esa-sceva/satcom-chunk-collection huggingface dataset:
# Clinical Applications of Optical Coherence Angiography Imaging in Ocular Vascular Diseases
## 2 Diabetic Retinopathy
### Role of OCTA in Diabetic Retinopathy
#### 2.2.1 OCTA for Non-Proliferative Diabetic Retinopathy  
The role of OCTA has been extremely useful, especially in imaging the milder stages of diabetic retinopathy where FA may not be indicated. Studies have shown that OCTA can detect retinal vascular abnormalities including the location of microaneurys, capillary non-perfusion, changes in the FAZ, alterations in the deep retinal layer, and impairment in the choriocapillaris before clinically detectable disease occurs [26; 27; 28]. [PERSON] et al. showed that ultra-high-speed swept source OCTA was able to identify the majority of microaneurysm s noted on FA and localize the microaneurysm s to a specific vascular plexus using en face depth-resolved imaging [29]. [PERSON] et al. also found that microaneurysm s take on different shapes on OCTA despite their similar appearance as round dots on FA [30].  
The deep capillary plexus (DCP) has been demonstrated to be more severely affected in diabetes compared to the intermediate (MCP) and superficial plexuses (SCP) [31]. Recently, [PERSON] et al. showed that the parafoveal vessel density in the DCP was the parameter that was most closely associated with ETDRS grading [32]. Another study showed that the \(3\times 3\)-mm SCP and the DCP vessel density were the most accurate parameters for the detection of high-risk DR [33]. Using a statistical model, another study identified the superficial capillary plexi of the FAZ area, DCP vessel density, and acircularity as parameters that best distinguished between DR severity groups [34]. Increasing interest in the MCP has shown that both the MCP and DCP show parallel changes to DR progression. Therefore, including the MCP within the SCP may cause confounding results [35]. However, the automated quantification of MCP is not widely available on commercial OCTA platforms at present.
## 3 Results  
Prior to the analysis, the yields of tars produced by the co-pyrolysis of nitrogen-containing compounds and tar-rich coal at different temperatures were explored, as shown in the blue part of Figure 2. At temperatures below 600 \({}^{\circ}\)C, the yields of all six nitrogen-rich tars increased with increasing temperatures, especially between 450 and 600 \({}^{\circ}\)C, where the yields of nitrogen-rich tars had a significant increase. When the temperature exceeded 600 \({}^{\circ}\)C, the yield of nitrogen-rich tar also decreased, mainly because too high a temperature would lead to the secondary cracking reaction, resulting in a decrease in the yield of nitrogen-rich tar. Therefore, the subsequent co-pyrolysis experiments were carried out at 600 \({}^{\circ}\)C.  
This was explored as the pyrolysis experiments were carried out on an indigenously constructed pyrolysis oven with a different pyrolysis time than the fixed bed reactor, as shown in the green part of Figure 2. When the co-pyrolysis was carried out at 600 \({}^{\circ}\)C and the pyrolysis time was lower than 15 s, the tar yield was at a low level, and it increased more slowly. As the pyrolysis time increased to 60 s, the tar yield increased rapidly; as the pyrolysis time continued to increase, although the tar yield increased, the growth rate became very slow until it stabilized, so the subsequent experiments chose the pyrolysis time to be 4 min.  
In addition, the ratio between the reactants is also very important in the pyrolysis process. In this paper, tar-rich coal: nitrogenous compounds = 1:1, 1:2, 1:3, 1:4, 1:5 was investigated, as shown in the pink part of Figure 2. It was found that when the ratio of nitrogen-containing compounds gradually increased, the content of nitrogen-containing compounds in nitrogen-enriched tar also increased, and the increase became very slow when the ratio was 1:3, so the subsequent experiments were carried out by using oil-enriched coal: nitrogen-containing compounds = 1:3. It is worth noting that the types of nitrogen-containing compounds in the three groups of nitrogen-enriched tars are almost the same, indicating that under the conditions of this paper, increasing the ratio of nitrogen-containing compounds can only increase the content but not change the types.
