esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Materials and Methods
### Ground Data  
Observed precipitation is used to validate the IMERG products and to act as an input into the SWAT model. In this study, 31 precipitation gauges from 2010 to 2016 were collected from the Malaysian Meteorological Department (MMD) and Department of Irrigation and Drainage (DID) Malaysia. Distribution of these precipitation gauges is shown in Figure 1. Daily precipitation data of MMD was collected at 08:00 a.m. local time [29], which is similar to the daily IMERG products collection time at 00:00 Coordinated Universal Time (UTC). Meanwhile, DID's daily precipitation data were reproduced by summing the hourly precipitation data at 00:00 UTC to match with the IMERG products. In January 2015, Malaysia only supplied precipitation data from 24 gauges for the GPCC product development [7]. Out of these precipitation gauges, only one station is located in the northern region of the Kelantan state. This precipitation gauge is located outside of the KRB, so we assumed that all of the 31 precipitation gauges are independent from the gauge-adjusted IMERG_F product.  
Daily maximum and minimum temperature data are available at the Kuala Krai station (longitude: 102.2\({}^{\circ}\)E and latitude: 5.53\({}^{\circ}\)N). Daily precipitation, maximum, and minimum temperature are the main climate inputs to the SWAT model. In addition, SWAT modelling also requires a DEM, a land use map, and a soil map as geospatial inputs. [PERSON] et al. [30] reported that the SRTM DEM is suitable to be used in SWAT modelling for Malaysian river basins, so the latest SRTM DEM with a spatial resolution of 30 m was used. The land use and soil maps were collected from the Ministry of Agriculture and Agro-based Industry Malaysia (MOA). In addition, the river network data of KRB was digitized from the topographic map collected from the Department of Survey and Mapping Malaysia (JUPEM). The digitized river network was burned into the DEM during the basin delineation to improve the delineation process and river network formation in low elevation regions. Streamflow data at Jambatan Guillermard (Figure 1) was collected from DID for SWAT calibration and validation purposes. Averaging of daily streamflow data at 00:00 UTC was performed to match with the daily precipitation gauges and the IMERG products.
## 4 Results
### Evolutions of SKPIs
\begin{table}
\begin{tabular}{l l l l l} \hline \hline \multicolumn{1}{c}{\multirow{2}{*}{**Year 2015**}} & \multicolumn{2}{c}{**Year 2016**} & \multicolumn{2}{c}{**Year 2017**} & \multirow{2}{*}{
\begin{tabular}{l} **Variance** \\ **Explained** \\ \end{tabular} } \\
**SKPI** & \begin{tabular}{l} **Variance** \\ **Explained** \\ \end{tabular} & **SKPI** & \begin{tabular}{l} **Variance** \\ **Explained** \\ \end{tabular} \\ \hline Logistics\_SKPI\_1 & 8.084\% & Finance\_SKPI\_1 & 9.084\% & Production\_SKPI\_1 & 10.041\% \\ Maintenance\_Finance\_SKPI\_2 & 7.086\% & Finance\_Logistics\_SKPI\_2 & 8.086\% & Production\_Planning\_SKPI\_2 & 6.630\% \\ Finance\_Planning\_SKPI\_3 & 7.004\% & Logistics\_SKPI\_3 & 7.938\% & Finance\_Logistics\_SKPI\_3 & 8.051\% \\ Finance\_SKPI\_4 & 6.938\% & Planning\_SKPI\_4 & 7.244\% & Finance\_SKPI\_4 & 8.014\% \\ Logistics\_Finance\_SKPI\_5 & 6.244\% & Production\_SKPI\_5 & 7.004\% & Maintenance\_SKPI\_5 & 7.092\% \\ Production\_SKPI\_6 & 6.128\% & Production\_Planning\_SKPI\_6 & 6.524\% & Logistics\_Maintenance\_SKPI\_6 & 6.064\% \\ Logistics\_Finance\_Maintenance\_SKPI\_6 & 4.524\% & Finance\_Planning\_SKPI\_7 & 6.128\% & Finance\_Planning\_SKPI\_7 & 5.524\% \\ Maintenance\_SKPI\_8 & 4.344\% & Maintenance\_SKPI\_8 & 5.344\% & Logistics\_SKPI\_8 & 4.072\% \\ Production\_Maintenance\_SKPI\_9 & 4.236\% & Maintenance\_Planning\_SKPI\_9 & 4.820\% & Logistics\_Planning\_SKPI\_9 & 3.741\% \\ Planning\_SKPI\_10 & 3.820\% & Logistics\_Maintenance\_SKPI\_10 & 4.236\% & Planning\_SKPI\_10 & 2.946\% \\ Logistics\_Maintenance\_SKPI\_11 & 3.230\% & Logistics\_Finance\_Maintenance\_SKPI\_11 & 3.230\% & \
\begin{tabular}{l} Logistics\_Finance\_Maintenance\_ \\ SKPI\_11 \\ \end{tabular} &
\begin{tabular}{l} **Available** \\ **Available** \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Evolution of SKPIs from the year 2015 to 2017.  
