esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Amidst the evolving communication technology landscape, conventional distribution networks have gradually metamorphosed into cyber-physical power systems (CPPSs). Within this transformative milieu, the cyber infrastructure not only bolsters grid security but also introduces a novel security peril--the false data injection attack (FDIA). Owing to the variable knowledge held by cyber assailants regarding the system's network structure, current achievements exhibit deficiencies in accommodating the detection of FDIA across diverse attacker profiles. To address the historical data imbalances encountered during practical FDIA detection, we propose a dataset balancing model based on generating adversarial network-gated recurrent units (GAN-GRU) in conjunction with an FDIA detection model based on the Transformer neural network. Harnessing the temporal data extraction capabilities of gated recurrent units, we construct a GRU neural network system as the GAN's generator and discriminator, aimed at data balance. After preprocessing, the balanced data are fed into the Transformer neural network for training and output classification to discern distinct FDIA attack types. This model enables precise classification amidst varying FDIA scenarios. Validation involves testing the model on load data from the IEEE 118-bus system and affirming its high accuracy and effectiveness in detecting power systems after multiple attacks.  
cyber-physical power systems; false data injection attack; attack modeling; generative adversarial network; gated recursive unit; the Transformer neural network detection +
Footnote †: journal: Applied Sciences  
**Citation:** [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] The Detection of False Data Injection Attack for Cyber-Physical Power Systems Considering a Multi-Attack Mode.  
**Cityper-Physical Power Systems Considering a Multi-Attack Mode.**  
**Appl. Sci. 2023, 13, 10996.  
and incorporated into robust control methodologies. Furthermore, the establishment of bidirectional communication networks is actively pursued to neutralize cybersecurity vulnerabilities. Equally vital is the need to institute preemptive strategies for averting and detecting false data injection attacks and other cyber-physical risks. By judiciously adopting pertinent security mechanisms and deploying discerning attack detection frameworks, the stable operation of the smart grid is assured, thereby mitigating potential disruptions to power systems stemming from cybersecurity threats. We list the abbreviations in this paper in Abbreviations.  
The inception of false data injection attacks was initially introduced by [5]. In the work presented, the authors delineate this phenomenon as a prototypical assault on the data integrity of power system state estimation. The realm of CPPS has, as a consequence, engendered substantial scholarly interest in exploring avenues of investigation surrounding concealed and arduously detectable FDIAs. As described in [6; 7; 8], extant methodologies for the detection of FDIAs predominantly encompass approaches grounded in model-based prediction and data-driven machine learning paradigms.
## 2 Genetics of Pustular Psoriasis
### Il36 RN  
IL-36 cytokines are relatively novel and belong to the IL-1 family, which has members that are produced by many sources, such as epithelial cells, myeloid dendritic cells, and monocytes. IL36 RN encodes for IL-36 Ra, which inhibits the pro-inflammatory effects of IL-36 cytokines by binding their receptors, then preventing the release of mediators that stimulate the pustule formation seen in GPP [26].  
[PERSON] et al. reported that IL-36 RN mutations can cause sporadic GPP, and according to their study, IL-36 mutations underline sporadic European GPP, as well as Tunisian autosomal recessive GPP [12]. Additionally, the first Asian case of GPP associated with IL36 RN mutations was reported in 2012, therefore indicating that IL36 RN mutations are common in some GPP cases worldwide [27]. The prevalence of IL36 RN mutations among pustular psoriasis subtypes is different; patients with GPP have the highest prevalence of these mutations (23.7%). This is followed by ACH, which has the second-highest prevalence (17.4%), and lastly, PPP demonstrates the lowest prevalence of these mutations (5.1%) [8].  
Hence, in order to ascertain if IL36 RN alleles are the crucial determinants of pustular psoriasis across various disease subtypes, a regression analysis was carried out, incorporating clinical diagnosis as a covariate [28]. Individuals with homozygous mutations of IL36 RN tend to experience more severe disease manifestations compared to those with heterozygous mutations, and these mutations are inherited through an autosomal recessive pattern [29]. Another study indicated that IL36 RN mutations are almost not seen in individuals with both PPP and GPP [30]. Accordingly, this finding suggests that a large proportion of cases of GPP alone are caused by homozygous or compound heterozygous mutations of IL36 RN.  