# Evaluation of PID-Based Algorithms for UGVs
## 3 Methodology
### Process of Calculating PID Coefficients  
To calculate the respective gains of the controller, there are several methods, which can be divided into two categories: closed-loop and open-loop methods. Numerous methods can be implemented for PID controller tuning, such as the Ziegler-Nichols (Z-N) method, Tyreus-Luyben (T_L), C-H-R, Cohen and Coon (C-C), Fertik, and Ciancone-Marlin (C-M) [42].  
The Ziegler-Nichols (Z-N) and Cohen-Coon (C-C) controllers are simple and easy to implement. The Cohen-Coon method for PID control is an extension of the Ziegler-Nichols method. This technique improves the steady-state response, especially when there is a large delay in the process compared to the open-loop time constant [43]. To achieve this, the PID parameters are obtained through open-loop experiments, as shown in Figure 15.  
From the obtained data, it is possible to calculate the associated gains, as shown in Table 3.
## 5 Three-Dimensional Path Planning Algorithms
### Machine Learning-Based 3D Path Planner
#### 5.1.1 Open Obstacles  
In this type of obstacle, there is a possibility in which the UAV has no other option but to pass through the opening, such as in the case when the mission is to enter or exit a closed room through open window. Missions like in-house first aid or disaster monitoring may encounter such a scenario. In this case, the algorithm determines the relative position of the UAV with respect to the center of the bounding box around the obstacle. The center of the bounding box, as shown in Figure 4, has coordinate axes \((x_{c},y_{c},z_{c})\) with respect to the ZED mini stereo camera frame, whose origin is located at the center of left camera.  
The \(x\)  
The UAV's local position \((x_{l},y_{l},z_{l})\) is acquired from GPS embedded in the Pixhawk 4 autopilot, LiDAR and ZED mini stereo camera. Before the UAV tries to pass through the window, it has to align itself with a vector normal to the plane of the window through appropriate attitude and altitude changes. In the figure, the setpoint \((x_{l},y_{s},z_{s})\) is sent by the 3D path planner to the autopilot to command the UAV to adjust itself before advancing forward. The variables \(y_{s}\) and \(z_{s}\) are the y and z axes' setpoint values, respectively, obtained as follows:  
\[y_{s}=y_{l}-y_{c}\qquad\text{and}\qquad z_{s}=z_{l}-z_{c} \tag{3}\]  
While the UAV is responding to the command, the ultrasonic sensors mounted on the sides of the UAV check whether there are objects or not in the way. Once alignment is done, the UAV advances through the window with the setpoint \((x_{s},y_{s},z_{s})\), where \(x_{s}\) is the relative depth of the bounding box with clearance.  
\[x_{s}=\mid x_{l}-x_{c}\mid+\quad obj_{clr} \tag{4}\]  
The variable \(obj_{clr}\) is a minimum object clearance or distance of the UAV behind the window that ensures the UAV has completely passed through the window with clearance. Moreover, to confirm the passage of the UAV through the window, the readings from the ultrasonic sensors mounted on the left and right sides of the UAV are considered. This method is implemented in cases like passing through tunnels or holes alike.
# Security Architecture for Swarms of Autonomous Vehicles in Smart Farming
## 1 Introduction  
The global population is constantly growing. In 2019, the United Nations estimated that the world's population is expected to increase by 2 billion persons in the next 30 years, reaching 9.7 billion in 2050 and potentially reaching 11 billion around 2100 [1]. Many of the fastest growing populations are in the poorest countries, which presents threats to sustainable development [2]. Moreover, by 2050, it is projected that 68 percent of the world's population will live in urban areas (an increase from 54 percent in 2016) [3].  
