esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Conclusions  
A non-anthropomorphic 3-DOF upper-limb exoskeleton with an internally rotated elbow joint for material handling was proposed and analyzed based on the typical anthropomorphic 5-DOF upper-limb exoskeleton for power-assisted activity in this paper. The conclusions are as follows:  
Figure 23: Statistical analysis results of the action completion time.  
Figure 22: A complete hanging action: 3-DOF (**a**–**g**), 5-DOF (**h**–**n**).  
1. The proposed 3-DOF exoskeleton had a reduced self-weight by removing two joints, and their corresponding actuators and eliminated singularity in the workspace. In addition, it is not necessary to avoid singularity in the workspace by means of rotating the base coordinate axis or the design of redundant degrees of freedom.
2. The kinematics and dynamics analysis showed that the 3-DOF upper-limb exoskeleton had the same actual workspace as the 5-DOF upper-limb exoskeleton; compared with the 5-DOF upper-limb exoskeleton, the maximum joint torque of the 3-DOF upper-limb exoskeleton decreased by 50%, and the elbow external-flexion/internal-extension and the shoulder flexion/extension power consumption decreased by 55% and 46%, respectively, which will further reduce the exoskeleton weight.
3. The experimental results showed that the angle \(\alpha\) between the forearm of the 3-DOF upper-limb exoskeleton and the sagittal plane and the ulnar deviation \(\beta\) had no influence on operating tasks; therefore, the proposed 3-DOF upper-limb exoskeleton with an internally rotated elbow joint had the same manipulability as the 5-DOF upper-limb exoskeleton for the hanging action process.  
The concept of a non-anthropomorphic 3-DOF upper-limb exoskeleton with an internally rotated elbow joint was verified using simulations and experiments in this paper. For future work, a non-anthropomorphic 3-DOF upper-limb exoskeleton with actuators and sensors will be designed and manufactured; the exoskeleton control will also be studied and discussed.  
This work is supported by the National Key R&D Program of China \"The study on Load-bearing and Moving Support Exoskeleton Robot Key Technology and Typical Application\" under Grant No. 2017 YFB1300500. We would like to thank the editor and all anonymous reviewers for their constructive suggestions. All authors made significant contributions to this article. [PERSON] was responsible for developing the exoskeletons, performing the experiments, and writing the paper, [PERSON], the corresponding author, was mainly responsible for the revision of the paper, [PERSON] was responsible for dynamics simulation, conceiving and designing the experiments, and [PERSON] was responsible for performing the experiments and processing the experimental data.  
The authors declare no conflict of interest.
# Vortex-Induced Vibration of Deep-Sea Mining Pipes: Analysis Using the Slicing Method
## 2 Theoretical Analysis
### Introduction of Relevant Parameters  
(1) Reduction velocity, \(\mathrm{u_{r}}\)  
In the study of vortex-excited vibration, due to the differences in structural parameters such as riser diameter, the actual velocity is usually dimensionless to generate an important dimensionless parameter called reduced velocity, \(\mathrm{u_{r}}\), which is defined as follows:  
\[\mathrm{u_{r}}=\frac{\mathrm{u}}{\mathrm{f_{n}d}}, \tag{1}\]  
where \(\mathrm{u}\) is for incoming flow velocity, \(\mathrm{m/s}\); and \(\mathrm{f_{n}}\) is the natural frequency of the riser structure, Hz.  
(2) Displacement root mean square value, \(\mathrm{y_{RMS}}\)  
Subtract the average value of each instantaneous displacement of the riser, sum it after square, divide it by the quantity, \(\mathrm{n}\), and take square root to calculate the root mean square of the transverse flow displacement, which is defined as follows:  
\[\mathrm{y_{RMS}}=\sqrt{\frac{1}{n}{\sum_{i=1}^{n}}[\mathrm{y_{i}(t)-\overline {y}}]^{2}}, \tag{2}\]  
where \(\mathrm{y_{RMS}}\) represents the root-mean-square value of the displacement of the riser in the \(\mathrm{y}\) direction, \(\overline{y}\) represents the average displacement of the riser in the \(\mathrm{y}\) direction, and \(\mathrm{y_{i}(t)}\) indicates that the riser is shifted in the \(\mathrm{y}\) direction at every moment.
