esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experimental Results and Analysis
### Hyperledger Fabric, Results and Analysis  
Figure 5 shows that the peak and initial throughput of Hyperledger Fabric is more or less indifferent to message size (2 KB and 10 KB) because Apache Kafka is the underlying messaging protocol. However, throughput reduces after the first 35-45 s, struggling under sustained load. Table 5 shows that peak throughput is significantly larger than average  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Size** & **Block Size** & **Max (tps)** & **Average (tps)** \\ \hline \multirow{2}{*}{2 KB} & 2 MB & 337 & 164 \\  & 4 MB & 472 & 120 \\ \hline \multirow{2}{*}{10 KB} & 2 MB & 76 & 31 \\  & 4 MB & 58 & 29 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Tendermint throughput results, averages and maximums for 2 KB and 10 KB transaction sizes.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Test Scenario** & **Type** & **Max (tps)** & **Average (tps)** \\ \hline \multirow{2}{*}{2 KB} & Calldata & 622 & 360 \\  & Memory & 620 & 323 \\  & State & 205 & 182 \\ \hline \multirow{3}{*}{10 KB} & Calldata & 139 & 125 \\  & Memory & 139 & 120 \\ \cline{1-1}  & State & 43 & 39 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Quorum throughput results, averages and maximums for 2 KB and 10 KB transaction sizes.  
performance, and that Hyperledger Fabric handles large increases in message-size well. As noted by [PERSON] et al. [36] and [PERSON] et al. [40], Hyperledger Fabric can utilise channels to run multiple \"contracts\" in parallel and in so doing increase transaction throughput. We increased the number of channels but our experimental results did not reflect this finding. This may be because of limited computing power. To test this assumption, tests were re-run with a larger machine as the orderer and we observed that performance increases 3% for the single channel case, and 18% for the four channel case, indicating that channels have some overhead, but result in increased throughput when used with more computing power. As noted by [PERSON] et al. [36], the endorsement policy is identified as a performance bottleneck, however, our tests show no significant difference between the policies \"majority\" and \"any\". Comparing our results to the literature [36; 38; 39; 40; 42], and Hyperledger's own Caliper tests [45], similar performance is observed. [PERSON] et al. [37] present a study of the bottlenecks in Hyperledger Fabric, which can be summarized as message communication overhead internal to its architecture.
## 3 Results
### Results Based on Threshold Segmentation  
The results of segmenting 10 images taken from different environments and different color spaces, as well as the effective segmentation area data, are shown in Table 5. The segmentation results of four images are shown in Figure 14. According to Table 5, the segmentation accuracy of HSV-based threshold segmentation on different images was relatively stable, with an average segmentation accuracy of 86.12%, which was 7.91% higher than the result from RGB-based threshold segmentation, and 10.01% higher than the result from LAB-based threshold segmentation, respectively. In Figure 14, it can be seen that the threshold segmentation based on HSV accurately segmented the plastic film area, which was close to the manually calibrated area range. The threshold segmentation based on RGB did not have clear boundaries in the internal area of the plastic film, making it difficult to accurately segment the internal contours. While the threshold segmentation based on LAB could accurately segment the internal contour of the plastic film there were, however, many noise points in the image.  
Figure 12: The [EMAIL_ADDRESS] value change curves of different models.  
Figure 14: Segmentation results in images based on different color spaces.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Index** & **Pixels** & **TP (HSV)** & **Accuracy** & **TP (RGB)** & **Accuracy** & **TP (LAB)** & **Accuracy** \\ \hline
1 & 3012153 & 2800914 & 92.98\% & 2832167 & 94.02\% & 2659164 & 88.28\% \\
2 & 3355487 & 3012671 & 89.78\% & 2801252 & 83.48\% & 2548802 & 75.96\% \\
3 & 3087489 & 2645590 & 85.69\% & 2125831 & 68.85\% & 2728891 & 88.39\% \\
4 & 3293901 & 2571284 & 78.06\% & 2598723 & 78.89\% & 2236581 & 67.90\% \\
5 & 3401765 & 2993142 & 87.98\% & 2182361 & 64.15\% & 2347390 & 69.01\% \\
6 & 2967825 & 2613487 & 88.06\% & 2491673 & 83.96\% & 2239916 & 75.47\% \\
7 & 3097104 & 2547923 & 82.27\% & 2087631 & 67.41\% & 2679143 & 86.50\% \\
8 & 3260913 & 2778392 & 85.20\% & 2918763 & 89.51\% & 2089894 & 64.09\% \\
9 & 3178657 & 2785319 & 87.62\% & 2709341 & 85.24\% & 2406246 & 75.70\% \\
10 & 3310468 & 2761925 & 83.43\% & 2214305 & 66.89\% & 2309133 & 69.75\% \\ Avr & & & 86.12\% & & 78.21\% & & 76.11\% \\ \hline \hline \end{tabular}
\end{table}
Table 5: Segmentation results based on different color spaces.  
