esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussion
### Coherent Noises Associated with the Impact of Medium Inhomogeneities during Hologram Recording
#### 4.2.2 Non-Stationary Object  
In the case of multiple scattering on fine inclusions of the medium, both the object and the reference waves may be so distorted that the averaging methods will be ineffective. A laboratory experiment using a digital holographic camera was conducted to assess the impact of the extension of the scattering medium and its turbidity on the image quality of a particle reconstructed from a digital hologram. The DHC design provides for a discrete variation of the length of the test volume. The length of the test volume (\(z_{H}\) in Figure 1) may equal 51 mm, 202 mm, 349.4 mm, or 498.6 mm. In laboratory conditions, we simulated water with different turbidities, into which the DHC was placed, by adding pure tap water to turbid lake water (dense microalgae hydrosol). The water turbidity was measured using the 2100P Turbidimeter for six samples: 7.48 nephemometric turbidity units (NTU), 6.60 NTU, 5.46 NTU, 4.65 NTU, 2.03 NTU, 0.53 NTU, and 0.1 NTU. The maximum turbidity corresponded to lake weed water, while the minimum corresponded to water from the water line.  
A model particle of a caliber (Figure 1b) located near the illuminating laser (Figure 1a) was used as an object of holography in all experiments. Digital holograms of this opaque model particle of a square shape with a size of 500 \(\times\) 500 \(\mu\)m were recorded for different sizes of the test volume and different turbidities of the medium. The dependencies of the particle distinctive ability on the length of the test volume were obtained for different turbidities of the water with model particles at the hologram recording stage. Considering that the shape of a square particle is recognized at a distinctive ability of not more than 0.2 [23], the dependence of the length of the studied volume on the medium turbidity according to the distinctive ability of 0.2 was obtained (Figure 17). The graph in Figure 17  
Figure 16: Results of averaging of reconstructed images recorded using a multispectral, three-line source and fiber coupler.  
allows optimizing the length of the registered medium volume (depth of the holographic scene) depending on turbidity.  
In the graph in Figure 17, \(z_{Hmax}\)--maximum possible length of the working volume ensuring the specified distinctive ability in clean water (turbidity--0.1 NTU), \(z_{Hmin}\)--minimum length of the working volume ensuring the specified distinctive ability in turbid lake water (turbidity--7.5 NTU). The dependence shown in the figure is asymptotic both with respect to the length of the working volume and turbidity. Extreme turbidity is determined in relation to the minimum \(z_{Hmin}=50\) mm and maximum \(z_{Hmax}=500\) mm lengths of the working volume.
# Dual Residual Denoising Autoencoder with Channel Attention Mechanism for Modulation of Signals
## 3 DRdA-CA Architecture Details
### The Encoding and Decoding Blocks
First, the compressed feature map \(g_{c}\) is fed into a convolutional layer with \(C/r\) output channels, namely a dimensionality-reduction layer with reduction ratio \(r\). Then the convolution result is activated using the LeakyReLU function, and finally the activated feature graph is dimensionally augmented through a convolution layer with \(C\) output channels to guarantee channel dimensional consistency between the output \(s_{c}\) and feature map \(v_{c}\). The excitation function \(F_{ex}\) is given by  
\[s_{c}=F_{ex}(g_{c},W)=\sigma(W_{2}\delta(W_{1}g_{c})) \tag{4}\]  
Figure 3: Improved SE-Resnet and Original SE-Resnet modules. “\(\oplus\)” represents addition operation. (a) Original SE-ResNet module. (b) Improved SE-ResNet module (encoding block). where \(W_{1}\in\mathbb{R}^{\tilde{\xi}\times C}\) and \(W_{2}\in\mathbb{R}^{C\times\frac{\zeta}{\tilde{\tau}}}\) are the weight values of the two convolutional layers around the LeakyReLU layer, and \(\delta\) is the LeakyReLU function with slope parameter \(\alpha=0.001\). The LeakyReLU function solves the problem of neuron necrosis in the ReLU function, and \(\sigma\) represents the sigmoid activation function. Finally, the feature map \(s_{c}\) output by the sigmoid function is used to adjust the channel weights of feature map \(v_{c}\), and the calculation process is given by  
\[\tilde{x}=F_{scale}(v_{c},s_{c})=s_{c}v_{c} \tag{5}\]  
Based on above analysis, the excitation operation can map the feature map \(g_{c}\) to a set of channel weights \(v_{c}\). The decoding block of the noise reduction autoencoder is depicted in the Figure 4. It is clear that the decoding block is similar to the encoding block, but it adopts TConv instead of one of the convolution layers in the original encoding block to realize the upsampling operation. Like the convolution operation, TConv is learnable. The advantage of transpose convolution is that it can theoretically obtain the upsampled value that is most suitable for the current dataset through continuously updating the parameters.
