esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Proposed Approach
### Events and Sensing Priorities  
This work is centered on the definitions of sensors, \(s\in S\), and events of interest, \(e\in E\). For the proposed approach, some additional information is required for sensors and events. Every considered sensor is defined as \(s=(p,c,x,y)\); a sensing priority \(s.p\) is a positive integer value within a pre-defined range, with \(P_{min}\leq s.p\leq P_{max}\) and \(P_{min}<P_{max}\), while the value of \(s.c\) is used as a persistence variable (further described in this section) and \(s.x\) and \(s.y\) are the 2D coordinates of the sensor (treated as a point) within a Smart City scenario. On the other hand, an event is defined as \(e=(p,x_{1},y_{1},x_{2},y_{2})\); besides the computed priority \(e.p\), an event also defines an area of influence represented by a rectangle with \((x_{1},y_{1})\) and \((x_{2},y_{2})\) as the left-bottom corner and top-right corner coordinates, respectively. The area of influence of any event is of paramount importance, since sensors in that area will be associated to the computed priority.  
Sensing priorities are computed based on two characteristics: (a) the potential impact that a detected event has to harm people, defined as _Severity_ (\(sev(e)\)); and (b) the potential number of people to be affected by the event, defined as _Scope_ (\(sco(e)\)). Obviously, critical events (e.g., an explosion) in crowded areas are more relevant to be monitored and thus they receive higher priority, when compared with events with low impact on people integrity (e.g., traffic jams). Therefore, a priority \(e.p\) is computed considering the _Severity_ and _Scope_ of the event, as defined in Equation (1). The variable \(\alpha\) is  
Figure 3: Schematic operation of the proposed approach.  
used to calibrate the impact of \(sev(e)\) and \(sco(e)\) on the computed value of _e.p_. When both parameters have the same impact, \(\alpha=0.5\).  
\[e.p=\alpha\times sev(e)+(1-\alpha)\times sco(e) \tag{1}\]  
The output of the Priority Computing Unit block is \(EoI\), a list of all detected events \(e\), according to the sample frequency employed in the Event Detector Grader module, defined as \(\Delta_{t}\). Such a list, defined as \(ListEoI\), may change every \(\Delta_{t}\) seconds, according to the processed _Twitter_ data flow.  
After assigning priorities to source nodes, applications need to define some use for the priority indexes, usually in the form of one or more optimizations [42]. As any type of priority-based optimization may be employed, the TwitterSensing approach does not specify any mandatory optimization mechanism. Actually, every sensor node must implement the adopted priority-based optimization so it can be used. For the sake of numerical verifications of the proposed approach, some reference optimizations will be defined further in this article.
## 2 Construction of Subdistribution Competing Risk Model with Zero Traumatic Failure Data
### Construction of Subdistribution Competing Risk Model with Zero Traumatic Failure Data
#### 2.2.1 Collection of Product Degradation Information with Zero Traumatic Failure Data
According to Equations (6) and (13), a subdistribution competing risk model with zero traumatic failure data can be obtained as follows:  
\[R(t|X(t))=R_{0}(t)^{exp\big{(}\sum\limits_{j=1}^{p}\beta_{j}X_{j}(t)\big{)}}= exp\bigg{(}\frac{ln(\gamma)}{n}\bigg{(}\frac{t}{t_{0}}\bigg{)}^{m}\bigg{)}^{ exp\big{(}\sum\limits_{j=1}^{p}\beta_{j}X_{j}(t)\big{)}} \tag{14}\]  
Because the subdistribution competition risk model has no restrictions on the distribution of outcome variables (reliable lifetime in this paper), therefore, COX proposed a partial likelihood function [39] to evaluate \(\beta_{j}\):  
\[L(\beta)=\prod\limits_{i=1}^{n}\Bigg{\{}exp\bigg{(}\sum\limits_{j=1}^{p}\beta _{j}X_{ij}\bigg{)}/\sum\limits_{s\in C(t_{i})}exp\bigg{(}\sum\limits_{j=1}^{p} \beta_{j}X_{sj}\bigg{)}\Bigg{\}} \tag{15}\]  