Figure 13: Scatter plot of different models between detection speed and accuracy.
# Low-Complexity Channel Codes for Reliable Molecular Communication via Diffusion
## 2 Materials and Methods
### Types of Pulses  
For the proposed MC system, in addition to the usual non-return to zero (NRZ) pulses exemplified in Figure 2a, Manchester pulses are also considered. These types of pulses, as in Figure 2b, have their wave boundaries always between 1 and \(-1\); therefore, the decisions are usually taken in the middle of each bit [30]. These features will provide more robustness to the MC communication system in the experimental implementation.  
Figure 1: Example illustrating the expected and observed concentrations for a diffusion-based MC channel, assuming number of molecules (\(N_{mcalc}\)) = 10,000, \(d\) = 0.3 \(\upmu\)m, \(D\) = 450 \(\upmu\)m\({}^{2}\)/s, and \(a_{rx}\) = 50 nm.
## 3 Results
### Geometrical Deviations Evaluation  
#### 3.1.1 Roundness Deviations  
The deviation of the constant Z strategy and circular pocket strategy is shown in Figure 12, and the deviation of the constant stepover-pocket auto border strategy and spiral strategy is shown in Figure 13, which represents a 5 th measured circle from BS Plane at a scale of 100:1. The feature was created by LSQ method. The resulting shape of the curves is caused by interpolation in SolidCAM.  
The roundness values for the single measured circular paths are shown in Table 3. The results show that the test sample produced by the constant Z strategy has the smallest deviations in shape.  
Figure 12: Roundness deviations (**a**) Constant Z strategy; (**b**) Circular Pocket strategy.  
Figure 13: Roundness deviations (**a**) constant step over–pocket auto border strategies; (**b**) spiral strategy.  
The results show that when the UPR filter is used, the roundness value is approximately \(\frac{3}{4}\) smaller, and the standard deviation is 10 times larger than without the filter used. Roundness deviations using the UPR filter are displayed in the form of a graph in Figure 17.
## 3 Preliminaries
### Verifiable Matrix-Vector Multiplication (VerM)  
Verifiable computation is a computational model that allows the client to delegate computational tasks to a computational entity and then to verify the correctness of the computed result without re-executing the entire computation task. The schemes in [32; 33] propose a critical observation of matrix-vector multiplication, and the scheme in [9] proposes a solution for publicly verifiable matrix-vector multiplication computation.  
The following describes the publicly verifiable matrix-vector multiplication scheme that we use based on [9], which consists of four algorithms: \(VerM=(Setup,\textit{Prepare},\textit{Compute},\textit{Verify})\). Table 3 shows the data size in the scheme, and the details are given below:  
* \(Setup(\lambda)\to pp\): The security parameter \(\lambda\) is taken as input, and a \(\lambda\)-bit large prime number \(q\) is chosen. Let \(\mathbb{Z}_{q}\) be a finite field and \(\mathbb{G}=\langle g\rangle\) be a cyclic group of generator \(g\) and order \(q\). Let \(m,d>0\) be integers, and then output \(pp=(q,m,d,g)\).
* \(Prepare(\mathbf{F})\rightarrow\textit{ver}\): The function owner takes their matrix \(\mathbf{F}\in\mathcal{F}\), where \(\mathcal{F}=\mathbb{Z}_{q}^{m\times d}\) is the set of all \(m\times d\) matrices over \(\mathbb{Z}_{q}\) as input to generate the public verification data _ver_ (once and for all). A vector \(\mathbf{t}=(t_{1},\dots,t_{m})\stackrel{{\mathsf{S}}}{{\leftarrow}} \mathbb{Z}_{q}^{m}\) is uniformly sampled, and \(\mathbf{s}=(s_{1},\dots,s_{d})=\mathbf{t}\cdot\mathbf{F}\) is computed. The obtained output is \(\textit{ver}=(\mathbf{T},\mathbf{S})\), where \(\mathbf{T}=g^{t}=(g^{t_{1}},\dots,g^{t_{m}})\), \(\mathbf{S}=g^{s}=(g^{s_{1}},\dots,g^{s_{d}})\).  
