esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Experiments on Robotic Cutting of Artificial Vertebrae
The data analysis reveals that the average cutting force for the cortical bone model hovers around 1.3 N, whereas for the cancellous bone model, it is approximately 0.5 N. In contrast, the average cutting force for the cortical bone of the porcine spine is around 1.5 N, and for the cancellous bone, it is roughly 0.8 N. These findings validate the cutting force predictions derived from the bone density models, confirming the effectiveness of the predictive model in simulating real bone cutting characteristics. Despite the overall success of the cutting experiments, several unexpected challenges arose. During some trials, minor discrepancies in force measurements were observed, particularly when cutting regions with complex microstructures. These variations could be attributed to the inherent differences in the microstructures of artificial vertebrae compared to biological bone. Moreover, the surface roughness and material composition of the 3D-printed models occasionally resulted in deviations from expected cutting forces. The comparison of cutting forces between the artificial models and the porcine spine provides crucial insights into the accuracy of the predictive model. The relatively small differences in force magnitudes between the two suggest that the model can reliably predict cutting behaviors. However, the observed discrepancies, though minimal, highlight areas for potential refinement in material selection and model parameterization. Further investigation into how material anisotropy and microstructural properties affect cutting force signals is necessary to enhance the reliability of the robotic cutting process. From the experimental findings, it is evident that a significant disparity exists between the cutting forces of the cortical and cancellous bone models, mirroring the substantial  
Figure 25: Cutting force signals for cortical and cancellous bone. (a) For cortical bone; (b) for cancellous bone. differences observed in real bone. This similarity in cutting force characteristics between the models and real bone validates the practicality of the method for preparing artificial vertebrae, which is grounded in bone density predictive modeling. Overall, while the cutting force data validate the accuracy of the bone density-based predictive model, further refinement is needed to address the minor discrepancies and ensure greater consistency in the cutting outcomes for future applications.
# Optimization of the 3D Point Cloud Registration Algorithm Based on FPFH Features
## 4 Experimental Evaluation
### Analysis of Rough Registration Experiment  
In this section, the improved rough registration algorithm in this paper is compared with the above four existing algorithms through experiments, and the results are analyzed, the comparison results are shown in Table 2. The evaluation index adopts root-mean-square error (RMSE), which is defined as follows:  
\[RMSE=\sqrt{\frac{\sum\limits_{i}^{N}{\left|{\frac{{{\it{T}_{p}}}}{{{\it{T}_{p} }-{{\it{q}}_{i}}}}}\right|}^{2}}{N}} \tag{14}\]  
In Equation (14), it is the closest point of the target point cloud to the point in the source point cloud, \(n\) is the number of point clouds, and t is the translation matrix.  
This paper registers the bunny point cloud data set of Stanford University with the free-form surface point cloud data set independently measured. It can be seen from the data in Table 2 that compared with existing methods, the improved rough registration method has a shorter iteration time, faster registration speed, and better accuracy and robustness.
## 4 Implementation Details and Evaluation Metrics
### Evaluation Metrics  
Evaluation metrics and the protocol prescribed for the ISBI public database [4; 5] were employed to validate the cephalometric landmark detection methods in this study. The validation was based on the radial error (RE), calculated as the Euclidean distance \(d()\) between the estimated, **EST**, and ground-truth landmark location, **GT** (i.e., the 2D point). A basic metric mean radial error (\(MRE\)) is derived from this error, where the MRE is calculated as the average of radial errors over \(L\) observed landmarks, which is written formally as  
\[MRE=\frac{1}{L}\sum_{i=1}^{L}d(\textbf{EST}_{i},\textbf{GT}_{i}), \tag{4}\]  
where \(\textbf{EST}_{i}\) in \(\textbf{GT}_{i}\) denote the estimated and ground-truth locations for the \(i\)-th landmark. It should be stressed that \(L\) denotes the number of landmarks, and does not necessarily represent the number of different types of landmarks observed in each X-ray image (this is denoted as \(N\) in this article).  
Two additional statistics of radial error were calculated besides the mean (and the standard deviation) in this research, namely, the median and the 90 th percentile of radial error. All the mentioned measures can be estimated per landmark type, per image, or even per all landmark types and all images (i.e., over all landmarks in all images in the database). These metrics are presented either in pixels or in mm if the spatial resolutions of the images are known.  
