esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
The probe fit and insertion depth were ascertained by making sure all participants had low absorbance at the lowest end of the frequency (\(\leq\)10% or 0.1 absorbances). This method resulted in excellent test-retest reliability in our previous dataset, as well as this study. WBT was performed twice in every ear. Between measurements, the probe was removed and reinserted to capture variability secondary to probe placement. The duration of each run was approximately 30 s.  
Statistical analyses  
Absorbance as a function of frequency (226-8000 Hz) was compared for the three groups: normal-hearing ears, implanted ears, and impaired-hearing ears that were not implanted. Absorbance was evaluated independently for the two pressure settings under the WBT tab only. Comparisons were also performed between (a) the two repeated measurements of the same ear, with removal and re-insertion of the probe to evaluate test-retest reliability; (b) right and left ears in participants with normal hearing, to exclude differences originating from laterality; (c) implanted ears and non-implanted ears with hearing impairment, to assess the effect of cochlear implantation on-ear mechanics; and (d) non-implanted hearing-impaired ears vs. normal-hearing ears, to evaluate the effect of SNHL per se on WBT results. Statistical analyses included descriptive statistics, a chi-square test for categorical variables, a repeated-measures ANOVA test for comparison of test-retest reliability and the effect of laterality, and a mixed model of the ANOVA for the WBT results. The Greenhouse-Geisser correction was used to compensate for variability in the equality assumption. To evaluate differences between groups at singular frequencies, the Tukey honestly significant difference (HSD) post hoc analysis was performed. All analyses were performed using SPSS Ver. 25 (IBM SPSS Statistics, New York, NY, USA). Institutional Review Board approvals were granted at the University of British Columbia, Canada (H17-00388-A001), and the University of Haifa, Israel (281/15).
# New Advances and Challenges of Fall Detection Systems: A Survey
## 3 Bird's Eye View of Fall Detection Research from 2014
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Reference** & **Sensors** & **Algorithms** & **Accuracy** & **Counts of Citations** & **Time** \\ \hline [PERSON] and [PERSON] [10] & Kinect & Decision Tree (DT) & N/A & 195 & 2015 \\ \hline [PERSON] and [PERSON] [11] & Kinect & Threshold and Shape Features & 100\% & 191 & 2014 \\ \hline [PERSON] et al. [12] & Wi-Fi & Random Forest & & & \\  & & Algorithm (RFA) & 94\% & 122 & 2017 \\ \hline [PERSON] et al. [13] & Kinect & Ad-Hoc Segmentation & N/A & 83 & 2014 \\ \hline [PERSON] et al. [4] & Kinect & \begin{tabular}{c} Shape Features and Artificial \\ Neural Networks (ANN) \\ \end{tabular} & 86.83\% & 65 & 2014 \\ \hline [PERSON] et al. [14] & Accelerometer & Thresholds & 97.5\% & 59 & 2014 \\ \hline [PERSON] and [PERSON] [15] & Accelerometer and & Support Vector & 98.33\% & 57 & 2014 \\  & Kinect & Machine (SVM) & 97.60\% & 55 & 2015 \\ \hline [PERSON] et al. [16] & Kinect & Decision tree (DT) & 97.60\% & 55 & 2015 \\ \hline [PERSON] et al. [17] & Doppler Radar & Discrete Wavelet & 89.37\% & 49 & 2015 \\  & Transform (DWT) & 97.55\% & 46 & 2014 \\ \hline [PERSON] et al. [18] & Accelerometer & Decision Tree (DT) & 97.55\% & 46 & 2014 \\ \hline [PERSON] et al. [19] & Accelerometer & \begin{tabular}{c} Discrete Wavelet \\ Transform (DWT) \\ \end{tabular} & 92\% & 44 & 2015 \\ \hline [PERSON] et al. [20] & Accelerometers and & \begin{tabular}{c} Decision tree (DT) \\ Video cameras \\ \end{tabular} & 79.57\% & 43 & 2014 \\ \hline [PERSON] and [PERSON] [21] & Kinect and & k-Nearest Neighbor (kNN) & 100\% & 40 & 2014 \\ \hline [PERSON] et al. [22] & Accelerometer & \begin{tabular}{c} Support Vector \\ Machine (SVM) \\ \end{tabular} & 95.19\% & 40 & 2015 \\ \hline [PERSON] et al. [23] & Kinect & \begin{tabular}{c} Nonnegative Matrix \\ Factorization (NMF) \\ \end{tabular} & 98\% & 38 & 2014 \\ \hline [PERSON] et al. [24] & Video cameras & \
\begin{tabular}{c} Shape Features \\ (DWT) and Support Vector \\ Machine (SVM) \\ \end{tabular} & N/A & 34 & 2016 \\ \hline [PERSON] et al. [26] & Accelerometer & Thresholds & N/A & 33 & 2015 \\ \hline [PERSON] and [PERSON] [27] & Kinect and &
\begin{tabular}{c} k-Nearest Neighbor (kNN) \\ and Support Vector \\ Machine (SVM) \\ \end{tabular} & 95.77\% & 31 & 2015 \\ \hline [PERSON] and [PERSON] [28] & Accelerometer & Thresholds & 97.83\% & 31 & 2015 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Twenty most cited papers on fall detection published from 2014.
