esa-sceva/satcom-chunk-collection huggingface dataset:
# Metal Recovery from Wastes: A Review of Recent Advances in the Use of Bioelectrochemical Systems
## 1 Introduction
In addition to the terms \"Metal\", \"Recovery\", and \"Bioelectrochemical\", which were included as search criteria, other frequently occurring keywords in this research field include \"recovery\", \"metal recovery\", \"recycling\", and \"electrodeposition\", reflecting the central focus and themes within the field. \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline
**Technology** & **Process Description** & **Advantages** & **Disadvantages** \\ \hline Hydrometallurgy [22; 34] & Metal extraction via aqueous solutions primarily involving leaching, solvent extraction, and precipitation. & High purity, effective impurity control, lower energy costs & - Extensive use of chemicals - Secondary pollution and wastewater generation \\ \hline Ion Exchange [35] & Metal ions are exchanged with ions on a solid resin, allowing for selective recovery. & - High selectivity - Reusable resins & - High operational cost - Requires regeneration of resins - Less effective for high metal concentrations \\ \hline Electrochemical Recovery/electrowinning [36; 37] & Metals are plated onto cathodes via redox reactions in electrochemical cells. & - High purity of recovered metals - Can be coupled with renewable energy & - Reduced efficiency for dilute solutions due to mass transport limitations. \\ \hline Membrane Filtration [35; 38] & Separates metals via physical barriers (e.g., reverse osmosis, nanofiltration, ultrafiltration). & - High removal efficiency - Suitable for a wide range of metals & - High CAPEX and OPEX - Prone to membrane fouling - Requires high pressure and energy input \\ \hline Bioleaching [22; 39] & Uses microorganisms to extract metals through biological oxidation/reduction processes. & - Environmentally friendly - Cost-effective for low-grade - Minimal energy input & - Slow process - Requires specific conditions (e.g., pH, temperature) - Limited to certain metals \\ \hline Bioelectrochemical Systems (BES) [28; 40] & Integrates bioprocesses with electrochemical techniques for metal solubilization and recovery. & - Energy-efficient or energy-generating - Simultaneous wastewater treatment and metal recovery & - Slow kinetics due to microbial activity - Limited scalability - Low technological readiness level \\ \hline \end{tabular}
\end{table} 
Table 1: _Cont._In this context, the main aim of this paper is to review the most recent developments in bioelectrochemical technology with applications for metal recovery from waste, with a focus on the last 5 years. According to the most common topics of recent research work, the articles have been classified into two main groups: (1) the use of pure bioelectrochemical systems for metal recovery and (2) the coupling of bioelectrochemical systems with other technologies for metal recovery.
# 3D Reconstruction of Celadon from a 2D Image: Application to Path Tracing and VR
## 5 Conclusions  
This paper presents a general guideline for generating a 3D celadon model from a single 2D image and further illustrates how to apply the model to various scenes rendered with a path tracer, along with a VR celadon museum. Our approach involves feature extraction from the 2D image and the detection of the profile curve. This curve is approximated by a B-spline curve, which offers a higher curve representation and flexibility compared to other approximation algorithms. This leads to the ability to generate 3D celadon models at any desired resolution. The texture coordinates of the 3D model are also automatically calculated, eliminating the need for any further inputs.  
The resulting 3D models can be easily used as VR content, for example, in cultural heritage applications. People can examine the celadon models in the VR celadon museum, facilitating a deeper understanding of the celadon's intricate details and cultural significance. However, the original celadon images were captured via perspective projection with a possibility that celadons can contain specular lightings in the images, causing distortions in the profiles and textures, reducing the reconstruction accuracy. In future work, we plan to calibrate the original images captured via perspective projection and develop an automated method for generating a VR celadon museum.  
The following supporting information can be downloaded at: [[https://doi.org/10.5281/zenodo.7932434](https://doi.org/10.5281/zenodo.7932434)]([https://doi.org/10.5281/zenodo.7932434](https://doi.org/10.5281/zenodo.7932434)) (accessed on 13 May 2023).  
All authors contributed to this work by collaboration. [PERSON] and Y.P. implemented the proposed method, performed the experiments, and wrote the paper. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Dong-A University research fund.  
Not applicable.  
The data presented in this study are openly available in Goryeo Celadon Museum, reference number [34].  
The authors thank [PERSON] for the technical support of Unreal Engine 5.  
