esa-sceva/satcom-chunk-collection huggingface dataset:
# Crack Location and Degree Detection Method Based on YOLOX Model
## 5 Conclusions and Future Work  
Structural damage detection and evaluation have always been a research concern in the field of SHM and are also a significant part. The traditional visual structural crack detection method is time-consuming and laborious. Additionally, most of the SHM methods based on image processing are only for a single image, a process which is non-automatic and inefficient. To address these issues, a detector based on YOLOX is utilized to detect different degrees of crack in this study. This method can detect and assess the structural state quickly, accurately, and automatically. An image dataset with different extents of crack is constructed in this paper. The dataset is mainly marked according to the width of cracks, which are divided into five types: super wide cracks, wide cracks, normal cracks, thin cracks, and spalling, respectively. It is a bold attempt to combine crack detection and assessment. This work can directly identify and evaluate different degrees of defect, rather than recognize crack first and then assess the characteristics of the detected crack. Moreover, some advanced deep learning models were systematically compared to illustrate the superiority of using YOLOX as the detector. From the training and testing, the mAP values of YOLOX exceed 88.5% on a whole, and the maximum mAP value is 91.05% in YOLOXx, whereas the mAP values of YOLOv5 and DSSD network are relatively stable at about 86% and the mAP value of the Faster R-CNN model is only 69.77%. Compared with other algorithms, the FPS and reference time of the proposed model are relatively shorter, which can meet the needs of real-time detection. Furthermore, thin crack detection is a challenge in object detection. The proposed method is especially strong at detecting thin cracks. For example, the AP values of thin cracks from YOLOX are at least 74%. Comprehensively, the proposed model demonstrates the most balanced detection performance and detection speed. Finally, the feasibility of the proposed method is proved by the crack degree transformation test.  
This study is groundbreaking in that it combines damage detection and assessment. However, one common limitation of almost all deep learning approaches is that they require numerous training data to obtain excellent results. Therefore, a long-term data collection plan is inevitable and will be pursued.  
Conceptualization, J.L. and F.K.; methodology, L.W.; validation, L.W.; formal analysis, J.L. and F.K.; writing--review and editing, L.W. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China grant numbers 51779035, 51769033, and 51979027, 52079022; Fundamental Research Funds for the Central Universities grant number DUT21 TD106.  
Not applicable.  
We thank [PERSON] and [PERSON] for their help in the manufacturing of concrete slabs.  
The authors declare no conflict of interest.
## 4 Methodology
### General Architecture
#### 4.1.4 Receiver  
The receiver is the middle OR. After the middle OR receives \(\mathbf{n}\) slices, where \(\mathbf{n}\leq\) **(s+n)**, it needs to go through the receive buffer, data recovery (layer two decryption and slice recovery), and the send buffer. After the data is transmitted through the network layer and reaches the TLS read buffer of the middle OR, it will be read into the input buffer in cell units. After the decryption operation is executed according to the path direction, the \(\mathbf{n}\) slices are restored to the original message data according to the redundant decoding algorithm. It does not matter if some of the data slices are lost, the original data can still be recovered according to the remaining slices, which solves the problem that the data slices are not recoverable when they are lost and the data is not recoverable in disorder. Of course, \(\mathbf{n}\) has a critical threshold, if \(\mathbf{n}\leq\mathbf{k}\), (the minimum number of recoverable slices is \(\mathbf{k}\)), the message \"Decoding failed, request retransmission\" will be returned to notify the sender. The user's OP then regenerates the interaction information between the two parties. After the data is recovered, the cell is put into the corresponding queue according to the Circ_ID field of the cell. This is because the middle OR is responsible for forwarding the data received from all parties to the corresponding exit OR. The cells in the output buffer are written to the TLS buffer, encrypted, and transmitted to the exit OR via the TCP protocol. The exit OR works normally and is not affected in any way.
