esa-sceva/satcom-chunk-collection huggingface dataset:
## Appendix C Fine Range Measurement--A Numerical Example  
Let us consider the two carrier frequencies \(f_{1}=2050\) MHz and \(f_{2}=2250\) MHz applied for frequency diversity. Note that these S-band frequencies are in the frequency range recommended by the Space Frequency Coordination Group for in situ lunar links [30]; thus most likely these frequencies may be used for proximity communications. The resulting wavelengths \(\lambda_{1}\) and \(\lambda_{2}\) are equal to 145.85 mm and 132.89 mm, respectively. The resulting virtual wavelength according to Equation (9) is \(\lambda_{virtual}=12.93\) mm, while the maximum range, \(R_{max}\), is approximately 3 m according to Equation (10). Let us consider the actual distance \(D\) to be 2 m. For this distance, wavelength \(\lambda_{1}\) and \(\lambda_{2}\) will have \(k_{1}=13\) and \(k_{2}=14\), respectively, full cycles--these are the unknown to be determined, while the remaining phases are approximately 0.61 and 0 rad/s (these are the input for the disambiguation algorithm). Runing the algorithm in Algorithm 1 will return the full cycles on the two wavelengths. Knowing the number of cycles and the remaining phase, thefine measurement procedure returns the estimated distance \(d\). This numerical example is summarized in Table 11.  
Let us consider a second scenario, where the carrier frequencies are \(f_{1}=2050\) MHz and \(f_{2}=2150\) MHz, and the distance in question is 1.56 m. In this case the virtual wavelength results in 6.8 m, while the maximum range is about 6 m. There are 10 and 11 full cycles, respectively, on the two waves, and the phase measurements are 0.6 and 0.15 rad/s. The disambiguation algorithm returns 10 and 11 cycles, just as expected, while the estimated distances on the carriers are 1.48 m and 1.56 m, respectively. This numerical example is also summarized in Table 11.
# A Discrete Hidden Markov Model for SMS Spam Detection
## 3 The Discrete HMM for SMS Spam Detection
### Label Each Word in Observation Sequence for HMM Learning  
Among these training SMS from the UCI repository dataset, some of them are labeled as spam and the others are labeled as ham. The labeled SMS dataset can be described as:  
\[\begin{array}{cccc}\mathit{SMS}_{1}&w_{1}^{\text{SMS}_{1}},\ w_{2}^{\text{ SMS}_{1}},\ w_{3}^{\text{SMS}_{1}},\ \cdots,\ w_{Length/\text{SMS}_{1}}^{\text{SMS}_{1}}&\mathit{ham}\\ \mathit{SMS}_{2}&w_{1}^{\text{SMS}_{2}},\ w_{2}^{\text{SMS}_{2}},\ w_{3}^{\text{ SMS}_{2}},\ \cdots,\ w_{Length/\text{SMS}_{2}}^{\text{SMS}_{2}}&\mathit{spam}\\ \cdots&&\cdots&\cdots\\ \mathit{SMS}_{i}&w_{1}^{\text{S}_{i}},\ w_{2}^{\text{SMS}_{i}},\ w_{3}^{\text{ SMS}_{i}},\ \cdots,\ w_{Length/\text{SMS}_{i}}^{\text{SMS}_{i}}&\mathit{ham}\\ \cdots&&\cdots&\cdots\\ \mathit{SMS}_{M}&w_{1}^{\text{SMS}_{M}},\ w_{2}^{\text{SMS}_{M}},\ w_{3}^{\text{ SMS}_{M}},\ \cdots,\ w_{Length/\text{SMS}_{M}}^{\text{SMS}_{i}}&\mathit{spam}\end{array} \tag{3}\]  
Take a look at the instances above again. The SMS, _What you thinked about me. First time you saw me in class._, is labeled as _ham_ and the other one, _Are you unique enough? Find out from 30 th August. www.areyounique.co.uk_, is labeled as spam. Thus, it is represented as [_What, you, thinked, me, First, time, you, saw, me, class_] with the label _ham_ and [_Are, you, unique, enough, Find, 30 th, August, www, areyounique, co, uk_] with the label _spam_.  
