esa-sceva/satcom-chunk-collection huggingface dataset:
# A Review of the Optimal Design of Neural Networks Based on FPGA
## 3 Application of Neural Networks Based on FPGA
### Application of RNNs Based on FPGA
The whole FPGA cluster shared an RNN model, and thus each FPGA processed only a part of the large RNN model, which reduced the processing burden of FPGA. The parallelism of the FPGA cluster and the time dependence of RNN made the delay basically unchanged when the number of RNN layers increased. Compared to Intel CPU, 31 times and 61 times speedup were achieved for single-layer and four-layer RNNs, respectively. In December of the same year, [PERSON] et al. [62] proposed a lightweight gated recursive unit (GRU)-based RNN accelerator, called EDGERDNN, which was optimized for low-latency edge RNN inference with batch size 1. EDGERDRNN used a delta network algorithm inspired by pulsed neural networks in order to exploit the temporal sparsity in RNN. Sparse updates reduced distributed RAM (DRAM) weight and memory access by a factor of 10, and reduced latency, resulting in an average effective throughput of 20.2 GOP/s for batch size 1. In 2021, [PERSON] et al. [63] implemented an efficient and reconfigurable RNN inference processor AERO on a resource-limited Intel Cyclone V FPGA on the basis of the instruction set architecture that specializes in processing the raw vector operations that constitute the data stream of RNN models. The vector processing unit (VPU) based on the approximation multiplier was used to complete the approximate calculation in order to reduce the resource usage. Finally, AERO achieved a resource utilization of 1.28 MOP/s/LUT. As the authors stated, the next step can be achieved by the multi-core advantage of FPGA clusters in order to achieve higher reasoning speed. In June 2022, [PERSON] et al. [64] proposed a subgraph segmentation scheme based on the CPU-FPGA heterogeneous acceleration system and the CNN-RNN hybrid neural network. The Winograd algorithm was applied to accelerate the CNN. Fixed-point quantization, cyclic tiling, and piecewise linear approximation of activation function were used to reduce hardware resource usage and to achieve high parallelization in order to achieve RNN acceleration. The connectionist text proposal network (CTPN) was used for testing on an Intel Xeon 4116 CPU and Arria10 GX1150 FPGA, and the throughput reached 1223.53 GOP/s. Almost at the same time, [PERSON] et al. [65] proposed a new LSTM accelerator called \"Spartus\" that was again based on spatiotemporal sparsity, with it utilizing spatiotemporal sparsity in order to achieve an ultra-low delay inference. Different from the pruning method used by [PERSON] [59] and [PERSON] et al. [60], they used a new structured pruning method of column balancing target dropout (CBTD) in order to induce spatial sparsity, which generates structured sparse weight matrices for balanced workloads and reduces the weight of memory access and related arithmetic operations. The throughput of 9.4 TOp/s and energy efficiency of 1.1 Top/J were achieved on a Xilinx Zynq 7100 FPGA with an operating frequency of 200 MHz.
## 4 Discussion  
### Optimization of Ecological Concrete Properties and Module Design  
The systematic optimization of ecological concrete properties revealed crucial insights into the relationship between composition and functionality, with the C25 grade emerging as the optimal formulation after comprehensive testing of three strength grades (C20, C25, and C30), aligning with recent findings on optimized cementitious matrices [19]. The C25 grade achieves a critical balance between structural integrity and environmental performance, featuring precisely calibrated proportions of cement (378 kg m\({}^{-3}\)), reinforcing agent (12 kg m\({}^{-3}\)), aggregate (1650 kg m\({}^{-3}\)), and water (137 L m\({}^{-3}\)). These proportions maintained both structural stability with 28-day compressive strength of 20-30 MPa and desired porosity (>15%) while achieving permeability coefficients above 1 \(\times\) 10\({}^{-4}\) cm s\({}^{-1}\), significantly exceeding the minimum threshold required for biological processes in engineered systems [20].  
