esa-sceva/satcom-chunk-collection huggingface dataset:
# A Survey on Traffic Prediction Techniques Using Artificial Intelligence for Communication Networks
## 6 Linear Regression
### Relevant Papers  
While the most common case for using regression models in communication networks is failure detection or network health prediction analysis [39], a few papers discuss the application of linear regression towards traffic prediction.  
[PERSON] in [40] proposed a machine learning regression model to obtain the best weights combination that minimizes the network fragmentation. He developed a fragmentation-aware algorithm showing that the proposed metric and assigned fiber weights decrease network fragmentation. Consequently, the proposed solution allows for bandwidth blocking probability reduction in the short term compared to the reference methods.  
Figure 6: Example scatterplot with a linear trendline.  
Ref. [41] describes how linear regression support can be used and shared in optical networks and the assessment of transmission quality (QoT), data traffic patterns, and crosstalk detection to help to route and distribute resources.  
Finally, [PERSON] et al. [42] propose a solution to reduce power excursions in dynamic optical networks that are equipped with an Erbium-Doped Fiber Amplifier (EDFA). An EDFA system automatically maintains power levels in network amplifiers but faces the issue of undesired power excursions. Power excursions occur when an increase in overall power results in a high-gain channel stealing power from a low-gain channel. A linear regression model is trained on 400 existing training data points until mean square error (MSE) convergence. After convergence, the model recommends channel slots to add or drop, considering their estimated standard error values. The model's accuracy is verified with known power standard deviations measured over a week. This approach is expected to be applicable for more extended periods with different EDFA network designs.
## 5 Experiment Evaluation  
To verify the rationality of the proposed strategy in this paper, we compared it with the most mature anchor strategy, whose anchor interval is 5 min [6], and the API strategy proposed in [7]. The DSA strategy proposed in this paper is an improvement strategy for the anchor strategy. The proposed scheme improved the anchor interval to 2.5 min by the anchor optimization selection strategy formulated in Section 4. These two strategies share the same data prefetching mode. Peers with these two strategies download the anchor data before the current playing point with the surplus download bandwidth after downloading the urgent data. The API strategy is also an improvement strategy for anchor strategy, which aims to increase the user satisfaction and reduce the playback delay. In this strategy, peers downloads both the anchor at intervals of 5 min and the video segments that are popular with users in the current system before the current playing point.  
We implemented the P2P-based VoD system and data-prefetching algorithm using Java. In the comparative experiment, we simulated the VCR operation process when the user watches a video in a P2P-based VoD system, and the test video bit rate is 600 kbps. After the video was played for 1 min, the user randomly performed 5 times VCR operations every 15 s.
# Modified Generalized Feistel Network Block Cipher for the Internet of Things
## 4 Statistical and Cryptographic Analyses
### Randomness Test  
According to research by [PERSON] and [PERSON] [58], a block cipher must meet the minimum security requirement of randomness. Therefore, a randomness test using the NIST Statistical Test Suite is conducted on the proposed MGFN lightweight block cipher algorithm to access the cipher randomness characteristics. Several algorithms have been assessed using the NIST Statistical Test Suite including SIMON [59; 60], PRESENT [61], RECTANGLE [62], and 3D-RECTANGLE [57]. The method for randomness testing consists of several stages, which include selecting sample sequences from the algorithm, conducting statistical tests using the NIST Statistical Test Suite, and assessing the outcomes of these tests to evaluate the level of statistical randomness achieved.  
Nine data categories of the block cipher were implemented to generate the keys and plaintexts i.e., strict key avalanche (SKAva), strict plaintext avalanche (SPAva), plaintext/ciphertext correlation (PtCtorr), cipher block chaining (CBChain), random plaintext/random key (RandPtRandK), low-density key (LowDKey), high-density key (HighDKey), low-density plaintext (LowDpt), and high-density plaintext (HighDpt). Due to its specific function, each data set was selected. Table 4 summarizes the sequence of the output generated in each category of data based on the input data. Each data category created 100 unique samples, with each sample containing 2081 to 15,744 ciphertexts, or 133,184 to 1,007,624 bits of data. The lengths of the block and key determine the total number of blocks in each sample. The blocks were derived by adding the ciphertexts in order to generate lengthy bit sequences.  
