esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Results
### Effectiveness Comparison  
In this section, the evaluation results for the anomaly and threat identification obtained for IoT-23 and SIMARGL2021 datasets are presented separately. It can be seen (see Tables 1 and 2) that the proposed approach achieved very good results for both datasets (the F1-score metric was higher than 0.9 for all classification tasks).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Class** & **Recall** & **Precision** & **F1-Score** \\ \hline Benign & 0.9980 & 0.9968 & 0.9974 \\ Anomaly & 0.9974 & 0.9984 & 0.9979 \\ \hline CNC & 1.0000 & 0.9957 & 0.9978 \\ DDOS & 1.0000 & 0.8814 & 0.9369 \\ Okiru & 1.0000 & 0.9990 & 0.9995 \\ Torii & 1.0000 & 1.0000 & 1.0000 \\ PortScan & 0.9972 & 0.9954 & 0.9963 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Detection effectiveness of the proposed approach (IoT-23 dataset).  
In the experiments, two classification scenarios were considered. One was focused on anomaly detection, while the other was related to thread identification. In the first case (anomaly detection), all the samples that indicated any kind of malicious behaviour were given the label 'anomalous', and genuine traffic samples were indicated as 'normal'. Having prepared the data, the ML-based model was trained according to the procedure presented in the previous section. For the second case (threat identification), the ML models were trained in a one-to-many fashion. In other words, a dedicated classifier was trained for each threat (cyber-attack).  
In both experiments, there were cyber threats that were conceptually similar in terms of techniques used by the adversaries. In particular, the malware included in the IoT-23 datasets conveyed network reconnaissance, which relied on network scanning, which, in turn, was included in the SIMARGL2021 dataset. The RUDY and Slowloris traces from the SIMARGL2021 dataset should resemble the samples that were indicated as DDoS.  
However, both datasets were significantly different in terms of the tools used to implement the attack, as well as the technical means to record the network traces (e.g., to record SIMARGL dataset, nProbe was used, while IoT-23 utilized Zeek/Bro firewall).
# Comparison Study on Coarseness Index and Maximum Diameter of Rock Fragments by Linear Cutting Tests
## 4 Comparison of Coarseness Index and Maximum Diameter by Cutting Conditions
### Effect of Spacing  
In rock cutting, the spacing (\(s\)) between adjacent cuts significantly influences the excavation efficiency and size of rock fragments. The size of rock fragments can be increased by increasing the spacing between adjacent cutting tools. However, a spacing that is too narrow reduces the excavation efficiency. Thus, the _CI_ and \(d_{\text{max}}\) were analyzed with respect to spacing.  
Figure 8 shows the relationship between spacing and _CI_ and the relationship between spacing and \(d_{\text{max}}\). [PERSON], et al. [16] and [PERSON], et al. [19] reported that _CI_ and \(d^{\prime}\) increased as the spacing increased. Similarly, _CI_ and \(d_{\text{max}}\) increased with increasing spacing in this study. The _CI_ increased with a power relationship, and \(d_{\text{max}}\) increased linearly. In contrast with the relationship with penetration depth, the correlation between spacing and _CI_ (R\({}^{2}>0.50\)) was greater than that of \(d_{\text{max}}\) (R\({}^{2}<0.49\)).  
Figure 7: Relationship between penetration depth and (**a**) _CI_; (**b**) maximum diameter.
# Automatic Detection and Unsupervised Clustering-Based Classification of Cetacean Vocal Signals +
### Automatic Detection  
The automatic detection process contains two steps: endpoint detection and reconstruction. Firstly, all the WAVE files are read into digital format by MATLAB function \"audioread\", and the Hamming window frames each sound to exclude some long-duration sounds. Then, a dual parameters-dual thresholds endpoint detection method based on EMD (empirical mode decomposition) [32] is conducted to detect the frequency variation within the sounds. Finally, the potential vocal signals are extracted and reunited to create a new acoustic signal. Consequently, background noise is removed from the audio files, and only the vocal signals are left in the new acoustic signal [33].  
The methodology details of the EMD-based endpoint detection method for cetacean vocal signals are presented in Appendix A. We introduce the processes briefly in this section.  
