esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Preliminaries and Motivations
### Problems Definition
#### 3.2.2 Children-Unreachable Problem in Storing Mode  
Oppositely, the storing mode can avoid source routing overheads, as well as the long-path problem. In fact, it performs similarly to the traditional store-and-forward routing function on the Internet. However, every node assumes the duty to store routing information of all its children in a subtree. If a network is large scale and P2P communications happen frequently, it can run out of the memory space of some nodes, which makes those nodes unable to support application-level functions. Moreover, the downward route information of some children might be lost due to the lack of memory space to store, which is defined as the children-unreachable problem.
# Concept Drift Adaptation with Incremental-Decremental SVM
## 4 Adaptive-Window Incremental-Decremental SVM (AIDSVM)  
We are now ready to introduce the AIDSVM algorithm, which is a generalization of the CP algorithm for concept drift, using an adaptive shifting window. Using the classification terminology [7; 8; 9], AIDSVM is a window-based active approach. It uses a window of the most recent samples to construct the classifier, and reacts to the concept drift by discarding the oldest samples from the window, until the Hoeffding condition (1) is met. The AIDSVM method is presented in Algorithm 1. A high-level diagram is also shown in Figure 2. The algorithm starts with an empty window; the samples are added progressively as they arrive. The window should have a minimum length, such that the statistical test could always be performed on a relevant number of samples. Below this minimum, the drift detection is not employed. For every sample added, several tests are performed on the current window. The window is partitioned into two splits, \(W_{0}\) and \(W_{1}\). As the partition moves, the length of split \(W_{0}\) increases, and the length of split \(W_{1}\) decreases. For a window width of \(n\) data vectors, where we keep at least \(m\) elements in the split, there are exactly \(n-m-1\) possibilities of constructing the \(W_{0}\) and \(W_{1}\) window splits.
# A Study on Defect Detection in Organic Light-Emitting Diode Cells Using Optimal Deep Learning
## 1 Introduction
In addition, defects can arise during the encapsulation process owing to the presence of impurities. This complexity makes it difficult to perfectly detect and discriminate defects, resulting in dark spots within the panels. Dark spots are formed because of cell discoloration within the panel when defects occur. This study compared and analyzed deep learning algorithms to identify and measure the shape and size of dark spots formed within cells. These dark spots were used as criteria for determining cell defects. We conclude that improving the yield of OLEDs and reducing the quantity of defective products during the manufacturing process is feasible by using deep learning algorithms to enhance defect detection and classification. Therefore, this study explores an optimal deep learning algorithm for detecting defects in OLED cells and discriminating between them to improve the yield. The VGG-16 deep learning algorithm was used to classify the dark spot images. A model was developed to distinguish between acceptable and defective cells by considering the shapes and sizes of the dark spots.
###### Abstract  
The feasibility of employing a birefringent fiber loop to enhance the performance of a directly modulated reflective semiconductor optical amplifier is experimentally demonstrated for the first time. The birefringent fiber loop acts as an optical filter of opposite slope than that of the reflective semiconductor optical amplifier electro-optical response and counteracts the finite reflective semiconductor optical amplifier modulation bandwidth of only 0.89 GHz. By proper adjustment of its detuning, the birefringent fiber loop tailors the spectral components that physically manifest due to the reflective semiconductor optical amplifier dynamic perturbation subject to direct modulation in the saturated gain regime, and suppresses the pattern-dependent distortions in the time domain. In this manner, the birefringent fiber loop manages to significantly improve the quality characteristics of the encoded signal at higher data rates than those enabled by the reflective semiconductor optical amplifier limited modulation capability. Owing to the birefringent fiber loop, the reflective semiconductor optical amplifier modulation range is extended to 4 Gb/s at the raw bit error rate of \(1.0\times 10^{-9}\), and to 11 Gb/s at the forward error correction limit of \(3.8\times 10^{-3}\). These results, which are unique against the evaluation criterion adopted in the first case, and the modulation speed achieved with post-filtering schemes in the second, highlight the beneficial role that the birefringent fiber loop can play in supporting reflective semiconductor optical amplifier operation for intensity amplification and modulation purposes.  
birefringent fiber loop; electrical modulation; offset filtering; pattern effect; reflective semiconductor optical amplifier +
Footnote †: journal: Applied Physics Letters  
1
# Recall Mechanism and Multi-Head Attention for Numerical Reasoning
## 2 Related Works
### Reasoning Module  
The reasoning module in deep neural networks typically incorporates a decoder, which can be based on feed-forward neural networks (FFNNs), recurrent neural networks (RNNs), multi-head attention, transformers, or graph neural networks (GNNs). Table 1 summarizes the performance of various approaches.  
