esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 ARIMA-ML Framework
### Feature Extraction Module  
The general ML classification process is described as extracting features from the raw data, determining the mapping relationship between the features and their labels, and finally tuning the models to obtain the best mapping function. [PERSON] et al. [2] and [PERSON] et al. [3] presented promising detection results of feature extraction using combinations of the first several AR coefficients of ARMA model. However, such features may not be sufficient for general structural systems, where damage patterns are not simply represented by reduced cross-sections as conducted in many idealized laboratory experiments.  
It is known that the AR coefficients contain information about the modal frequencies and damping ratios [26], and the changes (indicative of damage) in the stiffness of the structure are accordingly reflected by the changes in these coefficients [2], which can be used for damage detection. However, from Sections 2.2 and 2.3, if an ARMA/ARIMA is used as a TS model, the MA parts may also have certain contributions. Therefore, in this module, all coefficients of the AR, \(\alpha_{i}\) (\(i\in\{1,2,\ldots,p\}\)), and MA, \(\beta_{j}\) (\(j\in\{1,2,\ldots,q\}\)), from the ARIMA model are collected and concatenated into a \((p+q)\)-dimensional feature vector where several ML classifiers are applied on the whole feature vector.  
If only applying a simpler AR/ARI model without considering the MA part, all \(\alpha_{i}\) coefficients form a \(p\)-dimensional feature vector. In addition, since a fixed \((p,q)\) pair is applied to all TS segments, some segments may still not be well-fitted. Thus, similar to the model parameter determination module, a residual check using Ljung-Box statistics is conducted after model fitting, and the segments violating the acceptable residual assumptions are discarded.
## 4 Results and Discussion
### Velocity Profile Measurement at Two-phase Bubbly Flow
#### 4.3.2 Measurement at a Different Void Fraction  
The experiment was conducted on \(U_{L}\) = 200 mm/s at different void fraction (\(\alpha\)); 0.25%, 0.50% and 1.00%. From the visual observations as shown in Figure 18, the bubble shape was mostly spherical. In the present study, the effects of different bubble shapes will not be taken into account. It was confirmed that the flow regime was mono-dispersed flow regime with the bubble diameter was about 2-3 mm as represented in Figure 19. In this flow regime, the bubbles are uniformly distributed in the cross-section of the pipe, traveling vertically with minor transverse and axial oscillations [34]. Figure 20 represents the measurement results obtained by Developed-UVP. The graph shows the average velocity profile of both phases. The liquid velocity profile is the mean of 5000 profiles and bubble velocity profile is averaged by the amount of data obtained as shown in Figure 21.  
The result of bubble velocity in each void fraction was almost coincident with each other except the data of \(\alpha\): 0.25% at center pipe region. The fluctuation within this zone was caused by a small number of averaging data. Furthermore, the liquid velocity profiles could be obtained separately. The profile of liquid rises obviously when void fraction increases due to the enhancement of several bubbles. Hence, It can be concluded that the void fraction with constant bubble diameter affects the velocity level of the liquid. Besides, it influences measurement error of bubble velocity, if the void fraction is very low.  
Figure 17: Average liquid and bubble velocity distribution in bubbly flow at \(U_{L}\) = 300 mm/s and \(U_{G}\) = 5.3 mm/s; (**a**) velocity profile, (**b**) discrepancy of bubble velocity, (**c**) discrepancy of liquid velocity.  
Figure 19: Bubble diameter distribution.  
Figure 20: Velocity profile of bubble and liquid at a different void fraction.  
Figure 18: The image of bubble distribution in a different void fraction.
