esa-sceva/satcom-chunk-collection huggingface dataset:
# Boosting Targeted Black-Box Attacks via Ensemble Substitute Training and Linear Augmentation
## 2 Threat Model  
With the popularity of DNNs on image classification, some researchers found that DNNs are vulnerable. A taxonomy of adversaries against DNN image classification is found in [10]. In our work, the adversaries attempt to find a small perturbation added on the input image that is not visible to human eyes but can force the DNN classifier to classify the input into any specific class. To achieve this, we consider a weak adversary with access to the DNN output only, which is called black-box assumption. In addition, in the context below, we will describe our threat model detailed and introduce some notations that may be frequently used in the following sections.  
For an image classification system \(O:x\to y\), it outputs a label \(y\) or the predicting vector \(\hat{y}\) corresponding to the input \(x\). In adversarial attacks, our aim is to craft adversarial examples \(x^{*}=x+\delta_{x}\), where \(\delta_{x}\) is the perturbation. Corresponding to the adversarial goals, there are two kinds of adversarial examples: _non-targeted adversarial examples_ and _targeted adversarial examples_[21]. As the names suggest, the difference between these two kinds of examples is whether the adversarial examples is just misclassified or classified to any other desired class \(t\) by the target classifier.  
Aiming at black-box attacks, we cannot acquire the inside parameters of target model \(O\). In addition, here we consider targeted model outputs a probability vector \(O(x)\) when given an input image \(x\). In addition, \(O(x)_{i}\) indicates the confidence of \(O\) classifying \(x\) into the component index \(i\), which represents a specific class. Thus, we can easily know the predicted label, denoted as  
\[O^{*}(x)=\arg\max(O(x)). \tag{1}\]  
In addition, we consider that the adversary can only **query**\(O\) to acquire the pair \((x,O(x))\). They do not have access to the internal information of the classifier \(O\) or the training dataset. In addition, to make our attack closer to the real case, we assume that the adversary can only query the target model no more than 10% of its training dataset, which is are much stricter rules that many other works did not keep.
# Positioning-Based Uplink Synchronization Method for NB-IoT in LEO Satellite Networks
## 2 System Model
### Downlink NPSS in NB-IoT  
This study focuses on utilizing the NPSS received by NB-IoT terminals to estimate time-delay and Doppler shift observations. Figure 2 illustrates the structure of the downlink NPSS in NB-IoT systems. In NB-IoT, each radio frame (RF) has a duration of 10 ms and is composed of 10 subframes (SFs), each lasting 1 ms. Each subframe consists of two 0.5 ms slots, and each slot contains seven OFDM symbols. The NPSS is transmitted in the fifth subframe of every radio frame, with a repetition period of 10 ms.  
The NPSS in the NB-IoT downlink is generated based on an 11-length short Zadoff-Chu (ZC) sequence using the following formula:  
\[S(k)=e^{-\frac{i\text{{\rm{\rm{\rm{\rm{\rm{\rm{\rm{\rm{\rm{\rm{\rm{\rm{\rm{ \rm{\rm{\rm{\rm{\rm{\rm{\rm{\rm{\rm{ \rm{  }}}}}}}}}}}}}}}}}},k=1,2,\ldots,11 \tag{1}\]  
Here, \(k\) denotes the sequence index, and \(u\) is the physical root index used to generate the ZC sequence. In the NB-IoT system, \(u=5\). The resulting 11-point short ZC sequence is transformed into the time domain using the inverse fast Fourier transform (IFFT). By adding a CP and repeating the signal 10 times according to the rules, we obtain the NPSS \(x(n)\). The corresponding signal received by the NB-IoT terminal, denoted as \(y(n)\), is expressed as [15]:  
\[y(n)=\sum_{l=0}^{L-1}h(l)x(n-\delta-l)e^{l2\pi nx/N}+\omega(n) \tag{2}\]  
where \(\delta\) represents the normalized symbol timing offset, \(\varepsilon\) represents the normalized carrier frequency offset, \(n\) denotes the sample index, \(N\) denotes the point of FFT, \(h(l)\) represents the impulse response of the multipath channel with \(L\) uncorrelated taps, and \(w(n)\) is additive white Gaussian noise (AWGN) with zero mean and variance \(\sigma^{2}\).  
