esa-sceva/satcom-chunk-collection huggingface dataset:
# Feasibility of a Drug-Releasing Radiofrequency Ablation System in a Porcine Liver Model
###### Abstract  
The objective of this study was to investigate the feasibility of a newly developed anticancer drug-releasing radiofrequency ablation (RFA) system in a porcine liver model. A 15-gauge drug-releasing cooled wet electrode (DRCWE) was newly developed to improve the RFA efficacy for creating a large ablation as well as for simultaneously delivering an anticancer drug to the tumor margin. Nine ablations in three pigs were performed by the DRCWE. The sectioned liver specimens were evaluated by measuring the ablation zone by a positron emission tomography/magnetic resonance imaging examination to investigate whether \({}^{18}\)F-fluorodeoxyglucose was exactly diffused. Volumes of the ablation zones released drug injection volumes, circularity, retention rate defined as the ratio between an estimated and injection dose, and the standard uptake value were assessed. The drug-releasing RFA was technically successful without procedural-related complications. During the procedure, the color changes of the ablated zones of the liver were observed in all specimens. The mean drug injection volume was higher than the ablated volumes (17.21 \(\pm\) 2.85 vs. 15.22 \(\pm\) 2.30 cm\({}^{3}\)) and the circularity was 0.72 \(\pm\) 0.08. Moreover, the retention rate was 72.89% \(\pm\) 4.22% and the mean standard uptake value was 0.44 \(\pm\) 0.05. The drug-releasing RFA system was feasible not only for local ablation but also for the delivery of anticancer drugs. The results of this study indicate that this novel strategy of localized RFA with a drug delivery system could be a promising option for the prevention of local recurrence rates.  
drug-releasing radiofrequency ablation; anticancer drug-releasing cooled wet electrode; local recurrence rate +
Footnote †: journal: 7
chemoembolization, saline injection, and percutaneous ethanol injection, can be helpful to improve clinical outcomes [6; 7; 8; 9; 10; 11; 12].  
Several RFA electrodes have been developed to improve this weak point and expand the ablation range, e.g., the RFA with new types of electrodes (e.g., the multitier expandable needle electrodes with saline infusion and perfusion electrodes) and multiple electrodes for different applications of RF energy (e.g., the multipolar and the switching monopolar modes) [13; 14; 15; 16; 17]. Similarly, this study evaluated a new drug-releasing RFA system, which can deliver an anticancer drug to achieve sufficient treatment around the targeted tumor to increase clinical outcomes with a low recurrence rate. This preliminary study started with the hypothesis that anticancer drugs used in combination with RFA could lead to therapeutic outcome improvement and recurrence rate decrease for the local treatment of primary HCC (Figure 1). Therefore, this study aims to investigate the feasibility of a newly developed anticancer drug-releasing RFA system in a porcine liver model.
## 2 Data and Methods
### Methods
#### 2.3.1 The SM2 RAIN Algorithm
Thus, SMAP retrievals from 2017 to 2018 were selected for parameter optimization, and 2015-2018 for precipitation estimation. The in situ soil moisture observations, SMAP retrievals, and PML-V2 evapotranspiration were spatially aggregated into 100 km averages using a simple arithmetic method to facilitate uncertainty quantification.
## 3 Results and Discussion  
When material (1# sample) was used for our experiment, its low-energy raw image, high-energy raw image, low-energy image with filtering and high-energy image with filtering are shown in Figure 7a-d, respectively. Each image selected is grayscale image featured by 320 \(\times\) 320 pixels and 16-bit pixel depth data intercepted by high 8 bits of each pixel. Among them, the longitudinal dark stripes in the low-energy raw image (a) and the longitudinal light stripes in the high-energy raw image (b) were caused by the inherent cross-talk noise of the detector. From the filtered images in (c) and (d), it can be seen that median filtering (with 5 \(\times\) 5 observation window) benefitted the filtering of cross-talk noise and random noise and protect edge information, improving material identification to a certain extent.
