esa-sceva/satcom-chunk-collection huggingface dataset:
# Performance of Warm-Mixed Flame Retardant Modified Asphalt Binder
## 3 Test Results and Analysis of the Limit Oxygen Index Test of Asphalt Binder  
The limit oxygen index (LOI) was adopted to evaluate the flame-retardant properties of the asphalt binder in this study. Generally, it is determined using the oxygen index test method. LOI refers to the lowest oxygen concentration in the oxygen-nitrogen mixed gas inlet when it is just enough to keep the material burning under the required test condition. The calculation equation is shown as Equation (1).  
\[\text{LOI}=\frac{[\text{O}]}{[\text{O}]+[\text{N}]}\times 100\% \tag{1}\]  
In Equation (1), [O] indicates the volume flow of oxygen in mixed airflow at critical oxygen concentration; [N] indicates the volume flow of nitrogen in mixed airflow at critical oxygen concentration.  
According to _Determination of the Combustion Performance of Rubber_ (GB/T 10707-2008) [19], XZT-100A oxygen index determinator was adopted in this paper to determine the limit oxygen index (LOI) of SBS modified asphalt binder, Sasobit modified asphalt binder, and warm-mixed flame-retardant asphalt binder at different concentrations. In the test, the asphalt binder was poured evenly on the surfacing mat of fiberglass to make samples (100 mm \(\times\) 5.5 mm). The apparatus of the oxygen index test is as shown in Figure 1, the samples are as shown in Figure 2, and the test results are as shown in Table 3.  
Figure 1: Apparatus for oxygen index test.  
Figure 2: Samples for the determination of limit oxygen index.  
As seen in Table 3: 1 The limit oxygen index increased with the addition of flame retardant, which indicates that flame retardant can improve the flame resistance of asphalt binder, and the higher the concentration, the better the flame resistance. In addition, the limit oxygen indexes of warm-mixed flame retardant asphalt binder at different concentration all conform to the requirement of _Concrete of Flame Retardant Asphalt for Road Engineering_ (GB/T 29051-2012) that the limit oxygen index of flame retardant asphalt binder shall not be less than 23% [20]; 2 the limit oxygen index decreased from 23.0% to 22.8% after adding Sasobit into SBS modified asphalt binder, which means that Sasobit could have an adverse impact on the flame retardant properties, and according to the preliminary analysis, it is concluded that Sasobit has certain combustion-supporting effects on asphalt binder because it is a wax-based material.
# Two-Layer-Graph Clustering for Real-Time 3D LiDAR Point Cloud Segmentation
## 3 Proposed Method
### Category Update
In Algorithm 2, the visit map is initialized first (line 1), all nodes are in the unvisited state, a category label is assigned the value of 0 (line 2), and each channel is searched (line 3). If the current node has been visited in the visit map (line 5), the node is skipped (line 6). Find a node that has not been visited, add 1 to the category label, and add it to the queue. The visit map corresponding to the node is marked as visited (line 7-10). When the node queue is not empty, take the first node of the queue and mark its category as the current category label (line 11-14). Search the nodes near the current node (the nodes in upper and lower adjacent channels, which are connected to the current node and the first unconnected node on both sides, as shown in Figure 7). If the node is not visited or meets the requirements of node merging, join the node queue (line 15-19). Repeat until the queue is empty. Compared with BFS, not all connected nodes need to be traversed (the same channel is not traversed), and some nodes that are not connected can also be traversed (the first unconnected node on both sides of the adjacent channel).