On the other hand, the presence of IL36 RN disease alleles demonstrated a dose-dependent influence on the age of onset across all types of pustular psoriasis [28]. According to genetic analyses, the frequency of IL36 mutations plays a role in differentiating pustular psoriasis subtypes [8]. [PERSON] et al. found that the percentage of individuals carrying IL36 RN disease-associated alleles was higher in those with GPP and ACH. Individuals with GPP and ACH were more likely to have biallelic mutations compared to those affected by PPP.
## 2 Related Work
### Hybrid Approaches and Emerging Trends  
Recent research has increasingly focused on hybrid approaches that combine multiple methodologies to leverage their complementary strengths. Ref. [31] proposed an integrated framework combining graph-based anomaly detection with reinforcement learning, showing improved performance in detecting sophisticated attack patterns. Additionally, ref. [32] developed a system incorporating behavioral analytics with real-time monitoring, demonstrating enhanced capabilities in identifying and responding to multi-stage attacks. These hybrid approaches show promise in addressing the limitations of individual methods, though they introduce additional complexity in system design and optimization.
## 2 Materials and Methods  
The samples were multilayer thin films on Si substrate: 15\(\times\)(Zr/Ti), 15\(\times\)(Ti/Zr), 8\(\times\)(Zr/Cr/Ti), with the topmost layer being Zr, Ti and Zr, respectively. The total thickness of each multilayer structure was aimed to be \(\sim\)500 nm, making the thicknesses of individual layers--controlled by the deposition rate--about 17 nm for bi-layer samples and \(\sim\)21 nm for tri-layer sample. The samples were exposed to femtosecond beam which generated the LIPSS.  
The laser source was the Pharos SP Yb:KGW laser system from Light Conversion. The surfaces of the thin films were irradiated at normal incidence in open air by focusing a linearly polarized pulsed beam with a 1 kHz repetition rate, 160 fs pulse duration, and 1030 nm central wavelength to a 43 \(\upmu\)m Gaussian spot (1/e\({}^{2}\)) diameter. The samples were mounted on a motorized, computer-controlled, X-Y-Z translation stage, which enabled scanning (3 mm/s) and positioning out-of-focus to widen the scanning line. For higher precision, the irradiations were conducted at identical conditions, covering a surface of 5 \(\times\) 5 mm at a pulse power of 2.5 mW (fluence of 0.662 J/cm\({}^{2}\)) and scan velocity of 3 mm/s with a constant distance between the lines of 15 \(\upmu\)m.  
In order to determine the effects of chemical composition and morphology on wetting, the drops of distilled water were placed onto both the unmodified and laser-processed surface areas in open air. The volume of each droplet was of 3 \(\upmu\)L, controlled by a motorized syringe, while the shape was determined by micro-photographing in the horizontal direction by using Data Physics OCA (optical contact angle) Series device. The values of contact angles were estimated from the images by the utilization of the Gwyddion software [24].
## 4 Discussion and Conclusions  
A fully differential multipath CFIA for a bridge resistive sensor readout IC is presented. The proposed CFIA has the ability to reduce external and internal offsets, thereby achieving low-noise characteristics. To attenuate the internal offset, the chopper stabilization technique is implemented. To reduce the output ripple caused by chopper up-modulation, an RRL is used. A multipath architecture is employed to compensate for the notch in the chopping frequency band of the transfer function. To calibrate external offset, a 12-bit R-2R DAC is adopted. The proposed resistive sensor readout IC is implemented using 0.18 \(\upmu\)m CMOS technology. The current consumption of CFIA is 169 \(\upmu\)A with a 3.3 V supply. The simulated input-referred noise and NEF are 28.3 nV/V/Hz and 14.2, respectively. The CFIA achieves 112 dB PSRR+ and 162 dB CMRR. The proposed CFIA, which achieves low-noise characteristics, is expected to be employed in various resistive IoT sensor applications with high precision.  
Conceptualization and supervision, H.K.; investigation and writing--original draft preparation, Y.K.; and writing--review and editing, H.K., J.K., K.H., D.Y., H.H., and D.-i.D.C. All authors have read and agreed to the published version of the manuscript.  
