esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Examples of Datasets and Metrics  
Below, we present examples of datasets acquired using the described data-collection and processing system. The goal of this effort was to test system functionality for an extended period (up to 6 months) under realistic conditions and determine if the data collected could be used to track changes in a user's health in near real time. A total of 47 individuals (23-78 years, mean: 38.9 years; demographic information was not collected for 20 individuals in the initial pilot study) were enrolled into data-collection studies that were approved by the RTI's internal review board. Individuals provided informed consent and were asked to wear a Garmin Penix 6 smartwatch as continuously as possible as they went about their normal life and to complete a daily health survey.  
As described in Section 3, data streams collected with the S+ app contained the IBI value for each detected heartbeat, and SpO\({}_{2}\), respiration rate, and steps counts every 1 min. Figure 6 presents plots of the original data collected for 1 day selected from the total monitoring period as an example. Figure 7 shows selected HRV metrics extracted from a dataset collected for 1 week; all metrics were calculated in 5 min epochs, as described in Section 3. Also shown in Figure 7 are plots of IBI data quality metrics: RDF, VDF, and ADF averaged hourly.  
Figure 6: Example of the data stream collected from a healthy volunteer for a period of 1 day. Data include step counts every 1 min, each detected interbeat interval (IBI), and respiration rate (Resp) and SpO\({}_{2}\) every 1 min.  
To illustrate the best-case scenarios for the described data acquisition and processing system, the dataset presented in Figures 6 and 7 was selected from among the best data quality datasets we acquired when testing the system. The RDF and VDF are close to 100% on average, indicating good wear compliance on the part of the user, no issues in synchronizing the data on the watch with the smartphone, and low ADF.
# A Simple Neural-Network-Based Decoder for Short Binary Linear Block Codes
###### Abstract  
The conventional soft decision decoding (SDD) methods require various hard decision decoders (HDDs) based on different codes or re-manipulate the generator matrix by the complicated Gaussian elimination technique according to the bit reliability. This paper presents a general multi-class neural network (NN)-based decoder for the short linear block codes, where no HDD and Gaussian elimination are required once the NN is constructed. This network architecture performs multi-classification to select the messages with high occurrence probabilities and chooses the best codeword on a maximum likelihood basis. Simulation results show that the developed approach outperforms the existing deep neural network (DNN)-based decoders in terms of decoding time and bit error rate (BER). The error-correcting performance is also superior to the conventional Chase-II algorithm and is close to the ordered statistics decoding (OSD) in most cases. For Bose-Chaudhuri-Hocquenghem (BCH) codes, the SNR is improved by 1 dB to 4 dB as the BER is \(10^{-4}\). For the (23, 12) quadratic residue (QR) code, the SNR is improved by 2 dB when the BER is \(10^{-3}\). The developed NN-based decoder is quite general and applicable to various short linear block codes with good BER performance.  
neural network; deep learning; binary linear block code; soft decision decoding Article  
[PERSON]; [PERSON] [PERSON]; [PERSON]; [PERSON] A Simple Neural-Network-Based Decoder for Short Binary Linear Block Codes  
Bleok Codes. _Appl. Sci._**2023**, _13_, 4371. [[https://doi.org/10.3390/appl374371](https://doi.org/10.3390/appl374371)]([https://doi.org/10.3390/appl374371](https://doi.org/10.3390/appl374371))
## 1 Introduction
Experimental results demonstrate the effectiveness of the proposed design. Beyond its compact size and straightforward implementation, this 4-port MIMO array exhibits exceptional electromagnetic isolation, even with closely spaced radiators. This observation suggests that both the axial configuration and the integrated stubs effectively minimize electromagnetic interaction between the radiating elements.
# Positioning Energy-Neutral Devices: Technological Status and Hybrid RF-Acoustic Experiments
## 3 Hybrid Acoustic-RF Approaches for Positioning Energy-Neutral Devices
### RF Backscattering  
Antenna scattering theory has gained a lot of interest in recent years as a compelling research domain for (near) passive communications. Stemming from the RFID research wave of the 1990s, where the backscattering of incoming antenna signals was used for identification [44], more recent research [45] has focused on data-intensive wireless communication. Eliminating active RF transceivers could resolve major hurdles in massive IoT and its future applications, targeting near passive communication between sensor/actuator devices and the Internet [45; 46]. We here elaborate on the potential of backscattering approaches to achieve full energy neutrality for ranging operations, which can be further processed for determining the position of devices. Thereto, we first clarify the antenna scattering principles and its major parameters, and we then apply these to the case of backscattering modulation to be used for hybrid RF-acoustic ranging.
