esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Operational Efficiency Evaluation of Power Grid Enterprises
Although there was more investment in a low assessment region in 2013, the income was not obvious, and the efficiency evaluation results were low. When considering the delay of investment benefit, the dynamic calculation obtained a higher evaluation efficiency. Through the comparison of static and dynamic evaluation efficiency, the improved DEA model considered the impact of the previous year's investment on the operating result of the following year. Using Henan Province as an example, by comparing the static and dynamic efficiency evaluation of the two methods, the dynamic efficiency evaluation result improved with the modified DEA method that considered power grid structure and regional economic factors; the results of the dynamic efficiency evaluation did not change with the SFM model, which indicated that these factors were not taken into account. Therefore, the improved DEA model was more consistent with the factual data. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Number**} & \multirow{2}{*}{**DMU**} & \multicolumn{2}{c}{**D-DEA**} & \multirow{2}{*}{**Variation**} & \multicolumn{2}{c}{**SFM**} & \multirow{2}{*}{**Variation**} \\ \cline{3-3} \cline{5-8}  & & **Static** & & & & **Dynamic** & & **Rate** \\ \hline
1 & Anhui & 0.616 & 0.643 & 0.044 & 0.622 & 0.613 & \(-\)0.014 \\
2 & Henan & 0.752 & 0.783 & 0.042 & 0.745 & 0.745 & 0.000 \\
3 & Heilongjiang & 0.815 & 0.802 & \(-\)0.016 & 0.803 & 0.765 & \(-\)0.048 \\
4 & Hubei & 0.799 & 0.802 & 0.005 & 0.817 & 0.817 & 0.000 \\
5 & Hunan & 0.933 & 0.903 & \(-\)0.032 & 0.918 & 0.918 & 0.000 \\
6 & Jilin & 0.788 & 0.754 & \(-\)0.044 & 0.873 & 0.873 & 0.000 \\
7 & Northern Hebei & 0.855 & 0.867 & 0.014 & 0.873 & 0.873 & 0.000 \\
8 & Liaoning & 0.753 & 0.776 & 0.031 & 0.784 & 0.784 & 0.000 \\
9 & Sichuan & 0.665 & 0.703 & 0.057 & 0.684 & 0.709 & 0.037 \\
10 & Chongqing & 0.636 & 0.645 & 0.014 & 0.657 & 0.657 & 0.000 \\
11 & Beijing & 0.965 & 0.903 & \(-\)0.064 & 0.913 & 0.913 & 0.000 \\
12 & Tianjin & 0.931 & 0.890 & \(-\)0.044 & 0.943 & 0.943 & 0.000 \\
13 & Shanghai & 0.923 & 0.890 & \(-\)0.036 & 0.932 & 0.932 & 0.000 \\
14 & Fujian & 0.965 & 0.880 & \(-\)0.088 & 0.946 & 0.954 & 0.009 \\ \hline \hline \end{tabular}
\begin{tabular}{c} 1.000 \\ 0.950 \\ 0.900 \\ 0.850 \\ 0.750 \\ 0.650 \\ 0.550 \\ \end{tabular}
\end{table} 
Table 7: Comparison of static and dynamic results for efficiency evaluation. Figure 7: Evaluation comparison of static efficiency and dynamic efficiency.
## 3 Results and Discussion  
Table 5 presents the results of the 28 days chloride migration coefficient (\(D_{cl,RCMT}\)) from the RCMT, and the 90 days chloride diffusion coefficient (\(D_{cl,IT}\) and \(D_{cl,WDT}\)), surface chloride content (\(C_{s,IT}\) and \(C_{s,WDT}\)) and penetration parameter (\(K_{cr,IT}\) and \(K_{cr,WDT}\)) from the IT and WDT. The coefficient of variation (CV) of the RCMT was lower than 9%, which is within the maximum of 15% indicated in literature [5].  
Figure 2: WDT tank with the solution containment tank.  
