esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Results and Discussion
### Correlation Analysis (Spearman's Rho)  
Spearman's rho correlation matrix, analyzing the relationships between various framework attributes, demonstrates internal consistency [83]. Strong positive correlations emerged between key constructs as follows: Q1 (framework clarity/documentation) and Q11 (Selected BIM, Lean Tools, and Emerging Technologies) (\(p=0.826\)) highlighted that a well-structured framework directly facilitates appropriate BIM, Lean, and Emerging Technologies adoption, while Q20 (flexibility/scalability) and Q19 (automation enhancement)  
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Null Hypothesis** & **Test** & **Sig.** & **Decision** \\ \hline The median of Q1 equals 3.000. & 0.002 & \\ The median of Q2 equals 3.000. & 0.002 & \\ The median of Q3 equals 3.000. & 0.006 & \\ The median of Q4 equals 3.000. & 0.003 & \\ The median of Q5 equals 3.000. & 0.001 & \\ The median of Q6 equals 3.000. & 0.010 & \\ The median of Q7 equals 3.000. & 0.003 & \\ The median of Q8 equals 3.000. & 0.004 & \\ The median of Q9 equals 3.000. & 0.003 & \\ The median of Q10 equals 3.000. & One-Sample Wilcoxon & 0.004 & Reject the null \\ The median of Q11 equals 3.000. & Signed-Rank Test & 0.002 & hypothesis \\ The median of Q13 equals 3.000. & 0.004 & \\ The median of Q14 equals 3.000. & 0.002 & \\ The median of Q15 equals 3.000. & 0.005 & \\ The median of Q16 equals 3.000. & 0.004 & \\ The median of Q17 equals 3.000. & 0.004 & \\ The median of Q18 equals 3.000. & 0.001 & \\ The median of Q19 equals 3.000. & 0.001 & \\ The median of Q20 equals 3.000. & 0.001 & \\ The median of Q21 equals 3.000. & 0.001 & \\ \hline \hline \end{tabular}
\end{table}
Table 14: Hypothesis Test Summary.  
(\(\rho\) = 0.816) suggest that perceived scalability is closely tied to technological integration and operational efficiency. Similarly, the link between Q4 (ease of understanding) and Q15 (cross-project applicability) (\(\rho\) = 0.738) reinforced that clarity drives practical utility. A cohesive cluster of moderate correlations connected Q2 (visualization), Q5 (terminology), Q7 (workflow alignment), Q11, and Q14 (decision making), suggesting these elements collectively support framework efficacy. Notably, Q13 (collaboration) and Q12 (process optimization) correlated moderately with multiple items, emphasizing their integrative role. Overall, [PERSON]'s Rho analysis supports the construct validity of the framework, with clear clusters of interrelated items and a general pattern of positive associations, especially among core functional and strategic dimensions. Table 15 demonstrates the correlation analysis.  
Overall, these results support the validity and perceived effectiveness of the proposed framework in facilitating a more integrated and efficient approach to construction process optimization through the convergence of BIM, Lean Construction, and Emerging Technologies.
## 3 A Comprehensive Monitoring System for Landslides in 'Space-Sky-Ground-Underground'
### Underground-Based Monitoring  
Various acoustic, optical, electrical, and other sensors deployed inside the landslide are used in underground-based monitoring technology to obtain multi-field information such as deformation, seepage, temperature, and geoelectricity of the underground rock and soil mass of the landslide. Based on this, it is easy to grasp the changes between the various rock layers and their interfaces in the deep part of the landslide. Commonly used sensors include inclinometers, osmometers, thermometers, and electrodes. Especially of note, DFOS technology has developed rapidly in recent years, and has been well applied in various landslide hazard and slope monitoring due to its unique advantages of distribution, anti-interference, high accuracy, and long distance. DFOS uses light waves as a carrier and optical fibers as a medium to sense the changing processes of multiple physical parameters such as stress, vibration, temperature, and seepage inside landslides (Figure 15), and critical, innovative results have been achieved [41; 42; 43]. Based on this, scholars have researched the mechanism of landslide hazards, which provides essential technical support for promoting deep monitoring of landslides. In landslide monitoring, selecting the particular sensing optical cables based on different geological conditions and monitoring environments is necessary to achieve a comprehensive and accurate perception of the state information of deep rock and soil in landslides.  
