esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
In the course of this research, the potential of activated carbon from rice husk was examined as being a phenol removal medium from an aqueous solution in a fixed-bed adsorption column. The activated carbon was characterized through FESEM (Field-Emission Scanning Electron Microscopy) and BET (Brunauer-Emmett-Teller) surface area. According to the FESEM micrograph and BET surface area, RHAC (rice husk activated carbon) had a porous structure with a large surface area of 587 m\({}^{2}\).g\({}^{-1}\) and mean diameter of pores of 2.06 nm. The concentration effects on the influent phenol (100-2000 mg\(\cdot\)L\({}^{-1}\)), rate of flow (5-10 mL\(\cdot\)min\({}^{-1}\)), and bed depth (8.5-15.3 cm) were examined. It was found that the capacity of bed adsorption increased according to the increase in the influent concentration and bed depth. However, the capacity of bed adsorption decreased according to the increase in the feed flow rate. The regeneration of activated carbon column using 0.1 M sodium hydroxide was found to be effective with a 75% regeneration efficiency after three regeneration cycles. Data on adsorption were observed to be in line with many well-established models (i.e., Yoon-Nelson and Adams-Bohart, as well as bed depth service time models).  
activated carbon; breakthrough curve; column study; phenol removal; rice husk
## 4 Results and Discussions
### Scenario II: Identification of Gap Size
#### 4.3.2 Separability Analysis Using Relative Recall
This demonstrates that the Noise cluster is well separable, and therefore, the weld-relevant information is sufficiently available in segments longer than 10 ms. It is worth noticing that the Gap 0.3 class achieves almost 100% recall even with 1 ms segments, equivalent to 0.2 mm of the weld seam. This is another indicator that the acoustic emissions induced by a large-sized gap are not merely the absence of emission but also exhibit unique signal characteristics. Lastly, a few remarks on the effect of noise should be added. Both results in Figure 8 reveal that the separability of different classes becomes more challenging if segments are shorter than 5 ms, which is equivalent to 1 mm weld seam. Because such short segments tend to be misclassified as the Noise class, these results imply that the effect of noise becomes more crucial when segments become shorter. Figure 8: Average relative recall with regard to the Zero Gap class (top row (**a**)) and the Noise class (bottom row (**b**)) over varying duration \(T\) after 50 realizations. The results show how easy (high recall) or difficult (low recall) it is to distinguish between the reference class and another class. This serves as an indicator of how separable the cluster of the reference class is from that of another class. The results show how the relative recall changes depending on the segment duration for each channel: SB1 is a structure-borne sensor at the start of a specimen, and AB is an airborne sensor.
## 4 Experiments and Discussion
### Traffic Flow Decomposition and Reconfiguration
and period components obtained from the joint STL decomposition of the reconstructed components are shown in Figure 8.  
After multiple experiments to determine the time step of the model input, see Table 4, the reconstructed components are combined with the feature set \(T\) and input into the model for prediction. The selection of feature sets \(T\) is related to the input dimension of each component; RBIMF1 has the feature set \(\left[\mathrm{H}_{(t-1)\sim(t-8)},\mathrm{J}_{(t-1)\sim(t-8)},\cdots,\mathrm{ d}_{2,(t-1)\sim(t-8)},\mathrm{d}_{3,(t-1)\sim(t-8)}\right]\) with 136 features, RBIMF1 has 153 features, and RBIMF1 has 136 features. To improve the training efficiency of the model, principal component analysis (PCA) is used to extract the main components in the feature set, and as shown in Table 5, the first 10 components are selected for \(T_{1}\) and \(T_{3}\), and the first 11 components are selected for \(T_{2}\), which can ensure the prediction effect of the model and also save computational resources significantly. \begin{table}
\begin{tabular}{c c} \hline
**Portion Size** & **Input Compulations** \\ \hline \(\dot{Y}_{t}\) & \((x_{t-1},x_{t-2},x_{t-3},x_{t-4},x_{t-5},x_{t-6},x_{t-7})\) \\ \(\dot{Y}_{c}\) & \((x_{t-1},x_{t-2},x_{t-3},x_{t-4},x_{t-5},x_{t-6},x_{t-7},x_{t-8})\) \\ RBIMF1 & \((x_{t-1},x_{t-2},x_{t-3},x_{t-4},x_{t-5},x_{t-6},x_{t-7},x_{t-8},\mathrm{f}_{1})\) \\ RBIMF2 & \((x_{t-1},x_{t-2},x_{t-3},x_{t-4},x_{t-5},x_{t-6},x_{t-7},x_{t-8},\mathrm{f}_{2},\mathrm{g}_{1},\mathrm{g}_{2})\) \\ RBIMF3 & \((x_{t-1},x_{t-2},x_{t-3},x_{t-4},x_{t-5},x_{t-6},x_{t-7},x_{t-8},\mathrm{f}_{3} )\) \\ \hline \end{tabular}
\end{table} 
Table 4: Inputs and feature combinations for each component. Figure 8: Reconstructed component results. \begin{table}
\begin{tabular}{c c c} \hline
**Feature Set** & **Ingredient Selection** & **Cumulative Contribution Rate** \\ \hline \(T_{1}\) & 1\(\sim\)10 & 93.05\% \\ \(T_{2}\) & 1\(\sim\)11 & 93.12\% \\ \(T_{3}\) & 1\(\sim\)10 & 93.05\% \\ \hline \end{tabular}
\end{table} 
Table 5: PCA dimensionality reduction analysis.
