esa-sceva/satcom-chunk-collection huggingface dataset:
# The Progress of Cobalt-Based Anode Materials for Lithium Ion Batteries and Sodium Ion Batteries
## 3 Preparation Methods of Synthesizing Cobalt-based Active Materials
### Hydrothermal/Solvothermal Methods  
As a typical synthetic method, hydrothermal/solvothermal reactions are mainly used to prepare materials by providing appropriate temperatures and times [53]. Under the circumstances of moderate temperatures and high pressures, various structured cobalt-based materials have been synthesized with high-phase purity and crystallinity. Moreover, hydrothermal/solvothermal methods are environmentally benign and cost-efficient due to their own characteristics [21; 54]. Many cobalt-based anode materials with various morphologies have been synthesized by these methods, like nano-sized Co-Sn alloy, single-crystal intermetallic Co-Sn nanospheres and single-crystalline Co\({}_{3}\)O\({}_{4}\) nanobelts [55; 56; 57].  
[PERSON]'s group synthesized a facile structure with CoS\({}_{2}\) confined in the graphitic carbon walls of porous N-doped carbon spheres (CoS\({}_{2}\)-in-wall-NCSs) by the hydrothermal method associated with carbonization and sulfurization, as shown in Figure 4a-c [58]. During the process of hydrothermal, Co-containing melamine-phenolic resin spheres formed under the condition of high temperatures and high pressures. Followed with carbonization and sulfurization, CoS\({}_{2}\) nanodots were evenly distributed throughout the carbon sphere. Figure 4d,e reveals the worm-like sample with CuCo\({}_{2}\)S\({}_{4}\) nanocrystalline (5-20 nm) evenly anchored on the carbon nanotubes (CNTs) synthesized by [PERSON] et al. through solvothermal treatment [59]. CoSe@ carbon spheres (denoted as CoSe@CSs) were prepared through a facile solvothermal treatment by adding Co(NO\({}_{3}\))\({}_{2}\cdot\)6H\({}_{2}\)O and H\({}_{3}\)O\({}_{2}\)Se to a moderate isopropanol solution [60]. The TEM image (Figure 4f) reveals that CoSe nanoparticles uniformly embedded in amorphous carbon spheres (100 nm). [PERSON]'s group synthesized a pure orthorhombic CoP film of nanorod arrays through a hydrothermal method [61]. As displayed in Figure 4g, these CoP nanorods were evenly distributed and interconnected with others to form a 3D interwoven. [PERSON] and coworkers reported a facile solvothermal reaction to synthesize Co\({}_{3}\)O\({}_{4}\)/CNT nanocomposites [62]. In Figure 4h, the cyboidal Co\({}_{3}\)O\({}_{4}\) nanoparticles were evenly anchored on the surface of the CNT. The elemental mapping of the sample reveals the distribution of C, Co and O.
## 2 Dense Feature Extraction and Matching Theory and Mismatch Cause Analysis
### Mismatch Mechanism and Causes of Mismatch Analysis in SIFT Algorithm  
SIFT and FLANN can still perform well in feature point extraction and matching with varying shooting angle, distance, and illumination during image capture. However, slight  
Figure 2: Feature descriptions and direction histogram. (**a**) Feature descriptions in the neighborhood. (**b**) Gradients’ histogram.  
image grayscale variations inevitably change the value of feature point descriptors, and the distances between corresponding points in two images are not zero. Therefore, if the distance between two features is small enough, and if the ratio of the nearest neighbor distance \(d_{1}\) to the second nearest neighbor distance \(d_{2}\) satisfies:  
\[\frac{d_{1}}{d_{2}}<\tau \tag{4}\]  
where \(\tau\) is usually taken as 0.8, and it is considered as the nearest neighbor matching.  
FLANN is an approximation algorithm and may lead to matching errors, and SIFT features are multiple scale features imperceptible to the human eye, making it difficult to check and interpret matching outliers in practical applications.  