# Unsupervised Color-Based Flood Segmentation in UAV Imagery
## 1 Introduction  
Natural disasters have historically exerted profound and far-reaching impacts on humanity. Recently, climate change has intensified weather phenomena, exacerbating the frequency and severity of natural disasters. Sudden and massive rainfall in mid-summer triggers devastating floods, while dry conditions paired with unseasonal strong winds ignite uncontrollable wildfires. Additionally, the occurrence of powerful earthquakes, volcanic eruptions, and hurricanes has surged. These disasters result in significant loss of life and property, disrupt essential services, such as water supply, electricity, and transportation, and pose serious health risks. The economic and psychological impacts on affected populations are enormous [1]. The extent of damage caused by natural disasters is heavily influenced by the readiness and risk reduction strategies of a region, which can vary significantly over time. Floods and hurricanes inflict the greatest damage. The spatial distribution of these disasters is uneven, with notable patterns in the relative distribution of disaster types and their occurrences across continents. Specifically, floods account for 32% of disasters, tropical storms for 32%, earthquakes for 12%, droughs for 10%, and other disasters for 14%. Geographically, Asia experiences the highest share at 38%, followed by the Americas at 26%, Africa and Europe each at 14%, and Oceania at 8% [2]. These statistics underscore the critical importance of tailored disaster preparedness and mitigation efforts across different regions. Efforts to mitigate the impact of natural disasters include early warning systems, improved infrastructure resilience, disaster preparedness education, and internationalcooperation for humanitarian assistance. Preparedness and response strategies are crucial to minimize the human toll and to facilitate faster recovery from such events. Natural disaster detection systems contribute to early warning, risk reduction, efficient resource allocation, and community preparedness. Using technology and global cooperation, these systems play a vital role in minimizing the impact of disasters on both human populations and the environment. Technological advancements and collaborative technologies contribute to the sharing of disaster information benefiting from different types of media. Deep learning (DL) algorithms show promise in extracting knowledge from diverse data modalities, but their application in disaster response tasks remains largely academic. Systematic reviews have evaluated the successes, challenges, and future opportunities of using DL for disaster response and management [3], while also examining machine learning (ML) approaches [4], offering guidance for future research to maximize benefits in disaster response efforts. In this work, we specifically focus on flood segmentation. The relevant research undertaken is presented below, with a summary provided in Table 1.
## 2 Biomedical Image Segmentation
### Traditional Approaches to Biomedical Image Segmentation
#### 2.1.2 Region-Based Methods  
Region-based methods constitute a prominent category within traditional biomedical image segmentation techniques. Unlike thresholding, which relies on pixel intensity values, region-based methods consider groups of pixels with similar characteristics, emphasizing spatial coherence. These methods partition an image into regions based on criteria such as intensity homogeneity, texture, or other feature similarities.  
Region-based segmentation typically involves an iterative process, where regions evolve to maximize homogeneity and distinctiveness. This approach is particularly useful when regions of interest exhibit variations in intensity within their boundaries.  
[PERSON] et al. discuss texture-based region segmentation methods and their applications, providing insights into the importance of texture features in biomedical image analysis [19]. [PERSON] and [PERSON] present a region-based segmentation method known as Seeded Region Growing, which iteratively grows regions from user-defined seed points based on pixel similarity [20]. [PERSON] et al. introduce active contour models, or \"snakes\", which are region-based deformable models used for contour delineation in medical image segmentation [21]. The watershed transformation, a region-based segmentation method rooted in mathematical morphology, is discussed in [22], offering insights into its application in biomedical image segmentation. Ref. [23] provides a computational model of visual segmentation, encompassing region-based methods, and discusses their relevance to understanding human perception of visual scenes.
# GNSS Performance Modelling and Augmentation for Urban Air Mobility +
## 5 Conclusions  
GNSS performance degradation in urban environments is a critical challenge to overcome to support safe urban air mobility. A focused study on GNSS performance for UAS navigation in urban areas was proposed and numerically assessed. The analysis supported the development of a framework which allows prediction of performance and a corresponding trajectory generation strategy. The key GNSS error sources and fault modes affecting navigation performance in urban environments were presented, along with metrics for predicting navigation performance over short UAS missions. In particular, NLOS/multipath characteristics in an urban canyon scenario were modelled, including the impact of building facade, ground and airframe reflections. A guidance strategy accounting for the predicted GNSS performance in urban airspace was developed. The presented methodology allows the assessment of trade-offs between distance and navigation performance in dense urban environments. User-requested routes can be assessed by the UTM operator against the  
Figure 13: PDOP and Number of visible satellites for A* and Nav-A* paths.  