\(Compute(\mathbf{F},\mathbf{x})\rightarrow\mathbf{y}\): The function \(\mathbf{F}\in\mathcal{F}\) and the input \(\mathbf{x}=(x_{1},\dots,x_{d})^{\top}\in\mathbb{Z}_{q}^{d}\) are taken as inputs. The result \(\mathbf{y}=\mathbf{F}\cdot\mathbf{x}=(y_{1},\cdots,y_{m})^{\top}\) is obtained.
* \(Verify(\mathbf{ver},\mathbf{x},\mathbf{y})\rightarrow(0,1)\): The public verification data _ver_, the input \(\mathbf{x}\) and the result \(\mathbf{y}\) are taken as input. This convinces the user to accept the result if and only if \[\prod_{i=1}^{d}g^{s_{i}\cdot x_{i}}=\prod_{i=1}^{m}g^{t_{i}\cdot y_{i}}\] (7) If the equation is valid, then the output is 1; otherwise, the output is 0. The correctness of the verification is as follows: The left side: \[\prod_{i=1}^{d}g^{s_{i}\cdot x_{i}}=\prod_{i=1}^{d}g^{(\sum_{j=1}^{m}t_{i}^{j} \mathbf{F}_{i\mathbf{j}})x_{i}}=g^{\sum_{i=1}^{d}\sum_{j=1}^{m}t_{i}^{j} \mathbf{F}_{i\mathbf{j}}x_{i}}.\] (8) The right side: \[\prod_{i=1}^{m}g^{t_{i}\cdot y_{i}}=\prod_{i=1}^{m}g^{t_{i}\cdot(\sum_{j=1}^{d }\mathbf{F}_{i\mathbf{j}})}=g^{\sum_{i=1}^{m}\sum_{j=1}^{d}t_{i}^{j}\mathbf{F}_ {i\mathbf{j}}}.\] (9) If \(\mathbf{y}=\mathbf{F}\cdot\mathbf{x}\), then it is easy to verify that Equation (7) holds.
# Decimal Multiplication in FPGA with a Novel Decimal Adder/Subtractor
###### Abstract  
Financial and commercial data are mostly represented in decimal format. To avoid errors introduced when converting some decimal fractions to binary, these data are processed with decimal arithmetic. Most processors only have hardwired binary arithmetic units. So, decimal operations are executed with slow software-based decimal arithmetic functions. For the fast execution of decimal operations, dedicated hardware units have been proposed and designed in FPGA. Decimal multiplication is found in most decimal-based applications and so its optimized design is very important for fast execution. In this paper two new parallel decimal multipliers in FPGA are proposed. These are based on a new decimal adder/subtractor also proposed in this paper. The new decimal multipliers improve state-of-the-art parallel decimal multipliers. Compared to previous architectures, implementation results show that the proposed multipliers achieve 26% better area and 12% better performance. Also, the new decimal multipliers reduce the area and performance gap to binary multipliers and are smaller for 32 digit operands.  
decimal multiplication; decimal adder parallel multiplication; excess-3 coding; FPGA +
Footnote †: journal: Computer Vision and Pattern Recognition  
1
# High-Resolution Pixel LED Headlamps: Functional Requirement Analysis and Research Progress
## 3 Research Progress of High-Resolution Headlamps
### Structure and Performance of Existing High-Resolution Headlamps
The horizontal angle of the left front light is \(-28^{\circ}\)-\(5^{\circ}\), the angle in the vertical direction is \(-2^{\circ}\)-\(6^{\circ}\), the resolution of each pixel is 0.25\({}^{\circ}\), the maximum illuminance value at 25 m is 110 lx, and the total luminous flux is 5700 lm. Since the EVIYOS chip has an aspect ratio of 1:1, in most resolutions at present, in order to cover the recommended 40\({}^{\circ}\), the left and the right headlamps can be superimposed asymmetrically [46], thus the 10\({}^{\circ}\) vertical illumination range VFOV is determined by an aspect ratio of light distribution of at least 4:1. [PERSON] et al. proposed the concept of deforming projection to reduce the final adjustment work and increase the illuminance at the same time [47; 48; 49]. [PERSON] et al. tested the feasibility of anamorphic lenses through three modules, two of which are rotationally symmetrically applied to most of the light emission; the third lens achieves a 2:1 ratio, and the resolution of each pixel is 0.6\({}^{\circ}\)[50]. With a luminous flux of 4423 lm and a maximum illuminance of 144 lx at 25 m, the experiment shows that the horizontal illumination ranges of the left and the right headlamps are \(-20^{\circ}\) to \(10^{\circ}\) and \(-10^{\circ}\) to \(20^{\circ}\), respectively, and the two headlamps are superimposed asymmetrically. When the HFOV's illuminance is increased by four times, the horizontal and the vertical pixels are also increased, and the desired light distribution can be achieved by projecting the field of view with a half-pixel offset in horizontal and vertical directions. Figure 7: Simulated view of headlamp light distribution with Osram’s EVIYOS chip. CREE exhibited an LED car headlight with 4000 independently controlled pixels in cooperation with Valeo at the 2019 Consumer Electronics Show but did not elaborate on related products [51]. [PERSON] announced the launch of Osram's second-generation EVIYOS smart car headlamps [52]. Compared with the first-generation EVIYOS, the second-generation EVIYOS has 25,600 independently controllable pixels, and the entire area is only about 40 mm. Due to the size limitation of the LED chip, the existing matrix headlights cannot achieve high pixels, and it is difficult to achieve the requirement of a resolution less than 0.05 degrees, therefore making it impossible to implement the projection function. Only some simple functions such as anti-glare and corner assist can be realized. Pix LEDs are a relatively innovative solution that uses the intelligent ADB system as a light source. However, due to the limitations of current technology and other factors, it is currently difficult to achieve its mass production.
