esa-sceva/satcom-chunk-collection huggingface dataset:
# Dynamic Range Compression and the Semantic Descriptor Aggressive
## 4 Perceptual Listening Experiments
### Listening Experiment 2 Method  
The previous test demonstrated that vocals compressed with settings measured to have greater than or equal to 0.5% THD were rated as being the most aggressive. However, it could be argued the timing behavior of the 1176, particularly when working in all-buttons mode, plays a role in the result. Therefore, a second test was devised, which aimed to decouple distortion and timing behavior and answer whether distortion, timing behavior or a mixture of both were the key components in the creation of an aggressive sound quality. The experiment made use of the APE listening test interface and had participants rate the vocal tracks of three separate songs on the aggressive quality of the vocals. The two songs used in the previous experiment were utilized again, as well as a third new rock song, which was added to give the results more validity over a wider range of test scenarios. Participants were also asked to comment on the audio they were hearing using up to three descriptors.  
During the previous experiment, it was found the time constant settings had no significant effect on an aggressive sound quality; therefore, the vocal tracks were compressed with the hardware 1176 using only the A3R7 time constant (which measured highest for THD) and in 4:1 and all-buttons ratio modes. In addition, the vocals were compressed with the Klanghelm DC8C software compressor, using settings that emulated the timing behaviour of the 1176 in both ratios, and set to measure 0% THD. The timing behaviour was emulated by feeding the hardware 1176 and the software compressor a tone burst and adjusting the parameters of the software compressor until the software closely resembled the timing curve of the 1176 in both settings (see previous work by the author where the tone burst method is used and discussed in detail [31]). While this method did not allow for the exact matching of the 1176's timing curve, it did create very similar results. A more robust method could make use of a specifically designed software compressor algorithm that allows the experimenter to simply turn distortion on and off, but this would require close modelling of the 1176, which was beyond the scope of the current study. Finally, all audio used in Experiment 2 was recorded and processed at 24 bit/44.1 kHz.
# Estimation of the Measurement Accuracy of Wireless Passive Resonance Sensors
## Appendix E Algorithm to Fit the Frequency Samples Around the Maximum with a Lorentz Curve  
In order to achieve a resolution close to the Cramer-Rao lower bound, we have to fit the sample values in the frequency domain around the maximum with a Lorentz curve and then calculate the maximum from this analytically. [PERSON] presents in [39] and [40], based on preliminary work by [PERSON] and [PERSON], which is an extremely efficient iterative algorithm to calculate the maximum of the approximate Lorentz curve from two DFT values. These values must lie on both sides of the maximum, and their distance must be one step size of the spectrum calculated using an FFT. Below, we will present a brief derivation of [PERSON] algorithm. If we neglect noise, we can calculate analytically the Fourier coefficients \(S_{k}\) in the frequency domain from sample values \(s_{n}\), as defined by Equation (25), with  
\[S_{k} = \sum_{n=0}^{N-1}s_{n}e^{-j2\pi\frac{n}{N}}\] \[= \sum_{n=0}^{N-1}\tilde{b}e^{(-\alpha+j\phi)nT+j\phi}e^{-j2\pi\frac {n\phi}{N}}\] \[= \tilde{b}e^{j\phi}\frac{N-1}{n=0}e^{(-\alpha+j2\pi\left(f-\frac{ k}{N!}\right))nT}\] \[= \tilde{b}e^{j\phi}\frac{1-e^{\left(-\alpha+j2\pi\left(f-\frac{ k}{N!}\right)\right)NT}}{1-e^{\left(-\alpha T+j2\pi\left(f-\frac{k}{N}\right) \right)}} \tag{101}\]  
\(|S_{k}|^{2}\) would be maximum if \(f=\frac{k}{NT}\). Let \(m\) be the index of the maximum of the Fourier coefficients; then, the best estimator of the resonance frequency is given by  
\[f=\frac{m+\delta}{NT},\ \ \text{with}\ \ -0.5\leq\delta\leq+0.5 \tag{102}\]  
The maximum of the periodogram is less than half a step size away from \(m\). If we insert this relation in Equation (101), we obtain  
\[S_{k}=\tilde{b}e^{j\phi}\frac{1-e^{\left(-\alpha+j2\pi\frac{n\phi-k}{NT} \right)NT}}{1-e^{\left(-\alpha T+j2\pi\frac{n\phi-k}{N}\right)}}=\tilde{b}e^{ j\phi}\frac{1-e^{-\alpha NT}e^{+j2\pi\left(m+\delta-k\right)}}{1-e^{-\alpha T}e^{+j2 \pi\frac{n\phi-k}{N}}} \tag{103}\]  
For \(k=m\), we obtain  
\[S_{m}=\tilde{b}e^{j\phi}\frac{1-e^{-\alpha NT}e^{+j2\pi\delta}}{1-e^{-\alpha T }e^{+j2\pi\frac{k-1}{N}}}=\tilde{b}e^{j\phi}\frac{1+e^{-\alpha NT}e^{+j2\pi \frac{k-1}{N}}}{1-e^{-\alpha T}e^{+j2\pi\frac{k-1}{N}}} \tag{104}\]  
Half-step sizes in the frequency domain can be achieved either by zero padding N points before a FFT or by evaluating the DFT, as shown in Equation (100) for \(k=m\pm\frac{1}{2}\).
