esa-sceva/satcom-chunk-collection huggingface dataset:
# Tracking and Registration Technology Based on Panoramic Cameras
## 3 Method
### Loss Function  
During camera pose estimation, it is assumed that the illumination of the same scene point remains constant across different viewpoints, due to the predominantly diffuse nature of surfaces in real-world environments. Based on this assumption, to enable unsupervised learning across the entire network, a spherical photometric consistency loss is employed as a supervisory signal. This loss is computed using the input image, its corresponding reconstructed images, and occlusion masks. Let \(p\) denote a pixel at the same coordinate in the input image \(I_{t}(p)\), the reconstructed images \(I^{\prime}_{t-1}(p)\), \(I^{\prime}_{t+1}(p)\), and the occlusion mask \(M(p)\). The spherical photometric consistency loss is (Equation (3)):  
\[\updownarrow(I_{t,s})=\sum_{s\in\{1,2\}}\sum_{p\in G}M_{s}(p)\cdot\delta(I_{t, s}(p)) \tag{3}\]  
where \(s\), as defined in Equation (1), represents different resolution levels, and \(G\) denotes the set of all pixel coordinates in the image. The term \(\delta(I_{t,s}(p))\) represents the sum of absolute photometric differences between the input image and its forward and backward reconstructed views (Equation (4)):  
\[\delta(I_{t,s}(p))=\sum_{s\in\{1,2\}}\big{|}I_{t,s}(p)-I^{\prime}_{t-1,s}(p) \big{|}+\big{|}I_{t,s}(p)-I^{\prime}_{t+1,s}(p)\big{|} \tag{4}\]  
where \(I^{\prime}_{t-1,s}(p)\) and \(I^{\prime}_{t+1,s}\) denote the images reconstructed at scale \(s\) from the \(t-1\)-th and \(t+1\)-th frames, respectively, targeting the current frame.  
In addition to the spherical photometric consistency loss, two regularization terms are introduced to constrain the predicted occlusion mask and depth map. To prevent the predicted occlusion mask from collapsing to zero during training, a binary cross-entropy loss is applied to multi-scale occlusion masks (Equation (5)):  
\[\updownarrow_{m}(M_{s})=-\sum_{s\in\{1,2\}}\sum_{p\in G}\log(M_{s}(p)) \tag{5}\]As can be seen from Equation (5), the regularization term imposes a larger penalty when the occlusion mask values are small. To reduce noise in the predicted depth map, a smoothness regularization term is also applied (Equation (6)):  
\[\updownarrow_{sm}(D_{s})=\sum\
olimits_{s\in\{1,2\}}\left|\
abla_{x}D_{s}+ \
abla_{y}D_{s}\right| \tag{6}\]  
where \(\
abla_{x}D_{s}\) and \(\
abla_{y}D_{s}\) denote the horizontal and vertical gradients of the predicted depth map \(D_{s}\), respectively. By linearly combining the above three loss terms, the final loss function is defined as (Equation (7)):  
\[\mathcal{L}=\sum\
olimits_{s\in\{1,2\}}\updownarrow_{sp}(l_{t_{s}})+w_{1} \cdot\updownarrow_{m}(M_{s})+w_{2}\cdot\updownarrow_{sm}(D_{s}) \tag{7}\]  
where \(w_{1}\) and \(w_{2}\) are hyperparameters that balance the occlusion mask regularization and depth smoothness regularization, respectively.
# Condition Monitoring of Wind Turbine Blades Using Active and Passive Thermography
## 4 Quantitative Evaluation  
The SNR of the images is a measure of the quality of data. The traditional definition of SNR is the ratio of the average magnitude of a signal to the magnitude of the background noise, determined as described in Section 5. If a distorted signal, \(y(n)\), is considered as:  
\[y(n)=x(n)+u(n), \tag{9}\]  
where \(x(n)\) is a signal and \(u(n)\) is the background noise, the SNR can be written for \(N\) samples as [35]:  
\[\text{SNR}=\frac{\sum_{n=0}^{N-1}\left|\left[x(n)\right]\right|^{2}}{N\sigma^ {2}}, \tag{10}\]  
where \(\left|\left[x(n)\right]\right|\) is the magnitude of a signal and \(\sigma^{2}\) is the variance of the background noise. Since most signals have wide dynamic ranges, the SNR is usually given in decibels (dB), [36]:  
\[\text{SNR}=10\log\left(\frac{\Delta^{2}}{\sigma^{2}}\right), \tag{11}\]  
where \(\Delta\) is the peak value of the signal. Equation (11) can be simplified as:  
\[\text{SNR}=20\log\left(\frac{\Delta}{\sigma}\right). \tag{12}\]
## 5 Adsorption Isotherms Models
### Dubinin-Radushkevich Isotherms (D-R Isotherm)  
The D-R isotherm is used to describe the mechanism of adsorption onto a heterogeneous surface by a Gaussian energy distribution [136].  
