esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 TBM (Tunnel Boring Machine) Data Processing
### Data Preprocessing
#### 3.2.2 Outlier Processing  
Statistical analysis revealed the presence of outliers in TBM operational data. These anomalies may arise from sensor malfunctions, operator errors, or system glitches, all of which hinder subsequent analysis. Therefore, these outliers must also be removed. As illustrated in Figure 3, the blue boxes highlight the detected outliers.  
The boxplot is a simple, efficient, and intuitive data visualization tool commonly used to describe data distributions and identify outliers. It is constructed from several  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Cutterhead**} & \multirow{2}{*}{**Sample**} & \multicolumn{3}{c}{**Sample Cutterhead Speed**} & \multicolumn{3}{c}{**Sample Cutterhead Torque**} \\ \cline{3-8}  & & **Size** & **Max** & **Mean** & **Std** & **Max** & **Mean** & **Std** \\ \hline
0–1 & 118 & 0.995 & 0.506 & 0.248 & 452.739 & 95.290 & 53.241 \\
1–2 & 131 & 1.998 & 1.563 & 0.279 & 268.378 & 124.274 & 37.192 \\
2–3 & 185 & 2.995 & 2.511 & 0.289 & 329.793 & 136.090 & 40.722 \\
3–4 & 443 & 3.999 & 3.614 & 0.282 & 861.905 & 174.697 & 97.073 \\
4–5 & 2524 & 4.999 & 4.481 & 0.25 & 849.932 & 309.051 & 172.227 \\
5–6 & 3738 & 5.999 & 5.517 & 0.29 & 1018.987 & 447.003 & 249.512 \\
6–7 & 7776 & 6.999 & 6.592 & 0.277 & 1344.626 & 736.204 & 320.109 \\
7–8 & 8019 & 7.79 & 7.29 & 0.198 & 1400.759 & 932.366 & 295.201 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Statistical Distribution of Disc Speed and Torque.  
Figure 2: Raw torque line chart.  
key quartile values, including the first quartile (\(Q1\)), median (\(Q2\)), and third quartile (\(Q3\)), as well as the upper and lower whiskers, which represent the range of typical data values. These values effectively display the central tendency, dispersion, and shape of the data distribution.  
A boxplot divides the data into quartiles, where the data inside the box is considered normal. The whiskers reflect the approximate range of the data distribution. The boxplot's shape indicates whether the data distribution is symmetric and identifies outliers falling outside the whiskers.  
The boxplot is particularly advantageous because it is robust to variations in data distribution, making it ideal for summarizing the distribution characteristics of data, comparing differences between datasets, and filtering outliers. In this study, the boxplot method was employed to eliminate outliers. The results of the outlier removal are shown in Figure 4.  
Figure 4: Comparison of data before and after boxplot outlier removal.  
Figure 3: Line chart highlighting data outliers.
## 3 Results
### Network Throughput Calculation  
Figure 10 shows the simulation results of the network throughput under different loads in a scenario without microbursts. As shown in the figure, when the network load is \(1.04\times 10^{9}\) b/s, the network throughput after optimization has increased by \(2.41\times 10^{7}\) b/s compared to the throughput before optimization. Therefore, the optimization algorithm proposed in this paper can increase the network's throughput, enabling the network to support larger-scale data transmission.  
Figure 8: Total queuing delay for multi\(-\)node end\(-\)to\(-\)end transmission.  
Figure 9: Packet loss rate for multi\(-\)node end\(-\)to\(-\)end transmission.  
The rate of traffic pouring into a single egress port may exceed the maximum bandwidth of that port, which can cause microbursts. And microbursts are becoming increasingly frequent in high-speed networks. Under the granularity of seconds, network traffic is usually smooth. However, when observed at a finer time granularity, network traffic becomes more bursty. [PERSON] [23] et al. found through research that on a time scale of 110 to 100 microseconds, traffic exhibits significant burstiness.  
