esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Testing Results
### Deformation and Elastic Modulus
#### 3.1.2 Relation between Strain and Loading-Unloading Cycle Number  
Figure 3 shows the evolution characteristics of axial, circumferential, and volumetric strains of the No. 4 coal specimen as the cycle number increases. In Figure 3, the symbol \"N\" represents the loading-unloading cycle number, \"\(\varepsilon_{1}\)\" represents the axial strain, \"\(\varepsilon_{3}\)\" represents the circumferential strain, and \"\(\varepsilon_{v}\)\" represents the volumetric strain. Additionally, in Figure 3, the \"cyclic initial axial strain\" refers to the axial strain value at the beginning of a cycle, the \"cyclic maximum axial strain\" refers to the axial strain value at the maximum stress point of a cycle, and the definitions of circumferential and volumetric strains are the same as those of axial strain. From Figure 3, the axial strain is very well linear with the cycle number, and the circumferential and volumetric strains are approximately quadratic functions with the cycle number. The determination coefficient \"R\" of the axial strain versus cycle number fitting curve is higher than those of the circumferential and volumetric strains. So, under cyclic loading and unloading, the axial strain evolution of  
Figure 1: Cyclic loading and unloading path diagram.  
Figure 2: Relation curves between axial, circumferential, and volumetric strains and axial stress.  
coal specimens can be better predicted. However, the change in circumferential strain with cycle number is more obvious and sensitive. Therefore, if the variation characteristics of axial and circumferential strains are considered together, it can improve the prediction reliability of coal specimen failure under cyclic loading and unloading.
# Left Ventricular Non-Compaction Cardiomyopathy-Still More Questions than Answers
## 5 Genetics  
LVNC is a rare genetic form of cardiomyopathy. In the German TORCH registry [61] and the European EUROP cardiomyopathy registry, 5% or 4.1% of cases were diagnosed as LVNC patients, respectively. Recently, in a large retrospective study 327 unrelated LVNC were tested for disease-related DNA variants. About 66 genes were considered to  
Figure 6: An Electrocardiogram of the patient from Figure 1C–E shows regular sinus rhythm 75/min. PQ 190 ms, QRS 100 ms, QT 460 ms, QTc 514 ms. Note nothing of the R-wave of the QRS in lead I, II, III, aVF, and V4 (arrow) as well as prolongation of QTc.  
be associated with LVNC, 82% of those are coding for sarcomeric proteins [62]. Of note, in pediatric LVNC-patients, gene variants classified (probably) pathogenic (ACMG 4 or 5) were more frequently found than in adult LVNC. The genes associated with LVNC show considerable overlap with other forms of cardiomyocytes and especially with DCM and HCM. In a systematic meta-analysis, [PERSON] et al. evaluated 66 genes in 561 patients from 172 studies [11]. They found that 52% of affected genes are coding for sarcomeric proteins, and 9% for arrhythmia-associated genes. Non-sarcomeric, mitochondrial, and complex genotypes were found in 6-8% of cases, respectively. The most frequently affected genes were _MYH7_, _MYBPC3_, and _TTN_. In pediatric cases of LVNC, the genetic background of the disease is frequently associated with complex congenital syndromes, mitochondrial diseases, chromosomal defects, or X-linked. Disease-related variants in the genes _MYBPC3_, _TTN_, arrhythmia genes, or X-linked genotypes are associated with a high risk for major adverse cardiac events [11]. Although the correlation of genetic variables with the clinical outcome is impressive, little is known about the pathomechanisms of the variants. In addition, the penetrance of variants in cardiomyocytes is frequently incomplete and dependent on the noncompaction phenotype. Complex genotypes are rare. The highest degree of co-segregation was found in isolated LVNC and cases with the combined phenotype of DCM and LVNC [63]. This underscores that genetic testing in LVNC is of clinical relevance and should be considered.