# Elastic CRFs for Open-Ontology Slot Filling
## 1 Introduction
A widely used method is to implement a CRF layer on top of features generated by a RNN [1]. These recent neural CRFs are different from conventional CRFs, which mainly use discrete indicator features. However, these recent CRFs still work with a closed set of labels. In this paper, we propose a novel neural CRF model, called elastic CRF (eCRF), for open-set sequence labeling, by leveraging label descriptions inspired from [12]. The key idea of eCRFs is to use slot descriptions to create semantically meaningful IOB tags [3], which are further used for a new calculation of potential functions in the CRF framework. Compared to traditional fixed IOB tags in original CRFs, our eCRFs are able to process new slots unseen during training without retraining the model. Such flexibility is the motivation for calling it an \"elastic\" CRF model. The eCRFs are powerful models for open-ontology slot filling. Intuitively, the node potentials of eCRFs combine the neural features of both the utterance and the slot descriptions, and the edge potentials model the interactions between different slots. In the experiments, we make use of the Google simulated dataset [13], and re-split the dataset according to the in-domain task and the cross-domain task, which focus on the challenge of handling unseen values and unseen slots, respectively. The results show that eCRFs significantly outperform not only a BiLSTM baseline but also the concept tagger (CT) in [12] for both tasks, especially in predictions of unseen slots and values. In Section 2, we discuss related work. The new eCRF model is detailed in Section 3. Section 4 describes the dataset and task formulations. Section 5 presents the experiments, followed by the conclusion in Section 6. Figure 1: An example of slot filling in the movie domain.
# Deploying CPU-Intensive Applications on MEC in NFV Systems: The Immersive Video Use Case
## 2 Related Work
### MEC and NFV Framework Integration  
ETSI MEC has carried out a specific activity to integrate MEC and NFV; Reference [10] summarizes the results obtained and Figure 3 shows the proposed MEC in NFV reference architecture.  
The ETSI MEC in an NFV framework relies on three basic assumptions:  
* The ME platform is deployed as a VNF;
* ME apps are deployed as VNFs;
* The virtualization infrastructure is deployed as an NFVI, and is managed by a VIM.  
Unfortunately, such simple statements have a significant impact on the original MEC architecture outlined in Reference [8], bringing about relevant changes in the scheme shown in Figure 1[10].  
In particular, in MEC in NFV, the ME host is no longer present, since both the ME platform and the ME apps run as VNFs in the NFVI. Moreover, the ME platform manager becomes the \"Mobile Edge Platform Manager--NFV\" (MEPM-V) shown in Figure 3, delegating application lifecycle management to one or more VNFM(s). Finally, the Mobile Edge Orchestrator is transformed into the \"Mobile Edge Application Orchestrator\" (MEAO) [10].  
The MEAO is connected with the MEPM-V through Mm3, to carry out configuration activities. In Figure 3, Mm3 is indicated as Mm3*, with an asterisk to highlight that this reference point will necessarily be changed, due to the split between MEPM-V and VNFM introduced by Reference [10].  
