esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Simulation Protocol and Performance Quantification  
Here, the different seizure-generating scenarios and initial detection parameters \(\{T,N,n\}\) are introduced.  
Figure 4: Real (**top frame**) and synthetic (**bottom frame**) epimarker. In both plots, the epimarker is the blue trace and the vertical axes indicate its values. Note that the negative values on the top frame are due to a conveniently introduced offset as a compensation for white noise background. The horizontal axes on both plots represent the real and simulation time (sample steps), correspondingly. The vertical red line on the top frame represents the beginning of the seizure as detected in real time by the detector. On the bottom frame, the two red lines are at the beginning and the end of the model seizure event; the red star is the detected onset by the algorithm.  
Unless otherwise stated, the typical stimulation session includes a number of epochs, each of a length of 3600 simulation steps. In each epoch, we randomly generate five seizure events according to the stochastic rules explained in the previous subsection. After each epoch, we detect the seizures with the current detector parameters according to Section 2.2. Based on the data-generation log, only the true positive events are selected to enter the parameter optimization algorithm. Next, a minimum number of 20 retrospectively detected true events is required in order to start performing the dual detection parameter optimization procedure explained in Section 2.3. If the number of events is lower than 20, the current parameters are kept. The update is performed after each new simulation epoch; no more than 60 previously detected true events are taken, in order to account for possible nonstationary conditions.  
After each epoch, the detector performance is evaluated by collecting the statistics from the last 8 epochs (40 generated true events) according to the following standard definitions for the sensitivity and specificity:  
\[\begin{array}{l}Sens\equiv\
icefrac{{TP}}{{(TP+FN)}}\\ Spec\equiv\
icefrac{{TP}}{{(TP+FP)}}\end{array} \tag{11}\]  
In Equation (11), \(TP,FP,FN\) are the true positive, false positive, and false negative (undetected events) detections, correspondingly. The ground truth for the events is in accordance with the simulation log.  
All the calculus is performed in Matlab(r), version 2024a. The statistical distribution plots are produced by the standard \"boxplot\" function from the \"Statistics and Machine Learning Toolbox\" version 24.1.
# _Sci_
## 3 Results
### Technical Challenges of Employing Lasers versus Radio Frequency Communications  
The transition from \"traditional\" radio frequency waves to near- and in-band visible light waves invokes the inverse relation of frequency and wavelength--as frequency increases, wavelength decreases. The significant advantage of employing laser communications comes from increased bandwidth, and that comes from increased frequency (and with laser communications the ability to employ the entire bandwidth). Laser crosslinks will jump from millimeter wave-generated high giga-hertz bandwidths to infrared and visible light-generated tera-hertz bandwidths, enabling data rates from optical carriers to be from two-hundred to two-thousand times greater than \"traditional\" radio frequency [51].  
However, as the history above shows, the benefits are yet to be realized as the maturation of the technology remains sluggish. The use of a low divergence beams, a traditional benefit of lasers, complicates linking two moving targets. Additionally, radio waves are less affected by atmospheric interference, something necessary for our most sensitive and critical missions. Additionally, while the increased frequency is desired for increased bandwidth, it comes with a cost of increased free space path loss as a function of the base-ten logarithm log10(_f_).  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Level** & **Color** &
\begin{tabular}{c} **Research and Development** \\ **Probability of Success 1** \\ \end{tabular} \\ \hline
1 & Low & 99 \\
2 & Moderate & 90 \\
3 & Moderate-to-difficult & 80 \\
4 & Difficult & 50 \\ \hline
1 & Low & 99 \\
2 & Moderate & 90 \\
3 & Moderate-to-difficult & 80 \\
4 & Difficult & 50 \\ \hline
3 & Medium-difficult & 10-10 \\ \hline \hline \end{tabular}  
* Taken from [54] corresponding to Figure 3.  
\end{table}
Table 1: Technical Risk in Terms of Complexity.  
Figure 3: Technology Complexity Levels (see Table 1) for radio frequency and optical communication systems for various curves of constant equivalent isotropic radiated power, **(a)** RF communications systems, **(b)** Optical communication systems.  
As depicted in Figure 4, taken from [55], radio frequency transmission through the atmosphere is well-known and concentrates on \"windows\" of transmission, especially for long ranges. This section of the manuscript consolidates the concerns for transmission of laser communications signals.
