esa-sceva/satcom-chunk-collection huggingface dataset:
# A Semi-Supervised Lie Detection Algorithm Based on Integrating Multiple Speech Emotional Features
## 1 Introduction
Despite some achievements in semi-supervised research, the exploration of multi-feature lie detection algorithms under a semi-supervised framework has been neglected. A single feature cannot effectively represent lie information, so some scholars have considered combining multiple features for lie detection. Researchers at Columbia University considered combining lexical, prosodic, and acoustic features for lie detection research [15]. [PERSON] et al. [16] proposed a Transformer-based deep hashing multi-scale feature fusion (TDH) algorithm. This algorithm utilizes hierarchical Transformers to capture both global and local features of images to obtain richer information. [PERSON] et al. [17] considered the complementarity between audio and video modalities and proposed an LSTM-based joint cross-attention model to capture intra-class and inter-class temporal dependencies. This paper aims to combine feature fusion methods with semi-supervised learning techniques to address the challenges in speech lie detection research, prevent dimensional disaster caused by multi-feature fusion when labeled data is insufficient, and fully utilize the potential feature information in unlabeled data to obtain robust feature representations in a semi-supervised learning environment. Based on this, this paper proposes a semi-supervised lie detection algorithm based on integrating multiple speech emotional features. Firstly, LSTM network and self-coding network AE are used for feature extraction, followed by the introduction of joint attention method, which utilizes the correlation between different features to assist the feature fusion task and improve the accuracy of speech lie detection. Subsequently, Jefferys [18] loss is introduced to improve the supervised loss, which maximizes the target value of the output distribution and reduces the overfitting; Local Maximum Mean Discrepancy (LMMD) [19] is introduced to improve the unsupervised loss, which enables the model to learn richer feature information from unlabeled data. Experimental results demonstrate the effectiveness of the proposed algorithm.
# Experimental Evaluation of the Packet Reception Performance of LoRa
## 5 Results and Analysis
### The Impact of CR on the Packet Reception Performance of LoRa  
To improve the anti-interference ability, LoRa has added forward error correction (FEC) technology to the data transmission. The method used is to add redundant bits in the data bits, that is, 4 bits of data are added to the redundant bits to form 5, 6, 7, or 8 bits of data. In other words, if the coding rate is 4/n (n is equal to 5, 6, 7, or 8), the encoder generates a total of n bits of data, of which 4 bits are useful information and n \(-\) 4 is redundant. The Coding Rate (CR) can be set to 4/5, 4/6, 4/7, or 4/8. Although this method improves the reliability of communication, its disadvantage is also obvious, that is, the transmission time increases. We conducted extensive experiments on the different CRs, and the experimental results found that, as expected, as CR decreases, PER also decreases. In addition, it is worth noting that only under a little strong interference (PER > 0.5) will it be obvious that the difference in CR leads to a difference in PER. For example, the experimental settings included SF = 7, BW = 125 kHz, and a packet length of 32 bytes. When SNR = 0 dB, the PER of CR = 4/5 and 4/8 is 0.0013 and 0.0004, respectively. However, when SNR = \(-\)9 dB, the PER of CR = 4/5 and 4/8 is 0.7077 and 0.5380, respectively. Figure 8 shows the results of the impact of different CRs on the packet reception performance of LoRa.
