esa-sceva/satcom-chunk-collection huggingface dataset:
# Investigating a Toolchain from Trajectory Recording to Resimulation
## 3 Results  
### Recorded Data  
First, the scenarios were recorded with optical sensors. The camera position resulted in a view as shown on the left side of Figure 3. From the rooftop, it was not possible to film the scenery in a top view. Therefore, a laser scan of the T-junction was made; see Figure 3 in the middle. The image transformed by homography is shown on the right side of Figure 3.  
Next, trajectories of road users are extracted from the recorded videos. Figure 4 shows the raw data points of a trajectory, here exemplified for scenario 2. The corresponding corrected trajectories are presented in Figure 5. Finally, Figure 6 shows the traveled distance of a car between x = 0 m and x = 60 m with a speed of around 35 km/h (9.72 m/s).  
With increasing traveled distance of the car, the distance between the recording system and the car is also increasing. For smaller distances between the recording system and the car, the derived speeds seem to be quite accurate compared with the real speed, with variations between 34 km/h (9.44 m/s) and 36 km/h (10 m/s). With increasing distance between the recording system and the car, the variation of the derived speeds is increasing up to a range between 36 km/h (10 m/s) and 43 km/h (11.94 m/s). In addition to the increasing variation in the derived speeds for higher distances between the recording system and the investigated traffic participant, the derived total speed is increasing, too. It is especially difficult to estimate the speed of vehicles that are far away from the camera, as the distance each pixel represents in the 2D projection becomes larger. A complete trajectory is shown in Figure 5, which is computed from data as shown in Figure 4. The results of the trajectory derivation are manually checked for plausibility in the videos. For this purpose, all detected objects are projected into the frames of the videos. As these are initial investigations, no ground truth has been created yet.  
In the following, the simulation results are presented next to the recorded trajectories for the three scenarios under consideration.  
Figure 4: Original image of the T-junction with raw data of three different trajectories shown as blue, green, and red lines.  
Figure 3: The input image from the perspective of the recording camera (**left**), the laser scan image (**middle**), and the resulting merged view obtained through transformation (**right**) are shown.
# Multimode Guided Wave Detection for Various Composite Damage Types
## 3 Detection of Various Damage Types Using ABT-ABT on a 2-mm Quasi-Isotropic Composite Plate
### Pure Mode Generation Using ABT-ABT
#### 3.2.1 Pure S0 Mode Generation  
First, the adjustable ABT pair was used to generate pure S0 mode in the 2-mm quasi-isotropic CFRP composite plate. To generate and receive the pure S0 mode in the 2-mm quasi-isotropic composite plate, 500 kHz broadband angle beam transducers (A413S-SB, Olympus, USA) mounted on adjustable wedges (ABWX-2001, Olympus, USA) were used as the transmitter and the receiver. Figure 9 illustrates the pitch-catch experimental setup for guided wave generation and detection using ABT-ABT. The coupling between wedges and the specimen was attained with coupling gel (Olympus Sound Safe). For the excitation of pure S0 mode at 500 kHz, the incident angle of the wedge was set to 26\({}^{\circ}\) based on [PERSON]'s law. The excitation signal applied to the transmitter ABT was a narrow-band three-count tone burst at a central frequency of 500 kHz generated by a function generator (AFG3052C, Tektronix, USA) and amplified by a high-speed bipolar amplifier (HSA4014, NF, Japan). The response signals from the receiver ABT were collected by a digital oscilloscope (MDO3024, Tektronix, USA), which used 100 averages to improve the signal-to-noise ratio. The function generator also sent a trigger signal to the oscilloscope for synchronization.  
Figure 8: Dispersion curves of the 2-mm [\(-\)45/90/45/0]\({}_{\rm{z}}\) quasi-isotropic composite plate: (**a**) phase velocity; (**b**) ABT tuning angle; (**c**) group velocity.  
To validate the excited guided wave, the response signals at five different locations were measured by moving the receiver ABT at a constant interval of 10 mm, as presented in Figure 9. Experimental group velocity measurements of the S0 mode excitation are shown in Figure 10.  
Figure 10: Experimental group–velocity measurements of the S0 mode excitation: (**a**) waveforms at various locations; (**b**) correlation between distance and time of flight for the group velocity calculation.  
Figure 9: Experimental setup for the excitation and detection of pure S0 mode on a 2-mm [\(-\)45/90/45/0]\({}_{\text{2s}}\) quasi-isotropic composite plate using ABT–ABT.  
