esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Methods
### Architecture  
Figure 1 shows the architecture of the proposed G-UNETR++, consisting of a hierarchical encoder-decoder structure with skip connections between the encoder and decoder, followed by convolutional blocks to generate segmentation results, based on the recently introduced UNETR++ [20]. Instead of a single encoder in UNETR++, we added another two gradient-based encoders in parallel. In our G-UNETR++ architecture, each encoder consists of four stages. In the first stage, patch embedding is performed to divide volumetric input into 3D non-overlapping patches, followed by the EPA block in UNETR++. In the patching embedding step, the volumetric input \(f\in\mathbb{R}^{H\times W\times D}\) is  
divided into 3D patches \(f_{p}\in\mathbb{R}^{N\times(P_{1},\,P_{2},\,P_{3})}\), where (\(P_{1}\), \(P_{2}\), \(P_{3}\)) is the resolution of each patch and \(N=\left(\frac{H}{H}\times\frac{W}{H}\times\frac{D}{P_{3}}\right)\) denotes the length of the sequence. The 3D patches are then projected into C_1 channel dimensions to obtain a \(\frac{H}{P_{1}}\times\frac{W}{P_{2}}\times\frac{D}{P_{3}}\times C_{1}\) feature map. The same patch resolution (4, 4, 2) as in UNETR++ is used in our G-UNETR++. For each of the remaining three stages, downsampling non-overlapping convolution layers are used to decrease the resolution by a factor of two, followed by the EPA block. In each of the four stages, the outputs of the three EPA blocks from the three parallel encoders are added.  
The EPA block consists of two attention modules, spatial and channel attention, to learn enriched spatial-channel feature representations from both spatial and channel dimensions. The two attention modules share the keys and queries but use different value layers to generate feature representations more effectively and efficiently. A more detailed description of the EPA block can be found in UNETR++ [20]. Like the encoder, the decoder is structured into four stages as well. Each stage comprises an upsampling layer with deconvolution to increase the resolution of the feature representations by a factor of two, followed by the EPA block. For the last decoding stage, no EPA block is included. For each stage, a skip connection is used to connect the encoder and decoder to merge the feature maps at different resolutions to recover the spatial information lost in the downsampling procedures to obtain more accurate segmentation results. Between the two stages in the decoder, the number of channels is decreased by a factor of two. In the last decoding stage, the output of the previous decoding stage is fused with the convolutional feature representations from the encoder to recover spatial information. Then, the fused outputs are fed to a \(3\times 3\times 3\) and a \(1\times 1\times 1\) convolutional block to obtain the voxel-wise segmentation results.
## 2 Materials and Methods
### Data and Preprocessing
#### 2.3.2 SAR Data Preprocessing  
Sentinel-1 data preprocessing [26] involves track correction, thermal denoising, radiometric calibration, multi-look processing, filtering, topographic correction, and backscatter normalization. The ENVI-based SARscape module is used in Sentinel-1A data preprocessing. This module can comprehensively preprocess and analyze polarimetric radar data in various bands. SARscape currently supports the import, geometric correction, multi-look processing, registration, denoising, geocoding, and polarization decomposition of radar data and a series of basic processing functions. Because the study area is mainly in the plain with relatively flat terrain and mountainous area less than 3% of the total area, the slope effect cannot be considered in the processing. Preprocessing is made for VV polarization and VH polarization at the same time. The preprocessing process mainly includes the import, multi-look processing, registration, polarization, filtering, and geocoding of the data (Figure 3).  
Taking VV and VH data on May 19 as the benchmark image, five images from June to October were registered. Time-series filtering [27] was conducted on the registered images to remove the speckle noise of the radar image. This method can be applied to single-band and multi-time-phase radar images. Multi-time-phase radar filtering includes De Grand and anisotropic nonlinear diffusion. In this study, De Grand was adopted to process multi-time-phase SAR in the study area. GAMMA-MAP filtering was used in this study. The window was set to 5 \(\times\) 5. Three RGB channels were used to display VV polarization and VH polarization in the study area after preprocessing.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Imaging Mode** & **Acquisition Time** & **Track Number** & **Resolution** & **Multi-Look Resolution** & **Polarization Type** \\ \hline IW & 19 May 2017 & 128 & 10 \(\times\) 9.99 & 20 \(\times\) 19.988 & VH/VV \\ IW & 12 June 2017 & 128 & 10 \(\times\) 9.99 & 20 \(\times\) 19.988 & VH/VV \\ IW & 18 July 2017 & 128 & 10 \(\times\) 9.99 & 20 \(\times\) 20.002 & VH/VV \\ IW & 11 August 2017 & 128 & 10 \(\times\) 9.99 & 20 \(\times\) 19.988 & VH/VV \\ IW & 16 September 2017 & 128 & 10 \(\times\) 9.99 & 20 \(\times\) 20.003 & VH/VV \\ IW & 10 October 2017 & 128 & 10 \(\times\) 9.99 & 20 \(\times\) 19.988 & VH/VV \\ \hline \hline \end{tabular}
\end{table}
Table 2: Sentinel-1A data.  
