esa-sceva/satcom-chunk-collection huggingface dataset:
# Neuroimaging of Pediatric Cerebellum in Inherited Neurodegenerative Diseases
## 1 Introduction
### Cockayne Syndrome  
Cockayne Syndrome (CS) is a rare autosomal recessive multisystem disorder caused by mutations in \(CSA\) and \(CSB\) genes, where proteins are involved in the nucleotide excision repair pathway. Clinical characteristics include neurodevelopmental delay, cutaneous photosensitivity, pigmentary retinopathy, neurososensory hearing loss. The disease encompasses a wide spectrum of clinical severity, divided into four overlapping clinical subgroups of decreasing severity: (1) COFS is the most severe form, starting in utero or the neonatal period with a rapidly fatal outcome; (2) CSII, begins in the first year of life and leads to death in infancy; (3) CSI is the classic form that manifests during infancy; death occurs in  
Figure 5: GDC1a. Brain MRI scan of a boy. At 10 years (top), the exam demonstrates only slight cerebellar atrophy and hyperintensity in the dentate nuclei. The exam at 12 years (bottom) shows a severe worsening of cerebellar atrophy with the persistence of normal brainstem volume.  
the first decade of life. (4) CSIII shows milder neurological signs, and affected patients may reach adulthood [24].  
Brain CT and MRI findings include diffuse cerebral and cerebellar atrophy, calcifications in the basal ganglia, and lack of myelination of the white matter [25]. These cardinal neuroimaging features are not all simultaneously present: brain atrophy is probably the earliest detectable sign. Brain calcifications are typically bilateral and symmetric, ranging from punctate to large, and are most frequently seen in the putamen. Calcifications, which are progressive, may be seen in the white matter-cortex junction, dentate nuclei, pallida, and more rarely in white matter and thalami. Diffuse brain atrophy with white matter loss and ventricular dilatation is observed in the very early stages of the disease and always progresses; the brainstem and the cerebellum usually show moderate atrophy with enlarged cisterna magna. White matter abnormalities attributed to hypomyelination, or demyelination involve the corpus callosum, the posterior limb of the internal capsule and the external capsule, whereas the cerebellum and brainstem always show normal myelination. After several years of the disease, the signal intensity of the hemispheric white matter is very high on FLAIR images, suggesting progressive astrogliosis [25].
# Low-Profile Antenna System for Cognitive Radio in IoST CubeSat Applications
## 3 Antenna Fabrication and Discussion of Results
### MIMO Diversity (ECC)  
The diversity of the MIMO antenna in terms of envelope correlation coefficient (\(ECC\)) is calculated to show how much antenna elements are independent in their performance. The values are found to be very low, less than 0.02, ideal for the MIMO operation. ECC values are calculated based on the radiation patterns of antenna elements as given below:  
\[ECC_{ij}=\frac{\left|\int_{0}^{4\pi}\left[\overrightarrow{E_{i}}(\theta,\ \varphi)\times \overrightarrow{E_{j}}(\theta,\ \varphi)\ \right]d\Omega\right|^{2}}{\int_{0}^{4\pi}\left| \overrightarrow{E_{i}}(\theta,\ \varphi)\right|^{2}d\Omega\int_{0}^{4\pi} \left|\overrightarrow{E_{j}}(\theta,\ \varphi)\right|^{2}d\Omega} \tag{1}\]  
where \(\overrightarrow{E_{i}}(\theta,\ \varphi)\) is the radiation pattern of the \(i\)th antenna element. Figure 17 shows the ECC curves for both sensing and frequency reconfigurable antenna elements. It is evident that both antenna elements are performing well over the desired bands of operation.  
Figure 16: Simulated and measured radiation patterns of the narrowband antenna in xz- and yz-plane. (**a**) P-3 and (**b**) P-4.  
Figure 17: ECC curves between various antenna elements.
