esa-sceva/satcom-chunk-collection huggingface dataset:
# SC-SA: Byte-Oriented Lightweight Stream Ciphers Based on S-Box Substitution
## 2 SC-SA Cipher
### The Probability of Index Swapping during the RF Phase Analysis  
The RC4 algorithm has long been known to have security flaws, such as the weak key problem, and it no longer meets the security thresholds it was designed for. These issues are caused by biases in the key scheduling algorithm (KSA) that propagates through the Glimpse theorem. If the corresponding biases in the KSA can be eliminated, the biases in the key stream will also disappear. Under these circumstances, RC4 + [24] and its variants were proposed but they have security issues similar to those of RC4. SC-SA does not aim to become a variant of the RC4 algorithm but since its design ideas are similar, it should avoid similar attacks.  
In sequence cipher algorithms similar to RC4, the probability of using index values to swap values inside the S-box is an important basis for measuring the security of the Key Scheduling Algorithm (KSA). If index values are used frequently, biases are likely to occur. SC-SA adopts a design approach similar to RC4, where the indices used for swapping values in the RF phase are A and V. Under ideal security conditions, in 80 iterations, the values of A and V are different, meaning each index is swapped only once, at which point the cipher's security is at its highest. By randomly generating 10,000 keys, the SC-SA cipher algorithm calculates the probability of the number of swapped positions in the S-box under different key influences, as shown in Figure 7. It is evident that the SC-SA cipher algorithm provides a confusion capability of at least 115!, which is significantly higher than the security protection capability of 28.
## 2 Measurement Approach
### Wedge Detection Algorithm  
In order to detect turbulence wedges in thermograms, a model-based algorithm is proposed, which uses a technique called template matching. Template matching finds image parts that match the used template, which means that the proper choice of the template on the basis of a priori knowledge is crucial for the functionality of the algorithm. As a preface, the wedge-shaped templates are described in Section 2.3.1. Then, the two parts of the wedge detection algorithm are explained: the detection of the turbulence wedge's position in Section 2.3.2 and the determination of the wedge's size in Section 2.3.3.
## 3 Results and Discussion
### The Comparison with Other Methods  
This section compares the proposed model with existing models, including ATV [4], ITV [5], L0 TVPADMM [9], and HNHOTV-OGS [10]. Nine distinct IR images were selected as test images. The truck, the buildings, the car, and the figure images were 384 \(\times\) 288 pixels, and the garden, the stairs, the corridor, the road, and the zebra crossing images were 506 \(\times\) 408 pixels. The experimental results are listed in Table 4 and the unit of PSNR as dB. ITV performed the worst, whereas Lp_HOGS outperformed the other four methods in terms of PSNR, SSIM, and GMSD. The PSNR of Lp_HOGS under 30%, 40%, and 50% salt-and-pepper noise was at least 1.2 dB higher than that of L0 TVPADMM, which had the second-best PSNR. Therefore, the proposed Lp_HOGS model achieved better performance for removing salt-and-pepper noise than the other state-of-the-art methods.
# Impact of Gait-Synchronized Vibrotactile Sensory Feedback on Gait in Lower Limb Amputees
## 3 Results
### Results: Functional Gait Assessments
#### 3.3.2 Four Square Step Test--Coordination  
Except for two subjects (18%, p# 2 and 4), all subjects (82%) showed an absolute decrease in the required time to perform the FSST after the intervention (see Figure 7). Three subjects (27%; p# 5, 6 and 7) could decrease the time needed to perform the test by up to 10%, five subjects (45%; p# 1, 3, 8, 9 and 10) by up to 10-20%, and one subject (9%; p# 11) by 27.3%. Subject 4 showed the maximum relative increase (19.2%) after the intervention, and subject 2 showed a relative increase of 10.6%.  
For the FSST, the correlation analyses revealed a moderate, non-significant negative correlation between individual performance and VTFS usage time (r = 0.518, \(p\) = 0.102) and a very weak, non-significant positive correlation between performance and time since amputation (r = \(-\)0.154, \(p\) = 0.651).  
In contrast to the TUG, no significant difference was observed in the medians of the entire cohort (n = 11) before and after the intervention in the FSST (see Figure 5, \(p\) = 0.0537; medians (IQR): 13.51 s (4.54 s) vs. 11.33 s (5.55 s)). The difference in medians between the two measurements for the entire cohort showed an absolute decrease in the test performance time of 2.18 s.  
