esa-sceva/satcom-chunk-collection huggingface dataset:
# Localization of GNSS Spoofing Interference Source Based on a Moving Array Antenna
### Our Contributions  
The article presents a method to implement the recommended antenna model and navigation signal capture tracking method and proposes our approach based on this. Theproposed method leverages the consistent emission of deceptive satellite signals from a common source of interference, effectively extracting and confirming these misleading signals through statistical analysis of the double-differenced carrier phase. The article then formulates a cost function to address the direct positioning of spoofing interference with a moving antenna array and integrating carrier phase single-difference raw observation data from multiple observation points, while also presenting a solution methodology for this cost function. Additionally, the article also provides the Cramer Rao Bound (CRB) for the localization method of GNSS spoofing interference sources.  
The effectiveness of the proposed method has been verified through simulations, and the simulation results demonstrate that the proposed method can effectively achieve the detection, extraction, and localization of GNSS spoofing interference sources. In comparison to the conventional two-step methods, the proposed method has stronger adaptability toward larger carrier phase measurement errors (weaker deceptive interference signal strength) and antenna positioning errors. Moreover, the localization accuracy achieved by this approach closely approximates that of the global search method and CRB. It also examines the performance of the proposed method on typical localization observation paths and suggests optimal localization paths. By conducting experiments to assess the effect of varying the number of observation points on the localization accuracy, we propose optimal configurations for the number of observation points in this particular scenario.  
The subsequent sections of this paper are structured as follows. Section 2 initially presents the system model of our approach, encompassing the antenna model and methods for navigation signal acquisition and tracking. Building upon this foundation, Section 3 proposes a method for localizing GNSS spoofing interference sources using a moving array antenna. Section 4 of this paper presents the experimental chapter for the proposed method, which validates the effectiveness of the method and performs a comprehensive and systematic simulation analysis of the key factors affecting its performance. Section 5 is devoted to the conclusion of this paper.
## 2 Materials and Methods
### Materials and Chemicals  
Dulbecco's modified eagle medium (DMEM) and fetal bovine serum (FBS) from Gibco (Carlsbad, CA, USA). Dimethyl sulfoxide (DMSO), lipopolysaccharide (LPS), 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolzoiumbromide (MTT) and dexamethasone (DXM) from Sigma-Aldrich (St. Louis, MO, USA). Penicillin-streptomycin from Welgene (Gyeongsangbuk-do, Korea). Tin protoporphyrin IX (SnPPIX) from Cayman Chemicals (San Diego, CA, USA). NF-kB p65 (ab7970) antibody from Abcam (Cambridge, UK). Antibodies against iNOS (#13120), COX-2 (#12282), IkB\(\alpha\) (#4812), p-I-kB\(\alpha\) (#2859), HO-1 (#70081), Lamin A/C (#4777), NRF (#12721), HRP linked anti-mouse IgG (7076), and HRP linked anti-rabbit IgG from Cell Signaling Technology (Bervely, MA, USA). Antibodies against \(\beta\)-actin (A5411) from Sigma Aldrich (St. Louis, MO, USA), KEAP1 (sc-514914) from Santa Cruz Biotechnology (Dallas, TX, USA), and HSP90 (13171-1-1 AP) from Proteintech (Rosemont, IL, USA). 15-PGDH (160140) and mPGES-1 (9160615) antibodies from Cayman Chemicals (San Diego, CA, USA).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameters** & \multicolumn{2}{c}{**Conditions**} \\ \hline Analytical column & TSK-gell ODS-80 Ts (4.6 mm \(\times\) 150 mm) \\ Column temperature & & 35 °C \\ Injection volume & & 10 \(\mu\)L \\ UV Wavelength & & 277 nm \\  & Final time (min) & Water in 0.1\% \\  & 0.0 & 80 & 20 \\ Mobile phase & 30.0 & 20 & 80 \\  & 30.1 & 0 & 100 \\  & 40.0 & 0 & 100 \\  & 40.1 & 80 & 20 \\ Flow rate & & 1.0 mL/min \\  & Capillary & & 3.00 kV \\ Voltages & Cone & & 40 V \\  & Extractor & & 2 V \\  & RF Lens & & 0.2 V \\ Source Temperature & & 120 °C \\ Desolvation Temperature & & 400 °C \\ Gas flow & Desolvation & 600 L/h \\  & Cone & & 30 L/h \\ \hline \hline \end{tabular}
\end{table}
Table 1: The analysis conditions of liquid chromatography–mass spectrometry.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Compounds** & \begin{tabular}{c} **Linear Range** \\ **(ug/mL)** \\ \end{tabular} & **Regression Equation** & **R\({}^{2}\)** & \
\begin{tabular}{c} **LOD** \\ **(ug/mL)** \\ \end{tabular} &
\begin{tabular}{c} **LOQ** \\ **(ug/mL)** \\ \end{tabular} \\ \hline baicalin & 62.5–1000 & y = 428.15x + 4238.5 & 1 & 13.09 & 39.67 \\ baicalein & 12.5–200 & y = 529.06x – 8488.3 & 0.9883 & 47.94 & 145.27 \\ wogonin & 6.25–100 & y = 983.39x – 328.62 & 0.9999 & 1.66 & 5.02 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Calibration curves, correlation coefficients, limit of detection (LOD) and limit of quantitation (LOQ) of baicalin, baicalein, and wogonin.