## 4 Results and Discussion  
### Maintenance Improvement of TR-7A Scaper Conveyer Using Cause-Effect Diagrams (Ishikawa)  
Figures 3-6 show Ishikawa diagrams that point out the causes leading to the failure of the following elements of the TR-7A type conveyer: conveyer chain, hydraulic coupling, electric actuating installation, and the actuating motor (burnout).  
The diagram in Figure 3 highlights that the interruptions in operation due to the chain have the following causes: operating personnel (negligent, unresponsive, and poorly trained); the conveyor has inadequate technical characteristics, especially gauge dimensions; materials and manufacturing technologies are inadequate (the material heats up by friction against the conveyor chute, has inclusions, and has a defective heat treatment); and the maintenance and repair methodology is inadequate (without adequate equipment).  
The diagram in Figure 4 highlights that the interruptions in operation due to the coupling have the following causes: operating personnel who are careless, unresponsive, or poorly trained; the conveyer's technical characteristics, in particular, its insufficient transport capacity; the materials, especially the oil used in the coupling, are inadequate; and there is no appropriate equipment and a plan for revisions and repairs.  
Figure 4: Ishikawa diagram (cause–effect) for coupling failure.  
Figure 3: Ishikawa diagram (cause–effect) for chain break failure.  
The diagram in Figure 5 highlights that the interruptions in operation due to the electrical components have the following causes: the measurement and monitoring of the control elements are defective; the operating staff is poorly trained; the conveyer has many interruptions in operation due to the operating conditions with humidity and dust; and the electrical materials and components (cables and switches) do not work properly.  
The diagram in Figure 6 highlights that the interruptions in operation due to the combustion of the electric motor have the following causes: operating conditions in humidity and dust; operating personnel who are negligent, unresponsive, or poorly trained; the conveyer's insufficient transport capacity; the measurement and monitoring of the electrical parameters are deficient; and the insulating varnish is inadequate.  
Figure 5: Ishikawa diagram (cause–effect) for electric failure.
## 2 Instrument Design
### Critical Components
#### 2.2.3 Filters and Isolators  
The insertion loss \(L_{\rm BPI}\) of the 4-Section band-pass filter BP1 in the front-end of the MA contributes significantly to the residual noise \(T_{\rm RM\,0}\) of the radiometer, while the losses of the filters after the front-end are no longer critical. The selectivity and \(L_{\rm BP1}\) of BP1 are coupled such that higher selectivity implies higher losses. The BP1 was selected to minimize \(L_{\rm BPI}\), while maintaining acceptable selectivity outside of the protected band. To minimize the noise \(T_{\rm BP1}\) of the BP1, a high quality silver-plated cavity filter was selected with rated \(L_{\rm BP1}=0.77\) dB (corresponding to the power transmission factor of \(t_{\rm BP1}=0.84\)). For \(T_{0}=313\) K, this yields \(T_{\rm BP1}=T_{0}\)(1 - \(t_{\rm BP1}\)) \(\approx 50\) K, which is 40% of the estimated \(T_{\rm RM\,0}=125\) K. For the same reason, ISO1/2 were tuned to have very low insertion losses \(L_{\rm ISO}\) \(<\) 0.20 dB within the protected band (1,400 MHz-1,427 MHz), resulting in the relatively low noise \(T_{\rm ISO}\) \(\approx 14\) K for \(T_{0}\) = 313 K.
# Multipath Data Storage by Third-Order Nonlinear Optical Properties in Zinc Oxide Nanostructures
## Materials and Methods  
### Sample Synthesis  
The solid film ZnO samples deposited on silica substrates were prepared by a starting solution with zinc (II) acetate [Zn(O\({}_{2}\)CCH\({}_{3}\))\({}_{2}\)] exhibiting 0.2 M in a mix of deionized water, methanol [CH\({}_{3}\)OH] and acetic acid [CH\({}_{3}\)CO\({}_{2}\)H] in a proportion of 1:8:1 volume relation, respectively. In parallel, silver nitrate [Ag (NO\({}_{3}\))] with 0.25 M was dissolved in a mix of deionized water and acetic acid [CH\({}_{3}\)CO\({}_{2}\)H] (1:1 volume relation). We employed a constant [Ag]/[Zn] atomic percent ratio of 0.1 at. % in the preparation process in order to modulate the NO\({}_{3}\) action. A flow rate of 0.5 L/min of N\({}_{2}\) was used as the solution carrier and director gas in the deposition. A piezoelectric transducer with 1.2 MHz and 120 W of ultrasonic power in the synthesis process was employed for the preparation of the samples [23].  
