esa-sceva/satcom-chunk-collection huggingface dataset:
# Adaptive Beamforming Damage Imaging of Lamb Wave Based on CNN
## 4 Discussion and Analysis of Results
### Construction of Experiment Platform  
As shown in Figure 4, an aluminum plate with a length of 1000 mm, width of 1000 mm, and thickness of 2 mm was used in the experiments. The mechanical parameters of the material were the same as those used in the numerical simulation part. A uniform linear array of N = 9 PZT array elements was glued in the plate at positions consistent with the simulation coordinates, and the dimensions of the PZTs were \(\Phi\)7 mm \(\times\) 0.5 mm, numbered from left to right from 1 to 9. The damage was simulated with a magnet with a diameter of 5 mm, whose position can be adjusted according to the experimental validation needs.  
Figure 3: Finite element simulation model of a 9-element linear array.
## 3 Adaptive Control of PEMFC Power Supply
### Adaptive SMC Design
\begin{array}{l}u_{v}=-\frac{1}{v_{fc}\delta_{1}\delta_{2}}\big{\{}\gamma_{v }(-\alpha_{v}z_{1,v}+e_{2,v})-v_{o}\hat{\theta}_{2}\hat{\theta}_{1}-\hat{ \theta}_{3}\xi_{2}^{T}\hat{\theta}\\ +\alpha_{v}\xi_{2}^{T}\hat{\theta}+\xi_{2}^{T}\hat{\theta}-\alpha_{v}\dot{v}_ {0,ref}-\ddot{v}_{o,ref}+a_{v}[w_{v}+b_{v}sign(w_{v})]\Big{\}}\end{array}  \tag{43}\]  
where \(a\) and \(b\) are positive constants. Substitute Equations (40)-(43) into derivative of Equations (38) and (39), \(\dot{A}_{3}\) can be obtained by:  
\[ \begin{array}{l}\dot{A}_{3,i}=-\alpha_{i}z_{1,i}^{2}+z_{1,i}e_{2,i}-a_{i}w_{i}^ {2}-a_{i}b_{i}|w_{i}|\\ =-Z_{i}^{T}\left[\begin{array}{cc}\alpha_{i}+a_{i}\gamma_{i}^{2}&-\frac{1}{2}+ a_{i}\gamma_{i}\\ -\frac{1}{2}+a_{i}\gamma_{i}&a_{i}\end{array}\right]Z_{i}-a_{i}b_{i}|w_{i}|-z_{1, i}\xi_{1}^{T}\widetilde{\vartheta}\\ =-Z_{i}^{T}\mathbf{T}Z_{i}-a_{i}b_{i}|w_{i}|-z_{1,i}\xi_{1}^{T}\widetilde{ \vartheta}\end{array}  \tag{44}\]  
Similarly,  
\[\dot{A}_{3,\
u}=-Z_{\
u}^{T}\mathbf{Y}Z_{\
u}-a_{\
u}b_{\
u}|w_{\
u}|-z_{1, \
u}\xi_{2}^{T}\widetilde{\vartheta} \tag{45}\]  
where \(\mathbf{Y}=\left[ \begin{array}{cc}\alpha_{i}+a_{\
u}\gamma_{\
u}^{2}&-\frac {1}{2}+a_{\
u}\gamma_{\
u}\\ -\frac{1}{2}+a_{\
u}\gamma_{\
u}&a_{\
u}\end{array} \right]\). If \(\alpha\), \(a\), and \(\gamma\) are satisfying \(\alpha a-\frac{1}{4}+a\gamma>0\), the matrix \(\mathbf{T}\) and \(\mathbf{Y}\) are positive define matrix. Then,  
\[\dot{A}_{3,i}\leq-a_{i}b_{i}|w_{i}|-z_{1,i}\xi_{1}^{T}\widetilde{\vartheta} \tag{46}\]  
\[\dot{A}_{3,\
u}\leq-a_{\
u}b_{\
u}|w_{\
u}|-z_{1,\
u}\xi_{2}^{T}\widetilde{ \vartheta} \tag{47}\]  
Moreover,  
\[z_{1,i}\xi_{1}^{T}\widetilde{\vartheta}=\Big{(}i_{L}-i_{L,ref}\Big{)}\Big{(}v _{fc}-v_{o}\Big{)}\bigg{(}\frac{1}{L_{0}+\Delta L}-\frac{1}{\dot{L}}\bigg{)} \tag{48}\]  
\[z_{1,\