Moreover, numerous matches can be obtained even between two completely unrelated images of a panda and a calf, as shown in Figure 3a, which are inexplicable, meaningless, or even absurd. In Figure 3b, the left and right parts are symmetric along the central axis, and the right part can be obtained by clockwise rotating the left part by 90 degrees. There are ambiguities in matching results between the two parts of the image, and the symmetry or the rotation of the matching results is inconsistent. For example, point A and point D both have two matching results and show the presence of mismatches.  
The mismatches generated by the aforementioned feature extraction and matching methods can lead to errors in structural deformation measurement, which require high accuracy and unique matches. Therefore, it is essential to introduce more explicit constraints to remove the mismatches and ensure the accuracy of structural displacement measurements.  
A sufficient number of markers pre-installed on structures is a prerequisite for structural health monitoring and damage localization [24; 25]. In machine vision method, feature extraction and tracking algorithms such as Shi-Tomasi can extract only a finite number of feature points, which limits their application in structural health monitoring. Therefore, it is necessary to develop dense feature extraction and matching algorithms to trace the motion of a large number of displacement measurement points. The feature extraction method based on SIFT and FLANN matching may lead to mismatches and errors in structural displacement measurement. This paper proposes a full-field dynamic structural deflection measurement method by dual-step mismatch removal (DSMR) of the RANSAC algorithm and Structural Displacement Continuity Constraints (SDCCs).
# Error Resilient Coding Techniques for Video Delivery over Vehicular Networks
###### Abstract  
Nowadays, more and more vehicles are equipped with communication capabilities, not only providing connectivity with onboard devices, but also with off-board communication infrastructures. From road safety (i.e., multimedia e-call) to infotainment (i.e., video on demand services), there are a lot of applications and services that may be deployed in vehicular networks, where video streaming is the key factor. As it is well known, these networks suffer from high interference levels and low available network resources, and it is a great challenge to deploy video delivery applications which provide good quality video services. We focus our work on supplying error resilience capabilities to video streams in order to fight against the high packet loss rates found in vehicular networks. So, we propose the combination of source coding and channel coding techniques. The former ones are applied in the video encoding process by means of intra-refresh coding modes and tile-based frame partitioning techniques. The latter one is based on the use of forward error correction mechanisms in order to recover as many lost packets as possible. We have carried out an extensive evaluation process to measure the error resilience capabilities of both approaches in both (a) a simple packet error probabilistic model, and (b) a realistic vehicular network simulation framework. Results show that forward error correction mechanisms are mandatory to guarantee video delivery with an acceptable quality level, and we highly recommend the use of the proposed mechanisms to increase even more the final video quality.  
video streaming; vehicular networks; error resilience; error concealment; video coding; RaptorQ +
Footnote †: journal:  
0000-0001-4507-2278][PERSON]
# Stability of Running Stride Biomechanical Parameters during Half-Marathon Race
## 2 Materials and Methods
### Procedure  
A descriptive study was conducted through a field session, wherein all participants competed in the same official race under identical weather conditions and at the same time of day. To record biomechanical parameters, each athlete was equipped with the Stryd Summit Power Meter. This device was attached to the shoelace of the right foot and calibrated before the race started, following the manufacturer's guidelines. The field session, the \"International Half-Marathon Villa de Santa Pola\", was held on Sunday, 22 January 2023. This event is sanctioned by the Royal Spanish Athletics Federation and spans 21,097 m. The race features a total elevation change of 72 m, unfolding over a two-lap, urban circular route. Weather conditions on the day of the competition were optimal, with a temperature of 9 \({}^{\circ}\)C, relative humidity of 28%, wind speed of 3.5 m/s, and no precipitation (0.01/m\({}^{2}\)).  
For the analysis of biomechanical parameters and subsequent statistical analysis, the race was segmented as follows: Segment 1 data were collected from the race start to the first quarter (5274 m), Segment 2 data spanned from 5275 m to the midpoint (10,548 m), Segment 3 data covered from 10,549 m to the three-quarter mark (15,822 m), and Segment 4 data were gathered from 15,823 m to the race's conclusion.
