esa-sceva/satcom-chunk-collection huggingface dataset:
# Concerto: Dynamic Processor Scaling for Distributed Data Systems with Replication
## 3 Related Work
### Power Management for Data Centers  
Several studies have focused on power management in large-scale data centers. [PERSON] et al. identified that the CPU no longer accounts for the majority of the energy consumption on servers and presented a virtual machine consolidation concept that can be used along with DVFS. By migrating virtual machines off to the under-utilized machines, they enable more idle machines to be turned off, leading to great energy savings in data centers [31]. [PERSON] et al. presented a method to coordinate individual power management approaches in data centers so as to prevent them from interfering with each other in unexpected ways and, thus, to realize overall energy savings [32]. [PERSON] et al. further elaborated on power management techniques for computer systems by considering that frequent on-off activities can accelerate the wear-out of internal components, resulting in additional costs for repair and replacement [33].  
Although a number of studies have been conducted to reduce the energy consumed by computer systems, there is no prominent study that exploits the disparate requirements of data nodes in distributed systems for this goal. The need to take advantage of the performance relaxation in replicas for other benefits arises in the non-academic literature. The Redis community advises that the AOF (Append-Only-File) feature be disabled in the master node, which logs updates into persistent storage, as this aggravates the burdens of the master node [6]. Ceph supports the configuration of storage devices differently depending on the requirements of the application [34] and/or its functionality [35]. As an example, in relation to this method, the concept of device classes was recently introduced, with user level interfaces provided with which users can deploy the best-fitting storage devices for their objectives [34]. By making use of this approach, Ceph users can assign state-of-the-art storage devices, such as SSDs, to the primary data node while using massive and inexpensive storage devices, like HDDs, for the remainder of the data nodes [36; 37]. These approaches and our work have similar motivations, but they were introduced at different levels. To the best of our best knowledge, Concerto is the first approach to consider clock-speed scaling from the viewpoint of the distributed systems, focusing on heterogeneous performance constraints across data nodes.
## 4 Results and Discussion
### Reliability and Validity Analysis  
The reliability of the questionnaires, measured by Cronbach's Alpha, indicated high internal consistency across both experimental and control groups for sports identification and the willingness to spectate. For sports identification, [PERSON]'s Alpha values were 0.903 and 0.805 for the experimental and control groups, respectively, at the pre-test, and 0.903 and 0.754 at the post-test. For the willingness to spectate, reliability scores were similarly high, with 0.895 and 0.847 for the experimental and control groups, respectively, at the pre-test, and 0.878 and 0.816 at the post-test, showcasing the questionnaire's robustness. All the reliabilities were above the recommended threshold of 0.70 [19; 20].  
Table 3 presents the results of the validity analysis. All measures of construct validity exceed the recommended threshold of 0.70 [21]. For convergent validity, assessed using the average variance extracted (AVE) scores, all values are above 0.50, except for sports identification in the post-test, which recorded 0.49. Regarding discriminant validity, the AVE for each construct should exceed the shared variance with other variables [21]. This requirement is met in all cases, except for sports identification, where the AVE is lower than the shared variances with playing intention in both the pre-test and post-test. In summary, the convergent validity of sports identification is slightly lower than expected, which affects its discriminant validity with playing intention. More attention may be needed for the cognitive item related to understanding baseball or badminton in the sports identification scale, as this item had the lowest standardized loading among the eight items.
# Impact of Gait-Synchronized Vibrotactile Sensory Feedback on Gait in Lower Limb Amputees
## 3 Results
### Results: Functional Gait Assessments
#### 3.3.3 10 Meter Walk Test--Walking Speed  
Consistent with the findings of the FSST, all subjects except for two (18%; p# 2 and 4) demonstrated an absolute improvement in walking speed over a 10 m distance following the intervention period and while utilizing the vibrotactile feedback system in the 10 MWT at Follow-Up (see Figure 8). However, the median walking speed decreased by 0.09 s from the initial to the follow-up measurement. Three subjects (27%; p# 8, 9 and 11) increased their walking speed by up to 10%, one (9%; p# 1) by over 10% and the remaining four subjects (36%; p# 3, 5, 6 and 7) walked more than 30% faster in the follow-up measurement. The maximum relative increase in walking speed was 100.8% (p# 7). Subjects 2 and 4 slowed down by 21.2% and 12.5%, respectively.  
