esa-sceva/satcom-chunk-collection huggingface dataset:
# Assessment of BeiDou-3 and Multi-GNSS Precise Point Positioning Performance
## 5 Data Tests and Results Analysis
### Positioning Accuracy and Convergence Time
#### 5.1.1 Static Results and Analysis
Additionally, it is clear that the GPS/BDS-2/BDS-3/GLONASS/Galileo PPP positioning accuracy is as good as dual-constellation PPP. Figure 4: Number of satellites and PDOP at BRECH for GPS-only, BDS-2-only, BDS-2/BDS-3, GLONASS-only, Galileo-only, and GPS/BDS-2/BDS-3/GLONASS/Galileo on DOY 024 in 2019. Figure 6 depicts the convergence time for GPS-only, BDS-2-only, BDS-2/BDS-3 GLONASS-only, Galileo-only, GPS/BDS-2/BDS-3, GPS/GLONASS, GPS/Galileo, BDS-2/BDS-3/GLONASS, BDS-2/BDS-3/Galileo, GLONASS/Galileo, and GPS/BDS-2/BDS-3/GLONASS/Galileo static PPP processing while using real observation data collected at 16 stations over 30 days. In this research, the convergence criterion is defined when the component of positioning errors is less than 0.1 m and keeps within 0.1 m in the subsequent epochs. It is seen that the BDS-2-only PPP takes a much longer time to converge over the GPS-only, GLONASS-only, and Galileo-only PPP. With the development of BDS, the BDS-2/BDS-3 PPP reduces the convergence time by 27.15%, 27.87%, and 35.76% over the BDS-2-only PPP in three coordinate components, respectively. Whilst the BDS performance improves a lot, it is nevertheless a crucial problem that the convergence time is a little poorer than GPS-only PPP. When considering that the precise orbit determination strategy of BDS-3 is still immature and the BDS-3 constellation is not completed, this could be better with the development of the BDS. The performance of Galileo is better in terms of convergence time with the completion of Galileo constellation. Additionally, the convergence time of dual-constellation PPP is reduced over the single-constellation PPP and the GPS/BDS-2/BDS-3/GLONASS/Galileo PPP reduces the convergence time by 20.12%, 27.93%, and 46.09% over GPS-only PPP in the north, east, and up directions, which does further shorten the convergence time over single-constellation and dual-constellation PPP. Figure 5: The root mean squares (RMS) error for GPS-only, BDS-2-only, BDS-2/BDS-3, GLONASS-only, Galileo-only, GPS/BDS-2/BDS-3, GPS/GLONASS, GPS/Galileo, BDS-2/BDS-3/GLONASS, BDS-2/BDS-3/Galileo, GLONASS/Galileo, and GPS/BDS-2/BDS-3/GLONASS/Galileo static PPP processing using real observation data collected at 16 stations over 30 days.
## 1 Introduction
The observation intervals and the pixel representative area of satellite SIF are relatively large, which may result in a mixture of different vegetation cover types, leading to a poor match with ground eddy covariance flux data [30]. Ground-based SIF observations have temporal continuity and spatial representativeness [31], which can refine ground observation datasets and provide a suitable choice for thorough analysis of the relationship between SIF and Tr. The temporal scale can influence the relationship between SIF and Tr, with a higher correlation being observed at lower spatiotemporal resolutions [32]. At a detailed temporal scale, the estimation capability of SIF for Tr declined, which could be due to the complex response of the relationship between photosynthesis and transpiration to environmental changes. Moreover, because SIF signals are substantially weaker than background radiance, random noise in high-temporal-resolution measurements can significantly compromise the SIF-Tr relationship. [PERSON] et al. [18] found that vapor pressure deficit (VPD) may affect SIF-Tr, and [PERSON] et al. [26] enhanced the relationship between SIF and stomatal conductance (gc) after incorporating VPD. Similarly, the asynchronous response of SIF and photosynthesis to environmental changes can introduce significant uncertainty when utilizing SIF. Comparing different ecosystems revealed that the correlation between SIF and Tr is higher in croplands and forests, while it is lower in grasslands or sparse woodlands with lower canopy coverage [24]. Overall, the spatiotemporal scale and type of vegetation affect the relationship between SIF and Tr. Previous research has primarily focused on comparisons between different vegetation functional types, often overlooking the impact of species-specific factors. Compared to croplands, forests have more complex canopy structures and physiological variations, leading to greater inter-species differences in the ability of SIF to estimate Tr. Therefore, refining observations of SIF and Tr for different tree species at high temporal resolution can enhance the understanding of how the relationship responds to environmental factors and improve the capability of SIF in estimating Tr. Based on the differences in water-conducting tissues, woody plants can be categorized into ring-porous, diffuse-porous, and non-porous (tracheid) species. Trees adopt specific coping strategies under drought, which lead to differences in stomatal behavior and water use [33]. We chose Chinese cork oak (_Quercus variabilis_ var. _variabilis_, ring-porous), poplar (_Populus nigra_ var. _italica_, diffuse-porous), and arborvitae (_Platycladus orientalis_, non-porous) plantations to clarify the relationship between SIF and Tr and its response to environmental changes in different forest systems. The aims of this study are (1) to compare the temporal dynamics and patterns of SIF, Tr, and SIF-Tr relationship in three plantations; (2) to analyze the response of the SIF-Tr relationship to environmental factors; and (3) to evaluate Tr model predictability driven by SIF and environmental factors.