## 2 Flexible Electronics Based on GnPs
### Flexible Electronics Outlook  
In brief, GnPs have a high potential for flexible electronic devices. EMI shielding, antennas, supercapacitors, and bendable electrodes for solar cells are the most promising applications. The employment of large scalable production processes, together with the industrial availability of GnPs and the high resistance to mechanical stresses (e.g., bending cycles and abrasion), are all encouraging for the large-scale expansion of GnP-based flexible electronics. The large number of work dealing with the functionalization of cellulose and/or the employment of biopolymers and green methods is also promising for the future of sustainable electronics [68; 69].  
Figure 7: GnPs-cellulose nanocomposite used as electrode for surface electromyography. (**a**) Photograph displaying a standard electromyography titanium electrode (Ti electrode) and the cellulosic biocomposite; (**b**) Photograph showing both electrodes strapped to the arm side by side; (**c**) Signal acquired from the titanium conductor; (**d**) Signal recorded from the biocomposite; (**e**) Superimposed signals from both conductors. The signal rests at zero when the wrist is not flexed (inset on the left). The signal saturates when the wrist if flexed (inset on the right). Reprinted with permission from Advanced Electronic Materials, 2, 11, 1600245. Copyright 2016 Wiley.
## 6 Lifetime Estimation  
The orbital decay's primary influencing characteristics (orbit altitude and mass) for the proposed nanosatellite were taken into consideration as precisely as possible to eventually obtain a reliable prediction of lifetime (orbit decay), which is displayed in Figure 20.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Components** & **Mass (g)** \\ \hline
**Chassis** & 155 \\ \hline
**Solar panels** & \(50\times 5\) \\ \hline
**ADCS** & 351 \\ \hline
**EPS (with accumulators)** & 163 \\ \hline
**Communication System** & 75 \\ \hline
**Payload** & 55 \\ \hline
**OBC** & 56 \\ \hline
**Antenna** & 85 \\ \hline
**Hamesing** & 15 \\ \hline
**Margin** & \(\pm 10\%\) \\ \hline
**Total** & 1325.5 \\ \hline \hline \end{tabular}
\end{table}
Table 14: Mass budget for the proposed nanosatellite.  
Figure 19: Profiles of power consumption, power generation, and battery capacity.  
Figure 20 compares the lifetime predictions for six different atmospheric models, including the widely used Jacchia-Roberts, the most evolved NRLMSISE 2000, and the oversimplified Standard 1976. When the findings of two atmospheric density models are compared, the disparities are obvious. As illustrated below, the standard 1976 model is systematically overly pessimistic and should be avoided. With the NRLMSISE 2000 model, the nanosatellite's lifetime is predicted to be roughly two and a half years for the 400 km CO. The practical instance is obtained by employing the Jacchia-Roberts algorithm, which provides a lifetime of around two years.  
A crucial step in determining the viability of the nanosatellite mission and its lifetime is the reliable assessment of the battery lifetime to orbital cycles and discharge capacity. If the battery is discharged faster, its lifetime will be reduced, as the results of the following analysis, shown in Figure 21, demonstrate the feasibility of the proposed mission.  
According to the results presented in Figure 21, it can be noticed that the battery can resist up to 1.5 years, with 1C of battery discharge, due to limitations imposed by DoD-rated value, which is 30%.  
Figure 21: Battery capacity degradation according to the orbit cycles.  
Figure 20: Altitude decay according to the orbit cycles.