However, each word in the observation sequence should be marked as ham or spam for the HMM learning. As the UCI repository dataset only has a label for each SMS, we use a compromised method to label all words in the observation sequence, i.e., labeling the words in the SMS based on the label of the SMS. The labels for the observation sequence can be represented as:  
\[\begin{array}{cccc}\{\mathit{ham},\ \mathit{ham},\ \mathit{ham},\ \mathit{ \cdots},\ \mathit{ham},\\ \cdots,\\ \mathit{ham},\ \mathit{ham},\ \mathit{ham},\ \mathit{ham},\ \mathit{\cdots},\ \mathit{ham},\\ \cdots,\\ \mathit{spam},\ \mathit{spam},\ \mathit{spam},\ \cdots,\ \mathit{spam}\}\end{array} \tag{4}\]  
For the instances above, as the first SMS is a ham one, each word in the refined sequence is labeled as _ham_. As the second SMS is a spam one, each word in the sequence is labeled as _spam_. That is, the part of observation sequence [_What, you, thought, me, First, time, you, saw, me, class_] is labeled as [_ham_, _ham_, _ham_, _ham_, _ham_, _ham_, _ham_, _ham_, _ham_, _ham_, _ham_, _ham_, _ham_, _ham_, _ham_, _ham_, _ham_] and [_Are, you, unique, enough, Find, 30 th, August, www, areyounique, co, uk_] is labeled as [_spam_, spam_, spam, spam, spam, spam, spam, spam, spam, spam_, spam].
## 3 Experiments and Results
### Experimental Results  
#### 3.2.1 Error Calibration Results  
This section focuses on the correction results of the power-on clock error \(Fr(c\Delta dt_{ij})\) and hardware delay error \(Fr(\eta)\) in Section 2.3. Experimental data were collected on 7 November 2022. Figure 9 shows the calculation results of \(Fr(c\Delta dt_{ij})\) for multiple satellites at the three frequency points of GL1, B1I, and B1C. Table 1 shows the calculated values of GL1 Power-on clock corrections between AB, AC, and AD. Figure 10 shows the average value of \(Fr(c\Delta dt_{ij})\) over a period of time calculated using data from three frequency points of three baselines.  
Figure 8: Experimental scene.  
Figure 9: Power-on clock corrections for three frequency points of multiple satellites between \(A\) and \(B\).  
The following experiment is carried out by taking the GPS L1 frequency point as an example. Experimental data were collected on 24 September 2022. The experiment lasted five days, and the sampling frequency was 1 Hz. Figure 11 is the true value of the carrier difference between \(A\) and \(B\) calculated according to Formula (3), corresponding to \(\Delta\varphi_{ij}^{s}\) in Formula (22). Figure 12 is the carrier difference in Formula (22), which is corrected by using the power-on clock correction, corresponding to \(Fr\left(\Delta\widehat{\varphi}_{ij}^{s}+c\Delta dt_{ij}\right)\). Figure 13 is the calculation result of \(Fr(\eta)\), which is the difference between the data in Figures 11 and 12.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \multirow{2}{*}{**GL1**} & \multicolumn{2}{c}{**AB**} & \multicolumn{2}{c}{**AC**} & \multicolumn{2}{c}{**AD**} \\ \cline{2-7}  & **Ave** & **Std** & **Ave** & **Std** & **Ave** & **Std** \\ \hline G01 & 0.0123 & 0.0078 & 0.0891 & 0.0132 & 0.2016 & 0.0178 \\ G10 & 0.0093 & 0.0056 & 0.0898 & 0.0086 & 0.1987 & 0.0173 \\ G12 & 0  
Figure 11: The true value of carrier difference between \(A\) and \(B\).  
Figure 12: Calculated value of carrier difference between \(A\) and \(B\).
## 3 Noise in PLC Networks
### Noise Library for NBPLC-PRIME  
#### 3.2.1 Noise Categorization  
Reference [54] presents a well-established categorization of noise sources for the NBPLC frequency band, in general, and for the PRIME frequency band, in particular. It must be considered that in this study: (1) the equipment involved were legitimately CE (European Conformity) marked and the related standardized emission limits and immunity requirements were met; and (2) events related with an EMI due to current/voltage components in the frequency range from 2 to 150 kHz, exceeding the immunity of an equipment in normal operation status, were analyzed. Such a categorization is summarized next [54]:  
* EMI due to conducted emissions
* EMI due to lighting equipment
* EMI due to electricity meters
* EMI due to mains communication systems
* Power supplies
* TV antenna system
* UPS for computers/servers
* Camera surveillance system
* Satellite receiver amplifier
* TV receiver
* DVD player
* LAN router/switch
* 4G/LTE base station
* Battery charger / Voltage converter
* Antenna amplifier
* Short range effect (in MV/LV secondary substations)
* Power systems: loads and inverters
* Frequency inverters in an industrial plant
* Frequency-controlled ventilation
* Frequency-controlled water pump
* Inverters in a waste water treatment plant
* Inverter in a heat plant
* Several inverters in a rural supply area
* Commercial washing machine
* Variable Frequency Drives in a pump station
* Lighting equipment *
* Energy efficient lighting
* Fluorescent lights
* Emergency lighting
* Other EMI sources
* Fiber switch
* Signal attenuation by IT equipment
* Circuit breaker
* EMI due to medical equipment
* Ultrasonic equipment
* Electrocardiograph (ECG)
* EMI due to radiated emissions
* Broadcast time-signal systems
* Contactless magnetic card reader
* Mobile radio receivers
* Traffic control system.
