esa-sceva/satcom-chunk-collection huggingface dataset:
## 8 RAN Decomposition Scheme
### Performance Requirements
#### 8.3.2 Latency Requirements  
In general, latency varies from one application, service, and mobile network topology to the other. Thus, the MNOs have to make sure that the multi-access edge computing or user plane functions physical locations should be as close as possible to the user so as to meet the latency requirements for the 5G use cases. However, this can pose a stringent constraint on the level of functions that could be centralized [424].  
The HLS offers comparatively relaxed latency to the network. For instance, Option 2's maximum end-to-end (e2e) latency is unconstrained by the HARQ cycle, and it has the ability to tolerate high transmission latency. Its maximum transmission latency is estimated to be about 10 ms [430]. Option 6 presents stringent signaling and data timing requirements owing to the centralized HARQ [8]. Subject to the employed transmission time interval, about 250 us e2e maximum latency can be tolerated for this option. Moreover, like Option 6, as the HARQ is centralized [8], considering the required time for the process latency as well as transmission between DU and CU in options 7-1, 7-2, and 8, the estimated maximum e2e latency for each option is restricted to 250 us [430].  
In general, a dynamic split point not only aids in meeting the MFH requirements but also facilitates effective RAN virtualization with various degrees of centralization gain [426]. In the following subsection, we present the concept of RAN virtualization and its implementation for different deployment scenarios.  
\begin{table}
\begin{tabular}{l l} \hline \hline \multicolumn{1}{c}{**Parameter (UL/DL and 5G/LTE)**} & **Typical Value** \\ \hline Baselines bandwidth & 100 MHz \\ Number of sub-carrier & 1200 SC/20 MHz \\ OFDM symbol rate & 14 symbol/ms \\ MIMO layer & 8 \\ Number of antenna ports & 32 \\ Modulation scheme & UL & 64 QAM \\  & DL & 256 QAM \\  & UL & 30 Bytes \\ Average content size & DL & 20 Bytes \\  & UL & 50 Mbps \\ LTE peak data rate & DL & 150 Mbps \\  & UL & 24 Mbps, 2640 Mbps \\ Scheduling/control signaling overhead & DL & 16 Mbps, 133 Mbps \\  & UL & 80 Mbps, 120 Mbps \\ MAC information & DL & 121 Mbps, 713.9 Mbps \\  & 5G & 99\% \\ Spectrum Usage & LTE & 90\% \\  & 5G & 120\% \\ Upper bound estimation margin & LTE & 100\% \\ Maximum number of UE & & 1000 \\ \% UE reporting requests & & 10\% \\ \hline \hline \end{tabular}
\end{table}
Table 14: Typical transmission parameters considered.  
Figure 29: Required MFH capacity for different split options.
## 3 Materials and Methods
### Simulations
#### 3.1.3 CoMTUS through Layered Media  
To investigate the effect of layers with different speeds of sound in the medium, three different kinds of tissue were defined in the propagation media (general soft tissue, fat and muscle). The imaging depth was set to 75 mm with a configuration of the arrays in space that defines an effective aperture of 104.7 mm with a 45.3 mm gap. The acoustic properties assigned to each tissue type were chosen from the literature [27] and are listed in Table 1. A medium defined only with the soft tissue properties was used as the control case. Then, clutter effects were analysed by using heterogeneous media in which two layers with the acoustic properties of muscle and fat were introduced into the control case medium. In the different studied cases, the thickness of the muscle layer was set to 8 mm while fat ranged from 5 to 35 mm in thickness. Figure 2 shows an example of the propagation medium with a muscle layer of 8 mm in thickness and a fat layer of 25 mm.  
Aberration parameters were estimated using a one-dimensional version of the reference waveform method [28]. In these measurements, a source located at the centre of the shared FOV of both arrays that emits a spherical wave pulse that propagates through the different tissue layers and is received by the CoMTUS effective aperture (i.e., the two linear arrays) was simulated. Then, the arrival time fluctuations (ATF) across the receiving effective aperture caused by each layered medium were calculated by subtracting a linear fit from these calculated arrival times. Energy level fluctuations (ELF) in the data were calculated by summing the squared amplitudes of each waveform over a 1.5 ms window, which isolated the main pulse, converting to decibel units, and subtracting the best linear fit from the resulting values. More details of these measurements can be found in [29].
