esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Wastewater Treatment Process: Case Study  
The research addresses the study of the primary and sludge treatment in a WWTP for human consumption. The first stage considers a preliminary treatment, which consists of the removal of coarse fine solids, and sand. The preliminary treatment is followed by the sedimentation process, removing approximately 40% of biochemical oxygen demand (BOD) and 50% total suspended solids (TSS). After the primary treatment by flocculation and settling process, the water effluent continues its course to the secondary treatment. At the same time, the WWTP also considers the biologic treatment of sludge generated from the lamellar settling stage, going through an anaerobic digestion process, until its final disposal in a storage silo to become a sanitary landfill. The primary wastewater treatment process is set up from the stages described in Figure 1.  
In the WWTP understudy, it is possible to consider three fundamental processing lines: wastewater, biogas, and sludge treatment. The sludge is produced as waste from the primary treatment phase and is transformed to treated sludge or safe biosolids, through three main processes: sludge thickening, anaerobic digestion, and dehydration using centrifugal pumps for proper handling and final disposal.
## 4 Cases Study  
### Data Description  
The thickness of the PCCS determines whether it should be replaced or not. When the lower limit of the PCCS is reached, maintenance needs to be scheduled to replace it with a new one. The initial thickness minus the minimum required thickness 4.5 mm is taken as the failure threshold of the PCCS.  
The wear data are collected from the Guangzhou Metro. The wear value is recorded by the mileage (km) the train runs. The collected wear data are unequal intervals since the inspection interval of the PCCS is not fixed strictly. Here the H-type PCCS is taken as an example to demonstrate the  
Figure 7: Flow chart of RUL prediction.  
proposed method. The wear development trends of 12 samples for H-type PCCS are illustrated in Figure 8.  
As is shown in Figure 8, the wear development trends of the twelve samples are similar, but the wear rates are very different. In this paper, two PCCS samples (as marked in the plot) with large differences in wear rates are selected for this case study. The selected wear data of PCCS are shown in Table 4. The wear loss (Z(\(\tau_{k}\))) is obtained by using the original thickness minus the thickness of each point. The running miles as the unequal interval mileage point (\(\tau_{k}\)) are given by using the original mileage minus the mileage of each mileage point. The original unequal interval wear series are taken as the input. Then the proposed method is used to predict the wear loss at a specific point and the prediction of the remaining useful mileage life. Furthermore, the estimation of the parameters, PDF of the RUL, and other related parameters are also obtained.
# Multi-Segmental Motion in Foot during Counter-Movement Jump with Toe Manipulation
## 3 Results
### Landing Phase
#### 3.2.2 Foot Inter-Segment Kinematics  
Figure 3 exhibited the foot inter-segment angle comparison during landing under **NTM** and **TM** conditions. The greater pronation after manipulation was found in HXFFA with \(p=0.002\) and \(p<0.001\) (0%-26% and 50%-100%). The range 4%-6% showed a smaller supination with \(p=0.05\), for FFHFA. Pronation was smaller after manipulation in 64%-95% of the frontal plane of FFTBA (\(p<0.001\)). Adduction was smaller with \(p<0.001\) in the transverse plane at 62%-91% of landing.  
Figure 4 showed the sagittal HXFFA angle against the frontal HXFFA angle during the take-off and landing phases. The starting and ending coordinates of the take-off phase was (1.10, -11.19) and (2.93, -22.05) for **NTM**, and (2.34, -8.79) to (1.30, -19.07) for **TM**. During the landing phase, (5.05, -19.29) to (0.41, -8.64) with hallux abduction manipulation compared to (5.47, -23.65) to (-0.34, -14.59). The range of motion (ROM) was (1.83, 10.86) in the **NTM** session and (1.04, 10.28) in the **TM** session during the take-off phase, (5.81, 9.06) and (4.64, 10.65) in the landing phase.  
Figure 3: The foot inter-segment angle differences during landing in Non-Toe-Manipulation (**NTM**) and Toe-Manipulation (**TM**) sessions. Blue lines highlight areas that indicate significant differences according to spm1d with \(p<0.05\). Abbreviations: DF: dorsiflexion, PF: plantar flexion, Sup: supination, Pro: pronation, AD: adduction, AB: abduction, Inv: inversion, Ev: eversion, Int: internal rotation, Ext: external rotation.