The silica substrates have 5 cm\({}^{2}\) of area and the thickness of the substrate was 1 mm. During the processing route, the substrate temperature was 400 \({}^{\circ}\)C and, after deposition, the samples were annealed in the same temperature for 4 h in order to promote crystallization. Thin solid films were obtained and, by using ellipsometry evaluations, samples with 500 nm of thickness were selected for the study of the optical properties. Morphology observations were carried out by a scanning electronic microscopy (SEM) studies in a FEI Quanta 3D FEG microscope system in STEM mode (scanning transmission electronic microscopy). X-ray diffraction (XRD) measurements were undertaken by a Phillips X'pert diffractometer in order to reveal the crystalline structure of the ZnO thin films. The parameters of operation of the XRD system correspond to 40 kV and 30 mA using Cu K\(\alpha\) radiation.  
Single pulses below the ablation threshold of the nanostructures were propagated through different regions of the film in order to promote a potential laser annealing process. Single-shots with maximum fluence of energy per pulse 3.5 J/cm\({}^{2}\) at 532 nm and 4 nanosecond of pulse duration were employed in a focused beam waist of 1 mm of diameter. More than a hundred regions were explored. The ablation threshold of the sample was measured to be 4 J/cm\({}^{2}\) in the same laser conditions of wavelength and pulse duration. The modification of the nonlinear optical properties exhibited by as-prepared samples and the optically irradiated samples were systematically compared.
# A Survey of Vehicular VLC Methodologies
## 6 Vehicular VLC Channel Modeling and Propagation Characteristics
### Existing Vehicular VLC Channel Models
As a result, the received energy shows a periodic pattern (sinusoidal behavior), and the closed form of the path loss equation of the model can be defined as follows [83]:  
\[h_{dB}=C_{P}z+C_{SA} \tag{13}\]  
where \(z=cos((2/d_{T})d)\), \(d\) is the distance the vehicle travels from one streetlamp to another, \(2C_{P}\) and \(C_{SA}\) are denoted as the peak-to-peak change in received power and the sinusoidal axis by the notation, respectively. \(C_{P}\) and \(C_{SA}\) can be determined for any given scenario by  
\[C_{P}=0.5f\big{(}d_{y}\,D_{r},H_{D},S_{p},b_{1}\big{)}-0.5\big{(}d_{y}\,D_{r},H_{D},S_{p},b_{2}\big{)}, \tag{14}\]  
\[C_{SA}=0.5f\big{(}d_{y}\,D_{r},H_{D},S_{p},b_{1}\big{)}-0.5\big{(}d_{y}\,D_{r},H_{D},S_{p},b_{2}\big{)}, \tag{15}\]  
Here,  
\(f\big{(}d_{y},D_{r},H_{D},S_{p},b_{i}\big{)}\), \(i\) = 1,2 is defined as  
\[f\big{(}d_{y}\,D_{r},H_{D},S_{p},b_{i}\big{)} \tag{16}\] \[Pr=\ Pt\ \underbrace{Ad^{-2B}\ \exp(-cd)}_{p} \tag{8}\]0 \[Pr=\ Pt\ \underbrace{Ad^{-2B}\ \exp(-cd)}_{p} \tag{8}\]1 \[Pr=\ Pt\ \underbrace{Ad^{-2B}\ \exp(-cd)}_{p} \tag{8}\]2  
where \(dy\) is the lateral shift function, \(Dr\) is the receiver aperture, \(H_{D}\) is the height difference between streetlamp and receiver, \(S_{p}\) is the separation between streetlight poles, and \(b_{i,j}\) is the \(j^{\text{th}}\) element of \(\mathbf{b}_{i}\).
