esa-sceva/satcom-chunk-collection huggingface dataset:
# A Review of Sharp-Switching Band-Modulation Devices
## 4 Sharp-Switching Performance
The fine-tuning of the ground-plane voltage offers yet another solution for the suppression of the leakage current. However, a too negative V\({}_{\text{BG}}\) counteracts the effort of the front-gate to set a strong hole-injection barrier at the anode, whereas a too positive V\({}_{\text{BG}}\) hampers the creation of an electron barrier at the cathode. Once again, the dual ground-plane variant (Figure 3a) shows improved performance [32]. It is the ground-plane located beneath the gated region that makes the difference. When V\({}_{\text{BG}}\) = 0 the leakage is high, but it decreases by orders of magnitude for V\({}_{\text{BG}}\) = +2 V (Figure 15b). This ground-plane can guarantee a strong barrier at the anode, even though the front-gate voltage is kept small (V\({}_{\text{FG}}\) = 0.5 V), suitable for low-voltage applications. Note that the ground-plane biasing does not impact the I\({}_{\text{ON}}\) current. The I\({}_{\text{ON}}\)/I\({}_{\text{OFF}}\) current ratio can exceed six orders of magnitude. The proper selection of anode, gate, and ground-plane voltages is even more crucial in short-body devices, where the energy barriers are narrower and, thus, more transparent. While I\({}_{\text{ON}}\) current is improved, the I\({}_{\text{OFF}}\) leakage current tends to increase more quickly unless the gate voltages are increased. In summary, the Z\({}^{2}\)-FET stands as a surprisingly solid contender in the arena of sharp-switching devices. Its main asset is its performance at very high frequencies: (i) sharp turn-off enabled by higher energy barriers in the pulsed mode, (ii) easy turn-on thanks to the gate discharge current, and (iii) short I\({}_{\text{ON}}\) state duration, to save energy. A further aspect appealing for low-power applications is the devices capability to switch on and off at gate and drain voltages in the sub-1V range.
## 3 Results and Discussion
### Effect on Oxygen Supply to Soil  
As can be seen in Figure 6, soil moisture content decreased linearly with time. The two roughly parallel fitting lines (dotted straight lines) indicate that it decreased at approximately the same rate for the two water cycles. In water-saturated soil (about 48% moisture content), whether the bottom of the plate was covered by the film or not, soil oxygen concentration decreased rapidly from 20.1% to 0.2-0.5% in about 7 h, and then remained steady. However, when the soil moisture content decreased to about 44% (estimated results based on linear fitting equation) 19 h later, the oxygen concentration in the soil that was in close contact with the film (i.e., oxygen concentration at the film surface), padded on the bottom surface of the plate, increased sharply from 0.5% to 20.1% in less than 1 h, and then remained steady. The same changes in oxygen concentration at the film surface (i.e., rapid decline--steady anoxic state--sharp rise--steady oxygen saturation state) occurred again in the second water cycle. The oxygen concentration in soil that was directly in contact with the plate bottom (i.e., oxygen concentration at the plate bottom surface) that was not covered with the film, however, always remained at 0.2% for both water cycles, regardless of the decline in soil moisture content.  
Figure 5: Pore size distribution (**a**) and permeability (**b**) of the ramie fiber/starch nonwoven film.  
Figure 6: Changes in oxygen concentration and soil moisture content with time.  
The rapid decline in oxygen concentration in water-saturated soil in this study fully accorded with the reported fact that water-saturated soil becomes anoxic as soon as there is persistent depletion of oxygen by soil aerobic microorganisms. This is because the diffusion of oxygen into water-saturated soil is extremely slow (the effective diffusion coefficient is on the order of \(10^{-9}\) to \(10^{-8}\) m\({}^{2}\)/s), meaning that an adequate compensation of oxygen supply cannot be obtained [33]. Considering that the oxygen depletion by soil aerobic microorganisms continued when the soil water content reduced, the persistent high oxygen concentration at the film surface indicates that there was a continuous adequate oxygen supply. When considering the contemporaneous constant anoxia at the bottom surface of the plate, it can be safely concluded that it was the film that increased the supply of oxygen to the bottom soil.
