esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Characterization of Preforms  
The combination of the MCVD technology with nanocrystal doping of YAG nanocrystals resulted into the formation of highly transparent and homogenous preforms. Selected properties of the preforms are summarized in Table 1. Figure 5a shows the effect of the deposition flow rate of SiCl\({}_{4}\) on refractive index profiles of the preforms. The refractive index gradually increased with increasing flow rate of SiCl\({}_{4}\) reaching a maximal value of 1.466. As can be seen in Figure 5b, for the preform prepared at the flow rate equals to 400 sccm, the dopants were homogeneously distributed in the preform core and the refractive index profile followed the distribution of the dopants. The effect of the flow rate of SiCl\({}_{4}\) on the maximal core-cladding refractive index difference and on the maximal concentration of the dopants is demonstrated in Figure 6. The refractive index differences and maximal concentrations of dopants were proportional to the deposition flow rate of SiCl\({}_{4}\).  
Figure 4: TEM visualization of YAG nanoparticles. (**a**,**b**) Overall view. (**c**) Diffraction pattern recorded on one of aggregated nanocrystals. (**d**) Diffraction pattern recorded on nanoparticles smaller than 10 nm.  
However, the highest concentrations of Y\({}^{3+}\) and Al\({}^{3+}\) ions achieved were 0.40 and 0.979 at. %, respectively. These concentrations correspond to a ratio of Y\({}^{3+}\):Al\({}^{3+}\) equal to 3:7.3, which is lower than the expected ratio of 3:5, which corresponds to the stoichiometric formula of YAG, indicating a surplus of Al\({}^{3+}\) ions in the preform core attributed to the alumina nanoparticles.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Sample \#** & **1** & **2** & **3** \\ \hline Deposition flow rate of SiCl\({}_{4}\) (sccm) & 100 & 250 & 400 \\ Maximal core-cladding \(\Delta\)n & 0.0042 & 0.0066 & 0.0090 \\ Maximal concentration Al\({}^{3+}\) (at. \%) & 0.339 & 0.578 & 0.979 \\ Maximal concentration Y\({}^{3+}\) (at. \%) & 0.110 & 0.210 & 0.410 \\ Maximal concentration Er\({}^{3+}\) (at. \%) & 0.004 & 0.006 & 0.011 \\ \hline Numerical aperture & 0.111 & 0.139 & 0.162 \\ Attenuation coefficient at 850 nm (dB\(\cdot\)m\({}^{-1}\)) & 0.015 & 0.018 & 0.022 \\ Attenuation coefficient at 980 nm (dB\(\cdot\)m\({}^{-1}\)) & 0.210 & 0.620 & 1.540 \\ Attenuation coefficient at 1310 nm (dB\(\cdot\)m\({}^{-1}\)) & 0.027 & 0.075 & 0.138 \\ Cutoff wavelength of LP\({}_{11}\) mode (nm) & 1080 & 1140 & 1175 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Selected parameters of the preforms and optical fibers.  
Figure 5: (a) The effect of the deposition flow rate of SiCl\({}_{4}\) on the refractive index profiles of the preforms. (b) Refractive index profile (left scale) and the corresponding concentration profile of dopants (right scale) of the preform prepared flow rate of SiCl\({}_{4}\) equal to 400 sccm.  
Figure 6: The effect of the deposition flow rate of SiCl\({}_{4}\) on the maximal core-cladding refractive index difference (left scale) and on the maximal concentration of the dopants (right logarithmic scale).
