esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Incremental Improvement of Market Mechanism
### Analyzing Agents' Equilibrium Behavior  
The experimental results of the penalized DA mechanism demonstrate that truth-telling buyers are better off than untruthful buyers in the heterogeneous population of buyers. For the next step, we need to understand the interactions between buyers and sellers with various bidding strategies to ensure quasi incentive compatibility of buyers along with other favorable properties, such as efficiency and stability, in our online markets of perishable goods. The Nash equilibrium is an appropriate solution concept for understanding and characterizing the strategic behavior of self-interested agents. However, computing the exact Nash equilibria is intractable for a dynamic market with non-deterministic aspects [24]. We analyze the market through simulations across the restricted strategy space to evaluate its quasi incentive compatibility for buyers. In the experiments, buyers adopt CONR \(\beta\) strategies, in which buyer \(j\) bids \(\vartheta_{j}^{t}=\beta\upsilon_{j}^{p}\), with five different values of \(\beta\) (i.e., 0.2, 0.4, 0.6, 0.8, and 1.0), and sellers use four types of bidding strategies (i.e., MODE, TRUE, MONO, and STEP) explained in Section 3.2. In the \(n\)-th market of the experiments, seller \(i\)'s valuation is \(\upsilon_{i}=60+2(i-2n)\) and buyer \(j\)'s valuation is \(\upsilon_{j}=60-2(j-2n)\) as explained in Section 3.1. Because agents on each side are faced with the same utility maximization problem, they follow the same strategy in the equilibrium analysis. Therefore, we consider symmetric strategy profiles for each side of agents. Table 1 shows the payoff matrix between sellers and buyers in the online market with low risk of trade failures shown in Figure 1c. Buyers have more chances of increasing their surpluses by underbidding in the lower-risk markets. Each cell in the table represents the result of interactions between the sellers and buyers with the corresponding strategy. The cell is separated into the following two parts:  
* The upper part of the cell shows the following three types of information: (1) the average clearing price; (2) the average matching rate; and (3) the total utility that is the sum of traders' utility and penalties paid by buyers to the auctioneer. * The lower part of the cell represents utility of trader agents. The bottom left corner shows the averaged utility of seller agents, and the top right corner reveals that of buyer agents. Standard deviation of the average utility is shown inside parentheses. In the table, numbers in boldface represent utilities of the agent's best response to the other agent's bidding strategy, and cells in gray show Nash equilibria. It is noteworthy that the results in the first row of the table, where sellers adopt MODE strategy, are the same as those by the naive DA mechanism because there are no matching failures and thus, no penalty for buyers when sellers bid zero valuation. Figure 4: Simulation results in online markets with penalized DA mechanism. (**a**) Buyers’ surplus; (**b**) Buyers’ resale profit; (**c**) Buyers’ penalty; (**d**) Buyers’ utility.
###### Abstract  
Today, facility location planning primarily pertains to the long-term strategic and operational decision-making of large public and private organizations, and the significant costs associated with facility location, construction, and operation have turned location research into long-term decision-making. Presenting a hub location model for the green supply chain can address the current status of facilities and significantly improve demand coverage at an acceptable cost. Therefore, in this study, a network of facilities for hub location in the service site domain, considering existing and potential facilities under probable scenarios, has been proposed. After presenting the mathematical model, validation was performed on a small scale, followed by sensitivity analysis of the main parameters of the model. Furthermore, a metaheuristic algorithm was employed to analyze the NP-Hardness of the model. Additionally, two metaheuristic algorithms, NSGAII and MOPSO, were developed to demonstrate the efficiency of the model. Based on the conducted analysis, it can be observed that the computational time increases exponentially with the size of sample problems, indicating the NP-Hardness of the problem. However, the NSGAII algorithm performs better in terms of computational time for medium-sized problems compared to the MOPSO algorithm. These algorithms were chosen due to their proven efficiency in handling NP-hard optimization problems and their ability to balance exploration and exploitation in search spaces.  
