esa-sceva/satcom-chunk-collection huggingface dataset:
# SDNPS: A Load-Balanced Topic-Based Publish/Subscribe System in Software-Defined Networking
## 2 Related Work
### SDN-Based Publish/Subscribe Systems  
The current network architecture has seriously affected the network expansion because of its insufficiency to cater to the massive proliferation of services and users. As an evolution of programming networks, SDN has successfully attracted momentous attention from both academia and industry. On the academic side, the OpenFlow Network Research Center [22] has been created. Some work on standardization for SDN at the Internet Engineering Task Force(IETF), the Internet Research Task Force (IRTF) and other organizations was also developed. On the industry side, the Open Network Foundation was created for promoting SDN and standardizing the OpenFlow protocol.  
The work in [23] first studied the impact of SDN on the design of future message-oriented middleware, such as publish/subscribe systems. This paper pointed out that publish/subscribe systems could adopt a logically centralized controller model w.r.t. maintenance, monitoring andcontrol for the overlays. The authors presented a new publish/subscribe model, which is SDN-like with some properties borrowed from SDN.  
The Google Corporation deployed a private WAN called B4, which adopted a software-defined networking architecture [24] to connect Google's data centers across the planet. Through the mechanism of centralized traffic engineering service, B4 achieved nearly 100% link utilization.  
[PERSON] _et al._ proposed a reference architecture for building middleware, which befitted future Internet applications, accompanying a solution for realizing content-based routing at the line-rate relying on this architecture [25]. This architecture had some reference significance for us, even if it was for a content-based publish/subscribe system and there were no experimental data to support it.  
[PERSON] _et al._ implemented a multicast forwarding fabric called LIPSIN, which was suitable for large-scale topic-based publish/subscribe systems [26]. By placing a Bloom filter into data packets, LIPSIN achieved efficient multicasting on the network layer. The work in [27] proposed and evaluated a content-based publish/subscribe middleware in SDN, which achieved high performance in forwarding speed and bandwidth management. The work in [28] proposed a methodology for both vertical and horizontal scaling of the distributed control plane as an expansion to [27].  
[PERSON] _et al._ proposed a data-centric publish/subscribe paradigm for proactive overlay SDNs and presented a solution for realizing an SDN controller that operated in a distributed manner [29]. [PERSON] _et al._ proposed an end-to-end orchestration for IoT services using an SDN/NFV-enabled edge node under SDN [30].  
[PERSON] _et al._ proposed a particular architectural context for ICNconcerning how SDN and ICN could concretely be combined and outlined a possible realization in a novel design for ICN solutions [31]. They also pointed out several possible testbed deployments. However, they did not implement it, and there was no experimental verification as a consequence.
# Characterization and Optimization of PLA Stereocomplexed Hydrogels for Local Gene Delivery Systems
## 1 Introduction
In our previous work, the combination of triblock copolymers, PLLA-PEI-PLLA and PLLA-PEG-PLLA, has shown to produce nano-sized three-layered micelles (3 LM), where the DNA/PEI polyplex is localized within a hydrophobic capsule [31]. The 3 LM with relatively less toxic low MW linear-PEI has incorporated high concentrations of DNA. Additionally, 3 LM proved to possess high stability in neutral pH, while its encapsulated payload was released through a pH mediated trigger allowing for controlled release properties. We further reported the initial studies of the thermo-gelling system between the DNA-loaded 3 LM and the stereoisomeric block copolymer, PDLA-PEG-PDLA [32]. Overall, these systems have shown a great potential as non-viral gene delivery vectors. In this paper, we focus on the relationships of the 3 LM compositions and their hydrogel properties to understand the role of PLA stereocomplexation in the system. The sol-to-gel transition behavior as a function of block MW and polymer composition in 3 LM is studied. Additionally, to precisely tune the PLA stereocomplex derived hydrogels, the chemical and physical mechanisms of gel forming and degradation processes are investigated.
## 3 The Circuit-Partitioning Algorithm Based on Partition Connectivity Clustering and Tabu Search
### Refinement Partitioning Algorithm Based on Tabu Search
#### 3.4.4 Perturbation Process  
If the current best partition \(P_{G_{2}\text{best}}\) has not improved after \(\gamma\) iterations, a perturbation process is introduced to increase the diversity of the search. The perturbation process works by moving a selected number of \(p_{\text{str}}\) vertices (perturbation strength) to the specified subset.  
