esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction  
The use of a transjugular intrahepatic portosystemic shunt (TIPS) is an effective treatment for portal hypertension and its associated complications, which has made it a widely used tool worldwide since the 1980s [1; 2]. Multiple large clinical trials have confirmed that TIPS procedures play a major role in mitigating the severe consequences of portal hypertension, such as variecal bleeding and intractable ascites [3; 4; 5; 6; 7; 8]. However, despite the numerous advantages of TIPS procedures, severe peri-procedural complications have been reported to occur in over 10% of cases, including transcapsular puncture, the accidental puncture of non-target structures and the puncture of the extrahepatic portal vein (PV) [9; 10; 11; 12].  
The primary source of procedure-related complications is associated with the most technically challenging step of the TIPS procedure: gaining access to the PV, which is often referred to as a \"blind puncture\" [9; 13; 14; 15]. In this step, the operator has no visualization or real-time information of the spatial relationship between the systemic and portal venous systems. This \"blind puncture\" can make it more likely that the operator punctures areas other than the PV, such as the liver capsule, bile duct or hepatic artery and the extrahepatic PV, which may result in massive intraperitoneal hemorrhage or death. In rare cases, operators have been reported to make up to 50 attempts before successfully accessing the PV [16], which increases the risk of puncture-related complications. The difficult nature of this procedure can largely depend on the following factors: the malformation/transformation of a chronically diseased liver, the displacement of the liver due to ascites, thrombosed or cavernous portal/hepatic veins and the anatomic and pathologic variants of the portal architecture. In some of these cases, the selection of the correct PV may be critical for the effectiveness of the TIPS procedure [17; 18], suggesting that the current standard technique of blind punctures may be inadequate. The use of a direct intrahepatic portosystem shunt (DIPS), which is a technical variant of the TIPS procedure that relies on intravascular US, has been proposed as being preferable for certain patients, but has the same inherent limitations as current image-guided TIPS procedures [19].  
In our study, we developed a new technique for targeting the portal vein during TIPS procedures. By fusing angiographic imaging (cone beam computed tomography; CBCT) and cross-sectional 2D images with CBCT, MR or computed tomography (CT), we aimed to evaluate the safety, reproducibility and accuracy of a real-time 3D fusion image-guided (FIG) TIPS procedure in vivo.
###### Abstract  
The fraud detection of cargo theft has been a serious issue in ports for a long time. Traditional research in detecting theft risk is expert- and survey-based, which is not optimal for proactive prediction. As we move into a pervasive and ubiquitous paradigm, the implications of external environment and system behavior are continuously captured as multi-source data. Therefore, we propose a novel data-driven approach for formulating predictive models for detecting bulk cargo theft in ports. More specifically, we apply various feature-ranking methods and classification algorithms for selecting an effective feature set of relevant risk elements. Then, implicit Bayesian networks are derived with the features to graphically present the relationship with the risk elements of fraud. Thus, various binary classifiers are compared to derive a suitable predictive model, and Bayesian network performs best overall. The resulting Bayesian networks are then comparatively analyzed based on the outcomes of model validation and testing, as well as essential domain knowledge. The experimental results show that predictive models are effective, with both accuracy and recall values greater than 0.8. These predictive models are not only useful for understanding the dependency between relevant risk elements, but also for supporting the strategy optimization of risk management.  
**Keywords:** fraud detection; predictive modeling; bulk cargo theft; Bayesian network; port
# Customer Churn Prediction Approach Based on LLM Embeddings and Logistic Regression
## 3 Preliminaries
### Classification Algorithms
#### 3.2.4 Zero-Shot Classification  
Zero-shot classification is a machine-learning approach that aims to classify and predict unseen data. Unlike traditional classification tasks where the model is learned on labeled data from all classes, zero-shot classification enables the model to generalize to unseen classes by leveraging semantic information or embeddings associated with each class [42]. As illustrated in Figure 1, in this technique, no training is required; each feature element and target class are converted into vectors using embedding methods, particularly for textual data. Subsequently, it calculates the similarity between each element vector and class vectors using cosine similarity. The cosine similarity then acts as the prediction probability, with the predicted class being the one that achieves the highest similarity ratio.
