esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Methodology
### Segmentation of the Vertebrae  
The Residual U-Net (Res U-Net) architecture features a U-shaped design that effectively captures both contextual information and precise localization required for segmentation tasks. The contracting path begins with repeated 3 \(\times\) 3 convolutions each accompanied by a rectified linear unit (ReLU) activation that is followed by 2 \(\times\) 2 max-pooling operations with a stride of 2 for down sampling as illustrated in Figure 2. With each down sampling step the number of feature channels is doubled which enabling the network to learn increasingly abstract features. The expanding path mirrors the contracting path that involves up-sampling the feature map followed by a 2 \(\times\) 2 convolution to reduce the number of feature channels. This is then concatenated with the cropped feature map from the corresponding level of the contracting path that followed by two 3 \(\times\) 3 convolutions and ReLU activations. The final layer employs a 1 \(\times\) 1 convolution to map each 64-component feature vector to the required number of classes.  
The proposed architecture includes several key components. Convolutional Blocks (CA Blocks) contain two successive 3 \(\times\) 3 convolutions with ReLU activations, which is essential for capturing spatial context and extracting features. Max-Pooling Layers perform down-sampling by selecting the maximum value in each 2 \(\times\) 2 block, which reduces spatial dimensions and enabling hierarchical feature learning. Up-Convolution Layers restore spatial dimensions through up-sampling and 2 \(\times\) 2 convolutions that reduce feature channels [13]. Concatenation Layers combine feature maps from the contracting path with up-sampled feature maps by retaining high-resolution features for accurate localization. The Hierarchical Dense Aggregation (HDAC) layer aggregates multi-scale features efficiently which enhances the network's learning capacity.  
The U-Net model was designed using the Deep Network Designer app in MATLAB R2023a, providing a visual environment for designing, analyzing, and training deep learning networks. This facilitated experimentation with various architectures and configurations. The input to the network consists of CT scans with a resolution of 512 \(\times\) 512 pixels which ensures high-detail images for segmentation tasks. This high resolution is crucial for accurately identifying and segmenting anatomical structures within the scans.  
The validation strategy involved monitoring the Dice Similarity Coefficient (DSC) and other performance metrics. The model was validated using both the VerSe'20 dataset (214 CT scans) and a separate dataset of 25 patient scans collected from Kasturba Medical College. These datasets represent a wide range of anatomical and imaging variations, ensuring that the model is robust across different clinical settings.
## 3 Results
### Quality Assessment Methods
#### 3.2.2 Segmentation Evaluation Method  
As in 3.2.1, our segmentation standard is based on manual segmentation by professional doctors. Therefore, in the doctor's standard image segmentation, there will inevitably be a small error term, which we do not discuss here. The area overlap accuracy and contour accuracy are used to evaluate the TRUS graphic segmentation results. These general evaluation methods include the Hausdorff distance [9] (HD), Mean Absolute Distance [10] (MAD), Dice Similarity Coefficient [11] (DSC), Sensitivity [12] (SN), specificity [13] (SP), and Relative Standard Deviation (RSD). Various evaluation indicators are  
\[\text{SP}=\frac{\text{TN}}{\text{TN}+\text{FP}}, \tag{8}\]  
\[\text{SN}=\frac{\text{TP}}{\text{TP}+\text{FN}}, \tag{9}\]  
where TN represents the public area outside the true segmentation of standard and manual segmentation, and FN represents the algorithm segmentation region. These regions do not include non-public regions other than the standard segmentation region. TP is characterized by a common area representing standard segmentation and algorithmic segmentation, while FP is characterized by an algorithmic segmentation region other than standard segmentation.  