u}\xi_{2}^{T}\widetilde{\vartheta}=\Big{(}v_{o}-v_{o,ref}\Big{)}\bigg{[} i_{L}\bigg{(}\frac{1}{C_{0}+\Delta C}-\frac{1}{\dot{C}}\bigg{)}-v_{o}\bigg{(} \frac{1}{(R_{0}+\Delta R)(C_{0}+\Delta C)}\,-\frac{1}{\dot{R}\dot{C}}\bigg{)} \bigg{]} \tag{49}\]  
According to Assumption 5, \(-z_{1,i}\xi_{1}^{T}\widetilde{\vartheta}\) and \(-z_{1,\
u}\xi_{2}^{T}\widetilde{\vartheta}\) are bounded, and the upper bound are defined as:  
\[-z_{1,i}\xi_{1}^{T}\widetilde{\vartheta}\leq\overline{N}_{i} \tag{50}\]  
\[-z_{1,\
u}\xi_{2}^{T}\widetilde{\vartheta}\leq\overline{N}_{v} \tag{51}\]  
If \(a\) and \(b\) are satisfied as:  
\[\overline{N}_{i}-a_{i}b_{i}|w_{i}|\leq 0 \tag{52}\]  
\[\overline{N}_{v}-a_{\
u}b_{\
u}|w_{\
u}|\leq 0 \tag{53}\]  
Then:  
\[\dot{A}_{3,i}\leq\overline{N}_{i}-a_{i}b_{i}|w_{i}|\leq 0 \tag{54}\]  
\[\dot{A}_{3,\
u}\leq\overline{N}_{v}-a_{\
u}b_{\
u}|w_{\
u}|\leq 0 \tag{55}\]  
Yielding,  
\[\dot{A}_{3,\
u}\leq-a_{\
u}b_{\
u}|w_{\
u}|-z_{1,\
u}\xi_{2}^{T}\widetilde{ \vartheta} \tag{47}\]0  
According to Lemma 1, \(ab|w|-\overline{N}\) converges to zero. The system is stable asymptotically.
# Two-Dimensional Digital Beam Steering Based on Liquid Crystal Phase Gratings
## 2 Materials and Methods
### Device Characteristics  
The active area is defined by the pixel width and the total number of individual pixels. The employed nematic LC requires that the incoming light must be polarized. The birefringence of the LC delay the light polarized along the switching plane. The delay, \(\delta(V)\), can be calibrated by measuring the interference between the light polarized along the ordinary and the extraordinary axes, respectively, using crossed polarizers as a function of the applied voltage [23]:  
\[\delta(V)=\frac{2\pi}{\lambda}\cdot\left[n_{eff}(V)-n_{o}\right]\cdot d\, \tag{1}\]  
where \(\lambda\) is the wavelenght of the incoming light, \(d\) is the thickness of the LC cell, and \(n_{eff}(V)-n_{o}\) is the effective birefringence of the LC at a voltage, V.  
The intensity variation follows the relationship in Equation (2):  
\[I\propto\sin^{2}\frac{\delta(V)}{2}\, \tag{2}\]  
In the diffractive phase grating, the continously varying phase is _\"warapped\"_ between \(0\pi\) and \(2\pi\)[32] (Figure 3). When the pattern corresponds to an integer number of pixels, a recurrent phase delay structure is observed along the grating. In this paper, the term \"topology\" is used to describe the total retardation of an equivalent wedge prism in which the maxima is \(t\cdot 2\pi\); \(t\) being the topology number. The developed driver, which allows for individual addressing in the LC cell, means that the topology can be any fractional number. As an arbitrary convention, the positive sign of the topology has been assigned to deviation to the right and up along the two axes, respectively; Figure 4.  
Figure 2: LC beam steering device. The LC cells are in their holder, with the electronic driver. The variable attenuator employed in the setup is also visible.  