Figure 6: Scatter plot visualizing the mean performance of individual subjects, along with the median and IQR of the cohort’s overall performance in the Timed “Up and Go” Test (TUG) at baseline (light gray) and follow-up (dark gray). Subjects whose performance changes exceeded the minimum detectable change (MDC) after the intervention are highlighted with a star (\(\bigstar\)).
# Model Predictive Control of Grid Forming Converters with Enhanced Power Quality
###### Abstract  
This paper proposes an enhanced finite control set model predictive control (FCS-MPC) strategy for voltage source converter (VSC) with a LC output filter. The proposed control scheme is based on tracking the voltage reference trajectory by using only a single-step prediction within the controller horizon. Besides, the suitability of different frequency control schemes with the proposed scheme to prevent from inherent variable switching behaviour of conventional FCS-MPC is investigated. Based on that, the proposed method targets two major factors influencing power quality in grid forming applications by enhancing the output voltage harmonic distortion and also preventing variable switching behaviour of FCS-MPC. Although compared to multi-step prediction approaches, only a single-step multi-objective cost function to improve computation efficiency is utilized, the introduced control schemes are able to deliver higher power quality compared to its counterpart methods as well. Furthermore, the effect of different applied cost functions on the transient response of the system is studied and investigated for the future use of the VSC in microgrids (MGs). The effectiveness of the proposed scheme was assessed by simulation using MATLAB/SIMULINK and experiment using a 5.5 kVA VSC module and the results were in good agreement.  
Keywords:finite control set (FCS); grid-forming converters; model predictive control (MPC); cost function (CF); power quality; microgrid (MG) +
Footnote †: journal: Applied Soft 2020, 10, 6390; doi:10.3390/app10186390
## 4 Pool-Experiments Verification and Discussion
### The Joint Control Pool-Experiments of the Underwater Manipulator
#### 4.1.1 Experiments of Joint Step Response  
For the experiments of the joint step response, the manipulator was fixed on the UVIC-I AUV and the each joints move from the initial angle to the target angle. The two sets of initial and target angles of (neck, shoulder, elbow, wrist) joints in this paper are considered as follows  
1 st. initial angle (45.0\({}^{\circ}\), 80.0\({}^{\circ}\), 80.0\({}^{\circ}\), 60.0\({}^{\circ}\)) and target angle (115.0\({}^{\circ}\), 110.0\({}^{\circ}\), 35.0\({}^{\circ}\), 130.0\({}^{\circ}\)).  
2 st. initial angle (80.0\({}^{\circ}\), 120.0\({}^{\circ}\), 35.0\({}^{\circ}\), 100.0\({}^{\circ}\)) and target angle (8.0\({}^{\circ}\), 165.0\({}^{\circ}\), 150.0\({}^{\circ}\), 10.0\({}^{\circ}\)).  
The experiment results of the proposed method and the method in [17] are shown in Figure 3. For the convenience of analysis, the relevant indicator data in Figure 3, such as the overshoot, steady state error, is summarized in Tables 2 and 3.  
Note: 1, 2, 3, and 4 are the step response curves of (neck, shoulder, elbow, wrist) joints of the proposed method, respectively. The step response curves of (neck, shoulder, elbow, wrist) joints of the method in [17] are 5, 6, 7, and 8, respectively  
From Figure 3, it can be seen that the angles of the joints were uniformly converged. In addition, compared with the method in [17], the joint angular velocity (i.e., to illustrate the response speed of system) in this paper had a certain increase. This is due to the fact that the PID sliding manifold in the  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Overshoot** & **Steady State Error** \\ \hline The proposed method & (0.36, 0.62, 0.43, 0.55)\({}^{\circ}\) & (0.025, 0.017, 0.017, 0.025)\({}^{\circ}\) \\ The method in [17] & (0.68, 1.02, 0.98, 1.38)\({}^{\circ}\) & (0.035, 0.024, 0.026, 0.031)\({}^{\circ}\) \\ Reduced by & (47.06\%, 39.22\%, 56.12\%, 60.14\%) & (28.57\%, 29.17\%, 34.62\%, 19.35\%) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Statistical Results of Figure 3a.  
Figure 3: Step response curve of each joint of underwater manipulator. (**a**) Step response curve of 1 st and (**b**) step response curve of 2 st.  
paper could improve the system response speed of each joint of the manipulator. From the Tables 2 and 3, it is shown that the proposed method greatly improved the overshoot and steady state error of the joint of the underwater manipulator. The above data show that the proposed method could effectively improve the joint control accuracy, and verified the effectiveness of the proposed method.