Thus, this study simulated the network throughput in the microburst scenario. Figure 11 presents the simulation results of network throughput in microburst scenarios with different microburst durations and peak loads. It can be observed from the figure that when the microburst duration reaches 200 us, the optimized network throughput has increased by \(3.37\times 10^{8}\) b/s, which is an improvement of 62.72% compared to the throughput before optimization. Therefore, the optimization algorithm proposed in this paper can enhance the network's throughput during microburst events, mitigating the problem of network congestion in microburst scenarios.  
Figure 11: Network throughput for microburst scenarios at different microburst durations and peak loads.  
Figure 10: Network throughput under different loads in a scenario without microbursts.
# Therapeutic Potential and Challenges of Pioglitazone in Cancer Treatment
## 3 Therapeutic Use of Pioglitazone
### Therapeutic Effect on Different Types of Cancer
#### 3.6.4 Hepatocellular Carcinoma  
Assessing the preventative potential of pioglitazone, metanalytical results indicate that the use of TZDs in patients with T2 DM is associated with protection against liver cancer [81]. Complementary rodent studies indicated prevention of liver fibrosis and carcinogenesis following cirrhosis [82]. Results originating from preclinical studies showcase pioglitazone's anticancer properties. The increased presence of the receptor for advanced glycation end products (RAGE) is deemed as a negative biomarker for the progression of hepatocellular carcinoma which possibly indicates invasion. Pioglitazone decreased the expression of RAGE along with HMGB1, NF-\(\kappa\)\(\beta\), and p38 MAPK, resulting in reduced proliferation, invasion, and metastatic potential [83]. Additionally, it was reported that metabolomic alterations triggered by pioglitazone, mostly regarding the metabolism of lipids, leading to the death of chemically induced hypoxic HepG2 cells from oxidative stress [84].
# VANET Clustering Based Routing Protocol Suitable for Deserts
## 7 Conclusions  
This paper presented a novel clustering-based VANET routing algorithm protocol (CBVRP) appropriate for desert scenarios. CBVRP is mainly based on a vehicle's equipment, location and velocity, which play a major role in cluster classifications and CHE. A stable clustering approach is used to reduce on-demand routing. Furthermore, a local route maintenance mechanism was utilized to reduce the delay caused by the new route finding process. The routing algorithm approach proposes that when communication is within a single cluster, the CH directly selects the most appropriate vehicle to serve as the next hop in the route to the destination. In order to communicate with the outside, the CH uses flooding methods towards its cluster members, seeking a route using the nearest suitable vehicles able to communicate with the outside. This route remains stable until the cluster structure changes. Other available routes are stored as backup. CBVRP is evaluated using simulation by comparing it to other alternatives and against other protocols. Evaluation results show that the proposed algorithm characteristics have high efficiency and scalability. A VANET using CBVRP obtains higher stability, significant success rates of data transfer, lower routing cost and decreased packet transmission delay. Further research can focus on studying the capability of using CBVRP bidirectional routing, considering security issues and enhancing the CHE process by adding more priority factors, such as signal strength.  
This work is supported by the National Natural Science Foundation of China (No. 61471164, 61201175, 61171081).  
The authors contributed equally to this work.  
The authors declare no conflict of interest.
# Three Rapid Methods for Averaging GPS Segments
## 5 Conclusions  
Three fast segment averaging methods were proposed to handle complex curve segments. The two simple methods use linear model as the baseline and either median or medoid as the backup plan when linear model does not work well enough. The third method uses more complex divide-and-conquer technique to divide the problem into sub-problems.  
The results showed that the simplest variant (RapSeg-A) achieves similarly good accuracy as the more complex variant (RapSeg-C): 62.0% vs. 62.2%. All methods are fast, simple to implement, and do not suffer serious oversampling. The processing time of RapSeg-A is an order of magnitude faster (12 s) than the comparative methods: Medoid (1 h), Marteau (30 min), and CellNet (49 s).  