## 5 Bayesian Prediction
### Two-Sample Prediction  
Firstly, we consider a censored sample represented as \(X=(X_{1},\ldots,X_{r+1},\ldots,X_{m})\), with a size of \(m\), drawn from a population characterized by the generalized Pareto distribution. We also denote \((W_{1},\ldots,W_{K})\) as the ordered failure times of a future sample, where \(W_{1}\leq W_{2}\leq\cdots\leq W_{K}\). Additionally, we define \(W_{r}\) as the \(r\)-th failure time from this future sample. The density function of \(W_{r}\) is outlined as follows:  
\[f(w_{r}|\alpha,\lambda) =r\frac{m!}{r!(m-r)!}f(w_{r}|\alpha,\lambda)[1-F(w_{r}|\alpha, \lambda)]^{m-r}[F(w_{r}|\alpha,\lambda)]^{r-1} \tag{57}\] \[=r\frac{m!}{r!(m-r)!}f(w_{r}|\alpha,\lambda)\sum_{j=0}^{r-1}[1-F(w _{r}|\alpha,\lambda)]^{m-r+j}. \tag{58}\]  
Then, the posterior prediction density function of \(W_{r}\) is as follows:  
\[f^{*}(w_{r}|\hat{x})=\int_{0}^{\infty}\int_{0}^{\infty}\pi(\alpha,\lambda|x) f(w_{r}|\alpha,\lambda)\mathrm{d}\alpha\mathrm{d}\lambda. \tag{59}\]  
Utilizing the MH algorithm, the approximate result can be efficiently calculated as follows:  
\[f^{*}(w_{r}|\hat{x})=\frac{1}{M-MI}\sum_{j=M_{0}+1}^{M}f(w_{r}| \alpha_{j},\lambda_{j}). \tag{60}\]  
Furthermore, we derive the posterior survival function of \(w_{r}\), which is:  
\[S^{*}(w_{r}|\hat{x})=\int_{0}^{\infty}\int_{0}^{\infty}S(w_{r}|x) \pi(\alpha,\lambda|x),\mathrm{d}\alpha,\mathrm{d}\lambda=\frac{1}{M-MI}\sum_{ j=M_{0}+1}^{M}\int_{w_{r}}^{\infty}f(u|\alpha_{j},\lambda_{j})\mathrm{d}u, \tag{61}\]  
where  
\[S(w_{r}|\alpha,\lambda)=\int_{w_{r}}^{\infty}f(u|\alpha,\lambda) \mathrm{d}u. \tag{62}\]  
A Bayesian predictive interval for \(w\), denoted as \((L_{2},U_{2})\), is now constructed at the \(100(1-\tau)\%\) level by obtaining the solutions to the following functions:  
\[S^{*}(L_{2}|\hat{x})=1-\frac{\tau}{2},S^{*}(U_{2}|\hat{x})=\frac {\tau}{2}. \tag{63}\]  
The next step involves obtaining the predictive estimation of the \(r\)-th future ordered lifetime as follows:  
\[\widehat{w_{r}}=\int_{0}^{\infty}w_{r}f^{*}(w_{r}|\hat{x})w_{r}= \int_{0}^{\infty}\int_{0}^{\infty}\int_{0}^{\infty}w_{r}f(w_{r}|\alpha,\lambda )\pi(\alpha,\lambda|\hat{x})\mathrm{d}w_{r}\mathrm{d}\alpha\mathrm{d}\lambda. \tag{64}\]  
The predictive estimation of \(W_{r}\) is derived using the MH algorithm as follows:  
\[\widehat{w_{r}}=\frac{1}{M-MI}\sum_{j=M_{0}+1}^{M}\int_{0}^{\infty}w_{r}f(w_{ r}|\alpha,\lambda)\mathrm{d}w_{r}. \tag{65}\]
## 3 System Model
### Network Model  
We model the three-dimensional non-uniform distribution of 100 acoustic sensors (\(N_{node}=100\)) randomly deployed in 250 m \(\times\) 250 m \(\times\) 250 m. There is one surface sink on the sea surface, at the top of the network. Nodes are fixed to the ocean floor, and we presume that sensor locations will not change over time. A sample 4-clustered 3D network topology consisting of 100 underwater sensor nodes is shown in Figure 1. Cluster members are connected to their cluster heads via single hop.  
Data aggregation and the transfer of aggregated data to the gateway are handled by cluster heads. There are several rounds to complete this operation. A frame consists of one packet of \(L\) bits transmitted by each node, and a round is divided into many successive frames. Keep in mind that nodes use energy for data transmission, reception, and aggregation. The length of time that a packet is transmitted and received is shown by  
Figure 1: 3D Sample network topology showing gateway and sub-sea node placements.  
\(\tau_{tx}=L/R\), where \(R\) is the sensor nodes' bit rate. Moreover, all nodes harvest energy only in their idle periods.  
In our system, the network is divided into 4 regions by the k-means clustering method. The surface gateway divides the network into k clusters by computing the elbow method and initializing clusters [18]. Each node in the network learns their clusters from the broadcast message of the gateway station. As the next step, each node determines whether to become the cluster head for the current round. With prior knowledge of members in its cluster, cluster heads assign a unique time slot for each node in the cluster. Cluster members send their data to the cluster head in their programmed time slots to avoid collisions. After data transmission, each node becomes idle and harvests ambient energy until the next frame of data collection.  