[PERSON] et al. [44] monitored multiple field factors such as deformation, temperature, stress, and seepage of the Majiagou Reservoir landslide in the Three Gorges area by using DFOS. Figure 16 shows the temperature profile monitored by optical fiber inside the landslides. At the same time, they conducted comparative verification through the indoor physical model experiments. The landslide deformation mode and key deformation positions were determined through large-scale and fully distributed fiber optic monitoring. Subsequently, combined with quasi-distributed fiber optic monitoring, accurate real-time information on multiple fields at key landslide locations were obtained. The research results indicate that rainfall can significantly reduce the effective stress inside the soil, thereby putting the landslide in a state of easy instability. In addition, considering the particularity of the influence of reservoir water level on the reservoir bank landslide, the rapid rise and fall of the reservoir water level will also lead to a significant decrease in the stability coefficient of the landslide. The above research results provide important references for underground-based monitoring of reservoir bank landslides.  
Figure 14: Analysis of landslide scale based on 3D laser scanning technology [40].
## 4 Discussion  
Regarding the on-site results presented in Section 3.4, we considered the link closure with a minimum margin of 3 dB, which allows a lower technical complexity modulator system. On the other hand, if we considered an approach to maximize throughput, where we could transmit a larger volume of data, including playback recorded passages, we would have to adapt to higher-order MODCODs demanding a modulator of higher technical complexity for this development. This trade-off must be re-evaluated when considering satellite reception systems. Therefore, the results show the minimum MODCOD adaptation to support the INR as a function of antenna elevation for the azimuth point of highest interference.  
Furthermore, as shown in Figure 8, the on-site measurements indicate that the interference behavior around the receiving antennas does not have theoretically predicted distributions. These variations are due to the reflections around the ERG and the different front-back ratios of the antennas. Additionally, the signals can be received by the secondary lobes of the antennas.  
In the emulated scenario of Section 3.5, the end-to-end communication system allows the variation of SINR for constantly monitoring the image scenes processed from the WFI camera simulator. Consequently, for the moving window of WFI images without failures shown in Figure 13, we set a link margin of \(M=3\) dB for the specified BER = \(10^{-7}\) in the link budget. In other words, considering QPSK modulation and no error correction code, the SINR relation is given by:  
\[\frac{E_{b}}{I_{0}+N_{0}}=\frac{E_{b}}{N_{0}}(required)+M=11.3+3=14.3\text{ dB}. \tag{13}\]  
As a secondary measurement step, in order to find the threshold level, we increase the interference until we have the no-failure threshold, as shown in (14) and demonstrated in Figure 14.  
\[\frac{E_{b}}{I_{0}+N_{0}}=8.2\text{ dB}\Longleftrightarrow BER\cong 1.2\times 1 0^{-4}\;. \tag{14}\]To illustrate the failures in the image processing, we reduce the SINR to 5.2 dB, and we have the moving window depicted in Figure 15, which shows the processing failures in the image. The black stripes in the image highlight these failures.
# Workflow Trace Profiling and Execution Time Analysis in Quantitative Verification
## 5 Time Analysis
### First Passage Time  
Time metrics of trace components can be formalised as a group of fundamental measures in SMC, namely, _first passage times_ (FPTs) [14], which informally express \"the randomised time until the trace traverses a goal (i.e., absorbing) state for the first time\".  
To define an FPT, we first introduce a real-valued function, which is defined _almost everywhere_ on \(Path_{s}\) for some state \(s\) in \(\mathcal{M}\) (with respect to the probability measure \(Pr\)):  
\[time_{G}(\pi)=\begin{cases}0&\text{if }s_{0}\in G,\\ \sum_{j=0}^{i}t_{j}&\text{if }s_{i+1}\in G\text{ and }s_{j}\
ot\in G\text{ for all }0\leq j\leq i.\end{cases}\]  
Intuitively, \(time_{G}(\pi)\) is the total time duration along with the path \(\pi\) until an absorbing state is reached. Note that if the first state in \(\pi\) is a goal state, \(time_{G}(\pi)=0\) by definition. This is slightly different from the standard FPT definition in the literature, where the FPT at an goal state is the time of leaving that state. Also note that, although \(time_{G}\) is not defined on paths that do not reach any absorbing states, the set of those paths has a probability measure of zero (with respect to \(Pr\)), and thus \(time_{G}\) is defined almost everywhere on \(Path_{s}\). Therefore, we can define a random variable  
\[X_{s}:Path_{s}\to\mathbb{R}_{\geq 0} \tag{3}\]  
such that \(X_{s}(\pi)=time_{G}(\pi)\), where \(\pi\in Path_{s}\). Intuitively, \(X_{s}\) means the FPT for the absorbing states from state \(s\). Thus, the FPT of \(\mathcal{M}\) (to the absorbing states) is a linear combination of \(X_{s}\) with a weight given by \(\alpha(s)\) for all \(s\in S\):  
\[Y=\sum_{s\in S\backslash G}\alpha(s)X_{s}\;. \tag{4}\]  
Therefore, our objective is to compute the moments \(E[Y^{j}]\), where \(1\leq j\leq k\) for some \(k\).
