esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Energy Efficiency Optimization
### Joint Optimization of Density and Transmit Power  
In Sections 4.1 and 4.2, we have solved the optimization problem formulated in Equation (14) with respect to \(\lambda_{2}\) for a given \(P_{T2}\) and the optimization problem formulated in Equation (16) with respect to \(P_{T2}\) for a given \(\lambda_{2}\), respectively. In this section, we tend to find the optimal pair \(\left(P_{T2}^{opt},\lambda_{2}^{opt}\right)\) that jointly maximizes the EE in Equation (13). The joint optimization problem can be formulated as  
\[\begin{array}{ll}\underset{\lambda_{2},P_{T2}}{\max}&\eta_{EE}\\ &s.t.\ P_{\min}\leq P_{T2}\leq P_{\max},\lambda_{\min}\leq\lambda_{2}\leq \lambda_{\max}\end{array} \tag{18}\]  
By using these results obtained in Sections 4.1 and 4.2, we propose an alternating optimization method to solve the joint optimization problem given in Equation (18). This method iteratively optimizes \(\lambda_{2}\) for a given \(P_{T2}\) and \(P_{T2}\) for a given \(\lambda_{2}\) until convergence of the EE within a desired level of accuracy. The algorithm that solves Equation (18) based on the alternating optimization method is given in Algorithm 1.  
```
Step 1: Let \(P_{T2}\in[P_{\min},P_{\max}]\), \(\lambda_{2}\in[\lambda_{\min},\lambda_{\max}]\) Step 2: Set \(\lambda_{2}^{opt}\in[\lambda_{\min},\lambda_{\max}](initial);E=0;\varepsilon=10^{ -5}\); Step 3: Set \(E_{0}=E\); Step 4: Solving Equation (16) with given \(\lambda_{2}=\lambda_{2}^{opt}\), denote optimal solution as \(P_{T2}^{opt}\), get \(\widetilde{P_{T2}^{opt}}=\max\{P_{\min},\min\{P_{T2}^{*},P_{\max}\}\}\), solving Equation (14) with given \(\mathrm{P_{T2}}=\widetilde{P_{T2}^{opt}}\), denote optimal solution as \(\lambda_{2}^{*}\), get \(\widetilde{\lambda_{2}^{opt}}=\max\{\lambda_{\min},\min\{\lambda_{2}^{*}, \lambda_{\max}\}\}\); Step 5: \(E=\eta_{EE}(\lambda_{2}^{opt},\widetilde{P_{T2}^{opt}})\); Step 6: If \(|E-E_{0}|/E>\varepsilon\), jump to step 3; Step 7: Return \(\lambda_{2}^{opt}=\lambda_{2}^{opt},\widetilde{P_{T2}^{opt}}=\widetilde{P_{T2}^ {opt}}\).
```  
**Algorithm 1** Alternating Optimization of Density and Transmit Power  
First, the optimization range of variables is given as \(P_{T2}\in[P_{\min},P_{\max}]\), \(\lambda_{2}\in[\lambda_{\min},\lambda_{\max}]\). Second, the initial iteration value of density is given as \(\lambda_{2}^{opt}\in[\lambda_{\min},\lambda_{\max}]\), the initial iteration value of EE is given as \(E=0\), and the desired level of accuracy is given as \(\varepsilon=10^{-5}\). Third, we iteratively optimize \(\lambda_{2}\) for a given \(P_{T2}\) and \(P_{T2}\) for a given \(\lambda_{2}\) until convergence of the EE within a desired level of accuracy.  
Last, the optimal solution pair and optimal pair \(\left(P_{T2}^{opt},\lambda_{2}^{opt}\right)\) meeting the desired accuracy requirement is obtained.
Sigmoid Activation Implementation for Neural Networks Hardware Accelerators Based on Reconfigurable Computing Environments for Low-Power Intelligent Systems  
[PERSON]  
1 Faculty of Innovative Technologies, Tomsk State University, 634050 Tomsk, Russia; [EMAIL_ADDRESS] (D.S.)  
[PERSON]IL_ADDRESS] (S.S.)  
[PERSON]  
1 Faculty of Innovative Technologies, Tomsk State University, 634050 Tomsk, Russia; dshashev@mail.  
Such devices require specialized hardware accelerators and fine-tuned algorithms to use machine learning algorithms effectively.  