# Global Buckling Resistance of Cold-Formed Steel Beams with Omega-Shaped Sections
## 2 Normative Procedures for Assessing Buckling Safety
### ABNT NBR 14762:2010  
Annex C of ABNT NBR 14762:2010 [6] introduced a method for the design of cold-formed steel profiles subjected to simple bending, known as the Direct Strength Method (DSM). This method serves as an alternative to the Effective Width Method (EWM), and the prescriptions included in Annex C of this standard can be applied to calculate the resistant bending moment of the profile \(\left(M_{Rd}\right)\).  
The critical elastic local buckling moment \(\left(M_{t}\right)\), distortional buckling moment \(\left(M_{dist}\right)\), and global buckling moment \(\left(M_{e}\right)\) should be calculated through an elastic buckling analysis, typically performed using computational tools, such as CUFSM [20], ABAQUS [23], and GBTUL [24]. If the profile does not exhibit one of the three buckling modes in the analysis, there is no need to consider the corresponding resistance.  
The characteristic resistant bending moment \(\left(M_{Rk}\right)\) is taken as the smallest value calculated for global, local, and distortional buckling \(\left(M_{Re},\ M_{Rl},\ M_{Rdist}\right)\), respectively. The calculated resistant bending moment \(\left(M_{Rd}\right)\) is given by \(\frac{M_{Rd}}{\gamma}\), where \(\gamma\) is a partial safety factor that equals to 1.10. The equations for determining the value of the resistant bending moment to buckling are presented below.
# Cross-Layer Energy Optimization for IoT Environments: Technical Advances and Opportunities
## 2 Significance of Energy Efficiency in IoT Use Cases
### Case Study: Smart Agriculture  
Water and electricity both are the most prominent input parameters in agricultural systems. In a situation of inadequate rainfall, crop production is restricted due to inefficient usage of water resources and irrigation scheduling. From this perspective, the Cloud of Things (CoT)-based smart irrigation system has been introduced to utilize water and energy in an effective and efficient way, while enhancing agricultural production [16]. The irrigation system becomes more efficient and less costly by smart water usage (supervising and monitoring water capacity, timing, location and duration of flow on the basis of data analytics). The combination of data analytics collection from the CoT (weather situation, type of soil, land condition, crop features, etc.) and data capture by sensors measuring moisture, heat, water stress, chemicals, etc., provide practical information. This information helps farmers utilize fertilizers, water and pesticides in appropriate locations and quantities.  
Figure 5 illustrates a real time CoT-based smart irrigation system, by enabling task automation based on the data received by the sensors and the devices. In addition, performance statistics and  
Figure 4: Quality sensing and communication for product boxes.  
reports are generated, while providing real time information to the farmer based on the business activity, which helps the farmer to make timely and well-informed decisions. Then the farmer can benefit in terms of data access, storage, synchronization and cost, when farming applications are moved to the cloud daily. In the applications related to smart agriculture, energy usage is also one of the most crucial parameters in terms of lighting, boosters, pumps and other purposes. In the future, energy-efficient smart agricultural system should be proposed, while considering other parameters such as water based on IoT capabilities.
## 3 Results  
### Results of Lower-Limb Biomechanical Characteristics  
#### 3.1.1 Results of Hip, Knee, and Ankle Joint Angles During the Landing Phase  
The Shapiro-Wilk test indicated that the joint angle data during landing followed a normal distribution.  
A paired t-test was conducted to compare lower-limb joint angle changes during different landing phases before and after fatigue. After applying the Benjamini-Hochberg false discovery rate (FDR) correction, the hip joint angle at the initial contact (IC) phase was significantly lower post-fatigue (FDR-adjusted \(p\) = 0.023, t = 3.452), while its range of motion (ROM) significantly increased (FDR-adjusted \(p\) = 0.0115, t = 2.164). The knee joint angle at the IC (FDR-adjusted \(p\) = 0.0157, t = 3.377) and takeoff moment (TM) (FDR-adjusted \(p\) = 0.0088, t = 5.426) was significantly lower post-fatigue, whereas the knee ROM increased significantly (FDR-adjusted \(p\) = 0.0063, t = 6.213). The ankle ROM also showed a significant increase (FDR-adjusted \(p\) = 0.0077, t = 2.216). The detailed results are shown in Table 2 and Figure 2.  