# Real-Time Implementation of Multiband Spectrum Sensing Using SDR Technology
## 3 Implementation of MBSS Technique Using SDR Technology
### Impulsive Noise Reduction
#### 3.3.3 Noise Inhibition through Coefficients  
To reduce high-frequency noise, principally abrupt changes (impulsive noise), in the PSD, the values of the detail and approximation coefficients are reassigned. This task is the most important of this block of noise reduction. The flow chart that describes this submodule is shown in Figure 8.  
Once the detail coefficients \(dd_{ji}\) have been scaled for each device \(i\), a double-threshold condition, \(L_{1}<dd_{ji}<L_{2}|L_{1}=0.01,\ L_{2}=0.4\), is applied to the scaled coefficients \(dd_{ji}\), and these indices must not be consecutive. The indices that satisfy both conditions are stored in the variable \(ind\).  
Figure 8: Operation of the noise inhibition submodule.  
Figure 6: PSD decomposition through the MRA.  
Figure 7: Detail coefficient scaling submodule.  
The detail coefficients \(dd_{ji}\) of the \(ind\) localities and those that are around, previous, and next, are assigned the value of \(1\times 10^{-15}\). Practically, the detail coefficients are turning off, such that these components do not affect the next signal to be reconstructed, which is where high-frequency noise exists. In the remaining localities of \(d^{\prime}_{ji}\), the original value of \(d_{ji}\) is assigned.  
With the previous processing, the \(ind\) indices of the detail coefficients coincide with the indices of the approximation coefficients, which also have to be modified to remove some abrupt changes when reconstructing the signal; this coincidence is due to the fact that the level of decomposition is \(L=1\). To modify the approximation coefficients \(a^{\prime}_{Li}\), a close colony of three samples of approximation coefficients is replicated in the \(ind\) indices mentioned. In the remaining localities of \(a^{\prime}_{Li}\) the original value of \(a_{Li}\) is assigned.  
The result of this block corresponds to the approximation coefficients \(a^{\prime}_{Li}\) and the detail coefficients \(d^{\prime}_{ji}\) with the corresponding attenuations to eliminate the different noise variants mentioned above. Figure 9 shows an example of the difference between the original coefficients and those modified by this block.
## 2 Material & Methods
### Participants
#### 2.1.1 Normal-Hearing Subjects  
Twenty NH subjects participated in this experiment. Their age ranged from 18 to 33 years old, with an average of 25 years. They were recruited among the students of the Claude Bernard Lyon 1 University, through a recruitment notice sent via email. An otologic examination was performed before entering the study in order to exclude subjects with previous pathologies or deafness. All these subjects were considered to have normal hearing according to recommendations of the International Bureau for Audio-Phonology, as their auditory thresholds were below 20 dB HL for all frequencies between 250 and 8000 Hz.
## 4 Experiments  
### Description of Dataset  
In this experiment, the real vibration data of the centrifugal pumps is utilized to verify the effectiveness of out proposed fault diagnosis method. The vibration signals are acquired from an installed accelerometer, with a sampling rate of 10.24k Hz, and their engineering unit is millivolt (mV), which is shown in Figure 5.  
There are 5 typical modes of centrifugal pumps in our dataset, which are respectively the inner-race bearing fault, outer-race bearing fault, ball fault, impeller fault and normal status. For each mode of the centrifugal pumps, we divide and obtain 60 samples according to the standard of 5120 points per sample, which means that there are a total of \(5\times 60=300\) samples in our dataset.  
In order to test the performance of the proposed method under the scenarios of few failure samples, we divide the raw dataset into the training set and the testing set according to the ratio of 1: 5, which means that there are 10 samples under each fault mode to be used to train the fault classification model. In addition to that, in the Section 4.3, we set up additional several sets of experiments with training samples of smaller sizes, to further compare the adaptability on the extremely few failure samples between the proposed method and other existing mainstream ones.
# Comparative Analysis of Image Classification Models for Norwegian Sign Language Recognition
## 3 Methods
### KNN Model
#### 3.2.6 Experiment 1: 64 \(\times\) 64 Images  
Figure 7 displays the accuracy for the different values of K and the different metrics. Upon completion of the randomized search and cross-validation process, the optimal hyperparameters for the KNN model with a 64 \(\times\) 64 resolution were identified as follows:'metric': 'Manhattan', 'n_neighbors': 4, and 'weights': 'uniform'. By training the KNN model with these parameters, we achieved an outstanding accuracy of 99.9% on the unseen test dataset. This accuracy illustrates the effectiveness of the methods we have implemented in this model.  