# Shared Logistics--Literature Review
## 3 Substantive Analysis
### Physical Internet as Future of Logistics  
The highest form of shared logistics is the Physical Internet. The supply chains can be more efficient, resilient, and transparent by implementing industry 4.0 technologies [72].  
[PERSON] and [PERSON] [25] proposed a web architecture for a fresh food distribution network to meet the key challenges of maximizing the freshness of the delivered product and minimizing waste. The physical architecture of the Internet (PI) is based on the fundamental assumptions of sharing infrastructure between different parties, standardized addressing of all entities, and modularized activity. In addition, according to [PERSON] et al. [26], the Physical Internet (PI) provides a connected, shared, and adaptable logistics system that has great potential to significantly increase the reliability and resilience of supply chains.  
Areas that may be worth more insightful analysis in the subject of shared logistics include the following:  
* Fleet sharing: as it is one of the most common manifestations of shared logistics, and the fact that only two of the analyzed papers deal with the issue, more research on fleet sharing would be beneficial to understand the benefits, challenges, and best practices of this aspect of shared logistics.
* Safety and security issues: as shared logistics processes involve multiple parties and the sharing of resources, it is important to understand the potential security risks and how they can be mitigated.
* Barriers to implementation: understanding the reasons why companies may be hesitant to adopt shared logistics, and how those barriers can be overcome, would be valuable to help more companies implement shared logistics.
* Data security: as shared logistics involves the sharing of sensitive information among multiple parties, it is important to understand how data security can be ensured and how logistics agents can be convinced to share resources.
* Shared packaging: given the widespread efforts of companies to reduce waste and cut costs, understanding how shared packaging can be implemented and how it benefits the supply chain would be valuable.
* Fleet sharing in urban areas: as the density of the population and the infrastructure in the city is different from the rural areas, understanding how fleet sharing can be effectively implemented in urban areas would be valuable.
## 6 Simulation Results and Discussions
### Tradeoff between Spectral Efficiency and Delay Spread  
Figure 12 shows the average SE and delay spread for one vehicle pass with different \(K\). It can be seen that the case with \(K=16\) obtains the highest SE (i.e., 4.7 bit/s/Hz), but also the largest delay spread (i.e., \(2.6\times 10^{-6}\) s). Increasing \(K\) cannot further improve the SE duo to transceiver HWI. This result reveals that when we design a multi-RIS system, the number of RIS \(K\) should be carefully considered in terms of the tradeoff between SE and delay spread.
# Performance Evaluation of Multi-UAV Network Applied to Scanning Rocket Impact Area +
## 2 Communication Networks for UAVs
### Fanet
#### 2.1.1 Characteristics of FANET  
An essential characteristic of a FANET is that the communication among UAVs must be implemented through an ad hoc network which does not fully depend on infrastructure connections. This network works through the collaboration and coordination among the nodes. Otherwise, it cannot be categorized as a FANET [11].  
Figure 2 represents a simple composition of a FANET. The ad hoc network is composed of five UAVs and one ground control station (GCS). In this case, UAV1 and UAV2 have one direct link with GCS. The remaining ones need to use them as routers to send or receive information from the GCS. The network topology is dynamic and can be changed at any moment but it is absolutely necessary that at least one UAV provides connectivity with the GCS.
## 3 Materials and Methods
### Performance Metrics  
It is necessary to carry out evaluations of the values obtained by the regressors to predict values with the minimum error. The performance metrics considered are the following [35]:  
* Mean Absolute Error (MAE): This is the average of the absolute differences between the forecast value and the actual value.
* Mean-Squared Error (MSE): This is the average of the squared differences between the forecast value and the actual value.
* Mean Absolute Percentage Error (MAPE): This is the absolute average of the differences between the forecast value and the actual value in percentage terms.  
The next metrics were used to evaluate the performance of the anomaly-detection models [36]:  
* Accuracy is the metric that measures the proportion of correct predictions considering the total predictions made by a model. That is to say, of all the predictions made, enumerate the correct ones.
* Precision is the proportion of correct positive predictions considering the total positive or true predictions made by the model. That is to say, of all the positive predictions made by the model, enumerate the correct ones.
