esa-sceva/satcom-chunk-collection huggingface dataset:
# Information Consumer Experience: A Systematic Review
## 2 Background
### Customer Experience  
Despite the popularity of the Customer Experience (CX) approach in the literature, there is not a standardized definition yet. As a complex concept, authors have given different definitions of CX in the literature, but the most accepted definition of CX refers to a customer's perception produced by their interactions with any product, system, and service provided by a brand [20]. These perceptions have been classified into six dimensions [21]. (1) An Emotional Dimension that involves customer feelings, (2) a Sensorial Dimension related to the stimulation of customer senses, (3) a Cognitive Dimension associated with the customer's thinking processes, (4) a Pragmatic Dimension related to accomplishing a specific task, (5) a Lifestyle Dimension that involves customers beliefs and values, (6) and a Relational Dimension related to the customer relationships with other people. All these kinds of interactions have been called touchpoints [22]. A set of touchpoints can describe a journey followed by the customer (Customer Journey) based on a set of interactions produced before, during, and after the consumption of any brand product, system, or service. In this work, we define the ICX concept as an extension of the CX concept, where information consumers differ from customers based on the scope of the products or services with which they can interact. Therefore, information consumers can only interact with information products, systems, or services, meanwhile, customers can interact with any kind of product, system, and service.
# Probabilistic Models for the Shear Strength of RC Deep Beams
## 4 Probabilistic Shear Strength Models
### Development of Probabilistic Models
#### 4.2.3 Probabilistic Models  
The proposed model without a prior model can be obtained by substituting the simulation results of the parameter into Equation (3) and performing the exponential operation. The proposed model took the form  
\[V_{\text{MCMC}}=0.0031\cdot(f_{t})^{0.8957}\cdot(b)^{1.0385}\cdot(b)^{0.8349} \cdot\left(\frac{a}{d}\right)^{-0.6137}\cdot\left(\frac{b}{h}\right)^{-0.0647} \cdot e^{0.1837p\cdot}\cdot e^{0.0561p\cdot h}\cdot\rho^{0.3481} \tag{19}\]  
The proposed model with many parameters was very complicated. Therefore, some parameters were eliminated to simplify the probabilistic model with the parameter removal process. Table 4 illustrates the stepwise removal process. According to CoVs, \(\theta_{6}\), \(\theta_{7}\), \(\theta_{8}\), and \(\theta_{9}\) were removed sequentially. The removal process stopped after the third removal (SD = 0.2396) because the fourth removal (SD = 0.2914) caused a significant increase in the posterior SD. \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline
**Model Parameter** & \(\theta_{1}\) & \(\theta_{2}\) & \(\theta_{3}\) & \(\theta_{4}\) & \(\theta_{5}\) & \(\theta_{6}\) & \(\theta_{7}\) & \(\theta_{8}\) & \(\theta_{9}\) & \(\sigma^{2}\) \\ \hline Mean & \(-5.770\) & 0.896 & 1.039 & 0.835 & \(-0.614\) & \(-0.065\) & 0.184 & 0.056 & 0.326 & 0.082 \\ SD & 0.221 & 0.040 & 0.029 & 0.033 & 0.034 & 0.040 & 0.029 & 0.020 & 0.022 & 0.005 \\ CoV & 0.038 & 0.045 & 0.028 & 0.040 & 0.054 & 0.621 & 0.158 & 0.358 & 0.067 & 0.055 \\ Naive SE (\(\times 10^{-4}\)) & 9.880 & 1.809 & 1.299 & 1.479 & 1.501 & 1.797 & 1.295 & 9.002 & 0.969 & 0.201 \\ Time-series SE (\(\times 10^{-4}\)) & 9.880 & 1.809 & 1.305 & 1.468 & 1.797 & 1.797 & 1.295 & 9.002 & 0.969 & 0.204 \\
2.5\% quantile & \(-6.202\) & 0.817 & 0.981 & 0.769 & \(-0.680\) & \(-0.143\) & 0.127 & 0.017 & 0.283 & 0.074 \\
97.5\% quantile & \(-5.333\) & 0.975 & 1.095 & 0.899 & \(-0.548\) & 0.014 & 0.240 & 0.096 & 0.368 & 0.092 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Calculation results of probabilistic shear strength model parameters. Figure 3: \(Cont\). Figure 3: _Cont_. From Table 4, the uncertainty may be reduced for the model using fewer terms of parameters because unnecessary parameters may prevent the model from effectively describing the behavior. On the contrary, removing a key parameter also makes the model perform poorly and thus increases the uncertainty. Table 5 shows the stepwise removal process and explanatory terms that survived the modified models. Some explanatory terms were removed in the process, which implies that the base models have already considered their effect on shear strength or are insignificant. For example, the modified probabilistic model based on ACI 318-14 removed the descriptive terms \(a/d\), \(\rho_{\text{v}}\), and \(\rho_{\text{h}}\). Bias correction terms for simplified probabilistic models are listed in Table 6.
