esa-sceva/satcom-chunk-collection huggingface dataset:
# Understanding Balance Control in the Context of Riding a Personal Mobility Device
## 3 Results
### Weight-Shifting Task
#### 3.1.4 Subjective Measures  
The ANOVA results showed that the target distance (PP: F(2, 38) = 4.32, \(p\) = 0.020, \(\text{np2}\) = 0.19; ET: F(2, 38) = 17.47, \(p\) < 0.001, \(\text{np2}\) = 0.48; PS: F(1.45, 27.50) = 43.29, \(p\) < 0.001, \(\text{np2}\) = 0.70; PC: F(2, 38) = 25.96, \(p\) < 0.001, \(\text{np2}\) = 0.58), target direction (ET: F(4, 76) = 4.34, \(p\) = 0.003, \(\text{np2}\) = 0.19; PS: F(3.03, 57.54) = 10.83, \(p\) < 0.001, \(\text{np2}\) = 0.36; PC: F(4, 76) = 9.58, \(p\) < 0.001, \(\text{np2}\) = 0.34), and interaction of the target distance and direction (PP: F(8, 152) = 2.48, \(p\) = 0.015, \(\text{np2}\) = 0.12; ET: F(8, 152) = 3.76, \(p\) < 0.001, \(\text{np2}\) = 0.17; PS: F(8, 152) = 5.88, \(p\) < 0.001, \(\text{np2}\) = 0.24; PC: F(8, 152) = 5.58, \(p\) < 0.001, \(\text{np2}\) = 0.23) had significant effects on the subjective measures of the weight-shifting task. In the post-hoc tests on the main effects, the subjective ratings decreased when the goal target was located farther and increased when the target was located in the E and W directions compared to those in the other directions (Table 2). In the post-hoc tests on simple effects (Table 3), the PP values were significantly different by distance in the N and NE directions. By direction, the PP values were significantly different at distance 3 but not at distance 1 and distance 2. At distance 3, the N and NE direction PP values were significantly small compared to those in the other directions. The ET values were significantly different by distance in the N, NE, and NW directions. By direction, they were significantly different at distance 3 but not at distance 1 and distance 2. At distance 3, the ET values decreased from the left and right directions (W and E) to the forward directions (NW, NE, and N). The PS values were significantly different by distance in all directions; however, by direction, they were only significantly different at distance 3. At distance 3, the PS values were significantly small in the N direction compared to those in all other directions. The PC values were significantly different by distance in all directions except for those in the W direction. In the NE direction, the PC values between distance 1 and distance 2 were not significantly different, but they were significantly larger than those at distance 3. In the NW direction, the PC values were not significantly different at distance 2 and distance 3 but they were significantly smaller than those at distance 1. A comparison of the values in the directions at each distance showed that the PC in the E and W directions were significantly larger than those in the other directions.
## 6 Conclusions  
In this paper, we proposed and evaluated the performance of a multi-layer VR distribution service that utilizes both multicast and unicast transmissions to deliver the service to the users. Our numerical results indicate that the proposed grouping scheme providing closed-form expressions for the number of groups and their configurations is characterized by similar performance as iterative algorithms provided in the literature. At low UE densities, the use of multicast service for distributing the basic layer is ineffective. The highest gains are observed when the demand for the base layer is much higher than for enhancement ones. QoS profile, as well as UE density, have a profound impact on the required density of NR BS. At the same time, the effect of blockers density is non-linear having the greatest impact on strict QoS profiles. In general, depending on the parameters the required density of NR BSs may vary in the range of 20-250 BS/km\({}^{2}\).  
The presented study provides a simple and efficient algorithm to evaluate the density of NR BSs required to support a given density of multi-layer VR UEs with prescribed QoS guarantees. One of the potential extensions of the study is related to considering the 5G NR deployments serving a mixture of unicast and multicast traffic.  