## 2 Materials and Methods
For this analysis, the modified Becker-Kistiakowsky-Wilson (MBKW) equation of state is used, given that earlier research demonstrated that it best explains the behavior of low-density explosives [35]. Different approaches have been used to estimate the strength of an explosive relative to TNT based on the results of thermochemical calculations. Basically, all the approaches are based on the interpretation of the relative strength in terms of certain detonation properties [23; 24]. Most frequently, the heat of detonation (Q) is used to estimate the TNT equivalent [23]:  
\[TNT_{e}(Q)=\frac{Q}{Q_{TNT}}\times 100 \tag{4}\]  
In [24], the authors analyzed the accuracy of several approaches for the evaluation of relative strength in terms of the Trauzl test and found that the best concurrence between experimental results and calculation is obtained using the product between the heat of detonation (Q) and the square root of the volume of detonation products (V\({}_{0}\)):  
\[TNT_{e}(QV_{0})=\frac{QV_{0}^{0.5}}{\big{(}QV_{0}^{0.5}\big{)}_{TNT}}\times 100 \tag{5}\]  
In this study, we also used the detonation energy, \(E_{0}\) (i.e., maximum available energy for doing mechanical work), the detonation pressure (p\({}_{\text{CJ}}\)), and velocity of detonation (D) to estimate the TNT equivalent:  
\[TNT_{e}(E_{0})=\frac{E_{0}}{E_{0(TNT)}}\times 100 \tag{6}\]  
\[TNT_{e}\big{(}p_{CJ}\big{)}=\frac{p_{CJ}}{p_{CJ(TNT)}}\times 100 \tag{7}\]  
\[TNT_{e}(D)=\frac{D}{D_{(TNT)}}\times 100 \tag{8}\]  
The detonation energy in Equation (6) is derived from the expansion isentrope of detonation products as described in [36].
# Data Synchronization: A Complete Theoretical Solution for Filesystems
## 5 The Reconciler--Synchronizing Two Replicas
This is because these sets are disjoint and if \(\sigma\) in the first set and \(\tau\) in the second set are on comparable nodes, then they are \(\ll\)-connected (as both are in the canonical set \(M\)), thus in any ordering of \(M\) which honors \(\ll\) they are in a fixed order, but in \(A_{2}\circ(B_{2}\smallsetminus A_{2})\)\(\sigma\) comes before \(\tau\), and in \(B_{2}\circ(A_{2}\smallsetminus B_{2})\)\(\tau\) comes before \(\sigma\), which is a contradiction. Let \(\Phi\) be a filesystem to which both \(A\) and \(B\) can be applied. Again by Proposition 10, \(A_{2}\Subset A\), thus \(A_{2}\) is also applicable to \(\Phi\), and similarly \(B_{2}\) is applicable to \(\Phi\). By Proposition 11, \(M=A_{2}\cup B_{2}\) is also applicable to \(\Phi\), which is the first claim in Theorem 2 (c). For the other two claims observe that \(A_{1}=A\smallsetminus M=A\smallsetminus A_{2}\) and \(B^{\prime}=M\smallsetminus A=M\smallsetminus A_{2}\). We know \(A_{2}\Subset A\), thus \(A\circ A_{1}^{-1}=A_{2}\circ A_{1}\circ A_{1}^{-1}\), which is applicable to \(\Phi\) and gives \(A_{2}\Phi\). Since \(A_{2}\Subset M\) and \(M\) is applicable to \(\Phi\), \(M\smallsetminus A_{2}=B^{\prime}\) is applicable to \(A_{2}\Phi\). All together, \(A\circ A_{1}^{-1}\circ B^{\prime}\) is applicable to \(\Phi\), as claimed. Similar reasoning gives that \(B\circ B_{1}^{-1}\circ A^{\prime}\) is also applicable to \(\Phi\).
