esa-sceva/satcom-chunk-collection huggingface dataset:
Various plant parts, such as leaves, roots, seeds, fruits, bark, and flowers, have been used in the synthesis of metal NPs [6]. _Melaleuca quinquenervia_ leaf extract (MQLE) is rich in phenolic acids such as gallic acid, ellagic acid, 3-O-methylleagic acid, caffe acid, vanillic acid, ferulic acid, and rosmarinic acid, as well as flavonoids such as luteolin, kaempfer, quercetin, catechin, kaempfer-3-O-glucoside, quercetin-3-O-glucuronopyranoside, apigenin, rutin, hesperidin, and naringin [7]. These compounds facilitate the reduction in metal ions into stable metal NPs. The synthesis of metal NPs from plant extracts typically occurs in three stages: (1) the activation stage, characterized by the reduction in metal ions and the nucleation of reduced metal ions, during which the plant extract serves as a reducing agent; (2) the growth stage, characterized by the spontaneous aggregation of smaller particles into larger ones (Ostwald ripening); and (3) the stabilization stage, during which the plant extract serves as a stabilizing agent [8]. These stages aim to minimize pollution, enable rapid synthesis, and produce NPs with low cytotoxicity and broad-spectrum efficacy. To achieve these goals, advanced methods such as microwave-assisted synthesis and ultrasound-assisted synthesis have been developed. The aforementioned techniques reduce the duration of NP synthesis, ensure the production of relatively small particle sizes, and enhance the physiological activity of NPs [9,10]. Au and Ag NPs have distinct advantages and disadvantages. Au NPs exhibit minimal to no bactericidal activity and have high raw material costs but exhibit biocompatibility, low toxicity, and strong antioxidant properties, which contribute to their pronounced anti-inflammatory effects and decontamination abilities [11,12]. By contrast, Ag NPs exhibit strong antibacterial activity and moderate antioxidant activity but pose toxicity risks and exhibit low catalytic activity, which reduce their pollutant removal efficiency [13,14]. To capitalize on the benefits of both metals, Au-Ag alloy NPs can be synthesized using microwave-assisted techniques and MQLE. In this study, we initially performed microwave-assisted extraction to obtain MQLE, which was subsequently used as both a reducing agent and a capping agent in the microwave-assisted green synthesis of Au-Ag alloy NPs. Our approach facilitated the rapid production of Au-Ag alloy NPs while enhancing their diverse physiological activities.
# Amplitude Distribution of Partial Discharge Signals on Tunnel-Installed High-Voltage Cables
## 3 Amplitude Distribution Characteristics of Signals on Cross-bonding Wires
### Influence of Cable Laying Parameters on Amplitude Distribution  
In a three-phase tunnel-installed cable system, the amplitude distribution of the PD current in the adjacent cross-bonding wires is only related to the characteristic admittance ratio \(\alpha\). Therefore, by studying the influence of different laying parameters on \(\alpha\), including the tunnel radius \(R\), the earth resistivity \(\rho\), the cable center spacing, \(d\) and the distance from the cable to the inner wall of the tunnel \(R\)-\(b\), different amplitude distribution characteristics under different conditions can be obtained. The 220 kV XLEp cable listed in Table 1 is selected as an example, and the typical laying parameters are also given as the reference condition.  
As shown in Figure 5a,b the influence of the tunnel radius \(R\) and earth resistivity \(\rho\) on \(\alpha\) is generally small, within about 5%. However, it can be seen from Figure 5c that the influence of the cable spacing \(d\) on \(\alpha\) is very large. When the cables are getting closer to each other, \(\alpha\) rises rapidly and reaches a maximum at 2 \(r\). When the distance \(d\) increases, \(\alpha\) approaches a fixed value. The variation of \(\alpha\) in Figure 5d is similar to that in Figure 5c. When the distance from the cable to the inner wall of the tunnel \(R\)-\(b\) is \(r\), that is, when the cable sheath is in direct contact with the inner wall of the tunnel, \(\alpha\) reaches the maximum. As the cable moves away from the wall, \(\alpha\) drops rapidly and tends to a fixed value.  