The 28 days dry density and compressive strength are also presented in Table 5. Depending on the type of binder and w/b, the compressive strength of NWC varied between 30.7 and 78.0 MPa, within the strength classes C20/25-C60/75, according to EN 206 [50]. The dry density and compressive strength of LWAC varied between 1600 and 1920 kg/m\({}^{3}\) and 26.1 and 66.1 MPa, ranging from density classes D1.6-2.0 and strength classes LC20/22 to LC60/66, according to EN 206 [50], respectively. Moreover, the \(D_{cl,RCMT}\) varied between 5.9 \(\times\) 10\({}^{-12}\) and 25.2 \(\times\) 10\({}^{-12}\) m\({}^{2}\)/s, covering concrete of high to low quality, according to the classification of Cjorv [33] (Table 1). Thus, it was possible to cover a wide range of common structural concretes, broadening the scope and validation of this study.
## 4 Outlook
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multicolumn{2}{c}{**Noise Measurement Technique**} & \multicolumn{2}{c}{**Parameters Required in**} & **Whether It Is Applicable in** \\  & & **Traditional OFCs** & **Novel OFCs** \\ \hline \multirow{3}{*}{Timing jitter measurement} & BOC & Peak power: \textgreater{}2 kW a ** \\ \hline \multirow{3}{*}{Carrier envelope phase noise measurement} & _f-2f_ interferometry based on waveguides & Pulse energy: 1 nJ \({}^{\text{c}}\) & \(\times\) d \\  & _f-2f_ interferometry based on waveguides & Pulse energy: 30\(-\)100 pJ & \(\surd\) \\ \cline{2-4}  & Linear method, electrical & & \\ \cline{2-4}  & mixing, OH and time domain & Average power: mW level e ** & \(\surd\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of key parameters that required for noise measurement and whether it is applicable in novel OFCs.
# 100 Picosecond/Sub-10\({}^{-17}\) Level GPS Differential Precise Time and Frequency Transfer
## 1 Introduction
However, as it is affected by the noise and multipath of pseudorange, it is difficult to fix the SD integer ambiguity. For the problem of integer ambiguity resolution, [PERSON] et al. [15] proposed a least-squares ambiguity decorrelation adjustment (LAMBDA) method based on integer Gaussian transformation in 1995. Since then, the LAMBDA method has been widely used for DD integer ambiguity resolution in RTK positioning [16; 17; 18]. In 1999, [PERSON] et al. [19] further summarized integer rounding, integer bootstrapping, and integer least squares for a class of integer estimators and demonstrated the best performance of the integer least-squares estimator. In this study, the LAMBDA method was used for the determination of double-difference (DD) ambiguities. When estimating SD ambiguities from pseudorange measurements, we employed the integer rounding estimator based on the K-means algorithm to determine their integer values and then achieved differential precise time and frequency transfer (DPT). To verify the performance of DPT, common clock time transfer experiments were analyzed and calibrated, as well as three stations' closure time transfer results. In addition, we evaluated time and frequency transfer performances for four baselines over 30 days. The discussion and conclusion are given in the final section.
# Survey of Dense Video Captioning: Techniques, Resources, and Future Perspectives
## 5 Evaluation Protocols and Metrics
### Key Metrics
#### 5.1.4 ROUGE-L  
ROUGE (Recall-Oriented Understudy for Gisting Evaluation) is a collection of metrics designed to assess automatic summarization, and it has been adapted for evaluating video captioning as well [11]. Specifically, ROUGE-L measures the longest common subsequence (\(LCS\)) between a generated caption and reference captions, with a particular focus on the recall of content. This metric is valuable for gauging the completeness of the generated captions. ROUGE-L offers both _Recall_ (\(R_{lcs}\)) and _Precision_ (\(P_{lcs}\)) scores, and the overall ROUGE-L score is typically reported as their \(F\)-measure (\(F_{lcs}\)).  
The computation of ROUGE-L involves three steps:  
1. _LCS_ Calculation For two sequences \(X\) and \(Y\), let \(LCS(X,Y)\) denote the length of their longest common subsequence.