# Comparison of the Sentinel-3A and B SLSTR Tandem Phase Data Using Metrological Principles
## 5 Discussion
### Uncertainty Validation  
A key objective of this study was to apply metrological principles to the comparison of satellite observations. As previously described, within this context, uncertainty validation provides the key result of the comparison exercise, quantitatively determining if the sensors are meeting their expected performance. Since uncertainty information is available in both the SLSTR L1 and L2 products, such analysis was possible for the radiometric and SST data looked at in this study. The valuable quantitative conclusions that have been drawn on the basis of such uncertainty analysis demonstrates a key utility of the provision uncertainty information in satellite data products.  
At L1, it was found that the estimated uncertainties were reasonable for the S8 nadir channel and S7 channel in both views. Aside from the stray light issue previously described for the S8 and S9 oblique channels, the consistent 50 mK difference for the S9 channel was not explained by the uncertainties. The latter could be due to uncorrected detector temperature sensitivity, which should be further investigated. For the L2 SST data, the analysis was somewhat limited by a large component of covariance between SLSTR-A and B observations, but it has been shown that the theoretical uncertainties as provided likely need updating. At the very least, the N2 and D3 theoretical uncertainties will need updating being seemingly too large and too small, respectively.  
Beyond the current uncertainties in the data, the tandem data are also showing that there may be missing components of uncertainty in the SST data. It is clear from the statistics that in moving to a lower quality level there is an increase in the standard deviation that an extra component of uncertainty should be associated with a lower quality level. While this may seem obvious, with the tandem data, we can begin to provide an estimate of how large such a component should be and therefore we have the possibility of adding a quality-level-related uncertainty estimate. In addition, on top of this, the tandem data are also showing evidence of the extra uncertainty associated with the cloud probability, which is again a currently missing component of uncertainty. While the data used is not sufficient to pin this down fully because the SLSTR-B SST is not derived using the correct retrieval coefficients, the use of tandem data will clearly have a role in estimating a more complete SST theoretical uncertainty in the future.
## 4 Results and Analysis  
### Simulation Results of Arch Culvert Construction  
#### 4.1.1 Displacement of Strata  
Figure 12 shows the contour map of the foundation settlement under construction load. The settlement of the foundation soil on the left and right sides is roughly symmetrical. Because the height of the right filling is slightly higher than that of the left, the settlement of the right foundation is slightly larger than that of the left. There are two different types of arch culverts in the arch culvert area, and the sizes of arch culverts numbered 4-9 are larger than others. Due to the varying sizes of arch culverts, the soil in the surrounding area will be discontinuous and uneven, resulting in different load responses. The foundation below the junction of different types of arch culverts has the largest settlement. Figure 13 shows the horizontal displacement distribution of the foundation under construction backfill load. The maximum horizontal displacement occurs below both ends of the foundation, about 30 m from the bottom of the foundation, which is three times the depth of the foundation pit, and about 17 m from the bottom of the composite foundation. The settlement deformation depth affected by construction is 50 m below the bottom of the foundation, which is about five times the depth of the foundation pit, and the influence range of horizontal displacement deformation is about twice the length of the foundation pit. The specifications dictate that the post-construction settlement for general sections of first-class highways should be no more than 30 cm, while the post-construction settlement for culverts, box culverts, and channels should be no more than 20 cm [30]. The calculation results show that the settlement caused by the construction of the arch culvert on the soft soil composite foundation meets the requirements of the code.  
Figure 11: Data recording points: (**a**) Road surface, subgrade, and foundation; (**b**) Steel corrugated pipe construction.  
Figure 12: Settlement contour (unit: mm).
