esa-sceva/satcom-chunk-collection huggingface dataset:
## Evaluation of the Cultivated Mushroom
###### Abstract  
Fruiting bodies (basidiocarps) of the cultivated mushroom _Pleurotus ostreatus_ (16 strains) were characterized by vibration spectroscopy and chemometrics. According to organic elemental analysis and Megazyme assay, the basidiocarps contained \(\sim\)6.2-17.5% protein and \(\sim\)18.8-58.2% total glucans. The neutral sugar analysis confirmed that glucose predominated in all the samples (\(\sim\)71.3-94.4 mol%). Fourier-transformed (FT) mid- and near-infrared (FT MIR, FT NIR) and FT Raman spectra of the basidiocarps were recorded, and the characteristic bands of proteins, glucans and chitin were assigned. The samples were discriminated based on principal component analysis (PCA) of the spectroscopic data in terms of biopolymeric composition. The partial least squares regression (PLSR) models based on first derivatives of the vibration spectra were obtained for the prediction of the macromolecular components, and the regression coefficients \(R^{2}\) and root mean square errors (RMSE) were calculated for the calibration (cal) of proteins (\(R^{2}_{\rm cal}\) 0.981-0.994, \(\rm RMSE_{\rm cal}\)\(\sim\)0.3-0.5) and total glucans (\(R^{2}_{\rm cal}\) 0.908-0.996, \(\rm RMSE_{\rm cal}\)\(\sim\)0.6-3.0). According to cross-validation (CV) diagnosis, the protein models were more precise and accurate (\(R^{2}_{\rm cv}\) 0.901-0.970, \(\rm RMSE_{\rm cv}\)\(\sim\)0.6-1.1) than the corresponding total glucan models (\(R^{2}_{\rm cv}\) 0.370-0.804, \(\rm RMSE_{\rm cv}\) -4.7-8.5) because of the wide structural diversity of these polysaccharides. Otherwise, the Raman band of phenylalanine ring breathing vibration at 1004 cm\({}^{-1}\) was used for direct quantification of proteins in _P. ostreatus_ basidiocarps (\(R\) -0.953). This study showed that the combination of vibration spectroscopy with chemometrics is a powerful tool for the evaluation of culinary and medicinal mushrooms, and this approach can be proposed as an alternative to common analytical methods.  
o _et al._
## 5 Results and Discussion
### Implications of Standardization  
Based on the insights from developing the prototype, we subsequently discuss advantages and disadvantages of a potential standardization of CLS control boxes that mediate between aEMPs and DERs. In the regulation, the design of a control device acting as middleware to DERs is not addressed. Despite the above discussed need to freely design the application layer protocol to foster innovation, standardization can bring benefits in use cases where a large set of DERs is controlled based on an identical command set. One concrete example for such a use case is the control of DERs that are controllable according to SS 14a of the EnWG like heat pumps, night storage heater, or EV charging stations. Based on the named law, energy consumers pay lower grid fees when they give distribution system operators a certain authority over the control of their DERs. To this day, this is realized by ripple control. Usually, a dedicated ripple control receiver is used for receiving demand response commands which trigger state changes of one or two relais, thereby allowing one to switch between different operational states of the connected DERs. For example, power inverters of (rooftop) photovoltaic power plants can be operated in four states based on two relais: 100%, 60%, 30% or 0% of the maximum possible feed-in power. As in such use cases, an interface to a large number of DERs with almost identical control options is required, the SMGW-based control via a dedicated application protocol can be meaningful. However, in this context, it is worth noting that SS 14a EnWG is expected to be subject to major changes in the near future [14; 42], and that these changes could incentivize the usage of EMSs that control multiple DERs on their own [35].  
The FNN is an industry-representing committee of the German VDE Association for Electrical, Electronic and Information Technologies, which is--among other things--working on standardizing a CLS control box [43], and communication concepts. In this context, the FNN has published two notes, [44; 45], and a concept [46], mainly describing processes and requirements concerning the application layer on top of the transport layer. Special emphasis is put on the coordination between market participants and grid operators ensuring grid stability. In particular, the proposed coordination function in [45] is intended to ensure a regulated use of DERs. It is crucial to understand the significant difference between such a control box as it is proposed by the FNN and an EMS: the control box only forwards and translates control signals (or more advanced information such as price signals), while an EMS implements algorithms for the intelligent control based on the received parameters. In order to allow for the development of advanced energy management solutions that are not in scope of the FNN consortium, such as electricity market based optimization beyond pure time-of-use price signals, or the provisioning of ancillary services, it seems important to allow the development and usage of alternative CLS solutions.