required navigation performance for a given airspace sector and either accepted/rejected/amended. The methodology utilizes 3D models of the navigation environment, GNSS almanacs and models characterizing receiver characteristics to predict positioning error over a specified mission timeline and mission area. The error prediction model accounts for both satellite elevation dependencies and signal reflection from urban structures in generating the UERE. A primary deliverable of the proposed methodology is the improved ability to predict positioning performance over the intended operational area accounting for all significant ranging error sources. The study confirmed the overall feasibility of the approach and sets the foundation for targeted experimental verification in future work. Another area of future work is the investigation of an optimization strategy to balance navigation performance and trajectory length.  
Conceptualization, R.S. and S.B.; Methodology, S.B.; Software, S.B., A.G.; Formal analysis, S.B.; Writing--original draft preparation, S.B.; Writing--review and editing, A.G. and R.S.; Supervision, R.S. and A.G.  
This research received no external funding  
The authors declare no conflict of interest
## 3 Materials and Methods
### Machine Learning Model Selection and Fitting  
In this study, the extra trees classifier (ET) was selected as our predictive model due to its superior performance across various metrics, specifically the F1-score, as it provides a more robust evaluation with imbalanced data. Utilizing the PyCaret library [52], we automated the evaluation process for multiple machine learning models, thereby streamlining our workflow and objectively assessing model performance based on key metrics such as accuracy, AUC, recall, precision, and F1-score. Among the 14 models evaluated--which included the extra trees classifier (ET), light gradient boosting (LightGBM), random forest classifier (RF), quadratic discriminant analysis (QDA), extreme gradient boosting (XGBoost), gradient boosting classifier (GBC), AdaBoost classifier (Ada), Ridge classifier (Ridge), linear discriminant analysis (LDA), K-neighbors classifier (KNN), decision tree classifier (DT), logistic regression (LR), naive Bayes (NB), and support vector machines (SVM) with a linear kernel-the extra trees classifier consistently demonstrated superior performance compared to other models in terms of average metric values across each quadrant. The results for the mean values of the calculated metrics in all four quadrants are shown in Table 3.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **Accuracy** & **AUC** & **Recall** & **Precision** & **F1-Score** \\ \hline ET & 0.8794 & 0.9471 & 0.8797 & 0.8800 & 0.8744 \\ RF & 0.8659 & 0.9263 & 0.866 & 0.8734 & 0.8645 \\ LightGBM & 0.8458 & 0.9257 & 0.8745 & 0.8319 & 0.8480 \\ GBC & 0.8429 & 0.9235 & 0.8723 & 0.828 & 0.8459 \\ XGBoost & 0.8448 & 0.9243 & 0.8589 & 0.842 & 0.8453 \\ QDA & 0.8689 & 0.9412 & 0.7387 & 0.9947 & 0.8438 \\ Ridge & 0.8155 & 0.8783 & 0.8682 & 0.7896 & 0.8235 \\ LDA & 0.8146 & 0.8737 & 0.8719 & 0.7805 & 0.8207 \\ Ada & 0.7949 & 0.8726 & 0.8196 & 0.7898 & 0.7971 \\ LR & 0.7585 & 0.8223 & 0.7831 & 0.7521 & 0.7615 \\ KNN & 0.6868 & 0.7516 & 0.8111 & 0.6502 & 0.7193 \\ DT & 0.7184 & 0.7178 & 0.73 & 0.7137 & 0.7166 \\ NB & 0.6733 & 0.7797 & 0.7654 & 0.6649 & 0.6844 \\ SVM & 0.5736 & 0.6693 & 0.5023 & 0.5726 & 0.4514 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Results for the model selection process.  
It is critical to select a model that exhibits high predictive performance to ensure the reliability of XAI techniques SHAP and LIME. These values aim to accurately reflect the significance and contribution of each feature to the model's predictions. Choosing a poorly performing model could result in misleading interpretations, as the SHAP values would be describing an inaccurate representation of the model's behavior.  
