esa-sceva/satcom-chunk-collection huggingface dataset:
# Time Slotted Channel Hopping and ContikiMAC for IPv6 Multicast-Enabled Wireless Sensor Networks
###### Abstract  
Smart buildings benefit from IEEE 802.15.4e time slotted channel hopping (TSCH) medium access for creating reliable and power aware wireless sensor and actuator networks (WSANs). As in these networks, sensors are supposed to communicate to each other and with actuators, IPv6 multicast forwarding is seen as a valuable means to reduce traffic. A promising approach to multicast, based on the Routing Protocol for Low Power and Lossy Networks (RPL) is Bidirectional Multicast RPL Forwarding (BMRF). This paper aimed to analyze the performance of BMRF over TSCH. The authors investigated how an adequate TSCH scheduler can help to achieve a requested quality of service (QoS). A theoretical model for the delay and energy consumption of BMRF over TSCH is presented. Next, BMRF's link layer (LL) unicast and LL broadcast forwarding modes were analyzed on restricted and realistic topologies. On topologies with increased interference, BMRF's LL broadcast on top of TSCH causes high energy consumption, mainly because of the amount of energy needed to run the schedule, but it significantly improves packet delivery ratio and delay compared to ContikiMAC under the same conditions. In most cases, the LL unicast was found to outperform the LL broadcast, but the latter can be beneficial to certain applications, especially those sensitive to delays.  
Keywords:IPv6 multicast; wireless sensor and actuator networks; bidirectional multicast RPL forwarding; time slotted channel hopping; Orchestra; ContikiMAC +
Footnote †: journal: Sensors
## 2 Study Area and Data Sources
### Data  
#### 2.2.1 Data Collection  
The data sets for this case study include: OD transport flows between tollgates on the expressway network, toll gate locations and county-level socioeconomic data. There were 334 expressway tollgates throughout the Jiangsu Province. The OD transport flows between all 334 tollgates were processed from the transaction records of each tollgate. Each transaction record contains the vehicle ID and the time the vehicle entered or exited the tollgate. There were only two records per car--one for each entry to the tollgate and one for the exit tollgate. These two records define the individual OD travel of the vehicle. There were 235 million individual OD trips between these tollgates in 2014.  
Administrative units in China were mainly composed of provinces, prefectures and counties. The county-level unit was the smallest spatial unit in the national socio-economic census surveys.  
Figure 1: Study area (Jiangsu province) and spatial units of county.  
In order to model the interdependence between transport and economy on a regional scale, it was necessary to summarize the transport flow at the county level. There were 63 counties in the Jiangsu Province, but this study only included 59 counties. This is because there were no transactions in four counties (Rudong, Funing, Jinhu and Gaochun) in 2014. The total transport volume at the county level during the year was processed into a \(59\times 59\) flow matrix. Relevant socioeconomic statistics were from Jiangsu Statistical Yearbook 2015 [27].
## 4 Simulation Results and Discussion
The suggested TETSO will identify a set of Pareto optimal solutions that are the optimal threshold values. Figure 2 illustrates the search history of the TETSO over 15,000 simulations with the points indicated by red stars being the Pareto optimal answers. Pareto optimal solutions of multi-objective PSO with Subfigures are shown in Figure 3. Each subfigure emphasizes different angles. Mean squared error (MSE) calculates the mean of the squares of between the actual values and the estimated values. The position MSE against different values of the \(\Delta_{j,k}\) among the ternary levels of the TETSO is presented in Figure 4. The comparison of position MSE among different transmission rates is presented in Figure 5. The results demonstrate that TETSO shows remarkable performance in terms of reducing the \(\Delta_{i,k}\) rates compared to the other filters. The results show that the suggested architecture contributes to a lower communication rate and surpasses its counterparts, which verifies that the suggested approach is effective. Figure 4: The MSE comparison across various values of the \(\Delta_{j,k}\). Figure 3: Pareto optimal solutions of multi-objective PSO with some marked designs. Each subfigure emphasizes different angles. Table 2 demonstrates the analogy between the set-valued, quantize-valued, and point-valued measurements against the changing values of \(\Delta_{j,k}\), of which the results in the eight best solutions are shown. It should be noted that the suggested framework has the potential to decrease the overall communication rate by transmitting more quantized measurements and decreasing the measurement's transmission rate in the case of the event epochs.
## 2 Materials and Methods  
### Compressed Sensing Technique  
CS enables the reconstruction of under-sampled discrete-time signals when the original signals satisfy the relevant conditions for sparsity and incoherence [1]. For the former condition, a signal is considered to be \(K\)-sparse if the number of non-zero values is \(K\)[1].  
