esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Connection with Limbic Structures
#### 3.4.3. Functional Diversity of Limbic Connections  
A study by [PERSON] et al. (2022) [68] utilized 7T probabilistic tractography to create a detailed structural brainstem connectome. The authors distinguished two parts of the substantia nigra--SN1 (corresponding to the SNr) and SN2 (SNc)--and described their structural connectivity with the brainstem nuclei, cortex and limbic regions. Whilethe first part (SN1) exhibited predominant connections with motor areas, the second part (SN2) demonstrated direct connectivity with the amygdala, the ventral tegmental area, the periaqueductal grey, the reticular formation, and other nuclei implicated in emotion regulation, stress response, and sleep. The results provide support for the hypothesis that the SNc is not only part of the motor and reward circuits, but also an integral node of broader limbic circuits that may be relevant to mood disorders, anxiety, REM atonia, or attention disorders.
# Data-Driven Calibration Estimation for Robust Remote Pulse-Oximetry
## 4 Discussion
### Two versus Three Wavelengths  
As explained earlier, the rationale to use three instead of the common two wavelengths is that a higher-dimensional (pseudo)-color space allows projections that are orthogonal to more independent distortions, e.g., caused by subject-motions or illumination changes [8]. The number of calibration parameters, however, increases which could affect the performance when not set correctly. The results show that performance with three wavelengths is slightly better, even when the subject is static. Figure 10 shows that a miscalibration does not affect pulse quality, but only the SpO\({}_{2}\) error when using two wavelengths. For three wavelengths, both the pulse quality and the SpO\({}_{2}\) error are affected. This can be explained by the 'flat' optimum when using \(>\)2 wavelengths. With two wavelengths, there always exists an optimum because of the one-dimensional space. With \(>\)2 wavelengths, a miscalibration leads to'squeezing' of the signature vectors, which could result in a combination of a bias and an increased RMSE.
## 4 Discussion
### Challenges of the PFT Approach in Forest Models  
The categorization into PFTs is often based on inventory data. This is the reason why forest gap models are sometimes blamed to be 'data-hungry' [48; 49]. However, species grouping is a suitable and efficient approach for dealing with species-rich ecosystems--very often the only option for modeling species diversity at all. [PERSON] et al. [12] found that modeling studies have used between five and up to 50 PFTs. Finding the right balance between appropriate and necessary accuracy has not yet been treated satisfactorily. The study by [PERSON] et al. recommends applying a grouping which is based on successional status (three groups) and maximum height (five groups). This classification is in accordance with our finding that species should be grouped by successional status or light demands. However, the concept by [PERSON] et al. is related with a high parameterization effort--reducing the number of height layers in this case could be a good suggestion. In addition, the assignment of species to a single functional group is sometimes challenging [14] in the case that intra-specific variability of functional traits is higher than the inter-specific variability.  
For a single study, it is quite straightforward to define clear grouping criteria (e.g., maximum attainable height of each species) and to implement a categorization (e.g., maximum height between 10 m and 20 m). Grouping strategy depends always on the research question and study site, which means that there can be no general criteria for an appropriate species grouping [14; 50]. Therefore, it is challenging to transfer an established PFT classification from one study site to another region [51]. In a recent study [44], a method has been developed on how to transfer a local forest model parameterization across an entire region. In this regionalization approach, environmental conditions have been taken as a proxy for the mortality rate (instead of constant mortality rates for the entire biome), which is normally a site-specific parameter. Thereby, the forest model is able to reproduce spatially variable forest dynamics.  
Moreover, different species grouping concepts can be available for the same study region [15]. In the study by [PERSON] et al., five different species groupings were compared for the same tropical forest site. The groupings agreed mainly on tree height classification but differed with respect to light requirements of the trees. Again, various research questions can of course lead to different species grouping even for the same study site.  
There are many published species grouping around for different regions in the tropics which can serve as a starting point if a new site-specific grouping is needed. These different groupings should be analyzed and compared in future. It could even turn out that these published groupings may not be so different at all.