The innovative modular design incorporates \(\varphi\)61 mm \(\times\) H60 mm surface cavities in 100 mm \(\times\) 100 mm \(\times\) 100 mm concrete blocks, representing a significant advancement in ecological engineering. These cavity dimensions resulted from extensive optimization studies that the 61 mm diameter was specifically chosen to maximize the contact area between contaminated water and biochar while maintaining structural integrity, as verified through computational fluid dynamics modeling of flow patterns [21]. The 60 mm depth precisely accommodates 30 g of biochar while ensuring a sufficient base thickness of 40 mm for structural support, creating an optimal balance between treatment capacity and durability. This enhanced performance can be attributed to its optimized pore structure, as evidenced in the microscopic analysis showing interconnected pore networks with optimal tortuosity factors [22].  
The relationship between pore structure and permeability follows the modified Kozeny-Carman equation [23]:  
\[k=(\varepsilon^{3}/S^{2})\times[1/(c\tau^{2})] \tag{2}\]  
where \(k\) represents the permeability coefficient, \(\varepsilon\) denotes porosity, \(S\) indicates specific surface area, \(c\) represents the shape factor, and \(\tau\) signifies the tortuosity factor.  
The experimental results showed that the C25 grade achieves optimal values for these parameters, with porosity (\(\varepsilon\)) maintained above 15%, specific surface area (\(S\)) ranging from 1.2 to 1.5 m\({}^{2}\) g\({}^{-1}\), and tortuosity factors (\(\tau\)) between 1.8 and 2.2, results that align with theoretical predictions.
# Opportunistic Multipath Routing in Long-Hop Wireless Sensor Networks
## 4 Performance Evaluation
### Packet Delivery Ratio and Energy Consumption Versus the Number of Source Nodes  
In this section, we demonstrate the effect of the number of source nodes on the packet delivery ratio and energy consumption when the link reliability is 90%. Figure 10 shows the packet delivery ratio versus the number of source nodes. To investigate the variation in the packet delivery ratio, the failed packets do not retransmit. The packet delivery ratio of the protocols is degraded when the number of source nodes increases owing to the buffer overflow caused by frequently collision avoidance (especially nearby destination) by operation of carrier sense multiple access with collision avoidance (CSMA/CA). Thus, the packet delivery ratio of RGMR, which constructs the most paths, decreases the most. For the proposed scheme, the packet delivery ratio drops slightly due to congestion around the sink node. REER also suffers a slight decline in the packet delivery ratio because the number  
Figure 8: Average energy consumption vs. end-to-end distance with for 90% of the link reliability.  
Figure 9: Average energy consumption vs. end-to-end distance with for 80% of the link reliability.  
of paths increases. As RRDLA exploits only a single path to transmit the packet, less congestion and interference occur compared with the other protocols.  
Figure 11 shows the energy consumption versus the number of source nodes. To investigate the energy consumption when achieving the same packet delivery ratio, the failed packets are retransmitted. When the number of source nodes increases, the energy consumption also increases. However, the increase is not proportional to the number of source nodes. In the case of RGMR, the protocol exploits the maximum number of paths to satisfy the required packet delivery ratio. However, as most of the constructed paths by different source nodes overlap, collisions and interferences occur. That is, as the maximum number of transmissions and retransmissions occur, RGMR consumes the most energy. As REER and RRDLA construct the lowest number of paths compared with the results of RGMR, they consume a much smaller amount of energy. However, they cannot avoid congestion around the sink node. This leads to an increase in the number of retransmissions and energy consumption. The proposed scheme also suffers from the same problem. Thus, energy consumption of the proposed scheme slightly increases due to the collisions and interference around the sink node.
# A Study on the Design of Fog Computing Architecture Using Sensor Networks
## 3 Proposed System  
### Platform Design  
As shown in Figure 7, the platform divided into the role-based hierarchy of the proposed object Internet consists of the cloud, the proxy server, and the device. A proxy server has a server to analyze data and a storage to save data. A proxy server conducts re-encryption. A proxy server monitors device information and controls devices according to the information analyzed.  
Connection types of devices differ according to the specifications of the connected device. Unlike a high performance device, as a low performance device is configured with simple sensors and an actuator only, it is difficult to communicate with a proxy server. To deal with this issue, select a high performance device as a small segment of a proxy server and make it play an intermediate role for a proxy server. The system proposed consists of three layers, as illustrated in Figure 8. Each layer has a cloud, a fog, and a device layer.  
