esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experimental Evaluation
### Channel Estimators with Linear Equalization  
After addressing the frame synchronization problem, we evaluated the effectiveness of the proposed CSI estimators. Therefore, subsequent simulations evaluated the BER results of the TIBWB-OFDM modulation scheme in a set condition without perfect channel-state knowledge, and compared them with the case of perfect CSI.  
Figure 4: Correct frame detection probability as function of cross-correlation threshold value for peak detection.  
Figure 5 compares TIBWB-OFDM BER performance with perfect CSI and ZC sequences as preambles in order to perform channel estimation. For a low computational scenario, Algorithm A was chosen as the first estimation approach, where the goal was to find how ZC sequences would perform in channel estimation for TIBWB-OFDM modulation with MMSE equalization.  
Although the optimal CSI was not possible, good performance approximation comes with the price of necessary increased power of the preamble sequence. Having as reference the average power of the TIBWB-OFDM data block, we tested 3 situations where the appended ZC preamble was sent with the same power as data (i.e., 0 dB case), or 3 dB and 6 dB higher. System performance showed evident improvement with plus 3 dB power amplification. For 6 dB amplification, slight improvement was possible that almost reached the reference performance (assuming perfect synchronization and channel estimation).  
Despite additional power was was wasted for good system performance, this only applies to the preamble of the sub-block, which reflected approximately 2% of additional wasted total system power in the 3 dB scenario, and 5% in the 6 dB scenario for the considered TIBWB-OFDM frame structure. From a practical point of view, due to the recognized high PAPR of OFDM symbols and the TIBWB-OFDM being composed of several OFDM symbols, adding the amplified preamble did not degrade the PAPR performance since the signal's peak power was dictated by the component OFDM blocks and the robustness of the TIBWB-OFDM technique to imperfect CSI. Hence, it was not critical to increase the preamble power since the total overall power-efficiency calculation was weighted on the data frame and not on the preamble sequence.
## 1 Introduction  
The importance of assessment of periapical lesions in clinical decision-making is well known. Osteolytic lesions form as periapical lesions in response to inflammatory infiltrates and are often associated with morbidity of the root canal pulp [1; 2; 3; 4]. Recognition of osteolytic changes provides important information about the viability of a tooth, which influences decision-making during the treatment process. The relevant anatomical structures themselves are often small, which hinders acquisition of adequate anatomical outline. Moreover, the relative complexity of the region is increased by the presence of superimposing structures that result in \"anatomical noise\". All of these factors contribute to the difficulty in recognition of bone resorption on radiographic images, which impedesthe accuracy of diagnosis using digital intraoral radiography (DIR) images and may result in periapical lesions going undetected or detection is inadequate [5; 6; 7].  
All of the above-mentioned factors contribute to the relatively low sensitivity of lesion detection of 70% reportedly associated with DIR images, which is markedly less than that of cone-beam computed tomography (CBCT) [8]. While visualization of lesions on CBCT is superior to that on DIR, the radiation dose the patient is exposed to via CBCT is considerably higher than that associated with conventional radiography. Therefore, use of CBCT is questionable, especially as a follow-up modality. In the current work, we present how effective DIR image analyses are with the use of image post-processing in order to refine the acquired information.  
Texture feature analysis was first used to evaluate the structure of osteoporoptic bone [9; 10; 11], where fractal dimension and 13 Haralick features were used for osteoporosis classification on mandibular X-ray images [9]. Similar techniques were applied to analyze periapical bone loss [12; 13; 14; 15], where the Gray-Level Co-occurrence Matrix and Fractal Brownian Motion Model were used for bone-loss area detection [15] and localization [12]. Numerous features formed the basis for segmentation [13] and bone loss degree measurement [14]. Other applications of texture analysis were used for periapical bone healing [16; 17; 18], where radiological assessment of treatment effectiveness of guided bone regeneration was measured. The most similar research to the presented is considered in [19; 20], where the authors tried to detect the type of cyst using the Gray-Level Co-occurrence Matrix and its related properties. By using textural analysis to enhance bone representation derived from DIR images, trabecular structure may be depicted more informatively, and the shapes of different anatomical structures may be determined more accurately. Such techniques may also facilitate more precise determination of changes in the periapical bone region.  
