esa-sceva/satcom-chunk-collection huggingface dataset:
## 7 Challenges of Edge AI in Early Health Prediction and Future Directions
### Model Bias  
AI algorithms may exhibit bias when trained on datasets with inherent biases. This can result in predictions that are also biased, leading to unfair results, especially in early-stage health prediction, where the detrimental effects of erroneous predictions can be quite significant. Here are the key aspects to consider:* **Dataset Bias:** AI models are trained on datasets that may not be representative of the diverse population they aim to serve. Biases in the data, such as under-representation of certain demographics or health conditions, can lead to biased predictions and inequitable healthcare outcomes [100].
* **Algorithmic Bias:** Biases can also be introduced through the design and implementation of AI algorithms [101]. If the training data contain inherent biases or if the algorithm itself is biased, this can perpetuate and amplify existing disparities in healthcare.
* **Interpretability and Transparency:** Lack of interpretability and transparency in AI models can make it challenging to identify and address biases. Understanding how the model makes predictions and uncovering any underlying biases require explainable AI techniques [102].  
Future research directions to address model bias challenges in Edge AI for early health prediction include:  
* **Bias Detection and Mitigation:** Designing methods to detect and quantify bias in AI models and developing techniques to mitigate its impact. This can include techniques like debiasing algorithms, data augmentation, and fairness-aware feature selection.
* **Diverse and Representative Datasets:** Collecting and using diverse and representative datasets that encompass different demographics, health conditions, and socio-economic backgrounds. This helps reduce dataset bias and improves the generalizability of AI models.
* **Ethical Guidelines and Regulations:** Establishing clear ethical guidelines and regulations for the development and deployment of AI in healthcare. This can help address biases, promote fairness, and ensure accountability and transparency in early health prediction.
* **Collaboration and Interdisciplinary Research:** Encouraging collaboration between AI researchers, healthcare professionals, ethicists, and policymakers to collectively address model bias challenges. Interdisciplinary research can provide a holistic perspective and help develop comprehensive solutions.
# Generative AI in Medicine and Healthcare: Promises, Opportunities and Challenges
## 3 Current Efforts of Applying Generative AI and LLMs in Medicine and Healthcare
### Synthetic Data Generation  
Syntegra Medical Mind [37] utilizes generative AI to produce realistic synthetic patient records from real healthcare data like EHRs while protecting patient privacy. Healthcare professionals can access and analyze this data for research, education, and decision-making without compromising confidentiality. The synthetic records match the statistical properties of the original data, including rare cohorts and outliers, aiding specialists in understanding diverse disease patterns. Syntegra also addresses data bias and improves algorithmic fairness, promoting equitable treatment plans. The synthetic data layer breaks barriers to data access, fostering innovation and enhancing patient care. [PERSON] et al. [38] demonstrated the potential to advance dementia research through virtual cohorts synthesized with Syntegra.  
DALL-E 2 [39] is another OpenAI model for text-to-image generation. It was trained on billions of text-image pairs to learn to create realistic synthetic images. Thanks to its extensive pretraining, DALL-E 2 has the exciting potential of creating or augmenting medical data that is often sparse or limited in medical research and education without compromising patient privacy. [PERSON] et al. [40] investigated the DALL-E 2's domain knowledge in radiology by testing the output medical images generated from short descriptions and those from reconstructing existing radiological images with missing areas. The study showed that even though generation performance suffered for complex images such as CT, MRI, and ultrasound, DALL-E 2 could produce x-ray images that maintained similar style and anatomical proportions to authentic images, given that they were without pathologies. Despite the limited capabilities of directly applying DALL-E 2 in medical image generation, it showed promising potential to be further fine-tuned with medical data and related terminology to create a customized model for radiological data generation and augmentation.
# Evaluation of Light Pollution in Global Protected Areas from 1992 to 2018
## 3 Methodology
### The Temporal Trends of Nighttime Light  
We explored the temporal trend of NTL change using the Theil-Sen slope and Mann-Kendall test in polluted PAs (i.e., continuously and discontinuously polluted). We measured the slope of NTL change using the Theil-Sen trend approach, a robust and simple linear regression method [50]. We also calculated the significance level using the p-value provided by the Mann-Kendall approach [51]. Combining these two approaches for trend analyses is extensively used in studies such as ecological and environmental change [52; 53].  