Fourteen (14) different statistical analysis tests as listed in Table 5 were conducted on the complete 24 encryption rounds of the MGFN lightweight block cipher. These tests were categorized into two: eight were non-parameterized test selections, which do not require any user input, while the remaining six were parameterized test selections, which require an input of parameter values.  
In order to conduct the randomness tests, a significance level, \(\alpha\), must be established. For this particular experiment, the significance level was set at 1% (0.01). Therefore, the necessary number of samples for the experiments was calculated as \(1/0.01=100\) samples. According to [PERSON] and [PERSON] (2016), if the \(p\)-value obtained from a test is greater than or equal to \(\alpha\), then the sample is considered to be random with a confidence level of 99.9%. Conversely, if the \(p\)-value is less than \(\alpha\), then the sample is deemed to be non-random. Therefore, Table 5 also provides the corresponding \(p\)-values produced by each statistical test.  
The confidence interval formula is used to calculate the range of acceptable proportions for the ciphertext in this analysis, as described by (Kal
## 2 Related Work  
### Rule-Based Methods  
Rule-based methods [3] typically count on the port number or protocol identifier of the traffic to identify the application, such as HTTP traffic on port 80 or port 443. However, these methods are almost ineffective in encrypted traffic identification [4] because encryption protocols obscure port information and protocol flags.
# SIV: Raise the Correlation of Second-Order Correlation Power Analysis to 1.00
## 2 Related Works
### Correlation Power Analysis  
The CPA is a statistical method that analyzes a huge amount of power traces \(\mathbb{T}\) of cryptographic device encrypting different plaintexts \(\mathbb{P}\) to reveal the secret key \(K\)[6]. It is based on the fact that \(\mathbb{T}\) is related to the intermediate values \(f\left(\mathbb{P},K\right)\) calculated when encrypting \(\mathbb{P}\). The power consumption model is a method that describes the relationship between \(\mathbb{T}\) and \(f\left(\mathbb{P},K\right)\). Typically, the Hamming weight model is utilized as the power consumption model in software implementation. It assumes that the power consumption is linearly related to the number of 1's in the binary representation of the intermediate value [12]. Therefore, when data \(d=\left(d{{d}_{6}}{{d}_{5}}\cdots{{d}_{0}}\right)_{2}\) is stored in an 8-bit register, the power consumption \(P_{d}\) is linearly related to the Hamming weight of \(d\)\(\text{HW}\left(d\right)=\sum_{i=0}^{7}{{d}_{i}}\). \[P_{d}=\epsilon\times\text{HW}\left(d\right)+P_{noise}.\]  
The brute-force attack must guess the entire secret key to determining whether the supposed key is the right key, whereas the CPA guesses a much shorter partial key \(K_{i}\) and decides that. Thus, the CPA applies the divide-and-conquer algorithm that recovers the \(K_{i}\) and combines that to disclose the whole secret key \(K\). An analyst calculates the set of Hamming weights of an intermediate value \(\text{HW}\left(f\left(\mathbb{P},GK\right)\right)\), where \(GK\) is the guessed key. If \(GK=K_{i}\), the set of Hamming weight is linearly related to \(\mathbb{T}\). Therefore, the analyst can confirm that \(GK\) is the secret key by Pearson's correlation coefficient \(\text{Corr}\left(\mathbb{T},\text{ HW}\left(f\left(\mathbb{P},GK\right)\right)\right)\). The Pearson's correlation coefficient is a measure of linear correlation with a value between \(-1\) and \(1\). Masking is a countermeasure against the CPA performed by randomizing the power consumption unrelated to the guessable intermediate value. The Boolean masking conceals sensitive data \(x\) by XORing a random value (mask) \(m\) to the data \(x\oplus m\). Thus, the intermediate value \(x\) is split into more than two random variables \(r_{1},r_{2},\cdots,r_{n},r_{0}=x\oplus r_{1}\oplus r_{2}\oplus\cdots\oplus r_{n}\). The analyst cannot disclose the key via the CPA because the intermediate value, which is related to power consumption, is not guessable. Typically, the 8-bit masked AES is implemented as a schema proposed by [PERSON] [7]. As the schema can efficiently resist the first-order CPA (FOCPA) with only six masks. Figure 1 shows the
