esa-sceva/satcom-chunk-collection huggingface dataset:
# Optical and SAR Image Registration Based on the Phase Congruency Framework
## 2 Proposed Method
### Review of PC Theory  
The 2D log-Gabor filter plays an important role in the PC theory, and it can be defined as follows:  
\[LG(w,\theta)=\exp(\frac{-(\ln(w/w_{0}))^{2}}{2(\ln(\sigma/w_{0}))^{2}})\cdot \exp(\frac{-(\theta-\theta_{0})^{2}}{2\sigma_{\theta}^{2}}) \tag{1}\]  
where \(w_{0}\) is the centre frequency of the 2D log-Gabor filter; \(\sigma\) is the bandwidth; \(\sigma/w_{0}\) is a constant; \(\theta_{0}\) is the filtering orientation; and \(\sigma_{\theta}\) is the standard deviation of the Gaussian function. In the spatial domain, the 2D log-Gabor filter can be represented as follows:  
\[LG(x,y)=LG_{s,\rho}^{even}(x,y)+i\times LG_{s,\rho}^{odd}(x,y) \tag{2}\]  
where \(LG_{s,\rho}^{even}\) and \(LG_{s,\rho}^{odd}\) stand for the even-symmetric and the odd-symmetric log-Gabor wavelets in scale and \(o\) orientation, respectively.  
Assuming that \(I(x,y)\) is an image, convolving \(I(x,y)\) with \(LG_{s,\rho}^{even}\) and \(LG_{s,\rho}^{odd}\) yields the response components \(e_{s,\rho}(x,y)\) and \(o_{s,\rho}(x,y)\) as follows:  
\[[e_{s,\rho}(x,y),o_{s,\rho}(x,y)]=[I(x,y)*LG_{s,\rho}^{even}(x,y),I(x,y)*LG_{ s,\rho}^{odd}(x,y)] \tag{3}\]  
Then, the amplitude component \(A_{s,\rho}(x,y)\) and the phase component \(\phi_{s,\rho}(x,y)\) of \(I(x,y)\) at scale \(s\) and orientation \(o\) can be obtained by the following:  
\[A_{s,\rho}(x,y)=\sqrt{e_{s,\rho}(x,y)^{2}+o_{s,\rho}(x,y)^{2}} \tag{4}\]  
\[\phi_{s,\rho}(x,y)=\arctan(o_{s,\rho}(x,y)/e_{s,\rho}(x,y)) \tag{5}\]  
where \(A_{s,\rho}(x,y)\) is the function that will be used in the descriptor construction process. Finally, the 2D PC model \(PC(x,y)\) can be defined as follows:  
Figure 1: Framework of the proposed algorithm.  
\[PC(x,y)=\frac{\sum\limits_{s}\sum\limits_{o}w_{0}(x,y)\lfloor A_{s,o}(x,y)\Delta \Phi_{s,o}(x,y)-T\rfloor}{\sum\limits_{s}\sum\limits_{o}A_{s,o}(x,y)+\xi} \tag{6}\]  
where \(w_{0}(x,y)\) is the weighting factor; \(\lfloor\cdot\rfloor\) refers to the fact that the enclosed quantity is equal to zero when its value is negative; \(T\) denotes the noise threshold; \(\Delta\Phi_{s,o}(x,y)\) is a phase deviation on the scale \(s\) and orientation \(o\); and \(\xi\) is a small value.  
The PC maximum moment \(M\) to be used in the proposed algorithm is calculated as follows:  
\[a=\sum\limits_{\theta}(PC(\theta)cos\theta)^{2} \tag{7}\]  
\[b=2\sum\limits_{\theta}(PC(\theta)cos\theta)(PC(\theta)\sin\theta) \tag{8}\]  
\[c=\sum\limits_{\theta}(PC(\theta)sin\theta)^{2} \tag{9}\]  
\[M=\frac{1}{2}(c+a+\sqrt{b^{2}+(a-c)^{2}}) \tag{10}\]  
Thus, the PC maximum moment \(M\) and the amplitude component \(A_{s,o}(x,y)\) that will be used in the process of keypoint detection and descriptor construction are all obtained.
