esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Pore-Structure Characterisation and Analysis of Permeability Data
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{
\begin{tabular}{c} **Percentage** \\ **Addition** \\ \end{tabular} } & \multirow{2}{*}{\(\mathbf{r_{c}}\) [\(\upmu\)m]} & \multirow{2}{*}{\(\mathbf{r_{p}}\) [\(\upmu\)m]} & & \multicolumn{2}{c}{**k\({}_{\text{o}}\)/10\({}^{\text{-9}}\)**} & \multicolumn{2}{c}{**Ap [-]**} & \multirow{2}{*}{\(\mathbf{f}\)**p [Hz]**} \\  & & & & & **[m\({}^{2}\)]** & & & **[m\({}^{2}\)]** & \\  & & & & & **(Darcy)** & & & & **Hz)** & \\ \hline \multirow{8}{*}{L-L [0\%S]} & 10 & 1160 & 141.59 & 63.46 & 1.03 & 1.825 & 0.504 & 0.611 & 0.975 & 950 \\  & 20 & 1160 & 138.62 & 63.58 & 1.47 & 1.818 & 0.480 & 0.580 & 0.998 & 1050 \\  & 30 & 1160 & 222.29 & 63.51 & 1.95 & 1.818 & 0.456 & 0.548 & 0.997 & 1000 \\  & 45 & 1160 & 261.33 & 63.60 & 3.57 & 1.817 & 0.395 & 0.474 & 0.956 & 1050 \\  & 80 & 1160 & 323.86 & 64.08 & 7.03 & 1.810 & 0.318 & 0.388 & 0.853 & 1100 \\ \hline \multirow{8}{*}{S-S [100\%S]} & 10 & 360 & 74.28 & 65.90 & 0.24 & 1.788 & 0.528 & 0.621 & 0.736 & 2500 \\  & 20 & 360 & 97.52 & 66.08 & 0.39 & 1.782 & 0.549 & 0.656 & 0.810 & 900 \\  & 30 & 360 & 113.30 & 67.45 & 0.69 & 1.760 & 0.545 & 0.664 & 0.932 & 950 \\  & 45 & 360 & 129.02 & 67.93 & 1.06 & 1.757 & 0.524 & 0.643 & 0.983 & 1000 \\  & 80 & 360 & 152.23 & 69.79 & 1.84 & 1.731 & 0.487 & 0.604 & 0.997 & 1050 \\ \hline \multirow{8}{*}{6.25\%S} & 10 & 570 & 111.77 & 68.00 & 1.44 & 1.754 & 0.502 & 0.617 & 0.997 & 1050 \\  & 20 & 570 & 147.14 & 68.88 & 2.74 & 1.744 & 0.441 & 0.548 & 0.975 & 1100 \\  & 30 & 570 & 172.19 & 69.20 & 3.29 & 1.739 & 0.420 & 0.525 & 0.956 & 1150 \\  & 45 & 570 & 200.04 & 69.31 & 4.59 & 1.737 & 0.379 & 0.479 & 0.909 & 1150 \\  & 80 & 570 & 243.4 & 69.90 & 8.26 & 1.729 & 0.306 & 0.401 & 0.801 & 1150 \\ \hline \multirow{8}{*}{12.5\%S} & 10 & 430 & 99.39 & 70.56 & 1.74 & 1.720 & 0.495 & 0.616 & 0.998 & 1050 \\  & 80 & 430 & 214.09 & 72.32 & 7.55 & 1.694 & 0.322 & 0.428 & 0.813 & 1200 \\ \hline \multirow{8}{*}{25.0\%S} & 10 & 420 & 89.08 & 73.95 & 1.56 & 1.670 & 0.519 & 0.654 & 1.000 & 1100 \\  & 80 & 420 & 188.12 & 74.53 & 4.43 & 1.661 & 0.397 & 0.520 & 0.903 & 1200 \\ \hline \multirow{8}{*}{50.0\%S} & 10 & 390 & 80.71 & 73.33 & 1.27 & 1.679 & 0.535 & 0.669 & 0.997 & 1050 \\  & 20 & 390 & 106.05 & 75.40 & 3.18 & 1.648 & 0.443 & 0.575 & 0.948 & 1150 \\ \cline{1-1}  & 30 & 390 & 123.16 & 75.56 & 3.04 & 1.645 & 0.450 & 0.583 & 0.954 & 1150 \\ \cline{1-1}  & 45 & 390 & 147.53 & 74.77 & 3.18 & 1.657 & 0.441 & 0.571 & 0.950 & 1150 \\ \cline{1-1}  & 80 & 390 & 167.15 & 76.53 & 3.88 & 1.630 & 0.421 & 0.554 & 0.919 & 1200 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Tabular representation of pore-structure-related information and sound absorption properties of monomodal and bimodal virtual macroporous structures.