There are many papers about various optimizations of hardware and machine learning algorithms for low-power applications. Hardware optimizations mainly mean replacing powerful but energy-intensive graphics processing units (GPU), which are popular in the cloud and desktop systems, with more efficient devices, such as field-programmable gate array (FPGA) and application-specific integrated circuits (ASIC) [4; 5; 6; 7; 8; 9; 10; 11]. At the same time, to use hardware resources efficiently, applied algorithms are adapted to the features of the chosen platform. In addition, the quality of NN models is often acceptably downgraded to reduce computational complexity. For example, authors of [12; 13] propose to reduce the bit-lengths of numbers in a model to 8 and 4 bits, respectively, and in [14; 15], binary NNs are presented. In [16], the complexity of a NN is reduced using sparsing, which is the elimination of some weights to reduce the number of parameters.  
Such comprehensive solutions show good results in the implementation of concrete NN architectures. However, in practice, complex intelligent systems can face various tasks that require different NN architectures. Sometimes, it is impossible to predict which architectures will be required in the future. The problem is especially acute if the autonomous system is remote and difficult to access. The simplest solution is to implement many hardware subsystems for the most probable architectures, although this approach affects weight, power consumption, reliability, and complexity of the device.  
Another way to solve the problem is to use dynamically reconfigurable hardware accelerators that can change the current model by an external signal at runtime. Application of such accelerators offers great opportunities for performance, energy efficiency, and reliability. Therefore, numerous research has been conducted in this area. We explore dynamically reconfigurable accelerators based on the concept of reconfigurable computing environments (RCEs).  
One of the significant parts in developing RCE-based hardware accelerators is the implementation of neuron activation functions. There are many different activations now, and one of the most popular among them is the sigmoid activation (logistic function), which is widely used in an output layer of NNs for classification tasks. However, the original form of the activation is difficult to compute in hardware, so simplified implementations are usually used. This paper proposes two implementations of sigmoid activation for dynamically reconfigurable hardware accelerators.
# Optimization of Storage Medium for Hematological Reference Samples in External Quality Assessment
## 1 Introduction  
Hematology testing has been essential in the paraclinical field, contributing greatly to the diagnoses and treatments for hematologic diseases. Hence, hematology test results need to be reliable and standardized with frequent internal control and external evaluations [1, 2, 3]. The research and manufacture of hematology EQA samples have been conducted on many kinds of hematocyte originating from both human and animals. For instance, a hematology EQA sample based on the use of human and animal red blood cells (RBCs) as alternatives for white blood cells (WBCs) and platelets (PLTs) was generated [4, 5]. The RBCs originating from sharks, alligators, and turkeys were readable by automated hematology analyzers from Abbott and Streck [4, 5, 6]. In Japan, fresh blood samples from patients were used as references for nine laboratories; however, the analyses were suggested to be carried out within 8 h after collecting [7]. In Vietnam, suitable references for ethnic variation are still lacking, and all of them are purchased from foreign countries. Therefore, studies on establishing hematological reference samples in Vietnam could help to facilitate the initiative and to mitigate the expenses. Geese and goats are not only common livestock in Vietnam, their RBC sizes are also similar to human WBCs and PLTs, respectively. Accordingly, they could be utilized as assumed cells for the reference samples. One of the requirements foran appropriate reference sample is stability for at least 3 months at 2-8 \({}^{\circ}\)C. Storage media are expected to be isotonic so that they can minimize the hemolysis process [3; 8].  
The Plackett-Burman designs are known as an efficient way to select key factors from a large pool of variables for screening purposes [9]. Factors affecting the stability of a blood cell preparation include the type of storage medium, antibiotics, antifungal agents, and other stabilizing agents. Response surface methodology-central composite design (RSM-CCD) has been used extensively for choosing optimal values of different factors in a wide variety of experiments. The results of factor selection can be shown on a surface coordinate system, in which the optimal point was the extrema of each system [10].  
The current study aimed to examine various factors influencing the stability of hematology reference samples, using a Plackett-Burman matrix. RSM-CCD was used subsequently to optimize the concentrations of the influencing factors.
## 4 Numerical Analyses of a WFP Structure Upgraded with Slit-Plate Dampers (SPDs)
### Numerical Model of WFP System with Dampers  
A numerical model of the WFP structure with SPDs was built adding the dampers calculated in Section 4.1, modeled with the \"Two Node Link Elements\" implemented in OpenSees [24], to the numerical model described in Section 2.2 and depicted in Figure 3. The constitutive model of the \"Two Node Link Elements\" that represents the SPDs is the Uniaxial Material Steel 02 described in Section 3, with, \(sN_{y}=17.72\) kN and \(sK_{y}=3.31\) kN/mm; the values of the other dimensionless parameters of the model are indicated in Table 1.  
