esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### HRV Analysis
They suggested that the observed increase in heat rate was not interpreted as stress, but rather as a physiological response reflecting positive emotional states similar to those experienced in natural environments [26]. Several studies have reported that moderate levels of wood coverage in indoor environments mayinduce physiological relaxation effects. Visual stimulation by the 30% wood-covered room was found to reduce diastolic blood pressure and pulse rate, indicating a physiological relaxation effect [27]. It remains to be determined whether the results observed in the 90% wooden environments are due to a saturation effect or to positive psychological responses; this issue will be explored in further study. Some researchers have pointed out that there are limitations in interpreting physiological changes using only a single or a few indicators. For this reason, there is a growing need to integrate a wider range of physiological indicators such as HRV, blood pressure, skin conductance, skin temperature, and salivary cortisol levels. Moreover, several studies have combined physiological indicators with psychological tools like POMS (Profile of Mood States) analysis to produce more comprehensive results [28]. However, since this study limited its physiological evaluation to a single indicator, utilized a small sample size, and had a short exposure duration, it has limitations in providing a comprehensive interpretation of the effects of wooden environments on the physiological response. In addition, the relatively short exposure period may have constrained the observation of longer-term autonomic adjustments. Further studies should consider longer exposure durations or repeated visits and include a broader range of physiological indicators to achieve more robust and comprehensive analyses of the impact of wooden environments.
# Adaptive Modem Based on LSTM-AutoEncoder with Vector Quantization
## 6 Conclusions  
This paper used a variant of the autoencoder to solve the problem of rapid generation of unified framework communication waveforms, developed an LSTM autoencoder framework that supports fast switching of multiple waveforms, and introduced vector quantization technology to further reduce resource overhead. Through theoretical analysis with simulation tuning, its optimal structural parameters were obtained. The test results show that the lightweight model based on vector quantization LSTM-AE can achieve the same or better modulation and demodulation performance as the existing model, and the training convergence speed is significantly improved. It can be applied to demodulation systems with limited resources or scenarios with higher performance requirements.  
This research reduces the resource consumption of adaptive modulation technology, improves the overall efficiency of the communication system, and brings more possibilities for its application in scenarios such as military satellite communications. The requirements for channel information perception accuracy, fast feedback, and increasing real-time requirements of update mechanisms also bring more challenges. The proposed model will be further studied in the future to be extended to a wider range of application scenarios, including waveform generation with more orders, a joint channel and source coding and modulation unified autoencoder framework, and generation with specific frequency domain constraints (such as continuous phase) modems and other anti-jamming technologies to deal with increasingly intelligent jamming methods.  
Conceptualization, W.G. and S.X.; methodology, W.G.; software, W.G.; validation, H.W. and Y.Z.; writing--original draft preparation, W.G.; writing--review and editing, S.X. and Y.L.; visualization, W.G. and Y.L.; supervision, H.W. and Y.Z. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The data presented in this study are private, from the School of Electronics and Information Engineering, Nanjing University of Information Science and Technology, and 63 rd Research Institute, National University of Defense Technology.  
The authors declare no conflicts of interest.