## 4 Comparison of Prediction Accuracy  
### Fitting Method  
In order to predict anisotropic rock strength, indispensably, the parameters embedded in the selected model should be carefully fit by accurate experimental data of anisotropic failure. In this paper, the least-squares method is adopted as the data fitting approach to determine the strength parameters. The three types of criteria above are applied to fit the 12 different datasets of transversely isotropic rocks, wherein the parameters of the models  
Figure 2: Summary of coefficient of variation for different rock types.  
are selected to provide a global minimum magnitude for the root mean squar error, shown in Equation (6):  
\[RMSE=\sqrt{\sum_{i=1}^{N}\left(\sigma_{1}^{t}-\sigma_{1}^{p}\right)^{2}/N} \tag{6}\]  
In the equation, RMSE represents the root mean square error of the errors, in MPa; \(\sigma_{1}^{t}\) denotes the tested peak strength of shale, in MPa; \(\sigma_{1}^{p}\) signifies the predicted peak strength of shale, in MPa; and N stands for the number of strength tests. The range of these strength parameters is refined using MATLAB 2022a, and the optimal parameters with the lowest RMSE are identified iteratively.  
Following the derivation of the best-fit parameters for strength criteria, the square of the correlation coefficient between experimental and predicted values is computed to evaluate the predictive accuracy of different anisotropic strength criteria, as shown in Equation (7).  
\[R^{2}=1-\sum_{i=1}^{N}\left(\sigma_{1}^{t}-\sigma_{1}^{p}\right)^{2}/\sum_{i=1 }^{N}\left(\sigma_{1}^{t}-\overline{\sigma_{1}^{t}}\right)^{2} \tag{7}\]  
In the equation, \(\overline{\sigma_{1}^{t}}\) represents the average test value of rock peak strength at bedding angle \(\beta\), in MPa. Another error evaluation metric is the mean absolute error (MAE), as shown in Equation (8).  
\[MAE=\sum_{i-1}^{N}\left|\sigma_{1}^{t}-\sigma_{1}^{p}\right|/N \tag{8}\]  
Based on the aforementioned indicators, a smaller RMSE and MAE, along with a larger square of the correlation coefficient, indicate that the theoretical calculated values are closer to the test values, reflecting a better fitting effect.
# Guided Self-Accelerating Airy Beams--A Mini-Review
## 2 Results and Discussion
### Dynamic Linear Potential Guidance
When we set \(d(z)=1\), the trajectory of the beam is \(x_{p}=r_{0}\), that is, a straight line, so the autofocusing does not occur during propagation, and the propagation can be described by Equation (45). For this case, the linear potential exerts a \"pulling\" influence that can balance the virtual force which makes the beam focus. While, if the potential is a periodic function \(d(z)=1+4\pi^{2}\cos(\pi z)\), as in the former case, the potential also exerts a pulling effect, and the trajectory is a cosine-like curve, which is also periodic. The corresponding results are displayed in Figure 8.  
