esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
### Reconstruction  
The reconstruction process was adapted to the needs of long measurements. It fixes specific trustworthy regions based on the accelerometer data and minimizes the baseline differences in between these trustworthy parts. ABAMAR (as AMARA) does not necessarily correct the baseline at every detected MA, because it determines if the baselines difference exceeds 2.6 \(\times\) SD (Equations (13) and (14)). MARA and ABAMAR are based on a forward reconstruction, which adjusts the baseline of the next segment to the previous one (Figure 4a). This is practical for functional brain activation studies, where the total measurement time is less than 30 min and the absolute time course is not of interest. However, as shown in Figure 4b, for measurements of longer duration (hours) this method tends to drift continuously and leads to unrealistic values, e.g., the [O\({}_{2}\)Hb] and [HHb] showed a decreasing instead of an increasing trend. This may be related to a very slow hemodynamic response after a MA: often a drift occurs for several minutes before returning to baseline levels [63]. Such a drift was not visible after every artifact and was non-linear. Thus, the true baseline cannot be determined and it is difficult to detect such movement-induced slow artifacts. They may be caused by a physiological reaction, e.g., due to a change in head position [67]. Although the light coupling is canceled by the employed self-calibrating algorithm [44], under certain circumstances, light coupling changes still have a small influence on [O\({}_{2}\)Hb] and [HHb]. On the other hand, such drifts may also reflect real changes in the brain oxygenation, for example caused by transient changes in breathing and/or arterial CO\({}_{2}\)[39; 68; 69], but it is most likely a combination of all these factors. AMARA could have been implemented in a way to eliminate any drifts occurring during a few minutes (e.g., 2 min) after an artifact--but also real signal changes would have been suppressed. In Figure 4b, it is obvious that parts of the signal were missing. Due to saturated detector channels, parts of the signal were lost and were not displayed. These missing data, however, have no influence on the reconstruction, whenever the length of the missing data is either short or the missing data are surrounded by \"condition free\" segments (both is the case in Figure 4b). In the former case, a small signal change can be assumed for the missing data. In the latter case \"true\" fixation points exist and the data still can be adjusted properly. If both conditions are not fulfilled, e.g. the missing data length is longer than for the longest \"condition free\" segment and there is no \"condition free\" segment on one side of the missing data, the reconstruction could get instable and signals drifts to unrealistic values.
## 3 Methodology
### Semantic Extraction: Recognizing Objects, Places and Their Relations  
In order to work with the high-level semantic features, it is important to have thorough information regarding the composition of each frame (e.g., objects, persons, and places in the frame). Since broadcast videos do not carry that much frame-level semantic information, it is necessary for our pipeline to have a good model that can predict, with high accuracy,the objects and places in a frame. As seen in Figure 2, frame-level semantic extraction is a common step for all the tasks dealt with in the paper--from shot/LSU boundary prediction to object timeline generation.  
Feature Extraction  
We make use of low-level and mid-level visual information for predicting the high-level features that are necessary to determine the semantic composition of a logical story unit. In our approach we use object, person and location tags as high-level features for detecting the LSU boundaries. To obtain the object and person annotations, the latest version of the YOLO object detector [12], pre-trained on the COCO dataset [22], is used. COCO stands for Common Objects in Context. The dataset comprises of 1.5 million object instances covering 80 object classes. Along with the object detector, the place or the location of the scenes are predicted using the ResNet-50 CNN architecture, pretrained on the places-365 dataset [11]. This dataset contains more than 10 million images in total, comprising 400+ unique scene categories [23].
## 3 Experiments and Discussion
### Evaluation of the Performance of Measurement Uncertainty Modelling Using the Gaussian Process  
To evaluate the performance of the Gaussian process model for modelling the measurement uncertainty for a multi-sensor CMM, a repeated measurement experiment using a touch trigger probe was conducted and compared with the specification of the sensor and the result of using Gaussian process modelling. A workpiece was designed as shown in Figure 16 and eight points (marked A-H) were measured 50 times with a purposely-designed DMIS (Dimensional Measuring Interface Specification) programming language [25]. The eight points were chosen to be on a flat surface and curved surface with different tilting angles. The results show good agreement with similar distribution and Figure 17 shows one of the measurement results in a histogram and fitted Gaussian distribution. The standard deviation of the fitted Gaussian distribution is about 0.7 \(\upmu\)m, which is at the same level of the specification of the tough trigger probe (0.65 \(\upmu\)m).  