The deviation angle, \(\theta\), generated by the blaze is a function of its period and working wavelength [33]:  
\[\sin\theta=\frac{\lambda}{\Delta}\, \tag{3}\]  
Hence, the longer the wavelength or the shorter the period, the larger the diffraction angle, as long as the period is significantly larger than the wavelength. Additionally, the smaller the period, the smaller the pixels, in order not to compromise the diffraction efficiency, which is the real practical limiting factor in passive ITO based devices such as this one.
###### Abstract  
As technology evolves, embedded systems access more networks and devices, which means more security threats. Existing security-monitoring methods with a single parameter (data or control flow) are not effective in detecting attackers tampering with the data or control flow of an embedded system. However, simply overlaying multiple security methods will result in excessive performance overhead for embedded systems. In this paper, we propose a novel hardware security-monitoring architecture that extracts DI (data integrity) digests and CFI (control flow integrity) tags to generate reference information when the program is offline. To monitor the indirect jumping behavior, this paper maps the legal target addresses into the bitmap, thus saving the search time. When the program is loaded, the reference information and the bitmap are safely loaded into the on-chip memory. The hardware monitoring module designed in this paper will check the DI summary and CFI tags in real time while executing the program. The architecture proposed in this paper has been implemented on the Xilinx Virtex 5 FPGA platform. Experimental results show that, compared with existing protection methods, the proposed approach in this paper can effectively detect multiple tampering-type attacks on the data and control flow of the embedded system, with a performance overhead of about 6%.  
embedded system; security monitoring; data integrity (DI); control flow integrity (CFI)
###### Abstract  
Background: Implementing automatic classification of short texts in online healthcare platforms is crucial to increase the efficiency of their services and improve the user experience. A short text classification method combining the keyword expansion technique and a deep learning model is constructed to solve the problems of feature sparsity and semantic ambiguity in short text classification. Methods: First, we use web crawlers to obtain patient data from the online medical platform \"Good Doctor\"; then, we use TF-IWF to weight the keyword importance and Word2 vec to calculate the keyword similarity to expand the short text features; and then we integrate the cue learning and deep learning models to construct a self-adaptive attention model to solve the problem of sparse features and unclear semantics in short text classification in the adaptive-attention-Prompt-BERT-RCNN model to realize effective classification of medical short texts. Results: Empirical studies show that the classification effect after keyword expansion is significantly higher than that before expansion, the accuracy of the model in classifying medical short texts after expansion is as high as 97.84%, and the model performs well in different categories of medical short texts. Conclusions: The short text expansion methods of TF-IWF and Word2 vec make up for the shortcomings of not taking into account the keyword rarity and the contextual information of the subwords, and the model can achieve effective classification of medical short texts by combining them. The model further improves the classification accuracy of short text by integrating Prompt's bootstrapping, self-adaptive attention's keyword weight weighting, BERT's deep semantic understanding, and RCNN's region awareness and feature extraction; however, the model's accuracy in individual topics still needs to be improved. The results show that the recommender system can effectively improve the efficiency of patient consultation and support the development of online healthcare.  
online healthcare; keyword expansion; text categorization; BERT-RCNN +
Footnote †: journal: Applied sciences  
Article
## 2 Related Work
### Pam4  
Improved modulation methods are attempted owing to the increase in data demand. The NRZ-type modulation scheme has been most commonly used in data transmission.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **SOA**[9; 10] & **APD**[6; 7; 8] & **PIN-PD** \\ \hline
**Cost** & Highest & High & Low \\ \hline
**Package** & Bulky & Chip scale & Chip scale \\ \hline
**Power consumption** & SOA + TEC current &
\begin{tabular}{c} APD bias current \\ DC/DC converter \\ \end{tabular} & \(\sim\)mA \\ \hline
**Additional Circuit** & \
\begin{tabular}{c} SOA bias controller \\ TEC controller \\ \end{tabular} &
\begin{tabular}{c} APD bias controller \\ (DC/DC converter) \\ \end{tabular} & Nothing \\ \hline
**Operating Distance** & \(>\)40 km & 40–60 km & 10 km \\ \hline \hline \end{tabular}
\end{table}
Table 1: The long-reach transmission technology of an optical transceiver.  