Feed-Forward Neural Networks: Models like MHST use multi-head classifiers to identify arithmetic operations and relevant numbers from questions and documents [23]. GDS introduces a goal-driven approach to generate expression trees by recursively decomposing target outcomes into subgoals [9]. FinMath adopts a similar strategy for financial numerical reasoning [26].  
Recurrent Neural Networks (RNNs): The deep neural solver (DNS) combines an encoder-decoder architecture with a similarity-based retrieval model, achieving accuracies of 64.7% and 70.1% on Math23K and Alg514, respectively [10]. NeRd integrates BERT for encoding and an LSTM-based programmer for generating multi-step reasoning programs [21]. ELASTIC employs a GRU-based decoder with an attention layer and a memory register, achieving 68.96% execution accuracy on the FINQA dataset [6].  
Transformers: Transformer-based decoders, such as those used by [PERSON] et al., generate mathematical expression trees but often underperform compared to LSTM-based decoders [27]. Ensemble models combining multiple encoder-decoder architectures have shown improved results on datasets like Math23K.  
Graph Neural Networks (GNNs): Doc2 SoarGraph converts embeddings into hierarchical graphs (e.g., quantity comparison, text relation, and date comparison graphs) and processes them using a graph convolutional neural network (GCNN) [7]. QDGAT constructs graphs from questions and documents, using a question-directed layer to simulate reasoning processes [22].  
Prompting: Prompting is a technique used to guide large language models (LLMs) to generate more accurate and structured responses without modifying the model's parameters. By carefully designing the input query, users can significantly enhance the model'sreasoning ability in numerical tasks. There are several strong prompting techniques that improve LLM performance in numerical reasoning. Chain-of-thought (CoT) [28] demonstrates how step-by-step reasoning enhances model performance. [PERSON] et al. proposed self-consistency in \"Self-Consistency Improves Chain of Thought Reasoning in Language Models\", where multiple reasoning paths are sampled to select the most consistent answer [29]. Program-aided language (PAL) models explore integrating code execution with language models, enabling precise calculations by generating and running code [30].  
Despite these advancements, challenges such as information loss in deep networks and suboptimal multi-step reasoning in numerical reasoning remain significant. Inspired by ELASTIC [6], we propose a recall mechanism and multi-head attention for numerical reasoning (RMMANR) to address these limitations.
## 6 System Evaluation
### Xbee 868 MHz Radio Module  
According to the results of the ZigBee communication tests, another alternative must be considered to ensure a more powerful node-to-train communication. The XBee 868 radio modules have been tested. According to the manufacturer, they allow the use of high gain antennas, reaching a range of up to 80 km.
## 4 Experimental Methodology and Measurement Results
### RRU Synchronism
#### 4.2.1 Jitter Measurement  
The DL bitstream contained PRBSs to keep the CDRs on both RRUs locked. The carrier frequency clocks of both RRUs were connected to the real-time oscilloscope (RTO) (_Lecroy LabMaster_ 10-65 Zi-a). RRU1's clock (labeled in yellow) was the trigger signal. Figure 8 shows the experiment setup and the results averaged over 20 s (the longest limited average duration of the RTO).  
\begin{table}
\begin{tabular}{l c c c} \hline \hline  & \multicolumn{3}{c}{**UL (RRU to DU) Fiber Length**} \\  & **Back-to-Back** & **30 m MMF** & **100 m MMF** \\ \hline DL Back-to-Back & 2.615\% (\(-\)31.65 dB) & 2.672\% (\(-\)31.46 dB) & 2.684\% (\(-\)31.42 dB) \\  & & & Constellation: Figure 7b \\ \hline DL 100 m MMF & 2.621\% (\(-\)31.63 dB) & 2.641\% (\(-\)31.56 dB) & 2.648\% (\(-\)31.54 dB) \\  & & & Constellation: Figure 7c \\ \hline \hline \end{tabular}
\end{table}
Table 3: Uplink performance in EVM.  
Figure 7: Constellation diagrams of the demodulated OFDM signals of the worst cases of the downlink (Table 2) and uplink performance measurements (Table 3).  