# Nonsmooth Optimization-Based Hyperparameter-Free Neural Networks for Large-Scale Regression
## 3 Theoretical Background and Notations
### Neural Networks for Regression  
Let \(A\) be a given data set with \(n\) samples: \(A=\{(x^{i},y_{i})\in\mathbb{R}^{m}\times\mathbb{R}\mid\ i=1,\ldots,n\}\), where \(x^{i}\in\mathbb{R}^{m}\) are the values of \(m\) input features and \(y_{i}\in\mathbb{R}\) are their outputs. In regression analysis, the aim is to find a function \(\varphi:\mathbb{R}^{m}\rightarrow\mathbb{R}\) such that \(\varphi(x^{i})\) is a \"good approximation\" of \(y_{i}\). In other words, the following _regression error_ is minimized:  
\[\sum_{i=1}^{n}|\varphi(x^{i})-y_{i}|^{p},\quad p>0. \tag{1}\]  
Applying NNs to the regression problem can lead to significantly higher predictive power compared with traditional regression. In addition, it can model more complex scenarios just by increasing the number of nodes. This concept is proved in the universal approximation theorem, which states that a single hidden layer feedforward network of sufficient complexity is able to approximate any given regression function on a compact set to an arbitrary degree [41].  
In practice, NNs take several input features and, as a part of the learning process, multiply them by their weights and run them through an activation function and a loss function, which closely resemble the regression error (1). The loss function is used to estimate the error in the current model so that the weights can be updated to reduce this error on the next evaluation. The most commonly used loss function for NNR problems is the _mean squared error_ (MSE or \(L_{2}\)-norm). However, there are at least two valid reasons to choose the _mean absolute error_ (MAE or \(L_{1}\)-norm) over the \(L_{2}\)-norm: first, the \(L_{1}\)-norm makes the loss function with the ReLU activation function simpler, and second, the regression models with the \(L_{1}\)-norm are more robust to outliers (see, e.g., [42]). Once the NN is trained, the optimal weights for the model (regression coefficients) are found to fit the data.
# Deriving a Dilution of Precision Indicator for GNSS Factor Graph Optimization Solutions +
## 3 Results
### Validation of the FGO DOP Formula in a Varying Constellation Case  
In this section, the nominal GPS constellation from IS-GPS-200M is considered. The scenario considers a fixed receiver in Toulouse on the the 1 st of January 2023 with a 5-min interval over the first hours of the day. This scenario allows to have varying and realistic constellation geometries, leading to temporal variations of the snapshot DOP.  
Figure 2c shows the exact HDOP and the HDOP computed using Equation (16) for a fixed value of \(K=50\) and various values of \(q/\sigma\). Again, the HDOP values obtained with the formula are close to the exact values.  
An interesting observation can be made. For high accuracy motion constraints (e.g., \(q/\sigma\leq 0.1\)), variations of HDOP are attenuated and the batch HDOP reaches a floor value for central epochs. In this particular cases, the accurate motion constraints allow to have a good relative accuracy between consecutive position estimates, and the observations from every epoch to contribute to the absolute accuracy of the whole trajectory.
## 4 Characterization of the Optical Memory Buffer
### Measuring the Delay Tuning Range  
We start our characterization by measuring the added delay for each of the FBG channels using an electrical vector network analyzer as shown in Figure 3a. We send the electrical vector analyzer waveform to the RF input of the optical memory buffer. We modulate the waveform on a tunable laser source carrier and analyze the waveform phase at the buffer  
Figure 2: (**a**) The experimental setup of the optical memory buffer for delaying a linear frequency-modulated wide-band pulse using carriers from a Kerr comb and off-the-shelf LC-FBG and CS-FBG. We modulate the LFM pulse on optical Kerr comb carriers and send the signal through a concatenation of LC-FBG and CS-FBG. (**b**) The optical spectrum of the generated soliton Kerr comb using the microresonator. (**c**) A zoomed-in spectrum for the Kerr comb carriers in the C-band (1527.7–1567.6 nm). (**d**) The spectrum of the light reflection from the concatenation of the FBGs.  