Before detection, the collected waveform is initialized by eliminating the DC (direct current) components and normalizing the amplitudes. Firstly, the PAM data are transformed into EMD signals. Then, two adaptive thresholds are computed for automatic detection. Finally, the vocal signals are extracted from PAM data, and the clutters are removed. This method grabs the acoustic features of vocal signals and distinguishes them from the background clutters of the ocean environment. Two parameters--TKEO and short-time average ZCR (Zero crossing rate)--are computed and set as thresholds based on the IMF (intrinsic mode function) components. The average TKEO energy for each IMF component is calculated according to the frame size, and the frame length is set at 25 ms. These parameters are used to determine the front end and back end of vocal signals as well as filter out the background clutters without any prior information. We not only detect the endpoints of vocal signals but also extract the signals between the front end and back end points. The application of the TKEO on an underwater acoustic signal containing all IMF components reflects the energy distribution of the transient signal and produces an output for endpoint detection. More details of the endpoint detection method can be found in [32].
## 3 State of the Art
### LTE and Wi-Fi in a Nutshell
#### 3.1.1 LTE  
Upon a transmission, LTE uses the multi-user versions of the Orthogonal Frequency Division Multiplexing (OFDM) digital modulation scheme, called Orthogonal Frequency Division Multiple Access (OFDMA) for the DL and Single-Carrier Frequency Division Multiple Access (SC-FDMA) for  
Figure 2: Taxonomy of the techniques that can be applied to co-located LTE and Wi-Fi networks.  
the UL [8]. The available spectrum is divided into subcarriers, and each subcarrier occupies 15-kHz of bandwidth. The time domain is organized in timeslots of 0.5 ms in duration. One timeslot corresponds to seven OFDM symbols when the normal Cyclic Prefix (CP) is used and six OFDM symbols when the extended CP is used. Combining the subcarriers and timeslots, LTE defines the Resource Block (RB). The RB is a unit of transmission resource and consists of one slot in the time domain and 12 subcarriers in the frequency domain. An LTE radio frame has a duration of 10 ms and consists of 10 sub-frames, each of which lasts 1 ms corresponding to two slots. Figure 3 shows the structure of resource blocks in the frequency-time domain and how they are scheduled for different User Equipment (UE).  
LTE sends user traffic in the DL and in the UL using the Physical Downlink Shared Channel (PDSCH) and the Physical Uplink Shared Channel (PUSCH), respectively. In addition to user traffic, several RB are allocated for control traffic in dedicated channels. Such control traffic includes among others the transmission of synchronization signals and reference signals.  
LTE is a scheduled technology designed to operate in the licensed spectrum. Therefore, it does not require sensing the medium before transmission. The scheduling in LTE is performed by the LTE base station named the evolved NodeB (eNB) on a subframe basis. That means that every 1 ms, the assignment of the subframes to the active UEs can change.
## 4 Performance Assessment
### Service Dissemination  
The service dissemination for the proposed work is high for varying users and scheduling intervals. Here, power management determines appropriate user forwarding at the mentioned time. Power allocation is estimated for probabilistic balancing and examines the training data for the successive interval, and it is represented as \((s_{u}+i_{d})\times\left(\frac{N+i_{u}/\chi}{40}\right)\). Service distribution is performed by evaluating IoT sharing and resource access. The computation is conducted on the SDN-based IoT application, ensuring valid access. Sharing is derived from the number of users in the network and better deploys power allocation. The current state estimates scheduling based on the queuing process and deployed probabilistic learning. Here, service utilization is performed for service sharing, and resource access is achieved for the training data. In this case, prediction is performed for the prior and current states and provides access to the required resources. Sharing is conducted by determining the point-of-contact-based infrastructure that performs concurrent service sharing and resource access. Scheduling is performed to initialize devices and power allocation is carried out during this process (Figure 6). The different fluctuations in the service dissemination comparison curve in Figure 6 likely result from a combination of factors related to network dynamics, resource management, algorithm efficiency, user behavior, and environmental conditions.
## 5 Real Data Analysis
### Comparison Analysis  
For performance evaluation and comparison purposes, we create an oracle model by fitting a logistic regression model using data from between March 2012 and February 2017, 60 months in total, utilizing all explanatory variables listed in Table S8. Then, the oracle response of ArrDelayLabel for the entire dataset is generated accordingly based on the oracle model.  
Assuming that we do not know the true link (i.e., logit), we fit the Bernoulli model with one of four different links (namely, logit, cloglog, probit, and cauchit) using four approaches: (1) iterative reweighted least square (IRLS) on the combined data; (2) the mean representative (MR) approach; (3) the score-matching representative (SMR) approach; and (4) RASMR with the delta ratio split and exponential learning rate decay.  
