esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Simulation Results
### Physical Simulation Results
#### 4.2.4 Line Formation in a Complex Water Scene  
The start point and final point in the complex water scene were set as \((x,y)=(-5.7\) m, \(-7.4\) m) and \((x,y)=(-5.7\) m, \(-7.4\) m), respectively. The formation shape was a line, as shown in Figure19a. The initial position of the three spherical robots were \(position_{1}=(-6.5\) m, \(-10\) m), \(position_{2}=(-7.5\) m, \(-11.5\) m), and \(position_{3}=(-7\) m, \(-13\) m). Figures 23-26 show the simulation results of the three spherical robots, which formed a linear formation shape in a complex water scene. As shown in Figure 23, the initial positions of the three robots showed a triangular formation and deviated from their respective reference trajectories. Under the adjustment of the controller, the robots accurately converged to their respective expected trajectories and maintained a line formation. The trajectory tracking errors during the formation process are shown in Figure 24a. The tracking errors of the state variable \(x\) and \(\phi\) basically converged to 0, while the state variable \(y\) had a convergence error of 0.2 m. The formation error is shown in Figure 24b. It can be seen that the state error of any of the two spherical robots converged to zero. In addition, Figure 26 is a screenshot of the linear formation process. At approximately \(t=20\) s, the three robots formed a linear formation and maintained this formation shape to track the desired trajectory. The control input is shown in Figure 25. As can be seen, the input values fluctuated up and down but remained within the constraint range. The fluctuations of the control input were mainly caused by the curved formation reference trajectory.  
Figure 23: Real formation tracking result in a complex water scene.  
Figure 24: State errors during the formation tracking in a complex water scene. (**a**) Trajectory tracking error. (**b**) Formation error.  
Figure 25: Control inputs in a complex water scene.  
Compared with the formation in a single water scene, the heading angle of the formation reference path in complex environments was constantly changing. In this complex water scene, the three spherical robots can accurately and stably form the desired formation shape and track the desired trajectory, which further proves the feasibility and validity of the VS-ESO-MPC formation controller.
## 4 The Second Workshop: Structure and Contents of the STEW Guide
### The Contents of the BIM Service Wrapping-Up Guide (BWG)  
The BWG was established to guide the POs regarding BIM deliverables. As the suppliers implement BIM services according to the BEP, the BIM deliverables as planned are delivered to the POs gradually. The POs should perform quality verification of the outputs at the stages specified in the BEP, offer guidelines to ensure the desired quality at each stage, and provide timely payment for the suppliers on the basis of the verification results. The POs should make sure that the BEP clearly defines the deliverables that are to be transmitted to them at each stage of a BIM service contract. The deliverables should be compensated for the payment to be processed at each stage. Requiring a deliverable is not adequate. The POs should specify the level of development, the quality desired, and the format desired. The BWG needs to include the guidelines on which the POs can understand and prepare these tasks.  
There are many types of deliverables from BIM services, such as design intent models, construction models, as-built models, record models, operations and maintenance data, etc. [51]. Record models represent BIM models regarding the facility elements, surrounding conditions, and assets of a facility. The record models contain information relating to the architectural, structural, and MEP systems. The POs need to clearly understand that the deliverables to be transmitted to them must be different in accordance with the project delivery method implemented. In the case of a DB or a TP mechanism, in addition to the design intent model, a construction model, as-built model, and record model will be included. Therefore, the POs have to comprehend the differences between the deliverables, and differentiate them in conjunction with project scope and delivery methods.  
As stated above, using lists of the deliverables furnishes the POs or their representatives with the means to check whether they have received the deliverables. If the BWG provides various sample tables of the deliverables at each stage of a BIM contract, the POs can more conveniently develop their own scheme with confidence. Providing the deliverables in compliance with the stages described in the BEP, a supplier should submit a written report confirming that consistency checks have been completed. The BWG needs to provide instructions on checking for consistency of BIM modeling results, including clash checking at each stage, comparison checking for the modified BIM model after clash checking, etc.  
Furthermore, the BWG needs to ensure that the deliverables and their deadlines are better aligned with project milestones, e.g., schematic design, detailed design, construction documents, bid/procure, contract, notice to proceed, construction, substantial completion, commissioning, final inspection, occupancy/operations and maintenance, and warranty review [1; 20; 21; 51]. In general, the payment for certain services in public entities is likely to be implemented at the milestone schedules.
