esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
The mean difference was \(-80.94\) for BMD-QCT and \(-80.57\) for TBS-QCT, indicating that QCT systematically overestimates values compared to DXA and TBS. This is shown in Figure 3. The limits of agreement demonstrated an increasing dispersion at higher values, suggesting a proportional bias. This means that the discrepancy between QCT and DXA/TBS is not constant but tends to increase as BMD/TBS values rise. Additionally, the mean absolute difference (MAD) was 80.94 for BMD-QCT and 80.57 for TBS-QCT, confirming a consistent discrepancy between the methods, regardless of the direction of the difference. Figure 3: Combined Bland–Altman plot for QCT vs. aBMD (DXA) and TBS. The x-axis represents the mean of measurements (aBMD/TBS and QCT), while the y-axis shows the difference for each pair of measurements. The dashed black line represents the mean difference for aBMD-QCT (\(-80.94\)), while the dash-dot black line is the mean difference for TBS-QCT (\(-80.57\)). Figure 2: An example of a DXA mismatching case due to severe scoliosis and spondylarthosis. Image (**A**) (left side) is the QCT scan showing very low vBMD values in the range of osteoporosis, according to the American College of Radiology criteria (16). Image (**B**) on the right side shows the corresponding DXA scan with T-score values in the range of osteopenia, due to the BMD overestimation caused by spondylarthrosis. The Bland-Altman plot (Figure 4) for DXA aBMD vs. DXA TBS shows a mean difference (bias) of \(-0.367\), with limits of agreement (LoA, \(\pm 1.96\) SD) of [\(-0.654\), \(-0.079\)]. The mean difference (bias) is negative, indicating that aBMD values by DXA are consistently higher than TBS values. The limits of agreement are relatively wide, suggesting considerable variability between these two parameters. ROC curves were calculated to assess the capability to discriminate between participants with and without fragility fractures. The best performing parameter was the QCT-derived vBMD, with an area under the curve (AUC) of 0.748. In contrast, the diagnostic performance of DXA-based parameters was slightly worse, namely with an AUC of 0. 575 for aBMD and 0.650 for TBS. Figure 5 illustrates the comparison between the ROC curves for the three parameters. Volumetric BMD by QCT demonstrated the highest sensitivity (65.3%) in identifying fragility fractures, followed closely by TBS (63.3%), while aBMD by DXA showed the lowest sensitivity (55.1%). Figure 4: Bland–Altman plot comparing DXA BMD and DXA TBS. The x-axis represents the mean of DXA aBMD and DXA TBS, while the y-axis shows the difference (DXA aBMD-DXA TBS) for each pair of measurements. The red dashed line indicates the mean difference (bias), and the blue dashed lines represent the limits of agreement (\(\pm 1.96\) SD). Figure 5: ROC curves of aBMD (pink), TBS (blue), and vBMD (green) to evaluate the capability to discriminate between individuals with and without fragility fractures. Reference lice in orange.
## 5 Example Analysis
### Results and Analysis of Siting and Sizing
#### 5.2.3 Scheme II: Economical Customization  
According to initial investment budget, when planning WT/PV, considering the economic indicators (in millions of yuan) and stability, the ES is planned to be 30% of the total WT/PV capacity. The multi-objective particle swarm optimization algorithm embedded in the power flow calculation was used to obtain the Pareto optimality-of-economic-and-stability indicator, when the investment budget was limited between RMB 250 million and RMB 350 million (approximately USD 60 million), as shown in Figure 19.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Various Indicators**} & \multirow{2}{*}{**Original Working**} & \multicolumn{2}{c}{**Reduction of Gas Engine**} \\  & & \multicolumn{2}{c}{**Output**} \\ \cline{3-5}  & & **30\%** & **50\%** & **70\%** \\ \hline Power and capacity permeability/\% & 0 & 55.43 & 62.80 & 99.40 \\ \hline Energy permeability/\% & 0 & 34.84 & 39.42 & 61.10 \\ \hline Stability index & 0.094 & 0.053 & 0.057 & 0.073 \\ \hline \multirow{3}{*}{Loss/MWh} & Before storage & \multirow{3}{*}{103.2} & \multirow{3}{*}{53.5} & \multirow{3}{*}{49.4} & \multirow{3}{*}{44.6} \\  & connection & & & \\ \cline{1-1} \cline{3-5}  & After storage connection & & & & \\ \hline Voltage & \multirow{3}{*}{
\begin{tabular}{c} Before storage \\ function/p.u. \\ \end{tabular} } & \multirow{3}{*}{11.76} & \multirow{3}{*}{7.58} & \multirow{3}{*}{9.23} & \multirow{3}{*}{9.57} \\ \cline{3-5}  & & & & \\ \cline{1-1} \cline{3-5}  & After storage connection & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of various indicators before and after WT/PV ES connection.  
