esa-sceva/satcom-chunk-collection huggingface dataset:
# Numerical Simulation Study on the Volumetric Efficiency Loss of CO2 Swash Plate Axial Piston Pumps
## 4 Numerical Simulation Method and Simulation Method Validation
### Numerical Simulation Method for Fluid Domain of Friction Subfilm
In order to verify the reliability of the numerical simulation method for the liquid film leakage volume loss, a liquid film model and the working conditions of a port pair in the literature [30] were selected to obtain a curve of the leakage volume with the thickness of the liquid film. A comparison between the simulation results and experimental results from the literature is shown in Figure 9. It can be seen from Figure 9 that with an increase in liquid film thickness, the leakage amount was basically the same. The maximum error was 5.88%, indicating high accuracy and a reliable simulation method.
# Multi-Path Data Distribution Mechanism Based on RPL for Energy Consumption and Time Delay
## 3 Adaptive Multipath Traffic Loading Based on RPL, AMTL-RPL
### Energy Balance Based on the RPL
#### 3.1.1 Node Energy Consumption Model  
Let us take the network topology of Figure 3 as an example. Node A is the sending node; B1, B2 and B3 are its parent nodes; and C1 and C2 are bottleneck nodes.  
Figure 3: The node’s energy consumption.  
The node's consumption can be classified as three parts, the receiving consumption for new data, the calculation consumption and the sending consumption. We take node C1 as an example to calculate the three different kinds of energy consumption. The notations used in the article are shown in Table 1.  
Step 1: Node C1 receives the data which the descendant node sends; its energy consumption mainly comes from the consumption in receiving data:  
\[E_{rec}(c_{1})=\sum_{Parents(b)=\text{C1}}\frac{Tat(b,c_{1})}{PRR(b,c_{1})} \times\frac{E_{r}}{D_{r}} \tag{1}\]  
Step 2: Node C1 handles the cache queue; we can calculate its energy consumption by:  
\[E_{cmp}(c_{1})=N_{task}(Buffer\_Use(c_{1}))\times E_{c} \tag{2}\]  
Step 3: Node C1 sends data to its descendant nodes after finishing to process the cache queue, which energy consumption is:  
\[E_{txt}(c_{1})=\frac{Tat(c_{1},d)}{PRR(c_{1},d)}\times\frac{E_{t}}{D_{d}} \tag{3}\]  
Thus, node C1's residual energy is:  
\[E_{res}(c_{1})=E_{crt}(c_{1})-E_{txt}(c_{1})-E_{cmp}(c_{1})-E_{rec}(c_{1}) \tag{4}\]  
Here, \(E_{res(c_{1})}\) represents the residual energy after handling a transmission task or the energy used to clear up the cache. This metric can affect the route choice of bottleneck nodes.
## 3 Results
### Experimental Results
However, the AIR decreases rapidly when the distance is larger than 3 m. The linear interpolation brings a relative huge error at the point of 3.5 m. With nonlinear interpolation, the maximum error of AIR decreases from 0.11 Gb/s to 0.04 Gb/s, which is a decrease of 63.6%. Figure 8: The distribution of PS constellations in the highlight subcarriers in Figure 7c with different GMI: (i) 7.13 bit/Hz, (ii) 6.66 bit/Hz, (iii) 5.68 bit/Hz, and the bitloading modulation in the same subcarriers with different QAM order (iv) 7 bit/Hz, (v) 6 bit/Hz, (vi) 5 bit/Hz. \begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Distance(m)**} & **Average GMI of** & **Average Order of** \\  & **PS Bitloading (bit/Hz)** & **Classical Bitloading (bit/Hz)** \\ \hline
1 & 5.974 & 5.232 \\
2 & 6.002 & 5.268 \\
3 & 5.417 & 4.500 \\
4 & 3.944 & 3.377 \\
5 & 3.699 & 2.989 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The information entropy in each subcarrier of PS bitloading and classical bitloading.