# A Novel Low-Cost DIC-Based Residual Stress Measurement Device
## 1 Introduction
\begin{table}
\begin{tabular}{c c} \hline \hline
**Strain Gauges** & **DIC** \\ \hline \multicolumn{3}{c}{**Measurement errors:**} \\ \hline Errors due to misalignment between & No requirement for exact drilling-hole \\ drilled hole and strain gauge rosette & alignment relative to the sensor (as long as it \\  & remains in the field of view) \\ \hline Manual application of strain gauges may lead to & Repeatable measurements at the location of \\ imprecision in location, contact effectiveness, etc. & interest—alignment performed digitally \\ \hline \multicolumn{3}{c}{**Type of strain:**} \\ \hline Single point measurement of strain—averaging & Full-field strain and deformation \\ over a region & measurement \\ \hline Unable to quantify stress state in inhomogeneous & Full-field residual stress data, suitable for \\ or anisotropic materials & inhomogeneous and anisotropic materials \\ \hline \multicolumn{3}{c}{**Economical aspects:**} \\ \hline High cost per measurement (\(\ell 50+\)) & Low operation cost per measurement (\(\ell 1\)) \\ \hline Preparation time for applying the strain gauge & Fast and simple preparation of object \\  (Est. 2 h per point) & surface(45 min per measurement) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Benefits of using DIC over strain gauges for strain relief quantification.
# Comparison of Manual and Automated Capillary Morphometry Measurements in Oral Mucosa: A Pilot Study
###### Abstract  
This pilot study sought to evaluate and compare the performance of an automated method, developed using a neural network trained at the University of Palermo, with a traditional manual method for assessing capillary morphology in the oral mucosa. The analysis was conducted using videocapillaroscopy, a non-invasive imaging technique that allows for detailed visualization and quantification of microvascular structures. Three key parameters were examined: capillary caliber, length, and density. Measurements were taken across ten patients in four distinct anatomical sites within the oral cavity: the right buccal mucosa, left buccal mucosa, lower labial mucosa, and upper labial mucosa. The primary objective was to determine whether the automated method could serve as a reliable and efficient alternative to the manual approach, particularly in clinical and research settings where precision and reproducibility are paramount. The findings revealed that the automated method consistently produced higher values for capillary caliber and length compared to the manual method, suggesting a systematic overestimation of these parameters. This discrepancy may be attributed to the way the neural network identifies and processes capillary structures, potentially including more complex or tortuous segments that the manual method might overlook or exclude. In contrast, the two methods showed good agreement in the assessment of capillary density, with no significant differences observed. This indicates that the automated method performs well in quantifying capillary density, a critical parameter in microvascular studies. In conclusion, while the automated method shows promise as a faster and more efficient alternative to manual measurements, its systematic overestimation of certain parameters underscores the importance of continued development and validation. The availability of a non-operator-dependent, rapid, repeatable, and thus objective method represents a significant step forward in the assessment of microcirculation. However, careful consideration must be given to the limitations and potential biases of these tools to ensure their reliability and applicability in both research and clinical practice.  