Figure 3: Sentinel-1 SAR data preprocessing process.  
On the basis of the image after geocoding, RGB channels were used to display the composite graph of VV and VH polarization (R (05/19), G (06/12), B (07/18)) in the study area. The results are shown in Figure 4.
# Soil Moisture Monitoring at Kilometer Scale: Assimilation of Sentinel-1 Products in ISBA
###### Abstract  
Observed by satellites for more than a decade, surface soil moisture (SSM) is an essential component of the Earth system. Today, with the Sentinel missions, SSM can be derived at a sub-kilometer spatial resolution. In this work, aggregated \(1\) km \(\times\) 1 km SSM observations combining Sentinel-1 (S1) and Sentinel-2 (S2) data are assimilated for the first time into the Interactions between Soil, Biosphere, and Atmosphere (ISBA) land surface model using the global Land Data Assimilation System (LDAS-Monde) tool of Meteo-France. The ISBA simulations are driven by atmospheric variables from the Application of Research to Operations at Mesoscale (AROME) numerical weather prediction model for the period 2017-2019 for two regions in Southern France, Toulouse and Montpellier, and for the Salamanca region in Spain. The S1 SSM shows a good agreement with in situ SSM observations. The S1 SSM is assimilated either alone or together with leaf area index (LAI) observations from the PROBA-V satellite. The assimilation of S1 SSM alone has a small impact on the simulated root zone soil moisture. On the other hand, a marked impact of the assimilation is observed over agricultural areas when LAI is assimilated, and the impact is larger when S1 SSM and LAI are assimilated together.  
S 2023154329 1
# Integration of Machine Learning-Based Attack Detectors into Defensive Exercises of a 5G Cyber Range
## 7 Generation of ML Toolboxes for SPIDER Exercises: Smart Traffic Analyzers
Next, the Tagger adds, automatically and without human intervention, labels to each flow using the log information output by the Launcher during the execution of each experiment (e.g., the destination IP and port of a cryptomining server are used to add a \"crypto\" label to all connections that match these two values). In the last step, using as input the labeled datasets output by the Tagger, the training and testing of supervised machine learning models are conducted. Figure 6: Traffic generation and labeling. To complement this explanation, we detail the realization of a cryptomining attack scenario on top of the Mouseworld Lab. This scenario, based on the generic setup of Figure 4, will allow us to generate network traffic corresponding to normal connections and cryptomining ones. After collecting all packets in a pcap file, the STA module will be generated following the process described above. Both the pcap file and the STA module will be stored in the SPIDER cyber range to be used later in the cryptomining exercises. With the exception of the Machine Learning Model Builder, which is not represented in the figure and can be run on a separate infrastructure, the rest of the components for the generation of the cryptomining STA are deployed and run in the Mouseworld Lab. Note that the training and testing processes of the Machine Learning Model Builder, which tend to involve the execution of complex deep neural network architectures, are performed more efficiently in GPU-based infrastructures. Therefore, this last stage of the process can be executed on a more specialized computation infrastructure. When the training and testing processes are finalized, the resultant model is exported as a file using a model-specific format. The file containing the model will be stored later in an STA. Recall that STAs can be configured with several ML models that can be selected and activated on demand, one at a time, during the execution of the STA. Although several file formats are currently available to store ML and DL models (e.g., \"h5\" files using the Hierarchical Data Format (HDF), and \".pkl\" files using the Pickle serialization model), we strongly suggest using secure and open formats, such as ONNX [33].