## 5 Discussion
This finding may guide researchers in developing ensemble deep learning [48; 49] strategies for computer-aided BCa diagnosing systems. The higher F1-score and AUC achieved by Model A indicate its superior overall performance, showing a stronger capacity to differentiate between positive and negative classes across varying thresholds. In contrast, while Model B maintained a balanced tradeoff between TP and FP, this came at the cost of a lower F1-score and AUC. This finding suggests that the choice of loss functions and weight parameters may affect consistency in feature learning. Finetuning model architecture based on the respective fusions is thus recommended. Finally, the benchmarking confirmed previous findings [16, 17, 21], where ResNet-50 consistently outperformed almost all the other DL models, regardless of multi-band visual fusion strategies. The CAM heatmaps generated by these models closely aligned with pathologist interpretations, effectively highlighting diagnostically relevant areas. Key characteristic features include irregular, pleomorphic tumor cells actively invading surrounding breast tissue, which are critical indicators of IDC progression [50]. This consistency reinforces the potential of the proposed models to support clinical decision making, particularly in cases where subtle morphological patterns may otherwise be overlooked. This study utilizes a publicly available dataset to encourage reproducibility, benchmarking, and development in IDC diagnosis. The selection of small patch sizes aligns with the original dataset's intent to validate DL models of varying complexity. While individual patches may not fully capture morphological and anatomical contexts, some studies [20, 21, 45] have explored distilled classification, where adjacent patches are hierarchically labeled to provide a more comprehensive delineation of pathological lesions. Recognizing the generalization challenges associated with small image datasets, future research will focus on improving applicability by utilizing larger images and incorporating additional morphological and anatomical contexts.
# Advances in Quantitative Earth Remote Sensing: Past, Present and Future
## 1 Introduction  
The field of Earth remote sensing has been revolutionized during the past several decades due to advances in sensors, satellites and information technologies. This Special Issue of _Sensors_ is devoted to innovative papers based on multispectral remote sensing measurements of the Earth system enabled by a variety of optical, microwave and laser sensors and a combination of large-, small- and micro-satellites. These measurements are advancing our understanding of the fundamental processes that control Earth's planetary system, and its components (atmosphere, land, oceans, and polar regions)and their interactions, which in turn control the variability and change of these components and the entire Earth system. The scientific knowledge and resulting data are essential for developing and evaluating the performance of the Earth system models, the only tool available for the prediction of Earth's weather and climate conditions, water and energy cycles, which have profound socioeconomic importance. For this special issue, we invited papers based on the use of observations from long-term records (e.g., Landsat and Earth Observing System, EOS), multiple satellites (EOS A- and B-Trains, and combined optical and microwave measurements, such as European Copernicus), and satellites in polar, geostationary and other orbits. Studies focused on the innovative use of remotely sensed observations in deriving useful information were also encouraged (e.g., sun-induced fluorescence, vegetation chemistry and composition, species habitat, landscape-level ecosystems genotype and phenotype). We welcomed papers focused on the previous and next generation of Earth-observing sensors and satellites, and/or based on a combination of sensors and satellites (e.g., microsensor/satellite constellations), together with the use of advanced information technologies and systems (e.g., neural networks, artificial intelligence and deep learning). A summary of papers accepted and published thus far is presented in the next section.
###### Abstract  
Reinforcement Learning (RL)-based routing protocol has been proposed to establish paths in mobile ad hoc networks. However, due to the overhead of updating reward values according to frequent topology changes, existing protocols based on RL suffer from scalability problems with a large number of state and action spaces. To defeat this problem, in this paper, we propose a new resilient routing protocol by applying Unsupervised Learning (UL) prior to Deep Reinforcement Learning (DRL). In the former scheme, each node is clustered by mobility-resilient parameters. A reliable path that consists of only robust nodes in UL is decided by DRL with reasonable weight value through Multi-Objective Decision Making (MCDM). This approach leads to a reduction in update cost for reward value by excluding nodes that are considered severely affected by mobility. The comparative simulation results demonstrated that the proposed scheme outperformed the existing scheme in the aspects of Packet Delivery Ratio (PDR) and energy consumption. Our protocol demonstrates up to 35% higher PDR and reduces energy usage by approximately 20% under high-mobility conditions compared to Q-Learning-based protocols.  