The Dubinin-Radushkevich equation looks like this:  
\[\ln\ q_{\rm e}=\ln\ q_{\rm th}-K\cdot\epsilon^{2} \tag{4}\]  
Here, q\({}_{\rm e}\) is the amount adsorbed at equilibrium (mg/g), q\({}_{\rm th}\) is adsorption capacity (mg/g), K is the activity coefficient related to the mean free energy of adsorption or D-R isotherm constant (mol\({}^{2}\) k/J \({}^{2}\)), and \(\epsilon\) is the Polanyi potential or adsorption potential (kJ/mol) that is determined by  
\[\epsilon={\rm RT}\ln\left(1+1/C_{\rm e}\right) \tag{5}\]  
_R, T, and Ce are universal gas constants (8.314 l/mol/K), Kelvin temperature (K), and adsorbate equilibrium concentration (mg/L) in that order._ The approach is usually applied to distinguish the physical and chemical adsorption of metal ions with their mean free energy.  
Additionally, the adsorption energy E (kJ/mol) is the free energy change needed to move 1 mol of ions from the adsorbate to the adsorbent surface. This equation expresses this term.  
\[\text{E}=1/2\text{ K} \tag{6}\]  
Plotting \(\ln\) q\({}_{\text{e}}\) against \(\epsilon^{2}\), the constants K and q\({}_{\text{th}}\) can be calculated from the slope and intercept, respectively. This means that the free energy can determine the type of adsorption; if 8 < E < 16 kJ/ mol, the process is chemisorption; if it is less than 8 kJ/mol, the process is physisorption.  
In one of the research articles, the authors discussed the synthesis of polyacrylonitrile nanofiber by treating it with sodium bicarbonate and examining the adsorption efficiency of the modified adsorbent for the removal of lead and nickel ions from aqueous solutions. Different isotherm models were used in this research to study whether the sorption takes place by a physical or chemical method. The authors illustrated that the Langmuir isotherm was well fitted for the lead ions, suggesting monolayer adsorption, and the Freundlich isotherm adsorption values were well-fitted for the nickel ions, suggesting multilayer adsorption. The D-R isotherm determined the mean free energy of adsorption and suggested that both heavy metal ions are physically bound to the modified polyacrylamide surface. The estimated mean free energy of adsorption by the D-R isotherm was, E < 8 kJ/mol, clearly showing that both heavy metal ions were physically attached to the surface of the modified polyacrylamide. In the literature, several investigations based on the adsorption of metal ions on modified polyacrylamide surfaces have been published. In Table 2, a few research datasets have been summarized.
## 3 Industry Specification Analysis
As energy systems are part of the critical infrastructure, the highest security standards need to be adopted, and given specifications need to be fulfilled, e.g., [75]. In addition, data leaks have to be prevented to ensure that personalized information does not fall into the wrong hands. 5. Credibility and trustworthiness: The pursued solution should offer the possibility to purchase and sell energy flexibility automatically. Therefore, signing legally-binding digital contracts is an additional essential requirement. For this purpose, appropriate services and processes need to be established that at the same time provide proper technical solutions to ensure trustworthiness in terms of traceability and transparency. 6. Market entry threshold: The specification analysis also involved the current market design and resulted in requirements for key changes. In order for demand flexibility to be used to its full extent, sanctioning flexibility by network charges needs to be eliminated. Furthermore, a non-discriminatory access to all flexibility markets needs to be ensured. 7. General architecture requirements: Considering the discussed requirements for IT-based automated industrial DR, additional specifications regarding the architecture can be derived. In particular, modularity and extensibility are very important in order to provide the required reusability, adaptability, and scalability of the solution. These requirements can be summed up with the concept of service-oriented architectures. In addition, near real-time processing, as well as robust and reliable communication flows are essential. Furthermore, different deployment models, e.g., private or public cloud solutions, should be possible.