## 4 Results and Discussion
### Qualitative Interpretation
#### 4.1.2 Regional CBA Anomaly Map  
The regional CBA anomaly map in Figure 6b has a gravity anomaly range from \(-12.5\) to \(18\) mGal and can be classified as two main regions: LG (\(-12.5\) to \(0\) mGal) and MG (\(1\) to \(18\) mGal). The gravity reading increases towards the western part indicating the development of crustal density towards the sea. Moreover, this phenomenon has proved the statement regarding the thickening of Mesozoic sediment towards the western region. The dense contour in the central part of the map, which split the negative and positive anomalies, suggests the extension of granite and surrounding rock formation contact at depth. The zone of LG and MG has an equal part whereby the increasing gravity anomaly readings were increasing proportionally from east to west, or from high topographic to low topographic regions. However, in the vicinity of the Muda River basin, the LG zones dominated in approximately \(80\%\) of the area.
# Last-Mile Optimization Using Neural Networks
## 3 Proposed Method
### Classification Methods Used
#### 3.5.4 K-Nearest Neighbors  
In the context of our study on optimizing last-mile delivery processes in urban environments, the KNN is a generalization algorithm for nearest neighbor rules. The output of KNN is the class label that appears most frequently among the K nearest neighbors. Unlike the basic nearest neighbor rule, which only considers the closest neighbor, the KNN algorithm expands the search to K neighbors in the decision-making phase. This extension allows the algorithm to utilize more information from the surrounding data points, resulting in better classification accuracy [40].
## 3 Model and Methods
### Bathymetric Mapping Based on Active-Passive Remote Sensing Data  
[PERSON] et al. [25] proposed a bathymetric mapping method based on a large amount of active and passive remote sensing data to remove the effects of complex underwater optical paths and sea surface instabilities on satellite images. This method integrates several years of ICESat-2 data with Sentinel-2 data, enabling large-scale and efficient bathymetric mapping in the absence of in situ data. Specifically, in this study, available Sentinel-2 and ICESat-2 data from 2019 to 2022 were selected. The ICESat-2 data undergo a series of preprocessing steps, including DBSCAN denoising, tidal correction, and multi-orbit data fusion, to serve as prior data. The resulting available ICESat-2 data were then partitioned into training and validation datasets. Subsequently, L2A data from Sentinel-2 are directly accessed online via the GEE platform. Clouds and cloud shadows are removed based on the Cloud Score+ QA band [34], and glint correction is applied. For the extensive Sentinel-2 dataset, the median value \(W_{\text{md}}\) of the Water Depth Index (WDI) for each pixel is calculated, and the coefficients of the QPRM are fitted through combination with the ICESat-2 training datasets, resulting in bathymetric inversion imagery with a spatial resolution of 10 m.
## 5 Results and Discussion
### Knowledge Graph Validation  
The knowledge graph (KG) validation process in DKPS ensures predictions align with logical learning paths. The system exhibited significant improvements in its performance  
\begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Iteration** & **Accuracy (\%)** & **Precision** & **Recall** & **F1-Score** \\ \hline
1 & 70 & 0.68 & 0.75 & 0.71 \\
2 & 73 & 0.71 & 0.77 & 0.74 \\
3 & 76 & 0.74 & 0.79 & 0.76 \\
4 & 79 & 0.77 & 0.81 & 0.79 \\
5 & 82 & 0.80 & 0.83 & 0.81 \\
6 & 85 & 0.82 & 0.85 & 0.84 \\
7 & 87 & 0.84 & 0.86 & 0.85 \\
8 & 89 & 0.86 & 0.87 & 0.87 \\
9 & 91 & 0.88 & 0.88 & 0.88 \\
10 & 93 & 0.90 & 0.89 & 0.89 \\ Average & 83 & 0.80 & 0.83 & 0.81 \\ \hline \hline \end{tabular}
\end{table}
Table 16: Performance metrics of ensemble models.  
metrics: accuracy, precision, recall, and F1-score over iterations. Accuracy, which measures the percentage of predictions aligning with valid paths in the KG, improved from 80% to 95% as depicted in Figure 7. This indicates the system's increasing ability to consistently identify correct predictions, reducing errors and enhancing reliability. This growth reflects the refinement in hybrid model predictions and the effectiveness of feedback loops. Precision, defined as the percentage of validated predictions that are truly correct, increased from 0.75 to 0.92. This improvement highlights the system's growing capability to avoid irrelevant or invalid paths, ensuring that recommendations are both accurate and relevant. Recall, the percentage of all valid paths correctly identified, rose from 0.70 to 0.88. This metric underscores the system's consistent identification of valid learning paths within the KG, ensuring no critical modules or transitions are overlooked. F1-score, which balances precision and recall, increased from 0.72 to 0.90. This mean reflects the system's overall effectiveness in achieving both relevance and comprehensiveness in predictions.  