## 3 Learned Separation
### Model Fitting
#### 3.2.1 Parameter Representation  
We first have to decide which parameters are stochastic and which ones are deterministic. The fundamental frequency parameter \(f^{\circ}_{j,1,k}\) is clearly one of those in which \(y\) is not convex; however, it does have a useful gradient on a local scale. We therefore split this parameter into two parameters as \(f^{\circ}_{j,1,k}=\beta(\
u_{j,k}+\tilde{\
u}_{j,k})\), where the values for \(\
u_{j,k}\in\mathbb{N}\) are discrete and those for \(\tilde{\
u}_{j,k}\in\mathbb{R}\) are continuous. We treat \(\
u_{j,k}\) as a stochastic parameter and \(\tilde{\
u}_{j,k}\) as a deterministic one.  
If we limit each instrument to exactly one tone (therefore, \(m=N_{\text{ins}}\)), the instrument parameter \(\eta_{j,k}\) is not required mathematically. However, it makes sense to include it from a practical algorithmic perspective since this allows for a network architecture that sequentially extracts one tone after another while freely choosing the extraction order (see Section 3.2.2). As \(\eta_{j,k}\) is discrete, it is impossible to obtain a gradient. Thus, we model it as a stochastic parameter following a categorical distribution.  
For \(\sigma_{j,k}\), while it is possible to find examples in which the distance \(d^{\text{\tiny{d,abs}}}_{2,\delta}(Y,y)\) is not convex with respect to it, the gradient around the theoretical value is usually good, so we can treat it as a deterministic parameter.  
The inharmonicity parameter \(b_{j,k}\) is also continuous and has a good gradient around the optimum, but depending on the characteristics of the instrument, there can exist local optima. Therefore we do not rely on backpropagation and instead treat it as a stochastic parameter following a gamma distribution (see Figure 2). The reason why we chose a gamma distribution is that it is non-negative and it can have two qualitatively different shapes:Either it tends towards \(\infty\) at zero (which is useful to model tones without inharmonicity), or it is bell-shaped around a finite maximum (to model tones with inharmonicity). Approaching infinity, it always decays exponentially, and in the edge case between the two shapes, it matches an exponential distribution.  
In the tone amplitudes \(a_{j,k}\), the problem is convex, and therefore they are treated as deterministic parameters. In total, the stochastic parameters for each tone are \(\varpi_{\mathrm{s},j,k}=(v_{j,k},\eta_{j,k},b_{j,k})\), and the deterministic parameters are \(\varpi_{\mathrm{d},j,k}=(a_{j,k},\tilde{v}_{j,k},\sigma_{j,k})\). When it is clear which time frame we are considering, we can drop the dependency on \(k\) and summarize:  
\[\varpi_{\mathrm{s}}=(\varpi_{\mathrm{s},1},\dots,\varpi_{\mathrm{s},m})=( \varpi_{\mathrm{s},1,k},\dots,\varpi_{\mathrm{s},m,k}), \tag{13a}\] \[\varpi_{\mathrm{d}}=(\varpi_{\mathrm{d},1},\dots,\varpi_{\mathrm{d },m})=(\varpi_{\mathrm{d},1,k},\dots,\varpi_{\mathrm{d},m,k}). \tag{13b}\]
## 3 Full-Scale Testing
### Elliptical Tube Geometry
\begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline \multirow{2}{*}{**Specimen**} & \multirow{2}{*}{\(2a\)} & \multirow{2}{*}{\(2b\)} & \multirow{2}{*}{\(a/b\)} & \multicolumn{6}{c}{**Wall Thickness \(t\)**} & \multirow{2}{*}{\(P_{\mathrm{m}}\)} & \multirow{2}{*}{\(A\)} \\  & & & & & & & & \\ \cline{5-10}  & & **(mm)** & & & & & & \\ \hline EHS01-PLA1-100-50-3.0 & 100.14 & 50.01 & 2.0 & 3.00 & 3.17 & 0.11 & 3.48 & 233 & 737 \\ EHS02-PLA1-90-60-2.0 & 90.17 & 60.09 & 1.5 & 2.00 & 2.07 & 0.08 & 3.96 & 232 & 478 \\ EHS03-PLA1-90-60-2.0 & 90.02 & 60.11 & 1.5 & 2.00 & 2.08 & 0.06 & 2.73 & 