esa-sceva/satcom-chunk-collection huggingface dataset:
# DimAug-TimesFM: Dimension Augmentation for Long-Term Cloud Demand Forecasting in Few-Shot Scenarios
## 4 Proposed Method
### Dimension-Augmented TimesFM
#### 4.2.2 Implementation of DimAug-TimesFM  
To mitigate the performance degradation of TimesFM caused by distributional discrepancies in cloud resource demand forecasting, we propose DimAug. This method enhances the training set by incorporating relevant cross-pool information while preserving the integrity of the time series pattern, which encompasses two key steps, as illustrated in Figure 4:  
1. **Alignment and Adjustment of Resource Pool Data:** Let \(\mathcal{D}_{\text{target}}=\{y_{t}\}_{t=1}^{T}\) denote the univariate time series of the target resource pool, where \(T\) represents the total observation length. We first define a critical parameter \(L_{\text{train}}\) (training window length) based on the target resource pool's delivery period characteristics. For auxiliary resource pools \(\{\mathcal{D}_{i}\}_{i=1}^{N}\), their delivery period sequences \(\{x_{i,t}\}_{t=1}^{L_{i}}\) are processed as follows: \[\mathcal{D}_{i}^{\text{aligned}}=\begin{cases}\text{TimesFM}(\{x_{i,t} \}_{t=1}^{L_{i}})\rightarrow\{x_{i,t}\}_{t=1}^{L_{\text{train}}},&\text{if }L_{i}<L_{\text{train}}\\ \text{RandomSegment}(\{x_{i,t}\}_{t=1}^{L_{i}},L_{\text{train}}),& \text{if }L_{i}\geq L_{\text{train}}\end{cases}\] (1) where \(\text{TimesFM}(\cdot)\) generates synthetic values to extend short sequences, and \(\text{RandomSegment}(\cdot)\) extracts contiguous subsequences of length \(L_{\text{train}}\). This ensures uniformity in temporal resolution across heterogeneous pools.
2. **Differential Processing and Similarity Matching:** For each aligned series \(\mathcal{D}_{i}^{\text{aligned}}\), we compute its differenced representation \(\Delta x_{i,t}=x_{i,t}-x_{i,t-1}\) to emphasize trend dynamics. The similarity between the target resource pool \(\Delta y_{t}\) and auxiliary resource pool \(\Delta x_{i,t}\) is quantified using Dynamic Time Warping (DTW): \[\text{DTW}(\Delta y,\Delta x_{i})=\min_{\mathbf{P}}\sum_{(m,n)\in\mathbf{P}} \left|\Delta y_{m}-\Delta x_{i,n}\right|^{2}\] (2) where \(\mathbf{P}\) is a warping path adhering to boundary, continuity, and monotonicity constraints. The top-\(K\) most similar resource pools \(\{\mathcal{D}_{i}^{\text{aligned}}\}_{i=1}^{K}\) (minimizing DTW) are selected and concatenated as exogenous variables: \[\mathbf{X}_{\text{aug}}=\left[\Delta y\,\|\,\Delta x_{1}\,\|\,\cdots\,\|\, \Delta x_{K}\right]\in\mathbb{R}^{L_{\text{train}}\times(K+1)}\] (3) This multi-dimensional input enables the model to capture cross-pool temporal dependencies while retaining univariate forecasting capability.  
Figure 4: The workflow of _DimAug-Timesfm_. The method processes target and auxiliary resource pools through (a) time series padding or segmentation, to standardize sequences to \(L_{\text{train}}\) length (Equation (1)); (b) DTW similarity calculation between pools (Equation (2)); and (c) dimension augmentation by integrating the K most similar pools with the target pool (Equation (3)).
