esa-sceva/satcom-chunk-collection huggingface dataset:
Sensors_**2010**, _10_, 8332-8347; doi:10.3390/s100908332  
_Sensors_  
**ISSN 1424-8220**  
www.mdpi.com/journal/sensors  
_Article_  
**Sturdy Positioning with High Sensitivity GPS Sensors Under Adverse Conditions**  
**[PERSON] *, [PERSON] and [PERSON]**  
University of Ljubljana, Faculty of Civil and Geodetic Engineering, Jamova 2, SI-1000 Ljubljana, Slovenia; E-Mails: [EMAIL_ADDRESS] (O.S.); [EMAIL_ADDRESS] (B.S.)  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS]; Tel.: +386-1-4768-648; Fax: +386-1-4768-545.  
_Received: 8 July 2010; in revised form: 20 August 2010 / Accepted: 20 August 2010 / Published: 3 September 2010_  
**Abstract:** High sensitivity GPS receivers have extended the use of GNSS navigation to environments which were previously deemed unsuitable for satellite signal reception. Under adverse conditions the signals become attenuated and reflected. High sensitivity receivers achieve signal reception by using a large number of correlators and an extended integration time. Processing the observation data in dynamic and rapidly changing conditions requires a careful and consistent treatment. Code-based autonomous solutions can cause major errors in the estimated position, due primarily to multipath effects. A custom procedure of autonomous GPS positioning has been developed, boosting the positioning performance through appropriate processing of code and Doppler observations. Besides the common positioning procedures, robust estimation methods have been used to minimise the effects of gross observation errors. In normal conditions, differential GNSS yields good results, however, under adverse conditions, it fails to improve significantly the receiver's position. Therefore, a so-called conditional DGPS has been developed which determines the position differentially by using data from the strong signals only. These custom-developed procedures have been tested in different conditions in static and kinematic cases and the results have been compared to those processed by the receiver.  
**Keywords:** GNSS; GPS positioning; high sensitivity; differential GPS; adverse conditions; Doppler observations
## 1 Introduction
Moreover, the phase noise was superior to \(-117.6\) dBc/Hz at 10 kHz frequency offset when the generated signal frequency was 15 GHz. The phase noise value was 13.7 dB lower than that obtained in the open-loop structure and 8.9 dB lower than that obtained in the single-loop OEO. Furthermore, by utilizing an FP-LD in the proposed OEO structure, a dual-frequency dual-loop OEO with different frequency configurations could be tuned up to 12 and 18 GHz. It should be noted that the tunability for the proposed microwave signal generation is only limited by the electrical bandwidths of the amplifier, electro-optic modulator and photodetector. Thus, higher microwave frequencies could be obtained using the devices with larger bandwidths in the OEO loop.
# Constant Transmission Efficiency Dimming Control Scheme for VLC Systems
###### Abstract  
As a novel mode of indoor wireless communication, visible light communication (VLC) should consider the illumination functions besides the primary communication function. Dimming control is one of the most crucial illumination functions for VLC systems. However, the transmission efficiency of most proposed dimming control schemes changes as the dimming factor changes. A block coding-based dimming control scheme has been proposed for constant transmission efficiency VLC systems, but there is still room for improvement in dimming range and error performance. In this paper, we propose a dimming control scheme based on extensional constant weight codeword sets to achieve constant transmission efficiency. Meanwhile, we also provide a low implementation complexity decoding algorithm for the scheme. Finally, comparisons show that the proposed scheme can provide a wider dimming range and better error performance.  
visible light communication (VLC); dimming control; constant transmission efficiency; error performance; light-emitting diode (LED) +
Footnote †: journal: _Photonics_  
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# Physical Side-Channel Attacks on Embedded Neural Networks: A Survey
## 6 Discussion and Future Research Leads
### Challenges of Studied Attacks  
Physical SCA attacks exploiting power and EM traces constitute an emergent research field that is rapidly gaining interest. These attacks have shown that they are a real and dangerous threat and that mitigation techniques are undoubtedly required. However, several limitations of the studied attacks can be identified.  
**Reproducibility:** Very few works reference (dedicated articles and/or source code) the DNN model and the implementation details for their evaluation. Experimental results, however, might significantly vary depending on implementation details.  
