esa-sceva/satcom-chunk-collection huggingface dataset:
# From Barriers to Breakthroughs: Rethinking Autonomous Vehicle Design for Visually Impaired Users
## 1 Introduction  
Worldwide, more than 285 million people experience visual impairment, including 39 million with total blindness [1]. As populations age, this number is expected to increase. Visual impairment varies from total blindness to low vision and amblyopia, significantly restricting mobility and independence compared to non-disabled individuals [2]. Visually impaired people often rely on canes, guide dogs, or human assistance for daily movement, particularly in public transportation where they face challenges such as identifying stops, accessing vehicles, and disembarking safely.  
The emergence of ride-sharing services like Uber and Lyft has improved mobility by offering features such as vehicle location alerts, pre-trip planning, and real-time notifications, which reduce the need for physical assistance during transit [3]. Advancements in autonomous vehicle (AV) technologies, particularly SAE Level 3 and above, present further opportunities to enhance independent mobility for visually impaired users by eliminating the need for a driver [4]. However, despite these technological advances, most AV designs and shared mobility services remain tailored primarily for non-disabled users, limiting their inclusivity.  
To make autonomous vehicles fully accessible, design strategies must directly address the unique needs of visually impaired individuals. Inclusive vehicle design can reduce mobility barriers, expand movement range, and foster greater social inclusion. However, the complexity of visually impaired users' mobility challenges requires specialized designand evaluation methods. Existing UX frameworks often overlook the diverse sensory and interaction needs of this population, highlighting a need for more inclusive approaches [5; 6; 7; 8].  
This study proposes a future mobility concept tailored for visually impaired users by modifying the Double-Diamond design framework. The model was adapted to simultaneously address problem identification and solution development, ensuring both user needs and technical feasibility are considered throughout the process. Key pain points were identified through interviews and expert workshops, guiding the design of vehicle concepts with features such as multimodal feedback, voice navigation, and tactile guidance.  
By focusing on the specific needs of visually impaired users, this research aims to advance the design of autonomous vehicles, ensuring accessibility and enhancing the mobility experience. The findings contribute to the ongoing development of inclusive transportation technologies, emphasizing the importance of user-centered design in the evolution of autonomous systems.
# Investigation of Oil Extracted from Roasted and Unroasted Oats with Use of Chemometrics
## 3 Results and Discussion
### Statistical Modeling  
#### 3.5.1 Discriminant Model. MODEL 1  
Based on spectral data alone, discriminant analysis (DA) was performed to find differences between oil originating from roasted by 60 min and unroasted oat samples; 80% randomly selected by the TQ Analyst software samples were used for model calibration (squares and triangles in Figure 7), and the remaining 20% were used for calibration validation (crosses in Figure 7). The model constructed, named MODEL 1, classifies samples into adequate homologous groups with 100% effectiveness, which means all samples were assigned to appropriate groups without a single misclassification. The spectral data from the scanned spectral region, which is 4000-400 cm\({}^{-1}\), were used to calibrate MODEL 1. Ten principal components were used, and the model describes 99.8% of the overall variability. Results obtained with spectral data alone (no reference data are included in MODEL 1) are graphically presented in Figure 7. Two distinct groups, depicted squares and triangles for roasted and unroasted samples, respectively, are identified and circled to show they form homogenous groups. During the validation process, 20% of raw spectral data assigned to the validation group were considered unknown samples and used for validation. Addi  
Figure 6: For example, infrared spectra of oils extracted from unroasted and different time-roasted oat grain samples.  
tionally, new samples not included in the calibration or validation set (unknown samples) were controlled with the constructed model. All were correctly assigned to appropriate groups (see, the square number in Figure 7 is greater than the number of triangles). Five additional samples of oil extracted from 60 min roasted oat grains are presented in Figure 7. MODEL 1 demonstrates that IR spectral data alone can determine whether the oil is derived from roasted or unroasted oat grains. Figure 7 provides a clear visual representation of the effectiveness and versatility of MODEL 1. Mahalanobis distances were calculated, analysed, and compared. Notably, the Mahalanobis distances within the roasted samples indicate significant differences among them, greater than adequate distances in unroasted samples. This is due to variations in the final chemical composition of oil in grains after the temperature treatment despite the consistent heating conditions of 60 min at 160 \({}^{\circ}\)C for all samples. It finally means that unroasted samples are more similar. At the same time, the roasted samples are less identical to each other. Figure 7 presents two homogenous groups of samples. One group contains samples of oil extracted from unroasted grains triangles), while another contains oil extracted from roasted oat grains (squares).
