esa-sceva/satcom-chunk-collection huggingface dataset:
# Optimizing IoT Video Data: Dimensionality Reduction for Efficient Deep Learning on Edge Computing
## 3 Materials and Methods
### Features  
With the advent of deep learning technologies, a wide range of pre-trained models are available for various modalities and purposes. For instance, in Natural Language Processing (NLP), models such as BERT [52], LLaMA [53], and Gemini [54] have been developed. These models are trained on extensive datasets and can capture and encode the most relevant information from an input, leveraging the knowledge acquired during their training phase.  
Similarly, for video analysis, several large pre-trained models can be used for feature extraction and classification tasks. These models have been trained on the Kinetics Dataset [55], enabling them to learn general patterns that can be effectively leveraged for transfer to other tasks, for example, bird behavior classification. These models rely on diverse backbone architectures, such as 3 DCNNs [56] and Video Transformers [57]. Examples of 3 DCNNs include ResNet3D [7] and Video S3D [8], while Video Transformers include models like Video Swin Transformer [9] and MViTv2 [10].  
The _Features_ dimensionality reduction technique involves utilizing these models to extract internal representations of videos, generating embeddings that encapsulate essential features. These embeddings serve as input into an MLP for the final classification task. This architecture processes a sequence of 16 frames, each with dimensions of \(16\times 3\times 224\times 224\), and transforms it into a single embedding with a size of 400. This transformation achieves a dimensionality reduction of over 6000 times compared to the original video sequence, substantially decreasing the input size while preserving critical information. An overview of the proposed architecture is shown in Figure 1.
## 3 Long-Term Durability of Sensor Coupling and Mounting and of the Measurement Equipment
### Durability of Electronic Data Acquisition Equipment for AE and GUIW
The service lifetime of electronic components, modules and systems can be extended, if components are not operated at the limit of their specified rating. This means that the design shall consider a certain amount of so-called \"derating\" with respect to electric and thermal loads. This may further require, e.g., specific cooling of the equipment in operation. Protection against electrostatic discharge, on the component and module level, is also recommended. Effects of ambient humidity or humidity variation during service operation may also play a role. These can be considered in the design phase by utilizing, e.g., the choice of the packaging rating of the components. If commercial AE and GUW equipment instead of in-house developed instrumentation are installed, there may be the question of how long the commercial manufacturer will provide equipment support and related services such as calibration, repair or extension and upgrades. Another aspect is long-term data storage, including both raw data and analysis results. Of course, all data storage shall have a backup. Whether cloud services are an acceptable option may be questionable. If AE or GUW monitoring effectively yields data at rates of \(1\) GBit/second or more, the data storage necessary becomes impressive. Again, data storage demands for the deep space network provide a nice example [117] for comparison.
# Cyclic Detectors in the Fraction-of-Time Probability Framework
## 2 Materials and Methods
### Cyclic Statistical Functions Estimators
#### 2.3.4 Cyclic Spectrum  
The cyclic spectrum (22) can be consistently estimated using the frequency-smoothed cyclic periodogram [1], [2] (Section 5.2.3), [44] (Chapter 13), [45] (Section 4.6) or with the time-smoothed cyclic periodogram [1] (Chapter 13), [2] (Section 5.2.4), [45] (Section 4.5), [46].  
Let \(\Delta f\) be the spectral frequency resolution of the cyclic spectrum estimator. This is coincident with the width of the frequency-smoothing window for the frequency-smoothed cyclic periodogram and with the reciprocal of the block-length for the time-smoothed cyclic periodogram. For both estimators, under mild assumptions, the normalized error estimates are asymptotically \((T\to\infty)\) complex normal, provided that \(\Delta f=\Delta f_{T}\to 0\) and \(T\Delta f_{T}\to\infty\).