# Research Progress in Fluid Energy Collection Based on Friction Nanogenerators
## 2 Recent Developments in Triboelectric Nanogenerator-Based Fluid Energy Harvesting Systems
### Wind Energy Collection  
Wind energy has the benefit of sustainable supply, as is well known as being a clean energy source. The electromagnetic induction generators and turbines seen in modern wind power plants are advantageous due to their big power production and reliable operation [21]. The installation site is very demanding, and the electromagnetic induction generator performs poorly at low speeds, thus a more complicated ramp-up mechanism is necessary, which is an inherent disadvantage [22; 23]. The high-speed rotation of the propeller or the movement of the large-size propeller causes noise problems that can only be solved by optimization methods [24; 25]. The simple structure and low cost of nanogenerators can complement traditional wind power generation. The composition of nanogenerators also has the advantage of being green and environmentally friendly, and organic or inorganic materials that are easily absorbed and degraded by the environment and minimize dependence on precious metals can be selected, and environmentally friendly friction layer materials can be continuously developed [26; 27; 28; 29]. However, the collection of wind energy is difficult, especially at lower wind speeds, which has not been widely covered in current research, and with the flow of air currents, a large amount of energy is not collected and wasted, as a result, the utilization rate of wind energy is still not high [30]. In this section, we will discuss the topologies of wind nanogenerators and divide them into rotating structures, flutter structures, and other structures that are not easy to classify.
## 2 Background  
Training in neural networks is an iterative process in which the parameters are updated in order to minimize an objective function. Given a subset of the training dataset \(X\subseteq T_{r}\) with input-output pair samples \((x_{p},o_{p})_{p\in X}\) drawn at random from the training set \(T_{r}\) and error function \(E_{p}(\mathbf{w};x_{p},o_{p})\) parameterized by a vector \(\mathbf{w}\in\mathbb{R}^{d}\), the objective function to be minimized is defined as  
\[E(\mathbf{w})=\frac{1}{b}\sum_{p\in X}E_{p}(\mathbf{w}), \tag{1}\]  
where \(b=|X|\), is the batch size. In full batch, \(X=T_{r}\) and \(b=n\) where \(n=|T_{r}|\). In gradient based methods, the objective function \(E(\mathbf{w})\) under consideration is minimized by the iterative formula (2) where \(k\in\mathbb{N}\) is the iteration count and \(\mathbf{v}_{k+1}\) is the update vector, which is defined for each gradient algorithm.  
\[\mathbf{w}_{k+1}=\mathbf{w}_{k}+\mathbf{v}_{k+1},\quad\text{for}\quad k=1,2,...,k_{max}\in\mathbb{N}. \tag{2}\]  
**Notations**: We briefly define the notations used in this paper. In general, all vectors are denoted by boldface lowercase characters, matrices by boldface uppercase characters and scalars by simple lowercase characters. The scalars, vectors and matrices at each iteration bear the corresponding iteration index \(k\) as a subscript. Below is a list of notations used.  
