esa-sceva/satcom-chunk-collection huggingface dataset:
# Underwater SLAM Meets Deep Learning: Challenges, Multi-Sensor Integration, and Future Directions
## 2 Core Principles of SLAM: Front-End and Back-End
### SLAM Back-End
#### 2.2.1 Filtering-Based SLAM  
Filtering-based SLAM uses a probabilistic framework to represent uncertainties in both the robot's pose and the map. Various filtering methods are commonly employed in this approach. One widely used method is the extended Kalman filter (EKF) SLAM, which uses a state-space representation to estimate both the robot's pose and the map's landmarks. While EKF is computationally efficient, it struggles with non-linearities, which can affect accuracy in more complex environments [14]. The unscented Kalman filter (UKF) SLAM addresses these limitations by applying a deterministic sampling technique, making it better suited for non-linear models.  
Another prominent approach is particle filter (PF) SLAM, which represents the robot's pose and map using particles. PF-based SLAM handles non-linearities effectively but can be computationally expensive in large environments [15]. The Rao-Blackwellized particle filter (RBPF) SLAM combines particle filters for pose with Kalman filters for landmarks, offering an efficient solution.  
Several studies have advanced filtering-based SLAM:  
* Stachniss et al. [16] presented a grid-based FastSLAM for exploration, enabling active loop closure.
* Grisetti et al. [17] developed an RBPF integrating odometry and laser data for improved sampling.
* Heshmat et al. [18] used camera oscillations to enhance depth estimation.
* Sadeghzadeh-Nokhodberiz et al. [19] proposed an RBPF for quadcopters addressing sensor faults.
* Nie et al. [20] introduced LCPF, an RBPF LiDAR SLAM with loop detection.
# Optimization of Nonspherical Gold Nanoparticles for Photothermal Therapy
## 2 Light Absorption by Nonspherical Gold Nanoparticles
Cubic spline interpolation was used to calculate the complex refractive indices at intermediate wavelengths, when the data are not available directly from [PERSON] and [PERSON]. The computer codes for the calculation of \(\alpha_{abs}\) were developed based on the free T-matrix codes developed by [PERSON] ([[https://www.giss.nasa.gov/staff/mmishchenko/t_matrix.html](https://www.giss.nasa.gov/staff/mmishchenko/t_matrix.html)]([https://www.giss.nasa.gov/staff/mmishchenko/t_matrix.html](https://www.giss.nasa.gov/staff/mmishchenko/t_matrix.html))) [21]. Furthermore, the shape of the nanorods and the size-dependent complex refractive index of the metal nanoparticles were added to the source code. The main input parameters for the code included the light wavelength \(\lambda\), the aspect ratio \(R\), the length \(L\) of particles, and the refractive indices of particles and the surrounding medium, \(n_{p}\) and \(n_{m}\), respectively. In most calculations, the refractive index of the surrounding tissue was taken as 1.44, which is the refractive index of the subcutaneous fat [22]. Nanoparticles with length within the range of 30-130 nm were considered, and the aspect ratio was selected between 2 and 8. The step sizes of the length and the aspect ratio were considered to be 1 nm and 0.1, respectively.
# Semantic 3D Reconstruction with Learning MVS and 2D Segmentation of Aerial Images
## 5 Results and Discussion
### Discussion
#### 5.2.1. Parameter Selection for K-Nearest Neighbors  
There are two criteria for judging neighbor points. As the name k-nearest neighbors itself indicates, the maximum number of neighbors is \(k\). Besides that, the absolute distance in 3D space should also be limited. We down-sample the point could again with a rate of 0.001 to build a small KD-tree. Then we calculate the average distance of these points, setting the value to be the threshold of absolute distance. As indicated in Figure 7, the Pixel Accuracy firstly increases with the growth of \(k\), and reaches its peak with \(k=15\). After crossing the peak, accuracy decreases as \(k\) increases. This is because as \(k\) increases, the local method negatively optimizes for small areas such as vehicles and narrow roads.
# Approximate Image-Space Multidirectional Occlusion Shading Model for Direct Volume Rendering
## 3 Image Space Multidirectional Occlusion Shading Model
### Algorithm Overview  
As mentioned earlier, the ISMOS model is defined in the image-space, and there is no need to change the rendering pipeline to apply the model in the existing system since the model is decoupled from the rendering system. The ISMOS model utilizes only color and depth buffers to compute the amount of light transported to a pixel. Therefore, a scene in the rendering system is simply rendered to the color and depth buffers.  