## 2 Materials and Methods
### Measurement of PAHs  
Based on the chemical characteristics of the binding of PAHs to PM among 16 PAHs classified as priority pollutants [4], Fluoranthene (Flt), pyrene (Pyr), benz[\(a\)]anthracene (BaA), chrysene (Chr), benzo[\(b\)]fluoranthene (Bbf), benzo[\(k\)]fluoranthene (BkF), benzo[\(a\)]pyrene (BaP), benzo[\(gh\)]perylene (BghiP) and indeno[1,2,3-\(cd\)]pyrene (IcdP) were included in the nine individual PAH compounds measured using high-performance liquid chromatography (HPLC) equipped with fluorescence detection, as described previously [16]. In brief, each filter was cut into approximately 1 cm\({}^{2}\) in a glass flask, and an aliquot of the PAH internal standards (Pyr-\(d_{10}\) and BaP-\(d_{12}\)) was added. A total of 80 mL ethanol:benzene (purity99.5%; FUJIFILM Wako Pure Chemical Industries Ltd., Osaka, Japan) (_v/v_ 1:4) was used to extract PAHs by sonication, and the extract was cleaned with a buffer with 5% NaOH and 20% H\({}_{2}\)SO\({}_{4}\). The organic phase was evaporated under nitrogen gas until 100 uL before reconstituting with 900 uL acetonitrile and filtering into a vial. During the HPLC procedures, SS EPA 610 PAH Mix (Supelco, Bellefonte, PA, USA) was used as a standard of PAHs. To check the quality assurance/control of the data, the recovery test was performed by spiking the standard internal mixture with known concentrations on blank and sample filters. As a result, the recovery was 110-120%. Also, the reproductivity test was conducted by analyzing the same samples using the same methods at two facilities at Kanazawa University. The reproductivity range was 90-115%. Furthermore, at each sequence of analysis, a standard mixture was run and compared with the previous analysis. The relative standard deviation was within 15%. Total PAH concentrations were defined as the sum of these 9 PAHs.
## 1 Introduction  
A Wireless Sensor Network (WSN) is a type of wireless _ad hoc_ network that deploys a large number of low-cost sensor devices distributed over an area of interest. Collaboratively, they report sensor readings to a data collection sink or Base Station (BS), regularly or based on demand. The potential uses of this network range from military to civil applications. Their data can be as simple as measurements of physical parameters, such as temperature, pressure, relative humidity, _etc_, to as complex as multimedia content, as in recent years we have seen the exploration of wireless video/visual sensor networks for a wide range of applications [1, 2].  
Beside their memory capacity limitations, these low-cost sensors are limited in computation, communication capability and are generally battery-powered devices. Therefore, such resource-constrained devices require energy-aware protocols. Furthermore, WSNs deployments are commonly characterized by hostile and unattended environments, and since low-cost sensors are not tamper-resistant devices, consequently this type of network is vulnerable to a variety of attacks. Therefore, WSNs not only need energy-aware protocols, but also the secure ones [3].  
As WSNs are deployed to sense and collect information from certain areas of interest for particular applications, secure routing protocols are of important parts since they help govern how those sensor readings can be securely delivered to the BS. A number of secure routing protocols have been proposed [4].  
Data communication in WSNs constitutes a great share of the network energy consumption. A Sensor Network Encryption Protocol (SNEP) simulation shows that data communication consumed 71% of the energy cost of computation and communication. SNEP is one of the base components of a set of Security Protocols for Sensor Networks (SPINS) [5], one of the secure routing protocols for WSNs. Therefore in-network process/data aggregation really relieves network burden since it reduces the amount of data that travels to the BS and enforces short range communication for a large amount of sensor nodes, thus conserving network energy. Many secure data aggregation protocols have been proposed as well [6, 7].  
In this field, our interest is in the combinations of secure data aggregation protocols and secure routing protocols. It is very important to provide a secure routing protocol in order to optimize the security of any data aggregation protocol. The remainder of this article is organized as follows: Section 2 presents the related works. A literature review is described in Section 3. Section 4 includes our security analysis on the reviewed protocols. Our secure routing protocol that considers secure data aggregation is described in Section 5, followed by evaluation of the proposed protocol in Section 6 and finally the conclusions in Section 7.
