esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results  
### Data Acquisition  
In the experimental setup configuration, the developed system including humidity, temperature, CO\({}_{2}\), light, and proximity sensors is implemented in a working office, located in the University of Rwanda, KARISIMBI Building for around 15 days. The sensors are connected to an ESP8266 microcontroller board with its WIFI module enabled to send data to the local configured MongoDB database server. The selected indoor environmental parameters are considered by different researchers to detect the presence of human beings. However, the key additional parameter is the use of a proximity sensor to detect any human presence along with the other captured variables. The geo-location information is captured to allow localization of the victims in case of a fire or any incidences that occur. The output of the system displays on the web interface linked to the google map of the experimented building location.  
The presence of human beings is indicated by the increase of CO\({}_{2}\) as it is the gas produced during exhalation [42]. The other is used to infer the knowledge of human presence in a specific room also. The experiment did not consider the windows open or closed which can reduce the amount of CO\({}_{2}\) concentration in the room. The position of the kit was centered in the room. The room is a shared room with a proximity sensor is placed near the door to capture any movement of humans.  
Figure 8 shows the developed system in its operational mode during the testing phase. The data are sent to the back-end database in 1-minute time interval to avoid redundancy of similar records. Figure 9b shows the close-up of sensor node localization on the google map with minimum average parameters.  
Figure 8: (**a**) Prototyping setting up for data acquisition. (**b**) Closeup side of the data acquisition system.  
Figure 9: (**a**) Sample of captured data appealing on MongoDB user interface. (**b**) Close-up geographical location map of the sensor node placement with some parameters omitted.
## 3 Results
### Offline Case Study Evaluation
**(Left)** CNN + MFCC; **(Right)** CNN + LM. Figure 5: Confusion matrices in Audioset dataset: **(Left)** CNN + MFCC; **(Right)** CNN + LM. \begin{table}
\begin{tabular}{l c c c c} \hline \hline  & **Accuracy** & **Precision** & **Recall** & **F1-Score** \\ \hline CNN + MFCC model (ad hoc dataset) & 0.99 & 0.99 & 0.99 & 0.99 \\ CNN + LM model (ad hoc dataset) & 0.96 & 0.96 & 0.96 & 0.96 \\ CNN + MFCC model (Audioset) & 0.23 & 0.25 & 0.23 & 0.23 \\ CNN + LM model (Audioset) & 0.29 & 0.36 & 0.29 & 0.32 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Classification metrics from offline case study evaluation.
# Beyond Stochastic Gradient Descent for Matrix Completion Based Indoor Localization
## 3 Proposed Matrix Completion Based Localization
### Matrix Completion: Optimization over GD and Its Variants
#### 3.2.3 Adaptive Gradient (Adagrad)  
[PERSON] et al. [35] introduced Adagrad algorithm in the context of projected gradient method. Adagrad aims to adapt the learning rate to the updated parameters, performing low learning rate (i.e., smaller updates) when the memory of squared gradients is high and larger updates conversely. Adagrad update rule is as follows:  
* We set \(\mathbf{E}^{(t)}\) to be the gradient of the objective function with respect to the parameter \(\mathbf{\hat{X}}^{(t)}\) \[\mathbf{E}^{(t)} =\frac{\delta l(\mathbf{\hat{X}}^{(t)})}{\delta\mathbf{\hat{X}}^ {(t)}}\] \[=(1-\mathbf{H})\odot(\mathbf{\hat{X}}^{(t)}\cdot((\mathbf{\hat{X }}^{(t)})^{T}\cdot\mathbf{\hat{X}}^{(t)}+\epsilon\times\mathbf{I})^{-0.5}),\] (23)
* we compute the memory of squared gradients over time as \[\mathbf{G}^{(t)}=\sum_{i=1}^{t}(\mathbf{E}^{(i)})^{2},\] (24)
* we modify the general learning rate \(\alpha\) at each time step t for every parameter \(\mathbf{\hat{X}}^{(t)}\) based on the sum of the squares of the gradients that have been computed for \(\mathbf{\hat{X}}^{(t)}\) up to time step \(t\). \[\mathbf{W}^{(t)}=\frac{1}{\sqrt{\mathbf{G}^{(t)}+\epsilon\times\mathbf{I}}} \odot\mathbf{E}^{(t)},\] (25) while \(\epsilon\) is a regularizing term used to avoid division by zero. It is worth mentioning that we do not need to adjust the learning rate.