The core of CS theory is finding a solution to the following equation:  
\[\mathrm{y}=\mathbf{A}\mathbf{x}, \tag{1}\]  
where \(\mathrm{y}\) is an \((M\times 1)\) vector containing the compressive measurements from the original signal, \(\mathrm{x}\) is the original \((N\times 1)\) discrete-time signal, and \(\mathbf{A}\) is the \((M\times N)\) measurement matrix, in which random sampling is generally modeled with the Gaussian random distribution or the Bernoulli random distribution.  
If \(\mathrm{x}\) is \(K\)-sparse, there is a high probability that it can be completely recovered from \(\mathrm{y}\) if \(M\), which is the number of compressive measurements, satisfies the condition that \(M\geq O(K\log(N/K))\). While \(\mathrm{x}\) is not necessarily sparse in its original form, specific transformations exist to make it sparse in other domains. When these are applied, \(\mathrm{x}\) is expressed as follows:  
\[\mathrm{x}=\mathbf{B}^{-1}\mathrm{f}, \tag{2}\]  
where \(\mathbf{B}\) is an \((N\times N)\) basis (transformation) matrix, and the \((N\times 1)\) vector, \(\mathrm{f}\), is the coefficient for the specific basis matrix. To apply CS theory, a basis matrix should be chosen such that \(\mathrm{f}\) satisfies the \(K\)-sparse condition. In this case, with the matrix \(\mathbf{T}=\mathbf{A}\mathbf{B}^{-1}\), Equation (1) can be rewritten as follows:  
\[\mathrm{y}=\mathbf{A}\mathbf{B}^{-1}\mathrm{f}\,=\mathbf{T}\mathrm{f} \tag{3}\]  
In reconstruction, \(\mathrm{f}\) is first estimated from \(\mathrm{y}\), before \(\mathrm{x}\) is recovered using Equation (2). In general, Equation (3) has an infinite number of solutions, since this problem is underdetermined. However, if \(\mathbf{T}\) satisfies the restricted isometry property (RIP) condition, it becomes possible to determine a unique solution using optimization techniques. This condition can be expressed as follows:  
\[(1-\delta_{2K})\|\mathrm{f}\|_{2}\leq\|\mathrm{T}\mathrm{f}\|_{2}\leq(1+ \delta_{2K})\|\mathrm{f}\|_{2}, \tag{4}\]  
where \(\delta_{2K}\) is the RIP constant, located in the range \(0<\delta_{2K}<1\).  
Several different optimization approaches exist for determining a unique solution to Equation (3), including \(l_{0}\)-minimization and \(l_{1}\)-minimization. While \(l_{0}\)-minimization is not practical, since it is a nondeterministic polynomial-time hard problem [20], \(l_{1}\)-minimization becomes a convex optimization problem by solving the linear programming in Equation (5) [21,22]:  
\[\mathrm{\tilde{f}}\,=\mathrm{argmin}\|f\|_{1}\text{ subject to }\mathrm{T} \mathrm{f}=\mathrm{y} \tag{5}\]
# Enhancing Street-Crossing Safety for Visually Impaired Pedestrians with Haptic and Visual Feedback
## 2 Related Works
### Challenges in Mobility for Visually Impaired Individuals
#### 2.2.1. _Haptic Perception Mechanisms and Assistive Applications_  
To fully leverage the advantages of tactile feedback in enhancing the safety of visually impaired individuals during mobility, researchers have designed and developed a variety of assistive devices. These devices can be broadly categorized into two groups: portable devices, including enhanced white canes [48; 49] and handheld devices [50]; and wearable devices, which encompass belt-based systems [51; 52; 53; 54], vest-based systems [55; 56; 57; 58; 59], head-mounted systems [60; 61], earphone-based systems [62], footwear-based systems [63], and necklace-style systems [64]. In the field of portable navigation devices, [PERSON] et al. proposed a haptically enhanced white cane that provides visually impaired users with obstacle distance information via vibration feedback on the handle [49]. Experimental results demonstrated that users could recognize changes in obstacle positions with 90% accuracy [49]. To improve navigation precision, [PERSON] et al. designed a handheld tactile navigation device featuring a rotatable tactile pointer to provide real-time directional feedback. Experiments showed that users were able to maintain their position within 30 cm of the central path for 92.6% of the navigation time [65]. Additionally, [PERSON] et al. developed a virtual white cane system that uses tactile feedback to simulate the sensation of a traditional cane touching obstacles [66]. Testing revealed that this system effectively helped blind individuals identify obstacles and door locations in indoor environments [66].  