To identify those buffers with lit pixels that have different NTL intensities around PA, we proposed two intervals of buffers (i.e., the first polluted and the high-intensity intervals) that can well-characterize the spatial extent of those sensitive regions. The first buffer in a specific year was defined as the nearest buffer with lit pixels. Thus, the first polluted interval (Figure 3a) was defined as between the furthest (e.g., the 30 th buffer in 1992) and the nearest (e.g., the buffer in 2018) during the whole period. This interval reflects the NTL impact on PAs from a spatial distance, and those PAs having closer buffers with lit pixels are easier to be affected by the light pollution. Differently, we also defined the interval using the intensity of NTL (Equation (1)), in which both the DN values of lit pixels and their distances to PAs were considered (Figure 3b). Similarly, we calculated the minimum (e.g., the 50 th buffer in 1992) and maximum (e.g., the 20 th buffer in 2018) buffers from 1992 to 2018.  
Figure 2: Conceptual illustration of light pollution categories of non-polluted (**a**), continuously polluted (**b**), and discontinuously polluted (**c**).  
to 2018 and defined their interval as the high-intensity interval (e.g., the 20 th to 50 th buffer in Figure 3b).  
\[I_{i}=\frac{NTL_{sum}(B_{i}-B_{i-1})}{B_{i}} \tag{1}\]  
where \(I_{i}\) is the intensity of \(i^{th}\) buffer, \(B_{i}\) is the \(i^{th}\) buffer, and \(NTL_{sum}\) is the sum of NTL in the buffer ring \(i\) (i.e., \(B_{i}-B_{i-1}\)).  
We analyzed the temporal trend of NTL in different regions for continuously and discontinuously polluted PAs. Due to urban sprawl and human activities, PAs are likely to be affected by NTL over the past decades, i.e., the first polluted buffer becomes close to PAs with increasing intensity of NTL over the years. Therefore, for continuously polluted PAs, we analyzed the temporal trend of NTL inside the PA and the high-intensity interval because the first polluted buffer was merged with PA since 1992. However, for discontinuously polluted PAs, we investigated the temporal trend of NTL in the first polluted interval and high-intensity interval (a detailed case of the high-intensity interval can be referred to Seattle in Figure S3).
## 4 Results and Discussion  
To evaluate the effectiveness of the proposed methods, this section conducts several simulation experiments. We set up test scenarios in STK software by adding different numbers and types of EOSs, payloads, and targets, as shown in Figure 5. Mission requests (including targets geographical locations, observation types, priority, resolution requirement, etc.) are generated worldwide by a random uniform distribution. During the simulation period (from 8 March 2021 16:00 to 9 March 2021 16:00, universal time coordinated), the observation satellites composed of a Walker constellation in a 600 km height sun-synchronous orbit (the inter plane true anomaly increment is 2\({}^{\circ}\), the RAAN increment is 4.5\({}^{\circ}\)) are automatically generated. We can modify the scale of scenarios by adjusting the number of satellites in an orbital plane and orbital plane number to adjust the size of the constellation.