Therefore, the following work will involve traditional analytical methods only. The processing of such data obtained by coherent reflectometric systems, in addition to the traditional fast discrete Fourier transform, in some cases includes the use of fairly simple software filters based on a moving differential or a moving averaging, as well as some nonlinear transformations [38; 39; 40; 41]. Previously, in [38], simple digital filters were applied to increase the SNR of a cost-effective DAS system for applications such as biology and agriculture. The sensor was exposed to single-frequency acoustic events and their combinations. The use of multiple algorithms together resulted in a significant system SNR increase (by 8-10 dB). It should be noted that the event's spatial localization efficiency was not studied in the previous work [38]. Later, it was found that the proposed combination of software filters is able to distort the spatial distribution of events. Another identified drawback of the algorithms sequence from [38] is the insufficient program code execution time when processing large data arrays. Currently, most distributed fiber optic sensors are specialized. This also applies to distributed acoustic sensors. Various simplifications, design features, and signal processing regimes in many cases limit the scope of such systems to one of the following: flaw detection, geophysics, perimeter security, etc. It has been already noted that this trend is a condition of the cost reduction and, as a result, of the spread of distributed acoustic sensor technology in various branches of science and technology. But is this condition essential? Our study below shows that even when using a simplified setup, one can receive a signal with a quality level that is satisfying for customers from different industries. Some processing methods, including a modified one, in various combinations will be used to solve the problems that arise in various areas of research. Thus, it will be shown that their productive variation ensures the achievement of the maximum signal-to-noise ratio. In the future, this will allow one to create a methodology for choosing the optimal algorithm for each case (our research team has already solved this problem for distributed fiber optic sensors based on the stimulated Brillouin scattering principle, where it is also possible to use simple and cheap laser sources [42; 43; 44]). The purpose of this work was to develop an algorithm devoid of the above disadvantages, which would allow us to increase the DAS signal SNR without distorting the events' spatial pattern within a reasonable operating time. We propose a modified 2D version of the filtering sequence as such an algorithm. In the field of distributed sensors, this approach is called \"image processing\" by analogy with photo processing algorithms, where the subject being studied is a two-dimensional data array [45; 46; 47].
# An Improved Back-Projection Algorithm for GNSS-R BSAK Imaging Based on CPU and GPU Platform
## 1 Introduction
In the pre-processing program, to ensure the synchronization of the direct signal and the echo signal, the echo signal is synchronized with the accurate pseudorange and code phase information in the direct signal. The precision ephemeris of the GPS is introduced to improve the orbit accuracy of the navigation satellites and the positioning accuracy of the receivers, which improves the imaging quality of the BPA algorithm. Secondly, the parallel acceleration strategy of BPA in CPU and GPU heterogeneous architecture is designed and implemented. In fixed station mode, the GNSS-R SAR system may work for thousands of seconds on a single mission resulting in the acquisition of data that can reach hundreds of gigabytes. A stream processing structure was used to process such a large amount of data. According to the memory size of the GPU, the original data are divided into several subdata in chronological order and transmitted to GPU. To optimize calculation efficiency, the preprocess program is allocated to the CPU for execution, after which the BPA is allocated to the CPU for execution. To prove the feasibility of the proposed imaging algorithm, simulations and experiments were carried out using a GPS-L5 signal in the ground fixed station mode. In simulation and experiment, the time consumed by the proposed algorithm is 163.96 and 156 times less than that consumed by BPA running only on CPU, respectively. However, the image quality has not been reduced. The simulation and experimental results prove the feasibility and effectiveness of the proposed algorithm. This paper is organized into six sections. In Section 2, the signal model of GNSS-R BSAR is reviewed briefly. In Section 3, the improved BPA is discussed in detail. A GPU parallel acceleration method compatible with the improved BPA is proposed and analyzed in Section 4. The simulation and experimental results are provided in Section 4. A further discussion of the proposed algorithm is presented in Section 5, and conclusions are drawn in Section 6.