# Feature-Model-Based In-Process Measurement of Machining Precision Using Computer Vision
## 2 Measurement Algorithm
### Actual Machining Feature Extraction
#### 2.5.1 Hough Transform line Feature Detection  
The straight line passing through points \((x,y)\) can be represented in Cartesian coordinates as:  
\[y=kx+b \tag{17}\]  
where \(k\) is the slope of the line, and \(b\) is the intercept. The parameters of all lines passing through two points A \((x_{0},y_{0})\) and B \((x_{1},y_{1})\) satisfy Formula (16), which determines a family of lines. Formula (16) can be rewritten as:  
\[b=-kx+y \tag{18}\]  
Points A and B can be regarded as a straight line in the parameter plane of Figure 6b, where \(-x\) is the slope of the line, \(y\) is the intercept, and \(k\) and \(b\) are variables, as shown in Figure 6. Points in the image space correspond one by one to the lines in the parameter space. In Figure 6, it can be seen that the lines corresponding to point A and point B in the image space intersect at a point in the parameter space, which is the line determined by AB and corresponds to a unique point in the parameter space. The coordinate values \((b_{0},k_{0})\) of this point are also the parameters of line AB.  
However, in practical applications, the parameter space cannot choose a Cartesian coordinate system because the special line \(x=c\) (perpendicular to the \(x\)-axis, with an infinite slope and a constant \(c\)) in the Cartesian coordinate space of the original image cannot be represented in the parameter space based on the Cartesian coordinate system. To avoid the inability to recognize parallel lines, polar coordinates are used as parameter space, and Formula (18) is replaced by a straight-line polar coordinate equation:  
\[r=x\cos\theta+y\sin\theta \tag{19}\]  
where \(r\) is the distance from the line to the coordinate origin, and \(\theta\) is the angle between the horizontal direction and the perpendicular of the detection line. The representation in the polar coordinate system is shown in Figure 7, and the Hough transform can be determined based on the intersection point of two sine curves \(r_{1}\) and \(r_{2}\) in polar coordinates.  
Figure 6: Linear parameter representation and parameter space: (**a**) image space; (**b**) parameter space.
## 3 System Model and Methodology
### Channel Model  
The conventional layout for 5G wireless communication systems now includes antenna arrays. Such wireless communications systems are sometimes referred to as multiple input and multiple output (MIMO) systems since an antenna array contains several elements. By examining the redundancy across the various transmit and receive channels, antenna arrays can aid in increasing the SNR. Additionally, they enable the system's spatial and temporal information to be recycled in order to increase coverage. The system is deployed at 60 GHz in the line-of-sight propagation (LOS) and multipath propagation channel. The LOS path is the direct path between the transmitter and receiver assuming perfect channel conditions. Scatters are placed in the channel between transmitter and receiver, and when the data are passed through each scatter, they form a path that results in a multipath propagation channel.  
The transmitter is placed at a distance of 100 m in an indoor scenario. Antenna arrays have been used to reduce the BER as they consist of multiple elements that provide multiple inputs and outputs [27]. To investigate the performance with wavelet transform, the considered transmitter and receiver antenna array was a two-element ULA with half-wavelength spacing. The channel matrix for the MIMO channel with transmit and receive array for the millimeter wave channel model was adopted with the Scatteringchanmtx MATLAB function, The function returns the channel matrix components based on elements of transmit and receive array. The received signal Y can be given as  
\[[\mathrm{Y}]=[\mathrm{hchan}]\times[\mathrm{x}]+[\mathrm{n}] \tag{6}\]  
The hchan can be 2 \(\times\) 2 channel matrix for MIMO channel, and it can be 1 \(\times\) 2 or 2 \(\times\) 1 channel matrix for SIMO and MISO channels, respectively; n is the AWGN noise. The transmit and receive element position are found based on the wavelength, which depicts the frequency of millimeter wave communication system.
