esa-sceva/satcom-chunk-collection huggingface dataset:
# Efficient I/O Performance-Focused Scheduling in High-Performance Computing
## 3 Design
### Scheduling
#### 3.3.3 Comparison of Job Scheduling Methods  
As can be seen by examining the structural design plot of our scheme, scheduling jobs in this way is more efficient than using the basic scheduling model with the first-come first-served (FCFS) algorithm.  
Figure 3 represents ten job requests that arrive in time order. In the figure, each box represents a job, the width of the box corresponds to the runtime of the job, and theheight corresponds to the required IOPS. By representing job requests as boxes, we can intuitively understand the time and resource allocation each job occupies. As can be seen from the figure, in this assumed environment there is no limitation on IOPS; thus, jobs can be executed immediately as they arrive. However, supporting such an environment is very expensive. To maximize resource utilization and optimize costs, scheduling is necessary. Figure 4 shows the scheduled job requests from Figure 3 using two methods: Graph A represents scheduling based on the FCFS approach, while Graph B represents scheduling based on our scheme. In the figure, each time interval represents a check time and the red dotted line indicates the maximum IOPS.  
As shown in Figure 3, jobs (a,b,c,d,e) arrive in that order. In Graph A, jobs (a,b,c,d,e) are executed in the order they arrive at each check time. However, in Graph B they are executed in the order (a,c,e,d,b). Although jobs b and c both arrive after the first check time, job a is executed first in both graphs. At the second check time, the difference becomes apparent; in Graph B, job c is executed before job b because it has a higher IOPS, while in Graph A job b is executed first. This then affects the third check time; in Graph A,  
Figure 4: Here, jobs are scheduled with limitations on IOPS. Both graphs use the same jobs as those in Figure 3, with the original request times for each job also matching those in Figure 3. The upper graph shows the job execution simulation using the FCFS method, while the lower graph illustrates the simulation when using our scheme to prioritize jobs with higher IOPS requirements.  
Figure 3: Without any IOPS limitations, jobs start working immediately upon arrival. Here, jobs are represented as boxes; the width corresponds to the runtime of the job, while the height represents the required IOPS. Thus, a wider box indicates a longer runtime, while a taller box signifies a job with high IOPS demands, requiring prompt assignment.  
jobs d and e cannot be executed due to insufficient remaining IOPS after job c. In Graph B, however, because job b, which has a lower IOPS, was executed earlier, enough IOPS remains to simultaneously execute jobs d and e. Therefore, the jobs are completed more quickly in Graph B, as demonstrated by the shorter end times shown in Figure 4. Even though this example uses only ten jobs, there is still a noticeable difference. As the number of jobs increases, the efficiency of our scheduling method becomes even more apparent.
# Ocean Currents Reconstruction from a Combination of
## Data and Methods
### Methods: Optimal Reconstruction Quality Assessment  
Using the daily data detailed previously and accounting for Equations (3) and (4), we computed one year (2017) of optimal currents. The performances of the optimal reconstruction were evaluated by means of qualitative and quantitative assessments, respectively via visual inspection and by computing the Root Mean Square Differences (RMSD, Equation (6)), Percentage of improvement (PI, Equation (5)), temporal standard deviation (STD, Equation (7)) and spatial spectral properties of the altimeter-derived, optimal currents and CMEMS PHY modelled surface currents (our validation benchmark):  
\[\mathrm{PI_{U,V}=100\left[1-\left(\frac{RMSD_{U,V}^{OPC}}{RMSD_{U,V}^{SAC}} \right)^{2}\right] \tag{5}\]  
\[\mathrm{RMSD_{U,V}^{SAC/OPC}=\sqrt{\frac{\sum_{i=1}^{N}[(U_{i},V_{i})^{SAC/OPC }-(U_{i},V_{i})^{PHY}]^{2}}{N}}} \tag{6}\]  
\[\mathrm{STD_{U,V}^{SAC/OPC/PHY}=\sqrt{\frac{\sum_{i=1}^{N}[(U_{i},V_{i})^{SAC/ OPC/PHY}-\langle(U_{i},V_{i})^{SAC/OPC/PHY}\rangle_{t}]^{2}}{N}}} \tag{7}\]  
In (5)-(7):  
* the superscripts SAC, OPC, PHY, respectively, stand for synthetic altimeter currents, optimal currents, and CMEMS PHY modelled surface currents;
* U,V respectively indicate the zonal and meridional flow;
* the i index goes from 1 to N = 365, i.e., the number of the daily surface currents data during 2017;
* the \(\langle\rangle_{t}\) operator in (7) indicates a time average over the year 2017;Briefly, Equation (5), based on inputs provided by (6), quantifies the ability of the optimal reconstruction to reproduce the CMEMS PHY currents compared to the altimeter estimates. This is achieved computing a percentage of improvement (PI) for every grid point of our simulation area. On the other hand, Equation (7) is used to quantify the enhancement of the optimal currents temporal variability compared to the altimeter system. Indeed, the STD is computed with respect to the 2017 time averaged surface currents: higher STDs will correspond to higher temporal variability of the signal under evaluation.  