# RAC1B: A Rho GTPase with Versatile Functions in Malignant Transformation and Tumor Progression
## 4 Biochemical Properties, Generation and Degradation of RAC1B
### Regulation of RAC1B Biological Activity by Subcellular Localization  
RAC1B biological activity is strongly regulated by distribution in distinct subcellular compartments. Thus, membrane (and nuclear) localization promotes activity whereas cytoplasmic localization inhibits it. Most RAC1B protein is usually bound to the plasma membrane and is not sequestered by RHO-GDI in the cytoplasm which is a consequence of the inability of RAC1B to interact with RHO-GDI [7; 8]. Membrane localization of RAC1B also appears to be dependent on the cellular microenvironment and requires that the cells are cultured on stiff substrata or in collagen-rich regions as they are found in stroma-rich tumors [17]. At the membrane, RAC1B forms a complex with NADPH oxidase and promotes the production of reactive oxygen species (ROS), expression of Snail, and activation of epithelial-mesenchymal transition (EMT). In contrast, soft microenvironments inhibit the membrane localization of RAC1B and subsequent redox changes [17]. Stiff substrata are also a prerequisite for MMP3 induction of RAC1B. In rigid microenvironments, MMP3 upregulates expression of RAC1B, which translocates to the cell membrane to promote induction of ROS and EMT. While \(\alpha\)5-integrin maintains RAC1B at the membrane and is required for the promotion of EMT by fibronectin, \(\alpha\)6-integrin sequesters RAC1B from the membrane and is required for inhibition of EMT by laminin [18]. Interestingly, the subcellular distribution of RAC1B can have prognostic value with respect to patient outcome [19] (see Section 7.5).  
RAC1B, like RAC1, possesses a polybasic region that contains a canonical nuclear localization signal (NLS), (K(K/R)X(K/R)), responsible for nuclear localization. Its mutation resulted in loss of RAC1B stimulatory effects on transcription and suppressive effects on adhesion, indicating, in addition to membrane localization, the importance of nuclear localization of RAC1B [20].
# Dependence Representation Learning with Convolutional Neural Networks and 2D Histograms
## 2 Dependence Representation Learning  
### Method  
In this section, we define dependence representation learning, which allows neural networks to extract task-specific dependence representations from visualizations of feature pairs. The neural networks are trained in a supervised manner, but the targets for supervised learning can be generated without manual effort.  
Given a feature pair \((F_{x},F_{y})\), a visualization matrix \(V\) is computed by  
\[V=visualize(F_{x},F_{y}),\]  
where the function '_visualize_' is a visualization method, such as a 2D histogram. Given a dataset of 2D array values, a 2D histogram shows the number of elements in each bin, by drawing colors of corresponding intensity. In image processing, a 2D histogram shows the relationship of intensities at any position between two images. In our work, the 2D histogram of two random features is used for training the CNNs to classify the dependence types and predict correlations.  
\(F_{x}\) is the feature plotted on the \(x\)-axis of the visualization, and \(F_{y}\) is the \(y\)-axis. The dependence representation vector \(Z\) is extracted from the visualization \(V\) using a neural network \(\mathcal{N}_{\theta}\) with parameters \(\theta\):  
\[Z=\mathcal{N}_{\theta}(V).\]  
We then compute the desired dependence target of the feature pair (e.g., dependence type, correlation, or usefulness of the pair):  
\[T=target(F_{x},F_{y}),\]  
where '\(target\)' is a function that outputs the target. Finally, the neural network \(\mathcal{N}_{\theta}\) is trained by minimizing the objective function defined as follows:  
\[\min_{\theta,w}\mathbb{E}_{F_{x},F_{y}\sim\mathbb{F}_{pair}}\mathcal{L}(T, \phi(w\cdot Z)), \tag{1}\]  
where \(\phi\) is an activation function and \(w\) is a weight vector that maps representation \(Z\) onto target \(T\). \(\mathbb{F}_{pair}\) is a set of feature pairs, and \(\mathcal{L}\) is a loss function for a specific task, such as the cross entropy or mean squared error (MSE). The dependence representation learning algorithm is described in Algorithm 1.  
