esa-sceva/satcom-chunk-collection huggingface dataset:
# Semantically Controlled Adaptive Equalisation in Reduced Dimensionality Parameter Space +
## 2 Background  
### Semantically-Controlled Audio Effects  
Engineers and producers generally use a wide variety of timbral adjectives to describe sound, each with varying levels of agreement. By modelling these adjectives, we are able to provide perceptually meaningful abstractions, which lead to a deeper understanding of musical timbre and systems that facilitate the process of audio manipulation. The extent to which timbral adjectives can be accurately modelled is defined by the level of exhibited agreement, a concept investigated in [3], in which terms such as _bright_, _resonant_, and _harsh_ all exhibit strong agreement scores, and terms such as _open_, _hard_, and _heavy_ all show low subjective agreement scores. It is common for timbral descriptors to be represented in low-dimensional space; _brightness_, for example, is shown to exhibit a strong correlation with spectral centroid [4; 5] and has further dependency on the fundamental frequency of the signal [6]. Similarly, studies such as [7; 8] demonstrate the ability to reduce complex data to lower-dimensional spaces using dimensionality reduction.  
Recent studies have also focused on modification of the audio signal using specific timbral adjectives, where techniques such as spectral morphing [9] and additive synthesis [10] have been applied. For the purposes of equalisation, timbral modification has also been implemented via psychoacoustic measurements such as loudness [11], spectral masking [12], and semantically-meaningful controls and intuitive parameter spaces. SocialEQ [13], for example, collects timbral adjective data via a web interface and approximates the configuration of a graphic equaliser curve using multiple linear regression. Similarly, subjEQt [14] provides a two-dimensional interface, created using a Self-Organising Map, in which users can navigate between presets such as _boomy_, _warm_, and _edgy_ using natural neighbour interpolation. This is a similar model to 2 DEQ [15], in which timbral descriptors are projected onto a two-dimensional space using Principal Component Analysis (PCA). The Semantic Audio Feature Extraction (SAFE) project provides a similar non-parametric interface for semantically controlling a suite of audio plug-ins, in which semantics data is collected within a given Digital Audio Workstation (DAW). Adaptive presets can then be selectively derived based on audio features, parameter data, and music production metadata.
# Spatial Unmasking Effect on Speech Reception Threshold in the Median Plane
## 2 Method  
Measuring SRT is a common method to determine the accuracy of identification of a speech source in the presence of a masker. SRT is defined as the lowest SNR at which 50% of the speech is correctly recognized [13]. The dB difference in SRT at different positions indicates the level of spatial unmasking. In the context of the present experiment, SRT is the difference of the level of a target speech signal, presented at 0\({}^{\circ}\) elevation in the median plane, to the constant level of a noise presented at -30\({}^{\circ}\), 0\({}^{\circ}\), 30\({}^{\circ}\), 60\({}^{\circ}\) or 90\({}^{\circ}\) in the median plane to provide 50% speech identification.
## 4 Experimental Results and Discussion
### Performance in Extremely Dark Conditions  
We measured detection performance levels for systems that processed images from extremely dark conditions under severely insufficient lighting. By using these measurements, we compared the model trained with PASCAL VOC 2007 [6] and the system trained with our nighttime data. An efficient nighttime vehicle detection system should provide satisfactory results under dark conditions. The system should detect a relevant object, even if the features of the object cannot be extracted precisely and easily. Data can be used for improving the architecture of the feature extractor and developing systems with superior performance in extremely dark conditions. The data used to train the model are an important consideration in this study.  
As displayed in Figure 24; Figure 25, the proposed system trained with our nighttime data can detect relevant objects under severely insufficient lighting even if those objects (such as the row of motorbikes shown in the aforementioned figures) are typically difficult to recognize. Our system's performance levels differed from those of the models trained with the PASCAL VOC 2007 data set [6] (Figure 3; Figure 4), which may provide unfavorable localization performance. Because the photos in the PASCAL VOC 2007 data set were usually taken under sufficient lighting, the model trained with this data set could not ideally localize objects under dark conditions.  