In Table 3, we present the performance of the 64 \(\times\) 64 KNN model. The classification report from sklearn metrics provides a comprehensive view of the precision, recall, F1-score, and support for each sign, as well as the number of errors made by the model.  
Figure 7: Result of RandomizedSearchCV for our 64 \(\times\) 64 KNN model.  
Precision, which measures the model's ability to identify positive instances correctly, is at 1.00 for all signs, indicating that the model has a high degree of accuracy when it predicts a sign. Recall, which measures the model's ability to find all the positive instances, is also at 1.00 for all signs, showing that the model is highly effective at identifying all instances of a sign in the dataset. The F1-score, the harmonic mean of precision and recall, is also at 1.00 for all signs, indicating a balance between precision and recall in the model's performance.  
Despite the model's high accuracy of 99.99%, it is important to note that it took the model 188 s to make predictions on the unseen data. This trade-off between accuracy and time is a consideration for the practical implementation of the model. However, the overall performance of the model demonstrates its robustness and effectiveness in NSL recognition.  
To further confirm the model's accuracy, we aimed to display correctly and wrongly predicted images. Figure 8 shows randomly selected correctly predicted images, where the actual image is displayed as the original image input, and the predicted image is presented as the image version of the HOG features. Figure 9 displays the wrongly predicted image found in the entire model, highlighting the exceptional overall accuracy achieved by the model.
# Broadband Power Line Communication for Integration of Energy Sensors within a Smart City Ecosystem
## 6 Experimental Simulation Results
### Modulation Sweep and Physical and Application Throughput Simulation  
The first simulation considers all available modulations. Figure 2 demonstrates BPL throughput on the application layer. The communication includes the UDP/IP protocol, thus the shown rate is considered as the application rate. Input parameters for simulation are described in Table 8.  
Figure 7 displays the results of the modulation sweep simulation. The QAM4 modulation has tan application rate around 40 Mbps with a reach of approximately 40 m. With multi-state modulation QAM8 the range increased by 60 m and with QAM16 by 150 m from QAM4. A significant throughput drop was reached at 200 m, when the difference of the two closest modulations was approximately 20 Mbps.  
Figure 7 displays the comparison of physical and application rate with QAM4096 modulation. The UDP frame headers show the differences of values, where the biggest was 54 Mbps.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Access method & CSMA/CA \\ Cable type (LV) & NAY7 4 \(\times\) 150 SE \\ Bandwidth & 25 MHz \\ Modulation & QAM4–QAM4096 \\ Transmit PSD & \(-\)55 dBm/Hz \\ Background noise & \(-\)80 dBm/Hz \\ \hline \hline \end{tabular}
\end{table}
Table 8: Input channel parameters of modulation sweep.
# Improved Multi-GNSS PPP Software for Upgrading the DEMETRA Project Time Monitoring Service
## 2 Updated Atomium PPP Algorithms  
### Atomium Galileo PPP  
As for GPS, the P(Y) code and carrier phase signals ionosphere-free combination on L1 and L2 band are used in the old version of Atomium PPP. The code and carrier phase ionosphere-free measurements of Galileo on E1 and E5a band are added in the new Atomium Galileo PPP computation. The post-processed products of GNSS satellite clocks and orbits are selected for the PPP computation to provide the clock solution with the highest possible stability. To include Galileo, the PPP uses the post-processed satellite products from the IGS in the frame of the MGEX (Multi-GNSS Experiment), which contains products for all the GNSS constellations [28].  
To deal with both GPS and Galileo signals in PPP to generate a coherent receiver clock solution, an additional parameter called the inter-system bias (ISB) between GPS and Galileo measurements must be estimated in addition to the antenna position, the receiver clock, and the troposphere delay as the outputs of the least square computation. This bias is the sum of both the differences between the hardware delays in the receiving chain for GPS and Galileo signals and the bias between the time references for the GPS and Galileo satellite clocks in the MGEX products. This ISB is considered as a constant value during the estimation in one daily data batch, based on the assumption that the receiver delay is stable enough during one day, and the same handling scheme is used in the MGEX analysis center [29].  
Note that the observed distance between satellite and receiver are measured between the phase centers of the antennas at both ends, while the satellite positions provided by IGS are at the center of mass, they need to be corrected to the phase center positions. The file of IGS phase center corrections for the GNSS antennas at receiver and satellite is utilized. It contains the mean phase center offset and phase center variation corrections and is available on the IGS FTP [30] in ANTEX format (current version is igs14.atx).  
