esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Computational Experiments
### Experimental Results and Discussion
#### 6.2.2 Performance Evaluation of B-DMOEA  
This section analyses the performance differences between the B-DMOEA and each competitor in solving the D-HRDLB problem. Tables 6 and 7 provide the MIGD results (mean and standard deviation) for the three groups of paired algorithms, including Tr-NSGA-II versus B-NSGA-II, Tr-RVEA-i versus B-RVEA-i, and Tr-IBEA versus B-IBEA, on small-scale and large-scale problem instances. The better mean for each instance is highlighted in boldface.  
values in 13, 9, and 14 of the 15 small-scale instances, respectively. The proportions of the large-scale instances where the three B-DMOEA algorithms, including B-NSGA-II, B-RVEA-i, and B-IBEA, outperform their competitors are 18/24, 16/24, and 14/24 for MIGD, respectively. To intuitively describe the distribution of solutions in three-dimensional objective space, we use D-HRDLB problem S2 as an illustration. Figure 7 plots the final of the six evolutionary algorithms. Figure 7a,b) plot the final solutions of Tr-NSGA-II and B-NSGA-II, respectively. As shown in Figure 7c, we merge the solutions in (a) and (b) to find the nondominated solutions among them. B-NSGA-II can find more nondominated solutions, which means that B-NSGA-II can cover the Pareto front better. From the remaining subplots, we can draw similar conclusions.  
Figure 7: Normalised nondominated solution set of problem S2. (**a**) Tr-NSGA-II; (**b**) B-NSGA-II; (**c**) Tr-NSGA-II and B-NSGA-II; (**d**) Tr-RVEA-i; (**e**) B-RVEA-i; (**f**) Tr-RVEA-I and B-RVEA-i; (**g**) Tr-IBEA; (**h**) B-IBEA; (**i**) Tr-IBEA and B-IBEA.  
Tables 8 and 9 give the MHV results for all six algorithms on small-scale and large-scale problem instances. Similarly, the three B-DMOEA algorithms outperform their corresponding competitors on most test instances. B-NSGA-II, B-RVEA-i, and B-IBEA achieve better MHV values than their competitors in 13, 13, and 14 of the 15 small-scale instances. Additionally, for large-scale instances, the winning ratio of the three B-DMOEA algorithms B-NSGA-II, B-RVEA-i, and B-IBEA against their competitors is 21 to 3, 18 to 6, and 20 to 4 in the 24 comparisons, respectively.  
These experimental results demonstrate that B-DMOEA can perform better than TrDMOEA. B-DMOEA combines the marginal and conditional distributions to learn the common features between the domains, making knowledge transfer more effective and further improving the performance of MOEAs compared with Tr-DMOEA. In addition, whether in small-scale or large
## 3 AIGC's Approach to Global Stress Reconstruction
### Fine-Tuning of Initial Network  
The ANN for global stress was established using simulation output data, and it requires fine-tuning before it can be deployed to physical structures. The reasons are as follows: (1) there are slight differences between the material properties, boundary conditions, mesh partitioning, load simulation, and other information used in simulation calculations and the real physical world; (2) the actual measurement point location and orientation may have geometric errors compared to the ideal placement state.  
Figure 2 illustrates the process of fine-tuning the ANN trained by simulation data (initial network). To fine-tune the initial network for deployment to physical structures, measurement points are randomly selected from areas of high stress. The actual sensor  
Figure 1: Technical path in establishing an ANN for global stress.  
data are compared to the predicted values of the initial network, and a fine-tuning matrix is added outside the output layer of the network through supervised learning. This process creates the final artificial intelligence network (referred to as the \"final network\") that can be deployed to physical structures.  
The process of fine-tuning the initial network is similar to the process of establishing the initial network, as both require a dataset for error comparison. The goal is to minimize the loss function through optimization algorithms and improve the ANN. The key difference arises in the dataset used for validation, which plays a crucial role in achieving optimal performance.  