# A Symbolic Encapsulation Point as Tool for 5G Wideband Channel Cross-Layer Modeling
## 5 Numerical and Simulation Results
### V2V Scenario  
In this scenario, besides the moving receiver, the moving transmitter is included, also. Therefore, for each sample point at the receiver, we dislocate the transmitter for the length it travels with some constant velocity, different from the velocity of the receiver, and calculate distance matrix and hence, the complex envelope, again. For the sake of simplicity, the assumption is that both stations are moving in the same, or opposite, but parallel direction and we use the same absolute values of velocities. No line-of-sight (LOS) is available, so only scattering rays are present, either from roof tops, groups of trees, or, rarely, between buildings (Figure 1b).  
This time, time-varying frequency response was generated in order to visualize amount of non-stationarity, conventionally. It is noticeable the difference of number of crossings in this scenario depending on the directions of velocity vector, as shown in Figure 7a,b. When moving in the same direction, relative speed is lower, and so it is the influence of Doppler frequencies. Variations of channel become more degrading when moving in opposite, resulting in fades deeper than 50 dB during 100 ms period, compared to same direction movement, Figure 7e,f. Observing these cases across scanned frequency bands, the signal differently stays under the fading area (Figure 7c,d), suggesting the creation of virtual resource blocks, by adequate mapping of good PRBs in one continual resource. AFDs, in some regions, are almost ten times longer than others (\(10^{4}\) vs. \(10^{5}\) wavelengths) and consequently assumed to have incremented PER, for example. Other simulations stay further available to try, e.g., by different velocities or incorporating arbitrary moving directions, which is outside of the present observation.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Numerology** & **Case A** & **Case B** & **Case C** \\ \hline SCS (kHz) & 15 & 30 & 60 \\ Bandwidth (MHz) & 3.6 & 7.2 & 14.4 \\ Resolution (kHz) & 180 & 360 & 720 \\ Symbol duration (\(\upmu\)s) & 66.7 & 33.3 & 16.7 \\ Simulation time (4 symbols) & 200 & 100 & 50 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Simulation setup parameters.
# Radar Emitter Structure Inversion Method Based on Metric and Deep Learning
## 3 RES Modeling
### RES Model
#### 3.1.2 RF Link Model  
Practically, the signal generated by the signal source should undergo multi-stage amplification to achieve the transmission power required for the transmission distance. The parameter simulation of each component is performed using the S2P file. The latter is composed of the S parameter (S\({}_{11}\), S\({}_{12}\), S\({}_{21}\), S\({}_{22}\)) matrix, and the value of the S parameter is obtained by the manufacturer's field measurement of the component. Therefore, an S2P file can be approximately considered as the reflection of a field measurement component in the software.  
The RF link model is built using different amplification stages and module parameters. The RF mixer module is set as a linear device, and the RF amplifier module is set as a nonlinear device, which includes the following: the PA1 model is a first-stage amplifier, which uses the simulation amplifier; the PA2 model is a two-stage amplifier, which uses the HMC637 ALP5E GaAs power amplifier and simulation amplifier; and the PA3 model is a three-stage amplifier, which uses the HMC637 ALP5E GaAs power amplifier, the HMC8500 PM5E GaN power amplifier, and the simulation amplifier.  
The individual differences of each class of RES models are simulated by slightly adjusting the phase noise level and frequency offset of the RF mixer and the IP3 value of the RF amplifier. The specific parameters of each module are shown in Table 1. The reference impedance is 50 ohms, the noise figure is 8 dB, and the operating frequency of these devices is 2.1 GHz.
