esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Low-Dimensional Reconciliation
### Achieving the Uniform Distribution
#### 3.2.1 Gaussian Cumulative Distribution Function  
On [PERSON]'s and [PERSON]'s side, the Gaussian CDF function can be used to reach the uniform distribution of the correlated raw data. Since we assumed reverse reconciliation, let us to start the description from [PERSON]'s perspective. Let [PERSON]'s raw data unit \(X^{\prime}_{j,i}\) with Gaussian random distribution \(\mathbb{N}\big{(}0,\sigma_{X^{\prime}}^{2}\big{)}\). The Gaussian CDF-transformation \(C(\cdot):\mathbb{R}\rightarrow\mathbb{R}\) for a unit \(X^{\prime}_{j,i}\) is as follows:  
\[C\big{(}X^{\prime}_{j,i}\big{)}=\frac{1}{2}\left(1+erf\left(\frac{X^{\prime}_{ j,i}}{\sqrt{2\sigma_{X^{\prime}}^{2}}}\right)\right),\,\text{for}\,\,i\in[d], \tag{37}\]where  
\[erf\left(\frac{X^{\prime}{}_{j,i}}{\sqrt{2\sigma_{X^{\prime}}^{2}}}\right)=\frac{2 }{\sqrt{\pi}}\int_{0}^{X^{\prime}{}_{j,i}}\sqrt{\
icefrac{{2\sigma_{X^{\prime}}^{ 2}}}{{2\sigma_{X^{\prime}}^{2}}}}\ e^{-t^{2}}dt \tag{38}\]  
is the Gauss error function, and \(C\big{(}X^{\prime}{}_{j,i}\big{)}\in\mathbb{R}\) is a real variable from the range of \([0,1]\), with \(\mathcal{U}\) uniform distribution (for a plausible example Section 5). The quantity \(C\big{(}X^{\prime}{}_{j,i}\big{)}\) will be referred as the _CDF_-transformed unit.  
[PERSON] also applies the CDF transformation, and takes into account her raw data variance \(\sigma_{X}^{2}\) for the units of \(X_{j,i}\) to get \(C\big{(}X_{j,i}\big{)}\):  
\[C\big{(}X_{j,i}\big{)}=\frac{1}{2}\left(1+erf\left(\frac{X_{j,i}}{\sqrt{2 \sigma_{X}^{2}}}\right)\right)\text{, for }i\in[d], \tag{39}\]  
and the result of (37) and (39) is the correlated uniform raw data \(C\big{(}X_{j,i}\big{)}\approx C\big{(}X^{\prime}{}_{j,i}\big{)}\). In the reconciliation process, only [PERSON] can correct \(U^{\prime}{}_{j}\) into \(U_{j}\), because nobody knows the CDF-transformed raw data units \(C\big{(}X_{j,i}\big{)}\), except [PERSON].  
For a given \(\mathbf{X}_{j}\in\mathbb{R}^{d}\), the CDF function \(C(\cdot):\mathbb{R}\rightarrow\mathbb{R}\) reads as  
\[C\big{(}\mathbf{X}_{j}\big{)}=C\big{(}X_{j,0}\big{)},\ldots,C\big{(}X_{j,d-1} \big{)}=\frac{1}{2}\left(1+erf\left(\frac{X_{j,i}}{\sqrt{2\sigma^{2}}}\right) \right)\in\mathbb{R},\text{ for }i\in[d], \tag{40}\]  
Applying the results for [PERSON]'s raw data the CDF-transformed vector is:  
\[C\big{(}\mathbf{X}^{\prime}{}_{j}\big{)}=C\big{(}X^{\prime}{}_{j,0}\big{)}, \ldots,C\Big{(}X^{\prime}{}_{j,d-1}\big{)}=\frac{1}{2}\left(1+erf\left(\frac{ X^{\prime}{}_{j,i}}{\sqrt{2\sigma^{2}}}\right)\right)\in\mathbb{R},\text{ for }i\in[d]. \tag{41}\]  
The CDF-transformed \(C\big{(}\mathbf{X}_{j}\big{)}\), \(C\big{(}\mathbf{X}^{\prime}{}_{j}\big{)}\) raw data vectors each consist of \(d\) real \(\mathbb{R}\) variables as:  
\[C\big{(}\mathbf{X}_{j}\big{)}=\Big{(}C\big{(}X_{j,0}\big{)},\ldots,C\Big{(}X_{ j,d-1}\big{)}\Big{)}^{\text{T}},C\big{(}\mathbf{X}^{\prime}{}_{j}\big{)}= \Big{(}C\big{(}X^{\prime}{}_{j0}\big{)},\ldots,C\Big{(}X^{\prime}{}_{j,d-1} \big{)}\Big{)}^{\text{T}}. \tag{42}\]
## 2 UWA Communication by Generalized Sinusoidal Frequency Modulation  
The GSFM, which has a generalized form of SFM, is suggested for robust UWA communication. The GSFM waveform has much-lower-range sidelobes and maintains the mainlobe width without using the tapering function in the ACF compared with the LFM waveform.