## 3 Results  
### Principle and Design of the Glucose Sensor  
The glucose sensor electrode was made by a stainless-steel acupuncture needle with a diameter of 0.3 mm (Figure 1a). Platinum nanoparticles were deposited on the needle to enhance electrical conductivity of electrode, catalyze H\({}_{2}\)O\({}_{2}\) and enlarge the sensor surface area. Nafion permselective inner film was decorated on platinum layer to increase the quantity and activity of immobilized enzyme as well as to avoid the interference.  
According to Figure 1b and chemical Equations (1) and (2), accumulation of H\({}_{2}\)O\({}_{2}\) during the reaction leads to an inaccurate result and a narrow detection linear range. Hence, the SHPM-OS membrane was introduced. Porous inner PU with larger holes supported the Cat immobilization and  
Figure 1: Schematic diagram of the SHPM-OS glucose sensor. (**a**) functional layers of glucose electrode. The needle is modified by Pt nanoparticles, Nafion, GOD and SHPM-OS membranes, which consists of porous inner PU with large-sized pores, Cat and porous outer PU with small-sized pores. (**b**) schematic diagram of electrochemical reaction and element transfer on different layers. Glucose transmission was regulated by both outer and inner membranes and then glucose reacts in the GOD layer to produce electrons and H\({}_{2}\)O\({}_{2}\), which was catalyzed by Cat to replenish the oxygen.  
accelerated the diffusion of H\({}_{2}\)O\({}_{2}\) and oxygen. As shown in reaction (3), Cat in the sandwich layer decomposes H\({}_{2}\)O\({}_{2}\) to replenish oxygen required for reaction real timely. Porous outer PU with smaller holes adjusted the concentration of glucose spreading to enzyme layer, which improved the sensitivity and linearity of the glucose sensor.  
\[H_{2}O_{2}\ \xrightarrow{Cat}2H^{+}+O_{2}\ +2e^{-} \tag{3}\]
## 2 Materials and Methods
### Eddy Covariance Measurements  
As part of the TERENO/ICOS observatory network [28; 32], the station was established to derive carbon and water fluxes between the forest ecosystem and the atmosphere together with driving variables and parameters. EC measurements were carried out since summer 2014 at 49 m above the ground, about 20-25 m above the can-opy. The EC technique is used to assess intra- and inter annual changes of the carbon balance of the ecosystem.  
Fluxes of carbon dioxide (as well as latent and sensible heat and momentum) are determined with the EC technique [16; 33; 34] using an ultrasonic anemometer for the high-frequency wind components and high-frequency temperature (Campbell CSAT-3, Campbell Scientific Inc., Logan, UT, USA) and an open-path infrared gas analyser (LI-7500, LiCor Inc., Lincoln, NE, USA) for the concentrations of carbon dioxide and water vapour Flux computation from high frequency (20 Hz) raw data is performed with the EddyPro(r) software (v. 7.0.6) [35; 36]. After removing physically unrealistic flux values from the time series, subsequent post-processing steps such as estimating the u*-threshold, gap-filling and flux partitioning were performed with the REddyPro (v. 1.2.2) package [37] using R (v. 4.0.3) and Rstudio (v. 1.3.1056). Gaps in CO\({}_{2}\), air temperature and shortwave downward radiation (SWDR) were filled using the marginal distribution sampling method [21] as implemented in REddyPro. Additionally, some gaps in precipitation and PPFD were filled using the Kalman filter from imputeTS (v. 3.2) [38]. From the flux partitioning, _GPP_ and _R\({}_{\alpha\alpha}\)_ can be derived according to [PERSON] et al. [21], representing the gross fluxes in and out of the ecosystem as 30 min averages for an area of about 10 ha around the tower depending on the footprint climatology [17]. These half-hourly values for _NEP_, _GPP_ and _R\({}_{\alpha\alpha}\)_ were summed up on daily time steps (g C m\({}^{2}\) d\({}^{-1}\)). For comparison with the model output, the sign for _NEP_ was changed indicating positive values as carbon sequestration of the forest ecosystem [39].