After the initial preprocessing, the dataset was partitioned into training and testing sets in an 80:20 ratio, yielding 374 rows for training and 94 rows for testing. Subsequently, we applied the ET with its default parameters to each quadrant individually within our dataset. This step was crucial to evaluating the model's performance across different segments of the data and ensuring robustness in its predictive capabilities.
# Melanoma Metastases Arising at Unexpected Sites: Gallbladder and Uterus
## 3 Results  
### Case no. 1  
A 71-year-old man was admitted to the hospital with a complaint of upper right abdomen pain. Ultrasound showed gallbladder enlargement and a hypoechoic polypoid lesion located in the fundus. Cholecystectomy was performed. Grossly, the gallbladder exhibited a polypoid lesion of the fundus measuring cm 2.3 in its greatest diameter. Histological examination showed a hypercellular neoplasm exhibiting epithelelioid cell morphology (Figure 1A), located at the gallbladder submuc (Figure 1B) and focally eroding the upper mucosa. The neoplastic cells were large polygonally shaped cells with moderately eosinophil, hyperchromatic and irregular nuclei, and prominent nucleoli. No melanic pigment was observed.  
The neoplastic cells were diffusely and strongly stained with vimentin, s100, Melan-A (Figure 1C), HBM45, and SOX10 (Figure 1D), while no immunostaining was observed for pancytokeratins and LCA.  
The morphological and immunohistochemical features of the tumor were consistent with a diagnosis of metastatic melanoma with epithelelioid cell morphology of the gallbladder. While no anamnestic information was known at the time of the histological examination, from discussion with the clinicians we learned that the patient had undergone a previous cutaneous excision with a diagnosis of MM in the right axillary region approximately 15 years earlier. Only the pathology report of the previous MM was available, which exhibited the same epithelelioid morphology and lacked both ulceration and regression (Breslow thickness: mm 0.8; Clark level 3). _BRAF_ molecular tests were performed on paraffin  
Figure 1: (**A**) High magnification shows a hypercellular and slightly pigmented tumor composed of epithelelioid cells with large polygonal eosinophilic cytoplasm and vesicular nuclei with prominent nucleoli (H&E; original magnification 200\(\times\)); (**B**) a thin flap of overlying atrophic gallbladder mucosa, immunoreactive for pancytokeratins, compressed by the tumor, is seen (immunoperoxidase; original magnification 200\(\times\)); (**C,D**) neoplastic cells are diffusely and strongly positive for Melan-A (**C**) and SOX10 (**D**) (immunoperoxidase; original magnifications 200\(\times\)).  
tissue samples from the metastatic tumor, and _BRAFV600K_ (1799 GT\(>\)AA) mutation was found. Unfortunately, no additional patient follow-up and treatment data were available.
# A Cascaded Ensemble of Sparse-and-Dense Dictionaries for Vehicle Detection
## 2 Related Works
### Application Approaches of Detection on Autonomous Systems  
As for the special case of application on autonomous systems, various detection approaches have been proposed with special designs to ensure the efficiency and reliability of inference. [PERSON] et al. [24] proposed to use fish-eye camera input to perform human detection around heavy machines like forklifts; to deal with the distortion in fish-eye images, they use deformable part-based models (DPMs) [2] with different part anchor settings for different places in the camera view; they further introduced LiDAR input on which initial regions of interest are generated and perform finer detections within these regions, resulting in enhanced time performance and reliability. [PERSON] et al. [25] proposed a hybrid model of convolutional neural networks and recurrent neural networks to perform abnormal event detection in autonomous shuttle mobility infrastructures; they adopted a special auto-encoder which is comprised of both convolution layers and ConvLSTM [26] layers to extract spatiotemporal features for video sequences within shuttle infrastructures, and fed the video features to subsequent stacked LSTMs [27] to produce \"normal/abnormal\" classification results. [PERSON] et al. [28] proposed to deal with the car rotation problem in detection on unmanned aerial vehicles' imaging by adjusting the images according to road direction which is estimated through straight line detection; they also proposed a switching algorithm to choose between two candidate detectors based on their time performances under different scenes, in order to maintain detection rate.  
