esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Dataset and Experimental Setup
### Dataset Summary  
Table 2 provides a comparative overview of the datasets used in our evaluation, including their source, feature types, class distributions, and sample sizes.  
\begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Dataset** & **Source** & **Features** & **Classes** & **Samples Used** \\ \hline \multirow{3}{*}{CICIDS2017} & \multirow{3}{*}{Public} & Network & Normal, DDoS, DoS, & \multirow{3}{*}{\(\sim\)3 million} \\  & & flow-level features & PortScan, Web Attack, & & \\  & & (80+) & Bot, and Brute Force & \\ \multirow{3}{*}{TON\_IoT} & \multirow{3}{*}{Public} & Sensor logs + & Normal, reconnaissance, & \multirow{3}{*}{-500,000} \\  & & system telemetry & password cracking, and & & \\ \multirow{3}{*}{Synthetic (custom)} & \multirow{3}{*}{Lab-generated} & Spoofed MAC/IP, & \multirow{3}{*}{Multi-attack labels} & \multirow{3}{*}{-Varied} \\  & & DDoS burst, and & & \\ \cline{1-1}  & & model poisoning & & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Overview of the datasets used for training and evaluating the Edge-FLGuard framework. The table includes the dataset source, feature types, class distribution, and the approximate number of samples utilized in experiments.
## 2 Platform Configuration and Mooring Layout
### UMaine-Hywind TLP  
In terms of the weight and the size, the UMaine TLP is much smaller and lighter than the MIT/NREL TLP. It is also a ballasted cylindrical platform, except for only three legs protruding from this base. The vertical tensioned tendon extends from the tip of each leg to the seabed, to which it is anchored. As a result of excess buoyancy, the total tension of the mooring system for the UMaine TLP is less than half of the tension for the MIT/NREL TLP. The tower properties of the NREL 5-MW wind turbine were modified to conform to the UMaine TLP. The dimensions of the UMaine TLP and the layout of the taut-leg mooring system are shown in Figure 3.  
Figure 2: The schematic diagrams of the NREL 5 MW wind turbine mounted on the MIT/NREL TLP with the taut-leg mooring system in different views. (Unit: m).
# Efficient Multi-Identity Full Homomorphic Encryption Scheme on Lattice
## 6 Conclusions  
Aiming at the problem that low efficiency of trapdoor function and sampling algorithm in lattice-based multi-identity fully homomorphic encryption scheme, this paper first constructed an efficient and transformable IBE scheme based on MP12 trapdoor, which solves the problem that the trapdoor of IBE scheme is difficult to realize and the preimage sampling is complex. Based on the LWE hardness problem, it is proved that the scheme is IND-sID-CPA security under the standard model. Then, the IBE scheme is transformed into IBFHE scheme by using the approximate eigenvector to eliminate the evaluation key and the preimage matrix. This IBFHE scheme satisfies the homomorphism operation. Finally, the constructed masking scheme and the extended ciphertext method are used to transform the IBFHE scheme into mIBFHE scheme. Compared with similar schemes, our scheme is more concise and efficient, and the parameters are more compact.  
Conceptualization, H.F. and R.H.; formal analysis, H.F.; funding acquisition, R.H.; methodology, H.F.; validation, H.F., R.H. and F.L.; writing--original draft, H.F.; writing--review & editing, H.F. All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by the National Natural Science Foundation Project of China under Grant No. 62062009 and the Guangxi Innovation-driven Development Project under Grant Nos. AA17204058-17 and AA18118047-7.  
Not applicable.  
The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Scheme** & **Dimension** & \(q\) & **Size of \(\hat{\mathfrak{s}}\)** & **Size of \(\hat{\mathfrak{C}}\)** & **Noise Rate** \\ \hline
[12] & \(6n\log q\) & \(8\omega\beta(DN+1)^{L}\) & \(ND\) & \(DN\times DN\) & \(DN+1\) \\ Ours & \(2n\log q\) & \(5\omega\beta\left(\sqrt{DN}+1\right)^{L}\) & \(m^{\prime}D\) & \(Dm^{\prime}\times DN\) & \(\sqrt{DN}+1\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of main parameters of mIBFHE scheme.
