esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
The high level of noise in helicopter cabins considerably compromises the comfort and safety of the pilot and passengers. To verify the feasibility and effectiveness of microperforated panel composite sound absorption structures for noise suppression in helicopter cabins, simulation and experimental studies were conducted on a model of a light helicopter cabin. First, three microperforated composite sound absorption structures for the helicopter cabin wall panel were designed. Then, a finite element model of the main gear/body acoustic vibration coupling was established to obtain the target frequencies of the microperforated composite sound absorption structures; the acoustic effect was verified via simulation. Finally, a model helicopter cabin equipped with the three microperforated composite sound absorption structures was built, and a cabin noise test was performed. The test results showed that the combined microperforated panel acoustic structure and microperforated panel-porous material composite structure realized an overall cabin sound pressure level attenuation of 8-10 dB, on average, in a wide frequency range of 500-2000 Hz, with an amplitude of more than 20 dB. The microperforated panel-acoustic supermaterial composite structure achieved low-frequency sound absorption in the frequency range of 300-450 Hz. The sound absorption effect reached 50%, and it also exhibited good noise reduction effects in the middle- and high-frequency bands.  
helicopter; cabin noise reduction; microperforated panel; composite acoustic structure; sound field analysis; acoustic metamaterials +
Footnote †: journal: Applied sciences
## 2 Problem Description
### Simulation Challenges
#### 2.1.2 Accurate Models of Link Performance  
Error models mapped to the instantaneous signal-to-noise ratio (SNR) are often used in packet-level simulators, although this approach neglects some of the important effects caused by channel fading. Finite-state Markov models [3] are appealing from an analytic perspective, but they require separate parametrization for every possible combination of channel models and physical layer transmission schemes. Currently, they cannot cope well with frequency-selective fading channels, either. The most accurate results can be obtained by performing so-called Monte Carlo simulations of the actual transmitter and receiver transmissions, down to the level of modeling the transmission of individual bits and radio frames. Although computationally very intensive, this approach provides the highest fidelity, and the performance of specific receiver algorithms can be considered accurately, such as using coherent or noncoherent detection, _etc_. Finally, certain transmission schemes allow for computationally-efficient abstraction of the physical transmission [4; 5]. This abstraction takes into account the instantaneous channel realizations, as well as the receiver characteristics, so that accurate error predictions can be obtained without performing bit-level simulations in the physical layer. Instead, one can statistically relate the probability for successfully transmitting entire frames to the instantaneous channel (fading) realization that characterizes the path between the transmitter and receiver. These approaches allow very  
Figure 1: Common building blocks of an industrial mesh sensor/control network.  
good accuracy with comparatively low computational complexity, at the expense of some loss in the generality of the model [5].  
Although current off-the-shelf wireless _ad hoc_ and sensor technologies are based on low-complexity, non-adaptive PHY and MAC schemes (such as those based on the IEEE 802.15.4 standard), more recent developments have begun to find their way into low-complexity, low-cost sensor and industrial networks. Recent amendments to the IEEE 802.15.4 standard bring more sophisticated transmission techniques, for example coded, adaptive multi-carrier modulation in [6]. These techniques effectively mitigate or even exploit channel fading and open additional degrees of freedom (such as frequency-domain channel-dependent scheduling). However, efficient exploitation and realistic evaluation of such schemes call for more complex channel and link performance models, which conventional packet-level network simulators often do not incorporate.
# Deep Reinforcement Learning for Intelligent Penetration Testing Path Design
## 3 Proposed Method  
### Penetration Testing Network Model  
In order to use deep reinforcement learning in penetration testing, the network model for penetration testing needs to be modeled as an MDP. The MDP model is generally described in the form of quintuple <S, A, P, R, \(\gamma\)> and policy \(\pi\).  
* S represents the set of network environment states, that is, the information obtained by the agent from the environment, including all host information, such as hostidentification, host privileges, vulnerability information, running services, and the asset values of hosts.
* A represents the set of actions, that is, the set of operations that the agent performs on the host, including file access, vulnerability exploitation, and privilege escalation to the host.
* P denotes the set of transition probabilities between the states and actions. P(_s_'_|_, \(s\), _a_) denotes the probability that the state transfers to _s_' when the agent is in the state \(s\) and takes actions \(a\).