## 3 Results and Discussion  
### Surface Morphology  
Figure 2 shows that in general, there was a significant difference in fiber diameter and pore size between the two membranes. On average (Figure 2B), the PCL-AC membrane possessed a fiber diameter that was approximately 4 times larger and pore size 7 times bigger in comparison to the PCL-7 AA/3 AC membrane. In particular, histograms illustrated an irregular trend in the distribution of fiber diameter and pore size in the PCL-AC membrane. The most frequent fiber diameter values were in three distinct ranges: 500-1000, 1500-2000, and 3000-3500 nm, while the dominant pore sizes ranged broadly from 6 to 14 \(\upmu\)m. For the PCL-7 AA/3 AC membrane, more than 75% of fibers were less than 500 \(\upmu\)m in diameter, and the dominant pore sizes fell between 1.2 and 1.8 \(\upmu\)m. Thus, it was clearly noticed that the addition of AA dramatically reduced the fiber diameter from the microscale to the nanoscale, which was also found in other reports [7; 12]. This could be attributed to the reported association between viscosity of the polymer solution and average fiber diameter [13]. At the same PCL concentration, the addition of AA in the solvent decreased the viscosity of the polymer solution compared to when using acetone alone [8]. This led to the increase in the solution's surface tension pointing inward and narrowing the fiber width [14]. Furthermore, another difference was the increase in conductivity of the PCL solution when using the AA/AC solvent. PCL chains carrying a negative charge from the carboxylic group of AA accelerated the stretching, elongation, and thinning of the PCL fibers [3]. The combination of the surface tension force of the solution and the repulsive electrostatic force among PCL chains resulted in a significant decrease in the fiber diameter of the PCL-7 AA/3 AC membrane [3]. On the other hand, the erratic size of PCL-AC fibers could be attributed to the fact that solution clogging occurred at the needle tip, which was not seen for the PCL-7 AA/3 AC membranes. This was due to a higher evaporation rate of AC compared to AA, relative to the solution feeding rate [15]. The excessive amount of polymer accumulating at the needle tip resulted in the formation of an unstable Taylor cone and thus produced larger fibers in the PCL-AC membrane [8].  
Figure 1: Schematic of the 3D-printed cell culture tool.
# Upgraded Combined Inject-and-Transfer System for Serial Femtosecond Crystallography
## 3 Results and Discussion  
### Current Status of BITS Hardware  
In the BITS system, two translation systems are required to align the injector and transfer the film holder to the X-rays [40]. In a previous study, the injector needle was mounted on a 3D printed needle holder, which was connected to a manual stage in the XYZ direction [40]. The film, where the crystal sample was deposited, was moved by a motorized piezo motor stage in the XY direction. As previously reported for BITS sample delivery, the needle tip and polyimide film are in contact, allowing the sample from the tip of the needle to be deposited directly on the film. When the needle tip and film are separated, the sample forms a drop at the needle tip, impairing data collection until it touches the film [40]. Therefore, for efficient data collection, it is important to ensure contact between the film and needle tip before the experiment and during data collection. Althoughthe needle tip and film are precisely aligned before data collection, sometimes the needle tip and film detach during data collection. This occurs when the film on which the crystals are deposited is not perfectly flat or when the needle tip moves when it touches the film, at times changing the path owing to friction. In this case, because the system must wait until the sample is stably deposited on the film, the beamtime efficiency is lowered, and unnecessary sample consumption occurs.  