# An Elliptical-Shaped Dual-Band UWB Notch Antenna for Wireless Applications
## 4 Discussion and Outcomes
### UWB Antenna Results
#### 4.1.1 VSwr  
Figure 10 compares simulated and measured VSWR. ROHDE & SCHWARZ ZVL network analyzer measures VSWR. At VSWR < 2, the simulated impedance bandwidth is 2.8-11.2 GHz, and the operational impedance bandwidth is 2.5-11 GHz. Simulations and measurements of VSWR exhibit a minor difference. This is due to the effect of soldering, the failure to account for the SMA connector during simulation, flaws in the dielectric constant of the substrate, and manufacturing tolerances.
## 6 Analysis of Results
### Energy Optimization
#### 6.3.2 Graphical Analysis  
Suppose that \(Nodes_{max}=450\), \(Tr_{min}^{n}=500\), \(Tr_{max}^{n}=900\), \(E_{initial}=50j\), \(E_{idle}=158\) mW, \(Header_{Size}=11\) bits, \(Payload=72\) bits, \(Dist_{S}^{F}=500\) m, \(E_{normalized}=0.7j\), so the below equations are obtained from the above constraints.  
\[7.5\leq Etr^{n}\leq 11.11 \tag{23}\]  
\[1.58\leq Ercv^{n}\leq 3.16 \tag{24}\]  
\[9.08\leq Etr^{n}+Ercv^{n}\leq 14.27 \tag{25}\]  
Equations (23)-(25) are drawn to find the feasible region, as shown in Figure 19. The optimal solution is validated by each vertex of the feasible region. The boundary points of the feasible region recognize the optimal solution. The points are: P1 (7.5, 1.58) = 9.08 J; P2 (7.5, 3.16) = 10.6 J; P3 (11.11, 1.58) = 12.69 J; P4 (11.11, 3.16) = 14.27 J.  
The bounded region in Figure 19 indicates that the formulation is valid and energy usage in that specified region will always result in optimal network lifespan.  
Figure 19: Feasible Region.
## 5 Performance Evaluation
### Comparison of Inference Performance  
In order to achieve the optimally distributed deployment scheme, the partitioning optimization and deployment optimization are considered either separately or simultaneously. The following experiments first compare the inferencing delay under a given partition scheme and then compare both inferencing delay and device average energy cost in considering partitioning and deployment simultaneously.
## 4 Extension to Multiple SWIPT AF Relaying Systems: Performance Analysis
### Approximation SWIPT Relay to AF Relay  
With careful inspection of (34), we gain insight into the SWIPT relaying system such that the asymptotic BER in (34) can also be obtained for a general AF relay system with the SR link average SNR of \(\tilde{\gamma}_{\rm srr}\), and the modified RD link average SNR of \(\tilde{\gamma}_{\rm rdr}^{\prime}\). At the general AF relay system, the \(r\)th indirect link SNR can be approximated as  
\[\gamma_{\rm idr}=\frac{\gamma_{r}\beta_{r}}{\beta_{r}+1}\approx\frac{\gamma_{ \rm srr}\gamma_{\rm rdr}^{\prime}}{\gamma_{\rm srr}+\gamma_{\rm rdr}^{\prime} }\simeq\min\Bigl{\{}\gamma_{\rm srr},\gamma_{\rm rdr}^{\prime}\Bigr{\}}= \gamma_{\rm idr}^{\rm AF} \tag{37}\]where \(\gamma_{\pi_{r}}\) and \(\gamma^{\prime}_{\rm td}\), are the \(r\)th SR and the modified RD link SNRs, respectively [16; 23]. Then, the PDF of \(\gamma^{\rm AF}_{\rm id,r}\) is given by \(f_{\gamma^{\rm AF}_{\rm id,r}}(x)=\frac{1}{\gamma^{\rm min}}\exp\left(-\frac{x }{\gamma^{\rm min}}\right)u(x)\) with \(\left.\tilde{\gamma}^{\rm min}_{r}\right|_{r=1}^{R}=\left(1/\tilde{\gamma}_{ \pi_{r}}+1/\tilde{\gamma}^{\prime}_{\rm rd,r}\right)^{-1}\) and \(\tilde{\gamma}^{\prime}_{\rm rd,r}=\tilde{\gamma}_{\rm rd,r}\Omega_{\rm sr,r}/ \ln\left(4\tilde{\gamma}_{\rm rd,r}\Omega_{\rm sr,r}+1\right)\).