# Numerical Investigation of Thermal-Flow Characteristics in Heat Exchanger with Various Tube Shapes
## 2 Problem Description  
### Geometry  
Figure 1 depicts the proposed three-row staggered FTHEs with flat tubes layout. Cold air flows around the annular -inned tube while hot fluid flows inside the tubes. The air-side thermal exchange rate and pressure loss are evaluated in cross-flow thermal devices for three configurations of tubes: oval shape, circular shape, and flat shape. Four distinct shapes of each flat and oval tube were compared with the conventional circular tube. The variation in the hydraulic diameters of both oval and flat-shaped tubes has been chosen  
based on the numerical study of [PERSON] et al. (2019) (Figure 2). Stainless steel (Aisi 304) was used for the solid body, including the annular fins, with a thermal conductivity of (\(k_{\text{ss}}=14.9\) W/m\(\cdot\)K). The values of the basic parameters of the present annular fins are listed in Table 1.  
In this research work, we investigated and compared nine geometries of annular finned-tube heat exchangers. Figure 3 reveals the 3D-computational domain, its size, and all boundary conditions selected for the simulation. In order to keep the computational domain as small as possible and to speed up the computation time, the symmetrical boundary is chosen for the four lateral boundaries of the computational domain. No-slip coupled walls are used at solid-fluid interfaces, including tube outer surfaces and annular fin surfaces.  
Figure 1: Scheme representing the geometrical parameters of the investigated annular fins with a flat tube.  
Figure 2: Schematic of different flat and oval tube geometries (RMN = minor axis radius, RMX = major axis radius).  
The present computational domain is extended by 1.2-times the fin diameter, from the center of first row, and the outlet region extended by 3.6-times the fin diameter, from the center of the last row, to ensure a uniform inlet velocity and to avoid the reverser flow at the outlet.
## 5 Performance Evaluation
### Throughput Analysis over Number of Iterations  
Figure 2(a) shows that the proposed cooperative reinforcement learning outperforms both the random allocation and the distributed reinforcement learning regarding average system throughput calculated by Equation (6) considering 12 D2D user pairs and other parameter values as in Table 2. We can observe that after the 30 th iteration (Figure 2(b)), our proposed learning algorithm outperforms other methods at almost every iteration when the algorithm reaches the convergence of learning. In addition, there are variations in throughput results for each method and also there are some points where distributed reinforcement learning outperforms proposed cooperative reinforcement learning due to the fact that we consider the heuristic action selection policy based on exploration and exploitation in Equation (13), which avoids to stuck the learning algorithm in a local optimum. Random allocation shows poor results since it does not act appropriately with the changes of the environment. Whereas, distributed reinforcement learning shows moderate results comparing with  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline \(P_{max}\) & 23 dBm \\ Number of resource blocks & 30 \\ Number of cellular users & 30 \\ Number of D2D user pairs & 12 \\ D2D radius & 20 m \\ Pathloss parameter & 3.5 \\ Cell radius & 500 m \\ \(\tau_{0}\) & 0.004 \\ \(\tau_{1}\) & 0.2512 \\ \(\tau_{2}\) & 0.2512 \\ \(l_{th}^{(r)}\) & 0.001 \\ \(W_{RB}\) & 180 kHz \\ Initial \(Q(s,a)\) & 0 \\ Initial \(e(s,a)\) & 0 \\ \(\epsilon_{max}\) & 0.3 \\ \(\epsilon_{min}\) & 0.1 \\ \(k\) & 0.25 \\ \(\rho\) & 1 \\ \(\gamma\) & 0.9 \\ \(\tau_{1}\) & 0.5 \\ \(\lambda\) & 0.5 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Simulation Parameters.  
the both methods. We consider 100 iterations here for the comparison, but the trend of outperformance of our proposed algorithm remains the same with additional iterations.  
Figure 2(b) shows the Q-values calculated by Equation (9) of distributed reinforcement learning and cooperative reinforcement learning over number of iterations for learning. We can observe that the cooperative reinforcement learning converges faster due to the sharing learned policies between devices. Further, our proposed learning algorithm provides better Q-value at each time step which imposes an impact on the overall improved system throughput. Moreover, higher Q-values denote that action scheduling strategies are performed much better in our proposed algorithm.