## 3 Evaluation
### Evaluation Metrics  
We evaluate the proposed system on both multi-pitch detection and voice assignment using the frame-based precision, recall and F-measure as defined in the Music Information Retrieval Evaluation eXchange (MIREX) multiple-F0 estimation evaluations [53], with a frame hop size of 20 ms.  
The F-measure obtained by the multi-pitch detection is denoted as \(F_{mp}\), and for this, we combine the individual voice ground truths into a single ground truth for each recording. For voice assignment, we simply use the individual voice ground truths and define voice-specific F-measures of \(F_{s}\), \(F_{a}\), \(F_{t}\) and \(F_{b}\) for each respective SATB vocal part. We also define an overall voice assignment F-measure \(F_{ua}\) for a given recording as the arithmetic mean of its four voice-specific F-measures.
## 3 Results
### Correction of Radar Penetration Differences between X-Band and C-Band  
We selected two test sites within the study area where the X-band SRTM DEM of the ascending and descending tracks overlap to inspect the bias due to the different penetration depths of X and C band radar. According to the results of our co-registration exercise, the X-band and C-band SRTM DEMs had almost no offset in the horizontal direction (Figure 8). Additionally, there was almost no offset in the vertical direction.  
We calculated the mean values of the X-band and C-band SRTM DEM differences for the glaciated areas within our two test sites, which were 2.81 m and 1.78 m, respectively. Therefore, by calculating the average value of the radar penetration difference for the two test sites, the value of the radar penetration difference in the SETP was \(-\)2.295 m. The main reason for the radar penetration error correction is that the TanDEM-X bi-static InSAR data in this paper are X-band data, while the external DEM data are C-band data. There is a large difference in the penetration depth of ice and snow between these two bands, which has become a large source of uncertainty in estimating glacier mass balance using satellite geodetic methods. Therefore, it is necessary to use this rigorously calculated radar  
Figure 5: (**a**) Glacier elevation changes in the Luolong region from 2000 to 2014, (**b**) and the Bomi region from 2000 to 2014.  
Figure 6: (**a**) Glacier elevation changes in the Basu region from 2000 to 2014, (**b**) and the Chayu region from 2000 to 2014.
# Comparative Analysis of Different Model-Based Controllers Using Active Vehicle Suspension System
## 3 Control Methods
### High Order Sliding Mode Controller with Super Twisting Algorithm  
To improve the system performance, second-order sliding mode controller based on STA is proposed. When the boundary is known STA generates control function continuously, thus sliding variable and its derivative are driven to zero in predetermined time in the presence of matching uncertainties with bounded gradient. Since the integrand function of STA contains discontinuous function, the chattering is not eliminated but is rather weakened. In contrast to other second-order sliding mode controllers, STA is suitable for controlling the system that appears in the first derivative of the sliding surface (\(s\)).  