output. We deduce the delay from the slope of the phase curve for each wavelength channel compared to the zero wavelength of the optical channel at \(\lambda=1527.7\) nm. Figure 3b shows the measured delay values and the delay trendline. We observe that the range of optical channels over 1527.7-1567.9 nm spans 76 ns, giving each of the 100 channels \(\sim\)0.76 ns of additional delay compared to its left-sided wavelength channel.
# Small Satellite Tools for High-Resolution Infrared Fire Monitoring
## 7 Attitude and Orbit Control System of the FireBIRD Satellites  
### Requirements of the Attitude and Orbit Control System of the TET-1 and BIROS Satellites  
The main requirements of the attitude and orbit control subsystem (AOCS) for an Earth observation mission in Low Earth Orbit (LEO) are basically derived from the mission analysis and from the pointing requirements of the main payload.  
A general mission analysis, as conducted for the FireBIRD satellites, showed that their attitude control (and navigation) must provide a three-axis stabilization of the multi-spectral camera's orientation within the orbital altitudes of 450 km to 850 km and orbit inclinations from 53\({}^{\circ}\) up to \(\sim\)96\({}^{\circ}\) (to secure a sun-synchronous polar orbit).  
Thus, the main requirements of the FireBIRD satellite attitude control subsystem are:  
* The FireBIRD satellites shall be able to adjust the payloads' viewing directions:
* towards the Earth (nadir, zenith);
* in the flight direction; and/or
* to turn and hold the solar cells towards the sun.
* The FireBIRD multi-spectral camera systems shall be able to acquire image data from different viewing directions with respect to the nadir vector. To accomplish this, the following maneuvers shall be feasible:
* roll around the satellite x-axis (\(\pm\)30\({}^{\circ}\)) to increase the Field of Regard (FoR); and
* pitch around the satellite x-axis (\(\pm\)30\({}^{\circ}\)) for stereo imaging.
* Design drivers of the attitude control subsystem are the accuracy requirements for the orientation and stabilization of the \"Line of Sight (LoS)\" of the FireBIRD multi-spectral camera systems, i.e., the main payloads. The main accuracy requirements for attitude control and orientation determination of TET-1 and BIROS are derived from the optical parameters of their main payloads and are detailed in the \"Mission Requirement Document\" [3] and in the \"Technical Specification Bus\" [13] and can be briefly described as follows:
* The orientation accuracy of the satellite: In the so-called Earth-Pointing Mode, the attitude control system must align the satellite with the multi-spectral cameras system, i.e., the +Z axis of the satellite in the body-fixed coordinate system, in the direction of nadir to Earth with an accuracy of 5 arcmin.
* The jitter of the alignment accuracy for Earth observation shall be less than 2 arcmin/sec.  
Figure 15: The BIROS propulsion system on top of the payload support structure, the main payload, and a secondary payload: the Single Picosat Launcher (SPL) with its open flap (marked in blue).  
The accuracy of orientation determination relative to Earth: The attitude control and navigation system shall determine the orientation of the satellite relative to the Earth with an accuracy of 24 arcsec.
* The accuracy of the position determination of an image point of the multi-spectral camera system on the Earth's surface in the coordinate system WGS 84 (World Geodetic System 1984) shall be \(<\)150 m.
# Automatic Building Height Estimation: Machine Learning Models for Urban Image Analysis
## 6 Case Study: Extraction of Building Height in Cityscapes  
### Results  
The semantic segmentation model achieved an 86.13% IoU metric (intersection-over-union) on the Cityscapes fine val set [26]. To illustrate the results, a case study with a Cityscapes image is presented and discussed. An image from the Cityscapes val set was run through the model as a (width \(\times\) height \(\times\) 3) matrix with values ranging from 0 to 1. The result is a (classes \(\times\) width \(\ast\) height \(\times\) 1) matrix ranging from 0 to 1. The values of the vector represent how likely the model considers a pixel to belong to a given class. In this case, there are three classes: sky, building and car. The vector is reshaped to the size (width \(\times\) height \(\times\) 1), there now being an integer representing the class with the highest probability for each pixel. The height vector is then obtained (Figure 9), comparing the pixels belonging to the building and the car class for each row, after discarding the columns with no pixels belonging to the sky class. In this example, having cleaned the height vector, the building height ranges between (3.8) and (4.7) with a mean of (4.34). This means that the buildings are around (4.34) times higher than a car in this street. Now, the predicted average building height for this street would be (4.34 \(\ast\) car height ). The car height that must be used would be calibrated through measurements of the city with buildings of known height. This method requires uniform street alignment and sufficient visible sky in the image. As street view images are cheap and simple to obtain, erroneous measurements from images with moving cars or a non-uniform street could be statistically discarded. This method could provide average building heights on a street with a maximum error of 3 m (around 1 floor) and a relative error of 9% in a given city with uniform streets.  