## 2 System Model
### Limited Feedback Method with a Two-Stage Precoder
In this case, the quantized CDI of \(\mathbf{w}_{g,k}\) in (14), i.e., \(\hat{\mathbf{w}}_{g,k}\), is obtained by  
\[\hat{\mathbf{w}}_{g,k}=\underset{\mathbf{c}\in\mathcal{C}_{g,k}}{\arg\max}\ \cos^{2}\Big{(}\angle(\hat{\mathbf{w}}_{g,k},\mathbf{c})\Big{)}=\underset{ \mathbf{c}\in\mathcal{C}_{g,k}}{\arg\max}\ \Big{|}\hat{\mathbf{w}}_{g,k}^{\dagger}\mathbf{c} \Big{|}^{2}, \tag{16}\]  
where \(\hat{\mathbf{w}}_{g,k}=\frac{\mathbf{w}_{g,k}}{\|\mathbf{w}_{g,k}\|}\). Then, the quantization error denoted by \(Z_{g,k}^{\hat{\mathbf{w}}}\in[0,1]\) is defined as  
\[Z_{g,k}^{\hat{\mathbf{w}}}\triangleq 1-\Big{|}\hat{\mathbf{w}}_{g,k}^{ \dagger}\hat{\mathbf{w}}_{g,k}\Big{|}^{2}. \tag{17}\]  
For an arbitrary codeword \(\mathbf{c}\in\mathcal{C}_{g,k}\) in (16), the value \(\Big{|}\hat{\mathbf{w}}_{g,k}^{\dagger}\mathbf{c}\Big{|}^{2}\) follows the beta distribution with parameters \((s_{g}-1,1)\) because it is the square of the inner product of two independent and isotropic unit-norm random vectors in \(\mathbb{C}^{s_{g}}\)[6; 7]. Consequently, a quantization error using \(B\)-bits RVQ, i.e., \(Z_{g,k}^{\hat{\mathbf{w}}}\) in (17), is the minimum of \(2^{s_{g}}\) independent beta distributed random variables, of which the complementary cumulative density function (CDF) is given by \(\Pr\left[Z_{g,k}^{\hat{\mathbf{w}}}>z\right]=\left(1-z^{s_{g}-1}\right)^{2^{B}}\), and the expectation is bounded as [6; 7]  
\[\mathbb{E}\left[Z_{g,k}^{\hat{\mathbf{w}}}\right]<2^{-\frac{B}{s_{g}-1}}. \tag{18}\]  
After receiving the feedback of \(\hat{\mathbf{w}}_{g,k}\), the BS obtains the quantized CDI of the dimension-reduced effective channel according to (14) given by  
\[\hat{\mathbf{h}}_{g,k}^{\text{eff}}=\frac{\mathbf{\Omega}_{g}\Sigma_{g} \hat{\mathbf{w}}_{g,k}}{\|\mathbf{\Omega}_{g}\Sigma_{g}\hat{\mathbf{w}}_{g,k} \|}, \tag{19}\]  
and the quantization error of the dimension-reduced effective channel \(\hat{\mathbf{h}}_{g,k}^{\text{eff}}\) denoted by \(Z_{g,k}^{\hat{\mathbf{h}}\text{eff}}\in[0,1]\) is defined as  
\[Z_{g,k}^{\hat{\mathbf{h}}\text{eff}}\triangleq 1-\bigg{|}\left(\hat{ \mathbf{h}}_{g,k}^{\text{eff}}\right)^{\dagger}\hat{\mathbf{h}}_{g,k}^{\text{ eff}}\bigg{|}^{2}. \tag{20}\]  
Note that the distribution of \(Z_{g,k}^{\hat{\mathbf{h}}\text{eff}}\) in (20) can be different from the distribution of \(Z_{g,k}^{\hat{\mathbf{w}}}\) in (17) (i.e., beta distribution) since the quantized CDI of the dimension-reduced effective channel is projected by the covariance matrix and the outer precoder.
## 2 Methodology
### Data Preprocessing  
Steps for preprocessing of the original vector map primarily include map blocking and feature vertex extraction. Map blocking refers to dividing the map into blocks for subsequent uses of embedding watermarks into each individual block, which is helpful to defend against map cropping attacks. Feature vertex extraction is to extract stable vertices that are key elements in vector maps for subsequent uses of watermark construction [42; 43].  