## 5 Discussion
### Validation Caveats  
The validation applied in this study is limited by (i) the relatively low number of VPs (a total of 31 snow probed GCPs and samples on the SDEMs comprising a total cumulative area of 78,581 m\({}^{2}\)) and (ii) the fact that the accuracy has a biased improvement around the position of the GCPs. On the other hand, the produced DEMs gain reliability if the GCP network is dense and properly distributed, with the GCPs' areal coverage being more important than the actual number of GCPs [32]. In our case, GCPs for most of the areas are relatively well distributed. Additionally, the snow-free validation provided extra information (Figure 5) on multiple points that were distant from the GCPs, as can be seen from the areas' orthomosaics (Figure 1). Even though these are not representative for snow-covered areas, they indicate a coherent SDEM reconstruction in terms of rotation, translation, and deformation of the generated model relative to the true topography.  
Many studies have used several markers as GCPs or other features spread around the snow surface, e.g., poles and crosses, to manually measure snow depth [15]. Our study comprised a reduced number of displaced GCPs markers in comparison to other studies that employed a very dense validation network grid (e.g., [18]). It is suspected that such a grid and its allocation process in our case would have greatly assisted the SfM reconstruction by disturbance of the snow surface, as shown by [23]. One of our aims was to leave the snow surface undisturbed to test the known SfM limitations relating to the absence of texture and features.  
Although the use of fast-static or RTK survey techniques post UAS-survey could have provided the desired positioning accuracy given the scale of the spatial resolution achieved, the allocation of a large number of features would have compromised the snow surface.  
While this study does not claim to provide a full accuracy evaluation for the method, it demonstrates its capability to capture complex snow depth variability for a set of previously unexplored survey conditions, using extremely low-cost solutions and in a logistically challengingenvironment. Notwithstanding, uncertainties remain and further validation methods should be tested by comparison with other spatially significant methods such as LiDAR (e.g., [49]) or the addition of non-invasive extra control points. The effect of increased flying altitudes on the performance of the proposed method should also be investigated.
# A Survey of Wi-Fi 6: Technologies, Advances, and Challenges
## 5 SR in Wi-Fi 6
### Mechanisms for Spatial Reuse in Wi-Fi 6  
Wi-Fi 6 exploits BSS coloring to differentiate traffic from different BSSs. Intra- and inter-BSS NAVs avoid erroneous NAV resets that cause collisions. Further mechanisms facilitate simultaneous transmissions on a channel by different BSSs.
## 2 Materials and Methods  
### Dataset  
To verify the effectiveness of the proposed AMDDATrack algorithm in multi-pedestrian tracking tasks, data from the publicly available CUHK-SYSU and PRW datasets were chosen as the training set and evaluated on MOTChallenge [31]. CUHK-SYSU is a large-scale benchmark dataset for people searches containing 18,184 images, 8432 pedestrian identities, and 99,809 well-labeled bounding boxes. The images are derived from two sources, films and TV dramas, and contain different perspectives, lighting, resolutions, occlusions, and backgrounds. Person re-identification in the wild (PRW), a dataset collected by Tsinghua University, is a 10-h video in which all pedestrians appearing in each frame are labeled with bounding boxes and assigned an ID at the same time. Multi-Object Tracking Challenge (MOTChallenge) is a publicly available benchmark platform for multi-object tracking. Taking MOT16 [32] as an example, 14 video sequences are provided, seven of which are used for training and seven for testing, all of which are still or moving images in an unconstrained environment. The tracking results are already given in the data, so there is no need to solve the problem of pedestrian detection during training, only tracking. In this paper, the proposed AMDDATrack algorithm is tested on the MOT16 and MOT17 datasets.
## 1 Introduction
At the same time, tall parts with small cross-sectional areas and thus short path lengths correspond to very short layer times and result in substrate temperatures far above the melt temperature, leading to disintegration of the part. If robust and controllable methods can be found to increase the substrate temperature for long layer times and to cool down the substrate temperature for very short layer times, the full potential of extrusion-based additive manufacturing systems can be realized. First attempts to reheat the substrate with pressurized hot air proved ineffective and resulted in only a slight increase in temperature. [PERSON] et al. [16] already found that the use of infrared heaters is an effective method to increase the substrate temperature and thus increase the mechanical properties. Therefore, infrared emitters, which can be easily integrated on the sides of the extrusion unit, were chosen as the dedicated method for reheating. Given the industrial setup of the FEM facility, only the use of pressurized air was feasible for cooling of the substrate temperature for short layer times. Transient thermal simulations can be used to analyze the Fused Filament Fabrication (FFF) and EAM processes [7]. However, the complexity of the simulations as well as a wide range of required material data make it difficult to correctly predict the resulting mechanical properties, especially on a large scale. Therefore, to quantify the effects of heating and cooling on the mechanical performance of the parts, mechanical testing had to be conducted. Common test methods for AM and composite parts include Double Cantilever Beam (DCB) tests [17], three-point bending tests [18; 19], and tensile tests [2; 20; 21]. Since anisotropy is most pronounced in tensile tests and these are also among the simplest test setupsrequiring little additional equipment, tensile tests were chosen for this study. While in FFF, researchers often directly print small dog bones for tensile tests, much larger parts must be manufactured on large-scale deposition systems from which dog bones or composite test specimens can be cut after fabrication. Based on prior investigations of tensile strength of unheated and uncooled parts across the entire process window, as well as substrate temperatures below the ideal temperature range, the results of the manipulated setups of this study were compared and evaluated. The hypothesis states that the tensile strength in the transverse direction depends primarily on the substrate temperature. Therefore, the tensile test results of the heated and cooled samples should correlate with the unaltered results manufactured at the final substrate temperature reached before deposition. Furthermore, a trend toward lower tensile strength for final substrate temperatures below the intended process window is expected.