Specifically, let the current partition be \(\{G_{1},G_{2},\cdots,G_{k}\}\), where \(G_{\text{max}}\) is the subset with the maximum vertex weight, i.e., \(G_{\text{max}}=\max_{i=1}^{k}\{A_{G_{i}}\}\). The perturbation process includes the following steps:  
1. Randomly select a subset \(G_{m}\in\{G_{1},G_{2},\cdots,G_{k}\}-\{G_{\text{max}}\}\);
2. Randomly select a vertex \(v\) belonging to subset \(G_{c}\), such that \(G_{c}\in\{G_{i}\in P\mid A_{G}^{i}>A_{G}^{m}\}\);Move the selected vertex \(v\) from subset \(G_{c}\) to subset \(G_{m}\);
* Repeat steps (1)-(3) \(p_{\text{str}}\) times.  
Similar to the neighborhood generation rule R1, the perturbation mechanism has a key difference: it is not constrained by the tabu list and allows a vertex \(v\) to move to subset \(G_{m}\), even if it is not a boundary vertex with respect to \(G_{m}\). This feature enables the algorithm to escape from local optima and explore a broader solution space, thus helping to avoid being trapped in deep local optima.
# Online Blended Learning in Small Private Online Course
## 5 Online Blended Learning Based on Network Experiment Courses
### Necessity of Peer Grading and Basic Environment  
We have designed an SPOC environment, which contains a relatively small number of students compared with massive open online courses (MOOCs). Although there are differences between SPOCs and MOOCs, SPOCs involve the same peer grading approach as MOOCs. This is because teachers must review hundreds of open-ended essays and exercises, such as mathematical proofs and engineering design questions, at the end of the final semester. Considering the scale of the submissions, if teachers have to check all the submitted content alone, they may become overwhelmed and not have enough time and energy to carry out other teaching tasks.  
In order to assign a reasonable grade to open coursework, some researchers have proposed the use of an autograder, but an autograder also has its own shortcomings: (1) compared with the feedback provided via manual review, automatic grading usually only assesses whether the answer is right or wrong, and cannot give constructive feedback; (2) it is also unable to judge open and subjective topics, such as experimental reports and research papers.  
Therefore, it is best to use peer grading for these kinds of open problems. Peer grading means that students are both the submitters and reviewers of the assignments--one submission is reviewed by multiple graders, and one grader reviews multiple submissions. Our network experiment course is designed such that one submission is reviewed by four graders, and one grader reviews four submissions.  
In establishing peer grading, we solved four problems, including the reliability estimation of the grader, the assignment of tasks, the issue of human-machine integration, and peer prediction.
## 4 Simulations and Performance Evaluations
### Effect of a Cooling Mechanism  
The excessive amount of heat generated by the computation hardware can have a severe impact on the environment and is underlined quite often. Such issues can also affect the performance of the device, which can impact the model training performance. For the case of FL, it is important to analyze the impact of such heating issues on the training performances given the involvement of a large number of sensory devices. Therefore, in Figure 5, we have presented the performance of two FL models with similar tasks and training environments. One of the FL studies involves the utilization of a cooling fan to reduce the heating issues of Raspberry Pi clients. As is evident from this figure, accuracy can be improved with the use of cooling devices, which can help to achieve model convergence rapidly. This highlights the importance of incorporating novel cooling mechanisms into end devices to enable efficient intelligent solutions in wireless networks. As such, all subsequent simulations were conducted in the presence of a cooling fan.
## 5 Experimental Evaluation
### Dataset Description  
To evaluate RPL-IIoT intrusion detection in heterogeneous IIoT networks, the experimental model generates an RPL-IIoT dataset with normal and abnormal traffic for an IIoT network using Cooja Simulator. The dataset generation process is for a heterogeneous IIoT network (i.e., AMI) with different samples and the same feature space. With the support of Contiki-NG, the RPL-IIoT dataset samples comprise intrusion and normal samples; the intrusions involve rank, version, hello flood, DoS, blackhole, Sybil, selective forwarding, sinkhole, theft, and wormhole attacks.