The stable waveforms imply that the RRUs were frequency-synchronized owing to the synchronization circuits (Figure 6).  
The RTO measured the delay between a falling edge of the trigger signal (RRU1's carrier frequency clock) and the first subsequent falling edge of the observed signal (RRU2's carrier frequency clock, labeled in pink) as marked in Figure 8. The mean values of the delay, indicating the average phase differences between two clock sources, were different for the three test scenarios because the PLLs had to re-lock each time we changed the fibers. Once the PLLs were locked, these mean values were stable and would be captured as the channel information. Hence, while the mean values remain stable, the transceiver coherency is guaranteed.  
The standard deviation can be considered as an indicator of the asynchronous jitter. For the optical back-to-back case (Figure 8a), the standard deviation was 7.69 ps. After a 100 m MMF was added to only the RRU2 DL optical link (Figure 8b), the standard deviation increased to 9.11 ps. When the DL optical links of both RRUs had 100 m MMFs, the standard deviation increased slightly to 9.43 ps.
## 2 Coupled Analysis of Turbine and Support Structure
### Brief Introduction of Offshore Structural Analysis Software X-SEA
\begin{table}
\begin{tabular}{c c} \hline \multicolumn{1}{c}{**Hydrodynamic Analysis**} \\ \hline \multicolumn{1}{c}{**X-SEA Element**} & \multicolumn{1}{c}{**Material Property**} \\ \hline Shell element & XSHELL3-QSI: 3 node quasi-conforming & Elastoplastic: Von Mises with strain hardening, Ivanov-Yulishin, \\ XSHELL4-ANS: 4 node assume natural strain & Concrete elasto-plastic and elasto-plastic fracture, laminate composite, concrete creep \\ \hline Frame element & XFRAME: 2 node frame element with warping (7 dof, shear deformation, tapered, offset) & Elasto-plastic: Von Mises, concrete creep \\ \hline Solid element & XSOLID4T \& XSOLID10T: 4 and 10 node, tetrahedral & Elasto-plastic: [PERSON], Mohr-Coulomb, \\  & XSOLID8-EAS: EAS 8 node & Drucker-Prager, Tresca \\ \hline Truss element & XTRUSS: 2 node three-dimensional element & Elasto-plastic: [PERSON] \\ \hline \multirow{2}{*}{Cable element} & XCABLE–Parbolic & \multirow{2}{*}{5-point nonlinear model} \\  & XCABLE–Catenary & \\  & XCABLE-Mooring & \\ \hline Spring element & XSPRING: 3D Spring & \\ \hline Link Element & Gap, hook, gap-hook & \\ \hline Interface element & 1D, 2D, 3D (bond-slip element) & \\ \hline Tendon element & 3D prestressing tendon & \\ \hline \end{tabular}
\end{table} 
Table 2: Element Library of X-SEA. \(\dot{U}\) is the structural velocity and \(\ddot{U}\) is the structural acceleration. In Equation (1), \(\widetilde{M}\) is known as the added mass and \(\widetilde{C}\) is the hydrodynamic damping. These are given as:  
\[\widetilde{M}=\rho(C_{M}-1)V_{M} \tag{2}\]  
\[\widetilde{C}=C_{D}\rho A_{D}\overline{u} \tag{3}\]  
The modeling of the structures having large dimensions relative to the wavelengths is considerably different from that of the jacket framed structures, because the Morison theory is no longer applicable, therefore a diffraction theory must be considered. A three-dimensional (3D) mesh is necessary for applying the wave diffraction theory. The diffraction wave pressure on an element area is written as [24]:  
\[P=\frac{\partial F_{z}}{\partial z\cdot\partial\dot{b}}=\rho gH\frac{A(ka)}{ ka}\frac{\cosh(ks)}{\cosh(kd)}\cos(\omega t-\delta) \tag{4}\]  
where \(A(ka)\) is the variable of interest versus the range of wave that may be obtained by the Bessel equation, \(s\) is the interest position of element and \(\delta\) is the random phase angle. The first step of the hydrodynamic analysis is to calculate the diffraction pressure on each of the shell and solid elements as the wave propagates through the offshore structure. The integration of these pressures then produces the net force on the structure, which is used to calculate the structure motions.