The integration of MEC and NFV has attracted the attention of many researchers, and a state-of-the-art analysis can be found in References [9; 36; 37]. The work described in Reference [9] provides an overview of MEC, and discusses the interaction with NFV, while Reference [36] introduces the adoption of the Open Baton orchestrator in MEC. However, Reference [36] focuses on supporting the deployment of container-based network services, while the MEC management aspects considered in this work are not discussed. Reference [37] proposes NFVO+, a single enhanced NFV MANO modified to deal both with VNFs and ME apps. The present paper, conversely, aims to present an MEC part that is agnostic with respect to the adopted NFV framework, and can thus be combined with any, even already deployed, NFVO.  
On the application side, MEC has been proposed in many fields, as a means to provide enhanced services to end users. In particular, MEC-based video applications have been widely investigated in the literature. In the following section, we provide a brief survey of the of the state of the art of video services and MEC, to highlight the advances that the framework proposed in this paper can offer with respect to already available solutions.
# A Layer-Wise Strategy for Indoor As-Built Modeling Using Point Clouds
## 4 Experiments
### Quantitative Evaluation  
To quantitatively evaluate the quality of modeling results, we used both realistic data and synthetic data. We took the distance from the point to its nearest reconstructing surface as the measurement metric. The reconstruction error could be seen in Figure 24. According to our test, the proposed method was to obtain averaging fitting errors at 3.35 cm.  
We also manually created the building models and sampled them to generate synthetic data to evaluate the performance of the proposed method against different levels of noises (See Figure 25). As shown in Table 4, we appended 10% Gaussian noises at \(\sigma\) = 5 mm, 10 mm, and 20 mm. The average fitting errors from the results to the ground truth were 0.407-3.213 cm.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Synthetic Data** & **Maximum Error (cm)** & **Minimum Error (cm)** & **Average Error (cm)** \\ \hline Data 1 (5 mm) & 0.620 & 0.105 & 0.407 \\ Data 2 (10 mm) & 2.412 & 0.219 & 1.576 \\ Data 3 (20 mm) & 4.171 & 0.629 & 3.213 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Evaluation of synthetic data with different Gaussian noise.  
Figure 24: Point clouds colored with distance between points and the reconstructed model.  
Figure 25: Accuracy evaluation on the synthetic data. First row: Point clouds with 5 mm, 10 mm, 20 mm Gaussian noise. Second row: Errors from the proposed method.  
To evaluate the robustness towards sparse data, we sampled the original data to 60%, 30%, and 5% (corresponding to Figure 26a-c). The results showed that our method could retain the major part of building even in 5% point clouds. However, with the decreasing of point density, some details were discarded (e.g., uneven ceiling depicted in blue rectangle in Figure 26). The errors and detail deficiency were attributed to the sparse supporting points because of the insufficient extraction of representative layers.
# Requirements Engineering for a Drone-Enabled Integrated Humanitarian Logistics Platform
## 3 AIRDROP Overview
### Area Scanning System  
The area scanning system is a critical AIRDROP service, leveraging sensors and cameras on UAVs, UGVs, and USVs for specialized disaster monitoring. In fire outbreaks, UAVs and UGVs, equipped with ML-assisted electro-optical and infrared cameras as well as dedicated gas sensors, swiftly detect fire, smoke, and gas leakage (CO and CO2).  
For SaR operations, real-time video analytics on these platforms locate missing persons or vehicles, while acoustic sensors identify human sounds. Miniature ground vehicles (MGVs) with chemical sensors complement UAVs and UGVs in detecting human presence through the detection of specific organic compounds found in breath and body odors. In the event of structural collapse, ground-penetrating radar (GPR) sensors on UGVs locate survivors buried under rubble. Furthermore, UAVs and UGVs, with ML-enhanced electro-optical and infrared cameras, identify locations (e.g., voids) where victims may be trapped. The presence of live cables covered with debris and electrical devices that can remain energized in spite of power being turned off due to poor wiring poses a substantial electrocution hazard for first responders. For that reason, UGVs are equipped with electromagnetic field (EMF) sensors designed to identify high voltages.  
In floods, UAVs and USVs work synergistically; UAVs capture aerial images to identify obstacles, landmarks, and victims, while USVs provide surface information via acoustic sensors for route re-planning.  
