esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Simulations of the Profile Reconstruction Using the Existing Method and the New Method
### The Existing Method Based on GTP Method  
The schematic of the existing method in [19] is shown in Figure 2, and, when we assumed the method is based on the GTP method to get a high lateral resolution, we summarize the reconstruction process of this method as follows. Different from the method shown in Section 2, this device consists of three displacement sensors as probes: two probes (\(P_{1}\) and \(P_{2}\)) are placed on one side of the stage, and the spacing between them is \(d\). \(P_{3}\) is placed on the other side of the stage and opposite to \(P_{1}\). Considering the straightness error \(e_{y}(x_{i})\), similar to Equations (1)-(4), the outputs of probes at each measurement position can be expressed as:  
\[m_{1}(x_{i})=f(x_{i})-e_{y}(x_{i}), \tag{9}\]  
\[m_{2}(x_{i})=f(x_{i}+d)-e_{y}(x_{i}), \tag{10}\]  
\[m_{3}(x_{i})=-[g(x_{i})-e_{y}(x_{i})]. \tag{11}\]  
According to the two-point method, the reconstruction profiles can be calculated by:  
\[mf(x_{i})=\frac{1}{d}[m_{2}(x_{i})-m_{1}(x_{i})]=f^{\prime}(x_{i}), \tag{12}\]  
\[Y_{f}(x_{i})=mf(x_{i-1})\cdot s+Y_{f}(x_{i-1}), \tag{13}\]  
\[Y_{g}(x_{i})=Y_{f}(x_{i})-[m_{1}(x_{i})+m_{3}(x_{i})]. \tag{14}\]  
During many processes of measurement and reconstruction in practical applications, \(s\) is always reduced to increase the density of sampling points and obtain a high lateral resolution, which makes  
Figure 1: The four-probe system for reconstruction of a pair of profiles: (**a**) the simulation diagram; and (**b**) the schematic.  
it satisfy \(s<d\) (the GTP method). It is known that the result of the GTP method suffers from data processing error, which means high-order harmonic component distortion exists (the frequency components with periods equal to the probe spacing and its harmonics are lost) and the reconstruction profiles will be smoothed compared with the actual profiles [3]. When there are stepwise variations, this type of error is more harmful.
## 7 Discussion
### Genesis of Cu-Sn Mineralization  
A cassiterite-chalcopyrite-quartz-arsenopyrite high-temperature mineral assemblage appears in the deep ore of the Shuangjianzishan mining area, which contradicts the previous understanding that the Shuangjianzishan deposit only has a medium-low-temperature mineralization stage [9]. Previous studies have shown that magmatic cassiterite is rich in Nb and Ta, but relatively poor in Fe and Mn, while hydrothermal cassiterite is the opposite [44]. In this study, cassiterite is richer in Fe and Mn, but relatively poor in Nb and Ta, indicating that it was formed in a relatively high-temperature hydrothermal environment, and tin mineralization belongs to the cassiterite-sulfide type. Most tin mineralization in the world is considered related to hydrothermal processes related to highly differentiated and relatively reduced granite bodies [45; 46]. Cassiterite related to granite is rich in Nb and Ta, and the  
Figure 10: W-Fe diagram of cassiterite in the Shuangjianzishan deposit (After [39]).  
Nb + Ta content is generally higher than 1%, while cassiterite related to sedimentary or metamorphic hydrothermal solution has a low content of high-field elements [44]. The cassiterite in the Shuangjianzishan deposit in this study has a low content of high-field-strength elements (Nb is 1.02 \(\times\) 10\({}^{-6}\), Ta is 0.02 \(\times\) 10\({}^{-6}\)). It is rich in Fe (7405.76 \(\times\) 10\({}^{-6}\)) and W (1193.93 \(\times\) 10\({}^{-6}\)), which is different from typical magmatic hydrothermal deposits and SEDEX/VHMS-type deposits. This shows that the enrichment degree of W, Nb, Ta, Zr, Hf, and other elements in cassiterite is not only related to the source, but also influenced by many factors such as temperature, pH, pressure, and oxygen fugacity, and the source cannot be simply judged by the content of trace elements [32]. [PERSON] [47] thinks that the Co/Ni ratio of chalcopyrite in Cu-Ni sulfide deposits is less than 1, while the Co/Ni ratio of chalcopyrite in porphyry, skarn, and epithermal deposits in hydrothermal deposits is more than 1. Because chalcopyrite contains Ni-rich micro-inclusion minerals in this test, the Ni content is not representative, but according to the data of trace elements of chalcopyrite in the Shuangjianzishan deposit [37], it conforms to the characteristics of chalcopyrite in hydrothermal deposits. The enrichment of high-temperature elements such as Sn, Se, and W in cassiterite and chalcopyrite indicates that the Shuangjianzishan deposit has a high-temperature metallogenic stage of Sn-Cu, which is not a simple medium-low-temperature magmatic hydrothermal deposit.