In the domain of wearable navigation devices, [PERSON] et al. found that a vibrotactile belt could provide five distinguishable tactile cues in a laboratory environment [67]. However, in real walking scenarios with noise interference, the number of effectively recognizable cues significantly decreased to fewer than two [67]. To explore more effective tactile feedback solutions, [PERSON] et al. proposed a wearable backpack-based navigation system that uses vibrations on the shoulder straps and waist to assist visually impaired individuals in recognizing and adjusting their direction, effectively supporting forward movement [29]. [PERSON] et al. further developed a wrist-worn tactile display that innovatively combined static and dynamic tactile modes to deliver navigation information [68]. Experiments demonstrated that visually impaired users could accurately perceive the tactile feedback with a high recognition rate of 94.78% [68].
# SpaFLEX: Field Campaign for Calibration and Validation of FLEX-S3 Mission Products +
## 3 Results
### Short Campaign  
Table 1 shows the standard deviation and Moran's index after regression and autocorrelation analysis of the biophysical variables for the study areas. A higher standard deviation implies greater spatial variability, and a lower Moran's index indicates less spatial clustering. In both cases, the most limiting variable was Cab, with higher standard deviation and lower autocorrelation values, which was used to calculate the final estimate of the number of ESUs, resulting in 4 ESUs per site with an acceptable error of 10%.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Location** & & **Cab** & **FCV** & **FAPAR** \\ \hline \multirow{2}{*}{Sarrion} & \(\sigma\) & 0.181 & 0.002 & 0.002 \\  & Moran’s index & 0.348 & 0.368 & 0.406 \\ \hline \multirow{2}{*}{Manzanera} & \(\sigma\) & 0.120 & 0.001 & 0.001 \\  & Moran’s index & 0.107 & 0.459 & 0.346 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Standard deviation and Moran’s index for biophysical variables per site.  
Figure 2: Time series of solar-induced chlorophyll fluorescence (SIF) and photosynthetically active radiation (PAR) measured in 2024 at the Manzanera site (Teruel, Spain) using a FLOX sensor. Fluorescence retrievals at the O\({}_{2}\)-A (red) and O\({}_{2}\)-B (blue) bands are shown alongside PAR (orange). Red shading: dry periods; gray shading: recovery periods.  
Based on these results, an interpolation of the Cab variable was performed using the SVM methodology. Two strata were selected (two ESUs per stratum) corresponding to the four ESUs to be distributed, one above and one below the mean to ensure the required sample size, and one replicate per stratum. The interpolation and stratification of each site and the distribution of ESUs are shown in Figure 3.  
To obtain the number of measurements in each ESU, the PRI and ChRE images were obtained from the hyperspectral image (Figure 4). These images will also be used in upscaling.  
Figure 4: From left to right, hyperspectral image (RGB composition), PRI and ChIRE in the following locations: (**a**) Sarrión 300 \(\times\) 300 m plot and (**b**) Manzanera 300 \(\times\) 300 m plot. ESUs are highlighted in yellow.  
Figure 3: From left to right, interpolated map of Cab, strata (green below the mean and yellow above) and distribution of ESUs over RGB composition in the following locations: (**a**) Sarrión 300 \(\times\) 300 m plot and (**b**) Manzanera 300 \(\times\) 300 m plot.  
Finally, the sample size formula was applied to the pixels within each ESU with a maximum acceptable error of 1% for PRI and 10% for ChIRE, resulting in approximately 16 sampling points per ESU (Table 2).
## 4 Discussion
### Manual RFF Estimation as a Gold Standard
Further investigation is necessary to examine the hypothesis that differences in boundary cycle identification may be attributed to the algorithms leveraging a band-pass filtered version of the microphone signal to reduce the impacts of vocal tract resonances, coarticulation due to concurrent frication and aspiration, and radiation of the lips. Such an investigation should include an analysis of both laryngeal imaging and acoustics to comprehensively assess the relevance and validity of manual estimation as the gold-standard technique for calculating RFF. Laryngeal imaging is a crucial component for this investigation, as this modality can provide physiological confirmation of vocal fold vibrations that are indirectly captured via RFF. In addition to comparing manual and semi-automated boundary cycle selections, this investigation should aim to compare the boundary cycles obtained via manual RFF estimation when using each version of the acoustic signal (i.e., microphone, accelerometer). In the event that manual estimation is no longer considered as gold-standard RFF method, efforts should be made to develop new metrics of algorithmic performance to replace those that are calculated in reference to RFF values obtained via manual estimation (e.g., root-mean-square error, mean bias error). Even though manual RFF estimation demonstrated the lowest voiced/unvoiced boundary detection accuracy, it should also be noted that this method is currently the only means by which RFF can be calculated on running speech. Whereas manual RFF technicians are trained to process both isolated VCV productions and VCV productions extracted from running speech, the semi-automated RFF algorithm has been designed, trained, and tested on isolated VCV productions since its origination (see [22]). Thus, although aRFF-AP and aRFF-APH demonstrated greater accuracy in capturing the voicing transitions necessary to compute RFF, both versions of the algorithm can only be used in scenarios when the voice samples are compatible. It is therefore recommended that the aRFF-AP algorithm--which was validated across a broad spectrum of vocal function and recording locations [24]--be used in future investigations when compatible voice samples (i.e., isolated VCV productions) are available. In scenarios that require RFF to be computed from running speech, it is recommended that manual RFF estimation be used.