# Drone Deployment Algorithms for Effective Communication Establishment in Disaster Affected Areas
###### Abstract  
Communication establishment is crucial for rescue operations in disaster affected areas. A standard tool for communication is the use of cell phones. However, they can be useless in situations where the cellular network's base stations are damaged in a disaster. A contemporary approach to re-establishing a communication network is by hosting base stations in drones. However, low battery life and difficulty in calculating the number of drones needed in different terrains are limitations of the above approach. This paper introduces a novel terrain-aware algorithm that calculates the minimum number of drones needed to cover an area with no voids in the network coverage. Our method ensures that the drones are deployed at optimal heights to maximize the average leftover energy in the network. We apply the algorithm for an actual location in _Pettimudi_, _India_ and find the optimal number and positions of the drones to cover the area effectively without voids. In addition, we provide a simulation of the the communication establishment using above drones, and our experiments yield an average network efficiency of 98%, showing the effectiveness of our method.  
d rone networks; drone-based disaster recovery; drone-based communication establishment; drone coverage filling algorithm Article
# A Complete Pipeline for Heart Rate Extraction from Infant ECGs
## 2 Materials and Results
### The HR Pipeline
#### 2.3.2 Correction for Missing and Additional Beats
The effects of the specific filter width and threshold are illustrated in Figure 12. The identified incorrect measurements were removed, and then estimated by local linear interpolation. If precise beat-to-beat comparisons are not required, a more liberal threshold over a wider beat-window can robustly account for noise. The first evaluation (Figure 12a) examines a clean ECG against a ground truth, and only deals with missing beats. The second evaluation (Figure 12b) compares the residual heart rate from a filter applied to the output of the ECG pipeline against a ground truth and represents the approach that should be taken with a less clean initial ECG.
## Materials and Methods
### Physical Property Evaluation
#### 2.3.2 Tensile Strength  
Compared with the Mullen burst pressure measurement, the tensile test provides more information through measurements. Therefore, a tensile test was performed to analyze the mechanical characteristics.  
The AMX membrane exhibits anisotropy with different strength characteristics in the longitudinal direction (MD: machine direction) and width direction (TD: traverse direction), owing to the characteristics of the woven backing fabric. In this study, only the MD was measured.  
A tensile test sample was cut from the alkali treatment sample that was well equilibrated with 0.5 N NaCl. The test specimen measured were 10 mm in width, 50 mm in length, and 20 mm in effective length. Tensile tests were conducted at a displacement rate of 50 mm/min to determine the tensile properties at room temperature. The thickness of each specimen was measured. A tensile tester \"Tensilon\" (manufactured by A & D, Tokyo, Japan) was used to perform the slack correction in a wet state. The stress and strain at break were calculated using Equations (2) and (3).  
\[\begin{array}{c}\text{Stress at break (N/mm}^{2})=\text{Load at break/Sample thickness/Sample width}\end{array} \tag{2}\]  
\[\begin{array}{c}\text{Strain at break (%) = Elongation at break/Effective sample length}\end{array} \tag{3}\]  
\[\begin{array}{c}\text{Young's modulus (N/mm}^{2})=\text{Slope of initial proportional section of stress-strain curve}\end{array} \tag{4}\]  
To discuss the changes in various physical properties after the alkali treatment, the stress, strain, area below the SS curve, and Young's modulus of each membrane were normalized by the stress at break, strain at break, area below the SS curve to break, and the Young's modulus of the pristine membrane.  
Figure 1: A Müllen burst pressure measuring device: (**top**) overall; (**left**) before test; (**center**) during test; (**right**) red indicator needle.
## 3 Objective Function  
The system consists of cascaded outer voltage and inner d-axis current and reactive power/q-axis current control loops. Due to the cascaded nature of the control system, it is highly required that the inner loop of the designed controller must follow the outer loop with a minimum amount of error. Generally, the errors are calculated using four renowned methods known as IAE, ISE, ITSE, and ITAE [15]. IAE and ISE put equal importance on error irrespective of the time, but ITSE and ITAE put different weight on error with respect to time due to the presence of time in the objective function. In ITSE, due to presence of square term, error drops drastically as the error converges to zero. ITAE is free from such a problem, as it puts different weight to error with respect to time without squaring the error. However, only using such criterions sometimes does not guarantee lesser peak overshoot. Hence, a peak difference term is also added in the objective function to minimize the peak overshoot along with ITAE.  
The proposed objective function is as follows:  
\[=\min\biggl{(}\mathit{Peak\ Difference}+\int_{0}^{d_{\mathit{dim}}}t\big{|}e(t) \big{|}dt\biggr{)}, \tag{10}\]  
where  
\[\text{Peak\ Difference}=\text{Peak\ Value\ of\ the\ response\ -\ Reference\ Value\ or\ final\ value},\]  
\[\text{and,\ }e(t)=\mathit{reference\ value\ -\ measured\ value}.\]  
Each portion of the Equation (10) is comprised of three PI/FOPI controllers' responses. That means three peak differences are calculated for outer voltage and inner current control loops (d- and q-axis), and that three ITAE are calculated as well.
