esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion  
### The Universality of the Research Findings  
[PERSON] et al. (1996) and [PERSON] et al. (2005) statistically analyzed the actual measurement results of world-class ski jumpers' posture parameters during the flight stage and provided the typical ranges of various posture parameters, including the attack angle \(\phi\) (ranging from 25\({}^{\circ}\) to 40\({}^{\circ}\)), the angle between the velocity and skis \(\alpha\) (ranging from 25\({}^{\circ}\) to 40\({}^{\circ}\)), the angle between the body and skis \(\theta\) (ranging from 10\({}^{\circ}\) to 20\({}^{\circ}\)), the bending angle of the upper body \(\beta\) (ranging from 10\({}^{\circ}\) to 25\({}^{\circ}\)), the skis' opening angle \(\lambda\) (ranging from 20\({}^{\circ}\) to 40\({}^{\circ}\)), and the velocity V (ranging from 25 m/s to 32 m/s) [(15; 32)]. Furthermore, [PERSON] et al. (2021; 2018) conducted CFD studies and recommended optimal ranges for the skis' opening angle \(\lambda\) (ranging from 24\({}^{\circ}\) to 32\({}^{\circ}\)), the bending angle of the upper body \(\beta\) (ranging from 14\({}^{\circ}\) to 18\({}^{\circ}\)), and the angle between the body and skis \(\theta\) (ranging from 16\({}^{\circ}\) to 20\({}^{\circ}\)) [(28; 29)]. In this study, the values of the posture parameters, including the attack angle \(\phi\), the angle between the velocity and skis \(\alpha\), the angle between the body and skis \(\theta\), the bending angle of the upper body \(\beta\), the skis' opening angle \(\lambda\), and the velocity V, all fall within the aforementioned ranges, indicating the good universality of the research findings.
# Partial Fake Speech Attacks in the Real World Using Deepfake Audio
## 4 Threat Model
### DNN-Based Speech Synthesis  
In our attack, we used \"zero-shot\" systems that require less than one minute of target data for voice cloning. Our focus is on peer-reviewed papers that have accessible code implementations and pre-trained models. Despite testing several TTS and VC systems, including [58; 59; 60; 61], we noticed that many did not effectively generalise to unseen speakers (individuals who are not in the training dataset). Generalisation is critical for low-resource attackers, as it provides flexibility in target selection. Ultimately, to quickly generate PF speech in a simple manner, we selected four systems that showed superior performance on unseen speakers: Tortoise-TTS [9], a text-to-speech system, FreeVC [49], an any-to-any and cross-lingual VC, DiffVC [62] a one-shot many-to-many voice conversion, and ppgVC [50], an any-to-many VC.  
_Tortoise-TTS:_ Tortoise TTS is an advanced zero-shot text-to-speech model that requires just a few (3-10) s of recording. It excels at providing high-quality voice cloning and proficient narration for large volumes of speech content, like books or articles. With its precise control over voice synthesis, Tortoise is suitable for a wide range of applications, including virtual assistants and audiobook creation. The Tortoise-TTS system consists of a pipeline with five individually trained neural networks: an autoregressive decoder, acontrastive language-voice pretraining (CLVP) model, a contrastive voice-voice pretraining (CVVP) model, a diffusion decoder, and a vocoder [9].  
_FreeVC:_ The training strategy is an end-to-end approach that utilises a combination of a Conditional Variational Autoencoder (CVAE) enhanced with Generative Adversarial Network (GAN) training. The model architecture features a bottleneck extractor and a normalising flow, which work together to refine the process of content information extraction. This refinement improves the clarity and separation of speaker-independent features [49].  
_DiffVC:_ DiffVC is a one-shot, many-to-many voice conversion model. It utilises WavLM to extract self-supervised learning (SSL) features from waveforms and incorporates a bottleneck extractor to capture content information from these features. Additionally, the model employs spectrogram-resizing (SR)-based data augmentation, which modifies speaker information without altering the content information. This approach enhances the model's ability to disentangle content and speaker characteristics [62].  
