esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Patterning towards Devices: Conventional and Unconventional Methods
### Lithographic techniques  
The term lithography indicates a technique used to transfer a pattern onto a planar substrate by means of an etching process. Such technique allows for achieving highly cross-linked organic patterns by performing a masked step under a radiation source. The process typically consists of the following steps: (i) coating a planar substrate (most commonly silicon wafer) of a film of liquid resist (e.g., spin coating) (ii) soft baking of the wafer in order to remove the excess of solvent from the resist film and improve its adhesion to the substrate; (iii) exposition of the organic film to the radiation source through a mask; (iv) development of the pattern. The radiation promotes a cascade of chemical processes in the exposed areas of the polymers, thus inducing, in these regions, modification of the physical and chemical properties of the organic matrix. The property that most commonly changes is the solubility of the exposed regions in a solvent developer. It is thus possible to wash away the exposed areas, in the case of a positive resist, and the unexposed regions, in the case of a negative resist, resulting in the development of positive and negative images, respectively.  
Typical resist formulations possess a viscosity suited for the deposition of spin-coated uniform, defect- and stress-free films, for developing the resist and for reducing lateral flow in fabricated features. On the other hand, a high Young's modulus of the resist guarantees the mechanical stability of the achieved structures and a high sensitivity, as well as a good conversion efficiency under irradiation is necessary to achieve both high contrast and high resolution structures. In this perspective, chemically amplified resist (_i.e.,_ SU8, PMMA, ORMOCERs) are now the most suited candidates for manufacturing structurable polymer based nanocomposites due the their interesting mechanical and rheological properties, high sensitivity, contrast and resolution [136].  
The lithographic techniques typically applied to polymer nanocomposites and here reported are photo-lithography (comprising UV-lithography (UVL) and X-ray-lithography (XRL)) and electron beam lithography (EBL).
# Distributed Reliable and Efficient Transmission Task Assignment for WSNs
## 3 Description of Reliable and Efficient Task Allocation in WSNs  
### Energy Consumption Model  
The total energy consumption of a task \(m_{i}\) is the sum of the energy consumed on all nodes that undertook such a task. That is, the energy consumption of a node includes computational and communication energy consumption associated with that task. For calculation purposes, the energy model depicted in [15] is used in this paper, so \(e_{ij}\) represents the energy consumed by the task \(m_{i}\) in node \(N_{j}\). When the node \(N_{j}\) is not selected by the task \(m_{i},e_{ij}=0\). Otherwise, \(e_{ij}\) is composed of the calculated energy consumption and communication energy consumption of node \(N_{j}\). The calculated energy consumption of \(N_{j}\), namely \(e_{ij}^{comp}\), is given by Equation (1):  
\[e_{ij}^{comp}=e_{j}\times t_{ij}^{comp} \tag{1}\]  
where \(e_{j}\) represents the average processing energy consumption of the nodes in the network, and \(t_{ij}^{comp}\) is the calculation time of task \(m_{i}\).  
The communication energy consumption of a node, namely \(e_{ij}^{comm}\), includes the energy consumption for transmitting and receiving data packets. According to the commonly used communication energy model [6], the energy consumption of transmitting and receiving data of length \(l_{ij}\) bit at the distance \(d\) is calculated by Equations (2) and (3):  
\[e_{ij}^{t}=\left(e_{elec}+\varepsilon_{amp}\times d^{2}\right)\times l_{ij} \tag{2}\]  
\[e_{ij}^{r}=e_{elec}\times l_{ij} \tag{3}\]where \(e_{dec}\) is the energy consumption of operating the radio model for each bit, and \(\varepsilon_{amp}\) is the coefficient of the transmit amplifier. The communication energy consumption of the node is calculated by Equation (4):  
\[e_{ij}^{comm}=e_{ij}^{t}+e_{ij}^{r} \tag{4}\]  
Thus, the energy consumption of task \(m_{i}\) in node \(N_{j}\) is then calculated using Equation (5).  
\[e_{ij}=e_{ij}^{comp}+e_{ij}^{commn} \tag{5}\]
# Resilience in the Context of Cyber Security: A Review of the Fundamental Concepts and Relevance
## 3 Cyber Resilience: Fundamental Concepts
### The Relevance of Cyber Resilience in Various Segments
#### 3.2.5 Industry  
In the industry sector, there are several studies that relate the concern between security and cyber resilience. One approach is cyber security and resilience mechanisms for manufacturing applications using software-defined networks (SDN) [49]. Another approach refers to resilience in the context of the cyber-physical system, based on approaches focused on the application domain and organizational aspects [50]. Industrial Control Systems (ICSs), despite their great capacity for interconnection and adaptability, are considered highly vulnerable, and attention needs to be paid to ICSs that deal with critical infrastructure assets [51]. The study of resilience, especially when related to physical infrastructure, has been extensively re-examined within risk analysis [52]. Industry 4.0 comes with the promise of reconciling performance and agility, but with the expected improvements also come new cyber threats, since there is a greater attack surface and number of access connections [53].