# Output Power Smoothing Control for a Wind Farm Based on the Allocation of Wind Turbines
## 3 Power Smoothing Control Strategy
### Power Allocation of the PWTs, CWTs and the BESS
In Figure 4, \(P_{\textit{power\_max}}\) and \(P_{\textit{control\_max}}\) indicate the maximum output power of the power wind turbines and control wind turbines, respectively. The maximum output power was based on the equation \(P_{w}=\rho\pi R^{2}C_{p}(\lambda,\beta)v^{3}/2\) while \(C_{p}\) is the maximum value (0.48 in the manuscript). In the practical application, the efficiency of the wind turbine \(\eta\) should be considered and then \(P_{\textit{power\_max}}=\eta\rho\pi R^{2}C_{\textit{pmx}}v^{3}_{\textit{power \_max}}/2\), \(P_{\textit{control\_max}}=\eta\rho\pi R^{2}C_{\textit{pmx}}v^{3}_{\textit{control \_min}}/2\). In the simulation, many losses, such as iron loss and mechanical loss, are not considered. Then the efficiency of the wind turbines were relatively high and the efficiency was set as 0.95 in this paper. The minimum output power of CWTs \(P_{\textit{control\_min}}\) can be changed according to the wind speed condition and wind farm or grid demand.
## 3 Experiments and Analysis
### Load Simulation Results and Analysis
\begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**Parameters** & **Elastic Modulus E\({}_{0}\)/Pa** & **Poisson’s Ratio \(\
u\)** & **Cohesion \(c\)/kPa** & **Friction Angle \(\varphi\)/\(\left({}^{\circ}\right)\)** & **Density \(\rho\)/\(\left({}\)kg/m\({}^{3}\right)\)** \\ \hline Pile & \(3.5\times 10^{10}\) & 0.30 & - & - & 2400 \\ Mudstone & \(8\times 10^{8}\) & 0.34 & 100 & 32 & 2080 \\ Sandstone & \(2.1\times 10^{9}\) & 0.29 & 150 & 37 & 2170 \\ Slate & \(1.6\times 10^{10}\) & 0.25 & 200 & 42 & 2300 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Material parameters for each model. Figure 9: Load–displacement curves of the piles. Figure 11 shows the side resistance of different pile sections at relative pile-rock displacements. It is evident from the figure that the curve of the 0\(\sim\)1 m pile section of the pile exhibits an inflection point, indicating that a greater side resistance is exerted at smaller displacements at this location. In contrast, the side resistance in the 4\(\sim\)5 m pile section increases in a continuous manner, and the curve does not show any discernible inflection point. This is attributable to the presence of reaming at this location, which exhibits the most pronounced roughness, resulting in a larger ultimate side resistance. The overall curve of the pile demonstrates increased hardening, especially in the lower half of the pile. Figure 11: Side resistance of different pile sections at relative pile-rock displacements. Figure 10: Unit side resistance with depth for the pile under different loads. Figure 12 shows the variation in the pile stresses under different loads. The figure shows that the pile axial force increases with increasing load, followed by a gradual decline in the depth direction. At each load, the pile shaft resistance and pile base resistance are present and less than the load, indicating that the shaft and base together carry their loads. The pile is an end-bearing friction pile. Moreover, the base carries a minimal percentage of the applied load, with the side resistance bearing the primary load. The degree of inclination of the pile shaft force along the depth direction is indicative of the degree of lateral resistance exerted on the pile. It was observed that as the magnitude of the applied load increased, the effect of side friction resistance also increased. Figure 13 shows the stress distribution of the pile-rock model under a load of 7 MPa. As shown in the figure, the stress distribution exhibits a similar phenomenon, with significantly higher stresses at the upper end of the region near the pile body, where most of the load is distributed. The pile stress decreases with depth, and the applied stress is gradually counteracted by the pile side resistance and transferred to the surrounding rock. Figure 12: Axial force distribution along the depth for the pile.