The mass-proportional damping model was redefined for the WFP system retrofitted with hysteretic dampers in order to keep the relative damping values established for the WFP system without dampers. The new elastic stiffness matrix led to new modal frequencies for the first and second vibration modes, respectively \(f_{1}=3.08\) Hz and \(f_{2}=3.95\) Hz. In order to keep the damping ratio of 3% in the second mode established in Section 2.3 for the WFP structure without dampers, the parameter \(a_{0}\) was updated for the new frequency \(\omega_{2}=2\pi f_{2}=2\pi 3.95=24.82\) rad/s, giving \(a_{0}=1.49\). Figure 9 shows the mass-damping model of the WFP system both with and without dampers. According to the new damping model, the value of \(\xi_{1}\) corresponding to the fundamental frequency \(f_{1}=3.08\) Hz is \(\xi_{1}=3.8\)%. As the WFP system with dampers enters in plastic range, the fundamental frequency decreases. It is expected that its lowest value be higher than the obtained at the collapse in the tested structure without dampers, i.e., \(f_{1}=1.3\) Hz. The value of \(\xi\) for \(f_{1}=1.3\) Hz in the damping model for the WFP system with dampers corresponds to 9% (Figure 9), which is considered an upper bound for the damping in the numerical model.  
Figure 9: Mass-damping model defined for waffle-flat-plate (WFP) system with and without dampers.
# An Analytical Multiple-Temperature Model for Flash Laser Irradiation on Single-Layer Graphene
## 5 Limitations of Current Model  
The equations used in the present model have a \"classical form\". Nevertheless, the solutions were figured out under quantum mechanical manner. For example, the heat operator is used to solve the heat equation.The following limitations were identified with respect to the application of this approach:  
* The irradiation time/pulse duration should be superior to 1/s. If simulations are carried out for _as_ pulse durations, the laser wavelength is quite close to the separation distance between nucleus and first electron and the concept of the temperature distribution becomes meaningless.
* The target size is confined to the (20-100) nm range. If the particle size is smaller than 20 nm, the Fourier law collapses and cannot provide reliable results. For more than 100 nm, the restriction to nano-object is infringed.
* In current study, single-layer absorption has been used, thus limiting our study to single layer graphene. For multilayer graphene, the analysis becomes intricate and difficult to calculate since the absorption law and heat-transfer coefficients, in real-time, should be reconsidered for each layer, demanding a quantum-field theory in solid-state treatment. This subject is laborious to handle in simulations.
# Resource Allocation and Offloading Strategy for UAV-Assisted LEO Satellite Edge Computing
## 5 Performance Evaluation  
Within this chapter, we examine the behaviour of the proposed DDPG-based scheme. First, we present the simulation scenario. Then, many simulation experiments are performed, and the results are compared and analyzed. Lastly, we verify the training efficiency under different parameters and find the optimal parameter settings, comparing the performance of various task offloading schemes and further demonstrating the method's effectiveness.
## 3 Results and Discussion
### Solvent Recycling  
Although DMM has proven to be a valid alternative to DMF, SPPS cannot be considered green due to the high costs of the solvent. One solution to reduce waste, costs, and PMI (process mass intensity) is solvent recovery. PMI is a parameter that measures the sustainability of a process. It considers all the materials used in a process and can be easily calculated using the ratio between the total mass of materials and the mass of the isolated product [31; 32]. DMM can be easily recovered by distillation, considering that its boiling point is quite different from that of the other volatile components (DMM: 175 \({}^{\circ}\)C, DIC: 145 \({}^{\circ}\)C, and piperidine: 106 \({}^{\circ}\)C). The green solvent recycled was directly analysed by 1H NMR spectroscopy (Figure S41) [32]. The recovered DMM can be used for further synthesis. Table 9 shows the PMI values of DMM before and after solvent recovery. Through recycling, it decreased from 3750.20 to 1370.68. These important data can certainly balance the high cost of DMM compared to those of DMF.
## 3 Methodology
### Experimental Setup
#### 3.4.4 Experiment 4--Retrieval Augmented Generation  
Experiment 4 used Retrieval Augmented Generation (RAG) [6] to evaluate model performance when accessing external resources relevant to the use case. RAG is a text generation paradigm that combines traditional retrieval techniques with LLMs. In RAG, the model retrieves relevant text segments from a large corpus of text data based on the input query and then uses these retrieved texts to guide and improve the generation process [6]. This approach helps to leverage external knowledge explicitly, influencing the quality and informativeness of the generated text by providing concrete references during generation. While RAG has the potential to improve accuracy, it presents challenges, particularly in aligning retrieved text with input prompts and addressing the formal legal language of GDPR texts. To mitigate these issues, the following strategies were implemented:  
* Contextual pre-processing. The GDPR text was segmented into semantically coherent chunks, ensuring retrievals were contextually relevant.