times, customer satisfaction, and other key objectives. Financial management and integrated planning variables continued to provide essential support to production variables during this period.
# Ship Route Oil Spill Modeling: A Case Study of the Northeast Brazil Event, 2019
## 2 Materials and Methods
### Oil Spill Model
The first term in the equation represents the advective step and the second term the turbulent step. By default, the model does not calculate the derivative of \(K_{x}\), so the equation that is effectively solved is:  
\[dX(t)=udt+\sqrt{6K_{x}(x)}dW(t), \tag{4}\]  
which corresponds to an advection-diffusion equation with constant diffusivity. The horizontal diffusivity coefficient \(K_{x}\) is calculated from data on dye dispersion [40] as reviewed by [41]:  
\[K_{x}=0.0027t^{1.34} \tag{5}\]  
As the variance of a pollutant cloud grows, the cloud is dispersed by turbulence associated with increasingly higher spatial scales, such that the apparent diffusion coefficient increases with time. A maximum value for \(K_{x}\) is set to be 100 m\({}^{2}\) day\({}^{-1}\) based on [42]. The vertical turbulent diffusion coefficient above is related to the wave conditions following [43]:  
\[K_{z}=0.028\frac{H^{2}}{T}e^{-2 kz} \tag{6}\]  
where \(H\) is wave height, \(T\) is wave period, and \(k\) is the wave number. The minimum \(K_{z}\) value is set to 10\({}^{-4}\) m\({}^{2}\) s\({}^{-1}\)[42], corresponding to a low mixing zone below the pycnodine.
# Dual-Path Beat Tracking: Combining Temporal Convolutional Networks and Transformers in Parallel
## 3 Transformer block  
The block consists of six stacked Transformer layers called \"RoFormer\" with rotary positional embeddings [33], processing 128-dimensional inputs using 16 attention heads (size 8) and a pointwise feedforward network (expansion factor 4). For a fixed transformer dimension, there is a tradeoff between the number of attention heads and the dimensionality of each head. Using more heads allows the model to focus on diverse aspects of the input sequence in parallel, which can enhance performance. Reducing the number of heads or transformer dimensions degraded performance. Increasing transformer dimensions required significantly more memory, which reduced the model's practicality without substantial performance gains. Sigmoid gating is employed within each Transformer layer. Diagram representing transformer block is presented in Figure 5.  
This configuration treats the input as a temporal sequence, focusing on temporal attention rather than explicitly addressing frequency information, aligning it with preceding convolutional blocks. While both TCN and Transformer focus on temporal information, their approaches differ. TCNs leverage inductive bias with localized patterns via dilated convolutions, which are well-suited for capturing structured temporal dependencies. In contrast, Transformers use the attention mechanism, which is bias-free, enabling flexible modeling of temporal dependencies across the entire sequence. Both architectures aim to process temporal data, but their methodologies and biases in handling temporal relationships differ fundamentally.