The NPSS is generated from an 11-point short ZC sequence. The ZC sequence has excellent correlation properties, as its autocorrelation function is nearly zero at all non-zero lags. However, in satellite channels with large Doppler shifts, the peak of the correlation function is significantly degraded, which affects synchronization accuracy.  
Figure 2: NPSS structure diagram.  
Compared with the 5G primary synchronization signal (PSS), which occupies 127 subcarriers in the frequency domain and spans 1 OFDM symbol in the time domain, the NB-IoT NPSS only occupies 11 subcarriers in the frequency domain but spans 11 OFDM symbols in the time domain. This design aligns with NB-IoT's narrowband characteristics--the system has a bandwidth of only 180 kHz, necessitating a longer time-domain signal duration to ensure sufficient detection performance. Therefore, the synchronization method proposed in Section 3 is specifically designed to exploit the time-domain symmetry of NPSS under large Doppler conditions.
## 2 Materials and Methods
### Statistical Analysis  
_S. mutans_ growth controls were used as a primary outcome variable. For each formulation, means and standard deviations were calculated for the quantitative experiments (MIC, BPC, MTT, CV). For each test, six different concentrations were used and compared to the absence of inhibitor; for each concentration, 6 replicates (n = 6) were analyzed. Statistical analysis was performed using R software for Windows (R version 4.2.1 64 bit, the R foundation for statistical computing). The Shapiro-Wilk and Levene tests were used to evaluate the normality and overall homogeneity of the datasets, respectively. Then, to evaluate the significance of results found in the differential inhibitory effect on mature biofilms, assessed through MTT and CV assays, the Student \(t\)-test was used. Eight replicates of each CHG concentration were considered for each formulation and tested to verify that the differences found were statistically representative. A value of \(p\) < 0.05 was evaluated as statistically significant. Finally, the chi-square and Fischer exact tests were used to assess if results obtained with undiluted formulations were statistically similar to those obtained with CHG reference compound (2 mg/mL). A value of \(p\) < 0.05 was evaluated as statistically significant.
## 4 Experimental Results
### Histogram of Images  
The histogram analysis helps to analyze the uniform distribution of the pixel intensities, which are important to conceal the image from attacks. The histograms for the encrypted image and the information embedded image are flat, which helps in protecting images from attack. The histograms for the _Airplane_, _Baboon_, _Boat_ and _Peppers_ images are shown in Figure 6. The figure shows the histogram outputs for the original, encrypted, and data-embedded images.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Name of** & **Entropy** & **Entropy** & **Entropy (Data** \\
**the Image** & **(Original Image)** & **(Encrypted Image)** & **Embedded Image)** \\ \hline
**Airplane** & 6.7025 & 7.9992 & 7.9992 \\ \hline
**Baboon** & 7.3583 & 7.9994 & 7.9994 \\ \hline
**Boat** & 7.1914 & 7.9993 & 7.9993 \\ \hline
**Peppers** & 7.5937 & 7.9992 & 7.9992 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Entropy measured on airplane, baboon, boat and peppers images.  
Figure 6: Histogram results of the four images: airplane, baboon, boat and peppers. Figure (**a–d**): Original image histograms, Figure (**e–h**): Encrypted image histograms, Figure (**i–l**): Histograms from the images after hiding the data.
## 2 Numerical Simulations
### Time-Step and Grid Independence  
The time step per iteration is appropriately selected to obtain details of the switching process of the supply jet and should be short enough to describe the movement with desired precision. It should be noted that divergence problems may occur when the time step is increased too much, since the moving grid is sensitive to the scale of the time step. According to the previous computational results, the time step of 1E-4 is reasonable for the current simulations [22; 25; 29].  
On the other hand, a fine grid (around 120,000) and a coarser grid (around 50,000 cells) have been created to investigate the grid independence. As shown in Table 1, the maximum difference between all of the obtained results was less than 6%. It has been observed that even the coarse grid can provide  
Figure 2: A typical grid model of flow domain and the imposed boundary conditions.  
a sufficient grid independency. Considering the reasonable computational time and the satisfactory model accuracy, the original computational model is indicated to be adequate.