where \(P\) is the number of covariates. \(n\) is the number of covariates degradation data. \(C(t_{i})\) is degradation data set without failure after time \(t_{i}\). Let \(\partial L(\beta_{j})/\partial\beta_{j}=0\), and the maximum partial likelihood estimation value can be obtained. Its numerical calculation can be carried out through SPSS by inputting covariates degradation data, degradation time, and degradation status. Through calculating in SPSS, the regression coefficients \(\beta_{j}(1\leq j\leq p)\), \(P\) value of \(\beta_{j}\), the model's \(-2\)log-likelihood function value, and the upper and lower confidence limits of \(exp(\beta_{j})(1\leq j\leq p)\) at the confidence level of 90% are obtained. When the \(-2\)log likelihood value of this model with covariates is smaller than the \(-2\)log likelihood value of the no-covariates model, this risk model is considered to be valuable, and all covariates in the model have a significant combined impact on the dependent variable, but not on the contrary. To each covariate, when its \(p\) value is smaller than 0.05, it means this covariate is a significant factor to the dependent variable when it is separated from other covariates, when its \(p\) value is larger than 0.05, it means that it has no significant effect on the dependent variable.
# Matching of Water Breakthroughs in a Low-Resistivity Oil Reservoir Using Permeability Anisotropy
## 3 Materials and Methods  
The objective of this study was to evaluate the remaining oil reserves by comparing the oil reserves before and after production. Core data from LHS formation, including porosity, permeability, Kv, and Kh, were obtained from four wells. Log data from seven wells, which were drilled before 1998, were used for the static modeling. Zonation and well correlation were performed using a gamma ray (GR) log. Clay volume (Vsh) was calculated using GR. A Juhasz plot was imported in Techlog to determine the clay type, adjust the baselines for calculations of clay volume, and choose the appropriate water saturation model [18]. Porosity (Phi) was calculated using a density log, which provided a good match with the core data, and permeability was calculated using correlations with porosity, as described in [14]. Water saturation was calculated using the Archie and Simandoux equations for comparison. Petrophysical and logging data were processed using Techlog software (2017) and further imported into Petrel software (2019).  
A fine-scale 3D grid consisting of 660,192 cells (69 \(\times\) 104 \(\times\) 92) oriented SW-NE at an angle of 50 degrees was generated to create 3D models of thickness. Petrophysical properties were populated using sequential Gaussian simulation. Each cell had a size of 100 m \(\times\) 100 m \(\times\) 1.6 m (max) and the model was optimized for computational efficiency through cell cut-off, the utilization of theoretical aquifers, grid size refinement, etc. Sensitivity analyses for porosity and permeability cut-offs and other main uncertainties such as the oil-water contact were also conducted. Details of the modelling and simulation procedures can be found in [14; 15].  
Dynamic simulation was carried out using Intersect software to history match the production data from the above-mentioned wells of LHS up to the year 1999 and to further evaluate the remaining oil reserves. The history match philosophy adopted was that a simple model capable of a general match is preferable to a complex model requiring heavy \"tuning\" to individual wells. The history-matched data in order of importance were the oil production rate (OPR) and the cumulative water cuts and water production rate (WPR), RFT pressures, GOR, productivity index, bottom-hole shut-in, and flowing pressures. To investigate the influence of permeability anisotropy, the model was history-matched by varying the Kv/Kh ratio from 1:10 to 20:1. This was followed by analyzing the sensitivity of relative permeability curves. Previous coreflood data indicated that the maximum water relative permeability (Krwmax) in the presence of irreducible oil saturation was 0.8. Therefore, five different relative permeability curves were generated by shifting both curves (Krw and Kro) to the left or right or by shifting a single curve at a time. The maps of initial water saturation and at the end of production are presented. The percentage of remaining reserves to the initial oil in place is also presented for all of the sensitized cases.