The aim of the present study was to examine the applicability of different texture analysis techniques to radiographic dental images for the refinement and possible differentiation of periapical lesions.
# Rethinking the Operation Pattern for Anomaly Detection in Industrial Cyber-Physical Systems
## 2 Related Work
### Manual Features  
In order to learn the normal pattern, [PERSON] et al. [23] proposed converting industrial traffic into 12 features. The evaluation results of these features, with six supervised machine learning algorithms, show the model achieved a good detection performance for binary classification. Recently, [PERSON] et al. [24] pointed out that ICPS operations repeated themselves within a well-defined timeframe and proposed detecting attacks using temporal patterns. They proposed five feature extraction methods to represent the temporal patterns and used the hidden Markov model (HMM) and an artificial neural network (ANN) to learn patterns. However, their method only used the operation sequence and time interval features and ignored the execution time taken by slave devices to process the received commands. Therefore, their model has limitations in detecting more elaborate attacks, such as the MITM attacks that increase the execution time.  
[PERSON] et al. [27] proposed modelling the sequential behaviour using out-of-sequence detection algorithm (OSDA) and detecting abnormal operations that conflict with the normal sequence. However, the complex algorithms make it difficult to interpret the classification results. [PERSON] et al. [13] proposed using the DFA to describe the normal operation sequence for each master-slave channel. Each DFA state is a 33-bit symbol (one bit for Q/R, eight bytes for function code, sixteen bits for reference number and eight bits for the bit/word count). However, this model neglected the inter-dependencies between slave devices. Moreover, this work did not consider the operation time interval and the execution time.  
In addition to modelling the sequential operation patterns, some works focus on modelling the amount of industrial traffic because they suppose that periodic operations could trigger traffic periodicity. [PERSON] et al. [28] combined the seasonal auto-regressive integration moving average (SARIMA) and long short-term memory (LSTM) to build a time series pattern. [PERSON] et al. [15] used a burst-deterministic finite automaton (DFA)model to learn the logic and structure of the burst packets. However, only server attacks cause the traffic variations; thus, they are insensitive when detecting attacks.  
Another work that uses normal behaviours to detect attacks can be found in [29]. They proposed that the response results reflect the internal representation of SCADA systems, and variants occur in fixed ranges and can be used to detect abnormal states that could be caused by cyberattacks. This work is significantly different from ours because the proposed work focused on detecting malicious operations that could cause abnormal states. Moreover, it could be too late to detect attacks based on abnormal states because the abnormal states only occur after the attacks have had a significant impact.
###### Abstract
Besides the influence of the rust and chloride contamination, the effects of environmental conditions such as temperature and relative humidity on the radar signal were also found in the work of several investigators such as [PERSON] et al. [19] and [PERSON] et al. [20]. Recently, researchers [21; 22; 23; 24; 25; 26; 27; 28; 29; 30; 31] have also applied intelligence-based methods to analyze the experimental data as well as to investigate the factors that affect the GPR signals. For instance, [PERSON] et al. [30] employed an ANN technique to process the experimental GPR data for examining the effects of concrete cover thickness on rebar reflection amplitude  
Figure 1: GPR unit and the data collection process [1]: (**a**) Configuration of an GPR unit; (**b**) Field GPR data collection practice. and establishing the relationship between the input variables and the output GPR signals. The results from the study showed that the ANN model produced a high level of accuracy in establishing the input and output correlation. In a recent study, [PERSON] et al. [31] utilized an adaptive linear neural network to evaluate the backscattering properties of GPR data. Results from the research revealed the inverted backscattering intensity with a relative error rate of less than three percent. A DBN model was employed in this study to analyze the GPR amplitude output from the various inputs. Experimental GPR data obtained from previous publications were used to establish the proposed model. Presently, very few intelligence-based models have been used to analyze GPR data [30; 31]. To the best of the authors' knowledge, no available publications employ the DBN method to analyze the experimental GPR data. The main aim of this research is to fill the knowledge of utilizing the machine learning-based technique to investigate the influences of the input factors on the output. Additionally, the authors would like also to create a non-linear relationship between the amplitude of GPR signals and the input variables.