The data blocks for MR, SMR, and RASMR are created by the following strategy. The original airline data are saved in monthly files, which are regarded as natural data blocks. Before applying MR, SMR, or RASMR, we further split each of the monthly files according to the distinct values of three categorical variables, namely, QUARTER, DayOfWeek, and DepTimeBlk. Then, each monthly file is divided into \(4\times 7\times 4=112\) sub-blocks. We then split each sub-block further according to the three continuous variables via one of the following two schemes. The first scheme is a correlation-based quantile split. That is, we split each data block at the 25%, 50%, and 75% quantiles of the three continuous variables as the cutting points, and obtain \(4\times 4\times 4=64\) sub-blocks. The second scheme is to apply \(K\)-means clustering to the three continuous variables with \(K=64\). As a result, each monthly file is divided into \(112\times 64=7168\) sub-blocks (see Section S1 in the Supplementary Materials for more discussion).  
We compare MR, SMR, and RASMR with four different data sizes, namely, 60 months, 120 months, 240 months, and 371 months. The IRLS estimates on the combined data are referred to as the full data estimate, but only available for 60-month and 120-month datasets. As a result, in Tables 9 and 10, we list RMSE of MR, SMR, and RASMR (with the delta ratio split) from the IRLS estimates for 60 months and 120 months, and their RMSE from the oracle parameter values for 240 months and 371 months.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Link Function** & **Logit** & **Cloglog** & **Probit** & **Cauchit** \\ \hline \(\widetilde{AIC}\) & 90284022 & 97172503 & 95844750 & 113052242 \\
5-fold CV with Cross-entropy Loss & 1.38441 & 1.7491 & 1.8228 & 2.1535 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Link function selection using \(\widetilde{AIC}\) and 5-fold cross-validation with RASMR.
# Satellite Attitude Determination and Map Projection Based on Robust Image Matching
## 5 Discussion
### Inlier Rates and the Number of Repetitions in Robust Estimation
These results were consistent with the statistical expectation numbers (19, 26, and 956 for the Kanto, Yosemite, and Kyushu cases, respectively), defined as the lowest repetition number that guarantees three correct pairs with more than 99.9% probability (see Appendix B). Note that the number of required repetitions for the baseline RANSAC can be very large (>1000) when the inlier ratio \(\left( \begin{array}{cc}L\\ 3\end{array} \right)/\left( \begin{array}{cc}N\\ 3\end{array} \right)\) is small. Conversely, a large number of repetitions indicates a small number of inliers even before we reach a loop end. Thus, we can consider to stop determining satellite attitude from the image due to an insufficient number of inliers, or to take additional approaches, such as combining prior observation results as discussed in the next section, instead of a simple RANSAC-based approach. PROSAC was more efficient than the baseline RANSAC in a small inlier ratio case. Table 5 shows the repetition numbers for rejecting outliers with the early stopping mechanisms integrated to the baseline RANSAC and PROSAC. Especially in the Kyusyu case which has the smallest inlier ratio, PROSAC reduced the number of repetitions and showed a stable performance with a small standard deviation. Although the central hypothesis in PROSAC (feature pairs with higher similarities are more reliable) remains to be validated with many examples for the implementation to an actual space mission, PROSAC is a considerable candidate for optimizing the outlier rejection part. In the experiments, the threshold \(L_{0}=10\) ensured that \(\overline{\theta}\) was stably less than 0.024\({}^{\circ}\). Optimizing the value of \(L_{0}\) for individual cases will be explored in future work.
## 3 Principle of the Watermarking Algorithm in Transform Domain
### Principle of Watermark Extracting  
When extracting the watermark, the watermarked audio \(A^{\prime}\) is divided into \(M\) audio fragments \(A_{i}^{\prime}\) (\(1\leq l\leq M\)) with \(N\) sample points, and then perform \(r\)-level DWT on each audio fragment to obtain the wavelet coefficient \(Ce^{\prime}(r)\) and \(De^{\prime}(i)\) (\(i\ =\ 1,2,\cdots r\)). Divide \(De^{\prime}(r)\) into the former packet \(De_{1}^{\prime}(r,j)\) and the latter packet \(De_{2}^{\prime}(r,j)\). Perform DCT on the two packets to obtain \(C_{1}^{\prime}(r,j)\) and \(C_{2}^{\prime}(r,j)\) and then calculate their average amplitudes respectively according to Formulas (6) and (7).  