```
1:\(\theta\leftarrow\) initialize network parameters
2:repeat
3:for\(i\in\{1,...,m\}\)do// / \(m\): mini-batch size
4: Sample a feature pair \(F_{x}^{(i)}\), \(F_{y}^{(i)}\) from set of feature pairs \(\mathbb{F}_{pair}\)
5:\(V_{i}\gets visualize(F_{x}^{(i)},F_{y}^{(i)})\)
6:\(T_{i}\gets target(F_{x}^{(i)},F_{y}^{(i)})\)
7:endfor
8: Update the network and \(w\) by descending their stochastic gradients: \(\
abla\frac{1}{m}\sum\limits_{i=1}^{m}\mathcal{L}(T_{i},\phi(w\cdot\mathcal{N }_{\theta}(V_{i})))\)
9:until convergence of \(\theta\)
```  
**Algorithm 1** Dependence representation learning.
## 6 Results of the Simulations  
In the simulations, the four pattern-matching methods described in Section 4 receive exactly the same input datasets of fingerprints for each tile arrangement (combination of the parameters \(S_{S}\) and \(N_{SS}\), introduced in Section 5.9) considered. So, the results obtained represent the capability of the method to estimate the position, along with the possible limitations due to improper selection of the NN model or due to non-optimal training of the NN model, for the system and assumptions considered. To minimize the effects of these limitations, we applied the mechanisms discussed in Section 5.7.  
Positioning performance is measured according to the positioning error distance, calculated using Equation (1). The error distances are calculated for all of the moving tiles in the MTT dataset in step 8 in the block diagram describing the model training execution flow (Section 5.6.3, Figure 15) for each one of the four pattern-matching methods and for all the tile arrangements given in Table 14. The error distances for all of the moving tiles of the MTT dataset are presented with a CDF for posterior comparison against the other pattern-matching methods and tile arrangements. In the results, we represent with CDF(\(e\)) the CDF of the error distances \(e\) corresponding to the given case, that is, pattern-matching-method, number of sub-tiles per side of the tile, and side length of the sub-tile.  
The subsections that follow summarize the results of the simulations. First, the error distance is calculated for trivial solutions, as an initial reference for benchmarking all of the pattern-matching methods. Next, the results are presented for each pattern-matching method, for all of the tile sizes given by the tile arrangements considered. The tile arrangements are divided into three groups, arranged by the three side lengths of the sub-tiles considered, \(S_{S}\), as summarized in Table 14. The different numbers of sub-tiles per side of the tile considered, \(N_{SS}\), are listed in the table. Given that there is a total number of 32 combinations of sub-tile sizes and numbers of sub-tiles per side of the tile, for simplicity and in order to summarize several results in a few figures, we work initially with a few cuts at different percentiles in the CDFs of the error distances. For the most notable and best results observed, we will work with the full CDF of the error distances. Finally, the results between the pattern-matching methods are compared; the particular details of the errors for the method 4-MultFingIn-ClassOut are discussed; different observations from the results for the cases with spatial side information and aggregation in the fingerprint domain are summarized; and information about the computing costs and the reproducibility of the results is provided.
## 5 Performance Evaluation
### Performance Metrics  
We adopt the following key performance metrics [1; 14; 40] for evaluating system performance.  
* **Packet Reception Ratio (PRR):** It is the ratio of the number of neighboring vehicles that received the transmitted packet to the total number of neighboring vehicles of the transmitting vehicle. In a better communication environment, a higher PRR is expected.
* **Packet Inter-reception time (PIR):** It is the elapsed time between two consecutive receptions of packets for a transmitting (Tx) and receiving (Rx) vehicles pair. We computed the average PIR for a Tx-Rx pair. A lower PIR indicates lower latency and, thus, a better communication environment.
* **Throughput:** It is defined as the number of bytes received per unit time for a Tx-Rx pair. A higher throughput indicates better communication and is desirable.
# Multi-Layered Satellite Communications Systems for Ultra-High Availability and Resilience
## 6 Future Research Directions and MLSS Technology Roadmap
In short summary, for successful and robust MLSS missions, what is needed in the coming years, from a technical perspective, is a sufficient level of integration in different domains regarding standardization for unifying all subnetworks properly on different layers, a spectrum-sharing framework for interference mitigation, a network architecture with flexible 3D network control (access, resources, and routing) and data architecture,  
Figure 15: Roadmap for the MLSS technology. and a unified terminal air interface with user identity/location to support dynamically changing cells and layers.