The freeform surface shown in Figure 5a was measured using the tough trigger probe with a dense measurement (0.5 mm pitch). The measured data were modelled using Gaussian process modelling and the mean surface and the associated uncertainty are shown in Figure 18. The RMS of the uncertainty is 0.74 \(\upmu\)m which is slightly larger than the specification of the probe and the experimental standard deviation value. The result demonstrates the effectiveness of the Gaussian process method to model measurement uncertainty. It should be noted that the uncertainties in the edge and corner areas are large due to the lack of data points in those areas and the measurement uncertainties are different with different measurement density, with less measurement points or larger sampling pitch, so it is clear that the measurement uncertainty is larger due to the reduction of measurement data.  
Figure 16: The designed and machined workpiece and the repeated measurement positions (A–H) (unit in mm).  
Figure 17: Evaluation result for the measurement uncertainty for the trigger probe using the repeated measurement method.
## 2 Research Area and Data
### Data Sources
\begin{table}
\begin{tabular}{c c c c} \hline
**Dimensionality** & **Variable** & **Calculation Method** & **Illustrate** \\ \hline \multirow{4}{*}{Density} & Resident population density (X1) & Distribution density of the resident population within the unit & Reflect the distribution characteristics of the resident population \\ \cline{2-4}  & Building density (X2) & Ratio of the sum of built-up area within the unit to the unit area & Reflect the degree of building coverage \\ \hline \multirow{4}{*}{Density} & Functional density (X3) & Total count of POIs within the unit to the unit area & Reflect the concentration degree of functional types \\ \cline{2-4}  & Unit area (X4) & Area of the spatial unit & Reflect the unit area \\ \hline \multirow{4}{*}{Design} & Building height (X5) & Mean value of the heights of all buildings in the unit & Reflect the three-dimensional form of buildings \\ \cline{2-4}  & & Mean value of the fractional vegetation cover (FVC) within the unit & Reflect the vegetation coverage \\ \hline \multirow{4}{*}{Diversity} & Morphological compactness (X7) & Degree of compactness of the spatial form within the unit & Reflect the compactness of spatial form \\ \cline{2-4}  & Functional mixing degree (X8) & Mixing degree of different types of POIs within the unit & Reflect the degree of functional diversity \\ \hline \multirow{4}{*}{Metro accessibility (X9)} & Linear distance from the spatial centroid of the block to the nearest metro station/Mean value of the kernel density of metro stations within the subdistrict & Reflect the convenience of metro transportation \\ \cline{2-4}  & & Linear distance from the spatial centroid of the block to the nearest bus station/Mean value of the kernel density of bus stations within the subdistrict & Reflect the convenience of bus transportation \\ \hline \multirow{4}{*}{Distance to Transit} & Road network density (X11) & Ratio of the total length of the road network within the unit to the unit area & Reflect the density of road distribution \\ \cline{2-4}  & Commercial facilities & Ratio of the total quantity of commercial facilities within the unit to the unit area & Reflect the distribution density of commercial facilities \\ \cline{2-4}  & & Public facilities & Ratio of the total quantity of public facilities within the unit to the unit area & Reflect the distribution density of public facilities \\ \hline \multirow{4}{*}{Destination Accessibility} & Cultural and leisure facilities (X14) & Ratio of the total quantity of cultural and leisure facilities within the unit to the unit area & Reflect the distribution density of cultural and