## 3 Our LSP Sensor
### Water Effect on the Sensor  
In the recent past, [PERSON] et al. group demonstrated that aligning water droplets in a pattern on a substrate resulted in a metamaterial absorber [117]. It is not uncommon to study water pattern, but we are interested in studying the influence of water droplets on the sensor rather than creating a new metamaterial design. Thus, we studied the water effect by dropping (a) a single 4 mL water droplet on the sensor in a random location, and (b) several water droplets in a pattern labelled from 1 to 9, as shown in Figure 5. The spiral LSP design was modified by replacing base substrate with a heavier Rogers RT5880 material of 254 um thick, similar to the substrate of the radial LSP sensor.  
Figure 5a shows the new spiral sensor placed in a small glass container, with a single water droplet on it. Figure 5b,c show the final photos of sensor with nine water droplets, which were dropped one after another on it. A baseline signal was initially obtained before the start of the experiments.  
In the first experiment, a water droplet was randomly dropped on the sensor and an output signal was obtained. Later the water droplet was wiped off using soft cotton before dropping another water droplet at another location. The process continued for eight more times to obtain eight more output signals. Figure 5d shows the RMSD curve representing the single droplet effect on the sensor at different random locations. It was observed that no two locations resulted in the same output signal. In the next experiment, water droplets were introduced one after another on the sensor in the order as labeled in Figure 5c. Each time, an output signal was obtained at increased water content on the sensor. Figure 5e shows a continuous RMSD variation due to increase in water content. The output signals obtained during increase in water content are different from the output signals obtained at random locations. Thus, it can be concluded from both experiments that the sensor is smart and works in the presence of rain or humid atmosphere unlike the other smart materials, especially  
Figure 5: Spiral-LSP sensor and water effect (**a**) single water droplet on the sensor; (**b**) nine water droplets, dropped one after another; (**c**) order of dropping water droplets on the sensor; (**d**) RMSD variations when water droplets increase on the sensor; and (**e**) RMSD variations for water droplets at random locations.  
the piezoelectric type sensors such as MFCs, piezoelectric diaphragms, PZT get short circuited and damaged in wet environments.
# The Impact of an Electric Machine Body on EM Wave Propagation in RTMS
## 3 Modeling Electromagnetic Propagation in a Rotating Machine  
### Modeling and Structure  
In this section, we will focus on modeling the asynchronous machine using the finite element method in the Ansys HFSS environment. Due to its high performance, Ansys HFSS allows for more precise analysis in motor design and modeling. Designing the electric machine model requires not only knowledge of its structure but also the specifications of the constituent materials and various geometric characteristics.  
Communication between the transmitter and receiver is complex due to several factors that increase the complexity of the radio link budget and degrade the overall transmission quality. Transmission within a machine is influenced by these main components:  
* [noitemsep,topsep=0 pt]
* Rotor: The metal part of the machine.
* Stator: A complex composite made of various materials that differ widely between models or manufacturers. The associated electrical information is difficult to access and study.
* Flange: A steel component fixed to the stator with bolts or rods.  
To examine the maximum power transfer between two monopole antennas--one inside the electrical machine and the other outside--we will develop the expression for the propagation factor, which is inversely proportional to propagation loss.  
To study the influence of electrical machine components on electromagnetic propagation, modeling the electrical machine is essential. This requires knowledge of its structure and the specifications of its materials and geometric characteristics [29; 30]. The HFSS modeling must consider the same material specifications and geometric characteristics used with Maxwell 2D [31].  
The model includes all elements of the machine, such as a steel shaft, an aluminum rotor, a copper layer, a ferromagnetic stator layer, an aluminum flange, and steel lamination. The shaft has a diameter of 21.6 mm and a length of 285 mm; the rotor has a diameter of 74.5 mm and a length of 79.22 mm; the stator has a diameter of 126 mm and a length of 69 mm; and the flange has a diameter of 135.7 mm and a length of 213 mm. The antenna is placed near the rotor location.  