## 3 Results and Discussion
### Photocatalytic Activity Tests  
#### 3.3.1 NO Decomposition  
The photocatalytic activity of the new photoactive cements was evaluated during the decomposition of NO(II) under UV radiation, the results are presented in Table 3. During the tests, no influence of cluster temperature on the degree of NO decomposition was observed. In the case where the research was conducted at lower concentrations of NO, it follows that at 5 wt% of the TiO\({}_{2}\) content the highest NO decomposition is achieved. The highest photocatalytic activity was achieved by the 700/5 material. During 30 min of UV exposure to eight plates of modified clinker in NO streams with a concentration of 0.2 ppm, the NO removal rate was 14.4%.
# Product Demand Prediction with Spatial Graph Neural Networks
###### Abstract  
In the rapidly evolving online marketplace, accurately predicting the demand for pre-owned items presents a significant challenge for sellers, impacting pricing strategies, product presentation, and marketing investments. Traditional demand prediction methods, while foundational, often fall short in addressing the dynamic and heterogeneous nature of e-commerce data, which encompasses textual descriptions, visual elements, geographic contexts, and temporal dynamics. This paper introduces a novel approach utilizing the Graph Neural Network (GNN) to enhance demand prediction accuracy by leveraging the spatial relationships inherent in online sales data, named SGNN. Drawing from the rich dataset provided in the fourth Kaggle competition, we construct a spatially aware graph representation of the marketplace, integrating advanced attention mechanisms to refine predictive accuracy. Our methodology defines the product demand prediction problem as a regression task on an attributed graph, capturing both local and global spatial dependencies that are fundamental to accurate predicting. Through attention-aware message propagation and node-level demand prediction, our model effectively addresses the multifaceted challenges of e-commerce demand prediction, demonstrating superior performance over traditional statistical methods, machine learning techniques, and even deep learning models. The experimental findings validate the effectiveness of our GNN-based approach, offering actionable insights for sellers navigating the complexities of the online marketplace. This research not only contributes to the academic discourse on e-commerce demand prediction but also provides a scalable and adaptable framework for future applications, paving the way for more informed and effective online sales strategies.  
demand prediction; Graph Neural Network; spatial information Article
## 2 Definition of Activity Recognition problem  
### Functional description  
In the proposed human activity recognition architecture (see Figure 1), the objective is to select from a set of activity labels \(A=\left\{a_{1},\ldots,a_{N}\right\}\) the one that is the most likely according to the signal \(p\left(t\right)\) retrieved by the sensors at each time step. Sensors could be video cameras, thermal cameras or indoor localization systems, depending of the available resources and the conditions of the environment. In case of imaging sensors, they get a frame on each time step, \(p\left(t\right)\equiv I\left(t,x,y\right)\). Using \(p\left(t\right)\), the system needs to extract the position and size \(b_{i}\left(t\right)\) of each human \(i\) in the environment. When using imaging sensors, \(b_{i}\left(t\right)\) may be obtained using a blob tracker [10]. Blob tracker takes an image sequence and provides the location of each object \(i\) moving in the scene, mantaining a temporal coherence of objects between frames. Human tracking is a hard task and, despite of the advances in the last years, tracking methods still provide a noisy output, caused in part by scene illumination changes or by the complexity of object movements [11]. Using \(p\left(t\right)\) and \(b_{i}\left(t\right)\), a set of additional features \(\zeta_{i}\left(t\right)\) may have to be extracted to be  
Figure 1: Overview of the activity recognition process from multiple sensors  
able to differenciate between the activities to be recognized. Finally, using \(\zeta_{i}\left(t\right)\), a function \(\Lambda\left(\zeta_{i}\left(t\right)\right)\), \(\Lambda:\zeta\to A\) is used to decide wich is the activity being performed.  