videocapillaroscopy; automated measurement; neural network-based method; capillaries
# A Multilevel Adaptive Path-Planning Model in Off-Road Environments
## 2 Overview of Ant Colony Algorithm and Improvements
### Improvements on ACA  
#### 2.2.1 Improved State Transition Probability Equation  
The driving cycle of off-road vehicles are not on paved roads, but a complex environment with grassland, bare land, mire, etc., and the traveling time of vehicles varies on different surfaces. Therefore, in path planning, it is necessary to consider heuristic information in terms of trafficking to improve the state transition probability equation. Equation (6) shows the improved state transition probability equation:  
\[P_{ij}^{k}(t)\ =\ \begin{cases}\frac{\pi_{ij}^{k}(t)\cdot\eta_{ij}^{k}(t)\cdot \eta_{ij}^{k}(t)}{\sum_{j\in allowed,k}\frac{\pi_{ij}^{k}(t)\cdot\eta_{ij}^{k}( t)}{\eta_{ij}^{k}(t)\cdot\eta_{ij}^{k}(t)}},j\in A_{k}\\ 0,else\end{cases} \tag{6}\]where \(p_{ij}\) is the trafficability heuristic information function.  
Figure 1 interprets the difference between the original algorithm and the improved one. The ant can move from the start point S to the current grid in seven possible directions, as shown in Figure 1a. When only the impact of expectation heuristic information on the ant's decision is considered, the ant is likely to move to a grid closer to the destination E, as shown in Figure 1b. Figure 1c shows the possible moving directions when the heuristic information is considered, which suggests that the heuristic information plays a guiding role in an ant's decision-making. In the improved ACA, both the expectation heuristic information and trafficability heuristic information are included in the state transition probability equation; in this case, the ant will move to a grid with better expectation and better trafficability, as shown in Figure 1d.
## 1 Introduction
However, they also present some issues, including tracking error range, output being out of phase from input, non-robust performance in the presence of disturbances, and parametric uncertainties. This research, therefore, focuses on the enhancement of tracking performance of the LLE in the presence of disturbances and uncertainties, and elimination of phase delay, especially in the case of a time-varying reference signal. This is achieved via the design of both the MB and MF robust control strategies. The performance of both of the closed loop systems was analyzed to identify the optimized robust control methodology for the LLE. The paper is divided into five sections, including the introduction given in Section 1. In Section 2, the design of the model-based control strategy is presented and the simulation results are analyzed. In the following section, the design of the model-free control strategy and the simulation results are presented. A comparative analysis of results is presented in Section 4 to identify the optimized methodology for robust control of the LLE, before the study is concluded in Section 5. See Figure 1.
# Photonics-Based Microwave Image-Reject Mixer
## 1 Introduction  
A frequency mixer is an essential module in modern microwave systems, such as radar, electronic warfare, wireless communication devices and satellite payloads [1; 2; 3; 4]. Frequency mixers facilitate frequency upconversion to generate a radio frequency (RF) signal at \(f_{\rm RF}=\left|f_{\rm RF}+f_{\rm LO}\right|\), where \(f_{\rm RF}\) and \(f_{\rm LO}\) are the frequencies of the intermediate frequency (IF) signal and local oscillator (LO) signal, respectively. Frequency mixers also facilitate frequency downconversion to generate an IF signal, described as \(f_{\rm IF}=\left|f_{\rm RF}-f_{\rm LO}\right|\). In recent years, the frequency mixers implemented, based on microwave photonic technologies, have attracted great interest due to advantages such as their large bandwidth, light weight, high isolation and electromagnetic interference immunity (EMI) [5; 6; 7]. In addition, the photonics-based microwave frequency mixer can be combined with radio over fiber (RoF) technology to realize remote fiber-optic antennas, avoiding any additional electrical-to-optical or optical-to-electrical conversion [8; 9]. However, the photonic microwave mixer is usually implemented through a heterodyne structure to obtain a nonzero IF signal, which can be easily interfered with by image signals. As shown in Figure 1, the image signal at \(f_{\rm IM}\), with a frequency above or below the LO frequency by an amount equal to IF, will be converted to the same IF band, together with the desired IF signal downconverted from the radio frequency (RF) signal at \(f_{\rm RF}\). Thus the downconverted image cannot be removed by filtering due to spectrum aliasing. Image-reject mixer (IRM) is a downconverter with a large suppression of downconverted products (\(f_{\rm IMC}=\left|f_{\rm IM}-f_{\rm LO}\right|\)) resulting in undesired image signal \(f_{\rm IM}\) while maintaining or enhancing the desired component. Typical electrical IRM structure is shown and analyzed in [10]. Due to electronic bottleneck, the working frequency and bandwidth are limited for electrical IRMs (i.e., IF bandwidth limited to no more than 160 MHz, and LO frequency limited to no higher than 14.0-18.0 GHz) [11]. For future RF systems, RF signals with large bandwidth will be used and systems will be operated in a complicated electromagnetic environment. Thus, image distortions will be serious, which creates an urgent demand for high performance photonics-based microwave IRM.  