When the color and depth buffers are obtained, a light source is placed on a hemisphere centered at the center of the buffers, as illustrated in Figure 1a. Each light source is designed as a circular shape with the normal vector facing towards the center of the buffers. By varying the size and position of the light source, different light types such as point, area, or ambient light can be simulated. The light source is discretized as a finite number of light samples. We computed the amount of occluded light along paths from the light samples to each pixel on the rendered buffer.  
In this work, we computed the approximate amount of occluded light for opaque and transparent materials. Full occlusion was associated with the opaque material and calculated by counting the number of light samples occluded on paths from the light samples to each pixel. In contrast, partial occlusion was associated with the transparent material and computed by estimating the intensity of the light arriving at a pixel from the light samples. The details of these computations are presented in Sections 3.3.1 and 3.3.2 for the full and partial occlusion, respectively. The depth buffer was utilized to determine whether the paths were occluded by materials in the volume, which is illustrated in Figure 1b,c. In the figure, the gray paths are fully occluded by object materials in the scene. The faded pink paths are partially occluded and pass through the transparent material, which is the green object in (b). Finally, the rendering is shaded by combining the color buffer with the amount of light transmitted to the pixels in the color buffer. Note that the amount of transmitted light was computed as 1.0--_occlusion_.
# A Double Rate Localization Algorithm with One Anchor for Multi-Hop Underwater Acoustic Networks
## 5 Experiment Results
### Ranging Experiment
#### 5.2.3 Topology 3  
The experimental result of topology 3 is shown in Figure 18 and the localization error is exhibited in Table 6.  
According to the analysis of the experimental results we can see that the localization errors of the designed system in each topology are 0.1938 m, 0.2227 m, 0.1610 m which are related to the ranging accuracy, the network topology and the transducer response.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline  & **Exp. 1** & **Exp. 2** & **Exp. 3** & **Exp. 4** & **Exp. 5** & **Mean Error** \\ \hline Error & 0.1999 & 0.2049 & 0.1454 & 0.1048 & 0.1500 & 0.1610 \\ \hline \end{tabular}
\end{table}
Table 6: The localization results of 5 tests in topology 3/m.  
Figure 17: The localization result of topology 2.  
Figure 18: The localization result of topology 3.
# Control of Flexible Robot by Harmonic Functions
## 2 Materials and Methods
### Invariant Control  
In this section, the control variable \(\mathbf{w}\) is assembled. There is a dynamic system to be controlled (derived from the previous section):  
\[\mathbf{\hat{p}} = \mathbf{w} \tag{13}\] \[\mathbf{\bar{q}}_{z} = \mathbf{M}_{zz}^{-1}(\mathbf{F}_{z}-\mathbf{M}_{za}\mathbf{w}). \tag{14}\]  
The input variable \(\mathbf{w}\) directly influences parameters of \(\mathbf{p}\). However, how can the zero dynamics coordinates \(\mathbf{q}_{z}\) be controlled? The proposed way is to split the input variable into arbitrary part \(\mathbf{w}_{A}\), which controls the parameters \(\mathbf{p}\), and invariant part \(\mathbf{w}_{I}\), which controls coordinates \(\mathbf{q}_{z}\), with specific influence on parameters \(\mathbf{p}\).  
\[\mathbf{w}=\mathbf{w}_{A}+\mathbf{w}_{I} \tag{15}\]  
The arbitrary part is assembled to reach the final configuration at time, \(T\). The invariant part must satisfy invariance conditions (16). Hence it modifies parameters \(\mathbf{p}\) and their time derivatives only inside the interval \((0,T)\). The invariancy conditions are the following:  
\[\begin{split}\int_{0}^{T}\mathbf{w}_{I}\,\mathrm{d}t& =\mathbf{0}\\ \int_{0}^{T}\!\!\int_{0}^{t}\mathbf{w}_{I}\,\mathrm{d}t& =\mathbf{0}.\end{split} \tag{16}\]  
If the input variable (15) is substituted into the (13), and the system of equations is integrated from \(t=0\) to \(t=T\), then the following is obtained:  
\[\begin{split}\mathbf{\dot{p}}(T)-\mathbf{\dot{p}}(0)& =\int_{0}^{T}\mathbf{w}_{A}\,\mathrm{d}t+\int_{0}^{T}\mathbf{w}_{I }\,\mathrm{d}t=\int_{0}^{T}\mathbf{w}_{A}\,\mathrm{d}t\\ \mathbf{p}(T)-\mathbf{p}(0)&=\int_{0}^{T}\!\!\int_{0 }^{t}\mathbf{w}_{A}\,\mathrm{d}t&\mathrm{d}t+\int_{0}^{T}\!\!\! \int_{0}^{t}\mathbf{w}_{I}\,\mathrm{d}t&\mathrm{d}t=\\ &=\int_{0}^{T}\!\!\!\int_{0}^{t}\mathbf{w}_{A}\,\mathrm{d}t& \mathrm{d}t\end{split} \tag{17}\]From (17), it is obvious that parameters \(\mathbf{p}\) and their time derivatives at the end (final) time depend only on the arbitrary part of the control (15). The arbitrary part \(\mathbf{w}_{A}\) could be arbitrarily chosen, just to fulfil the end position and velocities of the parameters \(\mathbf{p}\). After the selection of the arbitrary part of the control, it is a time-dependent part, and (14) changes into (18).  
