esa-sceva/satcom-chunk-collection huggingface dataset:
# Evaluating the Vulnerability of Several Geodetic GNSS Receivers under Chirp Signal L1/E1 Jamming
## 3 Setup and Measurement Campaign
### GNSS Geodetic Receivers Used in the Experiment  
Several different GNSS-receivers from Trimble Inc. (Sunnyvale, CA, USA), Javad GNSS Inc. (San Jose, CA, USA), Leica Geosystems AG (Heerbrugg, Switzerland) with specific antenna types were used in the experiments, namely: Trimble 4000 SSi (antenna type: TRM22020.00+GP), Trimble R8 (antenna type: TRMR8_GNSS), Trimble R10 (antenna type: TRMR10), Trimble R8s (antenna type: TRMR8S), Javad Triumph-VS (antenna type: JAVTRIUMPH_VS), Javad Triumph-LSA (antenna type: JAVTRIUMPH_LSA), Leica GS18 (antenna type: LEIGS18), Leica GS07 (antenna type: LEIGS07) and Leica GS15 (antenna type: LEIGS15) (see Figure 2). Some were only able to receive GPS signals (the oldest receiver, Trimble 4000 SSi), most of them received GPS and GLONASS signals, while some were also able to receive other signals, for example, from Galileo (Javad Triumph-VS, Javad Triumph-LSA, Trimble R10, Leica GS15 and Leica GS18). As can be seen from Figure 2, the instruments were set on tripods and tribrach (positions are given in Table 1) close to each other.  
Figure 1: Geographic position of the testing area: (**a**) location of the village Crhotic in Slovenia and (**b**) approximate location of the testing area (red circle) [42; 43].  
Figure 2: The instrument set up at point \(B\) from north-east (left) to south-west (right) at the location (own study).
# Power Consumption Modeling of Discontinuous Reception for Cellular Machine Type Communications
## 6 Simulation Setup and Results Analysis  
In order to validate the semi-Markov-based model proposed for analyzing a DRX mechanism in an LTE-A network, simulations were also performed using a simulation model developed in MATLAB. In the simulations, the packet arrival process was modeled according to the Poisson distribution. Thus, the packet inter-arrival time followed exponential distribution with common mean \(\frac{1}{\lambda}\). In addition, the different stages of the DRX mechanism in LTE-A were also determined through several parameters, including timer values, transition probabilities related to various power states, as well as holding times. Furthermore, the packets arrived at different stages of the DRX mechanism based on the packet inter-arrival time. In the end, the related statistics were computed using the Monte Carlo method. Further, if not additionally specified at the simulation results, the used DRX simulation parameters and corresponding values are shown in Table 4. The DRX-related parameters used for the simulations were standardized in Reference [29], while the values of tail time \(T_{tail}\) = 11.576 s and RRC connection time \(T_{RC}\) = 260 ms were specified in Reference [30] based on the measurement results.
## 2 Materials and Methods  
### Materials  
Aqueous sodium silicate solution (SSS; Na\({}_{2}\)O.SiO\({}_{2}\), 27% wt. % SiO\({}_{2}\)), titanium IV isopropoxide (TiPOT, Ti[OCH(CH\({}_{3}\))\({}_{2}\)]\({}_{4}\), 97%), 3-aminopropyltriethoxysilane (APTES; H\({}_{2}\)N(CH\({}_{2}\))\({}_{3}\)Si(OC\({}_{2}\)H\({}_{5}\))\({}_{3}\), 99%) were purchased from Sigma-Aldrich (Darmstadt, Germany).  
Absolute ethanol (EtOH; 99.5%) from Merck (Darmstadt, Germany) and bidistilled water (conductivity 0-2 \(\mu\)S/cm\({}^{3}\), pH 5.8-6.5) were also used.  