## 3 Results and Discussion
### Analysis of Contour Reconstruction Reliability for S04  
This scenario provided the lower contrast (considering the test target as a whole) within the limits of the present work (Figure 18). Moreover, it also provides greatest local differences in contrast, because the FBS illumination introduced a noticeable reflection in the neighbourhood of the lower arc and, at the same time, originated a shadow near the upper arc.  
Figure 17: Linear samples of grey-level intensity distribution in the original scan of S03 for different orientations: 0\({}^{\circ}\) (**top left**); 45\({}^{\circ}\) (**bottom left**); 90\({}^{\circ}\) (**top right**); 180\({}^{\circ}\) (**bottom right**).  
Figure 18: MQI results as a function of \(S_{f}\) and \(T\) for every \(M\) calculated for S04.  
The best coverage was obtained for extremely low \(T\) values (Figure 18). In fact, just a narrow interval of \(T=0\) combinations provided full coverage, with the only exception of some \(T=5\) and \(T=10\) combinations with extremely low \(S_{f}\) in the case of Canny and ApproxCanny methods. Nevertheless, results were very poor for this scenario, when compared with the previous situations. A compromise between coverage and accuracy could hardly be achieved because best coverage implied very poor \(\overline{D}\) and \(\sigma_{D}\) results. In the case of first derivative algorithms, 100% \(C_{f}\) increased \(\overline{D}\) over 100 \(\upmu\)m, whereas \(\sigma_{D}\) was always above 55 \(\upmu\)m. A similar situation was observed regarding ZeroCross. In the mentioned cases, a full coverage could only be achieved using \(T=0\), which implied a high sensitivity to weak gradients. The consequence, as it was described before, was a multi-contour detection issue that was directly related to poor accuracy results. On the other hand, although Canny and ApproxCanny improved these, results were still poor, such as those obtained for S03.  
The best \(\overline{D}\) and \(\sigma_{D}\) results were achieved with test combinations that provided very poor coverage (below the minimum 75%). This scenario also showed a highly anisotropic contrast, therefore it presented an even worse compromise between coverage and accuracy than \(S03\). Thus, coverage dramatically dropped with growing \(T\), and the number of valid combinations under the condition \(C_{f}>75\%\) was low, except for Canny and ApproxCanny methods. Nevertheless, even those methods presented deviations ranging tens of microns, which made them unsuitable for feasible contour characterisation.  
Under the coverage priority criterion (Table 6), the optimal combination used Canny, \(T=10\) and \(S_{f}=1\) (\(C_{f}=100\%\); \(\overline{D}=54.90\)\(\upmu\)m; \(\sigma_{D}=48.97\)\(\upmu\)m). Under the accuracy priority criterion, the optimal combination also used Canny, \(T=20\) and \(S_{f}=3\) (\(C_{f}=77.5\%\); \(\overline{D}=31.93\)\(\upmu\)m; \(\sigma_{D}=17.75\)\(\upmu\)m) (Table 7).
## 4 Simulation Results and Analysis
### Total End-to-End Delay  
The total E-2-E latency of the four schemes is interpreted in Figure 11. The CoDBR and the DBR protocols have more delay than the DMR and the CoDMR protocols. This is due to the fact that the CoDBR scheme works in cooperative mode and in DBR the source node sends information packet from highest depth to the lowest depth destination node in the non-cooperative fashion. Both the existing schemes follow the long transmission path, which consumes a long amount of tim for information to reach the desired destination node. In the CoDBR scheme, a data packet passes to the two relays and to  
Figure 10: Packet delivery ratio (PDR).  