During the establishment process, the dataset for validation can be easily obtained from simulation output data at a low cost, allowing for a larger number of elements to be used for validation. In this case, any element, apart from the feature and test elements, can be designated for validation purposes. This broad selection of elements facilitates a comprehensive evaluation of the network's performance.  
However, during the fine-tuning process, a much smaller number of elements are available for validation, as they require sensor measurements. This limited number of elements used for validation can potentially lead to the network becoming trapped in local optimization instead of attaining global optimization.
## 1 Introduction
The better the quality and parametric characteristics of the adders, the more direct their effect on the device used, and as a result, the device will be more popular. For this reason, the presentation of new structures for these circuits is of interest to integrated circuit designers. The main goal in designing digital adders is to increase speed and reduce power consumption. One of the factors indicating the efficiency and good performance of the adder block is its use in larger and more practical circuits. Among the challenges in the design of the composite layout and cells is the challenge of designing the crossovers of wires in the best way to reduce costs [9; 10; 11]. Nowadays, non-adjacent clock phases are used to design the crossover of passing wires in a single layer [9; 12], and the presence of crossover in the design increases the delay. As a result, we did not use any crossovers in our designs in the present research, which in turn reduced the number of cells, the consumption area, and delay; therefore, the designs are coplanar due to the lack of need for crossovers. Therefore, we designed a new XOR gate, and we designed and implemented two full adder (FA) circuits with this proposed XOR gate, which was designed without an inverter gate (NOT gate); with a reduced number of cells, reduced consumption area, less delay, and reduced cost function; without crossovers and rotated cells (cell-45); and with the coplanar method. The remainder of this article is organized as follows: an overview of QCA and previous works is given in Section 2. In Section 3, the design of the XOR gate is proposed, and then the architecture of the circuits of the full adder proposed by this research is presented. InSections 4 and 5, we compare the proposed designs with previous architectures. In Section 6, we conclude the article.
## 4 Results
### Relation between Travelling Index and Wave Number  
Carangiform fish have very good integrated swimming abilities in terms of their fast speed and high efficiency. Their lateral undulations are confined to the posterior third of the body [1; 2], and the mean propulsive wavelength is 98% of body length (BL; range 78%-106%) in mackerel [35]. [PERSON] [36] also found that the propulsive wavelength is slightly greater than the body length, with the exception of the Atlantic mackerel, with a propulsive wavelength of 93% of body length. As explained, the wavelength is varied with the fish species, and this difference in present study is analysed by the COD method. As illustrated in Equation (1), the motions of carangiform fish are decomposed into the standing part and travelling part. The separated parts are shown in Figure 9, and the calculated travelling index is 0.639. This indicates that the travelling part is larger than the standing part in the midline motions. Meanwhile, it is also demonstrated that the carangiform fish prefers a swimming motion with a large component of travelling wave.  
To study the relationship between wave number and travelling index, we selected the wave number as ranging from 0 to 14, while all other kinematic parameters are unchanged. The results show that the travelling index increases with the wave number until reaching a stable value, as displayed in Figure 10. Besides, if the wave number is zero, i.e., \(h(x,t)=\left(a_{1}+a_{2}x+a_{3}x^{2}\right)\sin(\omega t)\), this type of wave is a pure standing wave, and the travelling index is zero. For carangiform fish, the wavelength is around one body length, and it should be larger than 0.6 fish lengths, so the wave number is between 5.2 and 10.4 [1]. The range of travelling index is calculated as 0.52-0.78.  
Figure 8: Mean drag coefficient \(\overline{C}_{d}\) and RMS of lift coefficient \(C_{l}^{\prime}\) for flow past a transversely oscillating cylinder at different \(f_{e}/f_{0}\).