* Recall, or sensitivity, is the metric that measures the proportion of positive instances that a model can identify considering the total number of positive instances present in  
Figure 1: Methodology. Source: own elaboration.  
the dataset, in other words the measurement of how many of the total correct answers have been identified.
* The F1-score is a measure that combines precision and recall into a single value, calculated as the harmonic mean of both metrics.
# Predicting C/N0 as a Key Parameter for Network RTK Integrity Prediction in Urban Environments
## 5 Prediction Results
### Adjusting the Visibility Based on Prediction  
In Section 4.2, we see that the tracking of the phase observation is different from that of code observations. Looking at the phase observations from measured values by the receiver, we can detect a C/N\({}_{0}\) threshold of around 30 dB-Hz for the tracked phase. Now, we have some predictions for C/N\({}_{0}\), so we are able to apply such a threshold to our predictions to modify the visibility prediction (cf. Section 4.2). Table 4 shows the confusion matrix for the GPS phase observations applying a threshold of 30 dB-Hz.  
Comparing the adjusted values to those of Table 1, it can be observed that the true negative noticeably increased and the false positive decreased.
# Surround by Sound: A Review of Spatial Audio Recording and Reproduction
## 2 Binaural Recording and Rendering
### Binaural Recording and HRTF Measurement
#### 2.1.1 Measurement Resolution  
One problem for the HRTF measurement is that there is a lack of recognized standards, especially in terms of the measurement resolution, resulting in the fact that databases with varying sample positions and the number of samples [26]. Even though interpolation is widely used to generate HRTFs at non-measured positions [27], the spatial resolution for HRTF measurement greatly influences the success and quality of binaural rendering.  
For azimuth sampling, it is shown that an angular spacing of 5\({}^{\circ}\) or less is necessary to reconstruct the HRTF data up to 20 kHz in the horizontal plane (the plane perpendicular to a vertical direction and at the same height of a person's ear) [28]. [PERSON] and [PERSON] [29] further studied the maximal azimuthal resolution, or equivalently the minimum number of azimuthal measurements (MNAM), at each elevation plane. The MNAM increases with increasing frequency, i.e., from five measurements at the lowest frequency to more than 60 measurements at 20 kHz in the horizontal plane, while, for a fixed frequency, it decreases as the elevation deviates from the horizontal plane. [PERSON] et al. [30] investigated the directional resolution of HRTF measurement in the horizontal, frontal, and median plane. A resolution of 8\({}^{\circ}\) over the sphere was deemed to be enough without introducing audible interpolation errors in binaural rendering, but this resolution threshold was largely based on listening experiments.  
The Spherical Harmonics, an orthogonal basis function on the sphere, has been widely used for HRTF data representation [31; 32], based on which the HRTF dimensionality [33], measurement resolution [34], and sampling scheme [35] are proposed. The Spherical Harmonics based analysis shows that, in order to capture the HRTF variations for the entire audible frequency range (i.e., 20 Hz to 20 kHz), the measurement grid should have a spatial resolution of 3-4\({}^{\circ}\) in elevation and azimuth, with fewer measurement points required towards polar directions.
## 4 Proposed Data Collection Algorithms
### Grid and Probabilistic Neighborhood-Based Data Collection Algorithm with Layered-Scan(GPN-LSCAN)
#### 4.1.2. AUV Trajectory Planning Phase  
After dividing the entire 3D network into several grids, the AUV executes a traveling path to collect data from the whole network. Five kinds of paths have been described, called Layered-Curve, Triple-Curve, Layered-Scan, Triple-Scan, and 3D-Hilbert that can fill a given cubic area. In [29], [PERSON] et al. compared these five paths with difference parameters. The simulation results showed that, in these five kinds of proposed paths, Layered-Scan and 3D Hilbert showed the best performance in term of path length. Because here we only need to consider path length, we selected the Layered-Scan as the trajectory of the AUV in this paper. Dividing the 3D region into several layers along one axis, each layer is regarded as a 2D plane, which results in Layered-Scan paths as shown in Figure 4.