**Author Contributions:** The ideas and implementation of the algorithms originated from [PERSON] and developed jointly by all authors. [PERSON] and [PERSON] conducted the experiments. Illustrations were prepared jointly by all authors. [PERSON] was the principal author wringing the paper.  
**Funding:** This research received no external funding.  
**Acknowledgments:** We would like to thank [PERSON] for his financial support from the Faculty of Science and Forestry.  
**Conflicts of Interest:** The authors declare no conflicts of interest.
###### Abstract  
Restricted Boltzmann Machines (RBMs) have shown success in both the front-end and backend of speaker verification systems. In this paper, we propose applying RBMs to the front-end for the tasks of speaker clustering and speaker tracking in TV broadcast shows. RBMs are trained to transform utterances into a vector based representation. Because of the lack of data for a test speaker, we propose RBM adaptation to a global model. First, the global model--which is referred to as universal RBM--is trained with all the available background data. Then an adapted RBM model is trained with the data of each test speaker. The visible to hidden weight matrices of the adapted models are concatenated along with the bias vectors and are whitened to generate the vector representation of speakers. These vectors, referred to as RBM vectors, were shown to preserve speaker-specific information and are used in the tasks of speaker clustering and speaker tracking. The evaluation was performed on the audio recordings of Catalan TV Broadcast shows. The experimental results show that our proposed speaker clustering system gained up to 12% relative improvement, in terms of Equal Impurity (EI), over the baseline system. On the other hand, in the task of speaker tracking, our system has a relative improvement of 11% and 7% compared to the baseline system using cosine and Probabilistic Linear Discriminant Analysis (PLDA) scoring, respectively.  
speaker tracking; speaker clustering; speaker segmentation; restricted boltzmann machine adaptation; agglomerative hierarchical clustering +
Footnote †: journal: Accepted: 2 July 2019; Published: 9 July 2019
# Optical Bullets and Their Modulational Instability Analysis
## 2 Overview of Schemes
### Description of PREM  
Consider that the solution of Equation (5) possesses the form  
\[w(\xi)=a_{0}+\sum_{i=1}^{m}f^{i-1}(\xi)[a_{i}f(\xi)+b_{i}g(\xi)], \tag{6}\]  
where \(f=f(\xi)\) and \(g=g(\xi)\) satisfy the projective Riccati equations given by  
\[\begin{gathered} f^{\prime}(\xi)=pf(\xi)g(\xi),\qquad g^{\prime} (\xi)=R+pg^{2}(\xi)-rf(\xi),\\ g^{2}(\xi)=-p\bigg{[}R-2 rf(\xi)+\frac{r^{2}+\delta}{R}f^{2}(\xi) \bigg{]},\end{gathered} \tag{7}\]  
where \(R\) and \(r\) are real constants, and \(p=\delta=\pm 1\). The set of Equation (7) is found to accept the following solutions.  
**Case 1.** If \(p=-1\), \(R\
eq 0\),  
\[f_{1}(\xi)=\frac{RA}{B\cosh(\xi)+C\sinh(\xi)+Ar^{\prime}},\quad g_{1}(\xi)= \frac{\sqrt{R}\{B\sinh(\sqrt{R}\xi)+C\cosh(\sqrt{R}\xi)\}}{B\cosh(\sqrt{R}\xi )+C\sinh(\sqrt{R}\xi)+Ar}, \tag{8}\]  
where \(A\), \(B\), and\(C\) satisfy \(C^{2}=A^{2}+B^{2}\) as \(\delta=1\), and \(A\), \(B\), and\(C\) satisfy \(B^{2}=A^{2}+C^{2}\) as \(\delta=-1\).  
\[f_{2}(\xi)=\frac{R}{\sinh(\sqrt{R}\xi)+r},\qquad g_{2}(\xi)=\frac{\sqrt{R} \cosh(\sqrt{R}\xi)}{\sinh(\sqrt{R}\xi)+r}, \tag{9}\]  
demands \(\delta=1\).  
\[f_{3}(\xi)=\frac{R}{\cosh(\sqrt{R}\xi)+r},\qquad g_{3}(\xi)=\frac{\sqrt{R} \sinh(\sqrt{R}\xi)}{\cosh(\sqrt{R}\xi)+r}, \tag{10}\]implies \(\delta=-1\).  