Figure 18: Optimization of ES connection when the output power of gas turbine is reduced by 70%: (**a**) one-day total loss optimization; (**b**) optimization of total voltage fluctuation of all the nodes.  
It can be seen from Figure 19 that when the total WT/PV planning was 92 MW and the total ES planning was 27.6 MWh, the initial investment was RMB 306.772 million and the stability indicator was 0.0607. When the sizing of total WT/PV was 76 MW and the sizing of total ES was 22.8 MWh, the initial investment was RMB 258.607 million and the stability indicator was 0.0723. With the increase in the initial investment, the stability of the distribution network system improved. The sizing optimization results of WT/PV ES were selected according to the weighting of different objectives in the actual scenario.
# Effective Strategies for Enhancing Real-Time Weapons Detection in Industry
## 3 Materials and Methods  
This section outlines the complete experimentation for real-time weapons detection using CCTV. Initially, multiple weapons detection datasets were acquired and processed, involving the creation of new datasets and modifications to existing ones (Section 3.1). To fulfil the objective of real-time detection, we opted for the one-stage detector, YOLO. Experimentation involved testing several models from various versions of the YOLO family to identify the optimal model that achieves high accuracy while minimising inference duration (Section 3.2).  
To address the significant scale disparity issue in the primary dataset, where the scale difference between the weapon object images and the CCTV camera images was considerable, we employed the Scale Match method [27]. A modification was applied to the primary dataset using the Scale Match technique, facilitating seamless utilisation of the dataset with diverse models. This modification ensures consistent and precise weapons detection, regardless of scale deviations (Section 3.3).  
To tackle the challenge of reducing the false positive rate and enhancing accuracy in complex scenarios where the YOLO detector encounters difficulties, a comprehensive pipeline is developed in Section 3.4. This pipeline incorporates a secondary cascaded classifier (Section 3.4.1) and a temporal window approach (Section 3.4.2).
# Multi-Camera Person Re-Identification Based on Trajectory Data
## 2 Materials and Methods
### Trajectory Extraction
#### 2.2.2 Object Tracker  
ByteTrack was proposed in [11] and represents the current state of the art in the realm of object tracking algorithms. It adheres to the tracking-by-detection paradigm, which involves using bounding boxes generated with object detection algorithms to make the association of each bounding box over time based on numeric IDs. To take advantage of all detections generated by object detectors, ByteTrack classifies them based on confidence levels (low or high). The decision to include the detections with low confidence levels is grounded in the observation made by the authors that many of these detections often emerge from occlusions. This occurrence does not necessarily compromise or invalidate their utility. Consequently, bounding boxes with confidence surpassing a certain threshold are associated with predictions made with the Kalman Filter [12]. This association is based on either motion (IoU) or appearance (Re-ID) similarity. Then, the Hungarian Method [13] is used to assign the IDs based on the retrieved similarity information. If these associations are unsuccessful, the process is reiterated for detections with confidence below the set threshold through IoU in order to solve occlusions and background detections.  
Among object trackers, DeepSORT [14] also stands out for its effectiveness, as highlighted in various studies [15; 16]. However, given ByteTrack's superior performance over DeepSORT, we opted for ByteTrack [11] as the tracking algorithm in our research.
# Virtual Belt Algorithm for the Management of Isolated Autonomous Intersection
## 4 Collision Checking of Virtual Belts
### Collision Checking between Virtual Grids  
Consider two uniform VGs (\(G_{v}\) and \(G_{v}^{\prime}\)). \(G_{v}\) is born at \(t_{0}\) and \(G_{v}^{\prime}\) is born at \(t_{0}+\Delta t\). Depending on the VPs and the born times of these VGs. During the time of \((t_{0}+t_{\Delta},t_{0}+t_{c}+t_{\Delta})\), there are only two possible results of collision checking between the two VGs. The first one is that \(G_{v}\) collides with \(G_{v}^{\prime}\) in a certain time as plotted in Figure 8. The second one is that \(G_{v}\) never collides with \(G_{v}^{\prime}\) as shown in Figure 9.  
