esa-sceva/satcom-chunk-collection huggingface dataset:
## The Experimental Process and the FEM Model
### The FEM Model
#### 2.4.3 Material  
In previous research, the results of [11] showed material nonlinearity would have an effect on the displacement of the PCCV when the internal pressure was beyond 0.886 MPa. According to the results in [15], the radial displacement, the tendon strain and concrete strain changed a little from the pressure 0 MPa to 0.585 MPa. This meant that the material of concrete structure and the tendons was still in the elastic stage during this pressurization period. In addition, the results of paper [16] showed the radial displacement of containment. The strain of tendon changed little and seemed to be proportional to pressure before the pressure reached 0.6 MPa. According to the works mentioned above, the material of concrete and tendons was still in the elastic stage if the inner pressure was under 0.585 MPa. As a result, the elastic material property was adopted to simplify the FEM model. The material properties of FEM model are listed in Table 1.  
The PCCV was prestressed by 223 horizontal, 144 vertical and 174 dome tendons with the nominal cross section area \(2.85\times 10^{-3}\), \(5.4\times 10^{-3}\) and \(2.85\times 10^{-3}\) m\({}^{2}\). In order to simulate the prestressing force, thermal expansion coefficient was adopted. Through temperature fall, the prestressing force was applied on every prestress tendon. The temperature fall magnitude was calculated by Equation (2).  
\[\sigma=E\cdot\varepsilon=E\cdot\alpha\cdot\Delta T \tag{2}\]  
\(\sigma\) is prestress force, \(E\) is elasticity modulus of tendon, \(\varepsilon\) is tendon strain, \(\alpha\) is the thermal expansion coefficient, \(\Delta T\) is temperature fall magnitude. The prestressing force of all the tendons was 1413 MPa.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Material** & \
\begin{tabular}{c} **Elasticity** \\ **Modulus (MPa)** \\ \end{tabular} & **Poisson Ratio** & **Density (kg/m\({}^{3}\))** &
\begin{tabular}{c} **Thermal Expansion** \\ **Coefficient** \\ \end{tabular} \\ \hline Concrete & \(3.45\times 10^{4}\) & 0.2 & 2500 & - \\ Steel liner & \(2.1\times 10^{5}\) & 0.28 & 7850 & - \\ Prestress tendon & \(1.9\times 10^{5}\) & - & 7850 & \(1.2\times 10^{-5}\) \\ Rebar & \(2\times 10^{5}\) & - & 7850 & - \\ \hline \hline \end{tabular}
\end{table}
Table 1: Material properties of the finite element method (FEM) model.  
Figure 5: The geometric dimension of PCCV. (**a**) The cross section of PCCV (length unit: m); (**b**) Layout of prestress tendons in cylindrical wall (length unit: m).  
Through Equation (2), the temperature fall magnitude \(\Delta T\) was \(-\)603.846 \({}^{\circ}\)C. Besides, six kinds of reinforcements in two directions were modeled on the inner, outer and middle of concrete cylindrical wall.
## 4 Experimentation and Results  
Experimentation was performed using MATLAB (R2022a). While learning and using Python might be difficult, MATLAB specializes in mathematical computations. Matrix handling is not native to Python, although it is possible to do so using the NumPy library. Signal processing and image processing are two areas where MATLAB excels, but Python suffers and performs substantially worse in these fields. The main technical difference between MATLAB and Python is that everything is treated as an array in MATLAB, while everything is an object in Python. Since MATLAB was designed to operate with arrays, it is more suitable to deal with feature maps and matrices in image processing and precise classification tasks. Considering these features, we opted for MATLAB for image processing and precise classification of lung nodule detection.
