esa-sceva/satcom-chunk-collection huggingface dataset:
# Road Obstacle Detection Method Based on Improved YOLOv5
## 4 Results
### Experimental Results  
#### 4.5.1 Ablation Experiments  
To verify that the improvements made to the YOLOv5-EC3F algorithm enhance the performance of road obstacle detection, this paper conducts experiments based on the YOLOv5 algorithm. The experimental results for the test set are presented in Figure 9.  
The experimental results indicate that substituting the SPPF module with the C3 SPPF module leads to a 1.7% decrease in accuracy compared to the original algorithm. However, both recall and mAP see improvements of 6.9% and 2.9%, respectively. This suggests that the refined C3 SPPF module enhances the YOLOv5-EC3F algorithm's obstacle detection capabilities, particularly in leakage detection, while also improving its capacity to perceive detailed information.  
The implementation of the Multi-Scale Feature Fusion (MFF) module resulted in a marginal decrease in accuracy; however, it notably enhanced the recall rate and mAP by 2% and 0.2%, respectively, compared to the baseline algorithm. This improvement indicates that the information derived from the multi-scale feature fusion process is no longer singular but rather diverse. The incorporation of this multifaceted information significantly augments the YOLOv5-EC3F algorithm's discriminative capacity, thereby reducing its rate of false negatives.  
To further assess the effectiveness of the EMFF Module, the YOLOv5-EC3F algorithm demonstrated rises in accuracy, recall, and mAP by 4%, 1.4%, and 1%, respectively, when compared to the original algorithm. In contrast to the MFF, accuracy and mAP improved by 3.1% and 0.8%, respectively, while recall decreased by 0.6%. These enhancements highlight how the MFF module, augmented with the EMA attention mechanism, enables the YOLOv5-EC3F algorithm to concentrate on intricate details, thereby capturing texture and essential features more effectively, which in turn enhances target definition.  
Figure 9: Ablation experiment. Note: MFF is without adding EMA attention, and EMFF is MFF after adding EMA.  
Upon integrating the two modules, a notable enhancement in performance metrics was observed, with a 7% increase in recall and a 3% improvement in mAP. Conversely, the accuracy exhibited only a marginal reduction of 0.1% in comparison to the baseline algorithm. A thorough analysis of the experimental findings suggests that the fused module has a beneficial effect on the original algorithm's performance, thereby confirming the efficacy of the incorporated module. Furthermore, Table 2 delineates the specific metrics for each category of detection prior to and following the enhancements. The results reveal that, apart from a minor decrease in the \(p\)-value associated with the categories of stone and pothole, all other categories demonstrated significant advancements. It is further shown that the YOLOv5-EC3F algorithm has better results for detecting targets.
## 5 Results and Discussions  
### Numerical Technique  
The proposed scheme is employed for solving a system of boundary value problems. The scheme is comprised of two stages. The first stage requires the computation of the second-order derivative of the dependent variable. Since the proposed scheme combines implicit and explicit computations of terms, an iterative procedure is also employed for solving difference equations obtained by applying the proposed scheme. The iterative procedure finds the solution to the problem using an initial guess. The iterative procedure will stop if the given criterion is met. This criterion is based on the maximum of norms for the difference of solutions computed at two consecutive iterations. If this maximum of norms is less than some given tolerance, the iterative procedure will be stopped. Otherwise, it continues to find solutions iteratively. The solution at the next iteration is found by the information of the solution found at the previous iteration. The solution convergence also depends on the guess choice for Matlab solver fsolve. Since the proposed scheme finds the solution of only first-order differential equations, some strategy is adopted for solving the second- or high-order differential equations with boundary conditions. For this case, a shooting approach is considered. The shooting method is based on the proposed scheme for solving given ODEs, an iterative method for solving difference equations, and a Matlab solver fsolve for solving equations. A procedure is applied for solving second- and third-order differential equations that reduce these equations into first-order differential equations. However, this procedure also requires an initial condition for each first-order equation in the system. Since the given problem is a boundary value, unknown initial conditions are assumed at the leftmost boundary of the domain for those boundary conditions implemented on the rightmost boundary of the domain. The Matlab solver fsolve uses an initial guess for unknown initial conditions and finds the solution with given estimated initial conditions. The solver fsolve continues its iterative procedure until the residuals of boundary conditions tend to zero. The range of physical parameters depends on the convergence of the proposed scheme. So, any numerical value of parameters can be chosen if the scheme provides a stable solution. In addition, the numerical values of the parameter(s) depend on the considered case of fluid. So, if a particular fluid is chosen, corresponding values of parameters will then be checked to find a stable solution. The stability and convergence of the solution also depend on the step size. These types of numerical schemes have not been discussed widely in the literature. Mostly in existing schemes, first-order derivatives of the dependent variable are considered, but the proposed schemes considered the information of the second-order derivative of the dependent variable. So far, the scheme has the advantage of having third-order accuracy and faster convergence compared with the existing first-order Euler scheme.