leisure facilities \\ \cline{
# Optimal Sizing of Battery Energy Storage System for Implicit Flexibility in Multi-Energy Microgrids
## 2 Methodology
### System Modeling and Objective Function
#### 2.5.2 Thermal and Cooling Circuit  
For simplicity, the cooling power demand \(P_{u,t}^{COOL_{load}}\) is converted into its thermal equivalent \(P_{u,t}^{CO\widetilde{O}_{Load}}\). This assumption reflects the configuration of the system under analysis, which includes an absorption chiller that although not explicitly modeled in this study, can utilize the CHP's thermal output to produce cooling power. This potential use improves the overall efficiency of CHP self-production during summer and must be considered when optimizing system operations. Accordingly, Equation (54) ensures that the sum of the thermal load and the cooling demand (converted into thermal equivalent) is covered by the CHP and boiler units. Unlike the electricity vector, the thermal balance allows for excess production, accounting for the practical possibility of dissipating CHP heat surplus into the atmosphere:  
\[\begin{array}{l}\sum\
olimits_{u=1}^{U_{TH_{load}}}\left(p_{u,t}^{TH_{load}} \right)+\sum\
olimits_{u=1}^{U_{COOL_{load}}}\left(p_{u,t}^{C\widetilde{O}_{ Load}}\right)\leq\sum\
olimits_{u=1}^{U_{CHP}}\left(p_{u,t}^{TH_{CHP}} \right)+\\ \sum\
olimits_{u=1}^{U_{ boiler}}\left(p_{u,t}^{TH_{build}}\right).\end{array} \tag{54}\]
###### Abstract  
The Hongtoushan Cu-Zn volcanogenic massive sulfide (VMS) deposit, located in the Hubei granite-greenstone terrane of the North China Craton, has undergone a complex, multi-stage metallogenic evolution. The deposit comprises three main types of massive ores: Type-1 ores, characterized by a sulfide matrix enclosing granular quartz and dark mineral aggregates; Type-2 ores, distinguished by large pyrite and pyrrhotite porphyroblastsats and a small amount of gangue minerals; and Type-3 ores, mainly distributed in the contact zone between the ore body and gneiss, featuring remobilized chalcopyrite and sphalerite filling the cracks of pyrite. The metallogenic process of the Hongtoushan deposit is divided into three main stages: (1) an early mineralization stage forming Type-1 massive ores; (2) a metamorphic recrystallization stage resulting in Type-2 massive ores with distinct textural features; and (3) a late-stage mineralization event producing Type-3 massive ores enriched in Cu, Zn, and other metals. This study integrates sulfur isotope, trace elements, and fluid inclusion data to constrain the sources of ore-forming materials, fluid evolution and metallogenic processes of the deposit. Sulfur isotope analyses of sulfide samples yield \(\delta^{34}\)S values ranging from \(-0.7\) to \(4.2\) (mean: \(1.8\pm 1.5\), \(1\sigma\)), suggesting a predominant magmatic sulfur source with possible contributions from Archean seawater. Trace element analyses of pyrite grains from different ore types reveal a depletion of rare earth elements, Cu, and Zn in Type-2 massive ores due to metamorphic recrystallization, and a subsequent re-enrichment of these elements in Type-3 massive ores. Fluid inclusion studies allowed for identifying three types of ore-forming fluids: Type-1 (avg. T\({}_{h}\): 222.9; salinity: 6.74 wt.% NaCl eqv.), Type-2 (avg. T\({}_{h}\): 185.72; salinity: 16.56 wt.% NaCl eqv.), and Type-3 (avg. T\({}_{h}\): 184.81; salinity: 16.22 wt.% NaCl eqv.), representing a complex evolution involving cooling water-rock interaction and fluid mixing. This multi-disciplinary study reveals the interplay of magmatic, hydrothermal and metamorphic processes in the formation of the Hongtoushan VMS deposit, providing new insights into the fluid evolution and metallogenic mechanisms of similar deposits in ancient granite-greenstone terranes.  