# A Graph-Based Approach for Modelling Quantum Circuits
## 4 Modelling the Unitary Circuit Model of Quantum Computation
### Example Application  
In order to demonstrate the modelling capabilities of the proposed metamodel, and obtain some statistics on the usage of modelling resources that will allow us to compare all strategies, we will use the quantum circuit that solves the following riddle: \"the finalist of a TV quiz show arrives at the last test, in which the presenter shows him two boxes with two notes. On box A, the note states 'at least one of the boxes contains a car'. On box B,the other box contains a pair of shoes'. The presenter also adds that either both notes are true or false. Which box should the contestant open?\".  
The riddle can be solved by finding the solution to the following Boolean equation: \((A\lor B)\) XNOR \((\
eg A)\). The quantum circuit that solves the riddle, a satisfiability problem in disguise, by applying [PERSON]'s algorithm [4] is shown in Figure 3. Qubits \(q[0]\) and \(q[1]\) model boxes A and B, respectively, with value \(|0\rangle\) meaning 'do not open this box'.  
The measure of the top two qubits (the bottom ones are ancilla qubits, employed to perform intermediate calculations) always yields the result \(|10\rangle\), meaning that the princess can know for certain that box B contains the kitten. The numbers appearing above the circuit are used to refer to the gates that appear below them, and are employed in the graphs depicted below to better identify the part of the quantum circuit they refer to.  
Figure 4 shows, as graphs, the models of the quantum circuit that solves the riddle by employing each of the five strategies described above. For space reasons, and without loss of generality, the quantum circuit is modelled up until the Control-NOT (CX) gate that appears in vertical number 6. Dashed lines on the graphs represent the relationship that exists between a control gate and the gate being controlled. These lines are not Edges of the metamodel but rather a visual aid added in the figures to better display the relationships that exist among controlled gates in the graph representation of the quantum circuit. Ellipses represent Nodes that contain just one gate, while circles with horizontal lines represent Nodes that contain more than one gate.  
Labels inside ellipses follow either the convention \"(vertical):(gate)((qubit))\" for single qubit gates, or \"(vertical):c(qubit),(controlledQubit)\" for control gates. The label inside a circle has as many letters as qubits in the circuit, where the symbol '\(\
eg\)' indicates that no gate acts on that qubit, according to the order in which the letters are written. This notation has only an illustrative purpose, to make it easier to identify the correspondence between the nodes of the graph and the gates of the quantum circuit. It should be highlighted that the two \"linear\" graphs in the figure show one of the possible orderings of such graphs. Specifically, the one in which the quantum circuit is represented from top to bottom.  
Figure 3: Quantum circuit that solves the proposed riddle.
# Nucleofection as an Efficient Method for Alpha TC1-6 Cell Line Transfection
## 2 Materials and Methods
### Nucleofection  
For nucleofection, \(\alpha\)TC1-6 cells were grown to a confluence of 70-80%. Cells were washed in PBS and detached by cell dissociation, enzyme-free, PBS buffer (GIBCO, by Life Technologies). The optimization protocol was followed according to the manufacturer's recommendations (Amaxa(tm) 4D-Nucleofector(tm) Optimization Protocol for Cell Lines). The required cell number (3.4 \(\times\) 10\({}^{6}\) cells per 4D-Nucleofector(tm) X Solution) was centrifuged at 90\(\times\)\(g\) for 10 min at RT. Following the centrifugation step, cell pellets were resuspended in 340 \(\mu\)L of SE, SF, or SG 4D-Nucleofector(tm) X Solution with a supplement at RT (2 \(\times\) 10\({}^{5}\) cells in 20 \(\mu\)L 4D-Nucleofector(tm) X Solution with a supplement for one well in 16-well Nucleoetet(tm) Strips, if not specified otherwise). The supplement was added in a _viviv_ ratio of 1:4.5. Afterward, the cell suspension was mixed with 0.4 \(\mu\)g pmaxGFP(tm) (Lonza, 1 \(\mu\)g/\(\mu\)L in 10 mM Tris pH 8.0) plasmid DNA (if not indicated otherwise), transferred into the 16-well Nucleoet(tm) Strips and electroporated by using the 4D-Nucleofector(tm) X Unit (Lonza). Optimization of nucleofection included 15 different nucleofector programs and no program control. After nucleofection, cells were preequilibrated with RPMI for 10 min at 37 \({}^{\circ}\)C and then transferred into a prepared culture dish with a prewarmed DMEM medium. The protocol for optimization of nucleofection is available at [[https://bioscience.lonza.com/lonza_bs/US/en/download/content/asset/21451](https://bioscience.lonza.com/lonza_bs/US/en/download/content/asset/21451)]([https://bioscience.lonza.com/lonza_bs/US/en/download/content/asset/21451](https://bioscience.lonza.com/lonza_bs/US/en/download/content/asset/21451)) (accessed on 4 February 2022).  
