esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussion
### Discussion  
Presented method proposes the selection of the best assembly sequence based on the estimated assembly time for the selected product. It works on the basis of selected universal criteria for the evaluation of assembly sequences and their impact on the process time. In principle, its correct operation is based on constant production conditions, which is a prerequisite for its proper operation and correctness of the network learning process. Universal criteria for assessing the assembly sequence proposed in this paper can be effectively automatically retrieved from CAD documentation, although this is not the subject of the presented analysis. The obtained test results confirm that it is possible to develop procedures supporting the determination of the assembly sequence of mechanical products. The neural network model effectively predicts the time of the assembly process. Further research should focus on developing a more universal method and increasing the amount of data to enable network learning.  
The effectiveness of the method depends mainly on the number of cases teaching the neural network that are able to generalize the knowledge and the neural network to different products to be assembled. At the moment, the effectiveness of the network in the data verification group is 99%. Entering new data into the network will improve the efficiency of the time sensitive tasks and universally the possibility of applying the procedure to new, not considered cases.  
\begin{table}
\begin{tabular}{c c c} \hline
**Case No.** & **Expected Network Value** & **Network Output Value** \\ \hline
1 & 0.645 & 0.628 \\ \hline
2 & 0.635 & 0.598 \\ \hline
3 & 0.573 & 0.531 \\ \hline
4 & 0.595 & 0.610 \\ \hline
5 & 0.525 & 0.508 \\ \hline
6 & 0.656 & 0.689 \\ \hline
7 & 0.629 & 0.661 \\ \hline
8 & 0.595 & 0.619 \\ \hline
9 & 0.620 & 0.597 \\ \hline
10 & 0.532 & 0.559 \\ \hline \end{tabular}
\end{table}
Table 4: Assembly time values expected and obtained at the network output.  
Figure 7: Comparison of the expected and obtained assembly time values at the network output.  
Thus, a network constraint may be a greater number of errors when predicting assembly time for other products. The aim of the authors is to develop the conducted research and verify the operation of the network on a wide range of products. A neural network model was developed to meet the requirements of all mechanical parts. The goal was to develop an overall model. Then, its effectiveness was verified on the basis of one selected product--the door of a forklift truck.
## 2 System Model and Problem Analysis
### RF Front-End of a Sensor Node in CR-WSN  
A broadband reconfigurable direct RF front-end architecture is shown in Figure 1. The input signals from the antenna are amplified by the LNA. After that, a tunable anti-aliasing filter configures the center frequency to determine the frequency location to be sampled. Then, by a series of pulses, the pulse sampler down-converts the signals to a certain intermediate frequency (IF). The second band-pass filter removes the components outside the IF bandwidth so that signals can be sampled and converted to digital signals by ADC. Finally, baseband signals are available through DDC.  
It is worth noting that, except for the heterodyne receiver shown in Figure 1, zero-IF and low-IF receivers are also in general use in sensors. However, no matter which kind the receiver belongs to, the joint effect of the non-constant group delay distortion and nonlinear distortion could occur in the receiver. Strictly speaking, an ideal linearity condition is hardly met in a real RF front-end. Almost every part in a receiver, such as the LNA, mixer, BPF, and ADC, can generate nonlinear components. Moreover, as long as the RF front-end has a broad passband, group delay is supposed to vary in a large range that cannot be ignored and, especially, the BPF's nonlinear phase-frequency response and the nonlinear device's memory effect will both make it happen. Thus, in the actual receiver system, the joint effect certainly exists and becomes more complicated.  
Besides these two kinds of distortions, in zero-IF or low-IF receivers, another significant effect resulting in decreased SFDR is I/Q imbalance, which is due to an imprecise 90-degree phase shift in the I/Q mixer. The main objective of this paper is to analyze the joint effect of non-constant group delay distortion and nonlinear distortion of a broadband RF front-end and provide its corresponding solution. In order to highlight the key point and make the result of the proposed method simple and explicit, a heterodyne receiver is taken as the object of study.
In this study, an integrated remote real-time SHM system, including a remote real-time monitoring system and a developed data processing model, was developed to conduct condition assessment of composite road box-type structures in reclamation areas using fiber Bragg grating sensing monitoring technology by encapsulating the FBG sensors with stainless steel and adhesives. Taking a composite road box-type structure project in a reclamation area in Southern China as a case study, the developed system was implemented to investigate the effects of the change in tides, rainfalls, temperature and vehicle induced vibrations on the degradation of the structure simultaneously.