The commercial phospholipids 1,2-dioleoyl-_sn_-glycero-3-phosphocholine (DOPC) and 1,2-dioleoyl-3-trimethylammonium propane (DOTAP) for the biomimetic shielding of a-TiO\({}_{2}\)-NH\({}_{2}\) NPs were purchased from Avanti Polar lipids. 3,3'-Dioctadecyloxacarbocyanine Perchlorate (DiOC\({}_{18}\)) and ATTO550-NHS ester fluorescent dyes, purchased from ThermoFischer, were used for fluorescence microscopy colocalization experiments.  
All chemicals were used without any further purification.
# General Moving Object Localization from a Single Flying Camera
## 7 Discussion
### Near Constant Speed Problem  
As mentioned in Section 3.4, depth ambiguity happens when both the pedestrian and the drone travel with constant speed. Thus, the drone must be in a constant state of acceleration until it takes the entire image sequence. However, we observe that at around frame 25 of a sequence (any sequence), the acceleration of the drone has dropped to a fairly small value (i.e., it is almost reaching constant velocity). Figure 19 shows the images of frames 35 to 39 when the drone moves with an almost constant speed.  
In Figure 19, the pedestrian appears at the same location in multiple images due to the almost constant speed of the drone. Thus, it is important not to choose baseline fragments where the drone has almost reached a constant speed. This problem can be mitigated by tuning the flight parameters of the drone so that it accelerates at a slower rate, thus reducing the likelihood of attaining an almost constant speed before the end of the sequence. Alternatively, the drone can fly in a sine wave pattern (i.e., it will accelerate to almost constant speed and then begin to decelerate towards zero, and the cycle continues) so that a significant acceleration is always maintained.
# A Numerical Model to Estimate the Soil Thermal Conductivity Using Field Experimental Data
## Appendix A  
The [PERSON] model [35; 36; 37] for soil thermal conductivity is represented as:  
\[\lambda=\Big{(}\lambda_{sat}-\lambda_{dry}\Big{)}K_{e}+\lambda_{dry} \tag{1}\]  
where \(\lambda_{dry}\) and \(\lambda_{sat}\) are the thermal conductivities for the dry and saturated, respectively in units of soil (Wm\({}^{-1}\)K\({}^{-1}\)) and \(K_{e}\) is the Kersten number, defined by:  
\[K_{e}\approx 0.7\ \log_{10}S_{r}+1.0\ (S_{r}>0.5) \tag{2}\]  
\[K_{e}\approx\log_{10}S_{r}+1.0\ (S_{r}>0.1) \tag{3}\]  
where (\(S_{r}=\frac{\partial}{\partial s}\)) is the normalized soil water content, \(\partial s\) is the saturated soil water content and \(\theta\) is the soil water content, in units (m\({}^{3}\)m\({}^{-3}\)).  
The \(\lambda_{sat}\) parameter is obtained by a geometric mean equation:  
\[\lambda_{sat}=\lambda_{s}^{1-n}\lambda_{w}^{n} \tag{4}\]  
where \(n\) is the soil porosity (m\({}^{3}\)m\({}^{-3}\)), \(\lambda_{s}\) is the thermal conductivity of soil solids (Wm\({}^{-1}\)K\({}^{-1}\)) and \(\lambda_{w}\) is the thermal conductivity of the water (\(\lambda_{w}=0.594\) Wm\({}^{-1}\)K\({}^{-1}\) at 20 \({}^{\circ}\)C). The \(\lambda_{s}\) value is obtained by another geometric mean equation:  
\[\lambda_{s}=\lambda_{q}^{q}\lambda_{o}^{1-q} \tag{5}\]  
where \(q\) is the quartz content of the total solids content, \(\lambda_{o}\) is the thermal conductivity of other materials (Wm\({}^{-1}\)K\({}^{-1}\)) and \(\lambda_{q}\) is the thermal conductivity of the quartz (\(\lambda_{q}=7.7\) Wm\({}^{-1}\)K\({}^{-1}\)). \(\lambda_{o}\) was taken as 2.0 Wm\({}^{-1}\)K\({}^{-1}\) for soils with \(q>0.2\) and 3.0 Wm\({}^{-1}\)K\({}^{-1}\) for soil with \(q\leq 0.2\).  