# MPVF: Multi-Modal 3D Object Detection Algorithm with Pointwise and Voxelwise Fusion
###### Abstract  
3D object detection plays a pivotal role in achieving accurate environmental perception, particularly in complex traffic scenarios where single-modal detection methods often fail to meet precision requirements. This highlights the necessity of multi-modal fusion approaches to enhance detection performance. However, existing camera-LiDAR intermediate fusion methods suffer from insufficient interaction between local and global features and limited fine-grained feature extraction capabilities, which results in inadequate small object detection and unstable performance in complex scenes. To address these issues, the multi-modal 3D object detection algorithm with pointwise and voxelwise fusion (MPVF) is proposed, which enhances multi-modal feature interaction and optimizes feature extraction strategies to improve detection precision and robustness. First, the pointwise and voxelwise fusion (PVWF) module is proposed to combine local features from the pointwise fusion (PWF) module with global features from the voxelwise fusion (VWF) module, enhancing the interaction between features across modalities, improving small object detection capabilities, and boosting model performance in complex scenes. Second, an expressive feature extraction module, improved ResNet-101 and feature pyramid (IRFP), is developed, comprising the improved ResNet-101 (IR) and feature pyramid (FP) modules. The IR module uses a group convolution strategy to inject high-level semantic features into the PWF and VWF modules, improving extraction efficiency. The FP module, placed at an intermediate stage, captures fine-grained features at various resolutions, enhancing the model's precision and robustness. Finally, evaluation on the KITTI dataset demonstrates a mean Average Precision (mAP) of 69.24%, a 2.75% improvement over GraphAlign++. Detection accuracy for cars, pedestrians, and cyclists reaches 85.12%, 48.61%, and 70.12%, respectively, with the proposed method excelling in pedestrian and cyclist detection.  
3D object detection; autonomous driving; image; point cloud; multi-modal fusion +
Footnote †: journal: _Algorithms_
# Voting-Based Contour-Aware Framework for Medical Image Segmentation
## 5 Discussion

Table 6: Quantitative evaluation of different networks for the hippocampus dataset in terms of DSC (%). images. There are also some works to bridge the gap between 2D and 3D. VFN [54] fused the 2D segmentation results from multi-views. Residual DSM [55] and Distill DSM [56] used 2D convolution to segment by extracting information and sharing it with neighboring slices. Although our model achieved lower scores on the hippocampus dataset than EMONAS-Net and Distill DSM, the EMONAS-Net trained 3D CNN, which was able to incorporate 3D features. The Distill DSM needs to input a stacked slice volume into the network. Our model was designed to train 2D CNN and segment with 2D slices, as 2D CNNs are known to be computationally lighter and have faster inference time than 3D CNNs. We further measured the suitable value of \(\lambda\) and the appropriate parameter of the voting strategy. In Table 7, the results show that our contour-aware CNN based on nn-UNet has the best performance when \(\lambda=20\). When the value of \(\lambda\) is lower or higher, it decreases the performance of models. In Table 8, the results show that our framework based on nn-UNet prefers to learn half of the samples in each mini-batch. Learning 25% of cases per iteration may lead to underutilization of data and has the worst performance on both datasets. The 75% sampling rate results are slightly lower than the 50% sampling rate results on the CHAOS dataset, and they have the same performance on the Hippocamp dataset. It can be shown that our voting strategy performs well on both datasets when the experimental setting is greater than a 25% sampling rate. We first developed a novel joint optimization framework consisting of contour enhancement modules and a voting strategy in our study, which considered the contour features and a meaningful sample learning order. In actual clinical practice, we expect more accurate results, and we verified that the combination of these two techniques is effective. We demonstrated in ablation studies that each module is useful and improves the overall performance of state-of-the-art baseline networks at each stage on two public datasets. We assume that our framework can generalize to unseen datasets. Now that we have validated our framework on two public datasets, we will collect more datasets to validate our framework in the future. We expect that our work can be translated into high-precision segmentation tasks and can be used as a reference for future segmentation tasks that require high precision. The limitation of our framework is that the complexity is relatively high and the mechanism of action needs further study. The voting strategy does consume more computing resources and complicates training, but it should be adopted in deep learning in future research when high-precision results are required and the model size can be ignored.
