esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Conclusions  
In this paper, the effect of adding Electric Arc Furnace (EAF) slag or Graphene Nanoplatelets (GNPs) on the microwave heating and healing efficiency of asphalt mixtures was evaluated. The following conclusions can be drawn:  
* It was found that the higher temperatures enhanced the healing performances of the asphalt mixtures, with and without the addition of slag or graphene.
* Although the asphalt mixture without additives can be heated with microwaves, both the slag and graphene allow saving approximately 50% of the energy during the heating process.
* The addition of slag or graphene does not seem to enhance the healing rates of the mixture. However, for the same healing rate, the addition of slag or graphene can halve the energy consumption during pavements maintenance operations. In accordance with the laboratory findings, the addition of 3% of slag (o/aggregates) or 2% of graphene (o/binder) is sufficient to obtain these savings of energy.
* Finally, it was observed that apparently, the contribution of the electrical conductivity to the microwave heating is low. Therefore, microwave heat generation could be attributed mostly to the  
Figure 14: Effect of slag (a) and graphene (b) on the Healing Efficiency  
oscillating electromagnetic fields that excite molecules rather than to eventual electrical currents generated in the mixture and the resulting Joule's effect.  
The use of GNPs in the asphalt mixtures is an innovative field, and the results of this work can be a starting point for further investigations in this area. In future research, other types of nanomaterials and the optimum dosage can be analyzed and compared with other traditional materials used in asphalt pavements, such as EAF slag.  
Conceptualization, J.G. and L.P.-S.; methodology, J.G. and L.P.-S.; validation, F.G and J.C.; formal analysis, F.G., J.C.; investigation, F.G., J.G. [PERSON] C., and L.P.-S.; resources, J.G. and L.P.-S.; writing--original draft preparation, F.G.; writing--review and editing, F.G., J.C., J.G. and L.P.-S.; supervision, J.G. and L.P.-S. All authors have read and agreed to the published version of the manuscript.  
Fundacion Agustin de Betancourt and Spanish Ministry of Education.  
This investigation was possible thanks to Grant for PhD students by the Agustin of Betancourt Foundation and Grant n. PDI-18-0 HXUI-55-QZMWQL for Foreign Investigation Stays by the Spanish Ministry of Education.  
The authors declare no conflict of interest.
## 4 Typical Examples of Systems
### The System of the Human Eye  
Our approach makes it easy to treat aberrations, including aberrations of the eye (Figure 8). It makes it possible to treat extreme cases, such as the effect of the finite size of the pupil, something which is not easily treated by geometric optics. Consider the simplified eye model, what we call [PERSON]'s simplified schematic eye [29; 30; 31; 32]. Of course, the reduced eye model, and even other more complex models, such as the schematic eye, described by [PERSON] [33], can be analyzed by our approach.  
Given a field \(g(x,0)\) (Figure 8), \(g(x,z)\), at the anterior principal plane H, is calculated by the Fresnel transform (1). \(g^{\prime}(x,0)\), observed in the plane H', is nothing but the complex product of \(g(x,z)\), and the transmittance of the compound system, means \(\exp\left(-j\pi\frac{n_{2}x^{2}}{\lambda f^{\prime}}\right)\). The aperture function, or pupil, and wavefront aberrations can be included in the analysis. Then, \(g^{\prime}(x,z^{\prime})\), which is the diffraction field observed at distance z' inside the eye, is calculated by relation (1). Of course, \(\lambda\) should be replaced by \(\lambda/n_{2}\) in Equation (1). Let us suppose that one of the surfaces of the eye crystalline lens is affected by aberrations, as depicted in Figure 8. The aberration field, \(a(x)\), as per Equation (28), is integrated in the calculation of \(g^{\prime}(x,z^{\prime})\), in order to measure the effect of wavefront aberrations. It is worth  
Figure 8: The simplified schematic model of Gullstrand (the unit is the millimeter: f = \(-16.54\), f\({}^{\prime}\) = \(22.05\), h = \(1.55\), h\({}^{\prime}\) = \(-4.06\), n\({}_{1}\) = \(1\), and n\({}_{2}\) = \(4/3\). N and N’: nodal points. F’ and F’m are the back focal points of an emmetropic and a myopic eye, respectively.  
noting that aberrations, related to aberration data of the outer components, such as the anterior cornea surface, are easier to treat, compared to inner components, such as the anterior lens surface.  