# A Cyber-Physical System for Wildfire Detection and Firefighting
## 1 Introduction  
Forests are crucial ecosystems that provide numerous benefits to the environment and its inhabitants. They filter air and water, provide food and shelter for animals, and play a crucial role in regulating the climate. However, global warming and human error have increased the incidence of forest wildfires, which can cause significant damage to the environment, property, and human health. Once an ignition starts, it is essential to declare it as soon as possible so that it can be rapidly controlled and suppressed. Researchers have focused on providing first responders, firefighters, and decision makers with systems for analyzing the situation at a glance through visual analytics techniques [1, 2, 3, 4], sharing data [1], or systems that involve active citizenship for early warning [5, 6]. However, wildland firefighters often do not have access to accurate and real-time information about the situation because the location of the fire is remote or hard to reach, and if the fire is not brought under control in time, it can continue to grow, increasing the danger and the suppression costs. Detecting and controlling wildfires is a challenging task, as they can spread rapidly and are difficult to anticipate. Researchers are exploring the use of advanced technologies such as IoT, robotics, and drone technologies [7]. The synergy of these technologies converges in a new trend that can be called the Internet of Robotics and Drone Things [8]. The interconnection of all these heterogeneous devices and the integration with software components has led to the design and development of a Cyber-Physical System (CPS) forwildfire detection [9] and collaborative robots able to navigate autonomously and perform fire attacks on wildland fires before they burn out of control. However, there remains a significant challenge in providing accurate and real-time information to ground control stations and operators overseeing fleets of drones in collaborative environments. In [10], the authors introduce a human-machine interface that enables a ground control station to remotely monitor and manage a fleet of drones in a collaborative environment, with the involvement of multiple operators. Collaborative robots, also defined as unmanned systems, have the capability to execute a mission autonomously without human intervention, and commonly they include unmanned aircraft and ground robots [11].
# Multi-Class Strategies for Joint Building Footprint and Road Detection in Remote Sensing
## 2 Methods
### Multi-Task Strategy  
Briefly, multi-task learning [33] consists of solving multiple learning tasks at the same time, exploiting commonalities and differences across them. Multi-task architectures are usually classified depending on how the parameters of the hidden layers are shared, as shown in Figure 4, where two tasks are considered (semantic segmentation and pixel count). In hard parameter sharing [43] (Figure 4a), the hidden layers between all tasks are shared, keeping some task-specific output layers. Being the most commonly used type of multi-task architecture, this approach greatly reduces the risk of overfitting since there are several tasks that are learned simultaneously, and the model has to find a representation that captures all of them. In soft parameter sharing [44] (Figure 4b), there are as many models as tasks, each one with its own parameters. It must be noted that the distance between these parameters is regularized to avoid overfitting.  
In this work, we have opted for using the hard parameter sharing approach, considering that the soft parameter sharing one demands more resources and thus, becomes unfeasible when dealing with many classes. To further improve the performance of semantic segmentation models, the simplest complementary task is the prediction of the  
Figure 4: Multi-task architectures depending on how the parameters of the hidden layers are shared.  
Figure 3: OVA decomposition scheme adapted to a multi-class semantic segmentation problem, including building footprints and road networks.  
percentage of pixels assigned to each class. Therefore, Section 4 studies the application of this strategy to increase the performance of the base models. Figure 4 describes how this strategy is implemented. Accordingly, the features extracted by the encoder are passed through a linear layer that outputs the percentage of pixels assigned to the positive class.
