esa-sceva/satcom-chunk-collection huggingface dataset:
# Augmenting CCAM Infrastructure for Creating Smart Roads and Enabling Autonomous Driving
## 4 Literature Review
* A Dell Poweredge R430 server (Intel(R) Xeon(R) CPU), CentOS 7 using OSM v4, Debian 9 OS (GNU), and OpenStack Pike is used to run the infrastructure, which include four VNF types, i.e., vOBU, OBU, data analytics, vehicle monitoring service. * A MySQL database is used to store data, Grafana tool for monitoring services, and an Android application (on Samsung Galaxy S7) to access vehicle data from a mobile device. The vOBUs are evaluated by analyzing the request RTT in different network segments. In this connection, the RTT is maintained at around 230 ms by vOBU, while it ranges from 400 ms to 22 ms for OBU. When a delay threshold of 300 ms is reached, the vOBU software provides the cache data to measure the requests. Meaning thereby, 90% of the requests are solved by the vOBU at 300 ms. With the 4G connection, the new vehicle registers itself in just 400 ms.  
Based on what has been mentioned above in the literature review, we provide the most relevant directions to design our differentiator. In this connection, the relevant research contributions are directed:  
* To techniques of data fusion,
* To approaches for enabling the specific AD use-cases (e.g., remote driving),
* To creating the high level perception/map through crowdsourcing techniques,
* To focusing on the information collected from automated and connected vehicles,
* To solutions for addressing the computation limitation,
* To the limited services with no contribution towards CCAM infrastructure, etc. On the contrary to the above and similar approaches, our work provides the differentiator by: (i) creating the enriched perception through images, videos, and sensory data captured by the sensory devices mounted in the RSU; (ii) approaches to utilize the generated enriched perception for enabling new AD use-cases; (iii) suggesting the computation and communication-rich infrastructure in the classical roadside unit to enable the creation of enhanced roadside unit; (iv) contributing with enabling concepts that help realize the advanced use-cases of autonomous driving.
## 3 System Architecture
### Overall Architecture
#### 3.1.4 ICN Name Resolution System  
It is a native functionality of the ICN. The ICN architecture chosen in this paper is an evolutionary architecture with flat naming. When routing, it is necessary to first confirm the location based on the content ID, which requires a name resolution system. This system is responsible for maintaining the mapping between IDs and locations of all entities within the network for use in the subsequent routing stage.  
Figure 2: Service information exchange among network nodes in the same domain.
# Effect of Surfactant on Water Content of Phosphogypsum
## 4 Conclusions  
The effects of single surfactants SDS, SDBS, SR and PDMS and coupled surfactants SDBS+SDS, SDBS+SR and SDBS+PDMS on the water content of phosphogypsum were investigated. The results show that, during the leaching process, surfactant strengthened the interfacial interactions between molecules through hydrophobic and hydrophilic orientation on solid-liquid interface, reduced the  
Figure 8: And SR form complex on the water leaching solution interface.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**System** & **Water Content** & **Mean Deviation** \\ \hline No Surfactant & 20.30\% & 1.03\% \\ SDBS & 18.00\% & 1.21\% \\ SDBS+SDS(1:1) & 18\% & 1.24\% \\ SDBS+SR(1:1) & 17.30\% & 1.09\% \\ SDBS+PDMS(1:1) & 18.60\% & 1.28\% \\ \hline \hline \end{tabular}
\end{table}
Table 8: Rate of water content of phosphogypsum after the modification of the compound surfactants.  
surface tension and contact angle to improve the filtration rate and reduced the soluble phosphorus loss, thereby improving the leaching rate of phosphate rock and reducing the water content of phosphogypsum. Among them, the water content of phosphogypsum was better controlled by SR + SDBS than by other surface surfactants. Compared with the blank group, SR + SDBS increased the filtration rate of phosphogypsum by 24.34%, decreased the moisture content by 3%, and increased the phosphate leaching rate by 4.36%. In addition, the fractal dimension of phosphogypsum crystal increased with the increase of contact angle of solid-liquid interface, and the mathematical relationship between fractal dimension and contact angle was well fitted.  