## 5 Conclusions  
In this paper, we propose a GAN-based anomaly detection model by using a custom generator component to generate samples of videos presenting spatio-temporal defects. The two components (discriminator and generator) are adversely trained to simultaneously generate anomalies along with learning to perform anomaly detection. The proposed  
\begin{table}
\begin{tabular}{l l c c c c c} \hline \hline
**Dataset** & **Model** & **AUC** & **ACC** & **Precision** & **Recall** & **FPS** \\ \hline \multirow{6}{*}{UHCTD} & Cust. 3D-CNN & 0.9563 & 0.9530 & 0.8697 & 0.9632 & **489** \\  & C3D & **0.9828** & **0.9730** & **0.9836** & 0.9110 & 214 \\ \cline{1-1}  & MoViNet & 0.9040 & 0.8644 & 0.6590 & **0.9881** & 222 \\ \cline{1-1}  & 3D ResNet & 0.9572 & 0.9672 & 0.9373 & 0.9363 & 259 \\ \cline{1-1}  & AutoEncoder3D & 0.5800 & 0.7708 & 0.7385 & 0.1794 & 173 \\ \cline{1-1}  & 3D-GAN & 0.6048 & 0.6177 & 0.5789 & 0.0778 & 278 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison among the six experimented deep learning 3D methods on the external UHCTD dataset.  
method only requires normal data and few instances of the non-anomalous class in the training process. We applied our method to three datasets with defects of TV digital encoding which were introduced in this paper to simulate real-world industry-oriented failures. Moreover, we investigated the generalizability of representations learned by the proposed generator in an external dataset created to test camera tampering detection methods. Finally, we compared the results to a custom 3D CNN model, an autoencoder model and three pre-trained CNN, precisely C3D, MoViNet and 3D ResNet.  
The main results of our experiments successfully demonstrated that the proposed generator helped the 3D-GAN model to achieve a performance compared to the results of the supervised state-of-the-art 3D ResNet and C3D, as well as the lightweight MoViNet, without an increased time cost. However, due to the problem dependency of the proposed custom generator, we observed a significant degraded performance of the 3D-GAN in the external dataset. In the future, we will try to extend our approach to be a more general-purpose method for irregularity detection.  
Conceptualization, E.M.d.S., L.A., R.G., A.S.J., A.P. and R.J.S.B.; methodology, L.A.d.S., E.M.d.S., L.A., R.G. and O.A.d.S.; software, L.A.d.S., N.S.F., L.A., M.V. and A.S.J.; investigation, L.A.d.S., N.S.F., L.A., D.F. and A.S.J.; data curation, L.A.d.S., N.S.F., L.A., D.F., M.V., A.S.J., C.F.S.C., R.J.S.B. and O.A.d.S.; writing-original draft preparation. L.A.d.S., E.M.d.S., N.S.F., L.A. and M.V.; writing--review and editing, E.M.d.S., L.A., R.G., A.P., R.J.S.B., A.S.C. and O.A.d.S. All authors have read and agreed to the published version of the manuscript.  
Not applicable.  
The results presented in this paper were sponsored by ENVISION Industria de Produtos Eletronicos LTDA under the terms of Brazilian federal law No. 8.387/91 (SUPRAMA). This research was conducted by partners: UFAM/ICOMP, ICTS and ENVISION/TPV.  
The authors declare no conflict of interest.
# Revisiting Low-Resolution Images Retrieval with Attention Mechanism and Contrastive Learning
## 3 Results
### Implementations  
All experiments are implemented using Pytorch on a Titan V GPU with 12 GB memory. We use Python as the programing language for all experiments, including structuring model architecture, loading dataset, and evaluations. The deep learning framework used in this study is Pytorch. The source code is available at [[https://github.com/Ka0](https://github.com/Ka0) Ri/Contrastive-learning-for-image-retrieval]([https://github.com/Ka0](https://github.com/Ka0) Ri/Contrastive-learning-for-image-retrieval) (accessed on 20 May 2021).  