## 5 Experimental Results and Analysis
### Parameter Setting of Morfeco-Ms
#### 5.1.3 Determining the Parameters \(L\) and \(q\)  
Since this paper employs element sorting within cycles, parameter variations \(q\) and \(L\) significantly influence the algorithm's performance. Table 11 presents the outcomes corresponding to four distinct combinations of \(q\) and \(L\). Notably, the analysis indicates superior performance when \(L=5\) and \(q=20\). Consequently, we opt to set \(L=5\) and \(q=20\) accordingly.
## 4 Experiment and Results  
### Experimental Environment  
The Sunway TaihuLight computer system uses the SW26010 heterogeneous many-core processor, using the 64-bit independent SW (Sunway) instruction set, full-chip 260 cores, a chip standard operating frequency of 1.5 GHz, and a peak computing speed of 3.168 TFLOPS. The Sunway TaihuLight high-speed computing system has a peak computing speed of 125.436 PFLOPS, a total memory capacity of 1024 TB, a total memory access bandwidth of 4473.16 TB/s, a high-speed interconnection network halved bandwidth of 70 TB/s, and an input/output (I/O) aggregation bandwidth of 341 GB/s. The measured LINPACK continuous operation speed is 93.015 PFLOPS, the LINPACK efficiency is 74.153%, the system power consumption is 15.371 MW, the performance power consumption ratio is 6051.131 MFLOPS/W, the auxiliary computing system's peak operation speed is 1.085 PFLOPS, the total memory capacity is 154.5 TB, and the total disk capacity is 20 PB.  
The Sunway TaihuLight computer system supports parallel programming models that are internationally integrated, including MPI3.0, OpenMPI3.1, Pthreads, and OpenACCess2.0, and it also supports message parallel programming models, shared parallel programming models, and accelerated parallel programming models. It also provides an accelerated thread library programming interface customized for the structural characteristics of the SW26010 heterogeneous many-core processor.
###### Abstract  
Resource utilization of iron tailings powder is an effective measure to reduce the dam-break risk of an iron tailings reservoir. Adding polypropylene fiber to iron tailings powder can improve its shear performance. Direct shear tests were carried out on reinforced iron tailings powder with polypropylene fiber with dosages of 0%, 0.25%, 0.5%, 0.75%, and 1%, respectively. The normal stresses during the tests were 100, 200, 300, and 400 kPa, respectively. The test results show that with the increase of polypropylene fiber dosage, the cohesive force of iron tailings powder firstly increases and then decreases gradually, and the internal friction angle firstly decreases and then increases. The back propagation (BP)neural network was used to fit the shear force (\(F\)) and shear displacement (\(s\)) of the test to obtain the \(F\)-\(s\) function relationship that satisfies the accuracy. Based on the energy dissipation theory, the direct shear energy dissipation of polypropylene-fiber-reinforced iron tailings powder was calculated. The mathematical model of energy dissipation of fiber interfacial failure was derived by the fiber distribution model. The interfacial strength parameters of polypropylene fiber were calculated based on the direct shear test data and the mathematical model of fiber interfacial energy dissipation. The test results show that the addition of polypropylene fiber from the perspective of energy dissipation can improve the shear properties of iron tailings powder.  