f acility location; maximum coverage; telecommunication; hub location; NSGAII; MOPSO +
Footnote †: journal: _Algorithms_
# Sending-or-Not-Sending Twin-Field Quantum Key Distribution with Light Source Monitoring
###### Abstract  
Twin-field quantum key distribution (TF-QKD) is proposed to achieve a remote key distribution with a maximum secure transmission distance up to over 500 km. Although the security of TF-QKD in its detection part is guaranteed, there are some remaining problems in the source part. The sending-or-not-sending (SNS) protocol is proposed to solve the security problem in the phase post-selection process; however, the light source is still assumed to be an ideal coherent state. This assumption is not satisfied in real-life QKD systems, leading to practical secure issues. In this paper, we discuss the condition that the photon number distribution (PND) of the source is unknown for the SNS protocol, demonstrate that the security analysis is still valid under a source with unknown PND, and show that with light source monitoring, the performance of the SNS protocol can remain almost unchanged.  
quantum key distribution; twin-fields; sending-or-not-sending; light source monitoring 2020 IEEEexample: accepted: 25 December 2019; Published: 26 December 2019
## 3 Results  
### NMR Results  
A typical NMR spectrum of a beer sample is shown in Figure 1. The concentrations of several alcohols, organics, carbohydrates, and amino acids can be determined from a single spectrum using maleic acid as an internal standard. Concentrations were calculated by the comparison of integrals from unique assigned peaks from each component molecule (Table 2 shows the assignment), knowing the number of protons associated with each component signal (N), the number of mass of maleic acid in solution (10 mg added from a 100 mg/mL stock solution) and the volume of sample (175 \(\upmu\)L).  
Concentrations of major organic compounds are listed in Table 3 and appear to be in reasonable agreement with previously published data [12; 13].  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Component** & **Shift (ppm)** & **Multiplicity** & **J (Hz)** & **Assignment** & **N** \\ \hline \multicolumn{6}{c}{**Organic Acids**} \\ \hline Lactic Acid & 1.33 & d & 7.0 & CH\({}_{3}\) & 3 \\ Succinic Acid & 2.67 & s & - & CH\({}_{2}\) & 4 \\ Acetic Acid & 2.08 & s & - & CH\({}_{3}\) & 3 \\ Malic Acid & 2.90/2.85 & dd/dd & 16.5,4.6/6.7, 16.5 & CH\({}_{2}\) & 2 \\ Pyruvic Acid & & s & - & CH\({}_{3}\) & 3 \\ Pyrruvic Acid hydrate & 1.57 & s & - & CH\({}_{3}\) & 3 \\ Citric Acid & 3.02, 2.86 & d, d & 15.8 & CH\({}_{2}\) & 4 \\ Formic Acid & 8.45 & s & - & CH & 1 \\ \hline \multicolumn{6}{c}{**Alcohols**} \\ \hline Ethanol & 1.17 & t & 7.1 & CH\({}_{3}\) & 3 \\ iso-Butanol & 0.87 & d & 6.6 & CH\({}_{3}\) & 6 \\ iso-Pentanol & 0.88 & d & 6.6 & CH\({}_{3}\) & 6 \\
1-Propanol & 0.87 & t & 7.1 & CH\({}_{3}\) & 3 \\
2,3-Butandiol & 1.13 & m & - & CH\({}_{3}\) & 6 \\ \hline \multicolumn{6}{c}{**Carbohydrates**} \\ \hline Maltooligosaccharides & 4.3–4.8/4.9–5.5 & d & - & CH & 1 \\ Lactose & 4.45 & d & 7.7 & CH & 1 \\ Glycerol & 3.55 & m & 5.4 & CH\({}_{2}\) & 2 \\ \hline \multicolumn{6}{c}{**Amino Acids**} \\ \hline Histidine & 8.7 & s & - & CH & 1 \\ Uridine & 7.86 & d & 8.1 & CH & 1 \\ Tryptophan & 7.58 & s & - & CH & 1 \\ Phenylalanine & 7.37 & m & - & CH & 5 \\ Tyrosine & 7.17 & d & 8.6 & CH & 1 \\ Proline & 2.12/2.39 & m & - & CH\({}_{2}\) & 2 \\ Alanine & 1.55 & d & 7.3 & CH\({}_{3}\) & 3 \\ Valine & 1.06/1.02 & d/d & 7.1 & CH\({}_{3}\) & 3 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Assignment of unique marker signals for each molecular component quantified in the beer samples.  
