esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 The Algorithms: MC, MCQ, MCR, MCS and BBMC
### Bbmc
#### 2.4.1 BBMC in Java
The best CPU time in a row is in **bold** font, and when CPU time limit is exceeded, the largest maximum clique size is **emboldened**. Easy instances are not tabulated, _i.e._, those that took less than a second. Overall, we see that MCQ1 is rarely the best choice with MCSa1 or MCSb1 performing better. There are 11 problems where MCSb1 beats MCSa1 and 9 problems where MCSa1 beats MCSb1. Therefore, the DIMACS benchmarks do not significantly separate the behaviour of these two algorithms. These results conflict somewhat with those in [10]. There it is claimed that colour repair, when added to BBMC, results in a performance gain in dense graphs (\(p\geq 0.8\)). Results are presented for a subset of the DIMACS instances with some of the difficult instances absent (brock800-*, hamming10-4, keller5, keller6, johnson32-2-4, MANN-a81, p-hat1000-3, p-hat1500-2, p-hat1500-3) and for random graphs with a sample size of 10. ### BBMC vs. MCSa: A Change of Representation

What advantage is to be obtained from the change of representation between MCSa and BBMC, _i.e._, representing sets as \(ArrayList\) in MCSa and as a BitSet in BBMC? MCSa and BBMC are at heart the same algorithm. They both produce the same colourings, order the candidate set in the same way and explore the same backtrack tree. Figure 7 shows on the left the run time of MCSa1 (x-axis) against the run time of BBMC1 (y-axis) in milliseconds on each of the \(G(100,p)\) random instances and on the right for \(G(200,p)\). The dotted

Figure 7: Run time of MCSa1 against BBMC1, on the left \((G100,p)\) and on the right \(G(200,p)\). line is the reference \(x=y\). If points are below the line then BBMC1 is faster than MCSa1. BBMC1 is typically twice as fast as MCSa1. In Table 2 we tabulate _Goldilocks_ instances from the DIMACS benchmark suite: We remove the instances that are too easy (take less than a second) and those that are too hard (take more than 4 h), leaving those that are \"just right\" for both algorithms. Under each algorithm, we have: Nodes visited (and this is the same for both algorithms), run time (in seconds), and in brackets the size of the maximum clique. The column on the far right is the ratio of MCSa1's run time over BBMC1's run time, and a value greater than 1 shows that BBMC1 was faster by that amount. Again, we see similar behaviour to that observed over the random problems: BBMC1 is typically twice as fast as MCSa1.
## 2 Materials and Methods  
In practice, the most common detection procedure is the use of sticky yellow traps placed in the culture [27; 28; 31; 32], which are visually explored with the help of microscopes or magnifying glasses. This work is done by entomological specialists with years of experience and knowledge in the diversity of insects that affect crops. For this process, minutes or even hours are invested in each trap since the pest insects are very small [33]. This, in conjunction with the characteristics of the air, which can dirty each trap with dust, increases the level of complexity when detecting trapped insects [34].  
For the detection of the different types and incidence rates of insect pests, yellow sticky traps were placed 10 cm above the plant canopy of pepper plants grown under irrigation.  
In order to know the density and type of pests trapped in the experiment, the collaboration of the specialist in charge of the entomology laboratory of the National Institute of Forestry, Agricultural and Livestock Research (INIFAP, Norte Centro, Zacatecas, Mexico) was requested.  
The specialist counted and classified the insects trapped in each trap throughout the sampling period.  
Pests, such as psyllids (_Bactericra cockerell_), thrips (_Thrips tabac_), whitrelies (_Bemisia tabac_), potato flea beetles (_Epitix cucumeris_), pepper weevils (_Anthonomus eugeniti_), and aphids (_Myzus persicae_), were detected. Each trap was tagged and sorted according to the date of collection. Digital image processing was developed on the MATLAB platform (The Mathworks, Inc., Natick, MA, USA) to extract the invariant features of insects, using database images taken from the sticky traps located in the crop area. The experiment began with the distribution of 20 yellow sticky traps placed in the plot; the traps were collected at the end of 7 days, for 19 consecutive weeks.  
The digital image processing consisted of two stages, which are explained below.
## 5 Conclusions  
In this paper, we propose the Spatial-Temporal Fusion Detection (STFD) framework for small infrared target detection in diverse and cluttered backgrounds. STFD integrates Temporal Regional Contrast (TRC), Regional Intensity Accumulation (RIA), and the Gradient-Enhanced Local Contrast Measure (GELCM) to leverage both temporal and spatial information. TRC captures temporal intensity changes to highlight targets while suppressing transient clutter, RIA enhances weak targets through multi-frame energy accumulation, and the GELCM enhances spatial contrast for improved detection by analyzing local directional contrast and gradients. The combination of these components enables robust detection performance across various scenarios, including bright backgrounds, point-like clutter, and multi-target scenes, significantly outperforming mainstream methods.  