Figure 9: _Cont._
# Evaluation of the Acceleration Vibration Signal for Aggregates of the Horizontal Drilling Stand
## 3 Theoretical Background and Calculation
### Time-Frequency Domain Vibration Analysis  
When processing signals whose nature changes rapidly over time, it is often useful to consider the frequency content of the short signal segments. It makes it possible to formulate a more general spectrum as a two-dimensional function, dependent not only on frequency but also on time position. The time-frequency analysis is practically based only on the finite signal segments. Classical frequency analysis is about determining the harmonic components of different frequencies [50; 51; 52]. However, in time-frequency analysis, it is a matter of the most accurate localization of the occurrence of the signal's frequency components, both frequency, and time data. The spectrogram as a basis of time-frequency analysis is one of the important tools of technical diagnostics. The spectrogram expresses changes in the power spectrum of the signal over time. In the case of evaluating a vibration signal, time-frequency analysis can be useful in examining changes in the spectral properties of the signal due to a change in the properties of the object being monitored. The graphical representation of the spectrogram is in the form of image information. It can be understood as a highly integrative source of information. Its information value is high compared to simpler forms of information. The spectrogram of random vibration signal is a sequence of spectra calculated one after the other. Thus, a spectrogram can be expressed as a time sequence of its power spectra, where these power spectra have the structure of \(N/2\) element vectors (7).  
\[S_{xx}(f_{k},j)=\Big{[}S_{xx}(f_{0},j),S_{xx}(f_{1},j),\dots,S_{xx}\Big{(}f_{ \frac{N}{2}-1},j\Big{)}\Big{]}. \tag{7}\]  
The elements \(S_{xx}(f_{k},j)\) of the power spectrum are the file mean values associated with the \(j\)-th time moment and the \(k\)-th power frequency component of the signal \(f_{k}\). Parameter \(N\) represents the number of samples in the segment of this signal.
## 4 \({}^{29}\)Si MAS NMR Analysis
### Calculation of Conversion Fraction based on \({}^{29}\)Si MAS NMR Results
\[\sum\
olimits_{Q}=400\bigg{(}\frac{0.54}{0.228}+\frac{0.21}{0.172}\bigg{)}+70 \frac{0.612}{0.06}+445\frac{0.8}{0.06}(0.352)=4238\text{ from Equation }(2) \tag{6a}\] \[\sum\
olimits_{Q3,\ 4,\ Sand}=452\frac{0.8}{0.06}(0.352)=2089\text{ from Equation }(3)\] (6b) \[\sum\
olimits_{Q4,\ Pozzolans}=70\frac{0.612}{0.06}=714\text{ from Equation }(4)\] (6c) Theoretical \[Q^{3}+Q^{4}\] fraction by sand is \[\frac{2089}{4238}=0.493\text{ from Equation }(3)/\text{Equation }(2)\] (6d) Theoretical initial \[Q^{4}\] fraction by pozzolan is \[\frac{714}{4238}=0.168\text{ from Equation }(4)/\text{Equation }(2)\] (6e) \[Q^{3}+Q^{4}=60\text{ \% from NMR result for FA15}\] (6f) Table 4.0 fraction by remaining fly ash is Table 4.1 (6g) Table 4.2  
As shown in Equation (6), firstly, \(\Sigma Q\) can be obtained from Equation (2). Similarly, \(\Sigma Q_{3,4,Smd}\) and \(\Sigma Q_{4,Pozzolans}\) can be evaluated from Equations (3) and (4). The \(Q^{3}\) and \(Q^{4}\) from the NMR results shown in Table 4 include the \(Q^{3}\) and \(Q^{4}\) of the natural sand. Thus, the \(Q^{3}\) and \(Q^{4}\) in the remaining pozzolanic materials can be obtained by removing the \(\Sigma Q_{3,4,Smd}\). Finally, the conversion fraction of the pozzolans can be obtained based on the initial silicate tetrahedrons in the pozzolans, \(\Sigma Q_{4,Pozzolans}\), with the \(Q^{3}\) and \(Q^{4}\) of the pozzolanic materials remaining after the reaction. From the calculation results, the conversion fractions of the FA15, SF35, and NS20 specimens were 36.3%, 83.1%, and 97.4%, respectively. Thus, it can be concluded that most of the silica fume and nanosilica took part in the pozzolanic reaction.