###### Abstract  
In this paper, we propose the analysis of the target detection performance of air-to-air airborne radars using long-range propagation simulations with a novel quad-linear refractivity model under abnormal atmospheric conditions. The radar propagation characteristics and the target detection performance are simulated using the Advanced Refractive Effects Prediction System (AREPS) software, where the refractivity along the altitude, array antenna pattern, and digital terrain elevation data are considered as inputs to obtain the path loss of the wave propagation. The quad-linear model is used to approximate the actual refractivity data, which are compared to the data derived using the conventional trilinear refractivity model. On the basis of the propagation simulations, we propose a detection performance metric in terms of the atmosphere (DPMA) for intuitively examining the long-range propagation characteristics of airborne radars in air-to-air situations. To confirm the feasibility of using the DPMA map in various duct scenarios, we employ two actual refractive indices to observe the DPMA results in relation to the height of the airborne radar.  
airborne radar propagation; long-range radar target detection; abnormal atmosphere; elevated duct +
Footnote †: journal: Applied Sciences
## 2 Related Work and Background
Furthermore, we assume that: (i) D2D communications exploit uplink resources, and unused downlink resources are destined to other kinds of requests, and (ii) forwarding devices in charge of relaying the received data to D2D receivers operate in Single Frequency mode. By summarizing, with respect to the literature in the field, our proposed Energy-Efficient D2D over Single Frequency Network scheme (named as EED2D-SF) introduces the following contributes:  
* Focuses on a mMTC (rather than on a human-oriented) scenario where sensor/machine nodes are interested in file downloading, one of the key use cases for such a scenario. * Maximizes the network energy efficiency, as it represents a key requirement for the analysed scenario composed by a huge number of energy-constrained nodes. * Exploits D2D communication to both reduce the energy consumption and increase the system data rate by properly setting the multicast-area transmission and D2D communications from appropriately selected relay nodes. * Avoids interference between cellular operation and D2D communications by exploiting downlink subframes for the transmission from the gNodeB to the multicast receivers, and uplink subframes for data forwarding from selected relays to cell-edge devices. * Takes advantage from the single frequency operation for D2D communications in order to avoid interference among D2D groups without the need to perform power control and channel assignment. * Analyses all 5G frame configurations in the performance analysis.
# Scalability of Water Property Measurements in Space and Time on a Brackish Archipelago Coast
## 2 Materials and Methods
### In Situ Data  
The analyses were based on a pre-existing dataset from 2007, described in [PERSON] et al. [9]. This dataset was found to be suitable for our purpose since it included spatially dense and frequent in situ measurements of water properties in the topmost water column during one growing season. Water properties were measured in situ from week number 20 in mid-May to week number 40 in early October 2007. Weather and sea ice conditions during this period were in the normal range compared to long-term averages.  
The sampling was carried out on eight occasions, approximately every three weeks. This sampling frequency allowed us to examine the variation in water properties caused by the algal growth cycles [9]. Inside the study area, a network of 20 sampling sites was established (representing the regional scale (10\({}^{4}\) m); Figure 2). The selected sites were extracted from the national monitoring network (published by the Finnish Environment Institute SYKE), hence their nomenclature. The site \"NAU2361\" in the centre of the study area (Figure 2) is monitored by the national authorities continuously, the other 19 sites sparsely and irregularly.  
At each site, three sub-sites with 300 m distances from each other were measured each time (representing the local scale 10\({}^{2}\) m; Figure 2). In addition, measurements were  
Figure 2: (**A**) The 20 sampling sites in the SW Finnish archipelago in 2007. (**B**) Sub-site sampling pattern with three parallel depth profiles at one sampling site (redrawn and modified from [9]).  
repeated at all sub-sites at six depths: 1, 2, 4, 6, 8 and 10 m below the surface (Figure 2). The dataset thus had a three-dimensional statistical structure (space, time, depth) and the spatial dimension had a two-level nested structure (Table 1). The nested structure of the data was in principle balanced, with almost all sites (18 out of 20) and sub-sites (54 out of 60) having an equal number of observations (Table 1). The few missing observations were caused by harsh weather conditions [9].  