## 2 Materials and Methods  
The pre-existing CBCT records of 132 Saudi adults (60 male and 72 female; ages 18 to 68) were randomly selected. These records were screened and collected from the National Guard Hospital and College of Dentistry at King Saud Bin Abdulaziz University for Health Sciences in Riyadh, Saudi Arabia. The retrospective data were from 2017 until 2022. The study protocol was approved by the institutional review board of King Abdullah International Medical Research Center (KAIMRC) (SP21R/356/06). All subjects were Saudi Arabian and aged above 18 years, with pre-treatment CBCT radiographs that were taken from the same machine (Planmeca Promax 3D Proface, manufactured by Planmeca OY, Helsinki, Finland, 2015) and were obtained in a standard manner according to the manufacturer's instructions and oriented in natural head position with teeth in maximum intercuspation. In addition, CBCT images with a volume size of 23.0 \(\times\) 17.3 cm or above and a voxel size of 400 \(\upmu\)m were included. Two technicians experienced in CBCT imaging performed the scanning procedure utilizing the same protocol. Any subject with congenital anomalies, previous jaw pathology, previous trauma to the jaws, previous orthodontic treatment, or previous orthognathic surgery treatment was excluded from the study. In addition, any CBCT images with unclear landmarks were excluded. To standardize the 3D head orientation, we selected the Frankfort horizontal plane as our reference plane based on its common use and acceptance in orthodontics [1; 15]. Following data collection, the skeletal classification was based on the ANB angle of [PERSON]'s analysis, and it is a clinically appropriate method [16; 17]. The classification of subjects and data measurements were performed by one examiner (N.A.) since the author was the most experienced in CBCT data analysis. CBCT radiographs were divided into 3 groups using Romerxis imaging software (Romerxis V 6.1, Planmeca OY, Helsinki, Finland):  
Group 1: skeletal class I malocclusion (1 \(\leq\) ANB \(\leq\) 4). Group 2: skeletal class II malocclusion (ANB \(>\) 4). Group 3: skeletal class III malocclusion (ANB \(\leq\) 0). Then, the investigator ([PERSON]) was blinded to the skeletal groups by providing a new Microsoft Excel sheet (version 2011, Microsoft Corp., Redmond, WA, USA) without skeletal groups for cephalometric measurements. Definitions of cephalometric landmarks are presented in Table 1 and Figures 1 and 2. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Landmark** & **Abbreviation** & **Definition** & **Sagittal View** & **Axial View** & **Coronal View** \\ \hline \multirow{4}{*}{Sella-Nasion} & \multirow{4}{*}{S-N (mm)} & & Distance between the middle point of the anti-de
## 2 Related Works
### Topology-Based Routing Algorithms  
Topology-based routing algorithms make decisions based on the network's topological structure (such as the number of hops, connectivity). This type of routing strategy is divided into proactive and reactive approaches. Proactive routing algorithms require nodes to maintain a global routing table locally and generate path selection schemes based on this routing table, such as optimized link state routing (OLSR) and its improved algorithms [10]. Reactive routing algorithms, on the other hand, calculate routes on demand based on real-time node communication requests, such as ad hoc on-demand distance vector routing (AODV), and its improved algorithms [11]. [PERSON] tested the performances of AODV and OLSR in wireless self-organizing networks. The results showed that the maximum latency of AODV is about 4 s, and the packet delivery ratio is approximate 85%; the maximum latency of OLSR is nearly 2 s, and the packet delivery ratio fluctuates between 20% and 80% [12].
# Research on Path Planning Based on the Integrated Artificial Potential Field-Ant Colony Algorithm
## 5 Algorithm Simulation and Analysis
### Parameter Sensitivity Analysis  
The optimal selection of parameters in the ant colony algorithm does not have rigorous theoretical support at present and mainly depends on the statistical data as well as experience accumulation. However, the important parameters in the algorithm, such as the heuristic factor and the number of ant colonies, will have an important impact on the advantages and disadvantages of the results of the path planning; therefore, it is of great significance to analyze the sensitivity of the parameters.  
In this paper, we use the control variable method to simulate the number of ants, the information heuristic factor, the expected value heuristic factor and the volatility coefficient in the ant colony algorithm, in which the original parameters are set as \(m=30\), \(\alpha=1\), \(\beta=5\), \(\rho=0.25\). In order to ensure the reliability of the experimental results, we repeat the experiments 50 times to obtain the average value of each group of parameters and analyze the changes in the path length by changing the values of the parameters. To ensure the reliability of the experimental results, the average value of each set of parameters is obtained by repeating the experiment 50 times, and the change of the path length is analyzed by changing the parameter values.  
From Figure 4, it can be seen that when the number of ants is 50, the path is the shortest, and with an increase in the number, the obtained solution stabilizes around it. Considering the time complexity and the convergence speed of iteration, it is most appropriate to choose the ant colony number of 50; from Figure 5, it can be seen that when \(\alpha<1\), the path gradually converges to the optimal solution with the increasing value; from Figure 6, it can be seen that \(\beta\) should not be taken too small; in Figure 6, the minimum value should not be taken too small, the maximum value should not be taken too large, and the length of the path and the number of iteration are better; from Figure 7, it can be seen that \(\rho\) is not suitable to be taken too large or too small.  