## 4 Experiments and Performance Evaluation
### Implementation Details
Subsequently, we performed an engineering optimization based on dynamic spectral subtraction, which was integrated as a post-processing step. First, a simple voice activity detection (VAD) mechanism was employed to determine whether the current frame contains speech. For non-speech frames, the noise estimate was dynamically updated to adapt to changing acoustic environments. Then, spectral subtraction was applied to the current spectrum using an adaptively adjusted suppression factor \(\alpha\). Additionally, a spectral floor gating mechanism controlled by a threshold parameter \(\beta\) was introduced to effectively prevent quality degradation or residual noise caused by over-subtraction. With this post-processing strategy, the PESQ performance of the enhanced audio was improved to 3.103, surpassing all baseline models. For fair comparison, the same post-processing was applied to the GTCRN model. However, its PESQ performance slightly degraded. We attribute this to GTCRN's use of complex masking for simultaneous magnitude and phase estimation, which may already produce outputs near the performance ceiling. In contrast, our model reconstructs the enhanced waveform using the noisy phase during inference, leaving room for further noise suppression improvements. We explored phase reconstruction using the MISI (Multiple Input Spectrogram Inversion) algorithm, but no notable enhancement was observed. The Griffin-Lim algorithm yielded modest improvements, but at the cost of a significant increase in inference latency. These findings not only validate that our model achieves strong performance even under the noisy phase assumption, but also highlight the potential for future improvement through more effective phase estimation strategies. The experimental code and pre-trained models have been released and can be accessed at \"[[https://github.com/ZujieFan/TSDCA-BA](https://github.com/ZujieFan/TSDCA-BA)]([https://github.com/ZujieFan/TSDCA-BA](https://github.com/ZujieFan/TSDCA-BA)) (accessed on 20 June 2025) for reference.
# Proposal of an Automated Mission Manager for Cooperative Autonomous Underwater Vehicles
## 3 Overview of the SWARMs Architecture
### The Mission Plan  
The SWARMs ontology [42] defines a mission plan as a \"sequence of scheduled low-level tasks (operator and vehicle level tasks) that need to be carried out to achieve a mission, with dependencies between tasks and approximate time duration\". Figure 2 illustrates the SWARMs ontology for mission and planning.  
The mission plan is generated at the MMT using one of the available high-level planners [46; 47; 48], and sent to the MTRR through an Apache Thrift [49; 50] interface using an ad hoc format whose structure is shown in Figure 3.  
Each mission plan is identified by its _missionId_, and consists of a hierarchical list of actions and the list of vehicles participating in the mission. The rest of the attributes shown in Figure 3 are only used at the MMT to feed the planners with the areas and points of interest for a mission.  
Every action in the action list is assigned to a vehicle. They can also have a parent action, forming a hierarchical structure where the high-level actions are those without a parent, and the low-level actions are the vehicle-level tasks from the SWARMs ontology. A graphical depiction of this structure is shown in Figure 4. In this figure we can see a mission plan including three AUVs named NTNU, PORTO 1 and PORTO 2. For each AUV, we have a two-level hierarchical mission plan.  
Figure 3: Structure of a SWARMs mission plan.  
Figure 2: The SWARMs mission and planning ontology.  
For instance, the mission plan corresponding to the NTNU AUV in Figure 4 has a high-level plan consisting of three high-level tasks, starting with a _TRANSIT_, followed by a _SURVEY_ and finishing with another _TRANSIT_. And the low-level plan consists of splitting the high-level tasks into low-level ones: both _TRANSIT_ high-level tasks are split in one _GOTO_WAYPOINT_ low-level task each; the high-level _SURVEY_ is decomposed in a sequence of _FOLLOW_ROW_ and _GOTO_WAYPOINT_ low-level tasks.  
The vehicles in SWARMs may be equipped with an onboard planner, meaning that these vehicles are capable of understanding high-level tasks and generating the required low-level tasks. In consequence, only one of the two levels of the hierarchy needs to be dispatched to the vehicles: the high-level tasks when the vehicles have an onboard planner and the low-level tasks otherwise.