For the 10 MWT, there was a very weak, non-significant negative correlation between individual performance and VTFS usage time (r = \(-\)0.173, \(p\) = 0.61) as well as a very weak, non-significant positive correlation to the time since amputation (r = 0.099, \(p\) = 0.77).  
In line with the findings of the FSST, our study likewise observed a lack of significant difference in the medians of the cohort (\(n\) = 10) between the two measurements in the 10 MWT, with a difference of \(-\)0.02 m/s (see Figure 5, \(p\) = 0.106; medians (IQR): 1.10 m/s (0.44 m/s) vs. 1.07 m/s (0.65 m/s)).  
Figure 7: Scatter plot visualizing the mean performance of each subject, along with the median and IQR of the cohort’s overall performance in the Four Square Step Test (FSST) at baseline (light gray) and follow-up (dark gray). Subjects with performance changes surpassing the MDC after the intervention are highlighted with a star (\(\bigstar\)).
# A New Partitioned Spatial-Temporal Graph Attention Convolution Network for Human Motion Recognition
## 1 Introduction  
Human action recognition plays a very important role in computer vision [1]. With the development of deep learning, the traditional algorithm [2] improves the performance of the skeletal action recognition algorithm. However, it ignores the information between the nodes, and the improvement in overall performance is limited. [PERSON] et al. [3] proposed to use a self-attention mechanism in a graph convolution network. This method increases the flexibility of the graph convolution network. However, the recognition effect will have errors when the skeleton is blocked. [PERSON] et al. [4] proposed an adaptive graph convolutional network. It integrates an attention mechanism into the network. However, the accuracy of this network is slightly worse than that of the existing mainstream network. [PERSON] et al. [5] proposed the NST-GCN model. A new partition strategy is proposed, but the network cannot distinguish between similar behaviors, and the accuracy of identifying similar behaviors is poor. [PERSON] et al. [6] were able to obtain dynamic data information about the skeleton when combined with an attention mechanism so as to focus on each frame of bone data in varying degrees. However, this method only focuses on local features and ignores the relationship between the global and the local.  
To sum up, ST-GCN has a lot of research results in human action recognition. Compared with traditional neural networks, it can improve recognition performance. However, ST-GCN [7] is less effective in identifying similar and local actions [8]. The model training and inference will also take up a lot of computational resources at this stage.  
Our main contributions are summarized as follows.  
1. On the basis of the ST-GCN network, the CA attention mechanism [9] module is integrated into the network structure to assign appropriate weights to different joints, and the network after the introduction is optimized.
2. The sampling region is divided in a new way. This method increases the sampling distance, and the root node is closely connected with the farther node.
3. Finally, through a large number of experiments and analyses, it is proved that the accuracy of our model is improved compared with the existing model and that the robustness of the algorithm is improved.  
The content of this paper is distributed as follows: Section 2 introduces related work. In Section 3, ST-GCN is described in detail. The method for improving ST-GCN is introduced in Section 4. The experimental results and analysis of the proposed method are presented in Section 5. Finally, in Section 6, we summarize our work.
# A Cryptography-Powered Infrastructure to Ensure the Integrity of Robot Workflows
## 4 Application and Evaluation in a Real Robot System
### Application in an Industrial Robot System
#### 4.1.3 Integration into a Robotics Software Stack  
As basis of our use-case, we use our internal software stack further developed from what our group presented in [42]. It is a layered collection of re-usable components that can be applied to various robots and applications (see Figure 7). The bottom layer defines drivers for individual robots and other devices like sensors. In the case of CHIMERA, there are drivers for the UR arm and the MiR robot base (we can either choose a ROS-based driver or a driver that uses the MiR REST interface). Above that, the next layer defines various modules for integration of devices like one for whole-body compliance as presented in [43, 44] or sensor fusion and advanced motion planning.  