The \(\lambda_{dry}\) parameter was defined by [PERSON] [35; 36; 37] by a semi-empirical relation with respect to the soil density, as follows:  
\[\lambda_{dry}=\frac{0.135\ \rho_{b}+64.7}{2700-0.947\rho_{b}} \tag{6}\]  
where \(\rho_{b}\) is the soil bulk density (kg m\({}^{-3}\)).  
The [PERSON] model [36] uses the same [PERSON]'s (1975) equation (Equation 1), with a new Kersten number:  
\[K_{e}\approx e^{[\alpha[1-S_{r}^{(a-1.33)}]]} \tag{7}\]  
where \(\alpha\) is a parameter dependent on soil texture. In addition, [PERSON] et al. [36] introduced a new equation for \(\lambda_{dry}\), described as:  
\[\lambda_{dry}=-0.56n+0.51 \tag{8}\]  
with \(n\) the soil porosity (m\({}^{3}\)m\({}^{-3}\)).  
The Tong model [6] is an empirical model for soil thermal conductivity, defined by:  
\[\lambda=a-be^{(-c\theta)} \tag{9}\]  
where \(a\), \(b\)\(e\) are empirical parameters.
# Signal Design and Compatibility Assessment for LEO Navigation Augmentation System +
## 3 Signal Design Scheme
### Modulation Type
#### 3.2.1 Signal Components for High Precision Measurement  
For signal modulation type, GNSS mainly uses BPSK and BOC signals. However, for the L1 and L5 frequency bands, the current GNSS signals already occupied the central frequency band. To achieve compatibility, the LEO navigation augmentation signal can choose either BOC modulation or BPSK modulation with frequency shift. Using a BOC signal has more advantages than using BPSK modulation with frequency shift because there is more effective bandwidth. When only receiving a spectral side lobe of BOC signal, it can be treated as a BPSK signal.  
The following Figure 1 shows the spectrum of GNSS signals at L1 and L5 frequencies. The L1 signal has multiple signal components and needs to be separated from the main lobe, with less room for selecting spectral intervals. To achieve the fast acquisition, short codes are used with a code rate of 1.023 Mcsp or 2.046 Mcps as much as possible. The available options include BOC (2,1), BOC (2,2), BOC (4,1), and BOC (4,2) signals.  
For L5, it belongs to the aviation radio frequencies and needs to meet the EPFD limitation requirement. The farther away from the center frequency it is, the better the compatibility is. However, from the perspective of receiving processing, L5 and L1 use the same signal modulation, which can simplify signal receiving processing.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Frequency** & **Signal** & **Modulation** & \
\begin{tabular}{c} **Minimum Receiving** \\ **Power** \\ \end{tabular} &
\begin{tabular}{c} **Maximum Receiving** \\ **Power** \\ \end{tabular} \\ \hline L1 (1575.42 MHz) & BDS B1C & QMBOC(6,1,1/11) & \(-\)159 dBW & - \\ GPS L1C & TMBOC(6,1,1/11) & \(-\)157 dBW & \(-\)154 dBW \\ GPS L1C/A & BPSK(1) & \(-\)158.5 dBW & - \\ Galileo E1 OS & CBOC(6,1/1/11) & \(-\)157 dBW & \(-\)154 dBW \\ BDS B2a & QPSK(10) & \(-\)156 dBW & - \\ GPS L5 & QPSK(10) & \(-\)154.9 dBW & - \\ Galileo E5a & QPSK(10) & \(-\)155 dBW & \(-\)154 dBW \\ \hline \hline \end{tabular}
\end{table}
Table 1: L1 and L5 signal parameters.