**Scalability/genericity:** As this is still an emergent research field, the majority of attack works aim at providing a proof of concept for the feasibility of SCA attacks as well as their potential effect. However, the scalability and genericity, for instance from a different physical target or a more complex and realistic data set, are not often considered. Moreover, some attacks are specific to a certain physical target or to a certain design choice.  
**Metrics for evaluation:** Several metrics have been proposed for evaluating the efficiency of attacks including the accuracy of the recovered information, the portion of the recovered information and the similarity of results for victim and recovered networks (see Table 2). We can note that some metrics serve for evaluation purposes when showing a proof-of-concept but cannot be used in an actual attack. This is, for example, the case of the comparison on the accuracy of the recovered information and, for instance, of recovered weight values. Comparing them to the victim DNN, the actual values are not possible as this information will not be accessible to the attacker. Moreover, when looking at comparing attacks or at studying their feasibility on more realistic scenarios, it is necessary to be able to evaluate their cost and complexity. The number of traces required for performing an attack are usually provided. However, very few works consider the overhead in terms of memory storage or recovery techniques computation effort. Similarly, as mitigation techniques emerge, common metrics for evaluating the resilience of defenses and their cost will be of great matter.
# Two-Channel OTDM System for Data-Center Interconnects: A Review
## 2 Two-Channel OTDM System with Constant-Phase Pulse
### OTDM System Using a Multilayer Equalizer
(**b**) Structure of the demultiplexer as the first-layer equalizer. (**SPS: samples per symbol; NL: nonlinear; SW: switch). improve the BER performance, likely because of the unsuitable time delay of the OTDM transmitter. By contrast, when using the multilayer equalizer, the BER performance was slightly affected by the sampling position. Subsequently, the nonlinear MIMO equalizer could effectively lower the BER, as shown in Figure 7b. Figure 8a shows the BER curves of the 150 Gb/s OTDM-PAM-8 signals under the back-to-back condition when using a symbol-spaced demultiplexer with a MIMO equalizer [29]. The nonlinear MIMO equalizer could not improve the BER performance, consistent with the results shown in Figure 7a. Instead, the performance deteriorated because of overfitting. However, when the multilayer equalizer was used, the nonlinear MIMO equalizer with either \(\mathbf{M}=[12,12]\) or \([12,0]\) could improve the BER more effectively than the linear equalizer, as shown in Figure 7b, likely because it compensated for the impairments due to the unsuitable time delay of the transmitter. Therefore, a BER lower than \(10^{-3}\) could be achieved using the multilayer equalizer (consisting of an FFE-based demultiplexer and a nonlinear MIMO equalizer) even after transmission over a 1.9 km SSMF. Figure 8: BER curves of the 150 Gb/s PAM-8 signals under the back-to-back condition when (**a**) a symbol-spaced demultiplexer with a MIMO equalizer and (**b**) a multilayer equalizer. (**c**) BER curves after 1.9 km transmission. Figure 7: BERs depending on the sampling position when using (**a**) a symbol-spaced demultiplexer with a MIMO equalizer and (**b**) a multilayer equalizer. The received optical power was \(-1\) dBm under the back-to-back condition. (**c**) Intensity histogram measured at the optimal timing phase when using a symbol-spaced demultiplexer.
# Prediction of Self-Healing of Engineered Cementitious Composite Using Machine Learning Approaches
## 2 Experimental Program
### Sample Preparation and Crack Measurement  
A planetary-type mixer of 50 L capacity was used to produce the ECC specimens. During the mixing process, the solid ingredients including cement, mineral admixtures, and sand were initially placed into the mixer and dry mixed for 30 s. Then, the water with HRWR was added, and the mixture was mixed for 2 min. After that, the PVA fibers were slowly added, and mixing was continued until a uniform distribution of fibers in the mix. After mixing, ECC pastes were cast into standard molds with dimension of 100 mm \(\times\) 200 mm. The specimens were demolded 24 h after casting and stored in a curing room with a temperature of \(23\pm 2\)\({}^{\circ}\)C and a relative humidity (RH) of \(90\pm 5\)% for 28 dd. To prepare the self-healing test samples, the cylinder specimens were cut into 50 mm-thick slice samples using a diamond blade saw.  