VMS; sulfide; geochemistry; sulfur isotope; fluid inclusion Article
## 5 Life Cycle Impact Assessment Results
### Interpretation  
Regarding global warming (GWP), the life cycle of WCO decreases this indicator by 94.6% compared with RPO. The primary source of greenhouse gases in the RPO production cycle is methane production from wastewater and landfill emissions in the oil extraction phase. In terms of abiotic depletion, WCO performs 97% better than RPO because of the reduced use of processing facilities and the avoidance of fertilizers (mainly urea) in its production. RPO performed worse than WCO in terms of acidification, mainly because of ammonia release derived from the use of urea as a fertilizer and the use of pesticides in the agricultural production phase. Regarding eutrophication potential, RPO presents a 90% greater contribution than WCO because of the chemical oxygen demand (COD) and nitrogen present in the wastewater at the extraction phase. On the other hand, WCO performs 7.56 times better than RPO regarding ozone depletion over five years. In terms of human toxicity at 20 years, WCO performs 95% better than RPO. The results show 27% better performance of WCO when compared with RPO regarding photochemical oxidation. In terms of terrestrial and marine ecotoxicity, mainly because of the use of herbicides, RPO performs 98% and 71% worse than WCO, respectively. The results comparison for each impact category is illustrated in Figure 7.
# Hidden Markov Model-Based Dynamic Hard Shoulders Running Strategy in Hybrid Network Environments
## 4 Numerical Results and Discussion  
### Scene Introduction  
#### 4.1.1 Scene Characterization  
In this section, we analyze the effectiveness of the traffic breakdown prediction model and the hard shoulder opening strategy in the context of the hybrid network traffic flow described above. The experiment was carried out through the secondary development of SUMO and MATLAB, and the studied scenario is shown in Figure 6. The experimental scenario was selected from the basic section of the motorway, length = 5500 m, the number of lanes in the section was 3, and the speed limit value of the section was 110 km/h. The length of the hard shoulder was 400 m, and the speed limit value was 80 km/h(ref. [13] showed that the speed of vehicles on the hard shoulder tends to be lower than 80 km when the hard shoulder is open). The basic characteristics of the traffic flow in the simulation scenario at different penetration rates are shown in Figure 7. The basic plots of density versus traffic flow of the road section at different penetration rates are presented in Figure 7a,b, from which it can be seen that the capacity of the road section gradually increases with the increase in the CAV penetration rate. The speed versus roadway density and speed versus roadway flow for different penetration rates are shown in Figure 7c,d. From Figure 7c,d, as the CAV penetration rate increases, the road section density and flow rate increase at the same speed.  
In addition, the historical data were processed to calculate the average speed \(V_{mean}\) and the standard deviation \(\sigma\) of the corresponding speed within the road section every 5 min, as shown in Figure 8a. Since the speed crash corresponds to the time period when the speed shows a sharp decrease, the average speed corresponding to the time period with the largest standard deviation \(\sigma\) is defined as \(V_{c}\), the crash speed. In this experiment,  
Figure 6: Simulation scene construction.  
Figure 7: Basic characteristics of motorway traffic flow: (**a**) Density flow relationship at each \(\rho\); (**b**) Density-flow diagram; (**c**) Density-velocity diagram; (**d**) Velocity-flow diagram.  
the permeability was selected as 0, 0.2, \(\ldots\), 1, and the corresponding crash speed of the road section was analyzed as shown in Figure 8b, from which it can be seen that as the penetration rate increased, the crash speed of the road network also showed the above trend.
