esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Theoretical Analysis
### Security Analysis  
Within a DHT cluster, the reliability of the system is mainly dependent on the storage policy and the data recovery policy. The storage policy refers to the redundancy method and the node selection mechanism is used when nodes store data. In this scheme, the number of replicas of each sharding is \(s\). The larger the value of \(s\), the more replicas of the sharding, the greater the storage space consumption of a single node, and the higher the system reliability; a smaller value of \(s\) indicates fewer replicas of the sharding, less storage space consumption in a single node, and lower reliability of the system. The node selection mechanism adopts the last-bit XOR mapping method to distribute the blocks generated by the blockchain network evenly among the nodes in the network, to ensure reliability of data storage. The data recovery strategy means that the data maintain integrity even after failure of some nodes in the cluster. Suppose that among \(n\) nodes, \(x\) nodes are randomly selected as the failed nodes, while the remaining \(n-x\) nodes continue to keep all the blocks of the cluster intact. For a particular \(n\), the larger the value of \(x\), the higher the reliability of the system, and the redundancy in the system is greater at that time.  
When \(n\) is a fixed value, the system reliability is related to \(s\). When \(s=1\), each block is stored in only one node. At this time, \(x\) must be 0 and no node is allowed to fail, or such a failure will lead to unrecoverable blockchain data; when \(s=n\), at this time \(x\) has a maximum value of \(n-1\), and even if \(n-1\) nodes fail, the full blockchain data can be recovered.
## 3 Field Test  
In order to evaluate air quality and ventilation performance in the caverns, a field test of the air velocity, dust, and poisonous gas (methane, CO, SO\({}_{2}\), H\({}_{2}\)S) in the main cavern and the construction roadway was carried out. The main cavern was organized with a test cross-section every 100 m in the longitudinal direction, with a total of 9 test cross-sections. The test instruments and related parameters of air velocity and harmful gas are shown in Table 1. As the air velocity and harmful gas concentration vary with the location, it is important to increase the number of measuring positions to obtain the average air velocity and harmful gas concentration of the cross-section (the average values only refer to the space). Consequently, the cross-section of the main cavern is divided into nine parts, and the cross-section of the construction roadway is divided into four parts as shown in Figure 3. The air velocity and harmful gas concentration in each part are averaged to be \(V_{a}\) and \(C_{a}\) separately. Hence, the value of \(V_{a}\) and \(C_{a}\) can be calculated by the flowing equation:  
\[V_{a}=\frac{\sum A_{i}\times V_{i}}{\sum A_{i}} \tag{1}\]  
\[C_{a}=\frac{\sum A_{i}\times C_{i}}{\sum A_{i}} \tag{2}\]  
where \(i=1,\,\ldots,\,6\) for the section of the upper layer and middle layer, \(i=1,\,\ldots,\,9\) for the full section of the cavern, and \(i=1,\,\ldots,\,4\) for the construction roadway, and \(A_{i}\) is the area of part \(i\) in the cross-section.  
Figure 4 depicts the field test of the ventilation environment in the caverns. It can be seen that the air quality is satisfactory after 20 min of axial-flow gallery ventilation. The excavation face is clearly visible in the range of 200 m, and the visibility in the cavern is more than 300 m when mucking.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Test Content** & **Test Instrument** & **Test Resolution** & **Control Value** \\ \hline Air velocity & Hot wire anemometer & 0.01 m/s & / \\ CO & M4 gas detector & 0.1 ppm & \(\leq\)30 mg/m\({}^{3}\) \\ SO\({}_{2}\) & M4 gas detector & 0.1 ppm & \(\leq\)14.3 mg/m\({}^{3}\) \\ H\({}_{2}\)S & M4 gas detector & 0.1 ppm & \(\leq\)10 mg/m\({}^{3}\) \\ Above 10\% free SiO\({}_{2}\) dust & CCZ-1000 direct-reading dust detector & 0.1 mg/m\({}^{3}\) & \(\leq\)2 mg/m\({}^{3}\) \\ Below 10\% free SiO\({}_{2}\) dust & CCZ-1000 direct-reading dust detector & 0.1 mg/m\({}^{3}\) & \(\leq\)4 mg/m\({}^{3}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Test content and related parameters.  
Figure 3: Layout of test points in each section: (**a**) Section of the upper layer; (**b**) Section of the middle layer; (**c**) Full section of the cavern; (**d**) Construction roadway.
