esa-sceva/satcom-chunk-collection huggingface dataset:
# Drone SAR Imaging for Monitoring an Active Landslide Adjacent to the M25 at Flint Hall Farm
## 3 Materials and Methods
### Fieldwork Procedures
#### 3.4.2 Data Collection  
The corner reflectors are used as point targets for the drone radar. Figure 15 illustrates the side-looking viewing geometry when the drone passes the corner reflector, which is used to create the flight path. An \(\alpha\) of 52\({}^{\circ}\) is optimal for the corner reflector triple bounce mechanism and thus target visibility. \(\beta\) is therefore 38\({}^{\circ}\). \(a\) is the drone altitude of 30 m. The horizontal separation distance of the corner reflector and drone, \(b\), is 23.44 m. The slant range, \(c\), is the line-of-sight distance from the drone to the corner reflector of 38.07 m; this comfortably satisfies the requirement for the slant range to exceed the minimum antenna farfield distance of 1.67 m.  
The SAR is side-looking and slightly downward facing due to the mounting angle of the antennas on the payload. As the corner reflectors are installed at approximately east-west orientations, the drone flight path is oriented south to north, flying uphill with a side-looking viewing geometry. The minimum and maximum flight latitudes are determined by the separation distance from the road and the tree line at the north of the site,  
Figure 15: Schematic of drone flight geometry with corner reflector target.  
respectively. This provides approximately 75 m of straight-line flight. The drone maintains a constant, low flight speed of 1 m/s to minimise pitching caused by rapid acceleration.
## 5 Discussion  
Our results show a higher level of cost stickiness in the Nordic countries compared to Spain within the textile industry. These findings are consistent with the hypothesis that economic factors, labor market dynamics, corporate governance models, and managerial optimism about future demand, significantly influence cost stickiness. The pronounced contrast between the Nordic countries' focus on sustainability, innovation, and social welfare, and Spain's emphasis on flexibility and rapid market response, underscores the strategic decisions firms make in managing costs under uncertain demand conditions. Thus, our empirical evidence suggests that the Nordic textile firms' higher cost stickiness can be attributed to their strategic emphasis on long-term viability and sustainability. This approach is encouraged by a robust social welfare system and a corporate culture valuing innovation and technological development. Such an environment likely fosters managerial decisions inclined toward resource preservation, even in the face of declining sales, reflecting optimism about future demand recovery. Conversely, the Spanish textile industry, operating in a more volatile economic landscape, necessitates agile cost adjustments to maintain competitiveness, thus exhibiting lower cost stickiness.  
The findings also highlight the influence of labor market dynamics on cost management strategies. The Nordic countries' decentralized collective bargaining systems and strong social safety nets support employment preservation strategies, contributing to higher cost stickiness. In contrast, Spain's higher unemployment rates and structural labor market inefficiencies drive firms toward more aggressive workforce adjustments, reflecting a direct response to economic pressures.  
The study's results align with the notion that managerial intentions, shaped by the broader economic and cultural contexts, play a significant role in determining cost stickiness. In the Nordic countries, the collaborative corporate governance model, which integrates stakeholder interests, seems to mitigate the propensity for empire-building behaviors that could exacerbate cost stickiness.  
The difference in cost management approaches between the Nordic countries and Spain not only highlights the critical role of regional economic and cultural frameworks in shaping corporate strategies but also emphasizes the importance of adapting financial management practices to the specificities of each environment. These insights are crucial for firms navigating the complexities of global economic landscapes, offering valuable lessons on the adaptability and strategic foresight required for sustained competitiveness.
## 2 Methods
### Differences of Consecutive RP Scores and Entropy  
Given that the RP distribution is ordered, we can calculate the distribution of the differences between consecutive terms (\(Rp_{i+1}^{up}-RP_{i}^{up},\ i\in[1,\ N-1]\) when testing for upregulation), shown in Figure 2B, which is more intuitive to understand than the initial RP distribution and proves more apt for the partitioning process described below. The initial ascent, at least partly overlapping with any significant differentially behaving features, is represented by a descending distribution of RP differences. Features following the null hypothesis (undifferentiated behavior) are expected to achieve ranks at random, resulting in a set of mostly undifferentiated RP scores [18]. An extreme case of this behavior was observed on applying rank sums (which has a sparser set of values) on the GSE12288 data set (see \"Supplementary Material 1--Method\", Part 3), where lengthy areas of genes adhering to the null hypothesis resulted in equal rank sum scores. In less extreme cases, undifferentiated rank product (or sum) scores result in non-zero differences between these consecutive, \"null-hypothesis-abiding\" terms of the ordered set, stochastically oscillating around values near zero.  