The simplified HFSS simulation model of the electrical machine is shown in Figure 2. Modeling this configuration took approximately 3 h to simulate.
# Post-Quantum Digital Signature: Verkle-Based HORST
## 9 Verkle Tree
### Algorithm for Verkle-Based HORST
Verkle-based HORST is more feasible for resource-constrained contexts, such IoT devices, because the shorter authentication channels also require less storage. Lastly, because Verkle-based HORST only uses hash functions and vector commitments--both of which are impervious to quantum attacks--it maintains the post-quantum security of HORST. Though it has advantages, Verkle-based HORST adds complexity from vector commitments, thus complicating implementation and assessment. Its implementation in practical systems may be slowed down by its complexity. The practical implementation of Verkle-based HORST may be hampered by the limited usage of Verkle trees, a relatively new cryptographic primitive. Efficiency is boosted with Verkle-based HORST, but there may be other trade-offs, including a higher computational cost for creating vector commitments. When selecting Verkle-based HORST for particular applications, these trade-offs need to be carefully taken into account. Verkle-based HORST, with its smaller signatures, quicker verification, and scalability is a potential advancement over conventional HORST. It is a post-quantum secure signature technique with security predicated on the binding property of vector commitments and the collision resistance of hash functions. Although it adds some complexity, its efficiency improvements make it a solid contender for applications like blockchain, the Internet of Things, and secure communication protocols that demand scalable and small digital signatures.
# Coexistence in Wireless Networks: Challenges and Opportunities
## 4 Spectrum Sharing and Coexistence
### Coexistence in Radar Networks
A gradient projection method is used to address the power reduction problem while considering the effect of BS interference on MIMO radar systems. Additionally, the technique employs a strong power minimization strategy to deal with faulty channel information. The outcomes demonstrate that the suggested strategy preserves performance-complexity balance while saving energy costs.
# Improving Spatial-Temporal Data Fusion by Choosing Optimal Input Image Pairs
## Study Area and Data
### Satellite Data and Preprocessing
#### 2.2.3 NASS-CDL Data  
The Crop Land Data Layer (CDL) provides a crop-specific landcover data layer at 30 m spatial resolution (downloaded from [39]). The CDL is produced annually over the continental United States by the USDA National Agricultural Statistics Service (NASS) [31]. We used the CDL to evaluate data fusion results for different crop and land-cover types. Based on CDL data (Figure 1b), the landcover was ~55% corn and ~35% soybean over site 1. The remaining land cover types were grass, forest, and water. For site 2, deciduous forest was ~46% by area, evergreen forest was ~29% and mixed forest was ~8% in 2015. There were also small percentages of water, grass, and shrubland in site 2.
## 4 Materials and Methods
### Decomposition of the HRV Signals
#### 4.2.3 WPD  
The WPD method is an extension of the DWT method with the advantage of providing a more precise decomposition of the high-frequency components of the given signal [52]. It differs from the DWT method in the sense that the decomposition of both the approximation and the detail coefficients takes place at each level of WPD, which contrasts with the DWT method where only the approximation coefficient is decomposed. The decomposition process in WPD segregates the time-frequency plane into rectangles having a constant aspect ratio [19]. In other words, as the level of decomposition is increased, the side of the rectangle representing the time axis becomes broader, and the side representing the frequency axis becomes narrower. In this study, the decomposition of the HRV signals using WPD was performed with the help of MATLAB software (2015a, MathWorks Inc., Natick, MA, USA).
# High-Speed VCSEL-Based Transceiver for 200 GbE Short-Reach Intra-Datacenter Optical Interconnects
## 3 Experimental Results and Discussion  
Results from the two different experimental processes are discussed thoroughly in the following subsections. Moreover, overshoots can be observed in the case of 40 Gbaud, which derived from the VCSEL's bandwidth limitations.