In nuclear and industrial disasters, UAVs and UGVs equipped with specialized sensors detect hazardous chemicals and conduct radiological mapping.  
For infestation disasters, the system facilitates UAV-intensive searches using hyperspectral cameras and real-time video analytics to assess vegetation size and detect pest infestations.  
Figure 1: Overview of AIRDROP Control System service.  
It is important to monitor and record weather parameters on every flight in order to achieve a successful area scanning mission. Therefore, UAVs are equipped with weather sensors to aid mission planning.  
AR devices are given to first responders to enhance their situational awareness by visualizing important data from the above-mentioned area scanning subservices.  
Last, a tethered \"Mothership\" UAV acts as a continuous \"Eye in the Sky\", equipped with high-definition cameras, flood lights, megaphone, and Wi-Fi access for seamless data transmission via optical fiber to the GCS at disaster zones.
## 3 Case Study
### Implementation of Model  
In the present analysis, a model utilizing Bayesian network and non-linear regression methods was trained and assessed using the MATLAB program. As mentioned in the preceding section, the validation dataset was employed to address the overfitting issue, with a maximum validation failure set at 100. Back-propagation learning was conducted on a single hidden layer with 10 hidden nodes. A total of 476 data samples from 68 distinct diesel-powered Z-drive harbor tugboat series were collected for this investigation [6]. The dataset was partitioned such that 70% of the samples were allocated to the training set, 15% to the validation set, and another 15% to the test set. The training set exclusively served for training the network, while the test set was used to evaluate network performance. The activation functions \"logsig\" and \"purelin\" were applied to the hidden layer and output layer, respectively [5]. The distribution graphs of output parameters, specifically those  
Figure 8: Correlation matrix of the collected and pre-processed data (Dots: Variables; Lines: Least-squares references; Histogram; Distribution of a variables).  
Figure 7: Pearson correlation coefficients of the collected and pre-processed data.  
related to vessel length (L) with input parameters bollard pull (BP) and ship speed (V), are presented in Figure 6 along with their conditional probability distributions.  
The structure of a neural network (NN) comprises multiple processing units capable of bidirectional communication through connections with varying weighting factors. Generally, not all neural networks possess a structure that includes the following components: input layer, signals, hidden layer(s), and output layer. The number of neurons in the input and output layers depends on the nature of the problem being addressed, considering the number of variables and outcomes.  
Input values (\(x_{i}\)) from previous layers are processed in a single artificial neuron using bias (\(b\)) and weights (\(w_{i}\)), as seen in the below Equation (5) [17]:  
\[S=b+\sum_{i=1}^{n}w_{i}*x_{i}, \tag{5}\]  
The structure of the Bayesian network model for this investigation is depicted in Figure 9. This model has two inputs (bollard pull and speed) and ten hidden layers with five outputs (length, beam, draft, draft-maximum, and power).
# Real-Time Monitoring of Wind Turbine Bearing Using Simple Neural Network on Raspberry Pi
## 2 Methodology
### The Description of the Neural Network Model  
Many research works use neural networks for fault detection or the monitoring of bearings. [PERSON] et al. [26] monitored wind turbines with deep learning. In their paper, CNN and LSTM are used to analyze variables and monitor wind turbine gears. Gearbox bearing temperature data are processed for AI monitoring and troubleshooting. [PERSON] and [PERSON] [27] provide a basic three-dimensional CNN-based wake model to predict wind turbine wake flow fields. The model accurately predicts wake flow characteristics, showing its potential for wind turbine wake forecasts. [PERSON] et al. [28] proposed an attention mechanism-based CNN-LSTM wind turbine fault prediction model. The CNN extracts features, LSTM captures time sequence correlations, and an attention algorithm gathers fault-related target information from the ontology-annotated Semantic Sensor Network. The model was shown to be precise and generalized.  
In this paper, we adopted a real-time detection neural network model to assess wind turbine bearing failure, as shown in Figure 2. The model adopts the architecture of a Multi-Layer Perceptron (MLP) [29].  