## 4 Conclusions  
The data-balance method that references focal loss as a loss function can effectively improve the detection accuracy of the network. The classification accuracy of the four networks was improved by 3.60%, and the loss value also decreased, which proves that the focal-loss function can achieve the expected results. In addition, in this experiment, all four networks had high classification accuracy, but the optimized DenseNet network had the highest test accuracy, at 98.48%, and it can be used as the primary choice for soybean appearance quality detection.  
In the face of training with data-augmentation data sets in soybean binary classification experiments, VGG and AlexNet multi-convolution network structures cannot effectively convolute soybean species, because multi-layer convolution introduces non-classified features into convolution calculation causing a decrease in accuracy, and it is experimentally proved that a small number of convolution layers can be used to prevent non-image features from participating in convolution and protect the original image features when processing simple image classification. DenseNet and ResNet networks face the data-augmentation data set because residual blocks can effectively transmit information and protect the original characteristics of the image, the classification accuracy is less affected, and they will not have a serious impact on the overall accuracy of the network.  
In this paper, the loss function of the DenseNet121 network was modified so that it could more effectively deal with the problem of data imbalance. Through experiments,  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Data Set** & **Intact Soybean Sample** & **Broken Soybean Sample** \\ \hline Original data set & 249 & 243 \\ \hline Oversampled data set & 700 & 659 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Number of samples in the rice dataset.  
Figure 16: Iteration of classification accuracy of three networks using over-sampled rice data sets: (**a**) training result of the VGG19 network model, (**b**) training result of the Simple network model, and (**c**) training result of the DenseNet network model.  
the impression of the over-sampled image-enhancement method on neural networks was verified, which has potential in the face of image noise.  
Conceptualization, N.Z., E.Z. and F.L.; methodology and formal analysis, E.Z.; investigation, F.L.; writing--original draft preparation, E.Z.; writing and review N.Z., E.Z. and F.L. All authors have read and agreed to the published version of the manuscript.  
This work was supported by Xijing University and National Science and Technology Major Project.  
Not applicable.  
The data that support the findings of this study are available from the corresponding author upon reasonable request.  
The authors declare no conflict of interest.
# Enhancing Botnet Detection in Network Security Using Profile Hidden Markov Models
## 5 Results
### Comparison with Other Models  
The multiclass nature of our experiments makes botnet detection particularly challenging. In Table 12, we compare our HMM and PHMM approaches to other detection models. In terms of accuracy, the results obtained with HMM are comparable to k-Nearest Neighbors (kNN) and only inferior to Multilayer Perceptron (MLP). The PHMM, on the other hand, surpasses all other models except for MLP, to which it has a close accuracy (0.97 compared to 0.99 for MLP). When considering the F1 score, we observe the effect of class imbalance reducing the overall detection rate. The F1 score for the HMM is reduced to 0.78, which is comparable to Decision Trees, superior to Support Vector Machines (SVMs) and Naive Bayes, but inferior to kNN and MLP. The PHMM, however, has a score of 0.89, which is superior to all methods but still inferior to MLP.  
We can conclude that the PHMM, in particular, is a method that closely matches the performance we can achieve with MLP. The advantage in this case is its faster training time and the ability to easily update its model compared to retraining an MLP model. In fact, the original model can be updated with new sequences by simply continuing the learning process from where we left off.
# Chaotic Time Series Forecasting Approaches Using Machine Learning Techniques: A Review
## 1 Introduction
### Importance of Chaotic Time Series Forecasting
#### 1.2.2 Chaotic Time Series Forecasting in Oil and Gas  
It is well known that the intake flow of a gasoline engine directly impacts the accuracy of the air-fuel ratio management under transient situations [12]. As a result, precise control becomes extremely difficult because the air ratio is far from stoichiometry for various reasons. Thus, forecasting the engine's intake flow with greater accuracy in less time can improve the convergence rate. Additionally, it will be able to overcome the shortcomings of the airflow sensor's lag. This is because it allows an accurate forecast of the future airflow. Similarly, it is also well known that there are abnormal fluctuations in the ventilation air in the nonlinear coal mines' ventilation systems [17]. These fluctuations in the air are due to the mining depth and intensity gradually increasing and equipment aging. The abnormalities, as mentioned earlier, can affect the entire system, resulting in various underground accidents and lost coal mines' ventilation system stability. Therefore, timely air quality prediction in coal mines' ventilation systems can help adequately manage systems, which directly influences the safety and output of the coal mine.