Figure 5: Influence of laying parameters on characteristic admittance ratio \(\alpha\). (**a**) Tunnel radius. (**b**) Earth resistivity. (**c**) Distance between cable centers. (**d**) Distance between cable and tunnel.  
The above results can be explained by the characteristic admittance model. If any two conductors in a three-phase cable system are treated as parallel conductors, the characteristic impedance of the two-conductor transmission line will be reduced and the characteristic admittance will increase when the distance between the conductors is reduced. The cable center spacing, \(d\) mainly affects \(Y_{ss}\), and the distance to the inner wall of the tunnel \(R\)-\(b\) mainly affects \(Y_{se}\). Considering Equation (15), it can be seen that \(\alpha\) will become larger in this case, and the ratio of \(I_{\text{major}}\)/\(I_{\text{minor}}\) will increase accordingly.
## 5 Discussion  
### Comparison of Improved LOS and Traditional LOS  
Switching following errors are reduced to varying degrees when using the improved LOS method as compared with the traditional LOS method. Additionally, the convergence speed and path-following accuracy are significantly improved. The use of traditional LOS path following with a fixed acceptance circle radius is simple and straightforward and is capable of offering stable and predictable path following in certain application contexts. However, the fixed acceptance circle radius has some limitations: at high speeds, it may cause excessively aggressive path following, whereas at low speeds, it may lead to conservative path following.  
The adaptive acceptance circle strategy adjusts the radius of the circle based on the vehicle's motion. This allows the vehicle to use a smaller radius at the curved region of the path and a larger radius for the larger path corners to fit the path more closely. To better account for the relationship between the vehicle and the following path, it is recommended that a larger circle radius is used at the larger corners and that the following path segments are switched ahead. This enables the vehicle to stay closer to the path, reducing following errors and overshoot.  
The traditional LOS method uses heading angle error feedback for control to handle large position deviations. However, the method requires a longer convergence time and following distance. Moreover, the maximum following distance of the robot is restricted by the limited space defined by the segmentation of the power lines. The adaptive heading  
Figure 14: Path following results for point c under different acceptance circles. (**a**) _xoy_ plane projection; (**b**) _xoz_ plane projection; (**c**) following error of trajectory.  
control strategy uses the scaling coefficient based on the distance between the robot and the path. It adjusts the priority of parallel and vertical path following. As the distance between the robot and the following path increases, the priority of parallel path following also increases. This method can effectively overcome the issues of large following errors and slow convergence speeds, thereby achieving fast, stable, and accurate path following.  
The proposed improved LOS method is used to analyze the equation of the 3D path-following error and to design a following control law based on state feedback, with the following error as the control object. Stability analysis is then performed to ensure that the following control law meets the requirements of accuracy and reliability in 3D path following.
## 8 Comparison with Other Emerging Interconnect Technologies
As a representative of the THz band communication, we consider CNT-based nano-antennas. We adopt FDMA to enable concurrent communication among wireless nodes envisioning the use of CNT antennas tuned to different frequency bands [31]. To model the THz wireless multi-chip system for comparative evaluations, we consider the same system where the mm-wave transceivers are replaced at the central cores with CNT-based transceivers. A separate FDMA channel is reserved for each pair of communicating chips via their central cores. These systems are simulated using the cycle-accurate simulation methodology discussed in Section 5. Uniform random traffic is used with 20% packets generated from all cores being memory accesses. The memory accesses are randomly distributed among the memory stacks to emulate a shared memory system. From Figure 19 it can be seen that all the emerging technologies improve the performance compared to the conventional I/O-based system. Furthermore, mm-wave wireless technology outperforms the interposer-based system as it provides direct single-hop paths between internal cores of the chips. The mm-wave wireless interconnection outperforms the wired systems. However, the photonic and CNT-based systems perform better than the mm-wave technology due to higher channel capacity of optical and THz bands. The main limitation of the mm-wave wireless interconnection is the relatively less channel capacity of mm-wave bands compared to photonic waveguides using WDM. However,  
* Value is in 45 nm technology node. \end{table}
Table 4: Advantage and disadvantages of various integration and interconnection technology. the THz wireless inter-chip interconnections with CNT antennas can provide comparable performance to that of silicon photonics and has the advantage of not requiring physical layout of waveguides. The Scope of Inter-Chip Wireless Interconnect with Advancements in Orthogonal Technologies Such as IoT, Industry 4.0, Wearable Electronics and Neuromorphic Computing  
The world of IC design and communication is evolving at a rapid pace and several new directions seem to be emerging clearly, namely, IoT, Industry 4.0 and Neuromorphic Computing.