In this paper, we review recent developments in photonics-based microwave IRM with an emphasis on the pre-filtering method [12; 13; 14; 15; 16; 17] and the phase cancellation method [18; 19; 20]. Applicationsof photonics-based microwave IRMs in electronic warfare, radars, satellite communications, and so on, are described [31; 32; 43], with challenges in implementing photonics-based microwave IRMs to meet the requirements of RF systems. Finally, development trends of the photonics-based microwave IRM are discussed.
## 2 Materials and Methods
### Methodology
#### 2.3.1 SCADA Data Processing  
Of the entire dataset, a subset of six variables is used to model the generator: active and reactive power, temperature of nacelle and generator stator, as well as wind and generator speed. While the dataset was composed of more than 300 variables, just a small selection was kept. Processing all the variables would result in very large computation time and likely lead to overfitting of the data, interpretability of the predictions would also be not trivial since the number of inputs would be very large. The selection of the variables has been done choosing measurements related to the system under evaluation (generator speed, generator stator temperature) as well as context signals that determine the operating status of the turbine (active and reactive power, nacelle temperature and wind speed).  
The dataset is split into a training and a test set, the first 70% of the data was used for training and the remaining 30% for test. Data shuffling has been avoided, since the dataset is composed of timeseries and random selection of data could result in information leakage.  
Analysis of the maintenance and alarm logs allows to filter out abnormal operating conditions from the training set, as well as remove outliers caused by sensor malfunctions, thus creating a training set composed only by normal operation records. No imputation of missing data was performed. To filter data, pre-processing algorithms [23] are applied. In practice, a range of acceptable values for the input variable of the model is defined and all the data not conforming with this range has been filtered, considered as communication errors.  
A crucial part of pre-processing is normalization of data, the training set is used to determine the minimum and maximum value for each input variable, and these values are then stored to be used later on in the test set.
## 5 Case Study
### Experiment Results
#### 5.4.2 Atmospheric Error  
The ranging accuracy of 10 km baselines is referred to in reference [46]. The results showed that the ranging error caused by atmospheric medium is between 0.25 m and 0.75 m even with the high precision electromagnetic wave rangefinder. In this experiment, the launch distance between the base stations is 0-4 m. According to the latest national standard specification of the atmosphere, temperature, pressure, and humidity, these factors's variation with distance can be obtained in Matlab 2020b, as shown in Figure 15. The scattered points are the actual values measured by the sensors, and the straight lines are the atmospheric conditions obtained by theoretical fitting. It can be found that the range of temperature change is 0.02 \({}^{\circ}\)C, the range of air pressure change is 0.4 kph, the range of humidity change is 0.02%, and the range of atmospheric refractive index change is 0.00003%. According to Equation (11), the relation between the refraction error and the distance within 4 m is obtained, and the error range is 0.24 mm, as shown in Figure 16.  
Figure 14: Heat map of intelligent manufacturing experiment production line positioning error. Note, T1, T2,... T9 are the static points used for UWB-tag position calibration in this lab.  
Figure 15: Changes in indoor temperature, air pressure, humidity, and atmospheric refractive index from 0–4m.