## 3 Methodological Background
### K-Nearest Neighbor (KNN)  
KNN is a supervised machine learning method that can also be used both for classification and regression. The KNN algorithm determines the class of each observation in the analysis dataset based on the classes (e.g., PDO, injury, or fatality in the crash severity analysis) of K closest observations (nearest neighbors). The closeness is measured using some function of distance in a multi-dimensional space, with each dimension representing an explanatory variable (e.g., speed, traffic volume, geometric design characteristics, etc.). In classification algorithms, the class of an observation is determined as a majority class among its K nearest neighbors; in regression algorithms the class is determined as an average of classes of the K nearest neighbors [22]. To execute the KNN algorithm, two parameters must be specified: the value of K (i.e., how many neighboring observations should be considered for classification or regression), and the distance function (e.g., how will the distance between the neighbors be determined). Small values of K may create a model that is too specific to the training dataset, leading to overfitting and rendering the model unfit to properly classify datasets other than the one used in training. Large values of K, on the other hand, may lead to a weak, overly generalized model, unable to properly classify both the training and testing datasets. The value of K is therefore determined in an iterative process, in which different values of K are applied and the one that yields the best performance (e.g., achieves the highest accuracy of prediction) is selected for the model. As a rule of thumb, in classification models where there are only two classes, which is the case in this study, K should be odd to avoid ties [23]. As for the distance function, in this study the Euclidean distance was used, which is formulated as:  
\[dist\big{(}x_{i},x_{j}\big{)}=\sqrt{\sum_{k=1}^{p}\Big{(}x_{ik}-x_{jk}\Big{)} ^{2}} \tag{3}\]  
where \(dist\big{(}x_{i},x_{j}\big{)}\) denotes the distance between the observations \(i\) and \(j\), and \(x_{ik}\) and \(x_{jk}\) are the values of the \(K\)th factor for \(i\) and \(j\), respectively.
# Novel Feature Extraction Method for Detecting Malicious MQTT Traffic Using Seq2 Seq
## 5 Feature Extraction
### MQTT Message Type  
Third, we extracted the MQTT message type. As discussed in Section 4.2, a client sends many publish packets to the broker during a flood attack. As shown in Table 1, the message-type value of a publish packet is three.  
Additionally, as shown in Section 4.4, in a malformed attack, a client sends a subscribe request, followed by a publish packet, to the broker. Therefore, the client attempts to create an exception for the broker. We extracted the MQTT message types to detect flood and malformed attacks.
## 2 Technical Backgrounds
### Security Considerations and Requirements
#### 2.4.1 Availability  
The convergence of 5G communication and IoT technologies should be a network that can connect heterogeneous devices while requiring high communication performance, bandwidth, and low latency [17]. In a 5G environment where a significant number of devices are expected to be connected, rigorous optimization of the performance of the entire network must be performed to meet these requirements. In particular, 5G networks must be able to accommodate traffic from additional authentication. Moreover, the architecture for 5G communication should consider ways to solve the traffic load on the home network, reduce the amount of traffic delivered to the central network through the hierarchical structure, and have the structure to manage the end nodes efficiently [22].
# Topology Optimization of an Aerospace Bracket: Numerical and Experimental Investigation
## 3 Results and Discussion
### Experimental Validation
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Nr.** & **Original** & **Optimized (LSM)** & **Optimized (DBM)** \\ \hline
1 & 1.25 & 1.47 & 1.9 \\
2 & 1.39 & 1.44 & 1.47 \\
3 & 1.46 & 1.51 & 1.46 \\
4 & 1.71 & 1.44 & 1.56 \\
5 & 1.42 & 1.49 & 1.43 \\ \hline
**Mean** & **1.45** & **1.47** & **1.56** \\
**Std. Dev.** & **0.150** & **0.028** & **0.174** \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Displacement (mm) of tested specimens under a 1.5 kN compressive load. Figure 12: Setup for mechanical testing; (**A**): printed component, (**B**): fixture, (**C**): component/fixture assembly, (**D**): component under compressive test, (**E**): component failure. regions of failure are in alignment with the high stress concentration regions identified in the analysis. Figure 14: Comparison of the regions of failure with the corresponding regions of high-stress concentration in the analysis of the initial unoptimized bracket. (**a**,**c**) depict the estimated stress distribution with (**b**,**d**) depicting the failure locations on the tested components; red arrows indicate the correspondence between high-stress and failure regions. Figure 13: The indicative load–displacement curves for two specimens corresponding to each design: initial bracket model (solid lines), design optimized with the level-set method (dashed lines), and density-based optimization design (thin dotted line); the red straight line corresponds to the load used for comparisons in Table 1.
