esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Method
### Previous Works
#### 2.1.3 Flat-Field Correction  
After histogram modification, because some portion of the radiographic images were not bright enough near the center of the picture, the teeth appeared slightly darker. The pixel values were close to the pixel values of the mouth. After contrast adjustment, the teeth blended into the background in some cases. Flat-field correction was incorporated to solve this problem.  
The timing of flat-field correction usually takes into consideration the following:  
1. Non-uniform illumination;
2. Inconsistent response between the center and edge of the lens;
3. The image devices not responding consistently to each response;
4. Fixed image background noise.  
The non-uniform illumination affects the image, based on the BHR flat-field correction of the fixed kVp [13]. In digital static FPD radiography, the voltage, kVp, is used to calibrate and correct non-uniform illumination, in order to maximize the performance. It is modeled as follows:  
\[\begin{array}{l}I(r,t;E(r),h(r))\\ =f(J(r)+N(r,t)\end{array} \tag{3}\]  
Here,\(f\) is a function of the exposure scale's signal intensity and beam quality measurement, as specified independently at each pixel. Several plate filters are required to produce spectral calibration. Since 95% of the values are averaged to within 2 standard deviations,  
Figure 4: Adjustment of the image contrast. (**a**) Original image, (**b**) original image with the pixel value distribution, (**c**) histogram equalization processing, and (**d**) histogram equalized with the pixel value distribution.  
in most routine checks using FPD, \(h\) has an upper limit of about 2 and a lower limit of about 0. The number of thickness calibration filters is selected based on the following formula:  
\[h_{j}=\frac{\widetilde{j}-1}{n}\overline{h}=1,\ldots 2n+1,\text{ for some integer }n. \tag{4}\]  
where the higher the resolution of \(h\), the greater the demand is for \(n\), and the \(J\) filter is radiated and distributed over the detector's dynamic range. By using fixed values for the field calibration at each exposure level, based on this result, a calibration drift time flat-field image and its spatial mean value are obtained by:  
\[f^{-1}(r)\ =\ <F_{jk}(r)> \tag{5}\]  
The subset of \(F_{ik}\) is  
\[F_{ik}(r)=\ <r,t\ :\ E_{ik}h_{i}>_{t}-<D(r,t-\delta)>_{t} \tag{6}\]  
Therefore, Equation (5) can be written as follows:  
\[f^{-1}(r)\ =\ \sum_{j=1}^{2n+1}f_{j}^{-1}(x|r)\cdot w_{j}(h), \tag{7}\]  
where \(f_{j}^{-1}\) is the weight-correction function and x represents any original image with a dark field offset; therefore, \(w_{i}(h)\) is an h-based resolution. If a highly accuracy \(h(r)\) is given, the normalized distance weight is given by:  
\[w_{j}(h)=\frac{n}{\overline{h}}\{h_{i+1}-h,\ \ if\ j=1\ h-h_{i},\ \ if\ j=i+1\ \ 0,\ \ otherwise \tag{8}\]  
After calculation, the weight value is applied to the picture after the histogram processing, to obtain a picture with balanced pixel values, as shown in Figure 5.
## 7 Recommendations  
Based on the conducted analysis, the following recommendations can be made for network deployment and operations:  
Figure 11: Energy consumption of a remote radio head (**left**) and a micro base station (**right**).  
Figure 12: Energy consumption of pico (**left**) and femto (**right**) base stations.  
Figure 10: Energy consumption of a macro base station.  
When the power consumption is dependent on the data rate (as in Figure 7), the aim should be to find the sweet spots or data rate regions to use different air interfaces. In multi-RAT 5G networks this would mean analysis of all other radio interface options than the ones analyzed in this paper. However, the most important ones currently are the LTE and WiFi.
2. With the latest WiFi and LTE models, the best option for cooperative data delivery is to select a relay and then use LTE for D2D transmissions. WiFi is a good option only for very high data rates.
3. The base station results show that D2D transmission brings the largest gains in macro cells, up to 70% energy reductions. Small cells are already more energy efficient, but energy savings can still be significant, even 40% in the case of femto cells. Thus, it is recommended to use direct communication between devices in all cellular networks regardless of the type of base station.