Table 1 summarizes the strengths and weaknesses of detection approaches of the aforementioned methodologies and our CESDD approach. Compared with these approaches, our CESDD could be trained with a small amount of training samples and training time while still achieving good classification and detection performance.
## 3 Channel Model
### FSO Channel
#### 3.1.3 Pointing Error
The velocity variation is denoted as \(\mathbf{v}_{D}=\big{[}v_{Dx},v_{Dy}\big{]}\), where \(v_{Dx}\) and \(v_{Dy}\) follow the zero mean Gaussian distribution \(\mathcal{N}\big{(}0,\sigma_{Dv}^{2}\big{)}\), and \(\sigma_{Dv}^{2}\) is the velocity variance of GS. The displacement distances within \(\Delta t\) can be expressed as \(x_{D}=v_{Dx}\Delta t/2\) and \(y_{D}=v_{Dy}\Delta t/2\). Therefore, the variance of \(x_{R_{i}D}\) and \(y_{R_{i}D}\) can be expressed as  
\[\sigma_{R_{i}Dx}^{2}=\sigma_{Rx}^{2}+L_{RD}^{2}\sigma_{\theta}^{2}+\sigma_{Dv} ^{2}(\Delta t)^{2}/4 \tag{18}\]  
\[\sigma_{R_{i}Dy}^{2}=\sigma_{Ry}^{2}+L_{RD}^{2}\sigma_{\theta}^{2}+\sigma_{Dv} ^{2}(\Delta t)^{2}/4 \tag{19}\]  
The total radial displacement \(r_{j}=\sqrt{x_{j}^{2}+y_{j}^{2}}\), \(j\in\{SR_{i},R_{i}D\}\) follows the Beckmann distribution and can be approximated by the modified Rayleigh distribution as  
\[f_{r_{j}}\big{(}r_{j}\big{)}=\frac{r_{j}}{\sigma_{j}^{2}}\exp\left(\frac{-r_{ j}^{2}}{2\sigma_{j}^{2}}\right),r_{j}>0,\ \ \ j\in\{SR_{i},R_{i}D\} \tag{20}\]  
Figure 2: The Gaussian beam footprint at receiver aperture: (**a**) the receiver is on the \(i\) th UAV; (**b**) the receiver is on the GS. where \(\sigma_{j}^{2}\) is modified beam-jitter variance approximation, the modified jitter standard deviation of the AWACS-to-UAV link is \(\sigma_{SR_{i}}^{2}=\left(\frac{\sigma_{SR_{i},x}^{6}+\sigma_{SR_{i},y}^{6}}{2} \right)^{1/3}\), and the modified jitter standard deviation of the UAV-to-GS link is \(\sigma_{R_{i}D}^{2}=\left(\frac{\sigma_{R_{i},Dx}^{6}+\sigma_{R_{i},Dy}^{6}}{2 }\right)^{1/3}\). The PDF of \(h_{j}^{p}\) can be expressed as  
\[f_{h_{j}^{p}}\Big{(}h_{j}^{p}\Big{)}=\frac{g_{j}^{2}}{A_{0,j}^{ 2}}\Big{(}h_{j}^{p}\Big{)}^{g_{j}^{2}-1},j\in\{SR_{i},R_{i}D\}, \tag{21}\]  
where \(A_{0,j}=\left[erf\big{(}v_{j}\big{)}\right]^{2}\) represents the fraction of the collected optical power when the difference between the optical spot center and the detector center is equal to zero, and \(erf(\cdot)\) is the error function. \(v_{j}=\sqrt{\pi}a/\sqrt{2}\omega_{z,j}\) is the ratio between aperture radius \(a\) and beamwidth \(\omega_{z,j}\) at the distance of \(L_{j}\). Moreover, the pointing error coefficient \(g_{j}=\omega_{Z_{eq}}/2\sigma_{j}\), where \(\omega_{Z_{eq}}^{2}=\omega_{z,j}^{2}\sqrt{\pi}erf\big{(}v_{j}\big{)}/2v_{j}e^ {-v_{j}^{2}}\) is the equivalent beamwidth.