The waveforms at various locations are shown in Figure 10a. It was found that strong and nondispersive wave packets were obtained. The time of flight (TOF) of the direct wave packet was determined for each location and plotted as a function of distance from the excitation, as shown in Figure 10b. First, the received signals were processed using a continuous wavelet transform (CWT) to extract the signal envelopes. Then, the TOF was determined at the time position corresponding to the maximum amplitude of the signal envelope. Linear regression was used to estimate group velocity, yielding a value of 5.43 mm/us, which agreed with the theoretical S0-mode group velocity of 5.49 mm/us at 500 kHz, as shown in Figure 8c. Therefore, the excited guided wave in the 2-mm quasi-isotropic CFRP composite plate was in pure S0 mode, as expected.
# Void Hole Avoidance for Reliable Data Delivery in IoT Enabled Underwater Wireless Sensor Networks
## 5 Linear Programming-Based Mathematical Formulation
### Throughput Maximization
#### 5.2.1 Graphical Analysis  
Assuming a scenario where total bandwidth is between 2000 kHz and 4000 kHz, where \(B_{Frw}\) shows the bandwidth allocated to the forwarding nodes with high residual energy and \(B_{NFrw}\) is the bandwidth assigned to non-forwarding nodes, the bandwidth \(B\) allocated to \(B_{Frw}\) and \(B_{NFrw}\) is computed as follows using the aforementioned constraints in Equations (12a)-(12d):  
\[200\leq B_{Frw}\leq 1000 \tag{13}\]  
\[2000\leq B_{NFrw}\leq 3000 \tag{14}\]  
\[2200\leq B_{Frw}+E_{NFrw}\leq 4000 \tag{15}\]  
The feasible region is plotted in Figure 6, and points are extracted from Equations (13)-(15).  
\(P_{1}\left(200,2000\right)=2200\) kHz  
\(P_{2}\left(1000,2000\right)=3000\) kHz  
\(P_{3}\left(200,3000\right)=3200\) kHz  
Thus, selecting any value from these points results in maximum network throughput.
## 3 Calibration System and Improved Speckle Statistics
### Verification Experiments of System Calibration  
It was found that the temperature sensitive characteristics of the SM filter beam splitter and monitoring photodetector contributed significantly to the fluctuation of the normalized intensity ratio, according to the results of many OFLT experiments. The normalized intensity ratio has a large fluctuation, even if there was a slight difference in the beam splitting ratio between the on and off wavelengths--this difference may be temperature-dependent. A function for thermoelectric cooling (TEC) was added to the SM filter beam splitter, as shown in Figure 4, which changed the temperature from 2 \({}^{\circ}\)C to 24 \({}^{\circ}\)C. The fluctuations and temperature were clearly correlated with each other, as shown in Figure 5. Therefore, we continued to use the TEC to keep the temperature of the SM filter beam splitter stable in the subsequent experiments.  
Figure 4: Same experimental setup as Figure 3, but a TEC was added to the SM filter beam splitter.  
Figure 3: Schematic of the setup for the optical fiber link test (OFLT) unit.  
After a thermal control function was added to the SM filter beam splitter, we studied the temperature-dependence of the monitoring photodetector. A TEC was added to the monitoring photodetector, as shown in Figure 6, which changed the temperature from 2 \({}^{\circ}\)C to 25 \({}^{\circ}\)C. There was a high correlation between the intensity ratio fluctuation and temperature, according to the results in Figure 7. An unacceptable fluctuation of 0.015 dB occurred when the temperature changed by 23 \({}^{\circ}\)C. We believe that this was due to the etalon effect in the photodetector module. Following these results, a thermal control function was added to the monitoring photodetector as well to keep the temperature stable. With the above improvements, we measured the fluctuation of the normalized intensity ratio, and the results are shown in Figure 8. The fluctuation was reduced to 0.002 dB with no distinct drift, even if the room temperature changed by 10 \({}^{\circ}\)C, and the target precision was realized in the OFLT experiment. The results of the verification experiments show that our lidar system was perfectly calibrated after the thermal control of the SM filter beam splitter and the monitoring photodetector. [PERSON] et al. did not report the influence of the temperature-dependent characteristics of the beam splitter on the fluctuation of the normalized intensity ratio, indicating that there are slight differences in the calibration process of systems with different configurations, which is related to the components used in the system.  
Figure 5: Intensity ratio results with the same experimental setup shown in Figure 4.  
Figure 6: Same experimental setup as Figure 4, but another TEC was added to the monitoring photodetector.