_ppgVC:_ This VC allows for any-to-many voice conversion, meaning it can take a speech sample from any speaker and transform it into multiple target voices, rather than being restricted to a one-to-one conversion. It combines a sequence-to-sequence phoneme recogniser (Seq2 seqPR) with a multi-speaker duration-informed attention network (DurIAN) to facilitate synthesis. Additionally, this approach has been expanded to support any-to-any voice conversion [50].
## 4 Effect of Perturbations
* How should decisions be made effectively for the end user to efficiently utilize data with the given coverage of a constellation? * How can creative methodologies can solve in-orbit problems with small satellites specifically considering their compact sizes and resource constraints? Some research, such as Refs. [11; 12; 13] has contributed towards solving similar problems significantly and this research is an extension of the same vision insofar as it presents the impact of the trajectory enhancement of small satellites in a constellation.
## 4 Computer Simulations
### Simulation: High Precision Performance of Fine Tuning  
In this simulation, the _SNR_ was fixed at 30 dB and RMS error was compared between two cases. One is the case where the estimated zero order fringe peak \(\hat{p}_{0}\) was fine-tuned for resolution enhancement and the other case was where the estimated zero order fringe peak \(\hat{p}_{0}\) was not fine-tuned.  
Figure 13: Comparison between RMS error with fine tuning and RMS error without fine tuning.  
As shown in Figure 13, without fine tuning, RMS error of \(\hat{p}_{0}\) is approximately 0.3/\(f_{S}\) fringe and RMS error of \(\hat{p}_{0}\) totally depends on the sample rate. But this RMS error was reduced down to \(\sim\)0.0015 fringe when \(\hat{p}_{0}\) was fine tuned. Note that RMS error was not sensitive to the sample rate \(f_{S}\) over the range between 10-20 samples/fringe. This feature predicts that signal processing algorithm can use lower sample rate \(f_{S}\) for the faster signal processing, but still maintain lower RMS error by using fine tuning, which is comparable to the error of higher sample rate \(f_{S}\). In the proposed algorithm, the cross-correlation between \(i_{S}(n)\) and \(i_{R}(n)\) is most time-consuming part of signal processing algorithm, but the sample rate \(f_{S}\) can be lowered to speed up the signal processing without losing higher sample rate RMS error. One strong benefit of the proposed algorithm is that once zero order fringe is identified correctly then the resolution of the fine tuning can be adjusted as low as desired simply by changing the value of \(N_{sub}\) and high precision phase delay estimation can be obtained.
## 3 Performance Analysis Methods
### Expressions for Path Loss and Average Path Loss
#### 3.1.2 The Average Path Loss of the NLOS Link Formed by \(M\)  
In the proposed V2I-VLC system, the NLOS link light caused by \(M\) and \(RS\) reflections also has a large impact on the system performance. Using similar analytical methods, the overall average path loss closure expression for the NLOS link formed by \(M\) can be expressed as follows:  
\[\begin{array}{rl}h_{NLOS\_M}^{PL\_av}&=\frac{{{A_{M}}^{2}{D_{R}}^{2}{I_{V_{i }-PD}}^{2}}}{{2\left({L_{w}-w}\right)}^{2}+{4{I_{V_{i}-PD}}^{2}}}\left\{{ \frac{\left\{{Beta\left[\frac{{4{I_{V_{1}-PD}}^{2}}}{{4{I_{w}}^{2}+{I_{V_{i}- PD}}^{2}}},\frac{3}{2}+\frac{2}{\epsilon},\frac{1}{2}\right]-Beta\left[\frac{{I_{V_{i }-PD}}^{2}}{{4{I_{w}}^{2}+{I_{V_{i}-PD}}^{2}}},\frac{3}{2}+\frac{2}{\epsilon}, \frac{1}{2}\right]}\right\}}}{{\left({I_{V_{1}-M,1,\text{\emph{Local}}}2{I_{M }-PD,\text{\emph{Local}}}}\right)^{2}}}\\ &+\frac{\left\{{Beta\left[\frac{{4{I_{V_{1}-PD}}^{2}}}{{{(L_{w}+2w)}^{2}+{4{I_{ V_{i}-PD}}^{2}}},\frac{3}{2}+\frac{2}{\epsilon},\frac{1}{2}\right]-Beta\left[\frac{{I_{V_{1}- PD}}^{2}}{{4{I_{w}}^{2}+{I_{V_{i}-PD}}^{2}}},\frac{3}{2}+\frac{2}{\epsilon}, \frac{1}{2}\right]}\right\}}}{{\left({I_{V_{2}-M,2,\text{\emph{Local}}}2{I_{M }-PD,\text{\emph{Local}}}}\right)^{2}}}\\ &+\frac{\left\{{Beta\left[\frac{{4{I_{V_{1}-PD}}^{2}}}{{{L_{w}}^{2}+{I_{V_{i}- PD}}^{2}}},\frac{3}{2}+\frac{2}{\epsilon},\frac{1}{2}\right]-Beta\left[\frac{{I_{V_{1}- PD}}^{2}}{{4{I_{w}}^{2}+{I_{V_{i}-PD}}^{2}}},\frac{3}{2}+\frac{2}{ \epsilon},\frac{1}{2}\right]}\right\}}{{\left({I_{V_{2}-M,2,\text{\emph{Local}}}2{I_{M }-PD,\text{\emph{Local}}}}\right)^{2}}}\end{array} \tag{13}\]Note that the derivation process of the above expression is different from the LOS link. The cases of Tx1 and Tx2 need to be considered separately. Refer to Appendix B for the specific derivation procedure.
# Temperature Sensing with Nd\({}^{3+}\) Doped YAS Laser Microresonators
## 3 Materials and Methods  
### Microsphere Production  
The microspheres used in this experiment were produced from Nd\({}^{3+}\) YAS glass (YAS: Nd\({}^{3+}\)) with the following composition (mol%): 13.4 Y\({}_{2}\)O\({}_{3}\), 37.2 Al\({}_{2}\)O\({}_{3}\), 46.5 SiO\({}_{2}\), and 2.9 Nd\({}_{2}\)O\({}_{3}\) using the melt quenching method in a platinum crucible. This glass was synthesized using high-purity raw materials containing trace amounts of impurities such as rare-earth metals (Sm, Eu, Gd, Tb, Dy, Ho, Tm), as well as other elements (V, Fe, Ca, Co, Mg, Mn, Ni, Cu, Na, Ti). According to the raw materials' data sheet all impurities were on the level of a few ppm. The composition of the synthesized glass was examined through laser-induced breakdown spectroscopy using an LEA-S500 elemental analyzer from SOL Instruments. While the detection limit of this method varies for each element, it is at least 100 ppm. The intensities of all the spectral lines corresponding to the above-mentioned impurities were at the background level. Therefore, it is proposed that no additional impurities were introduced to the glass during the melting process. The bulk glass sample was reduced to small particles and separated attending to grain size by wet sieving. The powder was then exposed to plasma and the surface tension pulled the molten glass into a sphere that was rapidly quenched [20]. Microspheres with diameters of 30 \(\upmu\)m to 50 \(\upmu\)m were used in this study.
## 4 Analysis Area and Data  
The PVD employed in this study were collected from 400 buses, 250 trucks, and 50 SUVs on freeways. KEC installed C-ITS infrastructures on the road and in-vehicle units  
Figure 2: (**a**) Speed–spacing trajectories from aggressive, conservative, and ordinary drivers, (**b**) comparison of speed–spacing trajectories between an ordinary and a conservative driver.  
were installed in these 700 vehicles. The data were collected from three freeway sections, as illustrated in Figure 3. The total section length is 85.4 km, and the longest section is a part of the Gyeongbu Line that connects the capital of South Korea and Busan. The PVD were collected over 16 months in 2019 and 2020. Over the two years, the two experiments were performed, the first of which was from July 2019 to July 2020; vehicles equipped with C-ITS in-vehicle units received in-vehicle warning information (we call this period the \"with\" period in this study). However, from August 2020 to October 2020, the drivers did not receive the warning information because the in-vehicle unit was turned off over this period (we call this the \"without\" period in this study). However, there was a log in the data at the time when the warning information should have been provided. This data over the \"without\" period was used as a control group against the data during the \"with\" period to analyze the drivers' driving behavior when no warning information was provided.  