Figure 2: **(A)** Characteristic entropy distribution for GSE60767 data, winter 2009, upregulated. Parameters used were: window size 220, 20 bins for discretization and advancement of the sliding window gene-by-gene. The initial, low-entropy set is clearly defined (initial blue-colored area). The low-entropy area near the end is attributed to a single difference value oscillation, lowering the entropy of the 220 windows containing it. **(B)** The corresponding distribution of consecutive RP score differences. The initial, signal-dominated area always overlaps with genes selected by the original rank products. The latter, noise-dominated area represents features to be excluded.  
Given that RP scores reward systematic behavior of features across samples (and thus convey information on it), these two distinct patterns can be considered to represent the difference between systematic behavior, attributable to differentiated processes (evident in the steep descent of RP differences), and noise-dominated behavior following the null rank product hypothesis (evident in stochastically oscillating RP differences). On this basis, we aim to utilize the calculation of entropy to partition this distribution into an initial, low-entropy and information-rich area and a high-entropy, information-poor subsequent area. The former will be considered to be the set \(S\), containing \(n\) features. The latter will be considered the set of features with a low signal-to-noise ratio, excluded from further analysis.
# Active Gait Retraining with Lower Limb Exoskeleton Based on Robust Force Control
## 1 Introduction
On one hand, fuzzy logic controllers stand out for their ability to handle system uncertainties and adapt to various gait velocities. On the other hand LSTM networks are notable for their capacity to accurately predict movement patterns and trajectories by capturing temporal dependencies in the data. However, despite these advantages, these controllers still face challenges. For instance, they may struggle when applied to patients who differ significantly from the analyzed population or when the data collection conditions vary. Furthermore, multimodal exoskeletons have been developed that combine active and passive rehabilitation [35], achieving greater interactivity between the patient and the gait rehabilitation device. The use of these exoskeletons has demonstrated positive results in improving muscular coordination and enabling faster and more interactive rehabilitation for the patient [36]. However, these improvements are not yet conclusive and require more comprehensive studies on the benefits of those exoskeletons. Nevertheless, it is expected that such rehabilitation technologies will continue to improve, with the main objective of complementing traditional rehabilitation in a more comfortable, adaptive, and inclusive manner. The main contribution of this article is the development of a robust inner-outer loop controller for a lower limb exoskeleton designed to assist in active gait retraining. A robust sliding mode controller is proposed to enable robust and asymptotic convergence of the exoskeleton's angular trajectories. To prevent chattering effects from affecting the exoskeleton user and causing wear on the exoskeleton itself, two solutions are proposed: first, elastic serial actuators are used, and the control algorithm is modified to reduce or even eliminates chattering effects. In this way, a robust torque controller is designed to act in a cascade with the first controller, also based on sliding modes, to ensure robust and asymptotic torque convergence. This paper is organized as follows. A description of the exoskeleton and its dynamic model are presented in Section 2. Section 3 presents the design of the robust force control, while the design of the robust tracking control is presented in Section 4 and the results of the numerical simulation and experimental implementation are reported in Section 5. Finally, the conclusions are presented in Section 6.
## 2 Background for IoT Architecture
### The Network Layer  
The Network Layer serves as the backbone for data transmission in IoT systems, functioning as a central hub for aggregating data from the Perception Layer and transmitting it to the Middleware Layer. This layer employs a range of communication technologies, including Wi-Fi, RFID, Bluetooth Low Energy (BLE), cellular networks, and Low Power Wide Area Networks (LPWANs), to enable seamless data flow between devices [10]. In particular, technologies such as Narrowband IoT (NB-IoT) and Sigfox support low-power, long-range communication, which is ideal for applications in remote or hard-to-reach areas [11; 12; 13].  
Figure 1: IoT architecture.  