* R denotes the reward function. R(_s_'_|_, \(s\), _a_) denotes the reward for the state transfers to s' when the agent is in the state s and takes action \(a\), and R(s) represents the reward value obtained by the agent after reaching the state \(s\).
* \(\gamma\in\)[0,1] is the discount factor, which reflects how much the agent emphasizes long-term returns. The larger the discount factor, the more important the agent will be for future long-term returns.  
In this process, the agent selects actions based on the policy \(\pi\). As shown in Equation (3), the quality of the policy depends on the cumulative rewards obtained after long-term execution. The goal of the agent is to find an optimal strategy to maximize the cumulative rewards.  
\[G_{t}=R_{t+1}+\gamma R_{t+2}+\ldots=\sum_{k=0}^{\infty}r^{k}R_{t+k+1} \tag{3}\]  
By applying MDP principles, such as state representation, action space, rewards, and iterative algorithms, MDP-based penetration testing models provide a systematic framework for modeling and optimizing attacker's decisions in penetration testing.
# MSSP: A Blockchain Sharding Protocol Based on Multi-Shard Storage
## 5 System Analysis
### Performance Analysis  
A performance evaluation of MSSP is conducted by analyzing transaction throughput and latency. For blockchain systems employing sharding techniques, the performance will be affected by cross-shard transactions.  
To comprehensively analyze the performance of sharded systems, it is essential to consider the distribution of cross-shard transactions across the entire system. Define the transaction distribution \(\alpha=\{\alpha_{1},\alpha_{2},\ldots,\alpha_{S}\}\), where \(\alpha_{1}\) represents the percentage of internal transactions and \(\alpha_{j}\) represents the percentage of cross-shard transactions involving \(j\) shards.  
The nodes within the shard need to communicate and coordinate with each other in order to reach consensus. As the number of nodes within a shard increases, so does the complexity of communication among them, leading to increased delays in the consensus process. Define a function \(f(n)\)[33] to represent the time delay for one round of consensus in a shard with \(n\) nodes, where parameter \(C\) denotes the number of transactions processed in one round of consensus within a shard.  
In a traditional sharding system, if a relay mechanism is used to process cross-shard transactions, the cross-shard transactions between \(j\) shards need to be divided into \(j\) sub-transactions for processing. Therefore, the maximum transaction throughput of a traditional sharding system with \(S\) shards is  
\[\text{TPS}^{\text{traditional}}=\frac{SC}{f(N/S)}\Big{(}\alpha_{1}+\frac{\alpha _{2}}{2}+\cdots+\frac{\alpha_{S}}{S}\Big{)} \tag{3}\]  
In a non-sharding system, there are no cross-shard transactions, so the transaction throughput of a non-sharding system is \(\text{TPS}^{\text{non}}=\frac{C}{f(N)}\). Ideally, in a multi-shard storage system, cross-shard transactions can be processed directly without affecting the throughput. The throughput of a multi-shard storage system with \(S\) shards is \(\text{TPS}^{\text{MSP}}=\frac{SC}{f(N/S)}\). It can be seen that compared to traditional sharding systems and non-sharding systems, MSSP has significant advantages in terms of throughput.  
In traditional sharding systems, the submission of each sub-transaction in a cross-shard transaction may need to wait for the completion of other sub-transactions on different shards. This leads to the necessity of submitting sub-transactions round by round, resulting in a confirmation latency of traditional sharding systems as  
\[\text{CONF}^{\text{traditional}}=f\left(\frac{N}{S}\right)\sum_{1\leq i\leq S}i \alpha_{i} \tag{4}\]  
The confirmation latency in non-sharding systems is \(\text{CONF}^{\text{non}}=f(N)\). In a multi-shard storage system, if the cross-shard transaction is handled by the m-node that stores the related shards, it can be submitted within one round, significantly reducing the overall confirmation latency. Therefore, the confirmation latency in MSSP is \(\text{CONF}^{\text{MSSP}}=f(N/S)\). MSSP exhibits lower confirmation latency compared to traditional sharding and non-sharding systems.