# Prediction Interval Adjustment for Load-Forecasting using Machine Learning
## 3 Materials and Methods
### Data Preprocessing
#### 3.1.1 Load Data Embedding  
For preprocessing, the time series is ordered in a form of delay embedding. The selected number of periods for the delay embedding is represented by \(s\). For this paper, \(s\) is selected by the thumb rule described in [20]. The thumb rule states that for autoregressive models, at least 50 but preferably more than 100 observations should be taken. Based on this thumb rule, we decided to select 10 times the horizons \(h\) needed for this problem (8 horizons). Therefore, every example is conformed by vectors of 80 values, in which the last value is considered to be the dependent variable described by the rest of autoregressive values. Thus, for this paper \(s=79\). The final objective is to transform the original time series dataset into a table form. To achieve this transformation, the set of delayed time series is constructed as follows:  
Let \(L\) be set of load measurements:  
\[L=\{V_{1},V_{2},\ldots,V_{n}\} \tag{1}\]  
where \(n\) is the number of observations.  
Let \(D\) be the set of delayed time series:  
\[D=\{d_{1},d_{2},\ldots,d_{m}\} \tag{2}\]  
where \(m=n-s\) and \(d_{i}\) is defined as follows:  
\[d_{i}=\{V_{j}\in L\,|\,j\in\{t-s,\ldots,t-2,t-1,t\}\} \tag{3}\]  
\begin{table}
\begin{tabular}{l c} \hline \hline
**Name** & **Value** \\ \hline Minimum & 779.2 MW \\ Median & 1418.6 MW \\ Mean & 1501.8 MW \\ Maximum & 2641.0 MW \\ \hline \hline \end{tabular}
\end{table}
Table 1: Statistical properties of the dataset.  
Figure 3: The distribution of values in the whole dataset represented by means of an histogram.  
In summary, the constructed dataset \(D\) contains \(m\) delayed time series \(d_{i}(i\in\{1,2,\ldots,m\})\), and every delayed time series \(d_{i}\) contains values of the \(L\) dataset in the form of \(\{V_{t-s},\ldots,V_{t-2},V_{t-1},V_{t}\}\) where \(t=i+s\). It is important to note that every \(d_{i}\) is distinct because of \(t=i+s\), which means that even if every \(d_{i}\) is constructed by \(\{V_{t-s},\ldots,V_{t-2},V_{t-1},V_{t}\}\) values, \(t\) is different for every\(d_{i}\). In Figure 4, an example of a \(d\) delayed time series is shown.  
For every \(d_{i}\), \(V_{t}\) represents the dependent variable and \(\{V_{t-s},\ldots,V_{t-2},V_{t-1},V_{t}\}\) the independent variables. Also, every \(d_{i}\) is paired to its correspondent period \(p\in\{1,2,\ldots,96\}\). This pairing allows applying machine learning algorithms in subsets defined by each period. Specifically, in the case of Association Rules, it is necessary to apply a discretization method, albeit the format is essentially the same. In Section 3.3.1, this discretization process is explained. In the next section, the process of prediction interval estimation through artificial neural networks is explained.
## 3 System Model
### Quantum Layer
#### 3.1.3 Problem Statement  
While many QKD networks currently rely on trusted relay techniques, the security risks posed by partially trusted or untrusted relay nodes in large networks cannot be ignored. Addressing these risks requires a shift from the assumption that all relay nodes are fully trusted, prompting the development of zero-trust relay approaches.  
Figure 3: A typical structure of an MTD-based zero-trust relay QKD network. To ensure the security of the QKD, a zero-trust spatiotemporal diversification multipath–multi-key pairs framework is used for key distribution.  
Existing solutions, such as multipath QKD strategies, aim to protect key distribution in networks with partially trusted relays. These approaches can be categorized into network connectivity schemes, which tolerate a specified number of compromised nodes [16], and probabilistic approaches, which analyze key distribution security based on the security probability of untrusted relays and paths [17; 19]. However, these studies often overlook the impact of key distribution path selection, diverse user security requirements, and compromised node detection. To address these limitations and improve key distribution security while balancing efficiency, a multipath-based spatiotemporal diversification approach is introduced.
# Research on Path and Sequence Planning for Multi-Cabin Onboard Transportation of Large Cruise Ships
## 2 Mathematical Model of Multi-Cabin Transportation Path Planning  
The development of a mathematical model is a key step in optimizing the cabin transportation path, as it directly relates to the determination of the optimal transportation path for each cabin module. This model must accurately represent the path constraints, environmental factors, obstacle locations, transportation sequence, and physical characteristics of the cabin modules during the transportation process.