Conceptualization, B.L.; Methodology, H.P.; Validation, B.L.; Formal Analysis, J.G.; Investigation, J.G.; Resources, B.L.; Data Curation, J.G.; Writing--Original Draft Preparation, H.P.; Project Administration, B.L., H.P.; and Funding Acquisition, H.P.  
This work was supported by the Science and Technology Project of Chongqing, China (cstc2018 icyjAX0018), and Talent Introduction Project of Yangtze Normal University (2017 KYQD117).  
The authors declare no conflict of interest.
## 4 Results and Discussion
### Strain Criterion of Electrochemical/Mechanical Failures
Figure 6: (**a**) Evolution of strains along x direction in electrochemical cycles, and (**b**) evolution of strains along y direction in electrochemical cycles. Figure 7: (**a**) Evolution of mean strain gradient (MSG) along x direction in electrochemical cycles, and (**b**) evolution of MSG along y direction in electrochemical cycles. In Figure 6a,b, the evolution curves of strains in different directions have been presented, in which the abbreviations of max and min represent maximum and minimum values of the strains, respectively, while ave and std represent average values and standard deviations, respectively. The strains in lithiation process are obtained by differencing the locations of B state and C state (see Figure 5), while in delithiation process by differencing the locations of A state and B state. In Figure 7a,b, the evolution curves of MSGs in different directions have been presented. From the results, it can be summarized as follows:  
1. Both the out-of-plane displacement and the in-plane strain during the lithiation/delithiation process show great discreteness over a large surface area. Positive strains and negative strains exist simultaneously, which indicates that the stress distribution is inhomogeneous. 2. Average strains slightly fluctuate with the cycles evolving, which indicates that electrochemical failures are not uncorrelated with average strains. In other words, single point analysis of strain/stress is not feasible in investigating electrochemical failures of electrode. 3. MSG values mainly decreases with the cycle number increasing. The trend indicates that the strain distribution tends to be uniform with the electrochemical reaction of working electrode. Furthermore, the trend of MSGs are opposite in lithiation and delithiation processes, which demonstrates that MSG is sensible to the movement directions of Li-ions.
## 6 Results  
The result of the work presented in this paper is an omnidirectional robotic platform, whose first prototype still does not count all the required sensors for autonomous surveillance. As shown in Figure 10, a monitored surveillance was perform in the SPS accelerator by members of the robotic group of CERN. Throughout the tests, the operator remotely navigated the robotic platform from the control room. In this case, the operator had the information from the cameras; however, in future interventions a point cloud-based reconstruction system for three-dimensional environments will be available, thanks to the installed 3D LIDAR. This addition will help the operator and control to cross the gates along the tunnel. Due to the developed tests, it was tested that the robot design comply with the first, third and forth requirements of Section 1, which are related with surveillance time, robot speed and autonomy.  
The robot was tested in its most critical point, this is, during crossing the section doors. As shown in Figure 11, the robot was able to cross it, complying in this way with the spatial requirements (the second requirement).  
As explained in Section 1, the robot charges its batteries in one of the entrances of the SPS tunnel, where a charging station is available. In that moment, the robot folds the robotic arm over its top plate, as shown in Figure 12.  
Figure 10: SPS robot driving during the first test.  
Figure 11: SPS robot crossing the door during the first test.  
Figure 12: Robot in the charging station with the robotic arm folded over its top end plate.  
The kinematic and dynamic models were tested in a wide environment, obtaining the results shown in Figure 13. In that picture, the blue line represents the desired trajectory or ground truth, the red line represents the trajectory that the robot followed using the kinematic model, and the green line represents the trajectory using the dynamic model. The test was done within a wide environment, traveling a distance of approximately 90 m and changing the orientation in the meantime. It is calculated that the maximum error by the kinematic model during the given trajectory was 2.78 m and 5.1\({}^{\circ}\), while the dynamic model reduced this error to 0.89 m and 0.45\({}^{\circ}\).
## 5 Conclusions
### Future Work  
The results presented here are limited to QoE evaluation of VoD applications modelled using exponential functions. We planned to use the proposed approach on other video streaming applications i.e., passive gaming video streaming, video conferencing to reach a generic conclusion about how sampling errors in QoS affect QoE evaluation of different video applications. We also propose to use different modelling functions like power-law and polynomial functions to determine how modelling technique affects the propagated statistical errors in QoE.  