## 3 Results
### Strength Study of the Original Design of the ASC Crossbar
The pipes are only used as a connection between the point of application of the real force and the clamp joints. The force in the analysis is used as even, linearly rising to its nominal value. The frame load was applied to the ends of the bent tubes simulating the wheel units and their caster. These pipes were fixed in clamping joints. Vertical and horizontal forces applied to the end edge of the bent tubes were used as a load. The vertical force is determined by the factor of safety and maximum radial reaction obtained from the simulations in the MSC Adams software, for the appropriate steering angle of the ASC. The horizontal force represents the rolling resistance and always acts against the direction of rolling of the individual wheels. Rolling resistance is caused by the deformation of the rubber wheel on a rigid road [9; 11; 33] and is defined according to the relation (3)  
\[F_{T}=Z_{k}.\frac{\xi}{r}\quad, \tag{3}\]  
where \(F_{T}\) is the rolling resistance force (N), \(Z_{SCK}\) is the radial reaction (N), \(\xi\) is the rolling resistance arm 1.6 mm, and \(r\) is the radius of the support wheel ASC (mm). The mesh was created using a SolidWorks 2022 generator. Subsequently, critical areas of the structure (Figure 6) were pointed out, where the mesh was refined to achieve more accurate results. The mesh size of both the standard and refined regions have been optimized for the best possible ratio between the calculation speed and the accuracy of the results (Figure 7). Subsequently, a geometrically and materially nonlinear analysis (GMNA) was performed for the frame with the wheels turned to face the outside and the inside of the  
Figure 6: Critical points on the crossbar. Figure 7: The refined meshing of the half of the ASC crossbar. car, forward and rearward (Table 3). The most unfavourable variant was the variant with the wheels turned to face the outside of the car; therefore, this position was used in all subsequent analyses. The ASC crossbar design is suitable for a reserve against limit states, but the mechanical stress at several locations exceeds the maximum allowable stress. The highest stress of the structure was calculated at the node 46 439 (Figure 8). To obtain the load degree value for the limit state of stability _of_\(L_{Fst}\), tangents to the elastic and plastic part of the curve were drawn and the value of the Load Factor at their intersection point (Figure 9) was  
The permissible Load Factor calculated according to the Formula (2) is greater than 1, which means that this concept of the frame in terms of limit states complies with the requirements. Even though the maximum permissible load on the ASC wheels can be 13% higher for compliance, the design does not comply with the maximum allowable stress in certain areas; therefore, its optimization was also undertaken.
# Quantitative Study on the Impact of Energy Consumption Based Dynamic Selfishness in MANETs +
###### Abstract  
Cooperative communication and resource limitation are two main characteristics of mobile ad hoc networks (MANETs). On one hand, communication among the nodes in MANETs highly depends on the cooperation among nodes because of the limited transmission range of the nodes, and multi-hop communications are needed in most cases. On the other hand, every node in MANETs has stringent resource constraints on computations, communications, memory, and energy. These two characteristics lead to the existence of selfish nodes in MANETs, which affects the network performance in various aspects. In this paper, we quantitatively investigate the impacts of node selfishness caused by energy depletion in MANETs in terms of packet loss rate, round-trip delay, and throughput. We conducted extensive measurements on a proper simulation platform incorporating an OMNeT++ and INET Framework. Our experimental results quantitatively indicate the impact of node selfishness on the network performance in MANETs. The results also imply that it is important to evaluate the impact of node selfishness by jointly considering selfish nodes' mobility models, densities, proportions, and combinations.  
selfishness; MANET; impact; energy consumption; OMNeT++ +
Footnote †: This paper is an extended version of our paper published in [PERSON]; [PERSON]; [PERSON] Quantitative Study on Impact of Node Selfishness on Performance of MANETs. In proceedings of the 2020 IEEE International Conference on Smart Internet of Things (SmartIoT), Beijing, China, 14-16 August 2020; pp. 9-14, doi:10.1109/SmartIoT49966.2020.00011.