The used method of map blocking directly impacts on the distribution and capacity of the watermarks. Different algorithms of map blocking have been developed, primarily including uniform blocking (UNIF), quadtree blocking (QUAD), and modified quadtree block (MQUAD) [1]. UNIF divides the vector map into several rectangles with equal grid areas. QUAD and MQUAD divide the vector map adaptively by quarters to force the vertex number in each block to be greater than prescribed thresholds. Considering the complexity and inhomogeneity in the spatial distribution of vertices, this study applies the vertex-constrained blocking method [47], a UNIF-based method, to split vector maps into blocks. The method continues to divide the maps into blocks until the vertex number within individual blocks are less than a given threshold. The initial block area \(S\) and the threshold of vertex number \(P\) are important to map blocking. If the values of \(S\) and \(P\) are set too large, the number of watermarks in the data will be reduced, and if the values of \(S\) and \(P\) are set too small, the blocking efficiency is low and the blocks are too scattered. Compared with typical UNIF or QUAD methods,the vertex-constrained blocking method generates reasonably distributed blocks that contain similar vertex numbers as well as evenly distributed watermarks in subsequent analysis.  
Feature vertices of vector maps are key elements that contain the major feature information of the map and are not removable under attacks. The less vulnerable the selected feature points, the stronger the robustness of the corresponding watermark. Extraction of feature vertices could be viewed as the process to remove non-feature vertices. In this study, the Douglas-Peucker algorithm, a typical algorithm for simplifying line features, is used to extract feature vertices. Line simplification is also the basic method of vector map compression, the greater the threshold set by the Douglas-Peucker algorithm, the stronger the anti-compression attack ability of the watermarking scheme, but the fewer feature vertices obtained, the less the watermark capacity. Therefore, before zero-watermark is constructed, an appropriate distance threshold value needs to be selected through trial and error to ensure that there are sufficient and stable watermark information in each block. On this basis, the non-feature vertices can be obtained by erasing the feature vertices of the original vector map.
## 2 Materials and Methods
### In Silico Screening of the Typical 2-Cys Prx and Phylogenetic Analyses  
The sequences of the _S. cerevisiae_ Tsa1 (UniProtKB: P34760), Tsa2 (Q04120), _Salmonella typhimurium_ AhpC (P0A251), and _Staphylococcus aureus_ AhpC (P0A0B7) were used as eukaryotic and prokaryotic prototypes of typical 2-Cys Prx containing Thr or Ser in the catalytic triad. These amino acid sequences were used as query against TREMBL database using blastp [54] with a cutoff e-value of 10\({}^{-3}\). Then, to minimize misidentification issues, we identified Pfam domains out of the putative unique orthologs and retained only those presenting the domains found in Tsa1 and Tsa2, AhpC/Prx1 (Pfam: PF00578), redoxin (PF08534) and 1-Cys Prx_C (PF10417) with hmmscan (v.3.1b2) [55]. Sequences presenting other domains, except for SCO1-SenC (PF026300), which we observed in the _S. aureus_ AhpC, were excluded. Additionally, because Prx superfamily presents a conserved active-site cysteine P-X-X-X-(T/S)-X-V-C-P-T-E motif [56], known as C\({}_{p}\)-loop [20], we removed sequences lacking this motif. After these filtering steps, we retrieved taxonomic information of the accessions we obtained from the NCBI taxonomy database (database resources of the National Center for Biotechnology Information; June 2018).
## 2 Article Structure Description and Aircraft Dynamics Modeling
### Chirp-Z Transform  
When the time domain data are converted to the frequency domain, the most commonly used method is the Discrete Fourier Transform (DFT) method, but the DFT method has some shortcomings for aircraft real time identification. Because it is usually necessary to pay attention to a small range of frequency bands, while DFT will introduce interference information, the Chirp-z Transform (CZT) method is used here.  