# Immersive Virtual Reality Experience of Historical Events Using Haptics and Locomotion Simulation
## 4 Conclusions  
Our developed system utilized equipment for three different aspects of immersion. The HTC Vive HMD for visual and auditory representation, the Vive Trackers along with the haptic gloves for hand tracking and haptic feedback and finally the omnidirectional treadmill for locomotion. The application we designed was a virtual experience for discovering the historical artifact of the Antikythera Mechanism. It consisted of an underwater exploration of the shipwck, where the mechanism was found, and a visit in the 1 st century BC, where the mechanism was functional. The application was tested by 20 users, who then answered a questionnaire. The main reason for the survey was to study the level of immersion they experienced and evaluate the importance of the different aspects of immersion provided. Finally, we examined the user's perspective about VR in cultural heritage.  
This study not only provided insightful ideas into VR user experiences but also indicated the focus on further research. While it provided us with a fair impression of the important aspects of immersion, a comparative study to test the impact of our system should be of great importance. In Section 3.4 we already discussed how to better utilize the treadmill for our future implementations. Furthermore, greater use of the Vive Trackers could also improve our desired feel of immersion. Similar to [42], a visual representation for underwater drag force perception could enlarge the sense of presence in our shipwck exploration scene. Finally, users suggested object-interactions as the primary feature of involvement in a virtual environment, so additional development should also focus on that.  
Overall, our system proved to be very useful in raising interest in learning about cultural heritage and such applications could definitely benefit cultural institutions.  
Conceptualization, A.C., K.K.; Methodology, A.C., K.K.; Software, A.C., K.K.; Validation, A.C., K.K.; Formal Analysis, A.C., K.K.; Investigation, A.C., K.K.; Resources, A.C., K.K.; Data Curation, A.C., K.K.; Writing--Original draft preparation, A.C., K.K.; Writing--review and editing, A.C., K.K., K.M.; Visualization, A.C., K.K.; Supervision, K.M. All authors have read and agreed to the published version of the manuscript.  
This research has been co-financed by the European Regional Development Fund of the European Union and Greek national funds through the Operational Program Competitiveness, Entrepreneurship and Innovation, under the call Special Actions in aquaticfarming-industrial materials-open innovation in culture (project code: _T6 YBIT-00120-CURVE_).  
Not applicable.  
Informed Consent Statement: Informed consent was obtained from all subjects involved in the study.  
The data presented in this study are available on request from the corresponding author.  
The authors declare no conflict of interest.
# FSRFNet: Feature-selective and Spatial Receptive Fields Networks
## 4 Network Architecture  
An FSRF network (FSRFNet) can be constructed by simply stacking a set of FSRF blocks. For concrete examples of FSRFNet architectures, a detailed description of FSRF-50 is presented in Table 1. It is recommended that FSRFNet consists primarily of a bunch of duplicate bottlenecks called \"FSRF units,\" in a similar fashion to ResNeXt [9]. Each FSRF unit consists of a series of \(1\times 1\) convolutions, FSRF blocks, and further \(1\times 1\) convolutions. In ResNeXt [9], large kernel convolutions in all original bottleneck blocks are replaced by the proposed FSRF blocks. FSRF-50 uses [3; 4; 6; 3] FSRF units. Table 1 shows a 50-layer FSRFNet-50 architecture with four phases, using {3, 4, 6, 3} FSRF units. Different architectures can be obtained by changing the number of FSRF units per stage.  
Figure 4: The schema of an FSRF-ResNet module.  
In the FSRF units of the FSRF-50, the reduction ratio of the number of parameters in the control fuse operator determines the final setting of the FSRF block. There are two important hyperparameters that determine this final setting: the group number \(G\) that controls the cardinality of each path and the reduction ratio \(r\) of the number of parameters in the control fuse operator. In Table 1, we set the reduction ratio \(r=16\) and cardinality \(G=32\).