## 3 Test of Bio-Inspired Algorithms on a Transportation Planning Problem
### Comparative Experiments
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline  & & & \multicolumn{3}{c}{**GA**} & \multicolumn{3}{c}{**PSO**} & \multicolumn{3}{c}{**ACO**} & \multicolumn{3}{c}{**BBO**} \\
**Problem** & \multicolumn{3}{c}{**Size \(T^{\text{max}}\)**} & _best_ & _mean_ & _std_ & _best_ & _mean_ & _std_ & _best_ & _mean_ & _std_ & _best_ & _mean_ & _std_ \\ \hline \#1 & 3 \(\times\) 10 & 30 & 18.65 & 18.65 & (0.00) & 18.65 & 18.65 (0.00) & 18.65 & 18.65 (0.00) & 18.65 & 18.65 (0.00) & 18.65 & 18.65 (0.00) \\ \#2 & 5 \(\times\) 20 & 30 & 38.93 \({}^{\dagger}\)39.31 & (1.17) & 38.93 & 38.93 (0.00) & 38.93 & 38.93 (0.00) & 38.93 & 38.93 (0.00) \\ \#3 & 6 \(\times\) 30 & 60 & 21.90 \({}^{\dagger}\)26.03 & (2.00) & 20.93 \({}^{\dagger}\)21.11 (0.71) & 20.93 & 20.93 (0.00) & 20.93 & 20.93 (0.00) \\ \#4 & 8 \(\times\) 40 & 90 & 76.27 \({}^{\dagger}\)83.89 & (8.38) & 69.33 \({}^{\dagger}\)76.40 (4.61) & 68.98 & 69.52 (0.83) & 68.98 & 69.28 (0.35) \\ \#5 & 10 \(\times\) 50 & 120 & 30.20 \({}^{\dagger}\)34.90 & (4.21) & 27.56 \({}^{\dagger}\)30.90 (4.12) & 25.56 & 26.96 (1.67) & 25.56 & 26.76 (1.37) \\ \#6 & 12 \(\times\) 60 & 150 & 84.81 \({}^{\dagger}\)91.03 & (8.42) & 80.27 \({}^{\dagger}\)86.41 (7.60) & 72.35 & 78.96 (5.02) & 71.89 & 76.81 (5.22) \\ \#7 & 12 \(\times\) 75 & 160 & 68.26 \({}^{\dagger}\)73.72 & (8.06) & 60.30 \({}^{\dagger}\)66.67 (7.15) & 51.75 \({}^{\dagger}\)57.97 (4.83) & 51.75 & 55.06 (3.86) \\ \#8 & 15 \(\times\) 80 & 180 & 52.15 \({}^{\dagger}\)57.10 & (6.66) & 48.91 \({}^{\dagger}\)53.38 (5.75) & 40.27 \({}^{\dagger}\)46.49 (6.42) & 38.66 & 42.28 (4.96) \\ \#9 & 16 \(\times\) 100 & 240 & 83.25 \({}^{\dagger}\)90.03 & (9.73) & 77.88 \({}^{\dagger}\)85.16 (8.06) & 72.60 \({}^{\dagger}\)76.33 (5.85) & 70.58 & 72.27 (5.02) \\ \#10 & 18 \(\times\) 118 & 300 & 72.63 \({}^{\dagger}\)81.01 & (10.24) & 63.97 \({}^{\dagger}\)74.16 (8.08) & 60.37 \({}^{\dagger}\)65.05 (6.68) & 56.03 & 58.32 (5.92) \\ \hline \end{tabular}
\end{table} 
Table 1: The summary of experimental results. GA, genetic algorithms; PSO, particle swarm optimization; ACO, ant colony optimization; BBO, biogeography-based optimization; std, standard deviation. \({}^{\dagger}\) denotes that BBO has statistically significant improvement over the algorithm. performance among the competitive algorithms on the test problems. This also indicates that the new meta-heuristic has potential in solving a wide range of transportation problems and other related disaster relief operational problems.
# An Efficient Causal Group Communication Protocol for Free Scale Peer-to-Peer Networks
###### Abstract  
In peer-to-peer (P2P) overlay networks, a group of \(n\) (\(\geq\)2) peer processes have to cooperate with each other. Each peer sends messages to every peer and receives messages from every peer in a group. In group communications, each message sent by a peer is required to be causally delivered to every peer. Most of the protocols designed to ensure causal message order are designed for networks with a plain architecture. These protocols can be adapted to use in free scale and hierarchical topologies; however, the amount of control information is O(\(n\)), where \(n\) is the number of peers in the system. Some protocols are designed for a free scale or hierarchical networks, but in general they force the whole system to accomplish the same order viewed by a super peer. In this paper, we present a protocol that is specifically designed to work with a free scale peer-to-peer network. By using the information about the network's architecture and by representing message dependencies on a bit level, the proposed protocol ensures causal message ordering without enforcing super peers order. The designed protocol is simulated and compared with the Immediate Dependency Relation and the Dependency Sequences protocols to show its lower overhead.  
causal ordering; peer-to-peer; free scale topology Article
# Numerical and Experimental Studies of the LK Type Shaped Charge
## 4 Numerical Analysis
### Description of Materials  
The equivalent explosive charge in the form of trinitrotoluene (TNT), the so-called TNT equivalent, was assumed in modeling [46; 47; 48]. This equivalent refers to the determination of such a TNT mass for which the explosion effects are identical to those of the explosive being tested, namely as:  
\[\kappa_{T}=\frac{m_{T}}{m}=\frac{E}{E_{T}} \tag{8}\]  
Figure 9: Numerical models of the LK charge components: (**a**) plastic case; (**b**) explosive; (**c**) cumulative liner; (**d**) armor plate.  
where \(\kappa_{T}\) means the TNT equivalent of the explosive in question, \(m\) is the mass of the explosive in question, \(m_{T}\) is the mass of the reference explosive, \(E\) is the explosive energy of the charge in question, and \(E_{T}\) is the explosive energy of the referenced explosive.  
