esa-sceva/satcom-chunk-collection huggingface dataset:
# A Computational Study of Polymer Solutions Flow Regimes during Oil Recovery from a Fractured Model
## 3 Results and Discussion
### Investigation of the Effect of Polymer Solution Properties on Oil-Recovery Factor  
The effects of the polyacrylamide solution properties on the ORF and pressure drop have been studied. Three polyacrylamide polymer solutions with a weight concentration of 0.1% were considered; grades 2515, 2540, and 2020 polymer solutions with relative molecular weights of \(\sim\)9, \(\sim\)13, and \(\sim\)18 mln c.u. were considered in the computational study. The wetting angle and the surface tension, as well as rheological parameters, were experimentally determined for all polymer solutions considered. The calculations were performed for a capillary number of 4 \(\times\) 10\({}^{-5}\). Calculations were carried out for oil with a density of 900 kg/m\({}^{3}\) and a viscosity of 0.1 Pa\(\times\)s.  
Based on the calculation results, the phase distributions of the displacing agents and oil in the computational domain were obtained when displaced by three solutions of polyacrylamide with different molecular weights and the same concentration. Figure 13 shows a comparison of the distribution patterns of oil and displacing agents in the pore space for different points in time. Blue color shows the pore space filled with oil, red is the displacing agent, and white color corresponds to solid rock.  
To quantify the process of oil displacement from a fractured medium, the dependences of the ORF on the displacement time were obtained during the calculations. These dependences for water and the three polyacrylamide solutions are shown in Figure 14. For grades 2515, 2020, and 2540 polymer solutions with different molecular weights, the oil-recovery factors, obtained during calculations, were equal to 0.863, 0.85, and 0.88, respectively. Such close values of the oil-recovery factors were obtained due to a strong increase in the viscosity of polymer solutions at a concentration of 0.1%.  
Figure 12: Breakthrough time vs. polymer concentration.  
Figure 13: _Cont._  
Figure 14: Oil-recovery factor vs. time for polymer solutions with different molecular weights.  
Figure 13: Phase distribution of the displacing agent and oil in a fractured medium during the displacement process. Effect of polymer solution properties.  
In this case, the polymers used are very effective. As you can see, the oil recovery factor for them is close to 90%. A further increase in the displacement ratio is very problematic. And small changes in oil-recovery factors require very large changes in properties. Therefore, the differences between them are not so visible.
# XGB-RF: A Hybrid Machine Learning Approach for IoT Intrusion Detection
## 2 Materials and Methods
### Feature Selection
#### 2.3.2 Recursive Feature Elimination (RFE)  
There is a lot of irrelevant features in a large dataset. The redundancy of the features affects the efficiency of the classification algorithm. As a result, the prediction may get biased. To find the effective features which are mostly responsible for expected predictions, we use RFE (Recursive Feature Elimination). This helps to reduce the dimensionality of the dataset and keep the effective features derived from the more compact subsets of features. This study employs a step-wise technique based on recursive feature elimination (RFE) to eliminate less important features. RFE is believed to be beneficial in enhancing RF models for intrusion detection [26]. It is an efficient feature selection algorithm for selecting features from a training dataset. Removing features that aren't relevant to the task can produce an input feature-set without sacrificing classification accuracy. The name \"recursive\" stands for the iterative approach that is deployed until a specified number of features are found. After determining the importance of each feature based on their contribution in classification using RF classifier, at first, it generates a feature ranking (high to low). Then, the least important features are removed and the model is re-trained with the revised features and the classification performance is obtained with the new feature set. This is an iterative process that continues until the feature set becomes empty.  