## 4 New TIRA System Design
### Software Architecture  
With TIRA being an experimental system, it is expected that parts of the monitoring and control software will be modified frequently. It must be possible to adapt or replace individual software components without affecting the entire system. In particular, changes in one component must not impair the proper functioning of other components. This requires well-defined interfaces and good isolation between the software components. These requirements lead to a microservice-based architecture, which also integrates well with the distributed nature of the various control, monitoring, and signal processing components.  
The communication between the services uses the ZeroMQ messaging library and the Protocol Buffers' data serialization format. ZeroMQ offers a high degree of flexibility in the design of the communication architecture, while Protocol Buffers implements an efficient and compact format for structured data. Both technologies are well established and have previously been used in the context of large steerable antennas and telescopes [23; 24]. To ensure that the service interfaces are well defined, they are specified using a combination of the Protocol Buffers interface description language and a custom, YAML-based format.
# Towards a Digital Twin Warehouse through the Optimization of Internal Transport
## 2 Materials and Methods
### Definition of Nodes and Paths  
The \"N\" nodes (see Figure 3) are dynamically defined (depending on the chosen warehouse layout) at the aisle intersections. To model a behavior close to reality, the trajectories that the forklift will follow when attacking a column are complemented by two additional nodes. The first (D1 or A1) is located in front of the column in the furthest position, and the second (D2 or A2) is the end point where the forklift is positioned for loading or unloading. Note that D1-D2 is considered in loads/unloads in right-hand positions and A1-A2 in loads/unloads in left-hand positions.  
The paths that interconnect the nodes, as well as their restrictions and circulation directions, are dynamically modified in each warehouse layout, as well as according to the optimal path calculated.  
The maximum speed on the loading/unloading paths, i.e., between nodes D1 and D2 (or between A1 and A2), is limited to the value set for this variable at the beginning of the simulation.
## 2 Materials and Methods
### dGNSS Based Method  
The athletes' head trajectories were captured using a dGNSS with a receiver mounted on the back and an antenna on the helmet, as shown in Figure 2, and a base station positioned in the adjunct to the outrun of the hill. This setup allowed for a short baseline dGNSS calculation. The GNSS antenna (G5 Ant-2 AT1, Antcom, Torrance, CA, USA) was attached to the helmet of the athlete and connected to the GNSS receiver (Alpha-G3T, Javad, California, USA). The GNSS base station was mounted on a tripod and equipped with an antenna (GrAnt-G3T, Javad, San Jose, CA, USA) and a receiver (Alpha-G3T, Javad, San Jose, CA, USA). The GNSS on the athlete and base station logged 50 Hz, GPS/GLONASS dual frequency (L1/L2) GNSS data starting from 30 min prior to the first jump. Raw GNSS data was downloaded from the GNSS receivers and dGNSS solutions were calculated in post-processing.  
The GNSS base station was accurately positioned in an absolute global frame WGS 84 (Universal Transverse Mercator zone 32, Northern Hemisphere) by post-processing of its dGNSS position with data from the DPOS base station, Opera, Oslo, Norway provided by the Norwegian Mapping authorities (Henefoss, Norway) and the geodetic post-processing software Justin (Javad, San Jose, CA, USA). The GNSS antenna position was calculated by use of the geodetic post-processing software Justin in double difference carrier phase  
Figure 2: Ski jumpers with the dGNSS antennae mounted on their helmets and the receivers in backpacks that were carried under the ski jumping suit.  
mode. This was done to achieve accurate antenna positions from frequencies L1 or L1 and L2 and GPS and GLONASS satellite systems [39]. When the post-processing failed to fix ambiguities (integer ambiguities), float ambiguities (real number ambiguities) were calculated. The processed position data was transformed from the WGS 84 coordinate system to a local coordinate system with the origin at the in-run edge and \(x\) along the longitudinal axis of the jump and \(y\) along the gravity vector using a Helmert transformation. This local coordinate system was used for the dGNSS and PosEst analysis.