The starting point \(z_{0}\) of the CZT transform can be arbitrarily selected, so the narrow-band high-resolution analysis of the input data can be performed from any frequency, and the frequency resolution can be adjusted as needed, so that any frequency band of interest can be accurately analyzed.  
The results of the CZT transform are shown in Figure 3, where the red dots cover only one frequency range of interest.  
The steps of the CZT transform can be expressed as follows:  
Figure 3: CZT transform effect illustration.  
First, two L-point sequences \(g\left(n\right)\) and \(h(n)\) are constructed as follows:  
\[g(n)=\left\{\begin{array}{c}A^{-n}W^{\frac{n^{2}}{2}}\times(n),0\leq n\leq N-1 \\ 0,\ \ N\leq n\leq L-1\end{array}\right. \tag{11}\]  
\[h(n)=\left\{\begin{array}{c}h(n)=W^{-\frac{n^{2}}{2}},0\leq n\leq M-1\\ h(n-L)=W^{-\frac{(n-1)^{2}}{2}},M\leq n\leq L-1\end{array}\right. \tag{12}\]  
where L satisfies \(L\geq N+M-1\), and \(L=2^{M}\), \(M\) is an integer.  
The Fourier transform \(G\left(k\right)\) and \(H\left(k\right)\) of \(g\left(n\right)\) and \(h\left(n\right)\) are calculated, respectively, and \(Y\left(k\right)=G\left(k\right)H\left(k\right)\).  
Then, the L-point inverse Fourier transform of \(Y\left(k\right)\) is calculated by:  
\[X(z_{k})=W^{-\frac{n^{2}}{2}}y(n) \tag{13}\]  
In order to achieve the effect of real-time identification, the conventional CZT transform process needs to combine the recursive finite Fourier transform mentioned in [17]:  
\[X_{i}(\omega)=X_{i-1}(\omega)+x(i)e^{-j\omega i\Delta t} \tag{14}\]  
\[e^{-j\omega i\Delta t}=e^{-j\omega\Delta t}e^{-j\omega(i-1)\Delta t} \tag{15}\]
# Supervised Segmentation of NO2 Plumes from Individual Ships Using Troppomi Satellite Data
###### Abstract  
The shipping industry is one of the strongest anthropogenic emitters of NOx--a substance harmful both to human health and the environment. The rapid growth of the industry causes societal pressure on controlling the emission levels produced by ships. All the methods currently used for ship emission monitoring are costly and require proximity to a ship, which makes global and continuous emission monitoring impossible. A promising approach is the application of remote sensing. Studies showed that some of the NO2 plumes from individual ships can visually be distinguished using the TROPOspheric Monitoring Instrument on board the Copernicus Sentinel 5 Precursor (TROPOMI/SSP). To deploy a remote-sensing-based global emission monitoring system, an automated procedure for the estimation of NO2 emissions from individual ships is needed. The extremely low signal-to-noise ratio of the available data, as well as the absence of the ground truth makes the task very challenging. Here, we present a methodology for the automated segmentation of NO2 plumes produced by seagoing ships using supervised machine learning on TROPOMI/SSP data. We show that the proposed approach leads to more than a 20% increase in the average precision score in comparison to the methods used in previous studies and results in a high correlation of 0.834 with the theoretically derived ship emission proxy. This work is a crucial step towards the development of an automated procedure for global ship emission monitoring using remote sensing data.  
TROPOMI/SSP satellite; NO2; maritime shipping; supervised learning; remote sensing application; ship plume segmentation +
Footnote †: journal: Remote Sensing
## 4 Analysis of Simulation Findings  
In this study, the MATLAB2021b/Simulink environment is used to create the motor simulation model. The simulation model of PMSM speed control based on Fuzzy-ADRC under small disturbance is shown in Figure 5. The simulation time of the system is set to 0.5 s, and the variable step size automatic solving algorithm is selected. The main modules used in the simulation model are a DC power supply, a general-purpose bridge, a PMSM motor module, a Clark coordinate transformation module, a Park coordinate transformation module, an inverse Park coordinate transformation module, an SVPWM module, a PI controller, an ADRC module, FC, and so on.  