Figure 7: The VR celadon museum in Unreal Engine 5: (**a**) rendering the museum with wireframe; (**b**) rendering the museum in unlit mode; (**c**) a user wanders around the museum and appreciates 3D celadon models generated by our method.
## 3 Results and Discussion
### Analysis of Model Prediction Results
A method for further data analysis and hypothesis generation was developed by mining latent knowledge. These three proteins--P16081 from the MF dataset, Q8W0A1 from the BP dataset, and Q84 YK8 from the CC dataset--represent instances where extra functions were predicted by the model. The additional predicted functions hold significant value and implications for advancing our understanding of protein functionalities. However, the model failed to successfully predict the actual labels for some samples. For example, the Q10 RB4 (BGAL5_ORYSJ) protein in the BP ontology dataset was manually annotated with functions GO:0005975, GO:0044238, and GO:0008152. However, the model did not predict the function of GO:0005975, possibly because of the similarities between GO:0005975 and GO:0044238. Within the BP gene ontology hierarchy, the carbohydrate metabolism process (GO:0005975) was classified as the primary metabolic process (GO:0044238), and potential biases may exist in the model when predicting specific functional categories. Similarly, the Q7 XP59 (GLR31_ORYSJ) protein in the CC ontology dataset had a GO:0005886 function in the Swiss-Prot database, and GO:0016020 and GO:0110165 functions were manually annotated. However, the GO:0005886 function was not observed during the prediction process. Owing to the inherent hierarchical relationship between the two functions, more general or abstract functional categories are likely to be recognized by the model during predictions because they appear more frequently in the training data. We also predicted and observed the protein functions in the test sets of indica, maize, and wheat. For the indica proteins A2 YNH4 (6 PGL2_ORYSI) and B8 ATT7 (VLN4_ORYSI), the maize proteins B6 SU46 (AAMT2_MAIZE) and Q9 LX9 (RBR1_MAIZE), and the wheat proteins Q5I7K3 (RS29_WHEAT), O04706 (GAO1B_WHEAT), and Q41560 (HS16B_WHEAT), the model successfully and completely predicted their functions. For the P0C461 (RR12_ORYSI) and B8 BKI8 (MCM2_ORYSI) proteins of indica, the P06677 (ZEA9_MAIZE) and P33488 (ABP4_MAIZE) proteins of maize, and the Q8L803 (RK9_WHEAT) protein of wheat, the model predicted functions that had not been previously annotated. For the indica proteins A2Y9M4 (SSY1_ORYSI) and Q01 DX6 (DAO_ORYSI), the maize proteins P29390 (FRI2_MAIZE) and Q41764 (ADF3_MAIZE), and the wheat proteins B6 DZC8 (IFEH3_WHEAT) and Q01481 (WIR1B_WHEAT), the model did not fully predict their annotated functions. This prediction bias reflects the challenges faced by models when dealing with bioinformatic data with hierarchical structures and semantic associations. In summary, it was demonstrated that MMSNet generally performed well in predicting grain protein functions.
# Modeling of Vessel Traffic Flow for Waterway Design-Port of Swinoujscie Case Study
## 3 Results
### Influence of the Ship's Draught and Width  
It is a natural behaviour of mariners to navigate more cautiously on ships with less manoeuvrability. On the scatterplots in Figure (a)a,b, it can be seen that, as \(T\) increased, the distance of ships from the centre of the track decreased (for \(T>7\) m rapidly). For vessels with more than 8 m draught, the steering is more precise as the vessel follows the given track from the beginning of the fairway, as shown in Figure (b)b. Ships with a shallower draught can join the main stream in this section (N8). The case studied location was a straight fairway, where vessels do not normally need to alter their course during passage. However, it should be noted that the approach channel was relatively narrow, so there was less opportunity to change course, and doing so was likely only during a perceived emergency.  
Figure 11: (a) Mean versus distance to 10 m isobaths \(D_{10m}\), \(R^{2}=0.63\), \(p=0.0183\), \(F=10.3245\); Standard Deviation = 292.23 + 0.0416 * distance to isobaths. (b) Standard deviation to distance, \(R^{2}=0.62\), \(p=0.0197\), \(F=9.927\); Mean = 212.3691 + 0.0210 * distance to isobaths.  
Figure 10: The standard deviation of the ship’s distance from the centre to the length of the leg.  