## 3 Remote Sensing of Ecosystem Health
### Remote Sensing of Vigor  
Vigor can be measured through metabolism, yield, and soil fertility [31]. The most commonly used vigor indicator is Net Primary Productivity (NPP) or Gross Primary Production (GPP) of an ecosystem [9; 32]. Other indicators that are directly or indirectly associated with NPP are green vegetation cover, green vegetation biomass, NPV cover or biomass, green ratio (green/dead vegetation cover or biomass), bare soil cover and BSC cover in semiarid and arid regions, and vegetation biological properties (chlorophyll, nitrogen, phosphorous, and moisture content _et al._). In addition, increase in NPP sometimes does not mean an improved ecosystem if the increase is attributed to expansion of invasive plant species [32]. Therefore, distribution of invasive plant species is considered to be another potential indicator of ecosystem vigor.
# Design and Analysis on Decoupling Techniques for MIMO Wireless Systems in 5G Applications
## 5 Additional Effects of Decoupling Techniques  
### Analysis of External Decoupling  
The external decoupling technique is the most commonly used method for isolation enhancement. Although it has an excellent performance in decoupling, it can be observed the  
Figure 7: MIMO system based on CMA: (**a**) top view; and (**b**) back view (Results from [68]). additional effects and differences between various types of structures from the comparisons in Table 2.  
The techniques mentioned above regarding external decoupling are summarized in Table 2. From Table 2, we can analyze the following four points to pay attention to when adopting external decoupling. 1. The NLs and grounding branches maintain the compact MIMO size without destroying the completeness of the antenna substrate. The additional decoupling structures can be co-formed with antenna elements to demand the insufficient matching impedance and improve the effective length of the antenna. The height of the MIMO system is not increased by those metal lines with a low profile. However, it is displayed from simulation results that the bandwidth will become narrow due to the introduction of decoupling lines. This can be explained by the fact that decoupling lines can be equal to capacitance and inductance, which lead to a rise in resonant Q-value;
2. To resist mutual coupling, the design of the PDE shape is innovative and versatile. In the role of added metal patches, the impedance mismatch of antenna arrays can be solved and the impedance bandwidth can be extended. Moreover, the antenna elements loaded parasitic patches typically exhibit good gain, which is attributed to the effective suppression of the currents flow by the decoupling structure. To obtain a
## 2 Materials and Methods
### The Framework of Proposed Fusion Method
#### 2.3.2 Low-Frequency Sub-Band Fusion  
The low-frequency sub-band fusion adopts the weight fusion rule with a weight map. To enhance local information, CLAHE is calculated on the initial image, and its local variance is adopted to calculate the weight map \(W\).  
\[S(x,y)=\sum_{I_{CLAHE}(n,m)\in D_{x,y}}\frac{\left(I_{CLAHE}(n,m)-\overline{I}_ {CLAHE}\right)^{2}}{N} \tag{8}\]  
\[\overline{I}_{CLAHE}=\sum_{I_{CLAHE}(n,m)\in D_{x,y}}\frac{I_{CLAHE}(n,m)}{N} \tag{9}\]  
where \(I_{CLAHE}\) denotes the image processed with CLAHE and \((n,m)\) denotes the position of the pixel, \(D_{x,y}\) denotes a region centered on \((x,y)\) with the size of \(s\times s\), \(N\) denotes the number of the pixels in region \(D_{x,y}\). \(S(x,y)\) denotes local variance, and the weight map \(W\) is calculated by Equation (10) with the local variance \(S_{morp}\), \(S_{phase}\) and weight coefficient \(\alpha\). The fused image is fused by Equation (11) for each pixel by the weight \(W\). With the increase of \(\alpha\), the fusion effect is gradually improved and finally tends to be flat. In the experiment, \(\alpha=2\) is a value suitable for the fusion.  
\[W=\frac{S_{morp}^{\alpha}}{S_{morp}^{\alpha}+S_{phase}^{\alpha}} \tag{10}\]  
\[L_{fusion}=WL_{morp}+(1-W)L_{phase} \tag{11}\]  
Figure 2: Framework of the proposed method.
# A Survey on Energy-Aware Security Mechanisms for the Internet of Things
## 3 Security Protections in IoT Environments
### Defense at the Management Level
#### 3.7.2 Firmware Updates  
Firmware updates are the most directly useful for patching known security vulnerabilities [83]. As software vulnerabilities continue to be discovered on IoT devices, regular firmware updates can patch these vulnerabilities in a timely manner. In addition to patching vulnerabilities, firmware updates also introduce new security features and enhancements, such as improved encryption algorithms, stronger authentication mechanisms, and enhanced data protection. These updates strengthen the device's resistance in the face of unknown attacks.  
