esa-sceva/satcom-chunk-collection huggingface dataset:
# Adaptive Versioning in Transactional Memory Systems +
## 5 Optimizations on Basic Adaptive Versioning
### Better Switching Mechanism Design  
Switching between _Eager_ and _Lazy_ versioning should ideally be done with no overhead. However, there might be a possibility of continuous switching between the versioning methods for every new transaction. This may result a significant amount of overhead in total execution time of the transactions. Thus, the idea is to minimize the total number of switching between the versioning methods without affecting the total execution time of the transactions. The better switching mechanism avoids the possible continuous switching between the versioning methods for every new transaction, thus helps to reduce the overhead. The versioning method is switched from one to another only if the switching condition is satisfied continuously for a specified number of times (which we call a switching interval threshold \(SW\_INT\)). Let the current versioning method \(V_{cur}=Eager\). Suppose at time \(t\), Adaptive decides to switch to \(V_{new}=Lazy\). With better switching mechanism, Adaptive does not switch to \(V_{new}=Lazy\) at \(t\) but waits until a switching interval threshold \(SW\_INT\). We define \(SW\_INT\) as the number of transactions that execute using the current versioning method \(V_{cur}\) before switching to the new versioning method \(V_{new}\) after \(t\). When  
Figure 5: An illustration of the better time barrier design. The interval \(\delta_{*}\) between _Eager_ and _Lazy_ represents the time taken by in-flight transactions to finish their executions after versioning method is switched. The new transaction that do not conflict with transactions using previous versioning can execute concurrently with in-flight transactions.  
Figure 6: An illustration of the better switching mechanism. \(\lambda_{*}\) represents the time interval in which versioning is not switched. \(\delta_{*}\) resembles better time barrier of Figure 5.  
\(SW\_INT=0\), Adaptive does not wait for switching the versioning method. We use \(SW\_INT>0\) in the _better switching mechanism_ design. Let \(\lambda\) be the time interval during which \(SW\_INT\) number of transactions finish execution using the current versioning method \(V_{cur}=Eager\). For every next (new or restarted) transaction arrived during the interval \(\lambda\), if \(V_{new}=Lazy\) satisfies (i.e., \(V_{new}\
eq V_{cur}\)), then the versioning method switches to \(V_{new}=Lazy\) at time \(t+\lambda\). Otherwise, versioning method remains to \(V_{cur}\). We determine the switching interval threshold \(SW\_INT\) by using an empirical method, i.e., varying the value of \(SW\_INT\) from 2 up to 10 and picking the one with the best performance. Note that the time interval \(\lambda\) denotes the time elapsed until the consecutive \(SW\_INT\) number of transactions satisfy for the switching of the versioning method. So, the value of \(\lambda\) may not be necessarily the same for all switching events. Figure 6 illustrates the design of the better switching mechanism. The pseudocode is given in Algorithm 4.
## 2 Related Work  
The security of IoT systems has garnered significant attention in recent years, leading to a wide array of research efforts aimed at addressing the unique challenges presented by these environments. These efforts have focused on various aspects of IoT security, including cryptographic protocols, AI-driven threat detection, and, more recently, blockchain-based identity management. Table 1 summarizes key related works in IoT security, categorized by their primary approach, focus area, key technologies, strengths, and weaknesses.  
The analyses presented in Table 1 provide a comprehensive overview of current approaches in IoT security, highlighting the diverse strategies and technologies employed in the IoT. By examining the strengths and weaknesses of each approach, the analysis reveals that while each approach offers valuable contributions, no single solution addresses all the complex security needs of IoT devices, particularly those constrained devices involved in rich and complex interaction scenarios. Many of these existing solutions advance security in particular areas but have limitations in others, indicating the need for a more integrated and holistic approach to IoT security.  