## 1 Introduction
Acoustic emission (AE) can be used to detect damage and deformation of the micro- and meso-structure of concrete, and many studies have shown that acoustic emission is an efficient way of analyzing the effect of admixture in concrete. [PERSON] et al. [11] studied the mechanism of the reinforcement of aligned SFs on the mechanical properties of cement-based composite by AE signal analysis, and their results showed that the concrete with aligned SFs had more AE signals produced by fiber pull-out. [PERSON] et al. [12] studied the reinforced mechanism of concrete with basalt and SFs by comparing their AE counts and duration. The test results showed that there were satisfactory correlations between the curve characteristics of AE parameters and the failure loads of specimens. The literature authorized by [PERSON] et al. [13] focused on the effect of chemical coating and shapes of SFs on the AE characteristics monitored during the fracture process of multifunctional concrete. Their results showed that AE analysis could be used for interpretation of the fracturing stage and characterization of the fracture mode. An experimental study conducted by [PERSON] et al. [14] demonstrated that micro-damage of CF-cemented composite can be detected by AE. [PERSON] et al. [15] monitored AE activity during three-point bending tests on concrete with CB, and the effect of CB on micro-damage of concrete was analyzed. The above studies have demonstrated that incorporating additives like fibers changes the mechanical properties of cementitious materials, including CGCMs, significantly, and the AE technique is an effective measure for characterizing damage and fracture in cemented materials. However, most research has focused on single additive materials, and comparative studies between different fillers are scarce. Every piece of research has focused on the effect of fibers on the mechanical characteristics of CGCMs. To address this gap, this paper conducts an experimental study on the AE characteristics of CGCMs with various additive materials, including CFs, SFs, and CB, with plain CGCM specimens prepared for comparison. The effects of different additives and curing ages on compressive strength, mechanical properties, and failure mechanisms are analyzed, and the damage mechanisms are further examined using SEM. The test results can provide valuable references for enhancing the supporting ability of backfill materials and the comprehensive utilization of coal-based solid waste.
## 4 Discussion  
The multisystem integration is expected to ensure the positioning performance in constrained visibility environments. However, the benefits from multisystem combination for positioning and navigation are limited under extremely harsh environments due to the distinct time scales and receiver-specific hardware delays among different GNSSs. For example, the position solutions are unsolvable if the visible satellites only include a GPS satellite, a GLONASS satellite, a Galileo satellite, a BDS-2 satellite, and a BDS-3 satellite, as the additional ISB parameters need to be simultaneously estimated. The characteristic analysis indicates that the daily ISBs have a good long-term stability; thus, the predicted high-accuracy ISBs (obtained in advance) can be used as a priori constraints to improve the benefits from multisystem combination for real-time positioning and navigation under extremely harsh environments. The four-system integrated SPP considering a priori ISB constraints with a prediction period of a day can provide a positioning accuracy better than 1.6 and 3.8 m and 2.9 and 7.2 m in the horizontal and vertical directions at an availability of approximately 100.0% and 90.0% under the cutoff elevations of 40\({}^{\circ}\) and 50\({}^{\circ}\), respectively, which is of great significance for the satellite navigation and low-accuracy positioning users.