Quantitative NMR has been demonstrated previously to be a precise method with variations of less than 1.0% [14; 15]. To obtain an idea of the repeatability of the NMR data, the spectra were processed seven times, and the standard deviation and % Relative Standard Deviation (%RSD) were calculated. Table 4 shows the results obtained from one of the beer samples. High intensity component signals that are well resolved from other signals yield a low measurement to measurement error (<0.5% of absolute value). Overlapping low intensity signals yield standard deviations of less than 5% of the absolute value.
# The Fast Discrete Tchebichef Transform Algorithms for Short-Length Input Sequences
## 1 Introduction
### The Main Contributions of This Paper  
As previously mentioned, fast \(N\)-point DTT algorithms for short-length signals can be developed via data flow graphs with a butterfly architecture and sparse matrix factorization. However, for some values of \(N\), constructing such architecture can be challenging. To address this shortcoming, we propose using the structural approach in addition to sparse matrix factorization to develop fast real DTT algorithms for short-length input signals. This approach was successfully applied to factorize the matrices of trigonometric transforms in [37; 38; 39; 40]. As a result, the number of arithmetic operations for these transforms was significantly reduced. According to the structural approach, the rows and columns of the transform matrix are rearranged, possibly changing the sign of some rows or columns. Then, the predefined templates are searched and extracted from the transform matrix. Based on the predetermined factorization of templates, the factorization of the entire transform matrix is obtained.  
The structural approach to DTT matrix decomposition is due to the symmetrical structure of the DTT coefficients matrix. However, as \(N\) increases, template extraction becomes a cumbersome process. That is why it is relevant to integrate the structural approach into the construction of data flow graphs with butterfly architecture. The efficiency of this trick owes to the symmetry of DTT coefficients, which can be paired for constructing butterfly modules. Thus, this research aims to reduce the computational complexity of a 1D real DTT by developing fast algorithms. These algorithms combine sparse matrix factorization with the structural approach for input sequences of length \(N=3\), \(4\), \(5\), \(6\), \(7\), \(8\).  
The paper is organized as follows. The problem of reducing the computational complexity of DTT and the aim of the research are introduced in Section 1. Notations and a mathematical background are presented in Section 2. Next, in Section 3, fast algorithms for a real DTT are designed for \(N\) in the range from 3 to 8. A discussion of the research results is presented in Sections 4 and 5. Also, the computational costs required to implement the developed and existing algorithms are compared. In Section 6 the conclusions are provided.
## 4 Conclusions  
In summary, a high-efficiency and long-service-time GS-SSF physically coated with COF-TPB-DMTP was developed, which was etched via the two-step electrochemical etching method. The built SPME-GC-FID method was employed to determine five PAEs in bottled tea beverages. The TPB-DMTP-GS-SSF showed larger EFs than the bare GS-SSF and the two commercial fibers. For only the physical coating, the prepared TPB-DMTP-GS-SSF exhibited a sound service time, low LODs and reproducibility due to the firm combination between the COFs coating and the gully-like surface of the SSF.  