For the case with \(d(z)=13-12z\), from the corresponding trajectory \(x_{p}=r_{0}-z^{3}+3z^{2}\), we know that the beam will undergo autofocusing during propagation, so the process should be described by Equation (43). From the inset in Figure 9, the two components (\(\theta_{0}=0\), \(\theta_{0}=\pi\)) separate before the focusing point, and then converge. Since the slope of the components at the colliding point is much bigger than without a potential, as in [40], this can be viewed as the components acquiring a larger speed because of the \"pushing\" effect, hence the autofocusing is strengthened. Similar to the former study, the maximum of the beam intensity (MBI) is also a function of both \(r_{0}\) and the propagation distance, as displayed in Figure 9b; the MBI first increases and then decreases with the increasing of \(r_{0}\). Besides this effect, one can also see that the location of the MBI also changes with \(r_{0}\), the reason being that the autofocusing effect requires a longer distance to establish itself when \(r_{0}\) increases. As stated above, the transverse and longitudinal coordinates are normalized to some characteristic transverse width \(x_{0}\) and the corresponding Rayleigh range \(kx_{0}^{2}\). The unit of intensity of the beam is arbitrary. Figure 8: **(a)** Analytical intensity distribution of an AAF beam during propagation in the \(x-z\) plane at \(y=0\), according to Equation (43), for \(d=1\); **(b)** Same as (a), but for \(d(z)=1+4\pi^{2}\cos(\pi z)\). Reproduced with permission from [80], Copyright the Optical Society of America, 2016. Figure 9: Manipulation of an AAF beam by a dynamical linear potential. **(a)** Maximum of the beam intensity during propagation for \(d(z)=13-12z\) and \(r_{0}=5\). The maximum of the beam intensity at \(z=0\) is 1. Inset shows the propagation of the components corresponding to \(\theta_{0}=0\) and \(\theta_{0}=\pi\), and the corresponding theoretical trajectories; **(b)** The maximum of the beam intensity as a function of \(r_{0}\) and \(z\). The white dashed line corresponds to the curve in **(a)**. The decay parameter is \(a=0.05\). Reproduced with permission from [80], Copyright the Optical Society of America, 2016.
###### Abstract  
Remote Sensing (RS) allows the estimation of some important vineyard parameters. There are several platforms for obtaining RS information. In this context, Sentinel satellites are a valuable tool for RS since they provide free and regular images of the earth's surface. However, several problems regarding the low-resolution of the imagery arise when using this technology, such as handling mixed pixels that include vegetation, soil and shadows. Under this condition, the Normalized Difference Vegetation Index (NDVI) value in a particular pixel is an indicator of the amount of vegetation (canopy area) rather than the NDVI from the canopy (as a vigour expression), but its reliability varies depending on several factors, such as the presence of mixed pixels or the effect of missing vines (a vineyard, once established, generally loses grapevines each year due to diseases, abiotic stress, etc.). In this study, a vine removal simulation (greenhouse experiment) and an actual vine removal (field experiment) were carried out. In the field experiment, the position of the Sentinel-2 pixels was marked using high-precision GPS. Controlled removal of vines from a block of cv. Cabernet Sauvignon was done in four steps. The removal of the vines was done during the summer of 2019, matching with the start of the maximum vegetative growth. The Total Leaf Area (TLA) of each pixel was calculated using destructive field measurements. The operations were planned to have two satellite images available between each removal step. As a result, a strong linear relationship (R\({}^{2}\) = 0.986 and R\({}^{2}\) = 0.72) was obtained between the TLA and NDVI reductions, which quantitatively indicates the effect of the missing vines on the NDVI values.  
total leaf area; mixed pixels; Cabernet Sauvignon; NDVI; Normalized Difference Vegetation Index; precision viticulture 2020
# Finite Element Model of Soliosis Brace with Increased Utility Characteristics
## 2 Materials and Methods  
### Boston Spinal Bracing System  
Designed for the non-invasive treatment of scoliosis, the Boston Spinal Bracing System is particularly effective for spinal curves with apexes between the sixth thoracic and third lumbar vertebrae. Uniquely, the top of this brace is designed to be flexible and sit under the patient's armpits to provide stability and minimize movement [2; 8]. The current research focuses on a Boston brace tailored to treat a leftward curve in the lumbar spine. The shell of this brace was fabricated from a 4 mm thick polypropylene sheet, and the authors focus solely on the analysis of this shell.  
The corset in the variant used to treat double curvatures (left lumbar and right thoracic) was chosen for the study because it is the most advanced geometric design among the variants of the Boston system. This variant of the corset can be the subject of the authors' next work, with a consideration of more applied loads, especially for trochanter extension and for axillary extension, similar to more complicated designs such as the Cheneau brace, which uses a multipoint system of corrective forces. In this work, the brace uses a simpler three-point force mechanism [9] for a case of left lumbar scoliosis. The variant of corrective forces used can also be effective for additional low right thoracic scoliosis, despite the lack of force applied to the axillary extension. This arrangement of forces corresponds to the Boston Thoracic-lumbar Brace variant, according to the reference manual. No force was also applied to the trochanter extension due to the mainly auxiliary, stabilizing role of this part of the corset. The three applied forces are crucial in the correction of the lumbar curvature, while the other parts of the corset have an auxiliary function. The three-force design is a statically determinable system that allows the distribution of forces to be unambiguously determined in both experimental studies and numerical analyses.  