## 6 Conclusions  
In this paper, we proposed an architecture based on the coexistence of ICN and the types of EC to provide an efficient caching mechanism for the data-driven IoT applications and to deliver multimedia information with low latency in ICN. We applied the clustering mechanism in our proposed tier-based architecture. We also proposed a new mechanism to identify the popular content in the network and the content replacements were done based on the newly-proposed content popularity mechanism. The drawbacks of the existing on-path and the hash caching mechanisms were described in this paper. We showed that our proposed cluster-based approach required less content transfer time than existing caching schemes via simulation and mathematical analysis and the overall performance was better than other caching mechanisms in terms of content transfer time and efficient content caching. We also showed that our cluster-based approach solved the existing drawbacks of on-path and hash caching mechanisms. Our cluster-based mechanism ensured fast content retrieval from the nearby content provider to the end-users.  
We simulated several wireless scenarios including the LTE network to ensure the efficiencies of our proposed cluster-based efficient caching mechanism. We showed the performance results depending on the locations of the content providers in a hop-based manner, the impact of popular content caching in the edge of the core and access network devices, and how duplicate content changes the content access time. In every scenario, we confirmed that the content is accessed from the nearby content providers or popular cached devices. Therefore, the content access time is reduced and the probability of the content access from the nearby devices is increased. Moreover, the network traffic is reduced, the consumption of the network bandwidth is reduced, network congestion is reduced, the packet loss is also reduced, and the overall performance of the network is increased.  
Conceptualization, K.H. and S.-H.J.; methodology, K.H.; software, K.H.; validation, K.H. and S.-H.J.; formal analysis, [PERSON] and S.-H.J.; investigation, [PERSON] and S.-H.J.; writing--original draft preparation, [PERSON]; writing--review and editing, [PERSON] and S.-H.J.; supervision, S.-H.J.; project administration, S.-H.J.; funding acquisition, S.-H.J.  
This research was financially supported by the Ministry of Trade, Industry and Energy (MOTIE) and Korea Institute for Advancement of Technology (KIAT) through the International Cooperative R&D program. This work was supported by Hankuk University of Foreign Studies Research Fund of 2019.  
The authors declare no conflicts of interest.  
Figure 14: Performance of caching in the LTE network.
# Spider U-Net: Incorporating Inter-Slice Connectivity Using LSTM for 3D Blood Vessel Segmentation
###### Abstract  
Blood vessel segmentation (BVS) of 3D medical imaging such as computed tomography and magnetic resonance angiography (MRA) is an essential task in the clinical field. Automation of 3D BVS using deep supervised learning is being researched, and U-Net-based approaches, which are considered as standard for medical image segmentation, are proposed a lot. However, the inherent characteristics of blood vessels, e.g., they are complex and narrow, as well as the resolution and sensitivity of the imaging modalities increases the difficulty of 3D BVS. We propose a novel U-Net-based model named Spider U-Net for 3D BVS that considers the connectivity of the blood vessels between the axial slices. To achieve this, long short-term memory (LSTM), which can capture the context of the consecutive data, is inserted into the baseline model. We also propose a data feeding strategy that augments data and makes Spider U-Net stable. Spider U-Net outperformed 2D U-Net, 3D U-Net, and the fully convolutional network-recurrent neural network (FCN-RNN) in dice coefficient score (DSC) by 0.048, 0.077, and 0.041, respectively, for our in-house brain MRA dataset and also achieved the highest DSC for two public datasets. The results imply that considering inter-slice connectivity with LSTM improves model performance in the 3D BVS task.  