**Case 2.** If \(p=1,R\
eq 0\),  
\[f_{4}(\xi)=\frac{RA}{B\cos(\sqrt{R}\xi)+C\sin(\sqrt{R}\xi)+Ar},\quad g_{4}(\xi)= \frac{\sqrt{R}\{B\sin(\sqrt{R}\xi)-C\cos(\sqrt{R}\xi)\}}{B\cos(\sqrt{R}\xi)+C \sin(\sqrt{R}\xi)+Ar}, \tag{11}\]  
where \(A\), \(B\), and \(C\) satisfy \(A^{2}=-B^{2}-C^{2}\) as \(\delta=1\), and \(A,B\), and \(C\) satisfy \(A^{2}=B^{2}+C^{2}\) as \(\delta=-1\).  
\[f_{5}(\xi)=\frac{R}{\sin(\sqrt{R}\xi)+r},\qquad g_{5}(\xi)=-\frac{\sqrt{R}\cos( \sqrt{R}\xi)}{\sin(\sqrt{R}\xi)+r}, \tag{12}\]  
\[f_{6}(\xi)=\frac{R}{\cos(\sqrt{R}\xi)+r},\qquad g_{6}(\xi)=\frac{\sqrt{R}\sin( \sqrt{R}\xi)}{\cos(\sqrt{R}\xi)+r}, \tag{13}\]  
where (12) and (13) demand \(\delta=-1\).  
**Case 3.** If \(R=r=0\),  
\[f_{7}(\xi)=\frac{s_{1}}{-p_{5}^{x}+s_{2}}=s_{1}g_{7}(\xi),\qquad g_{7}(\xi)= \frac{1}{-p_{5}^{x}+s_{2}}, \tag{14}\]  
where \(s_{1}\) and \(s_{2}\) are arbitrary constants.  
Substituting (6) along with (7) into Equation (5), a polynomial in the variables \(f^{j}(\xi)\) and \(f^{j-1}(\xi)\,g(\xi)\) is obtained. Then, equating each coefficient of \(f^{j}(\xi)\) and \(f^{j-1}(\xi)\,g(\xi)\) in this polynomial to zero generates a system of algebraic equations for \(a_{j},b_{j}\). Finally, the solutions to this system of equations create various exact solutions to Equation (3) with reference to (8)-(14).
# Gait Phase Identification and Damping Control for Knee Orthosis Using Time Series Forest Classifier
## 3 Gait Phase Segregation and Percentage Share
The final objective of identifying the gait phase and finding the corresponding activation level for knee damping is achieved by averaging the moment associated with each gait subphase, and a lookup table is generated, as shown in Table 5, for referenced damping. Figures 10 and 11 represent the moment associated with both knees. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Phase** & **Data Points** & \begin{tabular}{c} **Gait Percentage** \\ **Including State 0** \\ \end{tabular} & \begin{tabular}{c} **Gait Percentage** \\ **Excluding State 0** \\ \end{tabular} & \
\begin{tabular}{c} **Rounded Value** \\ \end{tabular} &
\begin{tabular}{c} **Cumulative Value** \\ \end{tabular} \\ \hline
0 & 42,498 & 15.299763 & & & & \\
1 & 17,647 & 6.3531208 & 7.50071 & 8 & 8 \\
2 & 75,138 & 27.050535 & 31.9368 & 32 & 40 \\
3 & 26,308 & 9.4711793 & 11.182 & 11 & 51 \\
4 & 20,936 & 7.5371982 & 8.89867 & 9 & 60 \\
5 & 12,971 & 4.6697076 & 5.51322 & 5 & 65 \\  & 56,676 & 20.404005 & 24.0897 & 24 & 89 \\  & 25,595 & 9.2144912 & 10.8789 & 11 & 100 \\
**Total Excluding 0 state** & 235,271 & 100 & 100 & 100 & \\
**Total Including 0 state** & 277,769 & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Dataset description. Figure 9: Percentage share of gait phases from class 1 to 7. The mean time for one gait cycle ranges from 0.98 to 1.07 s [15]. The pace variation occurs for each stride. An algorithm that can predict the next gait subphase by classifying the present subphase and exploiting the Markov cycle nature of the controller gait is ideal for knee flexion damping where the next gait subphase can be known if the present subphase is identified. Table 5 shows the mean and median brake activation corresponding to each gait phase. This is independent of the method used for damping the knee flexion. For this application, the damping is achieved by the MR brake-mounted knee orthosis; any other system devised for arresting the flexion of the knee can leverage the same data.
