esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Proposed Method
### Routing Algorithm at Intersection Level
Moreover, the discount factor is calculated using Equation (16) based on the two parameters, namely vehicle density and the distance to the destination:  
\[\delta=\frac{\left(\frac{D_{R}(l)-\underset{R\in\text{\emph{Intersect}}_{i}} {\max\limits_{R\in\text{\emph{Intersect}}_{i}}}D_{R}(l)}{\underset{R\in \text{\emph{Intersect}}_{i}}{\max\limits_{R\in\text{\emph{Intersect}}_{i}}}D_{ R}(l)}\right)}{\left(\frac{\sqrt{\big{(}x_{intersect_{i+1}}-x_{D}\big{)}^{2}+\big{(}y_{intersect_{i+1}}-y_{D} \big{)}^{2}}}{\sqrt{\big{(}x_{intersect_{i}}-x_{D}\big{)}^{2}+\big{(}y_{intersect _{i}}-y_{D}\big{)}^{2}}}\right)} \tag{16}\]where \(D_{R}(l)\) is the vehicle density on the current road. \(\left(x_{intersect_{t+1}},y_{intersect_{t+1}}\right)\), \(\left(x_{intersect_{t}},y_{intersect_{t}}\right)\), and \(\left(x_{D},y_{D}\right)\) are the spatial coordinates of the current intersection (\(intersect_{t}\)), the next intersection (\(intersect_{t+1}\)), and the destination, respectively. After reviewing Equation (16), we find out that if the Euclidian distance between \(intersect_{t+1}\) and the destination is less than that between \(intersect_{t}\) and the destination, and the corresponding road section includes a sufficient number of vehicles (i.e., it has a suitable vehicle density), \(\delta\) has a large value. The agent must discover the network environment by performing different actions and locating in various states. Thus, it calculates a Q-value for each action and the corresponding state and maintains it in Q-table to be used in the routing decisions. After converging the I2I routine algorithm, Q-table is stored in the memory of RSUs at the intersections. They use this table to select the next intersection with the highest Q-value. Then, RSU sends packets to the next intersection using the routing algorithm at the road level described in Section 5.3. This process continues until packets reach the destination vehicle. Figure 7: Different components in I2I routing model in QFHR.
## 6 Security Solutions
### Service Security
#### 6.3.1 Access-Control Models  
Access-control mechanisms aim to achieve two goals: (_i_) authorized users are given access to sensitive information stored on IoT devices; (_ii_) authorized users are allowed to perform only authorized operations, such as issuing commands to smart devices. Access-control models are designed based on three attributes: subjects (users), objects (resources and services), and operations (actions). The subjects are the users or consumers of an IoT system. Resources and services provided by an IoT device are considered as objects. An object's actions toward other objects are defined as operations. An access-control method defines policies that determine an object's rights (or permissions) to perform operations toward objects.  
The existing access-control methods can be classified into two major categories: Role-based Access Control (RBAC) [162] and Capability-based Access Control (CapBAC) [163]. In RBAC, every entity of an IoT system, such as a user or device, is assigned a role. Next, permissions to perform certain operations on IoT services and resources are mapped to the role. Figure 19 presents an overview of the RBAC model. Let us consider that two types of roles are defined for a smart home: administrative role and guest role. The administrative role is privileged to perform all sorts of operations, such as read and write, on the smart home appliances. In contrast, the guest role is allowed to perform a particular operation, such as a read request, on certain appliances. [PERSON], the owner of a smart home, is assigned an administrative role; therefore, she can control all the smart devices of the home. On the other hand, [PERSON], [PERSON]'s partner, may not have access to the home security system as he is assigned with the guest role.  
In the CapBAC, every entity of an IoT system is given a capability token that cannot be forged and specifies access rights of the entity. An entity's rights to perform operations on IoT services are defined in its capability token, as shown in Figure 19. An entity sends a request to an IoT device and attaches its capability token with the request. An object  
Figure 19: RBAC vs. CapBAC model.  
(an IoT device) first verifies the authenticity of the token. Next, it evaluates the entity's capabilities to perform the requested operation. Finally, the object accepts or denies the request according to the verification result.
# Virtual Reality and Digital Twins for Catastrophic Failure Prevention in Industry 4.0
## 6 Conclusions  
The advance of Industry 4.0 has led to the implementation of technologies such as virtual reality and digital twins, which in the industrial context represents a significant advance in the digitization of maintenance, monitoring and training processes. This work has demonstrated, through the systematic application of the proposed methodology, the technical feasibility of integrating physical sensors, PLC processing and immersive visualization along with anticipated fin systems implemented in Unity through the use of open-source software, allowing the application of these technologies in a more extended way.  