The wider the ship, the closer to the centre of the track she navigates, as shown in Figure 12(a). The parameter \(B\) of the ships particulars had a similar effect to \(T\). The wider the ship, the more accurately she sails. The deviation from COG was much less, as shown in Figure 12(b). The coefficient of determination was low, but the \(p\)-value was 0.000, indicating that parameter \(B\) was statistically significant.
## Potential for Developing Biocarbon Briquettes for Foundry Industry
###### Abstract  
The foundry industry is currently facing challenges to reduce the environmental impacts from application of fossil fuels. Replacing foundry coke with alternative renewable carbon sources can lead to significant decrease in fossil fuel consumption and fossil CO\({}_{2}\) emission. The low bulk density, low energy density, low mechanical strength and the high reactivity of biocarbon materials are the main factors limiting their efficient implementation in a cupola furnace. The current study aimed at designing, optimizing and developing briquettes containing biocarbon, namely, biocarbon briquettes for an efficient use in cupola furnace. Laboratory hydraulic press with compaction pressure of about 160 MPa and stainless-steel moulds (\(\O\) = 40 mm and 70 mm) were used for compaction. The density, heating value, energy density, mechanical strength and reactivity of biocarbon briquettes were measured and evaluated. The compressive strength and splitting tensile strength of biocarbon briquettes were measured by a compression device. The reactivity of biocarbon briquettes was measured under controlled conditions of temperature and gas atmosphere using the thermogravimetric analysis technique (TGA). Different types of binders were tested for the compaction of commercial charcoal fines with/without contribution of coke breeze. The effect of charcoal ratio, particle size, binder type, binder ratio, moisture content and compaction pressure on the quality of the biocarbon briquettes was investigated. Molasses with hydrated lime and cement were superior in enhancing the biocarbon briquettes strength and energy density among other tested binders and additives. The briquettes' strength decreased as the biocarbon content increased. The optimum recipes consisted of 62% charcoal fines, 20% molasses, 10% hydrated lime and 8% cement. Cement is necessary to develop the tensile strength and hot mechanical strength of the briquettes. The charcoal with high ash content showed higher strength of briquettes but lower heating value compared to that with low ash content. Dispersion of silica suspension on charcoal particles during the mixing process was able to reduce the reactivity of biochar in the developed biocarbon briquettes. The biocarbon briquettes density and strength were increased by increasing the compaction pressure. Commercial powder hydrated lime was more effective in enhancing the briquettes' strength compared to slaked burnt lime. Upscaling of biocarbon briquettes (\(\O\) = 70 mm) and testing of hot mechanical strength under load indicated development of cracks which significantly reduced the strength of briquettes. Further development of biocarbon briquettes is needed to fulfil the requirements of a cupola furnace.  
biocarbon; biomass; foundry industry; cupola furnace; CO\({}_{2}\) emission; briquetting
# Estimation of Optimal Speed Limits for Urban Roads Using Traffic Information Big Data
## 2 Theory and Prior Studies
### Speed and Traffic Safety  
The accident rate increases with the speed dispersion, and the speed dispersion tends to become minimized when the difference between the design speed and the speed limit is between 5 and 10 mph on all road types [13; 14].  
In the relationship between speed and accident rate, the accident rate increases as the speed becomes lower or higher, and the number of accidents causing injuries and property damage increases exponentially with the speed [15; 16; 17].  
The relationship between speed and the number of accidents is either a simple linear relationship or a weak curve function. If the speed is higher or lower than the mean traffic speed, the accident rate increases or decreases proportionally, and the vehicles driving at the mean traffic speed do not have any advantage [18].  
High speed means more accidents, and the higher the speed, the faster the frequency of accidents increases with the speed [19; 20].  
When the mean speed increases by 5%, all injury accidents increase by 10% and fatal accidents increase by 20%; when the mean speed decreases by 5%, the number of accidents causing injuries decreases by 10% and the number of fatal accidents decreases by 20% [21].  
The number of accidents causing property damage changes similarly even with small changes in the travel speed, but fatalities increase at four times the rate of speed change [22; 23; 24; 25].