Due to labour shortage, depopulation of rural areas and increases in food demand among others, farming faces many challenges in terms of productivity and cost-effectiveness. The usage of autonomous vehicles in agriculture is the current means to overcome these limitations while fostering new opportunities for the farming industry to bring higher yields.  
Drones and autonomous tractors have started to be used in experimental farms to till, plant and harvest crops, as they can operate 24/7 [4, 5]. GPS (Global Positioning System) tracking and navigation systems, robotics, AI (Artificial Intelligence) and computer vision techniques can make precision farming possible and enable farmers to make informed decisions. While these implementations control tractors to prepare the ground, to sow theseeds, to apply precise amount of fertilizer and to maintain the crops, drones can survey the area to take samples and to analyse images to detect weeds or diseases. Drones can also fly to monitor the behaviour of livestock in a nonintrusive manner, e.g., when they are in heat or about to give birth, or even to look for lost cattle.
# Semantic Visual SLAM Algorithm Based on Improved DeepLabV3+ Model and LK Optical Flow
## 2 Relevant Theories
### Semantic Segmentation Network  
The overall architecture of the semantic segmentation network DeepLabv3+ model is shown in Figure 2, and the main body of its encoder is Deep Convolutional Neural Networks (DCNNs) with cavity convolution, which are generally adopted from commonly used classification networks such as ResNet [26] or Xception [27]; DCNNs processed into Atrous Spatial Pyramid Pooling (ASPP) [28] with cavity convolution, which introduces multi-scale information and reduces information loss; and a DeepLabv3+ Decoder module, which further integrates the shallow features with the deeper features to improve the segmentation accuracy at the target boundary.
# Effects of Epigallocatechin-3-O-Gallate on Bone Health
## 3 Biochemical Effects of EGCG
### Increasing the Level of Vascular Endothelial Growth Factor  
The inactivation of VEGF completely inhibits the invasion of blood vessels with the simultaneous impairment of trabecular bone formation and the expansion of the hypertrophic chondrocyte zone in the mouse tibial epiphyseal growth plate [100]. Within the VEGF family of proteins, the following can be found: VEGF-A, VEGF-B, VEGF-C, VEGF-D, and PIGF. These proteins are bound by the receptors VEGFR1 and VEGFR2. VEGFR1 is activated by VEGF-A, -B, and PIGF, while VEGFR2 is activated by VEGF-A, -C, and -D [101]. VEGF-A is one of the critical mediators of blood vessel invasion into the cartilaginous mould. During typical distraction osteogenesis, the expression of both VEGFR1 and VEGFR2 is induced [102; 103]. Furthermore, blocking VEGF-A activity in the distraction gap through the antibody-mediated inhibition of VEGFR1 and VEGFR2 significantly reduces bone formation and decreases the number of blood vessels [102; 104]. Another hypothesis, termed the \"spontaneous rupture theory\", was proposed by [PERSON] et al. [105]. According to them, VEGF increases MMP expression in endothelial cells and in tendon fibroblasts themselves. The invasion of vessels and the MMP expression lead to a weakening of the normal tendon structure, which further leads to a decrease in the mechanical strain and subsequently to the spontaneous rupture. However, [PERSON] and [PERSON] [106] in their study came to a different conclusion. They found that the effects of extracellular VEGF are dose-dependent: high levels of VEGF inhibit regenerative cell infiltration and the osteoinduction of mesenchymal progenitor cells, resulting in reduced bone formation. The proper levels of VEGF produced by osteoblastic cells are crucial for the coupling of angiogenesis and osteogenesis during the healing of small bone defects. VEGF produced by osteoblasts is important for the early angiogenic response and the infiltration of macrophages during the initial phase of inflammation. In wound healing and tumour progression, macrophage infiltration occurs before angiogenesis, and VEGF acts as a chemotactic signal for macrophages and monocytes. This is why the proangiogenic effects of osteoblast-derived VEGF involve macrophages, in addition to directly targeting endothelial cells. Optimal amounts of VEGF are crucial for therapeutic outcomes. Not only a dose dependency but also a mechanism of delivery were noted by [PERSON] et al. [107]. In an in vitro study performed on cloned osteoblast-like MC3T3-E1 cells derived from newborn mouse calvaria, it was shown that the enhancement of SAPK/JNK activation
## 3 Results and Discussion
### Further Parameterization of LUE  
[PERSON] et al. (2017) found that photoperiod explained more seasonal variation in leaf photosynthetic capacity (related to LUE) than temperature. [PERSON] et al. (2018) also derived a seasonally-varying photosynthetic capacity from satellite SIF and other measurements and showed that using this seasonal variation within a canopy transport model improved estimates of GPP and LUE. Functions of NDVI have been used to account for these seasonal variations in LUE ([PERSON] et al., 2018; [PERSON] et al., 2018).  