## 4 Recent Advancements in Applications  
Researchers and engineers employed MMS technologies for a vast number of applications. Different aspects of MMS widen the range of applications, e.g., MMS could easily be employed to capture time-lapsed information of target objects such as roads, buildings, cultural heritage, etc. An MMS could be easily customized to capture more layers of data whenever needed, e.g., a heat camera could be attached to a modular MMS to capture heat leaks in buildings, or a multi-spectral camera could be employed for complex surface classification tasks. A comprehensive review of MMS applications could be found, e.g., in [PERSON] et al. [23]. Here we mainly concentrate on completing this valuable review paper by stating a summary of published research works from 2022-2025. Table 2. briefly lists some of the recent applications. In this section, the most important categories are listed that cover most of the recent applications within the past few years. Four main major categories are considered in this table: 1--natural resource management (NRM), such as forest inventory or water monitoring, 2--road and tunnel mapping and asset management, 3--data, and 4--building and cultural heritage documentation. For each selected category, a list of applications is stated and briefly discussed.
## 3 RE Process as \"Game Changer\": Clinical Applications
### Diagnosis
#### 3.1.2 3D Analysis from IOS and FS  
In the era of patient-oriented medicine, facial morphology and aesthetics recognition have massive relevance in orthodontics and orthognathic surgery. Clinicians should not plan orthodontic strategies and biomechanics merely based on cephalometric parameters. Instead, they should integrate a deep analysis of facial soft-tissue characteristics, even considering potential benefits and consequences of treatment mechanics on facial aesthetics, especially in the facial lower third. This concept has been described by Dr. [PERSON] as \"facially driven orthodontics\" and followed by FACE philosophy [17; 18]. Before the 3D era, the data on facial soft tissue were acquired via 2D methods [11] using imaging and photographs. Nowadays, this data can quickly and easily be acquired into tridimensional face scanners [12]. Additionally, these data can be integrated with those acquired from IOS and, eventually, with CBCT scans to obtain the \"real\" 3D digital patient and comprehensively evaluate the skeletal, dento-alveolar, and soft-tissue components.  
A facial scanner is a noninvasive measuring tool that can acquire 3D facial models in an open data format with natural skin texture and color and a scanning process that is typically noticeably short. To obtain a comprehensive and more accurate 3D image, multiple scans can be acquired in different facial positions (rest position, smiling position, and cheek retracted position). The scans are then aligned using the forehead to obtain more information on the patient's smile line, tooth display, and soft tissue [13]. Professional 3D scanning can be divided into two categories: contact and non-contact. Contact scanning places the object on a precision surface and probes the object with touch. Non-contact active scanning uses light to probe an object, while non-contact passive scanning detects ambient radiation. Depending on the used light source, it is possible to distinguish between laser-based devices and light-based devices even if both systems use the triangulation process mentioned above to reconstruct a 3D image of the patient's face. Moreover, they are fixed and portable systems, and it is possible to obtain facial scans using smartphones with specific applications [6].  
The main difference between IOS and FS data is the format of the generated file; the first (IOS) produces a standard tessellation language or stereolithography (STL) or a polygon file (.PLY), the latter an object code file (.OBJ). The difference between.STL.PLY and.OBJ files is that the first encodes just the triangular mesh without any information regarding color and texture, the second includes color information, and the latter includes texture information [7]. By integrating FS and IOS datasets (Figure 7), clinicians can have a realistic 3D fully digital model at their disposal for deeply analyzing patients' characteristics and choosing the best treatment option in the short and long term [19].
## 4 Simulation Results
Figure 3a gives the change curves of the attitude angle \((\phi,\theta,\psi)\) and its desired angle command \((\phi_{cmd},\theta_{cmd},\psi_{cmd})\). Figure 3b gives the change curves of the attitude angular rate. The roll rate, the pitch rate, and the yaw rate are all less than \(0.05\)\(rad/s\). Figures 2 and 3 actually show that the trajectory tracking errors and the angle tracking errors rapidly converge to a small neighborhood of zero. Figure 4 shows the curves of the disturbance observer states \((e_{\phi},\widehat{e_{\xi}}),\widehat{\Delta_{\xi}},Z_{1},Z_{2}\). Figure 4a shows that \(\widehat{e_{\xi}}\) can realize the dynamic observation of \(e_{\xi}\). Figure 4b gives the estimate \(\widehat{\Delta_{\Xi}}\) of \(\Delta_{\Xi}\). The state variables \(Z_{1}\) and \(Z_{2}\) are shown in Figure 4c,d. The approximation effect of the uncertainty term caused by complex kinematics is shown in Figure 5. Figure 5a illustrates that our proposed method combined with the adaptive approach and the polynomial method has good effect, and the initial value of the disturbance estimation in the \(z\) direction of \(A_{p}(t,\hat{P})\) reaches about \(700\) due to the position feedback in approximation. The disturbance term \(B^{\omega}(t,\omega^{b})\) and its estimation are shown in Figure 5b. The control input curves of position \((x,y,z)\) and the attitude \((\phi,\theta,\psi)\) are given in Figure 6. Finally, the three-dimensional flight trajectory of the mass center of the HAUQ in the water surface take-off is shown in Figure 7 which means that the HAUQ can achieve climbing and hover drainage as the fixed point (0, 0, 0.5 m) with the proposed control algorithm, the uncertainty estimator, and the disturbance observer. However, the unmodeled items, the time\(-\)varying drainage mass \(M_{\Delta}\), the time\(-\)varying disturbance  
Figure 6: Control input variable of the hybrid aerial underwater quadrotor. Figure 7: Three-dimensional trajectory of water surface take\(-\)off. moment of inertia matrix \(J_{\Delta}(t)\), the external disturbance, and the time-varying change matrix of the arm of the force \(L_{\Delta}\), exist and are given by (69), (70), and (73), and the position and the attitude can achieve a successful nonlinear robust tracking of their desired values under the proposed algorithm with the robust adaptive law \(\widehat{\Gamma}\) and \(\widehat{\Xi}\) and the disturbance observer (43) in this simulation scenario.