## 3 Methodology
### Speed Computation and Data Augmentation Based on Movement Feature
#### 3.3.1 Data Augmentation Based on Driving Direction  
In the context of BFRS within trajectory recordings, a recording \(s\) is composed of \(n\) samples, denoted as \(s\big{\{}r_{1},\quad r_{2},\quad\cdots\quad r_{n}\big{\}}\), with driving directions indicated by time stamps. BFRS can be sampled at arbitrary positions in these segments due to different labeling methods. Typically, BFRS is positioned at the center of the recording during the manual labeling process. However, various scenarios exist for BFRS labeling, such as labeling at the beginning or end of the recording. To simulate these different scenarios, each recording undergoes a spatial transformation with a random time seed \(m\in[0,w]\), and this operation is repeated \(2k\) times (with \(k\) iterations for pre-resamples and \(k\) iterations for post-resamples). To maintain the length of the resampled recordings as the original, the samples on the other side of the resampled recording are repeated and mirrored. For instance, if the recording \(s\) is pre-resampled by \(n-m\), it is represented as \(s\big{\{}s_{pre},s_{n-m}\big{\}}\) (as depicted in Figure 3a). However, there is a lack of samples for the previous recordings \(s_{pre}\). Thus, the sampling \(s_{pre}\) is first represented as \(s_{m}\) and then mirrored as \(s_{m}^{-1}\) to maintain the connectivity of the recordings. The resampled recording \(s\) is then denoted as \(s\big{\{}s_{m}^{-1},s_{n-m}\big{\}}\) (illustrated in Figure 3b), and the entire process is represented in (8).  
\[S=\text{AugmentDirection}(S,m,2k)m\in[0,\ w],k\in N \tag{8}\]  
Considering a trajectory where the vehicle moves from \(r_{5}\) to \(r_{10}\), the augmentation process involves creating continuity in the sequence. For pre-resample augmentation, if the result lacks initial recordings (e.g., \(\{r_{2},r_{3},r_{4}\}\)), we mirror the existing sequence (e.g., \(\{r_{5},r_{6},r_{7}\}\)) to fill in the gaps. In the case of post-resample augmentation, where the recording extends beyond the original sequence (e.g., \(\{r_{11},r_{12},r_{13}\}\)), the same mirroring technique is applied to maintain continuity. This approach, though simple, ensures the original sequence's continuity and effectively simulates various labeling scenarios. The augmentation process is automated and repeated to ensure a diverse dataset.
## 3 Result and Discussion
### EK Remediation Experiments
#### 3.2.4 Mass Balance  
The mass balance of the copper and organic phenanthrene was calculated upon completing the EK remediation experiment. The copper and phenanthrene extracts remained in the FSA and the filling material in the PRB were measured using the KSTM (Korea standard test method). The anode and the cathode of reservoir were measured and the mass balance of the copper was detected to be 91.53%, 91.58%, 93.90% and 95.71% in Exp. 1, 3, 4 and 5, respectively, while that of phenanthrene was hardly detected. The results are provided in Table 5. The amount and percentage of copper  
Figure 6: PH distribution within the sample of the test (Exp. 1–Exp. 5). (**a**) Copper; (**b**) Phenanthrene.  
adsorbed in the FSA ranged from 105 mg to 121 (i.e., 73% to 84%, respectively) and the best removal of the copper was achieved with the acetic acid improv. Table 6 shows that the mass balance of phenanthrene was hardly detected, and this was because of the low recovery rate of phenanthrene in kaolinite, based on the results of study by [PERSON] et al. [28] and [PERSON] [29]. Therefore, further study of the pretreatment of phenanthrene may be needed to clarify the current results. Figure 7 shows that a certain amount of heavy metal remained in the contaminated sample, with reservoir and outflow confirming a large amount of copper remained in the FSA. However, phenanthrene was hardly detected and was unidentifiable.
# Fully Open-Source Meeting Minutes Generation Tool
## 4 Selection of Open-Source Models
### Selection of Scene-Change Detection Algorithms  
Since the algorithm needs to determine the transition points in video recordings based on scene changes, an accurate and fast technique is essential. However, while speed is important, accuracy must not be compromised, as it would impact the quality and disrupt subsequent processes. To meet these requirements, several algorithms from the _OpenCV_ and _Scikit-Image_ libraries were compared. The results are presented in Table 1.  
Table 1 demonstrates the superiority of _SSIM_ over other image comparison algorithms. _SSIM_ achieves perfect precision, recall, and _F1_ scores. In contrast, _Histogram Comparison_ and _MSE_ struggle, especially with brightness variations and compression artifacts, leading to misclassified frames. Though _SSIM_ requires a longer processing time than _MSE_, its structural approach results in higher accuracy, since it considers luminance, contrast, and image structure. This precision justifies the extra computation time and makes _SSIM_ the preferred choice for scene-change detection.