Given that underwater channels have limited capacity and high propagation delay, TDMA-based medium access protocols are widely used by cluster-based data collection [33]. These protocols have been chosen over collision avoidance-based protocols due to their poor performance in underwater acoustic networks [34]. Furthermore, time slot allocation has the potential to reduce energy usage by preventing contention and collisions among packets without extra overhead for channel reservation that requires short control packets.
# RIS-Assisted High-Speed Communications with Time-Varying Distance-Dependent Rician Channels
## 4 Performance Analysis  
In this section, we first give the analytical expression of the upper bound for the ergodic SE. Then, we analyze the effects of Rician factors when the vehicle is far from (near) the BS and the RIS. Besides, we show that the channel \(h(t)\) follows complex-valued Gaussian distribution, and so obtain the outage probability of the \(h(t)\). The results reveal that the distance-dependent Rician factor not only affects the ergodic SE but influences the outage probability.
## 4 Particle Filter Model for Experiments
### Resampling and State Estimation  
After weight updates, resampling was performed to remove particles with low weights and replicate those with higher weights.  
\[x_{t+1}=\frac{\sum_{i=1}^{N}\omega_{t}^{(i)}x_{t}^{(i)}}{\sum_{i=1}^{N}\omega_ {t}^{(i)}} \tag{3}\]  
Equation (3) represents the resampling process, where the new state estimate is calculated as the weighted average of the particles, minimizing uncertainty and focusing on accurate target tracking.
## 2 Proposed UWB Antenna with Quadruple Band Rejections  
The geometry of the UWB monopole antenna is shown in Figure 1. The antenna is fabricated on a Rogers RT/duroid5880 substrate with a relative permittivity of 2.2, and the loss tangent is 0.0009. The overall size of the antenna is 30 mm \(\times\) 35 mm \(\times\) 1.575 mm. The radiator and a feed line are printed on the front side of the substrate, and the ground plane is printed on the backside of the substrate. A quarter wavelength converter is connected in series at the end of the feeder to realize the characteristic impedance matching of 50 \(\Omega\). Due to the antenna's gradual change structure, it can match over a wide frequency range.
# Evaluation and Economics of Shale Gas Reserves in the Flysch-Eocene Formation of the Jaca Basin
## 4 Discussion  
### Impact on the Gas Sector  
In 2019, around 38,000 MMm\({}^{3}\) of natural gas [18] was consumed in Spain for electricity generation, and industrial and residential use, representing a growth of 14.57% over 2018. In 2020 it dropped to 34,000 MMm\({}^{3}\) due to the effects of the pandemic.  
Figure 8: NPV (Net Present Value) and IRR (Internal Rate of Return) of the well.  
Estimated first-year production rates vary depending on whether or not fracking is used, but range between 20,000 and 38,000 M\(\rm{M}\)m\({}^{3}\).  
Figure 6 shows a high gas production obtained in the first year, which declines rapidly during the following years. Economically, production could be stopped after the tenth year, due to the poor yield obtained. However, this decision would be subject to changes in gas prices and improvements in the techniques used to produce the field.  
The impact of the use of the fracking technique on the production of the well is also noted. The difference between case 1 and case 3 production in the first year is about 48%, a value similar to the difference in total production over the 20 years.  
To make a more accurate approximation of the impact, the average production from the second to the seventh year is used, since no more than 5000 vertical wells can be put into production at the same time in the first year. A more realistic production is considered to be the sum of wells that are starting production and wells that have been in production for several years or even abandoned wells.  
An average production between 4000 and 8000 M\(\rm{M}\)m\({}^{3}\) would represent between 10% and 20% of consumption over several years. A 100% stimulation with fracking is close to the annual gas consumption rate for electricity, which in 2019 was 28% of the total consumed [18].  
The peak of natural gas consumption in Spain was reached in 2008 with 42,000 M\(\rm{M}\)m\({}^{3}\). The country's 20-year growth projections could suggest that this target will be reached and may be exceeded. Overall, over 20 years, it is estimated that around 800,000 M\(\rm{M}\)m\({}^{3}\) of natural gas could be consumed.  
With the previously estimated 20-year production of the reservoir (Table 5), the percentage that its exploitation would represent varies between slightly less than 10% and 18%. These percentages are very representative of a single field, in addition to the positive economic impact it would have on the country. Since there are several natural gas fields within the Spanish territory, similar to the one studied, imports of this energy resource could be reduced, thus reducing energy dependence.
