esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experimentation
### Simulation Setup  
The simulation of image processing was conducted on a computer system running Windows 10 Pro. The computation time was measured using a computer equipped with an Intel(R) Core (TM) i5-6300U CPU @ 2.40 GHz 2.50 GHz and 8 GB of RAM. The experiment utilized the Jupiter Lab development environment, which is a web-based interactive IDE for scientific computing that is open-source. To access Jupiter Lab, an Anaconda emulator was used. Anaconda is a distribution of Python and R specifically designed for fields like data science, machine learning, and scientific computing. It provides a user-friendly environment for managing packages, handling dependencies, and executing code. The use of the Anaconda emulator ensures that Jupiter Lab is launched with all the necessary libraries and packages pre-installed, simplifying and streamlining the setup and execution process of the experiment.  
**Dataset Description:** The dataset used in this study consisted of X-ray coronary angiography (invasive coronary angiography) images obtained from two renowned hospitals in Peshawar, Pakistan: The Cardiology Centre of the Hayatabad Hospital Complex and the Teaching Hospital of Khyber Peshawar. These images depict coronary arteries that exhibit varying thicknesses and complex vascular structures against a background. The dataset primarily revealed information about the intensity of blood vessels. It distinguished between various types of blood vessels, including healthy ones, constricted (narrow) vessels, and obstructed (blocked) vessels. Additionally, the dataset contained numerous instances of noise. Each coronary artery image in the dataset had a size of 512 \(\times\) 512 pixels. The dataset contained information from a large number of patients, but in our study, we only utilized data from a couple of specific patients. The collection of angiographic images was performed under the supervision and guidance of a cardiologist.
# Thermographic Stall Detection by Model-Inspired Evaluation of the Dynamic Temperature Behaviour
## 4 Results
### Transient Thermodynamic State Condition
#### 4.2.2 Distinguishability  
For the comparison of the signal processing approaches in terms of distinguishability between the different flow regions for a transient input signal, the respective CNR is calculated and summarized in Table 2.  
Firstly, the enhanced convective heat input for the measurement with \(Re=3\cdot 10^{6}\) causes an increasing CNR of the mean temperature and the gradient-based signal processing approach. In contrast, the harmonic signal processing approach around an operating point provides a reduced CNR, compared to the near thermodynamic steady state condition at \(Re=2\cdot 10^{6}\), as a result of the enhanced heat transfer coefficients in the laminar and separated flow region. Therefore, the harmonic signal processing approach around an operating point is for measurements with a high convective heat input not suitable to increase the distinguishability between flow regions.  
Regarding the distinguishability of different flow regions, the gradient-based signal processing approach provides the highest CNR between turbulent-separated flow regions. Compared to the mean temperature approach, a significant improvement in the CNR of about 103 % is achieved. For the distinction between laminar and turbulent flow regions, the mean temperature signal processing approach provides a higher CNR than the gradient-based approach as a result of the relationship between the convective heat input and the surface temperature distribution. According to Equation (2), the convective heat input depends on the heat transfer coefficient \(\alpha\) as well as the temperature difference between the fluid temperature \(T_{f}\) and the surface temperature \(T_{s}\). In the presented case of a low CNR between laminar-turbulent flow regions when using the gradient-based signal processing approach, flow-dependent differences in convective heat input are reduced due to the high temperature difference between the considered flow regions. However, the aim to enhance the contrast for stall detection is realised with the gradient-based signal processing approach.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Approach** & **Pair of Flow Regions** & **CNR** & **Change** \\ \hline \(\overline{T}\) & laminar–turbulent & 6.88 & 227.62\% \\  & turbulent–separated & 3.39 & 37.8\% \\ \(\overline{dT}\) & laminar–turbulent & 2.39 & 419.57\% \\  & turbulent–separated & 6.88 & 514.29\% \\ \(\overline{P}\) & laminar–turbulent & 0.67 & \(-30.93\%\) \\  & turbulent–separated & 1.33 & \(-16.88\%\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of the contrast to noise ratio for the proposed signal processing approaches for a transient input signal: mean temperature \(\overline{T}\), gradient-based \(\overline{dT}\), harmonic signal processing approach \(\overline{P}\). Additionally, a comparison of the respective CNR change for the different measurement conditions.
## 2 Three Dimensional Printing Technologies
### Stereolithography (SLA) and Digital Light Processing (DLP)  
Stereolithography (SLA) is based on the use of a photocurable liquid resin as a printing material (photopolymer) [23]. Its working process consists of a layer-by-layer model printing starting from liquid resins exposed either to the photopolymerizing action of an ultraviolet laser source (SLA) or a visible light projector (DLP). The former modality requires a progressive movement of the laser across the layer, whereas the former guarantees the curing of an entire resin layer thus resulting in a faster process. In this way, the cross-linking of the polymer and its hardening are promoted by the laser beam, that belongs to the ultraviolet wavelength. A necessary step following SLA/DLP printing is represented by the post-processing of the printed model to avoid its shrinkage and distortions. Accordingly, an alcohol solution removes all the uncured resin, subsequently heat and ultraviolet light are required to harden the model, generally put inside an illumination chamber [6; 14; 17].  