Sensors_**2015**, _15_, 23376-23401; doi:10.3390/s150923376  
**ISSN 1424-8220**  
www.mdpi.com/journal/sensors  
_Article_  
**Dual-Stack Single-Radio Communication Architecture for UAV Acting As a Mobile Node to Collect Data in WSNs**  
**[PERSON] 1, [PERSON] 2, [PERSON] 1,3 and [PERSON] 1,* 1 Department of Automation and Systems, Federal University of Santa Catarina, Florianopolis 88040-900, Brazil; E-Mails: [EMAIL_ADDRESS] (A.S.); [EMAIL_ADDRESS] (J.P.B.) 2 Campus Ararangua, Federal University of Santa Catarina, Ararangua 88906-072, Brazil; E-Mail: [EMAIL_ADDRESS] 3 Instituto Senai de Sistemas Embarcados, Florianopolis 88040-900, Brazil * Author to whom correspondence should be addressed; E-Mail: [PERSON]]; Tel.: +55-4837217606. Academic Editor: [PERSON]  
_Received: 16 July 2015 / Accepted: 7 September 2015 / Published: 16 September 2015_  
**Abstract:** The use of mobile nodes to collect data in a Wireless Sensor Network (WSN) has gained special attention over the last years. Some researchers explore the use of Unmanned Aerial Vehicles (UAVs) as mobile node for such data-collection purposes. Analyzing these works, it is apparent that mobile nodes used in such scenarios are typically equipped with at least two different radio interfaces. The present work presents a Dual-Stack Single-Radio Communication Architecture (DSSRCA), which allows a UAV to communicate in a bidirectional manner with a WSN and a Sink node. The proposed architecture was specifically designed to support different network QoS requirements, such as best-effort and more reliable communications, attending both UAV-to-WSN and UAV-to-Sink communications needs. DSSRCA was implemented and tested on a real UAV, as detailed in this paper. This paper also includes a simulation analysis that addresses bandwidth consumption in an environmental monitoring application scenario. It includes an analysis of the data gathering rate that can be achieved considering different UAV flight speeds. Obtained results show the viability of using a single radio transmitter for collecting data from the WSN and forwarding such data to the Sink node.  
**Keywords:** mobile nodes; unmanned aerial vehicles; communication architecture; data collection; wireless sensor networks
## 1 Introduction
assessed effective fiber reinforcement (Dacron vs. LARS(r)) to improve the fatigue resistance of SIBS by minimizing stress concentration in the matrix. LARS(r)-reinforced valve demonstrated superior physical properties over Dacron-reinforced valve, but they were still not enough to eradicate the main problem [32]. [PERSON] et al. (2010) tested dimyristoyl phosphatidylcholine (DMPC) precoating of SIBS [33]. Despite good hemocompatibility, inferior functional properties limited its use for manufacturing polymeric heart valve potentially suitable for the clinical practice. Although several researches have been done on SIBS, no study to datehas performed comprehensive assessment of its properties as a potential material for manufacturing heart valve leaflets and has compared with Gore-texTM, a benchmark material routinely used in the clinical practice. Thus, our research is aimed at assessing physical properties, hemocompatibility and biocompatibility of SIBS to set reference range for selecting optimal nanofillers or functional fragments able to reinforce its structure, improve hemocompatibility and its application potential for manufacturing polymeric heart valve leaflets. In order to achieve the set aim, we synthesized SIBS with Mn of 33,000 and compared its properties with Gore-texTM, a polymer that is routinely used for manufacturing medical devices for cardiothoracic surgery. Surface topography of both materials was described with scanning electron microscopy and atomic force microscopy. Uniaxial tension allowed describing the mechanical properties. The water contact angle was used to evaluate hydrophilicity/hydrophobicity of the study samples. Hemocompatibility was evaluated using cell lines (Ea.hy 926), donor blood, and in vivo.
# Provisioning of Fog Computing over Named-Data Networking in Dynamic Wireless Mesh Systems
## 3 The Proposed Solutions
### Dynamic Face Management  
#### 3.1.1 The Conceptual Approach  
The workflow of the proposed solution, depicted in Figure 3, relies on continuous listening of the events on the channel. In particular, (1) when a wireless node comes into the coverage range of another one, the nodes trigger the Face Manager. Next, the Face Manager (2) creates a new Face for communication with the new neighbor node and (3) checks if the physical address (MAC address) of the peering node matches a record in the QDB. If the MAC address (remoteUri) of the peering node matches a record in the QDB, it means that these nodes have communicated recently, and related forwarding information is temporarily stored in the QDB after the communication channel was dropped. In this case, related forwarding information (4) migrates back from the QDB to the FIB while the matched record is (5) removed from the QDB. If (6) a link with one of the neighbor nodes drops, Face Manager deletes the Face associated with the dropped link. However, (7) Face configuration, including its remoteUri has to be stored in QDB for later use. Once done, FIB and PIT entries associated with the Face are also deleted, and the quarantine timer starts for the record created in the QDB. Once the timer expires, the entry is deleted from the QDB.  