## 3 Results and Discussion
### The Effect of Operational Conditions on the DCMD Process  
An orthogonal experiment was designed to study the relationships among the temperature of the hot side, the flow rate of the hot side, the flow rate of the cold side and the permeate flux. Table 4 shows the factors in the experiment. The solution to be separated was 3 wt% NaCl solution. PTFE membranes with a pore size of 0.22 \(\upmu\)m were selected. The cold side's temperature remained constant at 20 \({}^{\circ}\)C. The experiment was conducted with three different hot-side inlet temperatures (60, 70, and 80 \({}^{\circ}\)C) and three different inlet flow rates (600, 800, and 1000 mL/min). During the experiment, the conductivity of the produced water was controlled below 10. Table 5 shows the experimental results, from which the optimal combination A\({}_{3}\)B\({}_{1}\)C\({}_{3}\) can be obtained.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Level**} & **Hot-Side Temperature** & **Hot-Side Inlet Flow Rate** & **Cold-Side Inlet Flow Rate** \\  & **A** (\({}^{\circ}\)C) & **B** (mL/min) & **C** (mL/min) \\ \hline
1 & 60 & 600 & 600 \\
2 & 70 & 800 & 800 \\
3 & 80 & 1000 & 1000 \\ \hline \hline \end{tabular}
\end{table}
Table 4: The factors in the orthogonal experiment.  
Figure 8: Comparison of membrane permeate flux of different membrane materials. (**a**) 0.22 \(\upmu\)m, (**b**) 0.45 \(\upmu\)m, (**c**) 1.0 \(\upmu\)m.
## Weighted Geometric Dilution of Precision Calculations with Matrix Multiplication  
**[PERSON]**  
Department of Information Management, Tainan University of Technology, Tainan, 710 Taiwan;  
E-Mail: [EMAIL_ADDRESS]; Tel.: +886-6253-2106 (ext. 261)  
Academic Editor: [PERSON]  
_Received: 15 September 2014 / Accepted: 26 December 2014 / Published: 5 January 2015_  
**Abstract:** To enhance the performance of location estimation in wireless positioning systems, the geometric dilution of precision (GDOP) is widely used as a criterion for selecting measurement units. Since GDOP represents the geometric effect on the relationship between measurement error and positioning determination error, the smallest GDOP of the measurement unit subset is usually chosen for positioning. The conventional GDOP calculation using matrix inversion method requires many operations. Because more and more measurement units can be chosen nowadays, an efficient calculation should be designed to decrease the complexity. Since the performance of each measurement unit is different, the weighted GDOP (WGDOP), instead of GDOP, is used to select the measurement units to improve the accuracy of location. To calculate WGDOP effectively and efficiently, the closed-form solution for WGDOP calculation is proposed when more than four measurements are available. In this paper, an efficient WGDOP calculation method applying matrix multiplication that is easy for hardware implementation is proposed. In addition, the proposed method can be used when more than exactly four measurements are available. Even when using all-in-view method for positioning, the proposed method still can reduce the computational overhead. The proposed WGDOP methods with less computation are compatible with global positioning system (GPS), wireless sensor networks (WSN) and cellular communication systems.  
**Keywords:** weighted geometric dilution of precision (WGDOP); geometric dilution of precision (GDOP)
# OCRL1 Deficiency Affects the Intracellular Traffic of ApoER2 and Impairs Reelin-Induced Responses
## 2 Materials and Methods
### CRISPR/Cas9 OCRL Knock-Out  
CRISPR/Cas9 was employed to generate _OCRL_ KO H4 and iPSCs. First, two specific guide RNAs (sgRNA1: 5'-GCTGTTCCTTCTCTCAAC and sgRNA2: 5'-GCTGCAAAATTCGGGCG) targeting the _OCRL_ gene were designed using CHOPCHOP ([[https://chopchop.cbu.uib.no/](https://chopchop.cbu.uib.no/)]([https://chopchop.cbu.uib.no/](https://chopchop.cbu.uib.no/)) (accessed on 21 February 2021)) [69; 70] and Broad Institute platforms ([[https://portals.broadinstitute.org/gpp/public/analysis-tools/sgrna-design](https://portals.broadinstitute.org/gpp/public/analysis-tools/sgrna-design)]([https://portals.broadinstitute.org/gpp/public/analysis-tools/sgrna-design](https://portals.broadinstitute.org/gpp/public/analysis-tools/sgrna-design)) (accessed on 21 February 2021)) [71]. The sgRNAs were cloned into the pX-458 vector at the Bbs I restriction site, following the protocol described by [PERSON] et al. (2013) [72]. Lipofectamine Stem (Invitrogen, Waltham, MA, USA, STEM00001) was used to transfer the sgRNAs into iPSCs, and Lipofectamine 2000 (Invitrogen, Waltham, MA, USA, 11668019) was used for H4 cells. 24 h after transfection, GFP-positive cells were sorted using FACSAria II (BD Biosciences, San Jose, CA, USA) and plated into 96-well plates as two cells per well, collecting at least two 96-well plates per sgRNA. Few wells without cells or with two cells were discarded. Cells were cultured in a complete medium for 1-2 weeks, allowing them to form colonies. Subsequently, individual colonies were transferred to 6-well dishes and cultured until they reached confluency. Clones lacking the protein OCRL1 were identified by western blot. The selected clones were expanded and stored for further experimentation.  