However, the system has some limitations. The lack of haptic feedback and the dependence on connectivity with IoT platforms may affect accuracy and user experience in critical scenarios. In addition, quantitative metrics such as latency, error rate in alerts or user performance in a real job in the context of tasks related to their work were not evaluated, which is considered essential for future validations.  
Future work will focus on implementing a quantitative evaluation of the system's performance in industrial scenarios. This will include measuring sensor-to-visual latency, synchronization accuracy between real-world data and the digital twin model, and transmission delay through the communication layers. In addition, user performance during training sessions will be assessed using objective metrics such as task completion time, error rate and response to alerts. These indicators will provide a more comprehensive validation of the platform's effectiveness, both technically and pedagogically, beyond subjective perception.  
As directions for future work, we propose the following: (1) the incorporation of haptic feedback to enrich the interaction with the virtual environment, (2) the use of 5G networksand edge computing to reduce latency and improve system stability, (3) the integration of artificial intelligence algorithms for automatic diagnosis and the personalization of training according to the user's profile. These improvements would allow us to not only increase the fidelity of the environment but also move towards predictive maintenance and adaptive learning models, contributing to safer, more efficient and resilient industrial environments.  
Conceptualization, F.M.-L.R.; Methodology, V.R.C.; Software, V.R.C. and E.R.; Validation, N.N.; Writing--original draft, V.R.C.; Writing--review & editing, N.N.; Supervision, J.L.V., J.O. and C.I.G.K.; Project administration, C.I.G.K. All authors have read and agreed to the published version of the manuscript.  
The participants express their gratitude to the Pontificia Universidad Catolica de Valparaiso--Proyecto Centenario N\({}^{\circ}\) 39312.  
Not applicable.  
The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.  
The authors declare no conflicts of interest.
## 3 Description, Implementation and Results of Experiments
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Number of** & \(a_{p}\) & \(f\) & \(v_{c}\) & \(VB_{B}\) (mm) & \(\Delta_{d}\) (mm) \\ \hline
1 & 0.01 & 0.1 & 70 & 0.0601 & 0.0526 \\ \hline
2 & 0.01 & 0.2 & 120 & 0.1254 & 0.0020 \\ \hline
3 & 0.01 & 0.3 & 170 & 0.0518 & 0.1460 \\ \hline
4 & 0.01 & 0.4 & 220 & 0.1239 & 0.0627 \\ \hline
5 & 0.05 & 0.1 & 120 & 0.1141 & 0.0589 \\ \hline
6 & 0.05 & 0.2 & 70 & 0.0260 & 0.0923 \\ \hline
7 & 0.05 & 0.3 & 220 & 0.1638 & 0.0704 \\ \hline
8 & 0.05 & 0.4 & 170 & 0.1181 & 0.0614 \\ \hline
9 & 0.10 & 0.1 & 170 & 0.1843 & 0.0476 \\ \hline
10 & 0.10 & 0.2 & 220 & 0.1839 & 0.1062 \\ \hline
11 & 0.10 & 0.3 & 70 & 0.0698 & 0.0344 \\ \hline
12 & 0.10 & 0.4 & 120 & 0.1136 & 0.0662 \\ \hline
13 & 0.50 & 0.1 & 220 & 0.1990 & 0.1148 \\ \hline
14 & 0.50 & 0.2 & 170 & 0.1301 & 0.0792 \\ \hline
15 & 0.50 & 0.3 & 120 & 0.1073 & 0.0589 \\ \hline
16 & 0.50 & 0.4 & 70 & 0.0284 & 0.0036 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Experimental results for parameters—flank wear (\(VB_{B}\)) and diameter deviation (\(\Delta_{d}\)) according to Taguchi L16 orthogonal array. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Factor Level** & \(a_{p}\) **Depth of Cut** & \(f\) **Feed** & \(v_{c}\) **Cutting Speed** \\  & **(mm)** & **(mm per Rev.)** & **(m/min)** \\ \hline
1. & 0.01 & 0.1 & 70 \\ \hline
2. & 0.05 & 0.2 & 120 \\ \hline
3. & 0.1 & 0.3 & 170 \\ \hline
4. & 0.5 & 0.4 & 220 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Cutting process conditions for Taguchi L16 orthogonal array. The lowest average value of diameter deviation was measured at a cutting speed of 70 m/min, namely 0.0046 mm, the highest average value of the diameter deviation was measured at a cutting speed of 220 m/min (0.0877 mm). The main effect graphs show the uncertainty of the process parameter levels to be developed for turning steel C45 as a steel group standard, and they also show that for certain lower parameter levels the flank wear is lower, but at the same time a change in the size deviation of the diameter occurs. The measurement results show that level 3 of cutting depth, level 4 of feed, and level 1 of cutting speed are factors for achieving the smallest diameter deviation. The dependence is important for the defined initial parameters, because the wear of the flank side of the cutting insert influences the deviation of the diameter. Therefore, the study of this dependence and the development of optimal input factors is important, also with regard to the use of LTS in the measurement of qualitative parameters.