* iteration index \(k\in\mathbb{N}:k=1,2,...,k_{max}\in\mathbb{N}\)
* \(n\) is the number of total samples in \(T_{r}\) and is given by \(|T_{r}|\).
* \(b\) is the number of samples in the minbatch \(X\subset T_{r}\) and is given by \(|X|\).
* \(d\) is the number of parameters of the neural network.
* \(m_{L}\) is the limited memory size.
* \(\alpha_{k}\) is the learning rate or step size.
* \(\mu_{k}\) is the momentum coefficient, chosen in the range (0,1).
* \(E(\mathbf{w})\) is the error evaluated at \(\mathbf{w}\).  
* \(\
abla E(\mathbf{w})\) is the gradient of the error function evaluated at \(\mathbf{w}\).  
In the following sections, we briefly discuss the common first and second order gradient based methods.
# Co-Production of Hydrogen and Methanol Using Fuel Mix Systems: Technical and Economic Assessment
## 3 Results and Discussion
### Economic Analysis  
To evaluate the overall sustainability of the process, the capital (CAPEX) and Operational cost (OPEX) have been estimated for both cases. All of the equipment has been sized in Aspen Plus to find the flow rate and size of the equipment. The CAPEX of all equipment is calculated using the capacity, cost of equipment and cost index information, as represented in Equation (4), where the value of \(x\) is considered 0.6.  
\[\text{Cost}_{\text{New}}=\text{Cost}_{\text{Old}}\times\left(\frac{\text{ Capacity}_{\text{New}}}{\text{Capacity}_{\text{Old}}}\right)^{\text{x}} \times\frac{\text{CEPCI}_{\text{New}}}{\text{CEPCI}_{\text{Old}}} \tag{4}\]  
The results show that the CAPEX for case 1 and case 2 is estimated as 2337 Mc and 2578 Mc, respectively. Due to the installation of an additional reforming unit, case 2 incurs a higher CAPEX. Moreover, the flow rates of syngas are also higher in case 2, which increases the sizes of all equipment, which also affects the overall CAPEX. Similarly, the OPEX of both cases is calculated on the basis of several assumptions including the feedstock price, catalyst cost and other utility costs. OPEX/yr represents the annual cost of running the plant and is estimated as 270 Mc and 308 Mc for case 1 and case 2, respectively. As natural gas has been used in case 2 as a third fuel, the OPEX/yr for case 2 tends to be higher, as shown in Figure 7. For a reliable analysis, the combination of CAPEX/MT and OEPX/MT during the lifetime of the plant is estimated in terms of fuel production (\(\xi\)/MT) for both cases. The comparative analysis showed that the CAPEX/MT and OPEX/MT required for fuel production are 403.4 Mc and 373.9 Mc for case 1 and case 2, respectively.  
Figure 6: Comparison of carbon conversion and emissions.  
While evaluating the process economics, it has been seen that the production cost and selling price of fuels are highly influenced by the CAPEX and OPEX. Table 3 highlights the detailed results of the economic analysis for both cases. Keeping the discount rate at 10%, the selling price of fuel is calculated as 443.74 and 411.30 \(\epsilon\)/MT of fuel. The cost breakdown reveals that the methanol selling price is significantly lower for the case 2 design. On the other hand, the H2 selling price is lower for the case 1 design. While comparing the results obtained from economic analysis, case 2 reflected a better process feasibility and offers lower fuel production prices.
# Design and Testing of a Measurement Device for High-Speed Bearing Evaluation
## 3 Results
### Measuring Dynamic Response  
The next step within the experiment was to conduct a comprehensive measurement of the dynamic response to the bearings included in Group A (Figure 9) and Group B (Figure 10). This step was carried out with a wider range of speeds, specifically speeds ranging from 40,000 rpm to 110,000 rpm. The objective was to obtain more comprehensive and detailed data regarding the dynamic operational characteristics of these bearings over a wider range of speeds.  
Figure 10: Graphical representation of the dynamic response of bearings in Group B.  
Figure 9: Graphical representation of the dynamic response of bearings in Group A.  
From the obtained measurements, we averaged values of dynamic responses for each group of bearings. These average values were recorded during the ramp-up process, which reached a maximum speed of 110,000 rpm, and were represented in graphical dependencies. The dynamic responses were marked on these graphical dependencies with a thick blue line, allowing for a clear observation of the development of these characteristics as the speed increased.  