Table 1 shows vehicle records included in the PVD. The PVD originate from two subsystems, DTG, including general vehicle travel records (e.g., speed, acceleration, location, etc.), and ADAS, with records of FCWS (forward collision warning system), LDWS (lane departure warning system), TTC, and spacing. In total, 108 types of vehicle records are logged every second in the PVD. Of those records, this study analyzed timestamp, speed, spacing, and in-vehicle warning logs.  
In this study, the effect of FCWS to prevent direct or secondary accidents caused by other vehicle breakdowns or accidents downstream was evaluated in terms of driving behavior. The FCWS was activated whenever the forward collision risk increases due to the slowing or sudden stop of the preceding vehicle downstream, which results in short spacing in contrast to relative speed.  
To analyze driving behavior following the provision of warning information, this study took a 30 s record before and after FCWS was provided (refer to Figure 4). The reason why the time unit chosen was 30 s was to obtain a sufficient sample of the two variables (speed and vehicle spacing) and at the same time, it was short enough to fully analyze the impact of providing warning information. Since in-vehicle records are logged every  
Figure 3: Analysis area.  
second, in theory, two groups of 30 s data are analyzed. However, some samples were obtained where a few records were missed due to certain technical issues. Thus, this study considered a sample that had at least 15 s consecutive records both before and after the in-vehicle warning to be valid.  
Table 2 summarizes the total number of valid samples extracted in the analysis by each analysis period.
# Recoil Control of Deepwater-Drilling Riser with Optimal Guaranteed Cost \(H_{\infty}\) Control
## 2 Problem Formulation
### Whole Fluid Column Model of Fluid Discharge and Heave Motion Model of Platform  
It is known that the viscous resistance of fluid discharge and the heave motion of platform have obvious effects on the recoil response of the riser. In this paper, the whole fluid column model of fluid discharge is utilized to analyze the influence on the recoil response of the drilling riser. As stated in [20; 23], the characteristics of the fluid discharge process can be described by  
\[\begin{split}&\rho_{w}gA_{s}(L_{r}-L_{m})+\rho_{m}gA_{s}L_{m}- \rho_{w}gA_{s}L_{r}-F_{m}-F_{w}-F_{d}\\ &=[\rho_{w}A_{s}(L_{r}-L_{m})+\rho_{m}A_{s}L_{m}]a\end{split} \tag{1}\]  
where  
\[\left\{\begin{array}{ll} F_{m}=\frac{f_{m}A_{s}\rho_{m}v^{2}L_{m}}{2D_{s}}, & F_{d}=\frac{\rho_{m}A_{s}v^{2}}{2}\\ F_{w}=\frac{f_{w}A_{s}\rho_{w}v^{2}(L_{r}-L_{m})}{2D_{s}}\end{array}\right. \tag{2}\]  
where \(\rho_{w}\) and \(\rho_{m}\) are the densities of seawater and drilling mud, respectively, kg/m\({}^{3}\); \(L_{m}\) is the length of drilling mud, m; \(F_{m}\) is the friction force of drilling mud, N; \(f_{m}\) is the friction coefficient of drilling mud; \(A_{s}\) is the flow area of the riser, m\({}^{2}\); \(D_{s}\) and \(L_{r}\) are the hydraulic diameter and the length of the riser, respectively, m; \(F_{d}\) is the frontal force, N; \(F_{w}\) is the friction force of seawater, N; \(f_{w}\) is the friction coefficient of seawater; \(v\) is the discharge velocity of fluid at time \(t\), m/s; \(a\) is the acceleration of fluid at time \(t\), m/s\({}^{2}\).  
The drilling platform is connected to the deepwater-drilling riser by the tensioners; the heave motion of the drilling platform can be approximated as the superposition of the sine functions:  
\[w(t)=\sum_{i=1}^{n}\alpha_{i}\text{sin}(\omega_{i}t+\varphi_{i}) \tag{3}\]  
where \(\alpha_{i}\) is the random amplitude of heave motion, \(\omega_{i}\) is the random frequency of heave motion, \(\varphi_{i}\) is the random phase of heave motion, and \(n\) is a given positive integer.