# Semantically Controlled Adaptive Equalisation in Reduced Dimensionality Parameter Space +
## 5 Results
### Parameter Space Evaluation
#### 5.1.1 Low-Dimensional Mapping Accuracy  
From Table 2 we show that for _trustworthiness_, pPCA achieves the highest rating (0.8426), with the sAE also performing similarly (0.842). The rest of the techniques are also able to achieve a high score, ranging from 0.81 for kPCA to 0.839 for standard PCA. The only technique that does not perform to the same standard is LDA, as the algorithm maximizes the separability of classes in the data instead of preserving the structure of the original dataset unrelated to its classes. For _continuity_, we can see that the majority of the techniques perform similarly, with scores ranging from 0.943 for the sAE to 0.958 for kPCA. However, as was the case with _trustworthiness_, LDA does not perform as well (0.868), due to the map reduction process.  
_Trustworthiness_ and _continuity_ metrics were used with a varying number of neighbours, ranging from 1 to 250. Here, the sAE exhibits higher scores for a lower number of neighbours (\(<\)120), as shown in Figure 5a--a result that suggests the system is better at retaining the local structure of the data, which is a necessary goal for a successful mapping technique. Furthermore, while the _continuity_ score of the autoencoder is lower than the remaining dimensionality reduction techniques (Table 2), its error from the best performing technique in terms of _continuity_ (kPCA) is only 0.015, which is deemed negligible.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Technique** & **Trustworthiness** & **Continuity** & **1-NN Classification** \\ \hline Original & - & - & 91.21\% \\ PCA & 0.8398 & 0.9541 & 87.61\% \\ pPCA & 0.8426 & 0.9567 & 87.92\% \\ kPCA & 0.8102 & 0.9583 & 86.14\% \\ FA & 0.8337 & 0.9490 & 86.19\% \\ DM & 0.8395 & 0.9533 & 87.89\% \\ LDA & 0.7292 & 0.8684 & 85.40\% \\ sAE & 0.8420 & 0.9439 & 84.01\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: _Trustworthiness_ and _continuity_ scores for the different dimensionality reduction techniques (higher values are better) and classification accuracy of 1-NN classification
## 4 Fault Diagnosis Method  
### Principle of Box Diagram  
Using the SOC obtained from the EKF to calculate the remaining discharge capacity, the calculation formula is shown in Equation (15):  
\[Q_{i}=SOC_{i}\times c_{0} \tag{15}\]  
where \(Q_{i}\) is the remaining discharge capacity of the \(i\)th battery cell, \(SOC_{i}\) is the extended Kalman estimation value of the \(i\)th battery cell, and \(c_{0}\) is the rated capacity.  
Using the remaining discharge capacity as a fault feature, compare the inconsistency of battery cells on a medium to long time scale using a box plot. The specific formula is as follows:  
\[\left\{\begin{aligned} IQR=Q_{3}-Q_{1}\\ UL=Q_{1}-1.5 IQR\\ DL=Q_{3}+1.5 IQR\end{aligned}\right. \tag{16}\]  
where \(Q_{1}\) and \(Q_{3}\) are the upper and lower quartiles in the data, respectively, and \(IQR\) is the internal distance; \(DL\) and \(UL\) are the lower and upper bounds of the observations, respectively, and when the data value is outside of the interval of \(DL\) and \(UL\), then the data is judged as an outlier.
