esa-sceva/satcom-chunk-collection huggingface dataset:
# Ad-BBR: Enhancing Round-Trip Time Fairness and Transmission Stability in TCP-BBR
## 5 Experiment and Evaluation
### RTT Fairness  
To evaluate the fairness improvements of the Ad-BBR algorithm in multi-stream competition scenarios, this section designs two sets of experiments: static network experiments and dynamic network experiments. Throughput performance is a commonly used metric to evaluate RTT fairness in congestion control algorithms under multi-flow competition scenarios. For example, in the case of the BBR algorithm, BBR dynamically adjusts its sending rate by estimating the bottleneck bandwidth and round-trip propagation delay (BDP), which results in flows with different RTTs obtaining different BDP values and, thus, sharing bandwidth unequally, leading to noticeable differences in throughput performance. Moreover, [PERSON]'s fairness index, as defined in Equation (14) [33], is used to quantify the fairness performance of each algorithm.  
\[\text{J}=\left(\frac{(\sum_{i=1}^{n}d_{i})^{2}}{n\sum_{i=1}^{n}d_{i}^{2}}\right) \tag{14}\]  
Here, \(n\) represents the number of streams in the link, and denotes the throughput of the i-th stream. The parameter is defined over the interval \([0,1]\); a value closer to 1 indicates better bandwidth fairness among the streams.  
Figure 7: Wi-Fi network simulation: download speed comparison in 0.5/5 BDP buffer.
# Path Planning Based on NURBS for Hyper-Redundant Manipulator Used in Narrow Space
## 2 Kinematics Modeling and Workspace Analysis of UCR Parallel Mechanism
### Workspace Analysis Based on Simulation  
In this section, the 3D CAD model of the UCR parallel mechanism is imported into simulation, and the simulation platform is used to analyze its working space. The simulation platform is shown in Figure 4.  
Figure 3: Parasitic motion.  
Through the position sensor module, the position information of UCR parallel mechanism is obtained, and simulation is used to form the track point envelope. As shown in Figure 4(a)-d. The envelope is cloak-shaped and symmetrically divided into three equal parts along the central axis.
## 3 Literature Overview and Indicator Selection
### Indicator Selection  
Among the meso-level CE and IS indicators retrieved from the literature, the hybrid ISI indicator proposed by [PERSON] et al. [18] meets the requirements for achieving the study objectives. It estimates the dynamic behavior of a symbiotic industrial network by measuring the symbiotic level of activity and combines both quantitative CE criteria, such as the amounts of materials exchanged, and qualitative ones (e.g., legislation and environmental and human health related risks), allowing a wide perspective on the creation of circular exchanges.  
The methodology is based on the Environmental Impact Momentum (EIM) index number that considers by-product flow and environmental impact--combining material flow analysis (MFA), environmental impacts assessment (LCA), and dynamic decision support system (DSS) theories.  
MFA is a proven method that allows tracking of the flow of materials throughout their entire lifecycle (production, use, and end-of-life) towards environmental compartments (air, water, and soil) [31]. LCA is also a widely accepted technique to assess environmental impacts associated with all the stages of a product's life, from raw material extraction through to materials processing, manufacture, distribution, use, and end-of-life [32]. Previous research confirms that both methods are mostly applied in CE and IS contexts to unveil circular and symbiotic opportunities and analyze the state of industrial networks [28; 30; 33].  
The combination of MFA and LCA with dynamic DSS [34] supports the ISI indicator, a real and infinite number that measures the level of symbiosis, considering environmental impacts and amount of reused materials on a scale that increases continually as the level of symbiosis increases and new organizations are added to the park.  
Changes in the symbiotic level within a network could be used to support practitioner decisions about processes, for instance. By-product flow depends on the amount of exchanged material within an EIP, called inbound by-product (AiP), and the amount of material that is not re-used and leaves the park, called outbound by-product (AoP). The higher the internal flow of the inbound by-product and the lower the external flow of by-product, the higher is the value of the symbiosis indicator (i.e., most of the by-products generated in the EIP are reused within the park itself and little by-product leaves the park). Conversely, if the external flow of by-products is high, the level of symbiosis will be lower. Figure 2 outlines the rationale of the indicator.  
