum temperature \(Z_{Tm}\) is located, can be linearly fitted using the least squares method.  
In general, over-all good agreement was obtained in previous studies [41; 42]. Moreover, given the good agreement, insights regarding the smoke motion can be obtained from simulation results [43; 44]. The results in this paper could help researchers further understand the thermal smoke flow, as well as turbulent mixing in stairwells. Simulations and even experiments with more configurations will be conducted in the future to verify the proposed expressions and classify a new phenomenon.  
Conceptualization, J.Z.; data curation, R.W.; investigation, T.Z. and Q.C.; software, J.W.; supervision, D.O.; validation, J.W.  
This work was supported by the National Key R&D Program of China (No.2018 YFC0808600).  
The authors declare no conflict of interest.
# A Set of Active Disturbance Rejection Controllers Based on Integrator Plus Dead-Time Models +
## 4 Real-Time Experiments and Analysis
### Process Transients  
During the experiment, the reference was first set to \(y_{0}=32\ ^{\circ}\text{C}\) to reach the initial steady state. Then, the reference step change to to \(w=38\ ^{\circ}\text{C}\) was initiated at \(t=50\) s. At \(t=150\) s, the plant input disturbance was introduced by changing the fan control value from \(u_{fan}=5\) to \(u_{fan}=15\).  
**Property 1** (Basic feature of ADRC output setpoint responses).: _Samples of transients in Figure 5 show similar behavior as, for example, analyzed in [48]. However, the output setpoint responses clearly differ from the setpoint responses achieved by traditional PID control by the pure termination of transients after reaching the setpoint value. As shown by [49] on the example of the PI controller, in order to reduce the control signal after reaching the setpoint from the upper limit to a steady-state value, an over-regulation (negative values of the control error) must appear in the process. This basic overshooting of PI and PID control after reaching setpoint value can only be eliminated by using 2-DoF controllers, but this slows down the setpoint responses._
## 4 Data Analysis in Maritime IoT Applications
### Benefits and Limitations
#### 4.3.2 Improved Safety  
The maritime industry often faces diverse and extreme safety challenges, from unpredictable weather conditions to potential equipment failures. Data analysis provides an opportunity to mitigate these risks. By analyzing data from various sensors and IoT devices on a vessel, potential safety hazards can be identified and addressed promptly. This contributes to the enhancement of safety measures, reducing the risk of maritime accidents and ensuring the well-being of the crew and the integrity of the cargo [75].
## 5 Discussion and Conclusions  
In many application fields, fuzzy clustering, especially fuzzy C-means (FCM) clustering, is one of the important hot research branches. FCM clustering algorithm is the most widely-used one among various clustering algorithms and has been used to successfully solve several application problems. However, the FCM algorithm is essentially a local search optimization algorithm. Herein, if its initial value is selected improperly, it will converge to a local minimum. Aiming at the disadvantages of the FCM algorithm, a new kind of swarm-based metaheuristic search, called KHE, is proposed to solve the clustering problem. Elitism strategy used in the KHE method can prevent the krill population from degrading. In KHE, the well-selected parameters are used instead of originating from nature. Furthermore, the KHE method is applied to solve the clustering problem for the purpose of escaping a local minimum. Moreover, with the aim of showing the performance of KHE method, it is compared with six other metaheuristic algorithms through seven complicated benchmark problems. The results show that the KHE method performs well on given benchmark problems and fuzzy clustering analyses.  
Moreover, there are no additional operators added to the basic KH method. Therefore, the KHE method is simple and easy to implement.  
Despite the above advantages of the KHE method, two prospective research points should be oriented as follows. On the one hand, in the current work, there is no study of computational requirements. The research of computational requirements should be made in future. On the other hand, only a few test problems and the clustering problem is solved by the KHE method in the present work. More problems should be used to test the KHE method from various aspects, and then it is used to solve more application problems, such as image segmentation, constrained optimization, knapsack problems, scheduling, dynamic optimization, antenna and microwave design problems, and water, geotechnical and transport engineering.  
Figure 4: Clustering results of the KHE method.