The workflow manager on the workflow abstraction layer orchestrates the execution of pre-defined sequences of actions. The state provider on the same layer collects information of the internal components and provides that to all other components. Both components are exposed to external entities by REST-based APIs on the Execution layer (the workflow manager API and the state API). The workflow manager API is a re-usable module that can be configured according to the capabilities of the underlying robot system. In the context of the CHIMERA robot, it allows for e.g., triggering movements of the mobile base or the robot arm (in joint and cartesian spaces) as well as support functions like the detection of AR tags using the wrist camera. The API provides asynchronous handling methods by returning an ID for each triggered workflow and the ability to query the status of such a workflow as it is being executed. Similarly, the state API adapts to the state data available from the robot system and exposes this data to the outside.  
Above that, components like UIs or planners can be used to (i) instrument single robots and (ii) coordinate fleets of robots. Security-wise, the software stack is configured and deployed according to our recommendations published in [27]. The components below the execution layer are not accessible from outside the stack itself. Thus, the API provides a well defined access channel to the robot which is secured using encrypted communication via https as well as API tokens for authorization.  
As we mentioned above, sealing ROS applications is more difficult. However, within our software stack, we are able to securely do so. We already presented an architecture for how to securely modularize robotic applications [27] including ROS modules. Our software stack adheres to this concept. We can also use ROS in our stack as individual modules e.g., on the driver level. Above we  
Figure 7: A logical overview of the CHIMERA software stack.  
provide workflow abstractions of individual ROS actions, services and publications to make them accessible in the workflow manager. The abstraction in combination with security measures also enables securing ROS applications with our concept.
## 5 Energy-Efficient Routing Protocols (E2 RPs) for UWSNs
### Reliable Physical Distance-Based Energy-Efficient Routing Protocol (RPDE2 RP)
#### 5.3.1 Reliable Energy-Efficient Routing Protocol (R-E2 RP)  
The author of [147] suggested a reliable E2 RP for UWSNs, to reduce transmission delay by creating a path from the Tx anchor node to the sensor Rx node. RTS data packets are transmitted by the Tx anchor node to its nearby nodes. The nearby nodes responded with the delay of the data packet transmission to the Tx anchor node. The Tx anchor node chooses the subsequent hop as the one with the least data packet transmission delay by comparing the data packet latency issues. Each nearby node will receive a broadcast of the newly created path.  
Figure 13: Cooperative reliability-based E2 RPs (CO-RE2 RPs) [146].
## 4 Results and Discussion
### Three-Dimensional Unsteady Simulation  
LES is preferred for high accuracy unsteady simulation but requires many grids and a long simulation time. Also, it requires proper initial conditions for stable numerical simulation. Therefore, three-dimensional unsteady LES was performed with the initial condition of the preceding RANS simulation results. The total number of the grid was the same as the RANS simulation. The simulation time interval was set to be \(5\times 10^{-5}\) s. Figure 11 shows the iso-contours of pressure and vorticity magnitudes in the zoomed plot as well as in the entire computational domain. It can be found that a sudden pressure drop occurs at the nozzle throat, and strong vortices are formed downstream just behind the throat, which causes a large amount of fluctuating turbulent disturbance components to  
Figure 11: Unsteady Large Eddy Simulation (LES) results; (**a**) static pressure (Pa); (**b**) vorticity magnitude.  
Figure 9: Entire computational domain, numerical meshes, and boundary conditions.  
be convected in the downstream direction of the pipe. This turbulent disturbance generated acoustic pressure inside the pipe.  