# A Design of a Parcel Delivery System for Point to Point Delivery with IoT Technology
## 1 Introduction  
Thanks to the 4 th Industrial Revolution, Internet of Things (IoT) technology is expanding and prospering in many industrial sectors. As a result, a growing number of domestic and foreign companies are rushing to launch their own IoT platforms for the launch of new services. Currently, the data computing and storage processes are mainly done in the central cloud on the IoT platform [1]. The centralized IoT system, however, is causing many problems. It requires a huge central server that processes and stores data received from a number of devices This requires large administrative costs. Therefore, it wants to improve efficiency by utilizing IoT technology. Continuing to increase access to IoT devices is difficult because there is a limitation at the central server [2]. While central servers need to be expanded to address this problem, it is highly inefficient and it is not recommended to expand a central server that already has high-performance. In addition, IoT devices require efficient management of data and the role of the central server is very important because of real-time data processing. Therefore, if there is a problem with the central server, all IoT devices will be useless. To address these problems, distributed networking of IoT is expected in the future [3; 4; 5]. In this case, the central server's role will be reduced, and the portion of the work handled by the terminal device will increase. In the future era of IoT, firmware-based IoT devices, which only deal with simple tasks, will be raised to the level of the RTOS (Real Time Operation System) to secure safety and connectivity. We enhanced our parcel delivery system based on existing IoT platform provided by the SK Telecom(SKT) of Republic of Korea (Seoul, Korea). In addition, we implemented simple end-to-end IoT systems by using a centralized IoT System. The name of the IoT platform is ThingPlug which also provides LoRaWAN as a dedicated global network for IoT. In this paper, we propose an efficient parcel delivery system with IoT devices.  
Currently, the Parcel Delivery System consists of the Hub and Spoke method [6; 7; 8; 9; 10]. The Hub and Spoke method is constructed assuming a large amount of Parcel is delivered. The method has the advantage of being able to transport efficiently in many areas with a small hub, but it also has the problem of having to visit the hub on adjacent streets. The Point to Point System used in aviation systems connects Point and Point as much as necessary to facilitate use. We aim to make a new Parcel Delivery System by adding an IoT Device from the current Hub and Spoke method [11].  
Section 2 introduces related technologies in order to fulfill our purpose. Section 3 shows the design of our parcel delivery system. Section 4 describes our IoT device hardware design. Section 5 explains the process of connections between IoT devices and a distributed network IoT platform. Section 6 discusses technical issues to be solved for our IoT applications. Section 7 provides conclusions and mentions the future research.
## 4 Verification
### Simulation Validation Utilizing Synthetic PMU Data  
Using Equation (1) as the instantaneous signal model for generating simulated PMU data, we first set the initial parameters for the simulated PMU data. We set the rated frequency \(f_{N}\) of the system equal to 50. Then, we set the fundamental frequency \(f_{0}\) to be [49 49.5 49.7 50 50.5 51 51.5] Hz. The remaining parameters for the fundamental, subsynchronous and supersynchronous components are specified as follows: \((x_{0},\,\phi_{0})=(100,\,\pi/3)\), \((f_{\text{sub}},x_{\text{sub}},\phi_{\text{sub}})=(20,20,\,\pi/4)\) and \((f_{\text{sup}},x_{\text{sup}},\phi_{\text{sup}})=(80,30,\pi/4)\).  
During the verification process, the sampling frequency of instantaneous data was set at 1.6 kHz. Sampling the instantaneous data using DFT resulted in synthetic PMU data with an upload frequency of 100 Hz. In the process of determining the SSO characteristics through the application of a fitting-based ERA technique, a specified data segment 200 msin duration was employed, which encompassed precisely 21 consecutive synchrophasor measurements for analysis.  