# Integration of Organic Waste for Soil Stabilization through MICP
## 3 Results
### Properties of MICP-Treated Soil Samples with Waste Additives
#### 3.2.2 Calcium Carbonate Formation during MICP Treatment of Soil Samples with Additives  
Soil samples destroyed after compressive strength testing were analyzed for precipitated calcite content (Figure 7). The highest percentage of precipitated CaCO\({}_{3}\) was found in the sample with shredded wastepaper (Figure 7B), which was almost 1.5 times higher than that for the soil sample without additives (Figure 7A). The lowest percentage of precipitated calcium carbonate was found in the soil sample with sawdust additive (Figure 7D). The introduction of only a CS into soil samples without the addition of a bacterial consortium also led to an increase in the calcium carbonate content in all samples.  
Our results demonstrate that MICP treatment of soil led to a significant increase in the compressive strength and calcium carbonate content in all samples. Soil treatment with a CS only also increased the strength and CaCO\({}_{3}\) content of the samples, although to a lesser extent. Therefore, we can conclude that endogenous soil bacteria also have bioccmentation potential. However, analysis of the effect of additives used in this study  
Figure 6: Values for compressive strength (Rc) of MICP-treated soil samples with different additives: (**A**) without additives, (**B**) wastepaper, (**C**) flax shives and (**D**) sawdust. Soil samples treated with water and cementation solution (without MICP) were used as controls. * Significantly different (\(p<0.05\)).  
clearly showed that only shredded wastewater algorithm into MICP-treated soil samples increased strength and calcite content compared to samples without additives. On the contrary, the introduction of flax shives and sawdust into the MICP-treated soil noticeably reduced the strength characteristics of the samples.
## 3 Problem Formulation  
### System Model  
In the edge network, we divide the large model inference tasks, which require a huge amount of computation, into many subtasks based on the model mentioned in Section 3.2 to match the computing power of our edge servers, and the system model described in this chapter is used to offload these subtasks to the servers. In order to clearly demonstrate our partitioning and offloading methods, the following discussion will use CNN inference tasks as examples.  
As shown in Figure 1, we demonstrate a multi-user multi-edge server-edge computing system. These edge servers are devices installed on wireless access points, with computing and storage capabilities similar to the cloud. In this edge computing system, we have multiple CNN inference tasks that need to be offloaded to the server for processing, denoted as: \(U=\{U_{1},U_{2},\ldots U_{n}\}\), and an edge network: \(S=\{S_{1},S_{2},\ldots S_{m}\}\) connected by multiple edge servers.  
On the edge network S, there are edge servers \(S_{m}\), and each edge server can be expressed as \(S_{i}=\{P_{i},M_{i},N_{i},P_{i,com}\}\), where \(p_{i}\) represents the computation frequency of the \(i\)-th server's core, \(m_{i}\) represents the storage resources of the \(i\)-th server, \(n_{i}\) represents the number of cores of the \(i\)-th server, and \(P_{com}\) represents the working power of this server when performing inference tasks.  
In this scenario, the CNN inference tasks arrive in real-time, and we use \(t_{aj}\) to represent the arrival time of each large task. Additionally, we divide each large task into smaller subtasks for easier processing, denoted as: \(U_{j}=\left\{t_{j}^{1},t_{j}^{2},\ldots t_{j}^{2}\right\}\), where \(t_{j}^{K}\) represents the \(k\)-th task (this task is a divided subtask) of the \(j\)-th user. These tasks can also be expressed as \(t_{j}^{k}=\left\{C_{j}^{k},d_{j}^{k},X_{j}^{k}\right\}\), where \(C_{j}^{k}\) represents the computing resources required for this task, \(d_{j}^{k}\) represents the data volume of this task, and \(X_{j}^{k}\) is an m-dimensional decision variable, \(X_{j}^{k}=\left\{x_{j,k}^{1},x_{j,k}^{2},\ldots x_{jk}^{m}\right\}\). When the value is 1, it indicates that the \(k\)-th task of the \(j\)-th user is offloaded to the \(i\)-th server, and when the value is 0, it is the opposite. In addition, we have also created a \(y_{j}\) variable to represent the server closest to the \(j\)-th user in order to find the shortest propagation delay.