## 2 Materials and Methods
### Optical Fiber Array  
Segments of optical fibers, each about 5 m long, were prepared in advance. The ends of the optical fibers were cut at a right angle, and the quality of the cut and the integrity of the fibers were checked. The optical fibers were placed on a special ray that protected the fibers from possible damage, and they were delivered to the test site. The specimen was instrumented with twelve optical fibers. The optical fibers were bonded to the specimen over its perimeter at several (six) equidistant horizontal planes. A horizontal position for the optical fibers was chosen because cracks in masonry stacks subjected to a compression  
Figure 3: Brick masonry specimen instrumented with the optical fibers: (**a**) schematic; (**b**) photograph; (**c**) an enlarged view of the points of connection of optical fibers to the specimen (photos). (1) Brick masonry specimen; (2) segments of optical fibers bonded to masonry that serve as crack detectors (not visible under opaque epoxy adhesive); (3) RC plate; (4) ray with free ends of the optical fibers; (5) free ends of the optical fibers; (6) points of connection of the optical fibers to the masonry specimen.  
load arise predominantly in the vertical direction. Two optical fibers were bonded near each other in each of the six planes of the specimen at about a 70 mm distance, as shown schematically in Figure 3a, using a two-component industrial-grade epoxy adhesive.  
The optical transmission of all 12 fibers was verified after bonding and again after 24 h. The aim was to identify possible damage to the optical fibers that could occur during bonding to the brick specimen or could be caused by the volumetric shrinkage of the adhesive due to its polymerization. The check was carried out with a common LED lamp. Each optical fiber was illuminated at one end, and the light coming out of the other end was observed. The inspection showed that all the fibers transmitted light normally; that is, none of the fibers were damaged during the process of bonding and polymerization shrinkage.  
Figure 4: Brick masonry specimen: (**a**) instrumented with the optical fibers and LVDTs and installed in the test frame of the load machine; (**b**) the specimen front face (A); (**c**) the specimen back face (C). (1) Universal load machine; (2) brick masonry specimen; (3) opto-electronic monitoring system; (4) horizontal LVDT; (5) vertical LVDT.
# Building Realistic Mobility Models for Mobile Ad Hoc Networks
## 4 Manet Routing Protocol Evaluation
It can be affected by the size of the network in terms of physical distance and hop count but is also significantly influenced by the way the routing protocols handle data packets. A protocol that needs to rediscover routes frequently is likely to delay packet transmission, leading to longer end-to-end delays. Throughout is a measure of the number of data packets traversing the network in a given time. The underlying network's data rate could influence this, but it is also subject to the speed with which the packets are forwarded. This itself is governed by the routing protocol in use, the processing power available for routing, the amount of traffic, and hence, the length of queues. The total number of collisions within a simulation run provides an indication of the volume of a network's load. Due to the fact that both data and routing packets are carried on the same lower layers of the network, a routing protocol that generates large numbers of control packets will put a significant load on the underlying network layers. This leads to an increase in collisions and hence, an increase in data packet loss and a drop in data packet delivery ratio. The average hop count indicates that the average number of nodes through which a data packet travels before reaching its destination. This is dependent on the physical size of the network, the transmission range of the nodes, and the node density. When each intermediate node forwards a packet, it increases the network power consumption, and this could be critical in situations where the nodes have limited power (e.g., handheld devices). A larger hop count will also result in an increase in end-to-end delay, as data is buffered and routed at each node. The output from genstats11 also includes fixed data, such as the number of nodes in the simulation and the number of data flows that occur during the simulation run time. A subset of these metrics is commonly reported in the literature. Examples extracted from this literature survey are as follows ([PERSON] et al., 1998; [PERSON] et al., 2002, [77]; [PERSON] et al., 2006, [154]; [PERSON] et al., 2011, [155]; [PERSON] et al., 2012, [22]; [PERSON] and [PERSON], 2013, [156]; and [PERSON] et al., 2013, [157]): NRL; hop count; average delay; data packet load; packets transmitted; dropped packet count; collisions; packet layer metrics (PDR, throughput, and round trip time); and physical/MAC layer metrics (signal-to-noise ratio and bit error rate).
# Image Anomaly Detection Using Normal Data Only by Latent Space Resampling
## 4 Experiment
### Experimental Setup
#### 4.3.1 Data Augmentation  
To diversify the training set to make the model more generalizable, we use random transforms and rotations on the MVTec AD dataset to augment the training data. Specifically, applying random rotation selected from a set \(\{0^{\circ},90^{\circ},180^{\circ},270^{\circ}\}\) and random flip to texture categories and some object categories (bottle, hazelnut, metal nut, and screw). The remaining object categories are randomly rotated between the range \([-10^{\circ},10^{\circ}]\). Finally, all categories are rescaled to \(256\times 256\), where texture categories are achieved by random cropping.