These VGs move along the corresponding VPs with the same speed. If \(G_{v}\) does not collide with \(G_{v}^{\prime}\) during the time range of \((t_{0}+t_{\Delta},t_{0}+t_{c}+t_{\Delta})\), then \(G_{v}\) will not collided with \(G_{v}^{\prime}\) at the time range of \((t_{0}+t_{c}+t_{\Delta},t_{0}+2t_{c}+t_{\Delta})\). Consequently, it can be deduced that \(G_{v}\) and \(G_{v}^{\prime}\) will not collide forever. Similarly, if \(G_{v}\) collides with \(G_{v}^{\prime}\) during the time range of \((t_{0}+t_{\Delta},t_{0}+t_{c}+t_{\Delta})\), then \(G_{v}\) will collide with \(G_{v}^{\prime}\) at each time circle in future. In another word, the future collision between two uniform VGs can be predicted based the result of collision checking in a time circle.  
```
0: two VGs (\(G_{v}\) and \(G_{v}^{\prime}\))
0: whether two VGs will collide with each other in the future
1: Initial the positions of \(G_{v}\) and \(G_{v}^{\prime}\)
2: Divide the time circle (\(t_{init}\), \(t_{init}+t_{c}\)) into small enough segments: \(\textbf{t}=(t_{init},t_{init}+t_{\Delta},t_{init}+2t_{\Delta},\cdots,t_{init}+t _{c})\)
3:for\(t\) in t do
4: Calculate the positions of \(G_{v}\) and \(G_{v}^{\prime}\) at the time of \(t\);
5:if\(G_{v}\) and \(G_{v}^{\prime}\) collide with each other then
6:return true
7:endif
8:endfor
9:return false
```  
**Algorithm 1** Collision checking of two VGs.  
If \(G_{v}^{\prime}\) collides with \(G_{v}\) in a time circle, \(G_{v}^{\prime}\) is called a CVG of \(G_{v}\). The total CVGs of a VG is defined as the CVGR of the VG. CVGR provides a simple but effective method to record the collisions among VGs. As all VGs repeat the same motion, a CVG of \(G_{v}\) collides with \(G_{v}\) in each time circle. And a VG that not belongs to the CVGs of \(G_{v}\) will not collide with \(G_{v}\) forever.
## 3 Physiological Signal Monitoring and WBSNs Overall System
### Compression of Physiological Signals  
There are physiological signals with great demand in data collection and transmission rate, for example in [19], sample rates for ECG and EEG are set at 240 samples/s and 500 samples/s respectively. This amount of data is a major challenge in terms of battery life for wireless sensors. The power consumed by transmitting one bit wirelessly is at least 480 times the energy consumed by a simple 32-bit addition instruction; therefore, most of the battery energy is consumed in radio communications [23]. For m-Health services in an emergency scenario, major technological concerns are power battery, data transmission and limited network resources. Data compression is also a major concern and a growing trend under scenarios that collect, store, process and transmit huge amounts of data [24].  
Compression has been used in WBSNs, with the most common method being wavelets. It is reported that this method can achieve nearly 80% compression in combination with neural networks [25]. For this study, an optimum threshold was used by considering an acceptable max absolute difference between the original signal and reconstructed signal less than 20. Another technique, based on predictive coding (a lossless compression technique), exploits redundancy between samples and has the advantage of reducing the total power consumption in the sensor nodes [26]. Although the actual compression techniques can achieve great reduction in data capacity and power consumption, there are still some challenges in compression technology techniques, e.g., to achieve a high level of data reduction without compromising the physiological signal integrity, and to develop methods that operate with a small processing power requirement.
## 4 Discussions
### Solar Radiation Influence on MMHg Photodegradation and DGM Production in Surface Water  
Because solar ultraviolet (UV-B) increases by around 10 \(\pm\) 20% for every 1000 m increase in elevation [58; 59], UV radiation intensity at Lake Uru Uru (3686 m a.s.l) is expected to be at least 40 to 80% higher than that measured at sea level [18; 60]. The large diurnal and seasonal temperature amplitude gradients between the dry season (12.2 to 20.7 \({}^{\circ}\)C) and the wet one (10.0 to 13.3 \({}^{\circ}\)C, Figure 2a) broadly reflect these gradients in solar radiation.  
For both seasons, filtered and unfiltered MMHg/THg decreased with increasing temperature, with the exception of the filtered phase during the wet season (Figure 5). This trend illustrates the strong influence of UV radiation on MMHg photodegradation [61] at both the diel and seasonal scales, with enhanced degradation of MMHg during the day and the dry season [18]. One cannot exclude higher activation of demethylating bacteria at higher temperatures [62].  