# Feedback Approach for the Relay Channel with Noisy Feedback and Its Security Analysis
## 5 Numerical Results
Figure 6 shows the relationship between secrecy level \(\Delta\), blocklength \(N\) and decoding error probability \(\epsilon\) of the proposed SK-Type FBL coding scheme for the MISO FRC with noisy feedback and in the presence of Eavesdropper. From Figure 6a,b, we see that both the secrecy level in Case I \(\Delta_{I}\) and Case II \(\Delta_{II}\) increase with the increase of the blocklength \(N\) and decoding error probability \(P_{\epsilon}\). To be specific, when the blocklength \(N=60\) and  
decoding error probability \(\epsilon=10^{-3}\), the secrecy level in case I \(\Delta_{I}\) has already exceeded 0.99 (secrecy level equal to 1 corresponds to perfect secrecy). However, under the same parameters, the secrecy level in case II \(\Delta_{II}\) only approaches 0.7. This result indicates that the maximum secrecy level achieved in case I (Eavesdropper does not know the complex dither signal sequence \(V_{1}^{N-1}\)) is superior to that of case II (Eavesdropper knows complex dither signal sequence \(V_{1}^{N-1}\)), which is consistent with the theoretical analysis. Figure 7 shows the relationship between the secrecy level \(\Delta\) and blocklength \(N\) under various powers of the proposed SK-Type FBL coding scheme. Figure 7a compares the secrecy level in Case I \(\Delta_{I}\) under different powers of Source encoder \(P\). It is easy to see that secrecy level in Case I \(\Delta_{I}\) approaches perfect secrecy when the blocklength \(N\) is about 100, and a larger \(P\) causes secrecy performance degradation on small \(N\). Figure 7b, plots secrecy level in Case II \(\Delta_{II}\) under several powers of the feedback channel \(P_{f}\). From this figure, we can see that the secrecy level in Case II \(\Delta_{II}\) of the proposed scheme cannot achieve perfect secrecy in general cases. Actually, only when the channel quality of Destination-Eavesdropper is much poorer (i.e., \(\sigma_{fe}^{2}\gg P_{f}\)), the secrecy level in Case II \(\Delta_{II}\) may approach 1 as the blocklength \(N\) tends to infinity.
# A Meta-Analysis on Dual Protocols for Chronic Stroke Motor Recovery: Robotic Training and tDCS
## 3 Results
### Moderator Variable Analysis: Study Designs and Recovery Phase  
The first moderator variable analysis showed no significant long-term effects across different study designs: (a) nine RCT comparisons from eight studies (\(\mathit{SMD}=-0.006\); \(\mathit{SE}=0.114\); variance = 0.013; 95% CI \(=-0.229\)-0.218; \(Z=-0.051\); \(p=0.960\)); and (b) one non-RCT comparison from one study (\(\mathit{SMD}=1.113\); \(\mathit{SE}=0.553\); variance = 0.305; 95% CI \(=0.030\)-2.197; \(Z=2.015\); \(p=0.044\)). These findings indicate that different study designs did not change the sustained effects of tDCS combined with robotic training on the upper limbs post-stroke.  
Figure 2: Short-term effects of combining the protocols of robot training and tDCS on upper limb functions. The synthesized data derived from a random-effects model show no significant positive transient effects on upper motor rehabilitation. Note that the red diamond indicates overall effect size and squares denote individual effect sizes with 95% CI. More positive values indicate more treatment effects after the combined protocols [45; 46; 51; 52; 54; 55; 56; 58; 59; 60; 65; 66].  
The second moderator variable analysis revealed no significant long-term effects across different post-stroke recovery phases: (a) two subacute comparisons from one study (\(SMD=0.051\); \(SE=0.177\); variance = 0.031; 95% CI = \(-0.295\)-0.398; \(Z=0.290\); \(p=0.772\)); (b) seven chronic comparisons from seven studies (\(SMD=0.174\); \(SE=0.230\); variance = 0.053; 95% CI = \(-0.277\)-0.625; \(Z=0.755\); \(p=0.450\)); and (c) one subacute and chronic comparison from one study (\(SMD=0.008\); \(SE=0.417\); variance = 0.174; 95% CI = \(-0.810\)-0.826; \(Z=0.020\); \(p=0.984\)). These findings indicate that sustained effects on upper limb rehabilitation were not altered by different post-stroke recovery phases.