Figure 23: Inference on a 1920 \(\times\) 1080-pixel nighttime image.  
Figure 24: Outputs from the Faster R-CNN [5] with VGG16 [1] in our proposed system trained using our nighttime data.  
Figure 25: Outputs from the Faster R-CNN [5] with ResNet101 [2] in our proposed system trained using our nighttime data.
# The Impact of Spoofing Attacks in Connected Autonomous Vehicles under Traffic Congestion Conditions
## 5 Conclusions  
It is well known that there are security gaps in the case of spoofing attacks for VANETs. Our approach presented in this paper introduces a real traffic simulation scenario that reveals these gaps. We develop a robust simulation framework for a real-time vehicular network consisting of a number of vehicles and an RSU. Depending on the traffic conditions, we create a dynamic vehicles platoon. This simulation framework adeptly addresses VANETs' complexities, notably in vehicle-to-vehicle communications, and investigates the vulnerabilities that connected vehicles may have to DoS attacks by malicious users. Our findings demonstrate substantial increase of driving distance under the effect of a spoofing attack while at the same time, an investigation of other levels of the network stack (e.g., PDR) also show potential weaknesses of such a spoofing attack since the WSMs being exchanged increase quite a bit. This is an element that can help identify an attacker more easily in a future detection system.  
Summarizing, as a comparison to the current literature, we observe that there is no corresponding paper with a realistic simulation framework like the one proposed in this paper that takes into account real traffic conditions. The parameters also of volatility measures such as \(CoV\) are not completely close to zero, like in [8,12], which follow a theoretical approach, due to unexpected situations. When a DoS attack is implemented, the variability of speed and acceleration shows a small increase, leading to a larger \(CoV\).  
Finally, this work represents a preliminary version of a future holistic model that, in addition to implementing DoS attacks, will also contain a real-time attacker detection system using machine learning algorithms. Therefore, there are some limitations that will be improved, such as implementing attacks with a different number of attackers and with a different duration. Moreover, the implemented attacks will be tested under stochastic input models (e.g., the arrival rate of vehicles). Additionally, methods of authenticating the members of a platoon of vehicles with passive estimation methods of estimating parameters at the physical level (e.g., relative speed) [21] will be explored so that vehicles will be able to do cross-checking with the information exchanged by the messages. Our aim is to have future detection methods be based on AI techniques [22].  
**Author Contributions:** Made substantial contributions to conception and design of the study and performed data analysis and interpretation: Z.-R.T. and D.K.; Performed data acquisition, as well as provided administrative, technical, and material support: A.X. and C.C. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Data Availability Statement:** The raw data supporting the conclusions of this article will be made available by the authors on request.  
**Conflicts of Interest:** All authors declared that there are no conflicts of interest.
# Low-Cost Underwater Communication System: A Pilot Study
## 2 System Design
### Quadrature Phase Shift Keying (De)Modulator
First, the QPSK modulator part together with the channel and receiving transducer is simulated on PC computer. Then the resulting signal is injected into the microcontroller memory just after the ADC block (look at the Figure 2. The rest of the demodulation process is run on the microcontroller. After completing a frame of data--in this case, 128 symbols--the data are transferred through the debug channel and stored on the local disk of PC computer. Summarizing,  
Figure 1: The diagram of QPSK modulation process implemented with the use of hardware timers. in Figure 3, 128 points with value equal to \(\text{syb}(k)=I(k)+iQ(k)\) are shown. Points are grouped into four subsets, i.e., four states, which are relatively easy to distinguish. Each subset represents one of four possible two-bit symbols, namely, 00, 01, 11, and 10. Looking at this constellation, no one knows which group of points represents a particular symbol. It is because the internal carrier frequency generated is not synchronized with the carrier frequency of the received signal. This kind of demodulation is called a noncoherent demodulation technique. To overcome the synchronization problem, a training sequence is utilized. The training sequence is composed of four unique symbols transmitted with a constant order at the beginning of each frame. Thanks to the known order, the decision block can \"learn\" the placement of each symbol on an IQ plane, and next, it can properly decode the information from the received frame. To completely describe the QPSK demodulation thread, it is worth noting that the demodulation thread constantly waits for the next buffer of information given by the DMA interrupt routine. Remember that DMA gathers consecutive samples from ADC into the buffer. After receiving a semaphore from the DMA interrupt routine, the demodulation thread copies the buffer into the internal memory and next checks in the buffer to see if there exists a carrier frequency or not. In the case of a positive check, the thread raises an internal flag informing that the start of the frame has been detected. As a consequence, the demodulation procedure is started, which means that the internal carrier frequency generator starts to generate a cosine/sine waveform. Indeed, this way, carrier frequency synchronization cannot be guaranteed. However, it was mentioned that it is not necessary in this project. Figure 3: A constellation of integrated IQ signal of a received frame consisting of 128 symbols modulated with the speed of 2 kbps. Figure 2: The diagram of QPSK demodulation process. A traditional demodulation algorithm with the use of IQ signals is utilized.