\[DSC=\frac{2 TP}{(TP+FP)+(TP+FN)}, \tag{10}\]  
\[\text{HD}(\text{A},\text{B})=\text{max}(\text{h}(\text{A},\text{B}),\text{h} (\text{B},\text{A})), \tag{11}\]  
\[\text{h}(\text{A},\text{B})=\text{max}(\text{a}\in\text{A})\text{min}(\text{ b}\in\text{B})\|\text{a}-\text{b}\|, \tag{12}\]  
\[\text{h}(\text{B},\text{A})=\text{max}(\text{b}\in\text{B})\text{min}(\text{ a}\in\text{A})\|\text{a}-\text{b}\|, \tag{13}\]  
\[\text{A}=\left\{\text{a}_{1},\text{a}_{2},\text{L},\text{a}_{\text{p}},\right\}, \tag{14}\]  
\[\text{B}=\left\{\text{b}_{1},\text{b}_{2},\text{L},\text{b}_{\text{p}},\right\}, \tag{15}\]  
\[\text{MAD}=\frac{1}{\text{N}}\sum_{i=1}^{\text{N}}\|\text{d}_{i}\|, \tag{16}\]  
\[\sigma=\sqrt{\frac{1}{\text{N}}\sum_{i=1}^{\text{N}}(\text{X}_{i}-1)2}, \tag{17}\]  
where A is the combined coordinates of the manually segmented contour points, B is the combined point coordinates of the segmented contour of the algorithm, N is the number of sampled contour points for manual segmentation, dj is the distance between the manual segmentation of the first j contour point and the corresponding point obtained by algorithmic segmentation. We use Equation (8) and Equation (9) to evaluate classification accuracy. Equations (10), (11), and (16) are used to compare the algorithm segmentation results with the standard. Equation (17) is used to compare the fluctuation of the segmentation results generated by different methods in the test set. \(\sigma\) represents the result of standard deviation, x represents the segmentation result of each picture and the IOU value of ground truth (GT).
## 2 Materials and Methods
### Batch Experiments  
A precursor Cr(VI) aqueous solution was prepared by dissolving an appropriate amount of K\({}_{2}\)Cr\({}_{2}\)O\({}_{7}\) with distilled water, targeting an initial Cr(VI) concentration of 1 mg/mL in the precursor solution. Solutions with various concentrations were achieved by the dilution of this precursor solution with deionized water. Solutions with an initial Cr(VI) concentration of 6 ppm were used to investigate the optimum pH value for the adsorption process. The tested pH values were in the range of 1.5-5.5 and were adjusted using a 0.1 N HCl aqueous solution. The optimum pH value was 3. After this, hexavalent chromium aqueous solutions with the optimum pH value (i.e., pH = 3) and with various initial concentrations (i.e., 5.23, 14,55, 20,94, 26.73, 35.40, 45, and 54.14 ppm) were prepared. All these solutions were treated with AC@SCF at 21 \({}^{\circ}\)C. More specifically, AC@SCF powder was added to these solutions in a final concentration of 180 mg/L and stirred for 24 h at 21 \({}^{\circ}\)C in a lab-scale batch reactor. Sequentially, a sample of 5 mL was centrifuged immediately and a smaller amount from the overnatant solution was measured colorimetrically, while the rest of the sample was returned to the reactor. To carry out these colorimetrical measurements, the overnatant sample was mixed with 1,5-diphenylcarbazide for the formation of a chromium-1,5-diphenylcarbazide complex. This complex exhibited a red-purple colour, and the Cr(VI) concentrations were calculated via the light absorbance of such samples at 540 nm. A calibration curve was developed before these measurements using solutions with standard Cr(VI) concentrations (i.e., 0-1 mg/L). The interpretation of the light absorbance measurements led to adsorption kinetic results for Cr(VI) sorption process in AC@SCF.
# Datasets for Cognitive Load Inference Using Wearable Sensors and Psychological Traits
## 2 Related Work
### Open Datasets for Affective Computing
In addition, this study introduced \"a computed stress level\", which was calculated based on the situation on the road (e.g., number of cars, pedestrians, and signs). The Driving-distractions dataset is a driving-simulator study that analyzes the behavior of the drivers under different types of stressors (physical, emotional, cognitive, and none), and it can be used for development of machine learning models for monitoring driving distractions [47]. Three datasets--Stress-math [11], WESAD [48], and Non-EEG [49]--are collected in studies focused on psychological stress. In the Stress-math dataset, the participants solved simple mathematical questions under time and evaluation pressure. The goal of this study was to induce and recognize psychological stress. In the WESAD dataset, the participants experienced both emotional and stress stimuli. More specifically, WESAD contained three sessions for each participant: a baseline session (neutral reading task), an amusement session (watching a set of funny video clips), and a stress session (being exposed to the Trier Social Stress Test [50]). Similarly, Non-EEG is a dataset recorded during three different stress conditions including physical, cognitive, and emotional stressors. Different to the already available datasets in Affective Computing, this study introduces two new datasets that enable cognitive load monitoring with a wrist device in combination with personality traits. The Snake dataset is a labeled dataset of cognitive load measurements in which participants played a smartphone game. The CogLoad dataset is the first dataset that allows analysis of the cognitive load induced by six different tasks in relation to the physiological responses of individuals and their personality traits. To the best of our knowledge, the only other vaguely related dataset that includes personality traits is Amigos, which focuses on human emotions.