## 6 Performance Evaluation
### Performance Comparison between DETO and Other Baselines
#### 6.2.3 Effects of Different Numbers of UAVs  
The performance of DETO was evaluated across a range of UAV counts, varying from one to four, by considering the system objective, total latency, total transmission energy consumption, and total throughput. Figure 8a illustrates a decrease in the system objective as the number of UAVs rises. As the UAVs increase in number from one to two, then to three, and to four, the system objective of DETO decreases by 7.60%, 13.13%, and 4.53%, respectively. Similarly, GAETO decreases by 6.60%, 13.27%, and 4.60%, respectively, while the greedy approach records reductions of 5.97%, 13.21%, and 4.88%, respectively. Additionally, at the relevant UAV count intervals, DETO consistently outperforms the greedy technique by 2.33%, 4.03%, 3.94%, and 3.58%, respectively.  
For different numbers of UAVs, Figure 8b,c compare the total transmission latency and transmission energy consumption for all algorithms; significantly, DETO performs better than the alternatives. The transmission latency and the transmission energy consumption of MDs tend to reduce as the number of UAVs rises because the increased accessibility of UAVs enables more efficient task offloading. The average distance between MDs and UAVs reduces as the number of UAVs rises. Due to the shorter travel distance, data transfer takes less time and energy, resulting in reduced latency and lower energy consumption for MDs. For example, when the number of UAVs increases from three to four, the transmission latency of DETO, GAETO, and greedy decreases from 73.16 s to 69.84 s, from 74.53 s to 71.09 s, and from 76.16 s to 72.44 s, respectively. Similarly, the transmission energy consumption of DETO, GAETO, and greedy decreases from 7.31 J to 6.98 J, from 7.45 J to 7.10 J, and from 7.61 J to 7.24 J, respectively.  
As the number of UAVs increases, Figure 8d illustrates the corresponding growth in total throughput. This increase is because data transmission speeds can be boosted by reducing the distance between sending and receiving points with more UAVs. With the rise of UAVs, the performance of DETO improves by 17.02%, 21.84%, and 5.03%, respectively. Notably, the degree of improvement is slightly lower when the number of UAVs changes from three to four. This happens because the distances between MDs and UAVs do not change noticeably when there are three UAVs in the environment compared to when there are four UAVs. The DETO solution achieves higher throughput compared to GAETO and the greedy approach with the increasing number of UAVs. For example, the total throughput of DETO is 2.27% and 4.36% higher than that of the GAETO and greedy approaches, respectively, with four UAVs.
## 3 Results
### Adapting Model Based Approaches
#### 3.1.1 Adapting Traditional Filtering Strategies
The use of geographical information as complementary information have led to the approach achieving complete prediction accuracy. On the other hand, the approach is inapplicable on different scenarios unless the method is extended. In addition, the prediction accuracy needs significant improvement by considering the influences of contextual information.
## 3 Results and Discussion
### Partial Least Square Regression Prediction Analysis of SSCFC's Fluidity and Compressive Strength
#### 3.5.1 The Basic Principle of the PLSR Algorithm  
Given m dependent variables, \(y_{1}\), \(y_{2}\), \(\ldots\), \(y_{m}\), and n independent variables, \(x_{1}\), \(x_{2}\), \(\ldots\), \(x_{n}\), with k samples, form predictor matrix X and response matrix Y. Center and standardize the data in X and Y to obtain normalized matrices X0 and Y0. Extract first principal components U1 from X0 and V1 from Y0, requiring that U1 and V1 maximally capture variance within their respective matrices while achieving the highest mutual correlation. Establish regression equations between X0 and U1 and Y0 and V1, and then evaluate their regression accuracy. Terminate the algorithm if regression precision meets requirements; otherwise continue extracting components until satisfactory accuracy is achieved. After extracting p components U1-Up from X through iteration, perform multivariate regression of dependent variables using these components. Reverse the standardization process to obtain original-scale regression equations of y's versus x's [77].
## 3 Results  
For testing, the signal from the chip was directed outside through the gold leads on both sides, as shown in Figure 4a, and the chip was attached to a sub-board, as shown in Figure 4b. Figure 4c shows a cubic aluminum block with edges of 2 cm, placed on the bottom of the printed circuit board. The aluminum block provides a flat measuring environment and noise reduction. Figure 4d shows the Agilent 8719 ES vector network analyzer that was used to perform measurements on the fabricated BPF.  