Conceptualization, D.M. and Y.G.; methodology, D.M.; software, V.B.; validation, D.M. and Y.G.; formal analysis, V.B. and D.O.; investigation, V.B., R.K. and E.G.; writing--original draft preparation, D.M., V.B. and D.O.; writing--review and editing, D.M., A.C. and D.O.; visualization, R.K. and E.G.; supervision, D.M. and Y.G.; funding acquisition, Y.G. and A.C. All authors have read and agreed to the published version of the manuscript.  
This paper has been supported by the RUDN University Strategic Academic Leadership Program (recipient [PERSON]). The reported study has been funded by RFBR, projects 19-07-00933, 20-07-01064.  
Not applicable.  
The data presented in this study are available on request from the corresponding author.  
Figure 8: Comparison of multicast and unicast services for basic layer.
# LTmatch: A Method to Abstract Pattern from Unstructured Log
## 4 Experiments
### The Accuracy of Log Extracting Algorithms  
This section conducts the experiment on the accuracy of the log pattern extraction algorithm. The experiment selected dataset 1 as the benchmark, and the comparison methods were the five methods with the highest average accuracy shown in Zhu [17]. The experimental results are shown in Table 2. The first five columns in the table are five methods for comparison, and the methods in the last two columns are the methods proposed in this paper. In the contrast method, LenMa uses the clustering algorithm in traditional machine learning to determine the log type. The Sepli algorithm uses the longest common subsequence based on the prefix tree to determine the log type. The difference from the prefix tree in this paper is that the prefix tree is used to store the characters of the longest common subsequence that has appeared. The AEL algorithm uses a heuristic algorithm to get the log type. IPLoM uses iterative splitting technology to fine-grain the types of logs. Drain uses a fixed depth tree to store the log structure. The first layer of its storage tree stores the length of the log, so when comparing, it is guaranteed that each log to be compared is a log of the same length.  
In the results shown in Table 2, each row represents the type of experiment log, and each column represents a different log parsing method. To make the results clearer, the last row in the table (except the last result) shows the average accuracy of the method. The last column in the table is the best accuracy calculated in each log dataset, and the last value is the best average accuracy. For each dataset, the best accuracy is token by a sign \"\" and the best accuracy values are also shown in the last column of the table. In particular, the accuracy of all results greater than 0.9 is marked in bold, because these results can be considered to be excellent on these datasets.  
From the results of Table 2, we can find out that LTmatch has achieved the best average accuracy, and the algorithm also contains the most optimal accuracy. Compared with the Lmatch algorithm before optimization, the average accuracy is improved by 9.65%, which shows that the fine-grained division of the tree structure can improve the accuracy of log classification. Compared with other log parsing algorithms, the LTmatch algorithm we proposed is 2.67% better than the best algorithm Drain among the other methods. It proves that the algorithm in this paper can more accurately identify the log type by comparing the longest common subsequence based on the weight.
## 2 Preliminaries  
### Arbitrary Initialization and Scalability  
Arbitrary initial system configuration refers to a system configuration the system is in before it begins its execution, where system processes are in any arbitrary initial state, where no assumptions can be made about the states of the variables of system processes. Most distributed systems/algorithms make certain assumptions about the initial system configuration and hence employ distributed reset mechanisms [17] to guarantee a certain initial system configuration prior to their execution. At first, this approach seems to be viable; however, systems that require a considerable amount of time to initialize/set up or execute become more prone to transient faults perturbing initialized processes as the system size increases. The perturbation of already initialized processes nullifies the effect of the system initialization or setup; as a result, the safety and liveness properties of systems/algorithms can no longer be guaranteed. Since the ability of a system that requires initialization to ensure its safety and liveness properties decreases as system sizes and the likelihood of transient faults perturbing already initialized processes increase, the initialization requirement of a system reduces its ability to scale up. Examples of systems that are large, initialization is difficult or challenging, and execution or initialization takes a significant amount of time to reach all system processes include the ones given in [18; 19; 20]. Such systems provide additional benefits such as avoiding delays caused by initialization. In addition, they tend to be more adaptive as they eliminate some preconditions for newly joining processes by allowing them to be in any arbitrary initial state. Therefore, distributed algorithms that do not require initialization are crucial.  