# Study of Subsidence and Earthquake Swarms in the Western Pakistan
## 1 Introduction
### Quetta-Zirat Earthquake Sequence  
On 28-29 October 2008, three damaging and widely felt earthquakes (EQ) (EQ 1 Mw 5.3, EQ 2 Mw 6.4 and EQ 3 Mw 6.4) struck the rural, mountainous region of Quetta (Figure 1). Smaller magnitude EQ 4, 5, 6, 7 and 8 earthquakes subsequently occurred around the same location in November and December 2008. A total of eight earthquakes in this sequence had similar focal depths (~15 km). The fault plane solution determined by Global Centroid Moment Tensor (GCMT) solutions shows that the foreshock and main shock on 28-29 October occurred on a northwest-southeast-oriented right-lateral strike-slip motion with a steep dip (73\({}^{\circ}\)). Previous studies suggested that the earthquakes were caused by an unmapped right-lateral strike-slip fault and inferred basement faulting of 60 cm of total movement [22]. This unmapped fault could be the Urghargai Fault mapped by [PERSON] [23] as an active right-lateral strike-slip fault striking in a northwest-southeast orientation (Figure 1).
## 4 Discussion
Furthermore, recent studies have reported that coating techniques, such as hydroxyapatite coating and melted polymer sleeve, can be applied to pedicle screws for improving fixation and anchorage [11; 26]. However, the effect of screw materials and the application of coating techniques were not a concern of this study. Further studies are needed to investigate the influence of different biomaterials and coating techniques on screw pullout biomechanics. There are several limitations to this study. Firstly, this study used a relatively small sample of specimens, which might establish a bias on the statistical outcomes. Second, we focused on the pullout strength of the screws under axial loading only, which is not representative of the entire biomechanical performance of these screws. On the basis of the present results, we plan future studies with cadaveric specimens and finite element model (FEM) studies under different loading conditions. Finally, this was a biomechanical testing study, and the results based on laminated polyurethane foam may differ from those obtained in real surgical conditions.