In the ISO 9300 standards, it is required to measure the pressure at the positions 2D and 4D downstream away from the end of the critical nozzle, where D is the pipe diameter. The pressure fluctuations at the same locations denoted as Sensor 1 and Sensor 2 in Figures 3 and 11 are measured using the experimental setup described in Section 3. The experimental devices and methods follow the ISO 9300 and the experimental conditions are the same as the simulation conditions.  
Figure 12 compares the 1/3 octave band spectrum of pressure between the measured and the predicted ones at each sensor position. There is good agreement between two results at the position of Sensor 2 while there is a relatively large discrepancy between two results at the position of Sensor 1 where the numerical results over-predict in comparison with the measured data. This seems to be due to the fact that the Sensor 1 is located in the region where the strong vortex is observed in the simulation while the location of Sensor 2 is far enough away from the strong turbulence region. However, the predicted pressure levels at both locations become much lower than the measured one in the high-frequency region above 2000 Hz, which seems to be due to poor resolution of the grids in the corresponding frequency range. More precise numerical simulations can be performed by using more resolved grids and fewer time intervals but at a much more expensive numerical cost. Since the AIV phenomenon is known to occur in the frequency range of 500 Hz to 2000 Hz, the current numerical resolution was determined to be sufficient for that purpose.
## 4 Analysis Results and Discussion of Coupled Multi-Body Dynamics  
### Analysis of Thermo-Solid Coupling Characteristics during Braking  
#### 4.1.1 Temperature Change Results of the High-Speed Train Brake Disc  
The temperature on the surface of the brake disc changes with the braking time during the braking process of high-speed trains, and its temperature distribution also has certain rules. The temperature field distribution on the surface of the brake disc during the braking process of the high-speed train is shown in Figure 7. Figure 7a-d, are respectively, the temperature contours of the brake disc at the initial braking time \(t=0.5\) s, \(t=10\) s, the highest temperature time \(t=30\) s, and the braking end time \(t=56.6\) s.  
The temperature of the brake disc generally shows a trend of rising initially and then declining as braking progresses because of the continuous frictional heat generation between the brake disc and the brake pad, as well as the effects of convective heat transfer and radiation heat dissipation. The high-temperature area on the disc surface is mainly concentrated in the friction contact area between the brake disc and the brake pad and is distributed in a ring shape. This is mainly due to the rotational compaction effect caused by the friction force, which increases the local contact stress at the friction radius of the brake disc. Therefore, heat accumulates continuously at the friction radius, and the temperature rises, and the highest temperature reached 226 \({}^{\circ}\)C in 30 s. As the braking process proceeds, the heat is transferred from the high-temperature area of the braking interface to the surroundings and the interior of the brake disc through the heat conduction of the material. In different regions of the brake disc, the radial temperature distribution is significantly different, and the temperature gradually decreases from the friction radius to both sides, forming a large temperature gradient.  
Figure 6: Comparison of the model calculation and the braking test temperature curve.