\[\ddot{\mathbf{q}}_{z}=\mathbf{M}_{zz}^{-1}(\mathbf{F}_{z}-\mathbf{M}_{za}( \mathbf{w}_{A}(t)+\mathbf{w}_{I})) \tag{18}\]  
The invariant part of the control must fulfil the conditions of (16), and the control coordinates \(\mathbf{q}_{z}\) from (18) must reach the desired states at the end time, \(T\). The suitable functions are the following harmonic functions:  
\[\mathbf{w}_{I}=\sum_{i}\mathbf{A}_{i}\sin\left(2\pi f_{i}t+\phi_{i}\right) \tag{19}\]  
The parameters \(\mathbf{A}_{i}\), \(\phi_{i}\) of harmonic functions (19) are obtained from the optimization process, and the frequencies, \(f_{i}\), are based on the so-called natural motion.
# A Novel Real-Time Battery State Estimation Using Data-Driven Prognostics and Health Management
## 8 Conclusions and Future Work
### Proposed Work Continuation  
For the continuation of this work, it is envisaged to  
* Implement the PHM framework on other Lithium-ion battery chemistries, such as LFP (Lithium Iron Phosphate) and NMC (Manganese Cobalt Oxide), as well as different form-factors, such as VL6P, for instance.
* Expand the use of the PHM framework for real-time forecasting models, using multi-steps ahead implementation, to allow future predictions beyond \(k+1\), which are important aspects for critical safety applications.
* Advance the whole system design to evolve towards the full PHM capability and remaining useful life (RUL) determination for embedded systems.  
This research received no external funding.  
Not applicable.  
The dataset used in this work is the \"Panasonic 18650 pf li-ion battery data\", available at [[https://data.mendeley.com/datasets/wykht8y7](https://data.mendeley.com/datasets/wykht8y7) tg/1]([https://data.mendeley.com/datasets/wykht8y7](https://data.mendeley.com/datasets/wykht8y7) tg/1), accessed on 15 May 2024, under a CC BY 4.0 license.  
The authors acknowledge the University of Derby for the support of this research.  
The authors declare no conflicts of interest.
## 2 Hybrid Energy System Model
### Diesel Generator Model  
The diesel generator is in charge of satisfying the load that cannot be provided by the wind generator, the battery bank, or both. In addition, this task has to be done considering the technical constraints of the diesel unit. If only the effect of wind generator needs to be considered, NL is calculated according to (21):  
\[P_{N(t)}=P_{L(t)}-P_{W(t)}\ \forall\ t=1,\ldots,T; \tag{21}\]  
On the other hand, if the joint effect of the wind generator and BESS needs to be considered, NL can be defined using (22):  
\[P_{N(t)}=P_{L(t)}-P_{W(t)}+P_{B(t)}\ \forall\ t=1,\ldots,T. \tag{22}\]  
As aforementioned, the diesel generator has to supply NL as defined in (21) or (22), fulfilling the constraint (23):  
\[P_{D}^{min}\leq P_{D(t)}\leq P_{D}^{max}\ \forall\ t=1,\ldots,T. \tag{23}\]  
To determine the power dispatch of the diesel unit, the parameter \(P_{D}^{a}\) is defined according to (24):  
\[P_{D}^{a}=max\!\left(0,P_{N(t)}\right)\ \forall\ t=1,\ldots,T. \tag{24}\]Then, depending on the value of \(P_{D}^{a}\), diesel generation (\(P_{D(t)}\)), power surplus (\(P_{EXC(t)}\)), and power not supplied (\(P_{ENS(t)}\)) are determined by following (25-27), respectively,  
\[P_{D(t)}=\left\{\begin{array}{c}P_{D}^{min},\ P_{D}^{a}>0,\ P_{D}^{a}\leq P_{D }^{min}\\ P_{D}^{a};\ P_{D}^{a}>P_{D}^{min},\ P_{D}^{a}\leq P_{D}^{max}\\ P_{D}^{max};\ P_{D}^{a}>P_{D}^{max}\end{array}\forall\ t=1,\ldots,T;\right. \tag{25}\]  
\[P_{EXC(t)}=\left\{\begin{array}{c}P_{D}^{min}-P_{D}^{a};\ P_{D}^{a}>0,\ P_{D}^{a} \leq P_{D}^{min}\\ 0;\ P_{D}^{a}>P_{D}^{min},\ P_{D}^{a}\leq P_{D}^{max}\\ 0;\ P_{D}^{a}>P_{D}^{max}\end{array}\right.\forall\ t=1,\ldots,T; \tag{26}\]  