## 4 System Analysis
### Selection of Effective Sink Proxies  
The effective selection of sink proxies is expected to result in both balanced and decreased energy consumption of compared with the conventional S2 retrieval model. Because the use of sink proxies enables maximum data aggregation among various sensor nodes, it alleviates the heavy energy consumption near the sink. Hence, if the use of each selected sink proxy also causes a lower energy cost compared with the conventional S2 information retrieval approach, the proposed approach then achieves both balanced and decreased energy consumption. Therefore, the basic criterion for the selection of a successful sink proxy is that both the following two conditions are satisfied: (a) The energy cost of the S3 query is no larger than that of conventional S2 query; (b) The energy cost of the S3 data collection is not larger than that of the conventional S2 data collection.
# Matching the Ideal Pruning Method with Knowledge Distillation for Optimal Compression
## 5 Discussion
The results provide valuable insights into the effectiveness of combining specific pruning methods with KD for optimal model efficiency. Figure 6 illustrates the outcomes of our comprehensive t-test analysis. The t-test results, a fundamental tool in statistical hypothesis testing, offer valuable insights into the relative performance of these pruning methods. We delve into the implications of the depicted figures and what they reveal about the efficiency comparison. The \(t\)-test results reveal significant differences in efficiency between weight pruning and channel pruning for both the CIFAR-10 and CIFAR-100 datasets. For CIFAR-10, weight pruning outperforms channel pruning with a t-statistic of \(-5.60\) and a \(p\)-value of \(3.3\times 10^{-4}\), signifying high statistical significance. Similarly, for CIFAR-100, weight pruning demonstrates superior efficiency with a t-statistic of \(-7.06\) and an even lower \(p\)-value of \(5.90\times 10^{-5}\), indicating an extremely high level of statistical significance. These results consistently show that weight pruning is a more efficient choice, suggesting that when combined with knowledge distillation, it is likely to produce a model with a better  
Figure 5: Sparsity distribution. Removed parameter in each layer: (**a**) Weight pruning on EfficientNet-b2; (**b**) Weight pruning on EfficientNet-b0; (**c**) Weight pruning on MobileNet; (**d**) Channel pruning on EfficientNet-b2; (**e**) Channel pruning on EfficientNet-b0; (**f**) Channel pruning on MobileNet. balance between accuracy and reduced complexity. These findings provide valuable guidance for researchers seeking to optimize model performance and efficiency in different problem domains.
# Electronic and Transport Properties of Epitaxial Graphene on SiC and 3C-SiC/Si: A Review
## 3 Transport Measurement Methods
### Field-Effect Measurements  
The electric field-effect from an externally applied voltage to a gate enables the study of the extraordinary electrical properties of graphene [29; 58]. The first observation of the electric field-effect in graphene (using mechanically exfoliated graphene onto SiO\({}_{2}\)/Si) was reported by [PERSON] and [PERSON] [29], where the charge transport was switched between electron and hole gases via the gate voltage. [PERSON] et al. performed the first transport studies of EG on SiC(0001) and modulated the electron density via electrostatic gating, which demonstrated the 2D electron gas properties [4]. The deleterious anisotropy effects of step bunching on SiC(0001) on gated devices has been discussed by [PERSON] et al. [59]. In the case of graphene on SiC(0001), the non-uniform thickness limits gate device applications [60].  
Figure 5: Scanning electron microscopy images of Hall bars fabricated on MLG (**a**) on a flat terrace and (**b**) perpendicular to the step edges on 6H-SiC. Reprinted with permission from Reference [55]. Copyright (2011) Elsevier Ltd.  
Figure 6: Resistivity, \(\rho\), and mobility, \(\mu\), as a function of temperature, T for MLG on SiC, indicating that the measured properties are almost similar for vdP (squares) and Hall bar (circles) structures. Reprinted with permission from Reference [55]. Copyright (2011) Elsevier Ltd.  