## 4 Experiments  
### Dataset  
To validate the effectiveness of the proposed bimodal urban economic activity evaluation model, this experiment utilizes a multi-source urban dataset for training and evaluation. The dataset comprises data from two major sources:  
Satellite images: High-resolution satellite images were sourced from the Sentinel-2 satellite dataset, which provides multi-spectral images covering various economic zones  
Figure 6: Cross-modal alignment and multimodal integration module (CMAF) based on Swin Transformer V2: Introduced before the attention module, it uses cross-modal attention to align image and text features. Multimodal position encoding: add position encoding for different modalities.  
(e.g., commercial, residential, and industrial areas) in multiple cities. All images were resized to \(512\times 512\) resolution, and color space normalization was applied to standardize inputs.  
Street view images: Public street view datasets, the OpenStreetMap (OSM) data, were used to capture detailed information on urban infrastructures, including buildings, roads, traffic, and green spaces. A pre-trained Mask R-CNN model was employed for semantic segmentation and object detection, labeling buildings, roads, and green areas in the images, which resulted in the generation of scene graphs. By employing these publicly available datasets, the model can leverage diverse urban contexts and enhance the generalizability of the evaluation results across different economic zones.  
The dataset used in this study comprises high-resolution satellite images and street view images, which are classified into different categories based on urban regions, such as commercial, residential, and industrial areas. The data are labeled with semantic information that allows us to generate scene graphs for each image, capturing the spatial relationships between various objects. The dataset includes a total of 45,000 images, divided as follows: commercial areas: 10,000 images with 8000 scene graphs; residential areas: 15,000 images with 12,000 scene graphs; and industrial areas: 8000 images with 6000 scene graphs.  
Table 1 summarizes the dataset distribution across different categories, showing the number of images and corresponding scene graphs used for analysis. The dataset's diversity and large number of labeled scene graphs allow the model to be trained effectively on a variety of urban environments, ensuring the generalizability of the results.
## 3 Results and Discussion
### DNA Fingerprinting  
Five SSR markers were used to evaluate the diversity of the samples at the genetic level. Due to their high mutation rate and extensive genome coverage, these markers have been successfully adopted in various applications, including plant DNA fingerprinting [46]. In addition, the SSR markers assayed in this study belong to the robust and highly informative microsatellite-based genetic identity kit set up by [PERSON] et al. [47], and have been proposed as a reference for standardizing potato germplasm analyses across laboratories. In total, 21 alleles were identified, with an average of 4.2 alleles per locus. The number of alleles per marker varied from 2 (locus STP0 AC58) to 7 (locus STI 001) (Table 10, Appendix A). To evaluate the strength of the relationship among the analyzed samples, an UPGMA dendrogram was built (Figure 4). The genetic distances between potatoes studied here varied from 0.52 (between AG and TU) to 1.00 (GA and DE, PI and TU), with an average value of 0.74 (Table 11, Appendix A). Overall, the dendrogram allowed for distinguishing several clusters.  
Figure 3: Dendrogram showing the hierarchical clustering of potato samples based on the morpho-agronomic descriptors. Asterisks indicate the accessions from Majella National Park.  
In particular, as DNA markers (such as SSR) are not environmentally influenced [48], genotypes were clustered according to their genetic makeup, regardless of the sampling area. The DE and GA local accessions were grouped together with the control DE. Similarly, PI and TU fell into the same group due to their high genetic similarity. By contrast, MO and AG were sorted into different clusters, displaying an independent genetic status compared to the other genotypes. Our findings demonstrate that SSR analysis was useful for providing a reliable discrimination of potato accessions collected in Majella National Park and providing a clear picture of their genetic relationships with other varieties included in the study.
# A Multi-Machine and Multi-Modal Drift Detection (M2D2) Framework for Semiconductor Manufacturing
## 4 Mathematical Justification
### Long-Term Drift Detection Consistency  
In Phase 3, the system periodically collects samples \(\left\{\mathbf{h}^{(m)}(t)\right\}\) over a window \([T_{k},T_{k+1}]\) to form the empirical distribution \(P_{new}\), and compares it against a baseline distribution \(P_{base}\). The distance measure is defined as follows  
\[\Delta=W_{1}(P_{new},P_{base})\text{ or }\Delta=D_{KL}(P_{new}\|P_{base}). \tag{22}\]  
If \(\Delta\) exceeds the threshold \(\gamma\), the system declares the existence of a long-term drift [28]. The next theorem shows that if a permanent drift truly occurs, detection probability converges to 1 as \(k\to\infty\).  