The next metric that has been introduced for the ISBI database is the successful detection rate (SDR), which evaluates the precision (i.e., the positive predictive value) of landmark detection with respect to the radial error. The metric SDR is assessed typically in respect to the radial error up to 2 mm (Class 1), 2.5 mm (Class 2), 3 mm (Class 3), and 4 mm (Class 4) from the ground-truth landmark position. It should be noted that we were unable to determine this metric for the AUDAX private database, because the spatial resolution information was not known for this database.
## 4 Case Study
### Fuzzy Logic System According to the IEEE 1855-2016  
The described fuzzy variables and rules were represented in a FML file according to the IEEE std 1855-2016 specifications. As an illustrative example, some parts of the file are shown in the Code 1.  
Figure 4: Definition of fuzzy risk levels.  
```
<?xmlversion=\"1.0\"encoding=\"UTF-8\"?> <fuzzySystemxmlns=\"[[http://www.ieee1855.org](http://www.ieee1855.org)]([http://www.ieee1855.org](http://www.ieee1855.org))\"name= \"FFHrisk-Example\"> <knowledgeBase> <fuzzyVariablename=\"Harnessdetection\" domainleft=\"0.0\"domainright=\"1.0\"type=\"input \"> <fuzzyTermname=\"attached\"> <singletonShapeparam1=\"1.0\"/> </fuzzyTerm> <fuzzyTermname=\"unattached\"> <singletonShapeparam1=\"0.0\"/> </fuzzyTerm> </fuzzyVariable>... </knowledgeBase> <mandaniRuleBasename=\"rulebase1\" activationMethod=\"MIN\" andMethod=\"MIN\" orMethod=\"MAX\"> <rulename=\"rule1\" andMethod=\"MIN\" connector=\"and\"weight=\"1.0\"> <antecedent> <clause> <variable>Virtual fence distance</variable> <term>High</term> </clause> </antecedent> <consequent> <then> <clause> <variable>Risk</variable> <term>Low</term> </clause> </then> </consequent> </rule>... </mandaniRuleBase> </fuzzySystem>
```  
**Code 1**. Extract of FML file according to the IEEE std 1855-2016 designed for the fall from height.
# Self-Learning Salp Swarm Optimization Based PID Design of Doha RO Plant
## 2 Reverse Osmosis System  
The schematic block diagram of Doha reverse osmosis plant (DROP) system is provided in Figure 1[4]. The RO desalination plant comprises of four stages, namely, pre-treatment, high pressure pump, RO membrane assembly and post-treatment.  
During pre-treatment stage, the saline water undergoes filtration to remove any physical or chemical impurities. This stage is important to ensure membrane longevity. The organic chemicals which can burn holes in the membrane and can cause irreparable damage to membrane are trapped using activated cartridge filters. In addition, some inhibitors are added to decrease the pH of the feed-water which helps in reducing the scaling effect. Besides this, physical impurities like mud particles and microbial growth on membrane are also removed during this stage to increase the life of membrane.  
High pressure pump is utilized to provide the necessary pressure required for reverse osmosis. For brackish water, it is in the range of 15-25 bars while it is 54-80 bars for sea water. The pressurized feed-water is then fed to the membrane assembly which is made up of two or more hollow or spiral wound composite polyamide membranes. This is the main part of the RO system. It does not allow impurities to pass through it. The membrane assembly must be able to withstand the high pressure. The rejected brine from membrane assembly is sent to the discharge channel. In post-treatment stage, calcium and bi-carbonate ions are removed from the water. In addition, the pH is raised slightly above 7 and water is prepared for distribution. The two controlled variables (flux and conductivity of output water) are manipulated using two manipulated variables (feed-water pressure and feed-water pH) in case of DROP model.
## 2 Observations and Method for Data Analysis  
### SABER Temperature Data  
The SABER instrument on board the TIMED satellite measures \(CO_{2}\) infrared limb radiance from approximately 20 km to 110 km altitude, and kinetic temperature profiles are retrieved over these heights using LTE radiative transfer in the stratosphere and lowest part of the mesosphere (up to \(\sim\)60 km), and a full non-LTE inversion in the MLT [65; 66]. It provides 100 temperature profiles during every 90 min orbit. As noted by [PERSON] et al., 2013 [67], the SABER temperatures are valid below \(\sim\)110 km altitude, above which un-modelled atomic oxygen variations introduce errors into the retrieved temperatures and the data mostly reflect model values used in the retrieval process. Due to the spacecraft yaw cycles, continuous measurements are available only in the latitude range of \(\pm 50^{\circ}\). Using both ascending and descending orbital modes, it takes 60 days for SABER to sample the data over the full 24 h of local times.  