In addition, a positive correlation was found between DOC concentrations and percentages MMHg/THg (R = 0.66, \(p\) < 0.01) during the dry season, supporting the notion that rising DOC concentrations decreased the efficiency of MMHg photodegradation [63; 64]. Although DOC concentration was lower during the wet season, the absence of a relationship between MMHg and SW temperature or DOC for the filtered fraction suggests that photodemethy  
Figure 5: Plots of percentage MMHg/THg in (**a**) the filtered and (**b**) the unfiltered phases vs. surface water temperature. Crossed symbols indicate samples collected during the night-time period.  
sink for MMHg during this season. Indeed, lower insolation, higher water column, possible changes in the composition of photo-reactive DOC species, and/or different MMHg-DOC bindings [65] could have attenuated the radiation effect and reduced MMHg photodegradation efficiency. In particular, higher molecular weight dissolved OM, and a lower abundance of thiol functional groups (RS-) during the wet season compared to the dry one, could have attenuated MMHg photodegradation [66; 67]. As mentioned previously, one cannot exclude the possibility of lower biological activity in the wet compared to the dry season, which would also have resulted in lower MMHg biodegradation.  
As for MMHg, the overall low DGM (i.e., Hg(0)) concentrations in SW corroborate results from in situ incubations with Hg-enriched isotopes, which reported low biological and photochemical production of DGM in Lake Uru Uru [18]. Although DGM concentrations were low, the higher concentrations found during the dry season likely result from higher solar radiation, higher concentrations of suspended solids, as well as reducing bacteria/algae. Although solar radiations are the main fuel for DGM production during the day, rises in DGM found during the night support the notion of significant bacterial reductions in the dark [57].
# An Improved Multi-Chaotic Public Key Algorithm Based on Chebyshev Polynomials
## 2 Preliminary
### Chebyshev Chaotic Mapping
#### 2.1.2 Chaotic Property  
The Chebyshev map is a one-dimensional chaotic map with favorable nonlinear dynamic properties. Chaos occurs when the control parameter \(\mu\) is greater than \(1\). When greater than \(2\), the map remains in a chaotic state. Figure 2 shows the bifurcation diagram of the Chebyshev map. Figure 3 shows the bifurcation diagram of the improved Chebyshev chaotic map \(x_{n+1}=a\cdot(1-2x_{n}^{2})\).
###### Abstract  
This study investigates the transformation of biogas (methane and carbon dioxide) into high-value liquid products using Ni/Al\({}_{2}\)O\({}_{3}\), Fe/Al\({}_{2}\)O\({}_{3}\), and Ni-Fe/Al\({}_{2}\)O\({}_{3}\) catalysts in a non-thermal plasma (NTP)-assisted process within a dielectric barrier discharge (DBD) reactor, operating at room temperature and atmospheric pressure. We compared the effectiveness of these three catalysts, with the Ni-Fe/Al\({}_{2}\)O\({}_{3}\) catalyst showing the highest enhancement in conversion rates, achieving 34.8% for CH\({}_{4}\) and 19.7% for CO\({}_{2}\). This catalyst also promoted the highest liquid yield observed at 38.6% and facilitated a significant reduction in coke formation to 10.4%, minimizing deactivation and loss of efficiency. These improvements underscore the catalyst's pivotal role in enhancing the overall process efficiency, leading to the production of key gas products such as hydrogen (H\({}_{2}\)) and carbon monoxide (CO), alongside valuable liquid oxygenates including methanol, ethanol, formaldehyde, acetic acid, and propanoic acid. The findings from this study highlight the efficacy of combining NTP with the Ni-Fe/Al\({}_{2}\)O\({}_{3}\) catalyst as a promising approach for boosting the production of valuable chemicals from biogas, offering a sustainable pathway for energy and chemical manufacturing.  
Keywords:biogas; nonthermal plasma; catalysis; oxygenates; acetic acid Article
## 3 Results
### Technical Assessment on the Suitability of GS Incorporation in Structural Concrete  
Table 5 reports the results of apparent density, compressive strength, a slump of the concrete sample containing 5% \(w/w\) GS (Concrete 5% GS), and the reference structural concrete (C30/37.S4.D22.CI0,40).  
The addition of 5% \(w/w\) GS caused the decrease of the apparent density from 2408 kg/m\({}^{3}\) to 2383 kg/m\({}^{3}\). This decrease is due to the higher water requirement for the new concrete mixture, which passes from 166 kg/m\({}^{3}\) (C30/37.S4.D22.CI0,40) to 180 kg/m\({}^{3}\) (Concrete 5% GS). The higher water amount increases the theoretical W:C ratio from 0.52 to 0.56 and, as a direct consequence, reduces the compressive strength of Concrete 5% GS by a percentage of 10% when compared to C30/37.S4.D22.CI0,40). The compressive strength values decreased from 45 Mpa (C30/37.S4.D22.CI0,40) to 40.1 Mpa at 28 days of curing time in the concrete 5% GS sample.  