## 4 Validation of Proposed Method
### Comparison of Electromagnetic Torque  
Axial flux motors are used to drive loads for various rotational movements, so electromagnetic torque must be calculated in engineering design. The waveform of the electromagnetic torque calculated by the 2D and 3D methods is shown in Figure 16, which roughly appears to be almost completely coincident, exhibiting a 12 th harmonic within an electrical cycle. The electromagnetic torque amplitudes obtained by the two methods are 47.85 Nm and 47.63 Nm, respectively, with a numerical error of only 1.2% between the two. For torque fluctuation, the fluctuation value calculated by 2D is 4.85 Nm, with a fluctuation ratio of 9.24%, and the fluctuation value calculated by 3D is 4.72 Nm, with a fluctuation ratio of 9.90%. The calculation results of the two methods are very close, and the error meets the engineering requirements in preliminary design and optimization.  
Figure 16: Comparison results of electromagnetic torque.  
Figure 15: Comparison results of cogging torque.
# Malware Collusion Attack against SVM: Issues and Countermeasures
## 3 Motivation
### Collusion Apps to Evade SVM Detection
#### 3.3.2 Collusion Attack on SVM
**Fitness Function** It is used to measure the individuals and to guide the algorithm towards optimal solutions. Thus, we can simply define it as \(g(x)\). To be more specific, the fitness function can be formulated as:  
\[fitness(ind)=g(ind)=\frac{|F(M_{\phi_{A}(ind)})\bigcap F(M_{\phi_{B}(i_{th})} )|}{|M|}. \tag{5}\]  
**Other Parameters of Genetic Algorithm** We perform a standard Genetic Algorithm in our experiment. Apart from the definition of individuals and fitness function, we also need to set other parameters, such as the population size, mutation rate, etc. [PERSON] [25] have investigated how to choose the parameters of Genetic Algorithm and its impact on the results. In our experiment, we choose a set of typical parameters. We initial the population with random values, and then iterate for 2000 generations. The crossover probability and mutation rate are 0.5 and 0.2, respectively. **The effect of Simulated Collusion Attack** Before we conduct a simulated collusion attack, we train and test the SVM classification model with linear kernel on the dataset in Section 5. The accuracy is 94.6%. After our simulation collusion attack by running Genetic Algorithm iteratively, we can get the results as shown in Figure 2. By the 2000 th generation, it almost converged. The best individual's fitness value is 78.4%, which indicates that 78% of apps in our data set can be split successfully by our best individual feature splitting scheme. Our simulated collusion attack can make SVM accuracy drop from 94.6% to 31.6%. Furthermore, we change Equation (4) to support splitting into three sets. After iterating for 2000 generations, the result is shown in Figure 2, from which we can know that 94.8% of the apps can evade detection of current methods when they are split into three apps. Our simulated collusion attack demonstrated that a large majority of malicious apps could evade the detection of SVM by splitting their malicious behavior to two or more apps.