3D blood vessel segmentation; deep supervised learning; inter-slice connectivity; long short-term memory; medical imaging; semantic segmentation; U-Net +
Footnote †: journal: Medical Image Processing  
1
## 5 Industrial Case Study Implementation
### Digital Model  
After the description of the current manufacturing system, the flowchart of the digital model is depicted in Figure 9, while the development is demonstrated in Figure 10. The flowchart includes two production lines, presented in Figure 7 with different colors; in particular, (i) the green line is the production line of Product A, and (ii) the blue line is the production line of Product B. Both production lines are connected to the same production department equipment (black line). In Figure 9, the most important information/details for the development of the digital model is presented: (i) the time required to complete a process, (ii) the number of storage tanks, (iii) the throughput of machines, (iv) the specification of conveyors, (v) packaging/palletizing parameters and (vi) flow process for each type of bottle. Moreover, the simulation tool used to model and simulate the production lines is a commercial software called Witness [70]. The digital model is developed for a continuous production rate and the maximum possible productivity of machines. In the case that the productivity is measured in given pieces per time, these should be transformed in the cycle time to be introduced into the digital model. Cycle time is the time required to perform a process. The formulas for the calculation of machines' cycle time are presented in Equations (7) and (8) below:  
\[\text{Production line for Product A}:\;\text{Cycle Time}=\frac{\frac{1( \text{can-min})}{1(\text{min})}}{\frac{68(\text{bottles})}{1(\text{min})}}=1.47 \times 10^{-2}(\text{min}) \tag{7}\]  
\[\text{Production line for Product B}:\;\text{Cycle Time}=\frac{\frac{1( \text{can-min})}{1(\text{min})}}{\frac{101(\text{bottles})}{1(\text{min})}}=9.9 0\times 10^{-3}(\text{min}) \tag{8}\]  
Figure 8: Flowchart of the Process Industry Production Line Under Investigation.  
There are three quality controls that check the bottle sealing, product quality and packaging quality. The abbreviations for each quality control are introduced below, (i) bottle sealing (Quality_01), (ii) product quality (Quality_02), and (iii) packaging quality (Quality_03).  
Based on Figure 10, the mixing tanks are Tank001-2, the boiling tanks are Tank003-4, the homogenizer is Tank005_HO and the storage tank of the filling machine is Tank006. The abbreviation for filling machines is 'FM', cooler 'C001', chain conveyors 'CC001-5' and belt conveyors 'BC004-5'; label dispenser is 'LD001-2', packaging machines are 'PACK-01' and layer palletizers are 'LP002-3'. Furthermore, the quality control of bottle sealing, final product quality, and packaging quality. as a form of a pseudo-code for the acceptance or rejection of a bottle, are presented in Figure 11.  
Figure 9: Flow Process Chart of the Production Line.  
Figure 10: Digital Model of the Production Line in the WITNESS simulation environment.