# Bayesian Optimization Based Efficient Layer Sharing for Incremental Learning
## 2 Preliminaries
### Incremental Learning Algorithm Based on 'Clone-and-Branch'
#### 2.1.3 Problem Definition  
Based on what was explained in the prior subsection, 'clone-and-branch' optimizes the objective function as follows:  
\[n^{*}=\gamma\times(\theta_{n-base}/\theta_{base}), \tag{1}\]  
where \(\gamma\) is the approximate sharing configuration of the base network at the 'accuracy vs sharing' trade-off curve, \(\theta_{base}\) is the similarity score between new tasks and the base network, and \(\theta_{n-base}\) is the similarity score between specific new task '\(n\)' and the base network. Then, \(n^{*}\) is the optimal sharing configurations of base network for accommodating a new task '\(n\)'. As shown in Equation (1), the problem to select the number of global optimal sharing layers for learning new tasks is non-convex and non-differential which is difficult to be solved by using a proper optimization strategy. To solve this issue, we firstly define this as a discrete combinatorial optimization problem and propose a novel efficient incremental learning algorithm-based Bayesian optimization which guarantees the global convergence even though the problem is non-convex and non-differential. The proposed method will be described in more detail in the following sections.
# Analysis of Machine Learning Techniques for Information Classification in Mobile Applications
## 3 Architectures for Machine Learning on Mobile Devices
### On-Device Architecture
#### 3.2.1 On-Device Testing with Pretrained Models  
Making predictions on the device has its advantages. First, privacy is increased because the data do not leave the device at any time, and there is no need for an API for the application to communicate with the machine learning model, which helps to avoid the introduction of an element into the architecture that could put the user's data at risk. Secondly, it lowers the response time from the time the request is made to the model until a prediction is made, achieving latencies of the order of microseconds in particular cases, as well as improving the amount of bandwidth used. This low latency is fundamental for architectures for which response time is crucial, such as an autonomous car. In addition, because device models must be as small as possible, it affects the ability of inference accuracy and flexibility of use, as they are tailored to specific circumstances.  
In order to run machine learning models on a limited-resource device, it is necessary to convert the original model files into TensorFlow Lite files. This is a framework specially designed for running deep learning models on the device, storing models in a special low-storage file format that enables low execution rates by reducing computing and memory requirements [20]. As seen in Figure 3, to perform inference on a given image, the application must load the pretrained model from internal storage and then perform the necessary computations locally on the device. Compared to previous architectures, this one does not need to communicate with a remote server or API to perform its functions. The developer only needs to check if the pretrained model meets the application requirements, then load it into the application. If not, a custom model is needed, which can be trained and adjusted to the application's needs on a computer or in the cloud. The pretrained model can be a common model used for a specific purpose or tailored to research needs, such as in [21; 22; 23; 24; 25; 26].  
As previously discussed, an application that performs all data processing on the device using algorithms preloaded into the device's storage is appropriate when it is necessary to preserve the user's privacy, as the user's data never leave the device. An example is the solution presented in [27], in which the authors presented a solution for detection of spam in short text messages (SMS), as such messages may contain sensitive user information that, if disclosed, could compromise some aspect of the user's personal life.  
Figure 3: Machine learning model architecture on-device testing.
# Radar-Assisted Multiple Base Station Cooperative mmWave Beam Tracking
## 4 Numerical Results  
In this section, we conduct simulations in a radar-assisted multi-RSU cooperation V2I network under the mmWave channel and provide extensive numerical results to verify the effectiveness of the proposed method. In the simulated V2I network, each of the L RSUs equipped with \(N_{t}\) transmit antennas and \(N_{r}\) receives antennas to serve K M-antenna vehicles, while all RSUs are linked to ES as defined in the section II system model. Figure 5 illustrates the relative positions of the L RSUs and the K vehicles by the two-dimensional (2D) coordinate system for linear and curvilinear mobility scenarios, respectively. In a linear mobility scenario, 3 RSUs and 2 vehicles are considered, while the coordinates of the 3 RSUs are [300 m, 70 m], [260 m, 70 m], [280 m, 130 m] and the initial coordinates of the 2 vehicles are [340 m, 100 m], [330 m, 100 m], respectively. Similarly, in a curvilinear mobility scenario, 2 RSUs with the position coordinates [290 m, 230 m], [200 m, 270 m] and 2 vehicles with the initial position coordinate [300 m, 300 m], [290 m, 290 m] are considered, as shown in Figure 5. The other default parameters are the same for both scenarios, which are given by Table 1.