## 3 Mathematical Formulation/Performance Metrics
### Energy Model  
The energy model estimates the network router energy consumption, represented as follows:  
\[E_{B}=E_{s}B+E_{L}B \tag{6}\]  
where \(E_{B}\) consists of the switch energy \(E_{s}B\) and link energy \(E_{L}B\) of the NoC. It is this energy that is consumed to transfer one bit data between the source and the destination. The following equation computes the average network energy consumption, denoted as \(EB_{(p_{i},p_{j})}\), used for the transfer of one bit of data between source \((p_{i})\) and destination \((p_{j})\):  
\[E_{B(p_{i},p_{j})}=H_{count}E_{SB}+(H_{count}-1)E_{LB} \tag{7}\]  
where \(H_{count}\) represents the Manhattan distance between the source nodes \((a_{i},a_{j})\) and destination nodes \((b_{i},b_{j})\).  
\[H_{count}=|a_{i}-b_{i}|+|a_{j}-b_{j}| \tag{8}\]Consequently, the network's total energy consumption (\(ET\)) is determined by the average network energy and the link bandwidth \(BW_{(p_{i},p_{j})}\) from \(p_{i}\) to \(p_{j}\).  
\[E_{T}=\sum_{i,j}^{H_{count}}(E_{B(p_{i},p_{j})}\times BW_{(p_{i},p_{j})}) \tag{9}\]  
Hence, the final equation takes the form  
\[E_{T}=\sum_{i,j}\biggl{[}(H_{count}\times E_{SB}+(H_{count}-1)\times E_{LB}) \times BW_{(p_{i},p_{j})}\biggr{]}. \tag{10}\]  
For cost computation (\(CC\)), the following equation can be used:  
\[CC=\sum_{i,j}(H_{count}\times BW_{(p_{i},p_{j})}). \tag{11}\]  
Various mappings lead to distinct energy and communication cost values. The primary aim is to derive a mapping function for the NoC with minimal cost. In this research, we use communication cost as the main performance metric for distinct applications.
# P System with Fractional Reduction
## 2 Fundamentals of Membrane Computing
### Definition of the Cell-Like P System  
The cell-like P System is one of the most fundamental and earliest proposed models for membrane computation [19]. A cell-like P System \(\Pi\) of degree m (\(m\geq 1\)) is defined as:  
\[\Pi=(V,O,H,\mu,\omega_{1},...,\omega_{m},R_{1},...,R_{m},i_{o}) \tag{1}\]  
Among them,  
(1) \(V\) is a finite, non-empty alphabet whose elements are objects;  
(2) \(O\subseteq V\) is the set of output objects;  
(3) \(H\) is the set of membrane markers, \(H=\{1,2,\ldots,m\}\);  
(4) \(\mu\) is the membrane structure containing \(m\) membranes, where \(m\) is called the degree of \(\Pi\);  
(5) \(\omega_{i}\in V^{*}(1\leq i\leq m)\), denotes the multiset of objects contained inside membrane \(i\). For example, if membrane \(i\) contains 5 \(a\)-objects and 3 \(b\)-objects, then we have \(\omega_{i}=a^{5}b^{3}\). \(V^{*}\) is the set of arbitrary strings consisting of the characters in \(V\). If \(\omega_{i}=\lambda\), then it means that no objects exist inside membrane \(i\).  
(6) \(R_{i}(1\leq i\leq m)\) is a finite set of evolutionary rules inside region \(i\) in membrane structure \(\mu\). The evolutionary rules are a binary set \((u,v)\), usually written \(u\rightarrow\upsilon\), where u is a string in \(V^{*}\), \(\upsilon=v^{\prime}\) or \(v=v^{\prime}\delta\), where \(v^{\prime}\) is a string on the set \(\left\{a_{there},a_{out},a_{in_{j}}\ \middle|\ a\in V,1\leq i\leq m\right\}\), and here indicates that object \(v^{\prime}\) is still in the the region where the rule is used; out indicates that object \(v^{\prime}\) is moved out of the current region into the outer membrane containing the current membrane; and in\({}_{j}\) indicates that object \(v^{\prime}\) will be moved into membrane \(j\), which is directly contained by membrane \(i\). \(\delta\) is a special character not belonging to \(V\). When a rule contains \(\delta\), the membrane is dissolved after execution of the rule.  