iron tailings powder; polypropylene fiber; shear characteristics; energy dissipation; interfacial strength parameter +
Footnote †: journal: Applied Sciences
# LSTM Network-Assisted Binocular Visual-Inertial Person Localization Method under a Moving Base
## 2 Materials and Methods
### Basic Description of Visual-Inertial
#### 2.2.3 Loss Function  
The general loss function takes the mean squared difference as the loss function; however, this effect is not good. In this paper, based on the inherent characteristics of IMU data, the location loss function is proposed as follows in Figure 6:  
Two different loss functions were used in the training process: the mean square error (MSE) and the location loss function. The MSE loss on the training dataset is defined as:  
\[\text{Loss}_{MSE}=\frac{1}{n}\sum_{i=1}^{n}\|(a_{i},\omega_{i})-(\hat{a}_{i}, \hat{\omega}_{i})\|^{2} \tag{14}\]  
Here \(\left\{a_{i},\omega_{i}\right\}_{i\leq n}\) is the 6-dimensional IMU data provided by the visual-inertial SLAM, \(\left\{\hat{a}_{i},\hat{\omega}_{i}\right\}_{i\leq n}\) is the output 6-dimensional IMU data after LSTM training, and \(n\) is the number of data in the training set.  
Position loss function:  
\[Loss(d,\hat{d})=\frac{1}{n}\sum_{i=n}^{n}\left(\sum_{j=m}^{m+M}\left\|d_{j\to j +1}-\hat{d}_{j\to j+1}\right\|^{2}\right) \tag{15}\]  
Figure 5: LSTM architecture diagram.  
Figure 6: Schematic diagram of the position loss function.  
where \(d_{i}\) is the ground truth position provided by the visual-inertial SLAM and \(\hat{d_{i}}\) is the position solved by \(\{\hat{a_{i}},\hat{\omega_{i}}\}_{i\leq n}\). \(n\) is the batch size during training, \(m\) is the start window of the sequence, and \(M\) is the number of windows of the LSTM in the training set.  
To learn long sequences of human motion relationships and reduce the cumulative error over time, an absolute position loss function is used:  
\[L_{AL}^{MSE}\left(d,\hat{d}\right)=\frac{1}{n}\sum_{i=n}^{n}\left(\sum_{j=m}^{ n+1}\left(d_{m}^{j+1}-\hat{d_{m}^{j+1}}\right)\right) \tag{16}\]  
where \(d_{m}^{j+1}\) is the ground truth position provided by the visual-inertial SLAM from moment \(m\) to moment \(j+1\), and \(\hat{d_{m}^{j+1}}\) is the position solved from moment \(m\) to moment \(j+1\) based on inertial data.
## 3 Results  
### Study Selection  
The presentation of the systematic study selection process is shown in Figure 2, following that of [PERSON], et al. [19]:  
(1) Identification: after searching the keywords, as listed in the previous section, a total of 1373 studies, published after 2000, were identified. An additional seven studies were retrieved from a search of the reference lists included in the present work. Eight studies without the full text were removed. Thus, 1372 studies were left.  
(2) Screening: the 1372 articles were screened with titles and abstracts to see whether they were closely relevant to the current topic. One thousand two hundred fifty articles that were not closely relevant to the current topic were excluded. One hundred twenty-two articles remained.  
(3) Eligibility: the 122 full-text articles were assessed for eligibility. One hundred three studies were excluded for the following reasons: 77 studies failed to report evaluation details; 26 studies were not peer-reviewed; and no studies involved duplicated data. This left a total of 19 articles.  
(4) Included: the 19 shortlisted articles were thus included for systematic review.
## 4 Influence of Advance Rate on Front Abtment Pressure
### Numerical Simulation Study of Front Abutment Pressure Under Different Advance Rates  
Based on the numerical model established in Section 3.1, in accordance with the actual advance rate variations in the 12308 face, simulations were conducted for a 240 m face advancement under four advance rates: 4 m/d, 6 m/d, 8 m/d, and 10 m/d. To monitor stress evolution in the roof strata during advancement, a pressure monitoring line was installed above the roof. The front abutment pressure curves under different advance rates are shown in Figure 14, while vertical stress distributions are presented in Figure 15.  
As can be seen from Figure 14, at 4 m/d, the peak front abutment pressure reached 11.4 MPa, located 14 m ahead of the coal wall. At 6 m/d, the peak pressure increased to 13.5 MPa, with the peak position shifting to 12 m from the coal wall. At 8 m/d, the peak pressure rose to 15.7 MPa, positioned 8 m ahead of the coal wall. At 10 m/d, the peak pressure surged to 18.3 MPa, occurring 6 m from the coal wall.  