# Experimental Approaches in Delineating mTOR Signaling
## 4 Identification and Characterization of mTOR Signaling Pathway Components
### Bioinformatics Approaches-Mediated Identification of mTOR Signaling Components  
#### 4.6.1. CASTOR1 and CASTOR2 Were Identified as Arginine Sensors by a Bioinformatic Approach  
SLC38A9 was identified as a lysosomal arginine sensor that suppresses mTORC1 activation when arginine is not available [59]. The observation that mTORC1 could be activated by arginine in cells lacking SLC38A9 suggests the existence of additional arginine sensors [59]. Considering that GATOR2 serves as an antenna to receive inputs from amino acids, [PERSON] et al. searched the BioPlex database (a human protein-protein interaction database [96]) for GATOR2 binding proteins and found GATSL3 (CASTOR1) as a GATOR2 binding partner, while GATSL2 (CASTOR2) interacted with GATOR1 [97]. Further biochemical studies supported a role of CASTORs in sensing arginine by binding to a distinct region on GATOR2.  
6.2. SAMTOR Was Identified to Connect One Carbon Metabolism to mTORC1 Activity Control by a Bioinformatic Approach  
[PERSON] et al. searched for GATOR1 or KISTOR binding partners using the BioPlex database and found C7 orf60 (SAMTOR) was a common binding partner for both GATOR1 (Depdc5, Nprl3, and Nprl2) and KICSTOR (Kaptin, ITFG2, C12 orf66, and SZT2) [98]. Subsequent biochemical analysesindicated SAMTOR was a \(S\)-adenosylmethionine sensor that links methionine/SAM to mTOR1 activity control [98].
## 7 Discussion
### The Analysis Cost-Effectiveness and Feasibility  
(1) Cost-effectiveness: the initial investment in blasting equipment and the required safety measures must be balanced against the long-term benefits of improved roadway stability and reduced rock burst risk. While the cost of drilling and blasting operations may vary depending on the roadway size and the coal seam depth, the reduction in roadway deformation and the prevention of rock bursts can result in significant cost savings. These savings can stem from reduced maintenance costs, fewer mine closures due to instability and the prevention of costly damage caused by rock bursts. Additionally, improved worker safety can reduce the financial burden associated with accidents and downtime.  
(2) Feasibility: the proposed blasting technique can be implemented in various mining conditions, but its practicality will depend on specific site conditions, such as rock mechanics, coal seam depth and the availability of skilled personnel. In regions with challenging geological conditions, the technique may require additional investments in training, equipment and monitoring to ensure proper execution and safety. Additionally, the blasting parameters would need to be optimized for different mining environments, taking into account the local geology, rock type and seam characteristics to ensure both effectiveness and safety.
# Techno-Economic Analysis of a Solar Thermal Plant for Large-Scale Water Pasteurization
## 3 Plant Model Design
### Pumping System and Pipe Losses  
The shaft power required by the pumps depends on the mass-flow rate and on the total head losses as \(P_{p}=\dot{m}\,g\,H/\eta_{p}\) (with \(\eta_{p}\) being the efficiency of the pump). The total head has been evaluated as [42]  
\[H=\gamma\,\left(\frac{\dot{m}^{2}}{d^{n}}\right)\,L, \tag{16}\]  
with \(\gamma=0.00164+0.000042/d\) being the friction factor, \(d\) the inner tube diameter, \(n=5.08\) the exponent used for the considered pipes and \(L\) the total length of the piping, which is the sum of the physical length and the equivalent one. This last term, which takes into account the concentrated head losses, has been assumed equal to 10% of the real length [42]. The piping extension on the treatment line has been evaluated considering the whole length of the heating coils.  
The pump for the solar field has been sized considering the length of the piping within the solar collectors. The solar collectors manufacturer suggests a minimum value for the mass flow rate of \(\dot{m}_{0}=0.6\) L m\({}^{-2}\) min\({}^{-1}\) in order to avoid an excessively overheating of the aqueous coolant [31]. Starting from this value, the mass-flow rate in the solar field can be evaluated as \(\dot{m}_{sf}=\dot{m}_{0}\,A_{c}\,N_{c}\), being \(A_{c}\) the cross-sectional area of the collector and \(N_{c}\) the number of collectors. The pump is activated only if two conditions are met, namely the solar radiation is present and the temperature of the storage system is below an upper threshold (95 \(\,{}^{\circ}\)C). This latter condition is important to prevent the thermal storage temperature from exceeding the maximum value. The pump for the gas burner, on the other hand, is activated to provide the required mass-flow rate to the burner only if the temperature of the storage system is below a lower threshold (85 \(\,{}^{\circ}\)C).  