The energy efficiency of the Network Layer is influenced by the choice of communication protocols and routing mechanisms. For instance, NB-IoT offers a low-cost solution with extended battery life for devices by limiting data rates and coverage [14]. Similarly, LPWAN protocols are designed to optimise energy usage by reducing power requirements while maintaining sufficient communication range. Routing algorithms, which determine data paths based on factors such as distance, node energy levels, and bandwidth availability, also play a crucial role in minimising energy consumption. Efficient routing strategies can significantly reduce the power consumed by IoT devices, especially in large-scale, dense networks.
# Combined Use of MRI, fMRIand Cognitive Data for Alzheimer's Disease: Preliminary Results
## 2 Methods and Procedure
### Brain Volume Calculation and Mask Construction  
After the segmentation, the brain tissue images were used to quantify the whole brain volume and the intracranial volume. The whole brain volume was calculated as the sum of the volume of GM and WM; whereas the intracranial volume was calculated as the sum of GM, WM, and CSF.  
Figure 1: Preprocessing and elaboration steps for MRI and fMRI images.  
The total brain volume was normalized for the whole intracranial volume to eliminate gender disparity [51]:  
\[V_{n}=\frac{WM+GM}{WM+GM+CSF} \tag{2}\]  
In fact, as can be seen in Figure 2a,c, males and females presented different total brain volumes (\(p<0.05\)). After normalization, such a difference fell, as shown in Figure 2b,d (\(p>0.05\)). These results strongly agreed with those found in [52]. Afterwards, employing the Wake Forest PickAtlas software (fmri.wfubmc.edu) [53], the masks were limited to a priori Regions Of Interest (ROIs) through the AAL (Automated Anatomical Labeling) atlas [45]. This is one of the possible atlases to be used to extract masks, and it is often employed for studies involving Alzheimer's and MRI [54; 55] or fMRI [56]. This atlas includes 90 ROIs (45 per hemisphere). These masks were employed to quantify ROI volume and connectivity as in [54; 55]. The whole brain, intracranial, and ROIs volume were determined with the \(get\_totals\) script (cs.ucl.ac.uk), such as in [57; 58]. This script takes one segmented MRI set, i.e., all the images for a given brain tissue, as input and then returns its volume expressed in mm\({}^{3}\). Furthermore, to quantify ROI volume, the script takes the GM image and the ROI mask, and then, it calculates the intersection between these images, finally returning its volume in mm\({}^{3}\).
# A Scenario-Adaptive Driving Behavior Prediction Approach to Urban Autonomous Driving
## Materials and Methods
### Problem Formulation and Method Overview
#### 2.1.2 Overview of the Proposed Approach  
Figure 1 presents the architecture of the proposed driving behavior prediction approach, which can be divided into two phases: off-line phase and on-line phase. During the off-line phase, the road elements information was extracted from digital maps and described by the proposed ontology model. In order to learn the continuous features of driving behaviors, field data from typical urban traffic scenarios are collected first. Each type of driving behavior was learned by one HMM, i.e., a set of behavior models \(\mathfrak{M}\) can be trained. Semantic rules are edited based on a priori knowledge (traffic rules, driving experiences) to define the candidate behavior models, input features, and prior probabilities for candidate models using scenario-dependent characteristics. All these rules were stored in the knowledge base. During the on-line prediction phase, all the learned models and semantic rules in the knowledge base were loaded into the working memory during the initialization process. At each time step, the scenario is represented based on a pre-defined ontology model and sensing data. \(F^{i}_{s^{i}},\ M^{i}_{s^{i}}\), \(P^{i}_{s^{i}}\) are specified by the rule-based reasoning module. Then, each candidate learned model is tested to obtain the likelihood of each corresponding driving behavior \(p(M_{j})\). Finally, the behavior tag is decided by the behavior tagging model \(\mathcal{M}(\cdot)\), which considers both model testing results (a posteriori probabilities) and a priori probabilities.  
From the standpoint of the reasoning mode, the proposed driving behavior prediction system can also be divided into qualitative reasoning modules (enclosed by dotted boxes in Figure 1) and quantitative modules (enclosed by solid boxes in Figure 1). The qualitative modules act as a logical reasoner to infer the current scenario and generate \(\{F^{i}_{s^{i}},\ M^{i}_{s^{i}},\ P^{i}_{s^{i}}\}\). Quantitative modules mainly model or test the continuous features of different driving behaviors.