## 2 Related Works
### Optimization-Associated Research
So, there is a need to develop a new model which fulfills these criteria. Therefore, in our proposed work, the privacy-preserving mobility mode and optimization-based advanced cluster head selection are performed. ROA-based CH selection is done to improve efficiency and reduce the congestion and overhead in the network. The privacy-preserving mobility \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Ref. No** & **Method** & **Advantage** & **Disadvantage** \\ \hline
[24] & Destination aware context-based routing scheme & Packet delivery ratio is high and network delay is low & Throughput is low \\ \hline
[25] & Modified \(k\)-means algorithm and maximum stable set problem & Packet delivery ratio is high and throughput is high & Overhead and delay is high \\ \hline
[26] & Hyper graph clustering model & Efficiency, throughput, and packet delivery ratio is high & Delay and routing overhead is high \\ \hline
[27] & Fuzzy and game theory-based clustering & Lifetime and efficiency are high & Throughput and packet delivery ratio is low \\ \hline
[28] & Cluster-based routing protocol & Packet delivery ratio is high and network delay is low & Overhead is more and throughput is low \\ \hline
[29] & Multi-channel clustering-based congestion control algorithm & Energy consumption is low and energy efficiency is high & Throughput is low \\ \hline
[30] & Reliability aware multi-objective optimization & Packet delivery ratio is high & Routing overhead is high \\ \hline
[31] & Multi-objective Harris Hawks optimization & Packet delivery ratio is high, throughput is high, and network delay is low & Not suitable for a network with huge dynamic mobility model \\ \hline
[32] & Cuckoo search optimization & Packet delivery ratio is high & Overhead is more and throughput is low \\ \hline
[33] & Hybrid Genetic Firefly Algorithm-based Routing Protocol & Convergence speed of the network is high & Throughput is low \\ \hline
[34] & AODV with ant colony optimization & Throughput is high, packet loss is low & Routing overhead is high \\ \hline
[35] & Particle swarm optimization & Packet delivery ratio is high and throughput is high & Overhead is more \\ \hline
[36] & A grey wolf optimization-based clustering algorithm & Throughput is high & Efficiency is low \\ \hline
[37] & Ant colony optimization & Computational cost and end-to-end delay are low & Routing overhead is high, throughput is moderate \\ \hline
[38] & Quality of service (QoS)-based mobility management & Packet delivery ratio and network stability is high & Throughput is moderate \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of the Literature. model is used to improve the accuracy of the network by enhancing security. A detailed elaboration of the proposed method is given below.
## 3 Results and Discussion
### Effects of Resin Content
#### 3.4.3 Compressive Strength  
The compressive strength was plotted as a function of resin content for all produced particleboards in Figure 14b. The results proved that the effect of the resin content on compressive strength is very important. While increasing the UF content led to improved compressive strength of the produced particleboard, at a particleboard density of 0.7 g/cm\({}^{3}\), increasing the resin content from 10% to 14% led to increasing the compressive strength from 1.55 to 4.46 MPa.  
Figure 13: Bending properties of particleboard as a function of resin ratio of urea formaldehyde.