## 4 Evaluation of Model Developments
### Observational Data to Evaluate Model Developments
#### 4.2.6 ESA-CCI Soil Moisture  
The ESA-CCI soil moisture product ([[https://www.esa-soilmoisture-cci.org/](https://www.esa-soilmoisture-cci.org/)]([https://www.esa-soilmoisture-cci.org/](https://www.esa-soilmoisture-cci.org/)), 1 February 2021) is also used to evaluate the impact of the new developments on soil moisture. The ESA CCI soil moisture product is provided by the European Space Agency for the 1981-2019 period at 0.25\({}^{\circ}\) spatial resolution and daily temporal resolution. It is generated with soil moisture observations from active (ERS1-2 SCAT and MetOp ASCAT A-B) and passive (SMMR, SSM/I, TMI, AMSR-E, WindSat, AMSR2 and SMOS) microwave satellite sensors. The combined product is obtained through the assimilation process of both C-band scatterometers active observations that use the change detection algorithm and the passive microwave observed land surface brightness temperature. Further details on ESA-CCI SM data and its generation procedure are found in [PERSON] et al. [83], [PERSON] et al. [84] and [PERSON] et al. [85].
# Video Multi-Scale-Based End-to-End Rate Control in Deep Contextual Video Compression
## 3 The Proposed Algorithm
### Video Restoration  
To restore the high-resolution frame from the reconstructed frame, HEDS-Net is designed. The structure of HEDS-Net is similar to the skip connection of the residual network, where two parallel processes are designed to restore different features. For one process, the multi-branch distillation network and the upsampling network are connected to build the low-frequency features of the high-resolution frame. For the other processes, the lightweight attention LCA block is used to build the detailed features of the high-resolution frame. Then, the two processes merge together to generate the final reconstructed high-resolution frame.  
Figure 4: Two-branch regression-based network.  
1 Multi-branch distillation network  
The multi-branch distillation network aims to learn and extract detailed features with multi-layer interaction. The structure of the multi-branch distillation network is shown in Figure 5.  
In Figure 5, it can be observed that three residual blocks are used for extracting detailed features. The depthwise separable convolution in the residual block enhances the feature learning ability. Different residual blocks are connected by the concatenation operation to accumulate feature information for improving multi-scale expression.  
2 Lightweight attention LCA block  
The lightweight attention LCA block provides the main contribution by generating the detailed pixels of the high-resolution frame; it carries this out by combing local and global features. On the other hand, the residual recursion mechanism utilizes the lightweight attention LCA block to improve prediction accuracy, with network resources continuously optimizing. The lightweight attention LCA block is shown in Figure 6.  
3 Upsampling network  
The upsampling network is mainly used to polymerize the low-frequency frame feature. Two branches are used for the upsampling network. The left branch of the sub-pixel convolution carried out pixel rearrangement. The right branch of the transposed convolution implements the feature transform. The fusion of the two branches will improve the diversity of the upsampling feature. The upsampling network is shown in Figure 7.  
Figure 5: Multi-branch distillation network.  
Figure 6: Lightweight attention LCA block.  
As mentioned above, the loss function of HEDS-Net is defined as  
\[loss_{\text{HEDS-Net}}=\frac{1}{N}\sum_{n=1}^{N}\sqrt{\left(x_{n}-\hat{x}_{n} \right)^{2}+\left(\omega_{1}\cdot\left|\
abla(x_{n}-\hat{x}_{n})(i,j)\right|^{ \omega_{2}}\right)^{2}} \tag{17}\]  
where \(N\) is the number of the dataset, and \(x\) and \(\hat{x}\) are the predicted frame and the label frame, respectively: \(\omega_{1}\in[0.1,1]\) and \(\omega_{2}\in[0.2,0.8]\). \(\
abla\) denotes the gradient value of \((x_{n}-\hat{x}_{n})\) at position \((i,j)\).
## 2 Materials and Methods
### Signal Processing  
Signal analysis involved reading MEG data, and applying a Notch filter to eliminate 50 Hz power line frequencies and their harmonics. Artifacts from the electrooculogram (EOG) and electrocardiogram (ECG) signals were automatically identified and manually reviewed to ensure the inclusion of any potentially omitted artifacts. Signal-space projection (SSP) methods were applied to correct the artifacts by order.