A newly developed splitting tensile test apparatus was used to generate micro-cracks, as shown in Figure 3a. It consisted of a steel frame, top and bottom members, and pre-stressed loading steel plates (5 mm thick) on both sides with loading nuts and wire springs, as shown in Figure 3b. Both steel plates were connected to the steel frame by nuts and wire springs. The specimen was placed inside the steel frame and then pre-stressed by the steel plates from both sides, limiting the propagation and size of cracks and preventing excessive crack growth.  
Micro-cracks less than 150 \(\upmu\)m were produced by pre-loading the ECC samples up to 70% of their maximum splitting strength. A digital microscope was used to measure the crack width on the surface of the specimens, as shown in Figure 3c. After the pre-loading, the cracked specimens were subjected to wet-dry (W/D) cycles to promote self-healing. Each W/D cycle included 24 h of wetting followed by 24 h of drying in laboratory conditions at \(23\pm 2\)\({}^{\circ}\)C and an RH of \(50\pm 5\)%. After 10 W/D cycles, the cracks were measured again by the digital microscope to examine the extent of crack recovery. Figure 4 illustrates the self-healing of cracks of an ECC specimen before and after 10 W/D cycles.
## 1 Introduction
Multi-band OFDM is very similar to sub-carrier multiplexing (SCM), which recently attracts much attention [22; 23] due to its transmission reach improvement of about 20% compared to its high-rate single-carrier counterpart [22]. SCM is a transmission technique which optimally trades off intra- and inter-channel non-linear effects by an optimization of the baud-rate of its sub-bands [23]. In this paper, we experimentally investigate the performance of the 3 rd-order IVSTF-NLE in a WDM experimental using a 400 Gb/s, DP, 16 QAM, OFDM super-channel at the center of a WDM comb, after propagating through 10 \(\times\) 100 km of an SSMF [16]. In addition, its performance is compared with the conventional DBP-SSF method with multiple steps per span [9]. Then, we extend the comparison study, via Monte Carlo simulations, by replacing the SSMF with a large effective areafiber (LEAF) to test the robustness of the 3 rd-order IVSTF-NLE against the multi-span DBP-SSF NLE in the presence of stronger nonlinear effects. We observe that, in the presence of strong nonlinear effects, the 3 rd-order IVSTF-NLE performs similarly to the almost twice more complex DBP-SSF with two-steps-per-span, whereas the eight steps-per-span DBP-SSFR is only slightly better. Nonetheless, the later algorithm is very demanding, in terms of real multiplications, as we estimate in the present study. The rest of the paper is organized as follows: In Sections 2 and 3, the experimental and the simulation setups are described, respectively. In Section 4, we present and discuss the experimental and simulation results. In addition, we estimate the computational complexity of the NLEs in terms of the number of real multiplications. Finally, the conclusions are summarized in Section 5.
## 2 Data Analysis of Load
### Statistical Parameter Descriptions  
After outliers were detected and data were cleaned, the output was fed to various regression methods. A quantitative analysis of each regression method is presented. Quantitative analysis includes the calculation of RMSE, MAE, MSE, \(R^{2}\), and Prediction Speed. Errors were obtained from the true versus the predicted value of the system [10]. RMSE was obtained by the square root of the mean of all the errors as shown in (4). MAE predicts the average of the modulated difference between actual and predicted, which is computed in (5). The average of the squared difference between average and predicted values are given by MSE which is calculated in (6). \(R^{2}\) explains the variance in the linear dependency of a predicted value with respect to the true value which is determined in (7). It lies between 0 and 1, where 1 represents a perfect fit model and 0 represents a perfectly unfit model. A detailed explanation of all the statistical models is shown in [15]. After the best method was determined, the MSE was further minimized by tuning or varying hyperparameters such as leaf size. This was implemented by using three optimization techniques--Bayesian Optimization, Grid Search, and Random Search--after which optimal parameters were determined.  
\[\text{RMSE}=\sqrt{\sum\limits_{j=1}^{n}\frac{\left(\overline{y}_{j}-y_{j} \right)^{2}}{n}} \tag{4}\]  
\[\text{MAE}=\frac{\sum\limits_{j=1}^{n}\left(\overline{y}_{j}-y_{j}\right)}{n} \tag{5}\]  
\[\text{MSE}=\frac{1}{n}\sum\limits_{j=1}^{n}\left(\overline{y}_{j}-y_{j}\right) ^{2} \tag{6}\]\[R^{2}=1-\frac{\text{Anamolic Variation}}{\text{Total Variation}}, \tag{7}\]  
where \(\overline{y}_{j}\) and \(y_{j}\) indicate the predicted and true values of the data and \(n\) determines the total number of observations.