We use BiT [20] and EfficientNet [21] as the backbone network for comparison with the ViT [11] model. All the models are fine-tuned based on pre-trained weights on ImageNet. The comparison of the size of models is given in Table 1. For more details, we use ViT-B-16, the base version with 12 encoder layers, 12 self-attention heads per layer, and patchify an image by \(16\times 16\). We use BiT-M-R152 \(\times\) 4 architecture, a varied version of ResNet version 2 with 152 layers and a width factor of 4. BiT models maintain almost the same architecture as the original ResNet version 2, except for replacing batch normalization with group normalization and weight standardization. The BiT comes up with three versions, S,M, and L, where the pre-trained dataset scales up from S to L. The scale of BiT depends on which ResNet model is used; in our study, two factors are considered: depth factor, which reflects how deep the model is going to be (50, 101, 152 layers), and width factor, which defines how many channels are used in a residual model (\(\times 1,\ \times 2,\ \times 4\)). EfficientNets are a family of models resulting from extensive search by neural architect search to balance performance and computational resources usage. EfficientNet's scaling factors include depth, width, and resolution of a pre-trained dataset of use to maximize the model accuracy for any given resource constraints. EfficientNet is scaled up from MobileNet (B0) into seven versions from B1 to B7 where the number of parameters gradually increased from 5.3 M to 66 M. We experiment with EfficientNet and BiT along with ViT architecture to clarify the effectiveness of ViT's attention-based mechanism over residual-based and depth-wise convolution-based mechanisms. In this study, we use Efficient-B7 for a fair comparison with other types of architectures. ViT, BiT, and EfficientNets are models that challenge the recently developed state of the art in regards to the large-scale ImageNet dataset.  
We consider the setup generic for training hyperparameters in all experiments unless otherwise stated in the separated experiments. In the training phase, we use the AdamW optimizer [46] with cosine-annealing learning-rate schedule during the training process, the weight decay is set to \(10^{-6}\), and the other parameters of the optimizer are set to default. The learning is also warmed up during the first ten epochs from 0 to 0.001. The model is trained for 100 epochs with a batch size of 64.
# A Novel Cross-Layering Power Control Mechanism for AODV +
## 4 DPCM (Density Power Control Mechanism)
### Power Control Mechanism Definition
#### 4.1.3 Maximum Power  
An upper bound of the transmission power is necessary. The maximum transmission power for the IEEE 802.11a standard is 23 dBm. The ad-hoc vehicle networks book [16] indicates that the limit of transmission power in ad-hoc networks should be 20 dBm. Therefore, for the mechanism designed in this work, the upper bound is the value of 20 dBm.
# Near Real-Time Irrigation Detection at Plot Scale Using Sentinel-1 Data
## Materials
### Tarbes Dataset  
A field campaign was conducted in Tarbes, southwest France (Figure 1) over irrigated summer crops in 2017 where information about the existence of irrigation was registered for each plot. During this field campaign, 150 irrigated plots including 135 irrigated maize plots and 15 irrigated soya plots were localized. The surface area of the plots varies between 0.15 ha and 28 ha. Irrigation over Tarbes usually takes place between May and October of each year. The most common irrigation technique used in this site is the sprinkler irrigation technique. Unfortunately, irrigation frequency and dates were not available over these plots. For this reason, these plots were used for analyzing the performance of the proposed model.
# Medium- and Long-Term Load Forecasting for Power Plants Based on Causal Inference and Informer
## 4 Overall Flow and Discussion of the Experiment
### Overall Flow of the Experiment  
The overall flow of the experiment is shown in Figure 6.  
* Data collection: The data included daily load data from a power plant and data on external factors. The datasets provided a range from 1 July 2019 to 28 September 2022.
* Data preprocessing: The load and external factor data were obtained from various sources, and their time stamps had to be aligned when integrated. For data such as temperature, wind speed, and rainfall, the minimum time interval for processing was on a daily basis, and the specific value recorded was the maximum value per day.