_Algorithms_**2011**, \(4\), 239-261; doi:10.3390/a4040239  
_Algorithms_  
**ISSN 1999-4893**  
www.mdpi.com/journal/algorithms  
_Article_  
**Radio Frequency Interference Detection and Mitigation Algorithms Based on Spectrogram Analysis**  
**[PERSON] * and [PERSON]**  
Remote Sensing Lab (RSLAB), Department of Signal Theory and Communications (TSC),  
Universitat Polit\(\,\)ecnica de Catalunya (UPC) and IEEC/CRAE-UPC, Campus Nord, Building D,  
08034 Barcelona, Spain; E-Mail: [EMAIL_ADDRESS]  
* Author to whom correspondence should be addressed; E-Mail: [PERSON]\(\_\)[PERSON]\(\_\)[PERSON]ADDRESS]; Tel.: +34-934-017-361; Fax: +34-934-017-232.  
_Received: 26 August 2011; in revised form: 3 October 2011 / Accepted: 13 October 2011 / Published: 25 October 2011_  
**Abstract:** Radio Frequency Interference (RFI) detection and mitigation algorithms based on a signal's spectrogram (frequency and time domain representation) are presented. The radiometric signal's spectrogram is treated as an image, and therefore image processing techniques are applied to detect and mitigate RFI by two-dimensional filtering. A series of Monte-Carlo simulations have been performed to evaluate the performance of a simple thresholding algorithm and a modified two-dimensional Wiener filter.  
**Keywords:** radiometry; spectrogram; RFI; detection; mitigation
## 4 Experimental Results and Analysis  
### Experimental Protocols  
The test images we used in the experiments are from \"The Whole Brain Atlas\" of Harvard Medical School Image Library ([[http://www.med.harvard.edu/aanlib/home.html](http://www.med.harvard.edu/aanlib/home.html)]([http://www.med.harvard.edu/aanlib/home.html](http://www.med.harvard.edu/aanlib/home.html)) (accessed on 20 September 2021)). Due to limited space, we chose ten brain slices #022-#112 to demonstrate the performance of our proposed algorithm. These ten brain slices are shown in Figure 7. All the experiments in this paper are run on Intel(R) Core (TM) i7-700 HQ CPU @ 2.80 GHz 2.80 GHz, 16 GB RAM, windows 10 and programming language is Python3.6. The parameter settings of the proposed algorithm and the number of thresholds are shown in Table 1.  
\begin{table}
\begin{tabular}{c c} \hline
**Parameter Settings** & **Description** \\ \hline \(\delta=0.01\) & Value that stops the iteration for RGIIM \\ \(W=3\) & filter window size \\ \(K=2,\ 3,\ 4,\ 5\) & Number of the thresholds \\ \hline \end{tabular}
\end{table}
Table 1: Parameter settings of the proposed algorithm.  
Figure 6: An example of hybrid spatial filter. (**a**) Original images, (**b**) Part of the enlarged original images (**c**) The results obtained by hybrid spatial filter, (**d**) Part of the enlarged results images.
# Regional Seismicity of the Northeastern Tibetan Plateau Revealed by Crustal Magnetic Anomalies
## 1 Introduction
(2016) studied magnetic anomalies in the areas of the 2008 Wenchuan earthquake (Ms = 7.8; Sichuan) and Longmenshan Fault [16]. [PERSON] et al. (2021) used the NGDC-720 geomagnetic field model to study the relationship between crustal magnetic anomalies and strong earthquake activity in the southern part of the China North-South Seismic Belt [17]. In addition, the CPD is closely related to seismicity [18; 19; 20; 21]. However, different tectonic units exhibit different relationships between the CPD and seismicity [21]. The NETP is a tectonically active region with complex faults and intense seismicity. So far, works on revealing the regional seismicity of the NETP according to the crustal magnetic anomalies are still lacking. Satellite, ground, oceanic, and aeromagnetic surveys have been used to observe the geomagnetic field [7]. In particular, magnetic surveys by satellites (e.g., Orsted, CHAMP, SAC-C, SWARM, CSES-01, and MACAU-01) have accumulated a large amount of geomagnetic field data [22], leading to a series of high-quality geomagnetic field models being developed [23; 24; 25]. In 2017, the geomagnetic field model EMM2017 was established by combining data from satellite, ground, oceanic, and aeromagnetic surveys ([[https://www.ncei.noaa.gov/products/enhanced-magnetic-model](https://www.ncei.noaa.gov/products/enhanced-magnetic-model)]([https://www.ncei.noaa.gov/products/enhanced-magnetic-model](https://www.ncei.noaa.gov/products/enhanced-magnetic-model)) (accessed on 1 January 2025)). In that model, the order of the spherical harmonic functions is up to 790, and the resolvable spatial wavelength is as fine as 51 km. This model provides valuable data for the analysis of regional crustal magnetic anomalies. The NETP is a region where different tectonic blocks collide and intersect, and large earthquakes occur frequently. However, the mechanisms that generate the earthquakes remain unclear. In this study, we investigated the relationship between crustal magnetic anomalies and seismic activity in the NETP and adjacent areas using the EMM2017 model. We inverted the CPD through a spectral analysis of the magnetic anomalies and compared it with the regional heat flow, crustal structure, and seismicity.