# Deep Learning for Medical Image Cryptography: A Comprehensive Review
## 4 Deep Learning for Medical Image Cryptography
### Key Generation in Encryption Algorithms for Medical Image Analysis  
Key generation is a critical component of encryption algorithms used in medical image analysis. Encryption is employed to protect sensitive medical data and ensure its confidentiality, integrity, and authenticity. When it comes to medical image analysis, such as the processing and transmission of medical images, the use of encryption algorithms is particularly important. [PERSON] et al. (2020) proposed a novel deep learning-based key generation network (Deep-KeyGen) to generate the private key, which can be used for encrypting and decrypting of medical images. DeepKeyGen was evaluated on chest X-ray dataset, the BraTS18 dataset and the ultrasonic brachial plexus dataset. The experimental results also show that DeepKeyGen achieves higher levels of security when compared to other key generation algorithms. Using a neural network with key generation, [PERSON] et al. (2014) introduced a novel dynamic medical image encryption technique. The key generation neural network receives the seed value from the ROI of the image. Following the creation of the key, the input image is encrypted using our inventive encryption algorithm, which is represented here by an encryptor. To further strengthen security, the key is itself encrypted before being transmitted. Although the encryption strength and key strength of the suggested approach are superior, the lengthier encryption time needs to be reduced. To test the proposed dynamic image encryption technique, X-ray images are used in the experiments. Table 6 presents the summary of published work related to key generation algorithms while performing the encryption or security on deep learning-based medical image analysis. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Ref.** & **Year** & **Organ** & \begin{tabular}{c} **Image** \\ **Modality** \\ \end{tabular} & **Datasets** & **Metrics** & \
\begin{tabular}{c} **Encryption/Security** \\ **Algorithm** \\ \end{tabular} &
\begin{tabular}{c} **Key Generation** \\ **Algorithm** \\ \end{tabular} \\ \hline
[22] & 2020 & \begin{tabular}{c} Chest, Neck, \\ Brain \\ \end{tabular} & X-ray, MRI, US & \begin{tabular}{c} Montgomery \\ County’s chest X-ray \\ dataset [143], \\ Ultrasonic brachial \\ plexus dataset [145], \\ BraTS18 dataset [96] \\ \end{tabular} & \begin{tabular}{c} Montgomery \\ County’s chest X-ray \\ dataset [143], \\ Ultrasonic brachial \\ P-value \\ \end{tabular} & \
\begin{tabular}{c} Entropy, NPCR, \\ UACI, Learning Rate, \\ P-value \\ \end{tabular} & DeepKeyGen &
\begin{tabular}{c} Generative \\ adversarial \\ network (GAN) \\ \end{tabular} \\
[144] & 2022 & Hand & X-ray & Dataset from [146] & \
\begin{tabular}{c} Entropy, NPCR, \\ Encryption speed \\ \end{tabular} &
\begin{tabular}{c} AES-256 and Hill \\ cipher \\ \end{tabular} & GAN-based PRNG \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Summary of encryption/security algorithms along with key generation mechanism.
# Multimode Tree-Coding of Speech with Pre-/Post-Weighting +
###### Abstract
Section 8 develops the novel voice activity detection and mode detection procedures using the G.727 codec parameters and shows the classification results for the sentences used for the initial studies. Zeros were included in the original G.727 to maintain prediction performance when the all-pole predictor order was reduced for stability reasons. Zeros are part of the short-term predictor in the MMT-WP codec for the different reasons briefly discussed in Section 9. Performance analyses of the MMT-WP codec start in Section 10, where the different Comfort Noise Generation (CNG) methods and the corresponding bit rates are given and then the performance with the different CNG coding methods is presented. One of the goals of the current research is to reduce the codec complexity and Section 11 analyzes the complexity of the MMT-WP codec per component and per sentence. Section 13 presents comparisons of the operational rate distortion functions of the MMT-WP codec and the AMR-NB, G.727, and G.728 codecs for the utterances studied. Comparisons of the operational rate distortion functions with the rate distortion bounds are given in Section 14 to see how far away these codecs are from the optimum performance theoretically attainable for the source models developed using the approach of [PERSON] and [PERSON] [5]. In comparison to the conference paper in [3], this paper has an extensive discussion of speech-coding background and current work in Section 2, a clearer development of the voice activity detection and mode decisions in Section 8, a justification of using zeros in the predictor in Section 9, a new section, Section 12, describing current voice codecs and comparisons in terms of complexity, latency, performance, and functionality in relation to the goals of the current research, an expansion of the performance comparisons to other standardized codecs in terms of operational rate distortion functions in Section 13, and the inclusion of new results on the proposed codec compared to theoretically calculated rate distortion bounds in Section 14. Finally, some suggestions for extending the MMT-WP codec for wideband speech inputs and ideas for other future work are given in Section 15 along with conclusions. The following section on Speech-Coding Background, Section 2, places this research in the context of the various speech-coding standards and some on-going speech-coding research.