Figure 2: Inbound (AiP) and outbound by-products (AoP). Adaptation from [PERSON] et al. [18].
## 4 System and Stages in Digestate Production  
### Digestate Production Systems  
Digestate production systems, closely linked to biogas production, vary in design and scale depending on input materials and geography. These systems range from household digesters [111] to municipal-scale treatment plants [101; 102]. Despite differences in specific processes, digestate production follows four main stages: feedstock storage and conditioning, biogas and digestate production, digestate separation, and post-treatment.  
As shown in Figure 3, organic waste is collected--either onsite (manure for farm plants) or via transportation (20 km average for municipal and food waste plants). Upon arrival, feedstock undergoes storage and pretreatment, particularly in municipal solid waste facilities, where sorting, shredding, blending, and hydrothermal treatment optimize biogas yield [99]. Co-digestion enhances both biogas and digestate composition.  
In the anaerobic digestion (AD) stage, biogas and digestate are produced, refined into biomethane, electricity, or heat. Raw digestate may be directly applied to land [113; 114] or further processed into refined products [111].  
Digestate separation is essential for product refinement. It involves gravity-based methods (settling ponds and decanters) [106], filtration techniques [88], ultrafiltration, and reverse osmosis. Mechanical separation (centrifugation and pressing) is most widely used due to its efficiency and cost-effectiveness. The separated solid fraction (SF) and liquid fraction (LF) can be managed as waste, fertilizers, or further processed.  
Post-treatment methods for digestate depend on the fraction being processed and the intended final product. The solid fraction (SF) can undergo traditional vermicomposting or window composing, producing nutrient-rich organic fertilizers. Alternatively, it can be dried into powdered fertilizers or pellets for easier application. In less common cases, SF is converted into energy products such as biogas, bio-oil, or syngas through gasification, hydrothermal carbonization, or pyrolysis. The liquid fraction (LF) can either be reintroduced into the digester to regulate moisture levels or further processed to recover valuable nutrients, such as struvite and ammonia, or through membrane filtration. These treatments allow for safe water reuse in irrigation while maximizing resource efficiency.
# An AMR-Based Liquid Film Simulation with Surfactant Transport Using PLIC-HF Method
## 3 Numerical Methods  
### Spatial Discretization on Staggered Grid  
A staggered grid system was used in the present research for spatial discretization, as shown in Figure 3. The use of the staggered grid can avoid the problem of velocity and pressure decoupling and can provide better numerical stability. In a two-dimensional uniform staggered cell \(\left[i-\frac{1}{2},i+\frac{1}{2}\right]\times\left[j-\frac{1}{2},j+\frac{1}{ 2}\right]\), \(\Delta x=\Delta y\), which means that the grid spacing is the same in both the x-direction and y-direction. The scalar values pressure \(p\), density \(\rho\), volume fraction \(\phi\), level set function \(\psi\), and curvature \(\kappa\) are defined on the cell centers \((i,j)\). The components of the vectors, such as the velocity \((u,v)\), are defined on the corresponding cell faces.
# A New COVID-19 Detection Method Based on CSK/QAM Visible Light Communication and Machine Learning
###### Abstract  
This article proposes a novel method for detecting coronavirus disease 2019 (COVID-19) in an underground channel using visible light communication (VLC) and machine learning (ML). We present mathematical models of COVID-19 Deoxyribose Nucleic Acid (DNA) gene transfer in regular square constellations using a CSK/QAM-based VLC system. ML algorithms are used to classify the bands present in each electrophoresis sample according to whether the band corresponds to a positive, negative, or ladder sample during the search for the optimal model. Complexity studies reveal that the square constellation \(N=2^{2}\times 2^{2}\), \((i=3)\) yields a greater profit. Performance studies indicate that, for BER = \(10^{-3}\), there are gains of \(-10\) [dB], \(-3\) [dB], \(3\) [dB], and \(5\) [dB] for \(N=2^{2}\times 2^{2}\), \((i=0,1,2,3)\), respectively. Based on a total of 630 COVID-19 samples, the best model is shown to be XGBoots, which demonstrated an accuracy of 96.03%, greater than that of the other models, and a _recall_ of 99% for positive values.  
