esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Analyses  
### Numerical Model and Configuration  
A lifelike digital breast model is applied to verify the effectiveness of the proposed method. The digital breast model is generated from clinical magnetic resonance angiography data collected at Washington University School of Medicine in St. Louis. Magnetic resonance data are obtained from a breast with fibroglands in an extremely dense state [24]. Figure 3a shows the \(y-z\) slice of a dense fibrogland breast model at \(x\) = 4.0 \(\times\) 10\({}^{-2}\) m. For better method validation, the microvessels are deleted from the original digital breast model, and two tumors are added at the border between fat and fibroglands. The digital breast model contains five components: water, fibroglands, fat, skin, and tumor. There is inhomogeneous sound speed distribution in this model. As shown in Figure 3a, the approximate locations of the five components are marked according to the serial numbers of each component specified in Table 1.  
The digital breast model is simulated and reconstructed using the k-Wave toolbox in MATLAB R2019a [25]. The model is placed in a computational grid of 900 \(\times\) 900. Its size is 0.18 m \(\times\) 0.18 m. The two tumor sizes in the model are set as follows: tumor 1 is a circular tumor with a diameter of 8.0 \(\times\) 10\({}^{-3}\) m, and tumor 2 is an irregular tumor with a maximum diameter of 1.2 \(\times\) 10\({}^{-2}\) m. Figure 3b shows the sound speed distribution map of the real sound speed field. The sound speed values of various components are set with reference to the sound speed range of biological tissues in the published literature, as shown in Table 1[26]. The transducer is arranged in a ring array. A total of 800 ultrasonic transducers are uniformly arranged on a ring with a radius of 7.6 \(\times\) 10\({}^{-2}\) m centered on the center of the computing grid to collect the photoacoustic signal in the forward simulation process. After obtaining the photoacoustic signal, the TR algorithm is applied to image the model. The use of the same computational grid for forward simulation and reconstruction during the calculation may mask potential errors; therefore, the reconstruction grid is set to 700 \(\times\) 700 on the same model size.
# A Low-Power Read-Decoupled Radiation-Hardened 16T SRAM for Space Applications
## 3 Simulation Results and Analysis
### Hold Power Comparison  
Since SRAM cells remain in hold mode for most of their operation, the power consumed during this mode significantly impacts the total power consumption of the cell. Therefore, minimizing leakage power in the hold state plays a crucial role in reducing the overall power consumption of the cell. Hold power (HPWR) refers to the power consumed when an SRAM cell is in hold mode and is typically measured by applying a pulse from 0 to VDD to all nodes storing logic '1' and recording the power when the voltage reaches VDD. HPWR is primarily consumed due to bit line leakage and leakage within the inverter structure. In the bit line, NMOS transistors generally show higher HPWR than PMOS transistors because of their higher carrier mobility. Additionally, when there are more current  
Figure 8: Comparison of WWTV between the proposed LDRH16T and previous works.  
Figure 7: Comparison of RSNM between the proposed LDRH16T and previous works.  
paths from VDD to ground inside the cell, HPWR tends to be higher. If the resistance of the pull-up and pull-down paths in the inverters is high, the leakage power generated within the inverter structure can be reduced. A comparison of HPWR across all evaluated cells is illustrated in Figure 9. Among them, SAW16T and LDRH16T exhibit lower HPWR than all other reference cells. Despite having four NMOS access transistors, both cells include transistor stacking internally and only two NMOS transistors connected to ground, which helps reduce leakage [9; 23]. Additionally, LDRH16T connects only two PMOS transistors to VDD, thereby supplying less current from VDD and resulting in the lowest HPWR among all comparison cells [21].