If \(w(q)=1\), according to Formulas (6)-(9), the average amplitude of \(C_{1}^{\prime}(r,j)\) is  
\[\begin{array}{c}M_{c1}^{\prime}\ =\ \frac{2^{r+1}N}{\sum\limits_{j=0}^{2^{r+1}}|C_{1} ^{\prime}(r,j)|}\\ \\ =\frac{2^{r+1}N}{\sum\limits_{j=0}^{2^{r+1}}|C_  
According to Formulas (12) and (13), when \(\lambda>0\), \(M^{\prime}_{c1}\geq M^{\prime}_{c2}\).  
Similar to the above analysis process, if \(w(q)=0\), according to Formulas (10) and (11), the average amplitude of \(C^{\prime}_{1}(r,j)\) is  
\[M^{\prime}_{c1}\ =\ \frac{2^{r+1}}{N}\sum\limits_{j\ =\ 0}^{N/2^{r+1}}|C^{ \prime}_{1}(r,j)|\ =\ (1-\lambda)M_{c},j\ =\ 1,2,\ldots,N/2^{r+1} \tag{14}\]  
The average amplitude of \(C^{\prime}_{2}(r,j)\) is  
\[M^{\prime}_{c2}\ =\ \frac{2^{r+1}}{N}\sum\limits_{j\ =\ 0}^{N/2^{r+1}}|C^{ \prime}_{2}(r,j)|\ =\ (1+\lambda)M_{c},j\ =\ 1,2,\ldots,N/2^{r+1} \tag{15}\]  
It can be seen from Formula (14) and (15), when \(\lambda>0\), \(M^{\prime}_{c1}\leq M^{\prime}_{c2}\). Based on the above analysis, the binary watermark can be extracted from the audio fragment \(A^{\prime}_{i}\) according to Formula (16):  
\[w^{\prime}(q)\ =\ \left\{\begin{array}{ll}1&,\ \ \ \text{if}\ M^{\prime}_{c1} \geq M^{\prime}_{c2}\\ 0&,\ \ \ \text{if}\ M^{\prime}_{c1}<M^{\prime}_{c2}\end{array}\right.,q\ =\ 1,2,\ldots,L \tag{16}\]
# Potential Evaluation of Twin-Screw Air Expanders with Dual-Lead Rotors Used in PEMFC Systems
## 3 Results and Discussion
### Effects of the Geometric Parameters on the Thermodynamic Performance
#### 3.2.2 Effect of the Total Rotor Length on the Thermodynamic Performance  
As concluded in Section 3.1, the total rotor length would yield a significant effect on the expander's efficiency if variable-lead rotors are applied. Additionally, the severe leakage in the dry TSE would also vastly affect the expander's efficiency. Hence, the effect of the total rotor length on the isentropic indicated efficiency was investigated with and without considering the leakage, as shown in Figure 10. It can be seen that the maximum isentropic indicated efficiency existed for the TSE with dual-lead rotors, when the overall rotor length was varied and the leakage was considered. The larger the lead of the HP rotor segment is, the shorter the optimal overall rotor length is, mainly due to the larger suction pressure loss. However, the maximum isentropic indicated efficiency for the constant-lead TSE was always higher than the dual-lead TSEs, which means that the effect of the severe mismatch between the internal and external pressure ratios for dual-lead TSEs is not significant under the condition of considering the leakage. However, compared with the results in Figure 9b, it can be concluded that the severe mismatch between the internal and external pressure ratios for dual-lead TSEs would have a significant effect on the isentropic indicated efficiency, but the effect is balanced out by the severe leakage, as the larger lead and longer length of the HP rotor segment lead to an obviously larger leakage area via the rotor tip, as Figure 7c indicates. When the leakage is neglected, the maximum isentropic indicated efficiency of 0.992 occurred in the case of \(T_{HP}\) = 90 mm, \(L_{HP}\) = 50 mm and \(L_{r}\) = 105 mm, and the maximum isentropic indicated efficiency for the constant-lead TSE was 0.968 when the overall rotor length was 90 mm. By contrast, the increment rate was 2.5%. Therefore, the dual-lead TSE would contribute to the increment in expander efficiency only when the leakage is effectively weakened.