* Feature engineering: The external factors were filtered by means of the mutual information method to obtain six variables, namely ambient temperature, workday, servicing, GDP, wind speed, and rainfall.
* Data splitting: In the present study, the training, validation, and test set ratio was 7:2:1.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & x1 & Date (Use in Informer only) \\ \cline{2-3}  & x2 & Workday \\  & x3 & Servicing \\ Input & x4 & GDP \\  & x5 & Rainfall \\  & x6 & Wind Speed \\  & x7 & Temperature \\  & x8 & Load residuals (Use in Informer only) \\ \hline Output & x9 (Target) & Load \\ \hline \hline \end{tabular}
\end{table}
Table 1: Inputs and output of the model.  
Figure 5: The actual generation load curve.  
* Model training: The present experiments involved comparing Causal-Informer with Informer, LSTM, Prophet, and ARIMA to determine the optimal model.
* Model evaluation: MAPE, MAE, and RMSE were adopted as the evaluation metrics. MAPE is a percentile of error that removes the effect of data scaling. MAE is the factual error between prediction and actual data, which is dependent only on the volume of the data. RMSE ensures that each item is positive and distinguishable.  
Figure 6: Overall flow of the experiment.  
\[MAPE=\frac{100\%}{N}{\sum}_{n=1}^{N}\mid\frac{P_{n}-P_{n}^{\sim}}{P_{n}}\mid \tag{18}\]  
\[MAE=\frac{1}{N}{\sum}_{n=1}^{N}\mid P_{n}-P_{n}^{\sim}\mid \tag{19}\]  
\[RMSE=\sqrt{\frac{1}{N}{\sum}_{n=1}^{N}(P_{n}-P_{n}^{\sim})^{2}} \tag{20}\]  
\(P_{n}\) is the actual load value and \(P_{n}^{\sim}\) is the corresponding result of the model prediction. \(N\) in the above equation denotes the number of time steps predicted by the model at a time (hereinafter abbreviated as pred_length).
* Results analysis: Visualization of forecast results. Model performance can be more visually observable for analysis.
## 4 Proposed Model
### Model Testing Process  
The few-shot learning model classification effect was tested by \(N\)-shot \(K\)-way; the following test method is stated in reference [48]. In the one-shot \(K\)-way test, (1) the dataset composed of the support set has \(K\) classes, and each type has only one sample, expressed as Equation (10); (2) one sample \(I_{test}\) is obtained from the test dataset, and compared with the data of the support set one by one, and each sample in the test dataset should be compared with all data of the support set; (3) the result of comparison between each test sample and the support set is identified as the class according to the test sample most similar to the support set, expressed as Equation (11) [48].  
\[S_{one-shot\ K-way}=\{(I_{m},L_{k})\},m=1,k=1\dots K \tag{10}\]  
Figure 5: Residual learning.  
\[C(I_{test},S)=\underset{s}{argmax}(P(I_{test},I_{s})),I_{s}\in S \tag{11}\]  
where \(I_{test}\) is the test data sample, \(S\) is the support set, \(I_{s}\) is the one sample from the support set, and \(P(I_{test},\ I_{s})\) is the probability of sample similarity.  
In the \(N\)-shot \(K\)-way test, (1) the dataset composed of the support set has \(K\) types, and each type has \(N\) samples; (2) each test sample should perform the one-shot \(K\)-way test \(N\) times; and (3) \(N\) test results of each class (\(K\) classes) are added up, the highest total probability is the class identified by the model, expressed as Equation (12) [48].  
\[C(I_{test},\ (S_{1},\ldots,S_{N}))=\underset{s}{argmax}\Bigg{(}\sum_{n=1}^{N}P \big{(}\hat{I},\ I_{sn}\big{)}\Bigg{)},\ I_{sn}\in S_{n} \tag{12}\]  
```
0: The epoch \(e=100\), the iteration \(i\), the batch size \(b=32\), both of images and label \(X\), the margin \(m\), the feature extraction layer \(f_{w}\), Adam hyperparameter \(\alpha\), \(\beta_{1}=0.9\), \(\beta_{2}=0.999\).