For iPSC _OCRL_ KO cells, promising clones were subjected to PCR amplification of the region containing the target sequence of the sgRNA, which produced a 585 bp fragment. 10% of the volume from PCR was analyzed by electrophoresis on a 1% agarose gel, and the positive fragments were subsequently sequenced in ABI PRISM 3130 XL (Applied Biosystems, Foster City, CA, USA). A clone with a one-base addition resulting in an early stop codon was selected.
# A Review on Standardizing Electric Vehicles Community Charging Service Operator Infrastructure
###### Abstract
Motivated by this gap in the literature, we present a comprehensive survey on standardizing EVs charging infrastructure to minimize the CS deployment cost. We also present a taxonomy on charging levels, charging modes, charging standards, charging technologies based on the different charging types such as conductive charging and wireless charging, and types of vehicle, i.e., 2-wheeler (2W), 3-wheeler (3W), and 4-wheeler (4W). Moreover, we considered a scenario of EV community charging infrastructure similar to that of private charging with the advantage of the infrastructure being utilized by several households and managed by a community charging service operator. In this context, EVs of several households would be able to utilize the infrastructure for compatible and reliable charging to overcome the challenges associated with public and private charging infrastructure. To make this possible, we propose the architecture of standardized EV charging infrastructure to provide compatible charging standards for efficient EV charging. Therefore, standardized EV charging standards can be quite beneficial in providing adaptability for consumers as EVs associated with the various charging standards can opt for charging.
# Efficient FPGA Implementation of Automatic Nuclei Detection in Histopathology Images
## 2 Materials and Methods
### Masking  
A response map \(R_{i}\), \(1\leq i\leq 10\) corresponding to 10 response maps may have false regional maxima, due to noise in the input image. The _masking_ module eliminates the false maxima that are located outside nuclei masks \(T\) generated by the thresholding module. The output \(M\) of the module is calculated as follows:  
\[M_{i}(m,n)=R_{i}(m,n)\ \&\ T(m,n),\]  
where & is a logical AND operation. The pixel locations with \(M_{i}(m,n)=1\) correspond to nuclei seed candidates. The seed candidate locations from all \(M\) matrices are combined and stored in the FPGA RAM. Let the set of candidate nuclei coordinates be denoted by \(S\).
## 3 Results for the Research Questions
### RQ2: What Are the Expertise Sources Used for Experts Finding Systems?
#### 3.3.1 RQ3a: What Are the Differences between Expertise Retrieval and Expertise Seeking?
They used the following motivation factors: qualification, a friend of a friend, social exchange, birds of the feather, and following the crowd. The authors of references [20; 21; 39] combined the connectivity, relevance, and the quality to their respective expertise retrieval systems. For determining similar experts' tasks, the authors of reference [27] integrated expert-related factors, namely familiarity, availability, the topic of knowledge, perspective, reliability, up-to-datedness, approachability, contacts, cognitive efforts, physical proximity, and time-saving, along with the organisation-related factors media experience and organizational structure. Furthermore, for research and developmental tasks, the authors in reference [25] combined various expert-related factors, namely trust, quality, and relevance. In an enterprise context, the authors in reference [35] combined accessibility and quality factors into the expert finding system, while the authors in reference [22] integrated connectivity, productivity, and relevance factors into their proposed system. In the KSCs, the authors in reference [17] integrated authority, user reputation, and relevance into their system. The popular expert-related contextual factors, as shown in Table 7, indicate that the most frequently used factor was quality, followed by connectivity and relevance. To date, only one paper out of the 68 expert finding system-related studies developed an expert finding system based on the theories. In their study, the authors of reference [55] applied the Multi-Theoretical Multi-Level (MTML) framework, which used different results acquired from the various social theories and the network formation systems used in communities for constructing a catalogue of several individual motivations, namely discovering new people (novelty), exploring the existing resources, collaborating with people having similar characteristics (homophily), and showing a faster response. The researchers used various theories in their framework, including balance, self-interest, social exchange, homophily, and contagion theories.