In addition, the transport properties from field-effect measurement are dependent on geometry and electrostatics and are affected by the substrate [61]. This transport measurement method does not permit a systematic analysis of charge scattering within the system [21].
## 4 Specific Examples and Applications of Wearable Antenna Designs
In addition this antenna is light weight, flexible and low cost, thus making this antenna well suited for on-body use. Additionally a new wearable textile antenna based on a half-mode substrate integrated cavity was proposed in [86]. Various embroidered textile antennas have been proposed for different applications. A low frequency (COSPAS/SARSAT satellite beacon--406 MHz) embroidered spiral antenna is proposed in [87]. An embroidered wearable multi-resonant folded dipole antenna for FM reception was fabricated in [88]. Also a UWB embroidered antenna design where stainless steel thread was used for the conductive thread is presented in [89]. GSM and Wi-Fi embroidered textile antennas were fabricated onto regular fabrics using an automated embroidery procedure with high density stitching [90]. The proposed antennas offer user-comfort and flexibility. A distributed body-worn transceiver system for mobile communications with the use of electro-textile antennas was proposed in [91]. Two major features causing the popularity increase for the distributed body-worn system are: (1) the high efficiency of the electro-textile antennas that can be seamlessly embedded into human clothing and (2) significant system diversity gain due to spacing among antennas distributed on the body.
## 3 Simulation Model
### Comparison of System Operating Characteristics and Necessary Components by Case Types
#### 3.2.1 Case-1: Fault Management in an HB-MMC DC System Through DCCB  
For HB-MMC systems, DCCBs are a necessary requirement for effectively clearing the DC faults, as shown in Figure 4a.  
Figure 3: Comparison of overall operating characteristics and the need for a current-limiting reactor.  
Unlike AC circuit breakers (ACCBs), HVDC circuit breakers (CBs) must create a current zero and dissipate the energy stored in the DC network. Recently, industry and academia have extensively researched DCCBs to overcome various well-known limitations. Among the developed prototypes, HCBs, which can block a 9-kA fault current within 5 ms, are attracting attention as the most suitable circuit breakers for application in HVDC systems [22; 23].  
In this paper, we proposed an HCB due to its excellent interruption capability (large di/dt with large dv/dt) and small interruption times (<5 ms). In addition, a large current-limiting reactor was installed at the DC terminal of the MMC to assist the operation of the HCB.  
Figure 3(b) presents the structure of the HCB mentioned above. The HCB contains a load commutation switch (LCS), an ultrafast mechanical disconnector (UFMD), and a main breaker (MB). As shown in the operation characteristics of Figure 3(c), the detection time of the fault current is assumed to be 6 \(\upmu\)s. The converter is blocked within 0.5 ms after the detection of a fault. During the normal operation of the HCB, the load current flows through a nominal path that comprises of LCS and UFMD. When a DC fault is detected, the LCS opens immediately and the current is commutated to the MB. Then the UFMD opens within 2 ms and isolates the LCS from the faulted line. With the UFMD in an open position, the commutation path interrupts the fault current, and energy of the fault is absorbed by the arrestor bank. In this process, the DC current decreases quickly and can be cleared within a few milliseconds.
## 2 Methods
### Stacked Autoencoder  
#### 2.4.1 Autoencoder  
As shown in Figure 2, to build an autoencoder, three layers, namely, an input layer, a hidden layer and an output layer have to be established. The explanations of symbols used in Figure 2 are listed below:  
\(n\): the size of the input and output layer.  
\(m\): the size of the hidden layer.  
\(x\in\mathbb{R}^{n},\ h\in\mathbb{R}^{m},\ y\in\mathbb{R}^{n}\) stand for the data vector of the input, hidden and output layers, respectively.  
\(b\in\mathbb{R}^{m},\ c\in\mathbb{R}^{n}\) stand for the bias vector of the hidden and output layers, respectively.  
\(\mathbf{W}\in\mathbb{R}^{m\times n}\) stands for weights matrix between the input and hidden layer.  
\(\widetilde{\mathbf{W}}\in\mathbb{R}^{n\times m}\) stands for weights matrix between the hidden and input layer.  
From the input layer to the output of hidden layer, the input signal is encoded. And from hidden layer to the output, the output of hidden layer is decoded by:  
\[h=f(x)=s_{f}(\mathbf{W}x+b) \tag{10}\]  
\[y=g(x)=s_{g}(\widetilde{\mathbf{W}}x+c) \tag{11}\]  
In Equations (10) and (11),\(f()\) and \(g()\) stand for the encoding and decoding functions, respectively. \(S_{f}\) and \(S_{g}\) are the corresponding activation functions of the encoder and decoder. sigmoid function can be chosen as the activation function and \(\mathbf{W}^{T}\) can be taken as the weights \(\widehat{\mathbf{W}}\) of the decoder.  