```
1 Initialization: Deploy a complete set of classifier-attributes \(C\) with the training data. Compute measurements, which are reflective of model performances. Finally, identify variable rankings. Pre-process data \(N=len(C)\) where \(C\) = Total number of classifier-attributess for\(i=1\) to \(N\) and Subset size from classifier-attributes, \(X_{i}\)do
2 Initiate with \(X_{i}\) significant classifier-attributes
3 Deploy \(X_{i}\) classifier-attributes on the training dataset Compute measurements, which are reflective of model performances Identify variable rankings
4 end for Result: Classifier-attributes corresponding to optimal \(X_{i}\)
```  
**Algorithm 1**Followed steps for Recursive Feature Elimination (RFE)
## 5 Experimental Result
Due to the small influence of parameter perturbation, the variable load torque is taken as one of the operating conditions to study the anti-disturbance ability. Figure 10 presents the speed and torque waveforms of PI, SMC, and the proposed TSMC in the DITC strategy when the load torque changes from 0.3 Nm to 0.5 Nm at 0.25 s under 3000 r/min. As shown, the speed overshoot of the proposed TSMC is 140 r/min, while those of PI and SMC are 363 r/min and 269 r/min, respectively. When the load torque changes at 0.25 s, three controllers take about 0.17 s, 0.15 s, and 0.11 s to reach the steady reference speed, respectively. The experimental results indicate that the proposed TSMC shows better anti-disturbance ability and robustness when an external disturbance occurs compared with PI and SMC. The torque ripple ranges in the steady state of PI, SMC, and proposed TSMC are 0.6 Nm, 0.47 Nm, and 0.37 Nm under 0.5 Nm, respectively. Among them, the torque ripple range of the proposed TSMC can be reduced by 38% and 21% compared with  
Figure 9: Experimental results under speed change. **(a)** PI, **(b)** SMC, and **(c)** proposed TSMC. those of PI and SMC under variable load torque conditions. It can be clearly seen that the torque ripple range of the proposed method is minimal when the load torque changes. The improvement in terms of lower chattering for modified reaching law can still be guaranteed by introducing variable gain \(f(x,s)\) and \(\tanh(s)\). For a more intuitive comparison, the experimental results of three controllers under various working conditions are listed in Table 3. In summary, the proposed TSMC can remarkably accelerate the dynamic response, reduce the torque ripple, and improve the anti-disturbance ability and robustness, as shown in the comparison results. The effectiveness of advanced reaching law equipped with faster approaching speed and lower chattering correspondingly is validated from the output performance results under various working conditions.
## 1 Introduction
This article aims to investigate a method for modeling the canyon/valley environment appropriately, obtaining the composite EM scattering from a target above the canyon/valley environment efficiently. Meanwhile, full explorations and discussions of the composite scattering characteristics are necessary. Accordingly, the contributions and work of this article mainly include the following four parts:  
1. Based on the composite surface modeling theory, a method for modeling the canyon/valley environment is suggested, which can realize the typical composite rough surface containing two slopes and a bottom. 2. The traditional SBR algorithm is modified by introducing the high-order reflection model, which considers the roughness of the composite rough surface for EM scattering prediction. 3. The facet-based small slope approximation (FBSSA) scattering model combined with the modified SBR algorithm is proposed to calculate the composite EM scattering from a complex target above the canyon/valley environment, which has better computation accuracy without a noticeable increase in memory and time consumption. 4. In the framework of the environment modeling method and modified SBR-FBSSA algorithm, thorough analyses of the composite EM scattering characteristics of a target above the canyon/valley environment were carried out. We also investigated how the Brewster effect affects the canyon's or valley's EM scattering characteristics. A more intricate scattering mechanism will result from the complex target in this canyon/valley environment. Researching these scattering issues is essential from a practical standpoint; this article's discussions, distinct occurrences, and findings will give the radar detection and remote sensing industries a solid data foundation. The remainder of this article is organized as follows. In Section 2, the weighted composite rough surface contour function is introduced, and canyon/valley composite rough surface modeling is presented. Section 3 presents the composite scattering model of the ultra-low target above the canyon/valley environment and the modified SBR-FBSSA method. Verifications and numerical simulation results are presented and discussed in Section 4. Section 5 is devoted to the conclusions of this paper.