## 6 Conclusions  
In this paper, we solve the underwater data gathering problem in an efficient scenario, in which an AUV is utilized to visit part of the sensors and collect data via high speed MI communication, while the remaining nodes acoustically route their data to the ones that will be visited by the AUV. We propose both distributed and centralized algorithms HAS\({}^{4}\) to select sink sensor sets in this AUV-aid data gathering applications. By setting the trade-off factor, HAS\({}^{4}\) can achieve both energy and AUV efficiency.  
For data gathering scenarios, the cooperation among multiple AUVs or the partition of the gathering task with high delay, unreliability, and low bandwidth acoustic communication in large scale UWSNs is much more challenging. Thus, in the future, we will further examine underwater data collection, including multi-AUV, task partition and online path planning.  
Conceptualization, M.P.; Methodology, M.P. and X.W.; Writing--Original Draft Preparation, X.W. and D.W.; Writing--Review and Editing, X.W. and M.P.; Supervision, X.W. and J.C.  
This research was funded by the National Natural Science Foundation of China (NSFC) (Grants No. 61772228), National Key Research and Development Program of China (Grant No. 2016 YFB0201503).  
The authors declare no conflict of interest.  
Figure 10: Effect of parameter \(l\) in distributed HAS\({}^{4}\) on tour.  
Figure 9: Effect of parameter \(l\) in centralized HAS\({}^{4}\) on tour.
## 3 Results
### Relationships under Different Sky Conditions
Figure 11: Scatterplots of daily sunshine duration recorded by Campbell–Stokes recorder (CS) and SD obtained from global radiation measurements (GI) under different sky conditions for Larnaca during the period 2009–2012. \begin{table}
\begin{tabular}{l l l c c c c c c c c c c c c c c c} \hline
**Station** & **Period** & **Variables** & **Parameters** & **1** & **2** & **3** & **4** & **5** & **6** & **7** & **8** & **9** & **10** & **11** & **12** & **Annual** \\ \hline Larmaca & 2009-2012 & CS vs. GI & **a** & 0.153 & 0.610 & 0.724 & 1.135 & 0.588 & 1.940 & 0.268 & 1.115 & 1.280 & 1.597 & 0.806 & 0.259 & 0.698 \\  & & **b** & 1.009 & 0.946 & 0.910 & 0.897 & 0.942 & 0.825 & 0.941 & 0.891 & 0.849 & 0.823 & 0.929 & 0.979 & 0.922 \\  & & **R\({}^{2}\)** & 0.953 & 0.908 & 0.940 & 0.925 & 0.919 & 0.831 & 0.843 & 0.867 & 0.900 & 0.918 & 0.909 & 0.958 & 0.960 \\ \hline Larmaca & 2009-2012 & CS vs. & **a** & 0.699 & 1.246 & 1.475 & 1.497 & 2.006 & 3.287 & 6.023 & 2.725 & 2.185 & 1.671 & 1.530 & 0.344 & 1.038 \\  & & **b** & 0.959 & 0.889 & 0.899 & 0.878 & 0.853 & 0.759 & 0.525 & 0.775 & 0.768 & 0.832 & 0.851 & 0.974 & 0.915 \\  & & **R\({}^{2}\)** & 0.862 & 0.807 & 0.865 & 0.883 & 0.831 & 0.740 & 0.291 & 0.813 & 0.777 & 0.888 & 0.841 & 0.921 & 0.918 \\ \hline Larmaca & 2009-2012 & CS vs. & **a** & 0.519 & 0.048 & 0.416 & -0.298 & -2.416 & -0.332 & 2.918 & 8.278 & \(-\)6.478 & 2.087 & 1.379 & 0.968 & 0.229 \\  & & **b** & 0.929 & 0.921 & 0.862 & 0.966 & 1.168 & 1.028 & 0.786 & 0.326 & 1.514 & 0.715 & 0.829 & 0.988 & 0.944 \\  & & **R\({}^{2}\)** & 0.756 & 0.773 & 0.787 & 0.835 & 0.855 & 0.252 & 0.298 & 0.074 & 0.859 & 0.685 & 0.714 & 0.729 & 0.843 \\ \hline Fafos & 2009-2012 & CS vs. GI & **a** & 0.959 & 0.882 & 1.340 & 1.513 & 1.397 & 4.975 & 7.753 & 1.944 & 2.480 & 1.303 & 1.255 & 0.847 & 0.911 \\  & & **b** & 1.003 & 0.957 & 0.866 & 0.878 & 0.901 & 0.636 & 0.408 & 0.916 & 0.787 & 0.878 & 0.909 & 0.949 & 0.952 \\  & & **R\({}^{2}\)** & 0.925 & 0.874 & 0.909 & 0.908 & 0.867 & 0.617 & 0.293 & 0.488 & 0.731 & 0.919 & 0.935 & 0.951 & 0.938 \\ \hline Athalassa & 2012-2015 & CS vs. Pr & **a** & 0.510 & 1.066 & 0.704 & 1.390 & 0.797 & 3.233 & 4.333 & 4.789 & 1.661 & 0.703 & 0.877 & 0.277 & 0.809 \\  & & **b** & 0.994 & 0.933 & 0.945 & 0.888 & 0.977 & 0.749 & 0.671 & 0.624 & 0.852 & 0.958 & 0.937 & 0.114 & 0.954 \\  & & **R\({}^{2}\)** & 0.971 & 0.931 & 0.955 & 0.905 & 0.954 & 0.810 & 0.747 & 0.694 & 0.843 & 0.939 & 0.930 & 0.973 & 0.956 \\ \hline \end{tabular}
\end{table} 