The top layer is the cloud computing layer that receives information from a proxy server. It also plays the role of a storage device to save all data inside the cloud. The saved data can be used for other purposes, such as data mining and management. The fog layer is a highly virtualized platform that  
Figure 8: Proposed System Architecture.  
Figure 7: Role for Each Layer of the Proposed System.  
provides a number of services such as calculation, saving and networking services at the network edge near an IoT device. It makes cloud service available near IoT devices or mobile users.  
The device layer includes an m-proxy server with a limited amount of resources, which is installed at a store, a cafe or a public facility such as an intersection within one or two hops from a sensor or an IoT device to collect information. As calculation is conducted at a network edge near an IoT device, it reduces the amount of data transferred to a cloud and delivers services to many devices with powerful wireless connections. For example, traffic control can be automated by collecting data from cameras and sensors installed at a side of road in real-time. Sensors detect approaching objects including pedestrians and vehicles and measure the distance from/to or the speed of the objects. They send a signal to a smart traffic light to provide appropriate instructions to vehicles, based on the collected information. Although the areas covered by the edge layer are different, they could provide low network latency, sensor position recognition, wide geographical distribution and mobile service.
## 3 Simulation and Analysis  
A joint simulation platform combining Matlab/Simulink and BikeSim was developed to simulate and verify the dynamic control system. Three common scenarios were constructed: double lane change, dry curved road, and wet curved road. Urban road lane widths are typically recommended to range from 3.5 m to 3.75 m to accommodate a variety of vehicle types [39]. Therefore, in all three scenarios, a single lane width of 3.75 m was used, with the vehicle traveling along the centerline of the road, maintaining a distance of 1.825 m from both the left and right edges. The inner radius of the curved road scenario was set at 60 m, with an adhesion coefficient of 0.85 for dry roads and 0.3 for wet and slippery roads. The initial speed of the vehicle in both scenarios was 72 km/h, allowing for a comparison of the motorcycle's control effectiveness on curved roads with different adhesion coefficients. The basic parameters of the two-wheeled motorcycle used in this study are presented in Table 1. The controller developed in this study manages longitudinal motion through a pre-brake controller, utilizes the front wheel for steering control, and engages pre-emptive braking when the vehicle speed exceeds the desired level, thereby ensuring the vehicle maintains the target speed.
# An Emerging Role of Long Noncoding RNAs as Novel Biomarkers for Breast Cancer Metastasis
###### Abstract  
Breast cancer is a leading cause of cancer-related deaths among women both worldwide and in Poland. Consequently, ensuring equitable access to diagnostic tests for all populations is crucial, alongside the urgent need to develop new, minimally invasive methods for early cancer detection with a particular focus on metastasis. Long noncoding RNAs (IncRNAs) have emerged as critical regulators of cancer metastasis. This review aims to investigate the potential of lncRNAs as novel biomarkers for breast cancer, focusing on their mechanisms, clinical relevance, and therapeutic implications. A comprehensive literature search was conducted using PubMed and Google Scholar databases, targeting publications from 2013 to 2024. Keywords included \"IncRNA\", \"biomarker\", \"breast cancer\", \"metastasis\", \"prognosis\", and \"diagnosis\". A total of 111 articles were selected based on their relevance and quality. Recent studies have identified numerous lncRNAs such as HOTAIR, MALATI, BANCR, NEAT1, H19, and GAS5 as key regulators of various metastatic processes in breast cancer. They can be both upregulated and downregulated. Clinical studies have shown that abnormal lncRNA expression correlates with poor prognosis, higher metastatic potential, and therapy resistance in breast cancer patients. LncRNAs have significant potential as novel biomarkers for breast cancer metastasis because of their regulatory roles in metastasis-related processes and detectability in body fluids. Further research is essential to validate these findings in larger clinical studies and to develop lncRNA-based diagnostic and therapeutic tools, ultimately improving patient outcomes in breast cancer.  
breast cancer; metastasis; cancer marker; biomarkers; early detection
## 4 Cooling System Design and Cooling Simulation Based on UG and Moldflow
### Establishment of Finite Element Model
#### 4.1.2 Moldflow Cooling Analysis Type Selection  
The cooling process of the injection mold is a three-dimensional transient heat transfer process, and the transient temperature of the mold surface varies periodically with the injection process. In practical engineering applications, it is generally reduced to a steady-state heat transfer process. Among the cooling analysis types in Moldflow, the main types include BEM cooling analysis and FEM cooling analysis.  