Two forces, labeled \(F_{1}\) and \(R_{1}\), exert pressure on the ribs to influence the spine, while a third force, \(R_{2}\), acts on the pelvis (Figure 1). Force F\({}_{1}\) serves as the primary corrective element, while forces \(R_{1}\) and \(R_{2}\) provide counterbalancing support.  
The goal of this study is to refine orthosis design to improve utility while maintaining the distribution of corrective forces after optimization. This requires maintaining a consistent pressure distribution across the patient's torso. Although the model used in this study assumes point loads, it is recognized that in real-world applications, these forces would be distributed across the surface of the corrective pads, all while maintaining pressure within safe limits. It is worth noting that modeling brace pressure on the torso is a complex subject that has been the focus of extensive previous research [1; 5; 6]. However, the primary goal of this research is to optimize the structural and utility aspects of the brace shell, rather than to simulate the pressure exerted by the brace on the torso.
# Detection of Induced GNSS Spoofing Using S-Curve-Bias
## 5 Conclusions  
In this paper, an induced spoofing detection algorithm is proposed. By gradually adjusting its parameters, the induced spoofing can capture victim tracking loops without creating loss of locks. However, it will lead to a significant change in the SCB. The proposed algorithm is based on the change of SCB to detect induced spoofing. In other words, the proposed algorithm utilized the dynamic feature of gradual adjustment process. More specifically, a detection metric based on the first derivative of SCB is defined in this paper. When the detection metric exceeds a given threshold, an induced spoofing will be declared. A series of experiments with the Texas Spoofing Test Battery (TEXBAT) are performed to verify the effectiveness of the proposed algorithm. The experimental results demonstrate that the proposed algorithm can detect induced spoofing in a earlier stage.  
Future work includes deducing the theoretical threshold value for a given \(P_{fa}\) or \(P_{d}\). On the other hand, it is noted that the proposed algorithm detects induced spoofing based on the dynamic nature of gradual adjustment process. Then there is overlapping between the two correlation peaks corresponding to authentic signal and spoofing, respectively. When the induction process is finished, the correlation peak of spoofing will not be overlapped with that of authentic signal and the proposed algorithm may fail. Then the proposed algorithm could be combined with other techniques such as power level monitoring to detect spoofing. Therefore, another possible future direction could be combining the proposed algorithm with other detection methods.  
[PERSON] and [PERSON] raised the idea and drafted the paper; [PERSON] performed the experiments and analyzed the results; [PERSON] analyzed results and revised the paper. All authors reviewed the paper.  
This work was supported by the National Natural Science Foundation of China (U1833112) and by the National Science Foundation (Awards 1815349 and 1845833).  
**Acknowledgments:** Comments from the anonymous referees and the editor are also gratefully appreciated.  
Figure 11: The relationship between detection probability and false alarm probability for the first 294 s data. The red solid and blue dashed curves are separately corresponding to the proposed algorithm and the ratio method.
