esa-sceva/satcom-chunk-collection huggingface dataset:
# Model-Free Tracking Control with Prescribed Performance for a Shape Memory Alloy-Based Robotic Hand
## 4 Control Method Design
### Controller Design  
Consider a discrete terminal sliding surface:  
\[s(k+1)=s(k)+\rho_{1}\mu(k+1)-\rho_{2}\mu^{\alpha}(k), \tag{18}\]  
where \(\rho_{1}\) and \(\rho_{2}\) are positive constants, \(\alpha\) is the ratio of two odd integers, with \(0<\alpha<1\).  
Then, substituting (14) into (18) yields:  
\[s(k+1)-s(k)=\rho_{1}\frac{\kappa\epsilon(k+1)}{\sqrt{\
u^{2}(k+1)-\epsilon^{2 }(k+1)}}-\rho_{2}\mu^{\alpha}(k). \tag{19}\]  
The tracking error is proposed in the following:  
\[e(k+1)=y_{d}-y_{a}(k+1)=y_{d}-\hat{M}(k)-\frac{1}{\hat{B}(k)}\Delta u(k)-\hat{ D}(k), \tag{20}\]  
where \(y_{d}\) is the reference output. On the basis of the equivalent reaching law:  
\[\Delta s(k)=s(k+1)-s(k)=0. \tag{21}\]  
The control input is proposed in the following:  
\[u(k)=u(k-1)+u_{eq}+u_{sw}, \tag{22}\]  
where \(u_{eq}\) and \(u_{sw}\) are the equivalent control input and the switching control input, respectively. Then, from (17)-(19), one has:  
\[u_{eq}=\hat{B}(k)(y_{d}-\hat{M}(k)-\hat{D}(k)-\Gamma(k)), \tag{23}\]  
where \(\Gamma(k)=\frac{\rho_{2}\mu^{\alpha}(k)\
u(k+1)}{\sqrt{(\rho_{1}^{2}\kappa^{2 }+\rho_{2}^{2}\mu^{2}(k))}}\).  
To deal with the robustness of \(u_{eq}(k)\), define the following the switching control law  
\[u_{sw}=\xi\hat{B}(k)\text{sign}(s(k)). \tag{24}\]Substituting (18), (21) and (22) into (17) yields to:  
\[\begin{split}&\Delta s(k+1)=s(k+1)-s(k)\\ =&\frac{\rho_{1}\kappa\left(\Gamma(k)-\xi sign(s(k)) \right)-\rho_{2}\mu^{\alpha}(k)\sqrt{v^{2}(k+1)-\left(\Gamma(k)-\xi sign(s(k)) \right)^{2}}}{\sqrt{v^{2}(k+1)-\left(\Gamma(k)-\xi sign(s(k))\right)^{2}}}. \end{split} \tag{25}\]  
It is notable that \(\Gamma(k)\) is bounded, that is, \(|\Gamma(k)|<\Gamma_{0}\). Then, \(\xi\) can be assumed to meet the requirements as follows:  
\[\left\{\begin{array}{ll}\Gamma_{0}-\frac{\rho_{2}\mu^{\alpha}(k)\sqrt{v^{2} (k+1)-\left(\Gamma(k)-\xi\right)^{2}}}{\rho_{1}\kappa}<\xi<\frac{\rho_{2}\mu^ {\alpha}(k)\sqrt{v^{2}(k+1)-\left(\Gamma(k)-\xi\right)^{2}}+2v(k+1)}{\rho_{1} \kappa}-\Gamma_{0}&s(k)>0\\ \Gamma_{0}+\frac{\rho_{2}\mu^{\alpha}(k)\sqrt{v^{2}(k+1)-\left(\Gamma(k)+\xi \right)^{2}}}{\rho_{1}\kappa}<\xi<\frac{\rho_{2}\mu^{\alpha}(k)\sqrt{v^{2}(k+1 )-\left(\Gamma(k)+\xi\right)^{2}}+2v(k+1)}{\rho_{1}\kappa}-\Gamma_{0}&s(k)<0. \end{array}\right. \tag{26}\]  
Finally, the control input is proposed as follows  
\[u(k)=\hat{B}(k)(y_{d}-\hat{M}(k)-\hat{D}(k)-\Gamma(k))+\xi\hat{B}(k)\text{sign }(s(k)). \tag{27}\]  
The schematic diagram of the control system is shown in Figure 5. The proposed control method merely depends on the I/O data. According to the reference output \(y_{d}\) and the output of the SMA-based robotic hand \(y_{a}(k)\), the transformed error \(\
u(k-1)\) can be obtained. According to the I/O data of the SMA-based robotic hand, the estimation of time-varying parameters \(\hat{M}(k-1)\) and \(\hat{B}(k-1)\) can be obtained. Then, through sliding mode control, we can get the control input \(u(k)\).