## 3 Results and Discussion
With its wide and thin walls running along the diagonals of the cross section of the pyramid, this feeder antenna has a horned section with a flared pyramid top. A second method for designing the mm-wave feeding uses a platelet conical corrugated structure [38; 39]. Figure 12 shows the variation in S parameters by considering all-port excitation. The results show variations in individual port return loss and other active reflectance coefficient values. As shown in Figure 12, the return loss and other reflectance coefficient values are below \(-\)10 dB for all operating bands defined in Table 1. These results help us identify the active reflectance coefficient when all \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Reference** & **Substrate** & \
\begin{tabular}{c} **Gain** \\ **(dB)** \\ \end{tabular} & **Dimension (\(\upmu\)\(\times\)\(\upmu\))** &
\begin{tabular}{c} **Bandwidth** \\ **(THz)** \\ \end{tabular} \\ \hline This work & Polyimide & \(\sim\)7 & (620, 800) & 10.96 \\ \hline
[27] & Tetrafluoroethylene & - & (109.76, 150.93) & 0.13 \\ \hline
[28] & RT/Duroid 6006 & 9.7 & (600, 700) & 0.15 \\ \hline
[29] & Pyrex & 7.3 & (500, 500) & - \\ \hline
[30] & RT/Duroid 6006 & 3.09 & (1000, 1000) & 0.15 \\ \hline
[31] & Polyimide & 9.19 & (600, 600) & 0.2 \\ \hline
[32] & Polyimide & 8 & (600, 800) & 0.0364 \\ \hline
[28] & RT/Duroid 6006 & 9.7 & (600, 700) & 0.15 \\ \hline
[33] & RT/Duroid 6006 & 10.43 & (1000, 1000) & 0.155 \\ \hline
[28] & RT/Duroid 6006 & 9.7 & (600, 700) & 0.15 \\ \hline
[34] & Photonic crystal & 10.7 & (500, 500) & - \\ \hline
[29] & Pyrex & 7.3 & (500, 500) & - \\ \hline
[27] & Tetrafluoroethilen & - & (109.76, 150.93) & 0.13 \\ \hline
[35] & Polyimide & 5.7 & (300, 300) & 0.269 \\ \hline
[36] & Triethylamine & 9.70 & (400, 400) & 0.119 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparative analysis of the proposed structure with previously published articles. the ports are excited. The values of the active return loss of \(<\!-10\) dB indicate the overall wave interference performance of the proposed antenna when it is entirely operated with all-port excitation.
## 5 Conclusions and Future Scope  
The research presents an energy and exergy analysis of diesel and biodiesel blends. Transesterification was used to create the biodiesel in consideration of its compatibility. The engine was operated at 1500 RPM and 17.5 CR for diesel and different blends under four loading conditions.  
* The energy-exergy analysis and SI value of a direct-injected diesel engine have been significantly impacted by the characteristics of biodiesel.
* With a minor exception, the results for all blends were in good agreement with the pure diesel.
* The deviation of exergy efficiency and thermal efficiency was observed as 5.09 and 5.71% for Diesel and \(\text{SBF}_{20}\) respectively.
* Maximum destruction exergy was observed at the 100% loading condition with \(\text{SBF}_{100}\) fuel blend, at 46.60%, whereas maximum power output was for diesel and \(\text{SBF}_{20}\), with 31.06% and 30.79% of total fuel energy input.
* The sustainability index was found in the range of 1.27 to 1.45.
* The increase in engine load increases the sustainability index and exergy efficiency in all fuel blends.
* With the use of exergy analysis along with energy analysis, we were able to obtain findings that were more accurate and realistic.  
It was shown that the evaluated biodiesel's provide competitive energetic performance with diesel. The best alternative in terms of using renewable resources and reducing exhaust is biodiesel. Therefore, biodiesel may be seen as a future fuel that will become widely used when petroleum is overexploited to the point of no return. Assessment of energy and exergy distribution is important when evaluating the performance of a thermal engine operated with different types of fuels or under different operational systems, because it can give the researcher a clear picture of the energy conversion processes and recommend the best way to reduce emissions, energy losses, or performance optimization. The appropriate alternative fuel can be chosen for future studies by looking at the impact of energy losses on the sustainability index.  
Conceptualization: C.T.; Methodology, C.T. and G.D.; Formal analyses: C.T., G.D. and T.N.V.; Investigation: C.T.; Data curation: G.D., T.N.V. and P.V.; Writing--original draft: C.T.; Supervision and project administration and resources: G.D., T.N.V. and P.V.; Review, and editing: G.D. and P.V. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
All the data is provided within the manuscript.  
The authors declare no conflict of interest.