Moreover, in this work, we considered the QoE models for loss and jitter separately in Equations (3) and (4). Our results show that PLR effects propagated statistical error significantly as compared to jitter. This work shows that jitter and PLR are weakly correlated and has a tendency to appear together but this work does not evaluate the statistical errors when they exist together. For separate jitter and PLR models, this correlation has an insignificant effect on propagated errors in QoS. We propose to extend this further to evaluate QoE such that we considers the correlation and then see how the statistical errors differ from the results presented in this work.  
Furthermore, in this work, we have emulated packet loss as a random Bernoulli process. This poses limitations to the analysis as it does not mimic the real network behaviour. We propose to model and emulate bursty packet loss that takes place in real networks. In future, we planned to model QoE using bursty packet loss and compare the propagated statistical errors of newly yielded models with the current QoE model presented in this work.  
The idea of this project was proposed by [PERSON] and [PERSON] designed the experiments and carried out the analysis. N.A. contributed towards data analysis and correlation. All authors contributed towards writing and reviewing of the final manuscript. All authors have read and agreed to the published version of the manuscript.  
No External funding was given for this research.  
We are grateful to the School of Electronic Engineering and computer at the Queen Mary University of London for their help and support in this project. We extend our thanks to [PERSON] and [PERSON] for their contributions in the project. We will also like to thank Teragence for their collaboration and providing us with the active measurement data.  
There is no conflict of interest declared by this research or authors.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} ACR & Absolute Category Rating \\ CI & Confidence Interval \\ GoF & Goodness of Fit \\ ITU-T & International Telecommunication Union-Telecom \\ MOS & Mean Opinion Score \\ OTT & Over-the-Top \\ PCC & Pearson Correlation Coefficient \\ PLR & Packet Loss Ratio \\ QoE & Quality of Experience \\ QoS & Quality of Service \\ SLA & Service level Agreement \\ SOS & Standard deviation of Opinion Score \\ SRCC & Spearman Rank Correlation Coefficient \\ SSE & Sum of Squared estimate of Errors \\ UDP & User Datagram Protocol \\ TCP & Transport Control Protocol \\ VoD & Video-on-Demand \\ \end{tabular}
# Research Challenges for the Internet of Things: What Role Can OR Play?
## 3 How can OR Support the Future IoT?
### IoT Business Investment Studied Using Game Theory  
Establishing business models for the IoT is a key challenge that can be addressed by OR. Decision Analysis has already been applied to this area. However other techniques such as Game Theory (GT) and Discrete-event Simulation may also be applied.  
GT has been used to explore technical issues such as multipath selection [49] and data distribution [48] in IoT networks. It could also be used to determine payoffs from different application areas of the IoT. GT has long been applied for business [50] to determine optimal strategies. Major US corporations such as Coca Cola and Pepsi have applied GT to assess business tactics.  
[PERSON] et al. applied GT to study price competition of IoT sensing services noting that traditional system optimization may be unsuitable for IoT due to its complex, heterogeneous nature with multiple entities and incentive mechanisms [93]. Considering the spectrum of potential IoT applications and markets ranging from personal (wearable devices), home (automation, security), transport (driverless cars), enterprise (e.g., smart cities, healthcare) (see [94]), GT could be applied to assess market strategies for developing and timing the release of new IoT products. Home automation systems, for example, may not all be provided by the same supplier but will need to be interdependent and interoperable.  
Companies providing competing or complementary systems could use GT to determine appropriate strategies to maximize their market penetration and profits.  
Which games types might be most appropriate to assist with making IoT business strategies? Of the 13 game descriptors defined in Wikipedia (Game Theory: [[https://en.wikipedia.org/wiki/Game_theory](https://en.wikipedia.org/wiki/Game_theory)]([https://en.wikipedia.org/wiki/Game_theory](https://en.wikipedia.org/wiki/Game_theory))), the likely types to study business investment are: (1) cooperative/non-cooperative; (2) symmetric/asymmetric; (3) zero sum/non-zero-sum; (4) simultaneous/sequential; (5) perfect/imperfect information (note that games can have a combination of these characteristics). Consider a situation where three companies provide home automation systems. While each company can supply the full range of equipment and services, Company A specializes in internal devices (such as smart refrigerators and lighting systems), Company B specializes in external devices (watering systems, weather monitors), while Company C specializes in security systems (cameras, monitors, alarms) as shown in Table 6.  