# Named Entity Recognition in Online Medical Consultation Using Deep Learning
## 4 Research Design and Analysis of Results
### Experimental Results and Discussion
#### 4.4.3 Performance of the Proposed Approach on Different Medical Entity Types  
Table 9 illustrates the varied performance of the proposed approach across different medical entity types. Based on the data presented in Table 9, several conclusions can be drawn:  
* As Rows 1, 11, and 22 in Table 9 show, the performance indicators for entity types AMO, DRT, and REA not only have low mean values but also very high standard deviations. This is due to the scarcity of these three types of medical entities in the experimental dataset, which prevented the model from adequately learning effective features.
* As Rows 5, 7, 8, 13, 17, 19, and 20 in Table 9 show, the F1 scores of the entity types CON, DEG, DEP, DUR, FRE, PAS, and POS were all above 90%, indicating that the model has a strong ability to identify these entity types. This arises from the relatively small number of unique entities within these types, facilitating the model's ability to learn their intrinsic feature patterns.
* As Rows 10, 14, 15, 16, 18, 23, and 25 in Table 9 show, the F1 scores of the entity types DRD, EFF, EQU, FOG, MIC, SIG, and SYM were all below 80%. The reason for the weak identification ability of the model to the entity types is that they are easier to express freely in the context of online medical consultation, and the pronounced inconsistency in expression significantly hampers the model's ability to thoroughly learn the intrinsic feature patterns.  
\begin{table}
\begin{tabular}{c l l l l} \hline \hline \# & **Model** & **P (\%)** & **R (\%)** & **F1 (\%)** \\ \hline \multirow{4}{*}{1} & CBERT-SCNN-Local\_Context-BiLSTM- & \multirow{4}{*}{85.56 \(\pm\) 0.73} & \multirow{4}{*}{85.77 \(\pm\) 0.69} & \multirow{4}{*}{85.24(\(-\)0.23) \(\pm\) 0.30} \\  & Global\_Context-DeepFM-Fusion\_Context- & & & \\  & MCRF (ours) & & & \\
2 & -(DeepFM-Fusion\_Context) & & & \\
3 & -(DeepFM-Fusion\_Context)-(Local\_Context)- & & & \\  & (Global\_Context) & & & \\
4 & -(DeepFM-Fusion\_Context)-(SCNN- & & & \\  & Local\_Context)-(Global\_Context) & & & \\
5 & (BiLSTM-Global\_Context)- & & & \\
6 & -(DeepFM-Fusion\_Context)-(SCNN- & & & \\  & Local\_Context)-(BiLSTM-Global\_Context) & & & \\ \hline \hline \end{tabular}
\end{table}
Table 8: Ablation study conducted on the experimental dataset.