Activity recognition solves the correspondence:  
\[q_{it}=\arg\max_{a_{k}}P\left(\zeta_{i}\left(t\right),\zeta_{i}\left(t-1\right) \ldots,\zeta_{i}\left(1\right)\mid a_{k}\right) \tag{1}\]  
where \(q_{i}t\) is the activity that maximizes the probability of the observations \(\zeta_{i}\left(t\right)\),\(\zeta_{i}\left(t-1\right)\),...,\(\zeta_{i}\left(0\right)\) at instant \(t\) by individual \(i\).  
To build an activity recognition system, three processes have to be performed (see Figure 2). First, an extensive feature set \(Z_{i}\left(t\right)\) has to be extracted from \(b_{i}\left(t\right)\) and \(I\left(t,x,y\right)\). On a second step, the subset \(\zeta_{i}\left(t\right)\) has to be selected from \(Z\left(t\right)\), selecting the subset with the most predictive power for \(A\). The last problem to solve is to select the best classifier architecture for activity recognition.
## 3 Results and Discussion
### Scanning Electron Microscopy-Fractography Study
The fibrils' formation is apparent, presenting curved areas due to the polymer chains' relaxation after the failure. However, the fibrils' length seems longer, indicating a higher elongation at break and a homogeneous stress distribution in the polymer matrix. On the plane view of the fracture surface, it can also be seen that the GNPs act as a bonding agent preventing the voids from fully opening, thus acting as an obstacle to the crack formation during the stress application. The above leads to smoother surface areas around the fracture surface on the sample's plane view, as presented in Figure S3b. This indicates a better dispersion of the GNPs in the polymer and a stronger interfacial bonding of the filler to the matrix induced by the ball milling process. Figure 4: SEM images of the fracture surfaces of (**a**) SCB-PE/0.5 GNPs no BM and (**b**) SCB-PE/0.5 GNPs w BM. The fracture surface of SCB-PE/2.5 no BM is presented in Figure 5a. The abrupt cut is evident by the highly ununiformed length and thickness of the fibrils. Large voids appear on the plane view around the failure area due to the polymers' stretching. Similar fracture behavior also presents the SCB-PE/2.5 GNPs w BM as seen in Figure 5b. The GNP content in these cases prevents the molecular chains' movement during the stress application, while the denser dispersion of the filler can cause stress concentration in the matrix's volume hindering the alignment of the GNPs along with the stress's direction, as was in the case with lower filler content. One can identify identical characteristics to the fracture surface of SCB-PE/5 GNPs no and w BM, presented in Figure S4, in the supplementary data report. The difference occurs on the fibrils thickness, while in the case of the ball-milled sample, a possible detachment of the GNPs can be observed in the voids. In this case, the interfacial adhesion between the filler and the matrix was inadequate to prevent the voids' formation during the stress application due to the stress concentration around the GNPs area and their dense dispersion in the polymer's volume.
## 2 Theoretical and Methodological Considerations
### Cognitive Resource Allocation Across Translation Directions  
The role of translation direction\(-\)whether from L2 to L1 or vice versa\(-\)has been investigated across various dimensions, including metaphor processing [3], translation accuracy [3; 40-41] and cognitive load [17; 43-45]. For example, [PERSON] [42] utilized eye-tracking and fMRI to evaluate whether cognitive asymmetries found at the word level persist in full-text translation. His findings supported the views of the Revised Hierarchical Model. Expanding on this work, [PERSON] [3] investigated how directionality influences metaphor translation and attention distribution. Later, [PERSON] et al. [17] examined the differential cognitive demands of automatic versus controlled processing and found that translation into L2 required greater attentional resources. Interestingly, translations from L2 into L1 were often more accurate, likely due to greater familiarity with the target language's structure.  