Figure 5: Simulation model of PMSM speed control based on Fuzzy-ADRC under small perturbation.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameters** & **Numerical Value** & **Parameters** & **Numerical Value** \\ \hline \(\beta\) & 500 & \(r_{0}\) & 100,000 \\ \(K_{pu}\) & 0.004 & \(\delta\) & 0.01 \\ \(K_{iu}\) & 2 & \(\alpha\) & 0.95 \\ \(K_{pd}\) & 120.54 & \(\beta_{1}\) & 55,000 \\ \(K_{pq}\) & 120.54 & \(\beta_{2}\) & 36,000 \\ \(K_{id}\) & 70,440 & \(\beta_{3}\) & 53,000 \\ \(K_{iq}\) & 70,440 & \(b\) & 5000 \\ \(h\) & \(10\times 10^{-4}\) & \(K\) & 4 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Summary of control parameters for the simulation model.  
An actual PMSM is chosen for simulation in this research to ensure that the algorithm is functional. The motor model is 60 ST-M00630, and Table 4 lists the details of the motor.
## 2 Related Works
However, in wireless communications, the time intervals between transmission events are different depending on the used MCS levels because their transmission durations are decided by the selected MCS levels. To overcome this problem, the authors of G-ORS proposed sliding-window G-ORS (SW-G-ORS) in [20] that adopts a sliding window over time. They also suggested approximation methods for the implementation of the sliding window over time to reduce its computational cost. In [32], the authors proposed to use RSSI and ACK/negative ACK (NACK) signals to detect the changes of the channel status. Then, the results were used for a D-UCB-like rate adaptation. In [33], the authors proposed an adaptive online Bayesian learning to detect changes in non-stationary communication channels. The rate adaptation is conducted with a Thompson sampling-based MAB algorithm. Nonetheless, in our proposed MAB algorithms, we directly use the sliding window over time because it is simple and the time interval we use for the sliding window is not large (e.g., a few tenths of a second) considering the simulated network environments, as shown in Sections 4 and 5. Thus, we explicitly include the usage of the sliding window in the proposed MAB algorithms as described in Section 3. The contributions of this paper can be summarized as follows:  
* We propose the use of log values of data transmission rates in MAB algorithms for the rate control in CSMA/CA-based wireless networks. * We propose two MAB algorithms that embrace the use of the frame aggregations developed for high-throughput wireless standards (explained in Section 3). * We evaluate the performance of the proposed MAB algorithms using the ns-3 network simulator for IEEE 802.11g and 802.11 ax wireless networks with various scenarios.
## 2 Materials and Methods
### Outdoor Setup  
A stainless-steel tank located at the Hera WWTP (Ferrara, Italy) was used for the outdoor experiment. The tank was positioned 1 m above ground level and was equipped with an adjustable cover to prevent contamination from the environment. Natural light was supplemented with white LED lamps (final mean PAR during daytime: 30 \(\upmu\)mol\({}_{\text{photons}}\) m\({}^{-2}\) s\({}^{-1}\), 16:8 h L:D). Data concerning average hourly air temperature 2 m above ground (\({}^{\circ}\)C), along with average, minimum and maximum daily temperature 2 m above ground (\({}^{\circ}\)C) for the control unit located in Malborghetto (Ferrara, Italy, 44\({}^{\circ}\)85\({}^{\prime}\)89.2\({}^{\prime\prime}\) N 11\({}^{\circ}\)65\({}^{\prime}\)62.5\({}^{\prime\prime}\) E) were downloaded from \"text3r\", a web app for the extraction of weather data from ARPAE-Emilia Romagna database (Agenzia Regionale per la Prevenzione, I'Ambiente e l'Energia of Emilia Romagna region, Italy). The control unit was chosen considering both geographical proximity to the WWTP and the completeness of the data recorded ([[https://simc.arpae.it/text3r/](https://simc.arpae.it/text3r/)]([https://simc.arpae.it/text3r/](https://simc.arpae.it/text3r/)), accessed on 5 June 2023) [80]. Trends during the 20-day experiment of the average, maximum, minimum, and midday daily temperature are depicted in Figure 1. An 85 L amount of _Chlorella_-like algae suspension, cultured as above, and 450 L of WW were mixed to obtain a final volume of 535 L and an OD\({}_{750}\) of 0.3. In the following days, the culture was mixed manually before collecting the samples.