###### Abstract  
Introduction: The severity of carpal tunnel syndrome (CTS) is evaluated by electrophysiological examination as well as a patient-oriented questionnaire. We hypothesized that machine learning could predict postoperative electrophysiological severity as well as the scores of patient-oriented questionnaires. In this study, we developed machine learning models to predict postoperative changes in electrophysiological severity and changes in the Carpal Tunnel Syndrome Instrument (CTSI). Materials and Methods: Data from four hundred and twenty hands of individuals who had been diagnosed with CTS and undergone carpal tunnel release were used. The features used for the machine learning model were preoperative age, gender, distal motor latency (DML) value, sensory nerve conduction velocity (SCV) value, preoperative electrophysiological severity stage, CTSI-SS value, and CTSI-FS value. Logistic Regression (LR), ElesticNet (EN), Support Vector Machine (SVM), Random Forest (RF), and LightGBM (LGBM) were used as machine learning algorithms. A machine learning model was created to binary classify the electrophysiologic severity at one year postoperatively. In the second experiment, regression models were created to predict the change in CTSI-SS and CTSI-FS at one year postoperatively. Results: In the electrophysiological severity classification model, LGBM showed the highest score (AUC = 0.802). Preoperative DML, age, and preoperative electrophysiological severity were important factors for model prediction. RF model showed the best performance. In the regression model predicting the change in CTSI-SS or CTSI-FS (RMSE: 0.418, 0.333, respectively), preoperative age and CTSI-SS or CTSI-FS scores were important factors for model prediction. Conclusions: The machine learning model can predict postoperative electrophysiological severity and CTSI score with high accuracy.  
CTS; machine learning; nerve conduction study
# Panoramic Digital Image Correlation for 360-Deg Full-Field Displacement Measurement
## 3 Experiments and Discussions
### 3D Displacement Measurement of Log Specimen
#### 3.3.1 Merging Error  
By reconstructing the visible part of two adjacent stereo camera pairs, the merging error of different stereo cameras on the reconstructed local surface was evaluated. The overlap of reconstructed three-dimensional point clouds of two adjacent stereo camera pairs should be theoretically the same. Therefore, the Euclidean distance between the reconstructed overlapping points was represented by the merging error by two adjacent stereo cameras. In this study, three groups of overlapping three-dimensional point clouds were selected, and the average combined error was calculated as 0.5 mm.
## 1 Introduction  
Climate change and anthropogenic activities have significantly affected agricultural and forest production by imposing novel combinations of multiple stresses, which act to alter plant function and productivity. Under favorable conditions, solar light absorbed by leaf chlorophyll (Chl) is used to drive photosynthesis through photochemistry [1]. Plant photosynthesis is a fundamental biochemical process for carbon fixation that occurs within chloroplasts, primarily in the cells of the leaf mesophyll tissue [2]. Chl is arguably the key component enabling vegetation photosynthesis, and is therefore an important indicator of plant function and productivity [3; 4; 5]. Chl levels vary with foliage maturity, nutrient levels, environmental conditions, light availability, and also seasonally. Thus, plants continuously regulate their photosynthetic processes in response to the changing environmental conditions on diurnal and seasonal timescales. To capture the diurnal responses and seasonal changes in plant photosynthesis, there is a critical need for temporally dense remote sensing time series to accurately monitor the key parameters governing vegetation function. Moreover, observations at relevant time scales would enable the detection of diurnal and seasonal changes in plant condition that may otherwise be overlooked. Remote sensing approaches used to assess vegetation function over larger scales (e.g., regional to global) are commonly based on reflectance vegetation indices (VIs), and only recently on solar induced fluorescence (SIF). The normalized difference vegetation index [6] (NDVI, Table 1) is often used in agriculture and forestry to track changes in vegetation \"greenness,\" capturing the seasonal phenology of green biomass, from leaf unfolding to senescence. NDVI uses reflectance bands from the red and near-infrared spectral regions to detect structural changes, such as the variations in canopy closure and leaf area index (LAI) that occur with leaf growth and senescence, providing an indicator of the absorbed photosynthetically active radiation (APAR) [7]. However, NDVI is more sensitive to changes in canopy biomass or LAI and less sensitive to the changes in Chl content, and therefore less sensitive to the variations in photosynthetic function. The photochemical reflectance index (PRI), in contrast to NDVI, is able to detect subtle changes in the photosynthetic activity, primarily documented in evergreen species [7; 8; 9; 10; 11]. PRI is typically calculated as a normalized difference VI, utilizing reflectance at the 531 nm wavelength, as compared to a reference band at 570 nm. Diurnal dynamics in PRI are driven by changes in the xanthophyll cycle, associated with changing light levels and the pH of the thylakoid lumen [9; 10], while responses over seasonal timescales reflect the changes in photosynthetic pigment contents (e.g., chlorophyll and carotenoids). PRI is also affected by the changes in canopy structure, leaf pigments, and the background signal [12; 13], which vary significantly with season. Frequently, the VIs saturate and provide an inconsistent sensitivity to GPP across the phenology.