## 5 Results Analysis
### Simulation Results for Scenarios 1-5
#### 5.2.1 Delay Time
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Scenario**} & \multirow{2}{*}{**Average Speed**} & \multicolumn{2}{c}{**Average**} & \multirow{2}{*}{**Delay Time**} & \multirow{2}{*}{**Average Speed**} & \multirow{2}{*}{**Average**} & \multirow{2}{*}{**Delay Time**} \\  & **[km/h]** & & **[min]** & & **[km/h]** & **[min]** \\ \hline \multicolumn{8}{c}{**On the motorway (for all vehicles) without the use of the alternative itinerary**} \\  & & North direction (A \(\Rightarrow\) D) & South direction (D \(\Rightarrow\) A) & \\ Scenario 1 & 104.35 & 14.233 & 1.719 & 116.84 & 12.671 & - \\ Scenario 2a & 77.66 & 19.125 & 6.611 & 106.05 & 13.960 & 1.289 \\ Scenario 2b & 68.82 & 21.582 & 9.068 & 106.83 & 13.858 & 1.187 \\ Scenario 2c & 64.06 & 23.185 & 10.671 & 107.59 & 13.760 & 1.089 \\ \multicolumn{8}{c}{**On the motorway (for passenger cars only) when the alternative itinerary is activated for HGVs only**} \\  & & North direction (A \(\Rightarrow\) D) & South direction (D \(\Rightarrow\) A) & \\ Scenario 3 & 113.11 & 13.131 & 0.617 & 116.84 & 12.671 & - \\ Scenario 4a & 95.72 & 15.517 & 3.003 & 106.05 & 13.960 & 1.289 \\ Scenario 4b & 90.19 & 16.468 & 3.954 & 106.83 & 13.858 & 1.187 \\ Scenario 4c & 79.23 & 18.746 & 6.232 & 107.59 & 13.760 & 1.089 \\ \multicolumn{8}{c}{**On the alternative route ( itinerary: A-B-E-I-L-C-D of Figure 2) when activated for all vehicles (passenger cars and HGVs)**} \\  & & North direction  
The worst scenario, however, was found to be under the following hypotheses: (i) complete closure of the north tube; (ii) the adjacent tube is not used for bi-directional traffic but it continues to be characterized only by the traffic flow towards the south direction; (iii) all the traffic flow towards the north direction (i.e., both passengers cars and HGVs) is redirected to the alternative itinerary which is formed by the set of the following bi-directional road segments: B-E, E-F, F-G, G-H, H-I, I-L, and L-C of Figure 2. By including in our analysis also the two motorway sections upstream and downstream of the mentioned alternative route (i.e., the unidirectional motorway Sections A-B and C-D of Figure 2) we computed in simulation very high values of the delay time due to the reaching of congested traffic conditions. In particular, with reference to the total length of the road section investigated (i.e., itinerary: A-B-E-I-L-C-D of Figure 2) connecting the Nodes A to D of length equal to 27.349 km, the delay time was found to be equal to about 14, 25, and 31 min for a closure time of the north tube equal to 1, 2, and 3 h, respectively. The corresponding average travel speed was found to be respectively of 61, 44, and 37 km/h.
# Dynamic Collaborative Optimization Method for Real-Time Multi-Object Tracking
## 4 Experimental Results and Analysis  
This section presents a comprehensive evaluation of the proposed multi-modal tracking framework, demonstrating the effectiveness of its individual components and the overall performance advantages through experiments conducted on multiple benchmark datasets.
## 3 Materials and Methods
### Data Processing  
A five-step data processing workflow was performed for each of the surveyed areas(Figure 3): (1) snow imagery pre-processing (2) GNSS post-processing of GCP data, (3) Agisoft Photoscan Pro SDEMs and TDEMs generation [35], (4) snow depth calculation, and (5) error analysis and validation.
## 1 Introduction
Typically, the transmitting and receiving coils are not only laterally misaligned but also have azimuthal deviations, as depicted in Figure 1, which depicts a schematic diagram of wireless charging by an AUV when the angle is not aligned. In such a complex environment, any variation in the azimuthal deviation can alter the mutual inductance of the system, thereby significantly impacting the output power and transmission efficiency of the MCR-WPT system. Hence, exploring the transmission characteristics of HTS-WPT systems with transmission coils under spatial azimuthal misalignment conditions is essential for optimizing superconducting wireless energy transmission systems and understanding the laws governing their practical application scenarios. Figure 1: Wireless Charging System for Autonomous Underwater Vehicles. This paper focuses on examining the impact of coil orientation variation on the transmission performance of high-temperature superconducting MCR-WPT systems. The experiments investigate the transmission efficiency and load output power of the wireless transmission system under various lateral misalignment conditions of the system with different azimuth angle misalignment and provide a comparative analysis of the transmission performance of the superconducting system and the copper system. The findings presented in this paper offer valuable insights for the application of wireless power transmission systems with superconducting coils under complex spatial conditions.