# People Detection Using Artificial Intelligence with Panchromatic Satellite Images
## 2 Materials and Methods
### Methodology
The YOLO model reached a maximum AP of 36.32%, much lower than the AP achieved by the Faster R-CNN model with data augmentation and a 15 cm buffer. In addition, other widely used object detectors, such as RetinaNet and Single Shot Detector (SSD), were evaluated using ArcGIS Pro software (version 3.3.1) implementation. A class arcgis.learn.SingleShotDetector() allows the user to create an SSD object detector with the specified parameters, including grid sizes used for creating anchor boxes, zoom scales and aspect ratios of the anchor boxes, backbone models for feature extraction, and dropout probability. The SSD implementation supports the backbones from the ResNet, DenseNet, and VGG families. A class arcgis.learn.RetinaNet() creates a RetinaNet object detector with the specified zoom scales and aspect ratios of anchor boxes, as well as backbone models. Only backbones from the ResNet family are supported by RetinaNet implementation. The aspect ratio parameters were set to 1:1 to define the square shape of the labels. Multiple zoom parameters were tested to define the appropriate scale of the anchor boxes to cover the sizes of the detected people in the image. Table 5 summarizes the parameters setting used for the SSD model and the RetinaNet model. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & & \multicolumn{2}{c}{**Input Parameters**} & \multicolumn{2}{c}{**Optimization**} & \multicolumn{2}{c}{**Quality Assessment**} \\ \hline
**Variant** & **Buffer (cm)** & **Backbone** & **Batch Size** & **Epochs** & **Learning Rate** & **Average Precision Score (\%)** \\ \hline
26. & 15 & Darknet53 & 8 & 300 & 0.002512 & 2.26 \\
27. & 15 & Darknet53 & 16 & 300 & 0.002089 & 4.04 \\
28. & 15 & Darknet53 & 32 & 300 & 0.003631 & 36.32 \\
29. & 15 & Darknet53 & 64 & 300 & 0.000832 & 4.27 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Settings for the training of the YOLOv3 model using data augmentation. Figure 5: Image with mask (pixel resolution is 30 \(\times\) 30 cm). The above testing in ArcGIS Pro implementation yielded very poor results, with a maximal average precision value of only 0.1% for the SSD model employing the VGG19 backbone, a batch size of 16, a zoom of 0.5, and a grid of 8. Based on the poor results of the YOLO model and ArcGIS Pro implementations, only the Faster R-CNN model was further tested.
## 2 Flight Altitude Feature Extraction Method  
The time histories of flight altitude we often see are shown in Figure 1. These data come from real flight data with some sensitive information removed. Except for takeoff and landing, the altitude data for the two flight profiles differ significantly and do not have obviously similar features. The altitude history of each profile has typical non-stationary characteristics. Climb-descent flight training, often meaning that the maneuvering motion will change considerably from the previous action, is shown by the upper triangle or sine shapes with a chain line in Figure 1.  
Two thresholds, namely, the shortest time and the maximum altitude difference, need to be set to determine whether the maneuvering actions form a climb-descent process, such as 15 s and 10% of the limit altitude of an aircraft. For the same aircraft and engine models, the thresholds can be chosen by training experiences and the definition of height levels; for example, the maximum altitude of an aircraft is 12,000 m, and the threshold of 10% represents 1200 m or two height levels when a height level is defined as 600 m. Those pitches with instantaneous property or climb-descent actions with a maximum altitude difference less than the threshold belong to other types of training and are not counted in the FCDF, as shown in the ellipses with a dotted and dashed line in Figure 1a.  
This section presents an altitude feature extraction algorithm that can obtain the FCDF within a flight profile, including trend filtering and PV difference de-redundancy methods. The purpose of trend filtering is to remove thin jumps from the altitude curve and weaken the impact of other flight actions on the climb-descent signal, such as hovering, rapid diving, or pulling up. The PV difference de-redundancy method further filters small fluctuations  
Figure 1: Time history of flight altitude: (**a**) Profile 1; (**b**) Profile 2.  
out of the data after trend filtering, which extracts the PV of the compressed altitude data and then removes the redundancy from the PV using a climb-descent threshold so as to retain the PV points that meet these thresholds. After conducting the PV difference method, the statistics of FCDF based on altitude features will be achieved.