# Modeling Network-Controlled Device-to-Device Communications in SimuLTE
## 1 Introduction
It is based on the OMNeT++ framework [12], and is fully compatible with INET [13], another OMNeT++-based library that simulates standard Internet entities. We first provide a general outlook on SimuLTE, discussing the relevant modeling choices and their impact on performance, and then explain the modeling of D2D communications, and in particular: how to modify the LTE protocol stack to accommodate the coexistence of traditional and D2D communications, modeling resource allocation with frequency reuse, and message transmission. We then discuss profiling results of SimuLTE execution, assessing the impact of D2D communications on the simulator performance, and then show examples of simulations employing D2D. To the best of our knowledge, SimuLTE is the only simulation library modeling network-controlled D2D communications. There are two other major system-level simulation libraries, i.e., LENA for ns3 [14] and LTESim [15]. LENA has a D2D add-on [16] that deals with network-unassisted D2D communications, whereas LTESim does not seem to support D2D. The main contributions of the paper can be summarized as follows:  
* The documentation of an efficient modeling of D2D communications for the widely used SimuLTE system-level simulator that allows researchers to validate the modeling and to extend the tool for their own research purposes;
* A discussion of the performance of the tool, in terms of complexity and profiling of execution time;
* Exemplary results showing the type of scenarios that can be run involving D2D communications, and the type of insight that can be gained by system-level simulation; and An assessment of the effects of parameter selection on the observable performance (namely, interference radius vs. frequency reuse, transmission horizon vs. reception probability and execution speed in multihop D2D communications). The rest of the paper is organized as follows: Section 2 introduces the relevant background on the LTE-A technology. In Section 3, we review the related works in detail. Section 4 describes the general architecture of SimuLTE, whereas Section 5 discusses the modeling of D2D communications within it. Section 6 reports evaluation results, and Section 7 concludes the paper.
# Bandwidth-Based Wake-Up Radio Solution through IEEE 802.11 Technology
## 3 Bandwidth-Based Wake-Up Radio Solution through IEEE 802.11
### Wake-Up Radio Receiver
Finally, the outputs of these three chains consist of the inputs of the logical decision block, which takes the decision on the signal received and, thus, on the symbol and corresponding bit decoding. If the output of the first chain is below the corresponding threshold, the decision is that a 20 MHz signal is received, regardless of the output of the second and third chains. On the other hand, if the output of the first chain is above the corresponding threshold, and the output of the second chain is below the threshold, then the decision is that a 40 MHz signal is received, regardless of the output of the third chain. If the outputs of the first and second chains are above the corresponding thresholds and the output of the third chain is below the threshold, the decision is that an 80 MHz signal is received. Finally, in case the outputs of the three chains are above the corresponding thresholds, then it is decided that a 160 MHz signal is received. The decision flow is illustrated in Figure 7. The choice of cut-off frequencies and threshold values (explained in the following Section 4) is crucial for the correct discrimination among signals of different bandwidths and, thus, for obtaining good WuRx performance with regard to WuC decoding. Figure 6: WuRx for two bits per symbol. Figure 7: Decision flow performed at the WuRx.
## 3 Aircraft System Identification
### Total Energy Model  
In Equation (18), the total energy is proportional to the deflection of throttle, \(\Delta\delta_{\mathrm{T}}\). The main objective for this section is to find out the relationship between \(\Delta\dot{E_{\mathrm{s}}}\) and \(\Delta\delta_{\mathrm{T}}\), described by the parameter \(\mu\). The \(\Delta\dot{E_{\mathrm{s}}}\) is obtained by applying the \(\Delta\delta_{\mathrm{T}}\) as input with a time step of 3 s. An example of Pulse Width Modulation (PWM) input of \(\Delta\delta_{\mathrm{T}}\) with a magnitude of 15 \(\upmu\)s is shown in Figure 7. We assume that the width of the input is proportional to the deflection of throttle. For each input with different magnitudes, five data were collected to obtain the average value of \(\Delta\dot{E_{\mathrm{s}}}\), as listed in Table 2. The average values of measured \(\Delta\dot{E_{\mathrm{s}}}\) are plotted against the \(\Delta\delta_{\mathrm{T}}\), and the curve is fitted with a third degree polynomial, as shown in Figure 8. The slope of the fitted curve at zero PWM in Figure 8 is corresponding to the value of 0.0006522.  
Figure 7: The response of the \(\Delta\dot{E_{\mathrm{s}}}\) with an increment of throttle input.