# Beyond Stochastic Gradient Descent for Matrix Completion Based Indoor Localization
## 5 Method Verification Using Real Measurements  
For method verification, we consider a classroom of our university as an indoor area of 88 m\({}^{2}\) illustrated in Figure 7. The used technology is LoRa which uses 868 MHz as frequency band. We placed eight sensor nodes which can transmit and receive messages. We collect RSSI data during an afternoon. Then, we construct the RSSI matrix, in which there are some missing values which correspond to unknown detected sensors. This matrix is used to obtain the partially known EDM matrix. We mention that the used technology and the number of sensor nodes explored for experimental verification relates to the availability of such equipment in our laboratory. Thus, we do not consider the optimal conditions determined before. The aim is to validate the effectiveness of our method.  
When applying our localization algorithm (Adam is used to estimate the complete EDM matrix) to localize each sensor node, we obtain a mean localization error which is 1.5 m. This error reaches 3.8 m when using the classic trilateration. In Figure 8, we present the CDF corresponding to the classic trilateration (without MC i.e., without matrix completion) and the CDF corresponding to our algorithm (with MC, i.e., with matrix completion).  
Figure 8: CDFs for real measurements.  
Figure 7: Used configuration for real experimentations.
# AuxDepthNet: Real-Time Monocular 3D Object Detection with Depth-Sensitive Features
## 6 Conclusions  
In this study, we proposed AuxDepthNet, a framework for real-time monocular 3D object detection that eliminates the need for external depth maps or pre-trained depth models. By introducing the Auxiliary Depth Feature Module (ADF) and the Depth Position Mapping Module (DPM), AuxDepthNet effectively learned depth-sensitive features and integrated depth positional information, enhancing spatial reasoning with minimal computational cost. Built on the DepthFusion Transformer architecture, the framework demonstrated robust performance in object localization and 3D bounding box regression.  
Although AuxDepthNet does not rely on any externally pre-computed depth maps or pre-trained depth estimation networks, it still requires dataset-specific information, such as camera calibration parameters and anchor box statistics derived from the KITTI dataset. These configurations are standard hyper-parameters that can be re-estimated for other datasets with appropriate 3D annotations and calibration data. Therefore, the framework remains general in its architecture and learning process, without depending on handcrafted priors unique to KITTI.  
Despite its effectiveness, AuxDepthNet has limitations, including its focus on the KITTI dataset and potential challenges in generalizing to diverse environments. Future work will explore broader dataset adaptation, improve robustness under extreme conditions, and optimize computational efficiency for edge applications.  
Conceptualization, R.Z. and H.-S.C.; methodology, R.Z. and D.J.; software, R.Z. and S.-K.J.; validation, P.H.N.A. and Z.Z.; formal analysis, R.Z. and D.J.; data curation, D.J. and S.-K.J.; writing--original draft preparation, R.Z.; writing--review and editing, R.Z.; visualization, P.H.N.A. and Z.Z.; supervision, H.-S.C.; funding acquisition, H.-S.C. All authors have read and agreed to the published version of the manuscript.  
This research was supported by Korea Institute of Marine Science & Technology Promotion (KIMST) funded by the Korea Coast Guard (RS-2021-KS211488). Also, it was supported by the Unmanned Vehicles Core Technology Research and Development Program through the Na-tional Research Foundation of Korea (NRF) and the Unmanned Vehicle Advanced Research Center (UVARC), funded by the Ministry of Science and ICT, the Republic of Korea (NRF-2020M3C1C1A02086321).  
Not applicable.  
The data presented in this study are available in the article.  
The authors declare no conflicts of interest.
## 2 Related Work
### Research on Data Augmentation  
Current studies on SER often rely on foreign language DBs, such as EMO-DB [10] and IEMOCAP [2], to analyze emotion characteristics. However, these foreign language DBs have limitations in reflecting the characteristics of the Korean language, including linguistic features and accents. These limitations stem from the absence of SER DBs based on the Korean language [5; 12]. To address this issue, data augmentation techniques can be employed to expand available data.  