This work was supported by a grant to the Bio-Mimetic Robot Research Center Funded by Defense Acquisition Program Administration and by the Agency for Defense Development (UD160027 ID). The EDA tool was supported by the IC Design Education Center (IDEC), Korea.  
The authors declare no conflict of interest.
# Combined Navigation and Tracking with Applications to Low Earth Orbit Satellites +
## 2 Deep Aiding Architecture
### Line of Sight (LOS) Predictions  
At the receiver epoch \(t_{REF}\), let \(\mathbf{x}_{m}^{p},\ \mathbf{x}_{m}^{p},\ \mathbf{x}_{m}^{p}\) be the relative ECEF position, velocity, and acceleration between the satellite p (at epoch of transmission) and the receiver m. The geometric range, range rate, and acceleration can be derived as follows, where \(\mathbf{e}_{m}^{p}\triangleq\mathbf{x}_{m}^{p}/\rho_{m}^{p}\) is the line of sight (LOS) unit column vector [10; 11].  
\[\rho_{m}^{p}=\left\|\mathbf{x}_{m}^{p}\right\|\ \ \ \ \ \ \ \dot{\rho}_{m}^{p}=\left(\mathbf{e}_{m}^{p}\right)^{T}\!\! \dot{\mathbf{x}}_{m}^{p}\ \ \ \ \ \ddot{\rho}_{m}^{p}=\left(\mathbf{e}_{m}^{p}\right)^{T}\!\!\dot{\mathbf{x}}_{m} ^{p}+\frac{\left\|\dot{\mathbf{x}}_{m}^{p}\right\|^{2}-\left(\ddot{\rho}_{m}^{ p}\right)^{2}}{\rho_{m}^{p}} \tag{1}\]  
Range rate and acceleration are converted into predictions and expressed in frequency units by dividing by carrier wavelength \(\lambda^{p}\) and adding the receiver clock drift and drift rate: \(\dot{dt}_{m}\) and \(\ddot{dt}_{m}\), respectively. Code predictions are obtained from the range, correcting for both satellite and receiver clock biases, \(dt^{p}\) and \(dt_{m}\), and ionospheric and tropospheric errors: \(I^{p}\) and \(T^{p}\)[10].  
\[\begin{array}{l}\tau_{m}^{p}(t_{REF})=\rho_{m}^{p}+cdt_{m}-cdt^{p}+I^{p}+T^ {p}\\ F_{m}^{p}(t_{REF})=\frac{1}{\lambda^{p}}\left(\dot{\rho}_{m}^{p}-c\dot{dt}_{m} \right)\ \ \ \ \ \dot{F}_{m}^{p}(t_{REF})=\frac{1}{\lambda^{p}}\left(\ddot{\rho}_{m}^{p}-c\ddot{ dt}_{m}\right)\end{array} \tag{2}\]  
Typical Xona satellite profiles for frequency rates as from (1) and the corresponding elevations and frequencies are plotted in Figure 3.  
Figure 2: Combined navigation and tracking for LEO: reference block diagram.  
The accuracy at which LOS geometry can be calculated is not sufficient to produce reliable predictions for the carrier phase. However, the aiding applied to frequency can indirectly benefit the estimation of the carrier phase through classic Frequency Locked Loop (FLL)-aided Phase Lock Loop (PLL) feedback or other equivalent open-loop schemes. For this reason, a general improvement is expected in the quality of the carrier phase observables and the ambiguity-fixing capability.
# An Improved Model for Medical Forum Question Classification Based on CNN and BiLSTM
## 3 Proposed Architecture
### Question Classification Based on BiLSTM  
The LSTM model was designed to address the RNN's problem of memory and information storage limitation. LSTM comprises three gates: the input gate, the forget gate, and the output gate [42; 43]. The LSTM model can remember long-term information using its memory cells, and regulate this process through a gate mechanism. LSTM models are much better at handling long-term dependencies and much less susceptible to the vanishing gradient problem, and are very efficient at modeling complex sequential data. With a single LSTM, information is processed from only one forward direction, while for BiLSTM, it is processed from two directions: forward and backward. The forward LSTM deals with the input sequence of past data information, while the backward LSTM receives information on the input sequence of future data information [44; 45].  