# Generation of High Resolution Vegetation Productivity from a Downscaling Method
## 4 Discussion  
### Comparison of the Accuracy of GPP Estimates from STARFM  
In this paper, a framework to generate high resolution GPP and NPP dataset at 30 m was developed based on downscaling method from Landsat data and MODIS/GLASS data. Direct validation against ground measurement data and cross validation against high resolution images demonstrated that this downscaling framework could obtain time series high temporal and spatial resolution GPP and NPP datasets. Data fusion approaches have been demonstrated to be feasible and efficient ways to synthesize time series images from multi-scale remote sensing data at different spatial and temporal resolutions [26; 55; 56]. The STARFM has been found to be applicatable to produce an accurate interpolated NDVI time series in many studies [55; 57]. Furthermore, based on the time series high resolution NDVI generated from STARFM, and the liner relationship between FPAR and NDVI, some studies have been carried out to estimate GPP and NPP from the LUE model [26; 30; 58]. However, the relationship between MODIS and Landsat NDVI may vary, due to different acquisition dates and cloud contamination, aerosols, viewing angles and temporal compositing [58], and the relation between FPAR and NDVI also changes with vegetation type and solar zenith angle [59], all these factors can introduce uncertainties into the FPAR estimation and GPP/NPP estimation. In our study, time series FPAR were generated from MODIS/GLASS FPAR products, which have been temporal infilled of cloud-contaminated pixels, and have been validated to be in good quality with high accuracy. Owing to the lack of ground observations, the downscaled GPP were validated with MODIS GPP product at 1 km scale (R\({}^{2}\) ranged from 0.55 to 0.81, and RMSE ranged from 1.39 g C\(\cdot\)m\({}^{-2}\cdot\)d\({}^{-1}\) to 3.92 g C\(\cdot\)m\({}^{-2}\cdot\)d\({}^{-1}\)) [20; 21] or validated with Landsat derived GPP (R\({}^{2}\) ranged from 0.85 to 0.86) in previous studies (Table 2) [26]. In this study, the downscaled GPP were validated with both high resolution GPP at 30 m scale (R\({}^{2}\) ranged from 0.81 to 0.95, and RMSE ranged from 0.84 g C\(\cdot\)m\({}^{-2}\cdot\)d\({}^{-1}\) to 1.55 g C\(\cdot\)m\({}^{-2}\cdot\)d\({}^{-1}\)) and ground observed data (R\({}^{2}\) ranged from 0.71 to 0.90, and RMSE ranged from  
Figure 16: Comparison of estimated GLASS NPP, D\(\_\)NPP\(\_\)MODIS and D\(\_\)NPP\(\_\)GLASS: (**a**) Cropland; (**b**) orchard; (**c**) vegetable field; (**d**) wetland. Note that the NPP in cropland are the mean values of all sites in cropland.  
0.87 g C\(\cdot\)m\({}^{-2}\cdot\)d\({}^{-1}\) to 3.14 g C\(\cdot\)m\({}^{-2}\cdot\)d\({}^{-1}\)). The validation results demonstrated that both the GPP from downscaled GLASS data and from MODIS data had higher accuracy.
## 2 State of the Art
### Four-Dimensional Modelling
#### 2.2.6 Time  
Time and non-linear temporal changes are the main elements of history. Current 3D/4D reconstructions still focus mostly on specific timestamps. This is challenging, since it requires multiple sources for these reconstructions, often taken at very different times and with each source a singular document of the represented state. Beside the issue of inter- or extrapolating sources of different times to gain a coherent historical view, dating of sources is challenging. Historical imagery is still primarily dated via metadata captured at recording or amended at later points. Where metadata are not available or certain, change detection can be applied to image series, e.g., to assess if undated images show corresponding states of construction to the dated ones. Current algorithmic change detection focuses on homogenous quality images, such as time series of satellite images [168; 169; 170] or aerial photos [171; 172]. Approaches for heterogeneous photographs can deal with large-scale changes but are limited with subtle changes (overviews: [173; 174; 175]). Other change detection approaches work with 3D geometries (overview: [176]) or segmentation- and feature-based comparisons between different images to identify changes in architectural features [98; 130]. The aim is to detect subtle changes in heterogenous historical imagery over time.