## 3 Results and Discussion
### ZnO/g-C\({}_{3}\)N\({}_{4}\) Composites Characterization
Figure 4f shows the characteristic peak at approximately 1021.96 eV, which was assigned to the Zn 2p\({}_{3/2}\) species, confirming that Zn existed in the Zn\({}^{2+}\) oxidation state in the form of ZnO [49]. Figure 4: Characterization results of obtained ZnO/g-C\({}_{3}\)N\({}_{4}\) composites: (**a**) XRD of ZnO model (brown line), g-C\({}_{3}\)N\({}_{4}\) (yellow line), and ZnO/g-C\({}_{3}\)N\({}_{4}\) composites (green line); (**b**) FTIR spectra of g-C\({}_{3}\)N\({}_{4}\) (yellow line) and ZnO/g-C\({}_{3}\)N\({}_{4}\) composites (green line); (**c**–**f**) XPS spectra of C 1s, N 1s, O 1s, and Zn 2p, respectively. The results obtained from XRD, FTIR, and XPS analyses confirmed the correct synthesis and, therefore, the formation of the ZnO/g-C\({}_{3}\)N\({}_{4}\) composites using this method. SEM imaging was employed to analyze the morphology of the prepared ZnO/g-C\({}_{3}\)N\({}_{4}\) composites. The images obtained are presented in Figure 5a,b, where it was observed that the synthesized composites exhibited a porous structure characterized by thin nanosheets. This morphology closely resembles that reported by [PERSON] et al. [35], who synthesized composites of metal oxide nanoparticles and g-C\({}_{3}\)N\({}_{4}\) nanosheets (g-C\({}_{3}\)N\({}_{4}\)/Fe\({}_{2}\)O\({}_{3}\) composites) using a DES composed of FeCl\({}_{3}\), urea, and melamine. Moreover, this morphology agrees with the structure usually presented by g-C\({}_{3}\)N\({}_{4}\) obtained by the thermal decomposition of urea and melamine, as reported in several studies [50; 51; 52]. Figure 5b shows in detail some zones of more porous aggregate growth on the nanosheets. This could have formed during polymerization due to the gas bubbles produced [52]. Finally, to prove the distribution of the different elements and the presence of Zn in the composites, EDS mapping was performed. The results for Zn, C, and N are presented in Figure 5c and confirm their uniform distribution in the structures. Regarding the quantification obtained for these elements (Table in Figure 5c), the amounts of N and C obtained correspond to a C/N ratio close to the theoretical 0.75, suggesting the presence of g-C\({}_{3}\)N\({}_{4}\) structures [53]. In addition, the amount of Zn in the samples was found to be 21.1%. This value is similar to 23.41%, obtained by ICP-MS analysis, which confirmed the presence of Zn in the structure. These results confirmed the successful synthesis of the composites, finding metallic zinc particles on the g-C\({}_{3}\)N\({}_{4}\) structures.
## 1 Introduction
A figure of merit (FoM) was developed to determine the optimal STC parameters, and simulations and experiments were performed to verify the proposed STC. Section 2 presents the working principle and model of a PEC system for downhole casing inspection. Section 3 presents the proposed STC and FoM used to evaluate its performance. The simulations and results of the experiments are discussed in Section 4. Finally, the paper is concluded in Section 5.
# Channel Measurement and Modeling for 5G Urban Microcellular Scenarios
## 2 Urban Microcellular Measurement Campaigns
### Channel Sounder  
All measurements were performed with the same setup, namely the in-house \"High Performance Digital Radio Testbed\" (HIRATE). Each device contains a commercial field-programmable gate array (FPGA) board, a self-developed converter board and several plug-on modules like filters, clocks and modulators. Two fully parallel signal chains allow dual-channel/frequency measurements [25].  