* Prompt engineering. Prompts were refined to better align informal user queries with formal legal language, improving retrieval compatibility.
* Retrieval filtering. Retrieved segments were filtered for semantic relevance to reduce noise in the generation process.  
These measures aimed to compensate for the deficiencies in the retrieval process and enable RAG to make better use of structured legal language in GDPR. Experiment 4 investigated whether RAG improved the generation of data categories in GDPR compliance and identified further areas of refinement for retrieval-based approaches.
## 3 Results and Discussion
### Characterization of the Activated Carbons
Also, it is worth highlighting the S content of 13.15% measured in AC-Fe\({}_{2}\)(SO\({}_{4}\))\({}_{3}\), indicating that impregnation with Fe\({}_{2}\)(SO\({}_{4}\))\({}_{3}\) was efficiently carried out. On the other hand, according to the literature, washing with HCl promotes the production of  
Figure 2: FT-IR spectra (**a**) and thermogravimetric analysis (**b**) of the carbon materials. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**adsorbent** & **C (wt.\%)** & **H (wt.\%)** & **N (wt.\%)** & **S (wt.\%)** & **pH\({}_{\text{EF}}\)** \\ \hline
**AC-ZnCl\({}_{2}\)** & 55.60 & 2.55 & 4.00 & 1.82 & 2.14 \\
**AC-FeCl\({}_{3}\)** & 59.77 & 2.36 & 3.36 & 1.35 & 5.40 \\
**AC-Fe(NO\({}_{3}\))\({}_{3}\)** & 33.51 & 0.99 & 2.05 & 0.66 & 6.81 \\
**AC-Fe\({}_{2}\)(SO\({}_{4}\))\({}_{3}\)** & 31.71 & 1.05 & 1.74 & 13.15 & 3.08 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Elemental analysis results and isoelectric point (pH\({}_{\text{HE}}\)) values of the carbon materials. acidic adsorbents [36], although it is necessary to correctly remove ash residues that clog the pores of the final materials. Furthermore, independently of the washing conditions, the use of activating agents such as FeCl\({}_{3}\) and ZnCl\({}_{2}\)[37; 38] conditioned the preparation of predominantly acidic carbon materials, as can be observed from their isoelectric point (pH\({}_{\text{HEF}}\)) values.
## 2 Dataset and Experiments
### Experimental Design
#### 2.1.1 Dataset 1: Nearby Parking Lot  
This experiment is based on the Sentinel-1 dataset. As shown in Figure 1, the change of the number of vehicles is detected in the area of interest indicated by the red box in the nearby parking lot. The sequence of time-series images spans from 5 March 2020 to 14 November 2022, and it contains a total of 82 images.  
Six sets of ground truth data were acquired through on-site collection. As the Sentinel-1 satellite is a sun-synchronous orbit satellite [26], it always passes over a specific area and acquires images every 12 days. Therefore, by recording the number of parked vehicles every 12 days during the sensor passing time, the required ground truth data can be obtained. Figure 2a shows the field situation of collecting truth data, and Figure 2b is an image obtained by modeling the truth data to restore the vehicle distribution in the parking lot, which is helpful for subsequent error analysis.  
\begin{table}
\begin{tabular}{l l} \hline
**Parameter Name** & **Values** \\ \hline Acq. Mode & IW \\ Pol. mode & HH \\ Spacing [m] & \(10\times 10\) \\ Orbit Repeat Cycle & 12 days \\ Onboard sensors & C-Band \\ \hline \end{tabular}
\end{table}
Table 1: Sentinel-1 experiment parameters.  
\begin{table}
\begin{tabular}{l l} \hline
**Parameter Name** & **Values** \\ \hline Acq. Mode & SM \\ Pol. mode & HH \\ Spacing [m] & \(3\times 3\) \\ Orbit Repeat Cycle & 11 days \\ Onboard sensors & X-Band \\ \hline \end{tabular}
\end{table}
Table 2: PAZ-1 experiment parameters.  
Table 3 lists six sets of truth data recorded from 15 September 2022 to 14 November 2022.  
The Logarithm Background Subtraction method and the pairwise comparison method were used to detect the change in the number of vehicles in the parking lot, and the change curve of the number of vehicles was obtained. Finally, the obtained truth data are used to quantitatively evaluate and analyze the errors of the proposed method and the traditional method.