## 2 Materials and Methods
### Signal Processing Features
#### 2.1.3 Wavelet-Domain Features  
The wavelet-domain features refer to the features in the time-frequency domain. They are generated from wavelet transform. The features of the wavelet domain explored in this work are summarized in Table 4.
## 4 Results
### Results of Analyses of Effects of Two Factors
#### 4.2.4 Analysis of the Pair Camera Rotation and Contrast Ratio  
The next pair to be analyzed was the camera rotation and the contrast ratio. For this, the authors used groups for both variables as defined in Sections 4.1.3 and 4.1.4. The \"NO_R, NO_C\" group  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Camera Type and Device used** & **Estimate** & **Standard Error** & \(z\)**Value** & **Pr (>|z|)** \\ \hline Intercept & 0.61105 & 0.01752 & 34.870 & \textless{}2\times 10^{-16}\) \\ Orthographic, Gear VR & 0.07859 & 0.04255 & 1.847 & 0.0647 \\ Perspective, desktop display & 0.15872 & 0.02501 & 6.347 & \(2.20\times 10^{-10}\) \\ Perspective, Gear VR & 0.20891 & 0.03735 & 5.593 & \(2.23\times 10^{-8}\) \\ \hline \hline \end{tabular}
\end{table}
Table 16: Logistic regression results analysis by pairing the camera type and the device used.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Resid.** & **Df Resid.** & **Dev** & **Df** & **Deviance** & **Pr (>Chi)** \\ \hline
1 & 2706 & 10,103 & - & - & - \\
2 & 2705 & 10,102 & 1 & 0.25097 & 0.6164 \\ \hline \hline \end{tabular}
\end{table}
Table 15: Comparison of the variables camera type and contrast ratio by ANOVA.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Camera Type and Device Used** & **Estimate** & **Standard Error** & \(z\)**Value** & **Pr (>|z|)** \\ \hline Intercept & 0.61105 & 0.01752 & 34.870 & \textless{}2\times 10^{-16}\) \\ Perspective & 0.15872 & 0.02501 & 6.347 & \(2.2\times 10^{-10}\) \\ Gear VR & 0.07859 & 0.04255 & 1.847 & 0.0647 \\ Perspective and Gear VR & \(-0.02841\) & 0.05672 & \(-0.501\) & 0.6164 \\ \hline \hline \end{tabular}
\end{table}
Table 17: Logistic regression analysis results of the additive model of camera type and device used, allowing interactions.  
comprised 1005 tests, the \"NO_R, INC_C\" group 208 tests, the \"INC_R, NO_C\" group 471 tests, and the \"INC_R, INC_C\" group 1025 tests, as can be seen from Table 12. The results of the logistic regression analysis can be seen in Table 19.  
According to the results, every pair has a better probability than \"NO_R, NO_C\". The \(t\)-test does not indicate a difference between \"NO_R, NO_C\" and \"NO_R, INC_C\", but differences can be seen among \"NO_R, NO_C\", \"INC_R, NO_C\" and \"INC_R, INC_C\". These last pairs are not distinguishable. Table 20 shows the results while also taking into account the interaction of the variables.  
As can be seen, the influence of INC_R is strong. The influence of INC_C is smaller (as can be seen, the estimation of the coefficient is equal to 0.09323), and there is no significant interaction (\(p\)-value = 0.1667).  
Table 21 shows the results of their interactions.  
According to \(p\)-value = 0.1661, the authors concluded that the model which takes the interactions into account does not provide a better probability than the additive model.
# SDN Architecture for 6 LoWPAN Wireless Sensor Networks
## 6 Conclusions  
This paper presented the SD6 WSN architecture, developed to control data plane traffic in 6 LoWPAN according to the SDN approach. The design of SD6 WSN considers the specific characteristics of WSN devices, such as low data transfer rate, high latency, packet loss and low  
Figure 23: COOJA simulation and SD6 WSN controller integration.  
Figure 24: Average latency comparison between RPL and SD6 WSN WSNs.  
processing power. It also sought to take advantage of the flexibility provided by flow-based forwarding of the SDN approach, which allows the development of specific networking applications capable of routing packages in the most appropriate way depending on particular requirements.  