# Design of Nanosecond Pulse Laser Diode Array Driver Circuit for LiDAR
## 2 Circuit Principle  
The specifications for the drive circuit we designed are as follows: an output pulse width of \(\leq\)5 ns, an output peak power of \(\geq\)75 W, and a high-speed laser scanning frequency of \(\geq\)1 MHz. To meet these specifications, the drive circuit must include at least two major parts: the control circuit and the power circuit. The power circuit is primarily responsible for outputting the input control signal to drive the LD load, while the control part undertakes the function of outputting narrow pulses and performing repetitive refreshing. These two parts of the circuit also need to be powered by different power supply networks. The structure of the circuit we designed is shown in Figure 1. The circuit includes a boost circuit, a linear power supply, a gate driver, a GaN MOSFET switch, a pulse narrowing circuit, and an analog multiplexer. The controller is connected to the pulse narrowing circuit, which is connected to the analog multiplexer, and the analog multiplexer is connected to 8 gate drivers. The electrical pulse provides a square wave excitation signal (for example, a pulse width of 100 ns and a frequency of 1 MHz). After passing through the pulse narrowing circuit, the electrical pulse is output as an electrical pulse with a pulse width less than 5 ns. This pulse is sent to the high-speed analog multiplexer, which outputs pulse signals one by one to drive the gate drivers. The gate drivers output signals to drive the GaN MOSFET switch, thereby controlling the LD to output pulsed lasers. The linear power supply provides power to the pulse narrowing circuit and the gate drivers. The boost circuit boosts the power supply voltage to 15 V-50 V (adjusting the output voltage can control the output power of the pulsed laser) to power the LD.
## 3 Model and Methods
### Speed-Up Routines
#### 3.2.1 Lazy Constraint Attribute
Use of a \"Min-Max\" Problem for the Initial Feasible Solution  
We observed that if the objective function were formulated as the maximum (weighted) delay, the solver then found the optimal solution quicker than the formulation with total (weighted) delay as the objective function. Solvers can accept initial feasible solutions and use these solutions to expedite the solution procedure by providing good upper bounds. In all of these problems, before solving the actual problem with total weighted delay, a problem with the maximum weighted delay objective was solved as coupled with the heuristic problem reduction algorithm mentioned above. The optimal solution of this problem was then fed as the initial feasible solution. At that stage, a new constraint was added to the model that prevents the maximum delay value from being higher than the optimal value of the min-max problem. This procedure proved to be quite effective in reducing running time. It also provided the opportunity to obtain better-quality solutions (from a railway operation perspective, which prefers to distribute the delay among multiple trains over delaying a single train excessively). This is illustrated in Table 6. Among the two alternative solutions given in the table, if the objective is to minimize total delay, then the algorithm would choose Solution 2. However, from a railway operation perspective, Solution 1 is more preferable. Thus, a multiobjective optimization approach was adopted. The final objective is still to minimize total weighted delay, but the maximum weighted delay will be constrained not to be higher than the maximum weighted delay obtained in the initial feasible solution with the maximum weighted delay objective. Before presenting the numerical tests and results, as a summary, the entire algorithm works in the following execution order: gathering all parameter values, running the solution space restriction algorithm for meetings, running the solution space restriction algorithm for followings and overtakes, construction of the model, identification of lazy constraints, solving the model with the maximum weighted delay objective and solving the model with the total weighted delay objective.