# RECRP: An Underwater Reliable Energy-Efficient Cross-Layer Routing Protocol +
## 4 Simulation  
### Simulation Setting  
In order to verify the efficiency of RECRP, we simulated it using a Monte Carlo method. All numerical simulation results were obtained after 1000 calculations. In this simulation, relay nodes (ranging from 100 to 600) were randomly deployed in a 10 km \(\times\) 10 km \(\times\) 10 km area which made the network sparse. Fifty sensor nodes were fixedly deployed on the seafloor, and 10 sink nodes were randomly deployed on the ocean surface. The upper transmission power threshold was 90 dB re \(\upmu\)Pa, and the SNR threshold was 20 dB. The frequency was constrained within 0 to 35 kHz, which is available in the underwater acoustic channel. We also assumed that each node spent 1 W on sending and receiving a packet, which is caused by updating the routing table and other computation. The idle power was assumed to be 10 mW. The size of control packets and ACK packets were both 64 bits, and a data packet was 256 bits. BPSK modulation was used in this simulation, which means the bit rate was capped at 35 kbps. Then, each packet at least can be completely sent within 7.4 ms. We assumed that each sensor node sent sensed data every 1 min.  
We compared RECRP with DBR and HH-VBF, and the frequency of the channel used by DBR and HH-VBF was fixed at 16 kHz, the transmission power was 90 dB re \(\upmu\)Pa, and the pipeline radius in HH-VBF was 1800 m.  
DBR (depth-based routing) adopts a flooding routing mechanism and uses depth information for routing. When a node wants to send a packet, it will sense its current depth relative to the sink node and place this value in the packet header field and then broadcast it. Packets are forwarded when the depth is shallower than the sending node. HH-VBF (hop-by-hop vector-based forwarding) uses a similar concept of virtual routing pipe as used by VBF. VBF is a vector-based protocol where the source-to-sink vector acts as a virtual pipeline with a pre-defined radius to reduce flooding packets for energy efficiency. HH-VBF defines a per-hop virtual pipe for each forwarder.  
In this work, a kinematic model [34] is applied to model the ocean current as follows:  
\[\left\{\begin{array}{l}v_{0|x}=k_{4}+k_{1}\lambda vsin(kk_{2}x)cos(kk_{3}y)+k_ {1}\lambda cos(2 kk_{1}t),\\ v_{0|y}=k_{5}-\lambda vcos(kk_{2}x)sin(kk_{3}y),\end{array}\right. \tag{17}\]  
where \(v_{0|x}\) and \(v_{0|y}\) represent the velocity along the \(x\)-axis and \(y\)-axis, respectively. Parameters \(k_{1}\), \(k_{2}\), \(k_{3}\), \(\lambda\), and \(v\) characterize environmental factors such as the strength of tides and bathometry. \(k_{4}\) and \(k_{5}\) are random variables used to simulate the random factors. \(k\) is a coefficient applied to control the frequency of the changing direction. These variables are given as in Table 2, where \(\mathcal{N}(A,B)\) is a normal distribution with the mean \(A\) and variance \(B\).  
The noise decays linearly in the logarithmic scale, and can be expressed as follows [21]:  
\[10\text{log}N(f)=N_{1}-\eta\text{log}f, \tag{18}\]  
where \(N_{1}\) was set to 95 and \(\eta\) was set to 15.
## 2 Methodology
### ML Algorithms Considered
#### 2.1.6 Synthetic Minority Over-Sampling Technique (SMOTE)  
SMOTE is a popular data augmentation technique used in machine learning, specifically for addressing class imbalance problems [46]. In many real-world classification problems, the dataset may have imbalanced class distributions, where the minority class has significantly fewer instances than the majority class. This class imbalance can pose challenges for machine learning algorithms, as they tend to be biased towards the majority class, leading to poor predictive performance for the minority class. SMOTE addresses this issue by generating synthetic examples for the minority class, effectively oversampling the minority class to balance the class distribution. The technique works by creating synthetic samples that are interpolations between the feature vectors of neighboring instances from the minority class.