# Long-Distance Shield Tunnelling Performance Prediction Based on Informer
## 4 Results and Analysis  
### Effect of Sequence Length Parameter  
Based on the experimental design in Table 3, the impact of different data extraction methods on the performance of the model was explored.  
1. The effect of data interval  
The prediction results of the LSPP model at different sampling intervals are shown in Figure 8. It can be seen that the performance of the model improves as the sampling interval is reduced. The performance improvement of the model is particularly obvious when the sampling interval is reduced from 1200 mm to 240 mm. When the sampling interval is less than 120 mm, the model performance improves less when the sampling interval is further reduced. The reason for this phenomenon may be that when the sampling interval is too large, some important information may be lost, causing the prediction error of the model to increase.  
2. The effect of historical sequence length  
The effect of history sequence length on LSPP performance is shown in Figure 9. When the length of the used history sequence increases, it helps to improve the model prediction performance. As the sequence length increases from 1200 mm to 3600 mm, the range covered by each performance indicator shows an obvious contraction trend, but when the sequence length reaches 3600 mm and then continues to increase the history sequence length, the model performance improvement is not obvious. Therefore, appropriately increasing the length of the historical sequence is beneficial to improving the predictive performance of the model.  
3. The effect of label sequence length  
The prediction performance of the model for different label sequence lengths is shown in Figure 10. As the label sequence length increases, the corresponding four performance indicators of the model gradually increase, indicating that the prediction performance of the model has decreased. The main reason for this phenomenon may be that when the label sequence is too long, the correlation between the information contained in it and the future penetration decreases, causing interference with the prediction of the model, and so the chosen length of the label should not be too long.  
Figure 8: Effects of different data intervals on LSPP prediction.  
Figure 9: Effect of different history sequence lengths on LSPP prediction.  
Through the above experiments, within the data interval length range of 24-1200 mm, reducing the sampled data interval improves model performance. Additionally, when both the history sequence and label sequence lengths are less than 7200 mm, increasing the history sequence length enhances model performance. However, excessively long label sequences degrade model performance.
## 3 Results  
### Simulation Results for Acoustic Pressure  
Figure 6 shows the acoustic pressure fields at some chosen frequencies for the single-phase and the 1-3 composite transducers. In every frequency case, the 1-3 composite transducers exhibit much higher acoustic pressure outputs compared with the single-phase transducers, as shown in Figure 7. The percentile difference in the acoustic pressure output reduced as the operating frequency increased (Figure 7). However, the acoustic pressure output in the 1-3 composite transducer was still much larger than that of the single-phase device. For example, the acoustic pressure output in the 1-3 composite was approximately 160% greater than that of the single-phase one under the 5 MHz operation condition.  
Figure 6: Comparison of the acoustic pressure fields produced by the 1-phase and the 1–3 piezoelectric composite transducers under the operation frequencies of (**a**) 2 MHz, (**b**) 3 MHz, (**c**) 4 MHz, and (**d**) 5 MHz.  
Figure 7: Comparison of acoustic pressure outputs in the single-phase and the 1–3 composite transducers at selected frequency conditions.  
Figure 8 shows the variations of the acoustic pressure outputs of the 1-3 composite transducer with the width of the piezoelectric rods. The dimension ratio between the length and thickness of the piezoelectric rod had a significant effect on the acoustic pressure output. For example, the pressure level at the focal point was potentiated by decreasing the lateral dimension of each piezoelectric rod (i.e., lower dimension ratio). Table 2 lists the dimension ratios of the piezoelectric rod and the corresponding acoustic pressure outputs.