**Supplementary Materials:** The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/nano15050385/s1](https://www.mdpi.com/article/10.3390/nano15050385/s1)]([https://www.mdpi.com/article/10.3390/nano15050385/s1](https://www.mdpi.com/article/10.3390/nano15050385/s1)), Figure S1. The schematic diagram of the synthesis of COF-TPB-DMTP; Table S1 Average percentage increase in EFs of the TPB-DMTP-GS-SSF in five PAEs with different fibers.  
**Author Contributions:** Conceptualization, Y.Y. and B.L.; methodology, Y.Y. and B.L.; software, Y.Y.; validation, Y.Y. and K.Z.; formal analysis, Y.Y. and H.Z.; investigation, Y.Y. and K.Z.; resources, B.L.; data curation, Y.Y.; writing--original draft preparation, Y.Y.; writing--review and editing, B.L. and H.Z.; visualization, Y.Y.; supervision, Y.Y., B.L. and H.Z.; project administration, B.L.; funding acquisition, B.L. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by the National Natural Science Foundation of China, No. 21974043.  
**Data Availability Statement:** Data are contained within the article and Supplementary Materials.  
**Conflicts of Interest:** The authors declare no conflicts of interest.
## 4 Experimental Results
### Feature Importance
In addition, we constructed an ensemble model composed of a CNN model and a basic DNN model to improve the model performance. Finally, we identified and compared the important variables of the ML model and the DL model. Figure 9 shows the F1-score of the proposed model and the comparative models. F1-score can accurately evaluate model performance for imbalanced data and is calculated as the harmonic average of recall and precision. The F1-score of the proposed XmCNN model significantly outperformed the F1-score of the DNN model and the comparative ML models. We additionally confirmed that the ensemble approach, which combines DL models, was effective in maximizing the performance of DL models. As shown in Figure 10, the Ensemble (\(\text{CNN}_{5(3)}\), \(\text{CNN}_{5(4)}\), \(\text{CNN}_{5(5)}\), \(\text{XmCNN}\)) model presented the lowest FPR among all the models. In this study, the FPR represents the probability of incorrectly calling customers who do not require information regarding insurance policy loans. Outbound telemarketing is an effective promotion for potential customers but can also be considered advertising spam for uninterested customers. If a company continues to send outbound telemarketing to the incorrectly selected target audience, it can cause customer dissatisfaction and even damage the corporate image and associated brand perception. Therefore, successful targeting for outbound telemarketing is very important. Our proposed XmCNN model and the ensemble model not only contribute to improving the accuracy of outbound telemarketing predictions but also imply that the spam problem can be reduced by minimizing the FPR. The experimental results on the feature importance were different in the ML model and DL model. In the ML model, variables related to the past transaction amount or period appeared to be important. Because the reuse rate of insurance policy loans is high, past transaction patterns seem to be important. This result can be predicted to some extent through domain knowledge. On the other hand, the DL model yielded unexpected results from domain knowledge. The variables related to the channels for insurance contracts appeared to be important. In particular, variables related to the contract by financial planner or general agent were important. The financial planner is affiliated with only one insurance company and can advise only that company's products to customers. On the other hand, a general agent can partner with several insurance companies to advise products from various companies to customers. Compared to other channels, financial planners or general agents seems to perform well at guiding insurance policy loans when making insurance contracts. Figure 9: F1-score comparison of the overall models. Ensemble indicates Ensemble (\(\text{CNN}_{5(3)}\), \(\text{CNN}_{5(4)}\), \(\text{CNN}_{5(5)}\), \(\text{XmCNN}\)) model.