The physical meaning switching/discontinuous control \(U_{D}\) is to make the trajectories of the system to move towards (sliding surface) HOSMC can be expressed as follows:  
\[u(t)=U_{eq}+U_{D} \tag{35}\]  
The equivalent control is obtained by referring to (28)  
\[U_{eq}=\left[\frac{1}{M_{c}}+\frac{1}{M_{wh}}\right]^{-1}(y_{ref}-f_{1}+f_{2}+ \lambda\dot{y}_{ref}-\lambda(X_{2}-X_{4})) \tag{36}\]  
where  
\[\begin{split} f_{1}&=-\frac{C_{c}}{M_{c}}X_{1}- \frac{d_{c}}{M_{c}}X_{2}+\frac{d_{c}}{M_{c}}X_{4}+d\\ f_{2}&=\frac{C_{c}}{M_{wh}}X_{1}+\frac{d_{c}}{M_{wh} }X_{2}-\frac{C_{wh}}{M_{wh}}X_{3}-\frac{d_{c}}{M_{wh}}X_{4}-\frac{d_{wh}}{M_{wh }}X_{4}-\frac{1}{M_{wh}}u+\frac{d_{wh}}{M_{wh}}u_{d}-d\end{split} \tag{37}\]  
Super twisting algorithm is used for \(U_{D}\) representing discontinuous part of the total control law. The STA was introduced as follows [33]:  
\[U_{D}=-k_{1}\left|s\right|^{\frac{1}{2}}sgn(s)+\rho \tag{38}\]  
where \(\dot{\rho}\) is defined as follows:  
\[\begin{split}\dot{\rho}&=-k_{2}sgn(s)\\ &=-k_{2}sgn(y_{ref}-\dot{X}_{1}+\lambda(y_{ref}-X_{1}))\end{split} \tag{39}\]  
Here, \(k_{1}\) and \(k_{2}\) gains are designed and selected as \(1.5\sqrt{\theta}\) and \(1.1\sqrt{\theta}\), respectively, where \(\theta\) is the positive constant. This algorithm only works with bounded gradients when the controller is designed to maintain the sliding surface. By using equations of the system, equivalent control (33) and STA for the direct control (38), the control signal of the proposed HOSMC for active quarter-vehicle suspension system is calculated as follows:  
\[\begin{split} u(t)&=\left[\frac{1}{M_{c}}+\frac{1} {M_{wh}}\right]^{-1}(y_{ref}-f_{1}+f_{2}+\lambda\dot{y}_{ref}-\lambda(X_{2}-X_ {4})-k\left|\dot{y}_{ref}-\dot{X}_{1}+\lambda(y_{ref}-X_{1})\right|^{\frac{1}{ 2}}\\ &\quad sgn(\dot{y}_{ref}-\dot{X}_{1}+\lambda(y_{ref}-X_{1}))+\rho \end{split} \tag{40}\]  
If HOSM is properly designed the convergence of HOSMC can be asymptotic and it can totally remove the chattering effect.
## 3 Results
### Resource Efficiency Metrics
#### 3.2.4 Energy Consumption  
On the Raspberry Pi 4 board, current and voltage consumption are measured as I = 0.43 A, U = 5.24 V in idle mode and I = 0.7 A, U = 5.24 V during the execution of the LiteRT DNN model. This corresponds to an increase in power consumption of 1.42 W (2.25 W in idle mode vs. 3.67 W during execution), which can be attributed to the simultaneous activation of up to four processor cores during DNN analysis. Considering that the mean execution time is 1.064 ms, the total energy consumption of the Raspberry Pi 4 board for model execution is estimated at 3.91 mJ (Table 3).  
For the STM32F769 IDISCOVERY kit (including additional consumption from indicator LEDs and microcontroller dedicated for ST-Link connection), current and voltage consumption remains unchanged between idle mode and DNN execution, measured at I = 0.2 A, U = 4.76 V, resulting in a power consumption of 0.952 W. This is due to the single-core architecture, where the processor operates at a fixed clock frequency, maintaining relatively equal power consumption regardless of the executed instructions. Considering the model-specific mean execution times, the energy consumption is 4592 mJ for the non-quantized model and about 585 mJ for both quantized models (Table 3). This indicates that quantization reduces energy consumption for DNN execution by about 7.85 times.
## 5 Implementation, Evaluation Details
### Metrics for Retinal Vessel Segmentation
#### 5.1.4 F1 Score  
F1 Score is the harmonic mean of precision and recall, providing a balanced measure of the segmentation algorithm's performance. It is calculated as follows:
## 4 DASH7 Communication System
### Air Interface
#### 4.1.2 Channel Classes  
To avoid signal collisions, the DASH7 protocol divides the specific spectrum band into several channels. Therefore, the DASH7 protocol defines three channel classes that represent the data rates that can be used. These are the Lo-Rate, Normal-Rate, and Hi-Rate data rates. Each channel class defines a certain symbol rate, modulation index, frequency deviation, and occupied bandwidth [21]. These parameters as displayed in Table 4.  
Figure 2: Concept of an in-phase and quadrature modulator and demodulator.  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**RF Band** & **Lo-Rate (d)** &
\begin{tabular}{c} **Normal and** \\ **Hi-Rate (d)** \\ \end{tabular} & **Start (b)** & **End** \\ \hline
433 MHz * & 0, 1, \(\ldots\), 68 & 0, 8, 16, \(\ldots\), 56 & 433.06 MHz & 434.785 MHz \\ \hline
868 MHz ** & 0, 1, \(\ldots\), 279 &
\begin{tabular}{c} 0, 8, 16, \(\ldots\), 216, \\ 229, 239, \\ 257, 270 \\ \end{tabular} & 863 MHz & 870 MHz \\ \hline
915 MHz *** & 0, 1, \(\ldots\), 1039 & 0, 8, 16, \(\ldots\), 1032 & 902 MHz & 928 MHz \\ \hline \hline \end{tabular}
\end{table}
Table 3: DASH7 channel bands. The stars in the table indicate the following: * Worldwide coverage with local regulatory limitations; ** EN 300 220 (Europe) with local regulatory limitations; *** FCC part 15 in the United States of America.  