Based on previous research [17], \(f_{\text{sub}}\) and \(x_{\text{sub}}\) have the greatest impact on the identification accuracy of the synchrophasor parameters under SSO. Therefore, using the control variables method, we let \(f_{\text{sub}}\) vary at intervals of 1 Hz within the range of [5; 45] Hz and \(x_{\text{sub}}\) vary at intervals of 1 within the range of [5; 100]. After generating PMU data under the above conditions, the synthetic PMU data were used for SSO parameter identification based on the fitting-based ERA method. To avoid randomness to a certain extent, each case was recalculated 50 times, and the central tendency was taken. Additionally, to evaluate the accuracy of the improved algorithm in determining SSO parameters within a noisy environment, we embedded zero-mean white noise into the model of instantaneous signals in Equation (1) for analysis. Drawing from [16], which states that the measured signal-to-noise ratio (SNR) of PMU data approximates to 45 dB, a noise condition of 40 dB was chosen for the simulation.  
Table 1 presents the identification results of SSO parameters using the fitting-based ERA method (abbreviated as \"F-ERA\") and the existing ERA method for comparison.
# Optimizing Parameters for Enhanced Iterative Image Reconstruction Using Extended Power Divergence
## 2 Image Reconstruction  
Tomographic image reconstruction can be formulated as an inverse problem, where pixel values \(x\in R_{+}^{I}\) are estimated from projections \(y\in R_{+}^{I}\), following the model:  
\[y=Ax+\sigma. \tag{1}\]  
Here, \(A\in R_{+}^{I\times I}\) is the projection operator, \(\sigma\in R_{+}^{I}\) represents measurement noise, and \(R_{+}\) denotes the set of non-negative real numbers. When all elements of the noise vector \(\sigma\) are zero and Equation (1) is satisfied by \(x\), the inverse problem is considered consistent. Hereafter, when the set  
\[\mathcal{E}:=\left\{x\in R_{+}^{I}\ |\ y=Ax\right\} \tag{2}\]  
is non-empty, we denote an element of this set by \(e\).  
A transform method based on the projection-slice theorem is commonly used when the tomographic inverse problem is consistent, i.e., if projections are measured over an angular range of 180 degrees, information on the 2D Fourier transform of the pixel values can be obtained, allowing for the reconstruction of pixel values from the projections through Fourier transform (or convolution integrals) and back-projection. However, in cases of ill-posed [17] inverse problems, such as when the measurements contain noise, certain adjustments are required, such as selecting a reconstruction filter (reconstruction function) that suppresses high frequencies instead of the standard ramp function used in convolution integrals.  
Iterative methods are described by recursive algorithms, where desirable reconstructed images are obtained by solving an initial value problem of difference equations. Iterative algorithms have been proposed which vary depending on the objective function or the derivation principle of the tomographic inverse problem. In this paper, we define the MLEM and PDEM methods, which play an important role in our study.
# Fabrication Tolerances' Impact on an ODAC-Based PAM-4 Transmitter
###### Abstract  
Photonic integrated circuits (PIC) devices are impacted by fabrication tolerances and therefore, prior knowledge of such variations could improve the PIC fabrication process and overall yield. This paper presents a method for predicting the fabrication impacts on a telecommunication optical digital to analog converter (oDAC)-based pulse amplitude modulator level four (PAM-4) transmitter as a case study where the certainty of this passive device is subjected to random variation. Our findings allow us to estimate the production yield in a fabrication scenario using the symbol error rate (SER) benchmark and this contributes to the study of the viability of oDAC PAM-4 transmitters to replace conventional electrical digital to analog converter (eDAC) PAM-4 transmitters.  
Keywords:programmable photonics circuits; photonic integrated circuits; pulse amplitude modulation; optical digital to analog converter; fabrication tolerance; passive imbalances +
Footnote †: journal:
# Investigating Coherent Magnetization Control with Ultrashort THz Pulses
## 4 Discussion  
Given the pump fluence of our experiment we were not able to trigger a detectable coherent magnetization dynamics with a single frequency THz radiation pump pulse. Adding higher harmonics to the pump pulse resulted in a clearly visible reduction of the sample's magnetization occurring on the picosecond timescale. However, there is no signature of the magnetization to be directly affected by the magnetic field of the THz pump pulse, the observed dynamics appears to be driven by a pure demagnetization process. This observation indicates that the magnetic field strength has been too low at any given time to trigger a coherent response of the sample's magnetization, while incoherent energy transfer through energy absorption accumulating over the successive cycles of the THz pump pulse induced the observed demagnetization dynamics. To be able to observe the THz induced coherent magnetization dynamics in a follow-up experiment, it is therefore necessary to increase the THz fluence, which can be achieved by improving the focusing of the THz beam.  