# Reliability of NAND Flash Memories: Planar Cells and Emerging Issues in 3D Devices
## 3 NAND Flash Memory Reliability
### Disturbs
#### 3.6.2 Program and Pass Disturbs
In fact, because of short-channel effects in scaled technologies, the actual electron injection point into the floating gate can move farther into the string, affecting even inner cells [274]. Moreover, such hot carriers may be trapped into the oxide and be subsequently released, giving rise to detrapping-related retention degradation [275]. The full picture is further complicated by electrostatic coupling between adjacent strings, via the STI isolation: when a string is programmed, its channel is kept at zero voltage and it will behave like the gate of a parasitic transistor whose gate oxide is the STI isolation. Adjacent inhibited cells will therefore experience large vertical GIDL current, between the (boosted) surface of the channel and its bulk [276]. Air-gap isolation as well as optimization of channel implant and STI depth uniformity [277] have been suggested as remedies. A related effect has been noted in [278], where a negative \(V_{T}\) shift contribution was found and explained in terms of hot-hole injection from the STI edge to the control-gate [279; 280]. All this suggests that the detailed leakage mechanisms affecting the boosted channel potential must be studied with extreme care [281]. In local self-boosting schemes, where the channel of the programmed WL is isolated from the rest of the string by grounding the neighboring WLs and is boosted by the programmed WL, other issues arise, such as hot-electron injection via drain-induced barrier lowering (DIBL) [282; 283], that paved the way for proposals of hot-carrier based programming of NAND cells [284; 285]. In conclusion, this overview demonstrates that many reliability issues have threatened the scaling of the planar NAND technology. In turn, its successful scaling down to the 15 nm node is a tribute to the effectiveness of the many different solutions adopted to keep the failure rate under control. NAND-based SSDs are today the main candidates for mass storage, having reached reliability performance comparable to, or in some aspects even better than, HDDs [62]. In the following, we will extend this discussion to the current leading-edge technology for NAND: 3D.
# Parent and PHY Selection in Slot Bonding IEEE 802.15.4e TSCH Networks
###### Abstract  
While IEEE 802.15.4e Time-Slotted Channel Hopping (TSCH) networks should be equipped to deal with the hard wireless challenges of industrial environments, the sensor networks are often still limited by the characteristics of the used physical (PHY) layer. Therefore, the TSCH community has recently started shifting research efforts to the support of multiple PHY layers, to overcome this limitation. On the one hand, integrating such multi-PHY support implies dealing with the PHY characteristics to fit the resource allocation in the TSCH schedule, and on the other hand, defining policies on how to select the appropriate PHY for each network link. As such, first a heuristic is proposed that is a step towards a distributed PHY and parent selection mechanism for slot bonding multi-PHY TSCH sensor networks. Additionally, a proposal on how this heuristic can be implemented in the IPv6 over the TSCH mode of IEEE 802.15.4e (6 TiSCH) protocol stack and its Routing Protocol for Low-power and Lossy network (RPL) layer is also presented. Slot bonding allows the creation of different-sized bonded slots with a duration adapted to the data rate of each chosen PHY. Afterwards, a TSCH slot bonding implementation is proposed in the latest version of the Contiki-NG Industrial Internet of Things (IIoT) operating system. Subsequently, via extensive simulation results, and by deploying the slot bonding implementation on a real sensor node testbed, it is shown that the computationally efficient parent and PHY selection mechanism approximates the packet delivery ratio (PDR) results of a near-optimal, but computationally complex, centralized scheduler.  
TSCH; slot bonding; multi-PHY; PHY selection; parent selection; routing; Contiki implementation  
25 July 2021  
Published: 29 July 2021
# Development of Height Indicators using Omnidirectional Images and Global Appearance Descriptors
## 2 Omnidirectional Imaging and Global Appearance Descriptors
### Global Appearance Descriptors
#### 2.2.3 Spherical Fourier Transform  
Omnidirectional images can be projected onto the unit sphere when the intrinsic parameters of the catadioptric vision system are known. Being \(\theta\in[0,\pi]\) the colatitude angle, and \(\phi\in[0,2\pi)\) the azimuth angle, the projection of the omnidirectional image \(im(x_{i},y_{i})\in\mathbb{R}^{M\times N}\) onto the 2D sphere can be expressed as \(f(\theta,\phi)\). As shown in [38], the spherical harmonic functions \(Y_{lm}\) form a complete orthonormal basis over the unit sphere. Any square integrable function defined on the sphere \(f\in L^{2}(s^{2})\) can be represented by its spherical harmonic expansion as:  
Figure 3: Two panoramic images captured indoors from the same \((x,y)\) position in the floor plane and a change of orientation \(\phi=68\) deg. The result is a circular shift of the columns.  