Given firmware updates, the device is able to verify that the firmware was released from a trusted source. This advantage prevents malware or firmware from being injected by untrusted third parties, thereby avoiding the risk of the device being infected with malicious code [84]. Firmware updates are completed using encryption (typically public/private key encryption). When a device receives a firmware update, it verifies the signature using the publisher's public key. This process ensures that the firmware has not been tampered with during transmission, thus protecting the integrity of the firmware update.
# 6D Pose Estimation of Objects: Recent Technologies and Challenges
## 2 Learning-Based Approaches  
In recent years, machine learning-based algorithms have become a hot topic due to new concepts' emergence, such as deep learning and neural networks. Many scholars have applied machine learning-based methods to 6D pose estimation and achieved good results. Posenet [18] is a monocular 6D relocalization system that trains a convolutional neural network (CNN) to regress 6D poses. The network transformed the problem of 6D pose estimation into a regression problem for which the input is a single RGB image and the output is the camera's 6D pose by using an end-to-end approach. To address the limitation of the lack of training data, a method was proposed that could generate large regression datasets of camera pose automatedly based on structure from motion. [PERSON] [19] also trained a CNN (Figure 3) to predict the 6D poses of objects that are partially visible. The key idea of the method was to predict the 3D-2D projections of feature points on each part of the object. When the test image is partially visible, the method could measure the 6D pose according to the feature points of visible part. When the test image is fully visible, the method is able to achieve more accurate results by combining all the feature points of the part. Particle Swarm Optimization (PSO)-based methods [20; 21] demonstrate superior performance compared to Iterative Closest Point (ICP) algorithms. [PERSON] et al. [22] combined CNN with Simultaneous Localization and Mapping (SLAM), which improved the method [23] by adding a 6D object pose detector and measuring the 6D pose from different viewpoints to achieve a robust object detector system. However, there is a special problem for 6D pose estimation methods based on deep learning.  
Symmetrical objects' 6D poses (Figure 4) are difficult to measure correctly using normal deep learning methods [24], because the 6D pose of an object does not change from a fixed point of view when it is rotated 180 degrees. However, their actual ground truth is obviously different. For instance, if a network is trained to predict the pose using the squared loss between the ground truth poses and the predicted poses, it will converge to a model predicting the average of the possible poses for an input image, which is of course meaningless. [PERSON] et al. [25] proposed an efficient method combined with Faster-RCNN, which relies on the normalization of the pose rotation. [PERSON] et al. [26] proposed a method that was able to detect the rotation ambiguities and characterization of the uncertainty in the problem without further annotation or supervision. [PERSON] et al. [27] used the rigid transformation-invariant point-wise features of the point clouds as input features and used a hierarchical neural network that combined global point cloud information with the local patches to predict the key point coordinates.
## 3 Results and Discussion
### Experiment 4. Redefinition of the 2 Stage Classification  
In experiment 3, in the pre-classification stage, accuracies above 90% were obtained using the following configuration; the 90% accuracy threshold was selected observing that the maximum accuracy reported in the literature was 94% in the work in Reference [26] using a refined ANN. In the present work it was decided to round this value to the nearest ten below, since a non-refined ANN is being used:  
* Feature group \(c_{3}\);
* MLP classifier; and
* \(q_{3}\) (Phase 1, binary classifiers) and \(q_{4}\) (Phase 2, multi-class classifiers) architectures.  
In a new experiment, the same configuration was used, but this time the 3 wide groups were redefined during the training stage. This new group definition was based on the size characterization dictated by the System for Human Cytogenetic Nomenclature (ISCN) [40]. Changes to model previously presented in Figure 3, are shown in Table 5.  
In phase 1, the obtained accuracies were 99.49%, 97.85% and 98.64% for the groups 1, 2 and 3, respectively. In phase 2, the accuracies were 99.24%, 92.69% y 92.47% for the groups 1, 2 and 3. Results of experiment 4 are shown in Figure 11.  
The results of the classification of the whole chromosome set using the modified model yielded a accuracy of 88.45%, which is higher than the result of the classification using the initial two-stage classification by 12.01%.  
Figure 11: Experiment 4. Results of the classification in 3 wide groups using the model 2 on the groups 1, 2 and 3.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**New Class**} & \multicolumn{2}{c}{**Chromosomes**} \\  & **Model 1** & **Model 2** \\ \hline Group 1 & 1 to 7 & 1 to 5 \\ Group 2 & 8 to 15 & 6 to 15 and 23 \\ Group 3 & 16 to 24 & 16 to 22 and 24 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Redefinition of the initial two-stage model.  