By leveraging its modular design, STFD adapts to complex scenarios, with temporal components addressing background motion and dim targets, while the GELCM refines spatial details, reducing false alarms and missed detections. Experimental results showed that STFD achieves precise detection with minimal computational overhead. Future work may focus on advanced fusion strategies, precise shape modeling of dim targets, and neural network-based representations of temporal and spatial characteristics. STFD offers a promising foundation for real-time applications in infrared detection and tracking, particularly in challenging operational environments.  
Conceptualization, F.C. and L.L. ([PERSON]); methodology L.L. ([PERSON]) and X.N.; software, L.L. ([PERSON]) and R.Z.; validation, L.L. ([PERSON]); formal analysis, L.L. ([PERSON]) and X.S.; investigation, L.L. ([PERSON]) and X.N.; resources, L.L. ([PERSON]) and X.S.; data curation, L.L. ([PERSON]); writing--original draft preparation, L.L. ([PERSON]); writing--review and editing, L.L. ([PERSON]); supervision, F.C.; project administration, F.C. and L.L. ([PERSON]); funding acquisition F.C. and L.L. ([PERSON]). All authors reviewed the manuscript. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Natural Science Foundation of China under Grant No. 61975222 and 62405062, and the Strategic Priority Research Program of the Chinese Academy of Sciences under Grant XDA19010102 (CASEarth Minisatellite Thermal Infrared Spectrometer Project).  
Not applicable.  
The original contributions presented in the study are included in the article. Further inquiries can be directed to the corresponding author.  
The authors declare no conflicts of interest.
## 4 Research Clusters: Content Analysis  
The comprehensive search within the ISI Web of Science database identified an initial set of 238 articles pertaining to the green shipping corridors research domain. In order to narrow the scope to highly interconnected and influential works, a minimum threshold of 15 bibliographic coupling citations per document was applied in the VOSviewer software, reducing the dataset to 115 articles. The bibliographic coupling analysis is a bibliometrics technique that connects documents citing the same reference, thereby facilitating the creation of document clusters [43]. This indicates a likelihood that the linked documents address a related subject matter, that is, a research cluster. A graphical representation of the 115-article dataset regarding cluster formation is presented in Appendix A.  
Subsequently, an equal-weighted average approach--incorporating both total link strength and citation count--was employed to assess each publication's relative impact and interconnect-edness. This combined metric facilitated the selection of the top 20 articles deemed the most significant for further in-depth content analysis. These articles were then organized into four distinct clusters, each comprising five publications, as presented in Table 7.  
Figure 3: Three-fields plot diagram in the green shipping corridor domain; most influential scholars (left); principal keywords (center); and contributing countries (right).  
Table 7 assigns the selected articles to four thematic clusters, each representing a distinct dimension of green shipping corridor research, as follows:  
* **Cluster 1:**_Sustainable Green Shipping Practices and Research_; focuses on conceptual frameworks, performance metrics, and resource management in maritime operations.
* **Cluster 2:**_Alternative Fuels and Low-Carbon Strategies for Maritime Transport_; addresses comparative evaluations of fuel types and decision-based strategies for decarbonization.
* **Cluster 3:**_Green and Low-Carbon Maritime Development_; underscores policy perspectives, global industry trends, and advanced modeling for emission reduction.
* **Cluster 4:**_Environmental Sustainability in Maritime Shipping_; investigates governance mechanisms, financial decision making, and technological feasibility for greener maritime operations.  
Figure 4 provides a visual depiction of the clusters and respective subclusters, illustrating their interconnections and core themes.  
Due to the complexity of the green shipping corridor research domain, each of these four clusters also encompasses multiple subclusters, which will be discussed in detail in the subsequent sections of this paper.
## 7 Machine Learning Models
### Linear Discriminant Analysis (LDA)  
Linear discriminant analysis (LDA) classifier has been extensively used in sEMG pattern recognition for bionic arm control [42]. In the LDA classifier, a group of higher dimensional features are separated into classes and they serve as input to the classifier. Subsequently, a projection that optimizes the mapping between the raw features and a lower-dimensional phase space is found, while maintaining the same class structure. As a consequence, the within-class distances are minimized, while the between-class distances are maximized, achieving a maximized discrimination facility. Mathematically, this optimized projection is estimated by applying eigen value decomposition on the training data scatter matrices.  
LDA depends on the Bayes classification rule, in which states that, for a given vector \(x\), assign it to the class \(c_{k}\) when the following inequality is satisfied  
\[p(c_{k}x)>p(c_{j}x)\text{ {for all }}k\
eq j \tag{2}\]  
\begin{table}
\begin{tabular}{c c} \hline
**Frequency-Domain Features** & **Time-Domain Features** \\ \hline Kurtosis & Length of the signal \\ \hline Signal Power & Root Mean Square of each segment of the signal \\ \hline Deciles, & \\ \hline Median frequency & \\ \hline Frequency peak & \\ \hline Dissymmetry coefficient & \\ \hline Frequency Peak & \\ \hline \end{tabular}
\end{table}
Table 1: Summary of the Extracted features.  