## 3 Survey of Techniques for Updating
### Dissemination
#### 3.1.13 Parallel Diffusion Mechanism (PDM)  
PDM [40] uses dynamic transmit power control to improve dissemination performance through interference-free simultaneous transmissions. The inverse-squared relationship between distance and power is used to estimate the transmission region of each node. Code is propagated by forwarding nodes (F-nodes), which advertise a _ProgramID_ and _SectionID_. Nodes which have received code segments successfully become candidate forwarding nodes (CF-nodes) and may be selected as future F-nodes. Neighbour candidate forwarding nodes (NCF-nodes) are CF nodes with overlapping transmission regions. Unknown nodes (U-nodes) have not yet received the code segments, and public nodes (P-nodes) are U-nodes located in the overlapping regions of CF-nodes. U-nodes and P-nodes respond to advertisements, and CF-Nodes use these responses to estimate the minimum power level required for overlap-free transmissions. When NCF nodes overlap, one goes to sleep to save energy, and reduce overlap.  
The reduction in both latency and energy use, are valuable optimisations for software updating. The increased parallelism may provide a useful feature for autonomic behaviour.
## 4 Novel Digital Cash Paradigm Description
### e-Coin Composition  
Given vendor's public key, \(PK_{V}\), an e-coin is a tuple of the form represented in Equation (3). Components subindexed with \(S\) refer to a cryptographic key-pair used for 'Signing' a transaction. The mentioned key-pair is used by the customer to issue a signature when the e-coin is spent. Those tuple components subindexed with \(R\) are related to a key-pair used for 'Receiving' the acquired digital item. Digital items are encrypted by the vendor under public key \(Q_{R}\) before transmitting them to customers.  
\[\{v_{S},Q_{S},(X_{S},Y_{S}),v_{R},Q_{R},(X_{R},Y_{R}),Y,\text{Sign}_{PK_{V}}(Y ).\} \tag{3}\]  
All the e-coin components but the last one (\(\text{Sign}_{PK_{V}}(Y)\)) are always generated by the customer. If the e-coin is valued, signature \(\text{Sign}_{PK_{V}}(Y)\) is computed by the vendor; otherwise, it is simulated by the customer. Regarding the components of an e-coin,  
* \(v_{S}/Q_{S}\) is a private/public key-pair of a public key cryptosystem allowing digital signature computation. Hence, data signed with \(v_{s}\) can be validated under \(Q_{S}\). \(Q_{S}\) has been \(\text{OAEP}_{\text{PA}}\)-encoded (with plaintext-awareness) into \((X_{S},Y_{S})=\text{OAEP}_{\text{PA}}(Q_{S},r_{S})\) for some random \(r_{S}\).
* \(v_{R}/Q_{R}\) is a private/public key-pair of a public key cryptosystem allowing data encryption. Hence, data encrypted under \(Q_{R}\) can only be decrypted by providing \(v_{R}\). \(Q_{R}\) has been OAEP-encoded into \((X_{R},Y_{R})=\text{OAEP}(Q_{R},r_{R})\) for some random \(r_{R}\).