Practically, applications start in an arbitrary initial system configurations, where the processes' states are not known [21]. Starting in an arbitrary system configuration, some nodes/processes may experience partial failures while others continue to work as expected [22]. In addition, data due to arbitrary initialization and its transmission can expose the distributed systems to potential safety risks such as unauthorized access and malicious attacks [23; 24; 25; 26]. Therefore, an algorithm that does not require initialization needs to ensure that the processes that execute the algorithm are free of the interferences caused by arbitrarily initialized processes. For instance, in a broadcast algorithm, a process that received the broadcast with neighbors that did not receive the broadcast may experience interferences by the arbitrarily initialized processes and may prematurely end the broadcast.
# LSTM and Bat-Based RUSBoost Approach for Electricity Theft Detection
## 4 Simulation Results and Discussion
### Summary of Results  
The summary of results is given in Figure 12 and Table 13. In order to validate the performance of the proposed system model, it is compared with the state-of-art benchmark schemes. We use reliable performance metrics such as the precision, recall, F1-score and ROC curve. For comparison, the benchmark models used in this paper are LR, SVM and CNN-LSTM models. The results prove that SVM performs worst compared to the other benchmark schemes. This is due to the fact that SVM does not handle large time series data; for massive data, it causes overfitting. We see from Figure 9 that, for testing data, the result of SVM is shown to be the worst: i.e., it achieves an ROC curve of 57.2%. The CNN-LSTM model shows better performance compared to SVM and LR; i.e., the value of the ROC curve is 81% and recall is 85%. It is considered to be the best model among the benchmark schemes. The CNN-LSTM model performs better because it is a deep learning model and can handle large time series data well.  
The proposed LSTM-RUSBoost model is reliable and beats the given benchmark schemes in terms of all performance metrics. Our proposed model shows superiority to other models for many reasons; firstly, it can effectively handle imbalanced data well by the random under-sampling operation and then by using the adaptive boosting technique for classification. Secondly, the LSTM block efficiently extracts the relevant features during feature refinement. Finally, the optimization by the bat algorithm further improves the performance of our proposed system model.
# Design Methodology and Application Dynamics of Compact Quasi-Zero Stiffness Isolators
## 4 Summary and Outlook  
### Summary  
As one of the innovations in low-frequency vibration isolation technology, QZS isolation has made significant progress in recent years, and its design mechanism mainly relies on the use of negative stiffness elements, which can realize the effect of QZS through specific geometric configurations or material properties. In this paper, we systematically summarize the design mechanism, research status, and applications of compact QZS, such as special springs, magnetic, bionic, metamaterials, and origami structures for vibration isolation in space-constrained application scenarios. Special spring-type isolators have a compact structure, high space utilization, and adjustable preload, but the machining precision requirements are extremely high, and slight deviations can easily lead to performance degradation. Magnetic-type isolators demonstrate the advantages of non-contact and control, but the challenges of thermal demagnetization and electromagnetic saturation of permanent magnets remain to be solved. Metamaterial-based type isolators can realize multifunctional integrated designs but are limited by expensive manufacturing costs. Biomechanically inspired QZS isolators can realize multidimensional vibration isolation, but complex intrinsic models are difficult for engineering applications. Similarly, origami-inspired types break through the bottleneck of compact design; however, the configuration is still far away from engineering realization.  
In terms of the current compact QZS research dynamics, most of them utilize large stiffness components to replace the original QZS structure, and there are fewer studies on the nesting and coupling design of positive and negative stiffness components.  
Figure 19: HSLDS structure with no restrainers and guidance bars [98].  
However, this paper only summarizes the compact design, with less consideration of other key design factors such as stroke, load-bearing mass, and vibration amplitude. The subsequent study will focus on the multi-featured optimal design method.