# Impact of Road Traffic Tendency in Europe on Fatigue Assessment of Bridges
###### Abstract  
Fatigue load models for road bridges given in the Eurocode EN1991-2 have been calibrated considering real traffic measurements that became available around 1990. Since then, traffic composition has evolved considerably, also considering the issuing of the 96/53/EC Directive, which legitimated member states, on an equal and not discriminatory basis, to allow the circulation of Long and Heavy Vehicles (LHVs). Thus, the appropriateness of fatigue load models to cover also the effects of these vehicles, which are longer, heavier and potentially more damaging than common Heavy Goods Vehicles (HGVs), became an issue. The aim of the study is to assess how the evolution of European traffic influences the fatigue assessment of bridges. To capture the essence of the problem, three different real traffic measurements are compared in terms of fatigue damage: the Auxerre (FR) traffic, adopted to define fatigue load models in EN1991-2; the Moerdijk (NL) traffic, characterized by a high percentage of LHVs; and the Igualada (ES) traffic. To assess the current relevance of fatigue load models LM2 and LM4 of EN1991-2, the aptitude of these models to adequately reproduce the effects caused by LHVs is discussed in detail. The results demonstrate that the Auxerre traffic is still the most onerous; that the Moerdijk traffic is generally more severe than the Igualada traffic, and that the fatigue load models of Eurocode do not require major updates. The study is further supplemented by investigating the suitability of the formulae provided in the Eurocodes for the damage equivalence factors \(\lambda_{2}\) and \(\lambda_{3}\) to express the influence of the total lorry volume on the fatigue damage. In that latter case, the conclusion is that the formulae proposed in the Eurocodes, based on the assumption of a linear fatigue strength \(S\)-N curve with constant conventional slope \(m\), could lead to erroneous, even unsafe, estimates of the fatigue life, especially when details are characterized by constant amplitude fatigue limit \(\Delta\sigma_{D}\), thus calling for further improvements of the formulae themselves.  
traffic loads on bridges; fatigue assessment; fatigue load model; long and heavy vehicles; Weighing-In-Motion; damage equivalence method; damage equivalence factors +
Footnote †: journal: Applied Soft. 2020, 10, 1389; doi:10.3390/app10041389
# Stress Analysis with Dimensions of Valence and Arousal in the Wild
## 4 Baseline Model Experiments
### Experimental Results  
Table 7 presents the performance on stress recognition in terms of MSE, MRE, and PCC. In particular, the baseline model achieved the best performance for the arousal class of 1.13 MSE, 15.42% MRE, and 0.46 PCC. In addition, for the stress class, the model also showed an error of 1.66 MSE, 22.16% MRE, and 0.52 PCC, and for the valence class, the error rate of 1.23 MSE, 22.71% MRE, and 0.53 PCC.  
In addition, Figure 8 summarizes the results obtained for each example in the test set. Figure 8a shows the MSE of valence for the stress and non-stress samples, where the stress cases obtained lower error. Besides Figure 8b illustrates the results of arousal, where the non-stress samples achieved better performance than the stress cases.  
Finally, Figure 9 shows the baseline model's predictions with quite good predictable results in the left column's images, and a large gap between ground truth and prediction values (indicated in red) in the right column's images.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Dimension** & **MSE** & **MRE (\%)** & **PCC** \\ \hline Stress & 1.66 & 22.16 & 0.52 \\ Valence & 1.23 & 22.71 & 0.53 \\ Arousal & 1.13 & 15.42 & 0.46 \\ Mean & 1.34 & 20.10 & 0.50 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Baseline model’s performance on the test set in terms of MSE, MRE and PCC.