\[P_{ENS(t)}=\left\{\begin{array}{c}0;\ P_{D}^{a}>0,\ P_{D}^{a}\leq P_{D}^{min }\\ 0;\ P_{D}^{a}>P_{D}^{min},\ P_{D}^{a}\leq P_{D}^{max}\\ P_{D}^{a}-P_{D}^{max};\ P_{D}^{a}>P_{D}^{max}\end{array}\right.\forall\ t=1, \ldots,T. \tag{27}\]  
Once the mathematical model of HES has been defined, the optimization technique proposed in this paper will be clearly explained in the next section.
# From Feed to Fish--Nutrients' Fate in Aquaculture Systems
## 3 Results  
### Paper Selection and Nutrient Budget  
A total of 74 papers reported the cultivated fish species, provided the nutrient data from some of the pond's budget terms, or reported the whole nutrient budget of carbon, nitrogen, or phosphorus. Of the assessed papers, the majority (51) did not report some nutrient input and output pathways. In the 23 papers that reported a complete or nearly complete nutrient budget, feed (in twenty papers), fertilizer (in nineteen papers), inlet water (in fifteen papers), and stocked fish (in ten papers) were the inputs quantified in most of the papers, while precipitation and gas influx were reported in only four papers each. Regarding the outputs, the main reported components were outlet water (in eighteen papers), harvested fish (in twenty-two papers), and sedimentation (in fifteen papers), while fewer studies reported infiltration and gas efflux (four) (Figure 2 and Table S1 for more details).  
Our search yielded papers from 25 countries, of which India (ten), Brazil (nine), and China (nine) were the countries with more publications. The country where most studies reported complete or near complete nutrient budgets was Brazil (five) (Figure 3).  
Figure 2: Number of papers that reported specific nutrient input and output pathways in fishponds. C: papers evaluating only carbon; N: papers evaluating only nitrogen; P: papers evaluating only phosphorus; N+P: papers evaluating nitrogen and phosphorus; C+N+P: papers evaluating all three nutrients; CH\({}_{4}\) + CO\({}_{2}\): studies in which both gases were quantified; N\({}_{2}\) + NH\({}_{3}\): studies that quantified dinitrogen efflux and NH\({}_{3}\) volatilization. n.r.: not reported in the budget.
# Active Equalization of Lithium-Ion Battery Based on Reconfigurable Topology
## 3 Control Strategy  
### Control Strategy of the Inner Layer  
The conventional equalization strategy always isolates the battery cell with the lowest SOC from the group. The working part is formed by the remaining \(n-1\) cells connected in series to discharge. When the SOC of the isolated cell is equal to the cell with the highest SOC among the remaining cells, the new cell with the lowest SOC will be isolated, and the original isolated cell will be connected to the working part. The conventional equalization strategy is shown in the shaded area (dotted line) of Figure 6. This control strategy has disadvantages in that the system is always in the circulation of equalization, the cells in the working part are constantly switched, and there is always a cell isolated from the pack, which reduces both the balancing efficiency and the maximum output power of the battery group [32].  
Figure 6: Control strategy of conventional method (shaded area, dotted line)/control strategy of the proposed inner layer.  
In this study, an advanced control strategy is proposed. A monitoring section has been added to check whether the equalization of the inner layer is completed. It is considered that the equalization of the battery group is accomplished if the difference between the SOC of any cell in the group and the average SOC of the group is less than \(\varepsilon_{1}\). Then all \(n\) cells are connected in series to discharge.