# An Adaptive Routing Algorithm Based on Relation Tree in DTN
## 5 Algorithm Design
### Adaptive Replication Phase  
Whenever a source node initiates a forwarding task, it enters that phase first. In this phase, the most appropriate number of message copies is copied based on its own cache occupancy. The messages that occupy the node cache are mainly divided into two categories: one is the newly generated messages by the node and the other is the messages received by the node as a relay node. When a message expires, the node automatically discards the message, so expired messages are not included in the above two categories. The cache of each node in the network has an initial size: buffer size. The calculation formula of node cache occupancy rate, \(\beta_{now}\), is as in Equation (1):  
\[\beta_{now}\ =\ \frac{\sum_{i=1}^{X}Received\{m_{i}\}+\sum_{i=1}^{Y}Created\{m_ {i}\}}{Buffersize} \tag{1}\]  
where \(X\) represents the total number of messages successfully received by this node before the current time, and \(Y\) represents the total number of messages successfully created by this node before the current time.  
When the source node starts to send data to other nodes, it will copy L copies of the message. In the AR-RT algorithm, the source node can dynamically adjust L according to its own buffer occupancy. The calculation formula of L is as in Equation (2):  
\[L=L_{init}-\partial*(\beta_{now}\ -\beta_{best}) \tag{2}\]  
Figure 2: Maintenance of the Relation Tree.  
where \(L_{init}\) is an initial set value, \(\beta_{best}\) is the optimal buffer occupancy rate of all nodes that are initially set, and \(\partial\) is an adjustment factor. This phase provides negative feedback that helps maintain the overall network load level at an appropriate level.
## 4 Discussion
There could be a few different reasons for this issue: for e.g., our MC model assumed a flat velocity profile for correlation transport in the channel but a parabolic flow-profile may better physically represent our experimental system; the MC simulations also did not consider flow directions (relative to photon trajectories) in calculations of correlation transport in Equation (3); we also did not investigate simulations in semi-infinite media using random flow models to examine how they were impacted at short SDS. Lastly, the phantom models though were carefully constructed and measured still includes uncertainty about the depth the channel from DCS probe may impact these results. These topics will be examined in future investigations. Although Equation (2) is widely used to quantify DCS measurements mainly due to its simplicity and ease of use, there have been recent reports that have developed improved theoretical formulations to model DCS measurements directly using correlation transport modeled using the radiative transport theory [54; 55]. Such approaches would be better suited for analyzing DCS measurements made at short SDS separations and could be investigated in more detail in the future using the MC simulations and experimental platform presented here. In summary, we establish that the theoretical expression in Equation (2) used to model DCS measurements of dynamically back scattered reflectance from turbid media can be robust for fitting data obtained experimentally (and numerically) across a wide range of SDS. The parameters obtained from theoretically modeled fits of measurements can be used to quantitatively parametrize relative changes of fluid flow at short SDS only when the optical properties of the media are not changing, across changing flow conditions. Yet, derived flow coefficients using Equation (2) can be linearly corrected to provide absolute flow coefficients, if the optical properties and SDS are known, via MC generated calibration factors. Our findings show that such MC derived correction factors are applicable to DCS measurements that were made at with SDS as small as 0.15 cm. Both [PERSON] and [PERSON] contributed to study conceptualization, methodology, investigation, data curation, writing the original draft, visualization. [PERSON] was responsible for software, writing--review and editing, formal analysis, supervision and validation of data. This research received no external funding. We wish to thank [PERSON] for valuable inputs in interpreting and analyzing experimental data and to [PERSON] for helpful discussions about approaches for programming correlation transport into our Monte Carlo model. We also acknowledge support from [PERSON] from the Research Computing Group at Miami for providing guidance with running MC simulations in parallel on the redhawk cluster. The authors declare no conflict of interest.
# A Novel Algorithm of Haze Identification Based on FY3D/MERSI-II Remote Sensing Data
## 2 Materials and Methods  
### Study Domain  
Our study area, which is bounded by latitudes of 0\({}^{\circ}\) and 60\({}^{\circ}\)N and longitudes of 60\({}^{\circ}\)\(\sim\)160\({}^{\circ}\)E, mostly covers China, Mongolia, Korea, Japan, Southeast Asia, India and other regions. Among them, eastern China and India are often affected by anthropogenic source emissions, resulting in the frequent occurrence of haze. In northwest China, such as in Xinjiang, Gansu and other regions, dust and bare soil cover the land, the main aerosol type is the sand dust type, the amount of anthropogenic emissions is relatively small and the atmosphere is relatively clean. The Tibetan Plateau, which is 5 km above sea level, has a typical polar plateau climate, and it is often covered by clouds. In general, the study area selected in this paper includes a haze area, a cloud area and a clear sky area, which is conducive to a more comprehensive analysis of the performance of the recognition algorithm.