# Multi-Objective Edge Node Deployment Method Based on Improved Heuristic Algorithms for Smart Mines
## 2 Related Work
They proposed a dynamic virtual edge node placement scheme based on the predicted parameters, with the primary aim of simultaneously minimizing placement costs and network delays. Empirical results confirm the algorithm's superiority compared to traditional clustering-based node placement schemes. [PERSON] et al. [19] formulated the edge node deployment problem as a multi-objective optimization problem aimed at minimizing workload imbalance and access delay. They employed a genetic algorithm framework, further enhanced by the integration of a local search algorithm to facilitate node assignment. [PERSON] et al. [20] proposed a reinforcement learning framework to solve the edge node placement problem, which models real-world scenarios by constructing state and action spaces. [PERSON] et al. [21] proposed a dynamic node placement scheme, beginning with system initialization using a clustering algorithm. Following this, a graph-matching algorithm is employed to calculate the fitness function, and a nondominated sorting genetic algorithm is used to identify the optimal placement of nodes. Empirical results demonstrate that this approach significantly reduces task latency compared to conventional clustering-based methods. As previously noted, the study standardized the computational capabilities of edge nodes. However, in real-world scenarios, it is crucial to deploy nodes with varying computational capacities to meet the specific requirements of different tasks. [PERSON] et al. [22] explored the edge node deployment problem, addressing both node placement and service layout. They proposed a two-stage approach, incorporating clustering and nonlinear programming. This method accounts for multiple factors, such as the number of nodes, interactions between edge nodes and base stations, and the impact of the nodes' computational and storage capacities on the deployment process. Empirical results confirm that the algorithm significantly outperforms the baseline approach. It is noteworthy that this research primarily focuses on smart city environments, without addressing the unique characteristics of mining applications. This limitation hinders its capacity to meet the stringent real-time requirements and to manage the spatial constraints inherent in mining operations. As a result, a significant research gap remains in the development of effective edge node placement strategies specifically tailored to mining environments. Therefore, this study is framed within the context of the complex mining environment. Recognizing the distinctive characteristics of this setting, the research introduces two specific edge node deployment algorithms designed for fixed and mobile device scenarios. These algorithms focus on optimizing latency and cost efficiency, aiming to ensure rapid task responses during mining and transportation operations. The study also offers a valuable framework for the strategic deployment of edge computing nodes across various production environments in mining operations.
## 2 Antenna Configuration
### Antenna Analysis  
Figure 4 shows the step-by-step design of the triple-band UWB antenna with dual-notch band characteristics. Figure 5 shows a performance comparison for the steps involved in the dual-notch band UWB antenna using a reflection coefficient (\(S_{11}\)) plot. Table 2 compares the coverage of operational and rejected bands at every step involved in the design. Figure 5 and Table 2 show how different design steps for the proposed UWB antenna influenced the operational and rejection bands coverage. In step-1, the inverted triangular patch antenna with a partial ground plane is operating between 3.4-12 GHz. In step-2, the triangular slot on the radiating patch contributed to achieving dual-band operation with one rejection band between 5.7-8.3 GHz. In the final step, the insertion of 3-SSRRs with stub helps to achieve triple-band operation with two rejection bands between 4.2-5.45 GHz and 6.77-8.7 GHz, respectively.
# Chemotherapy Regimen Optimization Using a Two-Archive Multi-Objective Squirrel Search Algorithm
## 4 Experimental Setup and Results Analysis  
### Experimental Setup  
To evaluate the performance of the proposed TA-MOSSA algorithm in the optimization of chemotherapy dosing, the experiment applies it to patients simulated by a tumor growth model and compares the chemotherapy plans obtained with other multi-objective algorithms. For two different treatment scenarios, chemotherapy regimens are selected from the non-dominated solution set by considering different trade-offs in optimization objectives to compare their efficacy. Scenario 1 represents a more aggressive chemotherapy regimen, prioritizing tumor reduction and disease control, typically used in curative chemotherapy aimed at eradicating the tumor. It is suitable for patients in the early stages of cancer or for those without other medical conditions and with optimistic health conditions. Scenario 2 represents a milder regimen, prioritizing patient quality of life and comfort. Compared to the first scenario, the mild regimen minimizes bone marrow damage and is suitable for palliative chemotherapy aimed at relieving symptoms for late-stage patients or those with severe complications to prolong survival.  
In this context, the experimental results are expected to be in silico, as the evaluation is conducted using computational simulations rather than real-world experiments.  
The multi-objective algorithms compared in this study include classic algorithms that have been widely used in optimizing chemotherapy doses in recent years, such as the Non-dominated Sorting Genetic Algorithm II (NSGA-II) [4; 13; 22], the Multi-Objective Particle Swarm Optimization Algorithm (MOPSO) [13; 16; 38], as well as the improved Two Archive algorithm (Two_Arch2) [33], the Constrained Two-Archive Evolutionary Algorithm II (C-TAEA-II) [39] for constrained multi-objective optimization, and the original Multi-Objective Squirrel Search Algorithm (MOSSA) [37]. The latter three are included to illustrate the performance improvements of the TA-MOSSA. Algorithms involved are implemented with Python 3.9, and all experiments are performed on an Intel Core 2.30 GHz CPU with 16 GB of RAM and Windows 11 version 21H2 system. The implementation of the algorithms was carried out using PyCharm 2022.1.1 (Community Edition).  