COVID-19; CSK; QAM; VLC; BER +
Footnote †: journal: _Sensors_**2023**, 23, 1533. [[https://doi.org/10.3390/s23031533](https://doi.org/10.3390/s23031533)]([https://doi.org/10.3390/s23031533](https://doi.org/10.3390/s23031533))
# Programmable Deterministic Zero-Copy DMA Mechanism for FPGA Accelerator
## 4 Performance Evaluation
### Prototype System  
We implemented the prototype system on two types of FPGA boards, Virtex-7 X690T and X2000, respectively. The host configuration is an Intel(R) Core(TM) i7-10510U CPU, 1.8 GHz, 8 G RAM, 8 Cores, and the host OS is KylinOS, Linux 4.18.0-25-generic. The version of the zero-copy driver comes from dpdk-19.11. We instantiate a multi-queue FPGA accelerator that both configures one interface with various hardware queues and writes the configuration parameters of the accelerator to specific registers. The FPGA resources required for deploying various queues is shown in Figure 5.  
The zero-copy driver still follows the logic of DPDK. We made the main modifications in the following aspects:  
1. [label=()]
2. Added the device information corresponding to the FPGA board to the device driver list of DPDK. Provide driver capability for this device through PCI device registration;
3. Implemented device probe and release functions and mapped hardware addresses in the probe function;
4. Created the Ethernet device object of DPDK, read the parameters in the device register, and instantiated the object, mainly including the configuration of device functions, initialization of transceiver queue, port enable, Link, and UP/DOWN;
5. Implemented the device's bulk receives and sends to support packet sending and receive functions for each enabled network interface and hardware queue.  
Furthermore, the programmable multi-queue module in the accelerator is the most central component to achieve multiple parallels, programmable and deterministic. In the module, the Agent programs the flow table with the OpenFlow, and the Repeater working logic can be described by Algorithm 1.
## 1 Introduction
### Suggestions  
Considering that the ramp rate control is a quasi-steady-state phenomenon and that the electrical quantities within the modern converters are measured in the time frame of \(\upmu\)s, we firstly suggest that \(P_{\text{el}}(t)\) represents the average active power over the previous 200 ms (10 cycles) [10] in the case of DRES, since they will deal with rather fast evolving ramp rates. In the case of BESS it is suggested that this averaging window in increased to 1 s, since the BESS will deal with slower ramps in the time frame of 1-10 min.
## 7 Conclusions  
In this article, a low-complexity JCSCR was proposed and introduced into the CFSO communication system. The proposed algorithm only used addition and subtraction of real absolute values in the preprocessing stage to remove the signal modulation phase, reducing system complexity while avoiding the appearance of additional noise generated by cross-terms. In addition, a joint compensation error was proposed for use in the carrier-compensation stage to further reduce the complexity of the overall algorithm scheme. The number of multipliers required in the operation of the JCSCR in the complexity analysis was less than \(\frac{1}{3}\) of the 4 th-PD-VV. The results of the simulation demonstrated that the proposed algorithm can achieve high-precision carrier recovery with much lower complexity, even with large laser linewidths. Moreover, the FOE accuracy of the proposed scheme was improved by one order of magnitude for the same symbol-block length of FOE under strong turbulence. Compared with the traditional 4 th-PD-VV scheme, the transmitting power required at BER = 3.8 \(\times\) 10\({}^{-3}\) was reduced by 3.6 dB, 2.2 dB, and 1.2 dB for the strong-, medium-, and weak-turbulence states, respectively. The experimental results further verified the robustness and feasibility of the proposed scheme in the QPSK CFSO system. In addition, improvements of receiver sensitivity were also experimentally validated. The proposed scheme can significantly improve free-space link-transmission capacity and ensure communication reliability, which is expected to provide technical support for a large amount of high-speed satellite observation data transmission in real time and satisfy the development demand for a next-generation space optical communication system with a large capacity and high speed.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Scheme** & **Real Multiplication** & **Real Addition** \\ \hline
4 th-PD-VV & 12N1 + 8N2 + 6 & 8N1 + 6N2-4 \\ JCSCR & 4N1 + 4 & 15N1 + 2N2-3 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Computational complexity of different schemes.  