Figure 3: Workflow of the proposed solution.  
This dynamic Face management system enables wireless devices to utilize unicast mode, fully exploiting the benefits of advanced medium access mechanisms (such as RTS/CTS in WiFi) instead of using random access channel variations to transmit user data. Moreover, in unicast mode, wireless technologies support error-control methods, such as automatic repeat requests (ARQ), further improving communication reliability. Eventually, the dynamic Face management system promises a better user experience for customers in wireless mesh networking or other multi-connectivity scenarios. The next section provides an implementation example of the proposed solution for the case of WiFi.
## 4 Discussion
### Model Limitations and Mitigation Pathways  
However, the improved Informer algorithm has several large residuals (Figure 10).  
1 Ring 250:  
Root Cause: An unplanned 12 h stoppage (due to cutterhead jam) occurred. The binary \"special factor\" flag (Section 2.3.3) captured the event but failed to quantify stoppage duration-intensity impacts, leading to underprediction.  
Limitation: The algorithm's static event encoding cannot adapt to dynamic disruptions.  
Mitigation: Integrate duration-weighted event features (e.g., \"stoppage hours \(\times\) depth\") to enhance temporal sensitivity.  
2 Ring 315:  
Root Cause: Sudden transition from silty clay to a pebble layer (Figure 6, Stratum (c)). The model's soil classification (Section 2.3.1) provided categorical inputs but missed real-time stratigraphic heterogeneity, delaying response to altered ground reactions.  
Limitation: Geological characterization relies on interpolation between sparse boreholes.  
Mitigation: Couple with online drilling parameter monitoring (e.g., specific energy) to detect layer changes dynamically.  
On the other hand, despite the high accuracy of the improved Informer, prediction fidelity may be influenced by unmonitored parameters. For instance: i. chamber pressure differentials (not recorded in this project) could refine soil discharge dynamics, potentially enhancing much pressure modeling; ii. grouting viscosity (excluded due to measurement constraints) might improve the grouting pressure's representation of soil-filling efficiency. Future work should integrate these parameters via multi-sensor fusion to further minimize prediction uncertainty.
## 3 Hybrid Network Implementation Using LoRaWAN and LoRaMESH
### Implementation of LoRaMESH
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Specification** & **LoRaMESH** & **LoRaWAN** & **Gateway** \\ \hline Data Rate & 21,900 bits/s & 21,900 bits/s & 21,900 bits/s \\ Network Topology & LoRaMESH & Star & Star \\   Security Cryptography & AES Cryptography & AES Cryptography & AES Cryptography \\ Communication & ModBus & LoRaWAN & LoRaWAN \\ Protocol & 902.3–927.5 MHz & 902.3–927.5 MHz & 902.3–927.5 MHz \\ Channels & 64+8 channels & 64+8 channels & 64+8 channels \\ Operation mode & Hybrid mode & Hybrid mode & Hybrid mode \\ Channel widths & 125/250/500 KHz & 125/250/500 KHz & 125/250/500 KHz \\ Transmission power & 20 dBm & 20 dBm & +27 dBm \\ Sensitivity of & \(-137\) dBm & \(-137\) dBm & \(-137\) dBm \\ reception & & \(10\) dBm & \(10\) dBm \\ Maximum reception & \(10\) dBm & \(10\) dBm & \(10\) dBm \\ level & & & \\ Modulation & LoRa—CSS & LoRa—CSS & LoRa—CSS \\ ANATEL & 02021-18-07215 & 02021-18-07215 & 02053-18-07215 \\ Serial communication & 2 UART Serial & 2 UART Serial & SPI Interface \\ ports & & & \\ Digital Outputs & 6 & 6 & — \\ and Inputs & & & \\ Analog inputs & 2 & 2 & — \\ Processor & ARM0+ 32-bits & ARM0+ 32-bits & ARM0+ 32-bits \\ Transceiver & Semtech SX1272 & Semtech SX1272 & Semtech SX1272 \\ Power & 5 Vcc & 5 Vcc & 5 Vcc \\ Antenna & \(4\) up to \(12\)Vcc) & (4 up to \(12\)Vcc) & (4 up to \(12\)Vcc) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: LoRa Radioenge end-node specifications. Figure 13: LoRaMESH Architecture. up to 1023 devices on the network, and the amount of jumps is according to the number of nodes on the network. This process is done automatically, so this work didn't focus on the analysis and implementation of the routing protocol for LoRaMESH networks in this scenario.