# Shapley's Value as a Resource Optimization Strategy for Digital Radio Transmission over IBOC FM
## 2 Materials and Methods
### Spectrum for All-Digital Mode  
In the all-digital mode, the spectrum is constructed by completely eliminating the analog signal, thereby allowing the entire bandwidth to be utilized for secondary sidebands. This scheme facilitates an enhanced mode of operation, wherein broadcasters transition from the hybrid system to the all-digital system, thereby achieving superior quality levels [21]. Figure 3 illustrates the carrier structure for the all-digital mode, with each of the secondary sidebands comprising 10 main frequency divisions and 4 extended frequency divisions. Figure 3: Spectrum of the fully digital IBOC FM waveform. Figure 2: Spectrum corresponding to the IBOC FM extended hybrid model. Two reference carriers, numbered \(-279\) and \(279\), and twelve protected OFDM subcarriers are transmitted in an area of the spectrum least likely to be interfered with by analog and digital interference. Each secondary sideband comprises carriers \(1\) to \(190\) and \(-1\) to \(-190\). The extended secondary sidebands encompass subcarriers \(191\) to \(266\) and \(-191\) to \(-266\). The protected sidebands consist of subcarriers \(-267\) to \(-278\) and \(267\) to \(278\). The average power of the secondary subcarriers will be between \(5\) and \(20\) dB below the primary subcarriers [1]. In the IBOC FM system, different service modes associated with the operating modes and logical channels are defined. For the hybrid mode, the operating modes range from MPI to MP7. In turn, for the all-digital operating mode, service modes ranging from MS1 to MS4 are available. IBOC has four main logic channels (P1, P2, P3, and PIDS) and six secondary logic channels (S1, S2, S3, S4, S5, and SIDS). A logical channel is a signal path carrying data frames with a given quality of service. Channels P1, P2, P3, and P4 configure the different primary audio services, and the PIDS channel provides the primary data service (IDS). Channels S1, S2, S3, S4, and S5 are used exclusively in the all-digital system for the transmission of data or ambient sound (supplementary audio). The SIDS provides the secondary data service [22]. The system control channel (SCCH) carries the control and status information related to the operating mode and configuration parameters. Additionally, the OFDM subcarriers are arranged in groups called frequency divisions. Each frequency division consists of \(18\) data subcarriers and one reference subcarrier, with a subcarrier spacing of \(363.4\) Hz. Table 1 describes the most important parameters of the IBOC FM system [3]. Tables 2 and 3 depict the characterization of the primary and secondary logical channels, respectively, according to the established service mode for transmission.
# A Survey on LoRaWAN Architecture, Protocol and Technologies
## 2 LoRa & LoRaWAN Technologies
### Security
#### 2.6.3 LoRaWAN Simulation  
LoRaWAN designed to simplify Medium Access Control (MAC) to reduce complexity on the network and the end-nodes. However, this approach may lead to performance issues when a high number of active nodes participate in the network. For these reasons, we have implemented a MAC layer simulation of the protocol using Matlab to test efficiency. Simulation is configured as follows;  
* Each node is configured to simulate only Class A devices.
* Each node is configured to send packets ranging from 10 to 50 bytes (plus 14 bytes protocol overhead).  
* Each node is configured with random 7-12 SF values.
* Nodes has always a packet to transmit after \(Toff_{subband}\) time if there are no collisions. Equation (8).
* Frame air time is computed using Equation (2).
* Nodes are distributed to have a communication range with the GW. Ex. Figure 21.
* The GW is placed in the center of the simulation area. Ex. Figure 21.
* Simulation runs with single GW and it has 4 different sub-bands \(\{1,3,6,12\}\) configuration.
* Simulation is configured to simulayte a complete year (365 days).  
Also, we assume that the network runs only unconfirmed messages and nodes activated using ABP with predefined keys. Obtained results are presented in Figure 22. According to the simulation data, the node count directly affects the successful packet transmissions. This outcome can be improved by partitioning the network with small cells with multiple GW.