## 4 Conclusions  
In conclusion, the SPPs properties were investigated versus conductivity at the interface of nanocomposite and hypercrystal. The amplitude of complex conductivity significantly influenced the SPPs propagation. The real part of SPPs dispersion relation was very large in comparison with the value of the free space wave-vector \(\text{Re}(\beta)>k_{0}\). It is worthwhile mentioning that propagation of SPPs was achieved at the interface with variation of complex conductivity. The conducted study allows one to conclude on the conditions of surface waves propagation in the complex conductivity plane. Thus, \(\varepsilon_{nc}(\sigma)\), \(\varepsilon_{\perp}(\sigma)>0\) and \(\varepsilon_{||}(\sigma)<0\) in the case of hyperbolic regime and \(\varepsilon_{nc}(\sigma)\), \(\varepsilon_{\perp}(\sigma)>0\), \(\varepsilon_{||}(\sigma)<0\) for Dyakonov surface waves. The potential applications of this works are in the fields of the development of waveguides sources, near-field optics, surface-enhanced Raman spectroscopy, data storage, solar cells, chemical sensors and biosensors.  
Figure 10: Dependence of propagation length versus frequency for different filling factors (**a**) and versus conductivity for \(f=0.3\) (**b**). \(\varepsilon_{n}\,=\,11.8\), \(\varepsilon_{d}\,=\,2.25\). All the presented results are obtained for the ITO inclusions.  
**Author Contributions:** Conceptualization, T.I. and T.G.; methodology, T.I.; software, T.G.; validation, T.G. and E.R.; formal analysis, E.R.; investigation, T.I.; resources, E.R.; data curation, T.G.; writing--original draft preparation, T.I.; writing--review and editing, T.G. and E.R.; visualization, T.I.; supervision, T.G.; project administration, E.R.; funding acquisition, T.G. and E.R. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This project has received funding from the European Union's Horizon 2020 research and innovation program under the Marie Sklodowska Curie grant agreement No 713694 and from Engineering and Physical Sciences Research Council (EPSRC) (Grant No. EP/R024898/1). The work of [PERSON] was partially funded by the Ministry of Science and Higher Education of the Russian Federation as part of World-class Research Center program: Advanced Digital Technologies (contract No. 075-15-2020-934 dated 17.11.2020).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 4 Performance Analysis  
This section presents an analysis and evaluation of the fault recovery performance of the TFR approach in comparison to the standard PTP. We analyzed and evaluated the delay of detecting and recovering the failure with BC nodes in the sample network. TC nodes are considered to be the connection point; as previously mentionned, they are not considered in the synchronization mechanism.  
The sample PTP network shown in Figure 2 is considered in order to analyze the performance of the proposed TFR approach. It is assumed that the link distance between each node is 50 m and the node processing rate is 100 Mbps. Furthermore, the Sync message rate is assumed to be 128 messages-per-second and the Announce message rate is 8 messages-per-second, which was defined in the ITU-T G.8275.2 standard profile [16]. Therefore, the Sync-Interval will be determined as 1/128 that is equal to 0.0078125 s and the Announce-Interval will be determined as 1/8 that is equal to 0.125 s. Finally, we consider that the Hello packet interval duration of RSTP is 2 s, which was defined in the RSTP standard as a default value [13]. Additionally, the port identity size is 10 octets and the MC dataset size is 14 octets, which was defined in the standard [5].  
We considered multiple scenarios in which link failures occur between the MCs and SCs in the network. In the first scenario, a link failure only occurs in one node in the network; afterwards, we increased the number of link failures in the network until all the nodes in the network experience  
Figure 6: The TFR state machine. BMC: best master clock.  
a link failure, as shown in Figure 7. In each scenario, we determined the total delay duration for detecting and recovering the failure between the BC slave ports and the MC ports in the same communication path.  
We did not consider the case of an all-links failure for a certain node in the sample network in the performance analysis in order for this specific node to not be isolated from the network. Afterwards, this specific node will be considered as the root of the synchronization hierarchy for the isolated network segment that will cause local clock drifting in comparison to the GMC. Eventually, the TFR approach will not solve this issue unless there is a live link between the MC ports and the SC ports in the same network segment.
# Effect of Environmental Temperature on the Insulating Performance of Epoxy/MgO Nanocomposites
## Materials and Methods  
### Materials  
Bisphenol epoxy resin (Eponex 1513) E-51 was obtained from Changzhou Runxiang Chemical Co., Ltd., Changzhou, China. Methyllettrahydrophthalic anhydride (anhydride curing agent), an accelerator, was produced by Shanghai Aladdin Biochemical Technology Co., Ltd., Shanghai, China. 2, 4, 6-Tris (dimethyl amino) phenol DMP-30, MgO nanoparticles (500 nm) were acquired from Wuxi Zehui Chemical Co., Ltd., Wuxi, China.