# Improved Technology for Rounding Graphite: Machine Structure and Industrial Test
## 3 Results and Discussion
### Rounding Effect
#### 3.1.2 Effect of the Shaping Period on Graphite Rounding  
The determination of reasonable shaping strength is mainly to ensure that the particles are mainly subjected to shaping action rather than crushing. Under the setting condition, the shaping time mainly affects the shaping degree and yield of the final product. Figure 5 shows the effect of the shaping period on the graphite rounding performance when the rotating speed of the shaping disk was kept at 1833 rpm, and the rotating speed of the classifier was limited to 2646 rpm. It is observed that the diameters, including D\({}_{10}\), D\({}_{50}\) and D\({}_{90}\), all change negligibly with a slight decrease with the shaping period increases. For example, with the shaping period increases from 30 to 80 min, D\({}_{10}\) decreases from 11.12 to 10.54 um, D\({}_{50}\) decreases from 19.41 to 17.63 um, and D\({}_{90}\) decreases from 30.85 to 28.39 um. It indicates that under the present conditions, the graphite particles inside the rounding process are dominantly subjected to be shaped rather than pulverized. Although the diameter changes negligibly, the tap density increases dramatically from 0.924 to 1.004 g/cm\({}^{3}\) while the yield declined sharply from 53 to 41% with the increase of the shaping period from 30 to 80 min. The dramatic increase in tap density clearly points out that the irregular corners of the graphite particles are constantly worn away and form fine particles. The formed fine particles are captured by the classifier and collected by the dust collector. This is also the reason that leads to the decline of the final product yield. The diameter and tap density are the main factors influencing the product quality, and the yield  
Figure 4: Effect of the rotating speed of shaping disk on the graphite rounding performance.  
is the main factor impacting the cost. The specific parameter selection should be based on the performance and application requirements of the final product while taking into account the principle of cost-effectiveness.
## 4 OW-FxLMS Algorithm for ANC of Engine Noise  
The active noise canceller of the OW-FxLMS algorithm can be used to eliminate intense sinusoidal sounds with frequency modulation. This method is applied to engine noise cancellation inside a vehicle because the engine noise is dominant. The engine noise is comprised of multiple intense sinusoidal signals. This signal is synchronized with the rotating speed of the engine and is a frequency-modulated signal. Other noise sources such as wind noise, tire noise, and high noise environments interfere with the engine noise [1].  
Figure 10 illustrates an active noise canceller for the engine noise of a vehicle. The canceller employs controller area network (CAN) bus data or a directional sensor to measure the instantaneous  
Figure 9: Simulation for signal 2 controlled by the LMS-algorithm-based adaptive notch filter and the OW-LMS algorithm. (**a**) Time history controlled by LMS-algorithm-based adaptive notch filter; (**b**) Time history controlled by OW-LMS algorithm; (**c**) Short-time Fourier transform (STFT) of signal 2 controlled by OW-LMS (\(L=30\)); (**d**) STFT of signal 2 controlled by OW-LMS (\(L=300\)); (**e**) STFT of signal 2 controlled by LMS algorithm-based adaptive notch filter; (**f**) Comparison of PSD of signal 2 controlled by each algorithm.  
frequency \(f_{i}\) corresponding to the rotating frequency of the engine, and four microphones to measure the interior noise containing engine noise and other noise. The measured instantaneous frequency is used for the generation of the reference signal. A recursive discrete-time oscillator is used to generate the reference signal [23]. The reference signal is processed by the adaptive filter to equalize it with the engine noise component of the primary noise contaminated by the other noise signals. It is subsequently subtracted to cancel out the engine noise component in the primary noise.  
Figure 11 shows the block diagram of the FxLMS-algorithm-based adaptive notch filter and the OW- FxLMS algorithm for the application of ANC to engine noise inside a vehicle. In the FxLMS -algorithm-based adaptive notch filter, two adaptive weights, \(w_{i1}\) (\(n\)) and \(w_{i2}\) (\(n\)), were used for each order. In the OW- FxLMS algorithm, the adaptive weight vector w(\(n\)) with different filter lengths was used for each order. The length of each weight vector was related to the total delay of the ANC system. The secondary path was considered for the application of ANC to engine noise.  
Figure 11: Active noise cancelling system for attenuation of engine noise associated with multiple harmonic order components of the engine rotation frequency using (**a**) FxLMS-algorithm-based adaptive notch filter, and (**b**) the OW- FxLMS algorithm.  
Figure 10: Block diagram of active control of interior noise in a test vehicle based on the OW-FxLMS algorithm.