As can be seen from the collected data, the used bearings exhibit almost identical characteristics. This ensures the possibility of comparing the impact of re-lubrication on these bearings. The obtained characteristics resulting from this measurement have significant applications for future analysis and the comparison of the effects of different lubricants on the tested bearings. These data serve as reference information for analyzing the impact of re-lubrication on bearings with various lubricants and providing detailed information about the dynamic properties of the bearings depending on their rotational speeds.
# Opportunistically Exploiting Internet of Things for Wireless Sensor Network Routing in Smart Cities
## 4 The Integration Platform
### Traversing of Data Packets  
In a nutshell, data packets are sent towards the sink using two layered approach. Figure 6 depicts basic sequence of communication between Sensor nodes and an IoT devices. After discovery and negotiation with IoT, Sensor sends the packet to the IoT device. IoT acknowledges the receipt of packet to the sensor so that sensor does not have to forward the packet using default routing protocol. When the IoT device changes the direction, it sends the information (data packet) back to the WSN layer, where the receiving sensor repeats the process.  
Figure 5: Sensor communication frames. (**a**) Frame structure for sensors in the two activity modes. (**b**) Frame and slot length information.  
```
1:SN[] = //array of SN objects
2:SN.rssi = //received signal strength indicator value
3:SN.distance = //Maximum distance between two points minus IoT's distance from Sink
4:SN.energyLevel = //Remaining Energy of Sensor
5:proceduregetBestSN(SN[])
6:for each\(p\in SN\)do//remove SN replies with less than threshold RSSI.
7:if\(SN[i].rssi<\)thresholdRssi then
8:SN.remove (p)
9:endif
10:endfor
11:best = SN[1]
12:for each\(p\in SN\)do
13: bestValue = 2 * best.rssi + best.energyLevel + best.distance
14:pValue = 2 * p.rssi + p.energyLevel + p.distance
15:if\(pValue>bestValue\)then
16: best = p
17:endif
18:endfor
19:return best
20:endprocedure
```  
**Algorithm 2** getBestSN  
Figure 6: The communication steps between wireless sensor networks (WSN) and IoT layer elements.  
In case the IT Platform component in the Sensor is unable to find any IoT device that is moving towards the Sink, the sensor continues the multi hop routing using its default routing protocol in the WSN layer. When the IoT device changes its direction, IT Platform enables IoT to send data packet back to WSN layer. Algorithm 3 shows the IsDirectionToSink algorithm in the platform for the IoT device. The algorithm is used to decide if it is moving towards or away from the sink based on euclidean distance as shown in lines 2 to 3. Table 1, shows the list of messages used in communication during traversing of Data Packets.  
```
previousX = //previous X position of IoT device previousY = //previous Y position of IoT device currentX = //current X position of IoT device currentY = //current X position of IoT device sinkX = //Y position of Sink set glDistance(x1,y1, x2,y2) // returns euclidean distance between two points
```
1:procedureisDirectionToSink(data)
2: currentDistance = getDistance(currentX, currentY, sinkX, sinkY)
3: previousDistance = getDistance(previousX, previousY, sinkX, sinkY)
4:if\(currentDistance>previousDistance\)then
5: returnValue = True
6:else
7: returnValue = False
8:endif returnValue
9:endprocedure ```
**Algorithm 3** IsDirectionToSink
# Software-Defined Vehicular Cloud Networks: Architecture, Applications and Virtual Machine Migration
## 4 Migration Management of VM over SDVC
### Review of the State-of-the-Art of VM Migration Techniques over SDVC
#### 4.2.2 Simulation Tools for VM Migration Services over SDVC
Vehicular cloud computing uses mobility prediction not only to allow service migration of VM running RSU/cloud services but also mitigate the challenge of high mobility and heterogeneity of wireless interface equipped in vehicles. [PERSON] et al. [64] introduced the Road Side Cloudlet (RSC) in RSU cloud established between the vehicular cloud and central cloud computing. RSC-based VM service relieved the challenge of the low response-time problem and the high communication cost experienced for vehicular users requesting cloud-based vehicular applications. The authors of [64] considered that RSC-based VM service has a very high execution cost in long-distance networks between vehicular users and RSC. Therefore, two heuristic algorithms are proposed in [64] to improve VM migration cost among RSCs. The first relies on an algorithm that finds the shortest path according to the vehicle movement towards to the next RSC without dropping link robustness. The second heuristic algorithm is based on VM allocation cost in RSC. Vehicular Virtual Machine Migration (VMMM) was proposed by [PERSON] et al. [68] to address the issue of vehicle mobility for vehicles considered as VM hosts in the vehicular cloud. The authors of [68] introduced factors that affect the vehicle to successfully complete the migration of VM. These factors are: geographical mapping of the wireless coverage, congestion of the vehicles, vehicle speeds, and wireless communications technology equipped in vehicles. Based on these four factors, the authors proposed three migrations schemes: (1) VVMM-U (uniform) that includes vehicles migrating after the proposed VVMM algorithm selects the optimal vehicle destination; (2) VVMM-LW (Least Workload) that includes a vehicle that switches its workload by selecting the next destination to resume the workload using the proposed VVMM algorithm; and (3) VVMM-MA (Mobility Aware) that includes vehicles that entrust the mobility awareness of neighboring vehicles before attempting to migrate.