###### Abstract  
Concerns about the impact of space radiation on spacecraft and their internal instruments have prompted the need for effective protection. However, excessive protection can increase the costs and difficulty of space launches, making it crucial to achieve better shielding protection of lighter weights. In real space orbits, we observed the interference of charged particles on photon-counting imaging detectors and plan to address this issue by adding a shielding ring to the side wall of the detector input terminal. Additionally, a local protection structure was proposed for electronics, where the outer edge was increased to enable particles to reach the same thickness as the shielding box within the PCB range. This approach resulted in an omnidirectional spatial shielding thickness that was nearly identical at any point on the PCB surface. Furthermore, we used the Monte Carlo method to calculate the energy loss of electrons and protons in materials such as aluminum (Al), tantalum (Ta), and high-density polyethylene (HDPE). Through this analysis, we determined the optimal mass ratio of Al, Ta, and HDPE to achieve the lowest ionization doses at an object's location in the particle environment of the FY-3 satellite orbit. This protection strategy provides a useful design concept for photoelectric detection instruments with high sensitivity.  
photon-counting imaging; MCP detector; radiation effects; radiation shield; all-directional protection
## 7 Differentiation between Switching and Faulty Transients
### Load Switching  
The load at node 671 was switched off at the fourth cycle and switched on at the sixth cycle. The cCurrent signal recorded at node 650 was processed using the algorithm and FI was evaluated as described in Figure 15. It is established that FI has lower values than the threshold during the transients due to the switching off and on of the load. Hence, the proposed algorithm effectively discriminates the load switching transients from the faulty transients.
# Analysis of Maize Sowing Periods and Cycle Phases Using Sentinel 1&2 Data Synergy
## 1 Introduction
Recently, much attention has been given to active radar as a reliable instrument to retrieve sowing periods [23]. The analysis performed over the Bekaa Valley (Lebanon) by [PERSON] et al. [16] showed the reliability of multi-polarization active radar for the assessment of crop sowing periods. The main research question that the present work aims to address is: can crop-specific sowing periods be retrieved/inferred from an EO-method combining active radar and optical data? The present work also aims to exploit the potential of combined optical and active radar information to describe the vegetation cycle of maize, highlighting the strengths of each sensor to detect significant properties of growing phases. To address the above goals, the high-resolution information from Sentinel-1 and Sentinel-2 have been combined with ground information on land use to analyze the growth cycle of maize over a densely cultivated temperate region in Piedmont (North-West Italy). Since maize is sowed and harvested within the same year, the analysis was carried out for the year 2019: besides the availability of data, analyzing year 2019 avoids uncertainties related to the agricultural practices during the global pandemic occurring in 2020. In Section 2, we present the satellite data used for the study and the ground information which was used to derive the land classification. In the same section we detail the procedure adopted to identify the pilot fields, to describe the phenological cycle of maize and to derive sowing dates. In Section 3, the results of the applications are presented and discussed; first, the checking for rainfed areas in the largely irrigated region are addressed; the spatio-temporal variability of emerging and sowing periods is then discussed, comparing the results with available large-scale calendars and with the information provided by local farmers.
## 3 The Proposed Method
### Specific Details of the Fault Diagnosis Method (ISCV-ViT)
#### 3.2.2 Input Embeddings  
It is widely known that the input of the standard Transformer module is a two-dimensional matrix, which is a vector sequence (token). However, the data format of a two-dimensional image is a three-dimensional matrix \([H,\,W,\,C]\), which is inconsistent with the requirements of Transformer. Therefore, it is necessary to transform the data through the embedding layer. For ViT, the input embeddings consist of three parts: patch embeddings, position embeddings, and learnable embeddings.  
Patch embedding is a one-dimensional vector obtained by the linear projection of an image patch. Specifically, given a time-domain image \(x\in\mathbb{R}^{H\times W\times C}\), where \(H\) and \(W\) are the lengths of the image in the horizontal and vertical directions, respectively, \(C\) is the number of channels (an RGB image has 3 channels). First, according to the specified size \(p\ \times\ p\), we divide the image into \(N=HW/p^{2}\) patches \(x_{p}\in\mathbb{R}^{N\times(p*p*C)}\). Then, each patch is linearly mapped to a one-dimensional vector \(x_{p}^{\prime}\in\mathbb{R}^{N\times d_{\text{model}}}\), which is exactly the form of input that meets our requirements. In the algorithm, this is implemented through a convolution layer, which then flattens the output according to the \(H\) and \(W\) dimensions.  