# Improving Bundle Routing in a Space DTN by Approximating the Transmission Time of the Reliable LTP
## 3 System Analysis
### Optimal Bundle Routing  
In this work, optimality is linked to the minimum average flow delay. The routing problem can be expressed by the following optimization problem, which yields the set of forwarding matrices \(\mathcal{R}\):  
\[\underset{\mathcal{R}}{\text{argmin}} \sum_{(a,b)\in\mathcal{E}}\lambda_{(a,b)}T(\lambda_{(a,b)}),\] s.t. \[\lambda_{(a,b)}<X^{-1}_{(a,b)}\frac{C_{(a,b)}}{C_{(a,b)}+V_{(a,b )}}\;\;;\;(a,b)\in\mathcal{E},\] \[\sum_{b}R^{(i)}_{s_{i},b}=1,\;\;\;(s_{i},b)\in\mathcal{E},\forall i \tag{8}\] \[\sum_{a}R^{(i)}_{a,t_{i}}=1,\;\;\;(a,t_{i})\in\mathcal{E},\forall i\] \[\sum_{(c,a)\in\mathcal{E}}R^{(i)}_{(c,a)}=\sum_{(a,d)\in\mathcal{ E}}R^{(i)}_{(a,d)},\;\;\;a\in\mathcal{V}-\{s,t\},\forall i\]  
where \(\lambda_{(a,b)}=\sum_{i}R^{(i)}_{(a,b)}\lambda_{i}\) and \(T(\lambda_{(a,b)})\) is link \((a,b)\)'s response, time given by Equation (2). In addition to the constraints covered in Section 3.3, each link's load cannot exceed its maximum capacity. This observation is expressed by the first constraint listed in Equation (8).  
A link's capacity depends both on the extended service time due to the retransmission efforts and the link availability determined by the contact-disruption pattern.  
In addition, it is noted that Equation (8) yields the minimum average end-to-end flow delay \(\mathcal{T}\) because of [PERSON]'s law; that is, \(\mathcal{T}\) is the ratio of the total number of packets in the DTN to the total input traffic rate:  
\[\mathcal{T}=\frac{\sum_{(a,b)\in\mathcal{E}}\lambda_{(a,b)}T(\lambda_{(a,b)})}{ \sum_{l=1}^{w}\lambda_{i}} \tag{9}\]  
Note that the offered demand \(\lambda_{i}\) for all flows \(i=1,\ldots w\) is given as an input and therefore is not decided by the solution of Equation (8), unlike the forwarding matrices. However, the total load \(\lambda_{(a,b)}\) to be handled by each link \((a,b)\) is a controllable value, given that it depends on the choice of the forwarding matrices. The load \(\lambda_{(a,b)}\) then determines the link's response time, which in turn yields the average flow response time for the DTN.
## 4 Results and Discussion
### Air-Cooling BTMS with the AFHS of Gradient Porosity Designs
#### 4.3.2 AFHS with Gradient Porosity Designed in the \(X\)-Axis Direction (the Flow Direction)
The temperature uniformity coefficient \(\beta\) with AFHS with gradient porosity designed in the \(Z\)-axis direction is shown in Figure 18b. Obviously, using the AFHS of PDGP can significantly improve the \(\beta\) under the same equivalent porosity level. Meanwhile, when using AFHS with PDGP in the \(x\)-axis direction, the \(\beta\) with m = 10 was similar to that of m = 0.1, but less pumping power was required with m = 10. Thus, the AFHS of PDGP with m = 10 is a better design to reduce the battery temperature difference. Figure 19 shows the FOM with AFHS with gradient porosity designed in the \(X\)-axis direction. According to the above analysis, compared with the homogeneous porosity AFHS, the AFHS of PIGP in the \(X\)-axis direction strengthened the heat transfer effect, while the PDGP in the \(X\)-axis direction weakened the heat transfer effect. Since the pumping power was the same, the FOM of PIGP was greater than that of PDGP. Although AFHS of PIGP with m = 0.1 in the \(X\)-axis direction had the largest FOM, the battery with that had the worst temperature uniformity. To sum up, the use of AFHS in BTMS will significantly enhance the thermal performance, but it will also increase the flow resistance and pressure drop, resulting in additional energy loss. However, [PERSON] et al. [33] indicated that the parasitic power consumption of a battery pack with liquid cooling was 1200 w-2000 w, while the pump power of a uniform foam aluminium heat sink with 20 ppi was 730 w-1500 w. Thus, the use of foam aluminium heat sink is acceptable. The AFHS of gradient porosity design further reduce the pressure drop and battery temperature difference. In the process of practical application,  
Figure 18: (**a**) Temperature control coefficient \(\alpha\), (**b**) temperature uniformity coefficient \(\beta\) (with AFHS with gradient porosity designed in the \(X\)-axis direction). comprehensive optimisation can be carried out according to specific conditions to achieve the best cooling effect.