# Mitigating Class Imbalance in Network Intrusion Detection with Feature-Regularized GANs
## 4 Experimental Results
### Cse-Cic-IDS2018 Results  
#### 4.4.1 Performance on Minority Class  
Table 6 presents precision, recall, and F1-score comparisons for the minority classes (infiltration and Web Attack) across traditional oversampling methods and various GAN variants, including SNNL-enhanced versions. The best values are highlighted in **bold**, and the second-best are underlined.  
Figure 2: F1-score comparison by majority class on CSE-CIC-IDS2017 dataset.  
For the infiltration class, traditional methods achieve relatively high recall (0.480-0.557) but suffer from low precision (\(\leq\)0.087), leading to limited F1-scores (0.110-0.137). GAN-based methods notably improve precision, particularly WGAN-GP and WGAN-GP+SNNL (above 0.49), although recall remains low (\(\leq\)0.057), resulting in comparable F1-scores to the traditional baselines. Overall, none of the evaluated approaches yield clear gains on this class, and SNNL integration appears to have limited impact.  
By contrast, for Web Attack, GAN-based methods generally achieve higher F1-scores than traditional techniques. While ROS reaches 0.357, all GAN variants exceed 0.65. Among them, WGAN-GP+SNNL achieves the highest F1-score (0.833), attributed to improved recall (0.74) while maintaining high precision (0.953). This demonstrates that SNNL enhances generation quality and supports better minority-class separability for detecting Web Attack.
# A Novel Method Using 3D Interest Points to Place Markers on a Large Object in Augmented Reality
## 2 Proposed Method for Placing Multiple Markers
### Scoring Step
#### 2.2.2 Density of Interest Points  
The 3D interest points are characteristic positions extracted from the three-dimensional geometric information of the mesh (hereafter 3D interest point is referred to as interest point). There are various methods for extracting interest points [21; 22; 23], most of them extract partial features by analyzing the overall geometry of the mesh. This means that if interest points are extracted from a large object, various features may not be reflected from the perspective of the segmented area.  
Therefore, Mesh Saliency, which computes the degree of saliency for every vertex, is used to extract interest points [21]. The threshold for extracting interest points is the mean value of the total saliency at all vertices, as in [24]. In addition, because it is a large object, the local maxima are not considered to derive the interest points as much as possible. Figure 8 shows interest points extracted after preprocessing car models, which are specific virtual large objects.  
From Hypothesis 2, the more densely the interest points are around \(\mathbf{f}_{j,i}\), the more likely it is to be a suitable candidate. We define the degree of this suitability as density of interest points in this paper (hereafter DoI), which is expressed as Equation (4).  
\[\text{DoI}\big{(}\mathbf{f}_{j,i}\big{)}=\forall\text{p}\in\text{interest} \big{(}F_{j}\big{)},\;\sum\begin{cases}1,\;\big{\|}\text{position}(\mathbf{f}_ {j,i})-\text{p}\big{\|}<r\\ 0,\;\big{\|}\text{position}(\mathbf{f}_{j,i})-\text{p}\big{\|}\geq r\end{cases} \tag{4}\]  
where \(\text{interest}\big{(}F_{j}\big{)}\) denotes all the interest points in \(F_{j}\), p denotes a point iterating through these interest points, and r is the radius for determining whether the interest points are around \(\mathbf{f}_{j,i}\) (in this paper, twice the value of \(\varepsilon\) in the preprocessing is used).  
Figure 8: Example of interest points extracted from virtual large objects: (a) A-segment car model; (b): C-segment car model; (c): D-segment car model.  
Figure 7: Example of CSA computation.  
As with the CSA represented by normalization, the DoI is also normalized and is expressed as in Equation (5).  
\[\text{normalize}\big{(}\text{DoI}\big{(}\mathbf{f}_{j,i}\big{)}\big{)}=\frac{ \text{DoI}\big{(}\mathbf{f}_{j,i}\big{)}}{\text{max}\Big{(}\forall\mathbf{f}^{ \prime}\in\mathbf{F}_{j},\,\text{DoI}\big{(}\mathbf{f}^{\prime}\big{)}\Big{)}} \tag{5}\]  
where \(\mathbf{f}^{\prime\prime}\) denotes a \(\mathbf{f}\) iterating through \(F_{j}\).  
Figure 9 shows the normalized DoI computed by Equation (5) at a specific \(\mathbf{f}_{j,i}\).