# Harnessing FPGA Technology for Energy-Efficient Wearable Medical Devices
## 5 Power Optimization Strategies
### Energy-Efficient Task Scheduling
#### 5.3.2 Duty Cycle Optimization  
In contrast to the event-driven approach, where the activation of a component is based on specific events rather than a time period, the duty cycling approach involves switching between active and idle modes at predetermined intervals [118]. This approach effectively manages average power consumption by balancing active and idle periods in systems which can tolerate a wake-up delay. Respecting these conditions is critical; otherwise, the system might miss crucial events. Duty cycling approaches contribute to reducing average power consumption by allocating more time to sleep for energy-consuming components such as wireless communication modules, processing elements (PEs), and sensors.  
The author of [119] used the duty cycle optimization approach to reduce the power consumption of high LED currents in the acquisition of PPG signals. The red and IR LEDs were switched on and off alternatively while controlling the duty cycle through digital feedback using pulse density modulation (PDM). This approach avoids unnecessary turning on LEDs to avoid power consumption. Likewise, in [105], the authors applied duty cycle optimization in their system, which has two transmission modes for lossy and lossless compressed ECG data. They configured the Bluetooth transceiver in such a way that the connection interval of the transceiver was managed according to the transmission mode by adjusting the duty cycle. This approach further reduces power consumption when only lossy data are transmitted.  
Duty cycling introduces some latency due to the time needed to switch between modes, which should be acceptable in the context of real-time health monitoring. While the accuracy of results might not be directly affected, the timing and synchronization of data collection could be impacted. This could potentially lead to missed data points if not managed correctly. However, this impact can be minimized by ensuring the wake-up intervals are short enough to capture the necessary data.
## 3 Results
### Performance Metrics
#### 3.6.4 Standard Deviation (SD)  
It is the measure of the dispersion of statistical data. Dispersion computes the deviation of data from its mean of average position. A large standard deviation indicates that the data points can spread from the mean and a small standard deviation indicates that they are clustered closely around the mean.  
Let the sample size be n with a mean of \(\overline{Y}\), the standard deviation (SD) is calculated using the following equation:  
\[SD=\sqrt{\frac{1}{n}\sum_{i=0}^{n-1}\abs{y_{i}-\overline{Y}}^{2}} \tag{4}\]  
These performance measures give the indicators for the determination of the effectiveness of the model for forecasting the daily stock index [50; 51; 52]. The standard deviation has been well used in [53] for multivariate time series in smart grids.  
A new forecasting model claimed to outperform conventional autoregression and vector autoregression models is proposed [54]. This was made possible by incorporating the least absolute shrinkage and selection operator (LASSO) framework, as the case study involves sparse placements of wind power. This paper has inspired our future work when we are planning to expand the work across various places in the cities of Rwanda. The importance of Spatio-temporal autocorrelation arrived from various data sources will be added advantage to avoid inaccurate predictions [55]. The application explored is the energy domain, as mostly in these domains many papers have usually explored only temporal data. This work is reflected in this current research work through the adoption of Spatio-temporal data in our data set.  