## 5 Demonstration Use-Case
### The Experimental Scenario
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**CPS** & **Integration Type** & **CPS Controllers** & **Production Services** \\ \hline Ball–valve assembly station & PAS with HTTP & Robot Controller & M1—Calibrate \\  & & Human-directed device & M2—Assembly ball–valve \\  & & & H1—Pick pieces from AGV, place them on fixture and check position \\  & & & H2—Check tools and robot add-ons correct positioning \\  & & & H3—Load assembled ball–valve onto AGV \\ \hline Warehouse & PAS with OPC-UA & Human-directed device & H4—Check correct quantity of pieces in containers \\  & & & H5—Put ball–valve components on AGV \\  & & & H6—Pick and stock assembled ball–valve from AGV \\  & & & H7—Put components box on AGV \\  & & & H8—Pick and stock ordered box from AGV \\  & & & H9—Load fixture on AGV \\  & & & H10—Pick, stock pieces from AGV and return fixture to factory docking station \\ \hline Assembly line & External OPC-UA cell & PLC controller & F1—Execute assembly order \\  & & & F2—Robot block \\  & & & (activates the secure handling zone of manufactured \\  & & & pieces by the human operator) \\  & & & H3—Robot sublock (de-activates the secure zone) \\  & & & H11—Control correct flow of assembled pieces \\ \hline Box handler & PAS with HTTP & Box controller & C1—Pick up components from AGV (without robotic arm) \\  & & & C2—Manufacture pieces \\  & & & C3—Load final pieces onto AGV \\ \hline AGV Fleet & PAS with HTTP & Fleet controller & AGV1—Go and wait to a predefined location \\  & & & AGV2—Transport components to a destination, through \\  & & & an intermediate cell \\  & & & AGV3—Continue to destination \\  & & & AGV4—Finish (indicates that transport is completed) \\ \hline \hline \end{tabular}
\end{table} 
Table 5: List of published services for each CPS. timestamp, and the type specific parameters. This lightweight communication between the CPS and the MESS Core allows a reliable operation. The detailed use-case scenario has been executed flawlessly in an automatic and trackable manner.
###### Abstract  
The separation models are tools used in solar engineering to estimate direct normal (DNI) and diffuse horizontal (DHI) solar irradiances from measurements of global solar irradiance (GHI). This paper proposes two empirical separation models that stand out owing to their simple mathematical formulation: a rational polynomial equation. Validation of the new models was carried out against data from 36 locations, covering the four major climatic zones. Five current top minute-scale separation models were considered references. The tests were performed on the final products of the estimation: DNI and DHI. The first model (M1) operates with eight predictors (evaluated from GHI post-processed measurements and clear-sky counterpart estimates) and constantly outperforms the already established models. The second model (M2) operates with three predictors based only on GHI measurements, which gives it a high degree of accessibility. Based on a statistical linear ranking method according to the models' performance at every station, M1 leads the hierarchy, ranking first in both DNI and DHI estimation. The high accessibility of the M2 does not compromise accuracy; it is proving to be a real competitor in the race with the best-performing current models.  
diffuse solar irradiance; direct normal solar irradiance; separation model; diffuse fraction; clearness index +
Footnote †: journal: 2023, 13, 6588. [[https://doi.org/10.3390/app131658](https://doi.org/10.3390/app131658)]([https://doi.org/10.3390/app131658](https://doi.org/10.3390/app131658))
## 3 Experimental Settings and Results Analysis
### Experimental Results and Analysis
During the validation of the validation set, the network outputs the probabilities of each factor, as shown in Figure 15.  