## 4 Simulation Results
### Transmission Error  
We simulated transmission error by KISSsoft software, which is widely used and reliable in the gear field. A precise spiral bevel gear transmission system model was established before simulation calculation, and KISSsoft can calculate the difference value between theoretical and actual rotation angle during the meshing process, which considers the influence of bearings and the deformation of shafts and gears. According to the actual load condition, different moments (500 Nm, 1500 Nm and 4500 Nm) are set to simulate light, medium, and heavy loads, respectively.  
The transmission error curves of this gear drives under different moment are shown in Figure 14. In addition, all transmission error curves are translated to zero position for better comparison. We can know that the transmission error change period is synchronized with the pitch angle of the pinion. Due to the increasing elastic deformation of the gear and the rotating shaft with the load T increases, the absolute value and peak to peak of the transmission error curves become larger. The transmission  
Figure 13: Comparison of the maximum value of bending stresses on the tooth surface boundary: (**a**) maximum bending stresses value of gear, and (**b**) maximum bending stresses value of pinion.  
error curve under the heavy load (4500 Nm) fluctuates much more severely because of the increase of gear deformation and meshing impact.
## 2 Materials and Methods
### Hybrid Control System Architecture
#### 2.1.4 Smith Predictor with Enhanced Features  
The enhanced Smith predictor structure compensates for time delays while incorporating real-time model updates from the LSTM identifier. The closed-loop transfer function of the system is given by the following equation:  
\[\mathrm{Y(s)}/Y_{s}(\mathrm{s})[\mathrm{K(s)G(s)}]/\left[1+\mathrm{K(s)Gm(s)} \right]\left[\mathrm{G(s)-Gm(s)}e^{-sL}\right.\right] \tag{8}\]  
where G(s) is the actual process and Gm(s)\(e^{-sL}\) represents the reduced model with delay.
# Research on the Dynamics of the Space Tubular Expandable Structure Driving Deployment Unit
## 2 Methods  
### Motion Analysis of the Driving Deployment Unit  
The inner-drive unit within the deployable cylindrical structure of a space telescope plays a critical role in achieving both axial deployment and radial pre-tensioning capabilities for motion deployment and stowing functions. This deployable cylindrical structure is an essential part of a space telescope, enabling its optical components to be precisely positioned at designated locations. Figure 3 illustrates the driving deployment unit, which is distributed within the inner-drive unit. This unit is responsible for driving the axial deployment of the inner-drive unit inside the cylindrical structure, sequentially expanding each level of the cylindrical tube to facilitate the positioning of optical components at the end of the third-level cylindrical tube.  
In Figure 4, we present a simplified model of the driving deployment unit in the XOY coordinate system. Point A denotes the rotation point, while Point B serves as the pre-tensioning point providing radial pre-tensioning load. Point C represents the connection point between the driving arm component and the pre-tensioning component, and finally, Point D is the contact point between the driving deployment unit and the inner wall of the cylindrical structure.  
Figure 4: The triangular configuration of the unit.  
Figure 3: Motion states within the cylindrical structure.  
When the driving and retracting unit is working inside the sleeve, the driving force provides traction \(F_{q}\); the pre tightening component provides the radial pre tightening force \(F_{y}\); the reaction force \(F_{t}\) is formed on the inner wall of the sleeve; a reverse friction force \(F_{j}\) is formed between the traction force \(F_{q}\) and the inner wall of the sleeve; when working, it is subject to the gravity of the earth G. According to the force analysis when driving the retracting unit, with the decrease of sleeve diameter, point A rotates clockwise, and the included angle \(\beta\) reduce. If the length of \(L_{1}\) between two points AB is unchanged, the included angle \(\alpha\) will also decrease. If the included angle is maintained \(\alpha\), the length of \(L_{2}\) needs to be adjusted. In order to maintain a stable triangular configuration ABC, \(L_{1}\), \(L_{2}\), and \(L_{3}\) are set as variable beam elements to cope with the force changes caused by the change of sleeve diameter and maintain the stability of the driving retraction process.