* Let \(Y=Y_{S}\oplus Y_{R}\). Then, \(\{Y,\text{Sign}_{PK_{V}}(Y)\}\) is a digest-signature tuple which can be validated under \(PK_{V}\).
## 2 Literature Review
### Transaction Security  
Many well-known domestic and foreign scholars have pointed out that security and trust are the biggest considerations for consumers in their research on online transaction issues. [PERSON] and [PERSON] (2021) found that trust occurs when one party has confidence in an exchange partner's reliability and integrity [29]. Users need to utilize a new service with comfort, safety, and fewer feelings of risk. Many studies have concluded that trust has a positive effect on user intention to adopt mobile payment [30,31]. Security is the biggest consideration for customers when banking online, therefore privacy protection and information security management are key for users' confidence towards websites. Only when consumers' transaction security of payment can be assured will consumers dare to transact through online or mobile payment. [PERSON] (1996) pointed out that confidentiality and data security on the Internet is imperative [32]. This study refers to \"Secure Electronic Transaction\" released in 1996 regarding transaction security constructs, which are then modified to fulfill research-specific needs applicable to the mobile payment transaction security context. The five transaction security constructs such as confidentiality, integrity, authentication, transaction non-repudiation, privacy and other security requirements are summarized as follows:  
1. Confidentiality: Transactions cannot be traced over public networks, and information about transactions cannot be obtained by unauthorized intermediaries. All information transacted in an e-commerce environment is kept confidential.
2. Integrity: Transactions must not be disrupted or interfered with. It should be confirmed that the content of the electronic transaction has not been changed during the transmission between the client and the server--that is, the information cannot be arbitrarily added, deleted or modified during the transaction processing.
3. Authentication: There needs to be assurance that the identity of a subject or resource is characteristic of the person it declares. Authentication applies to users, programs, systems, and information.
4. Transaction non-repudiation: The sender of the transaction has its own unique electronic signature, making it impossible to deny the fact of sending this document.
5. Privacy: Transactions should remain inviolable, and messages sent and received over the Internet cannot be read, modified or intercepted by any other parties.  
In summary, in e-commerce and online banking services, security is regarded as a website's basic construction requirement, and its confidentiality and data security are very important issues in online services, and it is amongst clients' greatest concerns [33].
## 3 Explosion Test Program
### Fiber-Hybrid Effect Evaluation  
To assess the hybrid effect of PP and PVA-fiber against explosion loadings, Figure 14(a) compared the damage coefficient \(K_{a}\) while Figure 14(b) depicted the blast-resistant coefficient \(K_{b}\) for HFRC with same steel fiber content addition. It was interesting that as steel content raises, the damage coefficient \(K_{a}\) for PP-Steel HFRC tends to decrease while \(K_{a}\) for PVA-Steel HFRC almost remains constant. The least value for \(K_{a}\) occurred around 1.5% Steel + 0.5% polymer (PP or PVA) fiber content. For both PP-Steel and PVA-Steel HFRC panels, the \(K_{b}\) exhibited increase and then decrease tendency with increasing steel fiber content.  
The hybrid effect on the blast resistance of HFRC panels against contact detonation was evaluated by introducing the hybrid effect index \(\alpha\) with reference to [24,36]:  
\[\alpha=\frac{K_{H}-K_{0}}{\sum(K_{i}-K_{0})\beta_{i}} \tag{3}\]  
Figure 14: Damage coefficient \(K_{a}\) and blast-resistant coefficient \(K_{b}\).  
Figure 15: Hybrid effect in terms of damage effect and blast-resistance coefficient.  
where \(\beta_{i}=V_{i}/V\) denotes the fiber volume fraction of one kind fiber in the whole volume of fiber \(V\), \(V_{i}\) is the volume of Steel, PP or PVA-fiber, \(K_{i}\) is \(K_{a}\) or \(K_{b}\) of concrete incorporated with single kind fiber, \(K_{0}\) is \(K_{a}\) or \(K_{b}\) of control specimen (plain concrete), \(K_{H}\) represents \(K_{a}\) or \(K_{b}\) of HFRC. To exclude the effect of fiber content, the \(K_{H}\) calculation should correspond to \(K_{i}\) with the same volume fiber reinforcement. This work focused on the 2% content fiber-hybrid effect evaluation. If \(\alpha>1\), the fiber-hybrid effect is positive for blast-resistant improvement, otherwise the hybrid effect is negative.  