The classification algorithm used for this test was the MLP, that is an Artificial Neural Network (ANN). The default configuration of this algorithm in Weka is presented in Figure 12.  
This configuration uses 6 ANN, with 131 neurons in the input layer, corresponding to the size of the feature set. The number of neurons in the hidden layer was defined as: \(HL_{neurons}=(a+c)/2\), where \(a\) are the attributes and \(c\) the classes. Finally, the number of neurons in the output layer corresponds to the number of classification classes.
# A Novel Deep Learning Approach for Machinery Prognostics Based on Time Windows
###### Abstract  
Remaining useful life (RUL) prediction is a challenging research task in prognostics and receives extensive attention from academia to industry. This paper proposes a novel deep convolutional neural network (CNN) for RUL prediction. Unlike health indicator-based methods which require the long-term tracking of sensor data from the initial stage, the proposed network aims to utilize data from consecutive time samples at any time interval for RUL prediction. Additionally, a new kernel module for prognostics is designed where the kernels are selected automatically, which can further enhance the feature extraction ability of the network. The effectiveness of the proposed network is validated using the C-MAPSS dataset for aircraft engines provided by NASA. Compared with the state-of-the-art results on the same dataset, the prediction results demonstrate the superiority of the proposed network.  
remaining useful life; convolutional neural network; kernel Article
# Securing IPv6 Neighbor Discovery Address Resolution with Voucher-Based Addressing
## 5 Discussion
### Sintplicity, Privacy, and Flexibility
Additionally, by the irreversible nature of hashing functions, VBAs will not expose the underlying LLID of the generating interface, thus alleviating device-specific vulnerability exploitation concerns and other privacy concerns from leaked LLID values. Finally, flexibility is a paramount concern for researchers seeking the adoption of their proposals. Any specification that mandates an immediate, wholesale, strict adherence to itself is bound to fail. Networks are built to be dynamic, independent, and transitory between various protocols and specifications, and any given node may be at its own stage in the deployment of a software tool or specification. Any proposal which ignores a simple transition capability will never be adopted because forced compatibility violates any notion of flexibility. VBA employs IEMs to fulfill this need: the various interface modes allow an operating neighbor to decide which policies to enforce based on local settings or autoconfiguration alone, enabling per-interface choices about which rules to adhere to.
# Exploring Data Model Relations in OpenStreetMap
## 4 Experimental Analysis
### Relation Analysis
#### 4.2.4 Tagging of Relations  
Tagging in OSM has been well studied over the past number of years by many authors. See [29; 30; 31; 32] amongst others for recent treatments of this area. As discussed in Section 3, relations can have tags associated with them in the same way that ways and nodes in OSM have tags. The KeepRight QA tool [20] in OSM flags an error if it finds a relation with no tags. Figure 3 shows the distribution of the number of tags associated with each of the relations in the four cities. A familiar pattern is observed here for tags on relations which has been reported by several authors for nodes and ways in OSM [33; 34; 35]. We see that over 85% of relation objects in all four cities have 3 tags or fewer. Zurich and Vienna show a higher percentage of relations, with more than 3 tags. We believe that this may be related to specific tagging strategies for relations related to public transportation or buildings. However, a closer analysis of these objects is required in order to correctly ascertain why this is the case.  
In Table 3, we summarize an analysis of tags associated with the five most frequently occurring types of relations. These relations are identified using the type tag key. They are amongst the most frequently mapped relations in the global OpenStreetMap database [26]. For each polygon, we consider the other tags with co-occur with the type key. The table is organised as follows. For each city, the digit 1 following the city name indicates the most frequently occurring tag key with the corresponding type tag key for that city. The digit 2 following the city name indicates the second most frequently occurring tag key with the corresponding type tag key for that city. Table 3 exhibits a very consistent pattern  
Figure 2: Distribution of the number of members, membership size, in relations for all four cities where the number of members is fewer than or equal to 10.  
of tagging across all four cities and the five relation types. There is complete agreement in regards to the tagging of type=restriction relations. There are small variations in route, public_transport and route_master. However, all of the co-occurring tag keys are valid tag keys. It is interesting to note the co-occurring tag keys for type=multipolygon. We see agreement in Rome and Vienna on building and landuse, while we see Madrid and Vienna agreeing on building and addr:street. This variation may possibly be explained by the bulk import of openly available geographic data into OSM in Madrid and Vienna. Bulk imports for both areas or regions are reported on the Catalogue of Imports to OSM [36].