the destination node. The destination node checks all the packets channel condition and further forwards the optimal information to the sink node, which increases latency. While in proposed CoDMR algorithm; due to a single relay and the placement of minor sink nodes at the balance position, path length decreases, which reduces the delay during the packet transmission. The latency of the proposed DMR is minimum than the proposed CoDMR due to the single path routing. In each region, a node near to the minor sink nodes sends its information directly. While nodes away from the minor sink node, forward the data packets through cooperation with a single relay, by considering only the physical distance and time calculation. This technique reduces the transmission path and minimizes the delay. At round 600, the delay of the CoDBR protocol is much higher and is approximately \(10\times 10^{8}\) s, while the DBR scheme has the delay close to \(6.5\times 10^{8}\) s. At the same round, the CoDMR protocol has the delay lower than the DBR and CoDBR schemes and has \(5\times 10^{8}\) s. Moreover, the DMR scheme produces the lowest latency having the value of \(3.5\times 10^{8}\) s, which minimizes the delay in all rounds of the entire network and ensure fast packets transmission.
# From Nearly Zero Energy to Carbon-Neutral: Case Study of a Hospitality Building
## 5 Results
### Energy Simulation Results
#### 5.1.2 DHW Energy Use  
The electrical energy use related to the production of DHW was calculated starting from the load curve reported in Figure 6. The hourly coefficients were then multiplied by the overall daily water need corresponding to the considered scenario. As far as the nZEB was concerned, a baseline of 33,228 L/day was set, corresponding to a hot water need of 130 L/day for each bed, with a contemporaneity factor of 0.9. This value was also used to calculate the corresponding quantity for the CNB; nevertheless, in order to reduce the water consumption and the corresponding energy use, in the CNB scenario, water mist nozzles were considered as water taps and showers in every room, allowing a reduction in the water requirement. Even though commercial products declare a reduction between 65% and 98%, a more conservative value of 30% was assumed in this work, leading to a daily overall water need of 23,260 L/day for the CNB. Once the DHW daily load profile was obtained for both scenarios, the corresponding annual curve was achieved considering the yearly occupancy profile for the whole building and, finally, the corresponding thermal energy need, with the assumption of a cold-water supply temperature of 10 \({}^{\circ}\)C, and a set-point temperature of 42 \({}^{\circ}\)C. For each scenario, the corresponding generation system was then considered to achieve the overall electrical energy and natural gas use, according to the technology involved. Moreover, the energy consumption related to circulation pumps was considered, with the weighting factor calculated as the ratio between the instantaneous and the daily maximum water need.  
In the nZEB scenario, the electrical energy use related to hot water production comes from the electric heat pump (SCOP 2.8) and the circulation pump to the users (0.9 kW). In the CNB scenario, the base heat source to produce hot water consists of the 9 micro-CHP, with a total nominal thermal output of 8.1 kW, while the rest of the energy is provided by the GWHP (SCOP 4.3). Moreover, the circulation pumps were considered both for the CHPs and the users' sides (0.6 kW and 0.9 kW, respectively). According to these data, the overall electrical energy needed to produce DHW in the nZEB was 196.12 MWh\({}_{\text{EL}}\), whereas in the CNB, there was a combined consumption of electric energy and thermal energy (to run the CHPs), with annual total values of 80.46 MWh\({}_{\text{EL}}\) and 265.77 MWh\({}_{\text{TH}}\) (corresponding to an annual natural gas consumption of 19,710 m\({}^{3}\)).