# A Virtualization Infrastructure Cost Model for 5G Network Slice Provisioning in a Smart Factory
## 3 Cost Model
### Cost Components in a Cloud
#### 3.3.3 Software and VNF licenses  
Software licenses are assumed to contribute to annual OPEX and are directly dependent on total servers installed at any time [32]. Fixed operational costs per VNF are assumed and depend on the total VNFs installed; VNF license costs are based on [33]. For example, if a VNF license cost is x EUR/year, and the deployment scenario requires four VNFs, then the total annual VNF license costs are 4x EUR/year.  
\[\text{Annual Software costs}\ =C_{SL}\times N_{S} \tag{8}\]  
\[\text{Annual VNF license costs}\ =C_{VNF}\times N_{VNF} \tag{9}\]  
Apart from the major cost components of a data center described above, slice provisioning will require operational costs arising from personnel management costs and the orchestration of slices. The level of automation in management and orchestration is expected to increase, thus requiring less personnel costs in the future. These personnel costs are not included in the modeling as the focus is limited to the virtualization infrastructure costs.  
The costs of the virtualization infrastructure are then calculated as follows:  
\[\text{CAPEX}=C_{S}\times N_{S}+C_{SW}\times N_{SW}+C_{R}\times N_{R} \tag{10}\]  
\[\text{OPEX}\ =C_{SL}\times N_{S}+C_{VNF}\times N_{VNF}+\text{PUE}\times\text{ E}\times 24\times 365 \tag{11}\]  
\[\text{TCO}\ =\text{CAPEX}+\text{OPEX} \tag{12}\]  
The above calculations are performed for each slice type and each deployment scenario, and a TCO breakdown is also provided in terms of costs per slice type and costs per slice type for each device. Furthermore, a sensitivity analysis is performed by varying the device growth over the study period and scaling the number of slice customers, thus making the model applicable to different industry verticals, use cases, and scenarios. The developed cost model is applied to the subsequent smart factory case.
# Tightly-Coupled GNSS/Vision Using a Sky-Pointing Camera for Vehicle Navigation in Urban Areas
## 3 Vehicle Motion Estimation Using an Upward-Facing Camera
### Outlier Rejection  
Due to the dense traffic in the urban canyons, clutters and moving objects such as cars or pedestrians are captured by the camera. The detected corners on these objects will be considered by the tracking algorithm as fixed points, while in reality, the features are dynamic. This constitutes the major source of outliers in urban areas where humans move actively. Although our configuration (with a sky-pointing camera) is beneficial in the sense that it will capture very few moving objects (depending on the FOV of the camera), in order to make the developed method camera configuration independent, the random sample consensus (RANSAC) algorithm [52] is used. The advantage of this method is its robustness when facing a high number of outliers, as well as its low computational load. RANSAC randomly selects a subset of feature points from the full set of the tracked features. The non-considered feature points are then reprojected using the estimated states. The errors between the measured image coordinates and the computed reprojection coordinates are tested using the specified measurement accuracies, the estimated state accuracy, as well as the defined confidence level. The process is repeated to refine the remaining feature points that are roughly consistent with the previous estimates. The inliers are those feature points that are consistent, and the outliers are those that are not. This procedure is repeated a fixed number of times.
# Energy-Efficient User Pairing for Downlink NOMA in Massive MIMO Networks
## 4 Proposed User Pairing Solutions
### Hungarian Algorithm  
The Hungarian algorithm is used for solving single agent to single task assignment problem [22]. It is initially designed to solve minimization problems, so we generate an objective optimization matrix of EE calculations between cell-center users and cell-edge users as shown in Table 2. The target now is to pick the pairs that maximize EE. To convert the optimization problem to a minimization problem, the optimization objective matrix is modified and converted to the cost matrix \(\delta_{cost}\). The modification process starts with finding the maximum of each column, then all column elements are subtracted from their corresponding maximum value as shown in Table 3. The resultant matrix is the cost matrix to be minimized. Minimization of the cost matrix is a maximization of the optimization objective matrix. The Hungarian algorithm is summarized in Algorithm 2. The optimization problem is now formulated as follows:  
\[Minimize\sum_{u_{c}=1}^{U/2}\sum_{u_{c}=U/2+1}^{U}F_{u_{c},u_{c}}\delta_{cost} \tag{15}\]  
\[F_{u_{c},u_{c}}=\begin{cases}1&;\text{if the $u_{c}$ is already paired with $u_{c}$}\\ 0&;\text{if the $u_{c}$ is not paired with $u_{c}$}\end{cases}\]  