The fiber-hybrid effect evaluation was conducted with Equation (3), and the results for both PP-Steel and PVA-Steel HFRC were plotted in Figure 16. For all the hybrid fiber mixes, the hybrid effect index \(\alpha<1\), suggesting negative hybrid effect. Nonetheless, PP-fiber had an advantage over PVA-fiber in terms of fiber-hybrid effect on blast-resistance improvement.
## 7 Simulation Results Discussion: Analysis and Evaluation
### Evaluating RSU Availability and Overnight Connectivity
#### 7.2.1 Vehicle and Communications over Time with Limited Connectivity  
Figure 14 illustrates vehicle communication metrics over a 24 h period under a scenario of low connectivity with one RSU.  
The left graph in Figure 14 shows a time series over 24 h, charting the growth of CV and CPV (see Section 7.1 for abbreviations). The CV increases steadily, whereas the CPV count grows more slowly, which could be indicative of the limited presence of only one RSU. Despite the increasing number of vehicles, the ratio of CPV to CV remains constant, suggesting a uniform Pre-Signature distribution over time. This is further supported by the right graph, where the ratio between CV and CPV quickly converges to be constant.  
An argument could be made that the fraction/number of vehicles with a Pre-Signature is not the quantity of interest as some vehicles may never/rarely communicate with other vehicles, and the presence of an up-to-date reputation is less relevant in such a case. One should not expect the presence of a Pre-Signature to be independent from the amount of communication a vehicle carries out as an isolated vehicle far from a town is less likely to have a Pre-Signature and is expected to communicate less--and vice versa for a vehicle in a town.  
Figure 15 offers a view of various communication metrics over time. It encompasses the TC, depicted in blue, which represents the total number of communications. The RC, in green, indicates those communications deemed reputable; the goal of any reputation system is to have this value as high as possible. The ONRC, shown in purple, highlights reputable communications accessible online. This is the performance of a naive reputation system without Pre-Signatures. Crucially, the offline reputable communications ORC shown in red represents the reputable communications conducted offline, which were enabled by our Pre-Signature scheme. This metric, underpinning the Pre-Signature scheme, emphasizes the strength and reliability of communications in offline settings. The steady or increasing trend of the red line on the graph underscores the robustness and adaptability of our Pre-Signature scheme, ensuring effective and secure transactions even without online connectivity. In the graph on the right, the line displays the ratio of RC to TC, providing a measure of communication quality relative to its quantity.  
The ratios CPV:CV and RC:TC converge to similar values. However, there are two opposing effects at play. A vehicle could receive the Pre-Signature near the end of its lifetime, decreasing RC:TC relative to CPV:CV. Conversely, cars receiving Pre-Signatures are close to RSUs, which tend to be in busier areas, meaning a higher degree of communication for vehicles with a Pre-Signature. For the parameters chosen for this specific scenario,  
Figure 14: Vehicle Communication Activity Over Time with Limited Connectivity.  
they happen to cancel out; this is not generally the case. It is important to understand the relationship between the parameters.
## 2 Weather Data Super-Resolution Algorithm Based on CBAM-SRResNet
### SRResnet  
SRResNet aims to learn the mapping function between low-resolution and high-resolution weather data. The function is shown in Equations (1)-(6).  
(1) Feature Extraction  
\[F_{0}=\sigma(W_{0}*I_{LR}+b_{0}) \tag{1}\]  
where \(I_{LR}\) is the input low-resolution image, \(W_{0}\) is the first layer convolution kernel, \(\sigma\) is the activation function, and \(*\) is the convolution operation.  