2. _Recall_ and _Precision_ _Recall_ (\(R_{lcs}\)) is determined by the ratio of the length of the longest common subsequence (\(LCS\)) to the length of the reference sequence. _Precision_ (\(P_{lcs}\)), on the other hand, is calculated as the ratio of the length of the _LCS_ to the length of the candidate sequence. \[R_{lcs}=\frac{LCS(X,Y)}{|Y|},\] (11) \[P_{lcs}=\frac{LCS(X,Y)}{|X|}.\] (12)
3. \(F\)-Measure The \(F\)-measure (\(F_{lcs}\)) is the harmonic mean of _Recall_ (\(R_{lcs}\)) and _Precision_ (\(P_{lcs}\)). \[F_{lcs}=\frac{(1+\beta^{2})\cdot R_{lcs}\cdot P_{lcs}}{R_{lcs}+\beta^{2}\cdot P _{lcs}},\] (13) where \(\beta\) is a parameter that controls the relative importance of _Recall_ and _Precision_. In ROUGE evaluations, \(\beta\) is often set to a very large value (e.g., \(\beta=\infty\)), effectively making \(F_{lcs}\) equal to \(R_{lcs}\).
# Position Tracking Control of 4-DOF Underwater Robot Leg Using Deep Learning
## 2 Related Works  
[PERSON] et al. [13] introduced a preliminary model of drag and lift forces acting on joint legs using a model of the underwater walking robot Crabster, conducting torque simulations with joint robot arms. [PERSON] et al. [14] utilized MATLAB Simulink's Simscape toolbox to simulate fluid forces applied to the 3-DOF manipulator. In the study by [PERSON] et al. [15], the calculation method for inertia and viscous fluid mechanics of underwater manipulators was summarized for dynamic analysis. They explored dynamic modeling from two perspectives, strip theory, and non-strip theory, to comprehend how the fluid mechanics of manipulators influence behavior and to study optimization for this. While these studies focused on accurately analyzing fluids, they did not apply position-tracking control to robots in the real world.  
[PERSON] et al. [16] employed model reference adaptive control, adjusting in real time based on errors between reference and real models, to address challenges arising from non-linear dynamics and hydrodynamics. [PERSON] et al. [17] enhanced the control performance of the 4-DOF underwater manipulator by using dual integral sliding mode control in their study. [PERSON] et al. [18] introduced the Resolved Acceleration Control (RAC) method to improve the control accuracy of the end effector and compared its performance with the CTC using an underwater robot equipped with two arms. Studies by [PERSON] et al. [19] proposed a trajectory tracking plan for an Underwater Vehicle-Manipulator System (UVMS), combining CTC with a modified H-infinity controller, and non-linear disturbance observer, successfully applying it through an online Grey Wolf Optimizer (GWO). Studies by [PERSON] et al. [20] proposed a method that combines backstepping strategies with adaptive robust controllers for precise motion control of underwater manipulators. These studies overcome fluid-related challenges using model-based control, sometimes robust or adaptive control. However, the process has limitations, being overly complex and requiring extensive computation.  
In recent times, the analysis of robot dynamics through machine learning has led to the development of control techniques for walking robots [21; 22]. [PERSON] et al. [23] proposed deep learning algorithms for real-time dynamic learning, demonstrating that dynamic models adapt more effectively to real-time changes than other algorithms. However, these studies did not consider the underwater environment.  
This study employs a deep learning neural network to simplify the computation of the dynamic model, proposing a control method applicable in both air and water environments and evaluating its position-tracking performance. The evaluation involves a comparison of the results with a single task-space PD control using a 4-DOF underwater robot leg.
# A Systematic Approach to Studying Quark Energy Loss in Nuclei Using Positive Pions +
## 2 Methodology  
### The Data Treatment  
To ensure the quality and relevance of the data for analyzing the quark energy loss in different target types, a series of cuts were applied to the variables associated with the experiment (for definitions of these variables, the PDG's section on Structure Functions may be consulted). These cuts help isolate the desired physical processes and minimize background noise:  
* \(Q^{2}\): A cut is applied to the photon virtuality, restricting \(Q^{2}\) to values between 1 and 4 GeV\({}^{2}\). This range helps ensure the interaction is in the deep inelastic scattering (DIS) regime, as well as avoiding energy dependency in terms of \(Q^{2}\).  