Keywords:resiliency; clustering; reinforcement learning; multi-objective decision making; mobile ad hoc networks +
Footnote †: journal:
# Downregulation of Squalene Synthase Broadly Impacts Isoprenoid Biosynthesis in Guayule
## 4 Materials and Methods
### Rubber and Resin Extractions  
The top part (stems and leaves) of approximately two-month-old tissue culture plantlets or tissues separated from 5-month-old greenhouse/growth chamber plants were separated from the medium/soil and lyophilized for 48 h. The dried tissues were placed in a 50 mL stainless steel grinding jar containing a grinding ball, frozen in liquid nitrogen for 5 min, and finely ground using the mixer mill MM 400 at a frequency of 30/s for 1 min. Three hundred milligrams (0.3 g) of pulverized tissues were loaded into 11 mL stainless steel-sand containing extraction cells (Dionex, Sunnyvale, CA, USA). Three sequential extractions were performed: 1. acetone, to remove resinous material and the low molecular weight organic solubules; 2. methanol, to remove chlorophyll and other alcohol-soluble materials; 3. cyclohexane, to remove rubber. Natural rubber was quantified gravimetrically (from ASE vials). The percent (%) rubber is the amount (% dw) of cyclohexane extract from 0.3 g dried tissue.
# Secure Quantum Communication Technologies and Systems: From Labs to Markets
## 2 Quantum Key Distribution Systems and Networks
### Closing Remarks on QKD Systems  
Most of current quantum communication testbeds and early commercial systems rely on DV-QKD, which was extensively investigated and compared to its competitor, CV-QKD. However, DV-QKD requires dedicated devices, which may forever limit its application to niche markets, which are not too sensitive to costs. CV-QKD potentially overcomes this issue reusing device already developed for classical communication systems. However, while for DV-QKD many security proofs exist, the guaranteed security level of CV-QKD is more uncertain, making it questionable if it is worthwhile to introduce a further encryption layer besides the ones already existing (ranging from physical to packet layers) in classical communications networks.  
However, the most serious concern about QKD systems, regardless of whether they are based on discrete or continuous variables, is that they are conceived for short-distance, low-capacity point-to-point links. The ability to support high data rates over long distances remains unsolved. Optical networks where classical and quantum channels coexist are a possible answer, but the achievable performance is far from acceptable, as the experiments discussed in the previous sections show. Spatial division multiplexing (SDM) is another possibility to increase the capacity, splitting and sending the key over parallel multiple fibers or spatial modes. In [65], a 33.6 Mbit/s key rate was achieved over 9.8 km, using a 7-core multicore fiber. SDM may be an interesting technique for data center interconnection, characterized by relatively short point-to-point links and ultra-short chip-to-chip interconnection [66].  
It is still too early to declare a winner among the several proposed QKD options, especially considering the early maturity stages of some enabling technologies, such as quantum repeaters, which are the key to extending the link distance. This aspect will be discussed in the next section.
# A QoS-Guaranteed and Congestion-Controlled SDN Routing Strategy for Smart Grid
## 3 Design of QCORS Algorithm
### Sdn System Framework  
SDN is an open and programmable [40, 41] architecture, which can obtain the global topological structure of the whole network. It is flexible and controllable, with strong real-time performance and good robustness. The central idea of SDN [42] is to decouple the traditional network architecture that is closely connected with the network equipment at all levels, and obtain a completely disconnected data layer and control layer. Based on this, standardization is implemented at all levels to realize centralized management and control. The established SDN system consists of three parts: the application layer, the control layer, and the forwarding layer.  