The IASF-IoT framework proposed in this paper aims to address the limitations identified in the related works by integrating and enhancing existing security measures. This framework leverages the capabilities of AI, blockchain, quantum-resistant cryptography, federated learning, and Digital Twins to provide a robust and comprehensive security solution tailored to the unique challenges of the IoT. The following sections introduce the IASF-IoT framework and its architecture and components, along with a theoretical performance evaluation.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Approach** & **Focus Area** & **Key Technologies** & **Strengths** & **Weaknesses** & **References** \\ \hline Lightweight & Efficient cryptographic & & & Limited to specific & [29, 30, 31] \\ Cryptography & Protocols for IoT & & & & device types & [29, 30, 31] \\ \hline AI-Driven Threat & & & & & \\ Detection and Response & AI in IoT security & & & & \\ \hline Blockchain-Based & & & & & \\ Identity and Access & Identity and access & Blockchain, Merkle & Decentralized, & High energy & [34, 35] \\ Management & management & Trees & & & & consumption [36, 35] \\ \hline Quantum-Resistant & Future-proof & & & & \\ Cryptography & & & & & \\ algorithms & & & & & \\ \hline Federated Learning for & & & & & \\ Edge Security & Edge Security & Federated Learning, & Data privacy, local data & Communication & [39, 40] \\ \hline Digital Twins for & Predictive threat & & & & \\ Predictive Security & analysis & Digital Twins, MDP & & & \\ \hline Privacy-Preserving & & & & & \\ Techniques & Data privacy in IoT & & & & \\ Encryption & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Summary of related work in IoT security based on established security approaches.
## 10 GaN-Based LLC Converter
Besides, power added efficiencies higher than 53 % at 10 GHz and the maximum operating frequency around 100 GHz have been reported for GaN-based HEMTs in telecommunication applications [137; 138]. Since GaN HEMT-based resonant converters operate at much higher switching frequencies, an additional challenge in the bridgeless converters is the presence of high frequency Common-Mode (CM) noise at the AC input [139]. Thus, a CM filter should also be considered. It has been shown that GaN HEMT has a worse behavior on the EMI test, as compared with MOSFETs, due to constraints of parasitic parameters in the filter design [41]. GaN-based LED driver consisting of a buck-boost PFC and an LLC resonant DC-DC converter has been presented in [140] with the high power density of 50 W/inch3, while replacing bulky electrolytic capacitors with low energy density ceramic ones.
## 3 Laser Scribing Mechanism and Theoretical Model
### Theoretical Model of Laser Scribing
#### 3.2.2 Cumulative Scribing Depth Model of Pulsed Laser  
In the scribing process of a pulsed laser, the energy conservation Formula (2) can be expressed as:  
\[(1-R)P_{s}=\Delta xb_{c}h\rho[c_{p}(T_{m}-T_{\infty})+H_{m}+H_{v}]+P_{HL} \tag{6}\]  
\(P_{s}\) is the single pulse energy;  
\(\Delta x\) is the displacement distance of two pulses.  
Where,  
\[\Delta x=\
icefrac{{v}}{{f}} \tag{7}\]  
For a quasi-continuous pulse laser, the calculation formula of the single pulse energy is:  
\[P_{s}=P_{pk}\cdot(1/f)\cdot D \tag{8}\]  
where:  
\(P_{pk}\) is peak power;  
\(f\) is pulse frequency;  
\(D\) is duty cycle.  
The laser output power per unit time is:  
\[P_{av}=P_{s}\cdot f=P_{pk}\cdot(1/f)\cdot D\cdot f=P_{pk}\cdot D \tag{9}\]  
Substitute Formulas (7) and (8) into Formula (6):  
\[(1-R)P_{pk}\cdot(1/f)\cdot D=\frac{v}{f}b_{c}h\rho[c_{p}(T_{m}-T_{\infty})+H_ {m}+H_{v}]+P_{HL} \tag{10}\]  
Formula (10) can be simplified as:  
\[(1-R)P_{pk}\cdot D=vb_{c}h\rho[c_{p}(T_{m}-T_{\infty})+H_{m}+H_{v}]+P_{HL} \tag{11}\]  
Substitute Formula (12) for Formula (11), the model equation is as follows:  
\[(1-R)P_{av}=(1-R)P_{pk}\cdot D=vb_{c}h\rho[c_{p}(T_{m}-T_{\infty})+H_{m}+H_{v} ]+P_{HL} \tag{12}\]When \(P_{out}=P_{av}=P_{0}\), Formula (2) is exactly the same as Formula (13). Therefore, the laser frequency only changes the single pulse energy but does not change the average laser output power. The laser power is mainly determined by the duty cycle and peak power.  
It is generally believed that one pulse interval is sufficient for heat transfer; all ablation products and excess heat can be blown away by auxiliary gas. Therefore, it is assumed that the removal rate of the material will not be affected by the action of the previous pulse. It should be noted that during the processing of the quasi-continuous laser, if the laser frequency is too large and the pulse time interval is too small, all ablation products and excess heat are difficult to be blown away by auxiliary gas, there may be overheating in the process of laser radiation processing. Overheating leads to the accumulation of the edges of laser scribing, resulting in the irregular edges of laser scribing lines, which affects the final scribing quality.