4. The best option for energy saving in D2D communications using the latest LTE and WiFi models is to send the data with the best possible data rate.
5. There is clearly room for the creation of novel models with the latest 3 GPP releases after rigorous power consumption measurement work. The measurements should specifically consider D2D measurements with the release 12 and beyond devices.
# Current Context and Research Trends in Linear DC-DC Converters
## 2 Switch Mode Power Supplies (SMPS)
### Issues with SMPS
#### 2.1.3 Switch Elements  
SMPS converts energy using high-frequency switching. BJTs, MOSFETs, and diodes are the commonly used switching devices. Even though switching delays are simply neglected in the theoretical efficiency calculations in SMPS, in reality, there is a small delay while changing the operation state of the switching component where neither current nor voltage is zero across the switching device. As a result, power loss occurs during the transient time where the operation status of the switch changes from on-state to off-state or vice versa [9]. Switching loss is proportional to the switching frequency, as reflected in the efficiency curve in Figure 3. Furthermore, MOSFETs and BJTs contain internal capacitances and inductances. As a result, there are energy losses due to the charging and discharging of these internal capacitances and inductances and from their parasitic resistances. These losses are also proportional to the switching frequency.
## 6 Discussion on Gaps, Challenges, and Links to Future Works
### Lack of Data on Evacuation Behavior  
The availability of comprehensive data on evacuation behavior is crucial for developing robust evacuation models. Unfortunately, data related to evacuation movements, choice of destination, mode of transport, selected routes, and mid-way trips in cases of wildfires or fires in wildland-urban interface (WUI) areas is scarce. Existing behavioral data is often derived from surveys and questionnaires; however, the lack of a standardized structure for data collection introduces discontinuities in the datasets. Moreover, a lot of the acquired data in this regard are based on evacuation exercises, not actual wildfire evacuation, which can also create more divergence from reality. Addressing this challenge necessitates efforts to collect more extensive and standardized behavioral data, which can offer valuable insights into evacuee decisions and behaviors during wildfire evacuations in WUI areas. Questions of great significance need to be answered to understand evacuees' behavior better. For instance: which destination do they choose? What mode of transportation is used? Which communities are more reliant on public transportation? What effects does \"background traffic\" have on evacuation? Which routes are most likely to be chosen? Will evacuees cooperate with traffic management operations (road or junction closure, contraflow, use of road shoulder, etc.) in emergency situations? Do environmental changes posed by wildfires, such as smoke, firebrands, or location of rescue teams, affect evacuation, and how?
## 2 Routing Background
### OLSRv1 and OLSRv2 Routing Protocols  
OLSRv1 is the first version of OLSR defined in RFC 3626. It was developed by the French National Institute for Research in Computer Science and Control (INRIA) for MANETs. In OLSRv1, each node maintains topology information by periodically exchanging link-state messages. This characteristic lets OLSR have reliable routes to all nodes in the network when required. To adapt itself to the low bandwidth requirements of _ad hoc_ networks, OLSR uses multipoint relays (MPR). Using the MPR technique, OLSR reduces the control traffic overhead and minimizes flooding. These optimizations make OLSRv1 suitable for use in large and dense networks, as the MPR optimization is especially effective in such situations.  
HELLO messages are exchanged between neighbor nodes to obtain neighborhood information. By using this neighborhood information, each node selects a subset of one-hop away neighbors known as the MPR nodes set. In the MPR set, all two-hop away neighbors are reachable through any member of the MPR set. MPR nodes are the only ones to forward topology control (TC) messages periodically. The purpose of TC messages is to broadcast link state information and to transmit topology information to the entire network.  
OLSRv2 is the second version of the OLSR protocol defined in RFC 7181. OLSRv2 holds the key features of OLSRv1, such as MPRs, and it includes some improvements on the protocol, such as a more modular and flexible architecture.  
While in OLSRv1, HELLO and TC messages share a common header format, OLSRv2 uses the Generalized MANET Packet/Message Format defined in RFC 5444 [13], type-length-values (TLVs) specified in RFC 5497 [14] and, optionally, message jitter specified in RFC 5148 [15].  