While such studies have yielded valuable insights, they tend to underrepresent translators' subjective experiences and self-perceptions of cognitive load. To address this gap, we adopt a multimethod design that integrates eye-tracking, keystroke logging, and retrospective self-reflection. Keystroke logging captures behavioral indicators of cognitive effort, including typing speed, revision patterns, and pauses during target text (TT) production. In parallel, participants' introspective accounts provide qualitative insights into their perceived mental workload. This triangulated methodology enables a more comprehensive analysis of cognitive resource allocation in translation. Specifically, our study aims to examine (1) how attention is distributed between source text (ST) comprehension and target text (TT) production; (2) variations in attentional metrics such as total attentional (TA) duration, AU count, and pupil dilation; and (3) the extent to which introspective reflections align with empirical data. By bridging objective measurement and subjective experience, this study contributes to a more nuanced understanding of how translation directionality shapes cognitive demand.
# Ontology-Based Spatial Data Quality Assessment Framework
## 2 Materials and Methods
### Framework
#### 2.3.3 Spatial Data Quality Ontology

Table 4: Hierarchical implementation of the specifications. In most cases, different masks yield different OGC relations, but there are exceptions. The mask \"T*T***T**\" indicates that besides the interior intersection, the exterior of one intersects with the interior of the other one. Depending on the dimensions of the features, this can be both ogcsfCroses or ogcsfOverlaps. The mask \"T*T***F**\" can indicate a crossing type relation, if the dimensions allow. sdqocrosesOrOverlaps is an object property equivalent to super property that combines ogcsfOverlaps and ogcsfCroses, which can have similar masks. The object property sdqo:resultForData establishes a relationship between quality results and the corresponding data instances. It links the quality assessment results to the specific data they are associated with. Additionally, the object property sdqo:hasResult is used to relate data quality elements to the quality results they pertain to. It establishes a connection between the individual data quality elements (e.g., logical consistency) and the specific quality assessment results generated for them. To summarize, sdqo:resultForData links data with quality results, while sdqo:hasResult connects data quality elements with the corresponding results. The created SDQO is designed to be extensible to a broad range of domains. [PERSON] [43] extended SDQO for positional accuracy assessment of OSM data against General Command of Mapping Agency (GCM) 1/25,000 scaled topographical map data.
## 4 Discussion
### Uncertainty Analysis
#### 4.2.1 Comparison with CAMS XCO2  
We computed the mean and standard deviation of the absolute differences between CNN-STK XCO2 and CAMS XCO2 for each season to quantify the uncertainty of CNN-STK XCO2. The uncertainties for spring, summer, autumn, and winter are 0.54 (\(\pm\)0.46) ppm, 1.40 (\(\pm\)1.09) ppm, 0.63 (\(\pm\)0.55) ppm, and 0.61 (\(\pm\)0.51) ppm, respectively. Uncertainty is significantly higher in summer than in other seasons. The spatial distribution of uncertainties for each season is shown in Figure 9.  
The CNN-STK XCO2 uncertainties calculated from CAMS XCO2 have significant differences in geographical distribution. Overestimation is more prevalent in western China than in the central and eastern regions. Statistically, CNN-STK XCO2 exhibits more positive deviations in spring and winter, accounting for approximately 66% and 81% of mainland China. During this period, negative deviations are primarily clustered in south-central China. Conversely, negative deviations dominate in summer and autumn, covering approximately 88% and 61%, with positive deviations concentrated in the western regions. Moreover, we highlighted several key regions with large uncertainties. Firstly, the Qinghai-Tibet Plateau region in southwest China (the magenta boxes in Figure 9a-c), which has a complex topography and climate, shows the maximum overestimations in spring, autumn, and winter. Secondly, the Sichuan Basin in the south-central region (the bright blue boxes in Figure 9a-c), which is characterized by topographic occlusion, experienced maximum underestimations during spring, autumn, and winter. Thirdly, the northeastern region exhibits significant negative anomalies during summer, consistent with the biosphere disturbance error mentioned in Section 4.1.