## 2 Review of Cryogenic-Cooling Benefits
### Thermal Parameters
#### 2.2.4 Thermal Conductivity Temperature Dependence  
Many thermal conductivity models use the Debye high temperature limit of Equation (33). Since then \(C_{v}\) is independent of temperature, the phonon speed is nearly independent of temperature, and the mean free path is inversely proportional to the temperature \(T\), we find that in the high temperature region  
\[k_{2}=k_{1}\cdot\left(\frac{T_{1}}{T_{2}}\right)^{b} \tag{40}\]  
where \(k_{2}\) is the new thermal conductivity at temperature \(T_{2}\) relative to thermal conductivity \(k_{1}\) at temperature \(T_{1}\), and \(b=1\). This scaling rule is true only for \(T>>\theta_{\rm D}\). To test the validity of this rule, we have tried fitting Equation (40) to the undoped and 4 at. % and 15 at. % Yb data of [29]. The results are shown in Figure 7. For undoped YAG we have found that the best fit to the data is \(b=1.315\), a significant departure from Equation (40) with \(b=1\). For the 4 at. % Yb data, however, we find \(b=1\), and for the 15 at. % Yb data, \(b=0.8\). It is clear from this data that the exponent b in Equation (38) is significantly different than 1, a likely consequence of modeling data in the temperature range of about 100-300 K, whilst using a model that is strictly valid for \(T>>\theta_{\rm D}\). The decreased exponent for the doped cases, with b decreasing as doping is increased, is very likely due to the change in the Debye temperature as doping increases. Similar results may be obtained by using an exponential rather than Equation (40) to fit the data.
## 2 Circuit Implementation
### High-Frequency Path  
Figure 4 shows a schematic of the high-frequency path. The high-frequency path consists of a three-input transconductance stage and the [PERSON] integrator. The input, feedback, and low-frequency path output signals are converted to currents through Gm\({}_{11}\), Gm\({}_{12}\), and Gm\({}_{5}\), respectively, and entered into the [PERSON] integrator. At the Gm\({}_{3}\) stage, to achieve a fine-frequency response in the high-frequency path, nested Miller compensation is implemented [14]. To compensate for the drawbacks of the dead-band in the crossover area, a Monticelli class AB output stage is implemented (M17, M18, M19, and M20). It has the advantages of linear output characteristics and distortion reduction. Figure 5 shows a schematic of the high-frequency path common-mode feedback (CMFB) circuit. The gate voltage of transistors M13 and M14 is determined by the active CMFB circuit, which has the advantage  
Figure 3: Block diagram of the proposed current-feedback instrumentation amplifier (CFIA).  
of increasing bandwidth. To stabilize the output voltage, the CMFB circuit is also implemented at the output stage.
# Hybrid Window Decoding for Joint Source Channel Anytime Coding System
## Abbreviations  
The following abbreviations are used in this manuscript:  
AWGNadditive white Gaussian noise  
BERbit error rate  
BPbelief propagation  
BPSKbinary phase-shift keying  
CNscheck nodes  
CSIchannel state information  
D-LDPCdouble low-density parity check  
DP-LDPCdouble protograph LDPC  
JEWDjoint expanding window decoding  
JHWDjoint hybrid window decoding  
JSCCjoint source-channel coding  
JSCACjoint source-channel anytime coding  
LLRslog-likelihood ratios  
PJEWDpartial joint expanding window decoding  
PNsparity bit nodes  
INsinformation bit nodes  
SNRsignal to noise ratio  
SNssyndrome nodes  
SC-LDPCspatially coupled LDPC code  
SSIsource significance information  
SC-RAspatially coupled repeat-accumulate code  
UEPunequal error protection
###### Abstract  
Outflow boundaries induced by cold-pools are a key characteristic of convective systems related to microphysical and kinematic processes during the mature stage of their lifecycle. Over the ocean, such kinematic processes are associated with low-level wind modulations that are captured by scatterometers. This study investigates the ability of the Advanced Scatterometer (ASCAT) wind retrievals to detect the outflow boundary associated with an oceanic mesoscale convective system (MCS). Leveraging a new technique to identify cold pools that is based on features that enclose elevated magnitude of the gradient of the wind, termed as 'Gradient Feature' (GF), wind retrievals at 50-, 25- and 7-km spatial resolution were utilized to explore how the characteristics of the outflow boundary vary with resolution. Ground-based radar retrievals were also implemented to assess and correct, when possible, the performance of the ASCAT retrievals. The magnitude of the gradient of the wind for the coarser resolution was an order of magnitude smaller (\(10^{-4}\) s\({}^{-1}\)) than the finer ones (\(10^{-3}\) s\({}^{-1}\)). An increase in the magnitude of the gradient wind field associated with the outflow boundary was captured by all resolutions and a respective feature was identified by the GF method. The location of the features relative to the distance from the front edge of the MCS decreased with resolution, indicating the importance of the high resolution ASCAT product to capture their extent, as well as additional smaller scale features. The effect of the background wind field on the selection of the final wind field during the ambiguity removal process for the high-resolution product is also discussed.  