# A Solution Procedure to Improve 3D Solid Finite Element Analysis with an Enrichment Scheme
## 3 Improving 3D Solid FE Solutions
### Ad Hoc Problem  
The first example is the ad hoc problem shown in Figure 4. For a detailed description, see Section 3.1. Figures 7 and 8 show the distribution of the von Mises stress on the surface at \(x=0\) obtained using the meshes constructed by HEX8 and TET4, respectively. In addition, the order of the automatically selected cover functions at each node obtained from the proposed scheme is depicted. Figure 9 shows the convergence curve of the relative error in strain energy. Table 1 shows the error von Mises stress at point A. It can be seen that the proposed scheme significantly improves the accuracy of the finite element solution. Moreover, the distribution of the enriched nodes and the region of the high stress gradient match well, indicating that the cover selection scheme is well-designed.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Element**} & \multirow{2}{*}{**NE**} & \multicolumn{2}{c}{**Energy**} & \multicolumn{2}{c}{**von Mises Stress**} \\ \cline{3-6}  & & **Standard** & **Adaptive** & **Standard** & **Adaptive** \\  & & **Solution** & **Solution** & **Solution** & **Solution** \\ \hline \multirow{4}{*}{HEX8} & 6 & 9.66\% & 4.56\% & 60.48\% & 19.39\% \\  & 8 & 13.68\% & 1.46\% & 16.21\% & 12.10\% \\  & 10 & 11.48\% & 0.75\% & 8.03\% & 5.57\% \\  & 12 & 8.72\% & 0.53\% & 5.05\% & 4.03\% \\ \hline \multirow{4}{*}{TET4} & 6 & 58.36\% & 6.51\% & 46.86\% & 13.98\% \\  & 8 & 42.94\% & 1.96\% & 39.86\% & 9.72\% \\ \cline{1-1}  & 10 & 31.72\% & 0.65\% & 32.98\% & 4.26\% \\ \cline{1-1}  & 12 & 23.98\% & 0.25\% & 26.96\% & 1.86\% \\ \hline \multicolumn{2}{c}{Reference solution} & \multicolumn{2}{c}{\(E_{ref}=6.250\times 10^{13}\) N m} & \multicolumn{2}{c}{\(\tau_{ref}=1.538\times 10^{12}\) N/m\({}^{2}\)} \\ \hline \hline \end{tabular}
\end{table}
Table 1: Relative errors in strain energy (\(E_{h}\)) and maximum von Mises stress (\(\tau_{h}\)) at point \(A\) for the ad hoc problem using HEX8 and TET4 elements.  
Figure 7: von Mises stress (\(\tau_{vM}\)) distributions on the surface at \(x=0\) for the ad hoc problem. Solutions are obtained by using \(NE\times NE\times NE\) meshes of HEX8 elements (\(NE=6\), \(8\), \(10\), and \(12\)).  
Figure 8: von Mises stress (\(\tau_{vM}\)) distributions on the surface at \(x=0\) for the ad hoc problem. Solutions are obtained by using \(6\times NE\times NE\times NE\) meshes of TET4 elements (\(NE=6\), \(8\), \(10\), and \(12\)).