# Theoretical Zero-Thickness Broadband Holograms Based on Acoustic Sieve Metasurfaces
## 2 Theories and Methods
### Numerical Simulation  
A combination of the FEM and the BEM is used for the numerical simulations and the software used is COMSOL Multiphysics. During the simulation of holographic imaging, a zero-thickness rigid boundary with a special arrangement of small holes is used to simulate the proposed hologram. The space below the rigid boundary is simulated by the FEM, and the space above the rigid boundary is simulated by the BEM. The plane where the rigid boundary is located is the coupling plane between the two methods. The plane \(1/6\) wavelength below the rigid boundary is the emission plane. The emitted sound wave is a plane wave with a sound pressure amplitude of 1 Pa and its propagation direction (i.e., z-direction) is perpendicular to the rigid boundary (the rigid boundary is located on the plane of \(z=0\) and its center is at the point of (0, 0, 0)). All boundaries in the calculation part of the FEM are set as plane-wave radiation boundaries (they can also be set as perfectly matched layers, and the calculation results of the two settings are basically the same) to achieve an anechoic environment, and the whole space in the calculation part of the BEM is set as free open space. For domains, the mesh is set to free tetrahedral, and for boundaries, the mesh is set to free triangular. The maximum element size of meshes is set to be no larger than \(1/6\) wavelength.
## 2 Materials and Methods
### Biopsy Collection and Assembly of Hybrid Model  
Following Maastricht University Medical Center+'s medical ethical committee protocol #2018-0945, a pre-operatively signed informed consent was obtained. Patients underwent a unilateral temporal lobe resection including an amygdalapolippocampctomy as treatment of drug-refractory temporal lobe epilepsy [24]. This type of surgery commonly includes resection of the most anterior 4 cm approximately part of the temporal lobe. Part of the biopsies was used for routine histopathological analysis; the remainder was used for the current study. Histopathological evaluation of resected neocortical and hippocampal tissue typically showed hippocampal sclerosis. Immediately following resection of the brain tissue, the biopsy was transported from the operation theatre to the laboratory (approximately 10 min) in maintenance medium at room temperature and without oxygenation. In the laboratory, the collection tube was opened in a laminar flow hood, and the biopsy was placed in a sterile tissue culture dish and cut into 3D-tissue plugs using a 5 mm inner diameter biopsy puncher (Biopunch(r) Ted Pella, Inc., Redding, CA, USA). The plug dimensions were 5 mm in diameter and 3 mm in height. For the hybrid model, three plugs were transferred onto a neuronal layer grown on three separate MEAs (DIV16), covered with maintenance medium, and incubated for 30 min at 37 \({}^{\circ}\)C and 5% CO\({}_{2}\) prior to the first recording (Figure 1B,F). From this point onwards (only maintenance), CSF-free medium was used (and special CSF-like media was not needed). All MEA devices were carefully transferred from the incubator to a preheated MEA head stage and allowed 5 min of rest before recordings started. Cultures were maximally 15 min outside the incubator. As a control, a 2D layer of hiPSC-derived cortical neurons was kept in culture on three MEAs without the biopsy plug. A second control consisted of three biopsy plugs that were directly placed on a MEA, i.e., without the neuronal cell layer as interface.