# Detecting Change at Archaeological Sites in North Africa Using Open-Source Satellite Imagery
## 3 Results
### Jufra  
Eighty-two sites in the Jufra oases were analysed (Figure 16, Table 4). Our earlier research explored how many sites in Jufra had already been damaged over the course of the twentieth century [20]. At most sites, no change had occurred in the analysis period, and this was correctly identified by the change detection algorithm. A few sites where there had been changes were missed, but no false positives were detected (user's accuracy of 100 %). The graph (Figure 17) shows the distribution of the mean pre-thresholded values for each buffer; some of the true positives are at the lower end because  
Figure 15: New building under construction in Nag Al-Tawil after the period of change detection analysis. An area of vehicle tracks and a new roof on a building were detected. Google image 2020 imported as an xyz tile to QGIS. Photograph 26 January 2020.  
the 'any non zero' parameter allows even small instances of change represented by a single pixel to be picked up. The unaffected sites tend to have mean values below 0.1, and the false negatives were missed because their values were also low. A sample of sites was visited in the field by [PERSON] and the results checked.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{3}{*}{Algorithm} & \multirow{3}{*}{Change} & \multicolumn{3}{c}{Imagery Checked} \\ \cline{3-5}  & & Change & No Change & Total \\ \cline{3-5}  & & 15 & 0 & 15 \\ \cline{2-5}  & No change & 7 & 60 & 67 \\ \cline{2-5}  & Total & 22 & 60 & 82 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Error matrix of the Jufra results, with an overall accuracy of 91% (user’s accuracy 100%, producer’s accuracy 68%).  
Figure 16: Sites analysed in the Jufra area. Sentinel-2 composite 2019–2020.  
Figure 17: Mean change values (prior to thresholding) for each buffer for the Jufra results.
# AEF: Adaptive En-Route Filtering to Extend Network Lifetime in Wireless Sensor Networks
## 3 Proposed Work
### Energy Consumption Model  
For energy consumption analysis, the first-order radio model is used, having a free (\(d^{2}\) power loss) channel model. We only cater for energy dissipation related to radio components and circuits of sensor motes as illustrated in Figure 3. For the transmission of a \(k\) bits message carried to a \(d\) distance  
Figure 2: Route setup: (**a**) a conventional shortest path routing and (**b**) energy-aware routing.  
between the transmitter and receiver radios, the transmission energy necessary \(E_{T_{x}}\left(k,d\right),\) is given in Equation (3).  
\[E_{T_{x}}\left(k,d\right)= E_{elec}\times k+E_{amp}\times k\times d^{\lambda} \tag{3}\]  
In Equation (4), \(E_{elec}\) is the energy consumed by the circuit electronics and \(E_{elec}\times n\) is the energy required by the transmitter electronics to transfer \(k\) bits. Additionally, the energy required by the amplifier is \(E_{amp}\) and the path loss constant is represented by \(\lambda\). Similarly, \(E_{R_{x}}\left(n\right)\) indicates the required energy in \(k\) bits as follows:  
\[E_{R_{x}}\left(k\right)= E_{elec}\times k \tag{4}\]  
\(E_{amp}=100 pJ/bit/n^{2}\) is the transmitter amplifier energy for transmission. Moreover, \(50\)\(nJ/bit\) is the energy consumption by transmitter and receiver circuits. Assumptions related to radios such as energy of transmission and receptions for single bit will change the advantages of different protocols. Therefore, the above-selected acceptable energy-to-noise ratio could avoid otherwise biased values. The optimal values of \(E_{elec}\) and \(E_{amp}\) are chosen from Reference [12] to attain an acceptable \(E_{b}/N_{0}\). There is no hardware changes required to deploy this model on sensor nodes. An energy and network lifetime analysis of the first-order radio model is presented [11].
## 5 DASH7 Communication System Implementation
### The Transmitting Process  
This process constitutes three steps: a data formatting process, a symbols to waveform conversion, and a modulation step. These operations will be discussed in the following sections.
## 4 Solving the Warehouse Delivery Problem
### Fact Format  
We start with our fact format representing instances of warehouse delivery problems. For simplicity, in what follows, we identify semantic objects, like \(v\in V\) or \(f_{E}((v,v^{\prime}))\), with their syntactic representation. We represent a warehouse \((V,E,f_{E},C,R,f_{H},f_{S})\) by the following facts  
* For each edge \((v,v^{\prime})\in E\), we add edge\((v,v^{\prime},f_{E}((v,v^{\prime})))\).  
For instance, fact edge(w1,w2,20) indicates that there is an edge from w1 to w2 requiring the robot to travel at least 20 s.