## Materials and Methods
### Identification of Aroma-Active Compounds
#### 2.3.2 Identification of Aroma-Active Compounds by GC-O-MS  
GC-O-MS analysis was performed on an Agilent 7890B GC equipped with an Agilent 5977B mass selective detector and a sniffing port (ODP 4, Gerstel, Wurttemberg, Germany). The samples were analyzed on both a DB-FFAP column (60 m \(\times\) 0.25 mm \(\times\) 0.25 \(\upmu\)m, Agilent Technologies, Inc., Santa Clara, CA, USA) and an HP-5 column (30 m \(\times\) 0.25 mm \(\times\) 0.25 \(\upmu\)m, Agilent Technologies, Inc., Santa Clara, CA, USA). The carrier gas was high-purity helium (>99.999%) at a flow rate of 1.8 mL/min. The concentrated extract (1 \(\upmu\)L) was injected into the GC injector at 250 \({}^{\circ}\)C in splitless mode. The column temperature program was set as follows: the oven temperature was initially held at 45 \({}^{\circ}\)C for 2 min, then raised to 230 \({}^{\circ}\)C at the rate of 6 \({}^{\circ}\)C/min, and held for 15 min. The temperature of the ion source was 230 \({}^{\circ}\)C, and the ionization energy was 70 eV. The range of masses full-scan was used in the 35-350 amu, and a 5 min solvent delay was applied.  
Four trained panelists (2 males and 2 females, 25 years old on average) from the Laboratory of Brewing Microbiology and Applied Enzymology at Jiangnan Universitywere employed for GC-O analysis. During a GC run described above, a panelist placed his/her nose close to the sniffing pore and recorded the retention time, aroma descriptors and intensity value of the odor peak for each compound. A 6-point scale (0, 1, 2, 3, 4, 5) was used for intensity judgment: 0 = none, 1 = very weak, 2 = weak, 3 = moderate, 4 = strong, and 5 = very strong. Every sample was repeated twice. The Osme value was the average result of the 8 records. The compound could be confirmed only when the aroma was perceived 6 or more times by the panelists. Aroma-active compounds were identified by comparison of their mass spectra, odors, and RI with the corresponding pure standards. The RI values were calculated based on the linear retention times of n-alkanes (C\({}_{6}\)-C\({}_{30}\)) in both the DB-FFAP and HP-5 columns under the same chromatographic conditions.
# Bifurcation Flight Dynamic Analysis of a Strake-Wing Micro Aerial Vehicle
## 2 Bifurcation and Continuation Analysis
### Bifurcation Theory and Bifurcation Types
Starting from the asymptotic state approximation, for a set value of bifurcation parameters, the computer code, in the course of a continuation process, determines curves for the solution of \(\mathbf{x}(\mu)\), which constitute a set of solutions to non-linear algebraic equations (2). In the case of each of the points of the solutions to system (2) and (7), the stability of solutions is determined:  
\[\left\{ \begin{array}{ll}\text{Equilibrium points}:&\mathbf{f}(\mathbf{x}, \boldsymbol{\mu})=0\\ \text{Limit points}:&\mathbf{f}(\mathbf{x},\boldsymbol{\mu})=0\\ \text{and at least one of eigenvalues}:&\boldsymbol{\lambda}=0\\ \text{Hopf points}:&\mathbf{f}(\mathbf{x},\boldsymbol{\mu})=0\\ \text{and at least Re}\big{(}\boldsymbol{\lambda}_{ij}\big{)}=0\text{ of pair of complex eigenvluuws}:&\boldsymbol{\lambda}_{ij}=\pm 2i\boldsymbol{\pi}/T\\ \text{Periodic orbits}:&\mathbf{x}(T)=\mathbf{x}(0)+\int_{0}^{T}\mathbf{f}( \mathbf{x},\boldsymbol{\mu})dt\end{array} \right. \tag{9}\]  
The continuation process assumes that all functions of the system of Equation (2) are continuous and differentiable. Summarising, the numerical continuation methods are a set of tools that enables obtaining information required to conduct bifurcation analyses. These methods utilize the predictor-corrector technique for finding, tracking and constructing equilibrium curves or periodic orbits of a differential Equation (1), as solutions of a properly defined system of algebraic Equation (2). Information on the stability is calculated based on analysing the J eigenvalues of Jacobian matrix (5) for equilibrium states, or based on Floquet multipliers (in the case of periodic orbits). Bifurcations can be detected, as well as tracked at various bifurcation parameter values.