Figure 1: Overview of the system: (**a**) wind turbine; (**b**) modeling in python language; (**c**) neural network model; (**d**) Raspberry Pi implementation; and (**e**) predictions.  
Figure 2: Proposed network.  
The neural network model contains two fully connected layers (FC1 and FC2), and the second fully connected layer (FC2) acts as the output layer. The first full connected layer (FC1) has 64 input features and 20 hidden units, which are used to learn complex features in the input data. The output layer (FC2) is a layer that takes the 20 output features from FC1 and reduces them to two output features, which are used to map the learned features to faults and normal categories.
## 3 Results
### Canopy Height
#### 3.2.1 Slope  
Given the significant impact of terrain slope on full-waveform canopy height inversion, we calculated average slope values within each laser footprint using surface slope data from the study area. Slopes were classified into the six following categories: 0-5\({}^{\circ}\), 5-10\({}^{\circ}\), 10-15\({}^{\circ}\), 15-20\({}^{\circ}\), 20-25\({}^{\circ}\), and \(>\)25\({}^{\circ}\). We estimated forest canopy heights across different slope categories and analyzed their variation trends using CASAL waveform data. Table 6 and Figure 9 present correlation coefficients and root mean square errors between CASAL-RH90 and ALS-Hmax across slope categories. While ALS-Hmax was normalized to remove slope effects, CASAL-RH90 remains slope-sensitive, causing overestimation in some plots and consequently reducing the mean height difference between the two datasets. Total laser points across slope categories are slightly reduced compared to the complete dataset due to minor gaps in terrain slope data.  
Figure 8: Validation of spaceborne CASAL RH90 against airborne ALS canopy height metrics. Scatter plot comparing canopy height estimates between the spaceborne CASAL system (RH90 metric) and airborne LiDAR (ALS Hmax) across 14,516 footprints. The least-squares regression line (black) and 1:1 reference line (red) are superimposed for comparative analysis.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**Parameter** & **RH80** & **RH85** & **RH90** & **RH95** & **RH99** & **RH100** \\ \hline \(R^{2}\) & 0.499 & 0.512 & 0.512 & 0.501 & 0.484 & 0.481 \\ RMSE (m) & 2.89 & 2.75 & 2.70 & 2.76 & 2.86 & 2.88 \\ Bias (m) & \(-1\) & \(-0.64\) & \(-0.35\) & \(-0.06\) & 0.12 & 0.14 \\ MAD (m) & 2.17 & 2.13 & 2.13 & 2.17 & 2.21 & 2.21 \\ \hline \end{tabular}
\end{table}
Table 5: Comparison of CASAL-derived forest height percentiles with ALS maximum canopy height.  
Table 6 demonstrates that as slope increases, \(R^{2}\) between CASAL-RH90 and ALS-Hmax progressively decreases, while Bias and median values increase. RMSE and MAD values initially decrease before increasing with steeper slopes. Over 70% of footprints in this study had average slope values below 20\({}^{\circ}\). For slopes between 0-20\({}^{\circ}\), CASAL-RH90 and ALS-Hmax exhibited stronger correlations compared to the full dataset, indicating TECIS-CASAL's enhanced capability for stable and accurate canopy height inversion on gentle slopes. Numerous studies have consistently demonstrated improved canopy height inversion accuracy with decreasing slope gradients.
# Dynamic Mixed Reality Assembly Guidance Using Optical Recognition Methods
## 6 Discussion
### User-Action Validation  
The internal hand-tracking features of the HoloLens were used to track the users' hand movements. Accuracy results from the experiments showed that the application validated the experienced user's component selection actions correctly in 99% of cases and the inexperienced user's component selection actions correctly in 70% of cases. The discrepancy derives from awareness that experience gave on how to pick components, so that the index finger would be inside of the component bounding box, whereas the inexperienced users picked up components in a variety of different ways, which was not anticipated when the bounding boxes were designed. A higher validation success rate can be obtained using this method, given a better design of bounding boxes.