* For each robot \(r\in R\), we add robot(\(r\)). home(\(r\),\(f_{H}(r)\)). start(\(r\),\(f_{S}(r)\)). For instance, the facts robot(r1), start(r1,h1), and home(r1,h1) represent that there exists a robot named r1, which is currently located at vertex h1, which is also its docking station.
* For each \((v,v^{\prime})\in C\), we add conflict(\(v\),\(v^{\prime}\)). For instance, the fact conflict(s1,s2) expresses that vertices s1 and s2 cannot be accessed simultaneously. We represent the task execution graph (\(T\),\(D\),\(f_{D}\),\(f_{V}\)) as follows.
* For each \(t\in T\) and \((t^{\prime},t^{\prime\prime})\in D\), we add task(\(t\),\(f_{V}(t)\)). depends(\(f_{D}(t^{\prime},t^{\prime\prime})\),\(t^{\prime}\),\(t^{\prime\prime}\)). For instance, the facts task(t1,11) and depends(deliver,t1,t2) represent that there exists a task t1 that has to be executed at vertex l1, it has to finish before t2 can be executed, and they have to be completed by the same robot.  
For illustration, we give in Listing 1 the facts capturing our example warehouse and task execution graph in Figures 1 and 2. Note that Line 7 makes sure that the edge relation is symmetric and thus that the actual graph is undirected. Similarly, the rules in Lines 14-16 take care of the reflexivity and symmetry of the conflict relation.  
```
1 edge(l1,w1,15).edge(p1,w2,15).edge(h1,w3,15).edge(h2,w4,15).
2 edge(w1,w2,20).edge(w2,w3,30).edge(w3,w4,20).
3 edge(w1,w5,18).edge(w2,w6,15).edge(w3,w7,18).edge(w4,w8,15).
4 edge(w5,w6,10).edge(w6,w7,20).edge(w7,w8,30).
5 edge(w5,s1,15).edge(w6,s2,15).edge(12,w8,15).
7 edge(V,V',T):-edge(V',V,T).
9 robot(r1).home(r1,h1).start(r1,h1).
10 robot(r2).home(r2,h2).start(r2,h2).
12 conflict(s1,s2).conflict(w5,w6).
14 conflict(V,V):-edge(V,_,_).
15 conflict(V',V'):-edge(_,V',_).
16 conflict(V,V'):-conflict(V',V).
18 task(t1,l1).task(t2,s1).task(t5,l2).task(t6,s2).
19 task(t3,p1).task(t4,l1).task(t7,p1).task(t8,l2).
20
21 depends(deliver,t1,t2).depends(deliver,t5,t6).
22 depends(deliver,t3,t4).depends(deliver,t7,t8).
23 depends(wait,t1,t4).depends(wait,t5,t8).
```  
**Listing 1**Facts representing our example problem instance capturing the warehouse and task execution graph in Figures 1 and 2.
# Radiation Qualification by Means of the System-Level Testing: Opportunities and Limitations
## 4 Limiting Factors of Qualification
### Irradiation Source Issues
#### 4.3.2 Irradiation Source Issues in SEE Tests  
Beam parameters such as beam penetration range and beam size may be problematic particularly in SEE tests. Due to limited penetration of, e.g., heavy ions, component packages often need to be opened before irradiation (it was the case for the PoL tests at GANIL and UCL facilities). Sometimes chip dies need to be even thinned to assure that the ions reach the sensitive volume (s). Additional problems may arise when it comes to soldering the fragile component with the opened package on the PCB board. For system-level tests, it happens that it is not possible to irradiate all the components with the same LET value in one run, because components are on different layers of PCB. Furthermore, for systems that are not intended to be opened for testing (such as whole satellites or \"black box\" systems), penetration of the beam becomes a critical limitation for irradiating with high LET ions. (Considerations on the penetration of heavy ions in board-level tests, as well as related test methodology have been discussed in [14]). Another limitation comes from the particle beam size: it will be a problem with beam uniformity when irradiating larger components or systems, but for system-level testing, it may turn out to be impossible to irradiate the whole system at once and different parts of the big system shall be irradiated one after another. (See Tables 3 and 4 for the information on beam parameters of the chosen heavy ion and proton facilities.) However, the CHARM facility operating in mixed-field mode allows for the beam uniformity typically bigger than in standard facilities used for SEE testing.