**Author Contributions:** Conceptualization, X.T.; methodology, X.T. and L.W.; software, X.T. and Y.L.; writing--original draft preparation, X.T.; writing--review and editing, X.T., S.C. and W.Z.; project administration, L.W. and Z.Z.; funding acquisition, Z.Z. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was supported by the National Natural Science Foundation of China (General Program) (Grant No. 62271084) and the Joint Funds of the National Natural Science Foundation of China (Grant No. U22B2009).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The data presented in this study are available upon reasonable request from the corresponding authors.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 5 Experimental Validations
### Model Parameter Identification Results  
An experimental analysis was conducted using the algorithm introduced in Section 4, as shown in Figure 5. The results depict the parameter identification of the second-order equivalent circuit model for cell 1 during the first cycle using SSA. The five subfigures correspond to the variation of the parameters R\({}_{0}\), R\({}_{\text{p1}}\), C\({}_{\text{p1}}\), R\({}_{\text{p2}}\), and C\({}_{\text{p2}}\) at every 5% SOC of the battery pack, respectively. R\({}_{0}\) stays relatively smooth throughout the discharge process, the two parameters R\({}_{\text{p1}}\) and R\({}_{\text{p2}}\) show some fluctuations at different SOCs, and the two capacitance parameters C\({}_{\text{p1}}\) and C\({}_{\text{p2}}\) also show fluctuations during SOC variations, which indicate that the dynamic characteristics of the battery changes with the change of SOC [16].  
Figure 6a shows the cumulative error during parameter identification at every 5% SOC of the battery pack. The cumulative error is generally at a low level in the range of 8% to 5% SOC, staying within 10 V. However, it rises sharply at the last 5% SOC, and the cumulative error reaches 68.79 V. This is consistent with the sharp change of the R\({}_{0}\) parameter at a low SOC in Figure 5, which indicates that the identification of the model parameters becomes more difficult, and the error increases when the battery is discharged too close to 0%. This is due to the instability of the battery performance at a low SOC [17].  
Based on the results of parameter identification, the second-order model terminal voltage under each sampling point is calculated on the second-order model of the lithium-ion battery and compared with the actual voltage, and the absolute value in the error between the two is also given, as shown in Figure 6b. The small error between the calculated model terminal voltages and the actual voltages indicates that the parameters identified using SSA for the second-order equivalent circuit model of the lithium-ion battery are able to better characterize the voltage characteristics of the battery. The error plot shows that the error is small at most sampling points, and the error is kept around 0.01 V. However, the error increases significantly during the low SOC discharge process, especially near the end of the discharge, which is consistent with the error trend of the parameter identification at low SOC. Therefore, this paper intercepts the data of the battery pack with SOC greater than 5% for analysis.  
Figure 5: Parameter identification results.
## 2 Materials and Methods
### Two-Fluid Model
#### 2.2.3 CFD Model Setup  
A 3D mesh geometric model was established. The model starts from 50 m upstream of tunnel inlet to tunnel outlet, including the reservoir, pressurized tunnel, non-pressurized tunnel, air ducts no. 1 to no. 3 and aerator no. 1 to no. 4. The mesh is generated by the ICEM-CFD software in ANSYS15.0. As can be seen in Figure 2, the hexahedral grid is mainly used. The mesh refinements are carried out for the key parts including the aerators and air ducts. Furthermore, three sets of meshes for the 100% gate opening ratio are generated. The cell counts are 720,000, 2.03 million, and 3.23 million, and the corresponding equivalent grid sizes are 0.9816 m, 0.6940 m, and 0.5947 m, respectively.  
Under the Euler-Euler framework, water phase is set as the primary phase (or continuous phase), while air phase is set as the secondary phase (or dispersed phase). The inlet boundary of reservoir is set as pressure inlet with a triangular distribution of hydrostatic pressure. The pressure value of this inlet is realized by the DEFINE_PROFILE macro in the User-Defined-Function interface of the commercial solver Fluent 15.0. The pressure boundary condition of tunnel outlet and other air inlet are set as fix-value with the atmosphere pressure (0 Pa). The tunnel walls are all set as no-slip wall boundary with roughness height of 0.001 m. To speed up the calculation, the initial volume fraction of water is set as 1 for the reservoir and pressurized tunnel.