Several studies demonstrate the application of data augmentation in SER. For instance, [PERSON] et al. [13] highlight the importance of establishing a large-scale training set to account for the variability of objects in the real world. They augmented a dataset by applying techniques such as cropping, mirroring, and color conversion to generate a large-scale training set, resulting in enhanced performance of SER and prevention of overfitting issues. Another study [14] introduced a data augmentation algorithm specifically designed to improve SER. In this study, a dataset used to train a CNN model was augmented by applying formant boosting, a data augmentation technique. Consequently, the algorithm developed led to enhanced performance in SER. These examples highlight the importance of data augmentation in conducting emotion recognition based on limited speech DBs.  
Through studies on speech emotion recognition (SER) and data augmentation (DA), it has been confirmed that log mel-spectrogram and CNN-based models are effective for feature extraction in SER, and that data augmentation enhances model performance. Hence, our study proposes a ResNet model utilizing the DA-S technique on a Korean speech DB to enhance its performance in Korean SER. Furthermore, it indicates the effects of the DA-S technique on improving the proposed model's performance.
# Image Quality for Near-Eye Display Based on Holographic Waveguides
## 3 Results
### Third DOE Configuration  
Based on the configurations presented in Figures 6 and 7, a modification of the holographic waveguide shown in Figure 8 was proposed and tested. The central area (the light input) uses a superimposed diffraction structure consisting of two diffraction gratings arranged at the angle of \(\pm 60^{\circ}\). The DOE2a and DOE2b at the edges of the waveguide are one-dimensional gratings with direction angles of plus and minus \(60^{\circ}\), respectively.  
The DOE1 input area of the third configuration is combined with a part of the relay grating area, i.e., it is increased practically to the boundary of the DOE3 area, as displayed by Figure 8a. Increasing the area of the input superimposed structure leads to decreasing the observed brightness of the central stripe of the image, as in the above-described case. It is supported by introducing additional optical losses on the superimposed structure due to self-diffraction during propagation towards the DOE3 area.  
Relay DOE2a and DOE2b are free from overlapping at the sides and, accordingly, from the presence of parasitic periodicity. Increasing the DOE2a and DOE2b areas will result in decreasing the optical losses, and the slope of their overlapping borders allows for excluding the sharp changes in brightness of the observed image in the horizontal direction. For the third configuration of the holographic waveguide the achieved level of brightness non-uniformity was about 28% because light and dark stripes are explicit in the image (Figure 8c).  
Nevertheless, experimental verification of this configuration did not yield significant results, because different parts of the field (different TIR angles in the waveguide) work with different sections of the input and relay DOEs with one or two superimposed gratings in the case of exit pupil expansion along the horizontal coordinate. This feature affects the brightness of these image parts and, therefore, the equal-dimensional brightness of the image.  
Figure 8: The third configuration of the holographic waveguide: (**a**) DOE configuration scheme; (**b**) photo of a single holographic waveguide; (**c**) resulting AR image.
# Analysis of Grating Lobe Effects on GEO DSC Distributed Antennas
## 3 Analysis of Grating Lobes in DSC Distributed Antennas
### Grating Lobe Suppression  
As analyzed in the previous section, distributed antennas, as a type of ultra-sparse array, produce a large number of grating lobes after beamforming, which requires careful  
Figure 8: Interference in the far-field antenna pattern when the two satellites are at a distance of 100 km: (**a**) \(\varphi_{1}=0^{\circ}\), \(\varphi_{2}=0^{\circ}\); (**b**) \(\varphi_{1}=0^{\circ}\), \(\varphi_{2}=90^{\circ}\).  
Figure 6: Distribution of the two satellites.  
consideration of their impact. There are two approaches for mitigating grating lobes: one is to suppress them in the spatial domain and the other is to suppress them in the signal domain.
# Efficient Small-Object Detection in Underwater Images Using the Enhanced YOLOv8 Network
## 4 Experiments
### Implementation Details  
Table 1 provides details on the hardware and software setup used in the experiment.  