232 & 481 \\ EHS04-PLA1-90-60-2.0 & 90.06 & 60.08 & 1.5 & 2.00 & 2.04 & 0.09 & 4.35 & 232 & 473 \\ EHS05-PLA1-100-50-3.0 & 100.04 & 50.05 & 2.0 & 3.00 & 3.10 & 0.12 & 3.93 & 233 & 721 \\ EHS06-PLA1-100-50-1.5 & 100.06 & 50.08 & 2.0 & 1.50 & 1.45 & 0.07 & 5.02 & 238 & 344 \\ EHS07-PLA1-105-35-2.0 & 105.13 & 35.07 & 3.0 & 2.00 & 2.02 & 0.10 & 4.73 & 228 & 459 \\ EHS08-PLA1-105-35-1.5 & 105.08 & 35.01 & 3.0 & 1.50 & 1.39 & 0.15 & 10.91 & 230 & 344 \\ \hline EHS09-PLA2-90-60-1.0 & 90.15 & 59.71 & 1.5 & 1.00 & 1.13 & 0.03 & 2.65 & 234 & 235 \\ EHS10-PLA2-90-60-1.5 & 90.02 & 60.09 & 1.5 & 1.50 & 1.60 & 0.05 & 3.21 & 233 & 350 \\ EHS11-PLA2-90-60-3.0 & 90.03 & 60.01 & 1.5 & 3.00 & 3.03 & 0.05 & 1.63 & 228 & 686 \\ EHS12-PLA2-100-50-1.0 & 100.08 & 50.03 & 2.0 & 1.00 & 1.13 & 0.05 & 4.07 & 238 & 239 \\ EHS13-PLA2-100-50-2.0 & 100.06 & 50.01 & 2.0 & 2.00 & 2.26 & 0.07 & 3.16 & 235 & 472 \\ EHS14-PLA2-100-50-2.5 & 100.03 & 50.04 & 2.0 & 2.50 & 2.73 & 0.06 & 2.19 & 233 & 586 \\ EHS15-PLA2-105-35-1.0 & 105.05 & 35.03 & 3.0 & 1.00 & 1.26 & 0.04 & 3.45 & 230 & 231 \\ EHS16-PLA2-105-35-2.5 & 105.07 & 35.01 & 3.0 & 2.50 & 2.72 & 0.05 & 1.94 & 225 & 566 \\ EHS17-PLA2-105-35-3.0 & 105.02 & 35.01 & 3.0 & 3.00 & 3.20 & 0.07 & 2.03 & 224 & 675 \\ \hline EHS18-ABS10-50-1.0 & 99.70 & 50.65 & 2.0 & 1.00 & 1.21 & 0.05 & 3.71 & 239 & 239 \\ EHS19-ABS9-00-60-1.0 & 90.16 & 59.98 & 1.5 & 1.50 & 1.16 & 0.07 & 5.67 & 235 & 235 \\ EHS20-ABS90-60-1.5 & 90.07 & 60.14 & 1.5 & 1.00 & 1.79 & 0.05 & 2.87 & 233 & 464 \\ EHS21-ABS105-35-1.0 & 105.42 & 35.61 & 3.0 & 2.00 & 1.32 & 0.20 & 15.42 & 231 & 231 \\ EHS22-ABS105-35-2.5 & 105.23 & 35.12 & 3.0 & 1.00 & 2.75 & 0.07 & 2.43 & 226 & 566 \\ EHS23-ABS-100-50-1.0 & 99.60 & 50.15 & 2.0 & 2.50 & 1.11 & 0.04 & 3.36 & 238 & 239 \\ EHS24-ABS-100-50-2.0 & 99.86 & 50.31 & 2.0 & 2.00 & 2.19 & 0.08 & 3.57 & 236 & 472 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Measured cross-sectional properties of EHS stub column specimens.
# Degradation Prediction Model for Friction of Road Pavements with Natural Aggregates and Steel Slags
## 1 Introduction
[PERSON] et al. [13] developed a skid resistance predictive model for three types of aggregates: granite, limestone and basalt. The authors related the skid resistance value at 35\({}^{\circ}\) (SRV35), with the differential polishing stone value (PSV\({}_{\text{diff}}\)) before and after polishing, mean texture depth (MTD) and gradation. The model does not consider the traffic volume. [PERSON] et al. [14] in relation to the same aggregates, extended the predictive model for any traffic volume for dense-graded asphalt concrete with 9.5 mm or 12.5 mm aggregate maximum size. Although most studies show that skid resistance is related to traffic volume, an accurate skid resistance predictive model, in terms of traffic volumes and regarding many types of aggregates, has not yet been clearly defined. In particular, a relationship that links the degradation of skid resistance in the laboratory with the on-site one has not been defined. This paper mainly discusses the skid resistance performance of various aggregates (slag, limestone, silica-limestone, mixed slag-limestone, basalt, diabase and Criggion stone), assessed with the PSV test and examines the micro-texture decay curves. In particular, the main purpose of the study is to identify a decay curve (deterioration) of the friction indicators, taking into account the number of cumulative vehicle passages over time. The aim of the research was, therefore, to assess whether the use of steel slags can replace natural aggregates by comparing the decay of skid resistance both in laboratory and on site.