The heat dissipated from the piping has been calculated iteratively starting from the data sheet provided by the manufacturer, in which the thermal losses per unit length are listed as a function of the pipe diameter.
## 3 Methods
### Feature Extraction for Each Image Pixel
#### 3.2.3 Backscatter Features  
A single Sentinel-1 SAR image is usually contaminated by speckle noises, and previous studies demonstrated the effectiveness of SAR image enhancement using time series images [55]. Following [PERSON]'s work [39], we selected the mean values of the VV and VH time series images of 75-100% in ascending order at the pixel level to form the final VV and VH image over the whole study area.
###### Abstract  
To expound on the correlation between the microorganism communities and the formation of off-odour in Xuanwei ham, the microorganism communities and volatile compounds were investigated in the biceps femoris (BF) and semimembranosus (SM) of Xuanwei ham with different quality grades (normal ham and spoiled ham). The single molecule real-time sequencing showed that differential bacteria and fungi were more varied in normal hams than in spoiled hams. Headspace solid-phase microextraction-gas chromatography (HS-SPME-GC-MS) results indicated that aldehydes and alcohols were significantly higher in spoiled hams than those in normal hams (\(p<0.05\)). The off-odour of spoiled hams was dominated by ichtriblex, maloudourous, sweaty, putrid, sour, and unpleasant odours produced by compounds such as trimethylamine (SM: 13.05 ug/kg), hexanal (BF: 206.46 ug/kg), octanal (BF: 59.52 ug/kg), methanethiol (SM: 12.85 ug/kg), and valeric acid (BF: 15.08 ug/kg), which are positively correlated with _Bacillus cereus_, _Bacillus subtilis_, _Bacillus licheniformis_, _Pseudomonas_ sp., _Aspergillus ruber_, and _Monaxella solensis_. Furthermore, the physicochemical property and quality characteristics results showed that high moisture (BF: 56.32 g/100 g), pH (BF: 6.63), thiobarbituric acid reactive substances (TBARS) (SM: 1.98 MDA/kg), and low NaCl content (SM: 6.31%) were also responsible for the spoilage of hams with off-odour. This study provided a deep insight into the off-odour of Xuanwei ham from the perspective of microorganism communities and a theoretical basis for improving the flavour and overall quality of Xuanwei hams.  
Keywords:Xuanwei ham; microorganism communities; single molecule real-time sequencing; HS-SPME-GC-MS; correlation analysis +
Footnote †: journal: _foods_
# Inundation Extent Mapping by Synthetic Aperture Radar: A Review
## 2 Techniques to Produce SAR Inundation Maps
### Error Sources  
We attribute commonly seen errors to three sources: water-like surfaces, noise-like speckle, and geometric correction.  
Water is not the only surface that exhibits close-to-specular scattering. Smooth surfaces at the scale of the measuring wavelength and shadowed areas share almost identical scattering properties with water surfaces; we refer to them hereafter as water-like surfaces. Water-like surfaces vary slightly with frequency and flood mapping algorithms. With sufficiently little roughness relative to wavelength, even bare soil can be misidentified as water. In procedures to detect non-obstructed water, water-like surfaces can create \"false positives\" or \"over-detection.\"  
Noise-like speckle is encountered for almost all SAR applications and, in many, is considered a major disadvantage of SAR images over optical images. For example, unlike optical images, noncommercial SAR images with 5-10 m resolution, may not be suitable for detecting headwater  
Figure 1: Backscattering components from vegetation with a canopy layer in enhanced MIMICS.  