# Augmented Lagrangian-Based Reinforcement Learning for Network Slicing in IIoT
## 3 Proposed Augmented Lagrangian-Based Reinforcement Learning
### Proposed AL-SAC Algorithm
#### 3.3.2 Q-Value Network \(\mathcal{Q}\)  
To evaluate the reward function, a Q-value network is deployed to calculate the state-action value for each action, i.e, \(Q_{\bar{\psi}}(\boldsymbol{s},\boldsymbol{a})\), where \(\psi\) denote the parameter of the neural network. This network is trained by minimizing the soft Bellman residual  
\[J_{\mathcal{Q}}(\psi)=\mathbb{E}\Bigg{[}\frac{1}{2}\Big{(}Q_{\bar{\psi}}\big{(} \boldsymbol{s}^{(t)},\boldsymbol{a}^{(t)}\big{)}-\Big{(}r^{(t)}+\gamma \mathbb{E}_{\boldsymbol{s}^{(t+1)}}\big{[}V_{\bar{\phi}}(\boldsymbol{s}^{(t+1) })\big{]}\Big{)}\Big{)}^{2}\Bigg{]}, \tag{27}\]  
where \(V_{\bar{\phi}}(\boldsymbol{s}^{(t+1)})\) is target state value mentioned above to enhance the training stability. Then, this neural network is updated by  
\[\
abla_{\psi}J_{\mathcal{Q}}(\psi)=\
abla_{\psi}Q_{\bar{\psi}}\big{(} \boldsymbol{s}^{(t)},\boldsymbol{a}^{(t)}\big{)}\Big{(}Q_{\bar{\psi}}\big{(} \boldsymbol{s}^{(t)},\boldsymbol{a}^{(t)}\big{)}-r^{(t)}-\gamma V_{\bar{\phi} }\big{(}\boldsymbol{s}^{(t+1)}\big{)}\Big{)}. \tag{28}\]  
Figure 3: The architecture of proposed AL-SAC algorithm.
# Improved Short-Term Clock Prediction Method for Real-Time Positioning
## 4 Conclusions  
An improved model for short-term prediction based on the traditional linear model is proposed to overcome the problems of data stream interruption and delay in broadcast in real-time clock application. FFT is used to analyze the residuals of the linear prediction and obtain the periodic signals in them. The cumulative prediction residual is modeled, and the periodic term model is applied to the follow-up forecast to improve the prediction accuracy.  
The experimental results using IGS final products show that the 3-h accuracy is better than 0.85 ns. The improved model can thus replace the IGU-P product in real-time PPP. Besides, due to the higher  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & & \multicolumn{3}{c}{**BJFS**} & \multicolumn{3}{c}{**CEDU**} \\ \cline{2-7}  & & **E (m)** & **N (m)** & **U (m)** & **E (m)** & **N (m)** & **U (m)** \\ \hline With & Std & 0.022 & 0.019 & 0.106 & 0.020 & 0.033 & 0.081 \\ prediction & Max (absolute) & 0.053 & 0.055 & 0.282 & 0.085 & 0.106 & 0.229 \\ \hline Without & Std & 0.009 & 0.006 & 0.010 & 0.005 & 0.010 & 0.017 \\ prediction & Max (absolute) & 0.041 & 0.021 & 0.084 & 0.036 & 0.029 & 0.098 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Statistics of the kinematic PPP during the prediction period (1-h).  
Figure 10: Residuals of kinematic PPP for CEDU.  
Figure 9: Residuals of kinematic PPP for BJFS.  
short-term stability, Block IIF exhibits better prediction accuracy than Block IIR and Block IIR-M, and there is no significant difference between Block IIR and Block IIR-M.  
Compared with the accuracy of the traditional linear model, the accuracy of the static PPP solution using the new model of the 2-h prediction clock in N, E, and U directions is improved by almost 50%. When an interruption occurs in the real-time model, the accuracy of the kinematic PPP solution using 1-h clock prediction product is better than 0.2 m, without significant accuracy degradation.  
The residuals of linear forecast are modeled to improve the accuracy of subsequent forecasts. According to the experimental results of IGS precision and real-time clock products, we believe that the long-term trend, which is less than the sampling frequency in short-term clock forecast, can also be solved by referring to this method. The disadvantage of this method is that the clock characteristics need to be analyzed carefully before prediction. In general, this new method is simple and accurate and can meet the requirement of real-time clock prediction.  
The authors are very grateful to IGS for providing GPS product. This work is supported by the National Nature Science Foundation of China (No. 41574030).  
[PERSON] conceived this method, performed the experiments and wrote the paper. [PERSON] designed the experiments and helped wrote the paper. [PERSON] and [PERSON] analyzed the experimental data. [PERSON] and [PERSON] provided the guiding idea for this study.  
The authors declare no conflict of interest.