Table 6: Monthly and annual linear regression parameters between different pairs of sunshine duration recorders for different time periods at the three synoptic stations. Response variable is the CS recorder.
# Parallel Image Captioning Using 2D Masked Convolution
## 1 Introduction  
Image captioning (a task that allows a computer to automatically describe the content of an image in natural language) is a very challenging problem. To do this, the machine is required to not only recognize the objects contained in the image but to also understand the relationship between each object in the scene, along with certain attributes such as color, quantity, size, etc. As challenging as it seems, image captioning also holds great interest in the research community because it has many benefits, ranging from assisting the visually impaired in understanding image content to improving the image indexing and annotation tasks [1].  
For many years, the main approach to image captioning has always been a combination of a convolutional neural network (CNN) as an encoder and a recurrent neural network (RNN) as a decoder. This approach has shown great results in the image captioning task, and it has seen many different adaptations and improvements from the research community. However, RNNs have known limitations, such as the parallel training problem and vanishing gradients, which result from the sequential processing nature of the model [2]. Even though long short-term memory (LSTM) [3] was later used in place of the RNN to solve the vanishing gradient problem, LSTM itself requires a large amount of memory, since it has to compute multiple linear layers per cell during its sequential learning. For this reason, many studies have been done to find a way to solve this problem [4; 5].  
Recent research in neural machine translation has seen many successful attempts to replace LSTM with a convolutional-based architecture [6; 7]. Inspired by these studies, we also proposean end-to-end convolutional-based image captioning approach that has a result comparable to the traditional CNN-LSTM model while also enabling parallel graphics processing unit (GPU) computing, which reduces the model training time on a Microsoft Common Objects in Context (MSCOCO) dataset from two days to less than 20 h. On the MSCOCO test set, we achieved evaluation scores of 73.6 with the BLEU-1 metric, 25.9 with METEOR, and 99.4 with CIDEr, which outperforms the conventional LSTM and non-LSTM approaches by a considerable margin, and is comparable to the state-of-the-art image captioning model [8].  
This paper makes two main contributions to image captioning research. First, we created a convolutional-based image captioning model that gives state-of-the-art performance over non-LSTM based models in some evaluation metrics. Secondly, we created a model that requires fewer parameters and less training time, compared to the state-of-the-art LSTM model, while also enabling parallel GPU computation.