## 2 Methods  
### Double Interpolation of Optimization Local Mean Decomposition (LMD)  
#### 2.1.1 Review of Local Mean Decomposition (LMD)  
Any signal is divided into a set of numerous product function (PF) components by the adaptive signal decomposition technique known as local mean decomposition (LMD), along with a signal residual. Each PF component takes the form of the product of an envelope signal and a pure FM signal. The amplitude, frequency, and other information of the original signal can be directly obtained through the decomposition. For an arbitrary original signal, it can be decomposed as follows [27]:  
1. Find out all the extreme points of the signal \(x(t)\), and the mean value \(m_{i}\) and the envelope value \(a_{i}\) are calculated for every two adjacent extreme points (\(n_{i}\) and \(n_{i+1}\)): \[m_{i}=\frac{(n_{i+1}+n_{i})}{2}\] (1) \[a_{i}=\frac{|n_{i+1}-n_{i}|}{2}\] (2)All mean values are connected with a line and then smoothed through the moving average method to form a smoothly continuous local mean function \(m_{11}(t)\). The envelope values are smoothed in the same way to derive the envelope function.
2. Obtain the resulting signal \(h_{11}(t)\) by separating the local mean function \(m_{11}(t)\) from the original signal \(x(t)\), and then divide \(h_{11}(t)\) by the envelope estimation function \(a_{11}(t)\) to obtain \(s_{11}(t)\): \[h_{11}(t)=x(t)-m_{11}(t)\] (3) \[s_{11}(t)=\frac{h_{11}(t)}{a_{11}}(t)\] (4) (3) Repeat step (1) to solve the envelope estimation function \(a_{12}(t)\) corresponding to \(s_{11}(t)\); if \(a_{12}(t)=1\), illustrate that \(s_{11}(t)\) is a pure FM signal, and terminate this iteration; if \(a_{12}(t)\
eq 1\), then repeat step (1) and step (2) n times until \(a_{1(n+1)}(t)=1\): \[\left\{\begin{array}{c}h_{11}(t)=x(t)-m_{11}(t)\\ h_{12}=s_{11}(t)-m_{12}(t)\\ \vdots\\ h_{1n}(t)=s_{1(n-1)}(t)-m_{1n}(t)\end{array}\right.\] (5) \[\left\{\begin{array}{c}s_{11}(t)=\frac{h_{11}(t)}{a_{11}(t)}\\ s_{12}(t)=\frac{h_{12}(t)}{a_{12}(t)}\\ \vdots\\ s_{1n}(t)=\frac{h_{1n}(t)}{a_{1n}(t)}\end{array}\right.\] (6) (4) Multiply all envelope estimation functions generated by the above process to obtain the envelope signal \(a_{1}(t)\) of the PF component: \[\begin{array}{cc}a_{1}(t)&=a_{11}(t)a_{12}(t)\cdots a_{1n}(t)\\ &=\prod_{q=1}^{n}a_{1q}(t)\end{array}\] (7) (5) The envelope estimation function \(a_{1}(t)\) is multiplied by the pure FM signal \(s_{1n}(t)\) to obtain the first PF component: \[\begin{array}{c}PF_{1}(t)=a_{1}(t)s_{1n}(t)\end{array}\] (8) (6) Subtract \(PF_{1}(t)\) from the original signal \(x(t)\) to obtain the first residual component \(u_{1}(t)\), and repeat steps (1) to (5) with \(u_{1}(t)\) as the original signal until the residual component \(u_{k}\) is monotonous: \[\left\{\begin{array}{c}u_{1}(t)=x(t)-PF_{1}(t)\\ u_{2}(t)=u_{1}(t)-PF_{2}(t)\\ \vdots\\ u_{k}(t)=u_{k-1}(t)-PF_{k}(t)\end{array}\right.\] (9) Finally, the original is decomposed into a series of PF components: \[x(t)=\sum_{p=1}^{k}PF_{k}(t)+u_{k}(t)\] (10)
## 3 Results
### Comparison in a Controlled Environment of the RH Values Monitored by the 10 Sensors  
The same identical approach was used to check the behavior of the sensors and correct the difference in terms of RH monitoring. Figure 6 shows the lineplot of the RH values of the i-th sensor compared with the average trend.  