## 4 Discussion
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline  & \multicolumn{8}{c}{Mean (SD)} \\ \hline  & \multicolumn{8}{c}{Plaster} & \multicolumn{8}{c}{Digital} \\ \hline Little’s Index & A & B & C & D & E & A & B & C & D & E \\ \hline \multirow{2}{*}{Maxillary arch} & 7.30 & 13.98 & 7.54 & 7.13 & 18.87 & 8.13 & 13.81 & 7.79 & 7.86 & 19.23 \\  & (2.03) & (1.10) & (3.78) & (2.43) & (7.68) & (2.23) & (1.76) & (3.43) & (1.92) & (7.37) \\ \hline \multirow{2}{*}{Mandibular arch} & 7.49 & 9.12 & 10.85 & 14.61 & 11.07 & 7.92 & 9.47 & 11.04 & 14.53 & 11.33 \\  & (1.30) & (2.37) & (2.53) & (2.24) & (3.58) & (1.18) & (2.27) & (2.43) & (2.83) & (3.88) \\ \hline \hline \end{tabular}
\begin{tabular}{c c c c c c c c} \hline \hline  & A & B & C & D & E & \(p\) value \\ \hline \multirow{2}{*}{Maxillary arch} & \(-0.83\) & 0.17 & \(-0.25\) & \(-0.73\) & \(-0.36\) & NS \\ \hline \multirow{2}{*}{Mandibular arch} & \(-0.43\) & \(-0.35\) & \(-0.19\) & \(0.08\) & \(-0.26\) & NS \\ \hline \hline \end{tabular}  
* Units are represented in millimeters; “A” = group from \(\geq 0\) to \(\leq 1\) mm; “B” = group from \(>1\) to \(\leq 3\) mm; “C” = group from \(>3\) to \(\leq 5\) mm; “D” = group from \(>5\) to \(\leq 7\) mm; and “E” = group over \(>7\) mm. Negative difference = digital measurement is larger; NS = non-significant (\(p>0.05\)). Significance based on one-way ANOVA.  
\end{table} 
Table 4: Mean values and difference between the plaster and digital measurements (ABO score) for each group of crowding. The question if LII is a good parameter for using in clinical practice is still debatable [8; 11]. In fact, LII had some deficiencies, especially when there was a single tooth with severe displacement that produced a high LII score not correlated to the arch length discrepancy (ALD). A limitation of the LII is that this it is only a measurement of irregularity; thus, it does not take into account tooth rotations and axial inclinations [7; 21]. We found that LII digital measurements were more reliable when the contact point displacement was gradually increasing, especially for the mandibular arch. These findings are in agreement with the study of [PERSON] et al. [11], which suggested an improvement in LII reliability, using digital measurements, for a contact point displacement \(\geq\) 1 mm, due to a reduction in subjectivity in the points selecting and overcoming the difficulty of measuring the displacement of the contact points using a bulky caliper. Furthermore, using digital measurements, it is possible to obtain a magnification of the images and this permits to have a finer evaluation of the contact points despite the plaster models, in which the abnormal teeth's relationship may alter the correct localization of the ideal interproximal contact points [22; 23].
## 4 Discussion
### Limitations and Future Perspectives  
The results of this systematic review were affected by several limitations.  
Some of these limitations were intrinsic to the systematic review process, such as the potential presence of a publication bias, attrition bias, and selective outcome reporting; we tried to minimize the latter and the inflation of clinical heterogeneity--even if the protocol of this systematic review was not pre-registered--by focusing on specific lesion types and by distinguishing between different stiffness measurement methods.  
Some other limitations could be related to this particular systematic review, such as the availability of only a few studies (also conducted over a large period of time), the high heterogeneity of US elastography equipment, the differences in the acquisition protocols (number of samplings, size, and placement of ROI), and generally small study samples. Furthermore, several important data (e.g., patients' age and lesion size) were provided in an aggregated manner, limiting our meta-regression and subgroup analysis; thus, individual patient data meta-analysis should be performed to appropriately investigate the covariates that could influence stiffness values [40]. Of note, as the small number of studies (with generally small samples) and the continuous nature of US elastography data were known to substantially influence the power and reliability of the Egger test, we opted to avoid investigating publication bias.  
Another methodological limitation was the minimal presence of preliminary evaluations on the reproducibility of the measurements in relation to the experience of radiologists and sonographers. Future studies need to consistently address this issue while also standardizing the acquisition protocols regarding the preferred type of US elastography, the unit of measurement, the number of measurements per lesion, and the size and positioning of the ROI within the lesion.  
From a technical point of view, all included SWE studies used a p-SWE technique, which provided a quantitative measurement of a small, dimension-fixed ROI. Conversely, 2D-SWE, when matched with a confidence threshold, could increase its accuracy. Furthermore, elastography can be used as a complementary technique to improve the diagnostic accuracy of CEUS, which, on the contrary, has shown good results in the characterization of renal lesions, as suggested by [PERSON] et al. [17].  