# RSSI-Based MAC-Layer Spoofing Detection: Deep Learning Approach +
## 4 Detection Approach: Deep Authentication
### LSTM Autoencoder Anomaly Detector  
In the context of our work, let \(X=<x_{1},x_{2},\ldots,x_{n}>\) denote an ordered sequence of n RSSI values received by node s. Then, the LSTM autoencoder is trained to learn two functions, namely, encoder \(E(.)\) and decoder \(D(.)\) such that \(X\approx D(E(X))\). In other words, as depicted in Figure 3, the LSTM autoencoder learns an encoding state that best describes the structure of the training/input data and a decoding function that reconstruct the input sequence given the encoding state with minimal error. In general, large reconstruction errors occur when the input does not conform to the structure previously learned by the LSTM autoencoder. As such, a large reconstruction error can be used as a measure of input anomaly [16, 18, 19, 20].  
In order to build an RSSI profile of \(s\) (through the use of LSTM autoencoder), the receiving node \(r\) begins the process of collecting and assembling a time-series of RSSI values extracted from the data frames transmitted by \(s\). Then, using a _rolling_ window of size \(n\), the time-series is segmented into m different overlapping sequences (where the extent of the overlap is controlled by the shift constant of the rolling window), which are further used to train the LSTM autoencoder. Since the LSTM autoencoder is supposed to learn the reconstruction of the input sequences, the m training inputs are also supplied as the expected outputs to the training algorithm with the _mean squared error_ (MSE) as the loss function.  
At runtime (i.e., during the actual use of the trained LSTM autoencoder for the purpose of attack/anomaly detection), \(n\) most recently observed RSSI samples are supplied into the trained LSTM autoencoder and then the MSE of the reconstructed sequence (relative to the provided input) is computed. Our experimental investigations (as described in Section 5) have demonstrated that the MSEs of the training data, in the absence of attack/spoofed instances, form a normal distribution. Therefore, our system uses Z-score to measure deviation from the expected MSE as the decision function to differentiate between the spoofed and the normal traffic. Specifically, for a Z-score \(\geq l\) the system declares the inspected RSSI window as malicious, where \(l\) can be computed experimentally and set for the desired _false positive rate_.
## 2 Materials and Methods  
For the case study, three types of aggregates commonly used in road pavement were selected: (i) Criggion green basalt coming from Northern Scotland (ID: R); (ii) kinginzite obtained from a stone quarry located in Maida-CZ, Italy (ID: K); (iii) Grey limestone from Amenta-CS, Italy (ID: C) as shown in Figure 1. Moreover, EAF slags coming from the \"Ferriere Nord--Gruppo Pittini\" steelmaking industry (Potenza, Italy) were analyzed (identified as ID: A sample in the following). Main mechanical properties of all aggregates are shown in Table 2.  
Figure 1: Selected aggregates: (**a**) Basalt (ID: R); (**b**) EAF slags (ID: A); (**c**) kinginzite (ID: K); (**d**) limestone (ID: C).  
The first step of the experimental plan was the selection of aggregates with a specific stone dimension (7.2-10 mm), according to the UNI EN 1097/8 2001. Standardized specimens (90.6 mm \(\times\) 44.5 mm \(\times\) 12.5 mm) were prepared using a metallic mold. The granules were manually placed over the mold surface in a single layer in order to have from 36 to 46 granules for each specimen, placing the flat surface of each stone externally. Finally, the mold was filled with a bicomponent fiberglass and closed with a shaped cover in order to obtain rigid and non-deformable samples once hardened. Three specimens for each kind of aggregate were realized for a total of 12 samples.  