Each channel is defined by an index specifying its start frequency relative to the start of the DASH7 operating frequency band. The center frequency expressed in Megahertz (MHz) of each channel can be calculated as follows:  
\[f_{c_{channel}}=\text{Start}(b)+0.025d+\frac{\text{Channel Spacing}(c)}{2}, \tag{6}\]  
where \(b\) and \(d\) are the start frequency of the band and the channel index, as shown in Table 3, while \(c\) is the channel spacing of the selected channel class, which can be found in Table 4.
## 4 Experiment and Result Analysis
### Result Analysis
#### 4.3.1 Contrast Experiment  
In order to objectively demonstrate the improved effect of the proposed algorithm, the improved model was compared and analyzed with official models such as YOLOv7, YOLOv7-Tiny and YOLOv7x. The analyzed results of various indicators are shown in Table 1, and the parameters used by the model were the same. The experimental results show that, compared with the original YOLOv7 algorithm, the improved YOLOv7 algorithm sacrifices a detection speed of 0.001s but achieves the best performance in terms of accuracy, recall rate and mAP scores, including mAP_0.5 and mAP_0.5:0.95. It also outperforms other official YOLOv7 models across all four metrics. This demonstrates that the model exhibits excellent performance in terms of mean average accuracy and detection speed after loss function optimization and attention mechanism feature enhancement.
# A Geometric Error Measurement System for Linear Guideway Assembly and Calibration
## 3 Uncertainty Analysis
### Laser Source Setup  
During the measuring process, the laser source setup could be affected by the unstable foundation, machine operation, crane movement, staff walking, etc. The longer the distance is, the worse the measurement results; therefore, there is a need of an error analysis toward the laser source.  
As depicted in Figure 9, suppose the distance between the laser source and the fixed end is \(D_{lp}\), the error angle of laser source at vertical direction (\(\theta_{lp}\)) will generate an error \(\delta_{lp}\):  
\[\delta_{l}=D_{lp}\ \tan\theta_{l} \tag{5}\]  
Table 1 lists the setup errors in the vertical and horizontal directions of the laser source due to surface ground vibration with various measurement distances according to Equation (5).  
\begin{table}
\begin{tabular}{c c c} \hline \hline \(D_{lp}\) (mm) & \(\theta_{lp}\) **or \(\theta_{lh}\) (arcsec)** & \(\delta_{lp}\) **or \(\delta_{lh}\) (\(\mu\)m)** \\ \hline _1000_ & 0.1 & 0.5 \\ _1000_ & 0.5 & 2.4 \\ _1000_ & 1 & 4.8 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Error analysis of the laser source at vertical or horizontal directions.  
Figure 8: Beam path of parallelism measurement.
### Main Contributions  
As can be observed from the presented literature review, a vast family of methodologies has been created, some of them based on heuristic techniques such as GA and PSO, another group inspired by DP, and most of them based on MILP combined with Benders decomposition.  
In a general sense, the optimization technique to be selected strongly depends on the characteristics and assumptions of the ESS model, as well as the context (isolated or grid-connected system) and the information available.  
To take advantage of the vast family of BESS models, a recently developed version of binary PSO (BPSO), which incorporates a time-varying mirrored S-shaped (TVMS) transfer function, has been adopted in this paper. Consequently, hourly behavior of charging-discharging efficiency as well as the influence of charge controller on battery operation can be effectively incorporated. Additionally, the influence of wind-speed daily profile on battery schedule and GHG emissions is deeply analyzed. The impact of battery operation on the emissions of total hydrocarbons (THC), carbon monoxide (CO), oxides of nitrogen (NO\({}_{\text{X}}\)), CO\({}_{2}\), and particulate matter (PM) is investigated.  
The remainder of the paper is organized as follows. Section 2 describes the mathematical models of the system configuration under study. Section 3 explains the formulation of the optimization problem and its solution by TVMS-BPSO. Then, problem formulation is tested in Section 4 through a sensitivity analysis based on GA. As TVMS-BPSO is a novel version of BPSO, its performance is compared with GA in Section 5. Finally, conclusions and main findings are discussed in Section 6.