Strategies for each company to obtain a profitable share of the marketplace could be determined using the cooperative gaming approach while the decision to invest in their non-specialist areas could also be assessed using other gaming approaches such as constant sum games. In the latter case, this leads to \(2^{3}=8\) possible outcomes since each company has 2 strategies: to decide whether to diversify or not. A 3-dimensional payoff matrix is required to describe this case [95].
# Collaborative Analysis of Learners' Emotional States Based on Cross-Modal Higher-Order Reasoning
## 2 Related Works
### Cross-Modal Feature Fusion
#### 2.1.3 Hybrid Fusion  
The method of hybrid fusion combines the advantages of both early and late fusion, retaining the simplicity and directness of early fusion while enabling deep processing when necessary. In hybrid fusion, certain modalities may undergo simple fusion at an early stage, while others undergo deeper processing, and their features are then fused together at a later stage.  
The principle of hybrid fusion lies in flexibly selecting fusion strategies based on the specific task requirements and the relationship between modalities. Through early-stage simple fusion, multimodal information can be quickly integrated, reducing model complexity. Meanwhile, in the late fusion stage, the advantages of deep processing can be utilized to better explore the correlations and interactions between modalities, thereby improving the model's performance and effectiveness.  
The advantage of hybrid fusion lies in its ability to flexibly select fusion strategies based on specific task requirements and the relationships between modalities. This flexibility allows the hybrid fusion method to adapt to different application scenarios and achieve good results in various multimodal learning tasks. At the same time, hybrid fusion can fully utilize the advantages of both early and late fusion, thereby retaining simplicity and directness while enhancing the model's expressive power and generalization performance. The schematic diagram of hybrid fusion is illustrated in Figure 3.
# EMG-Assisted Algorithm to Account for Shoulder Muscles Co-Contraction in Overhead Manual Handling
## 4 Discussion
### Limitations  
This study had limitations that should be considered. The model calibration has been done with a task similar to those analyzed. Since this task required low sternoclavicular and acromioclavicular joint net moment, our study was limited to the GH joint. For future studies, a more appropriate calibration trial should be used, to account for the contribution of the trapezius and serratus anterior, as they are critical in rotator cuff pathologies [54]. Furthermore, the need of indwelling electrodes to calibrate the model is a practical limitation of the EMG-informed approaches for the upper limb. Further studies should be done to understand the sensitivity of each parameter of the calibration, and thus refine its process and reduce the number of required EMG signals, whether they require surface or indwelling electrodes. Additionally, while the symmetrical activity is confirmed in the literature for manual handling tasks [23], it would be interesting to compare calibration results as well as activation patterns between the dominant and non-dominant sides. The musculoskeletal model had some limitations. The first limitation was related to the geometry, as it was difficult to validate the muscle lengths and moment arms for all 36 participants due to large variability in their morphological features. Secondly, the GH joint stability was ensured only by MTUs, which neglected the effect of the labrum and ligaments [55]. However, it was expected that, in our range of motion labrum and ligaments, contributions were much smaller than muscles contributions [1,56]. Finally, the GH joint reaction magnitude in this study was relatively larger, it is expected that an improved calibration step would yield a joint reaction magnitude closer to those reported in the literature.