To solve this problem, in the current upgrade, when the film and needle do not come into contact during data collection, one of them is moved along the \(Z\)-axis to make contact again. If the fixed-target scanning stage is moved along the \(Z\)-axis, it may deviate from the focal point of the focused XEFL, which affects crystal diffraction. In addition, data processing may be affected as the distance from the sample to the detector changes. In contrast, the \(Z\)-axis movement of the syringe needle does not affect the crystal position at the XFEL focal point or the distance between the crystals and detector. Accordingly, the manual injector stage was replaced by the \(XYZ\)-axis motorized piezo stage for real-time alignment between the syringe needle and polyimide (Figure 1A). Thus, if the needle does not come into contact with the film during data collection, it can contact the polyimide film by moving the \(Z\)-axis of the piezo stage to which the syringe needle is coupled. As the \(Z\)-axis travel length of the piezo stage was 29 mm, the tip of the syringe needle could also be moved 29 mm in the \(Z\)-axis direction. This travel length indicates that they can be aligned by moving the needle tip even if the 80 mm polyimide film is scanned horizontally, with the sample holder tilted by 20\({}^{\circ}\). Taken together, the replacement of the manual injection stage with a motorized piezo stage reduces unnecessary sample consumption during data collection and allows for more efficient use of the beamtime.
# Optimal Vehicle Lane Change Trajectory Planning in Multi-Vehicle Traffic Environments
## 1 Introduction
from the National Defense Academy, Kanagawa [20] proposed a model predictive control algorithm for autopiloted aircraft, allowing multiple aircraft to fly without collisions in three-dimensional space. The algorithm requires high accuracy, and while it was difficult and time-consuming to solve the optimization problem, it was more difficult to target multiple models. Numerical optimization algorithms generally belong to online trajectory planning. [PERSON] et al. of Zhejiang University used a numerical optimization algorithm to solve the problem of multi-vehicle cooperative lane change trajectory planning [21]. Those authors noted that it was difficult to solve the numerical optimization problem, which will be a significant challenges in subsequent research. At present, the interpolation curve algorithm is the most popular in lane-changing scenarios involving intelligent vehicles. Professor [PERSON] of Tsinghua University proposed the replanning trajectories with the fifth polynomial, whereby the vehicle returns to the original lane in dangerous scenarios [22]. The lane-changing trajectory must satisfy not only the safety constraints, but also the efficiency and lateral stability of lane-changing in a multi-vehicle traffic environment. Single collision avoidance and optimization algorithms have been applied to lane-changing trajectory planning by many scholars, but in this paper, a new research method combining lateral stability constraints, a collision avoidance algorithm and an optimization algorithm is proposed. Our research method is divided into four steps: the first is to analyze and compare the polynomial lane changing model; the second defines the vehicle lateral stability constraints; the third step involves the online collision avoidance algorithm, designed to search for safe trajectory clusters; and in the final step, the multi-objective optimization problem is solved by the TOPSIS algorithm to obtain the optimal lane changing trajectory. Three typical working conditions were verified by simulations. The results show that the proposed algorithm can be used to solve the problem of collision-free trajectory planning problem in multi-vehicle traffic environments.
## 2 Legal Status of Mega-Constellations under International Space Law
### International Telecommunications Union in Regulating Mega-Constellations
#### 2.2.2 Compliance with International Law  
The ITU's regulations must align with broader international space law principles, such as those outlined in the Outer Space Treaty. The ITU's role in promoting international cooperation and equitable access helps to ensure that mega-constellations do not violate these principles by monopolizing orbital slots or frequencies.  
While the ITU coordinates international standards, it also recognizes the sovereign right of each state to regulate its telecommunications. This dual responsibility can create challenges, as national regulators may prioritize their own interests, potentially leading to conflicts with the ITU's goal of equitable resource distribution. For instance, the US Federal Communications Commission (FCC) assigns orbital shells on a first-come, first-served basis, which can lead to the de facto appropriation of these orbits, contrary to the Outer Space Treaty [2; 5].
# Piezomagnetic Anomalies Associated with the 2021 \(M_{\text{W}}\) 7.3 Maduo (China) Earthquake
## 2 Data and Methods
### Piezomagnetic Model  
The constitutive law of the stress and magnetization has been proposed as follows [36]:  
\[\Delta\mathbf{J}=\frac{3}{2}\beta\mathbf{S}\mathbf{\cdot}\mathbf{J} \tag{1}\]  
where \(\Delta\mathbf{J}\) is the change of magnetization, \(\mathbf{S}\) is the deviatoric stress tensor, \(\mathbf{J}\) is the initial magnetization, and \(\beta\) is the stress sensitivity.  