SABER results presented in this paper were computed from the publicly available version 2.0, and level 2A SABER temperature data were downloaded from: [[https://saber.gats-inc.com/data.php](https://saber.gats-inc.com/data.php)]([https://saber.gats-inc.com/data.php](https://saber.gats-inc.com/data.php)) (accessed on 10 April 2024). The details about the validation and error assessment of the SABER data can be found in [PERSON] et al., 2008, and [PERSON] et al., 2008 ([PERSON] et al., 2008,Garcia-Comas et al., 2008). However, those for the currently used version 2.0 has been reported by [PERSON] et al., 2018 ([PERSON] et al., 2018). These studies note that the possibility of systematic errors increases with altitude due to uncertainties in the collisional quenching rates and the atomic oxygen density. In addition, the information about kinetic temperature that can be determined from the non-LTE retrieval decreases in the thermosphere. The following uncertainties are obtained at mid-latitudes: at an altitude of 92 km, the error is \(\pm 1.3\) K; at 96 km, it increases to \(\pm 1.4\) K; at 100 km, it is \(\pm 3.6\) K; while at 104 km, it is \(\pm 8.3\) K. In this study, similarly to other ones on atmospheric waves, the analysis uses perturbation temperature, which reduces the impact of possible systematic errors.  
The data were sorted by: (i) latitude, at each 5\({}^{\circ}\) between 50\({}^{\circ}\) S and 50\({}^{\circ}\) N; (ii) longitude, at each 15\({}^{\circ}\); (iii) altitude, at each 2 km between 20 and 110 km; and (iv) time, for each UT hour.
## 2 Preliminaries  
### Algebraic Graph Theory  
Let \(\mathcal{G}=(\mathcal{V},\mathcal{E},\mathcal{A})\) be a weighted digraph with the set of vertices \(\mathcal{V}=\{1,2,\ldots,N\}\), the set of edges \(\mathcal{E}\subseteq\mathcal{V}\times\mathcal{V}\), and the weighted adjacency matrix \(\mathcal{A}=[a_{ij}]\). For any \((i,j)\in\mathcal{E}\), \(i\
eq j\), the element of the weight adjacency matrix \(a_{ij}\) is positive if vertices \(i\)th and \(j\)th can communicate with each other, while \(a_{ij}=0\) in the cases of \((i,j)\
otin\mathcal{E}\) or \(i=j\). The in-degree of vertex \(i\) is denoted as \(deg_{i}^{in}=\sum_{j=1}^{n}a_{ij}\), and the degree matrix of graph \(\mathcal{G}\) is defined as \(\mathcal{D}=\textit{diag}\big{(}deg_{1}^{in},deg_{2}^{in},\ldots,deg_{N}^{in} \big{)}\). The Laplacian matrix is defined as \(\mathcal{L}=[\ell_{ij}]=\mathcal{D}-\mathcal{A}\). Further, \(\mathcal{G}\) is called an undirected graph if and only if \(\mathcal{A}\) is a symmetric matrix. An edge of the undirected graph \(\mathcal{G}\) is denoted by an unordered pair \((i,j)\in\mathcal{E}\). The undirected graph is connected if there exists at least a path between any pair of vertices. The set of all neighbors of the vertex \(i\) is denoted as \(\mathcal{N}_{i}=\{j\in\mathcal{V}:(i,j)\in\mathcal{E}\}\).  
**Lemma 1** ([28]).: _If a graph \(\mathcal{G}\) is connected, then its Laplacian matrix \(\mathcal{L}\) is positive semidefinite. Moreover, \(x^{T}\mathcal{L}x=0\) if and only if \(x=a\mathbf{1}_{n}\) for some \(a\in\mathbb{R}\)._
## 2 Materials and Methods
### Analysis of Extracts by Thin-Layer Chromatography (TLC)  
To obtain thin-layer chromatography (TLC) fingerprints, crude acetone and ethanol extracts of _Antidesma venosum_, _Ervthirna caffra_, and _Searsia lancea_ were qualitatively screened using an established protocol [23]. The screening involved eluting 100 \(\upmu\)g of each extract, which was loaded on a 1 cm wide band of aluminum-backed silica gel plates, with three different solvent systems of diverse polarities. The solvent systems used were benzene: ethanol: ammonium hydroxide (18:2:0.2, BEA, non-polar, basic), chloroform: ethyl acetate: formic acid (5:4:1, CEF, intermediate polarity, acidic), and ethyl acetate: methanol: water (40:5.4:5, EMW, polar, neutral). The TLC plates were marked with visible bands under white light and ultraviolet light (254 nm and 360 nm wavelengths, Camag universal UV light lamp TL-600). Freshly prepared vanillin (0.1 g vanillin, 28 mL methanol, 1 mL sulphuric acid) spray reagent was applied to the plates, which were then heated to 110 \({}^{\circ}\)C to achieve optimal color development.  