The JWL mathematical model [34; 41; 44; 45] was assigned, as it allows for determining the pressure according to the following relation:  
\[p=A^{*}\bigg{(}1-\frac{\omega\eta}{R_{1}}\bigg{)}exp^{\frac{-R_{1}}{\eta}}+B^{ *}\bigg{(}1-\frac{\omega\eta}{R_{2}}\bigg{)}exp^{\frac{-R_{2}}{\eta}}+\omega \rho\kappa_{pd} \tag{9}\]  
where \(\eta\) is the ratio of the density (\(\rho\)) of detonation products to the initial density (\(\rho_{0}\)) of the original explosive, \(\kappa_{pd}\) is the pre-detonation bulk modulus, and \(A^{*}\), \(B^{*}\), \(R_{1}\), \(R_{2}\) and \(\omega\) are the fitting coefficients obtained from experiments.  
The air properties were described using the Mie-Gruneisen equation [49; 50] in accordance with the following relation:  
\[P=p_{0}+\gamma\rho_{a}E_{0} \tag{10}\]  
where \(p_{0}\) is the initial air pressure, \(\gamma\) is the Gruneisen coefficient, \(\rho_{a}\) is the density of air, and \(E_{0}\) is the initial internal energy.  
The cumulative insert and armor plate are described by the J-C model together with the J-C damage model per [51; 52], as follows:  
\[\sigma=(A+B\varepsilon^{n})\cdot\bigg{[}1+Cln\bigg{(}\frac{\dot{\varepsilon} }{\dot{\varepsilon}_{0}}\bigg{)}\bigg{]}\bigg{[}1-\bigg{(}\bigg{(}\frac{T-T_ {0}}{T_{m}-T_{0}}\bigg{)}^{m}\bigg{]} \tag{11}\]  
\[\varepsilon^{f}=(D_{1}+D_{2}expD_{3}\sigma^{*})\cdot\bigg{[}1+D_{4}
# StereoVO: Learning Stereo Visual Odometry Approach Based on Optical Flow and Depth Information
## 4 Experimental Results
### Dataset and Metrics  
Dataset: The KITTI odometry benchmark [34] comprises twenty-two image sequences captured by two cameras mounted on a car during driving scenes in suburban and highway environments. Sequences 00-10 provide ground truth information on camera position, whereas sequences 11-21 only offer stereo images. The KITTI dataset records at a 10 frames-per-second rate during driving and includes numerous dynamic objects, such as cars, motorcycles, bicycles, and pedestrians, with speeds between 0 km/h and 90 km/h, which makes it a challenging dataset for the VO algorithm. To train our proposed network, we leveraged sequences 00-06 and 08, while our validation process selected frames randomly from the training set. Meanwhile, sequences 07, 09, and 10 served as our testing datasets. Table 2 shows the number of frames in each sequence. The stereo images are resized to a resolution of 1024 \(\times\) 256. Labels from the KITTI dataset are absolute position according to the initial frame of the sequence; we transform labels to relatively frame-to-frame Euler angle and translation along the \(x\), \(y\), and \(z\) axles.  
Evaluation Metrics: We adopt the KITTI odometry criterion, which reports the average translational error \(T_{rot}(\%)\) and rotational errors \(R_{rot}(\text{deg}/100\) m) of possible sub-sequences of length (100, 200, \(\cdot\cdot\), 800) in meters.
###### Abstract  
Multi-constellation global navigation satellite systems (GNSSs) are expected to enhance the capability of precise point positioning (PPP) by improving the positioning accuracy and reducing the convergence time because more satellites will be available. This paper discusses the performance of multi-constellation kinematic PPP based on a multi-constellation kinematic PPP model, Kalman filter and stochastic models. The experimental dataset was collected from the receivers on a vehicle and processed using self-developed software. A comparison of the multi-constellation kinematic PPP and real-time kinematic (RTK) results revealed that the availability, positioning accuracy and convergence performance of the multi-constellation kinematic PPP were all better than those of both global positioning system (GPS)-based PPP and dual-constellation PPP. Multi-constellation kinematic PPP can provide a positioning service with centimetre-level accuracy for dynamic users.  