The change detection in the application that involves streaming data is significantly described [53]. The approach has improved the quality of results by overcoming false positive detection, additional changes in new incoming data. The paper will be crucial in our future enhancement when deployed across cities, which creates the possibility for more and more new data, whereby the training and test datasets need to be rewired accordingly.
## 1 Introduction
### Related Works
#### 1.1.3 Dense-Direct Methods  
Dense-direct techniques work directly with pixel information and can recover dense depth maps. Some of the main contributions in this field are the [PERSON] et al., DTAM, REMODE, and LSD-SLAM systems. [PERSON] et al. (2015) proposed one of the first real-time dense monocular SLAM systems. They introduced a variational framework to estimate the dense depth maps from multiple images using robust penalizers for both the data term and the regularizer. The key contributions were integrating multiple images for noise robustness and an efficient primal-dual optimization scheme. However, their method was limited to local dense tracking and mapping without global map optimization. The DTAM system proposed by [PERSON] et al. (2015) enabled real-time dense tracking and global mapping using a single handheld camera. They introduced the concept of dense model-based camera tracking by aligning live images to the textured 3D surface models synthesized from the estimated dense depth maps. The depth maps were computed by filtering over the small-baseline stereo comparisons from video. A key difference from [PERSON] et al. was maintaining a global map with pose graph optimization. The REMODE system of [PERSON] et al. (2015) also performed per-pixel Bayesian depth estimation but introduced a convex optimization-based smoothing step using the estimated uncertainty to enforce the spatial regularity. They demonstrated probabilistic updating, allowing online refinement and error detection. A key contribution was the derivation of a measurement uncertainty model. However, REMODE was limited to local mapping without global optimization. The LSD-SLAM of [PERSON] et al. (2015), integrated many of these concepts into the first direct monocular SLAM system capable of performing consistent global semi-dense reconstruction. The key novelties were the direct alignment on the \(Sim(3)\) handling scale drift and the incorporation of depth uncertainty into tracking. LSD-SLAM reached an outstanding outdoor performance by enabling large-scale accurate monocular dense reconstruction in real time. In summary, early works, like [PERSON] et al. and DTAM, introduced key concepts like multiple image integration, probabilistic depth estimation, and variational optimization, while later methods, like LSD-SLAM, were built on these concepts to enable globally consistent mapping and reconstruction, with fully direct approaches finally demonstrating accurate monocular dense SLAM at scale.
## 3 Results and Discussion
### Effect of Fiber Types  
Comparing the tensile failure results of NOL rings with different fiber types, it is found that SYT49s-12k fiber possesses the maximum failure strength, and the difference in failure strength of different fibers is not obvious. Therefore, separate experiments were designed for different fiber types. The influence of different kinds of fibers on the mechanical  
Figure 4: Failure load of NOL ring samples with different winding angles.  
properties of composite materials was studied by keeping the type of resin unchanged and setting the winding angle at 0\({}^{\circ}\).  
It can be seen from Figure 5 that TZT700s-12k fiber has poor performance and T-700-12k fiber is in the middle when HBV-SL-03-03 resin is used for circumferential wounding at room temperature. SY49s-12k fiber has the best mechanical performance, and its tensile strength is 32.3% higher than TZT700s-12k fiber.  
As can be seen from Figure 6, when HBV-SL-03-01 resin is used for circumferential winding at room temperature, the mechanical properties of TZT700s-12k and T-700-12k fibers are still inferior to those of SY49s-12k fibers. Therefore, under the current test conditions, the mechanical properties of SY49s-12k are the best.