Specifically, the forwarding layer includes some basic network equipments, which does not have the control ability. Its main target is to complete the data forwarding and processing according to the command of the control layer [43]. The application layer of SDN involves the application of various businesses. In particular, the application layer communicates with the control layer through the northbound interface, where the northbound interface is an upward interface provided to operators or users for access and management. The application layer is composed of various software written by users. The user can thus invoke the API provided by the controller according to his own needs to innovate the network service. The SDN controller creates a logically centralized plane, which provides global control over all SDN node-based physical topologies connected to the controller, making fine-grained control of SG services possible. The control layer is the core part of the whole network, including network link discovery, topology management, network routing strategy, etc. SDN conducts centralized management and control over the whole network through the control layer. The southward interface is a downward provided interface, and the control layer and the data forwarding layer communicate and interact with each other through this interface. Currently, the mature southward interface protocol is OpenFlow protocol.  
Due to SDN having coordinated the different types of devices and different communication protocols for data-forwarding layers by standardization, the devices are expected to receive instructions from the control layer. Then, the message can be smoothly transmitted according to the forwarding demand. This design pattern can support different types of traffic flows and develop an optimized routing algorithm.
## 4 Simulation Results  
In this section, we evaluate the proposed protocol by simulation using Spyder (Python 3.6) on a desktop PC (Lenovo, made in Beijing, China) with Intel(R) Core (TM) i3-4170 CPU @ 3.70 GHz, 8 GB RAM. The BS of this paper is located at coordinates (50, 45), and the area is a square of 100 m \(\times\) 90 m. The specific simulation parameters are shown in Table 1. (Note that J in this paper stands for Joule, which is a unit of energy.)  
We mainly compare the node deployment strategy proposed in this paper with the random node deployment strategy, the uniform node deployment strategy, and the 1-fold coverage-based node deployment strategy in a cellular network in terms of coverage, information integrity, information validity, and information redundancy. At the same time, this paper selects three classic protocols, including LEACH, Distribute Energy-Efficient Clustering (DEEC), and Group-Based Sensor Network (GSEN). Then, the protocol of this paper (EECRP-HQSND-ICRM) is compared with the three protocols and the protocol without the inter-cluster routing mechanism (ICRM) of this paper (EECRP-HQSND) to compare the network lifetime, network coverage, information integrity, network energy consumption, the iteration round at which the WSN starts to decline, and the network throughput to reflect the advantages of the protocol in this paper, and especially the feasibility of the ICRM.
# Radiation Hardened Read-Stability and Speed Enhanced SRAM for Space Applications
## 2 Proposed RHRSE-20T SRAM
### Read Operation  
To describe the process of the read operation, assume that the SRAM is in a state where '0' is stored (Q = S1 = S3 = 0, QB = S0 = S2 = 1). For the read operation, BL and BLB are pre-charged to '1'. Since WL and WWL are separate, WWL is not activated during the read operation. Therefore, only WL is HIGH, and the read operation utilizes only two pairs of access transistors. The access transistor connected to the node storing '1' maintains the voltage of BL at the pre-charged level. In contrast, BLB, which is connected to the node storing '0', will have a voltage lower than the pre-charged level. At this point, the sense amplifier detects the voltage difference between BL and BLB, and if the difference exceeds the specified threshold, it provides the output.  
The following subsections provide a detailed explanation of the soft-error recovery mechanism implemented in the proposed architecture. In this section, we examine the case in which the cell stores a '1' (Q = S1 = S3 = 1, QB = S0 = S2 = 0). The proposed design can  
Figure 3: Simulation results of normal write, hold, and read operations of the proposed RHRSE-20T SRAM.  
recover from all SNU cases. Additionally, with the aid of layout techniques, it can recover from all DNU and MNU cases.  
To simulate a radiation strike on sensitive nodes, an exponential current is applied, which can fully invert the state of the node. In such scenarios, the sensitive nodes of the cell will return to their original state through a recovery mechanism whenever data corruption occurs due to external disturbances. The self-recovery principle of the proposed RHBSE-20T cell is designed to automatically restore the cell to its original state in the event of data corruption caused by external disturbances, such as radiation.