## 3 Methods and Purposes of Eccentric Muscle Training
### Types and Classification of Eccentric Exercise  
Different types of eccentric exercise rely on mechanical characteristics, metabolic cost, myotendinous unit compromise, and training purposes [4; 18; 19; 29; 68; 69]. [PERSON] et al. state that eccentric exercise (with or without concentric phase and accentuated load) includes three different types of exercises: (i) eccentric exercises with constant speed (isokinetic), (ii) eccentric exercises with constant weight (isoweight), and (iii) eccentric exercises with constant inertia (isoinertial), with each one displaying diverse biomechanical patterns that result in different acute and chronic responses [69]. Also, [PERSON] et al. propose a simple reasoning or classification based on concentric load intensity [18]. They divide the eccentric exercise into sub-maximal (less than 100% of concentric 1 RM), maximum (100% of one repetition maximum concentric 1 RM), or supramaximal (typically 105-140% of concentric 1 RM), with the latter stimulating a greater increase in exercises involving both concentric and eccentric actions. Although this classification is helpful in clinical research, it is necessary to propose a broader and more accurate classification based on training purposes.  
In practical terms, our proposal is based on concentric one repetition maximum (1 RM), the rating perceived exertion (RPE), and exercise complexity (in single-joint and multi-joint exercises). Although 1 RM testing must be controlled to safeguard the subject and the surrounding conditions, it is critical in determining accurate muscular workload [32; 70; 71; 72; 73]. This test must be implemented carefully in special populations and subjects without previous strength training experience. Thus, protocols and predictive equations for 1 RM value are used successfully for these populations to avoid the challenge of direct 1 RM testing. One repetition maximum can be accurately estimated in men, women, and special populations from multiple repetition tests, with five to ten repetitions being the most sensitive values for estimating 1 RM [71; 72; 73]. To avoid excessive muscle damage and DOMS, which negatively affect participants' adherence and could harm those with pre-existing physical limitations [70; 71], an adaptation period would be necessary before testing or estimating 1 RM.  
Our proposal adapts the 1 RM classification, adding a sub-classification in the sub-maximal phase and considering the degree of complexity of the exercise, the RPE, and training purposes. The main classification is shown in Table 1. The complexity of the technical movement intersects with different stages of the subject's preparation or rehabilitation. Therefore, each type of eccentric exercise fulfills different functions according to those purposes. As a result, different methods and types of eccentric exercises can be used at different classification levels by modifying one or more load components (i.e., intensity, volume, rest, frequency) [4; 54; 69].
# Emotion Recognition from ECG Signals Using Wavelet Scattering and Machine Learning
## 4 Discussion
### Classification Performance in Two Dimensions  
When using two dimensions simultaneously (four classes), the performance of the KNN classifier dropped, possibly due to the similarity or low distance between features of each class. On the other side, the Ensemble Tree classifier accuracy was similar to that achieved in one-dimension classification, possibly due to the classifier being deep enough to capture the differences between classes of the scattering features of each signal.  