Let us consider an emmetropic eye (F' is located exactly in the retina), as shown in Figure 8. If the eye is myopic (nearsighted), the focal point F'm is located before the retina (Figure 8). On the other hand, if the eye is hyperopic, the focal point F'h is located behind the retina. Our approach remains the same for these two cases. Our diffractive approach is also easily applied to the eye corrected by an ophthalmic lens, contact lens, or any other element of correction. We can proceed according to two methods. The first method consists in calculating a compound system, including the correction itself. The second method involves an additional application of the Fresnel transform, expressed by Equation (1). In this case, we consider the corrective element and eye system separately. As a result, the Fresnel transform is applied three times, instead of two. Besides, if the correction itself is affected by aberrations, such as the case of intraocular lens, laser surgery, or corneal implant, our approach is straightforwardly applicable.
## 4 Precoding Matrix Design Based on Type II 5G-NR for MU-MIMO
The same rotated factors are used for all the \(L\) DFT beams to reduce feedback overhead. Then, these selected \(L\) beams are linearly combined with different amplitude and phase weights for each layer and polarization according to \(\mathbf{w}_{2n}^{[kd]}\) as follows  
\[\mathbf{w}_{2n}^{[kd]}=\mathbf{P}_{WB}^{[kd]}\mathbf{P}_{SB_{n}}^{[kd]} \mathbf{C}_{n}^{[kd]}. \tag{15}\]  
where \(\mathbf{P}_{WB}^{[kd]}\in\mathbb{R}^{2L\times 1}\) and \(\mathbf{P}_{SB_{n}}^{[kd]}\in\mathbb{R}^{2L\times 1}\) represent the wideband and subband amplitude coefficient, respectively, for the \(d\)th layer and \(k\)th UE. The wideband amplitude parameter \(\mathbf{P}_{WB}^{[kd]}\) is computed from the subindex vector \(\mathbf{i}_{14}\) which is quantized with 3 bits. Therefore,each element of \(\mathbf{P}_{WB}^{[kd]}\) takes values from the set \(\left\{0,\sqrt{1/64},\sqrt{1/32},\sqrt{1/16},\sqrt{1/8},\sqrt{1/4},\sqrt{1/2},1\right\}\). The subband amplitude coefficients \(\mathbf{P}_{SB_{n}}^{[kd]}\) are selected from the set \(\left\{1,\sqrt{0.5}\right\}\) according to the subindex \(\mathbf{i}_{22}^{n}\) that uses a single-bit quantization to reduce overhead. On the other hand, \(\mathbf{C}_{n}^{[kd]}\) denotes the inter-polarization co-phasing vector. Similarly to Type I, each value of \(\mathbf{C}_{n}^{[kd]}\) is given by the complex exponential \(e^{j\phi_{n}^{[kd]}}\). The phase values \(\phi_{n}^{[kd]}\) are obtained from an \(N_{PSK}\)-phase-shift-keying (PSK) alphabet, where \(N_{PSK}\in\{4,8\}\), according to the subindex vector \(\mathbf{i}_{21}^{n}\). In Release 16, the Type II codebook is enhanced to support a higher number of parallel layers (i.e., \(N_{s}=4\)) and a more accurate representation of the channel eigenvectors. In this sense, the amplitude and phase coefficients are obtained with a higher number of quantization bits. Furthermore, the frequency granularity is improved by the introduction of the \"frequency domain (FD) units\" concept. Consequently, the precoding matrices are designed per FD instead of per subband as in Release 15. Since the number of FD units can be twice the number of subbands, the frequency granularity can be improved by a factor of 2. However, this demands a higher CSI-RS feedback overhead. To overcome this problem a DFT-based compression technique has been proposed. For this purpose, the wideband \(\mathbf{W}_{1}\) and linear combination \(\mathbf{W}_{2}\) matrices reuse the same design concept of Type II codebook in Release 15 but they are providing the results per FD units. Moreover, an additional matrix is defined to perform the frequency compression \(\mathbf{W}_{f}\) of certain DFT basis vectors from the total of FD units. Furthermore, to keep the CSI overhead controllable, either a one step or two steps feedback scheme is used based on the number of FD units. In Release 17, the angles and delays reciprocity in FDD operation are exploited to reduce computation complexity at the UE side. In addition, enhancements of multiple transmission and reception point (mTRP) are addressed [26; 29; 32].
