esa-sceva/satcom-chunk-collection huggingface dataset:
# Considering the Optimization Design of Urban Bus Network Scheduling
## 2 Synchronized Optimization Model for Network and Scheduling
### Model Construction
#### 2.3.3 Synchronous Optimization Model for Both Network and Scheduling  
Based on the above, the synchronous optimization model for both network and scheduling in this paper is represented as follows:  
\[\begin{array}{l}Z=min(T+\lambda F)\\ \\ \text{S.t}:\\ \\ \begin{array}{l}f_{min}\leq f_{r}\leq f_{max}\\ l_{min}\leq l_{k}\leq l_{max}\\ l_{k}=\sum_{j}^{5}\sum_{j}^{5}y_{ij}^{*}l_{ij}\\ \left[\frac{l_{j}}{d_{r}}\right]\leq 1.4\\ O_{i}^{r}\
eq O_{j}^{r}\end{array};\begin{array}{l}i,j=1,...,S;\ i\
eq j;\ r=1,...,R \end{array}\end{array} \tag{13}\]  
The objective function in the synchronous optimization model constructed in this paper is divided into two parts: passenger time cost and bus operation cost.  
Passenger time cost includes: waiting time (Equation (1)), inter-stop time (Equation (2)), dwell time (Equation (3)); bus operation cost includes: vehicle procurement cost (Equation (6)), route operation cost (Equation (7)).  
Constraints include: route length (Equation (9)), headway (Equation (10)), non-repeating route stations (Equation (11)), non-linear coefficient (Equation (12)).
# A Swarming Approach to Optimize the One-Hop Delay in Smart Driving Inter-Platoon Communications
## 6 Conclusions  
In this paper, we proposed a swarming approach to optimize the one-hop delay of inter-platoon communications through adjusting the minimum contention window size of each backbone vehicle in two steps. In the first step, we set a small average one-hop delay of backbone vehicles as the initial optimization objective and then proposed a swarming approach to find a minimum average one-hop delay of inter-platoon communications through adjusting the minimum contention window of each backbone vehicle iteratively. In the second step, we first set the minimum average one-hop delay found in the first step as the initial optimization objective and then adopt the swarming approach again to optimize the one-hop delay of each backbone vehicle to get close to the minimum average one-hop delay. After the second step, we can obtain the optimal minimum contention window sizes, i.e., the adjusted minimum contention window sizes with which the one-hop delay of each backbone vehicle could get close to the minimum average one-hop delay. The simulation results indicated that the one-hop delay is optimized and the other performance metrics including end-to-end delay, one-hop throughput, end-to-end throughput and transmission probability are considerably improved by using the optimal minimum contention window sizes, as compared with the IEEE standard contention window size.  
Simulation codes are available online at [[https://codeocean.com/2018/06/28/code-for-colon-a-swarming-approach-to](https://codeocean.com/2018/06/28/code-for-colon-a-swarming-approach-to)]([https://codeocean.com/2018/06/28/code-for-colon-a-swarming-approach-to](https://codeocean.com/2018/06/28/code-for-colon-a-swarming-approach-to)).  
Conceptualization, Q.W., P.F. and Z.L.; Methodology, S.N.; Software, S.N.; Validation, H.L.; Writing--Original Draft Preparation, Q.W.; Writing--Review and Editing, P.F. and Q.F.  
This work was supported by the National Natural Science Foundation of China under Grant No. 61701197 and 61571108, the Open Research Fund of National Mobile Communications Research Laboratory, Southeast University, under Grant No. 2018D15, and the Fundamental Research Funds for the Central Universities under Grant No. JUSRP11742.  
The authors declare no conflict of interest.
## 3 Fuzzy Logic Controller
### Architecture of Fuzzy Logic Controller  
The main components of the fuzzy controller framework for any managed system are the fuzzifier, knowledge base, fuzzy rule base, and defuzzifier, as shown in Figure 9.  