## 4 Simulations and Experiments on Tri-Axis RINS
### Experiments and Discussion
\begin{table}
\begin{tabular}{l c c c} \hline \hline \multirow{2}{*}{**Experiment Number**} & \multicolumn{3}{c}{**Maximum Radial Position Errors (m)**} \\ \cline{2-4}  & **Scheme A** & **Scheme B** & **Scheme C** \\ \hline
1 & 2028.9 & 928.1 & 650.4 \\
2 & 1304.7 & 1143.7 & 303.3 \\
3 & 1716.4 & 947.2 & 467.6 \\ Mean value & 1683.3 & 1006.3 & 473.8 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Maximum radial position errors during 6 h navigation of the three schemes.
## 2 Precise Orbit Positioning Processing Approach
### Global Navigation Satellite System Observation Model
It should be mentioned that for GLONASS, because of its frequency-division multiple-access (FDMA) strategy, the receiver code hardware delay \(d_{r}^{R_{k}}\), as well as the phase delay \(b_{r}^{R_{k}}\), are different for satellites with different frequency factors \(k\), referred to inter-frequency bias (IFB) for code and carrier phase, respectively. Therefore, they cannot be removed by forming DD ambiguity unless, homogeneous receivers, i.e., the same type of receivers, are used [16]. Since the code and carrier phase IFBs of a particular satellite is the same for all involved receivers, they can be eliminated while forming difference between homogeneous receivers. Through this classification, the IFB causes no effect on WL fixing. Indeed, IFBs can also be estimated and calibrated for integer ambiguity resolution [17].
# Axial-Symmetric Diffraction Radiation Antenna with a Very Narrow Funnel-Shaped Directional Diagram
## 4 Radial Emitters with a Narrow Funnel-Shaped Radiation Pattern
The radiation pattern characteristic of this range is shown in the upper fragment of Figure 7. For \(k=4.115\), radiation efficiency is \(\eta(k)=0.34\). The frequency \(k=3.765\) corresponds to the maximum efficiency \(\eta(k)=0.97\); the corresponding radiation pattern is shown in the bottom fragment of Figure 7.  
Figure 6: Radiation efficiency of a radial antenna at aimed parameter values \(c=0.1\) (see Figure 5) and \(c=0.06\). Figure 8: Excitation of the radial antenna (see Figure 7) by a quasi-monochromatic \(TE_{01}\)-wave with a central frequency \(\widetilde{k}=3.765\) and \(\widetilde{k}=4.115\). Distribution of values \(E_{\phi}(g,t)\) in the computational domain \(\Omega_{\text{int}}\), \(t=125\). Figure 8 shows the spatial distribution of the radiation field at the instant \(t=125\), when the structure is excited by a quasi-monochromatic \(TE_{01}\)- pulse wave:  
\[ \begin{array}{l}V_{1}(g,t):\ v_{1}\big{(}z:\ g\in\mathrm{L}^{+},t\big{)}=P(t) \cos\Bigl{[}\widetilde{k}\Bigl{(}t-\widetilde{T}\Bigr{)}\Bigr{]}=F_{2}(t);\\ P(t)=\ 0.01-5-95-99,\ \ \ \widetilde{T}=0.5,\ \ \ \widetilde{k}=3.765\ \ \ \text{and}\ \ \ \widetilde{k}=4.115\end{array}  \tag{23}\]  
where \(\widetilde{k}\) is the central frequency of the signal \(F_{2}(t)\) and \(P(t):\ \ t_{1}-t_{2}-t_{3}-t_{4}\) is its trapezoidal envelope, which equals unit for \(t_{2}<t<t_{3}\), and is zero for \(t<t_{1}\) and \(t>t_{4}\). The fronts of the radiated waves are practically flat (in the representation for values \(\phi=const\)) and the orientation of the relevant planes corresponds to the previously determined values \(\overline{\theta}=10^{\circ}\) (for \(k=3.765\)) and \(\overline{\theta}=2^{\circ}\) (for \(k=4.115\)). Obviously, the non-uniform (especially in the case \(\widetilde{k}=3.765\)) distribution of the radiation field amplitude along the emitter aperture is connected, among other things, with a decrease in the spatial energy density carried by the surface wave of the radial waveguide as it moves along the axis \(\rho\). It will be necessary to consider the corresponding effect when creating actual antennas with a sufficiently large value of \(L\) determining the size of their apertures.