**Step 1**: Create a cost matrix.  
**Step 2**: Find the smallest element in each row and subtract it from every element of that row. Repeat this process for each column.  
**Step 3**: Tag all zeros as starred or primed, where the starred zeros represent an independent set of zeros and primed zeros represent the possible candidates.  
**Step 4**: Cover each column containing a starred zero. If \(\dfrac{U}{2}\) columns are covered. Go to done, otherwise go to step 5.  
**Step 5**: Prime an uncovered zero. If there is no starred zero in the row containing the primed zero, go to step 6. Otherwise, cover this row and uncover the column containing starred zero. Continue in this manner until there are no uncovered zeros left. Save the smallest uncovered value and go to step 7.  
**Step 6**: Continue generating a series of alternating primed and starred zeros until this series ends at a primed zero that has no starred zero in its column. Unstar each starred zero, star each primed zero, erase all primes, and uncover every line. Return to step 4.  
**Step 7**: Subtract the value found in step 5 from every element of each uncovered column and add it to every element of each covered row. Return to step 5 without altering any primes, stars, or covered lines.  
**Step 8**: The position of starred zeros in the cost matrix indicates the paired users that maximize EE.
# Resource-Efficient Multicast URLLC Service in 5G Systems
## 3 Analyses of Possible Solutions and Their Adaptation to mURLLC
### Sounding Period  
The choice of short-term transmission parameters, such as precoder and MCS (see the following sections for details), significantly depends on the accuracy of channel measurements available at the gNB. In the case of TDD, the gNB measures both DL and UL channelsbased on SRS signals periodically transmitted by each UE in UL slots. As we show in this paper, the choice of SRS period can significantly influence the overall channel resource consumption for a multicast stream. In particular, a low SRS period improves the accuracy of channel measurements and, therefore, reduces the DL resource consumption. However, it significantly increases UL resource consumption used for pilot signals, which scales with the number of UEs in the multicast group. In contrast, a high SRS period reduces sounding overhead but increases DL resource consumption because of selecting too-low MCSs. In Section 4, we consider scenarios with different UE mobility and study how to select the SRS period in order to find a good balance between DL resources consumed for data transmission and UL resources consumed for sounding.
# Mine Intelligent Receiver: MIMO-OFDM Intelligent Receiver for Mine Information Recovery
## 3 Network Model
### Multi-Label Classifier  
Considering the original bit stream data as the labels for model training, if viewed as a regular multi-classification problem, the number of categories to be classified is \(2^{M}\), which is difficult to estimate for both the model parameters and the model training cost. Multiple binary classifiers are used in the literature [20] to reduce the classification complexity,  
Figure 4: One-short aggregation module. F1x3 denotes the convolution operation with a convolution kernel of \(1\times 3\); Cat denotes the feature map connected in the channel dimension; Favg denotes the global average pooling; Fc is the fully connected operation; Act is the h-sigmoid activation function; X denotes element multiplication; + denotes element addition.  
Figure 3: One-short aggregation model.  
Figure 2: Dense aggregation model.  
but \(M\) classifiers are required. For this reason, we consider the problem as a multi-label classification problem and use the h-sigmoid activation function instead of the sigmoid activation function to output the feature map as a one-dimensional vector of length \(M\) that matches the original bit stream labels of \(M\) bits.  