\begin{table}
\begin{tabular}{c c} \hline
**Parameter** & **Value** \\ \hline  & \% \\ \hline F & 0.22 \\ Na\({}_{\text{2O}}\) & 3.25 \\ MgO & 0.43 \\ Al\({}_{\text{2O}}\)\({}_{\text{3}}\) & 15.62 \\ SiO\({}_{\text{2}}\) & 70.03 \\ P\({}_{\text{2O}}\)\({}_{\text{3}}\) & 0.53 \\ SiO\({}_{\text{3}}\) & 0.11 \\ C1 & 0.01 \\ K\({}_{\text{2O}}\)\({}_{\text{4}}\) & 5.94 \\ CaO & 0.62 \\ TiO\({}_{\text{3}}\) & 0.24 \\ Cr\({}_{\text{2O}}\)\({}_{\text{5}}\) & 0.01 \\ MnO & 0.02 \\ Fe\({}_{\text{2O}}\)\({}_{\text{3}}\) & 1.63 \\ CuO & 0.01 \\ ZnO\({}_{\text{2O}}\) & 0.012 \\ Ga\({}_{\text{2O}}\)\({}_{\text{3}}\) & 0.004 \\ Rb\({}_{\text{2O}}\)\({}_{\text{3}}\) & 0.048 \\ SiO & 0.007 \\ ZrO\({}_{\text{2O}}\) & 0.014 \\ Nb\({}_{\text{3}}\)\({}_{\text{5}}\) & 0.003 \\ BaO & 0.017 \\ PbO & 0.005 \\ \hline LOI & 1.220 \\ \hline \end{tabular}
\end{table}
Table 4: XRF analysis and Loss of Ignition (LOI) of GS sample.  
Figure 3: XRD of dried GS.  
Similarly, the spread of Concrete 5% GS increased, with a slump value changing from 190 mm (Concrete C30/37.S4.D22.CI0,40) to 210 mm (Concrete 5% GS) (Table 5). Anyway, to guarantee the same technical performance as the reference composition, the reduction of the compressive strength of the Concrete 5% GS can be compensated by increasing the percentage of cement or using additives in the mixture, which inevitably causes additional environmental and economic costs.  
It must be noticed that, before its use in concrete mixes, GS must be dried since this by-product is characterised by a residual moisture content of 23.5% \(w/w\) and can't be mixed wet in the common industrial mixing process. This implies the introduction of an additional drying step before GS incorporation in the concrete mixture, thus increasing the environmental and economic costs.
## 5 In-Structure Response Spectra  
### Dynamic Analysis  
Three slabs and a wall were selected for the location of the equipment where the ISRS was computed, as marked with red boxes in Figure 7. Considering that it is reported that the out-of-plane local vibration has a remarkable equipment-structure interaction effect, a slab with a larger span and a wall with a taller clear height were selected. The ISRS was calculated for two horizontal directions and one vertical direction at the center of each selected slab or wall. The three components of the ground motion were assumed to act simultaneously. Modal time-history analysis was performed using the Newmark method with an assumption of constant acceleration, and a total of 1783 vibration modes were included. The block Lanczos eigenvalue extraction method was adopted for large symmetric eigenvalue problems [36]. The time increment was set as 0.005 s. The mass of the equipment was assumed as 1%, 0.1%, and 0.01% of the entire building mass.  
Both decoupled ISRS that does not consider the equipment-structure interaction and coupled ISRS that considers the equipment-structure interaction were calculated and compared at the selected equipment locations. The decoupled ISRS was calculated based on the response acceleration at each equipment location, which was obtained from the response analysis of the auxiliary building decoupled from the equipment represented by Equation (7) using ANSYS. The coupled ISRS was calculated directly as the acceleration response of the coupled two-DOF model represented by Equation (18) using the MATLAB code. For this procedure, the mechanical impedance expressed by Equation (15) was calculated as the inverse of the complex displacement response at the equipment location to the complex harmonic force with unit amplitude at the same node using ANSYS. The mechanical impedance at equipment location 1 in the H1 direction is plotted in Figure 8. It is noted that all ISRS in this study were calculated at frequencies from 0 to 20 Hz with an increment of 0.01 Hz. The equipment frequency was varied by changing the equipment stiffness keeping the equipment mass constant. The mechanical impedance of the primary structure was calculated up to 50 Hz, which is higher than the Nyquist frequency of the 20 Hz oscillator, with an increment of 0.1 Hz.  
Figure 7: Monitored node: (**a**) 6 th-floor slab (25 m); (**b**) wall (north wall).