esa-sceva/satcom-chunk-collection huggingface dataset:
# Hyperspectral Python: HypPy
## 2 Design and Implementation
### Image Formats  
HypPy has adopted the ENVI formats as its native formats. The ENVI image and ENVI spectral library formats are binary files with an accompanying text header file. ENVI supports three different types of interleaving in its native format: band sequential (BSQ), band-interleaved by pixel (BIP), and band-interleaved by line (BIL).  
HypPy supports all three ENVI formats: BSQ, BIP, and BIL. In addition, HypPy can map an ENVI spectral library onto an image. The spectral library is then represented as a \(y\) by \(x\) by \(z\) image, in which \(y\) is the number of spectra stored in the spectral library, \(x\) is always 1 (the spectral library is visible as an image with just one column of pixels), and \(z\) is the number of bands in the spectral library. The advantage is that spectral libraries can be viewed just like spectral images. On top of that, a processing chain meant for spectral images can be tested on a spectral library first to see what effect the processing chain will have on the various minerals present in the spectral library.  
For consistency in coding, single-band images are mapped onto three dimensions as well, in which case the band dimension is always 1. There is one exception to this rule: classification images are mapped as two-dimensional one-band images. Classification images have a different use and semantics than hyperspectral data, and for this reason, classification images are treated in a different way in HypPy.  
Internally, HypPy maps all three formats, BSQ, BIP, and BIL, onto an array with a BIP structure, thus relieving the programmer of having to deal with three different formats.
## 4 Data
### Reliability and Validity Test  
Reliability refers to the consistency of the results obtained after repeated measurement of the same variable, which can reflect the authenticity of the characteristics of the respondents. Since the measurement of variables in this study was based on multiple items, and it was difficult to conduct a follow-up survey on respondents, Cronbach's alpha was selected as the test for initial scale reliability, and SPSS 26 software was used for analysis. The Cronbach's \(\alpha\) reliability coefficients of the attributional style of behavior outcome and policy response in the questionnaire are 0.622 and 0.872, respectively, indicating that the energy-saving behavior attributional style questionnaire and policy response questionnaire had an acceptable reliability level.  
To test the validity of the data, SPSS (Version 26.0. IBM Corp: Armonk, NY, USA) software was used in this study. According to the test result, the KMO value is 0.819, above 0.5, and the significance of the Bartlett sphericity test is 0.000 \(<\) 0.05. Therefore, it can be considered that the questionnaire has good structural validity, and the questionnaire questions can effectively detect the degree of things to be measured. Concerning the content validity of the questionnaire, it was first pretested and then changed based on the results of the pretest. The revision and development of the questionnaire were based on in-depth interviews and literature research regarding existing maturity scales. Additionally, invite professors and experts engaged in energy conservation research for a long time to evaluate and revise it. Therefore, it can be considered that all questionnaires in this study have good content validity.
# Spatial Shape-Aware Network for Elongated Target Detection
## 3 Method  
In our research, we focus on the issue of inadequate content analysis within anchor boxes for detecting elongated and rotated targets in RS tasks. To address this challenge, we introduce a Boundary-Guided Spatial Feature Sampling Module (BGSF), a Shape-Sensing Module (SSM), and a Potential Comparison Module (PEM). Specifically, BGSF utilizes supervisory information to guide the sampling positions of convolutional kernels, making feature extraction more aligned with the true shape of the target. To select samples adaptively, SSM modifies the decision threshold according to the shape features of the object. Subsequently, PEM estimates the potential of positive samples by evaluating their standardized shape distance relative to the target. The pipeline of the method is shown in Figure 2.  
The datasets used in this study, namely HRSC2016 [29] and UCAS-AOD [30], may contain inherent biases that could influence model performance. To ensure the robustness of SSAD-Net, we employed data augmentation techniques to simulate the variations in visual conditions encountered in real-world scenarios. These included scaling, rotation, and flipping operations to accommodate different target orientations and scales. Additionally, we applied normalization to standardize the input images, ensuring consistency across different types of remote sensing data.