\(\delta(x)\) is a sigmoid function, and it is very complicated to calculate the derivative of the sigmoid function directly. For this reason, the activation function can be redesigned in combination with the ReLU6 function to reduce the activation function derivative complexity and speed up the model training, as shown in Figure 5.  
\[\delta(x)=\frac{1}{1+e^{-x}} \tag{14}\]  
\[\text{ReLU6}(x)=\min(\max(x,0),6) \tag{15}\]  
\[h-sigmoid=\frac{\text{ReLU6}(x+3)}{6} \tag{16}\]  
Since the original bit stream is a sequence of [0 1], the label processing process is omitted and can be used directly as training labels. The network training loss function uses a binary cross-entropy loss function, which can be expressed as:  
\[\text{Loss}=-\frac{1}{N}\sum_{i=1}^{N}y_{i}\cdot\log(\hat{y_{i}})+(1-y_{i}) \cdot\log(1-\hat{y_{i}}) \tag{17}\]  
where \(y_{i}\) denotes the true label, \(\hat{y_{i}}\) denotes the predicted value, and \(N\) is the number of samples.
## 3 Materials and Methods
### Method for the Generation of High Temporal Resolution Time Series
#### 3.3.3 Quality Information  
The ESTARFM algorithm does not give an indication on the quality of the fusion. It is, however, important to know whether a given prediction was based on stable regressions or if errors are likely to occur. For that reason, a quality dataset is produced here as an additional output of the ESTARFM framework (Figure 3). With each synthetic image, a layer with the number of similar pixels used for the prediction of each pixel is obtained. Furthermore, a layer for each band is calculated storing the coefficient of determination from the linear regressions between Landsat and MODIS used for derivation of the conversion coefficient. The information in these layers is considered an indicator of the stability and goodness of the regression and therefore provides an indicator of the proper functioning of the algorithm for different areas of the image.
## 3 Results
### Hardness Distribution  
The hardness distribution across the weld metal and the heat-affected zone (HAZ) was measured using the Vickers HV10 method to evaluate the effect of each welding process on the local mechanical properties. Figure 8 presents the Vickers hardness (HV10) results for macro-sections 3-3, 6-3, and 9-3, which represent ARC CF (ceramic ferrule), ARC SG (shielding gas), and ARC SRM (radially symmetric magnetic field) welding processes, respectively. The corresponding average hardness values are summarized in Table 7.  
The results show a clear increase in hardness within the weld metal compared to the adjacent base material for all processes. The highest weld metal hardness was observed in sample 3-3 (ARC CF) at 295.0 HV10, which reflects the higher heat input and rapid solidification associated with deeper penetration and a larger molten zone, as discussed in Section 3.1.  
Sample 6-3 (ARC SG) exhibited the lowest weld metal hardness (235.0 HV10) and slightly greater variation between the left and right base material measurements. This may indicate localized thermal gradients and less stable arc behavior compared to the other techniques.  
The ARC SRM process (sample 9-3) demonstrated a balanced result, with weld metal hardness reaching 256.0 HV10 and a more consistent base material hardness on both sides of the joint. This suggests that the stabilizing effect of the radially symmetric magnetic field produces a focused, stable arc, controlling thermal diffusion and yielding a narrow HAZ.  
Figure 7: Stud welding in progress using the PH-3N SRM gun with shielding gas supply (**left**); completed test plates with welded studs arranged according to the predefined template for macrostructural and mechanical testing (**right**).  
Overall, the hardness measurements confirm that all three processes generated sufficient heat input to produce localized hardening of the weld metal, with ARC CF producing the highest peak hardness, while ARC SG and ARC SRM provided smoother hardness transitions and more uniform thermal effects. This behavior supports the suitability of SRM and SG techniques for applications where dimensional stability and controlled thermal impact are essential.
## 3 Short-Reach Systems Classification and Enabling Technologies
### Short-Reach Systems' Overview and Classification
#### 3.1.1 Intra-Data Center or Server-to-Server Links  
In a DCN, intra-data center or server-to-server links are the data links that connect one server to another and the racks together inside the DCN. These kinds of links are usually below 300 m, and they comprise the major amount of the DCIs. Currently, the links are dominated by the MMF due to the extremely cost-efficient vertical-cavity surface-emitting laser (VCSEL) transmitters' employment. Since the links are mainly based on the MMF, the major limiting factor is the associated intermodal dispersion that severely limits the data rate per wavelength to below 40 Gb/s [35,42].