Given input vectors, the autoencoder aims to minimize the difference between an input \(x\) and the output \(y\). The reconstruction error can be described by the cross-entropy function:  
\[L(x,y)=-\sum_{i=1}^{n}[x_{i}\log(y_{i})+(1-x_{i})\log(1-y_{i})] \tag{12}\]  
Figure 2: The structure of an autoencoder.  
For the training set, \(S\); the average reconstruction error can hence be established as:  
\[\mathcal{L}(\theta)=\sum_{x\;\in\;S}L(x,g(f(x))) \tag{13}\]  
By minimizing \(\mathcal{L}(\theta)\), the parameter \(\theta=\{\mathbf{W},b,c\}\) of the autoencoder can be fitted. The learning of an autoencoder does not need the label information, so it is an unsupervised procedure. The output of the hidden layer \(h\) can be seen as a representation of input \(x\).
# Multigap Resistive Plate Chambers for Time of Flight Applications
## 3 Applications in Large Experiments
### A Brief Summary
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Electronics** & **Time jitter (ps)** & **Time Reconstruction Algorithm** \\ \hline \multirow{2}{*}{**ALICE/STAR**} & NINO amplifier & \(\sim\)20 & \multirow{2}{*}{Time over threshold (ToT)} \\  & HPTDC & \(\sim\)25 & \\ \hline \multirow{2}{*}{**CBM**} & PADI & \(\sim\)10 & \multirow{2}{*}{Time over threshold (ToT)} \\  & GET4 & \(\sim\)25 & \\ \hline \multirow{2}{*}{**Future (SoLID)**} & Fast AFE & \(<\)5 & \multirow{2}{*}{Neural networks} \\  & waveform digitizer (SCA) & \(<\)5 & \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Summary of the electronics and timing methods for MRPC detectors. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **ALICE** & **STAR** & **HADES** & **CBM** & **Future (SoLID/EIC)** \\ \hline \multirow{2}{*}{**Gap Thickness (mm)**} & 0.25 & 0.22 & 0.3 & 0.25 & 0.1–0.16 \\ \hline \multirow{2}{*}{**Gas Gaps**} & 2 \(\times\) 5 & 1 \(\times\) 6 & 1 \(\times\) 4 & 2 \(\times\) 4 & 4 \(\times\) 8 \\ \hline \multirow{2}{*}{**Working Gas (C\({}_{2}\)H\({}_{2}\)F\({}_{4}\)/C\({}_{4}\)H\({}_{10}\)/SF\({}_{6}\))**} & 93/0/7 & 95/5/0 & 98.5/1/0.5 & 90/5/5 & 90/5/5 \\ \hline \multirow{2}{*}{**Working Field (kV/cm)**} & \multirow{2}{*}{96} & \multirow{2}{*}{107} & \multirow{2}{*}{107} & \multirow{2}{*}{110} & \(\sim\)150 \\ \hline \multirow{2}{*}{**Glass Type**} & Float & Float & Float & Low res. & Low res. \\ \hline \multirow{2}{*}{**Detection Efficiency**} & 99.9\% & 95\% & 95\% & 95\% & 95\% \\ \hline \multirow{2}{*}{**Time Resolution (ps)**} & 60 & 60 & 70 & 60 & 10–20 \\ \hline \multirow{2}{*}{**Counting Rate (Hz/cm\({}^{2}\))**} & 50 & 10 & 700 & 30 k & \(\sim\)10 k \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Parameters and features of different MRPC detectors used in several large experiments. The counting rate is the highest rate capability of MRPC detectors. performance stable. This is essential in the operation of large experiments. Thus, the idea of \"effective voltage\" \(V_{eff}\) is given to describe the relationship  
\[V_{eff}=V_{app}\times\frac{T}{T_{ref}}\times\frac{P_{ref}}{\text{P}} \tag{10}\]  
where \(V_{app}\) is the applied high voltage, \(T\) and \(P\) are the actual temperature and pressure, \(T_{ref}\) and \(P_{ref}\) are the reference values. The efficiency versus the high voltage at different temperatures [109] is reported in Figure 17b. The working voltage varies at different temperatures. For every 5 \({}^{\circ}\)C increase of temperature, the working points should be decreased by \(\pm\) 200 V. If not, the overvoltage will cause big sparks and streamers. Therefore, one must carefully take the environmental conditions into account when operating the MRPC detectors in large experiments.