The total size of the proposed BPF is 1.54 mm \(\times\) 0.81 mm, which corresponds to a size of (2.4 \(\times\) 10\({}^{-2}\)\(\times\) 1.3 \(\times\) 10\({}^{-2}\))\(\cdot\lambda_{\mathrm{g}}\), where \(\lambda_{\mathrm{g}}\) denotes the guided wavelength at a center frequency of 1.35 GHz. Figure 5a shows the scanning electron microscopy image that is used to verify whether the processing is defective or not, as this would be difficult to observe with the naked eye. The simulated current density around the resonant frequency is shown in Figure 5b, which indicates the current flowing through the center of the filter, and reflects the internal workings of the central region of the filter at resonance. The Q-factor represents the amount of energy that is lost on the filter, with higher values indicating greater loss. The Q-factor of the BPF can be calculated as:  
\[Q=\frac{f_{\mathrm{c}}}{f_{\mathrm{H}}-f_{\mathrm{L}}} \tag{10}\]  
where \(f_{\mathrm{H}}\) and \(f_{\mathrm{L}}\) are passband-edge frequencies in the upper and lower bands, respectively. As shown in Figure 6a, a 10 dB fractional impedance bandwidth of 29% was achieved, the fractional bandwidth is calculated by \((f_{\mathrm{H}}-f_{\mathrm{L}})/f_{\mathrm{c}}\)[18]. The insertion loss is as low as 0.26 dB, and the return loss is above 25.6 dB within the passband. A transmission zero is designed to locate at 3.7 GHz, where the admittance parameter Y\({}_{21}\) equals zero, with a magnitude of S\({}_{21}\) to be 36 dB in the rejection band. The location of transmission zero can be slightly changed with the dimension; however, it should be noted that it may affect the variations of other parameters, such as bandwidth and selectivity [18; 21]. A sharp roll-off is obtained both in the lower and upper bands, from 20 dB to 3 dB attenuation points, which confirms the good skirt performance. A relatively constant group delay over the entire passband  
Figure 4: Measurement setup. (**a**) Enlarged view of on-chip BPF with Au wire bonding; (**b**) Packaged device on a printed circuit board; (**c**) Side view of the final product with connectors; (**d**) Test setup.  
Figure 3: Employed advanced integrated passive device (IPD) technology. (**a**) Stack-up of device on GaAs substrate; (**b**) Flowchart of the IPD fabrication process.  
is plotted in Figure 6b, indicating the small distortion of the BPF. The negligible differences between the measurement and simulation results may be due to losses in the measuring instruments.
# Ext-LOUDS: A Space Efficient Extended LOUDS Index for Superset Query
## 1 Introduction
Most trie-based superset query algorithms focus on query performance and ignore the storage space overheads. In response to this shortcoming, we try to research efficient trie compression technologies which can compress the space overheads and support superset queries. Double array [11,12] and Level-Ordered Unary Degree Sequence (LOUDS) [13, 14, 15] are the two most eminent compressed trie representatives. Compared with double array, LOUDS has a much higher compression ratio; however, its retrieving performance is relatively low [16,17]. Many works are devoting to solve this problem. [PERSON] et al. [14] introduced double numbering which partitions LOUDS bit vector into 2 bit vectors according to runs of zeros and ones, then executes RANK & SELECT on shorter bit vector separately. Some recent works [18, 19, 20] researched efficient RANK & SELECT operations to improve retrieving performance of LOUDS. [PERSON] et al. [21] designed a novel succinct structure that supports the mapping between preorder ranks and level-order ranks of nodes in constant time. Differently, in this paper, we aim at exploiting LOUDS to facilitate efficient superset query. To do this, we design an efficient extended LOUDS index named Ext-LOUDS based on LOUDS. The core of Ext-LOUDS consists of 1 integer vector and 3 bit vectors. By developing efficient RANK and SELECT operations, the position of the parent node and child nodes of a certain node can be directly calculated. As a result, the pointer overheads can be easily avoided. In addition, extending trie nodes with bit vectors can further help us decrease the space overheads. Experimental results on two real datasets show that Ext-LOUDS can reduce space overheads by up to 50%-60% without significantly reducing query performance.