Alongside the necessity to sizably increase the study samples and plan appropriately-powered studies, further diagnostic improvements could come from the application of quantitative imaging models based on artificial intelligence [41; 42]; substantial gains in the characterization of the intra- and perilesional elasticity landscape could be granted by artificial intelligence tools that are able to work on the two-dimensional elasticity patterns provided by the 2D-SWE technique.
# Multi-Material Topology Optimization Taking into Account the Position of Material Interfaces in 3D
## 1 Introduction
The aim is to obtain designs in which the position of the interface is optimal in terms of resistance to separation. [PERSON] et al. [26] used a very similar method to optimize embedded components in a 2D host structure. [PERSON] et al. [27] developed an objective function called Interfacial Tension Energy Density (ITED), which is based on the observation that bonded bimaterial interfaces in 2D primarily fail under tensile stress. The objective function ensures that the interfaces are shifted into the compression areas. It is used to optimize bonded bimaterial interfaces. The advantage of the method is that it does not require specialized Finite Element (FE) methods or computationally intensive non-linear behavior in the FE. In summary, it can be stated that all approaches for the optimization of interfaces in multi-material topology optimization have so far considered the 2D case. It is pointed out in [22; 27], for example, that further research is required for the 3D case. The following is a summary of the challenges for the development of methods for the consideration of interfaces in 3D. Numerous methods (see, e.g., [23; 25]) use non-linear components in the FEM. On the one hand, the non-linear components greatly increase the computational effort, as additional inner iterations are required to solve each optimization iteration. Furthermore, the non-linear components make the optimization methods more sensitive to parameter settings, as they are generally less robust than purely linear methods. Furthermore, the methods (e.g., [25]) also use immersed FEM, which is still relatively uncommon in practical use. If standard FEM is to be used in the optimization methods, this is only possible by means of remeshing. However, the approach developed in 2D (e.g., [22]) cannot be transferred directly to 3D. In general, the optimization methods developed in 2D do not yet take into account the shear stress components on the interface. In this article, the approach first presented in [28] for the consideration of adhesive surfaces in the three-dimensional case in multi-material topology optimization is further developed. In the first step, a system analysis of an adhesive surface between two materials is carried out. In this analysis, the common load cases are analyzed and requirements for the approach are derived. The most essential components of the optimization method developed in [15] are then discussed. The requirements developed in the previous step are then used to motivate and describe the objective function. In Section 3.7, the sensitivities are then derived for the developed objective function. Two numerical examples are used to validate the developed approach. In addition, a parameter study is carried out to investigate the influence of a newly introduced parameter in the developed objective function on the optimization results.
## 5 Performance Analysis and Numerical Results  
### Results of V2X Performance Comparison  
#### 5.1.1 Data Throughput  
The macroscopic results of the grid road scenario and the adjusted geometric road scenario are illustrated in Figure 7, respectively. By analyzing the macroscopic data dissemination distances in the grid road scenarios depicted in Figure 7a,b, we can evaluate and compare the data throughput of 802.11 bd and 802.11p. In this set of experiments, data packets are emitted simultaneously from the same location, with all vehicles following an identical data packet processing mechanism (see Figure 6), while the data packet size transmitted by 802.11 bd is twice that of 802.11p (detailed settings in Table 3). After the same duration, it can be observed that the packet transmission distances of both standards are very close in all four directions (as indicated by the green line marks), with a statistical difference of only 3% in the number of vehicles that receive data packets. This substantiates the claim that 802.11 bd achieves nearly double the data throughput of 802.11p.  
In the simulation experiments conducted in the adjusted geometric road scenarios depicted in Figure 7a,b (detailed settings in Table 3), the performance is found to be consistent with that in the grid road scenarios: with 802.11 bd transmitting data packets twice the size  
Figure 7: Macroscopic results of throughput comparison simulation.  
of those in 802.11p, the dissemination effects are remarkably similar, as evidenced by the zoomed-in views in the two subfigures. The differences between the two are also minimal regarding the microscopic statistics, further confirming the significant data throughput advantage of 802.11 bd over 802.11p.