Meanwhile, the PAM4 scheme was attempted by a company that created the digital signal processing (DSP) integrated circuit (IC) for data modulation [12]. Figure 1. shows a comparison between the NRZ and PAM4 signals. Compared with NRZ, PAM4 demonstrates numerous benefits associated with having half the Nyquist frequency. These benefits include doubling the data density, achieving higher resolution using the same oversampling rate, and having the same total noise power spread over a wider frequency band.  
The NRZ modulation scheme encodes a binary pattern, such as 0110010, into a continuous sequence of fixed voltage levels of low and high voltages of 0 and 1, respectively. Assuming a bit rate of 25 Gbps, faster bit speeds and higher bandwidths are needed to transmit 50 Gbps, which is twice the bit rate. To double the bit rate without doubling the bandwidth, the signal level must be split in half and added to the signal. Thus, there are four signal levels, similar to that of the PAM4 signal, and 2 bits per level symbol is assigned. Meanwhile, the IEEE P802.3 bj KP4 standard defines the requirements for 28 Gbps PAM4 signals, whereas the IEEE P802.3 bs andOptical Internetworking forum(OIF) CEI-56G-PAM4 documents define standards for 56 Gbps PAM4 signals [13; 14].
## 3 Results and Discussion
### Morphological Analysis  
The morphological analysis of as-prepared samples was carried out by performing scanning electron microscopy, as shown in Figure 3. Spherical aggregated morphology with a high packing density and narrow particle size distribution can be seen for all synthesized samples. However, the diffusion of nanospherical aggregates was observed at multiple locations owing to the addition of nitrogen into the titania lattice. It is further observed that the addition of urea reduces the agglomeration of nanoparticles, with the increased size of nanoparticles. On further increasing the amount of urea, the nanoparticles dispersion deteriorates, and agglomeration again dominates but the size of nanoparticles becomes smaller, revealing a narrower particle size distribution. The average sizes of the nanoparticles estimated using SEM images (via Image J software) are found as: 146 nm for pure TiO\({}_{2}\), 116 nm for NT-1, 115 nm for NT-2, and 109 nm for NT-3 samples.  
Figure 1: XRD patterns of pure TiO\({}_{2}\), NT-1, NT-2 and NT-3, synthesized by using 0.0 g, 0.983 g, 1.965 g and 2.940 g of urea respectively (A: anatase phase, and R: rutile phase).  
Figure 2: Raman spectra of pure TiO\({}_{2}\), NT-1, NT-2 and NT-3, synthesized by using 0.0 g, 0.983 g, 1.965 g and 2.940 g of urea respectively.
# Improving Traffic Load Distribution Fairness in Mobile Social Networks
## 4 TraLDA Design
### Social-Relations Popularity Calculation
Finally, the social-relations popularity of node \(i\) in time window \(t\) is defined as follows  
\[P_{soc}^{t}(i)=\sum_{j\in F(i)}\ \ \frac{\overrightarrow{g_{ji}}\cdot w_{ji}}{ \sum_{k\in F(j)}\ \overrightarrow{g_{jk}}\cdot w_{jk}}\ \cdot\ \overline{P}_{global}^{t}\ (j) \tag{14}\]  
where \(\overrightarrow{g_{ji}}\) denotes the presence of a (social) influence of \(j\) towards \(i\): \(\overrightarrow{g_{ji}}\) = 1 for \(\overline{P}_{global}^{t}(j)>\overline{P}_{global}^{t}(i)\), or =0 otherwise, \(F(i)\) represents the set of \(i\)'s friends, \(w_{ji}\) is the connection strength of \(i\) and \(j\), and \(\overline{P}_{global}^{t}(j)\) is the cumulative mean of global popularity in time window \(t\) calculated as follows  
\[\overline{P}_{global}^{t}(j)=\sum_{t=1}^{t-1}P_{global}^{t}(j)/(t-1) \tag{15}\]  
where \(P_{global}^{t}(j)\) is the instantaneous global popularity of \(j\) in time window \(t\) computed using (16) below. To present an example of the calculation of node social-relations popularity, we consider a simple neighbourhood of node \(A\) in Figure 6, comprising four neighbours with different levels of global popularity at a time \(t\). Between a pair of nodes \(A\) and \(B\), a black line indicates the social connection between them, with \(w_{AB}\) representing their connection strength (e.g., measured in total contact duration (seconds)). A red dotted vector, on the other hand, denotes the influence of node \(B\) to \(A\): \(\overrightarrow{g_{BA}}\) = 1 if \(\overline{P}_{global}^{t}(B)>\overline{P}_{global}^{t}(A)\), and =0 otherwise. Finally, the social-relations popularity of node \(A\) at time \(t\) is calculated as:  