Our results also showed that the THz pulse envelope emitted by the FLASH undulator source is highly non-uniformly structured. This indicates that an analysis of an experiment of coherent THz control of magnetization dynamics at this source will require a detailed characterization of the THz pulse shape. Such a diagnostic is not yet permanently accessible at the THz beamline of FLASH, but the results reported here provide due to the rather simple setup an attractive way to do so.  
[PERSON] conceived the experiment with help from [PERSON], [PERSON] and [PERSON], [PERSON], [PERSON] and [PERSON] designed the experimental set-up. [PERSON] prepared the samples. [PERSON], [PERSON], [PERSON] and [PERSON] operated the beamline. [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON] and [PERSON] conducted the experiment. [PERSON] and [PERSON] analyzed the data. [PERSON] and [PERSON] prepared the original draft. [PERSON], [PERSON], [PERSON] and [PERSON] reviewed and edited the text. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the CNRS PICS (now IEA) program (STEROID), the Sorbonne Universite emergence program (MADAM), the ANR under grant ANR-15-CE24-0009 and the CNRS MOMENTUM program.  
Not applicable.  
Figure 6: Reconstructed electric field of the THz radiation obtained by fitting the results of Figure 5 by Equation (1) for fundamental wavelengths of 45 μm (red) and 100 μm (blue). The fit neglects the higher harmonics for simplicity. The intensity is not uniformly spread among cycles and the most intense cycles are not located at the same position within the pulse for the two different wavelengths.  
**Data Availability Statement:** The data presented in this study are available on request from the corresponding author.  
**Acknowledgments:** We acknowledge fruitful discussions with [PERSON], [PERSON], [PERSON], [PERSON], [PERSON] and [PERSON].  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 1 Introduction  
Microwave sensors operating at higher frequencies such as Ku-band (13.4 GHz, 2.1 cm wavelength) are especially sensitive to changes in snow (e.g., [1, 2]). Microwave sensors can be used to detect ongoing snowmelt, whereas the detection capabilities of optical sensors is limited to snow covered area under cloud-free conditions. The exploration of Ku-band scatterometer data started in 1996 with the launch of NSCAT (NASA scatterometer) onboard the Advanced Earth Observation Satellite (ADEOS). Although this type of sensor is designed for ocean applications and the footprint is in the order of 25-50 km, they are attractive for land applications due to the short (several measurements per day at high latitudes) revisit intervals. Thus NSCAT (1996/7) and its follow-up SeaWinds on QuikSCAT (1999-2009) have been used for snowmelt studies. Both are scatterometers, which are real aperture radars. To date there exists no spaceborne SAR (Synthetic Aperture Radar) operating at Ku-band which could allow higher spatial resolution analyses. The ten year continuous record from SeaWinds thus provides a unique dataset in spite of the large footprint. This paper reviews characteristics of the data from this specific sensor, processing approaches and change detection methods for snowmelt applications in high latitudes. NSCAT studies are included since all snow application methods are based on analyses of data from this preceding instrument. Applications concentrate on regions mostly above 60\({}^{\circ}\)N. Snowmelt plays an essential role for the hydrological cycle in these regions with significant impact on river runoff timing and magnitude [3]. Changes in river discharge have been reported from several river basins within the last decades [4, 5, 6]. The focus of this review is on unglaciated terrain, but several studies also demonstrated the use of Ku-band scatterometer data for ice cap [7, 8] and seasonal sea ice monitoring [9, 10].