## 3 System Model
### UAVs Energy Consumption Model  
The model supports two basic modes of operation for UAVs, i.e., flying and hovering. The power consumption in the flying mode is represented as \(P_{\eta}\), and that consumed in the hover mode as \(P_{\zeta}\). Here, it is assumed that the UAV's flight paths are uniform, requiring no modification in terms of acceleration and deceleration. It is further assumed that rotary wing UAVs are used in this scenario, flying at a speed \(V\). Thus, the energy consumption of the UAVs during flying, represented as \(E_{\eta}\) is defined as:  
\[E_{\eta}=\frac{P_{\eta}}{V} \tag{4}\]  
Here, the \(V\) is the speed of the UAV and is defined as:  
\[P_{\eta}=P_{1}(1+P_{2}V^{2})+P_{3}\Bigg{(}\sqrt{1+\frac{V^{4}}{P_{4}^{2}}}- \frac{V^{2}}{P_{4}}\Bigg{)}^{1/2}+P_{5}V^{2} \tag{5}\]  
where \(P_{i}\), \(i\) = 1,..., 5, are the parameters for the energy model specified in [65] and \(P_{1}(1+P_{2}V^{2})\) is the blade profile power. \(P_{3}\Bigg{(}\sqrt{1+\frac{V^{4}}{P_{4}^{2}}}-\frac{V^{2}}{P_{4}}\Bigg{)}^{1/2}\) is the induced power, and \(P_{5}V^{2}\) is the parasite power. The parasite power is the element needed to counter the parasite friction drag caused by the aircraft flying through the air, and the induced power is the element needed to counteract the induced drag produced during lift force to keep the aircraft in the air.  
The power consumption for rotary-wing UAVs is provided by the finite value \(P_{1}+P_{3}\) for the exceptional case when the speed of the UAV is zero. Such power use is consistent with the rotary-wing UAV hovering in one place. However, if the UAV's flight speed is not zero, then parasite power emerges. While the induced power reduces with UAV speed \(V\), both the blade profile power and the parasite power increase with UAV speed.  
_Hovering:_ The UAV interacts with each sensor only when it is hovering at one of the optimum hovering locations. The energy consumption when UAVs hover is represented by \(E_{\zeta}\) and is defined as:  
\[E_{\zeta}=(P_{h}+P_{c})T_{\zeta} \tag{6}\]  
where \(P_{h}\) is the power consumed in the hovering, \(P_{c}\) is the power consumed by the UAV communicating with the ground node and \(T_{\zeta}\) is the time of hovering. These parameters are expressed in [65].
# Self-Organized Fast Routing Protocol for Radial Underwater Networks
## 7 Computer Simulation Results
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Nodes (ND)** & **Packet Delay (PKD)** & \(\mathbf{PKD_{i}-PKD_{i-1}}\) \\ \hline
3 & 0.0122 & \\
4 & 0.0138 & 0.0016 \\
5 & 0.0154 & 0.0016 \\
6 & 0.0170 & 0.0016 \\
7 & 0.0186 & 0.0016 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Packet Delay Analysis. Figure 15: Number of Retransmitted Packets vs. Probability of Packet Loss. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Nodes (ND)** & **Convergence Delay (CD)** & \(\boldsymbol{\alpha=\text{CD}_{i}-\text{CD}_{i-1}}\) & \(\boldsymbol{\beta=\alpha_{i}-\alpha_{i-1}}\) \\ \hline
3 & 22.639344 & \\
4 & 32.694753 & 10.055409 & \\
5 & 42.841890 & 10.147137 & 0.091728 \\
6 & 53.080755 & 10.238865 & 0.091728 \\
7 & 63.411348 & 10.330593 & 0.091728 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Convergence Delay Analysis. Figure 17 shows the collision rate as the number of nodes per string increases from 3 to 10. There is no clear relationship between the number of nodes in a string and the collision rate. It seems that the collision rate remains roughly constant. The mean is \(\mu=2.325\) and the standard deviation was \(\sigma=0.135\). This was due to the fact the collision among the nodes mainly depends on the number of strings rather than the number of nodes in a string. The number of timeslots is an important factor in controlling the collisions between transmitted packets. Figure 18 shows the simulation results for different numbers of timeslots ranging from 10 to 80. This comparison shows the tradeoff between collision rate and convergence delay. It is clear from the graph that increasing the number of timeslots decreased the collision rate, but it increased the convergence delay. When using 10 timeslots, the convergence delay is minimum but the collision rate is maximum, whereas in case of 80 timeslots, the number of collisions was at a minimum but the convergence delay was at a maximum. From Figure 18, it is observed that the optimum number of timeslots is around 30. We have chosen 40 timeslots in order to have a low collision rate, though at the cost of a little higher convergence delay. Figure 19 compares the convergence delay obtained from the mathematical equations and from the simulations (see green bars). It can be seen that there was almost perfect agreement between these values. Figure 19 also includes a comparison between the simulated collision rate values and the calculated collision rate. It also shows that the simulated collision rate was in agreement with the theoretical analysis. It can be seen in Figure 20 that the average energy consumption for 10 timeslots was higher than for 20 or 40 timeslots. This happens because the number of collisions in case of 40 timeslots was lower than in the other scenarios and, as a result, the number of retransmitted packets was lower. Figure 16: Percentage of Collision Rate vs. No. of Strings. Figure 17: Collision Rate vs. Number of Nodes.