Crisp input values are converted to fuzzy values via the fuzzification process of the fuzzy controller. Inputs and outputs for each of them and the knowledge of fuzzy relationships are stored in the form of membership functions [28]. The fuzzy rule base uses the if-then rule to join input and output membership functions. The center of any fuzzy system is the inference engine, as it performs rough reasoning [29]. The defuzzification process is the final stage in the fuzzy model that transforms fuzzy values of the fuzzy inference engine into new values through the defuzzifier [30]. MATLAB's fuzzy logic toolbox is used for designing and implementing the fuzzy logic model.  
Figure 9: Structure of the fuzzy logic controller.
## 2 Background
### Identification of the Functional User Requirements (FURs)
#### Identification of the Functional Users  
The following functional users send data across boundaries, triggering processes in the analytics system.  
* Website Users: Human users who interact with websites by performing actions such as page views, button clicks, or form submissions. Their interactions trigger events that are captured by Google Analytics.
* Client-Side Devices (browsers): Software (e.g., web browsers) through which users access a website. These devices execute JavaScript tags and send data to Google Analytics when events (e.g., page loads and button clicks) are triggered.
* Google Analytics: The external system receives data from the website and processes them for analytics. This can be considered as a functional user in terms of its role in receiving and storing analytical data.
# Energy Saving Strategy of UAV in MEC Based on Deep Reinforcement Learning
## 6 Simulation Results
### Switching UAV On or Off  
During the operation of the MEC system, individual UAVs may shut down due to malfunction or loss of power. New UAVs may also be connected to the system. In this section, the stability of the DOTO algorithm will be analyzed when individual devices are turned off or new devices are connected during operation.  
In Figure 8, we show whether the training loss converges quickly after turning the device on or off. When the number of iterations is 400, one UAV is randomly turned off in the MEC system with six UAVs, and two UAVs are newly connected in the system with seven UAVs. At this time, the training loss fluctuates greatly, rising (or falling) rapidly, but it converges again after around 100 iterations. This shows that the DOTO algorithm automatically updates its own offloading strategy and converges to obtain the optimal offloading strategy under the new environmental state.  
In Figure 9, we further show the more complex case where one of the UAVs is randomly turned off and then turned on in an MEC system with 10 UAVs. Specifically, when the number of training steps is 900, one UAV is randomly turned off. Moreover, when the number of training steps is 1800, it is turned on and re-connected to the system. It can be  
Figure 8: Training loss obtained by turning UAV on or off in the MEC system at iteration 400.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Training Sample**} & **Ratio of Residual Energy to** & **Convergence Performance** \\  & **Optimal Decision** & \\ \hline
16 & & non-convergence \\ \hline
32 & 0.9985 & convergence fluctuates greatly \\ \hline
64 & 0.9990 & convergence \\ \hline
128 & 0.9991 & convergence \\ \hline
256 & 0.9990 & convergence \\ \hline
512 & 0.9988 & convergence fluctuates greatly \\ \hline
1024 & 0.9985 & convergence fluctuates greatly \\ \hline \hline \end{tabular}
\end{table}
Table 3: The effect of training sample size on algorithm performance. Here, we set the experience replay pool size to 1024 and \(N=8\).  
seen from Figure 9 that the training is in a convergent state at first. However, after randomly shutting down one device, the training loss fluctuates greatly and rises rapidly, reaching convergence again after around 100 steps of training. When the number of training times is less than 1800, we reopen the original device to access the MEC system. At this point, the training loss fluctuates again and quickly converges to the state of the original 10 devices. This shows that the DOTO algorithm has strong self-adjustment ability and can be well suited for complex MEC online offloading situations.  
The above two experiments fully demonstrate that the DOTO algorithm has good adaptability.
## 3 Simulation Results and Analysis
### Read Delay Simulation and Comparison  
Read delay, often referred to as read access time (TRA), is defined as the time interval that elapses from the moment the word line voltage rises to 50% of VDD after transitioning from an initial low state. This interval continues until a voltage difference of 50 mV is established between the bit line (BL) and the bit line bar (BLB). Generally, TRA is significantly influenced by the reading current flowing through the access transistors and pull-down transistors, as well as the capacitance associated with the bit line. This relationship underscores the importance of optimizing both the electrical characteristics of the transistors and the capacitive load to achieve better read performance.  