## 5 Discussion
### Limitations  
The evaluation was conducted by discussing the SemDaServ approach with experts from industry as well as academia (workshops and interviews). Therefore, SemDaServ was qualitatively evaluated using expert experience and logic. This research approach is feasible to evaluate the applicability in practice, usefulness, and logical correctness of the SemDaServ approach. To evaluate the effectiveness and efficiency in real-world SSSE projects, experiments (e.g., A to B comparisons with professionals specifying data needs without any guidance, data-driven AI training without any data specification, etc.) are needed to quantify the efficiency and effectiveness of SemDaServ. Especially the impact of SemDaServ regarding cost, quality, and time on real-world SSSE projects cannot be quantified yet. Additionally, the uncertainty factor of the data specification resulting from the SemDaServ approach cannot be quantified yet. To overcome these limitations, a significant number of case studies are needed to measure the quality of the smart services developed using the specified data needs resulting from the application of the SemDaServ approach.
## 2 Methodology of the Forward Modelling
### Basic Equations of Propagation Matrix Theory  
The matrix system for the forward modelling of the reflection and transmission coefficients using the propagation matrix can be represented as  
\[(\textbf{A}_{1}-\left(\prod_{i=1}^{nz}\textbf{B}_{iz}\right)\textbf{A}_{2,n}) \textbf{r}=-i_{\text{P}}. \tag{22}\]  
Matrix \(\textbf{B}_{iz}\) represents the propagation operator for the geological stratum. \(\textbf{B}_{iz}\) is represented as  
\[\textbf{B}_{iz}=\textbf{T}(0)\textbf{T}^{-1}(h_{iz}),iz=1,\ldots,nz, \tag{23}\]where \(h_{iz}\) represents the depth of the \(iz\)-th layer. \(T\) can be expressed as  
\[\mathbf{T}(z)=\mathbf{r}_{iz,1}\mathbf{E}_{iz,1}. \tag{24}\]
## 4 Problem Formulation  
The original problem of this paper can be expressed as follows:  
\[\mathcal{OP}: \underset{\Theta,\Psi}{\text{min}}_{sum}(\Theta,\Psi),\] (13a) s.t. \[\text{C1}: \ 0\leq x_{j},y_{j},z_{j},w_{j}\leq 1,\forall j\in \mathcal{J}, \tag{13b}\] \[\text{C2}: x_{j}+y_{j}+z_{j}+w_{j}=1,\forall j\in\mathcal{J},\] (13c) \[\text{C3}: \ 0\leq p_{j}\leq p_{j},\forall j\in\mathcal{J},\] (13d) \[\text{C4}: \ 0\leq p_{m}^{m}\leq\leq p_{n},\forall n\in\mathcal{N},m\in \mathcal{M},\] (13e) \[\text{C5}: \ 0\leq f_{j}^{l}\leq F_{j}^{l},\forall j\in\mathcal{J},\] (13f) \[\text{C6}: \ 0\leq f_{j}^{l},f_{j}^{m},f_{j}^{m},\forall j\in \mathcal{J},n\in\mathcal{N},m\in\mathcal{M},\] (13g) \[\text{C7}: \ \sum_{j\in\mathcal{J}}(f_{j}^{n}+f_{j}^{n,fd})\leq F_{n}, \forall n\in\mathcal{N},\] (13h) \[\text{C8}: \ \sum_{j\in\mathcal{J}}f_{j}^{m}\leq F_{m},\forall m\in \mathcal{M},\] (13i) \[\text{C9}: t_{j}\leq T_{j},\forall j\in\mathcal{J}, \tag{13j}\]  
where \(\Theta=\{x_{j},y_{j},z_{j},w_{j}\mid\forall j\in\mathcal{J}\}\) is the collection of the task splitting strategy. \(\Psi=\{p_{j},p_{n}^{m},f_{j}^{l},f_{j}^{m},f_{j}^{n,fd},f_{j}^{m}\mid\forall j \in\mathcal{J},n\in\mathcal{N},\text{and }m\in\mathcal{M}\}\) denote the collection of multiple resource allocation, including power control, computing resource allocation of IOT devices, HAP drones, and LEO satellites. In this problem, (C1) and (C2) are constraints on task partitioning, indicating that the task partitioning variables must be continuous values between 0 and 1, and for each task \(j\), the sum of \(x_{j}\), \(y_{j}\), \(z_{j}\), and \(w_{j}\) must be 1. (C3) and (C4) are constraints on transmit power, ensuring that the transmit power does not exceed the maximum available transmit power of the device. (C5) to (C8) are constraints on computation frequency, ensuring that the computation frequency allocated to each task by each IoT does not exceed its maximum computation capacity. (C9) indicates that the overall delay for each IoT must not exceed its maximum tolerable delay.
# Automatic Design of Collective Behaviors for Robots that Can Display and Perceive Colors
## 5 Results and Discussion
### Stop  
Figure 3 (left) shows the performance of TuttiFrutti and EvoColor in stop. In this mission, TuttiFrutti performs significantly better than EvoColor.  