These posterior probabilities cannot be directly measured, but they can be obtained from estimates of the priori probabilities and the distribution of the class according to the Bayes formula:  
\[p(c_{k}x)=\frac{p(c_{k})p(xc_{k})}{p(x)} \tag{3}\]  
Where \(p(c_{k}x)\) is the probability density function for the vector within \(k\) class, \(p(c_{k})\) is the prior probability for class \(k\) and is usually assumed to be equal for all classes, \(p(x)\) is the probability density function of the input space and it is also constant over all of the classes. Subsequently, the decision rule can be simplified to:  
\[p(xc_{k})>p(xc_{j})\text{ {for all }}k\
eq j \tag{4}\]  
In the implementation of LDA classifier, the probability density functions for all of the classes are assumed to follow a multivariate Gaussian distribution.  
\[p(xc_{k})=\frac{1}{\sqrt{(2\pi)^{f}det^{2}(C)}}exp(-\frac{1}{2}(x-\mu_{k})^{T }C^{-1}(x-\mu_{k})) \tag{5}\]  
where \(x\) is the vector to be classified, \(f\) is the dimension of the vector, \(C\) is the common covariance matrix of all the classes, \(k\) and \(\mu_{k}\) are the mean value of class \(k\).
# A Comparative Study of Control Methods for X3D Quadrotor Feedback Trajectory Control
## 3 X3D Quadrotor Controller Design  
For an X3D quadrotor control system, two control loops are suggested, as shown in Figure 4. The position controller in the outer control loop controls the system's slower dynamics (longitudinal and lateral translations), while the attitude/altitude controller in the inner control loop controls the system's quicker dynamics (attitude and altitude).  
The quadrotor's intended rotor speed is output by the attitude/altitude controller. The PID controller, LQR controller, fuzzy logic controller, and model reference adaptive PID using MIT rule controller were used in this research to stabilize the translation position and attitude/altitude of an X3D quadrotor.
###### Abstract  
This study proposes a novel point absorber wave energy converter (WEC) utilizing an inertial adjusting mechanism for performance evaluation. A conceptual design is introduced to explain the WEC's functionality. Adjustable masses are incorporated to tune the natural frequency of the power take-off (PTO) system, matching the excitation frequencies of incoming waves. To analyze the system's behavior, a coupled mechanical and hydrodynamic time domain simulation is presented. This simulation, built primarily in MATLAB/Simulink, focuses on a truncated floating buoy connected to a bidirectional gearbox. Since the WEC operates under various wave conditions, hydrodynamic parameters are determined and simulated in the frequency domain using ANSYS AQWA wave interaction software (version number 2021R1). Finally, a prototype is constructed and tested in a wave tank. Test results under different wave conditions are presented and compared to assess the proposed WEC's performance.  
point absorber WEC; mathematical model; overall efficiency improvement; experimental investigation; inertial adjusting mechanism; screw and nut mechanism; mechanical PTO
# Game-Theory-Based Task Offloading and Resource Scheduling in Cloud-Edge Collaborative Systems
## 7 Conclusions  
In this paper, we study the joint task offloading and resource allocation problem in a multi-user network under a cloud-side collaborative architecture with the objective of optimizing system utility. Since the problem is an MINLP problem, it is difficult to obtain an optimal solution, which is decomposed into two sub-problems of computational resources and task offloading, and the computational resource allocation problem is solved using the KKT condition. For the task offloading policy optimization problem, a GTPSO algorithm is proposed to reduce time complexity while ensuring better performance. The experimental results show that the GTPSO scheme has good performance with respect to delay, energy consumption and system utility.  
Since the algorithm proposed in this paper only considers the static scenarios of users, users may dynamically leave the system during computing offloading or resource allocation. This affects the performance of the solution, and it requires all end users to upload information to the cloud center, which causes user information to be safe. In future research, we will study end-user mobility and combine federated learning technology with edge computing technology to protect user privacy information during task processing.  
Conceptualization, S.W., Z.H., Y.D. and L.H.; Methodology, S.W., Z.H. and L.H.; Project administration, S.W.; Software, Z.H.; Validation, Z.H.; Visualization, S.W., Z.H. and Y.D.; Writing--original draft, Z.H.; Writing--review & editing, S.W., Z.H., Y.D. and L.H. All authors have read and agreed to the published version of the manuscript.  
This research was partially supported by the Natural Science Foundation Project of Hebei Province, China (No. F2021207005).  
Not applicable.  
The data that support the findings of this study are available on request from the corresponding author.  