## 1 Introduction
[23] have produced the most comprehensive radiation-equivalent and geometrically accurate 3D-printed, patient-specific chest phantom, incorporating bone, heart, skin, fat, muscle and silicone-cast lung parenchyma and nodules [22]. However, their phantom merely represents an axial slice rather than an entire torso and does not incorporate lung vessels. Others have developed radiation equivalent chest phantoms for a range of purposes, including to optimise low-dose CT pulmonary angiography (CTPA) protocols for the detection of pulmonary embolism [24] and for optimising CT surveillance scans post thoracic endovascular aortic repair (TEVAR) [25; 26]. Nonetheless, no study thus far has 3D printed a thoracic model with the simulation of lung nodules to study low-dose protocols for lung cancer screening [22]. Additionally, most studies 3D print discreet thoracic organs, with limited phantoms representing holistic thoracic structures [22]. Thus, the aim of this study was to 3D print a realistic and radiologically tissue equivalent patient-specific CT derived chest phantom that comprehensively incorporates the full gamut of thoracic structures as well as lung nodules in order to optimise LDCT protocols for lung nodule detection. It was hypothesised that through the use of the 3D-printed phantom, optimal LDCT protocols can be identified by reducing voltage and current, despite the associated increase in noise. Furthermore, it is anticipated that there will be a threshold at which the voltage and current can no longer be reduced further due to overpowering noise that would interfere with diagnostic capability.
# Spaceborne LiDAR Systems: Evolution, Capabilities, and Challenges
## 5 LiDAR in Space Missions
### Spaceborne LiDARs for Terrestrial Applications
#### 5.1.4 Cats--The Cloud-Aerosol Transport System  
The Cloud-Aerosol Transport System (CATS) is an elastic backscatter LiDAR instrument launched to the International Space Station (ISS) in January 2015. The Cloud-Aerosol Transport System (CATS) is an elastic backscatter LiDAR instrument launched to the International Space Station (ISS) in January 2015. Operating from a 415 km orbit with a 51\({}^{\circ}\) inclination, CATS aimed to provide near real-time aerosol data to support global climate modeling, fill temporal gaps left by missions like CALIPSO, and test new LiDAR technologies. Notably, it evaluated high-frequency lasers, photon-counting detectors, and multibeam systems. The processed aerosol data were delivered within approximately six hours to support the timely forecasting of aerosols [133; 120; 134].
## 2 Modeling and Controller Design
### Controller Design of MMC-HVDC System
#### 2.4.2 Low-Level Control  
The following equation represents the current control dynamics of MMC in dq frame in the steady-state  
\[\frac{L}{2}\frac{di_{sd}}{dt}+\frac{R}{2}i_{sd}=\frac{L}{2}\omega_{0}i_{sq}+V_{d }-V_{sd} \tag{7}\]  
\[\frac{L}{2}\frac{di_{sq}}{dt}+\frac{R}{2}i_{sq}=-\frac{L}{2}\omega_{0}i_{sd}+V_ {q}-V_{sq} \tag{8}\]  
The following equation governs the HVDC link voltage  
\[\left(C_{d}+\frac{6C}{N}\right)\!\frac{dV_{dc}}{dt}=I_{ext}-i_{sd} \tag{9}\]  
Equations (7) and (8) form the inner current control loop, as depicted in Figure 4, to produce the modulating signal for the MMC converter. In addition to inner current control, a circulating current controller is needed to ensure symmetric current between the upper and lower arm.  
The circulating current rotates at \(2\omega_{0}\) frequency, and its dynamics are controlled by the following equation [47].  
\[L\frac{di_{t1}}{dt}+Ri_{d1}=2L\omega_{0}i_{q1}+V_{d1} \tag{10}\]  
\[L\frac{di_{q1}}{dt}+Ri_{q1}=-2L\omega_{0}i_{q1}+V_{q1} \tag{11}\]  
Figure 4: Complete control of MMC converter.  