flood. Since the floodplain of headwater may be only one or a few pixels wide, the water pixels can be contaminated severely by speckle and surrounding strong scatterers. Speckle is not real noise. The formation of speckle was well explained by [PERSON] and [PERSON] [38] (p. 101): As \"the distances between the elementary scatterers and the radar receiver vary due to the random location of scatterers, the received waves from each scatterer, although coherent in frequency, are no longer coherent in phase. A strong signal is received if wavelets are added relatively constructively, while a weak signal is received if the waves are out of phase.\" As a result, homogeneous and continuous areas exhibit strong inhomogeneity in SAR images. As the sample number decreases with the improvement of spatial resolution, the backscattering of a pixel may not, be represented well by a known fully developed speckle model which increases the difficulty in classification. Although many SAR filtering techniques are developed, most of them reduces noise at the price of damaging image details, which can also propagate to inundation mapping. Modern techniques such as the selection of the optimal filter [39] and machine learning may offer some advanced way in suppressing speckle.  
The original geometry of SAR images is range-azimuth. To be georeferenced, SAR images are often corrected to ground distance before high-level products are generated. Limited by the accuracy of input elevation data, the geometric correction algorithm, and orbit accuracy, it is common to see location errors at the level of a few pixels, as demonstrated in Figure 2. The offset in 10-pixel order in Figure 2 reflects the cumulatively geometric error of using Sentinel-1 images from different orbits, which can be significantly reduced using image pairs from the same orbit.
# Swin-YOLO for Concealed Object Detection in Millimeter Wave Images
## 3 Theoretical Model
### The Proposed Object Detection Network
#### 3.2.1 Feature Representation Layers and Prediction Head for Small Objects  
The YOLOv5 network extracts features from three different-sized feature maps. The high-level feature map contains rich semantic information but has a lower resolution, resulting in imprecise object localization and a significant loss of information for small objects. In contrast, the low-level feature map has higher amounts of localization information but less semantic information, which is beneficial for detecting small objects.  
In MMW human body images, concealed objects are small in size and have complex features. To make better use of the shallow feature information extracted from the backbone, we added a feature representation layer and a detection head for small-scale objects on top of the original YOLOv5 neck network. The new added layers will compensate for the loss of information caused by multiple down-sampling. Furthermore, it can enhance the feature fusion effect of small objects to adapt to small object datasets in MMW images.  
The small-scale feature representation layer utilizes the low-level information. A horizontal residual connection path was established with the shallow layers of the backbone, obtaining high-resolution mapping. The small-scale feature representation layer connects the original feature extraction layer through a top-down pathway, which fuses low-resolution, high-semantic features with high-resolution, and low-semantic features. The small-object detection head is set at the newly added feature representation layer, making the object detector more sensitive to small objects. With the added feature fusion layer, the neck network now has a four-layer detection head structure, which is more suitable for detecting small concealed objects in MMW images.  
Figure 2: The architecture of Swin-YOLO. We added a small object feature extraction detection layer and additional detection heads, and used BiFPN to fuse features. The red lines represent additional feature fusion; Local Perception Swin Transformer Layers were introduced to the network neck; Coordinated Attention Modules were added at the connection between each detection head and neck network.
###### Abstract  
This paper presents a new coverage flight path planning algorithm that finds collision-free, minimum length and flyable paths for unmanned aerial vehicle (UAV) navigation in three-dimensional (3D) urban environments with fixed obstacles for coverage missions. The proposed algorithm significantly reduces computational time, number of turns, and path overlapping while finding a path that passes over all reachable points of an area or volume of interest by using sensor footprints' sweeps fitting and a sparse waypoint graph in the pathfinding process. We devise a novel footprints' sweep fitting method considering UAV sensor footprint as coverage unit in the free spaces to achieve maximal coverage with fewer and longer footprints' sweeps. After footprints' sweeps fitting, the proposed algorithm determines the visiting sequence of footprints' sweeps by formulating it as travelling salesman problem (TSP), and ant colony optimization (ACO) algorithm is employed to solve the TSP. Furthermore, we generate a sparse waypoint graph by connecting footprints' sweeps' endpoints to obtain a complete coverage flight path. The simulation results obtained from various scenarios fortify the effectiveness of the proposed algorithm and verify the aforementioned claims.  
coverage flight path planning; footprints sweep; waypoint graph; navigation; urban environments; unmanned aerial vehicles +
Footnote †: journal: Applied