## 4 Results
The pilot test transitions to a real scale, becoming a permanent implementation with control plans to monitor activities. After successfully implementing the new process, data are collected and compared with the results defined in the earlier phase to ensure the expected outcome. Operating procedures are documented and standardized, with a training plan to fine-tune the new, smart process quickly. After overcoming initial transitory performance challenges and reaching a self-sustained level, a performance evaluation routine is established. Periodic reviews inspect and criticize the performance evaluation system. Indicators with minimal changes can be removed, while those requiring a substantial quantity of information should remain. New indicators addressing unexplored strategic aspects can be added. Periodic reviews also set new targets for key variables following process evolutions. Changing targets involves feedback loops to previous steps [7]. It is worth noting that the guidelines allow for some flexibility. Given the continuous emergence of new technologies and methodologies, it is imperative to formulate guidelines that facilitate adaptation to evolving processes, encompassing alterations in raw materials, equipment, and operational conditions. To achieve this adaptability, consideration can be given to the implementation of a system that automatically triggers a review and potential adjustment of control limits in response to significant changes [14]. Alternatively, a systematic approach involves regular updates and refinements to the fundamental algorithms, aiming to enhance precision and relevance [40]. It is important that these flexible guidelines align rigorously with pertinent industry standards and regulations. Such alignment is not only pivotal for the maintenance of quality but also for the preservation of regulatory compliance [14]. Finally, regarding data security concerns in the integration of SPC with Industry 4.0, attention must be given to the gathering and sharing of sensitive production data. Sensitive data can raise concerns about privacy, data security, and the possibility of unauthorized access [41]. A strategic response to these concerns involves implementing cybersecurity measures such as access control [42], encryption [43], data masking, and anonymization [44] using secure data transmission protocols [45]. Additionally, it is crucial to adhere to established security regulations when implementing such types of measures [41].
## 5 Results and Discussion  
The ISA and GA algorithms are applied to maximize the total PCR of all road sections for three years and, at the same time, minimize the total cost of pavement maintenance. The PMMS model and ISA and GA algorithms are coded using MATLAB 2019b. The \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \# & **Street Name** & **Road Class** & \begin{tabular}{c} **Section** \\ **Code** \\ \end{tabular} & \begin{tabular}{c} **Length** \\ **(m)** \\ \end{tabular} & \begin{tabular}{c} **Width** \\ **(m)** \\ \end{tabular} & \begin{tabular}{c} **Area** \\ **(m\({}^{2}\))** \\ \end{tabular} & \
\begin{tabular}{c} **IRI** \\ **Condition** \\ \end{tabular} &
\begin{tabular}{c} **Road** \\ **PCR** \\ \end{tabular} \\ \hline  
asieryah & Local Access & 1/116 & 694 & 8 & 5552 & 3 & Good & 3 \\
31 & Al-Qaihra & Local Access & 1/112 & 329 & 6.5 & 2139 & 11 & Bad & 0 \\
32 & Al-Mohandis & Main & 1/150 & 1348 & 6.3 & 8492 & 3 & Good & 3 \\
33 & Al-Mahian & Main & 1/130 & 567 & 7 & 3969 & 9 & Poor & 1 \\
34 & Al-Mahjan & Main & 2/130 & 1028 & 7 & 7196 & 5 & Fair & 2 \\
35 & Qarm Habab & Main & 1/160 & 156 & 6.1 & 952 & 13 & Bad & 0 \\
36 & Qarm Habab & Main & 2/160 & 489 & 6.1 & 2983 & 11 & Bad & 0 \\
37 & Qarm Habab & Main & 3/160 & 169 & 6.1 & 1031 & 12 & Bad & 0 \\
38 & Qarm Habab & Main & 4/160 & 93 & 6.1 & 567 & 10 & Poor & 1 \\
39 & Na-aman & Local Access & 1/113 & 725 & 6 & 4350 & 10 & Poor & 1 \\
40 & Al-Heah & Main & 1/170 & 140 & 7.2 & 1008 & 7 & Poor & 1 \\
41 & Al-Heleh & Main & 2/170 & 307 & 7.2 & 2210 & 9 & Poor & 1 \\
42 & Al-Heleh & Main & 3/170 & 427 & 7.2 & 3074 & 8 & Poor & 1 \\
43 & Haorah & Local Access & 1/111 & 761 & 8 & 6088 & 2 & Good & 3 \\
44 & Haorah & Local Access & 2/111 & 352 & 8 & 2816 & 7 & Poor & 1 \\
45 & Al-Gharabi & Main & 1/180 & 1401 & 7.1 & 9947 & 8 & Poor & 1 \\
46 & Al-Gharabi & Main & 2/180 & 1926 & 7.1 & 13675 & 9 & Poor & 1 \\
47 & Al-Mé’enah & Main & 1/140 & 889 & 7 & 6223 & 10 & Poor & 1 \\
48 & Dhoa’a & Local Access & 1/114 & 315 & 6.1 & 1922 & 12 & Bad & 0 \\
49 & Qirwee & Local Access & 1/115 & 431 & 4 & 1724 & 11 & Bad & 0 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Road sections condition data population size of both algorithms is set to 100, and the maximum number of iterations is set to 10,000. Therefore, the number of function evaluations is 100 \(\times\) 10,000 = 1,000,000. The optimization process is executed using PC (Intel (R) Core (TM) i7-3770 CPU @ 3.40 GHz (8 CPUs), 16 GB, Windows 7-64 bits). Figure 4 shows that the attained cumulative PCR for 3 years and 49 sections using the ISA algorithm is 470, which is larger than that achieved by GA (PCR = 466).