\[f(\theta,\phi)=\sum_{l=0}^{\infty}\sum_{m=-l}^{l}\widehat{f}_{lm}Y_{lm}(\theta,\phi), \tag{3}\]  
with \(l\in\mathbb{N}\) and \(m\in\mathbb{Z}\), \(|m|\leq l\). \(\widehat{f}_{lm}\in\mathbb{C}\) denotes the spherical harmonic coefficients, and \(Y_{lm}\) the spherical harmonic function of degree \(l\) and order \(m\) defined by:  
\[Y_{lm}(\theta,\phi)=\sqrt{\frac{2l+1}{4\pi}\frac{(l-m)!}{(l+m)!}}p_{l}^{m}( \cos\theta)e^{im\theta}, \tag{4}\]  
where \(P_{l}^{m}(x)\) are the associated Legendre functions.  
It is also possible to build a rotationally invariant representation of omnidirectional images using the Spherical Fourier Transform (SFT). Considering \(B\) the band limit of \(f\), the coefficients of \(e=(e_{1},...,e_{B})\) are not affected by 3D rotations of the signal, where:  
\[e_{l}=\sqrt{\sum_{|m|\leq l}|\widehat{f}_{lm}|^{2}}. \tag{5}\]  
More information and examples of applications of the SFT in navigation tasks can be found in [39; 40; 41; 42]. [PERSON] et al. [39] introduce the estimation of 3D rotations extending the shift theorem to the SFT. [PERSON] et al. [40] present a rotation estimation algorithm based on the SOFT (SO(3) Fourier Transform), being SO(3) the 3D Rotation Group. On the other hand, [PERSON] et al. [41] and [PERSON] et al. [42] show a localization method using the SFT applied to omnidirectional images and a predictive model of Gaussian probabilistic regression.  
In this work, we take advantage of the rotational invariance properties of the DFT to describe the scenes, using \(e_{l}\) with this aim.
## 4 Reliable and Highly Sensitive Gas-Sensitive Materials for Intelligent Monitoring  
### Carbon Nanotube (CNT) Material  
One-dimensional carbon nanotube materials have a large specific surface area and good electrical properties [60]. Carbon nanotubes have good sensing properties for gases [61]. Carbon nanotube-based gas sensors have outstanding characteristics, such as faster response, lower operating temperature, and higher sensitivity [62; 63], which have attracted considerable attention. Single-walled carbon nanotubes (SWCNTs) are constructed from graphite sheets, while multi-walled carbon nanotubes (MWCNTs) [64] consist of an array of such nanotubes. The use of CNT-based sensors to detect SF\({}_{6}\) decomposition gas can be used as an indicator for monitoring the insulation of power equipment. By doping the sidewalls of carbon nanotubes with metals or nonmetals, their adsorption capacity can be changed. Coupling the doped atoms (s) to a carbon cage can form a mutual region that has a great influence on the adsorption behavior of the resulting gas carbon nanotubes [65]. [PERSON] et al. [66] doped N-3 with carbon nanotubes to study the adsorption of SOF\({}_{2}\) and SO\({}_{2}\). The results show that the adsorption activity was significantly enhanced after N-3 embedding.  
To explore the type and concentration of SF\({}_{6}\) decomposition products, based on density functional theory (DFT), [PERSON] et al. [67] modified a nickel-modified carbon nanotube (Ni-CNT) gas sensor to analyze the sensitivity and selectivity of SF\({}_{6}\) decomposition components. The results show that the nickel-carbon gas sensor has excellent gas sensing performance. The sensitivity is sorted as follows: H\({}_{2}\)S \(>\) SOF\({}_{2}\) \(>\) SO\({}_{2}\)\(>\) SO\({}_{2}\)F\({}_{2}\). The detection limit of nickel-carbon nanotube gas sensors can reach 1 ppmv.