Two longitudinal and three crosswise sections were identified on each specimen by means of a preset mask to carry out profilometric analysis of stone surface texture by means of a laser profilometer based on conoscopic olography before and after polishing (see Figures 2 and 3). Profiles were traced by using a lens with an objective focal length of 50 mm and a sampling resolution of 5 \(\upmu\)mm (20 points per millimeter on each profile) [26; 27]. This procedure was carried out in order to focus the investigation on PSV tire/test specimen interface (PSV, polished stone value according to the UNI EN 1097/8 2001, see Section 2.2).  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Test** & **Standards** & **Basalt** & **EAF Slags** & **Kinginzite** & **Limestone** \\  & & **(ID: R)** & **(ID: A)** & **(ID: K)** & **(ID: C)** \\ \hline Los Angeles & UNIEN 1097-2 & 14 & 15 & 23 & 22 \\ Micro Deval & UNIEN 1097-1 & 9 & 10 & 19 & 16 \\ \hline \end{tabular}
\end{table}
Table 2: Main mechanical properties of aggregates.  
Figure 2: Specimen equipped with preset mask by profilometric alignments (longitudinal A-A, B-B; and crosswise C-C, D-D, E-E) for surface texture investigation.
# TARS: A Novel Mechanism for Truly Autonomous Resource Selection in LTE-V2V Mode 4
## 4 System Model Assumptions and Proposed Mechanism
### TARS Mechanism  
Truly Autonomous Resource Selection (TARS) has an iterative process including (a) RB selection, (b) counter selection, (c) the transmission of the message, and (d) RB reselection, as depicted in Figure 8.  
The following steps are involved in TARS.
## 2 Principles and Methods
### Principles of Optimized Random Forest Algorithm
#### 2.2.2 Optimized Strategies
Genetic Operations and Iterations: Perform crossover operations on the selected excellent parameter combinations to generate new potential excellent parameter combinations. Optionally introduce mutation operations to increase population diversity and prevent local optima. Form a new generation population with the newly generated parameter combinations and the excellent individuals from the parent generation. Repeat evaluation, selection, and genetic operations until the preset maximum number of iterations or until the fitness improvement threshold is met. Step 4. Output Results: Select the parameter combination with the highest fitness (highest accuracy) from the final population. Output this optimal parameter set as the best configuration for the random forest algorithm. Identification Process of Bolt Loosening Damage in Steel Truss Structures Utilizing MFCC-WPES and Optimized Random Forest  
The structural diagram of the steel truss bolt loosening damage identification method proposed in this article is shown in Figure 2. Firstly, the sound signals collected under the set working conditions are subjected to preprocessing through framing, windowing, and feature extraction. Mel Frequency Cepstral Coefficient (MFCC) and Wavelet Packet Energy Spectrum (WPES) features are extracted from the signals. Subsequently, the MFCC and WPES features are jointly used to construct the MFCC-WPES combined feature training dataset. The obtained combined features are then utilized to train a Random Forest (RF) classifier for sensitive feature selection and damage signal identification. Utilizing random forest for feature selection generally involves two main processes: feature importance evaluation and feature elimination. After obtaining the importance of each feature and arranging them in descending order, a certain proportion of redundant features are eliminated to obtain a new feature set. This new set is then inputted into the random forest model for training. Concurrently, a genetic algorithm is employed to optimize the parameters \(n\), \(d\), \(s\), and \(m\) of the random forest model; this optimization aims to find the best parameter combination, ultimately resulting in the optimal recognition model. To enhance the efficiency of model testing, a preliminary assessment is conducted using short-time energy. Signal frames with short-time energy greater than a predetermined threshold T are selected for analysis. These selected frames are then inputted into the random forest model for testing. This approach ultimately achieves the goal of quickly identifying the sound signals indicating loose bolt damage. Figure 2: Structural diagram of bolt loosening identification method for steel truss structure.