# A Comprehensive Survey of Masked Faces: Recognition, Detection, and Unmasking
## 5 Masked Face Methods
### Face Unmasking Approaches  
In this section, we explore recent progress in deep learning models designed for removing face masks, treating them as a specialized form of image inpainting, specifically focusing on \"object removal.\" This technique offers promising opportunities not only for mask removal and restoring unmasked faces but also for applications like verification and identification systems. Figure 7 provides an overview of the GAN network as a representative example of the FU-Net. Additionally, Tables 5 and 6 outline popular models utilizing the GAN and diffusion methods, respectively. To further illustrate the capabilities of state-of-the-art face unmasking models, Figure 8 showcases a comparative example of three leading approaches: GANMasker, GUMF, and FFII-GatedCon. The first column presents the input masked face, while the second column displays the original unmasked face for reference. Each column demonstrates the output of a specific model, highlighting how these methods reconstruct essential facial features. Most recent models in the field of object removal predominantly leverage GAN networks, a trend observed even before the emergence of COVID-19, as evidenced by works such as [78; 79; 80; 81; 82; 83; 84; 85; 86]. These methods are tailored for object removal tasks in general. However, there exists specific research dedicated to face mask removal, exemplified by works such as [2; 3], along with diffusion-based models [87; 88; 89; 90]. Figure 7: Overview of the GAN network as an example of FU-Net for face mask removal. Upon delving into the realm of image inpainting methods, one encounters a diverse landscape of approaches pioneered by various researchers. Among the earliest methodologies stands the work of [PERSON]. [PERSON], [PERSON], et al. [78], who introduced a convolutional neural network (CNN)-based technique employing context encoders to predict missing pixels. Building upon this foundation, [PERSON] et al. [79] proposed a generative adversarial network (GAN) framework equipped with two discriminators for comprehensive image completion. Similarly, [PERSON] et al. [80] put forth a gated convolution-based GAN tailored specifically for free-mask image inpainting. [PERSON] et al. [83] devised a multi-stage approach involving an edge generator followed by image completion, facilitating precise inpainting. Additionally, [PERSON] et al. [81] contributed to the field with their work on free-mask inpainting, leveraging partial convolutions to exclusively consider valid pixels and dynamically update masks during the forward pass. Another subset of methods focuses on face completion or the removal of objects from facial images. [PERSON] et al. [82] innovatively combined Wasserstein GAN with a Reverse Mask  
Figure 8: Face unmasking outputs from three state-of-the-art models: GANMasker, GUMF, and FFII-GatedCon. The first column shows the input masked face, while the second column displays the original unmasked face for reference.
## 4 Conclusions  
In this study, multi-material 3D-printed foods using NCD and CP were successfully produced using extrusion-based 3 DFP with two nozzles. The results showed that %Hreduction and %Lreduction were not affected by the type of material used in the 3D-printed square pillars (NCD or NCD + CP). Nevertheless, the %Wloss, force at 40% strain, and force at 80% strain were affected by the type of material used, where 3D-printed square pillars made of NCD + CP produced less %Wloss and a higher force at 40% strain and 80% strain in comparison with the 3D-printed square pillars made only using NCD. Less %Wloss in the 3D-printed square pillars made of NCD + CP might have occurred because of the lower water content compared to the 3D-printed square pillars made of only NCD. Furthermore, the presence of CP, with different hardness values due to protein gelation and starch gelatinization in the mid-section of the multi-material 3D-printed foods, might have led to a higher force at 40% and 80% strain. Regarding cooking processes, higher water loss during WOC resulted in higher %Wloss and %Lreduction in comparison to SC. In contrast, SC produced harder 3D-printed square pillars at 40% strain owing to the presence of water in the steam-cooked 3D-printed square pillars compared to the water oven-cooked pillars, where the water inside their structure is replaced by air. However, the force at 80% strain was not affected by the cooking process because the water oven-cooked 3D-printed square pillars might be prone to higher densification owing to the absence of water in comparison to the steam-cooked ones. In general, SC was found to be the most suitable cooking process because the appearance, size, and shape of the 3D-printed foods were not significantly affected.  