Finally, a spatial spectral analysis is also performed. From the Kinetic Energy (KE) fields of the SAC, OPC and CMEMS PHY modelled surface currents, we compute the KE power spectral density (PSD) in two land free areas of the Mediterranean Sea (in the Levantine Basin and in proximity of the Algerian Current). The PSDs are obtained via spatial Fast-Fourier-Transform analysis using the same technique proposed by [9; 50]. Longitudinal KE samples are extracted from the KE fields; then, the corresponding PSD are computed for every latitude and time. The PSDs are finally averaged to obtain a single spectrum for each dataset under evaluation. With this approach, we determine the amount of KE spatial variance found at different scales in the three surface currents' datasets, allowing for estimating and intercomparing their effective spatial resolution.
## 3 Results and Discussion
### Hirudin Release In Vitro  
The release profiles of rH from rH/CS/PCL MPs were examined in pH 5.8 and 7.2 buffers (Figure 7). It can be clearly seen that all kinetic curves are similar in shape with two sectors. The initial burst release occurs within the first 8 h, followed by a sustained release and gradual stabilisation of the level after 48 h. The bursting effect was attributed to the presence of rH on or near the surface of the particles. All three samples followed the same trend irrespective of the original loading ratio. This may be due to the uniform distribution of hirudin in all three samples. It is noteworthy that at each given time, the cumulative percentage release of rH from rH/CS/PCL MPs was higher at pH 5.8 than  
Figure 6: Thermogravimetric Analysis (TGA) (**a**) and Derivative Thermogravimetry (DTG) (**b**) curves of hirudin, CS/PCL MPs and rH/CS/PCL MPs3.  
at pH 7.2, which is attributed to a difference in the release rate due to higher swelling of the particulate matrix at the lower pH. At pH 7.2, the amino groups of CS were not yet ionised, which resulted in difficulties in the diffusion of PBS into the particles. By contrast, when pH = 5.8 (pH \(<\) 6), CS began to dissolve and amino protonation occurred, resulting in repulsion and loosening the matrix, resulting in accelerated expansion of the particles, and rH was released from the particles [42; 43]. Atherosclerotic plaques in humans and mice have been shown to be acidic, with greater macrophage aggregation and apoptosis in these acidic areas [44]. In vitro release experiments showed that rH/CS/PCL MPs are pH-responsive, exhibiting differential drug release at different pH levels. pH decreases when the particles reach the atherosclerotic plaque region, at which point the particles release more of the drug and inhibit thrombosis.
## 6 Numerical Experiments
### Sensitivity Analyses
#### 6.5.1 The Average Minimum Passenger Detour Distance  
The average minimum passenger detour distance, denoted as \(\overline{Z}_{\text{min}}\), is the sum of the minimized total detour distance \(Z_{\text{min}}\) incurred by all the passengers in each selection weighted by the selection time \(S\) under a given \(N-P\) combination. To separately investigate the impacts of \(N\) and \(P\), we created a series of \(N-P\) combinations, with either one fixed, and the other varied. Specifically, we fixed \(P=10\) and let \(N\) vary from 10 to 100 at an interval of 10 to test the effects of \(N\), while \(N\) was fixed to 10 and \(P\) ranged from 10 to 100 for investigating the impacts of \(P\). The results are depicted in Figures 11 and 12, respectively.  