# An Experimental Survey of Extended Resolution Effects for SAT Solvers on the Pigeonhole Principle
## 4 Analysis Procedure  
We analyze the effect of extended resolution on the PHP, i.e., the effect of extended variables, by asking two questions: (1) Can extended variables speed up a solver's search? and (2) How do extended variables affect proof of a formula's unsatisfiability? For speeding up solvers, we introduce two ways to prioritize the extended variables as decision variables. Then, we observe whether the extended variables can really enhance the search efficiency by measuring processing times. Moreover, we also survey the solver DRUP's proof [13] that represents the resolution steps leading to an empty clause, i.e., unsatisfiability of the formula.
# Impact of Mining Disturbance on Highway Sustainability:
## 3 A Case Study
### Subsidence and Deformatons Impact on Highway
#### 3.5.4 Risk Assessment for Highway
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Excavation** & \multicolumn{4}{c}{**Primary Subsidence**} & \multicolumn{4}{c}{**Secondary Subsidence**} \\ \cline{2-9}
**Panel** & **U** & **N** & \(-\)**R** & **+R** & **U** & **N** & \(-\)**R** & **+R** \\ **(EP)** & **(mm)** & **(mm/m)** & **(km)** & **(km)** & **(mm)** & **(mm/m)** & **(km)** & **(km)** \\ \hline EP1 & \(-194\) & \(1.3\) & \(29\) & \(71\) & & & & \\ EP2 & \(-308\) & \(2.0\) & \(20\) & \(40\) & & & & \\ EP3 & \(-356\) & \(2.2\) & \(19\) & \(36\) & & & & \\ EP4 & \(-368\) & \(2.2\) & \(20\) & \(40\) & & & & \\ EP5 & \(-368\) & \(2.2\) & \(20\) & \(37\) & \(-347\) & \(3.3\) & \(36\) & \(61\) \\ EP6 & & & & & \(-427\) & \(4.9\) & \(36\) & \(61\) \\ EP7 & & & & & \(-607\) & \(6.4\) & \(20\) & \(31\) \\ EP8 & & & & & \(-757\) & \(4.7\) & \(20\) & \(20\) \\ EP9 & & & & & \(-1070\) & \(2.3\) & \(17\) & \(34\) \\ EP10 & & & & & \(-1152\) & \(4.2\) & \(17\) & \(35\) \\ EP11 & & & & & \(-1212\) & \(5.7\) & \(17\) & \(35\) \\ EP12 & & & & \(-1232\) & \(7.3\) & \(18\) & \(32\) \\ EP13 & & & & \(-1245\) & \(7.4\) & \(18\) & \(32\) \\ EP14 & & & & \(-1256\) & \(7.1\) & \(19\) & \(33\) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Predicted maximum subsidence and deformations values on the highway [87]. 3.5.5. Mitigating the Long-Term Impact of Residual Subsidence on Highway Stability  
The results of this study indicate that while the immediate effects of subsidence on the E75 highway can be predicted with high reliability, long-term residual subsidence may lead to gradual terrain deformation. This poses a risk of differential subsidence, crack formation, and localized structural instability over time. The cumulative nature of residual subsidence can also affect road drainage patterns, increasing the potential for water accumulation and pavement degradation. To mitigate these risks, continuous geodetic monitoring should be established along the highway section, allowing for real-time assessment of terrain movement and early identification of critical deformation zones. Additionally, engineering solutions such as controlled compaction of the subsurface, reinforcement of road foundations, and the implementation of flexible pavement structures should be considered. These measures can enhance the resilience of the highway and accommodate minor subsidence effects without compromising safety and functionality. A further mitigation approach involves adaptive land-use planning in the affected region. Restricting the construction of sensitive infrastructure in high-risk zones and implementing subsidence-resistant foundation designs for new structures can significantly reduce potential damage. These measures should be integrated into a proactive maintenance strategy to ensure that any emerging deformation trends are addressed promptly, reducing long-term repair costs, preventing significant infrastructure damage, and ensuring highway sustainability.
# Selecting Target Range with Accurate Vital Sign Using Spatial Phase Coherency of FMCW Radar
## 2 Methods
### Vital-Sign Measurement  
Figure 1 shows the experimental settings for FMCW radar based VS measurements. As shown in Figure 1a, subjects comfortably sat on a chair facing the radar about 1 m apart. The distance between parts of body and the radar depends on the build of subjects, but generally distance to the abdomen was about 0.6\(\sim\)0.8 m, and distance to the neck was about 0.9\(\sim\)1.0 m. The FMCW radar used for recording was BTS60 [48] (bitsensing Inc., Seoul, Korea ) shown in Figure 1b. To obtain ground truth of VSs, a Neulog Respiration Monitor Belt logger sensor (Neulog Inc., Rishon Lezion, Israel) was wrapped around the abdomen, and the ECG electrodes of a PolyG-A (Laxtha Inc., Daejeon, Korea) were attached to both wrists and the right ankle.  