## 4 Method
### Predicting Oil Spill Consequences Using ANFIS  
ANFIS consists of a FIS and an ANN used to solve nonlinear problems [77]. The ANFIS reference schema for n inputs and one output is presented in Figure 4.  
In ANFIS, fuzzy inference is based on the Takagi-Sugeno model incorporating fuzzy IF-THEN rules (Equation (18)). These rules consist of a consequence (i.e., an output), which is linearly dependent on a premise (i.e., an input) as follows:  
\[R_{i}\,:if(x_{1}\,\,is\,\,A_{1}^{i})\,\,\ldots\,\,and\,\,\ldots\,\,(x_{n}\,\,is \,\,A_{n}^{i})\,\,then\,f_{i}=a_{i}^{T}x+b_{i} \tag{18}\]  
where (\(x\,\,\in\,\mathbb{R}^{n}\)) is the vector of inputs in the premise characterized by an appropriate membership function (MF), and (\(a_{i},b_{i}\)) are the coefficients in a linear equation. Training ANFIS means determining the parameters belonging to the premise and consequence areas utilising a particular optimisation algorithm.  
The basic structure of ANFIS has five layers:  
1. Fuzzification, during which crisp inputs are fuzzified using Gaussian MF [78] as follows (Equation (19)): \[\mu^{Gaus}=e^{\frac{-(x-x)^{2}}{2\sigma^{2}}}\] (19) where \(c\), \(\sigma\) are the function coefficients which are adapted by a back-propagation algorithm during the learning process. Note that other types of MFs can also be used.
2. Evaluating the strength of rules, where for each node the strength, \(w_{i}\), is provided by multiplication (Equation (20)): \[w_{i}=\prod\mu_{i}(x)\] (20)
3. Normalisation, during which the rule strengths, \(w_{i}\), are normalized (\(\overline{w}_{i}\)) (Equation (21)): \[\overline{w_{i}}=\frac{w_{i}}{\sum_{i}w_{i}}\] (21)
4. Obtaining an output, \(f_{i}\), where the rules \(R_{i}\) (Equation (18)) are applied.
5. Obtaining global model response \(f\) using Equation (22): \[f=\sum_{i}\overline{w_{i}}f_{i}\] (22)  
Figure 4: The basic structure of ANFIS.  
Once the ANFIS training has been completed, the performance is determined by a specific statistical test, examples of which are presented in [79]. In this research, we use MSE (Equation (4)), \(R^{2}\) (Equation (3)), and NRMSE (Equation (2)).
## 2 Related Work
[37] presented the design of a wireless network that was responsible for monitoring the crops and their irrigation system; the network consisted of a WSN and a wireless mesh network (WMN). Existing works have studied the use of drones for imaging in precision agriculture [10; 11; 12; 13], the transmission of video images to a ground station [21], and the data acquisition of nodes deployed in remote areas for different types of purposes such as urban lawn monitoring [23] or archaeological projects [22]. Other works focus on the exclusive use of drones as a gateway with specially designed flight plans that determine the shortest flight necessary to fly over all the nodes or has the drone hovering over the node for more than a minute until all the data are transmitted [17]. In our work, we consider the challenge that is generated when combining both functionalities by contemplating the use of remote sensing drones as gateways for WSNs in precision agriculture. A remote sensing drone already has a defined flight plan to map the fields or perform pest or disease detection. Then, the gateway node is presented with the challenge of gathering the data in a short time as the drone does not circle or hover over the node on the field. Considering these restrictions, this work provides a guide to determine the best coverage time for the communication between sensor nodes and a drone where we define the speed and height at which the drone moves so that the collection of data obtained from the sensor nodes is reliable. As far as we know, the combined use of a single drone as a mobile gateway while maintaining the flying parameters previously set for picture gathering has not been presented.