Figure 4: Effect of parameter \(M\) on path length.  
Figure 5: Effect of parameter \(\alpha\) on path length.  
Figure 6: Effect of parameter \(\beta\) on path length.  
After the sensitivity analysis of the main parameters of the algorithm, the key configuration parameters selected for the specific test scenarios are listed in Table 1.
## 2 Results
### Volatile Compounds Analysis
 } & M-xylene & 19.42 & 848 & - & 3.8 \(\pm\) 0.01 A & 1.1 \(\pm\) 0.18 B & nd & nd \\ \cline{2-7}  & O-Xylene & 22.05 & 909 & 881 D & 1.4 \(\pm\) 0.69 & nd & nd & nd \\ \hline \({}^{*}\)BH & 6,7-Dimethyltetralin-1,5,8- & 44.72 & n/a & - & nd & nd & nd & 4.5 \(\pm\) 0.18 \\  & tione & 48.63 & 949 & 1084 C & 0.3 \(\pm\) 0.08 & nd & nd & nd \\ \hline _Epxide_ & \(\alpha\)-Pineinee oxide & 52.43 & 1578 & 1578 A & 0.2 \(\pm\) 0.01 B & 0.2 \(\pm\) 0.03 B & 0.5 \(\pm\) 0.16 A & 0.7 \(\pm\) 0.13 A \\  & Caryophyllene oxide & 52.00 & 1399 & 1379 C & nd & 20.6 \(\pm\) 0.03 A & nd & 19.7 \(\pm\) 0.05 B \\ \hline \({}^{*}\)FAME & \({}^{*}\)Methyl cinnamate & 52.00 & 1399 & 1379 C & nd & 20.6 \(\pm\) 0.03 A & nd & 19.7 \(\pm\) 0.05 B \\ \hline _Monoterpens_ & \(\beta\)-pinene & 16.90 & 986 & 981 A & 0.8 \(\pm\) 0.08 B & 1.2 \(\pm\) 0.22 AB & 0.9 \(\pm\) 0.18 B & 1.3 \(\pm\) 0.10 A \\  & Limneneon & 22.31 & 1025 & 1029 B & 0.8 \(\pm\) 0.01 A & 0.3 \(\pm\) 0.05 B & 0.7 \(\pm\) 0.07 A & 0.3 \(\pm\) 0.02 B \\  & 1,8-Cinee (Eucalyptus) & 22.96 & 1028 & 1029 B & nd & 0.1 \(\pm\) 0.10 & nd & 13.6 \(\pm\) 0.52 & nd \\  & \(\gamma\)-terpinene & 25.00 & 1056 & 1063 A & nd & 0.1 \(\pm\) 0.03 A & nd & 0.1 \(\pm\) 0.06 A \\  & Trans-\(\beta\)-coimee & 24.60 & 1040 & 1037 B & nd & 0.2 \(\pm\) 0.05 & nd & nd \\  & Cis-\(\beta\)-coimee & 24.70 & 1095 & 1057 A & nd & 0.2 \(\pm\) 0.38 & nd & nd \\  & o-cymene & 26.54 & 1029 & 1032 C & 1.8 \(\pm\) 0.35 A & 0.1 \(\pm\) 0.02 C & 0.5 \(\pm\) 0.03 B & nd \\  & Para-cymene & 26.76 & 1018 & 1105 C & 0.5 \(\pm\) 0.02 A & nd & nd & 0.2 \(\pm\) 0.01 B \\  & Neo-allo-coimee & 45.19 & 1135 & 1130 C & 0.2 \(\pm\) 0.00 A & nd & 0.2 \(\pm\) 0.12 A & nd \\ \hline _Monoterpenoids_ & \(\alpha\)-terpinolone & 27.10 & 1083 & 1079 C & 0.4 \(\pm\) 0.07 A & 0.1 \(\pm\) 0.01 B & nd & nd \\  & Verbenyl ethyl ether & 31.90 & 1382 & - & nd & nd & nd & 0.3 \(\pm\) 0.01 \\ \hline _Naphthalenes_ & Nphthalene,1,2-dihydro- & 52.40 & 1454 & - & nd & 0.2 \(\pm\) 0.06 & nd & nd \\ _Phenylpropene_ & Methyl eugenol & 52.93 & 1401 & 1402 C & 0.3 \(\pm\) 0.02 B & nd & 0.5 \(\pm\) 0.08 A & nd \\ \hline _Sesquiterpene_ & \(\alpha\)-copaene & 37.46 & 1378 & 1377 B & nd & nd & 0.8 \(\pm\) 0.08 B & 1.9 \(\pm\) 0.04 A \\  & \(\alpha\)-gurjunene & 39.40 & 1408 & 1409 C & nd & 1.2 \(\pm\) 0.19 A & nd & 1.4 \(\pm\) 0.08 A \\  & _Trans-\(\alpha\)-perzometene_ & 42.60 & 1438 & 1432 C & nd & 2.4 \(\pm\) 0.38 & nd & nd \\  & \(\alpha\)-guaiene & 42.90 & 1440 & 1437 C & nd & 0.4 \(\pm\) 0.04 A & nd & 0.4 \(\pm\) 0.