The transmitter (TX) device generates a periodic baseband sounding signal based on a Frank sequence of length 256. The signal is up-converted to an intermediate frequency (IF) of 2.4 GHz. External mixers and filters are used to convert the IF signal to 10 and 60 GHz, respectively. The signal is finally fed into power amplifiers and radiated via the transmit antenna. The RX amplifies the received signal with a low noise amplifier. It is down-converted to IF and fed into the second HIRATE device, where it is converted to baseband. An internal trigger allows capturing snapshots of the signal with a high repetition rate. Most of the processing to compute the channel impulse response (CIR) is done via off-line post-processing, besides a real-time averaging procedure. It performs an \(N_{\mathrm{avg}}\)-times waveform averaging before storing the digitized signal, improving the signal-to-noise ratio by an averaging gain of \(G_{\mathrm{avg}|\mathrm{dB}}=10\cdot\log_{10}N_{\mathrm{avg}}\). Synchronization between TX and RX is established via temperature-controlled rubidium clocks. The setup is calibrated on site via back-to-back measurement. Details on the HIRATE can be found in [26].
## 4 Results and Discussion
### Setting Shrinkage  
Drying shrinkage occurs in ordinary Portland cement concrete mixtures. In contrast, setting shrinkage occurs in PC. This drying shrinkage in Portland cement concrete can be considered similar to the setting shrinkage seen in PC. This setting shrinkage causes shrinkage stress, and cracking occurs when this shrinkage stress exceeds the tensile strength of the Portland cement mixture. Since water is not used in producing PC, its shrinkage mechanism is different. This setting shrinkage is known mainly to be influenced by the type and percentage of polymeric material [7]. Shrinkage is also an important property with regards to the dimensional stability of a combined structure that employs the two different materials. The interface of the combined structure or portions that are low in strength can fail due to internal stresses generated by drying shrinkage. The incompatibility caused by drying shrinkage is greater than that which results from the modulus of elasticity [20]. The test results for setting shrinkage are shown in Figure 8. The setting shrinkage of the AAMT hardener ranged from \(1246\times 10^{-6}\) to \(1497\times 10^{-6}\), and the setting shrinkage for the CAMT hardener ranged  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Filler Type** & **AAMT Hardener** & **CAMT Hardener** \\ \hline FA & 95.5 & 95.8 \\ BFS & 98.9 & 97.8 \\ GCC & 101.9 & 101.1 \\ SF & 108.6 & 103.8 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Compressive strengths by filler and hardener type (unit: MPa).  
from \(403\times 10^{-6}\) to \(547\times 10^{-6}\), a relatively significant difference (see Figure 9). By filler type, the order from highest to lowest setting shrinkage was SF, GCC, BFS, and FA, and the difference between each setting shrinkage was not significant. The difference by type of hardener was likely due to the various reaction mechanisms according to the hardener's chemical composition, while the differences between filler type were probably related to the mode of particle size. However, further study is needed to confirm these conclusions. In a previous study, [PERSON] et al. [7] reported that the setting shrinkage of polyester resin concrete ranged from \(2900\times 10^{-6}\) to \(4200\times 10^{-6}\), which was much higher than the setting shrinkage of EP polymer concrete of \(1100\times 10^{-6}\). This study also reported that the addition of CaCO\({}_{3}\) micro filler negatively affected the setting shrinkage of polyester concrete. Their results are similar to the test results obtained in the present study. The low setting shrinkage of EP polymer concrete using CAMT hardener is very favorable in terms of compatibility.  
Figure 8: Variations in setting shrinkage by hardener and filler type: (a) AAMT hardener and (b) CAMT hardener.
## 1 Introduction
An Az-containing SCLCP, shown in Scheme 1 (PAz), shows a homeotropic orientation as well [19], but interestingly, we have found that a cyanobiphenyl (CB) SCLCP with a polymethacrylate main chain exceptionally adopts a random planar orientation [22]. When the Az and CB mesogens are randomly copolymerized [39,40], the homeotropic or planar mesogen orientation is changed, depending on the copolymerization ratio [39]. Despite the accumulation of the above explorations, a systematic study on the effect of a topcoat with amorphous polymers on SCLCP films has not been conducted yet. In this context, we have conducted an investigation using various conventional amorphous polymers with different glass transition temperatures (\(T_{\rm g}\)) and poly(butyl methacrylate) (PBMA, \(T_{\rm g}\) = ca. 20 \({}^{\circ}\)C), poly(4-vinyl pyridine) (P4 VP, \(T_{\rm g}\) = 140 \({}^{\circ}\)C) and poly(vinyl pyrrolidone) (PVP, \(T_{\rm g}\) = 150 \({}^{\circ}\)C) as the topcoat material. These polymers were chosen for the feasibility of an additional topcoat using poor solvents for the underlying SCLCPs. In this study, most experiments were conducted with PAz, but in the examinations of in-plane photoalignment, a random copolymer of P(Az-CB) (Az:CB = 0.27:0.73) was further examined (see Scheme 1). This copolymerization ratio was chosen because this copolymer adopts a random planar orientation that is favorable for the in-plane photoalignment [39]. In this study, we have also found a significant stabilization effect of the LC phase by top-coating with an amorphous polymer.