Thus, the more access transistors that are used in a read operation, the more paths connecting to the bit line exist, so the delay tends to decrease; however, on the other hand, due to parasitic components of these transistors, the delay of the read operation may also increase [10]. Examining the graph of Figure 5, most cells show that using either one or two pairs of access transistors leads to similar levels of delay, as all are utilized for reading operations. RSLP16T has two pairs of access transistors, but due to considering read stability, only one pair is used for read operations. Consequently, the remaining pair of access transistors acts as parasitic components, resulting in a longer read delay compared to other cells.
## 5 Hybrid Modeling of Heterogeneous Servers with General Service Time Distributions
### Application to Non-Exponential Service Time Distributions  
In Table 5, the results of the simulation and model B are compared for exponential service time and also normal or lognormal distributions. The normal distribution can be appropriated for logistic or manufacturing systems, where the coefficient of variation (CV) in the range from 0.2 to 0.5 is realistic [38]. A 0.3 value was set for this study. The lognormal distribution was found to properly fit some service times, like call centers. Again, CV = 0.3 was used, based on [39]. For the application of the MCM, each point was obtained from 10,000 shots to the distribution.  
We note that sampling the service times from a normal distribution of a given mean provides a normal distribution of service times with the same mean. In the case of the lognormal distribution, in order to provide a mean service time m and coefficient of variation CV, the sampling distribution must sample from the mean \(\mu\) and standard deviation \(\sigma\) given by (8).  
\[\mu=\ln\left[\frac{m}{\sqrt{CV^{2}+1}}\right];\;\sigma=\sqrt{\ln(CV^{2}+1)} \tag{8}\]  
As a consequence of non-exponential service times, the throughput did not vary in a significant way with respect to the exponential times of the same average; therefore, the independence of exponential arrivals seemed to dominate over the non-exponential service times. The average difference between the discrete events simulation of the system and model B with MCM was 0.8% (ranging from \(-\)0.4% to 2.4%) for the normal distribution and 0.7% (ranging from \(-\)0.5% to 2.2%) for the lognormal distribution. The pattern of homogeneous server rates (_1, 1, 1_) offered better throughput in all the cases under analysis. The difference with the solution of exponential service times for every server's arrangement was very low; therefore, these results initially support the use of the transition matrix as a first approach to study non-homogeneous server systems M/Gi/k/k. Further testing would consolidate this possibility. While the equivalence of homogenous systems M/M/k/k and M/G/k/k is well established, the performance of heterogeneous systems  
Figure 10: Effect of arrival rate uncertainty in operating curve of model B.  
seems to also be mainly influenced by the average values and with little dependence on the service time distribution.
# Ru(terpy)-Based Conducting Polymer in Electrochemical Biosensing of Epinephrine
## 3 Results and Discussion
### Selectivity  
In order to evaluate the selectivity of the Au/pRuTt/Tyr biosensor, the effect of the simultaneous presence of some typical interfering substances was studied. Ascorbic acid (AA), uric acid (UA), tryptophan (Trp), and L-cysteine (L-cys) were considered since these are typical components of biological fluids. The DPV curves reported in Figure 5 and in Figure S4 suggest that the oxidation potentials of the EP and the considered interferets, although quite close, are different enough to allow a selective determination of the main analyte. Unlike the other interfering substances investigated, L-cys was not detected in a DPV scan in these experimental conditions, likely because the presence of the -SH groups in its structure deactivates the enzyme.  
As described in Section 2.5, changes in the concentration of interferets compared to a 10 umol dm\({}^{-3}\) EP solution were considered, and the results are summarized in Figure 6.  
As Figure 6 shows, the effect of the interfering substances is acceptably low (from +3% to \(-\)11%) when EP and interferets have the same concentration. On the other hand, when increasing the concentration of interfering molecules, their effect is also more evident, causing a decrease of up to 66% in the case of EP:L-cys 1:10. Furthermore, as is predictable according to its chemical structure, L-cys seems to have the highest interfering effect on the determination of EP.  