# Textile Flexible Job-Shop Scheduling Based on a Modified Ant Colony Optimization Algorithm
## 1 Introduction
[PERSON] et al. [23] proposed a novel bilevel ACO algorithm to deal with the vehicle routing problem. In traditional textile workshops, during a certain period, machines in a particular operation only produce one type of textile, resulting in some machines in other operations being underutilized due to differences in operating time between operations. This leads to overall low production efficiency in the workshop. To improve production efficiency in the textile workshop, adopting the strategy of FJSP is essential. This allows the workshop to alternate between producing different types of textiles during a single period, thereby fully utilizing idle textile machines by selecting the optimal sequence for processing textile products. This paper aims to discuss the textile-flexible job-shop scheduling problem (T-FJSP) by solving the problem of reasonable allocation of machines according to the operating time in textile workshops. In this paper, the traditional single ant colony is improved to multiple ant colonies, and multiple ant colonies search together to obtain the reasonable allocation of textile products on the machine. The local pheromone update rule is different from the independent update rule of the single ant colony, but it is updated in the form of the joint action of all the ant colonies. In order to solve the problem that the original ACO algorithm converges slowly and easily into the local optimum, the pheromone evaporates parameter in the algorithm is improved to make it variable, so as to synthesize the convergence speed and global search ability of the algorithm. The rest of this paper is organized as follows. Section 2 describes the constraint conditions between textile operations, textile machines, and textile products, as well as the establishment of the T-FJSP model. Section 3 elaborates on the product ant colony optimization (PACO) algorithm designed in this paper, detailing the improvements made to this algorithm. Section 4 tests the PACO algorithm through simulation experiments by simulating the operation of textile workshops. Finally, Section 5 concludes the paper and outlines future research plans.
## 5 Field Test
### Analysis of Test Results  
The acoustic holes and the acoustic testing process during the experiment are shown in Figure 17.  
Figure 16: Schematic diagram of acoustic wave detection scheme.  
The pre-blast and post-blast acoustic wave velocities measured in the same acoustic hole, plotted against hole depth on the same graph, were analyzed using Equation (1). The damage depth was determined based on the damage assessment criteria. Figure 18 shows the pre-blast and post-blast acoustic velocity curves for simultaneous initiation pre-splitting blasting, while Figure 19 shows the corresponding curves for sequential pre-splitting blasting.  
Within the industry, a 10% reduction in acoustic velocity is widely used as the benchmark for assessing the damage caused to the rock mass. The depth at which a 10% decrease in wave velocity occurs is considered the depth of blasting influence.  
Using the aforementioned method, pre-blast and post-blast acoustic wave velocity comparison curves can be obtained. Since the acoustic holes are not perpendicular to the pre-splitting surface, trigonometric function formulas need to be applied to convert the measurements and obtain the depth of damage caused to the pre-splitting surface. Based  
Figure 17: On-site acoustic hole and acoustic detection.  
Figure 18: Acoustic velocity curve before and after pre-splitting blasting with simultaneous initiation.  
on the test results, the average values were taken, and the damage results are presented in Table 13.  
Compared to simultaneous initiation pre-splitting blasting, sequential pre-splitting blasting results in a damage depth that is smaller in the retained rock mass after blasting, with a reduction of approximately 44% in the damage depth; this is in comparison to the pre-splitting surface.  
Figure 20 shows the condition of the pre-splitting surface after sequential pre-splitting blasting. It can be seen that the entire slope wall is relatively intact and well formed. No loose rocks or debris are present. Sequential pre-splitting blasting not only reduces the damage caused to the retained rock mass, but also achieves better pre-splitting blasting results.