The Contiki operating system served as the basis for implementing the proposed architecture in motes used in real-world implementations. We provide in this paper the detailed description of the SD6 WSN implementation in Contiki motes. We also provide the results of two performance evaluation experiments in the COOJA emulator. The overhead introduced by SD6 WSNP messages was evaluated in an AMI network scenario with four distinct propagation settings. The results indicate that the introduced overhead is not excessive, given the advantages that the SDN approach can bring. In the second experiment, a grid-topology was used to compare the average latency of peer-to-peer communication. In this scenario, the average communication latency of SD6 WSNP was significantly lower than that obtained with RPL.  
Due to the current memory limitation of the motes, some points have not yet been addressed in the SD6 WSN implementation. We are working especially on two of them: buffering the incoming messages in situations that generate a packet-in notification and the securitization of SD6 WSNP messages. In future works, with motes with greater memory capacity, we will include and evaluate both.  
SD6 WSN opens up many possibilities for research. Because future SD6 WSN networking applications will have a comprehensive and reasonably accurate real-time view of the state of the wireless network, they could plan the flows taking into account this unified view. Moreover, because SD6 WSN applications can act on the motes, for example by changing their transmission power or channel, it is possible to configure flows that would meet specific latency or packet loss requirements. Furthermore, because the routing behavior is fully determined by the controller, it is possible to configure flows to isolate the traffic among specific nodes, making it easier to improve security requirements.  
[PERSON] and [PERSON] conceived of and designed the SD6 WSN architecture. [PERSON], [PERSON] and [PERSON] conceived of and developed the Contiki implementation. [PERSON] and [PERSON] planned and performed the experiments.  
This research has been partially supported by the Conselho Nacional de Desenvolvimento Cientifico e Tecnologico - CNPq (Brasilia, Brazil) under grants 308240/2016-2 and 401710/2016-5.  
The authors declare no conflict of interest.
# A Study of LoRa: Long Range & Low Power Networks for the Internet of Things
## 3 The LoRa Physical Layer
### Physical Frame Format  
Although the LoRa modulation can be used to transmit arbitrary frames, a physical frame format is specified and implemented in Semtech's transmitters and receivers. The bandwidth and spreading factor are constant for a frame.  
A LoRa frame begins with a preamble. The preamble starts with a sequence of constant upchirps that cover the whole frequency band. The last two upchirps encode the sync word. The sync word is a one-byte value that is used to differentiate LoRa networks that use the same frequency bands. A device configured with a given sync word will stop listening to a transmission if the decoded sync word does not match its configuration. The sync word is followed by two and a quarter downchirps, for a duration of 2.25 symbols. The total duration of this preamble can be configured between 10.25 and 65,539.25 symbols. The structure of the preamble can be seen in Figure 2.  
After the preamble, there is an optional header. When it is present, this header is transmitted with a code rate of 4/8. This indicates the size of the payload (in bytes), the code rate used for the end of the transmission and whether or not a 16-bit CRCfor the payload is present at the end of the frame. The header also includes a CRC to allow the receiver to discard packets with invalid headers. The payload size is stored using one byte, limiting the size of the payload to 255 bytes. The header is optional to allow disabling it in situations where it is not necessary, for instance when the payload length, coding rate and CRC presence are known in advance.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**BW** & **SF** & **7** & **8** & **9** & **10** & **11** & **12** \\ \hline
125 kHz & \(-123\) & \(-126\) & \(-129\) & \(-132\) & \(-133\) & \(-136\) \\
250 kHz & \(-120\) & \(-123\) & \(-125\) & \(-128\) & \(-130\) & \(-133\) \\
500 kHz & \(-116\) & \(-119\) & \(-122\) & \(-125\) & \(-128\) & \(-130\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Semtech SX1276 LoRa receiver sensitivity in dBm at different bandwidths and spreading factors, taken from [19].  
The payload is sent after the header, and at the end of the frame is the optional CRC. A schematic summarizing the frame format can be seen in Figure 3.  