# Failure in Confined Brazilian Tests on Sandstone
###### Abstract  
Strength of rocks in the confined tension region, where the minimum principal stress is tensile, has only infrequently been measured and is not well understood. Quasi-static confined Brazilian tests under a range of confining stresses (2.76 to 27.58 MPa) where used to determine the strength of sandstone in the confined tension region. The test results indicate that the strength in the confined tension region was a strong function of the intermediate principal stress: increasing the intermediate principal stress significantly increased the strength of the sandstone. The strength data were well fit by the Mogi-Coulomb criterion, which accounts for the intermediate principal stress. Unconfined Brazilian strength data were not well fit to the Mogi-Coulomb criterion derived from the confined Brazilian test data, consistent with a transition from tensile to shear processes dominating failure with increasing confining pressure. Observations of post-failure fracture surfaces reveal more indication of shear processes with increasing confining pressure. Numerical simulations from combined finite-discrete element method are compared to the experimental results and reflect similar conditions for failure compared to the experimental tests in the confined tension region.  
strength; confined tension; confined extension; intermediate principal stress; Brazilian test; failure criteria +
Footnote †: journal: Applied Sciences
## 5 Performance Analysis
### Standard HSR Protocol  
Figure 7 shows the process of sending a unicast frame under the standard HSR protocol. When a source node sends a unicast frame to a destination node, the standard HSR protocol floods and duplicates the frame in all rings except for the destination ring.  
The network traffic when a source node sends a unicast frame to a destination node under the standard HSR protocol, denoted by \(nt_{hsr}^{1}\), is determined by:  
\[nt_{hsr}^{1}=n_{link}^{D}\ +\ \sum_{i\in DR^{-D}}2n_{link}^{i}\ +\ \sum_{i\in QR}2n_{link}^{i}, \tag{2}\]  
where \(n_{link}^{D}\) is the number of links in the destination ring, \(n_{link}^{i}\) is the number of links in the ith ring, \(DR^{-D}\) is a set of all DANH rings except the destination ring, and \(QR\) is a set of all QuadBox rings.  
\[\sum_{i\in DR^{-D}}2n_{link}^{i}=\sum_{i\in DR}2n_{link}^{i}\ -\ 2n_{link}^{D}, \tag{3}\]  
where \(DR\) is a set of all DANH rings.  
\[\sum_{i\in DR}2n_{link}^{i}\ +\ \sum_{i\in QR}2n_{link}^{i}=2n_{link}, \tag{4}\]  
where \(n_{link}\) is the total number of links in the network.  
Figure 6: The process of forwarding a unicast frame using the DSP-based approach.  
Therefore, \(nt_{HSR}^{1}\) can be re-calculated as follows:  
\[nt_{hsr}^{1}=2n_{link}\ -\ n_{link}^{D}. \tag{5}\]  
Generally, the network traffic when a source node sends \(N\) unicast frames to a destination node under the standard HSR protocol, denoted by \(nt_{hsr}\), can be calculated by:  
\[nt_{hsr}=N\Big{(}2n_{link}\ -\ n_{link}^{D}\Big{)}. \tag{6}\]  
For the sample network in Figure 5, \(nt_{hsr}=138N\ (frames)\).
# Design and Analysis of Joint Source-Channel Code System with Fixed-Length Code
## 4 Irregular Channel Code for Iterative Source Channel Decoding  
The EXIT chart is an efficient tool for designing iterative decoding systems [34], and it provides a fast approach for predicting the performance of the iterative decoder accurately. In this section, the channel encoder is optimized based on the EXIT chart analysis. In particular, the EXIT chart of the proposed system is first analyzed concerning the design guidelines. Then, an appropriate irregular channel code is designed to make the iterative decoding have a good convergence.
# Real-Time Object Tracking Algorithm Based on Siamese Network
## 4 Experiments
### Ablation Experiments  
In this section, ablation experiments are conducted in the OTB benchmark to verify the effectiveness of the module and prove that the design of SiamFC is reasonable. The result analysis included the success rate and accuracy of otb50 and otb2015. As shown in Table 1. In this paper, SiamFC is used as a baseline tracker. The success rates of baseline in otb50 and otb2015 were 0.519 and 0.586, respectively, and the accuracy was 0.693 and 0.772, respectively. In baseline +PW, the optical flow estimation network is added, and the success rate and accuracy are greatly improved, which can prove that the optical flow network can more accurately predict the movement trend of the target. In baseline +PW +FP, the input image is replaced by the feature pyramid, and the success rate and accuracy are improved, indicating that the feature pyramid can suppress the target loss caused by illumination changes. In baseline +PW+FP+Att, the dual attention mechanism is added, and the success rate and accuracy have been significantly improved, indicating that the ability to use the attention mechanism to resist background interference is enhanced. Overall, each module in SiamFP can significantly improve the performance of the algorithm.