# Ni-Al Alloys as Alternative EUV Mask Absorber
## 3 Film Characterization
### Film Morphology
Figure 4: (**a**) TEM images of Ni\({}_{\mathrm{3}}\)Al, NiAl and Ni\({}_{\mathrm{2}}\)Al\({}_{\mathrm{3}}\) film cross-section; (**b**) Comparison crystallinity of Ni\({}_{\mathrm{3}}\)Al, NiAl and Ni\({}_{\mathrm{2}}\)Al\({}_{\mathrm{3}}\). Compared to the reference Ni (111) peak, the peak of as-deposited Ni\({}_{3}\)Al appears much broader, indicating smaller Ni\({}_{3}\)Al crystal grains. As-deposited NiAl exhibit a peak more similar to the reference Ni film. For as-deposited Ni\({}_{2}\)Al\({}_{3}\), no clear XRD peaks can be observed, indicating poor condition for diffraction. Possible causes include nano-crystallinity, more variation in average grain size, and random orientation of the crystal lattice. After thermal loading and cooling down to room temperature, the peak position of both Ni\({}_{3}\)Al and NiAl remains the same, indicating no crystalline phase change occurred. The peak width has decreased slightly, suggesting the crystal grains have grown. The Ni\({}_{2}\)Al\({}_{3}\) peak however, has shifted and increased significantly in intensity after thermal loading, indicating recrystallization into a different crystalline phase. Figure 5b confirms Ni\({}_{2}\)Al\({}_{3}\) recrystallization started after 200 \({}^{\circ}\)C. Taking the IS-XRD results into consideration, it is unlikely that a mask absorber comprising of only Ni and Al, will remain amorphous under working conditions. However, we have achieved a reduction in crystal grain size by alloying Ni with Al under unequal nominal Ni:Al ratio.
# An FPGA-Based Four-Channel 128k-Point FFT Processor Suitable for Spaceborne SAR
## 3 Hardware Implementation of the FFT Processor
### Strategy of Fixed-Point FFT Word Length Adjustment  
It is well known that the width of a single-precision floating-point number is 32 bits, including 1 sign bit, 8 exponent bits, and 23 decimal bits. The data type used in this study is a 32-bit fixed-point number, including 1 sign bit and 31 decimal bits. Comparing the two data types, it can be seen that the floating-point numbers can represent a wide range of data because of their exponent bits, but they are not as accurate as the 32-bit fixed-point number. For example, when representing a number near 0.5, the floating-point number can be accurate to approximately \(10^{-7}\), while the 32-bit fixed-point number can be accurate to approximately 4 \(\times\)\(10^{-10}\). In fact, if the representation range of the data is known, we can specify the position of the decimal point in the fixed-point number and place the representation range of the fixed-point number near the data range, that is, artificially realize the exponent in the floating-point number. Combined with the analysis in Section 1, a 32-bit fixed-point number has the advantages of higher data accuracy, simpler implementation circuits, and shorter processing delays than a single-precision floating-point number. However, the representation range of a fixed-point number is limited, and overflow is prone to occur in the process of addition and multiplication. The word length must be adjusted after each operation to prevent overflow.  
The bit width before the data flows into the operation stage is assumed to be DATA_WIDE, which indicates the bit width of the real and imaginary parts of the data. When the data pass through the radix-2 butterfly unit, they participate in addition and subtraction operations, and the data bit width is extended by one bit to ensure that the calculation result does not overflow. When data are multiplied with a constant factor and a non-constant factor, since the modulus of the factor is one, the change ratio of the real part and the imaginary part of the data is up to \(\sqrt{2}\) times, so the data bit width only needs to be extended by 1 bit and it can ensure that the calculation does not overflow. A single-channel 32k-point FFT requires a total of five radix-\(2^{3}\) units to be cascaded. Figure 10 shows the data word length adjustment strategy of a one-stage radix-\(2^{3}\) butterfly unit.  
Figure 9: Schematic diagram of pipelined processing of each radix-2 butterfly unit (BF2).