\[P_{soc}^{t}(A)=\frac{\overrightarrow{g_{BA}}\cdot w_{BA}\cdot \overrightarrow{P_{global}^{t}(B)}}{\overrightarrow{g_{BA}}\cdot w_{BA}+ \overrightarrow{g_{BC}}\cdot w_{BC}}+\frac{\overrightarrow{g_{CA}}\cdot w_{ CA}\overrightarrow{P_{global}^{t}(C)}}{\overrightarrow{g_{CA}}\cdot w_{CA}+ \overrightarrow{g_{CB}}\cdot w_{CB}}+\frac{\overrightarrow{g_{DA}}\cdot w_{ DA}\cdot\overrightarrow{P_{global}^{t}(D)}}{\overrightarrow{g_{DA}}\cdot w_{DA}+ \overrightarrow{g_{DE}}\cdot w_{DE}}\] \[+\frac{\overrightarrow{g_{CA}}\cdot w_{EA}\cdot\overrightarrow{ P_{global}^{t}(E)}}{\overrightarrow{g_{CA}}\cdot w_{EA}+\overrightarrow{g_{CD}}\cdot w_{ ED}}\] \[P_{soc}^{t}(A)=\frac{1\cdot 2000\cdot 7}{1\cdot 2000+0\cdot 1200}+ \frac{1\cdot 800\cdot 10}{1\cdot 800\cdot 1\cdot 1200}+\frac{0\cdot 3000\cdot 3}{0 \cdot 3000+0\cdot 700}+\frac{1\cdot 2500\cdot 8}{1\cdot 2500\cdot 1\cdot 700}\] \[P_{soc}^{t}(A)=7+4+0+6.25=17.25\]  
Figure 6: A neighbourhood of node \(A\), comprising four nodes which gives social influences to node \(A\). Social influences (red dotted vectors) to node \(A\) exist when the global popularity of the neighbours is higher than \(A\)’s.
# Variational Mode Decomposition Based Time-Varying Force Identification of Stay Cables
## 2 Variational Mode Decomposition Analysis
The next step is to find the instantaneous frequency of each eigenmode component. Instantaneous frequency is an important physical quantity that characterizes the transient frequency characteristics of a signal at a local time point. For most signals, especially natural signals, the frequency generally changes with time. The instantaneous frequency is defined as the derivative of phase versus time, as shown in Equation (11):  
\[\omega(t)=\varphi(t)=\frac{\mathrm{d}[\tilde{f(t)}/f(t)]}{\mathrm{d}t} \tag{11}\]  
where \(\varphi(t)\) is the instantaneous phase and \(\tilde{f(t)}\) is the signal obtained from the Hilbert-Huang transform and can be calculated as Equation (12):  
\[\tilde{f(t)}=\frac{1}{\pi t}\otimes f(t)=\frac{1}{\pi}\int_{-\infty}^{+\infty }\frac{f(\tau)}{t-\tau}\mathrm{d}\tau \tag{12}\]  
where \(\otimes\) is the convolution operation. The obtained analytical signal \(Z(t)\) is calculated as Equation (13) and Equation (14):  
\[Z(t)=f(t)+if\tilde{f(t)}=a(t)e^{i\theta(t)} \tag{13}\]  
\[a(t)=[f(t)^{2}+\tilde{f(t)}^{2}]\frac{1}{2}\quad\theta(t)=\arctan(\frac{ \tilde{f(t)}}{f(t)}) \tag{14}\]  
Furthermore, the instantaneous frequency can be obtained by deriving the instantaneous phase as Equation (15) shows:  
\[\omega(t)=\frac{1}{2\pi}\frac{\mathrm{d}\theta(t)}{\mathrm{d}t} \tag{15}\]  
The Hilbert transform can adaptively generate the \"base\", which is the IMF component after the VMD algorithm is decomposed. Other signal decomposition methods include the Fourier transform, whose \"base\" is a trigonometric function, and the wavelet transform, whose \"base\" is a wavelet base that satisfies the tolerance condition, which is also selected in advance. In practice, it is difficult to choose the appropriate \"base\", so the Hilbert transform is relatively more suitable. In addition, the Fourier transform, short-time Fourier transform, and wavelet transform are all constrained by [PERSON]'s uncertainty principle. The product of the time window and the frequency window must be a constant. The Hilbert transform is not subject to this criterion, so it is also applicable to abrupt signals. After the instantaneous frequency of the cable vibration acceleration signal is obtained, the time-varying cable force can be calculated according to the Equation (16):  
\[F=4 mL^{2}\omega_{1}^{2}-EI\frac{\pi^{2}}{L^{2}} \tag{16}\]  
where \(\omega_{1}\) is the first-order instantaneous frequency of the stay cable.