Generalized MANET Packet/Message Format specifies the syntax of a packet that is able to carry multiple messages required by a MANET routing protocol. Each message consists of a message header to identify the message type and a message body.  
The OLSRv2 message header allows nodes to decide whether to process and/or to forward messages. The message body contains compressed addresses. If required, the message body also contains attributes. It is important to note that TLVs are used to represent these attributes.  
In Figures 1-3, an OLSRv1 packet format and Generalized MANET Packet and Message Formats used in OLSRv2 are shown.  
OLSRv2 also differs from OLSRv1 because it uses a link metric instead of hop count, as the OLSRv1 does, in the selection of shortest routes. Minimum hop routes are not often the best, for example three good links may be better than two poor links.  
Finally, OLSRv2 includes extensions for security. While OLSRv2 allows signatures to be attached as packet/message TLVs, OLSRv1's immutable packet/message format causes signature problems.
## 5 Experimental Validation (Simulated)
### Results  
The simulation generated insightful results regarding device performance impact and security responsiveness:  
* **Processing Impact**: The average computational overhead introduced by security checks was under 0.05%, confirming minimal interference with regular device operations (see Figure 4).
* **Memory Usage**: Security components utilized less than 0.1% of the devices' available memory, affirming their suitability for deployment on memory-constrained devices (see Figure 4).
* **Energy Consumption**: The daily energy overhead ranged between 0.3 and 1.5 mAh per device, indicating high efficiency suitable for long-term IoT deployments without significantly compromising battery life (see Table 4).
* **Security Effectiveness**: The adaptive security framework demonstrated excellent threat detection accuracy, consistently achieving detection rates between 85% and 99%. Response times to detected threats averaged approximately 2 s, showcasing the framework's rapid response capabilities (see Table 5 and Figure 5).
## 4 Evaluation over Use Cases
### ICT Gateway
#### 4.1.2 Risk Identification  
The _risk identification_ phase focuses on characterizing the system, its components, and the relationships between them to identify vulnerabilities that may pose security risks. For the ICT GW, this phase involves constructing a detailed model of the system, leveraging multiple data sources and tools to provide a structured overview of components and associated security claims. In this instantiation (Figure 5), we employ the ResilBlockly tool for a detailed analysis of vulnerabilities and attack paths, as well as for creating an extended behavioral profile. ResilBlockly [41] is a tool developed within the BIECO project designed to model highly complex and interconnected systems and infrastructures while significantly reducing the cognitive load typically associated with such activities. Additionally, it includes features for modeling and identifying threats, enabling security risk assessments of identified weaknesses and vulnerabilities.  
We describe the ICT GW in a YAML file following the format described in Listing 1 (lines 9-22), which follows the IETF notation, where? denotes optional fields and denotes repeatable fields. In this file, components are listed with their respective sensitivity scores (ranging from 0 to 10), indicating the criticality of each component. The _claims_ block links these components to both generic and vulnerability-specific claims, together with their impact levels across the different dimensions (safety, operational, financial, and privacy/legal compliance). Vulnerability information, including known vulnerabilities from public databases (e.g., CVE), is also included in this system description, allowing for integration with risk assessment tools like ResilBlockly for further analysis.  
Furthermore, we use the ResilBlockly tool to create a model of the TOE (available in [23]). From this model, ResilBlockly can execute a vulnerability and weaknesses analysis, identifying and associating them with components. In the analysis performed over the interface GUI-ICTGW, ResilBlockly was able to retrieve 152 CWEs and 11 CVEs from the catalogs. By carefully analyzing the CWEs and using an approach that involves selecting CWEs from the first level of a hierarchical weakness tree, the number of CWEs associated with the interface was reduced to 33. Finally, ResilBlockly assists in generating a preliminary extended MUD profile for the ICT GW, integrating the risk analysis outcomes and information from the system model. The complete MUD file and further details about the generation process of this file are given in [23].