The piezomagnetic potential \(\mathrm{d}W\) at point \(P\) (\(x_{0}\), \(y_{0}\), \(z_{0}\)) due to \(\Delta\mathbf{J}\) of a sub-block with coordinates of (\(x,y,z\)) can be expressed as Equation (2) [36], and the PMs can be obtained by differentiating the right-hand side of Equation (2).  
\[dW=\frac{\Delta\mathbf{J}\cdot\mathbf{\rho}}{\rho^{3}}dv=\Delta\mathbf{J}\cdot\frac{l(x_{0 }-x)+m(y_{0}-y)+n(z_{0}-z)}{\rho^{3}}dxdydz \tag{2}\]  
where (\(l,m,n\)) are the direction cosines of \(\mathbf{J}\), and \(\rho\) is the distance between point \(P\) and the sub-block,  
\[\rho=\sqrt{\left(x_{0}-x\right)^{2}+\left(y_{0}-y\right)^{2}+\left(z_{0}-z \right)^{2}}\]  
The total PMs at point  
are usually installed. The total intensity (\(F\)) and east component (\(Y\)) of PMs are calculated because \(Y\) is expected to be significantly changed under the east-west slip regime.  
The spatial distribution of the PMs is shown in Figure 3. Our results show that the pattern of PMs is consistent with the left-lateral slip. For \(Y\), negative changes appear in the compressional area, whereas positive changes appear in the extensional area. This is the opposite for \(F\), because \(F\) is primarily calculated from the north component (X) and the vertical component (\(Z\)) which are reverse variations with \(Y\) according to Equation (1).  
The large PMs occur in the near-field. The maximum amplitude of PMs is about 10 nanotelsa (nT) for \(F\) and 8 nT for \(Y\). Additionally, three peaks are in the regions with much larger slip mentioned above. However, PMs are small at the epicenter area.  
We also examine the \(Y\) changes on three profiles (Figure 4). Since the distributions of the \(F\) changes are opposite with the \(Y\) changes, we do not further analyze the \(F\) changes. The \(Y\) changes are more remarkable at the north side than that at the south side of fault trace. The \(Y\) changes on profile 1 and profile 2 are about zero at 20 km and 30 km from the fault trace on the south side and north side, respectively. The \(Y\) changes on the profile 3 are about 0.5 nT at 40 km away from the fault trace.  
Figure 3: Contours of calculated (**a**) total intensity and (**b**) east component of PMs obtained from the Maduo earthquake. Contour interval is 1 nT. Focal spheres represent the epicenter. Gray lines are the surface rupture. Dash lines are profiles.
## 1 Introduction
When ASR systems operate in a real environments, they have to face many problems such as overlapping, background noise, echoes, speaker variability, etc. If the training data are abundant and contain the expected variability [26], the resulting ASR system can be very robust to those sources of variability. However, it can be very difficult to collect a large amount of training speech covering all the possible situations. Therefore, it is very common to resort to the use of data augmentation methods to generate artificial data representative of the possible situations that a system may face in real operation. This approach, in combination with deep learning approaches, has proved to provide large improvements in multiple research works to compensate speech variability due to noise, reverberation, speaking rate, etc. [27; 28]. However, there are very few works in which data augmentation is applied to deal with the problem of packet losses and reduced bit rate codification. There are some related works such as [29], which tries to use data augmentation to increase robustness to deformations applying partial loss of information in time and frequency on the log mel spectrogram directly. It does not aim at reducing the effect of packet losses, but the removal of segments in time is similar to packet losses. Other works such as [30] apply data augmentation using audio codecs with different bit rates, sampling rates, and bit depths. The works that we have found more similar to our approach of applying data augmentation techniques to deal with the problem of packet losses are [31], which presents a study of different training approaches, including data augmentation approaches, to deal with packet losses in a emotion recognition task, and [17], which proposes and evaluates a deep learning based PCL system using ASR measured in terms of WER, and compares results without the PCL system with and without data augmentation in ASR training. We will discuss in more detail these works in comparison with our results at the end of the Results and Discussion section. The main motivation for this study on the possibility of increasing ASR robustness against packet losses and low bit-rate speech codecs using data augmentation techniques is based on two reasons. The first one is a real need to improve ASR accuracy in speech data recorded in call centers that presents these two types of issues, packets losses, and low bit-rate speech codecs, aside from all the other typical problems in these real situations. The second one is that, after studying the available scientific literature, we noticed that the research community has not extensively addressed this problem, despite it being a very common issue in the industry. The rest of the paper is organized as follows: Section 2 explains the methods used to train and evaluate the systems built, as well as the data used in each case and type of acoustic model applied. Section 3 presents the results obtained and finally Section 4 presents the conclusions obtained in this study.