The phase center variations applied to GPS and GLONASS satellite antennas are nadir-dependent, while the ones to the Galileo satellite antennas are both azimuth- and nadir-dependent. The azimuth angle of the satellite antenna is estimated in the satellite-fixed coordinate system [31].  
The phase center variations for the GNSS receiver antennas are elevation- and azimuth-dependent. However, few antennas are currently calibrated for Galileo signals in the IGS ANTEX file. In this paper, we use the same values in the receiver antenna phase center corrections on GPS L1 and L2 frequencies for the corrections on Galileo E1 and E5a, separately.  
Finally, the outputs of Galileo PPP are receiver antenna GPS/Galileo mean phase center position; receiver antenna ARP (antenna reference point) position; receiver clock errors to the MGEX time; troposphere zenith wet delay; ISB.
# Models versus Datasets: Reducing Bias through Building a Comprehensive IDS Benchmark
## 4 Experiments, Results, and Analysis
### Model Interpretation--Global Explanation  
Similar to the local explanations presented in Section 4.1, global explanations also interpret the model by providing insights on input features, patterns, and the output correlation to promote model behavior transparency [73]. SHAP provides various methods to explain models. This paper's scope is limited to deep learning models only, so we used DeepExplainer and GradientExplainer to extract important contributing features in the overall model output. Analyzing global explanations of models is important to understand general model behavior when dealing with large datasets and a large number of features. We implemented multiclass classification in our experiments and extracted the top 20 contributing features for each attack type. The experiments were run on all eight datasets and eight DL classifiers. To conserve space in the paper, we present the visual output of only two datasets in Figure 8. Figure 8a shows the top 20 most contributing features extracted from the BoT_IoT dataset, and Figure 8b shows the top 20 most contributing features extracted from the UNSW_NB15 dataset. Different target classes are shown in color legends as well.  
SHAP adds the absolute Shapley values per feature per target label to identify important features globally and sorts them with maximum values on the top and minimum values at the bottom. The visual graph in Figure 8 depicts important contributing features from top to bottom in different colors. The process is represented in Equation (3).  
\[I_{\text{j}}=\Sigma_{i=1}^{n}|\phi_{\text{j}}^{(i)}| \tag{3}\]  
where \(\phi_{\text{j}}^{(i)}\) refers to the Shapely value for feature j in the \(i\)-th data; N is the total number of samples in the dataset; and \(I\_\)j is the average Shapley value of the feature j. Briefly, Figure 8a depicts dur as the most important feature followed by sum, N_IN_Conn_P_DstIP, etc., in the BoT_IoT dataset. Similarly, Figure 8b depicts sttl as the most important feature, followed by ct_state_ttl, dttl, smeasz, etc. Further details and analysis of which features each classifier considered important are presented in Section 4.3. Global explanation helps to refine further models towards building an online IDS, which can look at only a limited set of network traffic features to make decisions quickly in an unsupervised manner.  
Figure 8: SHAP global explanation of top 20 features impact on model output magnitude.  
Figure 7: LIME local explanation of DoS vs. NOT DoS attack.
# Spatial Image-Based Walkability Evaluation Using Regression Model
## 2 Proposed Method  
This study proposes a walkability evaluation method using road images and Figure 1 shows the flowchart of study. The proposed method consists of data acquisition and feature analysis.  
For data acquisition, images of roads required for the walkability evaluation are collected, and walkability is evaluated through crowdsourcing. Semantic segmentation is employed to extract the walking environment features, and the TrueSkill algorithm is used to calculate scores for the images used in the walkability evaluations, thereby ranking them. Additionally, a web application is built to visually confirm the results of the walkability evaluation.  
Regarding feature analysis, a dataset for training the regression models is constructed by the 150 features obtained through semantic segmentation and the image scores calculated using the TrueSkill algorithm. The training dataset comprises a data identifier, image name, image score, and 150 features. The five regression models are trained on the dataset, and their performances in predicting walkability scores are compared. The regression models analyze features that significantly contribute to predicting walkability scores for evaluation and extract feature importance based on their contribution. To improve accuracy, the  
Figure 1: Flowchart of this study.  
five regression models are retrained using the extracted feature importance. Through this process, we conduct this study to find the spatial correlations existing in road images.