Figure 5: DPV curves of solutions of EP (10 μmol dm\({}^{-3}\), **Top Left**), ascorbic acid (AA) (500 μmol dm\({}^{-3}\), **Top Right**), uric acid (UA) (100 μmol dm\({}^{-3}\), **Bottom Left**), and tryptophan (Trp) (100 μmol dm\({}^{-3}\), **Bottom Right**) in 0.1 mol dm\({}^{-3}\) phosphate buffer at the Au/pRuTt/Tyr biosensor.
## 5 Results and Analysis
### The Results of the Impact on Horizontal Displacement  
Figure 12 displays a comparative chart of horizontal displacements for various excavation sequencing plans. Unlike the vertical displacement change curve, the trends and relative magnitudes of horizontal displacements at different tunnel nodes exhibit a more complex phenomenon for each excavation plan. Overall, all three plans show a continuous increase in horizontal displacement, indicating soil deformation during tunnel excavation. Sequential excavation, characterized by a step-by-step advancement method with the excavation of individual pits, maintains lower horizontal displacement throughout the entire construction period compared to the other two plans. This may be partially attributed to the gradual stress release in the soil under smaller stress impacts, reducing horizontal displacement. However, it is noteworthy that an increase in soil volume in the latter half of the construction period may lead to an accelerated increase in horizontal displacement, which needs consideration in engineering practice.  
Simultaneous excavation (Plan 2) generates larger horizontal displacements in the vertical direction, with the horizontal displacement of nodes (N5792, N7569) in the vertical direction of the tunnel being the largest among the three plans. Additionally, the horizontal displacement in the horizontal direction of node N5792 above the tunnel is 17.66% smaller than that of node N7569 below the tunnel. Comprehensive excavation (Plan 3) induces significant horizontal impacts in the horizontal direction, making horizontal displacements of nodes (N61174, N59140) the largest among the three plans. However, there is considerable variation in the horizontal displacement values between the two nodes. The horizontal displacement value of node N59140 on the side closer to the excavation pit is 33.85% larger than that of node N61174 on the far side.  
Figure 12: Comparison chart of horizontal displacement variation at various nodes for different excavation sequence scenarios. (**a**) Node: 61174, (**b**) Node: 5792, (**c**) Node: 59140, (**d**) Node: 7569.
# A Comparative Study of Machine Learning Techniques for Cell Annotation of scRNA-Seq Data
## 2 Materials and Methods
### Single-Cell Annotation and Pre-Processing of scRNA-Seq  
Single-cell analysis commences with the isolation of individual cells from diverse tissues. Subsequent to isolation, these cells undergo sequencing, resulting in the generation of a cell-by-gene count matrix, as shown in Figure 1a. In this study, we obtained the cell-by-gene count matrices of datasets from different sources. To ensure quality input for downstream analysis, we first performed standard pre-processing steps, including quality control, normalization, feature selection, and batch effect correction (Figure 1b). The process begins with quality control to remove low-quality or damaged cells based on metrics such as mitochondrial gene content, gene count, and unique molecular identifier (UMI) counts. High mitochondrial RNA content may indicate damaged cells, while too few or too many gene counts help identify empty droplets or doublets. Low unique molecular identifier (UMI) counts suggest poor-quality cells due to insufficient RNA capture or sequencing depth. Next, normalization is applied to adjust for sequencing depth using methods like log normalization. Then, highly variable genes (HVGs) are selected to retain biologically relevant features while reducing noise. Next, we used Seurat as batch correction methods to address technical differences across batches. Subsequently we applied Uniform Manifold Approximation and Projection (UMAP) as a dimensional reduction technique to reduce data dimensions for visualization and feature extraction. Cells were then clustered using the Leiden algorithm, a graph-based community detection method that captures local cell-cell similarity in expression space. Finally, cell type annotation was performed using reference-based methods, where clusters were assigned biological labels by comparing their expression profiles to curated reference datasets containing well-defined marker genes. This systematic and reproducible pipeline ensured that the data were clean, structured, and biologically interpretable for downstream analyses.