Figures 11 and 12 show distinct trends of the average minimum passenger detour distance as a function of \(N\) and \(P\). In general, we observe that \(\overline{Z}_{\text{min}}\) increases with \(N\) and decreases with \(P\). On the one hand, the positive effect of \(N\) on \(\overline{Z}_{\text{min}}\) can be explained by the fact that \(P=10\) indicates a very sparse city-wide distribution of parcel locker locations, so that the marginal requests are likely to have longer detour distances than the existing ones when \(N\) increases. On the other hand, the decrease in \(\overline{Z}_{\text{min}}\) displayed in Figure 12 is relatively intuitive because when the number of parcel locker locations is small, many passengers are pooled with sub-optimal parcel locker locations with longer
## 4 Experiments and Results
### RQ3 Which Algorithm Provides the Most Suitable Tracing?
#### 4.7.1 Tf-IDF  
The advantage of TF-IDF is that it can make decisions based on the importance of words. This is a well-utilized tactic in similarity judgment without missing security and system terminologies, which are important for linking CVE and CAPEC. On the other hand, factors that could not be identified by TF-IDF are due to the inability to understand the context. An example is CVE-2020-0601. CVE-2020-0601 is associated with CAPEC-475. CAPEC-475 is signature spoofing by improper validation. The description of CVE-2020-0601 is as follows:  
A spoofing vulnerability exists in the way that Windows CryptoAPI (Crypt32.dl) validates elliptic curve cryptography (ECC) certificates. An attacker could exploit the vulnerability using a spoofed codesigning certificate to sign a malicious executable, making it appear the file was from a trusted, legitimate source, aka \"Windows CryptoAPI Spoofing Vulnerability\" [43].  
Both CVE-2020-0601 and CAPEC-475 contain many words related to cryptography, such as \"cryptography,\" \"cryptographic,\" \"cryptographically,\" and \"cryptanalysis.\" TF-IDF cannot measure a high similarity because it does not understand the context. Context-sensitive algorithms such as SBERT can compensate for this shortcoming.
## 3 DoFs Angles Estimation  
Having modeled the upper limb as described in the previous Section, we present a methodology to estimate the angles' values of the defined DoFs for the upper limb model. This methodology starts using the inertial sensors measurements. If a 9-DoF inertial sensor is attached on a link of upper limb, the gathered measurements during this link's motion could give the link orientation in the 3D space. The calculation approach, which is described in the sequel, assumes that the coordinate frames of the sensor node resemble these of the MPU-9150 [39]. In case of a different sensor or coordinate frames configuration, the mathematical equations should be modified accordingly. Furthermore, an important step before the extraction of the orientation is the filtering process, as these are calculated by the raw sensors data, which suffer from gyroscopes drifting and accelerometers noise.  
Nevertheless, the calculation of each individual segment orientation is insufficient for the estimation of the human upper limb posture and orientation in the 3D space. Hence, in the proposed methodology, the orientation of each upper limb link is initially estimated individually based on the corresponding sensor node. Then, as each link is a part of the described in the previous Section upper limb model, we manage to get the related DoFs angles in a closed form. The intermediate calculation steps for each upper limb segment and the corresponding joints angles (3 DoFs for shoulder, 2 for elbow and 2 for wrist)  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Frames** & **Links** & \(\mathbf{a_{i}}\) & \(\mathbf{a_{i}}\) & \(\mathbf{d_{i}}\) & \(\mathbf{\theta_{i}}\) \\ \hline \([\)C\(]\) & 0 & 0 & 0 & \(l_{c}\) & \(-90^{\circ}\) \\ \hline \([\)S\(]\) & 1 & 0 & \(90^{\circ}\) & 0 & \(\theta_{1}+90^{\circ}\) \\ \hline  
are presented in Sections 3.2-3.4. At the last step, the calculated joint angles \(\theta_{i}\) are used to solve the Forward Kinematics of the upper limb model, following the procedure that was presented in Section 2. Thus, the reconstruction and representation of the upper limb motion in the 3D space is finally accomplished.
# Latency-Aware Accelerator of SIMECK Lightweight Block Cipher
## 4 Implementation Results and Comparisons
### Settings and Selections  
Before describing the implementation results, we provide the setup and settings to run the design synthesis/implementation. We selected Xilinx 7-series FPGA devices for our demonstrations. It is essential to mention that the synthesis for each device can be performed with different speed grades. According to [33], different speed grades result in distinct values for the design metric(s), i.e., high performance or low power. For instance, \(-1\), \(-2\), and \(-3\) rank the speed grade in slowest, mid, and highest for the Xilinx 7-series FPGA devices. The slowest specifies the longer critical path delay with minimum power consumption. The mid-rank results in the optimal frequency, and power values. The use of the highest speed grade shows a higher circuit frequency but with extensive power consumption. Hence, in our experiments, we run synthesis with a speed grade of \(-2\) to obtain the optimal results for our proposed SIMECK architecture.