# Landslide Prediction with Model Switching +
## 3 Model Switched Landslide Prediction System
### Switch-Based Prediction Model Design
Both of the classes \(T_{class}\) and \(T_{ADASYN}\) are integrated into the training set of the event-class predictor. Second, the event-class predictor is constructed using the BPNN model. Finally, the event-class predictor is used to predict the future class label, \(P_{class}\in\{0,1\}\), for the testing phase, where \(P_{class}=0\) represents that the prediction class label is stable and \(P_{class}=1\) represents that the prediction class label is unstable. The aim of our proposed method is to construct different pattern predictors, as shown in Figure 5. The steps of different pattern predictors are as follows. First, \(T_{class1}\) is used as training data to train a BPNN model. After training, the BPNN model is the stable pattern of \(x_{if,fs}\) (i.e., \(Feed-Forward\ BPNN_{Stable}\)). On the other side, \(T_{class2}\) is applied as training data to train another BPNN model. After training, the BPNN model is the unstable pattern of \(x_{if,fs}\) (i.e., \(Feed-Forward\ BPNN_{Unstable}\)). Thus, two BPNNs are built to deal with different patterns. As shown in Figure 6, this procedure can switch different pattern predictors, according to the predicted class, \(P_{class}\), that is obtained by the event-class predictor. When \(P_{class}=0\), the testing data, \(P_{test}=\{x_{t}\}\), is applied to predict the future FS using \(Feed-Forward\ BPNN_{Stable}\), where \(x_{t}=<x_{t,fs},x_{t,r},x_{t,sm},x_{sloper},y_{t}>\), \(x_{t,fs}\) is the testing data of the FS value, \(x_{t,r}\) is the testing data of the rainfall, \(x_{t,sm}\) is the testing data of the soil moisture, \(x_{t,slope}\) is the testing data of the slope gradient, and \(y_{t}\) is the testing data of the corresponding  
Figure 4: Flow of constructing the event-class predictor. class label. On the other side, when \(P_{class}=1\), \(P_{test}\) is employed to predict the future FS using \(Feed-Forward\ BPNN_{Unstable}\). Finally, the predicted FS, \(P_{output}=\{FS_{predict}\}\), can be obtained by using the proposed Switch-based Prediction Model. The main contribution of this work is that the proposed method can make highly accurate predictions, even in the case of highly imbalanced data. Two techniques were employed, ADASYN and an event-class predictor. Figure 5: Flow of constructing different pattern predictors.
# A Short Review of Through-Silicon via (TSV) Interconnects: Metrology and Analysis
## 4 Outlook
### New Design Tools and Approaches  
As the integration of integrated circuits increases, the use of 3D-ICs has become popular due to its capability of stacking multiple chips together vertically, resulting in a higher transistor density with reduced interconnect power and latency. However, this approach comes with several challenges that must be considered during the design process. The increased interconnect complexity poses a risk to signal integrity, and the noise coupling between TSVs, signal delays caused by thermal-electrical coupling, and the effects of stress on carriers must be taken into account. Furthermore, global routing has become a critical aspect, and the optimization of the cost function with layer assignments based on dynamic programming is essential to achieve less overflow and fast convergence [77; 78].  
The electrical state of TSVs is coupled with multiple physical domains, and highly inhomogeneous power densities and large temperature gradients must be avoided. Additionally, the coefficients of thermal expansion of the TSV material and the substrate material may introduce large mechanical stresses, leading to delay variations in the driver through electrical-mechanical coupling [79; 80]. Such delay variation may introduce bias for delay-sensitive clock tree designs, and, therefore, a robust physical design in 3D ICs with TSVs requires optimization in terms of coupling in the electrical, mechanical, and thermal domains [20; 81; 82].  
The deployment of signal TSVs across the 3D chip may result in uneven temperature differences, leading to significant clock offsets due to the electrical-thermal coupling of the signal TSVs. The stresses induced by the TSVs may also affect the mobility and latency of the driver, further worsening the clock offsets of the electrical-mechanical coupling of the driver across the 3D chip. Hence, conventional design approaches that do not consider temperature and stress gradients may become inaccurate and unreliable. Thus, there is a need for new design tools and methods to handle these issues.  
We have collected some new TSV performance with the new manufacturing process, as shown in Table 3.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **hTSV** & **RTSV** & **Equivalent Circuit** & **Insertion Loss S\({}_{\text{21}}\)** & **Reference** \\ \hline MW-CNT TSV & 100 \(\mu\)m & 10 \(\mu\)m & \(-\)0.1 dB (20 GHz) & [83] \\ Elliptic cylindrical TSV & 60 \(\mu\)m & 2.5 \(\mu\)m & \(-\)0.6 dB (20 GHz) & [84] \\ CMF-TSV & 100 \(\mu\)m & 10 \(\mu\)m & \(-\)16.8 dB (3.5 GHz) & [85] \\ Tapered TSV & 50 \(\mu\)m & 0.17 \(\mu\)m (min) & \(-\)0.18 dB (20 GHz) & [86] \\ PI-TSV & 300 \(\mu\)m & 30 \(\mu\)m & \(-\)2.63 dB (2.4 GHz) & [87] \\ \hline \hline \end{tabular}
\end{table}
Table 3: TSV parameters under different new processes.