Figure 2: Changes in lower-limb joint angles during the landing phase before and after fatigue. * Indicates that the results are statistically significant.
## 3 Results
### MERRA-2 GDB (SSR Trends) and Its Evaluation
#### 3.2.1 Evaluation of MERRA-2 GDB (SSR Trends)
More specifically, the BSRN sites indicate a GDB equal to 3.78 W/m\({}^{2}\), resulting from 8/14 stations with brightening/dimming. It should be noted that the apparent contradiction of more stations with dimming resulting in a total brightening does not exist in practice, since the sign of GDB is not only determined by the number of stations with brightening/dimming, but also by the magnitude of brightening/dimming of each station. On the other hand, the corresponding MERRA-2 pixels indicate a dimming equal to \(-\)2.43 W/m\({}^{2}\) resulting from 6/16 stations with brightening/dimming. Thus, overall and during the entire period 1992-2020, it is found that MERRA-2 Reanalysis reproduces the phases of GDB (brightening and dimming) recorded by the BSRN station measurements. However, in spite of this agreement and the satisfactory global distribution of BSRN stations, their small number (unlike GEBA) does not allow us to continue using them in the next steps of this study focusing on the evaluation of MERRA-2 GDB on a regional level.
## 4 System Architecture  
### Channel Coding  
In digital communication systems, bit mistakes are found and fixed via a method called channel coding, commonly referred to as forward error correction coding (FEC). Both the transmitter and the receiver carry out channel coding. Channel coding is known as an encoder on the transmit side, where additional bits (parity bits) are added to the raw data before modulation. Due to noise, interference, and fading during transmission, channel coding enables the receiver to find and correct errors. In order to improve the OCC system performance, channel coding is employed in this paper as Figure 1.  
Figure 1: System Architecture.
## 4 AC/DC Converter
### Architectures: Evaluation and Selection  
The selection of an AC/DC architecture aims to obtain the maximum efficiency. Four architectures were analyzed in order to achieve this goal. Although rectifiers were discarded previously, the first one  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**MOSFET**} & \(V_{in}\) & \(f_{in}\) & \(R_{load}\) & \(D\) & \(f_{sw}\) & \(Q_{g}\) & \(R_{ds}\) & \(V_{out}\) & \(I_{out}\) & \(P_{out}\) & \(P_{out}\) & \(P_{out}\) & \(P_{out}\) \\  & (**Vp**) & (**kHz**) & (**f**) & (**kHz**) & (**mC**) & (**mT**) & (**V**) & (**mA**) & (**mW**) & (**mW**) & (**mW**) & (**mW**) \\ \hline Si155 SDL[55] & 1.025 & 1.9 & 80 & 0.05 & 500 & 0.8 & 0.63 & 0.677 & 8.469 & 5.73 & 0.523 & 5.20 & 44.05 \\ Si155 SDL & 1.025 & 1.9 & 90 & 0.1 & 500 & 0.8 & 0.63 & 0.709 & 7.887 & 5.59 & 0.527 & 5.063 & 42.83 \\ Si8466 EDB [56] & 1.025 & 1.9 & 80 & 0.05 & 500 & 6.8 & 0.05 & 0.683 & 8.538 & 5.83 & 6.79 & -0.96 & -8.12 \\ SI8466 EDB & 1.025 & 1.9 & 90 & 0.1 & 500 & 6.8 & 0.05 & 0.719 & 7.997 & 5.74 & 7.313 & -1.57 & -13.30 \\ IRLU3802 [57] & 1.025 & 1.9 & 80 & 0.05 & 500 & 27 & 8.5 & 0.664 & 8.803 & 5.51 & 20.476 & -14.96 & -126.6 \\ IRLU3802 & 1.025 & 1.9 & 80 & 0.1 & 500 & 27 & 8.5 & 0.657 & 8.224 & 5.40 & 20.4 & -15 & -126.9 \\ Si1553 CDLN [58] & 1.025 & 1.9 & 80 & 0.05 & 500 & 0.55 & 0.578 & 0.68 & 8.51 & 5.78 & 0.341 & 5.43 & 46.01 \\ Si1553 CDLN & 1.025 & 1.9 & 90 & 0.1 & 500 & 0.55 & 0.578 & 0.715 & 7.945 & 5.68 & 0.350 & 5.33 & 45.09 \\ \hline \hline \end{tabular}
\end{table}
Table 19: Comparative results between different MOSFETs at 3 m/s.  