## 2 State of the Art  
As mentioned in the introduction, an ontology is a shared data schema that provides a common understanding of data descriptions. Data descriptions based on ontologies are reusable by any machine and understandable by humans. Thus, we examined ontologies already used in irrigation decision systems to reuse them as data modeling patterns to improve the results of our data modeling activity.  
Several expert systems already exist that can determine whether a crop needs water. For example, one expert system uses the common-KADS method to irrigate mango trees, as presented in [5]. Please note that this expert system is not connected to any sensors or actuators. Its goal is to help farmers decide when to irrigate. However, no information about the ontology publication is provided.  
Semantic web technologies are already used in CASs dedicated to irrigation. For example, the FIWARE platform that links Internet of Things (IoT) devices to the cloud has been used in several different irrigation experiments [6]. The FIWARE cloud platform contains the SEPA SPARQL-based engine, which represents information in an RDF format and provides SPARQL queries. To the best of our knowledge, no information about the ontologies used in these experiments is available online.  
The works of [7; 8] present some ontologies dedicated to hilly citrus tree cultivation. One ontology addresses the irrigation decision. The ontology stores the computations of relative soil moisture based on the soil type and the crop growth stage. When the soil moisture reaches a given threshold, the farmers receive an alert from the system. In this case, the ontology is not published; therefore, it is impossible to reuse it.  
To our point of view, the most successful CAS dedicated to irrigation is the one developed during the PLANTS project [9]. This system is installed in a greenhouse. It uses several types of sensors to observe raspberry plants. This system can control watering equipment to control irrigation in the four zones of the greenhouse. The PLANTS ontology aims to describe e-entities and their interactions [9; 10]. An e-entity is a virtual representation of a physical object that can be either a raspberry plant or equipment. Every measurement is defined as the parameter of an e-entity. Sensor streams are not aggregated. The last measurements are used to update a parameter. The rules are used to derive the states of raspberry plants from various parameters. Watering equipment is controlled by the detection of \"drought stress\" in a specific zone. Thus, the only part of the ontology that we want to reuse is the plant state hierarchy. Unfortunately, this ontology is unavailable on the Web because it is the schema of the facts-base implemented in the Jess inference engine.  
To conclude, while several works focus on processing sensor streams and computing aggregations, their results are not directly reusable, as our approach should handle the temporal and spatial aggregation of sensor streams for the irrigation use case. Additionally, the ontologies mentioned in previous works are either unavailable online or their domain is loosely related to our use case.