(2) Residual Block \(\times\) N  
The structure of each residual block is as follows:  
\[F_{i}(x)=W_{2}{}^{(i)}*\sigma(BN(W_{1}{}^{(i)}*x)) \tag{2}\]  
Figure 2: CBAM-SRResNet network structure.  
\[y_{i}=F_{i}(x)+x \tag{3}\]  
where \(x\) is the input of the \(i\)-th residual block, \(W_{1}{}^{(i)}\) and \(W_{2}{}^{(i)}\) are the weights of the two layers of convolution, and BN is Batch Normalization.  
(3) Long Skip Connection  
A convolutional layer after the residual block restores the dimension to the input feature dimension and then adds \(F_{0}\) (long jump connection):  
\[F_{1}=W_{res}*F_{res}+F_{0} \tag{4}\]  
where \(W_{res}\) is the convolution kernel.  
(4) Sub-Pixel Convolution  
Sub-pixel convolution, also known as Pixel Shuffle, is a method of up-sampling through convolution operations [36]. It is commonly used in image super-resolution reconstruction. Its structure is shown in Equation (5).  
\[F_{2}=PS(\sigma(W_{up1}*F_{1})) \tag{5}\]  
where PS is the Pixel Shuffle operation, and \(W_{up1}\) is a convolution kernel for up-sampling.  
As shown in Figure 3, the second and third parts are convolutional neural networks (CNNs), and the four-part sub-pixel convolution layer is a sub-pixel convolution. If low-resolution weather data are magnified by R times, it is necessary to generate a weather feature map of R\({}^{2}\) channels through a convolutional neural network and then arrange the meteorological feature maps of these R\({}^{2}\) channels in a specific way through pixel cleaning to obtain high-resolution weather data.  
(5) Output Layer  
The last layer is used to generate high-resolution images:  
\[I_{SR}=W_{out}*F_{2} \tag{6}\]  
where \(I_{SR}\) is the output high-resolution image, and \(W_{out}\) is the convolution kernel.
# Deep Learning-Based Detection Technology for SQL Injection Research and Implementation
###### Abstract  
Amid the incessant evolution of the Internet, an array of cybersecurity threats has surged at an unprecedented rate. A notable antagonist within this plethora of attacks is the SQL injection assault, a prevalent form of Internet attack that poses a significant threat to web applications. These attacks are characterized by their extensive variety, rapid mutation, covert nature, and the substantial damage they can inflict. Existing SQL injection detection methods, such as static and dynamic detection and command randomization, are principally rule-based and suffer from low accuracy, high false positive (FP) rates, and false negative (FN) rates. Contemporary machine learning research on SQL injection attack (SQLIA) detection primarily focuses on feature extraction. The effectiveness of detection is heavily reliant on the precision of feature extraction, leading to a deficiency in tackling more intricate SQLIA. To address these challenges, we propose a novel SQLIA detection approach harnessing the power of an enhanced TextCNN and LSTM. This method begins by vectorizing the samples in the corpus and then leverages an improved TextCNN to extract local features. It then employs a Bidirectional LSTM (Bi-LSTM) network to decipher the sequence information inherent in the samples. Given LSTM's modest effectiveness for relatively long sequences, we further integrate an attention mechanism, reducing the distance between any two words in the sequence to one, thereby enhancing the model's effectiveness. Moreover, pre-trained word vector features acquired via BERT for transfer learning are incorporated into the feature section. Comparative experimental results affirm the superiority of our deep learning-based SQLIA detection approach, as it effectively elevates the SQLIA recognition rate while reducing both FP and FN rates.  