Figure 30: Comparisons between diodes at 3 m/s and 10m/s. (**a**) Efficiency. (**b**) Output power.  
For the test case of minimum wind velocity, the maximum output power is 4.04 mW, and it is achieved for resistive loads of 30 \(\Omega\) or 40 \(\Omega\). The maximum voltage level obtained is 0.57 V for a 100 \(\Omega\) load and 3.26 mW is obtained at the output. The required resistive load value to achieve a 3 V level would be so big that the output power would be close to zero, i.e., an open circuit condition. Thus, this architecture is not valid. Nevertheless, simulations were carried out for comparison between architectures.  
For a wind velocity of 10 m/s, the output level of 3 V is only reached for a resistive load of 100 \(\Omega\), obtaining 93.70 mW. The maximum obtained power is 132.32 mW for 30 \(\Omega\) load and 1.99 V signal.  
Rectifier efficiencies are 38% and 61% for 3 m/s and 10 m/s wind velocities, respectively. The complete system efficiency is around 5% in both ambient conditions.  
In both cases, the input voltage is lower than the output voltage. Diode bridge architecture only rectifies input signal and would require a second stage to step-up the voltage; consequently, it is not a feasible alternative.
## 4 Optimum Arrangement of BRBs for RC Frames with Curtailed Walls
### Determination of Objective and Fitness Function  
The binaries number produced in step one is then transferred to the STERA_3D to recreate the frame with BRBs. The nonlinear dynamic response is then performed with ten ground motions to obtain the inter-story drift and the damage index of beams. These parameters were used to calculate the objective function, together with the total number of BRBs. The fitness function is defined as the sum of three conditions: the total number of BRBs, inter-story drift, and the damage index of the RC members, as shown in Equation (3).  
\[Minimize:f=w_{1}\phi_{1}+w_{2}\phi_{2}+w_{3}\phi_{3} \tag{3}\]  
where:  
\(w_{1},w_{2},w_{3}\): Weighting factors, with \(w_{1}\) = 0.3, \(w_{2}\) = 2, and \(w_{3}\) = 0.2 (based on the trial);  
\(\phi_{1}\): Condition for total number of BRBs;  
\(\phi_{2}\): Condition for story drift;  
\(\phi_{3}\): Condition for damage index of RC members.  
Figure 18: The general process of Genetic Algorithm sample.  
The parameter \(\phi_{1}\) is the contribution effect of the number of BRBs. The \(\phi_{1}\) is calculated using Equation (4).  
\[\phi_{1}=\frac{1}{5(N-k)}\sum_{i=k+1}^{N}n_{D,i} \tag{4}\]  
where:  
\(n_{D,i}\): Number of BRBs in \(i\)-th story;  
\(N\): Total number of stories;  
\(k\): Top story number of the curtailed wall.  
The parameter \(\phi_{2}\) is the effect of the maximum inter-story drift. The \(\phi_{2}\) is defined by Equations (5) and (6), where it is zero if the maximum inter-story drift is less than the allowable drift \(\delta_{allow}\), which equals \(1/100\) of the inter-story height.  
\[\phi_{2}=\frac{1}{(N-k)}\sum_{i=k+1}^{N}\Omega_{i} \tag{5}\]  
\[\Omega_{i}=\left\{\begin{array}{ll}0,&if\ \delta_{\text{max},i}<\delta_{allow} \\ \frac{\phi_{\text{max},i}}{\delta_{allow}},&if\ \delta_{\text{max},i}\geq\delta_{allow} \end{array}\right. \tag{6}\]  
where:  
\(\delta_{\text{max},i}\): The maximum story drift in \(i\)-th story;  
\(\delta_{allow}\): Allowable story drift.  