# Enhanced Tone Mapping Using Regional Fused GAN Training with a Gamma-Shift Dataset
## 4 Experiment Results
### Step-by-Step Results of the Proposed Method  
In this section, each step result of the proposed method is analyzed. Figure 7 displays the images resulting from the gamma shift to the RFT process in the proposed model section. Figure 7a is an input image and a dark environment, but the lighthouse correspond to a bright area. The brightness around the ground and trees is extremely dark, making it difficult to identify objects. Figure 7b displays the results of learning the gamma shift dataset using the proposed model. The image becomes brighter overall, but the brightness around the lighthouse and window also becomes oversaturated and the boundaries of the objects are very blurred, indicating that the network has difficulty in learning the local brightness of the image. We accordingly use the RWL to induce local tone mapping learning. Results, such as those shown in Figure 7c, which would have learned the gamma shift data and RWL process, are observed. The brightness of the light part remains unchanged, whereas the brightness of the dark part of the ground and the wood part increases.  
However, from the tonal compression perspective, the brightness of the dark area should be slightly increased. The training module must allow the local training rate of the images to be regulated, so we used the RFT method as the next process. Figure 7d is the result of adding RFT, which is the final training process. The brightness of the lighthouse is significantly improved while maintaining its brightness due to RFT--a process that reduces the learning speed of the light region of the image while increasing the learning speed of the dark region.  
Figure 6: Mask R-CNN processing for closed-circuit television.  
Figure 7: Part of each method’s result: (**a**) input, (**b**) gamma shift augmentation, (**c**) gamma shift with regional weighted loss (RWL) result, and (**d**) gamma shift and RWL with regional fusion training result.
# WaQuPs: A ROS-Integrated Ensemble Learning Model for Precise Water Quality Prediction
## 2 Related Works
It is crucial to clarify that the proposed work is primarily theoretical, offering a proposal for future research directions, and it does not present concrete evaluation results or empirical findings. For a practical examination of the subject, [PERSON] and [PERSON] [19] have undertaken a separate study in which they systematically assess and synthesize satellite data for predicting water quality. Their work consolidates findings from various literature sources, including Scopus, Web of Science, and IEEE, to assist environmental restoration programs in aligning with regulatory guidelines. They present several case studies that explore potential parameters and algorithms for predicting water quality, offering valuable insights into the intersection of technology and environmental policy.
# A Performance Analysis of VANETs Propagation Models and Routing Protocols
## 3 Proposed Work
### Propagation Models
#### 3.1.1 FRIS (Free Space Propagation Model)  
It is one of the earliest propagation models where radio waves move in an omnidirectional pattern from the source used in wireless networks, specifically in MANET. The radio waves move in free space without any obstacles, and the received power is computed upon power transmitted, antenna gain, and sender-receiver distance. This model revolves around a transmission range of the transmitter. If the receiver is present within the transmitter's range, the packet is received; otherwise, it is lost.  
\[P_{r}=\frac{P_{t}G_{t}G_{r}\lambda^{2}}{(4\pi d)^{2}L} \tag{1}\]  
Here, \(P_{t}\) is the transmit power (W), \(P_{r}\) is the reception power (W), \(\lambda\) is the wavelength (m). Transmit and receive gains are \(G_{t}\) and \(G_{r}\), respectively. The distance between transmitter and receiver is \(d\), and system loss is \(L\).  
As per Equation (1) [43], the received signal strength depends on the strength of the transmitted signal, the receiving and transmitting antenna gain, and the distance between the transmitter and the receiver.