Moreover, robustness experiments were conducted to showcase the applicability of the proposed TA-MOSSA algorithm across patients with diverse tumor growth conditions and to evaluate the algorithm's robustness. Various tumor growth scenarios were simulated by systematically adjusting the parameters of the tumor growth model.
# A Trusted Routing Scheme Using Blockchain and Reinforcement Learning for Wireless Sensor Networks
## 3 Approach
### Security Analysis  
We established a trusted routing information management system based on the PoA blockchain, where all the routing nodes and server nodes jointly maintained the routing transaction. In this subsection, we analyze it from six perspectives to show how our proposed scheme ensures the security of the system. The related security performance is shown as follows:  
* PoA consensus mechanism: The blockchain network is based on a consensus mechanism called PoA (proof of authority), and only the validation of more than half of the authenticated server nodes can allow a server node to upload the transaction and update the routing information. Therefore, any information on the blockchain cannot be tampered with by individuals.
* Transaction traceability: The server nodes record the transactions on the blockchain, including the transaction of releasing token contract, the transaction of routing node running functions on the contract, the transaction of transferring tokens. All the information about these transactions is recorded on the blocks and can be traceable across the blockchain network.
* Routing information source: Different from the traditional routing network, in our scheme, all the routing nodes get the relevant routing information from the blockchain network instead of its neighbor routing nodes. In this way, the routing information obtained by the whole network routing nodes is consistent and not determined by individuals.  
* Avoid the single point attacks: Our blockchain-based routing scheduling scheme does not require a trusted third-party central authority to manage routing information. The single point attack is prevented by the authentication of the transaction by multiple server nodes.
* No double-spending: The codes of our token contract specifies that each routing node address maps to only one address at each time slot, and that the routing node will not initiate token transactions to two other routing nodes at the same time slot.
* Self-adaptability: In the proposed routing scheduling scheme, the routing link with malicious nodes will not generate routing transactions. The reward value \(r\) of the routing link is very low based on our RLBC routing algorithm and the learning model will adaptively select the routing link of normal nodes.
# Development of a Staggered PCD End Mill for Carbon Fiber Reinforced Plastic
## 7 Conclusions  
1. A staggered PCD end mill is designed for CFRP milling. The milling process of the staggered PCD end mill is analyzed, and the structural parameters of the tool are designed as the tool diameter is 12 mm, the rake angle is 3\({}^{\circ}\), the rear angle is 10\({}^{\circ}\) and the inclination angle is 3\({}^{\circ}\). The cemented carbide K40 UF is selected as the tool body material.
2. Tool grinding and PCD composite cutting are conducted, and the welding process of the staggered PCD end mill is proposed. The tool fabrication is done using high-frequency induction brazing and cutting-edge grinding.
3. An empirical prediction model of milling force is established, and the significance test of the regression model is verified. It is found that the milling force decreases with the milling speed and increases with the feed per tooth and milling width.
4. In unidirectional CFRP milling, the main cutting force in down milling (0\({}^{\circ}<\) 0 \(<\) 90\({}^{\circ}\)) is larger than that in up milling (90\({}^{\circ}<\) 0 \(<\) 180\({}^{\circ}\)). During down milling, the cutting force increases first and then decreases with the increase in feed per tooth; during up milling, the cutting force decreases first and then increases with the increase in feed per tooth.  
The authors would like to acknowledge the support of the International S&T Cooperation Program of China (Grant No. 2014 DFA70400), the Program for New Century Excellent Talents in Heilongjiang Provincial University (Grant No. 1254-NCET-007), the Program for Harbin Science & Technology Innovation Talents (Grant No. 2015 RQQXJ039), the Fundamental Research Funds for the Central Universities, and the Shanghai Natural Science Foundation (Grant No. 14 ZR1443100).  
[PERSON] and [PERSON] designed the staggered PCD end mill in the work. [PERSON] conducted the milling experiment of CFRP. [PERSON] established the regression model of milling force. [PERSON] analyzed the effects of fiber direction on milling force.  
The authors declare no conflict of interest.