Coordinates were recorded at each sub-site during each field visit. The following water property variables were measured in situ with a YSI 6600 V2 multiparameter sonde (YSI Inc., Yellow Springs, OH, USA) at each depth (Table 2): temperature and salinity (specific conductance; sensor YSI 6560), pH (sensor YSI 6565) and chlorophyll-a (relative fluorescence units, RFU; sensor YSI 6025). The in situ measurements were compared to water samples in a certified laboratory setting (laboratory of the Water Protection Association of Southwest Finland). The calibration, validation and variable transformation procedures were described in detail by [PERSON] et al. [9].
## 3 Vehicle Control Unit Description
### Braking Management  
The braking system consists of two different parts, namely the electrical and pneumatic parts. The VCU deals with the management of these two components. During a braking procedure, three different phases can be distinguished. In the first one, only electrical braking is involved, i.e., the two motors are used as generators to recharge the battery  
Figure 3: Front to rear driving torque distribution as a function of speed and the direction of motion scheme (**a**). Repartition coefficient in forward (**b**) and reverse (**c**) condition.  
packs. In the second phase, when the speed is below a given threshold, the pneumatic braking system is also enabled and the pressure of the brakes increases according to a ramp function. When the minimum speed threshold is achieved (third phase), no more power is absorbed by the motors and only the pneumatic system is responsible for slowing the vehicle. In this way, a hefty burden is not placed on the pneumatic system and the braking power can be recovered by the motors to charge the batteries.  
Nevertheless, the above-described situation represents the most general case. If some battery limits occur while braking (i.e., maximum \(SOC\) or power reduction required by the BMS), the electrical braking is proportionally reduced according to the feedback provided by the batteries. At the same time, the pneumatic system compensates for this lack of braking torque.  
According to Figure 4a, the speed control loop requires a braking torque \(T_{b,ref}\) which is split between electric and pneumatic brakes as follows:  
\[\left\{\begin{array}{l}T_{b,e}=k_{e}T_{b,ref}\\ T_{p,e}=k_{p}T_{b,ref}\end{array}\right. \tag{2}\]  
where \(k_{e}\) and \(k_{p}\) are weighting coefficient that range from 0 to 1 and are function of the vehicle speed and battery \(SOC\) as reported in Figure 4b,c respectively. It must be pointed out that \(k_{p}\) and \(k_{e}\) are related as follows:  
\[k_{p}=1-k_{e} \tag{3}\]  
In this way, in steady-state conditions, i.e., neglecting the actuators dynamics, the provided braking torque, which is the sum of electric and pneumatic braking torque, is equal to the demanded braking torque.
## 5 Conclusions
The following abbreviations are used in this manuscript:  
LS land subsidence  
MC-TCGRU multi-component temporal convolutional gate recurrent unit  
STL seasonal-trend decomposition using LOESS  
EMD empirical-mode decomposition  
GPS global positioning system  
TS-InSAR Time-series interferometric synthetic aperture radar  
PS-InSAR persistent scatterer InSAR  
SBAS-InSAR small baseline subset InSAR  
LSP land subsidence prediction  
RNN recurrent neural network  
LSTM long short-term memory  
CEEMDAN complete-ensemble empirical-mode decomposition with adaptive noise  
IMF intrinsic mode function  
EEMD ensemble empirical-mode decomposition  
GRU gated recurrent unit  
TCN temporal convolutional network  
TCGRU temporal convolutional gated recurrent unit  
AEMs accuracy evaluation metrics  
MAE mean absolute error  
RMSE root mean squared error  
R\({}^{2}\) coefficient of determination  
MAPE mean absolute percentage error  
SMAPE symmetric mean absolute percentage error
# Numerical Analysis of the Bottom Thickness of Closed Rectangular Tanks Used as Pontoons
## 5 Conclusions  
The calculation results presented in this paper were obtained using the finite difference method in terms of energy, taking into account spatial static work.  
By analysing the presented solutions, the following findings were obtained:  
Figure 10: View of the tank during execution (**left**) and during launching at the test site (**right**) [7; 34].  
* Thickening of the bottom plate in relation to the constant and identical wall and upper plate thickness, at all points of the calculated tanks, led to a reduction in deflections as the bottom thickness increased.