# Towards a New Digital Era: Observing Local E-Government Services Adoption in a Chinese Municipality
## 4 Methodology
### Regression Analysis
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline
**Variable** & \multicolumn{3}{c}{**WeChat Adoption**} & \multicolumn{3}{c}{**App Adoption**} & \multicolumn{3}{c}{**Weibo Adoption**} & \multicolumn{3}{c}{**WeChat Adoption**} \\ \hline  & Coef & SE & t & Coef & SE & t & Coef & SE & t & Coef & SE & t \\ \hline
**Age** & 0.02 & 0.03 & 0.64 & 0.03 & 0.04 & 0.96 & 0.06 & 0.03 & 1.96 \({}^{*}\) & 0.10 & 0.03 & 3.32 \({}^{***}\) \\
**Gender** & 0.04 & 0.08 & 0.45 & 0.02 & 0.09 & 0.25 & 0.16 & 0.08 & 1.98 \({}^{*}\) & 0.12 & 0.08 & 1.51 \\
**Local Residency** & & & & & & & & & & & & & \\ (_for_ = non) & 0.27 & 0.08 & 3.10 \({}^{**}\) & 0.39 & 0.99 & 3.99 \({}^{***}\) & 0.15 & 0.09 & 1.99 \({}^{*}\) & 0.26 & 0.09 & 3.04 \({}^{**}\) \\
**Education** & 0.01 & 0.04 & 0.24 & \(-\)0.06 & 0.04 & \(-\)1.41 & 0.00 & 0.04 & 0.02 & 0.06 & 0.04 & 1.54 \\
**IT literacy** & 0.01 & 0.05 & 0.04 & \(-\)0.02 & 0.05 & \(-\)0.35 & \(-\)0.02 & 0.04 & \(-\)0.51 & \(-\)0.05 & 0.04 & \(-\)0.11 \\
**Awareness** & & & & & & & & & & & & \\ Web & 0.63 & 0.04 & 15.58 \({}^{***}\) & & & & & & & & \\ App & & & & & & & & & & & & \\ Weibo & & & & & & & & & & & & \\ WeChat & & & & & & & & & & & & \\
**Intention to Use** & & & & & & & & & & & & \\ Web & 0.12 & 0.04 & 2.77 \({}^{**}\) & & & & & & & & & & \\ App & & & & & & & & & & & & & \\ Weibo & & & & & & & & & & & & & \\ Webo & & & & & & & & & & & & & \\ Webo & 0.12 & 0.05 & 2.61 \({}^{**}\) & & & & & & & & & & \\ App & & & & & & & & & & & & & \\ Weibo & & & & & & & & & & & & & \\ WeChat & & & & & & & & & & & & & \\
**Constant** & 0.023 & 0.26 & \(-\)0.52 & 0.26 & & \(-\)0.54 & 0.23 & & \(-\)0.30 & 0.23 & & & \\
**Observations** & & & & & & & & & & & & & \\
**R-squared** & & 0.50 & & & & & & & & & & & \\ \hline \hline \end{tabular} \({}^{***}\)\(p<0.001\), \({}^{**}\)\(p<0.01\), \({}^{*}\)\(p<0.05\). Coef: regression coefficient; SE: standard error; t-statistics.  
\end{table} 
Table 2: Results of Regression Analysis on Determinants of Adoption of Four Local E-Services.
###### Abstract  
A truly universal system to optimize consumptions, monitor operation and predict maintenance interventions for internal combustion engines must be independent of onboard systems, if present. One of the least invasive methods of detecting engine performance involves the measurement of the exhaust gas temperature (EGT), which can be related to the instant torque through thermodynamic relations. The practical implementation of such a system requires great care since its torque-predictive capabilities are strongly influenced by the position chosen for the temperature-detection point(s) along the exhaust line, specific for each engine, the type of installation for the thermocouples, and the thermal characteristics of the interposed materials. After performing some preliminary tests at the dynamometric brake on a compression-ignition engine for agricultural purposes equipped with three thermocouples at different points in the exhaust duct, a novel procedure was developed to: (1) tune a CFD-FVM-model of the exhaust pipe and determine many unknown thermodynamic parameters concerning the engine (including the real EGT at the exhaust valve outlet in some engine operative conditions), (2) use the CFD-FVM results to considerably increase the predictive capability of an indirect torque-detection strategy based on the EGT. The joint use of the CFD-FVM software, Response Surface Method, and specific optimization algorithms was fundamental to these aims and granted the experimenters a full mastery of systems' non-linearity and a maximum relative error on the torque estimations of 2.9%.  