# Efficient Data Gathering in 3D Linear Underwater Wireless Sensor Networks Using Sink Mobility
## 4 Framework and Formal Definition of the Problem
### Energy Consumption Model in UWSNs
The MS broadcasts a control packet \(I_{c}(n_{i},d(n_{i}),t_{i},R)\), where \(I_{c}\) is a control packet, \(n_{i}\) is the current node ID and \(d(n_{i})\) is the distance between the node and MS. \(t_{i}\) is the time slot allocated to \(n_{i}\) for data transmission, and \(R\) is the transmission radius of the MS. Nodes that do not receive the control packet from the MS in a certain time \(t\) receive a control packet from \(C_{i}\), _i.e._, \(i_{c}(n_{i1},d(n_{i1}),t_{i1},r)\), where \(i_{c}\) is the control packet and \(n_{i1}\) is the current node ID pf the neighbor of \(C_{i}\). \(d(n_{i1})\) is the distance between the nodes and the CN. \(r\) is the transmission radius of the node.
# Compact Planar Super-Wideband Monopole Antenna with Four Notched Bands
## 4 Conclusions  
A compact SWB monopole antenna with quadruple notched-band characteristics is presented. The antenna radiator is fed using a tapered feed line for broadband impedance matching. Four notch bands are introduced in the proposed SWB antenna to eliminate the interfering radiations of WLAN (5.5 GHz), Wi-MAX (3.5 GHz), satellite communication X-band (7.5 GHz), and amateur radio (10.5 GHz) band. The interference of WLAN and satellite communication bands are notched using inverted U-shaped slots, while the Wi-MAX and amateur radio bands are notched through a meandered slot and an SRR, respectively. The presented antenna has a simple design, without any active components on the patch or the ground plane. The proposed wide bandwidth antenna can be used for a variety of applications such as cognitive radios spectrum sensing, defense systems, doppler navigation, personal communication, radio astronomy, and so forth.  
Conceptualization, S.K., H.C.C., and K.W.K.; methodology, S.K. and G.H.L.; software, S.K.; validation, D.H.K., N.S.H. and H.C.C.; formal analysis, S.K.; investigation, S.K. and K.W.K.; resources, G.H.L., D.H.K., K.W.K., and H.C.C.; data curation, S.K., D.H.K., and N.S.H.; writing--original draft preparation, S.K.; writing--review and editing, K.W.K. and H.C.C.; visualization, S.K. and G.H.L.; supervision, K.W.K. and H.C.C.; project administration, K.W.K. and H.C.C.; funding acquisition, K.W.K. and H.C.C. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Ministry of Education, Science and Technology and the Ministry of Education, Korea.  
This research was supported by the National R&D Program through the National Research Foundation (NRF) of Korea funded by the Ministry of Education, Science and Technology (No. NRF-2019M1A7A1A03088464) and the BK21 Plus Project funded by the Ministry of Education, Korea (No. 21A20131600011).  
The authors declare no conflict of interest.
# Development of a Staggered PCD End Mill for Carbon Fiber Reinforced Plastic
## 3 Fabrication of the Staggered PCD End Mill  
The fabrication of the staggered PCD end mill is as follows:  
1. Preparation and cutting of PCD composite film: according to the shape of the flute, the PCD composite film is cut into the designed shape by wire cutting.
2. NC machining of flute and insert pockets for the cemented carbide tool body: A 5-axis CNC grinding center is used to process the flute and insert pockets according to the design requirements.
3. Welding of PCD insert: Using high frequency induction brazing, silver-based solder and QJ102 silver brazing flux are used to weld the PCD inserts to the cemented carbide cutter tool body.
4. Grinding and passivation of the PCD cutting edges: The EWAG super-hard tool grinder is used to grind the flank of the PCD tool. The fabricated staggered PCD end mills are shown in Figure 6.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Rake Angle f\({}_{f}\)/f\({}_{p}\)** & **Relief Angle f\({}_{f}\)/ff\({}_{p}\)** & **Inclination Angle** & **Tool Length** & **Tool Diameter** & **Edge Radius** \\ \hline
3\({}^{\circ}\)/3\({}^{\circ}\) & 10\({}^{\circ}\)/10\({}^{\circ}\) & Staggered 3\({}^{\circ}\) & 70 mm & 12 mm & 10 \(\upmu\)m \\ \hline \hline \end{tabular}
\end{table}
Table 1: Structural parameters of the PCD end mill.  
Figure 5: Unfolding axial force analysis of cutting edges of the staggered PCD end mill.