## 3 Conventional Geolocation Techniques
### Frequency Difference of Arrival (FDOA)  
FDOA, sometimes called differential Doppler (DD), is a technique similar to the time difference of arrival (TDOA) method. The FDOA is the difference between two Doppler shifts of arrival signals [54; 55; 56]. Mathematically, the Doppler shift between the emitter and one of the sensors is defined as  
\[\partial f=v\bigg{(}\frac{f_{0}}{c}\bigg{)} \tag{7}\]  
where \(v\) is the velocity (scalar) of closure between the emitter and sensor, \(f_{0}\) is the signal of the carrier frequency, and \(c\) is the light speed. FDOA can be expressed as  
\[\text{FDOA}=f_{i1}=\partial f_{i}-\partial f_{1} \tag{8}\]  
From Figure 7, one can observe how the Doppler shift or FDOA could be extracted from LEO satellite signals of opportunity. In addition, the Doppler rate and Differential Doppler rate can also be extracted or estimated from the carrier phase of the received signal [57].  
A critical drawback of FDOA is that significant amounts of data must be moved between observation points to do the cross-correlation processing, which is used to estimate the location of the RFI. In addition, it is very expensive, and it is influenced by the NLOS error [58].
## 2 Materials and Methods
### Ratiometric Calcium Imagimolecular Weight ng  
Ratiometric Ca\({}^{2+}\) imaging analysis was performed as described previously [22; 23]. Dissociated SHED \(\beta\)-cells were loaded with Fura2-AM (Molecular Probes) for 40 min at 37 \({}^{\circ}\)C in a calcium imaging buffer (CIB) containing 130 mM NaCl, 3 mM KCl, 0.5 mM MgCl\({}_{2}\), 0.9 mM CaCl\({}_{2}\), 10 mM HEPES, 10 mM sucrose, 1.2 mM NaHCO\({}_{3}\) pH 7.45, and 320 mOsm (adjusted with mannitol). Ratiometric Ca\({}^{2+}\) imaging was performed using an inverted fluorescence microscope (Nikon Instrument) with an excitation filter changer (Sutter Instrument) and a CCD camera (Nikon Instrument) at room temperature. After a 15-min incubation period for de-esterification, the coverslips containing the cells were placed in a recording chamber, which was continuously perfused with CIB with or without drugs. Dual images (510 nm emission) were collected every 2 s using NIS Elements (Nikon Instrument). The Fura response was defined as the ratio of emissions measured during excitation at 340 and 380 nm, and the relative Fura response was defined as the ratio normalized to the baseline Fura ratio. To assess the amplitude of changes in the relative Fura ratio (\(\Delta\) response) in each exogenous application, baseline Fura ratios were calculated prior to stimulation, and the percent increase in the relative Fura ratio was calculated.
## 2 Materials and Methods
### Solar-Induced Chlorophyll Fluorescence (SIF) Observations  
Canopy-level SIF was monitored using an automated chlorophyll fluorescence observation system (AUTOSIF, Bergson Spectrum Science Limited., Beijing, China). The AUTOSIF system included a high-sensitivity spectrometer (QEPRO, Ocean Optics, Dunedin, FL, USA) for retrieving SIF in the O\({}_{2}\)-A absorption band, featuring a signal-to-noise ratio (SNR) of 1000, a wavelength range of 730-785 nm, and a spectral sampling interval of 0.07 nm. A high-throughput optical fiber was installed 5 m above the forest canopy, targeting a circular observation area with a diameter of 4.4 m. The downwelling irradiance was collected using a cosine-corrected optical configuration, while the target surface was observed via a bare optical fiber (Figure 1c,e). The fiber type used in the system was a 600 \(\upmu\)m core fiber (Ocean Optics, Dunedin, FL, USA). Radiometric and wavelength calibrations were performed using a mercury calibration lamp (HL-3 Plus, Ocean Optics Inc., Dunedin, FL, USA) and an integrating sphere (LightFluxColor LFC-illumina Plus2., Labsphere, North Sutton, NH, USA), respectively.
# Metachrylate-Based Polymeric Sorbents for Recovery of Metals from Aqueous Solutions
## 9 Overview of Characterization Methods
### X-ray Photoelectron Spectroscopy (XPS)
The La(III) and Y(III) sorption causes a shift toward lower BEs (binding energies) for P-O peaks, decreases the intensity of deprotonated phosphonate and increases of the intensity of protonated phosphate peaks. XPS and FTIR analysis confirmed the contribution of phosphonate groups in metal binding with the co-existence of different complexes or different interactions with the neighboring reactive groups. [PERSON] et al. also used analysis of XPS spectra for understanding the mechanism of adsorption of gold ions on poly(glycidyl methacrylate) functionalized with 2-aminothiazzo (A-PGMA) [59]. As a result of gold ions adsorption, two new peaks at 82.6 eV and 86.3 eV appeared in the Au 4f spectra. The appearance of a new peak at 167.7 eV in S 2p spectra as well as a peak shift from 399.16 eV to 400.30 eV in the N 1s spectrum were ascribed to chelating between sulfur and nitrogen atoms with the gold ions. In conclusion, XPS analysis revealed that the gold ions adsorption on A-PGMA proceeds via ion exchange and chelation between the sulfur and nitrogen atoms on the surface of A-PGMA and AuCl\({}_{4}\)-ions.