# A Signaling-Free Underwater Code Division Multiple Access Scheme
## 7 Conclusions  
In this paper, we proposed a CDMA scheme that removes signaling overhead associated with channel access and thereby increases throughput along with reducing delay. Sensor nodes are able to correctly estimate the number of nodes in transmission only by means of received power measurement. Computer simulation shows that the ratio of successful traffic estimation is above 95% for varied offered load conditions. With reference to throughput, the proposed scheme outperforms the schemes against which it was compared for almost all cases of offered load. We note that the proposed scheme can be implemented and evaluated in a real underwater environment. Underwater nodes consist of a commercial underwater modem and a DSP board where we can program the proposed MAC protocol algorithm. Then, each node is deployed in the underwater environment to build an underwater star topology network. After deployment, each node executes the MAC protocol operation and maintains a log of the events. Using the MAC protocol event logs, the performance of the proposed scheme can be evaluated. In a future study, the persistent transmission probability \(p_{tr}\) will be researched to make the proposed scheme more sustainable regardless of traffic conditions.  
[PERSON] performed computer simulations and wrote the paper, [PERSON] participated in data analysis and revision process. [PERSON] supervised the research and participated in problem formulation, data analysis and revision process.  
This research was supported by the project titled \"Development of Distributed Underwater Monitoring &Control Networks\", funded by the Ministry of Oceans and Fisheries, Korea.  
The authors declare no conflict of interest.  
Figure 6: Throughput of the node at different distances from the sink.
## 2 Fracture Parameter Solution Method Based on the Postprocessing Routine of ABAQUS
### Computation of Stress Intensity Factors  
After obtaining the interaction integral results, it is necessary to decompose and extract various types of stress intensity factors to determine the mixed-mode crack conditions. This is based on the relationship between the J-integral, strain energy release rate, and stress intensity factors in isotropic materials, as follows:  
\[J\ =\ G\ =\ \frac{1}{E^{\prime}_{\text{tip}}}(K_{\text{I}}^{2}+K_{\text{II}}^{ 2}), \tag{16}\]  
and  
\[E^{\prime}_{\text{tip}}\ =\ \begin{cases}\ E_{\text{tip}}&(\text{Plane stress})\\ \frac{F_{\text{tip}}}{1-\varepsilon_{\text{tip}}^{2}}&(\text{Plane strain})^{ },\end{cases} \tag{17}\]  
\(E_{tip}\) and \(v_{tip}\) are the [PERSON]'s modulus and [PERSON]'s ratio at the crack tip, respectively.  
The J-integral under the combined actual and auxiliary fields is expressed as follows:  
\[J^{S}\ =\ \frac{1}{E^{\prime}_{\text{tip}}}[(K_{\text{I}}+K_{\text{I}}^{ \text{aux}})^{2}+(K_{\text{II}}+K_{\text{II}}^{\text{aux}})^{2}]\ =\ J+J^{\text{aux}}+I, \tag{18}\]  
The J-integral due to the auxiliary field alone is expressed as follows:  
\[J^{\text{aux}}\ =\ \frac{1}{E^{\prime}_{\text{tip}}}[(K_{\text{I}}^{\text{aux}} )^{2}+(K_{\text{II}}^{\text{aux}})^{2}], \tag{19}\]  
The interaction integral is expressed as follows:\[I\ =\ \frac{2}{E^{\prime}_{\rm tip}}(K_{\rm I}K_{\rm I}^{\rm aux}+K_{\rm II}K_{ \rm II}^{\rm aux}), \tag{20}\]  
Substituting \(K_{\rm I}^{\rm aux}\ =\ 1,\ \ \ K_{\rm II}^{\rm aux}\ =\ 0\) and \(K_{\rm I}^{\rm aux}\ =\ 0,\ \ K_{\rm II}^{\rm aux}\ =\ 1\) into Equation (20), the Mode I and Mode II stress intensity factors are calculated via two interaction integral computations.  
\[K_{\rm I}\ =\ \frac{E^{\prime}_{\rm tip}}{2}I_{(1)},\ \ \ K_{\rm II}\ =\ \frac{E^{ \prime}_{\rm tip}}{2}I_{(2)^{\prime}} \tag{21}\]  
where \(I_{(1)}\) and \(I_{(2)}\) denote the interaction integral values obtained from the two calculations.  