###### Abstract  
In this paper, the accuracy of an in-house Actuator Line (AL) model is tested on aeroelastic simulations of a Wind Turbine (WT) rotor and a helicopter Main Rotor (MR) under uniform free-stream flow. For the scope of aeroelastic analyses, the AL model is coupled with an in-house multibody dynamics code in which the blades are modeled as beams. The advantage from the introduction of CFD analysis in rotorcraft aeroelasticity is related to its capability to account in detail for the interaction of the rotor wake with the boundary layer developed on the surrounding bodies. This has proven to be of great importance in order to accurately estimate the aerodynamic forces and thus the corresponding structural loads and deflections of the blades. In wind turbine applications, a good example of the above is the rotor/ground interaction. In helicopter configurations, the interaction of MR with the ground or the fuselage and the interaction of tail rotor with the duct in fenstron configurations are typical examples. Furthermore, CFD aerodynamic analysis is an obvious modeling option in which the above mentioned asset can be combined with the consideration of the mutual interaction of the rotor with the ambient turbulence. A WT rotor operating inside the atmospheric boundary layer under turbulent free-stream flow is such a case. In the paper, AL results are compared against Blade Element Momentum (BEM) and Lifting Line (LL) model results in the case of the WT, whereas LL and measured data are considered in the helicopter cases. Blade loads and deflections are mainly compared as azimuthal variations. In the helicopter MR cases, where comparison is made against experimental data, harmonic analysis of structural loads is shown as well. Overall, AL proves to be as reliable as LL in the canonical cases addressed in this paper in terms of loads and deflections predictions. Therefore, it can be trusted in more complex flow conditions where viscous effects are pronounced.  
actuator line; aeroelasticity; loads; deflections; uniform free-stream flow; wind turbine rotor; helicopter rotor; HeliNOVI
## 3 A Non-Real-Time DTN-Based Forwarding Algorithm
### Distributed Forwarding Algorithm
```
1:node start (initialization)
2:broadcast ID
3:listen for neighbors' IDs
4:wait for messages()
5:if message received then
6:save neighbors' IDs and contact time in neighbors list
7:update mean time between contacts
8:update availability and delivery probabilities
9:update number of contacts with this neighbor
10:if neighbor requests delivery probability then
11:if final destination = own ID then
12:send delivery probability 1
13:else if destination known then
14:return delivery probability (not through requesting node)
15:else if destination unknown then
16: do not reply
17:else if neighbor sends a message then
18:if it is for me then
19: read message
20:else
21: store message and send it to next node
22:else if node has message to send then
23: ask for delivery probabilities to destination
24: send a copy of the message to the two best delivery probability neighbors
25:if no delivery probabilities then
26: store message in buffer until a path to destination appears
```  
**Algorithm 1** Distributed algorithm for every node in the network.
# Ins and Outs of the Ankle Syndesmosis from a 2D to 3D CT Perspective
## 3 Results
### Interoposterior Translation
#### 3.3.2 The 2D Measurements Quantifying Syndesmotic Area  
Despite numerous linear measurements, [PERSON] et al. first described the area between the fibula and tibia 1 cm above the tibial plafond, i.e., the syndesmotic area, as depicted in Figure 2C and described in Table 1[35]. Subsequently, this measure has been used increasingly. Multiple articles describe injured and uninjured syndesmoses in both non-weight-bearing and weight-bearing conditions [13; 26; 31; 33; 34; 35].  
Within healthy syndesmoses, a mean area of 105.2 mm\({}^{2}\) and 106 mm\({}^{2}\) was found for CT and WBCT, respectively. In the presence of lesions, the area increases to 129.5 mm\({}^{2}\) on CT and 134.1 mm\({}^{2}\) on WBCT. Therefore, injured syndesmoses are, on average, 24.3 mm\({}^{2}\) (CT) and 28.1 mm\({}^{2}\) (WBCT) larger compared to non-injured.  
[PERSON] et al. examined one cohort of unilateral injured and contralateral healthy syndesmoses in addition to a second cohort of bilateral uninjured ankles. Merely a mean difference of 0.41 mm\({}^{2}\) was detected between the left and right normal syndesmotic areas, in contrast to a difference of 46 mm\({}^{2}\) of unilateral injury. These findings support the use of contralateral, uninjured syndesmosis as an internal control for injury assessment [33].  