# Occluded Pedestrian Detection Techniques by Deformable Attention-Guided Network (DAGN)
## 3 Deformable Attention-Guided Network (DAGN)
### Target Optimization  
#### 3.2.1 Loss Function  
Accurate localization is another important factor to improve pedestrian detection performance, especially in a crowded environment. To better optimize prediction localization, we apply the CIoU loss [13] to regress the predicted bounding box. The CIoU loss considers overlap area, central point distance, and aspect ratio, which are critical to measuring the similarity of the two boxes. It is defined as follows:  
\[L_{CIoU}=1-IoU+R\big{(}B,B^{gt}\big{)}+\gamma v \tag{2}\]  
where  
\[IoU=\frac{\big{|}B\cap\ B^{gt}\big{|}}{\big{|}B\cup\ B^{gt}\big{|}} \tag{3}\]  
\[R_{DIoU}\big{(}B,B^{gt}\big{)}=\frac{\rho^{2}\big{(}b,b^{gt}\big{)}}{c^{2}} \tag{4}\]  
In the above, \(B\) and \(B^{gt}\), respectively, denote the predicted box and target box, \(b\) and \(b^{gt}\) are the corresponding central points, \(\rho(\cdot)\) is the Euclidean distance, \(R_{DIoU}\big{(}B,B^{gt}\big{)}\) is the distance-IoU penalty term to minimize the normalized distance of the center points, \(c\) is the diagonal length of the smallest enclosing box covering \(B\) and \(B^{gt}\), and \(\gamma\) is the trade-off parameter which is defined as \(\frac{v}{(1-IoU)+v}\). We denote the width and height of the bounding boxes by \(w\) and \(h\), respectively. The consistency of the aspect ratio of bounding boxes is computed by \(v=\frac{A}{\pi^{2}}\left(tan^{-1}\frac{w^{gt}}{h^{gt}}-tan^{-1}\frac{w}{h} \right)^{2}\) as in [13].  
For classification, we adopt the binary cross-entropy (BCE) loss as shown in Equations (5) and (6). The parameter \(p_{i}\) is the predicted probability and \(y_{i}\) is the ground truth label for the class.  
\[L_{cls}=CE(\hat{p}_{i})=-log(\hat{p}_{i}) \tag{5}\]  
\[\text{where}\hskip 56.905512 pt\hat{p}_{i}=\left\{\begin{array}{cl}&p_{i}&,\ if\ y_{i}=1\\ &1-p_{i}&,\ otherwise.\end{array}\right. \tag{6}\]  
To sum up, the overall objective function is derived as given in Equation (7), where \(\lambda\) is a trade-off coefficient, which is experimentally set to 5.  
\[L_{total}=L_{CIoU}+\lambda L_{cls} \tag{7}\]
# DPtracer: Integrating Log-Driven Accountability into Data Provision Networks
## 6 Security Analysis
### Post-Process Tampering  
In DPTracer, an attacker cannot undetectably tamper with the log records of previous rounds. When the submitter sends the final result to the data requester, the final result encapsulates the last log entry and its commitment. Furthermore, the submitted data are assumed to be immutable in DPNs. For example, if the data requester operates within a blockchain system, the data become unchangeable because of the blockchain's inherent characteristics. Therefore, the Process Tree is finalized at the time of the submission. If attackers modify any log entries from earlier rounds, the root hash value will differ from the submitted commitment for that round. Other tampering attempts on the log entries, such as insertion, deletion, and re-ordering, will also result in noticeable discrepancies in the root hash value. The inspector must detect these inconsistencies. As described in Section 5.3, whenever the inspector retrieves data from the logger, it validates the interval proof against the stored commitment for that round. If the log entries have been altered, the original commitment cannot be recomputed from the interval proof because the attacker cannot find the hash collision in Process Tree.