# Conservative Interference Injection to Minimize Wi-Fi Sensing Privacy Risks and Bandwidth Loss
## 3 System Description
### Wi-Fi Sensing System  
As shown in Figure 2, Wi-Fi sensing is predicated on the propagation of signals (red) through a physical environment. As the signal encounters physical entities, its power and phase are altered, causing a disparity in the initial signal and that which is received by Rx. Where the physical entities are moving, such as the finger movements in Figure 2, the artefacts introduced in the wireless signal are also time varying as shown by the spikes in the signal (red) received by Rx. The relationship between the transmitted and received signals can be formally expressed as follows:  
\[Y(f,t)=H(f,t)X(f,t)+N(f,t) \tag{1}\]  
where the signal \(Y\) is received in continuous time \(t\) for all OFDM subcarrier frequencies \(f\). This received signal is equal to the transmitted signal \(X\) after it incurs the multipath transformation, represented by \(H\) (CSI). The Gaussian white noise in CSI is represented by\(N\). Using this relationship, tools such as Nexmon [12] are able to take'snapshots' of the CSI data (\(H\)) by inspecting the pilot bits of all received packets. The CSI data can be represented as \(H[f,k]\) where \(t=kT_{s}\). Here, we measure the CSI at \(k\) locations with a separation of \(T_{s}\) along the originally continuous domain \(t\). The frequency domain representation of \(H\) is thus the following:  
\[H[f,n]=\sum_{k=0}^{N-1}H[f,k]e^{-j\frac{2\pi}{N}nk} \tag{2}\]  
where for all subcarriers \(f\), \(H[f,n]\) is the Discrete Fourier Transform (DFT) of the sequence \(H[f,k]\). Equation (2) shows that the DFT representation of the underlying CSI is inherently dependent on the number of CSI snapshots used to compute the DFT, \(N\), and the discrete domain variable \(k\), which is itself dependent on the sampling period \(T_{s}\). As described in Section 2, existing techniques in Wi-Fi sensing rely heavily on frequency analysis of CSI to discern gestures and movements of different speeds. Hence, we conclude that Wi-Fi sensing ML algorithms are highly dependent on the CSI sampling rate.
# Informative Language Encoding by Variational Autoencoders Using Transformer
## 4 Experiments
We notice that the parameters of the Transformer-Decoder and LSTM without VAEs are smaller than those of the VAE-Transformer and VAE-LSTM with VAEs. As it is a pre-trained model, VAE-GPT is the largest model of all, and a direct model comparison is fair. However, we can check the effect of a pre-trained model in the variational autoencoder. The model dimension of the encoder and decoder of the Transformer model is 128, and its hidden dimension is 512. Furthermore, we use two multi-head attention mechanisms, the GeLU activation function from Ref. [23] in each layer, and the dimension of 64 for the latent variables. We set the encoder of the VAE-GPT model to be the same as the VAE-Transformer encoder and the decoder of the VAE-GPT model the same as in [16]. We set the encoder of the LSTM model to be bidirectional and the decoder to be unidirectional. We set the hidden dimension as 128, and 2 hidden layers for both the encoder and the decoder. The hidden dimension of the LSTM language modeling is set at 128, and the two hidden layers are set to be the same as the VAE-LSTM. Moreover, we use the same settings as that used with the VAE-Transformer for the Transformer-Decoder. Figure 7: Procedure of extrinsic evaluation in classification. We optimize the models by the AdamW optimizer [24] with cosine annealing [25] for a varying learning rate. We use an initial learning rate of \(1\times 10^{-3}\) for the PTB and Wikitext2 datasets and \(1\times 10^{-4}\) for the SST2 and AIR datasets. The final learning rate of \(1\times 10^{-7}\) is used, which restarts every 10 epochs for all datasets. We set the momentum parameters at 0.9 and 0.999, and the weight decay as 0.1. The batch size is 32, and our model is trained in 50 epochs. To prevent KL vanishing, we use linear annealing [2] and cyclical annealing [10]. With the annealing techniques, the objectives of the model (ELBO) are rewritten as in Equation (8). Notice that \(\alpha(t)\) represents a learning rate that varies with iteration \(t\). \[ELBO=E_{\pi\;q_{\phi}(z|x)}[\log(p_{\theta}(x|z))]-\alpha(t)\;KL(q_{\phi}(z|x) ||p(z)) \tag{8}\]  
For linear annealing, \(\alpha(t)\) starts at 0 and reaches 1 at a certain step, after which we set \(\alpha(t)\) at 1. For cyclic annealing, \(\alpha(t)\) is the same as in linear annealing, except that after reaching 1, it resets to 0 and repeats this several times, as shown in Equation (9). Figure 8 shows the comparison between linear annealing and cyclic annealing scheduling according to iteration \(t\). \[\begin{array}{rl}\alpha(t)&= \begin{cases}f(\tau),\;\tau\leq R,\\ 1,\;\tau>R,\end{cases} \\ \tau&=\frac{\mathrm{mod}\;(t,\lceil T/M\rceil)}{T/M},\end{cases} \tag{9}\]  
where \(T\) is the total number of training steps, \(f\) is a monotonically increasing function, \(M\) is the number of cycles, and \(R\) is the proportion used to increase \(\alpha(t)\) within a cycle. We set \(R\) at 0.5 and \(M\) at 5 for 50 training epochs, in the same way as [10].