# A Novel Protocol Using Captive Portals for FIDO2 Network Authentication +
## 6 Results
### Functional Prototype of FIDO2 CAP
#### 6.2.1 WebAuthn Authentication Web Application (WAWA)  
WAWA is a web application that allows a user to authenticate using FIDO2 Authenticators compatible with the WebAuthn standard, using a web browser. The registration of users, and their corresponding security keys, is performed by a registrar, which is a privileged user, implemented using RBAC roles.  
The developed server is compatible with the last W3C Recommendation of the WebAuthn standard L3 [29], implementing both discoverable and non-discoverable WebAuthn credentials. This feature makes all types of FIDO2 security keys or software \"passkeys\" compatible with the server registration and authentication procedures. In the developed system, modern \"passkeys\" or discoverable credentials are supported, so the user does not need to introduce a username in the authentication form. If the user has software or hardware not compatible with discoverable credentials, they can use the system with non-discoverable credentials by introducing their username (see Figure 8).  
Additionally, an administrator user in the server can list authenticated users in real time (see Figure 9). The session database allows users to open more than one authenticated web session, which are listed in the administration panel. Although the user can logout at any time during the session, the expiration time will force the end of the web session automatically.  
Finally, an administrator can register security keys. These can be associated with an existing user by specifying the username, or can be registered and associated to a new user. Therefore, a user can have multiple registered security keys. This feature allows users to have a backup security key, which can be used in case of device loss to securely gain access to the network.  
Figure 8: Developed captive portal UI. The WebAuthn login form uses discoverable credentials when the “I don’t need a username” option is selected.  
Figure 9: The administration interface of the developed authentication server. It allows the registration of security keys. The table of registered users lists the registered device of each user and the active sessions in real time. Admin column is used to configure admin privileges to users.
# RAC1B: A Rho GTPase with Versatile Functions in Malignant Transformation and Tumor Progression
## 7 RAC1B as a Prognostic Marker in Cancer
### Colorectal Cancer (CRC)  
RAC1B is overexpressed in some colorectal tumors predominantly in an active GTP-bound state which selectively promotes NF\(\kappa\)B activation and signaling [42]. This overexpression of RAC1B in colorectal tumors induces cell cycle progression and cancer cell survival [45], and has been associated with the _BRAF-V600E_ mutation. _BRAF_ mutations are in turn enriched within the consensus molecular subtype (CMS)1 group of CRC [69]. The CMS classification allows for a categorization of most CRC tumors into one of four subtypes with distinguishing features: CMS1 (MSI Immune), CMS2 (Canonical), CMS3 (Metabolic), and CMS4 (Mesenchymal). CMS1 is characterized by increased expression of genes associated with a diffuse immune infiltrate, strong activation of immune evasion pathways, defective DNA mismatch repair, overexpression of proteins involved in DNA damage repair, and hypermethylation. Patients with CMS1 tumors have very poor survival after relapse, in agreement with worse prognosis of patients with recurring MSI and _BRAF_-mutated CRC tumors [69]. It is conceivable that RAC1B expression, too, is overrepresented in the CMS1 group.  
The suitability of RAC1B expression as a predictor of chemotherapy efficacy in metastatic CRC (mCRC) was evaluated by [PERSON] and colleagues. RAC1B overexpression was a poor survival factor for overall survival and progression-free survival in K-RAS/B-RAF wild-type mCRC patients. However, RAC1B overexpression represents a marker of poor prognosis in K-RAS/B-RAF wild-type mCRC patients treated with first-line FOLFOX/XELOX therapy [70]. Hence, in K-RAS/B-RAF colorectal tumors and corresponding metastases, an overexpression of RAC1B has been suggested as a biomarker of poor prognosis [70].