During the training, consistent training parameters were applied to each experimental group to ensure the precision of the experiments. The input resolution was configured with the longer side set to 640 pixels, preserving the original aspect ratio of the images, and the batch size was fixed at 32. In the training process, if the model did not show an improvement within 50 epochs, the training was terminated early, with a maximum of 300 epochs allowed. Optimization of the loss function was achieved through the utilization of the Stochastic gradient descent (SGD) algorithm, incorporating a momentum value of 0.937 and a weight decay coefficient of 5 \(\times\) 10\({}^{-4}\). The initial learning rate was set at 0.01, and the confidence threshold was defined as 0.25. Mosaic data augmentation was employed, while all other parameters were kept consistent with those in YOLOv8.  
During the inference, a standardized input resolution with the longer side set to 640 pixels was used, while preserving the original aspect ratio of the images. The confidence threshold was precisely defined at 0.001, and the intersection over union (IOU) threshold was established at 0.7. In the context of speed testing, singular GPU utilization was implemented, and the batch size was specifically set to 1, denoting the sequential processing of individual images.  
In this context, we utilized the widely accepted metrics for object detection, as outlined in Table 2. The measurements for the parameters and FLOPs were evaluated with the longer side set to 640 pixels.  
\begin{table}
\begin{tabular}{c c} \hline
**Environment** & **Versions or Model Number** \\ \hline CPU & Intel(R) Xeon(R) Silver 4210R CPU @ 2.40 GHz \\ GPU & GeForce RTX 2080 Ti, Two GPUs, Memory of 11 G \\ OS & Ubuntu 18.04 \\ CUDA & V 10.2 \\ CUDNN & V 7.6.5 \\ PyTorch & V 1.12.1 \\ Python & V 3.8.16 \\ \hline \end{tabular}
\end{table}
Table 1: The experimental setting.  
\begin{table}
\begin{tabular}{c c} \hline
**Metrics** & **Description** \\ \hline AP\({}_{50}\) & The mean average precision (mAP) at an intersection over union (IoU) of 0.50. \\ AP & The mAP at IoU of 0.50:0.05:0.95. \\ Parameters & The overall count of parameters in the network. \\ FLOPs & Floating-point operations per second. \\ \hline \end{tabular}
\end{table}
Table 2: The metrics used in our experiments.
## 3 UWA Channel and Noise Models  
In this section, the channel models for the UWA system represented by the noise effect will be demonstrated using MATLAB R2023b simulations to show the matching characteristics. UWA noise modeling is performed based on [4] utilizing a MATLAB fitting tool, while the noise is measured at a depth of 4 m-12 m. The histogram function of MATLAB is utilized on the noise model at the receiver and compared with different PDFs with a t-distribution, that is [4],  
\[f_{r}(x,d)=\frac{\Gamma\Big{[}\frac{d+1}{2}\Big{]}}{\sigma\sqrt{\pi(d-2)} \Gamma\Big{(}\frac{d}{2}\Big{)}}\bigg{(}1+\frac{x^{2}}{\sigma^{2}(d-2)}\bigg{)} ^{-\frac{d+1}{2}} \tag{13}\]  
where \(\Gamma(\cdot)\) is the Gamma function, \(\sigma\) is the standard deviation, \(d\) is the degree of freedom for the t-distribution function and \(x\) is the random variable. The nearest model that is applicable to this model is a t-distribution with five and six degrees of freedom, that is:  
\[f_{r}(x,5)=\frac{0.49}{\sigma}\Big{(}1+\frac{(x+\mu)^{2}}{3\sigma^{2}}\Big{)} ^{-3} \tag{14}\]  
The histogram plot of the noise model vs. Equation (14) is shown in Figure 1, which demonstrates the close match between the two plots. In addition, to verify the close match between these plots, a Kolmogorov-Smirnov (KS) goodness-of-fit test [18; 19; 20] is used at a significance level of 5%. The test result was 0, showing that the two samples are close and cannot be rejected, especially at a degree of freedom of six.  
Figure 1: Comparing the histogram of the noise model with the t\(-\)distribution at five and six degrees of freedom.