# Integration of Machine Learning-Based Attack Detectors into Defensive Exercises of a 5G Cyber Range
## 6 5G Traffic Generation  
An essential part of the cyber range infrastructure is related to the possibility of emulating 5G networks and the correspondent traffic depending on the specific scenario being run in it. This means that the infrastructure must integrate the 5G network functions (NFs) that make up the core of this type of mobile network, and some mechanisms must bedesigned and deployed to generate and inject traffic through this network. It is possible to differentiate at least two types of traffic specific to 5G. On the one hand, the user-application traffic (i.e., traffic generated by standard applications in User Equipment, such as web browsers, streaming apps, etc.), and on the other hand, signaling traffic needed by the core Network Functions for the correct functioning of the network (that is, PDU session establishment, authentication processes, etc.).  
To address the need to execute ML-based exercises in real 5G environments, a complete 5G network stack has been added to the Mouseworld Lab. This stack was designed using VNFs and, therefore, is compatible with the NFV management framework. Additionally, we developed custom software that is able to emulate a range of user equipment in terms of signaling traffic while allowing us to capture and inject any user traffic from generic sources in the 5G network. The rationale behind this signaling traffic generator/user traffic injector tool is to be able to generate a wide range of different traffic scenarios without needing the deployment of full User Equipment emulators or actual User Equipment with radio access and gNB base stations to receive the traffic.  
As there will not be actual radio stations in the infrastructure, the software implements the protocols and functionalities between the stations and the network core, while the communications between User Equipment and gNB are not actually being emulated.  
We describe in the following subsections the 5G traffic generation tools that we have designed and implemented in Mouseworld. In addition, we detail the 5G infrastructure deployment.
## 4 The Prediction Model When Signal Approaches the Ground Station (Section B)  
Several steps predicted the signal attenuation after it passes construction. In this section, we describe in detail the entire process. Following, are the main process steps:  
Figure 6: (**a**) Comparison of the predicted data and the actual data for 2.4 GHz; (**b**) Comparison of the predicted data and the actual data for 10 GHz; (**c**) Comparison of the predicted data and the actual data for 23 GHz; (**d**) Comparison of the predicted data and the actual data for 48 GHz; (**e**) Comparison of the predicted data and the actual data for 72 GHz. Reprinted with permission from ref. [1]. Copyright 2021 IEEE.  
measuring the building shadowing loss, setting up the prediction model testbed, constructing the database for the learners, developing the predictive model, training the learning system, and aligning the satellite signal strength.
# A Selective Dynamic Sampling Back-Propagation Approach for Handling the Two-Class Imbalance Problem
## 5 Experimental Set-Up
### Classifier Performance and Significant Statistical Test  
The Area Under the receiver operating characteristic Curve (AUC) [44] was used as the criteria of measure for the classifiers performance. It is one of the most widely-used and accepted techniques for the evaluation of binary classifiers in class imbalance domains [10].  
Additionally, in order to strengthen the results analysis, a non-parametric statistical test is achieved. The Friedman test is a non-parametric method in which the first step is to rank the algorithms for each dataset separately; the best performing algorithm should have rank as 1, the second best rank as 2, etc. In case of ties, average ranks are computed. The Friedman test uses the average rankings to calculate the Friedman statistic, which can be computed as,  
\[\chi_{F}^{2}=\frac{12N}{K(K+1)}(\sum_{j}R_{j}^{2}-\frac{K(K+1)^{2}}{4}) \tag{6}\]  
\(K\) denotes the number of methods; \(N\) is the number of data sets; and \(R_{j}\) is the average rank of method \(j\) on all datasets.  