# A Malware Detection Framework Based on Semantic Information of Behavioral Features
## 4 Dataset and Evaluation Methods
### Experimental Results
#### 4.3.3 Model Evaluation  
We compare the performance of the algorithms in this experiment with the performance of other state-of-the-art malware detection methods in the existing literature to better evaluate the method proposed in this work. [PERSON] et al. [46] built a detection model based on a sequence of system calls, transferring the deep learning over. A unique binary vector was encoded for each API from the dataset using One-Hot coding. A deep neural network with two convolutions and one recursive neural network were used for malware classification. [PERSON] et al. [45] proposed a BLSTM-based malware detection method. The same APIs that appear more than twice in a row are removed and sorted by call time. Then, Word2 Vec is transformed into a unique binary vector for each API call, and finally a BLSTM neural network is used to implement malware detection. [PERSON] et al. [47] built a malware detection model using deep learning methods. The sequences of API calls they collected were detected with an embedding layer and a two-layer LSTM. [PERSON] et al. [48] presented an architecture for malware detection basing on the intrinsic features with API sequences. They designed a feature encoder consisting of three modules to express and assemble the inherent characteristics of API sequences. On the basis of the encoder, they implemented a deep learning model as a means of detecting whether a program is malware or not.  
We replicate the above methodology and conduct experiments using the same dataset. We mainly consider four metrics including accuracy, precision, recall, and F1-Score.  
As shown in Table 5, the model we proposed scores the highest in all metrics compared to all the other detection models. In comparison to the models based on the traditional approaches ([PERSON] et al. [46] and [PERSON] et al. [47]), our proposed approach learns more features and the model performance is significantly more efficient, which shows the importance of learning both semantic and structural information of APIs through word embedding models. [PERSON] et al. [45], using only semantic features, and [PERSON] et al. [48], using only structural features, both obtain good results, but neither is the best-performing method, suggesting that a combination of structural semantic information and statistical features of APIs can be used to learn API features more adequately.
# Research on Reliability Assessment of Mechanical Equipment Based on the Performance-Feature Model
## 4 Case Study  
### The Wear-Feature Model of Tools  
Milling experimental data came from [PERSON]'s team at the Singapore Institute of Manufacturing Technology [23]. The data set contained tool wear and vibration signals of 315 cutting tests. Three piezo accelerometers were used to measure the machine tool vibrations of the cutting process in different directions, respectively. The machining tests were carried out with cutting speeds of 4.7 m/min and spindle speed 23,600 rpm. In this experiment, tool life-cycle experiments were made by inspecting the tool wear. The cutter's wear was measured after a complete cutting distance using a LEICA MZ12 microscopy system (Leica Microsystems, Wetzlar, Germany). To decrease the influence of other factors on the vibration signals, the cutting parameters were same in the experiments for three samples. The collection frequency of experimental data was 50 KHz/channel.  
Figure 2: The reliability curve.  