Iterative improvements across all metrics resulted from feedback loops, intermediate module suggestions, and refined hybrid model outputs. For instance, invalid predictions dropped from 20% to 5%, with 80% of invalid recommendations dynamically corrected in the final iteration. Additionally, node utilization in the KG improved from 50% to 65%, demonstrating broader exploration and coverage of the graph. Meanwhile, the average validation time decreased from 0.7 s to 0.5 s, reflecting system optimization for real-time application as mentioned in Table 17.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline  & **Precision** & **Recall** & **F1-Score** \\ \hline
1 & 0.70 & 0.80 & 0.74 \\
2 & 0.72 & 0.82 & 0.76 \\
3 & 0.75 & 0.83 & 0.78 \\
4 & 0.77 & 0.85 & 0.80 \\
5 & 0.80 & 0.85 & 0.82 \\ \hline \hline \end{tabular}
\end{table}
Table 17: Performance metrics of models.  
Figure 7: Knowledge graph performance metrics.
## 3 The Proposed ReSiNet for Low-Memory Devices  
### Motivation  
The network structure of Tiny-YOLO is shown in Figure 3. This architecture consists of a total of nine convolutional layers followed by six max-pooling layers that are used to remove features of images along with one detection layer. This method uses convolutional layers containing 512 and 1024 filters that provide a large parameter density, large memory storage, and a lower detection speed. Another issue with Tiny-YOLO is its low detection accuracy. The network's irrational compression techniques may further decrease detection accuracies.  
Figure 3: The structure of Tiny-YOLO.  
Hence, in order to address such issues, ReStINet is introduced in this research, which directs towards the performance of the model's size as well as its accuracy. Algorithm 1 details the proposed ReStINet model.  
```
Input: Input(shape= (input_size, input_size,3)) Input: learning_rate, epoch, batch_size Input: iou_threshold, score_threshold Output: output_shape, mAP def fire_module(model, fire_id, squeeze, expand) def maxpooling (pool_size, stride) def resnet_block (model, filters, reps, stride) def mAP (model): map = model.evaluate (generator, iou_threshold, score_threshold, average_precisions) return map def layer(conv, batchnorm, activation, maxpooling, dropout) def main (){ create layer1: ([16,3,1], norm_1, leakyReLU[.1], 2, null) x \(\leftarrow\) layer1 for i in range(2,3,4,5): create layer(i): ([32*(2**i), 3, 1], norm_ + str(i+2), leakyReLU[.1], 2, [0.20]) x \(\leftarrow\) (x) (layer(i)) //return x create fire_module1: (x, 2, 16, 64) create fire_module2: (x, 3, 16, 64) create maxpooling1: (3, 2) create resnet_block1: (x, 64, 3, 1) create fire_module3: (x, 4, 32, 128) create fire_module4: (x, 5, 32, 128) create maxpooling2: (3, 2) create resnet_block1: (x, 128, 4, 2) create fire_module5: (x, 6, 48, 192) create fire_module6: (x, 7, 48, 192) create fire_module7: (x, 8, 64, 256) create fire_module8: (x, 9, 64, 256) dropout \(\leftarrow\) 0.50 return mAP(x), output_shape(x)]
```  
**Algorithm 1** ReStINet pseudocode  
The goal of ReStINet is to develop a model that is smaller, swifter, and more capable at detecting humans on lightweight devices. The network's optimization is carried out by performing a reduction in parameters to an acceptable level rather than blindly decimating the convolution layers. SqueezeNet's fire module compresses the framework using a bottleneck network layer and widens the network module without significantly sacrificing detection accuracy. As a result, the introduction of fire module was carried out to achieve the performance of a faster as well as a smaller network structure. ReStINet then seeks achieve a higher accuracy in detection while simultaneously minimizing the parameters. Study [53] achieved a higher accuracy with a smaller number of parameters in which residual blocks were integrated between fire modules in the VGG-16 network. Thus, in between the fire modules lies the residual block, which is used in ReSTINet to maximize the detection accuracy.