# Intelligent Monitoring of Tunnel Fire Smoke Based on Improved YOLOX and Edge Computing
## 1 Introduction
The primary motivation for this analysis stems from the unique challenges of fire detection in tunnel environments, including restricted airflow, complex lighting conditions, and high background interference, which make traditional detection methods less effective. To tackle these challenges, this study systematically examines feature engineering in tunnel scenes and fine-tunes the structure of the target detection model to enhance its adaptability to tunnel-specific conditions. By optimizing the utilization of limited hardware resources on edge devices, the proposed approach leverages computational efficiency and real-time processing capabilities while minimizing model parameters and computational complexity. To validate the effectiveness of this approach, extensive experiments were conducted on Nanjing Metro Line 3, where real-world deployment confirmed that the proposed intelligent tunnel fire monitoring method effectively balances detection speed and accuracy. The results demonstrate that the optimized model not only enhances detection precision and reduces false alarms but also fortifies the model's resilience in complex tunnel environments. This study provides valuable insights into the application of edge computing and deep learning technologies for real-time fire detection, offering a scalable and efficient solution for tunnel safety monitoring. In summary, the structure of this paper is as follows: Section 2 introduces the optimized feature engineering methods for flame and smoke detection, including frequency domain feature extraction and wavelet transform optimization, the fuzzy features and fuzzy loss method summarized during the experiments, and data optimization strategies based on the distribution characteristics of the dataset. Section 3 proposes an improved lightweight real-time detection algorithm based on YOLOX and evaluates its performance by comparing network parameter size and other key metrics against the baseline YOLOX model. Section 4 focuses on deploying the proposed model on edge computing devices using knowledge distillation techniques, aiming to reduce model size while improving inference speed on edge devices. Section 5 validates the feasibility and effectiveness of the proposed approach through experiments conducted in real construction site environments for smoke and flame detection. Finally, Section 6 presents the main research conclusions and discusses future research directions.
# Advances in Improving Energy Efficiency of Fiber-Wireless Access Networks: A Comprehensive Overview
## 4 Energy Efficiency Analyses of Radio-over-Fiber Networks
### Approaches for Improving the Energy Efficiency of Cloud Radio Access Networks
#### 4.2.2 Techniques for Improving Energy Efficiency in Cloud Radio Access Networks
Therefore, the authors in ref. [83] proposed a power model that maximizes the network energy efficiency through the estimation of the optimal number of active virtual machines within the BBU server (Table 3). To address the unsustainable growth of energy consumption caused by the increased mobile traffic, the C-RAN architecture separates the BBU from the RRH in the BSs and consolidates the BBUs into a BBU pool to allow low-utilized BBUs to enter sleep mode during decreased network activity, thereby reducing energy consumption. However, when a BBU is in sleep mode, the RRHs connected to it must be switched to another BBU, which can impact the QoS for those RRHs. To address this issue, the authors in ref. [84] have proposed a deep reinforcement learning (DRL) based BBU aggregation scheme that ensures both minimal energy usage of BBU and minimal migration of RRH traffic at the same time. Furthermore, several heuristic algorithms are introduced to assist with the DRL training. The proposed heuristic-assisted DRL (HA-DRL) approach is evaluated numerically and is found that the proposed approach outperforms the benchmarks by achieving the lowest cost for all scenarios. Additionally, the authors have found that the DRL agent is able to achieve better results by trading BBU power consumption for RRH traffic migration. The utilization of C-RAN has proven to be effective in improving network performance. This gain is due to the smart management of RRHs with regard to power consumption and on/off operation modes. However, conventional resource allocation techniques maximize network efficiency without taking into account the overhead of RRH switching in adjacent time intervals. Therefore, in ref. [85], the authors aim to optimize EE while adhering to per-RRH transmission power and user data rate constraints. To achieve this, authors have formulated the EE problem as a Markov decision process (MDP) and have implemented DRL techniques to gain cumulative EE rewards. Simulation results showed that a proposed double deep Q network (DDQN)-based framework outperforms traditional approaches due to its ability to consider future effects of actions, and the ability to overcome the issue of action overestimation. This leads to a significant improvement in EE compared to benchmarks. The presented analyses of related works indicate that many challenges related to improving EE in FiWI networks based on the C-RAN concept have not been addressed. It can be observed that further research activities related to the development of advanced architectures, signalizations, protocols and scheduling algorithms must be performed in order to make such FiWi networks more energy efficient.
## 4 Experiments and Results  
In this section, the basic settings including datasets, implementation details, evaluation metrics and comparison methods are first introduced. Then, the experimental results are presented and discussed, in which the experiments include two parts: (1) comparison of short-term OD prediction performance among the proposed PIANet and the three comparison methods; and (2) ablation studies.