# Gesture Recognition Based on 3D Human Pose Estimation and Body Part Segmentation for RGB Data Input
## 2 Related Works
The summary of the related works is given in Table 1.  
Most of the gesture recognition methods exploit the human pose estimation, however often the pose estimation in the video can often be inconsistent because of independent errors in the sequence of frames. Additionally, the extraction of skeleton joints may not be successful when the \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Author** & **Approach/Features** & **Details** & **Comments** \\ \hline \multirow{2}{*}{[PERSON] et al. [1]} & Support Vector Machine & Discriminative classification & \\  & (SVM) & from 3D skeleton joints & \\ \hline \multirow{2}{*}{[PERSON] et al. [2]} & \multirow{2}{*}{Bag of features} & Classification via & \multirow{2}{*}{Classical machine learning methods.} \\  & & bag-of-words vector & \\ \hline \multirow{2}{*}{[PERSON] et al. [3]} & \multirow{2}{*}{Hidden Markov Model} & HMM based on the likelihood & \\  & & threshold estimation & \\ \hline \multirow{2}{*}{[PERSON] et al. [4]} & Two-stream network & Extract motion from RGB & \\  & with separate ConvNets & images and optical flow & \\  & & images & \\ \hline \multirow{2}{*}{[PERSON] et al. [15]} & \multirow{2}{*}{Data level fusion} & Combine RGB and optical flow & \\  & & modalities with static images & \\ \hline \multirow{2}{*}{[PERSON] et al. [8]} & Long-term recurrent & CNN and LSTM for & \\  & convolutional network & spatio-temporal features & \\ \hline \multirow{2}{*}{[PERSON] et al. [17]} & \multirow{2}{*}{3 DCNN, SVM classifier} & Spatio-temporal features & \\  & & extracted by 3 DCNN & 3 DCNN to capture \\ \hline \multirow{2}{*}{[PERSON] et al. [16]} & Recurrent 3D & \multirow{2}{*}{Online gesture recognition by 3 DCNN} & spatial and \\  & Convolutional Neural & 3 DCNN & temporal features \\  & Networks & & \\ \hline \multirow{2}{*}{[PERSON] et al. [19], [PERSON] et al. [20], and [PERSON] et al. [21]} & RNN or LSTM, human skeleton joints & Capture temporal features from skeleton joints and depth & Deep learning + \\  & & skeleton joints and depth & human pose \\ \hline \multirow{2}{*}{[PERSON] et al. [9]} & Convolutional & \multirow{2}{*}{Combine the results from RGB} & \multirow{2}{*}{Multi-modal or multiple deep learning models} \\  & two-stream consensus & & \\  & voting network & & \\ \hline \multirow{2}{*}{[PERSON] et al. [10]} & Multi-stream model & Extract hand location & \\  & based on RNN & information from RGB-D input & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The summary of related works. background contains multiple objects or human beings. In this paper, we try to solve these problems through the use of segmentation of the target person. We propose a novel method for gesture recognition with multi-modalities: RGB images, color body part segmentation images, and 3D skeleton joints information.
## 5 Discussion
### Practical Application Value of the Research  
The student overall course performance prediction model proposed in this study holds significant practical value in educational management. The model's prediction results can effectively support educational institutions and teachers in their teaching decisions. By accurately forecasting student performance, teachers can identify students facing academic difficulties and adjust teaching methods or provide personalized tutoring based on the predictions, thereby improving teaching effectiveness. Furthermore, by identifying students at risk of academic failure in advance, schools can implement targeted interventions to reduce dropout rates and mitigate the risk of declining performance. Additionally, the prediction results can assist educational administrators in optimizing the allocation ofeducational resources. By precisely identifying students who require additional support, resources can be allocated efficiently to ensure that they are maximally utilized to assist students in need. Moreover, this study can be applied to course adjustments, teaching strategy optimization, personalized learning recommendations, and interventions, further promoting educational equity.