From visual inspection, TuttiFrutti produced control software that effectively uses the capabilities of the robots for displaying and perceiving colors. The swarm first disperses and homogeneously covers the arena--aiming to rapidly detect the stop signal. If a robot detects the stop signal, it stands still and disseminates the information by emitting a signal of an arbitrary color. When other robots perceive the signal emitted by their peer, they also transition to a standstill behavior and relay the signal. The process continues until all robots in the swarm are standing still. We consider that this behavior shows the potential of TuttiFrutti for producing event-handling collective behaviors. The swarm collectively transitions from coverage to standstill when the stop signal appears. As we expected, TuttiFrutti produces control software that establishes communication protocols by correctly pairing the color of the signals that robots emit and the behavior other robots must adopt when they perceive them--similarly to the results obtained by [PERSON] et al. [14] with Gianduja.  
EvoColor, unlike TuttiFruti, designed collective behaviors that do not respond to the stop signal. The swarm adopts a rather simplistic behavior in which robots move until stopped by the walls. They remain then in a standstill behavior because they persistently push against the walls--no reaction can be appreciated in the swarms when the stop signal appears. This behavior was observed too in the experiments with physical robots, and in many cases, robots maintained standing-still behaviors by pushing against other robots too.  
In the experiments with physical robots, both TuttiFruti and EvoColor showed a significant drop in performance with respect to the simulations. However, the difference in mean performance between simulations and experiments with physical robots is larger for EvoColor than TuttiFruti. Swarms deployed with the control software produced by TuttiFruti showed the same collective behavior observed in simulation, although the rapidness in discovering the stop signal and disseminating the information decreased. In the case of EvoColor, robots often do not reach the walls and they push against each other to remain still in place.  
Figure 4 shows an example of the control software produced by TuttiFruti for stop. Robots start in repulsion with no color displayed \((\gamma=\varnothing)\). They transition to stop and turn yellow \((\gamma=Y)\) when color-detection is triggered either by a green wall \((\delta=G)\) or by yellow robots \((\delta=Y)\). In this sense, robots change their behavior when they either perceive the stop signal or the yellow signals that other robots emit.
## 3 Results
### Microstructural Analysis  
The OM images in Figure 11 show the macrostructures on the two planes with and without WIP. In the S-plane without WIP, irregular pores and gaps formed between the inter-layers (Figure 11a). By contrast, in the L-plane without WIP (Figure 11c), although large pores due to lack of fusion did not appear, micropores existed nevertheless. However, after WIP, these critical large defects were dramatically diminished (Figure 11b,d). Figure 12 shows the microstructure obtained by means of SEM. Round secondary phase particles at the grain boundaries (indicated by arrows in Figure 12) and even in the grains were observed in both specimens. Moreover, the segregated secondary phase at the grain boundary is observable in Figure 12 with white color. The chemical composition of the secondary-phase particles is shown by the elemental maps in Figures 13 and 14; it was characterized by C, N, O, Cr, Fe, and Ni. The round particles mainly consisted of Cr and O, like in the chrome oxide phase; the segregated particles were mainly Cr. For these secondary phase particles formed during the sintering process [40], a significant difference in the characteristics such as size, shape and distribution was not found between the cases with and without WIP.  
Figure 11: Optical micrographs of specimens at (**a**) S plane without WIP; (**b**) S plane with WIP; (**c**) L plane without WIP; (**d**) L plane with WIP.  
Figure 12: Microstructural SEM images of specimens and secondary phase particles (white arrows) at (**a**) L plane without WIP; (**b**) L plane with WIP; (**c**) S plane without WIP; (**d**) S plane with WIP. (WIP: warm isostatic pressure.)Figure 14: Elemental maps at the specimen S plane (**a**) without and (**b**) with warm isostatic press. (SE: secondary-electron SEM image.)  
Figure 13: Elemental maps at the specimen L plane: (**a**) without and (**b**) with warm isostatic press. (SE: secondary-electron SEM image.)
# Signal Source Localization of Multiple Robots Using an Event-Triggered Communication Scheme
###### Abstract  
This paper deals with the problem of signal source localization using a group of autonomous robots by designing and analyzing a decision-control approach with an event-triggered communication scheme. The proposed decision-control approach includes two levels: a decision level and a control level. In the decision level, a particle filter is used to estimate the possible positions of the signal source. The estimated position of the signal source gradually approaches the real position of signal source with the movement of robots. In the control level, a consensus controller is proposed to control multiple robots to seek a signal source based on the estimated signal source position. At the same time, an event-triggered communication scheme is designed such that the burden of communication can be lightened. Finally, simulation and experimental results show the effectiveness of the proposed decision-control approach with the event-triggered communication scheme for the problem of signal source localization.  