## 3 Results and Discussion
### Influence of Process Parameters on the Resistivities of Printed Parts
explain the resistivity decrease from 154.87 \(\Omega\cdot\)m to 89.07 \(\Omega\cdot\)m when the raster width changed from 0.3 mm to 0.5 mm. For the structure of printed parts, current in the vertical direction must pass through the adjacent fiber layers to obtain electrical conductivity. For the simulation analysis, the increase of the resistivity in the vertical direction occurs between the fiber layers, and the presence of the contact resistivity between the fiber layers increases the resistivity of the whole structure. In addition, with the increase of the raster width, the contact area between the fibers increased, and the resistivity decreased. Hence, the evidence of simulation and electron microscopy analysis could demonstrate bonding conditions, including the bonding area and bonding quality, which are the most important factors that affect the resistivity in the vertical direction. On the other hand, the current in the horizontal direction cannot only spread along the fiber, but also transmit through the adjacent parallel fibers from the structure of printed parts. The presence of voids reduces the contact of the adjacent fibers, as well as the effective cross-sectional area of the fibers and, therefore, may increase the resistivity. Both simulation and electron microscopy proved the results. Thus, voids are supposed to be the most important factor to affect resistivity in horizontal direction. The electrical conductivity of ABS/CB is afforded by the conductive fillers and the structure of contact between carbon filler is a factor to affect the resistivity of printed parts, but the main issue we focused on were structural features within the scope of extruded fibers, and the influence of contact between carbon filler on resistivity could be found in other studies [37; 38]. In addition, although the shape of the actual fibers and the bonding condition between adjacent fibers had some inconsistencies, which may be caused by inaccuracies in the positioning stage or the extrusion rates, the effect of these deviations on resistivity was slight because the fibers are relatively small in size and the error due to each fiber was averaged over a number of fibers. Therefore, there is evidence that the resistivity in the vertical direction can be adjusted by changing the raster width.
## Appendix A Description of Brunei Education Management Information Systems (iNEIS(tm))
As the iNEIS(tm) has begun its full implementation, all primary schools must aim for a 95 percent pupil attendance rate, which the minister of education announced in a meeting with primary school leaders at Sengkurong Sixth Form Centre. The minister went on to say that the new key performance indicator (KPI) will help to measure students' achievements in their school attendance and for school leaders and parents to effectively monitor and support students' learning development. As reported in the Borneo Bulletin, the introduction of the Integrated National Education Information System (iNEIS(tm)) has evidently reduced the time needed to take attendance for teachers. This aspect was one of the methods taken by the Ministry of Education to lessen the burden of teachers who must complete extra administrative work in addition to teaching. More importantly, a ninety-five percent attendance rate across government primary and secondary schools has been recorded by the Ministry of Education (MoE) since the implementation of its online attendance monitoring system iNEIS(tm). Apart from the above, the MoE is compiling data on co-curricular activities in schools through an online system to encourage students to take up at least one co-curricular activity throughout their academic years. It is reported that the data are collected through the Integrated National Education Information System. iNEIS(tm) was launched to track and monitor students' attendance. The purpose is that the data-driven web application will ensure full participation of students in co-curricular activities. The id iNEIS(tm) will allow teachers to streamline the management of co-curricular activities and ensure that students in primary and secondary schools participate in at least one co-curricularactivity throughout their school years. This is to encourage students to stay active by making physical, mental or spiritual wellbeing a priority by participating in co-curricular activities at school. Aside from the description of the functions and characteristics above, iNEIST(tm) as an Educational Management Information System is also a set of formalized and integrated operational processes, procedures and cooperative agreements by which data and information about schools and schooling, such as teachers, students, learning activities and evaluative outputs, are regularly shared, integrated, analyzed and disseminated for educational decisions used of each level of the educational hierarchy. While the INEIST(tm) function is to provide an efficient administrative service for schools and MoE, the role is for teachers to focus more on teaching. iNEIST(tm) is designed as a centralized and integrated system which supports processes in schools and MoE to be more efficient with a high degree of accessibility.