Equation (3), derived from Semtech's datasheets [19; 20], gives the number of symbols required to transmit a payload \(n_{s}\), as a function of all of these parameters. This number should be added to the number of symbols of the preamble, in order to compute the total size of the packet in symbols. In this equation, \(PL\) is the payload size in bytes, \(CRC\) is 16 if the CRC is enabled and zero otherwise, \(H\) is 20 when the header is enabled and zero otherwise and \(DE\) is two when the low data rate optimization is enabled and zero otherwise. This equation also shows that the minimum size of a packet is eight symbols.  
\[n_{s}=8+max\left(\left\lceil\frac{8 PL-4 SF+8+CRC+H}{4\times(SF-DE)}\right\rceil \times\frac{4}{CR},0\right) \tag{3}\]
## 4 Issues of Opportunistic Routing in Asynchronous WSN
### Multiple Receiver  
In an opportunistic routing protocol, the sender broadcasts the packet opportunistically, so that if a potential forwarder wakes up earlier than other nodes and receives the packet, it can forward the packet successfully. However, because nodes wake up asynchronously and within a short time window, with a large number of nodes, there is a high probability that more than one node can wake up at the same time. As a result, when a sender broadcasts a packet, it is possible that more than one potential receiver can receive the packet and attempt to forward it upon successful reception. This problem is called a'multiple receiver' problem where more than one forwarder exists for forwarding one data packet at a time. This will cause collision and unnecessary data retransmission, which will lead to more energy consumption.
# A Review of Wireless Positioning Techniques and Technologies: From Smart Sensors to 6G
## 2 Localization Techniques
### Signal Measurements
#### 2.1.1 Time-Based Measurements  
The time-based measurements are signal propagation delay-based methods for estimating the time lapse between the transmitting and receiving nodes and, as such, can become highly biased from the environment. The time-based measurements include the time of arrival (ToA), the time difference of arrival (TDoA), and the round-trip time (RTT).  
Time of Arrival (ToA)  
The ToA is a one-way time measurement [30] of the signal propagation time and is used for estimating the distance between the transmitting and receiving node, as shown in Figure 2a. Precise time synchronization between the nodes is required, as well as the timestamp information to be included in the different packets exchanged within the  
Figure 1: Localization/Positioning Techniques Classification.  
network. The receiving node utilizes the received packets' timestamps for estimating its distance \(d\) from the transmitting node, using the following equation [5]:  
\[d=(t_{1}-t_{0})*v, \tag{1}\]  
where \(t_{0}\) and \(t_{1}\) are the transmitting and receiving time, respectively, and \(v\) is the velocity of the signal, i.e., \(3*10^{8}\) m/s for radio frequency (RF) or 350 m/s for ultrasound.
# A Secure and Scalable Authentication and Communication Protocol for Smart Grids
## 3 Methodology  
This section outlines the proposed cryptographic protocol designed for a smart grid environment. It facilitates interactions between distributed nodes and the central management server. It provides a step-by-step implementation and describes the key components involved in the cryptographic processes.
## 4 Experiments and Results
### Results of Image Quality Enhancement  
The parameters of the model used in the experiment are summarized in Table 1. The experiments were performed using a Resnet-based model with a backbone depth of 101 layers. The detection model needs pixel-based detection for each damage type, so it must be detected with instance segmentation. We studied the optimal model based on mask-R CNN [18], which is a popular method in instance segmentation, and Blendmask capable of real-time detection.  
Figure 6: Concept of combined detection model framework by members.
# Listeners' Spectral Reallocation Preferences for Speech in Noise
## 4 Expt. 2: Spectral Reallocation Preferences for Band-Limited Speech
### Results
#### 4.3.4 Relationship between Preference and Intelligibility  
Figure 6 shows the distribution of listener preferences and intelligibilities at each preference value. Two-sample Kolmogorov-Smirnov tests confirmed that listeners' preference distributions were non-uniform (all \(p<0.01\)). Unlike the mean preferences shown in Figure 5, modal choices tended to be at values most similar to the original speech, i.e., at adjustment values that produce the widest speech bandwidth. Indeed, for almost all of the LP, HP and BW adjustments (the exception being the HP\(/-6\) dB condition), the most  
frequently chosen adjustment led to the widest possible speech band. These preferences are apparent even when other choices lead to a similar intelligibility as seen by the generally uniform range of scores obtained for a range of adjustment values for many of the conditions. Intelligibility scores in the case where listeners were able to adjust bandwidth appear to be largely independent of the chosen bandwidth. In contrast, the relationship between intelligibility and preference is quite close for most of the other conditions. This correspondence is clearest in the CF condition.