# Devising Mobile Sensing and Actuation Infrastructure with Drones
## 3 Simulations
### Network Performance
#### Simulation Results in the Data Ferry Method  
We implemented two path-routing algorithms in the simulator, which are a nearest neighbor algorithm [29] and a rendezvous algorithm [30], for the comparison study, since these algorithms have been widely used for UAV path-finding. All three algorithms firstly visit the sensor network and then visit the actuator network. The nearest neighbor algorithm visits the unvisited nearest sensor node in a greedy manner and then visits the actuator network in the same way. In case of the rendezvous algorithm, eight sensors and four actuators are selected as rendezvous points. Then, the drone visits only 12 rendezvous nodes since other sensor nodes or actuator nodes are connected to rendezvous nodes. Our event-centric path-planning algorithm visits the sensor node following our algorithm, which is a little bit like the rendezvous algorithm, but it firstly visits the highest correlated actuator.  
The data transmission is not continuous in the WSAN. And this makes the network throughput comparison difficult. Therefore, to ease comparison of the performance, we adopt normalized cumulative priority service throughput (NCPST). With the NCPST, the weighted size of transmitted data could be obtained. As we can see from the result of the simulation in Figure 4a, our event-centric path-planning algorithm in DaaG shows the best performance compared to the other two algorithms in the simulation. With respect to the time it takes for the drone to send the sensing data to one of the actuators successfully, our algorithm is 6.5% earlier than the rendezvous algorithm and 23.2% earlier than the shortest-path algorithm.  
The rendezvous algorithm generates circular path which connects rendezvous nodes based on the geographic position of the rendezvous nodes. After the path has created, the drone follows the generated path to collect the data and returns to the starting point. However, our event-centric path-planning algorithm generates path based on the correlation between the actuators and the sensors. This is what differentiates our algorithm from the rendezvous algorithm. Therefore, the rendezvous algorithm's arrival time at the actuator network is delayed. In terms of the priority service throughput, that of DaaG showed the fastest growth rate. Because the first actuator that the other two algorithms visited is not a highly-correlated actuator, it has a low \(\omega_{i}\) value. The rendezvous algorithm achieved the maximum throughput at first since the total service delay is lower than the other algorithms, but this is not more important than the main service delay. Hereby, we can verify that our path-planning algorithm can increase the main throughput in the data ferry method.
# A Survey on Modeling Languages for Applications Hosted on Cloud-Edge Computing Environments
## 3 Results
### Hosts in Different Geolocations  
Geolocation can be used as a deployment criterion in certain CMLs, as hosts may be distributed across different regions. This feature proves advantageous in orchestrating application components. For instance, [PERSON] et al. [27] propose an extension of HOT that enables the deployment of services based on location constraints and location-aware elasticity rules.  
Similarly, CAMEL [1] includes a location package that allows for users to specify locations as geographical regions or cloud locations. This option allows for the description of parent regions, such as continents, sub-continents, and countries.  
In the model utilized by Aeolus Blender [33], there is the option to describe the locations of available hosts/virtual machines in a string format. Aeolus also includes a model that describes package repositories along with their geolocation information.  
Cloud platforms can leverage this feature to facilitate the deployment of location-based applications. Uni4 Cloud [13], for example, utilizes location information for internalization purposes, enabling the deployment of location-based applications with specific language packages.  
By extending the tosca.nodes.ComputeNode node type, TOSCA can be enhanced to support geolocation criteria. [PERSON] et al. [18] demonstrate an example of such an extension in their work, where a virtual machine on AWS is described, and the geolocation is represented by the availability zone. Similarly, [PERSON] et al. [17] introduce an extension of the tosca.nodes.ComputeNode in their research, specifically for EC2 resources, incorporating the region_name property to denote the region of the corresponding virtual machine. In their study [20], [PERSON] et al. devise a TOSCA extension for an instance level model, which introduces several processing node entities to describe distinct resources linked to various cloud providers. These entities incorporate a cloud section that allows for describing the type of cloud provider and facilitates specifying the cloud region.  
[PERSON] et al. in [21] employ region description techniques for compute nodes integrated within edge, fog, or cloud environments. In this context, the region description could specify a particular building, rooms of that building and other workspaces. Similarly, the research addresses geolocation by utilizing latitude and longitude parameters in IoT device descriptions.  