(7) \(i_{o}\) is the output region of the membrane structure that is used to hold the results of the computation, \(i\in H\).  
In addition to the rules introduced above, it is necessary to consider rules for creating membranes of the form: \(e\rightarrow\left[\ W\right]_{i}\), where \(e\in V,W\in V^{+}\), \(i\) is a number on a given list of  
Figure 1: Structure of the cell-like P System. (**a**) Cell membrane; (**b**) Abstraction of membranes.  
ordinal numbers. This rule indicates that the object \(e\) creates a membrane labelled \(i\) and the objects inside the membrane are denoted by \(W\)[20].  
In every membrane structure, the implementation of evolutionary rules will be guided by two principles:  
1. Non-determinism  
The P System will follow the non-determinism principle when executing evolutionary rules, which means that when multiple rules can be satisfied simultaneously, the rules chosen by the P System to execute are non-deterministic [3].
2. Maximum parallelism  
In the P System, each step of the computation follows the principle of maximum parallelism, meaning that all rules that can be executed must be executed at the same time.
# Exploring the Potential of Sensing for Breast Cancer Detection
## 5 Biosensors for Breast Cancer Detection
### Microfluidic Chip
Integrated microfluidic devices could differentiate breast cancer patients from healthy controls with high sensitivity (90%) and specificity (>95%), making this strategy applicable to the analysis of clinical specimens [127]. In [128], the authors considered a glass substrate and polydimethylsiloxane for constructing a microfluidic device applicable to quantifying the circulating exosomes. They collected and measured the circulating EpCAM-positive exosomes from six breast cancer patients and three healthy people and observed that the level of EpCAM-positive exosomes significantly increased in breast cancer patients compared to healthy people. Personalized breast cancer therapy is associated with molecular classification by HER2. The authors considered nineteen breast cancer patients with HER2-positive exosomes. They utilized a microfluidic device to discover the secret HER2-positive exosomes in breast cancer cells. They observed that the level of HER2-positive exosomes in SK-BR-3 CM is significantly higher than that in both MCF7 and M231 CM. In [129], the author designed a detection platform for miRNA biomarkers by considering a microfluidic chip and observed that the detection limit was 1 pM and the time was 30 min. This microfluid chip approach is suitable for detecting breast cancer biomarkers and quantifying the amount of existence.
# In-Situ Observation of Adhesion Behavior During Ultrasonic Al Ribbon Bonding
## 5 Conclusions  
The interfacial adhesion behavior during ultrasonic bonding between an Al ribbon and transparent silica substrate has been discussed based on the in-situ observation results, using a high-speed video camera with the different frame rates of \(10^{4}\) fps and \(10^{3}\) fps. The bonded area has been classified into four parts: the island streaks, the central belt zone, the peripheral bond area, and the outermost touch and detach area. The morphologies and the appearances of each bonded area appeared to be different and affected by the frame rate and illumination (luminous intensity); however, the essential qualities of each area were identified by analyzing the in-situ observation results. The primary results obtained are as follows:  
1. Immediately after introducing the ultrasonic vibration, the ribbon was largely shaken, that is, a large friction slide occurred. Initial poor adhesion was locally produced. Macro-folding arose and the apparent contact width increased.
2. The island streaks were clearly observed at \(10^{4}\) fps. Conversely, the central belt zone looked translucent at \(10^{4}\) fps; although it was clear when observed at \(10^{3}\) fps. The island streaks were unclear at \(10^{3}\) fps.
3. The island streaks were formed as a footprint derived from the Al surface waviness. The island streaks grew long and parallel to the direction of ultrasonic vibration.
4. The central belt zone was formed, normal to the direction of ultrasonic vibration. The island streaks did not change into the central belt zone. The central belt zone was clearly captured at \(10^{3}\) fps.
5. The central belt zone remained on the silica side after fracture in the twist and peel test.
6. The central belt zone was between the island streaks and the silica substrate. The central belt zone must be formed by the interfacial reaction.