## 3 Materials and Methods
### Visual Representation Techniques  
Utilizing visualization methods in the literature review is important because it allows researchers to gain a deeper, more comprehensive understanding of the literature that must be examined. Knowledge mapping is a novel approach developed within the fields of scientometrics and information metrology. This method illustrates the emergence and evolution of knowledge and reveals the structure of the knowledge landscape. CiteSpace version (6.2.6) is a prominent tool for visualizing citation networks. It is used to generate keyword atlases and time zone views of co-citation maps and to cluster citations, identifying nodes and their interconnections. Bibliometric R Package (R 4.3.2) and VOSviewer software (1.6.20) are reliable tools commonly used to create co-occurrence maps for authors, citations, and keywords. These maps spatially show how nodes cluster together and are connected based on their proximity and density, clearly demonstrating the connections between authors and institutions. Additionally, CiteSpace allows for contrasting and comparing the strengths and weaknesses of relationships among different themes using trees and lines, enabling an extensive analysis of theme relations. Meanwhile, VOSviewer is particularly helpful in understanding connections within each cluster, providing complementary information. Furthermore, the Biblioshiny interface utilizes trend analysis, allowing researchers to track research trends over time and examine how interest in particular areas or topic keywords evolves. Similarly, virtual online platforms have been used to advance investigations by analyzing more complex data handling methods, making the analysis more in-depth than ever before.
# An Adaptive Neuro-Fuzzy Propagation Model for LoRaWAN
## 2 Brief Review of Common Models
### [PERSON] and Hata Models  
[PERSON]'s signal propagation investigation has been a cornerstone in this field. [PERSON]'s model [2] was built upon a practical data collection in Tokyo, Japan, within the frequency range of 150-2000 MHz. In his model, the path loss estimation depends on the FSPL, antenna gain factor, propagation gains due to mobile/base-station antennae heights and collective correction gain. The latter, collective correction gain, is to compensate for the type of environment, average slope of terrain and finally land/sea parameters. This model is somewhat a more comprehensive version of the log-distance model. Like other nondeterministic methods, [PERSON]'s model lacks the higher accuracy of deterministic models. In addition, independent parameters of this model, such as frequency, and mobile and base station antenna heights, were limited in their range. [PERSON]'s findings then became the basis of the [PERSON] model [3], also known as the Okumura-Hata model. This model introduces many more independent parameters, such as reflection, diffraction and scattering factors. It suggests new correction factors for suburban and rural environments, and extends the range of the parameters of [PERSON]'s model, such as mobile and base station antenna heights. The International Telecommunication Union (ITU) model [4] was inspired by [PERSON]'s and [PERSON]'s models.
## 5 Experimental Evaluation
### Experimental Results  
To evaluate our model (V1), which considers only dynamic obstacles, we compare its performance with the baseline method DSRNN [43] (Table 1). We also compare our second model (V2), which considers both static and dynamic obstacles, with the baseline method SOADRL [42] (Table 2). The following metrics have been defined: (i) success rate, (ii) timeout rate, (iii) collision rate, (iv) contact loss rate, and (v) navigation time. In relation to the present system, it was imperative to assess the safety and temporal demands associated with patient mobility. Consequently, we incorporated various metrics, such as contact loss rate to ensure sustained grip of the robotic arm by the patient, navigation time to gauge the duration required to complete the walking task, timeout rate to prevent the reinforcement learning algorithm from learning inefficient policies that prolong short distance coverage, collision rate to evaluate the safety of robot navigation around obstacles, and lastly, success rate to comprehensively assess the system's overall performance. The success rate is the percentage of trials in which the robot reaches the goal without collisions with other humans while keeping contact with the patient. The timeout rate is the percentage of trials that require more than 100 timesteps for the robot to reach the goal. The collision rate is the percentage of trials where the robot collides with a human, and the contact loss rate is the percentage of trials where the robot moves away from the patient, and the contact is lost. The lower the contact loss rate, the better the model supports the patient throughout the trial. Finally, the navigation time is the time (in seconds) that the robot is required to reach the goal from the start.  
\begin{table}
\begin{tabular}{c} **Model V1** \\ \end{tabular} } \\ \hline
5 & 30 & **18** & NA & 18 & 0 & 0 & 70 & **82** & 12.58 & **10.13** \\ \hline \(10\) & 46 & **32** & NA & 12 & 0 & 0 & 54 & **68** & 13.06 & **11.93** \\ \hline \(17\) & 60 & **54** & NA & 10 & 0 & 0 & 40 & **46** & 15.05 & **12.07** \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison between the baseline model [43] and the proposed model V1 with FOV 70\({}^{\circ}\).  