# Effects of Acute Exposure to Virtually Generated Slip Hazards during Overground Walking
###### Abstract  
Postural instability and the inability to regain balance during slip-induced events are the leading causes of falls on the same level in occupational environments. Virtual reality (VR) provides the potential to be immersed in a realistic environment, exposing themselves to fall-risk hazards without the risk of injury real-world exposure may cause. Therefore, the purpose of this study was to compare the lower extremity joint kinematics of the slipping leg during real and virtually generated slip hazards. A secondary purpose was to investigate dynamic postural stability following acute exposure to real (REAL) and virtual (VR) environmental conditions. A total of 14 healthy participants' (7 men, 7 women; age: 23.46 \(\pm\) 3.31 years; height: 173.85 \(\pm\) 8.48 cm; mass: 82.19 \(\pm\) 11.41 kg; shoe size (men's): 9.03 \(\pm\) 2.71) knee and ankle joint kinematics were compared during exposure to both REAL and VR environments. Participants then completed a series of Timed Up-and-Go (TUG) variations (standard, cognitive, manual) at the beginning and the end of exposure to each environment. TUG-C involved backwards counting and TUG-M involved walking with an anterior load. Environmental exposure was selected in a counterbalanced order to prevent an order effect. Knee and ankle joint kinematics were analyzed separately using a 2 \(\times\) 3 repeated measure ANOVA to compare environments as well as gait types at an alpha level of 0.05. TUG variations were also analyzed separately using a 3 \(\times\) 3 repeated-measures ANOVA to compare TUG variations and environment. No significant differences were observed for knee or ankle joint kinematics between environments or gait types. There were also no significant interactions between environments and gait types. However, significant differences were observed for TUG-C following VR environmental conditions (\(p\) = 0.027). Post hoc comparisons revealed significantly lower times for TUG-C following VR exposure (\(p\) = 0.029). No significance was observed for TUG-S or TUG-M. Current findings suggest the potential effectiveness of VR as a means of fall prevention training for occupational populations based on improved TUG-C and similar lower extremity joint kinematics in REAL and VR conditions.  
virtual reality; gait training; postural stability; overground walking; slips +
Footnote †: journal: _Sciences_
# EOR: An Enhanced Object Registration Method for Visual Images and High-Definition Maps
## 4 Experiments
### Random Experiment  
The Random Experiment evaluated the performance of our method by generating abundant variations in camera pose for a single captured image frame. It aims to quantify the probability of retrieving a good pose by randomly sampling different camera poses.  
Table 4 summarizes the mean translation errors, rotation errors, and IoU results for EOR, MOR, and VisionHD, while Figure 11 provides visual insights into the error distributions and alignment performance across all dimensions (x, y, z, pitch, yaw, roll) and IoU. From Table 4, VisionHD achieves the lowest x-axis translation error (0.14 m), indicating superior lateral precision due to its reliance on lane markings. However, this comes at the cost of larger z-axis errors (2.47 m), highlighting its limitation in controlling longitudinal accuracy. The proposed EOR method achieves balanced performance across all dimensions. It reduces the mean z translation error to 1.48m and achieves comparable IoU (0.28) to MOR. Notably, EOR improves robustness with a 90% probability of IoU > 0.2, outperforming MOR (87%) and VisionHD (65%). The red shade in Figure 11 highlights the IoU > 0.2 range.  
Figure 11a,b visualize the error distributions and show that registered errors in all dimensions, except z, regress towards zero, demonstrating improved alignment. Figure 11c further highlights the overall alignment trends, reinforcing the quantitative results presented in Table 4.  
Figure 11: Random Experiment Result. (**a**) Scatter Distribution of 6-DoF Errors; (**b**) KDE Distribution of 6-DoF Errors; (**c**) KDE Distribution of IoU.  
Figure 10: Manual Annotated Process of Poles and Traffic Signs. (**a**) Accumulated Point Clouds; (**b**) Annotation; (**c**) Ortho View.