A significantly greater area (\(p<0.001\)) was identified on CT images in injured ankles relative to normal ones [26; 34; 35]. Similar significance (\(p<0.001\)) was achieved for WBCT [26; 34]. [PERSON] et al. reported a mean increase in the syndesmotic area of 8.8% and 19.9% for CT and WBCT, respectively [34]. In addition, a larger area difference was detected between non-injured syndesmosis for men compared to women, which approached near significance for CT (\(p=0.069\)) and WBCT (\(p=0.063\)) [31; 34]. Furthermore, weight-bearing induced a difference between normal and injured ankles that was significantly greater for men (\(p=0.04\)) [34]. [PERSON] et al. investigated the impact of a talocrural range of motion (ROM) on the syndesmotic area and found that the surface decreased on average by 26 mm\({}^{2}\) (\(p<0.001\)) going from dorsiflexion to plantar flexion [31].  
Excellent interobserver reliability was documented for syndesmotic area estimation for both CT (ICC = 0.96) and WBCT (ICC = 0.93).
## 4 Results and Analysis
### Debris Flow Characteristics
#### 4.1.3 Froude Number  
Figure 8 shows the Froude numbers calculated through the flume experiment observations and the change in Froude number due to the installation of the perm. Figure 8a,b show the Froude number according to \(\alpha\) and \(C_{V}\), respectively. Figure 8c,d show the change in the Froude number according to \(\alpha\) and \(C_{V}\), respectively, when the perm was installed. In the case of the straight channel test, the Froude number increased as \(\alpha\) increased (Figure 8a). With the perm installed, the Froude number decreased by 2.1-9.7% when \(\alpha\) was 25\({}^{\circ}\) and \(C_{V}\) was 0.50 or less (Figure 8c,d); however, when \(\alpha\) was less than 25\({}^{\circ}\), the Froude number increased by 9.7-58.0% due to the installation of the perm (Figure 8c,d). The average incremental increases in the Froude number were found to be 12.8%, 30.8%, and 19.5% for a \(C_{V}\) of 0.40-0.50 and \(\alpha\) values of 10\({}^{\circ}\), 15\({}^{\circ}\), and 20\({}^{\circ}\), respectively, and 8.4%, 12.3%, and 25.6% for an \(\alpha\) of 10\({}^{\circ}\)-25\({}^{\circ}\) and \(C_{V}\) values of 0.40, 0.45, and 0.50, respectively. In other words, the average increase in the Froude number due to the installation of the perm increased as \(C_{V}\) increased.  
Figure 7: **(a)** Flow depth according to \(\alpha\) (at \(C_{V}\) = 0.40–0.55); **(b)** flow depth according to \(C_{V}\) (at \(\alpha\) = 10\({}^{\circ}\)–25\({}^{\circ}\)); **(c)** percent decrease in flow depth due to perm according to \(\alpha\) (at \(C_{V}\) = 0.40–0.50); **(d)** percent decrease in flow depth due to perm according to \(C_{V}\) (at \(\alpha\) = 10\({}^{\circ}\)–25\({}^{\circ}\)).
# Adversarial Robust and Explainable Network Intrusion Detection Systems Based on Deep Learning
## 1 Introduction  
Cyber security has recently faced enormous attention from many researchers in the IT research community. The growth of computer computational power and the wide distribution of the internet of things have put cyber security in the limelight. With the advancement of IT infrastructure and telecommunication facilities distribution, there is a noticeable growth of cyber-attacks continually exploiting the weakness of the cybersecurity infrastructure. Network intrusion detection systems have recently become more popular because of their capabilities for detecting anomaly activities. These architectures are a significant component in the security infrastructure against network attacks. Cyber security researchers have been working tirelessly on new ways to counter the recent attacks with the increased complexity of the attacks. In addition, much research has been performed to augment their detection capabilities by incorporating machine learning and deep learning techniques into IDS.  
This incorporation has improved the NIDS detection capabilities even for zero-day attacks. However, new research findings from the mainstream ML and DL have shown that ML and DL models are vulnerable to adversarial attacks, defined as carefully crafted imperceptible changes of inputs used to fool or weaken the capabilities of ML or DL [1]. Adversarial examples can be prepared by intentionally imputing small perturbations to the original inputs. leading to the misclassification of deep learning models with high confidence. Fast gradient sign method (FGSM) [2], Projected Gradient Descent (PGD) [3],Jacobian-based saliency map attack (JSMA) [4], DeepFool [5], and C&W [6] attack are representative adversarial examples of generation methods.  