# Context-Driven Service Deployment Using Likelihood-Based Approach for Internet of Things Scenarios
## 3 Background
### Maximum Likelihood Estimation  
Maximum likelihood estimation (MLE) is a method for estimating parameters in a probability space from a data sample. In this method, the parameters are obtained and estimated by finding parameter values that the likelihood function will maximize. We define the sample set \(X=\{x_{1},x_{2},\ldots,x_{n}\}\) obtained from a probability model with a standard probability density function (PDF) \(f_{x}(x;\lambda)\), which depends on the parameter \(\lambda\) in the parameter space \(P_{s}\). The MLE is obtained according to the following procedure:  
1. The likelihood function \(L(\lambda)\) is defined as \[L(\lambda)=\prod_{i=0}^{n}f_{x}(x_{i};\lambda)\] (2) that is, it is the product of the \(n\) density function terms (where the \(i\)th term is the density function evaluated at \(x_{i}\)) viewed as a function of \(\lambda\).
2. Take the natural logarithm of the likelihood and collect terms involving \(\lambda\).
3. Find the value of \(\lambda\in P_{s}\), \(\hat{\lambda}\), for which \(\log L(\lambda)\) is maximized, for example by differentiation. If \(\lambda\) is a single parameter, find \(\hat{\lambda}\) by solving \[\frac{d}{d\lambda}\{\log L(\lambda)\}=0\] (3) in the parameter space \(P_{s}\). If \(\lambda\) is vector-valued, say \(\lambda=(\lambda_{1},\lambda_{2},\ldots\lambda_{k})\), then find \(\hat{\lambda}=(\hat{\lambda_{1}},\hat{\lambda_{2}},\ldots,\hat{\lambda_{k}})\) by simultaneously solving the \(k\) equations given by \[\frac{\partial}{\partial\lambda_{j}}\{\log L(\lambda)\}=0,\quad j=1,2,\ldots,k\] (4)in parameter space \(P_{s}\). If \(P_{s}\) is a bounded interval, then the MLE may lie on the boundary of \(P_{s}\).
4. We then have to check that the estimate \(\hat{\lambda}\) obtained from Equation (4) truly corresponds to a maximum in the (log) likelihood function by inspecting the second-order derivative of \(\log L(\lambda)\) with respect to \(\lambda\).  
In the proposed model, we use MLE to select the set of most-liked (most frequently availed) contexts of a node. This determines the set of contexts in which the node has a higher chance of being used in the near future. This, in combination with the concept of combined context, leads to a set of stable nodes to which we can distribute the registry shares if the nodes have enough residual memory.
# A Lightweight Perceptron-Based Intrusion Detection System for Fog Computing
## 4 Experimental Setup
### Software and Platform  
The experiments are implemented using Python 2.7, Numpy, and Scipy for reprocessing, ScikitLearn library for machine learning and Matplotlib library for visualization. The experiment is tested on Raspberry Pi 3, which is a small low-powered single-board computer with the size of a credit card, manufactured by Raspberry Pi Foundation in the UK. Raspberry Pi has been very popular among computer enthusiasts and has been used in countless number of IoT and robotics projects. Raspberry Pi comes with the low-power and powerful BCM2835 CPU, in addition to 40 General Purpose Input Output (GPIO) pin for connecting sensors, which makes it an ideal device for IoT application. In many IoT applications, Raspberry Pi has been deployed as a fog node, see for example [39; 40; 41]. In this experiment we used Raspberry Pi 3 which is the third generation Raspberry Pi with following hardware specifications:  
* A 1.2 GHz 64-bit quad-core ARMv8 CPU.
* 1 GB of RAM.
* SD card, 16 GB, class 10.
* 4 USB ports.
* 40 GPIO pins.
* Full HDMI port and Ethernet port.
* Video Core IV 3D graphics core.  
The Energy consumption of Raspberry Pi can be calculated using certain specifications of the Raspberry Pi should be known, namely, the Voltage (V), the Electrical Current (I), and the CPU time for the run of the experiment. The Voltage is given as a standard value for the Raspberry Pi model 3 B. As for the electrical current, it depends on what peripherals are connected, typicall ranging between 700 to 1000 mA for the 3 B model, with a constraint of 1.2 Amp as maximum current. The experiment's configuration includes HDMI port and a keyboard peripherals. The product of Current and the Voltage yields to the Power consumption, which is then multiplied by the CPU time to calculate the energy consumption [42].  
Figure 4 shows the locations of the major hardware components in Raspberry Pi 3 model B. The Raspberry Pi is running on Raspbian, a Debian Linux-based operating system optimized for the Raspberry Pi hardware.