# Synthesis of Novel Chalcones as Acetylcholinesteraseerase Inhibitors
###### Abstract  
A new series of benzylaminochalcone derivatives with different substituents on ring B were synthesized and evaluated as inhibitors of acetylcholinesteraseerase. The study is aimed at identification of novel benzylaminochalcones capable of blocking acetylcholinesterase achieving for further development of an approach to Alzheimer's disease treatment. These compounds were produced in moderate to good yields via Claisen-Schmidt condensation and subjected to an in vitro acetylcholinesterasecholinesteraseerase inhibition assay, using [PERSON]'s method. The in silico docking procedure was also employed to identify molecular interactions between the chalcone compounds and the enzyme. Compounds with ring B bearing pyridin-4-yl, 4-nitrophenyl, 4-chlorophenyl and 3,4-dimethoxyyl moieties were discovered to exhibit significant inhibitory activities against acetylcholinesterasecholinesteraseerase, with IC\({}_{50}\) values ranging from 23 to 39 uM. The molecular modeling studies are consistent with the hypothesis that benzylaminochalcones could exert their effects as dual-binding-site acetylcholinesterasecholinesteraseerase inhibitors, which might simultaneously enhance cholinergic neurotransmission and inhibit \(\beta\)-amyloid aggregation through binding to both catalytic and peripheral sites of the enzyme. These derivatives could be further developed to provide novel leads for the discovery of new anti-Alzheimer drugs in the future.  
benzylaminochalcones; Claisen-Schmidt condensation; acetylcholinesterasecholinesteraseerase; Ellman's method; \(\beta\)-amyloid aggregation; docking
# Towards Efficiently Provisioning 5G Core Network Slice Based on Resource and Topology Attributes
## 5 Performance Evaluation
### Evaluation Results and Analysis
The reason is that, as the slice arrives, the physical resources are consumed. The subsequent arriving slices are easy to be rejected, which decreases the provisioning revenue. When the simulation time reaches 10,000 time units, it tends to be stable because the arrival and departure of the slices reach a relatively balanced state. In the final steady state, the long-term average revenue of RT-CSP+ algorithm is 20.01%, 23.86% and 69.88% higher than those of VNE-DCC, NRM-VNE, and CC, respectively. Similar to the slice acceptance ratio and the long-term average revenue, the revenue-to-cost ratio also tends to be stable after 10,000 time units. Therefore, we show the average revenue-to-cost ratio histogram during the steady stage in Figure 3b. The RT-CSP+ and RT-CSP algorithms have better performance than others in terms of this metric. This is consistent with the long-term average revenue performance. Furthermore, since the revenue-to-cost ratio depends on the revenue and cost, the larger revenue-to-cost ratio is not only because our algorithms can achieve higher revenue, but also because it can reduce the provisioning cost. Figure 3: (**a**) Long-term average slice provisioning revenue; and (**b**) slice provisioning revenue-to-cost ratio. Figure 2: Slice acceptance ratio.
# Renewable Energy-Based Energy-Efficient Off-Grid Base Stations for Heterogeneous Network
## 2 System Architecture and Mathematical Modeling
### Reliability Analysis  
Annual capacity storage (\(E_{CS}\)) or 'annual capacity shortage' is a variable used to quantify the system's reliability. It can be calculated as:  
\[E_{CS}=\frac{E_{ED}}{E_{BS}} \tag{3}\]  
\(E_{ED}\) can be expressed as follows and calculated in kWh/yr:  
\[E_{ED}=E_{BS}-E_{gen} \tag{4}\]  
where \(E_{gen}\) is the generated electricity, which can be written for macro and micro BSs:  
\[E_{gen}=E_{PV}+E_{BG} \tag{5}\]  
For pico and femto BSs, the above equation can be modified as follows:  
\[E_{gen}=E_{PV} \tag{6}\]  
Figure 3: Average hourly solar power production over a day for 1 kW PV module size.  
To ensure reliability of the system, the proposed scheme is designed with sufficient backup power reserve over the entire project duration. The excess electricity, generated as backup power, can be estimated by:  
\[E_{excess}=E_{gen}-E_{BS}-C_{loss}-B_{loss} \tag{7}\]  
where \(C_{loss}\) and \(B_{loss}\) represent the losses associated with the converter and battery, respectively.