7. Microslipping and microfolding mechanisms can occur in the ultrasonic bonding.  
Conceptualization, Y.T. ([PERSON]); Validation, Y.T. ([PERSON]), K.T., K.M., Investigation, Y.T. ([PERSON]), K.T., K.M., Y.T. ([PERSON]); Writing--original draft preparation, Y.T. ([PERSON]); Writing--review and editing, Y.T. ([PERSON]).  
This research received no external funding.  
The authors declare no conflict of interest. The funders had no role in the design of the study, in the collection, analyses, or interpretation of data, in the writing of the manuscript, or in the decision to publish the results.
## 3 Numerical Tests
### Plate Shear Flow  
Plate shear flow describes the flow phenomenon in which a fluid undergoes shear motion due to externally applied shear force, as shown in Figure 3. In this study, the length of the plate \(L\) is 1.0, and the distance between plates \(d\) is 0.1; both inlet and outlet static pressures are 0, and the speed of upper plate \(u_{\rm u}\) is determined by the Reynolds number while that of lower plate, \(u_{\rm d}\), is zero; the fluid viscosity \(\
u\) is 1.0 \(\times\) 10\({}^{-3}\). The Reynolds number is defined with \(\
u\), \(d\) and \(u_{\rm u}\).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Scaling Factor** & **Reynolds Number** & **PINN-Predicted \(u_{\rm max}\)** & **Analytic Solution of \(u_{\rm max}\)** & **Error** \\ \hline \multirow{4}{*}{\(1.0\times 10^{-4}\)} & 139 & 0.1611 & 0.4167 & 61.34\% \\  & 313 & 0.2224 & 0.6250 & 64.42\% \\  & 553 & 0.2280 & 0.8333 & 72.64\% \\  & 1250 & 0.0280 & 1.2500 & 97.76\% \\  & 2000 & 1.7384 & 1.5811 & 9.949\% \\ \cline{2-5}  & 139 & 0.4291 & 0.4167 & 2.976\% \\  & 313 & 0.6572 & 0.6250 & 5.152\% \\ \(1.0\times 10^{-2}\) & 553 & 0.8743 & 0.8333 & 4.920\% \\  & 1250 & 1.2816 & 1.2500 & 2.528\% \\  & 2000 & 1.5779 & 1.5811 & 0.202\% \\ \cline{2-5}  & 139 & 0.4167 & 0.4167 & 0.000\% \\  & 313 & 0.6244 & 0.6250 & 0.096\% \\ \(1.0\) & 553 & 0.8331 & 0.8333 & 0.024\% \\  & 1250 & 1.2500 & 1.2500 & 0.000\% \\  & 2000 & 1.7196 & 1.5811 & 8.760\% \\ \cline{2-5}  & 139 & 0.4565 & 0.4167 & 9.551\% \\  & 313 & 0.6825 & 0.6250 & 9.200\% \\ \(1.0\times 10^{2}\) & 553 & 0.9065 & 0.8333 & 8.784\% \\  & 1250 & 0.8958 & 1.2500 & 28.33\% \\  & 2000 & 0.6527 & 1.5811 & 58.72\% \\ \cline{2-5}  & 139 & 0.4097 & 0.4167 & 1.680\% \\  & 313 & 0.5259 & 0.6250 & 15.86\% \\ \(1.0\times 10^{4}\) & 553 & 0.0439 & 0.8333 & 94.73\% \\  & 1250 & 0.6607 & 1.2500 & 47.14\% \\  & 2000 & 0.5080 & 1.5811 & 67.87\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of the maximum axial velocity of Couette flow between analytic solutions and PINN-predicted results by different scaling factors.  
Figure 3: Plate shear flow. Here, \(L\) is the plate length, \(d\) is the plate distance and \(u_{\rm u}\) and \(u_{\rm d}\) are the speed of upper and lower plates.  