compression-ignition engine; torque; exhaust gas temperature; thermocouples; CFD; FVM; RSM; indirect estimation; agricultural machines +
Footnote †: journal: Applied Sciences
# Pattern-Based Decoding for Wi-Fi Backscatter Communication of Passive Sensors
## 7 Conclusions  
In this paper, we proposed a pattern-based decoding algorithm for backscatter communications using ambient Wi-Fi signals. The proposed algorithm identifies the pattern of received signal samples, and is thus more robust against signal fluctuation with no need for tricky threshold configuration. Moreover, the pattern for a pair of adjacent bits is exploited, and the algorithm decodes a bit pair at a time, which enables the algorithm to make a decision on a bit from two patterns, and makes the decision more reliable against fluctuation. The experimental study with real Wi-Fi traffic demonstrated that the proposed algorithm has noticeable performance enhancement over conventional threshold-based decoding methods. The proposed algorithm can be combined with or extended for other techniques of a longer communication range, higher-order modulation, etc. to achieve higher overall performance.  
The work was realized with the collaboration of all the authors. [PERSON] contributed to the main results and prototype implementation. [PERSON], [PERSON], and [PERSON] organized the work, provided the funding, supervised the research, and reviewed the draft of the paper. All authors discussed the results, approved the final version, and wrote the paper.  
This work was supported in part by the Institute for Information and Communications Technology Promotion (IITP), grant funded by the Korean Government (MSIT) in 2018 (No.2017-0-00070, Maximization of wireless-communication efficiency based on compression sensing, nonpower supply, and superhigh-speed mobility), the Human Resources Development of Korea Institute of Energy Technology Evaluation and Planning (KETEP) grant funded by the Korean Government's Ministry of Trade, Industry, and Energy (No. 20174030201840), and a Research Grant from Kwangwoon University in 2018.  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} AP & Access point \\ BER & Bit error rate \\ CDF & Cumulative distribution function \\ CSI & Channel state information \\ MAC & Medium access control \\ OFDM & Orthogonal frequency-division multiplexing \\ PAPR & Peak-to-average-power-ratio \\ PSK & Phase-shift keying \\ RF & Radio frequency \\ RSSI & Received signal strength indication \\ SIC & Successive interference cancellation \\ USRP & Universal Software Radio Peripheral \\ \end{tabular}
# Energy-Efficient Edge Optimization Embedded System Using Graph Theory with 2-Tiered Security
## 4 Energy-Efficient Enabled Reliable Edge-SDN Protocol with Graph Theory
### Discussion
The proposed system uses the Krushkal algorithm to construct a routing path with a minimum weighted cost:  
1. Firstly, the source node selects the edge from its neighbor based on the minimum weighted cost \(min(w\_i)\)
2. Moreover, it is continuously monitored so that the selected edge does not create a loop. 3. Finally, the execution of the Krushkal algorithm stops if edges \(m-1\) are included in the route. Figure 2 depicts the flowchart of the proposed system in terms of subgraph-based data transmission for IoT networks. The proposed system comprised two main components. First, each node in the network initialization process needs to be registered with the nearest gateways. Later, nodes are organized into undirected weighted graphs and based on the optimal condition, and the proposed system extracts various spanning trees. Among these spanning trees, the proposed system selects the minimum one for routing the IoT data with the support of gateways.