# Advancing TinyML in IoT: A Holistic System-Level Perspective for Resource-Constrained AI
## 3 Key Challenges in AI for Constrained Devices
### Real-Time Constraints  
Real-time applications--such as robotics, autonomous systems, and certain industrial processes--add another layer of complexity to AI deployment on constrained devices. In these scenarios, decisions must be made within strict timing windows to ensure safe and effective operation [58]. Consider a robotic arm in a smart factory that must adapt its movements based on vision-based AI inferences: if the computations are delayed beyond a few milliseconds, the entire control loop could become unstable, leading to errors or safety issues. Balancing the intricacy of AI models, which often correlates with their predictive accuracy, against the system's real-time requirements is a key challenge in edge AI [24].  
Techniques to address this challenge typically involve model simplification or designing specialized hardware accelerators that can run inference quickly. Real-time operating systems (RTOSs) may also be employed to guarantee scheduling priorities for AI tasks, ensuring that they preempt lower-priority processes. In some instances, partial offloading strategies are used, where less time-critical workloads are sent to nearby edge servers or the cloud when feasible, while mission-critical tasks stay local [59]. Nevertheless, the margins for error are slim, and ensuring deterministic response times frequently pushesdevelopers toward rigorous profiling and optimization at every stage of the pipeline, from data acquisition to final output [16].
# Massive Data Capture Approach for Modeling Existing Building Stocks
## 2 Methodology
### S3--Mesh Optimization  
This third step is based on the last phases of the methodology that was previously developed by the authors' laboratory Virtual Innovation in Modeling the Architecture and the City lab (VIMAC) involved in this project in the modeling for 3D printing [PERSON]'s Dragon Door in Finca Guell [66] and to filter facade surfaces in surveys with overlapping in the Sagrada Familia Cathedral [43]. Mesh optimization is a proven technique that ensures compatibility for the BIM and also for meshing highly detailed geometries without instrumental errors or loss of the detail present in the model. This is a key step for obtaining the final mesh in a way that improves upon other previous methodologies. Other filtering methods, such as SOR or noise point removal, generate loss of information and decrease density in some areas.  
To be effective at cleaning the point cloud, the source of noise must be known. In the case of existing building stocks, noise is caused by instrumental error and ambient conditions. In addition, because the scanning and photographing are performed over a period of time, this new approach proposes introducing changes in error condition that allow for the determination of clean areas in the final unoptimized result, which corresponds to the zones that need to be preserved. This step uses the CloudCompare 2.13.2 [67] software tool as follows.  
First, meshing by the Poisson Reconstruction algorithm is undertaken [68]. This allows users to decide the details that they want to preserve based on an interpolation method. In the case of non-heritage building stock in need of rehabilitation, meshing at 1 cm allows for preservation of the main surfaces and avoids the introduction of elements such as coats, lights, or non-stable elements in the final point cloud. In the last sub-step, filtering the point cloud, the method meshes again using only the optimized and clean point cloud with the required final resolution of 1 cm. It is key to controlling the parameters of interpolation to remove unwanted points and obtain the desired level of detail. In contrast, in heritage cases, meshing would be under 1 or 5 mm, depending on the specific application [43].  
Second, the point cloud is filtered by comparing the distance between the temporary mesh and the unoptimized point cloud. The points at a greater distance from the threshold are deleted as defined in the previous meshing step. This survey method avoids open meshes; inverted normal, non-manifold edges; and the inclusion of unwanted artifacts.  
Finally, the method meshes again using only the optimized and clean point cloud with the final resolution that is needed. The use of this method enables clean meshes to be obtained, which are suitable for BIMs and compatible with 3D printing, without the need for other methods such as Quadric mesh decimation or retopology.