## 6 Undesired Effects
### Recording Noise  
Recording noise involves receiver self-noise, natural, and anthropogenic noise. Receiver self-noise is random noise caused by the sensor thermal noise and the amplifier voltage noise. Receiver self-noise increases with decreasing source frequency, until it steadies at frequencies above \(10^{-1}\) Hz [42]. Anthropogenic noise is periodic and/or random [44]. Periodic noise is generated by power lines, buried metallic cables, grounded electric devices, and so on. Most periodic noise can be heavily attenuated by applying digital filters to data. Random anthropogenic noise, on the other hand, can be caused by sources such as the motion of trains or trans on the rail tracks. Random noise can be diminished by stacking  
Figure 4: Sensitivity of time-lapse electric fields to volumetric and resistivity changes due to various temperature contrasts. The black dashed lines show the boundaries of the low-enthalpy reservoir at 2.3 and 2.5 km.  
repeated measurements to increase the signal-to-noise ratio. Increasing the source dipole moment also helps to increase the signal-to-noise ratio.  
To account for the recording noise that may not be removed via processing, we changed the amplitude of the electric field randomly in the range of \(\pm 1\%\), as follows:  
\[\eta=0.01[n_{1}\Re(E)+\mathrm{i}n_{2}\Im(E)], \tag{3}\]  
where \(n_{1}\) and \(n_{2}\) are random values between \(-\sqrt{0.5}\) and \(\sqrt{0.5}\); the two symbols \(\Re(E)\) and \(\Im(E)\) refer to the real and the imaginary parts of the calculated electric field response, respectively; and i refers to the imaginary unit. Different random multiplicative noise was added to the base and monitoring electric fields. The electric fields were determined using the setup detailed in Section 4.  
As shown in Figure 5, the resistivity change presented a time-lapse difference that was generally stronger than that of the synthesized recording noise. Random noise in the base and monitoring electric fields can either add or subtract, leading to the amplification or cancellation of the noise in the difference. It is better to reduce the random noise in the data, in order to avoid any possible amplification when calculating the time-lapse difference. After describing the irregular errors arising from random noise, we analyse the errors arising from unrepeatable survey factors.
## 4 Results
### Performance Comparison Based on Energy Concentration
#### 4.1.2 Simulated Signals  
The experiment is repeated for synthetic signals using the IPFM and the AM-FM model described in Section 3. The synthetic signals are sampled at 1.33 Hz and analyzed using the same set of TFDs that were used for the measured signals.  
The TF representations obtained using the IPFM and the AM-FM model are shown in Figures 3 and 4, respectively. In both cases, the ADTFD achieves the highest energy concentration of auto-terms for both strong and weak signal components, while significantly reducing cross-terms. The energy concentration measures for the IPFM and the AM-FM model are shown in Tables 2 and 3, respectively. The ADTFD achieves the lowest value, implying the highest concentration and thus confirming the results of the visual assessment.  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**TFD** & **ADTFD** & **AOKTFD** & **MBD** & **S-method** & **Spectrogram** \\ \hline EC (mean) & \(0.8554\times 10^{4}\) & \(1.4576\times 10^{4}\) & \(1.4129\times 10^{4}\) & \(1.4298\times 10^{4}\) & \(1.6417\times 10^{4}\) \\ EC (standard deviation) & \(1.5582\times 10^{3}\) & \(3.3485\times 10^{3}\) & \(2.2148\times 10^{3}\) & \(2.2651\times 10^{4}\) & \(2.9416\times 10^{3}\) \\ \hline \end{tabular} The time-frequency distributions (TFDs) used are adaptive directional time-frequency distribution (ADTFD), adaptive optimal kernel time-frequency distribution (AOKFTD), modified B distribution (MBD), 5-method and Spectrogram  
\end{table}
Table 1: Performance comparison of time-frequency distributions (TFDs) for the analysis of measured heart rate variability (HRV) signal using the energy concentration measure.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**TFD** & **ADTFD** & **AOKFTD** & **MBD** & **S-method** & **Spectrogram** \\ \hline EC & \(7.0524\times 10^{3}\) & \(1.5547\times 10^{4}\) & \(1.5241\times 10^{4}\) & \(1.4872\times 10^{4}\) & \(1.8622\times 10^{4}\) \\ \hline \end{tabular}
\end{table}
Table 2: Performance comparison of TFDs for the analysis of simulated HRV signal based on the integral pulse frequency model.  