For three-dimensional problems, the interaction integral and stress intensity factors satisfy the following relationship:  
\[I(s)\ =\ \frac{2}{E^{\prime}(s)}(K_{\rm I}K_{\rm I}^{\rm aux}+K_{\rm II}K_{ \rm II}^{\rm aux})+\frac{1}{\mu(s)}K_{\rm III}K_{\rm III}^{\rm aux}, \tag{22}\]  
and  
\[E^{\prime}(s)\ =\ \left\{\begin{array}{ll}E(s)&(\mbox{Plane stress})\\ \frac{E(s)}{|1-\
u^{2}(s)|}&(\mbox{Plane strain})\end{array}\right., \tag{23}\]  
\[\mu(s)\ =\ \frac{E(s)}{|2+2\
u(s)|}, \tag{24}\]  
Set \(K_{\rm I}^{\rm aux}\ =\ 1,K_{\rm II}^{\rm aux}\ =\ K_{\rm III}^{\rm aux}\ =\ 0\) in Equation (22) to obtain the Mode I stress intensity factor at point s. Similarly, adjust the coefficients to derive the Mode II and Mode III stress intensity factors.
## 3 AI/ML in the Standards and Industry
### Open RAN
#### 3.3.2 Use Cases and Challenges in Open RANs
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt}} \hline \hline
**Use Cases** & **Key Benefits and Enablers** \\ \hline Low-cost RAN White-box Hardware & COTS hardware reduces CapEx. It is easier to upgrade and inter-operable with network functions developed by different vendors. \\ \hline Traffic Steering & Faster response to data traffic variations using AI/ML-based proactive load-balancing yielding reduced OpEx, better network efficiency and user experience. \\ \hline QoE Optimisation & Prediction of degraded QoE for a UE using AI/ML and proactive allocation of radio resources to the UE. \\ \hline QoS-based Resource Optimisation & AI/ML-based allocation of radio resources to ensure that at least certain prioritised users maintain their QoS under data traffic congestion. \\ \hline Massive MIMO Optimisation & Adapting beam configuration and related policies, e.g., packet scheduling, for enhancing the network capacity. \\ \hline RAN slice SLA assurance & Maximise revenue with AI/ML-based management of network slices. \\ \hline Context-based dynamic handover management for vehicle-to-everything (V2X) & AI/ML-based handovers using historical road traffic and navigation data resulting to better user-experience. \\ \hline Dynamic resource allocation based on the flight-path for unmanned aerial vehicles (UAV) & AI/ML-based resource allocation using historical flight data and UAV measurement reports. \\ \hline Radio resource allocation for UAV applications & AI/ML-based resource allocation under asymmetric uplink/downlink data traffic. \\ \hline RAN sharing & Reduced CapEx due to multi-vendor deployments. \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Use cases considered by the O-RAN alliance [100].
## 4 Data Quality Checks and Experimental Corrections
### Atmospheric Absorption  
Noise data are corrected for atmospheric absorption following the procedure developed by [PERSON] [26] and based on the true radiation angles and distances as determined by the shear layer refraction correction.
## 3 Materials and Methods
### Data  
#### 3.2.1 Datasets  
An image is considered a two-dimensional array of values in range \([0,255]\). The following datasets were used in this work:  
* MaskedFace-Net [30; 31]
* Natural images [32]  
The MaskedFace-Net [30; 31] dataset was used to train and test the K-means classifier that is capable of detection if the face mask is worn correctly or incorrectly. This dataset consists of 137,013 images (as of 5 November 2020) that is based on the Flickr-Faces-HQ (FFHQ) dataset, that was originally created as a benchmark for the generative adversarial networks (GAN) [33]. The dataset consists of two subsets:  
* Correctly Masked Face Dataset (CMFD)-67192 images,
* Incorrectly Masked Face Dataset (IMFD)-69821 images. The Incorrectly Masked Face Dataset contains three subclasses:
* Face masks worn only on chin, leaving the mouth and nose uncovered--6243 images;
* Face masks covering both chin and mouth, leaving the nose uncovered--57,224 images;
* Face masks covering both nose and mouth, leaving chin uncovered--6354 images.  
The images are of high-quality, with a size of 1024 \(\times\) 1024 px. They also incorporate a large enough variation regarding age, ethnicity, and background, and also introduce glasses and headgear.  
The Natural images [32] dataset was used to train and test K-means classifiers supported by the sliding window technique that is capable of face detection. This dataset consists of 6899 images composed of eight subsets:  
* airplane,
* car,
* cat,
* dog,
* flower,
* fruit,
* motorbike,
* person.  
The images of people are of good quality, with a size of 256 \(\times\) 256 px. The images incorporate similar variation as the MaskedFace-Net [30,31] dataset. In this paper, the other classes are treated as one non-person class.