From an application and technology viewpoint, the findings obtained in this study suggest the possibility of producing 3D-printed foods using two food materials, NCD and CP, and improving their appearance and texture, as well as maintaining their 3D-shape stability through cooking processes including WOC and SC. These findings are expected to be useful for the innovative design of chickpea- and corn-based multi-material 3D-printed foods.  
Conceptualization, V.V.R.-H. and I.K.; methodology, V.V.R.-H., T.U. and H.K.; validation, V.V.R.-H. and H.K.; formal analysis, V.V.R.-H.; investigation, V.V.R.-H.; resources, I.K.; data curation, V.V.R.-H.; writing--original draft preparation, V.V.R.-H.; writing--review and editing, T.U., H.K. and I.K.; visualization, V.V.R.-H. and H.K.; supervision, I.K.; project administration, I.K.; funding acquisition, I.K. All authors have read and agreed to the published version of the manuscript.  
This research was partially funded by the Cabinet Office, Government of Japan, Moonshot Research and Development Program for Agriculture, Forestry, and Fisheries (funding agency: Bio-oriented Technology Research Advancement Institution), Grant Number JPJ009237. The first author appreciates the Government of Japan, Ministry of Education, Culture, Sports, Science and Technology (MEXT-Monbukagakusho), Grant Number 200500.
###### Abstract  
(1) Background: Container orchestration frameworks provide support for management of complex distributed applications. Different frameworks have emerged only recently, and they have been in constant evolution as new features are being introduced. This reality makes it difficult for practitioners and researchers to maintain a clear view of the technology space. (2) Methods: we present a descriptive feature comparison study of the three most prominent orchestration frameworks: Docker Swarm, Kubernetes, and Mesos, which can be combined with Marathon, Aurora or DC/OS. This study aims at (i) identifying the common and unique features of all frameworks, (ii) comparing these frameworks qualitatively and quantitatively with respect to genericity in terms of supported features, and (iii) investigating the maturity and stability of the frameworks as well as the pioneering nature of each framework by studying the historical evolution of the frameworks on GitHub. (3) Results: (i) we have identified 124 common features and 54 unique features that we divided into a taxonomy of 9 functional aspects and 27 functional sub-aspects. (ii) Kubernetes supports the highest number of accumulated common and unique features for all 9 functional aspects; however, no evidence has been found for significant differences in genericity with Docker Swarm and DC/OS. (iii) Very little feature deprecations have been found and 15 out of 27 sub-aspects have been identified as mature and stable. These are pioneered in descending order by Kubernetes, Mesos, and Marathon. (4) Conclusion: there is a broad and mature foundation that underpins all container orchestration frameworks. Likely areas for further evolution and innovation include system support for improved cluster security and container security, performance isolation of GPU, disk and network resources, and network plugin architectures.  
**Keywords:** container orchestration frameworks; middleware for cloud-native applications; commonality and variability analysis; maturity of features; feature deprecation risk; genericity
## 4 Results and Discussion
### Comparison of Two Combined Systems  
Using R600a and input values given in Tables 2 and 3, general comparison of proposed two systems can be seen from Table 8. A 44% decrease in COP\({}_{\text{sys}}\) occurs when dual evaporator is used instead of a single evaporator in refrigeration part of the combined system. Also, \(\eta_{\text{lex,sys}}\) decreases with the addition of an extra evaporator. Moreover, compressor power, total exergy destruction rate, and total mass flow rate are observed to be higher in dual evaporator system when compared to single evaporator system. The reason of increase in compressor power is that heat is removed at higher temperature in the second evaporator while vapor must be compressed from the lower pressure of the first evaporator.  
Figure 18: Second law efficiency (**a**) and degree of thermodynamic perfection (**b**) for each component in combined ORC-dual evaporator VCR cycle.