These results demonstrate that the advance rate significantly governs both the magnitude of peak front abutment pressure and its distance from the coal wall. As the advance rate increases, the peak pressure escalates while its location migrates closer to the coal wall. The simulation outcomes align closely with theoretical predictions and field measurements, validating the consistency of the findings.  
Figure 14: Front abutment pressure distribution curves under different advance rates.  
According to the above measured results, the data of the upper, middle, and lower brackets are analyzed and summarized, as shown in Table 6 below.  
The comparative analysis clearly demonstrates that during low-speed mining, the periodic weighting parameters average 12.3 cycles in frequency, 10.3 m in interval, and 14,226.7 kN in intensity. Upon transitioning to high-speed mining, the periodic weighting frequency decreases to 8.7 cycles (\(\Delta\) = \(-\)3.6 cycles), while the weighting interval increases to 15.1 m (\(\Delta\) = \(+\)31.8%). The weighting intensity exhibits a marginal rise to 14,332.2 kN (\(\Delta\) = \(+\)1%), indicating negligible enhancement.  
These findings confirm that increased advance rates reduce periodic weighting frequency while moderately extending intervals, with negligible impact on intensity.  
Figure 16: Relationship between advance rate and shield resistance (upper section). (**a**) Low-speed mining phase; (**b**) high-speed mining phase.  
Figure 17: Relationship between advance rate and shield resistance (middle section). (**a**) Low-speed mining phase; (**b**) high-speed mining phase.  
Figure 18: Relationship between advance rate and shield resistance (lower section). (**a**) Low-speed mining phase; (**b**) high-speed mining phase.
## 3 Results
### Outcome Evaluation  
The mean 3D distances between starting points \(PL_{1}\) and \(PF_{1}\) was 14.9 mm, between \(PL_{2}\) and \(PF_{2}\) it was 10.8 mm, between \(PL_{3}\) and \(PF_{3}\) it was 8.7 mm and between \(PL_{4}\) and \(PF_{4}\) it was 8.9 mm. The 2D angles between each pair of projected starting point connecting vectors \(\vec{V}L_{1}\) and \(\vec{V}F_{1}\) averaged at 5.3\({}^{\circ}\), the one between \(\vec{V}L_{2}\) and \(\vec{V}F_{2}\) at 1.0\({}^{\circ}\) and the one between \(\vec{V}L_{3}\) and \(\vec{V}F_{3}\) at 9.8\({}^{\circ}\). The mean absolute 2D angle deviations between \(SRL\) and \(SRF\) and between \(RIL\) and \(RIF\) were 4.6\({}^{\circ}\) and 9.1\({}^{\circ}\), respectively. For the fragment reorientation, the average absolute rotational errors in Euler angles were 6.7\({}^{\circ}\), 7.0\({}^{\circ}\) and 0.9\({}^{\circ}\) for the x-, y- and z-axis. The mean absolute difference between planned and performed LCE angles was 4.5\({}^{\circ}\).  