# Manufacturing of Micro-Lens Array Using Contactless Micro-Embossing with an EDM-Mold
## 3 Finite Element Model  
Between glass transition and melting temperature, PMMA shows as viscoelastic behaviors. In this study, the viscoelastic material was treated as a general Maxwell model. The temperature dependence of PMMA material can be treated as a Thermally Rhetorically Simplicity (TRS) model by the Williams-Landel-Ferry (WLF) equation [39]. C1 and C2 are the WLF equation constants that can be fitted.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Levels**} & \multicolumn{3}{c}{**Factors**} \\ \cline{2-4}  & \multicolumn{2}{c}{**Embossing Temperature (\({}^{\circ}\)C)**} & \multicolumn{2}{c}{**Embossing Force (N)**} & \multicolumn{1}{c}{**Holding Time (s)**} \\ \hline
1 & 105 & 200 & 120 \\
2 & 120 & 400 & 180 \\
3 & 135 & 800 & 240 \\ \hline \hline \end{tabular}
\end{table}
Table 2: List of factors, levels for orthogonal design.  
Figure 3: Schematic of micro-embossing process using electrical discharge machining (EDM) mold.  
The response of PMMA material to the continuous stress history can be expressed by the general Maxwell model  
\[\sigma(t)=\int\limits_{0}^{t}G(t-\tau)\frac{d\varepsilon(t)}{d\tau}d\tau+G(t) \varepsilon(0) \tag{1}\]  
\[G(t)=G_{i}\sum\limits_{i=1}^{N}w_{i}\exp(-t/\tau_{vi})+G_{\infty}, \tag{2}\]  
where \(t\) is the current time; \(\tau\) the past time; \(w_{i}\) are the weigh factors; and \(G\) is the modulus of material.  
Stress relaxation at different temperatures can be expressed by WLF Equation [39]  
\[\log a_{T}=-\frac{C_{1}(T-T_{ref})}{C_{2}+(T-T_{ref})}, \tag{3}\]  
where \(T_{ref}\) is the reference temperature and \(C_{1}\) and \(C_{2}\) are the WLF equation constants that can be fitted. As for the PMMA material, C1 and C2 can be taken as 17.4 and 51.6, respectively [40]. The mechanical properties were obtained from the literature [41,42].  
Figure 4 shows the initial geometry of the model, including the PMMA, the upper mold, lower mold, and mold core. In order to reduce the computation, one fourth of the coupled thermal-displacement model was developed in the software. The PMMA was modeled as linear viscoelastic material, and the molds were treated as elastic material. Two master-slave types of contact interaction pairs were established when the interface behaviors were treated as hard contact and coulomb friction model with penalty formulation. The PMMA/mold interface friction coefficient was obtained from the ring compression tests. The surfaces of upper and lower mold were defined as the master surface. The vertical displacement of the upper mold was fixed with the horizontal displacement to keep free. The heating mechanism in the PMMA embossing refers to gap heat conductance and radiation.
## 5 Evaluation
### Experimental Setup  
The NS-3 [22] network simulator was employed to develop the simulation environment for this experiment, replicating a blockchain network environment running on Hyperledger Fabric version 2.2. The program was deployed in a virtual machine hosted on a server with an Intel Core i5-10400F CPU and 32 GB of DDR4 3200 MHz RAM. The virtual machine was operating on Ubuntu 20.04, with 8 GB of RAM and a dual-core processor allocated to it. At the experiment's onset, the network comprised 100 nodes, all connected in a peer-to-peer architecture, representing nodes within the same member cluster in Hyperledger Fabric.  
Table 1 summarizes the basic parameters of the simulation experiment, including the network form, bandwidth, block size, interval of block sending, the initial number  
of peers, latency range, etc. Specifically, the initial number of peers refers to the count of peers present within the channel at the start of the blockchain network. The latency range delineates the span of propagation delay values between Peers in the channel, signifying the magnitude of a vector space with time delay as the Euclidean distance. Moreover, \(eps\) and \(min\)_Pts_ correspond to the neighborhood radius and the minimum aggregate count of objects within the neighborhood in the DBSCAN algorithm, respectively. As this experiment is simulated using software, its fidelity to the real Fabric network is constrained by environmental conditions, resulting in certain parameter settings that may deviate from actual values. Nevertheless, the parameters presented in the Table 2 are grounded on authentic Fabric parameters and represent the most suitable values determined through iterative experiments and comparisons, thereby enabling effective observation of the pros and cons of distinct methods.