Several adversarial defenses and attacks have been crafted and studied in different domains such as malware detection, computer vision, and voice recognition. However, some of these attacks are deemed ill-suited for intrusion detection systems. Hence, there have been few research works on implementing some attacks and defenses from other domains in intrusion detection systems. The main reason given by the IDS research community is that attacks in other fields modify feature vectors instead of real input space. In addition, in crafting attacks for IDS, there is a need to ensure no communication violation or compromise of maliciousness when modifying malicious traffic, which is not a problem for other non-security domains [7]. [PERSON] et al. [8] studied adversarial examples in NIDS. They showed that adversarial examples generated by FGSM and JSMA methods could significantly reduce the accuracy of deep learning models applied in NIDS. [PERSON] [9] explored the vulnerabilities of deep learning-based intrusion detection systems with JSMA attack, FGSM attack, DeepFool attack, and C&W attack. Their results further proved that the attacks algorithm proposed to fool the deep learning-based image classification can also be employed in intrusion detection.
## 5 Conclusions  
The present study proposed the BPANN for dealing with the complex input-output relationships in USM in the absence of an explicit mathematical model. We then compared the MRR prediction precision of the BPANN with the corresponding precisions obtained using previously proposed regression techniques with equivalent experimental parameters \(G\), \(P\) and \(FS\). Based on the conducted analyses, the following conclusions can be drawn.  
1. The BPANN model proposed in this study and the improved nonlinear regression model established in our earlier research are useful for predicting the MRR in the USM process, but the results of the present study demonstrate that the BPANN model provides the best results and requires no explicit mathematical function.
2. The method employing ANNs proposed in this study for predicting the MRR in USM can be considered as a guide for modelling complex general machining problems without explicit mathematical functions.  
**Acknowledgments:** This research was financially supported by the research projects: the Research Plan of Nanjing Agricultural University of China (No. 2015 CG01) and the Research Plan of State Key Laboratory for Manufacturing Systems Engineering at Xi'an Jiao Tong University of China (No. sklms2017008).  
**Author Contributions:** [PERSON] designed and performed the experiments, analysed the data and drafted the present report. [PERSON] contributed the experimental materials and analysis tools. [PERSON] proposed the method of ANN modelling.  
**Conflicts of Interest:** The authors declare no conflict of interest. The founding sponsors had no role in the design of the study; in the collection, analyses or interpretation of data; in the writing of the manuscript; nor in the decision to publish the results.
## 3 Results and Discussion
### Formulation Studies  
#### 3.4.1 Preparation Processing Method  
For the design of the liquid-solid extraction process, the appropriate solvent had to be selected for the quantitative and selective recovery of the active ingredients from the substrate (honey with or without chocolate coating). Based on their solubility properties (Table S2), water cannot be used as such, because on the one hand, it does not ensure the complete dissolution of the active ingredients, and on the other, it dissolves most of the components of the substrate (honey and chocolate) [52]. Therefore, two solvents were investigated, methanol and acetonitrile, of which methanol exhibits the greatest solvating capacity for both active ingredients but does not ensure the stability of ACA. Similarly, to investigate which of them does not dissolve honey, 500 mg of the substrate was weighed into two separate beakers, one of which was filled with 25 mL methanol and the other with acetonitrile. The two solutions were sonicated (15 min) and then placed in the freezer (30 min). According to Figure S3, methanol, unlike acetonitrile, does not show selectivity, since it completely dissolves honey.  
Therefore, methanol should, of course, be used in the extraction (as the optimal solvent of ACA, DIP) but in the minimum possible amount in order not to dissolve the substrate. Also, the sample, at this stage, should not be subjected to sonication for a long time in order not to destroy the ACA. Then, after the first dilution, the ratio of acetonitrile to methanol should be as high as possible to precipitate the honey. Various MOH-ACN ratios were tested (Table S4) and 1:24 \(v/v\) was considered optimal. The sample was then sonicated (30 min), frozen (30 min), centrifuged (5000 rpm for 15 min), diluted (1 mL in 10 mL of 60:40 \(v/v\) H\({}_{2}\)O-ACN diluent) and filtered before analysis by HPLC.  
The proposed method was applied to five chocolate-honey samples and the % recovery of the substances was calculated. According to the results, the % recoveries were found to be 97% (%RSD = 1.82) for dipyridamole and 100.1% (%RSD = 1.78) for acetylsalicylic acid.