Tables 1 and 2 show the results of the AR guided osteectomies and the AR guided fragment reorientation, respectively. Furthermore, as given in Table 3, we evaluated the LCE angles of the planned and performed reorientation. For a visual evaluation of the results please refer to Figure 8.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Measure** & **Planned** & **Performed** & **Cadaver 1** & **Cadaver 2** \\ \hline \multirow{4}{*}{3D distance (mm)} & \(PL_{1}\) & \(PF_{1}\) & 19.5 & 10.3 \\  & \(PL_{2}\) & \(PF_{2}\) & 17.2 & 4.3 \\  & \(PL_{3}\) & \(PF_{3}\) & 10.8 & 6.5 \\  & \(PL_{4}\) & \(PF_{4}\) & 11.3 & 6.6 \\ \hline \multirow{4}{*}{2D angle (\({}^{\circ}\))} & \(\vec{V}L_{1}\) & \(\vec{V}F_{1}\) & 3.3 & 7.4 \\  & \(\vec{V}L_{2}\) & \(\vec{V}F_{2}\) & 0.3 & 1.7 \\  & \(\vec{V}L_{3}\) & \(\vec{V}F_{3}\) & 13.9 & 5.8 \\ \hline \multirow{2}{*}{Absolute 2D angle deviation (\({}^{\circ}\))} & \(SRL\) & \(SRF\) & 3.6 & 5.7 \\  & \(RIL\) & \(RIF\) & 14.1 & 4.1 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Results of the AR guided osteectomies.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Axis** & **Cadaver 1** & **Cadaver 2** \\ \hline \multirow{4}{*}{Planned rotation (\({}^{\circ}\))} & x & \(-\)12.9 & \(-\)12.9 \\  & y & \(-\)9.2 & 9.2 \\  & z & \(-\)2.1 & 1.9 \\ \hline \multirow{4}{*}{Absolute error (\({}^{\circ}\))} & x & 7.7 & 5.8 \\  & y & 8.9 & 5.1 \\ \cline{1-1}  & z & 0.7 & 1.2 \\ \hline \hline \end{tabular}
\end{table}
Table 2: 3D results of the AR guided fragment reorientation.
## 4 Security Attacks
### Application Layer
#### 4.5.5 Malware Attack  
Malware attacks [31] on the application layer are designed to stealthily compromise the target device and potentially its connected network. In a blockchain context, malware can hijack a device's computational resources to perform unauthorized tasks, such as mining blocks in the background without the user's consent. Ransomware attacks are a common variant, wherein the malware locks the target out of its own system resources until a ransom is paid. These attacks not only disrupt normal device operations but can also lead to significant financial losses if critical assets or credentials are compromised. Implementing robust anti-malware solutions and maintaining up-to-date security practices are key measures in defending against such threats.  
In summary, we have examined a wide range of attacks spanning multiple layers of blockchain systems. These include network-level exploits--such as 51% majority attacks, Sybil, eclipse, DDoS, BGP hijacking, routing, and DNS attacks--as well as consensus and contract layer threats such as vector 76, selfish mining, block withholding, bribery, re-entrancy, overflow, and short address attacks. We have also discussed application-level vulnerabilities that target both cold and hot wallets. For a quick overview of these attacks, please refer to Table 2. This comprehensive analysis highlights the multifaceted security challenges inherent in blockchain technology. With a solid understanding of these attack vectors, we now shift our focus to vulnerability detection. The following section will explore state-of-the-art techniques and emerging approaches designed to identify, analyze, and mitigate these vulnerabilities, ultimately strengthening the overall security posture of blockchain systems.
## 3 Model Development and Validation
### Material Constitutive Models  
The von Mises yield criterion and bilinear model are utilized for simulating the behavior of the ED zone made of low yield point steel (LP100) and the SL zone made of Q345B. When the strain \(\varepsilon\) of the steel sheet is less than the ultimate strain, the actual stress \(\sigma\) of the steel sheet is equal to the elastic modulus multiplied by the strain \(\varepsilon\); when the strain \(\varepsilon\) of the steel sheet is greater than the ultimate strain, the actual stress \(\sigma\) of the steel sheet is equal to the elastic modulus multiplied by the ultimate strain. With the advancement of structural health monitoring technology, high precision strain measurement can be obtained [37; 38] to guarantee the quality and reliability of stress calculation under complicated load bearing situation. The elastic modulus of the steel is set to be \(=2.05\times 10^{5}\) MPa, the Poisson's ratio of steel is taken as 0.3 [39; 40].