We tested various expressions of seasonal variation of LUE in an attempt to further improve our satellite-based GPP estimates. We found that a small portion of the remaining variance in LUE can best be modeled as a simple polynomial expression of NDVI. In this model, we have for each of the two separate fits within the dual fit approach,  
\[\text{GPP}=\text{FAPAR}^{\prime}\times\text{SW}_{\text{TOA}}\times(a+b\text{ NDVI}+c\text{NDVI}^{2}), \tag{11}\]  
where FAPAR\({}^{\prime}\) is an FAPAR proxy such as NDVI\({}^{\prime}\) or multi-band proxies. Other LUE parameterizations tested included second order polynomials in \(\text{SW}_{\text{TOA}}\), \(\text{SW}_{\text{TOA}}\)/max(\(\text{SW}_{\text{TOA}}\)), or NDVI/max(NDVI) for each location as proxies for photoperiod or products of these predictors. However, the straight NDVI parameterization provided the best results.  
Figure 6 shows results for our dual fit seven-band variable LUE (Equation (11)) models that we will refer to as FluxSat-7, the dual fit NDVI\({}^{\prime}\)-based variable LUE model (FluxSat-N), as well as NIR\({}_{V}\)-based results at eight-day temporal resolution. At the 0.00833\({}^{\circ}\) spatial resolution of MCD43D, both FluxSat-7 and FluxSat-N were more centered about the 1:1 line than the NIR\({}_{V}\)-based model, and correlation differences between FluxSat and the NIR\({}_{V}\)-based models were statistically significant with very low \(p\)-values. The FluxSat-7 correlation improvement over FluxSat-N was significant with a \(p\)-value of 0.05. Improvements were obtained with the NDVI-dependent LUE as compared with the results in Figure 4.
# Collaborative Networks: A Pillar of Digital Transformation
## 7 Discussion of New Research Challenges
#### 3.3.1.1 Smartness and data-richness
The collaborative networks discipline is by itself the result of interdisciplinary efforts, but this effort needs to be further pursued bearing in mind the ongoing digital transformation. _Further research_: Continuously **re-enforce multi-disciplinary and interdisciplinary approaches**, seeking synergies from the combination of multiple knowledge areas and diversity of players. * **Further education and dissemination of a collaboration culture**  
_Context_: The effectiveness of the aimed industrial revolution does not only depend on technology. It requires new ways of working, the adoption of new methods and new processes, with a different mind-set, thus a \"new collaboration culture\". For a successful transformation journey, it becomes mandatory to create a \"culture of collaboration\" in industry and society. _Further research_: Establishing **educational curricula** on collaborative networks and elaborating a **portfolio of success stories** of collaboration. * **Responsibility, ethics and compliance**  
_Context_: Various earlier works on CNs focused on the needed legal frameworks to regulate their establishment and operation. Some countries (e.g., Portugal, Italy and some others) already have some laws covering both \"long-term strategic networks\" and \"goal-oriented networks\". With the increasing hyper-connectivity and systems integration (towards systems-of-systems), and the increasing levels of intelligence and autonomy of those systems, it is necessary to revisit and better understand issues of responsibility, compliance and ethics, and define novel regulatory mechanisms and frameworks. _Further research_: Developing **new conceptual and regulatory frameworks** to cope with responsibility, ethics, and compliance in hyper-connected systems with increasing levels of intelligence and autonomy. The above list of challenges, although not exhaustive, provides a representative overview of the new directions for research resulting from the \"coming together\" of CNs and digital transformation/Industry 4.0. As this list not only emerges from the identified gaps in literature but also reflects, to a large extent, the outcomes from multiple discussions in a series of international events organized by SOCOLNET (Society of Collaborative Networks), it demonstrates that the area has become an active research agenda for the coming years.