The boundary element method (BEM) is a numerical approach used to analyze the cooling process of plastic parts in injection molding. BEM cooling analysis is utilized to determine the average temperature of the plastic part during the steady state or the entire molding cycle. In the cooling simulation, the boundary element method cooling analysis can easily change the position of the cooling channel, obtain an effect of the position of the cooling channel on the cooling effect of the mold, and optimize the design of the cooling channel. The FEM cooling analysis calculates both steady-state and transient temperatures of the mold by the finite element method. In the steady state, the mold temperature is uniform, while in the transient state, the mold temperature varies with time.  
The finite element method (FEM) cooling analysis employs two main methods to analyze transient temperatures in molds: intra-cycle transient and transient after the start of production. Intra-cycle transient analysis simulates temperature fluctuations in the mold during a single cycle and calculates the deviation of the mold temperature from the cycle's average value. This analysis helps to identify and mitigate potential temperature-related issues during production. The other method, transient after the start of production, evaluates temperature changes in the mold after production has begun. These two types of analyses are commonly used in finite element cooling analysis to comprehensively understand and optimize the temperature dynamics of molds during different phases of production.  
The post-start transient calculates the change in mold temperature from the initial production cycle in the cold state to the time when the mold temperature stabilizes and reaches its optimal conditions, and it calculates the number of cycles required for the mold to reach its optimal production conditions. In order to accurately simulate and predict the temperature distribution of the mold cavity walls, this paper employs the FEM cooling analysis type for cooling simulation.
## 2 Improved AMCL Algorithm
### Improved Odometer Motion Model Sampling  
In the time interval \((t-1,t)\), the given motion information \(u_{t}\) is  
\[u_{t}=\begin{bmatrix}\overline{x}_{t-1}\\ \overline{x}_{t}\end{bmatrix} \tag{2}\]  
where \(\overline{x}_{t}\) is the coordinates inside the robot; the difference between \(\overline{x}_{t-1}\) and \(\overline{x}_{t}\) enables an estimate of the difference between the two poses. The relative distances can be calculated from three steps: initial rotation \(\delta_{rot1}\), translation \(\delta_{trans}\), and secondary rotation \(\delta_{rot2}\). Calculate one translation and two rotations by the given motion information \(u_{t}\).  
\[\begin{cases}\delta_{trans}=\sqrt{\left(\overline{x}-\overline{x}^{\prime} \right)^{2}+\left(\overline{y}-\overline{y}^{\prime}\right)^{2}}\\ \delta_{rot1}=arctan^{2}\left(\overline{y}^{\prime}-\overline{y},\overline{ x}^{\prime}-\overline{x}\right)-\overline{\theta}\ \ \delta_{trans}=\sqrt{\left(\overline{x}-\overline{x}^{\prime}\right)^{2}+\left( \overline{y}-\overline{y}^{\prime}\right)^{2}}\\ \delta_{rot2}=\overline{\theta}^{\prime}-\overline{\theta}-\delta_{rot1}\end{cases} \tag{3}\]  
Since the AGV runs for a long time, it will lead to cumulative errors. The actual values of translation and rotation can be obtained by subtracting the observed value from the interference error \(\varepsilon_{b2}\), the variance of which is \(b^{2}\). Therefore, the motion error model can be written as:  
\[\begin{cases}\delta_{trans}=\delta_{trans}-\varepsilon_{a_{1}\delta_{trans}^{2} }+\alpha_{4}\delta_{rot1}^{2}+\alpha_{4}\delta_{rot2}^{2}\\ \delta_{rot1}=\delta_{rot1}-\varepsilon_{a_{1}\delta_{rot1}^{2}}+\alpha_{2} \delta_{trans}^{2}\\ \delta_{rot2}=\delta_{rot2}-\varepsilon_{a_{1}\delta_{rot2}^{2}}+\alpha_{2} \delta_{trans}^{2}\end{cases} \tag{4}\]  
\(\varepsilon_{a_{1}}\sim\varepsilon_{a_{4}}\) is the cumulative error of motion.  