## 5 Step-Wise Integration of an IoT-Based Composing Set-Up to a Modern City and Its Ecosystem
### Energy Efficiency: SDG 7--Affordable and Clean Energy  
The use of forced aeration in composting piles of the facility is vital for the proper degradation of the waste and transformation into compost. As this process is automated and mechanically powered, it requires energy that can be sustainably obtained to reduce operational costs. For example, solar energy is used to power the composting facility's sensors and equipment and reduce the operation's gas emissions by replacing the gas generators used for mechanically aerating the compost piles [69]. Alternative energies such as thermal collectors, wind turbines, and biogas energy, other than fossil fuels, can be effectively used to power up the electronic components of the composting facility, improving energy efficiency and sustainability [104]. Furthermore, the design of the composting sensors and data collection devices for maximum energy efficiency can minimize the power consumption of these devices during their active usage within the composting facility. Furthermore, it is essential to implement sleep or low-power consumption modes when these devices are not collecting data. For example, intelligent composting bins for collecting compostable waste can be set up on sleep or low power modes when indicated empty [105].  
Materials Recyclability Andreusability: SDG 12--Responsible Consumption and Production and SDG 9--Industry, Innovation, and Infrastructure  
According to [26], the precious metallic components in IoT-based sensors and other electronic equipment can be extracted and reused in IoT-based sensors and other devices in the composating facility. For example, end-of-life electrical conductivity sensors and printers can extract carbon for remanufacturing electrical conductivity sensors for the composating facility [106]. The design of IoT devices, such as sensors, data storage units, and compostable waste collection bins for easy disassembly, saves labor, energy, and resources for recycling such end-of-life components in the composating facility [107]. Using reusable components in the composating ecosystem reduces the need for frequent replacements. For example, long-lasting and reusable polymers in composable waste collection bins can manufacture circuit boxes for IoT devices and insulation shields for composating vessels within the composing facility [106].
# Simultaneous Quantitation of Isoprenoid Pyrophosphates in Plasma and Cancer Cells Using LC-MS/MS
###### Abstract  
Isoprenoids (IsoP) are an important class of molecules involved in many different cellular processes including cholesterol synthesis. We have developed a sensitive and specific LC-MS/MS method for the quantitation of three key IsoPs in bio-matrices, geranyl pyrophosphate (GPP), farnesyl pyrophosphate (FPP), and geranylgeranyl pyrophosphate (GGPP). LC-MS/MS analysis was performed using a Nexera UPLC System connected to a LCMS-8060 (Shimadzu Scientific Instruments, Columbia, MD) with a dual ion source. The electrospray ionization source was operated in the negative MRM mode. The chromatographic separation and detection of analytes was achieved on a reversed phase ACCQ-TAG Ultra C18 (1.7 \(\upmu\)m, 100 mm \(\times\) 2.1 mm I.D.) column. The mobile phase consisted of (1) a 10 mM ammonium carbonate with 0.1% ammonium hydroxide in water, and (2) a 0.1% ammonium hydroxide in acetonitrile/methanol (75/25). The flow rate was set to 0.25 mL/min in a gradient condition. The limit of quantification was 0.04 ng/mL for all analytes with a correlation coefficient (r2) of 0.998 or better and a total run time of 12 min. The inter- and intra-day accuracy (85-115%) precision (<15%), and recovery (40-90%) values met the acceptance criteria. The validated method was successfully applied to quantitate basal concentrations of GPP, FPP and GGPP in human plasma and in cultured cancer cell lines. Our LC-MS/MS method may be used for IsoP quantification in different bio-fluids and to further investigate the role of these compounds in various physiological processes.  
LC-MS/MS; farnesyl pyrophosphate; geranyl pyrophosphate; geranylgeranyl pyrophosphate Using LC-MS/MS +
Footnote †: journal: Medical Cancer