## 2 System Model
### DD Domain Input-Output Relationship in OTFS Modulation
This transformation is executed through the application of the Wigner transform, as elaborated in reference [59]. It is paramount to underscore that this Wigner transform can equivalently be construed as the CAF, denoted as \(A_{g_{rx}r}(t,f)\). Furthermore, it may be formally defined as:  
\[ \begin{split}& Y_{\text{TF}}(t,f)=A_{g_{rx}r}(t,f)\\ &\overset{\Delta}{=}\int g_{rx}^{*}(t^{\prime}-t)r(t^{\prime})e^ {-j2\pi f(t^{\prime}-t)}dt^{\prime}.\end{split}  \tag{9}\]Upon performing the sampling of \(Y(t,f)\), we derive the discrete output in the following manner:  
\[Y_{\text{TF}}[n,m]=Y_{\text{TF}}(t,f)|_{t=nT,f=m\Delta f}, \tag{10}\]  
for \(m=0,\dots M_{5}-1\) and \(n=0,\dots,N_{T}-1\). Consequently, through the application of the SFFT to \(Y_{\text{TF}}[n,m]\), we obtain the discrete output symbols \(Y_{\text{DD}}[k,l]\) in the DD domain. \[Y_{\text{DD}}[k,l]=\frac{1}{\sqrt{N_{T}M_{5}}}\sum_{n=0}^{N_{T}-1}\sum_{m=0}^{ M_{5}-1}Y_{\text{TF}}[n,m]e^{-2\pi\left(\frac{uk}{N_{T}}-\frac{ut}{M_{5}} \right)}. \tag{11}\]  
In cases where an ideal PSF is employed, as elaborated in reference [61], the IO relationship between the OTFS symbols can be described as:  
\[ \begin{split} Y_{\text{DD}}[k,l]=\sum_{n=0}^{N_{T}-1}\sum_{m=0}^ {M_{5}-1}& X_{\text{DD}}[n,m]h_{\omega}[k-n,l-m]\\ &+Z_{\text{DD}}[k,l].\end{split}  \tag{12}\]  
Here, \(Z_{\text{DD}}\) adheres to a complex Gaussian distribution characterized by a mean of zero and a covariance of \(\sigma^{2}I\), representing the the DD domain effective noise. Additionally, \(h_{\omega}[k,l]\) is the EC in the DD domain, acquired via the following means:  
\[h_{\omega}[k,l]=\sum_{i=1}^{P}h_{i}\omega(k-k_{vi_{i}},l-l_{\tau_{i}})e^{-2\pi v _{i}\tau_{i}}. \tag{13}\]  
In this context, the function \(\omega(k-k_{vi_{i}},l-l_{\tau_{i}})\) serves as the sampling function. The parameters \(v_{i}\) and \(\tau_{i}\) are defined in accordance with Equation (8). When both the TF transmitter and receiver opt to employ a rectangular window, as extensively elucidated in reference [53], the sampling function takes on a more streamlined and simplified form, as follows:  
\[ \begin{split}&\omega(k-k_{vi_{i}},l-l_{\tau_{i}})=\mathcal{G}(k-k_{vi _{i}})\mathcal{F}(l-l_{\tau_{i}}),\\ &\mathcal{G}(k-k_{vi_{i}})\overset{\Delta}{=}\frac{1}{N_{T}}\sum _{k=0}^{N_{T}-1}e^{-\beta\pi\frac{(k-k_{vi_{i}})\mathcal{F}}{N_{T}}},\\ &\mathcal{F}(l-l_{\tau_{i}})\overset{\Delta}{=}\frac{1}{M_{5}} \sum_{l^{\prime}=0}^{M_{5}-1}e^{2\pi\frac{(l-l_{\tau_{i}})\mathcal{F}}{M_{5}} }.\end{split}  \tag{14}\]  
As expounded in reference [61], the non-zero components of the EC in the DD domain exhibit uniform localization patterns when both the rectangular and ideal PSFs are utilized. The primary distinction lies in the introduction of an additional phase offset. Consequently, the algorithms delineated in this study can be readily and seamlessly adapted to suit scenarios where a rectangular PSF is employed.