**Theorem 2**: (Long-Term Detection Consistency). _Consider an infinite sequence \(\left\{\mathbf{x}(t)_{t=1}^{\infty}\right\}\) originally drawn from \(p(x)\). Suppose that after some time \(t^{*}\), samples are drawn from a new distribution \(p(x)=p(x)\). Then, for any \(\epsilon>0\), there almost surely exists \(K\) such that for all \(k>K\),_  
\[P[\Delta>\gamma]>1-\epsilon, \tag{23}\]  
_meaning that the probability of detecting the drift approaches 1 for sufficiently large \(k\)._  
**Proof.**  
1. Under _i.i.d._ or weakly stationary assumptions, one can apply the law of large numbers or the Glivenko-Cantelli theorem to show that \(P_{new}\) converges to \(p\prime(x)\) as the sample size grows [29].
2. If \(p\prime(x)\
eq p(x)\), then \(W_{1}(p^{\prime},p)>0\) or \(D_{KL}(\,p^{\prime}\|p)>0\).
3. Due to the continuity of the distance measure, for sufficiently large samples, \(\Delta(P_{new},P_{base})\) almost surely exceeds any \(\gamma<\Delta(p\prime,p)\). Hence, the long-term drift will be detected.  
Therefore, under gradual or permanent drift, detection eventually becomes certain as \(k\rightarrow\infty\).
# Blind Phase Search with Angular Quantization Noise Mitigation for Efficient Carrier Phase Recovery
## 2 Principle of the Proposed Angular Quantization Noise Filter in F-BPS
Thus, being the number of employed test phases in the C-BPS CPR the limiting factor in terms of resolution. This, in practice, allows for a potential mitigation of the quantization noise as the C-BPS process provides a phase noise estimator with a lower resolution (<32 or <64 values for 16 QAM and 64 QAM respectively) than the resolution of the DSP ASIC's processor employed to physically perform the algorithm (~8 bits) [13]. It is important to note the difference between the proposed angular quantization noise filter in the F-BPS and the C-BPS block filter. The block filter is used to average the AWGN influence on the distance metric calculation, while the proposed angular quantization noise filter in the F-BPS scheme is employed to reduce the out-of-band angular quantization noise of the C-BPS phase estimator. In order to maintain an implementable low overall complexity of the proposed F-BPS, low pass filters with low complexity implementation (sliding average filter (SAF) without weighting coefficients) are used in this paper for both the C-BPS block filter and the additional proposed angular quantization noise filter of the F-BPS scheme. Optimization of both filters might further improve the performance, but at the cost of a larger implementation complexity and it is not the purpose of this paper. It is also noted that the proposed filter differs from the parabolic interpolation filter shown in [14] as the aim of this filter is to interpolate the error metric distance function to obtain a more accurate phase estimator. Instead, as mentioned earlier, the purpose of the proposed angular quantization noise filter in this paper is to mitigate the out-of-band-quantization noise by low pass filtering the C-BPS phase noise estimator. Although both filters might tackle similar problems, they are different in nature, have different implementation complexity, might perform differently, and the decision on which to implement corresponds to the ASIC designer. Figure 2: (**a**) FN-PSD of different tracked phases by the C-BPS algorithm employing different number of test phases; (**b**) time domain tracked phase by the C-BPS algorithm for different number of test phases.
## 2 Methodology
### Simulation  
Designing the optimised reflector happened in multiple stages in order to divide the research into small parts. Initially, a simplified simulation model was built in COMSOL(r). This model was used to validate all simulation parameters before starting the optimising of the reflector shape. Secondly, the ray-tracing model was combined with a batch-script to alter the parameters describing the parametric geometries. The batch-script made it possible to use parametric sweep over the parameters in order to find the reflector with the highest intensity in a predefined region.  
The following assumptions were made in order to design an optimised geometry:  
* The reflector is made of aluminium.
* The surface finish is very smooth, ensuring a reflection coefficient around 1.
* The halogen tube lamp emits electromagnetic-radiation uniform along its length.
* The heating source and reflector shape are considered to be indefinitely so the sides can be neglected.