# Progress in THz Rectifier Technology: Research and Perspectives
### THz Diode Technology: I-V Characteristics and Figures of Merit (FOM) for MIM Diode
#### 2.2.6 Nonlinearity (NL)  
All diodes must be fabricated with strong nonlinearity to achieve a large response, especially for applications such as EH where it is desirable to operate with no application of external DC bias. The nonlinearity in Equation (8) represents the ratio between the differential conductance and the conductance [7; 23; 27; 28; 29].  
\[NL\ (V)=\frac{\frac{dI}{dV}(V)}{\frac{I(V)}{V}};\text{ Nonlinearity}\ >\ 3 \tag{8}\]  
Equation (8) represents the deviation from a linear resistor. The introduction of dissimilar metal electrodes and thick tunneling layers allows the obtaining of high nonlinearity. Furthermore, this parameter increases with the insulator thickness and the low barrier height.
## Dubins Traveling Salesman Problem with Neighborhoods: A Graph-Based Approach  
**[PERSON] * and [PERSON]**  
Department of Electrical and Computer Engineering, University of California, Santa Barbara,  
CA 93106-9560, USA; E-Mail: [EMAIL_ADDRESS]  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS];  
Tel.: +1-805-893-7785; Fax: +1-805-893-3262.  
_Received: 31 October 2012; in revised form: 17 January 2013 / Accepted: 18 January 2013 / Published: 4 February 2013_  
**Abstract:** We study the problem of finding the minimum-length curvature constrained closed path through a set of regions in the plane. This problem is referred to as the Dubins Traveling Salesperson Problem with Neighborhoods (DTSPN). An algorithm is presented that uses sampling to cast this infinite dimensional combinatorial optimization problem as a Generalized Traveling Salesperson Problem (GTSP) with intersecting node sets. The GTSP is then converted to an Asymmetric Traveling Salesperson Problem (ATSP) through a series of graph transformations, thus allowing the use of existing approximation algorithms. This algorithm is shown to perform no worse than the best existing DTSPN algorithm and is shown to perform significantly better when the regions overlap. We report on the application of this algorithm to route an Unmanned Aerial Vehicle (UAV) equipped with a radio to collect data from sparsely deployed ground sensors in a field demonstration of autonomous detection, localization, and verification of multiple acoustic events.  
**Keywords:** traveling salesman problem; graph transformation; nonholonomic vehicles
## 4 Materials and Methods
### Luciferase Reporter Assay  
The HEK293T cells were co-transfected through the application of the wild-type psiCHECK-2, the mutant _PCSK7_ 3'-UTR, and the miR-overexpressing vectors so that the direct interactions of the nominated miRNAs with _PCSK7_ 3'-UTR could be investigated. In brief, 150 ng of the wild-type or mutated 3'-UTR constructs and 300 ng of the miRNA-expressing vectors were co-transfected in HEK293T-cultured 48-well plates using FuGENE (Invitrogen, USA). Additionally, the psiCHECK-2 and pEGFP-C1 mock vectors were transfected and utilized as controls for luciferase assay and transfection, respectively. Transfection efficiency was monitored by fluorescent microscopy (Nikon TE2000S, Japan) 36 h following the procedure.  
The psiCHECK-2 reporter construct plasmid contained the _Renilla luciferase_ gene upstream of _PCSK7_ 3'-UTR and an independent _firefly luciferase_ gene as an internal control for normalization. Forty-eight hours after HEK293T co-transfection, the luciferase reporter assay was conducted by employing the Dual-Luciferase Reporter Assay System (Promega, USA) with a luminometer (Titertek-Berthold, Pforzheimer, Germany) in accordance with the manufacturer's protocol. Each sample was performed in triplicate, and the experiment was repeated at least three biological times. In short, a lysis buffer was added to each well after the removal of the media of the cell. Then, LARII Reagent was added, and after 20 min, the firefly luciferase activity was measured as a control. Afterward, Renilla activity was determined using the Stop & Glo Reagent. The relative luciferase activity was calculated using the following formula:  
\(\Delta\)Fold Activity of Luciferase (Renilla/Firefly) = Average Renilla/Firefly from Samples A/B.