PCA can be used to reduce the dimensionality of the input vector, but it adds an additional step in the algorithm and tends to degrade classifier performance since reducing the number of variables of a dataset naturally comes at the expense of accuracy. In this  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline \multirow{2}{*}{**Classifier**} & \multirow{2}{*}{**Validation**} & \multicolumn{2}{c}{**Arousal**} & \multicolumn{2}{c}{**Valence**} \\ \cline{3-6}  & & **Acc.** & **F1** & **Acc.** & **F1** \\ \hline Naive Bayes [7] & LOSO & 0.69 & 0.545 & - & 0.551 \\ Nearest Neighbors [25] & N/R & - & 0.66 & 0.58 & 0.57 \\ Linear Discriminant [25] & N/R & 0.72 & 0.63 & 0.67 & 0.65 \\ Linear Support Vector [25] & N/R & 0.68 & 0.60 & 0.61 & 0.55 \\ Multilayer Perceptron [25] & N/R & 0.68 & 0.59 & 0.61 & 0.51 \\ AdaBoost [25] & N/R & 0.70 & 0.66 & 0.61 & 0.58 \\ Random Forest [25] & N/R & 0.68 & 0.67 & 0.59 & 0.59 \\ DCNN [25] & N/R & 0.81 & 0.76 & 0.71 & 0.68 \\ CNN w/o self-supervised [26] & 10 Fold & 0.837 & 0.828 & 0.809 & 0.808 \\ CNN self-supervised [26] & 10 Fold & 0.858 & 0.851 & 0.840 & 0.837 \\ XGBost [27] & LOSO & - & 0.561 & - & 0.633 \\ Bayesian DL [23] & 10 Fold & - & - & 0.90 & 0.86 \\ KNN [24] & 10 Fold & 0.558 & - & 0.597 & - \\ KNN (this work) & 10 Fold & 0.888 & 0.821 & 0.889 & 0.834 \\ Ensemble (this work) & 10 Fold & 0.902 & 0.894 & 0.904 & 0.896 \\ \hline \hline \end{tabular}  
* Acc.—accuracy; F1—F1-score; LOSO—Leave one subject out; 10 Fold—10-fold cross validation; N/R—not reported.  
\end{table}
Table 5: Comparison of the results from previous studies that use AMIGOS dataset.  
work, the accuracy was only reduced from 89.0% to 84.4% when using all 210 features, with only 50 components showing a reasonable trade of accuracy for simplicity.
## 3 Network Anomaly Detection  
In this section, we introduce the proposed anomaly detection model. Anomaly detection tasks currently face several challenges: (1) imbalanced class distribution with a large proportion of normal samples and a small number of anomalies, and (2) the lack of labeled information in network data. These challenges make it difficult to cover various types of anomalies that may arise in the future. To address these issues, the proposed anomaly detection model is a machine learning model that learns from the data itself, without the need for additional labels or prior knowledge about the data. In this section, we apply this approach for anomaly detection.
# Effects of Precipitation and Fire on Land Surface Phenology in the Brazilian Savannas (Cerrado)
## 2 Methodology
### Selection of the Study Area  
Created in 1961 and located in the state of Goias, in central Brazil, the ENP was chosen as the study area for three primary reasons (Figure 2). First, this protected area (a Conservation Unit) encompasses vegetation physiogonies that are representative of the Cerrado biome, including savanna grasslands and shrub savannas (referred here to as \"grasslands\") as well as wooded savannas and woodland savanna (referred to as \"woodlands\"). Second, the ENP is the largest protected park within this biome, covering an area of 1328 km\({}^{2}\), which facilitates observations using MODIS (250-m MOD13Q1 product) and Landsat/Sentinel-2 (30-m HLS product) data. Third, three large fire events were recorded during the study period (2002-2023), as detailed later, making the park an ideal site for our experimental design.  
Fire management is a common practice at ENP. Controlled burns and firebreaks (wide, vegetation-free corridors) are integral components of this strategy. When combined, these actions prevent the accumulation of dry biomass on the ground (fire fuel), creating also barriers that inhibit the spread of fire [35]. These practices have reduced both the frequency and impact of human-induced fires, which usually start in the vicinities of the park. Large fire events, affecting significant portions of the park, previously occurred every three years, having decreased in frequency in more recent decades.  
The ENP has well-defined rainy (October to May) and dry (June to September) seasons, with an average annual precipitation of 1600 mm. The mean annual temperature is 23 \({}^{\circ}\)C [36]. Most of the ENP is located at the top of a plateau with gentle terrain and altitudes ranging from 800 to 900 m, especially in grassland areas (Figure 2). At the northeast of the park (woodland areas), the relief is more undulated. The predominant soil types, according to the Brazilian System of Soil Classification, include _Latossolo Vermelho-Escuro dis  
Figure 2: Location of the Emas National Park (ENP) in the state of Goias, central Brazil. The vegetation map highlights the predominant occurrence of grasslands (savanna grassland and shrub savanna physiogonies) and woodlands (wooded savanna and woodland savanna physiogonies). Areas classified as ’Others’ include riparian forests and small patches of seasonal semideciduous forests. The vegetation map was adapted from Batista [36].  
trofico_ and _Latossolo Vermelho-Amarelo distrofico_[35; 37], or red and yellowish-red Dystrophic Ferralsol, respectively.