## 4 Experiment and Analysis
### Analysis of Performance Indices of Machine Learning Algorithms  
After testing the performance of the models when using each algorithm on sets of different sample numbers, the effect of each model in the same test set is further evaluated, so as to reflect the predictive abilities of the models to some extent and judge the generalization abilities of the algorithms. As shown in Table 3, the value of the accuracy index and recall index of each model in classifying water bodies and other ground objects are high, the accuracy index is in the range of 0.945-1, and the recall index is in the range of 0.911-1. However, the AUC index can better represent the comprehensive performances of the models and the higher the value, the better the performance [63]. There is little difference in the effect of each machine learning algorithm on the test set, and the AUC index ranges from 0.956 to 0.987; by analyzing AUC data, the logistic regression and XGBoost algorithm are found to perform best on the test set, followed by the SVM, the neural network, then the random forest, while the decision tree has (in general) the worst performance. Whether the evaluation of these algorithms in the test set can accurately represent the generalization abilities of the algorithms for classifying water bodies in the remote sensing images needs to be discussed and studied using remote sensing images acquired under different conditions.
# A Review of Posture Detection Methods for Pigs Using Deep Learning
## 2 Data Acquisition and Sub-Tasks for Pig Posture Detection
### Data Acquisition for Pig Posture Detection
#### 2.1.3 Depth Images from Depth Cameras  
In addition to the 2D optical pixel information, the depth image can provide the auxiliary distance information between pigs and devices at every pixel available to detectors, thereby providing 3D data about the pigs. Therefore, the depth image can improve the poor performance of 2D camera systems under certain situations, such as occlusion among grouped pigs and difficulty in distinguishing sternal and ventral lying from the top view [21]. Along with providing additional depth information, depth images can reduce the negative influence of poor light conditions [29]. Depth images were initially applied to animal behavioral research, where the Kinect depth sensor showed excellent performance in recognizing aggressive behavior and automatic detection of lameness by analyzing the walking patterns within depth images [30; 31]. Like the 2D camera system, depth camera systems are also commonly used in pig posture detection. [PERSON] et al. [32] used a top-view 3D Kinect camera to obtain depth images, which were then input into a manually designed recognition algorithm to recognize and classify pig behaviors and standing, sitting, and lying postures. In addition to the whole-body posture, to facilitate the prediction of tail biting in intensive pig farms, the posture or angle of pig tails can be easily measured by modeling analysis on depth images collected from time-of-flight 3D cameras [33].  
Even though depth images are less sensitive to lighting conditions, their quality is easily affected by the occlusion of moving noises caused by the disturbance of dust and dirt [34]. Using the spatiotemporal interpolation technique to remove noise, [PERSON] et al. [34] applied the background subtraction method to detect the standing and lying posture of pigs from depth images. In addition, depth images usually have lower resolution than traditional RGB images [29], which may decrease the quality of extracted features and then affect the detection of small or subtle changes in pig postures. This problem can be alleviated by powerful algorithm models. For example, [PERSON] et al. [29] used a deep learning model to process depth images acquired by Kinect v2 sensor, and [PERSON] et al. [35] inputted depth image data obtained by Azure Kinect DK depth camera into a designed model that integrates deep learning techniques. In their experiments, five postures of pigs could be detected well and classified under commercial conditions.
## 2 Materials and Methods
### Data Sets  
Two extensive field trials have been conducted by SherpaTT in the desert of Morocco (2019) and in the desert of Utah (2016). Both sites have been shown to be representative of Mars analogue terrain ([PERSON], 2019). During these field trials, different test tracks in natural terrain, GAP-modes and footprints have been tested. Since the focus of those campaigns was different from the aim of this research, only a small portion of the data logs can be effectively used for terrain estimation purposes. Table 1 sums up the datasets available for terrain and slippage classification, clarifying specific terrain, total run time and logged sensors. Specifically, the only available run in the Morocco campaign is performed on sandy dunes of flat/moderate slopes, while Utah data logs are available on flat, moderate sloped and steep sloped rocky terrain. Utah moderate sloped terrain presents an inclination less than 10 deg, while steep sloped terrain had a grade up to 28 degrees. All the available experimental runs are performed following a straight path. Sensor signals are logged together with a synchronized timestamp. Four sensor modalities are available on SherpaTT: the four FTSs, the body's IMU, a Differential Global Positioning System (DGPS) on the rover body and joint telemetry. The FTSs are six axial sensors able to measure the three cartesian components of forces and moments acting on each wheel. The IMU outputs the rover's attitude ad accelerations, while DGPS provides an absolute position of the rover and it is used for ground truthing. Joint position and speed, as well as each joint's electrical current and PWM duty cycle, are available for this study.  