# Robustness and Sensitivity Tuning of the Kalman Filter for Speech Enhancement
## 5 Results and Discussion  
### Objective Quality Evaluation  
Figure 8 shows the average PESQ score (found over all frames for each test condition in Section 4.1) for each SEA. It can be seen that the KF-oracle method exhibits the highest PESQ score for all test conditions. It is due to (\(\{a_{i}\}\), \(\sigma_{w}^{2}\)) and \(\sigma_{v}^{2}\) being computed from theclean speech and the noise signal. The improvement of the average PESQ score for the KF-non-oracle method is marginal as compared to the noisy one. The proposed SEA shows a considerable PESQ score improvement compared to the benchmark methods across the test conditions. The average PESQ score for the proposed method is also very similar to that of the KF-oracle method. It is due to the reduced-biased Kalman gain obtained by the proposed tuning algorithm being closely similar to that of the KF-oracle method (Figure 7). Amongst the benchmark methods, MDKF-MMSE [17] shows relatively competitive PESQ scores followed by AKF-RMBT [22] for all tested conditions (Figure 9a-d). On the other hand, the AKF-IT method [13] exhibits reduced PESQ scores than other benchmark methods across the test conditions due to suffering from _distortion_ and _musical_ noise in the enhanced speech. In light of this comparative study, it is evident that the proposed method has better quality with regard to enhanced speech than that of the competing methods for all tested conditions.  
Figure 8: Average PESQ score comparison between the proposed and benchmark SEAs on NOIZEUS corpus corrupted with: (**a**) _babble_, (**b**) _street_, (**c**) _factory2_, and (**d**) _f16_ noises for a wide range of SNR levels (from \(-\)5 to 15 dB).  
Figure 9 shows the average SDR (dB) score (found over all frames for each test condition in Section 4.1) for each SEA. Like the earlier experiment in Figure 8, the KF-oracle method shows an indication of the highest SDR score for all test conditions. Additionally, the noisy one shows the lower SDR scores for all tested conditions. The proposed SEA consistently demonstrates SDR score improvement from the competing methods across the test conditions. Amongst the competing methods, the MDKF-MMSE [17] show relatively competitive SDR scores for all tested conditions (Figure 9a-d). The noisy one shows the lowest SDR scores for all tested conditions. In light of this comparative study, it is evident that the proposed SEA exhibits less distortion in the enhanced speech than that of the competing methods for all tested conditions.
## 2 Background  
### Natural Language Understanding  
Natural language understanding (NLU) is a subfield of artificial intelligence (AI) that aims to enable machines to comprehend and analyze natural language based on concepts, entities, sentiments, and keywords [17] using machine learning and natural language processing (NLP) techniques. At the core of most chatbot applications lies NLU [18]. Chatbots need to allow the extraction of structured information from natural language inputs with unstructured semantic inputs, such as natural language inputs [19]. Nevertheless, developing an entire NLU system is challenging and expensive; therefore, the standard market practice consists of employing NLU solutions to create chatbots [20; 21].  
In the context of chatbots, NLU is utilized to analyze the question asked by the user and complete their requests [11]. To achieve this, NLU extracts domain-specific entities and intents from a text. Intents are the intentions or purposes with which the user asks a question. Intent recognition (IR) is a crucial component of NLU that involves identifying the intent behind a user's query [22]. Named entity recognition (NER) is another subtask of NLU that includes identifying and categorizing named entities in text or speech. Named entities are objects, people, locations, organizations, and other entity names in natural language [23; 24].  
To illustrate this with an example, consider the ATM maintenance process. In this context, the words \"on\" and \"off\" are classified as entities representing the status of a red LED on the receipt printer. These entities might carry different implications in different scenarios, highlighting the importance of context in NLU. Now, when a user inputs \"The LED is on\", the NLU system springs into action. The intent recognition (IR) component identifies the intent of the user's message--in this case, to report the status of the LED. Concurrently, the named entity Recognition (NER) component pinpoints the word \"on\" as an entity specifying the LED's current status.  
By utilizing these entities and intents, an NLU engine can generate a targeted response that can efficiently assist users with their queries [25]. Thus, IR and NER--intent and entity recognition--form the twin pillars of NLU. Their applications are extensive, ranging from information extraction and text understanding to the creation of knowledge bases [24].