## 6 Future Research Scope
### Non-Uniformity of Isothermal Heights  
Accurate estimation of the slant path length in the rain structure up to the cloud is essential. Unfortunately, this part exists in the vertical space and is hard to estimate. ITU-R [38; 41] has recommended that rain height is to be used only where another measured estimation is not available. Including ITU-R suggested techniques, many other rain height determination techniques are presented in Table 3. However, comparisons between these techniques are unavailable for different climatic zones. In this regard, there is research scope in a particular climatic region to compare rain height determination techniques or propose a better rain height estimation procedure.
# Broadband Microstrip Antenna for 5G Wireless Systems Operating at 28 GHz
## 6 Optimization Process and Discussion of Simulation Results
### Radiation Characteristics  
The radiation characteristics show how the antenna radiates energy depending on direction. It represents a standardized distribution of the electric field, or the relative distribution of surface power density. The characteristics are determined in two planes, i.e., horizontal and vertical, and can also be presented in a three-dimensional (3D) format [33; 34]. The designed antenna should have directional radiation characteristics. A three-dimensional chart of the proposed antenna's radiation characteristics for 27.51 GHz (a), 28.0 GHz (b), and 28.35 GHz (c) frequencies is shown in Figure 10. Figure 11 shows the standardized radiation characteristics of the proposed antenna for 27.51 GHz (blue line), 28.0 GHz (green line), and 28.35 GHz (red line) frequencies in the polar coordinate system for vertical polarization. Figure 12 shows the standardized radiation characteristics of the proposed antenna for 27.51 GHz (blue line), 28.0 GHz (green line), and 28.35 GHz (red line) frequencies in the polar coordinate system for horizontal polarization. The presented drawings show that the beam width at the \(-\)3 dB level for vertical polarization is 115.04\({}^{\circ}\) with the back lobe being \(-\)6 dB, while the beam width at the \(-\)3 dB level for horizontal polarization is 72.16\({}^{\circ}\) with the back lobe being \(-\)10 dB.  
Figure 9: Surface current distribution for the proposed 5G antenna at different frequencies. (**a**) 27.51 GHz; (**b**) 28.0 GHz; (c) 28.35 GHz.  
Figure 10: The three-dimensional (3D) view of radiation pattern for the proposed 5G antenna model at different frequencies. (**a**) 27.51 GHz; (**b**) 28.0 GHz; (**c**) 28.35 GHz.  
Figure 11: The normalized radiation patterns for the proposed antenna model operating in the 5G system for 27.51 GHz (blue line), 28.0 GHz (green line), and 28.35 GHz (red line) in polar coordinates for vertical polarization.  
Figure 12: The normalized radiation patterns for the proposed antenna model operating in the 5G system for 27.51 GHz (blue line), 28.0 GHz (green line), and 28.35 GHz (red line) in polar coordinates for horizontal polarization.
## 3 Results and Discussion
### Practical Prospects
#### 3.4.4. Applicability of EN 14793 within the VERA Protocol  
To fully qualify an analyser type for VERA, EN 14793 [13] requires that the results are reproducible across multiple devices of that type. Because only one device of each analyser type was available in this study, the reproducibility assessment could not be conducted. Nonetheless, this brings up a dilemma concerning the feasibility of proving the proficiency of an analyser instrument, as currently prescribed by the VERA test protocol. Most providers of gas analysis instruments do not have direct access to livestock test sites, whereas testing organizations in the agricultural sector are usually not in possession of multiple devices of the same type mainly because of the high costs involved. On the other hand, while the demonstrated reproducibility of an analyser type is undoubtedly helpful for the users to make an appropriate choice among the wide variety of NH\({}_{3}\) measurement devices, the longitudinal repeatability is arguably of the highest importance for conducting field measurements. It is, after all, impractical to perform frequent instrument checks and calibrations in the field, especially in the absence of reference alternative to the wet-chemistry method.
## 5 Discussion
### Layering Strategy
However, uncontrolled grain growth or orientation due to improper layering strategies might compromise both strength and ductility. Varying layering strategies can result in different residual stresses within the manufactured component. Controlling these stresses is crucial, as excessive residual stress can lead to cracking or reduced ductility. Strategies like controlled cooling rates or post-processing treatments may diminish these effects.