BiLSTM is more efficient than LSTM because it uses previous and succeeding information, and combines them to obtain the output. To control the state of memory cells, point-wise multiplication and sigmoid function operations are performed in each gate. The input data at the current state and the output from the hidden state of the previous layer enter all gates. The role of the forget gate is to decide whether the information should be kept or ignored. The output value of the forget gate is between zero and one. When the value is close to zero, the information is ignored. However, when the value is close to one, the information is kept.  
The BiLSTM model has two main gates that run in opposite directions at each time [41]. \(\vec{h}_{f}\) represents the forward output of LSTM at the time \(t\), \(\vec{h}_{b}\) represents the reverse output of LSTM at the time \(t\), and \(h_{t}\) represents the output of BiLSTM at the time \(t\). \(x_{t}\) is the input at time \(t\). Each time state in the BiLSTM model is calculated as follows:  
\[\vec{h}_{f}=LSTM\Big{(}x_{t},\vec{h}_{t-1}\Big{)} \tag{7}\]  
\[\vec{h}_{b}=LSTM\Big{(}x_{t},\vec{h}_{t-1}\Big{)} \tag{8}\]  
\[h_{o}=v_{o}\vec{h}_{f}+y_{o}\vec{h}_{b}+c_{t} \tag{9}\]  
where, \(v_{o}\) stands for the weight matrix of forward output; \(y_{o}\) stands for the weight matrix of the reverse output; \(c_{t}\) is the offset of \(t\) time.  
The main advantage of BiLSTM is its ability to capture dependencies from both past and future contexts, enabling the model to make more informed predictions. This is particularly beneficial in QAS tasks where understanding the full context of a sentence or document is important, such as sentiment analysis, called entity recognition, sequence labeling, and machine translation.
## 2 System Model
### Channel Model  
We adopted a geometric channel model, as in [17], for multi-user mmWave massive MIMO systems. In this model, each user is assumed to observe the same number of propagation paths, denoted by \(N_{p}\). The strength associated with the \(l\)th path observed by the \(u\)th user is represented by \(\alpha_{l}^{u}\sim\mathcal{CN}(0,1)\), and the random angle of departure (AoD), which is drawn independently from uniform distribution over \([0,2\pi]\), is denoted by \(\phi_{l}^{u}\). The channel of the \(u\)th user is given by \(\mathbf{h}_{u}^{H}=\sqrt{\frac{N_{t}}{N_{p}}}\sum_{l=1}^{N_{p}}\alpha_{l}^{u} \mathbf{a}^{H}(\phi_{l}^{u})\), where \(\mathbf{a}(\phi_{l}^{u})\) is the array response vector that depends only on the array structure. For a uniform linear array (ULA) antenna structure, the array response can be expressed as \(\mathbf{a}_{ULA}(\phi)=\frac{1}{\sqrt{N_{U}}}[1,e^{jkl\sin{(\phi)}},...,e^{j(N_{t}-1 )kl\sin{(\phi)}}]^{T}\), where \(k=2\pi/\lambda\), and \(d\) is the inter-element spacing [12].
## 4 Modeling and Implementations of Software-Defined Vehicular Networks for V2X
### Architecture Overview of Software-Defined Vehicular Networks  
Figure 3 depicts the components of various wireless communications in the software-defined vehicular network. To allow an SDN-based vehicular network (SDVN), simulation conducted on it leads to a certain number of SDN components. The SDN controller is the central logical intelligence of the SDN-based vehicular system. The SDN controller has a generalized and global view of the vehicular network and implements Openflow protocol to handle routing policies to eNB-type RSU controller on RSU. In fact, eNB-type RSU controller deployed on the edge of the vehicular network shortens the decision of generating new routing packets undefined in forwarding devices' flow tables. The SDN controller V2X network management conveys routing policies to UEs (vehicles) by implementing ITS's goals set up on the cloud or at the edge of the network for lowering processing decisions. The SDN controller is not only responsible to provide the whole performance but also provide routing rules for wireless devices (vehicles) selecting best routing paths to their destinations in VANET. OpenFlow enabled V2X-EU is the SDN wireless node and is responsible to control the data plane elements [12]. Data plane on vehicle implements OpenFlow protocol and is embedded in the OnBoard Diagnostic Unit (OBU). Furthermore, data plane elements are the VUEs that perform control message in term of routing policies from the eNB-type RSU controller to execute predefined actions which state ITS's goals once implemented in the application plane of the SDVN.