## 3 Results and Discussion
### Optical Microscopy (MO)  
In Figure 4, the cross-sections of the layers modified by machining in DF1-F solution with the tool electrode in negative (Figure 4A) and positive (Figure 4B) polarities are represented by MO. As shown in Figure 4, the modified layer (ML) of samples machined with DF1-F under both polarities was approximately 30 \(\upmu\)m thick and exhibited pores and surface irregularities. Under positive polarity, the surface appeared flatter, whereas samples machined under negative polarity displayed more re-entrant features and pronounced irregularities.  
Figure 4: Cross-sectional optical micrographs of workpieces EDMed at (**A**) negative and (**B**) positive polarity using DF1-F, and (**C**,**D**) machined with DF2-Ca in the negative and positive polarities, respectively, where the dotted dark line indicates the presence of the HAZ layer.  
For samples machined with DF2-Ca under negative polarity, the corresponding microstructure is shown in Figure 4C. In this condition, the recast layer was not evident and appeared to be absent along most of the surface. In contrast, for samples machined with DF2-Ca under positive polarity, the recast layer is visible in Figure 4D, with a measured thickness of approximately 45 \(\upmu\)m--greater than that observed in samples treated with DF1-F. Another relevant feature is the significant presence of micro-cracks within the recast layer, which may compromise structural integrity and increase the risk of premature material failure [68].
# Potential Metal Contamination in Foods of Animal Origin--Food Safety Aspects
## 2 Major Toxic Metals in Food
### Arsenic (As)
The total arsenic concentrations detected in marine fish, shellfish, and other edible aquatic animals were variable, such as 0.98 \(\pm\) 0.47 mg/kg in tuna (_Thunus albacares_), 3.26 \(\pm\) 0.39 mg/kg in sardines (_Sardina pilchardus_), 1.28 \(\pm\) 0.52 mg/kg in squids (_Loligo vulgaris_), 2.88 \(\pm\) 1.12 mg/kg in oysters (_Crassostra gigas_, _C. angulata_), and 3.01 \(\pm\) 1.46 mg/kg in shellfish species (_Mytilus galloprovincialis_, _M. edule_, _Venerupis philippinarum_, _Glycymerisglycym)_, respectively [26; 27; 28]. In freshwater fish, such as rainbow trout (_Oncorhynchus mykiss_), the measured arsenical content was 1.65 \(\pm\) 0.49 mg/kg [29]. The bioavailability of organic derivatives in fish is significant in humans. Between 66 and 86% of arsenic in fish cakes and flounder is absorbed, and 80% of arsenic sugar is excreted in the urine within 4 days [19; 30]. However, in sheep fed seaweed, arsenic sugar is more highly absorbed and metabolized, and significant arsenic concentrations can be measured in wool, blood, and urine, although arsenic sugar is not detected in the urine [31]. It is well distributed in the animal body; higher concentrations are found in the skin and horny issues. Significant amounts can also be present in the liver and kidneys, which may raise food safety concern. Organic arsenic compounds in marine fish and shellfish are less objectionable because they have lower toxicity and are rapidly eliminated from the human body. However, arsenical concentrations detected in the muscle of roe deer (_Carpelous caproellus_) ranged from 0.03 to 0.51 mg/kg wet weight (w.w.) [32]. The highest total arsenic levels have been detected in fish and seafood, alga-containing products, and cereal and cereal products, especially in rice and rice-based products with high concentrations. Exposure of inorganic arsenic from food and water ranges from 0.13 to 0.56 \(\upmu\)g/kg body weight (b.w. )/day in average consumers of EU countries; however, it is higher (0.37-1.22 \(\upmu\)g/kg b.w./day) in high consumers. Young children (<3 years) are most exposed to inorganic derivatives (0.50-2.66 \(\upmu\)g/kg b.w./day) [33].
## 3 Results and Discussion  
### Sample Characterization  
#### 3.1.1 XRD Analysis  
XRD patterns for the four analyzed biomaterials (Figure 1) exhibit characteristics typical of cellulosic materials. A common feature of all the patterns is the presence of an amorphous phase, observable within the 15\({}^{\circ}\)-25\({}^{\circ}\) 20 range, which corresponds to lignin, hemicellulose, and cellulose components [21; 22; 23]. Additionally, peaks attributed to the crystalline structure of cellulose 1a are detected [24; 25; 26].