## 2 Materials and Methods
### Quantitative Assessment of Caco-2 Cell Kinetics in xCELLigence RTCA S16
#### 2.4.3 Monitoring of Real-Time Caco-2 Cells Grow in E Plate 16  
The xCELLigence RTCA S16 system continuously monitored the cells during proliferation and confluence in real-time for 50 h. The media was not changed. Sweep (the single measurement of impedance) interval in the xCELLigence software was set to 30 min for 100 repetitions (50 h in total) for all cells densities.
###### Abstract  
The rapid development of location-based social networks (LBSNs) produces the increasing number of check-in records and corresponding heterogeneous information which bring big challenges of points-of-interest (POIs) recommendation in our daily lives. The emergence of various recommender techniques bridges the gap between the numerous heterogeneous check-ins and the personalized POI recommendation. However, due to the differences between LBSNs and conventional recommendation tasks, besides the user feedback, the spatio-temporal information is also significant to precisely capture the user preferences. In this paper, we propose a multi-task learning model based POI recommender system which exploits a structure of generative adversarial networks (GAN) simultaneously considering temporal check-ins and geographical locations. The GAN-based model is capable of relieving the sparsity of check-in data in POI recommender systems. The temporal check-ins not only present the preference but also show the lifestyle of an individual while the geographical locations describe the active region of users which further filters POIs far from the feasible region. The multi-task learning strategy is capable of combining the information of temporal check-ins and geographical locations to improve the performance of personalized POI recommendation. We conduct the experiments on two real-world LBSNs datasets and the experimental results show the effectiveness of our proposed approach.  
recommender systems; sequential analysis; attention; generative adversarial networks +
Footnote †: journal: Applied Sciences
## 5 Conclusions
### Evaluation of Indoor UAV Applications (Greenhouse)  
Despite the wide variety of work in the outdoor area, there are very few studies on the use of UAVs in the greenhouse, so UAV solutions in the greenhouse are insufficient. In addition, no studies have emphasized this deficiency. Previous review studies have often introduced UAV applications and different UAV models in precision agriculture. In fact, the greenhouse environment is well suited for autonomous tasks. Factors such as the lack of GPS information in an indoor environment, the presence of obstacles in the environment during flight and a limited flight area make UAV control difficult in indoor environments such as a greenhouse. However, the intended use of greenhouses requires the constant control and monitoring of the condition of plants or crops. Performing such applications in very large greenhouses with an autonomous flying UAV instead of being repeated by humans will make a great contribution to precision agriculture.  
As an example of UAV applications in the greenhouse, plant-based (individual) monitoring of the growing conditions of vegetables in greenhouses can be provided, and various metrics can be obtained using imaging techniques that are directly related to the growth of plants. Plants showing unexpected growth can be identified, and thus, crop control can be achieved. In addition, continuous control of products in the greenhouse through processes such as weed detection, yield estimation, disease detection and plant temperature stress can be provided. Although these are the first applications that come to mind, greenhouses have not been preferred in studies thus far. In fact, such tasks can be performed by a UAV with a camera using various artificial intelligence and image processing techniques. In this way, precision agriculture practices can be applied, and crop yields can be increased. However, using UAVs in a greenhouse environment involves many technical difficulties. These challenges range from the automation of the UAV flight controller to the development of the software necessary for archiving, retrieving and interpreting the collected big data. Determining the position of the aircraft will be a major challenge in indoor environments with limited flight space. The difficulties encountered in the real world, such as insufficient GPS signal, the presence of dynamic obstacles in the environment, static obstacles in the task area and lighting changes, require a large number of threads to work in harmony in the software.
# Development and Evaluation of Training Scenarios for the Use of Immersive Assistance Systems
## 4 Results
### Research Question 2.  
_What effect does the quality of training have on the human factors of acceptance and stress in an immersive assistance system?_  
**Answer 2.** According to the results, it was shown that QoT has a significant influence on acceptance (BI) and the subscales PE, EE, FC, and HM. This demonstrates that insufficient training not only reduces the acceptance of the immersive assistance system but also increases the fun and enjoyment of using it. QoT also has a significant influence on perceived stress and frustration when performing operational tasks. It was also shown that QoT has a significant influence on stress during operational tasks with an immersive assistance system.