Similar to Couette flow, a general expression of hard constraints is formulated, i.e.,  
\[\left\{\begin{aligned} \hat{u}=u_{\text{par}}+D_{u}(\mathbf{x}) \overline{u}=\left[u_{u}(\frac{\frac{4}{2}+y}{d})^{3}+u_{d}\left(\frac{\frac{4} {2}-y}{d}\right)^{3}\right]+a\left(\frac{d^{k}}{2^{k}}-|y|^{k}\right)\overline{ u}\\ \hat{v}=v_{\text{par}}+D_{v}(\mathbf{x})\overline{v}=0.0+a\left( \frac{d^{k}}{2^{k}}-|y|^{k}\right)\overline{v}\\ \hat{p}=p_{\text{par}}+D_{p}(\mathbf{x})\overline{p}=\left(\frac{x- x_{\text{in}}}{x_{\text{out}}-x_{\text{in}}}p_{\text{out}}+\frac{x_{\text{out}}-x }{x_{\text{out}}-x_{\text{in}}}p_{\text{in}}\right)+(x-x_{\text{in}})(x_{\text {out}}-x)\overline{p}\end{aligned}\right. \tag{23}\]
# Robust CNN Compression Framework for Security-Sensitive Embedded Systems
###### Abstract  
Convolutional neural networks (CNNs) have achieved tremendous success in solving complex classification problems. Motivated by this success, there have been proposed various compression methods for downsizing the CNNs to deploy them on resource-constrained embedded systems. However, a new type of vulnerability of compressed CNNs known as the adversarial examples has been discovered recently, which is critical for security-sensitive systems because the adversarial examples can cause malfunction of CNNs and can be crafted easily in many cases. In this paper, we proposed a compression framework to produce compressed CNNs robust against such adversarial examples. To achieve the goal, our framework uses both pruning and knowledge distillation with adversarial training. We formulate our framework as an optimization problem and provide a solution algorithm based on the proximal gradient method, which is more memory-efficient than the popular ADMM-based compression approaches. In experiments, we show that our framework can improve the trade-off between adversarial robustness and compression rate compared to the existing state-of-the-art adversarial pruning approach.  
model compression; adversarial robustness; weight pruning; adversarial training; distillation; embedded system; secure AI +
Footnote †: journal: Applied Sciences
# A Routing Algorithm Based on Real-Time Information Traffic in Sparse Environment for VANETs
## 1 Introduction
Since the analysis of node mobility model, we propose link Utility to reduce the packet loss rate. The main contributions of this paper are as follows:  
1. We propose a link utility algorithm in VANETs, which is used to minimize the number of hops while ensuring communication quality. In this algorithm, both the vehicle's speed, direction, and position are taken into account. 2. We propose an effective information traffic algorithm in VANETs, which is used to count the effective historical transmission information of each node, and indicate the connectivity of the node. In the algorithm, we define a function that can automatically attenuate to adapt to the change of connectivity caused by vehicle movement. 3. Based on 1 and 2, we propose a weight-based vehicle's utility algorithm, and the weights are the variances of effective information traffic and link utility. In the relay selection process, the node with the highest vehicle utility is selected as the relay node. The remaining of this paper is organized as follows: Previous related studies are reviewed in Section 2. The effective information traffic is described in Section 3. Unveiling the modules and the main algorithms of our relay selection scheme is the subject of Section 4. Section 5 presents the simulation and analysis of the experimental results. Finally, Section 6 concludes our work in this paper and points out the further works.
# In-Situ Nanoindentation Surface Topography of Lead-Free Piezoelectric Thin Films
## 2 Materials and Methods  
### Piezoelectric Surfaces  
For this study, four samples with lead-free piezoelectric thin films, composed of bismuth, sodium, and titanate (BNT), are studied. The films are deposited by radio frequency magnetron sputtering by varying thickness (220, 350, and 450 nm) on two types of substrate stacking layers, \(\text{LNO/SiO}_{2}\)/Si or \(\text{LNO/Si}\) (Table 1), where LNO (LaNiO\({}_{3}\)) is the bottom electrode. These piezoelectric samples are here studied as they represent typical configurations of piezoelectric films used in MEMS, taking into account environmental regulation (lead removing).  
BNTs are promising lead-free piezoelectric materials for a wide range of microelectronic applications: sensors, actuators, and energy storage [18; 19; 20]. The mechanical behavior of BNT piezoelectric materials has been extensively studied in the literature using local techniques such as indentation [21; 22] and scratching [21; 23]. These experimental techniques, which are particularly well suited to thin films and coatings, can be used to assess mechanical properties such as hardness and Young's modulus [22], residual stresses by measuring crack lengths [22], and the adhesion of the coating to the substrate [21; 23]. In most cases, these evaluations require sample preparation with cutting and polishing, which can lead to a modification of the mechanical behavior [24]. In our study, we propose to observe the evolution of the apparent mechanical properties as a function of the native surface roughness, without any post-deposition modification.