## 3 Results
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Test Statistic** & **Sig.** & **Adj. Sig. a** \\ \hline \multicolumn{3}{c}{**Maximum Load (N) across Group**} \\ \hline Group D-Group C & 10.30 & 0.05 & 0.29 \\ \hline Group D-Group B & 11.80 & 0.02 * & 0.14 \\ \hline Group D-Group A & 12.30 & 0.02 * & 0.11 \\ \hline Group C-Group B & 1.50 & 0.77 & 1.00 \\ \hline Group C-Group A & 2.00 & 0.70 & 1.00 \\ \hline Group B-Group A & 0.50 & 0.92 & 1.00 \\ \hline \multicolumn{3}{c}{**Elastic Modulus (MPa) across Group**} \\ \hline Group D-Group C & 8.00 & 0.13 & 0.76 \\ \hline Group D-Group B & 13.10 & 0.01 * & 0.07 \\ \hline Group D-Group A & 24.10 & \textless{}0.001 * & 0.00 \\ \hline Group C-Group B & 5.10 & 0.33 & 1.00 \\ \hline Group C-Group A & 16.10 & 0.00 * & 0.01 \\ \hline \multicolumn{3}{c}{**Graph B-Group A**} & 11.00 & 0.04 * & 0.21 \\ \hline \multicolumn{3}{c}{**Yield Strength (Offset 0.2\%) (MPa)**} \\ \multicolumn{3}{c}{**across Group**} \\ \hline Group D-Group C & 11.00 & 0.04 * & 0.21 \\ \hline Group D-Group B & 20.25 & \textless{}0.001 * & 0.00 \\ \hline Group D-Group A & 23.35 & \textless{}0.001 * & 0.00 \\ \hline Group C-Group B & 9.25 & 0.08 & 0.46 \\ \hline Group C-Group A & 12.35 & 0.02 * & 0.11 \\ \hline Group B-Group A & 3.10 & 0.55 & 1.00 \\ \hline \multicolumn{3}{c}{**Compressive Strength (MPa)**} \\ \multicolumn{3}{c}{**across Group**} \\ \hline Group D-Group C & 10.10 & 0.05 & 0.32 \\ \hline Group D-Group B & 18.80 & \textless{}0.001 * & 0.00 \\ \hline Group D-Group A & 25.50 & \textless{}0.001 * & 0.00 \\ \hline Group C-Group B & 8.70 & 0.10 & 0.58 \\ \hline Group C-Group A & 15.40 & 0.00 * & 0.02 \\ \hline Group B-Group A & 6.70 & 0.20 & 1.00 \\ \hline \hline \end{tabular} Each row tests the null hypothesis that the group 1 and group 2 distributions are the same. Asymptotic significance (2-sided tests) are displayed. * The significance level is 0.050. * Significance values have been adjusted using the Bonferroni correction for multiple tests.  
\end{table} 
Table 4: Pairwise comparisons of groups. Table 5 shows the distribution of fractures according to location in the specimens. Fracture type 12 (**multiple-direction fracture**) was the most common type for all groups. Fracture types 2, 3, 5, and 7 had not occurred in any specimens.
## 4 Methodology and Mathematical Model
### Objective Function  
The objective function, considered to evaluate the performance of a synchronization solution, aims to reduce both the number of workers on the line, the number of multi-manned workstations and to distribute workloads to workers as fairly as possible. It is based on three different key criteria. First, the line efficiency (LE) is the ratio of the time required to perform all assembly tasks to the total time available, as shown in Equation (1). It depends on the number of workers and the actual cycle time on the line (TMAX), i.e., the longest working time among the workers as defined in Equation (2). The second contribution is line length (LN), which is equal to the number of workstations (Equation (3)). Finally, the smoothness index (SI) considers the balance of the generated workloads and is given by Equation (4). These parameters are combined together to build the proposed objective function (Equation (5)), which is a non-dimensional amount greater than 1 to be minimized. It is calculated as the product of the three contributions given by the ratio between LE, LN, and SI and their most desirable values, which are LE\({}^{\max}\), LN\({}^{\min}\), and SI\({}^{\min}\), respectively. These serve to compare quantities with different measurement units using identical weights and they are defined by Equations (6)-(8), as a solution with 100% LE, minimum LN given by Equation (7), and SI tending to 0 (calculated as an objective fraction S of the cycle time) represent a perfectly synchronized line.  