Figure 2: Green: Electromagnetic shower counters, Blue: Cherenkov counters, Red: Time-of-flight scintillators, Purple: Drift chambers regions, turquoise: Superconducting toroidal magnet. Note that there are regions where we have few or no events due to the limited detector acceptance [7].  
* \(W\): Events with \(W>2\) GeV are selected to minimize the contributions of resonance, ensuring that we remain in the DIS region.
* \(x_{B}\): The Bjorken scaling variable is restricted between 0.12 and 0.57, firstly to ensure that the scattering occurs on a single nucleon within the nucleus and avoids elastic scattering or processes where \(x_{B}\) exceeds unity and secondly to avoid energy dependency.
* \(y_{B}\): A cut is applied to reduce radiative effects, which become increasingly significant at high values of \(y_{B}\); a cut is applied such that \(y_{B}<0.85\).
* cut and target identification: After selecting electrons, pions, and DIS events, it is necessary to identify the target where the event occurred--either the solid or liquid target. Both targets are positioned along the z-axis, corresponding to the beam axis. A vertex cut is applied to the \(Z\) variable, which represents the vertex's position along the beam axis, to accurately distinguish between interactions in the liquid deuterium target and the solid targets. This cut ensures that events are correctly attributed to the target they originated from, minimizing contamination and enhancing the reliability of the analysis by focusing on interactions within the expected boundaries of each target type.  
By implementing these cuts, we isolate events of interest to study the quark energy loss and minimize the effects of background noise and non-DIS contributions. Other cuts not mentioned here were applied following [9].
# A Practical Neighbor Discovery Framework for Wireless Sensor Networks
###### Abstract  
Neighbor discovery is a crucial operation frequently executed throughout the life cycle of a Wireless Sensor Network (WSN). Various protocols have been proposed to minimize the discovery latency or to prolong the lifetime of sensors. However, none of them have addressed that all the critical concerns stemming from real WSNs, including communication collisions, latency constraints and energy consumption limitations. In this paper, we propose Spear, the first practical neighbor discovery framework to meet all these requirements. Spear offers two new methods to reduce communication collisions, thus boosting the discovery rate of existing neighbor discovery protocols. Spear also takes into consideration latency constraints and facilitates timely adjustments in order to reduce the discovery latency. Spear offers two practical energy management methods that evidently prolong the lifetime of sensor nodes. Most importantly, Spear automatically improves the discovery results of existing discovery protocols, on which no modification is required. Beyond reporting details of different Spear modules, we also present experiment evaluations on several notable neighbor discovery protocols. Results show that Spear greatly improves the discovery rate from 33.0% to 99.2%, and prolongs the sensor nodes lifetime up to 6.47 times.  
Keywords:neighbor discovery; wireless sensor networks; communication collision; latency; energy consumption +
Footnote †: journal: Sensors
## 3 Results  
The _E. faecalis_ count with aPDT treatments using Photoactive+ activated by red wavelengths (635 nm and 660 nm) and combination of LED (420-480 nm) and red wavelengths (635 nm and 660 nm) were significantly reduced compared to the control group in both irradiation time (\(p<\) 0.05) (Figure 1). aPDT with LED (420-480 nm) decreased the bacterial count, but with no significant difference from the control group in both energy density of 26.4 and 76 J/cm\({}^{2}\) (\(p=\) 0.75, \(p=\) 0.5). The Photoactive+ without activation reduced the count of bacteria with no significant difference from control group. In addition, all light sources including 635 nm, 660 nm and LED (420-480 nm) showed no significant difference in bacteria reduction compared to control group. The greatest reduction in bacterial count was seen with 635-nm red laser activation for energy density of 76 J/cm\({}^{2}\) (83%) and lowest with the Photoactive+ without activation (0.8%). Conversely, considering the energy density, the higher energy density of 76 J/cm\({}^{2}\) showed a higher reduction of the bacterial count compared to lower energy density of 26.4 J/cm\({}^{2}\) in all groups (Figure 1). The mean and standard deviation of experimental groups were shown in Table 2.  