# SINR-Based MCS Level Adaptation in CSMA/CA Wireless Networks to Embrace IoT Devices
###### Abstract  
This paper presents an automatic modulation and coding scheme (MCS) level adaptation algorithm to embrace Internet of Things (IoT) devices by improving the area spectral efficiency of carrier-sense multiple access with collision avoidance (CSMA/CA) wireless networks. In the proposed algorithm, senders of CSMA/CA wireless networks use the signal to interference plus noise ratio of acknowledgment frames from their receivers to estimate channel statuses between the senders and the receivers. Using the estimated channel status of each receiver, senders control sending rates of traffic by adjusting MCS levels of packets destined for each receiver. We use Poisson point processes (PPPs) to model the locations of participating nodes (i.e., access points and wireless devices) in a given area. We evaluate the effectiveness of the proposed algorithm using an event-driven ns-2 simulator for various PPP densities of access points and wireless devices.  
Internet of Things; modulation and coding scheme; rate adaptation  
collision avoidance mechanism such as CSMA/CA. In this paper, we suggest leveraging the SINRs of acknowledgment (ACK) frames in CSMA/CA wireless networks to estimate the channel statuses between senders and receivers since most license-free wireless networks such as IEEE 802.11 and IEEE 802.15.4 adopt half-duplexing to more efficiently utilize frequency bands (i.e., both downlink and uplink traffic use the same frequency band). Here, we are assuming that there are high correlations between downlink channels for data packets and uplink channels for ACK frames on short time scales.  
The contributions of this paper can be summarized as follows:  
* Using the SINRs of ACK frames, we propose a novel modulation and coding scheme (MCS) level adaptation algorithm: senders adapt data sending rates by adjusting MCS levels of packets to be sent to their receivers based on the estimated channel statuses from the SINRs of ACK frames. In addition, senders decrease the MCS levels of packets in the absence of ACK frames after a given number of retransmissions (i.e., a retry limit event occurs).
* We demonstrate and evaluate the performance of the proposed algorithm using the ns-2 [13] network simulator (version 2.35) for IEEE 802.11a [14] wireless networks with various numbers of participating nodes (i.e., access points and wireless devices) distributed in a given area using independent homogeneous Poisson point processes (PPPs).  
The remainder of this paper is organized as follows: in Section 2, we survey related work on the research for adapting data sending rates in CSMA/CA wireless networks. In Section 3, we explain the proposed algorithm in detail with examples and pseudo code. In Section 4, we demonstrate the behavior of the proposed algorithm in both single cell and multi-cell environments using simulations. In Section 5, we evaluate the performance of the proposed algorithm with various PPP densities of access points and users. Section 6 concludes this paper.
# Overview of Compressed Sensing: Sensing Model, Reconstruction Algorithm, and Its Applications
## 2 Sensing Methods  
Sensing methods have always been a hotspot in CS research. The sensing process also affects signal sampling and the accuracy of signal reconstruction. The main operation of a sensing step is to correlate a sparse signal with a proper sensing matrix without any prerequisites. In this section, we briefly introduce several sensing methods.