\begin{table}
\begin{tabular}{l r r} \hline \hline
**Plants** & **\% Acetone** & **Extract Yield (g/g)** & **\% Ethanol Extract Yield (g/g)** \\ \hline _Antidesma venosum_ & 1.92 & 3.28 \\ _Elaedendron crocum_ & 4.96 & 8.12 \\ _Ervthirna caffra_ & 5.35 & 2.68 \\ _Indigofera frutsccens_ & 1.68 & 6.87 \\ _Pleurostylia capensis_ & 5.11 & 6.80 \\ _Searsia lancea_ & 5.49 & 10.75 \\ _Searsia leptolictya_ & 1.98 & 4.00 \\ _Trichilia emetica_ & 2.79 & 6.99 \\ _Ziziphus mucromata_ & 2.80 & 6.04 \\ \hline g/g = gram/gram. & & \\ \end{tabular}
\end{table}
Table 2: Crude extract yield of the selected plants.
## 2 Background
### Multi-Channel Operation
#### 2.2.3 Open Issues  
As mentioned above, the frequency used in both standards is in the 5.9 GHz band. In Europe, the basic strategy is to exclusively tune to CCH or tune on demand to one of the SCH channels with the help of SAMs transmitting on the CCH channel in case of congestion on the control channel. In the US, according to the 1609.4 protocol family, time is divided into CCH and SCH intervals, and the ITS system can switch between them with different techniques. Overall, while there are some differences in the Multi-Channel Operation between the EU and US standards, both systems have similar challenges and potential weaknesses with the MCO concept.  
One of the main issues is the bandwidth wastage at the end of a channel interval. Suppose the remaining time interval is not long enough to transmit an already established packet. In that case, the standard recommends postponing it to the next synchronization interval: this wastes channel bandwidth and results in an extended access delay [2].  
An adjacent channel may be physically close to another channel. In this case, interference generated by an adjacent channel can be so significant that it is detected by the CSMA/CA unit of the other channel and delays its transmission because it assumes that other services are using the channel. This is a serious problem because if a safety-related message is dropped due to CSMA/CA false detection, it could result in fatal consequences in traffic [19].  
Another topic is channel selection. Both standards suggest selecting the least congested SCH channel for service information transmission. However, this can be an issue since ITS-S systems can switch SCH channels on demand. The question is whether devices have to switch simultaneously to the advertised SCH or can switch asynchronously when an advertisement message (WSA/SAM) is received [2].  
SCH3 and SCH4 channels are located in the SRD (Short-Range Device) band. Since ITS systems can transmit with more power than SRD devices, it is necessary to set some kind of spectrum rules to ensure that these devices have free access to the bands. That is why SRDs are prioritized users of those channels, but only for a limited range; however, this range is not defined yet [28].
###### Abstract  
Geosynchronous equatorial orbit (GEO) satellite-derived AOD possesses huge advantages for monitoring atmospheric aerosol with high frequency; however, the data missing existing in the satellite-derived AOD products dramatically limits this expected advantage due to cloud obscuration and aerosol retrieval algorithm. In recent years, numerous AOD fusion algorithms have been proposed, while these algorithms are mostly developed to blend daily AOD products derived from low Earth orbit (LEO) satellites and generally neglect discrepancies from different categories of products. Therefore, a spatiotemporal fusion framework based on the Bayesian maximum entropy theorem, blending GEO with LEO satellite observations and incorporating data discrepancies (GL-BME), is developed to complementarily recover the Advanced Himawari-8 Imager (AHI) AOD products over East Asia. The results show that GL-BME significantly improves the average spatial completeness of AOD from 20.3% to 67.6% with ensured reliability, and the accuracy of merged AODs nearly maintains that of original AHI AODs. Moreover, a comparison of the monthly aerosol spatial distribution between the merged and original AHI AODs is conducted to evaluate the performance and significance of GL-BME, which indicates that GL-BME could further restore the real atmospheric aerosol situation to a certain extent on the basis of dramatic spatial coverage improvement.  