# Securing Wireless Sensor Networks Using Machine Learning and Blockchain: A Review
## 9 BC and Cyberattack Prevention
### BC in the Literature
This technique minimizes communication overhead, as sensing nodes are not connected directly to the unauthenticated public BC; therefore, frequent node authentication is not required. The focus of [146] was on securing an identity authentication scheme against worm attacks by using the IOTA Tangle BC to store the authentication data safely; however, the network proposed in [146] relies on a single point, namely, sink nodes, to authenticate other nodes, which means that the network has a centralized architecture. Another approach was proposed in [147], where the authors considered a sequential detection scheme that starts by validating the hash value of the node's ID, followed by validation of the node signature by each node, and ends with a voting technique that determines whether the node is malicious or benign. The results from the different stages are then used to decide whether a suspect node is kept or eliminated. The authors of [147] revealed that the security level of the proposed BC method is improved compared to other classical approaches in the literature; however, the latency introduced by the three combined phases could potentially be higher than classical approaches. The authors of [148] proposed another decentralized authentication and trust model, which stores the authentication and trust information in the BC and uses a subjective probability as a reputation level. The technique is limited by the origin block problem, which causes the system to misbehave in cases where malicious values are included in the first block in the chain. Localizing WSN nodes accurately is another application, and was addressed in [149] by investigating a decentralized BC-based trust management model. Their model relied on a trust value consisting of both behavior and data trust values evaluated by a selected number of trusted nodes, such as the number of successful and unsuccessful interactions between sensor nodes and feedback metrics related to the integrity of each beacon node. Though the simulated results in [149] indicate that the proposed scheme outperforms other current techniques in several aspects, it requires an additional number of transactions associated with the evaluation processes, and lacks a complexity analysis of the proposed technique.
# Hybrid Representation of Sensor Data for the Classification of Driving Behaviour
## 4 Experimental Evaluation
### Datasets  
The classification schemes investigated are evaluated on the publicly available UAH dataset [12], which has been acquired by means of mobile phone sensors, as well as on two datasets, which have been acquired by means of telematics sensors and have been created in the context of this work.
## 3 Experimental Results  
### Experimental Environment and Performance Evaluation  
In order to better prove the function of the module proposed in Section 2, we adopt the following experiments to verify the practicability of our proposed LDKA-NET network. The experiments were conducted using the PyTorch1.8.1 [15] framework. In the experiments, the batch size was set to eight, the momentum was set to 0.9, the weight decay was set to 0.0005, the learning rate was set to 0.001, and the optimizer used was SGD. The experiments utilized an NVIDIA GeForce RTX 3090 GPU, an Intel i9-11900k processor (NVIDIA Corporation, Santa Clara, CA, USA), and the operating system was Ubuntu 18.04.  
To objectively evaluate the advantages of the proposed algorithm, mean average precision (mAP), recall (R), and precision (P) were selected as evaluation metrics [16].
## 4 DC Characterization and Model Validation
### UTC Photodiode Compact Model Validation  
#### 4.3.1 I-V Characterization and Parameter Extraction  
Three UTC-PDs with active areas of 5 \(\times\) 15 \(\upmu\)m\({}^{2}\), 5 \(\times\) 30 \(\upmu\)m\({}^{2}\) and 7 \(\times\) 25 \(\upmu\)m\({}^{2}\) were characterized under a bias sweep in the range of (\(-\)3, 1) V. Current-voltage characterizations were performed on each device geometry at three temperatures: 15, 25 and 35 \({}^{\circ}\)C. Figure 6 compares measured the DC characteristics and our developed compact model described in Section 3.2, demonstrating good model accuracy over the entire range of forward and reverse bias at different temperatures. The photodiodes present very low dark current levels of around 4, 20 and 55 nA @ -2V for the geometries 5 \(\times\) 15 \(\upmu\)m\({}^{2}\), 5 \(\times\) 30 \(\upmu\)m\({}^{2}\) and 7 \(\times\) 25 \(\upmu\)m\({}^{2}\), respectively, which shows the quality of the fabrication and especially the effective passivation of the photodiode junction. The dark current is very low compared to the photocurrent used for mm-wave generation, which is usually in the mA range to generate sufficient power. Therefore, as the dark current is more than three orders of magnitude below the photocurrent, even if a detailed analysis of the photodiode noise was not realized, we can assume that the impact of a dark current is negligible compared to the global noise of the system. We would like to mention that in order to have a complete view of the impact of the photodiode on the performances of the radio link, one requires the analysis of the nonlinear behavior of the photodiode which is not in the scope of this paper.