# An Information Theoretic Approach to Privacy-Preserving Interpretable and Transferable Learning
### Methodology  
Figure 2 outlines the methodological workflow. For an information theoretic evaluation of the privacy leakage, interpretability, and transferability, we provide a novel method that consists of following three steps:
# A Novel Parallel Auto-Encoder Framework for Multi-Scale Data in Civil Structural Health Monitoring
## 2 Parallel Auto-Encoder Framework
### The Proposed Approach
#### 2.2.1 Parallel Sparse Dimensionality Reduction  
The main objective of this component is to perform scale-invariant, correlated and noise-robust features extraction with dimensionality reduction from the original input. The original input is firstly divided into several subsets as follows:  
\[Natural\;frequencies:\;\overline{q}^{r}=[q_{1}^{r},q_{2}^{r}\dots\;q_{i}^{r}]^{T} \tag{1}\]  
\[Mode\;shapes:\;\overline{m}^{q_{1}r}=[m_{1}{}^{q_{1}r}\dots\;m_{j}{}^{q_{1}r}]^{ T},\;\dots,\;\overline{m}^{q_{j}r}=[m_{1}{}^{q_{i}r}\dots\;m_{j}{}^{q_{i}r}]^{T} \tag{2}\]  
We denote \(\overline{q}^{r}\) as the natural frequencies input subset, where \(q_{i}^{r}\) is the \(i\)th (\(i=1\dots n\)) frequency in the \(r\)th sample and \(\overline{m}^{q_{i}r}\) is the mode shape parameters subset corresponding to \(i\)th frequency. Each mode shape subset has \(j\) number of parameters. For simplicity, we use \(\overline{c}_{ori}{}^{r}\) to denote the original input vector in the generic formulation.  
As described in Figure 2, each subset of input data is fed into a stacked sparse auto-encoder model with a deep neural network architecture for performing dimensionality reduction and they are conducted in parallel. In the proposed parallel structure, the 1 st hidden layer of each sparse auto-encoder model is to learn the local essential distribution from each input subset. Then the 2 nd to kth layers further compress the hidden features layer-by-layer, observed from the previous layer. Only the encoding layers of a stacked deep auto-encoder are shown in the dimensionality reduction component in Figure 2. Depending on the problem complexity, the \"deep architecture\" can be extended deeper to match the problem complexity. The brief introductions on the sparse  
Figure 1: Proposed parallel based sparse auto-encoder framework.  
auto-encoder, sparse activation function and the formulation of objective functions for the parallel dimensionality reduction component are provided in Sections 2.2.2 and 2.2.3, respectively.
###### Abstract  
The geodetic monitoring of local displacements and deformations is often needed for civil engineering structures and natural phenomena like, for example, landslides. A local permanent GNSS (Global Navigation Satellite Systems) network can be installed: receiver positions in the interest area are estimated and monitored with respect to reference stations. Usually, GNSS geodetic receivers are adopted and provide results with accuracies at the millimeter level: however, they are very expensive and the initial cost and the risk of damage and loss can discourage this approach. In this paper the accuracy and the reliability of low-cost u-blox GNSS receivers are experimentally investigated for local monitoring. Two experiments are analyzed. In the first, a baseline (65 m long) between one geodetic reference receiver and one u-blox is continuously observed for one week: the data are processed by hourly sessions and the results provide comparisons between two processing packages and a preliminary accuracy assessment. Then, a network composed of one geodetic and two u-blox receivers is set up. One u-blox is installed on a device (slide) that allows to apply controlled displacements. The geodetic and the other u-blox (at about 130 m) act as references. The experiment lasts about two weeks. The data are again processed by hourly sessions. The estimated displacements of the u-blox on the slide are analyzed and compared with the imposed displacements. All of the results are encouraging: in the first experiment the standard deviations of the residuals are smaller than 5 mm both in the horizontal and vertical; in the second, they are slightly worse but still satisfactory (5 mm in the horizontal and 13 mm in vertical) and the imposed displacements are almost correctly identified.  
low-cost GNSS receivers; u-blox; landslide monitoring; static surveying; time series analysis; displacements analysis +
Footnote †: journal: Sensors