# Effects of Wave-Induced Processes in a Coupled Wave-Ocean Model on Particle Transport Simulations
## Appendix A Wave-Current Interaction Processes  
As described in Section 2, the NEMO ocean model has been modified to take into account the following wave effects [11,18]: (1) the Stokes-Coriolis forcing; (2) sea-state dependent momentum flux, set as a scalar dependence of the flux from the atmosphere to waves and ocean or as a vector; and (3) a sea-state dependent energy flux. Below is the description of the wave-induced forcing and the processes of wave interaction with the ocean circulation.  
The surface Stokes drift \(u_{\text{st}}\) is defined by the following integral expression in the WAM model:  
\[\overset{\rightarrow}{u_{\text{st}}}=\int_{0}^{\infty}\ \frac{2 gk}{\omega tanh \ (2 kD)}\overset{\rightarrow}{k}E(f,\theta)\ df\ d\theta. \tag{1}\]  
Here \(E=E(\omega,\theta)\) is the two-dimensional wave spectrum which gives the energy distribution of the ocean waves over angular frequency \(\omega\) and propagation direction \(\theta\). Particle trajectories in water waves do not form entirely closed orbits because the particles spend more time forward under wave crests than backwards under wave troughs. This sets up a second-order effect, which leads to a discrepancy between the average Lagrangian flow velocity of a fluid parcel and the Eulerian flow velocity known as the Stokes drift. As is the case for the wind-induced currents, the Stokes drift also interacts with the Earth's rotation. This adds an additional veering to the ocean currents known as the Stokes-Coriolis force [66],  
\[\frac{D\mathbf{u}}{Dt}=\ -\ \frac{1}{\rho}\ \
abla p+(\mathbf{u}+\mathbf{v}_{ \text{s}})x\ f\hat{\mathbf{z}}+\frac{1}{\rho}\ \frac{\partial\tau}{\partial\hat{\mathbf{z}}} \tag{2}\]  
where \(\mathbf{v}_{\text{s}}\) is the Stokes drift vector, \(p\) is the pressure, \(\tau\) is the surface stress and \(\hat{\mathbf{z}}\) is the upward unit vector. Because calculating the full vertical profile is costly, the Stokes drift velocity profile was first calculated with an approximation from [10]. The Stokes-Coriolis force is also included in the tracer advection equations as described by [28]. In the present approach, the Stokes drift velocity profile is also considered [67,68].
## 3 Methods  
### Objectives  
The main objective of this study is to analyze the factors influencing the acceptance and use of artificial intelligence by Latin American university students, specifically Costa Ricanstudents, based on the UTAUT2 model and using a partial least squares structural equation modeling (PLS-SEM) approach. Alternative models, such as the technology acceptance model (TAM), primarily focus on perceived usefulness and ease of use, which may not fully capture the social and facilitating factors that influence AI adoption in an academic context. Similarly, innovation diffusion theory (IDT) provides a macro-level perspective on how technologies spread but lacks a structured framework to assess behavioral intention and actual usage at the individual level. Given the relevance of performance expectancy, effort expectancy, social influence, and facilitating conditions in predicting students' acceptance of ChatGPT, UTAUT2 was deemed the most appropriate theoretical framework for this study.  
To achieve this purpose, the following specific objectives are established:  
* Evaluate \"Performance Expectancy\" (PE) in the intention to use artificial intelligence in the university context, considering students' perceptions of the usefulness of tools like ChatGPT in their learning process.
* Analyze \"Effort Expectancy\" (EE) and its impact on AI acceptance, examining whether the simplicity of interaction with these technologies facilitates their adoption.
* Determine \"Social Influence\" (SI) in the intention to use AI, exploring the role that academic and social opinions and recommendations play in students' willingness to use artificial intelligence.
* Examine the effect of \"Facilitating Conditions\" (FC) on AI adoption, identifying the availability of resources, technological knowledge, and institutional support as key factors in the implementation of these tools.
* Measure \"Behavioral Intention\" (BI) and its relationship with \"Usage Behavior\" (UB) in artificial intelligence, analyzing whether students' willingness to use these tools translates into effective use in the educational field.
* Validate the structure of the UTAUT2 model in the context of higher education in Latin America, assessing the robustness of the model through the analysis of factor loadings, discriminant validity, and global fit using SRMR.