# Crowdsourcing Based Performance Analysis of Mobile User Heterogeneous Services
###### Abstract  
In mobile networks, crowdsourcing in Quality of Experience (QoE) assessment phase involves collecting data from the user terminals or dedicated collection devices. A mobile operator or a research group may provide applications that can be run in different mobility test modes such as walk or drive tests. Crowdsourcing using users' terminals (e.g., a smartphone) is a cheap approach for operators or researchers for addressing large scale area and may help to improve the allocated resources of a given service and/or the network provisioning in some segments. In this work, we first collect a dataset for three popular Internet services: on-demand video streaming, web browsing and file downloading at the user terminal level. We consider two user terminals from two different vendors and many mobility test modes. The dataset contains more than 220,000 measures from one of the major French mobile operators in the Ile-de-France region. The measurements are effectuated for six months in 2021. Then, we implement different models from the literature for estimating the QoE in terms of user's Mean Opinion Score (MOS) for every service using features at radio or application levels. After that, we provide an in-depth analysis of the collected dataset for detecting the root cause of poor performance. We show that the radio provisioning issues is not the only cause of detected anomalies. Finally, we discuss the prediction quality of HD video streaming service (i.e., launch time, the bitrate and the MOS) based only on the physical indicators. Our analysis is applied on both plain-text and encrypted traffic within different mobility modes.  
Keywords:data collection; \(LTE\) mobile network; Quality of Experience (QoE); root cause analysis (RCA); 5 Gmark tool; video streaming +
Footnote †: journal: _electronics_
## 3 Methodology
### Classification Using Machine Learning  
Four well-known machine learning methods have been used alongside transfer learning models: (1) KNN, which is based on similarities between data points, (2) SVM, which is based on mathematical model inference and boundary lines, (3) NB, which is based on conditional probability principles, and (4) LDA, which is based on statistical relationships. These methods have been used in this study because of their simplicity, effectiveness, and wide applicability. They have been applied in the classification experiments carried out on the final dataset obtained after the feature extraction and selection phases.  
KNN [62] classifies an unknown instance point according to the classifications of the nearest k points from a set of previously classified instances. It uses various distance measures such as Manhattan, Euclidean, and Cosine to determine these k neighbor points.  
SVM [63] is designed to find the optimal hyperplane separating the data into different classes and maximizing the margin between the classes [64]. Data points closest to the hyperplane are defined as support vectors. It can be efficiently used for both linear and non-linear classification tasks.  
NB classifier [65] is based on [PERSON] probability theorem with the conditional independence assumption. It calculates the probability of an instance belonging to a specific class.  
LDA [66; 67] can be used for identifying the most significant features with their level of significance and also classifying. It aims to maximize the separability of instances by finding linear combinations of features, in other words, by computing linear discriminant functions. The best separation of classes can be achieved by maximizing the distance between the means of the classes and minimizing the variance in each class [68; 69].
# Dosimetric Optimization of a Laser-Driven Irradiation Facility Using the G4-ELIMED Application
## 3 Results
### Lateral Profiles  
The lateral profiles represent the relative dose distributions measured along the transversal axes with respect to the proton beam direction (in our case, the X and Y axes). To irradiate cells, flat distributions with very sharp lateral penumbras are desirable to ensure a homogeneous irradiation over all the cells. In this study, to reduce the impact of the noise produced by a lack of statistics in the quality parameters, the lateral profiles were normalized to the average value of the signal. The obtained lateral profiles are shown in Figure 4 and the corresponding beam quality parameters are summarized in Table 3.  
Figure 4: Distributions related with the lateral profiles at the irradiation point: (**a**) Density of protons in the XY plane produced by the combination of different energy beams; (**b**) Normalized lateral dose profiles on the X-axis obtained by the combination of different energy beams at the irradiation point; (**c**) Normalized lateral dose profiles on the Y-axis obtained by the combination of different energy beams at the irradiation point.  
Figure 3: Layout of the in-air part of the beamline. The relative distances of the Secondary Electron Monitor (SEM), the Scattering Foil (SF), the Monitor Chamber (MC), the In-air Collimator, and the Irradiation Point (IP) are referred with respect to the Kapton Window (KW).  
As observed in Table 3, the only parameter which was not falling within the required tolerances was the flatness. But, as it is observed in Figure 4, the profiles had a certain noise which was probably generated by a lack of statistics in the simulated proton histories.