A learnable embedding is a vector specifically used for classification in ViT. Referring to the class token proposed in BERT [47], a token \(z_{0}^{0}\)= \(x_{class}\) independent of the image and with fixed position embedding is spliced with patch embeddings obtained by convolution. Moreover, the token is a randomly initialized, trainable embedding, so it can encode the statistical characteristics of the entire dataset. This makes the classification task more reasonable. At this point, the vector obtained by splicing is \(z_{t}\in\mathbb{R}^{(N+1)\times d_{\text{model}}}\).  
Position embedding is the same as position encoding in Transformer. ViT uses standard learnable one-dimensional embeddings \(E_{\text{pos}}\in\mathbb{R}^{(N+1)\times d_{\text{model}}}\), which are directly superimposed with the obtained patch embeddings. Thus, it is necessary to ensure that both dimensions are the same. Notably, the author of [34] conducted a series of comparative experiments in ViT to explain the reason for choosing one-dimensional position embedding. The results show that the accuracy is significantly improved compared with that without position embedding, but there is little difference in the accuracy when using one-dimensional or two-dimensional position embedding.  
Thus far, the input embeddings \(z_{0}\) meet the requirements.  
\[z_{0}=\left[x_{class};x_{p}^{1}E;x_{p}^{2}E_{i}\cdots;x_{p}^{N}E\right]+E_{pos} \tag{7}\]  
where \(E\in\mathbb{R}^{(p*p*C)\times d_{\text{model}}}\) is the learnable linear mapping used in patch embeddings.  
Figure 4: Normalized current signal time domain images: (**a**) Single-phase current; (**b**) two-phase current processed by ISCV.
# Analysis of the Channel Influence to Power Line Communications Based on ITU-T G.9904 (PRIME)
## 4 Workbench Description and Test Conditions
### Precision of the Simulations
#### 4.4.2 SNR Precision and SNR Range for Each Test  
For SNR computation, only the noise in band is considered, this is, the noise power within the bandwidth assigned to PRIME transmissions. The precision in the generated SNR magnitude is lower than 0.01 dB.  
The SNR increment for each test point is 0.1 dB. It has to be small enough as these digital communications tend to have great variations in 1 or 2 dB.  
The SNR range for each test has been experimentally estimated by performing a fast simulation of 100 or 1000 PPDUs every 1 dB of SNR in a very wide SNR range. Based on this estimation, the useful SNR range for the final simulation was selected.
# Simulation of Wind Speeds with Spatio-Temporal Correlation
## 3 Case Study  
The accuracy of the model and, consequently, of the simulation of the random data requires a good set of the necessary parameters. The framework of the results obtained in this paper is Galicia, the autonomous region located in the Northwest of Spain because this is one of the areas where wind energy has a greater potential. In fact, in 2019, Galician wind farms presented the third part of the total installed power in the region, exceeding the combination of conventional sources [33], with a total number of 4026 wind turbines installed [34].  
A large amount of the wind power installed in Galicia is located in the northeast of the region, and due to this, the study places belong to this area. From the meteorological stations of the MeteoGalicia network [35], the data from the Fragavella, Labrada and Lanzos stations corresponding to 2019 were selected. Once the locations to be considered in the study are known, the data collected are the wind speeds of each location. With them, it is possible to analyze the simultaneity, and as the height at which they were collected is known, this makes it possible to calculate the wind speeds at the study height. One of the objectives of this study is to determine the relationship between distances and correlations. Therefore, based on the coordinates of the meteorological stations, the straight line distance between each pair of locations was obtained. In Figure 6, the positions of the selected places and their distances in km are shown.  
The data collection height of the selected meteorological stations is different from the height of wind turbine hubs, i.e., the height of the study of the simultaneity of wind speeds. Therefore, it is necessary to characterize the friction factor of the terrain for each of the selected sites. In Table 1, the estimated friction factors and the different data heights available at the MeteoGalicia website are shown.  
Figure 6: Selected locations and distances between them.  
By analyzing the frequency of the wind speeds in the collected data, it is possible to affirm that the trend of the wind speed in all those locations is correctly described by the Weibull distribution. The shape and scale parameters of each location are calculated using Equations (3) and (4), and the results are shown in Table 2.