## 4 Discussion
### Volatile Secondary Metabolites  
The antagonism test analysed the interactions between the fungus _F. oxysporum_ and the _Bacillus_ species _B. amyloliquefaciens_, _B. subtilis_ and _B. thuringiensis_. They were inspired by literature reports on the fungistatic activity of volatile organic compounds (VOCs) produced by bacteria of the genus _Bacillus_[23; 34; 35; 36; 37]. The ability of some strains to produce a unique and reproducible VOC profile under certain conditions is an important antagonistic mechanism [38]. The antagonistic effect of VOCs includes the inhibition of mycelial growth and spore germination [34] or the inhibition of the gene expression of some fungal species [39]. When mycelial growth is inhibited, certain volatiles are produced in response to the presence of a particular fungal species. The specific profile of VOCs produced may be due to factors such as the response of the fungi to the presence of different components of the volatile mixture, the interaction site or the different abilities of the fungi to detoxify volatile metabolites. The inhibitory effect of _Bacillus_ species may be due to existing interactions between bacteria and fungi and belongs to species or even strain characteristics. The different degree of inhibition of mycelial growth depends, among other things, on the _Bacillus_ species and _Fusarium_[34].  
In the investigated case of the interaction between the bacterium _B. amyloliquefaciens_ and the fungus _F. oxysporum_, an inhibition of mycelial growth was observed 24 h after the start of the measurement at a high level of 60%-70%. After 96 h, however, it was 40%-50%, indicating that the duration of the experiment was sufficiently long and no further extension was required. Similar results were obtained by [PERSON] et al. [31], who observed that the _B. amyloliquefaciens_ NJN-6 strain they tested reduced the mycelial growth of _F. oxysporum_by 30%-40% after 3 days of the experiment compared to the control. In contrast, in the experiment by [PERSON] et al. [40], _B. amyloliquefaciens_ strain W19 inhibited the mycelial growth of _F. oxysporum_ f. sp. _cubense_ (FOC) by 21% during a 5-day antagonism test. Also, [PERSON] et al. [30] confirmed the fungistatic properties of a _Bacillus_ sp. strain (LPTC170) against _F. kalimantanense_, which is genetically closely related to _F. oxysporum_ f. sp. _cubense_ (FOC) and belongs to the _F. oxysporum_ species complex (FOSC). In in vitro tests, _Bacillus_ sp. (LPTC170) showed significant inhibition against _Fusarium_ strain LPTC130 at 41.23%. The slightly lower inhibition values reported by [PERSON] et al., [PERSON] et al. and [PERSON] et al. [30; 31; 40] compared to the results obtained in this study may be due to differences in the composition of the culture media used and the particular characteristics of the strain used in this study.
###### Abstract  
Optical satellite communications (OSC) downlinks can support much higher bandwidths than radio-frequency channels. However, atmospheric turbulence degrades the optical beam wavefront, leading to reduced data transfer rates. In this study, we propose using reinforcement learning (RL) as a lower-cost alternative to standard wavefront sensor-based solutions. We estimate that RL has the potential to reduce system latency, while lowering system costs by omitting the wavefront sensor and low-latency wavefront processing electronics. This is achieved by adopting a control policy learned through interactions with a cost-effective and ultra-fast readout of a low-dimensional photodetector array, rather than relying on a wavefront phase profiling camera. However, RL-based wavefront sensorless adaptive optics (AO) for OSC downlinks faces challenges relating to prediction latency, sample efficiency, and adaptability. To gain a deeper insight into these challenges, we have developed and shared the first OSC downlink RL environment and evaluated a diverse set of deep RL algorithms in the environment. Our results indicate that the Proximal Policy Optimization (PPO) algorithm outperforms the Soft Actor-Critic (SAC) and Deep Deterministic Policy Gradient (DDPG) algorithms. Moreover, PPO converges to within 86% of the maximum performance achievable by the predominant Shack-Hartmann wavefront sensor-based AO system. Our findings indicate the potential of RL in replacing wavefront sensor-based AO while reducing the cost of OSC downlinks.  
wavefront sensorless adaptive optics; reinforcement learning; optical satellite communications downlinks; fiber coupling