ocean surface winds; scatterometer; ASCAT; oceanic convection; mesoscale structures; outflow boundary; precipitation; radar +
Footnote †: journal: Remote Sensing  
1  
Using Scatterometer Winds at Different Spatial Resolutions
## 4 Experiments  
An anomaly detection dataset based on the full lifecycle data of CMGs on a spacecraft is produced for experimental validation of the proposed method. Existing spacecraft anomaly detection systems still rely mainly on manual evaluation. The dataset in the paper has been annotated with anomalies by qualified experts. CMG data were sampled every 10 days, for a total of 155 days of data. The CMG data is divided into 16 channels, including the gimbal motor current, rotor motor current, shaft temperature, speed, and other parameters. Since the data characteristics of each channel are different, the Sinc-LSTM network trains the anomaly detection task model separately for each channel data after pre-training. In the working condition classification stage, the data of each channel are first divided into hourly segments, and there are a total of \(155\times 24=3720\) segments. The data size of each segment after segmentation is about 16 channels and 6450 rows.  
Figure 5: Results (**a**) the original data and reconstructed sequences; (**b**) the smoothed result of the difference value.  
This section validates the effectiveness of each module of the proposed method and the advancement of the whole method, respectively, by setting up two sets of comparative experiments. The self-comparison experiments are conducted by ablating the work condition classification module, the migration learning method, and the SincNet filter layer of the model, and then comparing the experimental results to verify the contribution of each module to anomaly detection. The comparison experiments with state-of-the-art methods are conducted to verify the advancedness of the proposed method on the CMG dataset by comparing it with baseline and some of the latest anomaly detection methods.
## 4 Parameter Sensitivity Analysis
### Circumferential Grout Filling Phase
#### 4.1.2 Shield Tail Void Thickness \(b\)  
Based on existing studies, the shield tail void thickness ranges from 0.05 m to 0.2 m [20; 32; 38; 43]. The variation of the grout pressure \(P\) with the thickness of the shield tail void \(b\) is shown in Figure 11.  
When the thickness of the shield tail void \(b\) increases from 0.05 m to 0.1 m, the grout pressure increases slowly. This is because when the grout flows through the larger shield tail void, the average flow velocity of the grout will decrease. This means that the kinetic energy per unit volume of grout is lower. According to the conservation of energy, the total energy of the grout remains unchanged. Therefore, the lower the flow velocity, the greater the proportion of pressure energy, resulting in higher pressure. The grout pressure near the top (\(\theta=5^{\circ}\)), the sides (\(\theta=90^{\circ}\)), and the bottom (\(\theta=175^{\circ}\)) of the tunnel increases from 185.4 kPa, 273.0 kPa, and 365.7 kPa to 189.2 kPa, 279.1 kPa, and 371.5 kPa, respectively, with the increasing values of 3.8 kPa, 6.1 kPa, and 5.8 kPa.  
However, when the thickness of the shield tail void \(b\) increases from 0.1 m to 0.2 m, the grout pressure increases by 2.1 kPa, 3.0 kPa, and 2.0 kPa, respectively. The grout pressure tends to stabilize. As the shield tail void continues to increase, the rate of decrease in grout flow velocity will become smaller and smaller, which means that the trend of decreasing kinetic energy and increasing pressure will slow down. Therefore, 0.1 m can be set as a threshold value. When the thickness of the shield tail void reaches the threshold value, its effect on the grout pressure is limited. However, some studies have shown that a larger thickness of the shield tail void leads to larger grout settlement [44]. Therefore, the thickness of the shield tail void should be appropriately designed before tunnel construction to avoid extreme ground settlement.