# MSPNet: Multi-Scale Strip Pooling Network for Road Extraction from Remote Sensing Images
## 1 Introduction
A possible solution to these problems is to enhance the embedding of linear features within the CNN architectures. In this paper, we propose a multi-scale strip pooling network (MSPNet) to address the above-mentioned problems. We first introduce an encoder-decoder architecture network to learn the feature of roads, where the Pyramid Pooling module (PPM) is adopted to increase the receptive field of feature points and learning ability of complex features in different sub-regions. Inspired by the strip pooling being more aligned with the shapes of roads that are long-span, narrow, and distributed continuously, we propose a multi-scale strip pooling (MSP) module to learn the linear features of roads, which is placed in the skip-connection paths. The MSP focuses on establishing relationships in the elongated road region between road and occluded road pixels to guarantee the connectivity of roads. Extensive experiments on popular benchmarks in terms of several metrics demonstrate the superiority of our MSPNet compared with several state-of-the-art methods. The main contributions of this work are summarized as follows. * We propose an end-to-end multi-scale strip pooling network (MSPNet) with symmetric encoder-decoder network design for the task of road extraction. This network design can preserve spatial detailed information and therefore optimize the smoothness of roads. In addition, it is also suitable for processing large-scale images. * We develop a multi-scale strip pooling (MSP) module that utilizes strip pooling layers to aggregate multiple long-range contextual information. The linear features of roads are enhanced within CNN architecture, which thus improves the road connectivity. * Ablation studies and comparative experiments on a benchmark DeepGlobe data set are performed to verify the effectiveness of our proposed MSPNet. The remaining of this article is organized as follows. Section 2 introduces the related works of road extraction. In Section 3, we describe datasets, evaluation metrics, and implementation details, and we illustrate our proposed MSPNet in detail. Extensive experiments are performed to evaluate the performance of the proposed method for road extraction in Section 5. The conclusion and discussion are presented in Section 6.
## 4 Experimental Results
### Initial Module Detection  
In the initial detection process, \(99\) nodules are detected successfully, and the number of false positives is \(5,368\) per scan. Most of false negatives (FNs) have low intensity less than \(-600\) HU. One false negative has an irregular shape. Four of five FNs are at the agreement level 1 and the other is at the agreement level 3. Figure 10 shows the free-response receiver operating characteristic (FROC) curve of the initial detection of all the agreement levels. The numbers of FPs per scan at the TP ratios of \(95.20\)% / \(90.38\)% / \(86.54\)% are \(3,832\) / \(554\) / \(441\), respectively. The FROC curves of individual agreement levels are represented in Figure 11. At the agreement level 1, the numbers of FPs per scan at the TP ratios of \(86.21\)% / \(79.31\)% are \(1,744\) / \(454\), respectively, at the agreement level 2, those at the TP ratios of \(95.45\)%/ \(90.91\)% are \(1,338\) / \(376\), respectively. At the agreement level 3, the numbers of FPs per scan at the TP ratios of \(96.77\)% / \(90.32\)% are \(502\) / \(344\), respectively, at the agreement level 4, those at the TP ratios of \(100\)% / \(95.45\)% / \(90.91\)% are \(1090\) / \(696\) / \(556\), respectively.