\[\text{LE}=\frac{\sum_{\text{f}}\text{f}}{(\sum_{\text{k}}\sum_{\text{w}} \text{z}_{\text{wk}})\cdot\text{TMAX}},\ [\%] \tag{1}\]  
\[\text{TMAX}=\ \max_{\text{w}}\Bigl{\{}\sum_{\text{f}}\text{f}\cdot\text{z}_{ \text{wk}}\Bigr{\}},\ [\text{time unit}] \tag{2}\]  
\[\text{LN}\ =\ \sum_{\text{k}}\text{r}_{\text{k}},\ [\text{workstations}] \tag{3}\]  
\[\text{SI}\ =\ \frac{\sqrt{\sum_{\text{k}}\sum_{\text{w}}(\text{TMAX}-\sum_{ \text{j}}(\text{f}\cdot\text{z}_{\text{wk}}\cdot\text{z}_{\text{wk}}))^{2}}} {\sum_{\text{k}}\sum_{\text{w}}\text{z}_{\text{wk}}}\,\ [\text{time unit}] \tag{4}\]  
\[\text{Min}\ \Phi\ =\ \frac{\text{LE}^{\max}}{\text{LE}}\frac{\text{LN}}{\text{ LN}^{\min}}\ \frac{\text{SI}}{\text{SI}^{\min}} \tag{5}\]  
\[\text{LE}^{\max}\ =\ 100\%\,\ [\%] \tag{6}\]  
\[\text{LN}^{\min}=\left\lceil\frac{\sum_{\text{f}}\text{f}}{\text{c}}\right\rceil \cdot\frac{1}{\text{M}}\right\rceil,\ [\text{workstation}] \tag{7}\]  
\[\text{SI}^{\min}=\text{S-c },\ \text{S}\to 0\,\ [\text{time unit}] \tag{8}\]
## 4 Experiment and Analysis
### Evaluation Metrics  
The evaluation indices used in this experiment were Area Under the Curve (AUC) and Accuracy (ACC). AUC is the area enclosed by coordinates and axes under the ROC curve. Receiver operating characteristic curves are in the vertical and horizontal coordinates of the true positive rate (True Positive Rate) and the false positive rate (False Positive Rate), respectively. The area under the curve usually ranges from 0.5 to 1, and the higher the value range, the better the performance of the model.
# Dynamic Obstacle Avoidance and Path Planning through Reinforcement Learning
## 4 Discussion
### Research Gap in Reinforcement Learning Application in Mobile Robot Obstacle Avoidance  
Environmental complexity remains the major issue in mobile robot obstacle avoidance. Most research on reinforcement learning algorithms sometimes fails in a dead zone, such as a complex experimental environment, perhaps due to some inappropriate reward condition. This, however, makes it difficult for the robot to search the solution space; consequently, learning along the path of the wall and escaping the dead zone becomes difficult. Creating appropriate reward functions in the dynamic environment remains the main gap in research. However, the review of the included studies also shows that while authors are trying to help mobile robots plan their path and avoid obstacles in dynamic and static environments using the RL algorithm, little is known about the interaction of multiple mobile robots within a single static or dynamic environment. Few or no studies have demonstrated how multiple mobile robots can co-exist within a single environment, carrying out different tasks without clashing with one another while still avoiding obstacles. There is also the issue of robot cooperation, and more issues will need to be resolved. Hence, there is a need for more studies to investigate multiple-robot cooperation using the RL algorithm in a different environment. Another important gap considered by this current study is that most review studies on this phenomenon of obstacle avoidance use virtual and simulated robots and test environments. In contrast, few studies investigated the efficacy of the RL algorithm in a real environment. The autonomous navigation of a robot in dynamic surroundings could still use a predictive navigation framework that can forecast the robot's long- and short-term trajectories. However, this could enhance the robot's capacity for learning.