Figure 6: RL simulation environment for the patient-robot co-navigation training.
# E-GTN: Advanced Terrain Sensing Framework for Enhancing Intelligent Decision Making of Excavators
## 3 Methods
### Terrain Information Processing  
#### 3.1.1 Raw Point Cloud Acquisition  
Given the raw point cloud data \(P\), we apply a voxel grid downsampling method. For each voxel \(V\) with edge length \(l\), the subset of points \(P_{V}\subseteq P\) is replaced with their centroid \(c_{V}\) to obtain the downsampled point cloud \(P^{\prime}\):  
\[c_{V}=\frac{1}{|P_{V}|}\sum_{p\in P_{V}}p,\quad P^{\prime}=\bigcup_{V}c_{V} \tag{1}\]  
where \(|P_{V}|\) denotes the number of points in voxel \(V\). The next step involves estimating the normal at each point in \(P^{\prime}\). Using a neighborhood defined by a radius search in a KD-tree structure [11], the normal vector \(\vec{n}_{p}\) at point \(p\) is computed as the eigenvector corresponding to the smallest eigenvalue of the covariance matrix constructed from the neighbors of \(p\).  
For feature description, we compute Fast Point Feature Histograms (FPFHs) [12] for each point \(p\) in \(P^{\prime}\) based on the normal vectors and the relative positions within a neighborhood \(\mathcal{N}(p)\):  
\[FPFH(p)=SPFH(p)+\frac{1}{k}\sum_{p_{k}\in\mathcal{N}(p)}\frac{1}{\omega_{pk}} SPFH(p_{k}) \tag{2}\]  
where \(SPFH(p)\) is the Simplified Point Feature Histogram for point \(p\), \(\omega_{pk}\) is the inverse Euclidean distance between \(p\) and \(p_{k}\), and \(k\) is the number of neighbors.  
To align the point clouds into a common coordinate frame, we first apply an initial rough alignment using feature matching. A RANSAC-based approach with _FPFH_ features estimates the transformation \((R_{i},t_{i})\) by maximizing the consensus set of matched features:  
\[(R_{i},t_{i})=\arg\max_{R,t}|\{(p,q)\in P^{\prime}\times Q^{\prime}:\|(Rp+t)-q \|<\epsilon\}| \tag{3}\]  
where \(Q^{\prime}\) is the downsampled point cloud from the other laser scanner and \(\epsilon\) is a threshold for matching.  
Subsequently, the Iterative Closest Point (ICP) algorithm [13] refines the transformation \((R,t)\) by minimizing the mean squared error between the corresponding points:  
\[(R^{*},t^{*})=\arg\min_{R,t}\;\sum_{(p,q)\in\mathcal{C}}\|(Rp+t)-q\|^{2} \tag{4}\]  
where \(\mathcal{C}\) is the set of corresponding point pairs between the registered point cloud \(P^{\prime}\) and the target cloud \(Q^{\prime}\), and \((R^{*},t^{*})\) is the optimized transformation. Through these steps, we ensure the integrity and computational efficiency of the data, leading to a high-fidelity reconstruction of the excavation site suitable for further simulation and analysis.
# Routing Protocols for Delay Tolerant Networks:
## 3 Performance Evaluation
### Evaluation Using Synthetic Mobility Patterns
#### 3.2.2 Single Technique Protocols  
We now examine how simple protocols can be improved using just one of the proposed techniques (see Figure 3). As such, the protocols analyzed in this section implement either QM or FW or R.  