The parameter \(\phi_{3}\) is the effect of the damage index of the RC members. The \(\phi_{3}\) is defined by Equation (7). In this equation, the average damage index between the left side and the right side of the beam element is adopted.  
\[\phi_{3}=\frac{1}{5(N+1-k)}\sum_{i=k}^{N}\left(\sum_{j=1}^{5}\left(\frac{DI_{ BL,i}+DI_{BR,i}}{2}\right)\right) \tag{7}\]  
where:  
\(j\): The inspected busy number;  
\(DI_{BL,i}\): Damage index of beam on the left joint;  
\(DI_{BR,i}\): Damage index of beam on the right joint.  
The damage index (DI) is expressed by Equation (8) based on the structural deformation and the hysteretic energy response due to seismic excitations.  
\[DI=\frac{u_{m}}{u_{u}}+\beta\frac{E_{h}}{\Gamma_{y}u_{u}} \tag{8}\]  
where:  
\(u_{m}\): Maximum displacement response of structure element due to earthquake;  
\(u_{u}\): Ultimate displacement capacity under monotonic loading;  
\(E_{h}\): Hysteretic energy dissipated by the structural element;  
\(F_{y}\): Yield force;  
\(\beta\): Non-negative parameter based on repeated loading effect.
## 3 Results and Discussion
### Bending Moment-Curvature Relationship  
The accurate determination of the bending moment-curvature curve of the RC members is a reliable indicator of the load capacity of the concrete structure. Therefore, the measured values from the concrete strain gauges and embedded rebar strain gauges were compared. Furthermore, the values of individual gauges were measured until their readings became unreliable owing to the cracking on the concrete surface. Figure 6 shows the applied bending moment-curvature curve at the mid-span of the RC members. The results demonstrate that the slopes of the moment-curvature were similar before the initial cracking occurred, and the flexural rigidity did not exhibit any difference. When the heavyweight waste glass is replaced with fine aggregate, however, the moment-curvature curves changed. Namely, the heavyweight waste glass substitution ratio did affect the flexural rigidity of the RC members.  
The bending moment of the initial cracking load corresponds to 3.6-6.8 kN\(\cdot\)m in all RC members. The moment-curvatures exhibited a nonlinear relationship after curvature of the first cracking load.  
Figure 6: Moment-curvature curve for RC beam.  
In the nonlinear region, the curvature of the all the RC members increased suddenly. Conclusively, the moment-curvature curves of the RC member were reduced significantly when all the fine aggregate were replaced by heavyweight waste glass.
# Augmented Data-Driven Approach towards 3D Printed Concrete Mix Prediction
## 2 Methodology
### Dataset Extraction and Preparation
#### 2.1.1 Feature Selection  
Feature selection is one of the most crucial steps in machine learning which directly impacts models' performance and efficiency. Choosing the right features for the data enhances the models' interpretability for the key relationships [91]. For 3D concrete printing (3 DCP), this problem is further intensified by the lack of universal printable properties and standardized tests leading to most of the researchers generating a printable mix design with different assessment criteria to optimize cost and sustainability [30]. So, the experimental dataset was extracted from the literature [38; 92; 93; 94; 95; 96; 97; 98; 99; 100; 101; 102; 103; 104; 105; 106; 107; 108] with an emphasis on printability characterized by slump flow and strength shown by anisotropic compressive strength. A quality-over-quantity approach was taken during data extraction. The input features were mix design constituents, with a focus on constituents mostly used in the formulation of affordable and sustainable mix designs. Most importantly, the extracted dataset includes slump flow, which is essential to capture printability along with anisotropic compressive strength [109; 110]. For 3D printing a layer extruded by the setup shown in Figure 4a, the strength is maximum in the loading direction perpendicular to the plane of printing indicated by Dir.1 in Figure 4c, while for the other two loading directions (Dir.2 and Dir.3) as shown in Figure 4d,e, the strength is less than Dir.1 but is comparable to each other [96].