0: Initial feature extraction layer's parameters \(w_{0}\), initial learning rate \(a_{0}\).
1:while\(w\) has not converged do
2:for\(h=1,\ldots,e\)do
3:for\(u=1,\ldots,i\)do
4:for\(t=1,\ldots,b\)do
5: Training dataset \(T=\{I_{1},I_{2},\...,I_{2}\}\)
6:\(X=\big{[}\hat{I}_{1},\ I_{2},\ Y\big{]},\ \hat{I}_{1}\in T,\ \hat{I}_{2}\in T\)
7:\(O_{1}=f_{w}\big{(}\hat{I}_{1}\big{)}\), where \(\hat{I}_{1}\in X\)
8:\(O_{2}=f_{w}\big{(}\hat{I}_{2}\big{)}\), where \(\hat{I}_{2}\in X\)
9:\(MSD_{w}(O_{1},O_{2})=ED_{w}(O_{1},O_{2})+MD_{w}(O_{1},O_{2})\)
10:\(L^{(l)}\big{(}w,\ Y,\ \hat{I}_{1},\ \hat{I}_{2}\big{)}=0.5(1-Y)(MSD_{w})^{2}+0.5(Y)[max(0,\ m -MSD_{w})]^{2}\)
11:endfor
12:\(w\leftarrow\ Adam\Big{(}\
abla_{w}\frac{1}{b}\sum_{t=1}^{b}L^{(t)},\ w,\ \alpha,\ \beta_{1},\ \beta_{2}\Big{)}\)
13:endfor
14:\(\alpha=\alpha_{0}\times(0.5)^{\frac{d}{2b}}\), where \(d=20w,\ v\in h\ \cup\{0\}\)
15:endfor
16:endwhile
```  
**Algorithm 1** Residual VGG-Based Siamese Network. We default parameters of \(Y=0\) or \(1\)
## 3 Key Technologies of UAV-Assisted SAGINs
### Deployment of UAVs  
The deployment and placement of UAV is a critical research topic in UAV-assisted communication networks. It involves the planning of UAV positions and altitudes in 3D space. Unlike path planning, which focuses on finding efficient routes from start to finish, UAV deployment aims to devise a comprehensive task implementation scheme, including task allocation, take-off point selection, and network support. UAV deployment must meet constraints such as airspace regulations, UAV quantity, battery endurance, and task priority, enabling single or multiple UAV deployment orchestration. These problems are often highly complex and challenging to solve directly. This section reviews recent research on UAV deployment, and a list of relevant reviews is displayed in Table 7.
# A Two-Stage Optimization Model for Airport Stand Allocation and Ground Support Vehicle Scheduling
## 2 Literature Review
Research on ground support vehicles typically focuses on a single type of service vehicle or two interrelated types of vehicles. Few scholars have comprehensively considered multiple types of support vehicles within airports to develop integrated scheduling models. 3. Existing research tends to separate stand allocation and ground support vehicle scheduling, overlooking the fact that flight turnaround is a unified, multi-stakeholder collaborative process. As the location where ground support services are provided to aircraft, different stand allocation schemes directly affect the optimization of support vehicle scheduling. Additionally, given the limited availability of ground support resources, stand allocation should take into account the service capacity within a given timeframe to avoid flight delays caused by waiting for vehicles, which could disrupt the overall turnaround process. From the literature review, we conclude that there is a lack of coordinated scheduling mechanisms between airport ground operations and support resources. Moreover, current studies have not fully addressed the collaborative mechanism between different types of stand allocation and ground support vehicles scheduling. Therefore, in this paper, based on the shortcomings of existing studies mentioned above, we propose improvements from the perspective of integrated airport scheduling and explore solutions to optimize resource allocation.