## 4 Methodology
### Data Analysis
#### 4.2.2 Feature Selection  
Since a large number of features would negatively influence the computation time and would not improve the classification result due to redundant information, a feature selection is performed.  
There are many different approaches to feature selection in the literature. [PERSON] [25] and [PERSON] et al. [19] selected suitable features for classification using an analysis of variance. Here, features are either evaluated individually based on their suitability for classification (ANOVA), or the best feature combination is searched for iteratively (MANOVA) (see [32]). To further reduce the number of features and redundancy, [PERSON] et al. [19] combined correlated features and thereby achieve a 4.2% increase in tool wear prediction accuracy. [PERSON] et al. [8], on the other hand, fused the extracted features using principal component analysis. The features selected in this way achieved significantly better classification results than using all extracted features.  
All extracted features of the raw signal and one spectral transform each are used as input for feature selection. Since the raw signal, which is the basis of the spectral transformations, is always recorded, raw signal features are always used. For reasons of efficiency, only one spectral transformation is to be calculated in the application and its features are to be used. Two alternative methods of feature selection, MANOVA and a combination of ANOVA and rank correlation coefficient \(r_{s}\), are used to select information-bearing features from the input variables, which allow a good separability of the output variables into their classes. For the four output variables of radial feed, tool tumble, OTE, and total profile deviation, suitable feature combinations are selected separately in each case.  
The first feature selection method investigated selects the best feature combination for classification using MANOVA. Depending on the considered output variable, different numbers of features are selected by the MANOVA.  
In the second feature selection procedure investigated, the features evaluated in the ANOVA are sorted according to their suitability for classification, and redundant features are removed. Only features with a rank correlation coefficient of less than 0.5 are retained. Thus, the highest-ranked 60 least redundant features per combination of raw signal features and features of a spectral transformation were selected.  
Figure 8: Raw signal with a tumble of 0 \(\upmu\)m and a radial feed of 0 mm as well as overview of the time intervals used and the process steps of the hobbing process.
## 3 Results
### Fast Volumetric Imaging for Doppler Application  
The feasibility of the 4-D platform implementation is shown here on a blood vessel mimicking phantom (described in Section 2.4) for 3-D Doppler applications. The results indicate the capability to extract the Power Doppler information when fast volumetric imaging is performed. Two different cases are presented in Figure 8 the US probe parallel (first row) and tilted by an angle of 12\({}^{\circ}\) from the vessel phantom (second row). One PW was transmitted for each acquisition. In B-mode, the walls of the phantom are easily detected in all the planes without any distortion. The frame-rate available for volumetric acquisitions is sufficient to detect the blood mimicking fluid for power Doppler evaluation. Some artifacts are visible in the presented cases, on the distant wall of the imaged phantom.
# An Edge-Based Approach to Partitioning and Overlapping Graph Clustering with User-Specified Density
## 4 Proposed Partitioning and Overlapping Clustering Method
### Partitioning Clustering
#### 4.1.1 Determination of Edge Degrees  
The initial phase entails computing the degree of each edge (\(d(e_{i})\)) within a given graph (\(\mathcal{G}\)) using Equation (1). Figure 2(a) demonstrates the process of calculating edge degrees within a randomly generated graph. Equation (2) outlines the process for calculating the average edge degree (\(Avg_{deg}(\mathcal{G},e_{i})\)) based on the determined edge degrees within the graph(\(\mathcal{G}\)). Figure 2(b) provides an illustrative example, where the average edge degree is measured at 6.76.  