## 2 Literature Review
[30] created a comprehensive model based on repetitive scheduling, fuzzy set theory, and genetic algorithms to optimize cost, delays, and scheduling. Distributing resources according to constraints and leveling is a very complex problem that falls under the category of resource allocation scheduling problems in project management. The numbers of resources, activities, and execution modes make it a complex problem. Genetic-algorithm-based models [31] and NSGA-II applications have been developed extensively for different multi-mode project scheduling and resource leveling problems [32]. Recent research emphasizes incorporating resource allocation into time buffering via robust scheduling methods, which aim to increase efficiency between cost and quality in dynamic project environments. Through a comprehensive review of the literature on schedule compression and project scheduling, several observations (gaps) were identified, along with potential improvements that could enhance the efficiency and accuracy of project schedules. Table 1 provides a summary of the capabilities and limitations of the methods found in the most recent and relevant references in this field. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & & & & & & & & & \\ \cline{3-10}
The gaps identified in previous research can be summarized as follows:  
* Resource scheduling methods that are unconstrained by available resources. * Deterministic scheduling methods under resource constraints. * Scheduling methods that ignore delay penalties. * Scheduling methods that do not consider the additional costs associated with resource management, whether during resource acquisition (hiring) or release (firing). To address these research gaps, this paper presents a comprehensive approach that advances project scheduling models and schedule compression methodologies. The proposed framework effectively balances project duration, cost, and resource constraints, making it more applicable to real-world scenarios. This approach enhances execution efficiency and reduces overall project costs, providing an innovative model that surpasses the traditional scheduling methods in this field.
## 4 Sound-to-Image Translator
### Training Methodology
We implemented the moving-average adversarial loss \(L_{G}^{m}(r_{max},\ \hat{r})\) defined in Equations (4) and (5), where \(r_{max}\) is the maximum RC-score value, \(t\) is the current epoch number, \(\overline{L}_{Gi}\) is the average adversarial loss for epoch \(i\), and \(k\) is the number of averaged data-points. When employing the moving-average loss instead of the current batch loss \(L_{G}(r_{max},\hat{r})\) (Equation (4)) to train the generator, adversarial training instability was significantly attenuated. In our case, this was especially important becausethe ratio of generator/discriminator training update, which was 5, was higher than typically applied, causing even more instability than usual during GAN training. \[L_{G}(r_{max},\,\hat{r})=\frac{1}{b}\sum_{i=1}^{b}(r_{max}-\hat{r}_{i})^{2} \tag{4}\]  
\[L_{G}^{max}(r_{max},\hat{r})=\frac{L_{G}(r_{max},\hat{r})+\sum_{i=t-k+1}^{t-1} \overline{L}_{Gi}}{k} \tag{5}\]  
The final generator loss is expressed in Equation (6). The adversarial loss is scaled by a factor \(\lambda\) to balance the mean amplitude of the two terms. \[L_{G}=L_{G}\big{(}Y,\hat{Y}\big{)}+\lambda L_{G}^{max}(r_{max},\hat{r}) \tag{6}\]  
Leaving apart the audio encoder training, we present in Algorithm 1 a pseudo-code describing the main steps of the training of our translator. ```
Input\(b\) (\(b\) is the batch size) Input\(n_{g}\) (\(n_{g}\) is the number of training iterations of the generator) Input\(n_{gd}\) (\(n_{gd}\) is the number of training iterations of the generator per discriminator training) Input\(r_{min}\) (\(r_{min}\) is the minimum RC-score value) Input\(r_{max}\) (\(r_{max}\) is the maximum RC-score value) Input\(\lambda\) (\(\lambda\) is the adversarial loss scale factor) Input\(n_{g}\) iterations do
1: Get \(b\) spectrograms \(S\) from stored data: \(S_{batch}\) {\(S_{1}\), \(S_{2}\),..., \(S_{b}\)} \(\leftarrow\) data
3:with\(A_{E}\) frozen
4: Get \(b\) audio embeddings \(x\) from the audio encoder: \(N_{batch}\) {\(x_{1}\), \(x_{2}\),..., \(x_{b}\)} \(\leftarrow\)\(A_{E}\) (\(S_{batch}\))
5:endwith
6: Get \(b\) real images \(Y\) from stored data: \(Y_{batch}\) {\(Y_{1}\), \(Y_{2}\),..., \(Y_{b}\)} \(\leftarrow\) data
7:if current iteration number is multiple of \(n_{gd}\)then
8:with generator \(G\) frozen
9: Update the discriminator \(D\) to minimize: \((L_{D}(r_{max},\,D(Y_{batch},\,x_{batch}))+L_{D}(r_{min},\,D(G(x_{batch}),\,x_{ batch})))\)
10:endwith
11:endif
12:with discriminator \(D\) frozen
13: Update the generator \(G\) to minimize: \((L_{G}(Y_{batch},\,G(x_{batch}))+\lambda L_{G}^{max}(r_{max},\,D(G(x_{batch}),\,x_{ batch})))\)