multi-constellation; PPP; convergence time; positioning accuracy Article
# Feature-Based Approach to Change Detection of Small Objects from High-Resolution Satellite Images
## 2 Feature Point-Based Change Detection Method
### Feature Matching  
After feature extraction, we perform feature matching. Our purpose is to classify matched features whose pixel distance is within a small threshold in previous and recent images as unchanged, and remove them from further analysis. Matching performed over the entire image may produce false matches. Therefore, we divide the whole image into small sections called buckets, and limit feature matching to within each corresponding bucket. Figure 1(a) shows how an image is divided into buckets. Feature points within a bucket of a previous image are compared only with the feature points of the corresponding bucket from a recent image. Figure 1(b) is the result of feature matching within a bucket. The circles in the left and right images in Figure 1(b) are feature points extracted and lines connecting them show match results between feature points. Matched features are shown as the same color. The yellow boxes show successfully matched features.  
Feature matching is executed with brute force-based K Nearest Neighbors (KNN) [47]. KNN matching searches k pairs of feature points by matching one feature point to k feature points. Since matching is performed on multi-temporal ortho-images, the positions between matched feature points should be the same or similar for unchanged objects. Therefore, when the pixel distance between matched feature points is smal  
Figure 2: Feature point matching using a bucket: (**a**) bucketing the entire image, and (**b**) feature matching results in one bucket.  
can determine the feature points as the points from the same unchanged object. They are classified as unchanged and removed from follow-up change analysis.
## 3 Experimental Data and Evaluation of Results
### Source of Experimental Data
#### 3.1.2 Real Data  
The real data for the experiments were acquired from the DaISy, ADFECGDB, and NIFECGDB databases. The DaISy database was provided by Belgian scholar [PERSON] and is a single data set containing eight channels of skin potential recordings of a pregnant woman, of which the first five channels were collected at an abdomen electrode and the last three channels were collected at a chest electrode. The sampling rate is 250 Hz and the time is 10 s. The ADFECGDB database contains five data sets, namely, r01, r04, r07, r08, and r10. Each group of data contains one direct scalp measurement signal and four abdominal measurement signals. The sampling rate is 1 kHz and the time is 5 min. The NIFECGDB database consists of 55 multichannel abdominal FECG records, all from the same tester, between 21 and 40 weeks of gestation. Each set of recordings contains two chest signals and three or four abdomen signals. The sampling rate is 1 kHz and the resolution is 16 bit.  
Figure 5: Waveform and spectrum before and after resampling of Ch8 in the DaISy database.  
Figure 6: Waveform and spectrum before and after resampling of a103 in the MIT-BIH database.
## 2 Diagnosis Model Description
### Fundamental Equations of the Diagnostic Model
#### 2.2.4 Heat Transfer Model  
The heat transfer rate produced between the combustion blend and the cylinder walls is shown in Equation (7).  
\[\frac{dQ_{r}}{dt}=h_{c}\cdot A_{w}\cdot(T-T_{w}) \tag{7}\]where \(\rm{h_{c}}\) is the heat transfer coefficient, \(\rm{A_{w}}\) is the heat transfer surface area of the combustion chamber, \(\rm{T}\) is the average temperature of the gas inside the cylinder and \(\rm{T_{w}}\) is the chamber wall temperature of combustion.  
Equations (8) and (9) show the calculation of heat transfer coefficients.  
\[\rm{h_{c}}=C\cdot B^{m-1}\cdot P^{m}\cdot w^{m}\cdot T^{0.75-1.62m} \tag{8}\]  
\[\rm{w}=C_{1}\cdot v_{mp}+C_{2}\cdot\frac{V_{disp}\cdot T_{ref}}{P_{ref}\cdot V_ {ref}}\cdot(P-P_{r}) \tag{9}\]  
where \(\rm{B}\) is the diameter of the hole, \(\rm{P}\) is the pressure inside the combustion chamber, \(\rm{w}\) is the average velocity of the gases inside the cylinder and \(\rm{P_{r}}\) is the pressure of the combustion chamber if the engine was in a test. The constant \(\rm{C}\) takes a value of 3.26, while the constant \(\rm{C_{2}}\) takes a value of \(3.24\times 10^{-3}\) (during combustion) and zero (when no combustion is present), which is based on [PERSON]'s research [33].  
The constant \(\rm{C_{1}}\) is obtained by means of Equation (10).  
\[\rm{C_{1}}=C_{W1}+C_{W2}\cdot\frac{v_{u}\;(\rm{\theta})}{v_{mp}} \tag{10}\]  
where \(\rm{C_{W1}}\) and \(\rm{C_{W2}}\) are constants that depend on the stroke (compression, expansion, and admission) [33].