## 1 Introduction
We demonstrate that by using machine learning techniques and by simultaneously looking at the intensity distribution of the transmission eigenchannels and transmitted speckle-like intensity distribution, it is possible to classify transmission eigenchannels. Spatial profiles of the transmission eigenchannels of disordered systems depend on scattering strength, which dictates the energy density distribution inside the medium. The ability to classify spatial profiles is a viable technique to determine the strength and type of disorder in the system, which is of great importance for numerous applications ranging from random lasing [16] and biomedical sensing [17], white LEDs [18], to photovoltaics [19]. In addition, using transmitted speckle-like intensity distributions to perform classification of disorder strength using machine learning techniques can lead to advancements in speckle-based metrology. While efforts in this direction have been previously investigated, such as looking at speckle contrast and speckle statistics [20; 21], this is, to the best of our knowledge, the first time that machine learning techniques have been utilized to perform classification and identification of disorder strength using the spatial distribution of energy densities. Finally, we envision that our approach to machine learning-based classification could be generalized to other planar photonic systems. For example, metasurfaces are particularly promising, as the field distribution inside the meta-atoms that constitute the metasurface can also be expressed as a sum of multipoles, where the weights of these multipoles can be used as the input vector when performing classification [22].
# Silicon Waveguide Optical Isolator with Directly Bonded Magneto-Optical Garnet  
[PERSON]  
1 Laboratory for Future Interdisciplinary Research of Science and Technology (FIRST), Tokyo Institute of Technology,  
realized yet. By use of seed layer or post annealing process, depositions of poly-crystalline Ce:YIG or terbium iron garnet (TIG) on semiconductor substrates have been investigated continuously [10; 11].  
MO isolators had been realized with direct bonding techniques. Ce:YIG dies were successfully bonded on silicon or compound semiconductor waveguide by surface-activating process. It enabled us to use an MO upper cladding layer of single-crystalline garnet with negligibly thin interlayer, which resulted in an effective MO interaction with propagating light. A nonreciprocal phase shift (NPS) was a direction-dependent propagation constant because of the first-order MO effect induced in the transverse magnetic (TM) mode. A Mach-Zehnder interferometer (MZI) or a ring resonator with the MO cladding layer allowed a nonreciprocal optical propagation. A ring resonator enabled small footprint, whereas its narrow operating wavelength was sensitive to temperature and fabrication errors. [PERSON] et al. and [PERSON] et al. formed a planar spiral electromagnet on a ring resonator, which provided external magnetic field for the MO effect as well as controlled the device temperature by Joule heating [12; 13]. An MZI configuration was relatively robust for temperature and fabrication errors as long as they were balanced between the two arms. Therefore, the operation bandwidth could be wider.  
[PERSON] et al. firstly reported a Si MZI-based isolator in 2008 [14]. After that, they have demonstrated MZI-based isolators with an MO upper cladding layer having higher isolation, wider bandwidth, and temperature-insensitive operations [15; 16; 17]. In this article, we present an overview of the progress of MZI-based silicon waveguide isolator. The operating polarization is TM mode because NPS is induced in the TM mode for layered waveguide structures. We then present an integration of transverse electric (TE)-TM mode converter [18]. Finally, we discuss the possibility of low insertion loss.
# A Comprehensive Review of In-Body Biomedical Antennas: Design, Challenges and Applications
## 5 In-Body Antenna Applications
### Glucose Monitoring and Sensing  
In Ref. [99], a miniaturized single-fed wide-beamwidth circularly polarized implantable antenna working in the ISM band (2.40-2.48 GHz) is presented for subcutaneous real-time glucose level monitoring application. Figure 17 shows the proposed antenna with the dimensions of 8.5 mm \(\times\) 8.5 mm \(\times\) 1.27 mm employing four C-shaped slots and a complementary split-ring resonator (CSRR). Meanwhile, by adjusting the slits of the CSRR, circular polarization is realized. In this work, Rogers 3210 is selected as the substrate with \(\varepsilon_{r}=10.2\) and tan \(\delta=0.003\). The simulation results with a three-layer phantom demonstrate that the impedance bandwidth is 12.2%, with a peak gain of \(-17\) dBi.  
[PERSON] et al. in Ref. [100] presented a spiral antenna for wireless power and data telemetry for an injectable glucose sensing device. The size of the antenna is 3 mm \(\times\) 0.6 mm. It is operating at the 900 MHz ISM band, with the peak power gain of less than \(-30\) dB. The power transfer efficiency of the inductive link is 0.1%. The antenna is built on a silicon substrate through a photolithography process, as shown in Figure 18.