On the other hand, [PERSON] and [PERSON] [52] demonstrated that \(\chi_{F}^{2}\) has a conservative behavior. They proposed a better statistic (Equation (7)) distributed according to the \(F-\)distribution with \(K-1\) and \((K-1)(N-1)\) degrees of freedom,  
\[F_{F}=\frac{(N-1)\chi_{F}^{2}}{N(K-1)-\chi_{F}^{2}} \tag{7}\]  
In this work, the Friedman and Iman-Davenport tests are employed with the \(\gamma=0.05\) level of confidence, and KEEL software [51] is utilized.  
In addition, when the null-hypothesis was rejected, a post-hoc test is used in order to find the particular pairwise method comparisons producing statistically-significant differences. The Holm-Shaffer post-hoc tests are applied in order to report any significant difference between individual methods. The Holm procedure rejects the hypotheses (\(H_{i}\)) one at a time until no further rejections can be done [53]. To accomplish this, the Holm method ordains the \(p\)-values from the smallest to the largest, i.e., \(p_{1}\leq p_{2}\leq p_{k-1}\), corresponding to the hypothesis sequence \(H_{1},H_{2},...,H_{k-1}\). Then, the Holm procedure rejects \(H_{1}\) to \(H_{i-1}\) if \(i\) is the smallest integer, such that \(p_{i}\leq\alpha/(k-i)\). This procedure starts with the most significant \(p\)-value. As soon as a certain null-hypothesis cannot be rejected, all of the remaining hypotheses are retained, as well [54]. The Shaffer method follows a very similar procedure to that proposed by [PERSON], but instead of rejecting \(H_{i}\) if \(p_{i}\leq\alpha/(k-i)\), it rejects \(H_{i}\) if \(p_{i}\leq\alpha/t_{i}\), where \(t_{i}\) is the maximum number of hypotheses that can be true given that any \((i-1)\) hypotheses are false [55].
## 3 Results
### Results of Multiple Regression
#### 3.3.2 Multiple Regression Model for Fuel Mass Flow  
The model equation is shown below:  
\[\begin{array}{ll}RoI_{Flow}=&-\left(2.36\cdot 10^{-2}\right)+3.6\cdot 10^{-5} \ast\text{n}-\left(3.522\cdot 10^{-3}\right)P_{inj}+\left(3.135\cdot 10^{-3} \right)\text{m}\\ &+(0.376)\text{A}+\left(2.808\cdot 10^{-2}\right)\mu_{f}+\left(2.7\cdot 10^{-5} \right)P_{inj}^{2}-\left(3\cdot 10^{-6}\right)\text{m}^{2}\\ &-(2.886)\text{A}^{2}-\left(1\cdot 10^{-6}\right)\times\text{n}\times P_{inj}+ \left(7.66\cdot 10^{-4}\right)\times n\times\text{A}\\ &-\left(2.5\cdot 10^{-5}\right)\times\text{n}\ \times\mu_{f}+\left(6.74\cdot 10^{-3} \right)P_{inj}\times\ A\\ &+\left(4.79\cdot 10^{-4}\right)P_{inj}\times\mu_{f}-\left(1.29\cdot 10^{-2} \right)\text{m}\times\text{A}-\left(4.56\cdot 10^{-4}\right)\text{m}\times\mu_{f}\\ &-(0.15749)\text{A}\times\mu_{f}\end{array} \tag{4}\]  
An equation was also modelled to calculate the RoI based on the flow rate value in each injection, considering the five fundamental variables detailed in Table 10.  
The volumetric flow rate calculation for the injector and experimental results of the calibration injection bench is given in mm\({}^{3}\)/s. This rate was calculated by comparing the mass of fuel injected with the volume delivered per unit time. This comparison was then compared with the manufacturer's record to ensure accurate measurements.  
This model had a regression of 99.42% of the variance, which indicates that the validation model presented had a high assertiveness with an error factor of less than 1%, which allowed for the calculation of the RoI\({}_{\text{Flow}}\) for the fuel flow.  
Figure 9a shows the behaviour of the incidence of each variable presented in the correlation model for the variations or fluctuations in the data measured during each test, denoting that the most representative variable was the viscosity of the fuel, and Figure 9b shows the behaviour of the incidence of each variable presented in the correlation model  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Denomination** & **Variable** & **Units** \\ \hline \(RoI_{Flow}\) & Flow volume & mm\({}^{3}\) \\ \(n\) & Engine speed & s \\ \(P_{inj}\) & Pressure & MPa \\ \(m\) & Mass & mg \\ \(A\) & Area & mm\({}^{2}\) \\ \(\mu_{f}\) & Kinematic viscosity & mm\({}^{2}\) \\ \cline{2-3}  & & s \\ \hline \hline \end{tabular}
\end{table}
Table 10: Details of variables in Equation (4).  