## 2 CPW--Waveguide Transformer Design  
### Transformer Structure Design  
Figure 1 shows the structure of CPW transition to waveguide. The rectangular waveguide was a standard WR-10 waveguide, and a window connecting to the air cavity was opened in parallel with the H plane of the waveguide. The length of the window was the same as the CPW length, and the width and height of the window were \(w_{\text{s}}\) and \(l_{\text{a}}\), respectively. One end of the rectangular waveguide was blocked with solid metal block. An H plane bend structure was designed to facilitate the measure, and the bending radii were \(r_{1}\) and \(r_{2}\), as shown in Figure 1c. The fan-shaped radiation part connected to the end of the signal trace of the CPW was used to propagate the wave of the CPW into the waveguide, as shown in Figure 2. The E plane probe was designed on the high permittivity Al\({}_{2}\)O\({}_{3}\) substrate. The relative permittivity was 9.8, and the height was 127 \(\upmu\)m. The numerical analysis software Ansoft HFSS (High Frequency Structure Simulator) was used to simulate the configuration. The red parts in the Figure 1a are the wave ports, and the optimized detailed parameters of the transformer are shown in Table 1.  
Figure 1: The configuration of the transformer. (**a**) 3D view; (**b**) side view; and (**c**) H plane bend.
# Anomaly Detection of Metallurgical Energy Data Based on iForest-AE
###### Abstract  
With the proliferation of the Internet of Things, a large amount of data is generated constantly by industrial systems, corresponding in many cases to critical tasks. It is particularly important to detect abnormal data to ensure the accuracy of data. Aiming at the problem that the training data are contaminated with anomalies in autoencoder-based anomaly detection, which makes it difficult to distinguish abnormal data from normal data, this paper proposes a data anomaly detection method that combines an isolated forest (iForest) and autoencoder algorithm. In this method (iForest-AE), the iForest algorithm was used to calculate the anomaly score of energy data, and the data with a lower anomaly score were selected for model training. After the test data passed through the autoencoder trained by normal data, the data whose reconstruction error was larger than the threshold were determined as an anomaly. Experiment results on the electricity consumption dataset showed that the iForest-AE method achieved an F1 score of 0.981, which outperformed other detection methods, and a significant advantage in anomaly detection.  
metallurgical energy data; data anomaly detection; iForest algorithm; autoencoder algorithm 2022
## 4 Empirical Results  
### Testing Replicas and Correlation  
Table 3 illustrates the process of constructing replicas, and Tables 4 and 5 evaluate correlation coefficients. Moreover, Table 3 displays the resampling results of 500 and 5000 replicas obtained with a 95% confidence interval. Nevertheless, the number of replicas depends on the number of inputs, outputs, and DMUs [9]. Thence, to check the variations of scores by increasing the number of replicas is necessary. In the study, the results from the data analysis show that the difference was insignificant.  
The Pearson correlation coefficients are used to measure the isotonic relationship between input and output. Table 4 shows the average results of Pearson correlation coefficients obtained from the year 2013 to 2017. The correlation between input and output variables are positive and statistically significant at correlation. The Fisher 95% correlation matrix should be asymmetric and based on estimates of undistorted standard errors. Fisher 95% confidence intervals are presented in Table 5. The correlation coefficient is known to slightly underestimate the population correlation due to the asymmetrical being skewed toward zero of the distribution of correlation coefficient. The correlation coefficient implemented in the DEA model represents a linear relationship between input and output factors. The correlation coefficient \(i\) is between (\(-1\)) and (\(+1\)), and the closer the correlation is to (\(\pm 1\)), the closer it is to a perfect linear relationship.