\begin{table}
\begin{tabular}{c c c} \hline \multirow{2}{*}{**Groups**} & **CFU/mL (Mean \(\pm\) Standard Deviation) \(\times\) 10\({}^{5}\)** \\ \cline{2-3}  & **24.6 J/cm\({}^{2}\)** & **76 J/cm\({}^{2}\)** \\ \hline Control & \(13\pm 1.9\) & \(13\pm 1.9\) \\ Photoactive+ & \(12.9\pm 0.49\) & \(12.9\pm 0.49\) \\ R1 & \(12.35\pm 0.45\) & \(11.34\pm 0.3\) \\ R2 & \(12.2\pm 0.35\) & \(11.1\pm 0.15\) \\ Blue & \(12.46\pm 0.59\) & \(12.1\pm 0.45\) \\ R1 + Photoactive+ & \(4.3\pm 0.96\) & \(2.2\pm 0.48\) \\ R2 + Photoactive+ & \(5.9\pm 0.94\) & \(3.1\pm 0.8\) \\ Blue + Photoactive+ & \(12.5\pm 0.8\) & \(11.6\pm 0.65\) \\ Blue-R1 + Photoactive+ & \(6\pm 0.58\) & \(3.6\pm 0.77\) \\ Blue-R2 + Photoactive+ & \(7.5\pm 1.49\) & \(4.7\pm 1.17\) \\ R1–Blue + Photoactive+ & \(6.3\pm 0.85\) & \(3.3\pm 0.66\) \\ R2-Blue + Photoactive+ & \(7.3\pm 1.49\) & \(4.2\pm 0.66\) \\ \hline \end{tabular}
\end{table}
Table 2: Mean \(\pm\) standard deviation of experimental groups.  
The peak absorption of Photoactive+ which assessed by company was shown in Figure 2a (Source: [[https://www.wmedicalsystems.com/](https://www.wmedicalsystems.com/)]([https://www.wmedicalsystems.com/](https://www.wmedicalsystems.com/))) and our assessment confirmed its absorption in red (620-660 nm) and blue (400-450 nm) region of electromagnetic spectrum (Figure 2b).  
The results of MTT assay for Photoactive+ at concentration of 0.01-g/mL showed that after the incubation period, there was no significant reduction in the number of viable cells compared to the control group (\(p>0.05\); Figure 3a). Additionally, morphologically, HuGu of the control group showed a spindle-shaped cell and those in contact with the Photoactive+ demonstrated the typical stellate appearance of this type of cell (Figure 3b,c).  
Figure 2: (**a**) Absorption curve of chlorophyllin and phycocyanin. (**b**) Absorption curve of Photoactive+.
# The Role of Mobile Application Acceptance in Shaping E-Customer Service
## 4 Results
### Qualitative Results
#### 4.2.2 Factors Contributing to Increased Mobile App Acceptance in E-Commerce  
According to participants 2 and 3, the convenience associated with the mobile app is one of the primary factors contributing to its increasing use by customers who purchase products and services on online retailers. Customer usually receives the real-time response from the retailers regarding the price and availability of some brands in the market.  
Participant 2: _\"Most customers are embracing mobile app acceptance because of the convenience in accessing the products and service offered by online retailers.\"_  
One of the drivers accelerating mobile applications' acceptance in e-commerce among customers is the convenience linked to such applications. Unlike the traditional purchase system, where customers physically visit stores to buy services, mobile applications do not require the physical presence of clients for transactions to be completed. As a result, time and resources that would have been used by consumers to travel to service stores are channeled to more pressing needs. The results demonstrate that the convenience of using mobile applications is also linked to the geographical location and the number of times consumers purchase a given service. Therefore, mobile applications enable customers to order e-commerce services from any country worldwide as frequently as they desire without making many unnecessary trips to service firms.  