# A Framework and Benchmarking Study for Counterfactual Generating Methods on Tabular Data
## 3 Experiments  
### Experimental Design  
The experimental design is summarized in Figure 2, starting with the raw data processing, normalization by using the mean (\(\mu_{a}\)) and variance (\(\sigma_{a}^{2}\)) of each continuous feature (\(\textbf{a}_{norm}=(\textbf{a}-\mu_{a})/\sigma_{a}^{2}\)) and one-hot encoding of categorical features. Some counterfactual generating algorithms are not tailored towards multiclass datasets--as they, for example, do not explicitly allow for say: why class1 and not class2? However, rather: why class1 and not other than class1? Therefore, the target variable is made binary for multiclass datasets, considering the majority class versus all others. A training/validation/test split is made (60/20/20), followed by model training with definitions discussed in Section 2.3, factual data selection (the data instances to explain) and, finally, the counterfactual generation. The same models and data are used for all counterfactual explanation generation algorithms. This is an important configuration as different data processing or models can cause unreliable comparison results. Even simple modifications as scaling can lead to different distances between points which can lead to misleading results. The same can happen when working with different models, where different classification boundaries can lead to misleading results when comparing distinct counterfactual generators. The factual data consist of up to 100 random points for each dataset and class. The selection primarily relies on data from the test set; however, as the test set of some small datasets has less than 100 instances, additional data are obtained from the validation (and potentially even the training set). For the counterfactual generation, we produce one counterfactual for each factual row. Although some algorithms allow for generating multiple counterfactuals for a single point, we always select the first generated counterfactual and consider it as being the best counterfactual explanation the algorithm can produce. All steps can be verified in the provided repository. Moreover, this benchmarking framework can accept other algorithms as it is intended to be an open benchmarking tool for counterfactual explanation algorithms.  
Figure 2: Flowchart detailing the benchmarking process from taking the raw dataset to the counterfactual generation.
## 4 Results and Analysis
### Results of PS and SBAS-InSAR Interferometry
#### 4.2.2 Mean Deformation Velocities and Temporal Evolutions  
Analyses of the PS and SBAS-InSAR results from the ENVISAT-ASAR dataset show that significant surface uplift occurred over or near the oilfields over the study period (Figure 7). The spatial distributions and mean displacement velocity for the period from 30 September 2003 to 15 June 2010 are shown in Figure 7a (for PS-InSAR), Figure 7b (for SBAS-InSAR). Temporal decorrelation of  
Figure 6: (**a**) The representative interferogram generated from the data pair (29 November 2006 and 6 May 2008); (**b**) Close-up view of a typical fringe map, where the location is indicated by the black dashed lines in Figure 6a. Figure from (**c**) to (**k**) correspond to the main deformation area, which is highlighted in a red dashed line in (**a**), and they show the interferometric phase change in different time periods. The image in 29 November 2006 is the common master image.  
bare land and agricultural areas were excluded by the coherence threshold. Major uplifted areas were highlighted by both PS and SBAS-InSAR methods and indicated in red dashed circles in Figure 7a,b. Dense PS points were detected around the reservoir area, whereas the distributions of PS points were sparse in other areas. The maximum deformation velocity was approximately 24 mm/year. The locations and the mean deformation velocity detected by the SBAS-InSAR technique were much clearer than the PS-InSAR results (Figure 7b). SBAS-InSAR also revealed a major uplift area that was not detected using the PS-InSAR, as indicated by the black dashed circles in Figure 7a,b. The estimated maximum deformation velocity was approximately 33 mm/year in the LOS direction.  
Figure 7: Mean displacement map of ENVISAT-ASAR data from 2003 to 2010. (**a**) The mean displacement velocity map of the PS-InSAR method; (**b**) The mean displacement velocity map of the SBAS-InSAR method. Positive (negative) values indicates the ground close to (away from) the satellite. The points T1, T2, T3 and T4 are chosen for showing the accumulative time series land deformation pattern.  
Figure 8 shows the PS and SBAS-InSAR measured displacements over time, from 30 September 2003 to 15 June 2010, for four specific points located within the major deformation area (Figure 7). Three typical reference points were selected with the induced deformation as T1-T3 and one stable reference point as T4, which are shown in Figure 7a,b. Figure 8 illustrates that both PS-InSAR and SBAS-InSAR methods have good agreement in the deformation trend, with the time evolutions of each selected pixel sharing similar characteristics, such as increasing uplift. Despite a notable difference in values for T3, both PS-InSAR and SBAS-InSAR show a trend of consistently increasing deformation. The point T1 shows the largest deformation with the maximum uplift of 151.8 mm in 2010 in the LOS direction. The activity of the wastewater injection of the area may be responsible for the continuing land uplift.