To obtain the actual location \(x_{t}\) from \(x_{t-1}\) after the initial rotation angle \(\delta_{rot1}\), follow the pan delta \(\delta_{trans}\), then add to another rotation angle \(\delta_{rot2}\):  
\[\begin{pmatrix}x^{\prime}\\ y^{\prime}\\ \theta^{\prime}\end{pmatrix}=\begin{pmatrix}x\\ y\\ \theta\end{pmatrix}+\begin{pmatrix}\delta_{trans}\text{cos}\left(\theta+\delta_{ rot1}\right)\\ \delta_{trans}\text{sin}\left(\theta+\delta_{rot2}\right)\\ \delta_{rot1}+\delta_{rot2}\end{pmatrix} \tag{5}\]  
Figure 4: The effect of the improved algorithm.  
Then, the final odometer error is:  
\[\left\{\begin{aligned} &\delta_{trans}-\delta_{trans}\\ &\delta_{rot1}-\delta_{rot1}\\ &\delta_{rot2}-\delta_{rot2}\end{aligned}\right. \tag{6}\]
## 2 Overview of Research Directions Based on the Spec Issue Papers
The quality of online educational materials has become increasingly important in the recent period, and it is crucial that content is created that allows our students to learn effectively and enjoyably. The authors present the milestones of curriculum creation and the resulting model, the criteria of selecting online learning environments and also introduce some principles of instructional design, as well as a self-developed model that can be used to create effective online learning materials and online courses. [PERSON] et al. [2020] investigate and validate different virtual reality (VR) stressor levels from existing emergency spaceflight procedures. Experts in spaceflight procedures and the human stress response helped design a VR spaceflight environment and emergency fire task procedure. Since stress is a complex construct, physiological data (heart rate, heart rate variability, blood pressure, electrodermal activity) and self-assessment (workload, stress, anxiety) were collected for each stressor level. The results suggest that the environmental-based stressors can induce significantly different, distinguishable levels of stress in individuals. [PERSON] et al. [21] presents the proposal of a method to recognize emotional states through EEG analysis. The novelty of this work lies in its feature improvement strategy, based on multiclass genetic programming with multidimensional populations (M3 GP), which builds features by implementing an evolutionary technique that selects, combines, deletes, and constructs the most suitable features to ease the classification process of the learning method. After implementing the M3 GP, the results showed an increment of 14.76% in the recognition rate without changing any settings in the learning method. The proposed methodology achieves a mean classification rate of 92.1%, and simplifies the feature management process by increasing the separability of the spectral features. [PERSON] et al. [22] introduces and illustrates an approach to automatically detecting and selecting \"critical\" road segments, intended for application in circumstances of limited human or technical resources for traffic monitoring and management [23]. The presented approach is psychologically inspired to the extent that it introduces a clustering criterion based on the Gestalt principle of proximity.
# Scalable and Reliable Data Center Networks by Combining Source Routing and Automatic Labelling
## 4 Implementation
### Host Registration  
In eTorii, HLMAC addresses are assigned to network switches, but not to end hosts. Edge switches are usually responsible for registering their connected hosts and assigning them an HLMAC address, which is directly deduced from its own ones. In this regard, the developed application needs to register these hosts, associating an HLMAC to each physical MAC of the host, and saving its location together with the topological map. This action cannot be performed by LLDP, as this protocol is only capable of discovering network nodes directly connected to the controller, which is not the case of hosts. For this reason, the application registers the hosts indirectly via traffic snooping. In particular, our implementation considers any host will send an ARP message prior to any communication, hence the controller will monitor these events (as they will generate a PACKET_IN) and register the hosts accordingly.