Figure 3: TF analysis of simulated HR signals based on the integral pulse frequency model (IPFM); (**a**) ADTFD (\(a=3,b=8\)); (**b**) AOKFTD; (**c**) MBD (\(\eta=0.05\), lag window = 64); (**d**) S-method (STFT window length = 91, frequency axis convolution window length = 21); (**e**) spectrogram (window length = 91).
# Optimization of Design Parameters in LSTM Model for Predictive Maintenance
## 3 Optimization of Design Parameters for an LSTM-Based Predictive Maintenance Model
### Design of GA for Exploring Optimal Hyperparameters of LSTM
#### 3.3.2 Fitness Function  
A fitness function is used to evaluate whether the chromosome in the population is good or bad, which is important for proper design of the GA. Since we aim to optimize hyperparameters of LSTM, the fitness function should be able to evaluate chromosomes by using the accuracy of trained LSTM with hyperparameters recorded in them. Therefore, we suggest the following fitness function proportional to \(Acc\), where \(Acc\) means the accuracy and \(c\) is a constant.  
\[fitness\_function=\frac{1}{c}Acc. \tag{1}\]
# Review on Retrospective Procedures to Correct Retinal Motion Artefacts in OCT Imaging
## 3 Methods
### Validation Techniques
#### 3.5.4 Multimodality Comparison  
Imaging modalities such as FP or SLO have much shorter acquisition times than OCT volumes, and are often considered artifact-free. Thus, they can be used as a reference to infer the registration performance when compared to the OCT en face image. This analysis can be done visually or quantitatively.  
_Retinal trackers_: One special case of multimodality comparison is retinal tracking, which uses an additional imaging modality, typically SLO, to guide the OCT acquisition. Although these hardware approaches are out of the scope of this review, we must mention their usefulness for validation, as they measure the eye motion, effectively providing a reference for the registration.
## 2 Blood Pressure Estimation Methods
### Signal Preprocessing  
The obtained ECG and PPG signals (denoted as \(S^{c}\) and \(S^{p}\)) have zero values or meaningless values and these signal segments are removed. The main component of the PPG signals is between 0.5 and 11 Hz, and the main component of the ECG signals is 30 Hz or lower [18; 19]. Therefore, a low-pass filter with a cutoff frequency of 35 Hz is applied to the PPG signals. A high-pass filter with a cutoff frequency of 0.5 Hz and a low-pass filter with a cutoff frequency of 35 Hz are sequentially applied to the ECG signals.  
The ECG and PPG signals are segmented into sequences of ECG and PPG segments denoted as \(S^{e}=\left\{s_{1}^{e},s_{2}^{e},\cdots,s_{L_{s}}^{e}\right\}\) and \(S^{p}=\left\{s_{1}^{p},s_{2}^{p},\cdots,s_{L_{s}}^{p}\right\}\), respectively. The \(L_{s}\) is the number of segments of \(S^{e}\) and \(S^{p}\). The \(s_{i}^{e}\) and \(s_{i}^{p}\) segments are blocked into frames of \(L_{f}\) samples and each adjacent frame is separated by \(L_{k}\) samples. For each frame, the Hamming window is applied to reduce leakage, and then the fast Fourier transform is applied to estimate the corresponding frequency responses. Then, the frequency responses of \(s_{i}^{e}\) and \(s_{i}^{p}\) are denoted as \(f_{i}^{e}\) and \(f_{i}^{p}\). Therefore, the corresponding frequency input sequence for ECG and PPG signals can be found and denoted as \(F^{e}=\left\{f_{1}^{e},f_{2}^{e},\cdots,f_{L_{e}}^{e}\right\}\) and \(F^{p}=\left\{f_{1}^{p},f_{2}^{p},\cdots,f_{L_{s}}^{p}\right\}\). Finally, the inputs of convolutional neural networks are \(X^{s}=S^{e}\oplus S^{p}\) and \(X^{f}=F^{e}\oplus F^{p}\) for input vectors in the time domain and frequency domain, respectively.