Figure 8: Representativeness (**a**) and incidence (**b**) of variables in Equation (3).  
for the importance of each variable for the use of Equation (4), denoting that the variables with the greatest representativeness were the injection pressure and the speed of the motor.
## 3 Materials and Methods
### Evaluation Metrics  
For the binary classification problem, the samples can be divided into true positive (_TP_) cases, false positive (_FP_) cases, true negative (_TN_) cases, and false negative (_FN_) cases according to the combination of their real categories and prediction categories, where _TP_ represents the number of samples in which positive cases are correctly predicted to be positive, _TN_ represents the number of samples in which negative cases are correctly predicted to be negative, _FP_ represents the number of samples in which negative cases are incorrectly predicted to be positive, and _FN_ represents the number of samples in which positive cases are incorrectly predicted to be negative. There are many evaluation metrics to evaluate the ADHD classification performance.  
Accuracy (_ACC_) is the most common classification evaluation metric, which is the number of correctly classified samples divided by the number of all samples. It is measured as in Equation (4). Generally speaking, the higher the accuracy, the better the classifier.  
\[ACC=\frac{TP+TN}{TP+TN+FP+FN} \tag{4}\]  
Sensitivity (_SEN_) refers to the proportion of correctly identified positive cases in all positive cases, which is shown in Equation (5). High sensitivity means a low missed diagnosis rate.  
\[SEN=\frac{TP}{TP+FN} \tag{5}\]  
Specificity (_SPC_) refers to the proportion of correctly identified negative cases in all negative cases, which is shown as Equation (6). High specificity means a low misdiagnosis rate.  
\[SPC=\frac{TN}{TN+FP} \tag{6}\]
## 6 Machine Learning and 5G Security
### Machine Learning Applications in 5G Security  
Machine learning, with its advanced analytical capabilities, is instrumental in various applications within 5G security [120; 121]. These applications span across diverse domains such as intrusion detection, malware mitigation, and predictive security modeling. In the context of intrusion detection, machine learning algorithms analyze network traffic data to identify patterns that deviate from the norm. SVM and decision trees, for instance, have demonstrated effectiveness in classifying network activities and detecting potential intrusions [122; 123]. Such techniques greatly enhance the security system's responsiveness and accuracy, thereby minimizing the risk of successful cyberattacks. Machine learning is also pivotal in combating malware. It facilitates the identification and classification of harmful software based on their behavioral characteristics and binary features. For instance, deep learning methods such as CNNs are highly efficient in extracting and learning complex features from large-scale data, allowing for effective detection of zero-day malware [124]. Predictive security modeling is another area where machine learning holds significant potential. Leveraging algorithms such as logistic regression and random forests, predictive models can forecast potential vulnerabilities and threats based on historical data and ongoing network activities. This enables proactive defense measures, thereby improving the resilience of 5G networks against sophisticated cyber threats [124].
# A Neural Network Based Approach to Inverse Kinematics Problem for General Six-Axis Robots
###### Abstract  
Inverse kinematics problems (IKP) are ubiquitous in robotics for improved robot control in widespread applications. However, the high non-linearity, complexity, and equation coupling of a general six-axis robotic manipulator pose substantial challenges in solving the IKP precisely and efficiently. To address this issue, we propose a novel approach based on neural network (NN) with numerical error minimization in this paper. Within our framework, the complexity of IKP is first simplified by a strategy called joint space segmentation, with respective training data generated by forward kinematics. Afterwards, a set of multilayer perception networks (MLP) are established to learn from the foregoing data in order to fit the goal function piecewise. To reduce the computational cost of the inference process, a set of classification models is trained to determine the appropriate forgoing MLPs for predictions given a specific input. After the initial solution is sought, being improved with a prediction error minimized, the refined solution is finally achieved. The proposed methodology is validated via simulations on Xarm6--a general 6 degrees of freedom manipulator. Results further verify the feasibility of NN for IKP in general cases, even with a high-precision requirement. The proposed algorithm has showcased enhanced efficiency and accuracy compared to NN-based approaches reported in the literature.  
inverse kinematics; general six-axis robot; neural network; numerical error minimization 20222228909