## 2 B-Spline Corner Smoothing Method
### B-Spline Curve
#### 2.1.2. G3 Continuity  
In Figure 1, two linear tool paths are connected using the B-spline at junction points \(P_{j,0}(u=0)\) and \(P_{j,8}(u=1)\). \(P_{j,0}\), \(P_{j,1}\), \(P_{j,2}\), \(P_{j,3}\) and \(P_{j,5}P_{j,6}\), \(P_{j,7}\), \(P_{j,8}\) lie on the line segment, respectively. As mentioned above, the B-spline \(C(u)\) is 4 th order with nine control points to keep G\({}^{3}\) continuity at \((0,1)\). Since the derivative of the straight line segment is 0 at the connection points, the corresponding derivative of the B-spline should be 0 to obtain G\({}^{3}\) continuous tool path. That is,  
\[C^{\prime\prime}(s)|_{u=0,1}=0 \tag{12}\]  
\[C^{\prime\prime\prime}(s)|_{u=0,1}=0 \tag{13}\]  
where  
\[C^{\prime\prime}(s)=C^{\prime\prime}(u)\big{(}u^{\prime}(s)\big{)}^{2}+C^{ \prime}(u)u^{\prime\prime}(s) \tag{14}\]  
\[C^{\prime\prime\prime}(s)=C^{\prime\prime\prime}(u)\big{(}u^{\prime}(s)\big{)} ^{3}+3C^{\prime\prime}(u)u^{\prime}(s)u^{\prime\prime}(s)+C^{\prime}(u)u^{ \prime\prime\prime}(s) \tag{15}\]  
By mathematic calculation, the sufficient conditions for the establishment of Equations (12) and (13) can be given:  
\[C^{\prime\prime}(u)|_{u=0,1}=0,\ C^{\prime\prime\prime}(u)|_{u=0,1}=0 \tag{16}\]  
\[u^{\prime\prime}(s)|_{u=0,1}=0,\ u^{\prime\prime\prime}(s)|_{u=0,1}=0 \tag{17}\]  
For corner smoothing B-spline curve, the derivatives of curve parameter \(u\) with respect to arc length \(s\) can be described as follows.  
\[u^{\prime}(s)=\left\|C^{\prime}(u)\right\|^{-1} \tag{18}\]  
\[u^{\prime\prime}(s)=-C^{\prime}(u)C^{\prime\prime}(u)/\left\|C^{\prime}(u) \right\|^{4} \tag{19}\]  
\[u^{\prime\prime\prime}(s)=-\Big{\{}\Big{[}\big{(}C^{\prime\prime}(u))^{2}+C^ {\prime}(u)C^{\prime\prime}(u)\Big{]}\left\|C^{\prime}(u)\right\|^{2}-4\big{[} C^{\prime}(u)C^{\prime\prime}(u)\big{]}^{2}\Big{\}}/\left\|C^{\prime}(u) \right\|^{7} \tag{20}\]  
From Equations (18) to (20), it can be seen that the establishment of Equation (16) will lead to the establishment of Equation (17). Substituting Equations (5) and (6) into Equation (16) yields:  
\[\left\{\begin{array}{l}C^{\prime\prime}(u)|_{u=0}=15(-10c+20b)\overset{ \rightarrow}{T_{1}}=0\\ C^{\prime\prime}(u)|_{u=0}=(-(10/3)d+15b-20c)\overset{\rightarrow}{T_{1}}=0 \end{array}\right.\ \ \ \text{and}\left\{\begin{array}{l}C^{\prime\prime}(u)|_{u=1}=15(10c-20b) \overset{\rightarrow}{T_{2}}=0\\ C^{\prime\prime\prime}(u)|_{u=1}=((10/3)d-15b+20c)\overset{\rightarrow}{T_{2}}=0 \end{array}\right. \tag{21}\]  
Solving Equation (21) obtains,  
\[c=2b,\ d=3b \tag{22}\]