## 3 Results and Discussion  
To implement and construct our machine learning models, we used Python 3.9, version 1.1.2 of the Scikit-learn API [72] and version 1.6.2 of the XGBoost (extreme Gradient Boosting) library [62]. Other Python libraries such as NumPy 1.21.0, SciPy 1.7.0, and pandas 1.3.0 were also needed. The experiments were carried out on a Windows 10 64-bit PC with an Intel Core i7-1065G7 CPU at 1.30 GHz and 16 GB of RAM. To evaluate each model using Monte Carlo cross-validation, the sample was split into two datasets: the training set, which was used to estimate the new model, and a test set, which was used to evaluate the predictive ability of the model. The validation process was repeated 10 times, by randomly partitioning the sample into 75% for training and 25% for testing. Several experiments were also performed with repeated \(k\)-fold cross-validation, using different values of \(k\) (\(k=4\), \(k=10\)). The \(k\)-fold cross-validation procedure was also repeated 10 times. To enable a comparison and ensure the reproducibility of results, the same 10 seeds were used to perform the partitions in both cross-validation procedures (see also Section 2.1.6).  
At the preprocessing stage, normalisation was applied to scale the dataset. This is often useful to improve the accuracy of most machine learning algorithms, especially  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Measures** & **Estimate** \\ \hline \(R^{2}\) & 0.825 \\ MAE & 2.897 \\ MAPE & 15.85 \\ RMSE & 3.745 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Statistical measures for the best ANN model in [6]. “Adapted with permission from Ref. [6]. Copyright 2018, Elsevier”.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Technique** & **Model** & **Measures** & **Estimate** \\ \hline \multirow{8}{*}{MLR} & Without & \(R^{2}\) & 0.767 \\  & validation & MAE & 3.394 \\  & & MAPE & 19.04 \\  & & RMSE & 4.327 \\ \cline{2-4}  & Best model & \(R^{2}\) & 0.766 \\  & with validation & MAE & 3.396 \\  & & MAPE & 18.86 \\  & & RMSE & 4.332 \\ \hline \multirow{8}{*}{CHAID} & Without & \(R^{2}\) & 0.829 \\  & validation & MAE & 2.829 \\ \cline{1-1}  & & MAPE & 15.49 \\ \cline{1-1}  & & RMSE & 3.705 \\ \cline{1-1} \cline{2-4}  & Best model & \(R^{2}\) & 0.820 \\ \cline{1-1}  & with validation & MAE & 2.928 \\ \cline{1-1}  & & MAPE & 16.22 \\ \cline{1-1}  & & RMSE & 3.808 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Statistical measures for the best MLR and RT models in [19]. “Adapted with permission from Ref. [19]. Copyright 2019, Elsevier”.  
when there are variables with different units. In the same way as in [6], we used min-max normalisation in this work to scale the data to the range \([0,1]\).  
For each machine learning technique, an exhaustive analysis of its behaviour is presented below, including the values chosen for the parameters and hyperparameters involved in each method. Based on these analyses, the best models are selected in an attempt to avoid both overfitting and underfitting.
## 4 Parallel Connection of Power Routers  
We verify the circuit switching operation in the parallel connection of the power routers. Figure 6 shows a schematic diagram of this experimental configuration. The controller for the power router is located upstream. We have explained that the developed information terminal controls each power router directly. However, a personal computer with a PLC modem may apply the same for remote exchanging of connections. It is feasible to establish a remote controlled routing network using the same control protocol. The simulated power is operated at 50 Hz/101 \(\mathrm{V_{rms}}\). This implies that the system is under the out of phase synchronization scenario. A PLC modem is connected to the commercial PL. The communication between the power routers and the PC is established by the commercial PL by the PLC. Loads are connected to two output ports of the power router, A and B. The other output ports, _i.e._, C and D, are open circuits. We verify circuit switching by the power router in two cases that are different based on the connected load, _i.e._, incandescent bulbs or light-emitting diode (LED) bulbs. The incandescent bulb is considered to be an electric heating load. Meanwhile, the LED bulb is a constant current load that includes a regulator. For the setup, we measured the output voltages and currents of ports A and B to which the loads are connected.  
Figures 7 and 8 show the measured waveforms in the systems with incandescent bulbs or LED bulbs. From these figures, it is confirmed that circuit switching is successfully performed in both cases without switching failure. The LED bulbs generate the non-sinusoidal and intermittent current waveform because of the built-in regulator. From these results, it is noted that the circuit exchange can be performed after detecting the voltage cross point that is independent of the current waveform. However, note that the switching surge voltage would occur if there exists a difference between the input currents.  
Figure 6: Schematic diagram of a system in parallel connection.