* Hydrostatic load on tank walls and uniform load on the bottom combined with an increase in the bottom thickness led to an increase in bending moments acting in the middle of the bottom plate span towards a shorter span (\(\text{M}_{y}\)2, Figure 3), while reducing bending moments at other points of the tank.
* Uniform load on the upper plate in tanks with a thicker bottom led to an increase in bending moments, but only in the bottom, both in fastening moments (\(\text{M}_{\text{A2}}\) and \(\text{M}_{\text{B2}}\), Figure 3) and in the moment acting in the centre of the plate towards a shorter span (\(\text{M}_{y}\)2, Figure 3). Bending moments at other points of the tank practically did not change.
* An increase in the thickness of the bottom plate leads to a change in the size of the pontoon's tilt angle, which enables it to change easily, to smaller than required by applicable regulations (in this case \(\varphi\leq 6^{\circ}\)).  
The publication was co-financed within the framework of Ministry of Science and Higher Education programme as \"Regional Initiative Excellence\" in years 2019-2022, Project No. 005/RID/2018/19.  
The author declares no conflict of interest.
## 2 Confocal Ellipsoidal Densification Model (CEDM)
### Solution of Ellipsoid Parameters for Different Compacted Zones  
Six parameters, including radius of tamper, crater depth, initial dry density, maximum dry density, friction angle, and unit weight, were selected to solve the ellipsoidal parameters of HCZ and WCZ based on the following considerations: (1) The radius of tamper directly determines the initial contact area between the tamper and the ground surface, while the crater depth provides a measurable proxy for the penetration depth. These two parameters contribute to the horizontal and vertical extents of compacted zones, especially for the HCZ ellipse. (2) Initial dry density and maximum dry density define the densification potential of the soil. The difference between them governs the possible volumetric strain, which is crucial for calculating the extent and intensity of compacted zones. (3) Because friction angle reflects resistance to particle rearrangement and soil shear strength, it is the key to determining how compaction stress propagates and distributes spatially in the soil foundation. (4) Unit weight affects how impact energy translates into effective stress and densification. In summary, these parameters ensure that the ellipsoidal geometry derived in CEDM reflects external loading conditions, soil mechanical behavior, and densification. They also strike a balance between theoretical completeness and practical measurability in engineering applications.  
Herein, the trial-and-error method was used to solve Equation (22), representing confocal ellipse geometry, and Equation (23), representing mass balance. Figure 2 shows the solution procedure of the proposed CEDM for calculating the ellipsoid parameters of different compacted zones.
# Measurement of Head-Related Transfer Functions: A Review
## 6 Post-Processing of Measured HRTFs
### Others  
Some measurement inaccuracies caused by the misalignment between the subject and loudspeakers may cause the rotation (offset) of the entire HRTF database. [PERSON] et al. [137] proposed to compensate this small offset by calculating ITDs and ILDs around 0\({}^{\circ}\) azimuth and elevation angles of measured HRTF sets, and align the HRTF with the minimum value to 0\({}^{\circ}\) azimuth and elevation. However, for the measurement of human subjects or dummy heads with asymmetrical behavior, the offset correction can hardly be achieved by calculating binaural cues. Instead, a careful alignment between subjects and loudspeakers can be done by using a laser before the measurement [126; 136; 174; 188].  
The sound velocities are related to the environmental temperature, and the temperature change during the measurement can cause slight variations in onset delays of measured HRTFs. [PERSON] et al. [174] observed temperature variations of about 3.1 \({}^{\circ}\)C during their long-term HRTF measurements, leading to fluctuations in sound arrival times from the loudspeaker to the microphone of up to 27 \(\upmu\)s (1.2 samples with a sampling rate of 44.1 kHz). In that study, the onset delay of HRTFs was corrected by using a fractional delay according the temperature offset [174].  
Some psychoacoustic experiments revealed that HRTFs can be smoothed up to a certain degree without audible artifacts [189; 190]. Therefore, not all spectral information in HRTFs is perceptually relevant. For instance, [PERSON] et al. [106] smoothed HRTF spectra by applying a gammatone filter with one equivalent rectangular bandwidth (ERB) to remove perceptually irrelevant spectral details [190].