# Measuring Collision Risk in Mixed Traffic Flow Under the Car-Following and Lane-Changing Behavior
## 2 Existing Surrogate Safety Measures (SSMs)  
### Time-Based SSMs  
Time-based SSMs have been widely used in safety research, the most prominent example of which is Time to Collision (TTC). TTC was proposed by [PERSON] [18] and predicts the time remaining until a collision occurs by analyzing future vehicle trajectories. It assumes that both vehicles maintain their current velocities. TTC is especially useful for analyzing potential rear-end collision risks. The calculation of TTC is given by Equation (1).  
\[\text{TTC}=\frac{D}{\Delta v} \tag{1}\]  
where  
Figure 1: The flowchart of methodology.  
\(D=x_{l}-x_{f}-l\) is the distance between the leading vehicle and the following vehicle, calculated as the difference between the positions of the leading vehicle \(x_{l}\) and the following vehicle \(x_{f}\), minus the length of the leading vehicle \(l\).  
\(\Delta v=v_{f}-v_{l}\) is the relative speed between the following vehicle and the leading vehicle, where \(v_{f}\) is the speed of the following vehicle and \(v_{l}\) is the speed of the leading vehicle.  
It is important to note that TTC is only applicable when the relative speed \(\Delta v\) is non-zero. If \(\Delta v=0\), TTC cannot be defined as the two vehicles are moving at the same speed, and a collision will not occur under these circumstances.  
Modified Time to Collision (MTTC) was proposed to address the limitations of TTC [19], particularly its inability to account for the effects of acceleration and deceleration during potential collision scenarios. Unlike TTC, MTTC takes into consideration the acceleration differences between vehicles, making it more suitable for analyzing complex driving conditions such as lane changes, merging, and other dynamic traffic situations.  
The calculation of MTTC is based on the Equations (2) and (3):  
\[t=\frac{-\Delta v-\sqrt{\Delta v^{2}+2\Delta a\cdot D}}{\Delta a} \tag{2}\]  
where  
\(\Delta a=a_{f}-a_{l}\) is the relative acceleration, where \(a_{f}\) and \(a_{l}\) are the accelerations of the following and leading vehicles, respectively.  
The MTTC is then calculated as:  
\[\text{MTTC}=\begin{cases}\min(t_{1},t_{2}),&\text{if }t_{1}>0,t_{2}>0\\ \max(t_{1},t_{2}),&\text{if }t_{1}\cdot t_{2}>0\\ \frac{D}{\Delta v},&\text{if }\Delta a=0\end{cases} \tag{3}\]  
MTTC provides a more comprehensive estimation of potential collision risks by identifying both longitudinal and lateral collision scenarios. It can adapt to various driving conditions, addressing the limitations of traditional TTC in scenarios where acceleration plays a significant role.
# Modeling of Dry Clutch Wear for a Wide Range of Operating Parameters
## 2 Experimental Setup  
### Mechanical Subsystem  
The active part of disk-on-disk tribometer (Figure 1; [24;25]) consists of rotational and vertical servo-axes. The rotational axis carries the original dry clutch friction plate (Figure 1d), and it is placed on a rotating table powered directly by an electric servomotor and cooled by water supplied via a dynamic seal-based coupling. The vertical axis consists of the following elements given in bottom-up order: (i) pressure plate cut from the clutch flywheel and equipped with a temperature sensor positioned at 4 mm axial distance from the friction contact surface, (ii) water-cooled disk, (iii) custom-designed leaf spring suspension that mitigates the non-parallelism between the friction and pressure plate and related occurrence of hot spots, (iv) a set of three three-axial piezoelectric force sensors providing measurement of clutch normal force and torque, and (v) an electric servomotor and a spindle acting as a source of normal force.  
A set of linear guides is used to provide stiff mechanical support for the entire vertical axis. They are positioned between the force sensors and the source of normal force (green bell in Figure 1a) to avoid any parasitic transfer of forces from the pressure plate to the surrounding, thus facilitating high precision of normal force/torque measurement system. It should be noted that only one side of friction plate is in sliding contact with the pressure plate, so that the measured torque is a half of that occurring in the real clutch for the same normal force.
## 6 Dependability for MCAs in 6G
### Dependability Analysis of 6G MCAs
#### 6.2.2 Availability  
Bad architecture and low-quality hardware can also affect the availability of MCAs. As many use cases, such as telesurgery and autonomous driving, depend on external hardware (such as sensors and CPS components), it is important to ensure that these are trustable and will not hinder the execution of the MCA. Moreover, a proper architecture in the form of resource localization would ensure the MCA is capable of deploying high capacity node replacements, thereby offering adequate scalability. Depending on the use case, some MCAs might use EdgeAI approaches for improving their bandwidth and latency requirements. Proper resource and task allocation are needed in order to take advantage of these scenarios.