## 3 Background
### Discrete Cosine Transform (DCT)  
To illustrate an image as the sum of data points, the discrete cosine transform (DCT) is frequently used. The variable magnitudes and frequencies of sinusoids are shown here [58]. Separating an image into its component sub-bands or spectral sub-bands allows for more control over the image's overall visual quality. If one wants to move a signal or image from the spatial domain to the frequency domain, one can use the discrete cosine transform (DCT). The image can be separated into its high- and low-frequency components. Reflectance is reflected in the low-energy, high-frequency component. The image's brightness is conveyed via the low-frequency component, which is also quite energetic. Images may be compressed and watermarked using DCTs, just to name a couple of their many uses. The function of the DCT is shown in Figure 2. With the DCT, an image in the spatial domain is transformed into one in the frequency domain. Aside from its low mistake rate, it also features a high compression ratio and computational complexity. The following equation is used to get the DCT of an \(N\times M\) image.  
\[F(i,j)=\sqrt{\frac{2}{M}}\sqrt{\frac{2}{N}}G_{i}.G_{j}\sum_{i=0}^{M-1}\sum_{j -0}^{N-1}I(m,n)cos\Big{[}\frac{\pi u}{2M}(2m+1)\Big{]}cos\Big{[}\frac{\pi u}{2 N}(2n+1)\Big{]} \tag{1}\]  
where  
\[\begin{split} G_{i}&=\begin{cases}\frac{1}{\sqrt{2}} &\text{if }i=0\\ 1&\text{otherwise}^{\prime}\\ \end{cases}\\ G_{j}&=\begin{cases}\frac{1}{\sqrt{2}}&\text{if }j=0\\ 1&\text{otherwise}^{\prime}\\ \end{cases}\\ \end{split} \tag{2}\]  
The reconstruction of the image is achieved by applying the following equation:  
\[I(m,n)=\sqrt{\frac{2}{M}}\sqrt{\frac{2}{N}}\sum_{i=0}^{M-1}\sum_{j-0}^{N-1}G_ {i}.G_{j}.F(i,j)cos\Big{[}\frac{\pi u}{2M}(2m+1)\Big{]}cos\Big{[}\frac{\pi u}{ 2N}(2n+1)\Big{]} \tag{3}\]It is common practice to apply the DCT to each non-overlapping block of an image when using the popular block-based DCT transform. There are, therefore, three distinct frequency ranges resulting from this process: the low-frequency range, the intermediate-frequency range, and the high-frequency range. The foundation for DCT-based watermarking comprises a couple of simple facts. In the first place, an image's most crucial visual aspects are located in the low-frequency sub-band, where much of the signal energy is. The second reality is that compression and noise attacks typically get rid of the high-frequency components of an image. Therefore, the watermark is encoded by altering the coefficients of the middle-frequency sub-band, guaranteeing that it will remain in place through decompression and viewing.
## 5 Conclusions  
This study addresses long-range, low-cost coverage communication problems, such as those found in emergency communications. In the case of a UAV integrating green energy MEC networks, considering UAV energy levels and offload reliability, where UAV contains relay and edge server functions, a MENROM on the basis of the MDP algorithm is presented. Compared with other offloading methods, the multilevel offloading model reduces the energy burden of the UAV, obtains higher system utility, and provides a more reliable offloading strategy. Nevertheless, a green energy UAV can have longer working hours. More scientific optimization of UAV resources can maximize the system resources and improve efficiency, and these aspects are also the directions of subsequent research.  
Conceptualization, Z.C. and N.X.; methodology, Z.C. and N.X.; validation, Z.C., N.X.; formal analysis, N.X.; investigation, D.H.; resources, Z.C.; data curation, N.X. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Natural Science Foundation of China (Nos. 61601182 and 61771195), Natural Science Foundation of Hebei Province (F2017502059 and F2018502047), and in part by the Fundamental Research Funds for the Central Universities under Grant 2019 MS088.  
The authors declare no conflict of interest.