# Reducing the Uncertainty of Lidar Measurements in Complex Terrain Using a Linear Model Approach
## 5 Conclusions  
In this study, different methods for lidar-based wind field reconstruction in complex terrain were investigated. The main objective was to reduce uncertainties in the measurements by using optimized methods for wind field reconstruction. As a side product, the influence of the opening angle and the scanning duration could also be investigated. Two different methods were investigated and these were additionally coupled with the statistical _PRESS_ procedure. The result of the _CLS_ method in combination with _PRESS_ shows a very skewed distribution of high values of \(R^{2}\) based on the 10 min data, but this is not reflected in the correlation comparison with the in-situ measurements at the met mast. The results of the correlation are worse than the results of the _CLS_ method without _PRESS_ statistics. It could be shown that with the _linear_ model for wind field reconstruction the agreement with the measurements of the in-situ sensors can reach the level of measurements with homogeneous flow. Furthermore, the range of the 5-95 th quantile could be reduced by 32.2% on average compared to the reference VAD method. Furthermore, the influence of the opening angle on the measurement results could be shown. The agreement with the mast measurements and the width of the 5-95 th quantile was reduced with larger half opening angles \(\phi\). The influence of the scan duration on the uncertainties reduction could not be verified with the current data set and measurement set up. In future measurement campaigns with complex flow, the _linear_ model with an optimized trajectory should be used. However, this naturally also requires an appropriate lidar system in which the azimuth and elevation angle can be adjusted because for the application of the _linear_ model, three different half-opening angles \(\phi\) are necessary. This is currently not possible with the standard lidar wind profilers and it requires so-called all sky scanning devices.  
[PERSON] and [PERSON] conceived and designed the experiments; [PERSON] performed the experiments; [PERSON] and [PERSON] analyzed the data; [PERSON] wrote the manuscript; [PERSON] and [PERSON] read, reviewed, and approved the final manuscript and provided valuable input.  
This research was funded by by the German Federal Ministry for Economic Affairs and Energy (BMWi) grant number [0325519].  
The authors would like to thanks all the people who contributed to the measurement campaigns.  
The authors declare no conflict of interest.
# Projected Augmented Reality Intelligent Model of a City Area with Path Optimization
## 2 Model Proposed
### Use of GA and A* for Solving the Travel Salesman Problem  
The Traveling Salesman Problem (TSP) [18] was first introduced by [PERSON] in the 19 th Century, and it remains one of the classical problems in Artificial Intelligence.  
The goal in the problem is to find an optimal solution for a supposed salesman who needs to visit a number of different points of interest, starting and ending at a given point. An optimal solution will be a path that contains all the points of interest, in an optimal order, minimizing the total distance traveled. It is a combinatorial optimization problem belonging to the class of NP-complete problems.  
For a small number of points, the TSP problem can be resolved as a search problem, applying a search algorithm that tries all possible combinations and returns the path with the lowest cost (smallest distance to travel). As the number of places to visit grows, the search space becomes very large and the search tree grows exponentially. For the problem of visiting \(n\) points of interest, returning to the starting point, the search space has \(n!\) solutions. Another approach to solve the TSP problem, without using large search trees, is using adaptive algorithms such as Particle Swarm Optimization or Genetic Algorithms. The present research uses a GA.
# A QUBO Model for the Traveling Salesman Problem with Time Windows
## 3 The Classical Formulation of the TSPTW
### Small Scale Examples for the TSPTW
#### 3.1.1 A Four-Node Example  
Figure 1 is the graphical depiction of the network \(G_{1}\) consisting of the four nodes \(0,1,2\) and \(3\) together with the arcs that connect them. According to our convention, node \(0\) is the depot.  
Table 2 gives the quantitative characteristics of the above example: the X and Y coordinates of every node, as well as the Ready Time and the Due Date.  
Table 3 contains the cost \(c_{u,v}\) corresponding to each pair \((u,v)\) of nodes. This cost is taken to reflect the distance between the nodes, which is calculated using the Euclidean distance, given by the well-known formula: \(d(u,v)=\sqrt{(x_{2}-x_{1})^{2}+(y_{2}-y_{1})^{2}}\), where \(u=(x_{1},y_{1})\) and \(v=(x_{2},y_{2})\).  
A feasible solution for this particular TSPTW is given in Table 4. One can see that, if all time windows are respected in every step of a tour going from one customer to the next, a feasible solution can be constructed. Although this is a small scale example, we expect that it will enhance one's understanding of what is a feasible solution for the TSPTW.