# Effect of Buildings on the Radiation Characteristics of MF Broadcast Antennas
## 2 Materials and Methods
### Building Modeling  
A building may be modeled as a rectangular parallelepiped with facade length \(L\), width \(W\), and height \(H\). It is assumed to comprise from 2 up to 16 stories, each story being at least 50 m\({}^{2}\) and about 5 m high; thus \(L\), \(W\), and \(H\) may span the range \(10\leq L\leq 50\) m,  
Figure 1: Geometry of the antenna models. (**a**) Equivalent dipole of length \(2h\) and (**b**) equivalent two-dipole array.  
\(10\leq W\leq 50\) m, and \(10\leq H\leq 80\) m, respectively. An issue that arises when modeling a building is its orientation. As shown in Figure 2, the antenna is placed along \(z\)-axis, whereas the length \(L\) is assumed to be parallel to the \(x\)-axis, i.e., the facade of the building faces the antenna; \(W\) is taken parallel to the \(y\)-axis and \(H\) is parallel to the \(z\)-axis.  
The materials of buildings are, obviously, not uniform and difficult to model. They comprise several conductive (steel frame, copper cables, metal pipes, etc.) and dielectric (bricks, glass, concrete, marble, etc.) parts that may or may not exhibit a certain conductivity. Herein, the buildings are modeled as wire-grid bodies [27], for the sake of simplicity. It should be noted that, since the wires were taken as PEC, the aforementioned model constitutes the worst-case scenario in terms of the effect on the radiation characteristics of the antenna. The grid consists of similar wires at a distance \(\Delta\); the radius of each wire is \(a=\Delta/10\)[28]. Thus, the whole building comprises \((L/\Delta+1)\times(W/\Delta+1)\times(H/\Delta+1)\) wire segments arranged in a mesh, as shown in Figure 2.
## 5 Discussion  
### Integration and Optimization of Multi-Source LST Data for Geothermal Anomaly Detection  
#### 5.1.1 Anomaly Extraction and Method Optimization of Landsat-8 LST Data  
This study utilized the GEE platform for large-scale spatiotemporal data processing, successfully handling 134 scenes of long-term Landsat 8 daytime winter imagery. Compared to conventional methods, this approach significantly improved spatiotemporal coverage and data completeness for geothermal anomaly extraction.  
The results indicate that the multi-year winter average LST map (Figure 31) is more effective than single-year images in smoothing localized high-temperature anomalies, highlighting long-term thermal stability and overall distribution patterns in the region. This provides a more stable dataset for subsequent geothermal anomaly analyses.  
During data preprocessing, key steps, such as water body removal and elevation correction, significantly improved the accuracy of the temperature distribution. The processed surface temperature distribution became more uniform, particularly in mountainous areas, where high-temperature anomalies were greatly reduced, effectively minimizing the impact of non-geothermal factors. These preprocessing optimizations substantially enhanced the accuracy of geothermal anomaly extraction.  
Moreover, the local block threshold method, which assigns region-specific thresholds, addressed the global method's limited sensitivity to localized anomalies and enhanced the extraction of small-scale thermal anomalies. The extracted anomaly regions closely matched known geothermal hotspots, further confirming the method's reliability and effectiveness.  
Figure 10: (**a**) Mean temperature distribution map generated using density segmentation of MODIS nighttime monthly mean LST data from 2003 to 2023 (21 years, 252 months). Colors range from blue (low) to red (high), representing temperature in degrees Celsius; (**b**) Isothermal map based on the same dataset, with isotherms shown in a color scale from purple (low) to red (high), indicating a gradual temperature increase. The high-temperature zones in (**a**) are interpreted as potential deep magma chamber regions.