# Reinforcement Learning with Self-Attention Networks for Cryptocurrency Trading
## Appendix A Exchange Fee Minimization Algorithms
### No Inter-Asset Transactions  
In some markets, exchanging shares between assets is not allowed. Instead, an investor needs to sell some shares to obtain cash, and use that cash to purchase shares of other assets. This is equivalent to the condition, \(\tau_{ij}^{k}\) is zero if neither \(i\) nor \(j\) is zero. Applying this condition to Equations (2) and (4) results in Equations (A1) and (A2), respectively. We refer to this simplification of the fee minimization problem as 'the constrained case'.  
\[\rho^{k}w_{i}^{k+1}=\bar{w}_{i}^{k}-(1+f_{i0})\tau_{i0}^{k}+\tau_{0i}^{k},\ \text{for}\ i\
eq 0\] (A1)  
\[\rho^{k}=1-\sum_{i=1}f_{i0}\tau_{i0}^{k}-\sum_{i=1}f_{0i}\tau_{0i}^{k}\] (A2)Note, an agent cannot buy and sell shares of the same asset at the same time. In other words, if \(\tau_{i0}^{k}>0\), then \(\tau_{0i}^{k}=0\), and if \(\tau_{0i}^{k}>0\), then \(\tau_{i0}^{k}=0\). These conditions can be explicitly written in terms of the ReLU function from Equation (A1), as shown in Equation (A3), where the ReLU function is denoted by \((.)^{+}\).  
\[\begin{split}(\bar{w}_{i}^{k}-\rho w_{i}^{k+1})^{+}& =(1+f_{i0})\tau_{i0}^{k}\\ (\rho w_{i}^{k+1}-\bar{w}_{i}^{k})^{+}&=\tau_{0i}^{ k}\end{split}\] (A3)  
Next, Equation (A3) is substituted in Equation (A2) to obtain Equation (A4), where \(g_{i0}\) represents the expression shown in Equation (A5).  
\[\rho^{k}=1-\sum_{i>0}g_{i0}(\bar{w}_{i}^{k}-\rho^{k}w_{i}^{k+1})^{+}-\sum_{i>0 }f_{0i}(\rho^{k}w_{i}^{k+1}-\bar{w}_{i}^{k})^{+}\] (A4)  
\[g_{i0}\overset{\text{\tiny def}}{=}\frac{f_{i0}}{1+f_{i0}}\] (A5)  
Then, the well-known identity of the ReLU function, shown in Equation (A6), is substituted into Equation (A4), and the terms in the resulting expression are rearranged, obtaining Equation (A7).  
\[(a-b)^{+}=(b-a)^{+}+(a-b)\] (A6)  
\[\rho^{k}=\frac{1-\sum_{i>0}\Bigl{[}\left(g_{i0}+f_{0i}\right)\left(\bar{w}_{i }^{k}-\rho^{k}w_{i}^{k+1}\right)^{+}-f_{0i}\bar{w}_{i}^{k}\Bigr{]}}{1+\sum_{i> 0}f_{0i}w_{i}^{k+1}}\] (A7)  
Equation (A7) is used to iteratively search for the best possible \(\rho^{k}\), leading to the best possible values \(\tau_{i0}^{k}\) and \(\tau_{0i}^{k}\) for all \(i\). The procedure is summarized in Algorithm 2. This algorithm is similar to the one given in Equation (2.6) of [30]. However, in their formulation, \(f_{i0}\) and \(f_{0i}\) are assumed to be constant for all \(i\). Thus, the procedure in [30] is a special case of the proposed method. The proof of convergence of Algorithm 2 is given in Appendix B, where it is shown Algorithm 2 always converges to the optimal solution of the constrained case for the fee minimization problem when \(\rho_{0}\in[0,1]\).