Even though the discrepancy between each sensor and the average values is very small and corresponds to the standard ISO 7726 [35], we would like to reduce the difference between the sensors as much as possible. For this reason, we also performed a linear regression in this case over the first 50% of the data. Table 2 shows the linear regression coefficient (m) and the intercept considered (q) for all sensors.  
Figure 6: RH raw data—comparison in the climatic chamber: on the left, the lineplot of the i-th sensor when compared with the average values; on the right, the histplot and the boxplot of the absolute difference between values recorded by each sensor and the average values (the white dot indicates the mean value and the black line indicates the median).  
Figure 7 shows the results in relation to the remaining 50% of RH data linearized and confirms the possibility of using these 10 sensors in the further course of the experiment without considering a discrepancy in the measurements of RH due to the possible intrinsic error of the instruments used.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline  & **A** & **B** & **C** & **D** & **E** & **F** & **G** & **H** & **I** & **J** \\ \hline
**m** & 0.9954 & 1.0007 & 1.0127 & 0.9912 & 1.0045 & 1.0192 & 0.9753 & 0.9968 & 1.01 & 0.9832 \\
**q** & 0.766 & 0.3203 & \(-\)0.1757 & 0.2963 & \(-\)0.1073 & \(-\)0.4822 & 0.2039 & 0.8712 & \(-\)0.9748 & \(-\)0.0579 \\ \hline \end{tabular}
\end{table}
Table 2: RH values—Linear regression. Slope (m) and intercept (q) for all sensors from A to J.  
Figure 7: RH data after linear regression (LR)—comparison in the climatic chamber: on the left, the lineplot of the i-th sensor when compared with the average values; on the right, the histplot and the boxplot of the absolute difference between values recorded by each sensor and the average values (the white dot indicates the mean value and the black line indicates the median).
# Methodology for the Efficient Progressive Distribution and Visualization of 3D Building Objects
## 2 Related Work
### Storage Method of 3D City Models  
Today, 3D city models are generally stored in object-relational databases, such as Oracle and PostGIS and, in most cases, these databases are extended with dedicated city model tools, such as 3 DCityDB [45] and DB4 Geo [46]. [PERSON] and [PERSON] [47] introduced object-oriented databases for 3D model management; these databases have also been used for 3D building applications [48]. [PERSON] [49] implemented the CityGML schema in an object-relational database. [PERSON] et al. [11] proposed a 3D geospatial database engine based on Oracle 11 and a file system. [PERSON] and [PERSON] [50] recently used BaseX an XML database for CityGML. However, the CityGML schema is complex and leads to hundreds of tables in the database although most of these tables may not be used in most applications.  
Cloud computing and NoSQL technology have been developed to provide high performance, big data storage, high scalability and high availability. These properties make the technology suitable for 3D model management. [PERSON] et al. [51] proposed a 3D model management strategy based on the Hadoop distributed file system to tackle the big data problem which 3D model management is facing. [PERSON] et al. [52] developed and implemented a web-based 3D data processing system using Amazon's EC2 cloud computing environment. Their tests demonstrated the advantages of cloud computing over traditional approaches in terms of time, cost and performance. [PERSON] et al. [53] employed a NoSQL database (MongoDB) to store 3D models to assist public consultation. [PERSON] and [PERSON] [54] built their 3D data management system based on another NoSQL database called Hbase. Cloud computation methods, such as Map-Reduce, can be deployed on a NoSQL database to increase the analysis speed. It is suitable for big data applications, such as 3D city model generalization and visualization.  
In this paper, we use the NoSQL database MongoDB to store multiresolution 3D building objects. MongoDB is document-oriented and schema-less, which is suitable for the integration of different XML-based 3D city model standards, such as KML and CityGML. MongoDB has previously been used in various applications such as building information modeling (BIM) applications. [PERSON] et al. [53] developed a unified and integrated framework, called 3D Repo, which supports collaborative editing and the distribution of 3D assets, the results showing that MongoDB is suitable for 3D city model management. An advantage of NoSQL databases, in comparison with object-relational databases, is that they have a better capability for automatic data distribution and load balance. Furthermore, they are easier to extend and parallelize.