14:endwith
15:endfor
```  
**Algorithm 1** Pseudo-code for the training of the translator's GAN.
# How Can Radiomics Help the Clinical Management of Patients with Acute Ischemic Stroke?
## 2 Radiomics
### Time since Stroke Prediction  
Chen Y et al. attempted to estimate the time since stroke onset (TSS). They manually outlined the clot on CTA images of 221 patients and obtained 944 RFs. They divided the patients into two different groups: <4.5 h and >4.5 h since stroke onset. They analyzed the performance of the radiomics model and the combined model (radiomics + clinical data). The AUC was 0.803 for the combined model and 0.813-0.803 for the combined model (training and test cohorts). The clinical data used were age, diabetes and atrial fibrillation. They concluded that there was no significant difference between the radiomics and combined models. They also concluded that radiomics based on RFs obtained from CTA images can estimate TSS in patients with AIS [58]. Yao et al. also developed a CT-based radiomic model to classify the onset time of basal ganglia infarcts. They classified the patients into two groups (>4.5 h and <4.5 h). In the validation cohort the results were an AUC of 0.974, sensitivity of 0.951 and specificity of 0.961 [59]. Wen et al. also developed a CT-based radiomics model to predict infarct onset time in patients with MCAO. They obtained similar results with the combined model (AUC of 0.808-0.833) [60]. Finally, Zhang et al. also developed an MRI-based radiomics model to predict TSS with similar results [61].  
Determining the time since stroke onset may play an important role in strokes that occur during sleep. In this sense, knowing whether or not a stroke has occurred in time to receive recanalization therapy with atteplace is critical to the management of these patients.
# Numerical Study of Concrete: A Mesoscopic Scale Simulation Methodology
## 2 Materials and Methods
### Stage 2: Numerical FEM Model
#### 2.2.3 Material Constitutive Laws and Properties
Ansys employs the failure criterion due to a multiaxial stress state:  
Figure 3:0  
Here, \(F\) is a function of the principal stresses (\(\sigma_{x},\sigma_{y},\sigma_{z}\)), S represents the failure surface, and \(f_{c}\) is the uniaxial compressive strength, satisfied when the condition:  
Figure 3:1  
is met, where:  
Figure 3:2Regarding the material definition, in addition to the elastic modulus and Poisson's ratio, the coefficients of transfer of shear stress for open and closed cracks must also be introduced. With this model, Ansys nullifies the elastic modulus of concrete elements parallel to the principal tensile stress in the event of cracking. Conversely, in the case of crushing, the elastic modulus of concrete elements in all directions is nullified, equivalent to removing the element from the model. The material properties of the limestone aggregates, mortar and ITZ were assigned to the meshed model by APDL programming [22]. Based on authors' previous works [36], both numerical and experimental, and other research papers [20; 38; 39], the following material properties are adopted:  
* For coarse aggregate, a purely linear elastic model is employed due to its high strength. Additionally, for the contact between coarse aggregates and ITZ, a pure penalty algorithm between deformable surfaces with asymmetric behavior is utilized. * Furthermore, the relevant mortar material properties employed in the model include: Density of concrete: 2150 kg/m\({}^{3}\). [PERSON]'s modulus (\(E\)): 42,000 MPa (N/m\({}^{2}\)). Poisson's ratio: 0.2. Nonlinear properties for the Drucker-Prager material model: Ultimate uniaxial crushing strength: 42 MPa (N/m\({}^{2}\)). Internal friction angle (\(\varphi\)): 35\({}^{\circ}\). Cohesion angle (\(c\)): 35\({}^{\circ}\). * The ITZ properties used in the model are: Young's modulus (\(E\)): 25,000 MPa (N/m\({}^{2}\)). Ultimate uniaxial compressive strength: 37 MPa (N/m\({}^{2}\)). Internal friction angle (\(\varphi\)): 35\({}^{\circ}\). Cohesion angle (\(c\)): 19\({}^{\circ}\). * Additionally, the properties of the limestone aggregates employed in the model are: Young's modulus (\(E\)): 65,000 MPa (N/m\({}^{2}\)). Poisson's ratio: 0.18.