In many components of cutting vibration, the influence of tool wear on the vibration is obvious. However, the cutting vibration increases with cutting tool wear, despite some fluctuation. It is mainly related to factors such as material properties, built-up edge, and measurement error, which accumulate in the cutting process. However, the influence on the overall upward trend of cutting vibration is small.  
To show the feature trend with the number of milling significantly, vibration signals that had better linear relationships with the tool wearing process were decomposed, then the intrinsic mode functions (IMFs), frequency spectrums of IMFs, and the features relating to amplitude changes of frequency spectrum were obtained, thus feature value \(Gn\), as outlined in Reference [18] could be calculated using  
\[Gn(j)=\sum_{f=11.2\text{ KHIZ}}^{f=11.4\text{ KHIZ}}A_{j}(f)\,j=1,2,\cdots,P \tag{12}\]  
where \(j\) is the number of cuts, and P = 315 in Figure 3. \(A_{j}(f)\) shows the frequency spectrum amplitude of IMF1 decomposed from vibration signals at the \(j\)th milling in \(f\) Hz.  
The results obtained from preliminary calculation of \(Gn\) in the milling process, are shown in Figure 3. Despite the fluctuations in the amplitude, the amplitude of \(Gn\) is expected to continue increasing with the number of cuts in the milling process; and the results of the correlational analysis between \(Gn\) and tool wear show that there is a high correlation between the feature value and the tool wear.
## 2 Tri-Stable Stochastic Resonance Theory
### Principle of Stochastic Resonance
#### 2.2.2 Fokker-Planck Equation  
The nonlinear Langevin equation has no fixed solution, so scholars turn to study the evolution of the Langevin equation. Let \(\rho(x)\) be the distribution function of \(x\), and its Markov process is described as  
\[P(x_{n},t_{n}|x_{n-1},t_{n-1};\ldots;x_{1},t_{1})=P(x_{n},t_{n}|x_{n-1},t_{n-1 }), \tag{14}\]  
Its distribution function is  
\[\rho(x,t+\tau)=\int P(x,t+\tau|x,t)\rho(x,t)dx, \tag{15}\]  
Expanding the above formula can get  
\[\frac{\partial\rho(x,t)}{\partial t}=\left\{\sum_{n=1}^{\infty}(-\frac{ \partial}{\partial x})^{n}\lim_{\tau\to 0}\frac{M_{n}(x,t,\tau)}{n!\tau} \right\}\rho(x,t), \tag{16}\]  
From Equation (16), to obtain the solution of the function \(\rho(x,t)\), we need to know the transition moments \(M_{n}(x,t,\tau)\) of each order. Equation (13) at \(t+\tau\), the center distances of the centers centered on \(x\) are  
\[M_{n}(x,t,\tau)=\langle[x(t+\tau)-x]^{n}\rangle, \tag{17}\]  
\[x(t+\tau)-x=\int_{t}^{t+\tau}{[f(x(t^{\prime}),t^{\prime})]dt^{\prime}}, \tag{18}\]  
Substituting the above equation into Equation (16) yields the Fokker-Planck Equation (FPE) as  
\[\frac{\partial\rho(x,t)}{\partial t}=-\frac{\partial}{\partial x}\{[f(x,t)+D \eta(x,t)\eta^{\prime}(x,t)]\rho(x,t)\}+D\frac{\partial^{2}}{\partial x^{2}}[ \eta^{2}(x,t)\rho(x,t)], \tag{19}\]
# E2-VINS: An Event-Enhanced Visual-Inertial SLAM Scheme for Dynamic Environments
## 4 Experiments
### Quantitative Results
In contrast, the proposed event-based dynamicity metrics ensure that E\({}^{2}\)-VINS can measure feature points' dynamicity with high temporal resolution. The \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Level**} & \multicolumn{5}{c}{**Dense\_street\_day**} & \multicolumn{5}{c}{**Dense\_street\_night**} & \multicolumn{2}{c}{**Urban\_road\_day**} & \multicolumn{2}{c}{**Urban\_road\_night**} \\ \cline{2-10}  & **Easy\_a** & **Easy\_b** & **Medium\_a** & **Difficit** & **Easy\_a** & **Easy\_b** & **Easy\_a** & **Easy\_b** & **Easy\_a** \\ \hline ORB-SLAM3 [1] & fail & 8.558 & fail & 4.332 & fail & 4.482 & fail & fail & fail \\ DSO [3] & fail & 8.509 & fail & 7.647 & fail & 5.560 & 11.190 & 13.663 & 5.534 \\ VINS-FUSION [28] & fail & 3.428 & fail & 1.937 & 3.110 & 9.966 & fail & fail & 12.964 \\ USLAM [21] & fail & 9.435 & fail & fail & 5.363 & 6.141 & 7.947 & 8.861 & 3.939 \\ DynaVINS [8] & fail & 2.701 & fail & 3.472 & 3.500 & 5.014 & 9.239 & 10.318 & 6.496 \\ Ours & **1.356** & **1.562** & **2.404** & **1.393** & **1.427** & **2.736** & **7.339** & **9.933** & **3.159** \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Mean position error (%) of E\({}^{2}\)-VINS and compared SLAMs on the ECMD dataset [43]. \begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Level**} & \multicolumn{5}{c}{**City\_day**} & \multicolumn{5}{c}{**City\_night**} & \multicolumn{5}{c}{**Parking\_tot**} \\ \cline{2-13}  & **None** & **Low** & **Mid** & **High** & **None** & **Low** & **Mid** & **High** & **None** & **Low** & **Mid** & **High** \\ \hline ORB-SLAM3 [1] & 1.230 & 0.543 & 2.844 & fail & fail & fail & fail & fail & 0.194 & 0.231 & 0.191 & 0.256 \\ DSO [3] & 6.055 & 5.214 & 5.445 & fail & fail & fail & fail & fail & 3.106 & 1.762 & 8.440 & 5.999 \\ VINS-FUSION [28] & 0.133 & 0.115 & 0.355 & 0.323 & 0.198 & 0.224 & 0.276 & 0.280 & 0.134 & 0.182 & 0.932 & 1.497 \\ DynaVINS [8] & 0.142 & 0.106 & 0.098 & 0.231 & **0.114** & **0.109** & **0.111** & **0.155** & 0.128 & 0.158 & 0.156 & 0.196 \\ Ours+V2E [45] & **0.118** & **0.101** & **0.080** & **0.104** & 0.165 & 0.165 & 0.223 & 0.163 & **0.095** & **0.142** & **0.138** & **0.134** \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Mean position error (%) of E\({}^{2}\)-VINS and compared SLAMs on the VIODE dataset [42]. designed momentum weight factor and alternating optimization strategy ensure that the dynamicity weights of features are smoothly updated, demonstrating strong robustness in dynamic environments where visual-inertial SLAM methods struggle, thus resulting in the effectiveness of the proposed approach.
# Efficient Parallel Implementation of CTR Mode of ARX-Based Block Ciphers on ARMv8 Microcontrollers
## 3 Related Works
### Parallel Implementation of CTR Mode of Operation on the NEON Engine  
In this section, we propose the parallel implementation of CTR mode optimization. The CTR mode optimization is as follows. The input block of CTR mode is composed of a nonce and a counter, and the nonce part is a fixed value and the counter part is a variable value. When there is encryption in the CTR mode, the nonce part always comes out with a fixed value, so some round operations of the initial few rounds can be pre-computed. Therefore, when performing CTR mode encryption, it is possible to effectively reduce the computational overheads by looking up the pre-computation table rather than computing the round operation for the initial few rounds. We use the existing optimization of CTR mode for HIGHT and revised CHAM [6; 7], and, in the case of LEA, we optimize one more round than the existing work [7]. Through the CTR mode optimization method, the operations of the round function are effectively reduced by looking up the pre-computation table up until four rounds for LEA, five rounds for HIGHT, and seven rounds for revised CHAM. Furthermore, we apply the proposed parallel implementation to CTR mode optimization to encrypt multiple plaintexts simultaneously. We not only apply the CTR mode optimization, but also process massive amounts of encryptions in parallel, effectively improving the performance of the CTR mode in ARMv8 architecture.