GEO-LEO; AOD fusion; discrepancy quantification; Himawari-8 AHI Article
## 6 Results and Analysis of Numerical Simulation  
### Numerical Shear Characteristic of Discontinuities Possessing Different Joint Wall Strengths  
After each shear simulation was completed, the numerical test data were derived. Figure 12 presents the comparison between the experimental and numerical results of the shear curves of joint specimens.  
It is visible from Figure 12 that the numerical results are basically consistent with the experimental results. Both the test curve and the numerical curve had obvious peak values and showed a progressive failure process. The peak shear strength, residual shear strength and shear stiffness in numerical results increased significantly with the gradual increase in normal stress. Under high normal stress, the shear stress of discontinuities with JRC of 18.7 decreased steeply after reaching the peak value, which means the brittle failure was caused by the great difference in strength between the hard rock and the soft rock. That is to say, for discontinuities possessing different joint wall strengths with JRC of 18.7, the convex structure of the discontinuity would suddenly break under the  
Figure 12: Comparison results of numerical curve and test curve of discontinuities possessing different joint wall strengths. (**a**) GCR1; (**b**) SCR1; (**c**) GCR2; (**d**) SCR2; (**e**) GCR3; and (**f**) SCR3.  
effect of high normal stress. Therefore, the overall failure mode of discontinuity was manifested as brittle shear failure, which was consistent with the test results. It can be seen that the numerical results fully verified the accuracy of the test results, and further that the CY model in UDEC is completely applicable to the shear numerical simulation of discontinuities possessing different joint wall strengths. Figure 13 presents the shear stress contours of the discontinuities possessing different joint wall strengths when the shear displacement reached 5 mm.  
Figure 13: _Cont._  
It is visible from Figure 13 that in the case of equal roughness, the shear stress of discontinuities increased with the increase in normal stress. In the case of the same normal stress, the shear stress endured by discontinuities with large roughness was significantly greater than that of discontinuities with small roughness. Simultaneously, the shear stress endured by discontinuities was mostly concentrated on the asperity facing the shear direction. Comparing with the test results, the friction damage and cutting phenomena of asperities mostly occurred in the morphology facing the shear direction. Meanwhile, it can be realized from the shear stress contour that the shear stress endured by granite-mortar discontinuities was greater than shear stress endured by sandstone-mortar discontinuities under the same normal stress. This is owing to the strength difference between granite and mortar being greater, and the embedding effect and furrow effect of asperity being more significant in the shear process. Therefore, the shear stress endured by granite-mortar discontinuities increased obviously.
###### Abstract  
Sensing plays a vital role in enabling smart cities. The mobile surveillance of different sectors, the retransmission of radio signals, and package delivery are the main applications conducted by unmanned vehicles in smart cities. Multiple unmanned vehicles or miniaturized real-time flying machines with onboard sensors, whether land- or air-based, communicate with each other to form a flying sensor network. Almost all of these machines are battery-operated. Therefore, power preservation is an extremely important factor to be taken into consideration. This paper proposes a power-aware biologically inspired secure autonomous routing protocol (P-BIOSARP) that depends on enhanced ant colony optimization (eACO). eACO autonomously and securely routes the data packet, and the power awareness maintains the power consumption of the flying sensor network. The novel intelligent power-aware routing protocol was implemented in network simulator 2 to perform a number of experiments with different scenarios. The scenarios included varying numbers of total nodes and mobile nodes, different packet rates, mobile source nodes, multiple mobile routing nodes, and, on the side of security, the injection of malicious nodes. The proposed protocol is compared with BIOSARP, E-BIOSARP, and SRTLD in terms of energy consumption, the delivery ratio, and traffic overhead. The analysis shows that the P-BIOSARP remarkably reduces energy consumption compared to other well-known protocols implemented on real testbeds.  
ant colony optimization; energy; flying; intelligent; mobility; network simulation; routing protocol; security; surveillance
## 4 Voltage Vector Distribution Method  
This section deals with three different voltage vector distribution methods and their selection strategy. Because all these voltage vector distribution methods need to judge whether the inverter desired voltage vector is in the modulation range of the corresponding inverter, the judgmental algorithm will be introduced first.