After determining the degrees of all edges and identifying the average degree edge \(Avg_{deg}(\mathcal{G},e_{i})\), it serves as both the cluster (\(c_{i}\)) initialization and the starting edge for further expansion. It is worth mentioning that, in cases where an edge with the exact degree of \(Avg_{deg}(\mathcal{G},e_{i})\) does not exist, the algorithm selects the edge with the closest value to initiate the clustering process. In contrast, when multiple edges possess the same average degree, the algorithm randomly selects one of them. The resulting \(Avg_{deg}(\mathcal{G},e_{i})\) edge from this phase is characterized by its average degree and serves as the starting edge for expansion, functioning as the input for the second phase.
## 3 Methodology and Algorithms
### Theoretical Foundation
#### 3.1.3 Deep Learning Architectures for Sign Language Recognition  
Deep learning models have become foundational to the task of sign language recognition (SLR), as they offer powerful mechanisms for extracting and modeling spatiotemporal patterns from high-dimensional motion sequences. Unlike static image classification, SLR requires understanding structured motion over time, often involving complex hand articulation, temporal dependencies, and multimodal cues. To address these challenges, both convolutional and attention-based neural architectures have been extensively adopted.  
Convolutional approaches such as 3D Convolutional Neural Networks (CNN3D) capture spatiotemporal dependencies by applying convolutional filters across both spatial and temporal dimensions. This enables them to learn short-range motion patterns effectively while maintaining spatial coherence, making them suitable for recognizing locally structured gestures. In contrast, self-attention-based architectures like Transformers model global temporal relationships through attention mechanisms, allowing them to capture long-range dependencies without relying on recurrence or local convolutions. Their ability to process variable-length inputs and attend to both past and future frames makes them particularly effective for recognizing continuous, coarticulated gesture sequences where temporal context is critical.  
The role of these architectures in SLR extends beyond prediction; they serve as analytical tools for evaluating temporal consistency, identifying gesture boundaries, and quantifying the discriminability of learned representations. By learning to map raw motion data into structured latent spaces, deep models enable systematic assessment of gesture classes and can serve as a diagnostic mechanism for evaluating the quality and diversity of training datasets.
# Fault-Tolerant Tripod Gait Planning and Verification of a Hexopod Robot
## 3 Planning Method of F-TT Gait
### Phase Sequence Planning of F-TT Gait
#### 3.2.2 F-TT Gait in Leg 2 Fault State  
Based on the phase and the duty factor of the F-TT gait equation, the gait phase sequence diagram of the hexapod in the case of a leg 2 fault is obtained, as shown in the Figure 7. The F-TT gait locomotion process of the hexapod robot in the leg 2 fault state is shown in Figure 8; the dotted line in the figure indicates a leg in the transfer phase, the dot-dash line indicates a broken leg, and the solid line indicates a normal leg in the support phase.  
In the first process, legs 4 and 6 are lifted up to swing forward, legs 1, 3, and 5 are in the support state to push the body forward, and the robot posture is transformed from Figure 8a,b.  
In the second step, legs 4 and 6 touch down, leg 1 is lifted up to step forward, leg 5 is raised to step forward at a slower velocity, legs 3, 4, and 6 provide support on the ground. Meanwhile, the body is moved passively, and the attitude changes from Figure 8b,c.  
In the third process, leg 1 touches down, leg 5 continues to transfer forward while leg 3 is lifted to swing forward, legs 1, 4, and 6 support the body, which pushed the body to move forward, and the attitude changes from Figure 8c,d.  
After the three processes, a whole walking cycle of the F-TT gait movement under the fault condition of leg 2 is completed, reverting to the initial attitude in Figure 8a and looping the cycle.  
Figure 6: Transfer order of legs in fault tolerant tripod gait in leg 1 fault state.  
Figure 7: Phase sequence table of fault tolerant tripod gait in leg 2 fault state.
## 4 Discussion
### Limitations and Implications  
Although we studied the responses of vegetation dynamics to various drivers on the phenology-based seasonal scale, due to the complex driving factors, there are still some limitations and uncertainties in our study. Firstly, due to differences in sensor performance and soil background color, satellite-based NDVI data have inherent limitations [18]. Secondly, other factors, such as natural disturbance and human activities have a non-negligible impact on vegetation growth [1;50n51]. Taking all these factors into account is the most likely to yield more comprehensive and accurate outcomes. Finally, we noticed that climate factors have both a time delay effect and a cumulative effect on global vegetation growth. Our research only focused on the immediate and lagged impacts of climate. Therefore, in future work, we should discuss the influence of other variables in detail.