## 3 The Machine Learning Swarm Intelligence Algorithm
### ML Binary Operator  
The binarization process is driven by the Metaheuristic Binary Transformation (MBT) operator, detailed in the pseudocode of Algorithm 2. This operator refines the solution set, \(solSet\), from the previous iteration by applying the chosen metaheuristic strategy (\(MetaH\)), in this case WoA, in conjunction with the current optimal solution, \(optSol\), and the transition probability for each cluster, \(clustProb\). The metaheuristic, \(MetaH\), updates \(solSet\) by generating velocities, \(velSet\), which represent transition vectors obtained through \(MetaH\). These velocities are then processed using k-means clustering, where the number of clusters, \(Clusters\), is predefined as 5.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter** & **Description** & **Used Value** & **Explored Range** \\ \hline P & Particle number & 5 & [5; 10; 20] \\ K & Number of groups & 5 & [4; 5; 6] \\ I & Swarm iterations & 800 & [600, 800, 1000, 1200] \\ L & Local search iterations & 2000 & [1000, 2000, 4000] \\ a & Parameter \(a\) of WoA: controls & Decreases linearly from 2 to 0 & Decreases linearly from 2 to 0 \\ a & exploration and exploitation. & Decreases linearly from 2 to 0 & Decreases linearly from 2 to 0 \\ b & Parameter \(b\) of WoA: controls & 1 (constant) & 1 (constant) \\  & spiral shape. & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Setting binarization parameters for WoA.  
Each solution, \(sol_{i}\), across its dimensions \(d_{j}\), is assigned to a cluster. The clusters are associated with transition probabilities, \(clustProb\), which are ranked according to the magnitude of their centroids. The transition probabilities used are [0.10, 0.20, 0.40, 0.80, 0.90].  
The cluster with the smallest centroid is assigned the lowest transition probability, 0.10. Lower centroid magnitudes correspond to smaller transition probabilities, \(clustProb\). For each dimension \(d_{i,j}\) of \(sol_{i}\), a specific transition probability \(transProb_{i,j}\) is computed and compared with a randomly generated number, \(randNum\). If \(transProb_{i,j}\) exceeds \(randNum\), \(sol_{i,j}\) is updated to the best observed value; otherwise, it remains unchanged.
## 5 Empirical Studies  
The selection of the three datasets for the case study was based on their size, complexity, and historical change data availability. Vuze, Spring Framework, and Elasticsearch offer a wide range of historical change data due to their long-standing usage in the industry and open-source nature (see Figure 6).  
Vuze, known for its moderate size and architectural complexity, was chosen as a representative case study for moderately complex software systems. Historical change data for Vuze show a peak in development activity from 2003 to 2006, followed by a gradual decline until 2011. This extensive history provides a rich dataset for analysis, offering insights into how changes propagate over the lifespan of the software system. The Spring Framework, a widely used Java framework that facilitates the development of enterprise-level applications, represents large-scale software systems in our study. Its change history shows a unique pattern with a peak in activity from 2008 to 2009, followed by a decline and then a resurgence from 2012 to 2016. This pattern provides an opportunity to analyze change propagation in large-scale software systems undergoing periods of intense development and relative stability. Elasticsearch, a Java-based distributed search and analytics engine, offers another perspective on large-scale software systems. Its change history shows a different pattern compared to the other two datasets. There was a gradual decrease in activity from 2010 to 2012, followed by a period of high activity until 2020, and then a gradual decline. This pattern allows us to explore how change propagation evolves during periods of varying development activity.  
Figure 6: Ascending order of transactional changelogs spanning Vuze (10 July 2003–2 August 2014), Spring Framework (11 July 2008–3 May 2023), and Elasticsearch (8 February 2010–5 May 2023).  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{3}{*}{**Hyperparameters**} & \multirow{3}{*}{**Description and Default Value**} & \multirow{3}{*}{**Searched**} & \multicolumn{6}{c}{**Optimal Values**} \\ \cline{4-9}  & & & \multicolumn{2}{c}{**Vuze**} & \multicolumn{2}{c}{**Spring**} & \multicolumn{2}{c}{**Elasticsearch**} \\ \cline{4-9}  & & & & & **Fine** & **Package** & **File** & **Package** \\ \hline \multirow{6}{*}{\(n\_\)neighbors} & The number of dimensions in the vector space in which the word embeddings are represented. The default value is set to 100. & Fixed number calculated from the rule of thumb (37) or 200 as random choice. & 37 & 200 & 37 & 37 & 37 & 37 \\  & Number of neighbors to be used for K neighbor queries. Default value is set to 5. & Fixed number 10. & 10 & 10 & 10 & 10 & 10 & 10 \\ \hline \hline \end{tabular}
\end{table}
Table 5: _Cont._