Keywords:signal source localization; multi-robot system; event-triggered communication; consensus control +
Footnote †: journal: Applied Sciences
## 2 Novel Frameless Slotted-ALOHA Protocol
### Parameter Setting  
Based on the multi-user coding (MUC) scheme in [12], the authors of [13] proposed a hybrid random-access system to coordinate the burst transmission of multiple independent users sharing a single channel. The capacity of \(T\) users real adder channel [19] is used as an upper bound for the rate of \((N_{c},T)\) (\(T\) active out of \(N_{c}\)) codes and the codes given in [12] are used as a lower bound. The maximum stable throughput for the hybrid random-access system with an upper bound and lower bound are simulated, respectively. The simulation results show that the system obtains the maximum throughput when the number of active users is three for \((N_{c},T)\) codes. Therefore, our work is carried out under the maximum number of detectable conflicting users \(K=3\).  
We illustrate the advantages of such a method with a simple example. When the number of conflicting users is three, the signature codes and SIC are combined to recover these conflicting packets. The received signals in the first three slots are:  
\[\begin{array}{l}S_{1}=A+B+C+Z_{1},\\ S_{2}=A+C+Z_{2},\\ S_{3}=C+Z_{3},\end{array} \tag{1}\]  
where \(A,B\) and \(C\) represent packets sent by active users. \(Z_{1},Z_{2}\) and \(Z_{3}\) are noise in the first three slots, respectively. Recall that in a setup with the slotted-ALOHA protocol, the receiver has no knowledge about the set of transmitting users, e.g., in Equation (1), it does not know that \(A\) is sent in slots 1 and 2, \(B\) is sent in slot 1, etc. Therefore, it needs to wait for the reception \(S_{3}\) to decode \(C\) and learn from the signature codes of other slots that have sent \(C\) so that it can be canceled. The received signals \(S_{1}\) and \(S_{2}\) are buffered. If \(C\) is decoded successfully in slot \(S_{3}\), it can be removed from \(S_{1}\) and \(S_{2}\). Then, the receiver continues to decode \(A\) from the noise signal \(S_{1}-B-C=A+Z_{1}\). In this example, \(S_{3}\) is a replica, i.e., a clean copy of a conflicting packet, retransmitted by the sender. Obviously, this approach can make full use of the information in conflicting slots and reduce the number of retransmissions.
# Performance Evaluation of Routing Protocols for Underwater Wireless Sensor Networks
###### Abstract  
Underwater Wireless Sensor Networks (UWSNs) are emerging technology for disclosing multiple applications, such as oil, earth quick, and marine environments. All sensor nodes deployed in UWSNs operate through limited power batteries. Prolonging the network's lifetime in such environments is an essential task and a hot topic among researchers. Multiple routing protocols have been designed to overcome the limited power issue and reduce energy consumption. Each routing protocol evaluated different parameters, but the issue is still unclear as to which one is better. In this study, we evaluated multiple routing protocols to investigate which is better in terms of parameters, such as packet delivery ratio (PDR), energy consumption, end-to-end delay, and the number of alive nodes. The simulation results indicate that Reliability and Adaptive Cooperation for Efficient UWSNs Using Sink Mobility (RACE-SM) performs better in all performance metrics than other routing schemes.  
UWSNs; underwater; comparative analysis; routing protocols Article
# Beyond the Horizon, Backhaul Connectivity for Offshore IoT Devices
## 3 Results
### Additional Slow Fading in the Channel  
In addition to average path loss in ED, there is climate induced slow fading. It is noted that the changes in average path loss values due to slow fading are due to temporal changes rather than spatial. The transmitter and receiver are fixed and the change in average path loss is due to changes in ED characteristics only, mainly its height. The fast-instantaneous fading on top of the slow fading is also due to multipath effect within the channel due to sea wave movement. Additional slow fading at 40 km distance, approximated with 2-P probability distribution is shown in Figure 8. In Figure 8, it is visible that the PDF of the data is validated by Lognormal distribution (tail distribution). The 90% confidence interval lies up to 20 dB pathloss. However, we extended the confidence interval up to 40 dB to ensure 99% availability.  
Figure 8: Average random slow fading approximated with 2-P probability distribution with respect to EDH climatology at 40 km distance.  
Figure 7: Measured path loss for 500 samples values at 30 km, 40 km, 55 km, 65 km and 80 km.