# Wideband Circularly Polarized Antenna Based on a Non-Uniform Metasurface
## Antenna Geometry  
The geometrical configuration of the wideband CP antenna with uniform MS is illustrated in Figure 1. The antenna is constructed with a driven microstrip patch, an MS and a ground plane. The antenna is fabricated on two substrates with a dielectric constant of \(\varepsilon_{r}\) = 3.5 and loss tangent of tan \(\delta\) = 0.0018. The driven patch with truncated corners, acting as a primary radiating CP source for the proposed design, is located between the MS layer and the ground layer. The MS is formed by different sizes of unit cells, in which the four center cells are larger than the others. The antenna is excited by the 50 \(\Omega\) SMA (Sub Miniature version A) connector, whose outer and inner conductors are respectively connected to the ground plane and the driven patch. The high-frequency structure simulator (HFSS) is used to characterize the antenna and then validated by measurement.
## 4 Experiments and Analysis of Results
### Case Study 2: JNU Dataset  
In this section, the experiments utilize the bearing fault diagnosis dataset provided by Jiangnan University. The data acquisition setup is illustrated in Figure 14. The dataset encompasses four bearing operating states: normal (N), inner ring fault (IRF), outer ring fault (ORF), and rolling element fault (REF). Vibration signals were collected under various typical operating conditions at three motor speeds: 600 r/min, 800 r/min, and 1000 r/min. The signal data were recorded using a high-precision data acquisition device with a sampling frequency of 50 kHz, while a torque sensor was employed to measure power and speed during the experiments.  
As shown in Table 8, the experiment selected data from four operating states at rotational speeds of 600 r/min and 800 r/min. Given the relatively low rotational speed (600 r/min = 10 Hz), we ensured that each 4096-point sample (corresponding to 81.9 ms at 50 kHz) covers nearly a full rotation cycle. This temporal span allows the preservation of key low-frequency components associated with fault periodicity. Moreover, the use of FFT and STFT over the full window helps maintain adequate spectral resolution at lower frequencies.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multicolumn{2}{c}{**Comparison**} & \multicolumn{4}{c}{**Evaluation Metrics (\(\%\))**} \\ \cline{3-6} \multicolumn{2}{c}{**Models**} & **Accuracy** & **Precision** & **Recall** & **F1** \\ \hline Machine & RaF & 98.01 & 98.06 & 98.23 & 98.07 \\ learing & SVM & 98.11 & 98.08 & 98.11 & 98.11 \\ \hline \multirow{3}{*}{Deep} & HCNN & 98.01 & 98.08 & 98.01 & 97.99 \\  & 2D-CNN & 98.51 & 98.54 & 98.51 & 98.53 \\ learing & 2D-ResNet & 92.54 & 95.17 & 92.54 & 93.06 \\ \cline{1-1}  & Proposed & 99.89 & 99.91 & 99.89 & 99.92 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Comparison of different classification models.
## 4 Test on Beams
### Test Apparatus  
Three-point loading tests were performed on the beams DB1 and DB2 before retrofitting and on the beams DB1-R and DB2-R after retrofitting. Each beam was simply supported by a steel hinge support H and by a steel roller support R (Figure 5a,b). The span length (L) measured between the supports was 280 cm and 310 cm for the beams DB1 and DB2.  
The three-point loading test was carried out applying a vertical load (F) at the beam midspan by means of a 1000 kN hydraulic actuator supported by a vertical steel frame. This force produced two vertical reactions at the two beam supports. Vertical steel plates were added at the supports to avoid the beam torsion and lead plates regularized the contact surface between the beam intrados and the steel supports (Figure 5a). The apparatus setup is shown in Figure 5b and measured: the applied vertical load of a 1000 kN load cell placed between the actuator and a thick steel plate on the beam extrados at the midspan; the vertical deformations of each beam support by two vertical potentiometers (one for each beam side); the vertical beam deflections by 4 potentiometers at 0.25 L, 0.5 L (2 at the beam midspan) and 0.75 L for each beam side; 3 diagonal displacements along the potential shear cracks by 3 potentiometers for each beam side at each beam end (Figure 5b); the local axial strains of the CFRP fabric at bottom, middle and top position along each U-strip side at each beam end. The beams deflections measured along each beam were corrected considering the vertical deformations at the supports.  
Figure 5: Three-points loading test apparatus: (**a**) photo and (**b**) scheme of the system to apply the loads and to measure deformation of the beam and strain of the CFRP U-strips (frontal view).