# Evaluation of 5G Positioning Performance Based on UTDoA, AoA and Base-Station Selective Exclusion
## 2 Signal Model
### Observable Calculation
#### 2.2.2 Observable Based on Angle of Arrival
Once the orientation of the BS arrays is known, it is then possible to calculate the azimuth and elevation angles using the formulas:  
\[\phi_{n}=a\tan 2\left(\frac{Y^{\prime}}{X^{\prime}}\right), \tag{9}\]  
where \(Y^{\prime}=(y-y_{BS,n})\cos\zeta-(z-z_{BS,n})\sin\zeta\) and \(\zeta\) is the computed array orientation,  
\[\theta_{n}=a\cos\left(\frac{Z^{\prime}}{d}\right), \tag{10}\]  
where \(Z^{\prime}=(y-y_{BS,n})\sin\zeta+(z-z_{BS,n})\cos\zeta\) and \(d=\sqrt{X^{\prime 2}+Y^{\prime 2}+Z^{\prime 2}}\). Both \(Y^{\prime}\) and \(Z^{\prime}\) are derived from (7). Since the 5G signal for one symbol is completely known, the formulas of CRB for angular estimation are derived as in [18]:  
\[\sigma^{2}_{\theta_{n},CRLB}=\frac{6}{C/\sigma^{2}MNN_{s}(\frac{w_{c}d}{c})^{ 2}\cos^{2}\theta_{n}}\frac{1}{[(N^{2}-1)\sin^{2}\phi_{n}+(M^{2}-1)\cos^{2} \theta_{n}]}, \tag{11}\]  
\[\sigma^{2}_{\phi_{n},CRLB}=\frac{6}{C/\sigma^{2}MNN_{s}(\frac{w_{c}d}{c})^{ 2}\sin^{2}\theta_{n}}\frac{1}{[(N^{2}-1)cos^{2}\phi_{n}+(M^{2}-1)\sin^{2} \theta_{n}]}, \tag{12}\]  
where \(w_{c}=2\pi f_{c}\), \(C/\sigma^{2}\) is the SNR of the 5G signal and \(N_{s}\) is the number of subcarriers per bandwidth allocated. Note that increasing the number of antenna elements reduces the angle errors. The \(n\)-th 5G AoA observables of elevation and azimuth are computed using the following formulas, respectively:  
\[\theta_{obs,n}=dirBS2 UE_{\theta_{n}}+\sigma_{\theta_{k}}\cdot\alpha,\quad \alpha\sim N(\mu,\sigma^{2}), \tag{13}\]  
\[\phi_{obs,n}=dirBS2 UE_{\phi_{n}}+\sigma_{\phi_{k}}\cdot\beta,\quad\beta\sim N (\mu,\sigma^{2}), \tag{14}\]  
where \(\sigma_{\theta_{k}}\) and \(\sigma_{\phi_{k}}\) are the angle errors calculated in (11) and (12), \(dirBS2 UE_{\theta_{n}}\) and \(dirBS2 UE_{\phi_{n}}\) are the azimuth and elevation angles calculated in (9) and (10), respectively.
## 5 Discussion
The lower number of events available using only one level of completeness leads to larger confidence regions. In particular, for normal events, the confidence region computed with the classical method (light green curve in Figure 8) is much bigger than the one computed with the new method (green curve in Figure 8). As expected, a larger amount of available information leads to smaller uncertainties in the estimated parameters, especially in this case. Notably, central values (MLEs) also change, correcting potential biases. For example, the MLE for the entire catalog results outside the confidence bounds defined using more data. The different shapes of the confidence region considering the whole catalog and the sub-catalogs show the importance of separating the contribution of different classes of earthquakes to correctly interpret their behavior. Indeed, the averaged behavior estimated from the complete catalog is substantially incompatible with the actual behavior of each single seismicity class. Indeed, by applying the global statistics (obtained by the full catalog) to each individual class, we would implement the wrong statistics to different classes, impacting the hazard in a different way. In our case study for the Atlantic ridge, we would artificially increase the probability of high magnitude normal events. On the contrary, compared with strike-slip events, we demonstrated that normal earthquakes have a significantly smaller corner magnitude coupled with a significantly larger b-value, resulting in a smaller probability of high magnitude normal events. This not only may complicate the interpretation of the parameter estimation, but also may have a significant impact on hazard quantifications. For example, most of the recent ground motion prediction equations to estimate the attenuation of seismic waves from  
Figure 8: 95% Confidence region estimation (curves) and maximum likelihood estimations MLEs (dots) for the classical estimation approach (light colors: gray, pink, and light green) and the new estimation approach (dark colors: black, red, and green). As in Figure 7, we report both results using for the whole catalog (gray and black), and the sub-catalogs with strike-slip events (pink and red), and with normal events (light green and green). the source to target are dependent on faulting mechanisms, applying different attenuation laws to the different mechanisms (e.g., [30]). This is probably even more impacting is tsunami hazard, where different mechanisms have a different capability of deforming the sea bottom, resulting in different tsunamigenic capabilities (e.g., [31]). For example, normal events are typically more tsunamigenic than strike-slip events. For not introducing artificial bias in hazard quantification, it will be therefore fundamental to individuate potential mechanism-dependent variation of earthquake statistics and apply hazard models allowing for the aggregation of multiple classes of seismicity (e.g., [32]).