# Compliant Detachment of Wall-Climbing Robot Unaffected by Adhesion State
## Appendix B  
OIAC minimizes mission error and maintains single\(-\)limb dynamic stability by:  
\[J(t)=J_{c}(t)+J_{p}(t), \tag{1}\]  
where \(J_{c}(t)\) minimizes bias and impedance item error, and \(J_{p}(t)\) maintains single\(-\)limb dynamic stability. \(J_{c}\) is given by:  
\[J_{c}(t)=\frac{1}{2}\int_{t-T}^{t}\text{vec}^{T}(\tilde{K})Q_{k}^{-1}\text{vec }(\tilde{K})+\text{vec}^{T}(\tilde{D})Q_{d}^{-1}\text{vec}(\tilde{D})+\tilde{F }^{T}Q_{f}^{-1}\tilde{F}, \tag{2}\]  
where \(Q_{f}\), \(Q_{k}\), and \(Q_{d}\) are symmetric positive-definite matrices. The derivative of \(J_{c}\) is given by:  
\[\begin{split}\delta J_{c}=& J_{c}(t)-J_{c}(t-T)\\ =&\frac{1}{2}\int_{t-T}^{t}\text{tr}\Big{\{}\tilde{K} ^{T}(\sigma)Q_{k}^{-1}\tilde{K}(\sigma)-\tilde{K}^{T}(\sigma-T)Q_{k}^{-1} \tilde{K}(\sigma-T)\Big{\}}\\ +&\text{tr}\Big{\{}\tilde{D}^{T}(\sigma)Q_{d}^{-1} \tilde{D}(\sigma)-\tilde{D}^{T}(\sigma-T)Q_{d}^{-1}\tilde{D}(\sigma-T)\Big{\}} \\ +&\text{tr}\Big{\{}\tilde{F}^{T}(\sigma)Q_{f}^{-1} \tilde{F}(\sigma)-\tilde{F}^{T}(\sigma-T)Q_{f}^{-1}\tilde{F}(\sigma-T)\Big{\}} d\sigma\\ =&-\frac{1}{2}\int_{t-T}^{t}\delta\tilde{\Phi}^{T}( \sigma)Q^{-1}\delta\tilde{\Phi}(\sigma)d\sigma\\ -&\int_{t-T}^{t}\gamma(\sigma)\tilde{\Phi}^{T}( \sigma)\tilde{\Phi}(\sigma)d\sigma\\ +&\int_{t-T}^{t}\varepsilon(\sigma)\tilde{K}(\sigma )e(\sigma)+\varepsilon(\sigma)\tilde{D}(\sigma)\varepsilon(\sigma)+\varepsilon (\sigma)\tilde{F}(\sigma)d\sigma,\end{split} \tag{3}\]  
where \(\text{tr}\{\cdot\}\) stands for the trace of a matrix. \(\tilde{\Phi}(t)\) and \(Q\) are given by:  
\[\begin{split}\tilde{\Phi}(t)&=\Big{[}\text{vec}( \tilde{K}(t))^{T},\text{vec}(\tilde{D}(t))^{T},\tilde{F}(t)\Big{]}^{T}\\ &\quad Q=\text{diag}\Big{(}I\otimes Q_{k},I\otimes Q_{d},Q_{f} \Big{)},\end{split} \tag{4}\]and adaptive parameter \(\gamma(t)\) is given by:  
\[\gamma(t)=a^{-1}(1+b\|\varepsilon(t)\|^{2}). \tag{10}\]  
Similarly, \(\delta J_{p}\) and \(\delta J\) are given by:  
\[\delta J_{p}= -\int_{t-T}^{t}\varepsilon(\sigma)\tilde{K}(\sigma)e(\sigma)+ \varepsilon(\sigma)\tilde{D}(\sigma)\varepsilon(\sigma)+\varepsilon(\sigma) \tilde{F}(\sigma)d\sigma, \tag{11}\] \[\delta J= -\frac{1}{2}\int_{t-T}^{t}\delta\tilde{\Phi}^{T}(\sigma)Q^{-1} \delta\tilde{\Phi}(\sigma)d\sigma-\int_{t-T}^{t}\gamma(\sigma)\tilde{\Phi}^{ T}(\sigma)\tilde{\Phi}(\sigma)d\sigma.\]  
To decrease \(J\) with time, \(\delta J<0\) is necessary. So, the following formula must be satisfied:  
\[\gamma(\sigma)>0,\tilde{\Phi}^{T}\tilde{\Phi}\geq 0, \tag{12}\]  
so the parameter \(a\) and \(b\) must be positive.