According to participants 1, 3, and 4, mobile apps are increasingly used by customers because of the perceived ease of use. Customers easily access information regarding the price and variety of retailers' products within a short period through mobile apps. If a service transaction takes longer than expected to complete, there is an increased likelihood of most customers opting out of the purchase process. However, the provision of e-commerce services through mobile applications is associated with speedy transactions that are likable among consumers, thereby increasing the acceptance of mobile apps.  
Participant 3: _\"The increasing adoption of the mobile app is associated with the perceived ease of use and faster execution of transactions.\"_  
Customers prefer to use mobile applications for e-commerce dealings because of understandability, clarity, and reliability. Mobile applications are designed with objectivity in mind to ensure that every consumer can use them with the least possible assistance. Mobile applications are easy to understand and operate when making e-commerce transactions. As a result, customers accept the apps because they are dissociated with the need to seek guidance and directions from customer care assistants. Periodically, mobile application developers release updated versions of apps intended to make them more effective. Therefore, the findings imply that mobile applications are easily accepted by customers since they are reliable for ordering and paying services from e-commerce service firms.
## 4 Proposed Model
### Performance Comparison  
To evaluate the performance of proposed model, we compare BGRU with Decision Tree (DT), Support Vector Machine (SVM), Logistic Regression (LR), Random Forest (RF), CNN, LSTM, and Multilayer Perceptron-Convolutional Neural Network (MLP-CNN).
## 2 Materials and Methods
### Polymerase Chain Reaction (PCR) for Detection of Virulence Genes
#### 2.7.2 PCR Analysis  
The _Salmonella_ isolates were subjected to Polymerase Chain Reaction (PCR) to screen for the presence of the following eight virulence genes: _invA_, _agfA_, _lpfA_, _lhiA_, _sivH_, _sefA_, _sopE_, and _spvC_ as previously described [8] with slight modifications. Forward and reverse primer sequences for each gene are shown in Table 2.  
The 20 \(\mu\)L PCR master mix consisted of 10 \(\mu\)L NEB OneTaq 2X MasterMix with Standard Buffer (Catalogue No. M0482S), 1 \(\mu\)L Genomic DNA (10-30 ng/\(\mu\)L), 7 \(\mu\)L Nuclease free water (Catalogue No. E476) and 1 \(\mu\)L of each primer (10 \(\mu\)M forward and 10 \(\mu\)M reverse). The mixtures were subjected to amplification in a thermocyler machine and underwent conditions consisting of an initial denaturation at 94 \({}^{\circ}\)C for 5 min, followed by 35 cycles each of denaturation at 94 \({}^{\circ}\)C for 30 s, annealing at 50 \({}^{\circ}\)C for 30 s, elongation at 68 \({}^{\circ}\)C for 1 min and a final extension at 68 \({}^{\circ}\)C for 10 min. The amplicons were then held at 4 \({}^{\circ}\)C.
###### Abstract
As coordinating different parts of a QKD network is extremely complicated, an SDN orchestrator is implemented to manage multiple SDN controllers to ensure end-to-end QKD service provisioning between different parts of a QKD network with different vendors. For example, if there are multiple common trusted nodes to interconnect different parts of systems, a path must be calculated in advance for an end-to-end key delivery from an initiating application to the target application belonging to different parts of a network, meaning a common trusted node must be selected in advance. The SDN orchestrator is designed and implemented to perform this role with the information from each SDN controller from each part of the QKD network. The SDN orchestrator facilitates the integration and control of different networks composed of various QKD vendors, providing user portals for configuring services and an intelligent monitoring system for increasing operational efficiency and flexible network scalability. Additionally, the SDN orchestrator provides network information, such as topology, nodes, links, inventory, services, performance, events, and cryptographic key status, as well as offering automatic network monitoring and network management system (NMS) functions such as troubleshooting and incident management.