deep learning; SQL injection detection; TextCNN; LSTM +
Footnote †: journal: Applied Sciences
# Causal Inference-Based Self-Supervised Cross-Domain Fundus Image Segmentation
## 1 Introduction
However, existing methods [10; 11; 12; 13; 14] suffer from two major limitations: first, they predominantly utilize labeled source-domain data for supervision, lacking effective constraints on unlabeled images; second, the use of a shared network for processing images from different domains makes it challenging to completely eliminate interference caused by domain style differences. To tackle the issue of domain style interference in cross-domain segmentation, we propose a self-supervised cross-domain segmentation method based on causal inference. First, to generate more reliable pseudo-labels, we introduce a causal inference strategy. By constructing a structural causal model (SCM), we explicitly delineate the causal relationships among target images, feature maps, domain style interference, and prediction outcomes, and employ a back-door adjustment strategy to sever the confounding pathways introduced by domain style. This converts the conditional distribution of pseudo-label generation into an interventional distribution, thereby enhancing the reliability of the generated pseudo-labels and providing robust support for subsequent model training. In this process, we utilize the Fourier transform to achieve style transformation between source and target domain images, simulating the stratification operation in back-door adjustment so that the model can fully account for interference under different domain styles. Moreover, we design a dual-path segmentation network that processes source and target domain style images separately, ensuring that each network exclusively extracts features corresponding to a single domain style, thus further mitigating the adverse effects of domain style interference. In addition, we incorporate adversarial learning and cross-domain contrastive learning strategies. Through adversarial training, the model can align the feature representations of original and style-transferred images, thereby effectively narrowing the visual gap between domains and mitigating the adverse effects of style discrepancies on segmentation performance. Meanwhile, cross-domain contrastive learning minimizes the distance between positive sample pairs, thereby reinforcing the structural consistency between images of different styles. These strategies not only help to further reduce domain style interference but also enhance the model's generalization capability on cross-domain data. Our main contributions can be summarized as follows:  
* We propose a causal inference-based pseudo-label fusion module for self-supervised learning that effectively reduces domain style bias and imposes constraints on target domain images. * We introduce adversarial learning [15, 16] and cross-domain contrastive learning mechanisms, which reduce the distribution discrepancy between source and target domains. * We conduct extensive experiments on three publicly available datasets, and the results fully demonstrate the effectiveness of the proposed modules as well as a significant improvement in overall performance.
## 3 Results and Discussion
### Separation Rate, Mix Rate and Broken Rice Rate  
For the rice mill's design, separation rate, mix rate and broken rice rate are important evaluation factors. The simulation is conducted under an air speed of 6-8 m/s, and the jet air speed is 10-20 m/s. The separation rate, mix rate and broken rice rate under working conditions are shown in Table 5. When the air speed is 6 m/s, the jet air speed is 10 m/s, the separation rate is 95.9%, the mix rate is 2% and the broken rice rate is 5.9%. When the air speed is 6 m/s, the jet air speed is 20 m/s, the separation rate is 88.2%, the mix rate is 2.3% and the broken rice rate is 6.9%. When the air speed is 8 m/s, the jet air speed is 10 m/s, the separation rate is 96.8%, the mix rate is 3.2% and the broken rice rate is 5.5%.  
When the air speed is 8 m/s, the jet air speed is 20 m/s, the separation rate is 90.1%, the mix rate is 4.6% and the broken rice rate is 7.2%.  
Compared with the separation experiment's results, when the air speed is 6 m/s, the jet air speed is 10 m/s and the air speed \(v_{o}\) = 8 m/s, the jet air speed is 10 m/s, the separation rate is increased, the mix rate and the broken rice rate are reduced. When the air speed is 6 m/s, the jet air speed is 20 m/s and air speed is 8 m/s, the jet air speed is 20 m/s and the separation rate of the experimental results is higher than that of the simulation results. The results show that the separation rate and the mix rate increase when the upper air inlet's air speed increases. The larger the jet air speed, the weaker the airflow movement in the air duct. The influence of airflow on the particles in the milling chamber is greater, resulting in a decreasing separation rate and an increasing broken rice rate. The simulation results are consistent with the experiment results when comparing the evaluation factors in Tables 1 and 5. When the air speed is 6 m/s, the jet air speed is 10 m/s and the air speed is 8 m/s, the jet air speed is 10 m/s and the rice milling performance is better.