Further research is needed to identify the optimal method for expressing geolocation criteria, whether using latitude and longitude coordinates or specifying continent, country, region, and city names, or the availability zone. The selection of values would rely on the orchestration capabilities and architecture requirements of the cloud platform. In order to use geolocation parameters, a platform would need to have a monitoring mechanism in place to expose geolocation information, along with an intelligent reasoner to parse the geolocation data from the model and match it with the monitoring information.
## 3 Results and Discussion
### Total Serum Protein (TSP) and Total SERUM Glucose (TSG)  
The changes associated with TSP and TSG levels are summarized in Figure 4. Concentration-dependent depletion of TSP and elevated levels of TSG were observed in diazinon-exposed \(C\). _batchus_ compared to the control group (Figure 4). The significantly lower levels of complete serum protein in diazinon-exposed fish implicate increased energy demand typical of chemical-induced stress and toxicity [84; 85]. Similar patterns of lowered serum protein levels have been documented in other fish species exposed to diazinon [86].  
Also, in this study, the dramatically higher blood glucose levels in diazinon-exposed \(C\). _batchus_ highlight toxicity-related energy demand necessitating increased mobilization of glycogen into glucose for biotransformation of xenobiotics and sustaining compensatory responses. Under stress, adrenal tissue secretes more glucocorticoidoids and catecholamines, resulting in hyperglycemia [26]. Elsewhere, hyperglycemia following diazinon exposure has been attributed to limiting glucose metabolism due to a reduction in AChE (acetylcholinesterase) activity [87]. In addition, diazinon-induced oxidative damage in the liver and pancreas of the treated organism may also disrupt insulin and glucose homeostasis [88].  
Figure 3: (**a–c**) Mean and SD values of different hormones of _Clarias batrachus_ exposed to different concentrations of diazinon (0.64 and 1.28 mg/L) for different exposure times (15, 30, and 45 days). *—denotes significant differences to control within the same exposure time (* \(p<0.05\), *** \(p<0.001\) and *** \(p<0.0001\)); GH—G=growth hormone, T\({}_{3}\)—tri-iodothyronine, T\({}_{4}\)—tetra-iodothyronine (thyroxine).
# Time Delay Optimization of Compressing Shipborne Radar Digital Video Based on Deep Learning
## 3 Time Delay Optimization of Compressing Shipborne Radar Digital Video Based on Deep Learning
### Flow Chart of Compressing Delay Optimization Algorithm
In the process of compressing shipborne radar digital video, by limiting the CU search depth interval, a large amount of computational redundancy due to the cost of ergodic rate distortion is reduced. For example, when the prediction result is \(D_{2}\), the encoder stops traversing the RD-cost with a CU depth of 3 and skips the calculation of RD-cost with a CU depth of 0. This speeds up the current CTU encoding rate and reduces the compressed delay to a certain extent.
# Artificial Intelligence Applications in Smart Healthcare: A Survey
## 5 Challenges and Existing Solutions
### Continuous Learning and Adaptability
#### 5.5.2 Existing Solutions  
Continuous learning in AI involves adapting algorithms to continuously evolving data without a predefined number of tasks. In healthcare, this capability is vital due to the constant changes in patient data and clinical practices. For instance, IBM Watson for Oncology updates cancer treatment recommendations by integrating the latest medical research and patient data, quickly adapting to new immunotherapy advancements [124]. Similarly, Dexcom's glucose monitoring system adjusts insulin dosages in real time based on current blood glucose levels, optimizing diabetes management [125]. During the COVID-19 pandemic, AI systems like BlueDot refined predictive models and issued early outbreak warnings by analyzing real-time health data and news [126]. Tempus Labs uses AI to analyze genomic data, regularly updating its database with the latest genetic research to provide precise, personalized treatment recommendations. These examples highlight how continuous learning allows healthcare AI systems to stay up to date with evolving data and practices, enhancing the accuracy and effectiveness of medical services.  