Spray-and-Wait [36] implements the replication technique. It shows good performance apart from the delivery delay. The average overhead is bounded by the number of copies created in the sprayphase. Based on [36], the L parameter for Spray and Wait is recommended to be chosen for 10%-15% of the expected number of nodes. Therefore, given the network size, the node mobility patterns and application message generation rates, we set set \(L=4\), so that on average, three copies of the messages are forwarded in the spray phase. In the waitphase, those copies have to reach the destination in a direct delivery fashion. For this reason, the delay of Spray-and-Wait is comparable to that of Direct-Delivery; it is always smaller, because the multiple copies of the message in the network increase the probability of reaching the destination in less time.  
Figure 2: Synthetic traces. Random walk (**a,c,e**); Levy Walk (**b,d,f**). Simple protocols.  
PRoPHET [37] implements the forwarding technique. When the number of nodes increases, the forwarding technique has more candidates as possible effective relays among the contacts. This implies that the overhead increases (more relays imply more transmissions) and the delay decreases (more copies of the message increase the probability of reaching the destination). However, the effective management of the multiple copies of the message requires either a proper queue management technique or a big buffer size. In fact, for the small size of buffer considered, the delivery ratio of PRoPHET decreases due to message dropping.  
Fuzzy-Spray [25] implements the queue management technique. It is the best performer in terms of delivery delay, because (similarly to epidemic) there is an unlimited number of replicas in the network. QM tries to keep messages in the queue until there is a high confidence that they have been delivered already. As a consequence, even when the size of the queue is relatively small, the effects of buffer overflow and message dropping are less dramatic (note the differences with Epidemic). Indeed, the delivery ratio is always remarkably high, except for the highest density of nodes (70 nodes in Figure 3);  
Figure 3: Synthetic traces. Random walk (**a**,**c**,**e**); Levy Walk (**b**,**d**,**f**). Single technique protocols.  
this is because, in such a scenario, the QM policy employed by Fuzzy-Spray does not effectively handle all messages.
## 4 Methodology
### Data Encryption: Pre-Processing
#### 4.2.3 Model Quantization  
Concrete-ML quantizes the trained model, converting it into an integer-only format for inference. This quantization process is crucial for compatibility with FHE, ensuring that the encrypted data remain secure while allowing for accurate model predictions [43].  
\begin{table}
\begin{tabular}{c c c} \hline
**Sensor Type** & **Sensor ID** & **Sensor Location** \\ \hline M, LS & 001 & Entry \\ \hline M, LS & 002 & Kitchen \\ \hline M, LS & 003 & Kitchen \\ \hline M, LS & 004 & Dinning Area \\ \hline M, LS & 005 & Living Room \\ \hline M, LS & 006 & Kitchen \\ \hline M, LS & 007 & Kitchen \\ \hline M, LS & 008 & Living Room \\ \hline M, LS & 009 & Bedroom \\ \hline M, LS & 010 & Hallway \\ \hline M, LS & 011 & Bedroom \\ \hline M, LS & 012 & Bedroom \\ \hline MA, LS & 013 & Living Room \\ \hline MA, LS & 014 & Bedroom \\ \hline MA, LS & 015 & Toilet \\ \hline MA, LS & 016 & Kitchen \\ \hline D, T & 01 & Living \\ \hline D, T & 02 & Main Entry \\ \hline D, T & 03 & Toilet \\ \hline \end{tabular}
\end{table}
Table 3: Description of sensor placement in HH101 dataset.
# Collision Risk Evaluation and Verification of GNSS-Based Train Integrity Detection
###### Abstract  
To meet the demand for middle and low-density railway lines, a Global Navigation Satellite System (GNSS) based on a train integrity monitoring system (TIMS) is used for train integrity detection. Each system has to be analyzed before it is applied in practice. To evaluate the safety of the train integrity detection, a collision risk evaluation method is proposed based on the positioning errors and protection level, in which the Probability of dangerous Failure per Hour (PFH) is computed to quantify the the criteria of Safety Integrity Level (SIL). Then, an experiment-based simulation procedure is presented for safety verification. Statistics results have been obtained from field test data, and simulations are carried out using CPN and MATLAB to verify the collision risk of GNSS-based train integrity detection. The result showed that the GNSS-based train integrity detection satisfies the safety requirements in the system design phase for railway applications.  
Global Navigation Satellite System; train integrity; Colored Petri Net; safety verification +
Footnote †: journal: Applied  
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