## 4 Two Improved Decision-Level Fusion Criterions for MUD  
### The Problems in O-DFC and MV-DFC  
Although two decision-level fusion criterions (O-DFC and MV-DFC) have been analyzed above, their performances are not acceptable in our decision-level fusion based MUD model, due to the following reasons:  
(1) The optimal decision-level fusion performance that O-DFC can gain, is conditioned on the assumption that these local detectors are statistically independent with each other; meanwhile, the accurate BER value of each detector should be _a priori_ knowledge. To our knowledge, these two assumed conditions are unsatisfied in fact.  
On one hand, in Figure 1, the inputs of these sub-optimal detectors (SIC, DEC and MMSE) are all the outputs of CD. For this reason, their performances have a definite relationship with each other. Further, their correlation degree can be estimated [46] quantitatively:  
\[\rho_{n}=\frac{nN^{\prime}}{N-N^{\prime}-N^{\prime}+nN^{\prime}} \tag{26}\]  
where \(n\) is the total number of sub-optimal detectors, \(N\) is the total number of information bits for numerical tests, \(N^{\prime}\) denotes the number of bits that are detected wrongly by all detectors while \(N^{\prime}\) is the bits detected correctly by all. Figure 3 depicts the correlation degree \(\rho_{3}\) of SIC, DEC, and MMSE detectors _versus_\(E_{b}\)/\(N_{0}\), when 10 active users are in this system and 106 information bits are used for this simulation. From this figure, we can see that as the \(E_{b}\)/\(N_{0}\) increases, their correlation degree rises obviously until \(E_{b}\)/\(N_{0}=10\) dB (from 0.48 to 0.87), while after that, it fluctuates slightly above 0.9.  
Figure 3: The correlation degree of SIC, DEC, and MMSE detectors, when \(K=10\).  
On the other hand, since the communication channel is random, we are unlikely to estimate the BER of each detector accurately in practice.  
(2) Compared with O-DFC, MV-DFC does not need the accurate estimation of the BER of each detector. Moreover, from Equation (22), only the operation of addition is demanded. However, its defects are also evident: (i) to make a final decision, the number of its sub-optimal detectors should be odd; (ii) the assumption of statistical independence is still necessary; (iii) even though the accurate estimation of BER is not needed, the condition \(P_{eim}=P_{em}\), \(i=1,\,2,\,\ldots,\,n\), should still be satisfied, which is impossible for different sub-optimal detectors in our model.  
From all the analysis above, we can conclude that both the O-DFC and the MV-DFC are not acceptable in our decision-level fusion based MUD model, and this will also be authenticated by numerical simulations in Section 5.
## 4 Case Study: Damage Identification of a Jacket Platform
### Effect of Damage Identification for Damaged Elements at Different Locations  
Figure 3 shows that most structural components of the jacket platform are located below the sea level. Practically speaking, it is more demanding to install underwater sensors at deeper locations. Assuming that the 12 sensor positions are kept fixed, we can study the accuracy of the damage identification method considering different locations of the damaged components. One can intuitively expect that the prediction accuracy would be better when the damaged components are closer to the sensor positions.  
In reality, damage can occur to any structural components randomly. However, the components near the water level are relatively more vulnerable to damage because of higher exposure to corrosion and wave impact. Still, for the sake of simplicity, we divide the whole underwater structure into four areas from top to bottom: A, B, C, and D. In each area, eight elements are assumed to be damaged, and the corresponding element numbers are summarized in Table 4. Note that existing sensors are all located in area A. It is interesting to test the prediction accuracy when damaged elements are located in areas B, C, and D.  
We use the damaged values listed in Table 2 for the damaged elements in the four areas and use the training sample size of 7000, which has minimum MSE. Then we employ the proposed damage identification method to predict the structural damage for each assumed group of damaged elements. To compare the prediction accuracy, test samples with the same damaged values as those in Table 3 are considered. For each test sample D1, D2, D3, and D4 and for each damaged element in the assumed  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Area No.**} & \multicolumn{8}{c}{**Element No.**} \\ \cline{2-9}  & **1** & **2** & **3** & **4** & **5** & **6** & **7** & **8** \\ \hline A & E1,2 & E2,3 & E7,8 & E8,9 & E13,14 & E14,15 & E19,20 &  
Figure 6: Relative errors of the predicted damage for varying damaged elements.