The primary challenge in continuous learning models is their susceptibility to catastrophic forgetting or interference, caused by fixed and limited storage allocation. This occurs when training a model with new information interferes with previously learned knowledge, often leading to a sudden decline in performance or complete replacement of old knowledge with new information [127]. In continuous learning, various approaches are employed to balance old and new tasks and mitigate forgetting and interference problems. Regularization-based methods add explicit terms to the loss function to achieve this, while replay-based methods approximate and recover old data distributions. Optimization-based approaches manipulate optimization procedures, such as gradient projection or meta-learning. Representation-based techniques leverage representations for continual learning, incorporating self-supervised learning and pre-training. Architecture-based strategies allocate task-specific parameters to alleviate inter-task interference, thereby enhancing model flexibility and adaptability [128]. In practice, when implementing continuous learning, considerations should extend beyond fixed and limited storage allocation to include computational costs. Various methods achieve this balance. For instance, using a k-NN classifier continuously updated alongside a fixed, pre-trained feature extractor is ideal for adapting to dynamic data streams within limited computational budgets, ensuring retention of previously seen data [129].
# The Elasto-Plastic Contact Models of Cylinder-Based and Sphere-Based Fractal Rough Surfaces
## 3 The Revised Model of a Single Fractal Asperity Contact
### The Existing Conditions for Asperity Full Plastic Deformation  
When the interference is greater than the elasto-plastic critical interference, namely \(\omega_{n}>\omega_{nepc}\), the asperity of frequency index \(n\) is in full plastic deformation. The relation between the contact area and contact load can be expressed as follows [30]:  
\[a_{np}=2\pi R_{n}\omega_{n} \tag{29}\]  
\[F_{np}=Ha_{np} \tag{30}\]  
In full plastic deformation, the asperity contact area equals the asperity truncated area, namely \(a_{np}={a^{\prime}}_{np}\), so the relation between contact load and truncated area can be written as  
\[F_{np}={H{a^{\prime}}_{np}} \tag{31}\]  
As the asperity height is greater than its own elasto-plastic critical interference, namely \(\delta_{n}>\omega_{nepc}\), \(n>n_{epc}\), elastic deformation, elasto-plastic deformation, or full plastic deformation can take place in the asperity under contact load.  
From the above points, the elastic critical contact area \(a_{nec}\) and the elasto-plastic critical contact area \(a_{nepc}\) depend on the radius of curvature of the asperity \(R_{n}\). The \(a_{nec}\) and \(a_{nepc}\) are proportional to the radius of curvature of the asperity. The elastic critical area \(a_{nec}\) is less than the elasto-plastic critical contact area \(a_{nepc}\), i.e., \(a_{nec}<a_{nepc}\). Similarly, we can obtain identical solutions for asperity critical truncated areas, such as \({a^{\prime}}_{nec}\) and \({a^{\prime}}_{nepc}\). The single asperity undergoes a transition from elastic, elasto-plastic, to full plastic deformation with an increase in the load and contact area.
# Consistency Models of NoSQL Databases
## 3 Consistency Models
### Causal Consistency  
If some process updates a given object, all the processes that acknowledge the update on this object will consider the updated value. However, if some other process does not acknowledge the write operation, they will follow the eventual consistency model [16]. Causal consistency is weaker than sequential consistency but stronger than eventual consistency.  
Strengthening the Eventual Consistency model to be Causal Consistency decreases availability and network partitioning properties of the system.
## 3 Results and Discussion
### Statistical Analysis
In addition, this study conducted a comparative analysis of the simultaneous validity of joint angle (trajectory) and reliability in the test through 4 DEYE(r) based on a novel multi-view RGB sensor and VICON(r) with infrared-based marker attachment, which consists of hip flexion and knee flexion in the sagittal plane and trunk rotation in the transverse plane as one continuous motion as data on transverse plane movement, which could not be obtained in previous studies. Therefore, high reliability could be obtained for the joint measurement of the movement of all joints. In the field of rehabilitation science, identifying and improving joint movement damage and mobility are important clinical evaluation points. In this study, the hip rock motion was selected to measure the continuity of multi-joint composite movements and it involves the complex movements of knee flexion, hip flexion, and trunk rotation. The in-depth relationship between the VICON and 4 DEYE results is significant for moving away from marker-attached products, which are uncomfortable, when assessing joint movement damage or mobility evaluation and improvement. Motion-analysis equipment that is based on a non-marker and non-wearable multi-point image sensor can be used in various fields to improve the quality of human mobility and joint movement and can be systematically managed via quantification. In the case of trunk rotation, the existing pose-estimation engine cannot be used to target the pelvis; therefore, it may produce less accurate results than when it is used in other joints for inferring and calculating angles by using the indicators of other joints.