Proprioceptive data streams have been associated with a sequence of terrain patches with the same length. In this work, a patch length of 0.3 m is considered. In such a way, for each terrain patch, a set of features has been computed, as extensively explained in the following section.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Run ID**} & \multirow{2}{*}{**Test Site**} & \
\begin{tabular}{c} **Terrain** \\ **Type** \\ \end{tabular} & \multirow{2}{*}{**Slope**} &
\begin{tabular}{c} **Run** \\ **Time [s]** \\ \end{tabular} & \multirow{2}{*}{**FTS**} & \multirow{2}{*}{**IMU**} & \multirow{2}{*}{**DGPS**} & \multirow{2}{*}{**Encoders**} \\ \hline R01 & Morocco & Sand & Flat/Moderate & 381.5 & ✓ & ✗ & ✗ & ✓ \\ R02 & Utah & Rock & Flat & 214.8 & ✓ & ✓ & ✓ & ✓ \\ R03 & Utah & Rock & Flat & 214.8 & ✓ & ✓ & ✓ & ✓ \\ R04 & Utah & Rock & Moderate & 152.4 & ✓ & ✓ & ✗ & ✓ \\ R05 & Utah & Rock & Steep & 453.9 & ✓ & ✓ & ✓ & ✓ \\ R06 & Utah & Rock & Steep & 457.7 & ✓ & ✓ & ✓ & ✓ \\ R07 & Utah & Rock & Steep & 481.2 & ✓ & ✓ & ✓ & ✓ \\ \hline \hline \end{tabular}
\end{table}
Table 1: Datasets gathered by SherpaTT during field trials; for each test run, terrain description, testing time and available logged sensors are indicated.  
Figure 2: Examples of possible footprint configurations and resulting support polygons.
## 3 Results  
In this section, we introduce observational data obtained from Swarm L2\(-\)IBI and COSMIC satellites. Our essential aim is to study the morphology and occurrence of the plasma bubbles during three different levels of geomagnetic storms. First, we present a statistical IBI data obtained from the three Swarm satellites to study the variations of plasma bubbles with respect to latitude, longitude, altitude, and local time. Second, we investigate the variation of the ionospheric \(n_{e}\) profile of three geomagnetic storm levels using Swarm and COSMIC satellites.
# Recent Advances in Protein Caging Tools for Protein Photoactivation
## 2 Caged Proteins  
Methods for converting proteins to photo-responsive ones by reversible chemical modification have also been used as a complementary approach in which intact proteins with no modification are produced after photoactivation [10; 11; 12; 21; 22; 23]. In the field of chemical biology, inactivation of biomolecules by modification with photodegradable protective groups is called \"caging\", and such protected compounds are called caged compounds [10; 21]. Caging have been employed for activating biomolecules of interest at desired timing and location by exposure to light (Figure 1A). For example, a caged glutamine was reported to be introduced into a neuron, and then one of the dendrites was site-specifically exposed to light. As a result, the shape change of the light-exposed dendrites clearly indicated the molecular system in which glutamine affects the shape memory of neural spines [24]. Thus, caging has been utilized as a powerful research tool for giving new insight into molecular mechanisms in cell biology. Such a caging approach has also been applied to proteins, and caged proteins have been reported for more than a quarter of a century [22; 23]. The simplest strategy for preparing caged proteins is to randomly introduce a photodegradable protecting group to the amino group on the protein surface through the reaction with an active carbonyl group (Figure 1B) [25; 26; 27; 28]. In this method, in principle, almost all proteins can be caged without any pretreatment simply by mixing a reactive photodegradable protecting reagent (a caging reagent). After the protective group is degraded by light, the caged lysine residue returns to the original lysine one without leaving any trace, so there is no need to consider the effect of caging on the protein function after photoactivation. This is a great advantage when studying the function of a protein or using it as a drug. Actually, the polymerization activity of G-actin was photo-regulated by this caging strategy [25]. The selective binding function of antibodies were demonstrated to be remotely controlled by light exposure [27]. In a similar way, the carboxylic acid moiety on protein surfaces was reported to be randomly blocked with the diazo derivatives of photolytic protection groups [29]. This method realized photoactivation of hemoglobin. However, unlike small caged compounds, such random introduction of a small photodegradable protecting group onto the surface of a large protein often fails to fully inactivate the function of the protein (Figure 1C). In the case of enzymes, for example, this simple strategy based on random amine modification can function only on the enzymes that happen to have lysine residues at sites involved in catalytic activity or substrate binding (Figure 1B). Therefore, caging of proteins often requires the strategy of introducing the photodegradable protecting group specifically at the sites involved in their activity.