Since the validation set only contains binary data, \"1\" represents the factor being abnormal and \"0\" represents that the factor is normal. The obtained probability needs to be quantified into binary values--that is,  
\[\rho_{j}= \begin{cases}1,\ p_{j}\geq\delta,\\ 0,\ \ \text{other},\end{cases}  \tag{40}\]  
where \(p_{j}\) represents the probability of the corresponding factor experiencing an anomaly and \(p_{j}\) indicates whether the factor is anomalous. Specifically, \(\rho_{j}=1\) means the factor is \begin{table}
\begin{tabular}{c c c c} \hline
**No.** & **Factor** & **No.** & **Factor** \\ \hline
1 & Steam recovery amount & 9 & Out-of-furnace temperature \\
2 & Flue gas temperature & 10 & Out-of-steel temperature \\
3 & Heat loss & 11 & Process heating temperature \\
4 & Furnace refractory material loss & 12 & Actual hot charging temperature \\
5 & Steam recovery amount & 13 & Rolling ratio \\
6 & Time deviation in furnace & 14 & Billet unit weight \\
7 & Hot air release amount & 15 & Product specification impact \\
8 & Air–fuel ratio & & \\ \hline \end{tabular}
\end{table} 
Table 14: Output factor. Figure 15: Factor probability obtained by Bayesian neural network. anomalous and \(\rho_{j}=0\) means the factor is normal. The threshold \(\delta\) is set manually. The confusion matrix obtained from the validation set is shown in Figure 16.  
According to Table 15, the Adam optimizer shows significant advantages over the other three methods in terms of accuracy and other metrics. The causal path obtained in the previous section is \(T_{3},T_{2},V_{1},T_{1},V_{2},T_{6}\). The corresponding input vector to the network is \([0.67,0.33,0.50,0.83,1,0,0,0.17,0]^{\mathrm{T}}\). After inputting this into the network, the output is \([0,0,0,0,1,0,1,0,0,1,1,1,1,1]^{\mathrm{T}}\), corresponding to the factors such as steam recovery, hot air dissipation, steel temperature, process heating temperature, actual heat treatment temperature, rolling ratio, billet weight, and specification effects. This outcome aligns with the actual industrial conditions. As shown in the Table 16, compared with the existing methods commonly used in industrial sites, it can be seen that the method proposed in this paper has great advantages in accuracy, etc., which verifies the effectiveness of the method proposed in this paper. Figure 16: Confusion matrix obtained by different algorithms. (**a**) Adam (**b**) SGD (**c**) SGD with momentum (**d**) LBFGS. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Optimization Method** & **Accuracy** & **Precision** & **Recall** & **F1 Score** \\ \hline Adam & 0.774 & 0.683 & 0.684 & 0.683 \\ SGD & 0.755 & 0.518 & 0.532 & 0.526 \\ SGD with momentum & 0.594 & 0.431 & 0.422 & 0.426 \\ LBFGS & 0.505 & 0.369 & 0.543 & 0.440 \\ \hline \hline \end{tabular}
\end{table} 
Table 15: Comparison of abnormal factor identification effects.
## 3 System Model
### Communication Model
#### 3.1.3 Outage Analysis  
In wireless networks, outage events occur when the actual mutual information is less than the required data rate [30]. To ensure the reliability of information transmission, the SNR of the channel must be greater than the SNR threshold value \(\gamma_{th}\) to transmit. At time slot \(t\), the transmission condition for train \(T_{n}\) with wayside node \(R_{m}\) is \(\gamma_{T_{n},R_{m}}>\gamma_{th}\), and the transmission condition for wayside node \(i\) and wayside node \(j\) is \(\gamma_{i,j}>\gamma_{th}\). In particular, the maximum transmission distance \(R_{\max}\) between train and wayside relay while the train \(T_{n}\) is moving can be calculated as  
\[\frac{p_{T_{n}}\Big{|}X_{T_{n},R_{m}}d_{T_{n},R_{m}}^{-\beta/2}}{N_{0}}\geq \gamma_{th}. \tag{7}\]  
We can obtain the maximum distance \(R_{\max}\) as  
\[R_{\max}=d_{T_{n},R_{m}}=\left(\frac{N_{0}}{p_{T_{n}}X_{T_{n},R_{m}}}^{2} \gamma_{th}\right)^{-\frac{1}{\beta}}. \tag{8}\]