# An Efficient Approach to Manage Natural Noises in Recommender Systems
## 1 Introduction
The proposed scheme detects natural noises according to the inconsistency between the rating behaviors and characteristics. Different from previous studies, we divide users into three subcategories: Negative, Average, and Positive. Meanwhile, items are divided into No-Preferred, Av-Preferred, and Preferred. Then, we consider the probability that each user belongs to each subcategory and correct the natural noise with threshold values weighted by probabilities. Experimental results on different datasets indicate that our scheme can effectively and efficiently manage natural noises in recommender systems. The contributions of this paper are described as follows:  
* The rules for detecting natural noises are proposed based on the subcategories of users and items. The proposed scheme detects natural noises only depending on historical ratings in the recommendation and does not introduce additional information into the recommender system. * In the proposed natural noise management scheme, we do not eliminate it directly but correct the natural noise with threshold values weighted by probabilities. Compared with re-predicting methods, our scheme has a much higher efficiency. * Experimental results on different datasets indicate that our proposed scheme performs better than previous typical methods. It has high potential to be used as a scheme for managing natural noises and provides a great decision support for recommender systems. The remaining parts of this paper are organized as follows. Section 2 introduces the related works. Section 3 describes the proposed method of detecting and correcting natural noises in the ratings. Section 4 analyzes the performance of the proposed scheme on three datasets. The conclusion is drawn in Section 5.
# An Improved Multi-Chaotic Public Key Algorithm Based on Chebyshev Polynomials
## 2 Preliminary
### Chebyshev-ElGamal Public Key Cryptographic Algorithm (CEPKC)
#### 2.2.1 CEPKC Algorithm  
Assuming [PERSON] is the sender and [PERSON] is the receiver, the communication process of the ElGamal-type public key algorithm based on finite field Chebyshev polynomials (CEPKC) proposed by [PERSON] et al. [51] is as follows:  
1. Key generation. [PERSON] randomly generates a large prime number \(N\), chooses positive integers \(x\) and \(s\), such that \(x<N\), \(s<N\), and computes \(y=T_{s}(x)modN\). [PERSON]'s public key is \((x,N,y)\), and the private key is \(s\).
2. Message encryption. After receiving [PERSON]'s public key \((x,N,y)\), [PERSON] converts the message to be encrypted into an integer \(m(1<m<N)\). She chooses a random number \(r\), such that \(0<r<N\). Using the public key \((x,N,y)\), she calculates \(c_{1}=T_{r}(x)(modN)\) and \(c_{2}=(m\cdot T_{r}(y))(modN)\) then sends the ciphertext message \(c=(c_{1},c_{2})\) to [PERSON].
3. Message decryption. Upon receiving the ciphertext message \(c\), [PERSON] can decrypt it using the private key \(s\) to recover the plaintext message \(m=(c_{2}\cdot T_{s}(c_{1})^{-1})(modN)\).
# Energy-Aware Control of Error Correction Rate for Solar-Powered Wireless Sensor Networks
###### Abstract  
In a wireless sensor network (WSN) environment with frequent errors, forward error correction (FEC) is usually employed at the link layer to achieve reliable transmission. In the FEC scheme, the error correction rate varies depending on the length of parity used for the recovery of broken data. The longer the parity length, the higher the possible error correction rate. However, this also means that the energy consumption increases. Meanwhile, in a solar-powered WSN, the energy of each node can be periodically collected, but the amount of collected energy varies drastically depending on the harvesting environment, including factors such as the weather, season and time of day. Therefore, each node must control energy consumption according to the energy harvesting rate. The scheme proposed in this study executes this control by adaptively adjusting the parity length of FEC according to the given energy budget of a node for the next period. This means that the error recovery rate can be increased as much as possible without adversely affecting the blackout time. Simulation results show that the proposed scheme improves the amount of data collected from the entire network for each environment compared with previous schemes.  
solar-powered; wireless sensor network; energy-aware; forward error correction; Reed-Solomon; blackout time; throughput Article