## 2 Materials and Methods
### Differential Scanning Calorimetry (DSC)  
Differential scanning calorimeter DSC 7 (Perkin Elmer, Norwalk) equipped with an Intracooler II and Pyris software was used to investigate the water behavior in cheese samples. DSC was calibrated using indium (m.p. 156.6 \({}^{\circ}\)C, \(\Delta\)H\({}_{\text{f}}\) = 28.45 J/g) and n-dodecane (m.p. \(-\)9.65 \({}^{\circ}\)C, \(\Delta\)H\({}_{\text{f}}\) = 216.73 J/g). Cheese samples (9-10 mg) were weighed into 20 \(\mu\)L aluminum pans (Perkin Elmer, No. 0219-0062) and hermetically sealed. The reference was an empty, hermetically sealed aluminum pan. Nitrogen (99.999% purity) was used as the purge gas. The sample pan was placed in the calorimeter at 5 \({}^{\circ}\)C. The following time--temperature program was set: (1) heating and isotherm for 1 min at 70 \({}^{\circ}\)C; (2) cooling at 5 \({}^{\circ}\)C/min to \(-\)40 \({}^{\circ}\)C; and (3) heating at 5 \({}^{\circ}\)C/min to 70 \({}^{\circ}\)C. The following parameters were analyzed from the second melting curve: the temperatures of ice melting T\({}_{\text{onset}}\), T\({}_{\text{peak}}\), and the enthalpy \(\Delta\)H\({}_{\text{m}}\) of ice melting, calculated per 1 g of water. The content of freezable water (FW) in the water fraction was calculated as:  
\[\text{FW}=\left(\frac{\Delta\text{H}_{\text{m}}}{\Delta\text{H}_{\text{ref}}} \right)\times 100 \tag{1}\]  
where \(\Delta\)H\({}_{\text{m}}\) is the enthalpy of ice melting per unit mass of water contained in cheese (J/g), \(\Delta\)H\({}_{\text{ref}}\) is the enthalpy of ice melting for samples of pure water, equal to 333.7 J/g.  
The unfreezeable water content (UFW) was calculated as:  
\[\text{UFW}=100-\text{FW}\;(\%) \tag{2}\]  
where FW is freezable water.
# From Tilings of Orientable Surfaces to Topological Interlocking Assemblies
## 5 Conclusions and Outlook  
In this work, the versatility of the Escher-like approach for constructing topological interlocking assemblies (TIA) has been demonstrated. By considering both regular and semi-regular tilings, as well as extending the method to arbitrary orientable surfaces, a wide range of potential TIA configurations are constructed. The key findings of this work include:  
1. The successful construction of planar and non-planar TIA using the Escher-like method;
2. The exploration of a semi-regular tiling yielding an interlocking structure;
3. The extension of the method to non-planar surfaces, such as the unit sphere and various convex polyhedra, demonstrates the feasibility of constructing spherical and polyhedral TIA.
4. Practical insights gained from 3D-printed models provides a profound understanding of the assembly and interlocking processes.  
The Escher-like approach, including its modified versions, has proven to be a powerful tool for the construction of TIA, with practical implications for fields such as architecture and 3D printing; see [27; 33].  
Future research will focus on refining the described construction technique for given applications and for optimising mechanical properties tailored to practical scenarios such as the construction of domes with non-planar TIA, the investigation of columned-shaped TIAs for tunnel systems, or spherical-shaped TIAs for interlocking puzzles. For this, generalisations of reliable tools for fast evaluation of interlocking assemblies, like the \"Interlocking Flow Method\" presented in [28], should be generalised in order to gain a profound understanding of the assemblies constructed in this work.  
Figure 17: **(a)** Projection of a modified tile resulting from deforming the edges of a kite that forms a face of the deltoidal hexecontahedron (**b**) Different views of the block that results from interpolating between a kite that forms a face of the deltoidal hexecontahedron and a modified tile that is contained on the surface of a scaled deltoidal hexecontahedron.  
Figure 18: Two exploited views of the TIA that realises the surface of the deltoidal hexecontahedron.  
Moreover, future research will explore different materials for constructing TIA. One specific scenario could involve using additive manufacturing techniques, such as 3D concrete printing, to produce large-scale spherical TIA blocks.  
Conceptualization, methodology, writing (draft, review and editing) R.A., T.G. and A.C.N.; visualization and software R.A. and T.G.; funding acquisition A.C.N. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Deutsche Forschungsgemeinschaft (DFG, German Research Foundation)-SFB/TRR280, Project-ID 417002380 (A04).  
The data presented in this study are available on request from the corresponding author.  
The authors declare no conflicts of interest.