## 5 Conclusions  
In this paper, we presented a new way to mine human mobility patterns, which aims at identifying short-term tourists' nationalities from location-based trajectories. The proposed model was designed to capture the dependency of track points and to infer the latent patterns of users. We first transformed original trajectories into sequences of locations, unfolding in fixed time steps, then a Skip-gram Word2 vec model was used to construct the location embeddings, and finally we applied an LSTM neural network model for correctly labeling each sequence as the nationality of the user generating it.  
Defining the problem as a multinomial classification task, the reported methodology was shown to substantially outperform baselines, achieving promising results in terms of correct nationality detection.  
Potential extensions of this paper can go in multiple different directions. The first issue that is worth studying is the role of individual travelers and organized groups. Although the dataset used did not contain significant portions of synchronized traces (sequences with same place-time), with the exception of stationary traces, the granularity of the data was insufficient to detect the coordinated motion of groups with certainty. Therefore, the possible role of group motion in some specific situations is a valid motivation for a further investigation, which would require a more granular dataset. In addition, the study of tourists' motion activity at a smaller scale could be an interesting step to evaluate if finer trajectories in space and time (e.g., in an urban environment) still make it possible to identify visitors' information, such as their nationality; in particular, GPS data would allow finer resolutions than telecom data. Another direction could be to integrate explicit time information into the location sequences for assessing a possible performance improvement, or even analyzing detection variation over time (e.g., month by month). A final direction is to explore different types of information detection for user profiling. The same methodology could be utilized to infer user information in different use cases, not limited to tourism analysis.  
To conclude, the use of embeddings and LSTM, commonly adopted in the field of NLP, can potentially be successful in a wide range of applications dealing with mobility traces, and therefore extended to various tasks related to trajectory analysis and human motion behavior.  
[PERSON] conceived and designed the experiments, analyzed the data and wrote the paper. [PERSON] supervised the work, helped with designing the conceptual framework, and edited the manuscript.  
This research was funded by the Austrian Science Fund (FWF) through the Doctoral College GIScience at the University of Salzburg (DK W 1237-N23).  
The authors would like to thank Vodafone Italia for providing the dataset for the case study.  
The authors declare no conflict of interest.
# Shooting Prediction Based on Vision Sensors and Trajectory Learning
## 4 Shooting Results Prediction
### The Effect of the Density of Smooth Interpolation on the Prediction Results  
We can find that the number of basketball trajectory points obtained after image processing depends on the density of trajectory interpolation. The more the number of trajectory points, the smoother the trajectory curve that is obtained. The trajectory curve thus obtained is closer to the real basketball trajectory. Therefore, to explore this effect, in addition to the basic 43-point trajectory length mentioned above, we also introduced other interpolation points for prediction. Finally, we obtained the prediction results for  
Figure 7: Prediction accuracy based on (**a**) time t and coordinates (x, y), (**b**) time t and distance D, and (**c**) time and height Y of different trajectory points with KNN, NB, LDA, and CART.  
28, 79, and 116 points after interpolation of the total trajectory after stabilization. The final prediction results are shown in Figure 8.  
It can be seen that when the interpolation density is high (the total number of points is 116), the prediction accuracy of the CART algorithm is improved, and the prediction accuracy increases to 90%. After reducing the interpolation density (the total number of points is 43), the prediction accuracy of the algorithm is significantly reduced, and the prediction accuracy is 85%. When the interpolation density reaches 28 total trajectory points, the prediction accuracy is reduced to 79%. Therefore, it can be concluded that the accuracy of shot prediction is positively correlated with the interpolation density, which also reflects that the prediction accuracy may be related to the smoothness of the trajectory and the video frame rate.
# AI-Driven Innovations in Software Engineering: A Review of Current Practices and Future Directions
## 3 AI Applications in Software Engineering
### Deployment and Maintenance  
#### 3.5.1 Predictive Maintenance Using AI  
In the deployment and maintenance phases, AI significantly contributes to ensuring system reliability and performance. Predictive maintenance employs AI algorithms to analyze operational data and predict potential system failures before they occur.  
By continuously monitoring metrics such as system logs, resource utilization, network traffic, and user behavior patterns, AI models can detect anomalies and forecast issues. Machine learning techniques, such as time-series analysis and anomaly detection algorithms, enable the identification of patterns indicative of impending failures.  
This proactive approach allows teams to address problems pre-emptively, thereby minimizing system downtime, reducing maintenance costs, and enhancing user satisfaction. Furthermore, predictive maintenance facilitated by AI supports the optimization of system performance over time by enabling data-driven decision-making in maintenance scheduling and resource allocation.