In this equation, \(i_{d1}\) and \(i_{q1}\) are the dq current converted from the MMC three arms difference (between upper and lower arm) current. The PI controller produces the required adjustment in terms of \(V_{d1}\) and \(V_{q1}\), which further converts to modulating signal. The angle for circulating current conversion to dq frame and \(V_{dq1}\) conversion to modulating signal is \(-2\)g. After combining the modulating signal from inner current control and circulating current control, nearest level modulation (NLM) is employed to produce the number of submodules selection. Apart from the circulating current between the upper and lower arm of the MMC, submodule (SM) capacitor voltages within the upper or lower arms drift from each other. The uncontrolled charging and discharging are the reasons for such drifts. Therefore, it is expected to place a higher voltage sub module (SM) when the current leaves and a lower voltage SM when the current enters, to minimize the voltage imbalance among the SMs. This work employs a submodule-sorting algorithm (SSA) to place the appropriate SM from the set of SMs in the upper and lower arm based on current direction and current state of charge (voltage) of the SM capacitor. Figure 4 shows the complete control of MMC bridges. However, the AC side fault significantly reduces the converter's power transfer capability, which in turn raises the HVDC link voltage. This work employs active power reduction of PV to protect the HVDC link voltage. If the PCC voltage surpasses the threshold, the PV power is reduced to control the HVDC link voltage, whereas the system becomes normal if the PCC voltage goes below the threshold. MMC2 controller switches from fixed AC voltage to variable AC voltage control during the abnormal rise of HVDC link voltage under fault at PCC1.
# Cervical Cell Segmentation Method Based on Global Dependency and Local Attention
## 3 Proposed Method
### Channel Attention Module  
A certain intermediate input feature mapping \(X=[x^{1},x^{2},x^{3}\dots,x^{c}]\) is denoted as a combination of channels \(x^{i}\), \(x^{i}\in\mathbb{R}^{H\times W}\), and channel attention is obtained using a squeeze-excitation operation that transforms the input feature map into \(X_{CA}\in\mathbb{R}^{1\times H\times W}\) by globally  
Figure 3: Diagram of each attention sub-module, where (**A–C**) denote CA Model, GD Model, and SA Model, respectively.  
averaging the squeeze space dimensions as a combination of channel features \(x_{CA}^{i}\in\mathbb{R}^{1\times 1}\). The squeeze operation for each channel can be expressed as:  
\[x_{CA}^{i}(x_{i})=1/(H\times W)\sum_{j=1}^{N_{p}}x^{i}(j) \tag{3}\]  
After spatial squeezing, the feature vector is reduced by a bottleneck structure to limit the computational complexity of the model. Specifically, a dimensionality reduction operation is first performed on the channel dimension with a rate of r, plus _ReLu_ activation function for nonlinear transformation, and finally expanded to the original dimensionality. Subsequently, the sigmoid function is used to excite the channel so that the dynamic range of the eigenvector \(X_{CA}\) is brought to the interval [0\(\sim\)1]. This strategy makes it easy to identify subtle but dominant differences of similar objects. The overall process can be summarized as:  
\[F_{\text{C}}(X)=\sigma(W_{1}(ReLu(BN(W_{0}(X_{CA}))))) \tag{4}\]  
where \(\sigma\) denotes the sigmoid function. \(W_{0}\) and \(W_{1}\) are two linear transformation matrices, \(W_{0}\in\mathbb{R}^{C\times C/r}\) and \(W_{1}\in\mathbb{R}^{C/r\times C}\) used to construct the bottleneck, and \(BN\) is the batch normalization process. The structure diagram of the channel attention module is shown in Figure 3A. The excitation feature vectors represent the importance of the channels used to rescale the original features \(x^{i}\), enabling adaptive adjustment of channel weights to emphasize meaningful channels. In this paper, a 1 \(\times\) 1 convolutional layer is used to construct the bottleneck structure layer, and a BN layer is added for regularization after the dimensionality reduction operation using convolution. Since the smaller the channel compression ratio, the more detailed the differentiation of channel importance, the ratio r is set to 2.