The authors declare no conflict of interest.  
Figure 8: Delay and energy consumption versus weighting factors: (**a**) Average task latency at different \(\beta_{n}^{t}\); (**b**) Average task energy consumption at different \(\beta_{n}^{e}\).
## 3 Results and Discussion
### Model Results
#### 3.2.3 Electron Temperature  
In plasma dominated by electron interactions, its chemistry depends on the kinetic energy of these particles. The electrons take the energy from the electric field and share their energy with other particles by collisions. The electron temperature is a measure of the kinetic energy of the electrons.  
Figure 12 plots the dependence of the electron temperature on the positions for times 2, 6, 10, 14, and 18 ms. The highest values of the electron temperature corresponded to positions where this field was highest, i.e., the powered electrode for positive voltage and the ground electrode for negative voltage. Otherwise, the electron temperature in the inlet/outlet pipes was lower by the low electric field in these zones.  
The model results of electron temperature have also been averaged over the period (T = 20 ms). The averaged values for r = 15 mm are compared with the experimental measures of rotational, vibrational, and excitation temperatures in Figure 7b. The values of the theoretical electron temperatures were higher than these temperatures, which is typical of a 2-temperature plasma, characterized by the following relation between the temperatures: \(T_{e}\gtrsim T_{exc}>T_{p}>T_{r}\sim T_{g}\)[47].  
Figure 11: Spatial distribution of the electron density at time (**a**) 2 ms, (**b**) 6 ms, (**c**) 10 ms, (**d**) 14 ms, and (**e**) 18 ms for the reactor at 22 kV AC voltage and 1 cm distance between electrodes.
## 2 Related Work
### QSFP28 Optical Transceiver Software Standard  
The specifications for the operation and management of QSFP28 optical transceivers are defined in the SFF-8636 document [17]. The QSFP28 optical transceiver uses a 2-wire bus interface to communicate with the host. The 2-wire bus interface for QSFP28 is the same as that used in inter-integrated circuit (I2C) communications but with some differences in the write operation. The 2-wire interface does not support a \"combined format\" that can send continuous write addressing for writing. Therefore, under repeated start conditions, the firmware should be implemented to discard writing operation and to abort the data received. Figure 3 shows the procedure for a byte write behavior in the I2C interface between the host and the optical transceiver as defined in the standard. Figure 3a is a one-byte write operation, and Figure 3b is a sequential byte write operation. The host system becomes the master, and the QSFP28 optical transceiver is a slave. The I2C interface sends I2C frames in bytes, checks the opponent's acknowledgement in the ninth clock, and sends/receives additional data in bytes. It should be able to send up to four sequential bytes in a sequential write operation. Detailed procedures for the I2C interface protocol between the QSFP28 optical transceiver and the host are defined in the standard document.  
To control the operation of the QSFP28 optical transceiver through the host interface and to check the optical transceiver status, the QSFP28 optical transceiver configures the internal register map. Figure 4 shows the register map structure defined in the standard. The physical address for accessing the QSFP28 register with the I2C interface is 0 \(\times\) A0 in hexadecimal. The QSFP28 register consists of pages, and each page consists of 128 bytes. The register consists of one lower page and a number of upper pages, and an upper  
Figure 3: QSFP28 host two-wire interface (write operation) [17].  
page consists of a basic required page and an optional page. The address to access the register, lower page, and upper page ranges from 0 \(\times\) 00 to 0 \(\times\) FF, from 0 \(\times\) 00 to 0 \(\times\) 7F, and from 0 \(\times\) 80 to 0 \(\times\) FF, respectively. Data, such as interrupt flags and status monitoring, that require quick access are configured on the lower page for timely access. Page 01 h, corresponding to an upper page, can implement an application option table, and page 02 h acts as an electrically erasable programmable read-only memory (EEPROM) by allowing users to arbitrarily read and write. Pages 20 h to 21 h includes the information from additional monitored parameters for modules that have PAM4 such as alarm and monitoring parameters. This study includes support for additional monitoring parameters for QSFP28 optical transceivers. To access the upper page, The host can read or write the value to the upper page address after writing the upper page index value to access the lower page address 0 \(\times\) 7F.
## 1 Introduction
Vertices which belong to \(DS\) are able to perform tasks related to data routing and can serve vertices that are not in the set. \(DS\) formation reduces communication overhead, increases bandwidth efficiency and decreases energy consumption which ultimately increases network lifetime in UWSNs. Vertices in \(DS\) efficiently forward data towards MSs which are following pre-defined optimal trajectories. The rest of the paper is organized as follows. In Section 2, related work along with motivation is presented. In Sections 3 and 4, the channel model and the mathematical model are discussed. Section 5 presents a detailed explanation of our proposed scheme. Section 6 deals with the discussion of simulation results and Section 7 concludes the paper.