# Monitoring Cropland Abandonment in Hilly Areas with Sentinel-1 and Sentinel-2 Timeseries
## 2 Materials and Methods
### Method
#### 2.3.2 Sentinel-1 and Sentinel-2 Imgeries Processing
The Savitzky-Golay filter is a digital filter that can be applied to a set of images to smooth the image, i.e., to increase the precision of the image without distorting the phenology tendency. This was achieved, in a process known as convolution, by fitting successive subsets of adjacent data points with a low-degree polynomial using the method of linear least squares [50]. Many articles have proven  
Figure 3: The numbers of cloudless pixels of Sentinel-2 image in the study area from 2019 to 2021. (**a–c**) The numbers of cloudless pixels in 2019 to 2021, respectively. (**d**) The monthly numbers of cloudless pixels from 2019 to 2021.  
the excellent efficiency of Savitzky-Golay (SG) filtering to reconstruct remote sensing images, especially in areas seriously disturbed by clouds [30; 33; 51]. The Open Earth Engine Library (OEEL) is an open-source SG filter library which can be easily applied on GEE; an application example is given at [[https://code.eartheengine.google.com/?scriptPath=users/OEEL/examples:MODISFiltering](https://code.eartheengine.google.com/?scriptPath=users/OEEL/examples:MODISFiltering)]([https://code.eartheengine.google.com/?scriptPath=users/OEEL/examples:MODISFiltering](https://code.eartheengine.google.com/?scriptPath=users/OEEL/examples:MODISFiltering)), accessed on 22 June 2022. The moving window of observation was set to three, with a filter order of two, as the growth period of most crops exceeds 3 months [36]. After image processing, a set of commonly used indices were composited by Sentinel-1 and Sentinel-2. A summary of the indices and their expressions is reported in Table 2.
# INSPIRE-SAT 7, a Second CubeSat to Measure the Earth's Energy Budget and to Probe the Ionosphere
## 3 Description of the INSPIRE-SAT 7 Mission and Mission Operations
### Inspire-SAT 7 Mission Concept of Operations and Calibration and Validation (Cal/Val)
* A commissioning phase lasting approximately 1 month will be performed for INSPIRE-SAT 7 where all instrument operation aspects will be verified and in-orbit calibration and validation activities will be initiated with a validation of the 3-axis accelerometer/gyroscope/compass inertial measurement unit, a determination of the magnetic field of the Earth, a determination of the attitude of the satellite using the TRIAD and MEKF methods, a determination of the flux measured by the sensors (OSR, OLR, TSI, UV SSI), and a technological validation of the different instruments (Li-Fi payload led by LATMOS and Oledcomm, CUIONOI payload led by ONERA, SPINO payload led by LATMOS and Adrelys). * An exploitation phase that will extend for the duration of the mission in which calibration and validation activities will continue with the main goal to obtain the main scientific data products. A quantification of the instruments aging will be done. Possible corrections will be carried out.
# A New Sum-Channel Radiating Element for a Patch-Monopole Monopulse Feed
## 5 Conclusions  
The main contribution of this article is the introduction of a new sum-channel radiating element with a comparable shape complexity to the patch-monopole monopulse feed design. The new sum-channel patch shape allows for conductive separation of the patch to the other parts of the feed.  
Both sum and difference channel radiation patterns are as expected in the design phase. The sum-channel center-frequency LHCP gain of the feed is around 8 dBi. The feed exhibits a somewhat narrowband sum-channel in the 30 MHz range as it is primarily optimized to test achievable efficiencies of the design. The sum-channel aperture illumination efficiency is 75% at a target \(\frac{\mathrm{F}}{\mathrm{D}}\) ratio of 0.4. We were unable to measure radiation efficiency very well, but the design with low-loss dielectrics should not be problematic or limiting in this regard.  
Conceptualization, P.M.; methodology, P.M.; software, P.M.; validation, P.M. and B.B.; formal analysis, P.M.; investigation, P.M.; resources, B.B.; data curation, P.M. and B.B.; writing--original draft preparation, P.M.; writing--review and editing, P.M. and B.B.; visualization, P.M. and B.B.; supervision, B.B.; project administration, B.B.; funding acquisition, B.B. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Slovenian Research and Innovation Agency under grant J2-50072 and research core fund P2-0246.  
Data Availability StatementData are contained within the article.  
Authors [PERSON] was employed by the company Space-SI. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.