Optimized Protocol for Nucleofection for the \(\alpha\)TC1-6 Cell Line
* After reaching 70% of cell confluency, aspirate the medium from the flask. Wash the cells once with PBS and harvest them by using cell dissociation buffer.
* Determine cell density.
* Centrifuge 5 \(\times\) 10\({}^{6}\) cells per one 100 \(\mu\)L single Nucleoucvette(tm) at 90\(\times\)\(g\) for 10 min at RT and completely remove the supernatant.
* Add 100 \(\mu\)L 4D-Nucleofector(tm) SF Solution with supplement and 7.5-\(\mu\)g plasmids on dry cells pellet. Mix gently with pipetting.
* Transfer cells into the 100-\(\mu\)L single Nucleoucvette(tm).
* Place Nucleoucvette(tm) into the retainer of the 4D-Nucleofector(tm) X Unit and start the CM-156 nucleofection program.
* After run completion, add 400 \(\mu\)L of RPMI medium to each Nucleoucvette(tm) and incubate them for 10 min at 37 \({}^{\circ}\)C.
* Gently resuspend cells by pipetting two to three times and transfer cells in preincubated six-well plates with complete DMEM.
* Propagate cells until analysis.
## 3 Control Strategy of Variable Displacement Pump under Different Working Conditions
### Non Constant Power Stage  
As shown in Figure 2, in section \(MA\), the load in this stage is small. The system has not entered the constant power operation stage. The left end of the LS valve core is connected with the maximum load pressure \(P_{L}\), which is affected by the spring force \(F_{1}\). The right end is connected with the pump outlet pressure \(P_{\text{P}}\). The balance equation for the LS valve is obtained as Equation (13). LS valve ensures that the pump outlet pressure is always greater than the maximum load by a target value, which can be expressed as \(P_{\text{LS}}=F_{2}/A_{2}\).  
\[\begin{split} P_{\text{L}}A_{2}+F_{2}=P_{\text{P}}A_{2}\\ P_{\text{P}}=P_{\text{L}}+\frac{F_{2}}{A_{2}}\end{split} \tag{13}\]  
where \(P_{\text{L}}\) is the maximum pressure of load. \(F_{2}\) is the spring 2 force. \(A_{2}\) is the left end area of LS valve.  
Figure 3: Internal schematic diagram of load sensing pump.  
The left end of the constant power valve core is connected with the pump outlet pressure. At the same time, it is under the action of variable pressure thimble. Set the pressure of the oil chamber of the variable thimble as \(P_{\rm f}\), which is determined by the current \(I\) of the input electric proportional reducing valve. Set the cross-sectional area of the thimble oil chamber as \(S\). The force relationship of constant valve core can be deduced as (14). The constant power valve works in the right position.  
\[\begin{split} P_{\rm f}S+P_{\rm p}A_{1}<F_{1}\\ P_{\rm f}=f(I)\end{split} \tag{14}\]  
where \(P_{\rm f}\) is the pressure of the oil chamber of the variable thimble. \(S\) is the left end area of constant power valve. \(F_{1}\) is the starting force of the spring \(1.f(0)\) is the function between the pressure of the oil chamber and input current. \(I\) is the input current of reducing valve.  
The left end of the pressure cut-off valve spool is affected by the spring force \(F_{3}\). The right end is connected with the pump outlet oil circuit. At this time, the force relationship of the left and right ends of the pressure cut-off valve spool is deduced as (15). The pressure cut-off valve works in the left position.  
\[F_{3}>P_{\rm p}A_{3} \tag{15}\]  
where \(F_{3}\) is the spring force of the left end of the pressure cut-off valve. \(A_{3}\) is the area of cut valve.  
In this working stage, the oil chamber at the left end of the variable piston returns to the oil tank through the pressure cut-off valve, LS valve and constant power valve. The variable piston moves left to the maximum displacement, and the pump displacement works at the maximum displacement. With the increase in the pump outlet pressure, the pump displacement remains unchanged.