Figure 2 shows VS signals measured by FMCW radar. \(M(t,r)\) and \(P(t,r)\) for respiration and heartbeat are shown in Figure 2a,b. Because of high range-resolution of the radar, displacements in various parts of body are reflected in different range-bins. To identify the range-bin where the VS displacement exists, the temporal correlation coefficient [49] between \(P(t,r)\) at each range-bin and referenced VS signals was calculated as shown in Figure 2c,d. According to the conventional studies, respiration can be measured near the abdomen or thorax [39; 42], and heartbeat can be measured where the skin is thin, such as neck [43]. In our experiments, the correlation between \(P(t,r)\) and referenced respiration signal was high between 0.5 and 0.6 m, which is near the subject's abdomen. The correlation between \(P(t,r)\) and referenced heartbeat signal was high between 0.75 to 0.85 m which is near the subject's neck.  
In Figure 2e,f, the extracted VSs (respiration and the heartbeat) from various range bins corresponding to high and low correlation with referenced signals are compared. \(P(t,r=0.525,0.550)\) m in Figure 2e is highly correlated with referenced respiration, but \(M(t,r=0.525,0.550)\) m is not. \(P(t,r=0.775,0.800)\) m in Figure 2f shows a high correlation with a referenced heartbeat; however, \(M(t,r=0.775,0.800)\) m does not. At \(r=0.800\) m in Figure 2e and \(r=1.050\) m in Figure 2f, \(M(t,r)\) nor \(P(t,r)\) are associated with referenced VSs.  
To summarize, respiration and heartbeat signals can be observed in multiple range-bins of \(P(t,r)\), which is more accurate and reliable than \(M(t,r)\). In addition, displacement of VSs exists in certain range-bins, so the range-bin that contains accurate displacement of VSs must be chosen.  
Figure 1: Vital-sign measurement using FMCW radar. (**a**) Experimental settings for vital measurement; (**b**) FMCW radar.
# Energy-Efficient Multicast Precoding for Massive MIMO Transmission with Statistical CSI
## 1 Introduction
* We used the large-dimensional random matrix theory to deduce the deterministic equivalent (DE) of the optimization objective function to further reduce the computational complexity involved in sample averaging. The rest of this paper is organized as follows. We present the massive MIMO beam domain channel model in Section 2. In Section 3, energy-efficient multicast precoding for single-cell massive MIMO transmission is investigated. The eigenvectors of the optimal energy-efficient multicast transmit covariance matrix is identified in closed-form and a sequential beam domain power allocation algorithm is proposed. Numerical results are presented in Section 4. Finally, we conclude the paper in Section 5. The notations adopted throughout the paper are listed as follows: We denote by \(\mathbb{R}^{M\times N}\) the \(M\times N\) dimensional real-valued vector space and \(\mathbb{C}^{M\times N}\) the \(M\times N\) dimensional complex-valued vector space, respectively. We adopt upper-case boldface letters to denote matrices and lower-case boldface letters to denote column vectors, respectively. We adopt \(\mathbf{X}\succcurlyeq\mathbf{0}\) to denote that \(\mathbf{X}\) is a positive semidefinite matrix. We use \(\text{tr}\left\{\cdot\right\}\) to denote the matrix trace operation and \(\text{det}\left\{\cdot\right\}\) to denote the matrix determinant operation, respectively. We use \(\text{E}\left\{\cdot\right\}\) to denote the expectation operation. We adopt \(\mathcal{CN}\left(\mathbf{a},\mathbf{B}\right)\) to denote the circular symmetric complex-valued Gaussian distribution with mean \(\mathbf{a}\) and covariance \(\mathbf{B}\). We adopt \(\left(\cdot\right)^{H}\), \(\left(\cdot\right)^{T}\), and \(\left(\cdot\right)^{*}\) to denote the conjugate-transpose, transpose, and conjugate operations, respectively. We adopt \(\odot\) to denote the Hadamard product. We adopt \(\left[\mathbf{A}\right]_{m,n}\) to denote the \(\left(m,n\right)\)th element of matrix \(\mathbf{A}\). We adopt \(\triangleq\) and \(\sim\) to denote \"be defined as\" and \"be distributed as\", respectively.