Digital light processing (DLP) is a 3D printing technology that is quite similar to SLA, since a photopolymer is used as a printing material [23]. In particular, the latter is cured with a digital micromirror device (DMD). Different from SLA, the curing light is projected on the entire layer, while the former technique is based on a spot laser beam. This difference results in a faster procedure for DLP [24].
## 3 Modeling and Results Analysis  
### Analysis of the Control System  
The input from the driver's driving and steering signals can be interpreted as the desired speed input for the motors on both sides, and the variable pump controller sends a responsive displacement command to the variable pump mechanism, thereby regulating the speed of the motors in order to accomplish the intentions of the driver. The closed-loop control system of vehicle steering and driving is shown in Figure 4.  
As shown in Figure 4, the intention of the driver can be converted into the desired vehicle speed \(v\) and the desired steering angular velocity \(\omega\), which are decomposed into the desired track speeds on both sides of the tracked vehicle by means of the differential speed algorithm, and the actual speed of the driving wheels can be adjusted by means of feedback control in the driving wheels so as to achieve the required speed and steering control.
## 5 Applications and Services
### UAV Design
#### 5.2.1 UAV-BS  
Probably the common form of UAV-enabled wireless communication is utilizing UAVs as airborne BS. See Table 1 for the paper that studied the deployment of UAV-BS. However, the application of UAV-BS as active aerial BS can be computationally prohibitive, expensive, and inefficient in future B5G and 6G networks [144]. Because equipping UAVs with massive MIMO antennas for beamforming capability requires complex baseband processing and channel estimation. This seems impractical, as it makes the aerial package expensive and heavy. Notably, it is against UAV agility which is required to provide aerial LoS communication for intelligent vehicles [145]. Therefore, equipping UAVs with RIS as RISeUAV can be an alternative and cost-effective solution [41].
## 3 Results and Discussion
### Pairwise Comparison of Volatiles in Lapsang Souchong Black Tea of Different Grades  
The pairwise comparison of Lapsang Souchong black tea across different grades revealed 52 differential compounds (Table S4), including six alcohols, three arenes, four aldehydes, 11 phenols, 12 terpenes, three ketones, two esters, and 11 others. There were a total of 24 differential substances in A vs. B (20 up-regulated and 4 down-regulated), including seven terpenes, five phenols, two alcohols, four aldehydes, one aromatic hydrocarbon, and five others (Figure 3a). There are a total of 31 differential substances in A vs. C (19 up-regulated and 12 down-regulated), including three alcohols, one aromatic hydrocarbon, nine phenols, four terpenes, two aldehydes, three ketones, and eight others (Figure 3b). There were a total of 36 differential substances in B vs. C (9 up-regulated and 27 down-regulated), including four alcohols, three aromatic hydrocarbons, nine phenols, seven terpenes, one aldehyde, three ketones, one ester, and eight others (Figure 3c). There were fewer differential substances between A vs. B. Among them, 2-ethylhexanol, creosol, benzaldehyde-4-methyl- and phenol-4-ethyl- are less in A than in B (Figure 3d). The typical characteristics of these substances were sweet, woody, fruits, and slightly floral [25]. However, A contains more terpene and aldehyde substances with fruits and floral aromas, so the aroma score in the sensory evaluation score of A is higher. In addition, Figure 3e,f show that C contains a large number of phenolic substances such as \(p\)-cresol, 2-Methoxy-4-ethylphenol, 4-ethyl-2-methoxyphenol, eugenol, and trans-Isoeugenol with floral, spicy, and smoky aromas. [PERSON] et al. [7] research shows that smoking was conducive to the accumulation of phenolic substances in Lapsang Souchong black tea. This may be the reason why the aroma score of C was not as high as that of A and B.  
In conclusion, A contains a large number of terpene and aldehyde substances with fruits and floral aromas, while C also contains a large number of phenolic substances with floral and spicy aromas. Therefore, in the sensory evaluation, A has the highest score, and C has the lowest score.
###### Abstract
The proposed method was validated using a real-world example of a system deployed in Poland for storing medical patient records, where efficient resource utilization and cost-effectiveness are of paramount importance. The paper makes significant contributions in the following areas:  
* The development of a methodology for acquiring a load profile aligned with specified external conditions/restrictions, grounded in the maximum entropy principle (Section 4.1.1);
* Proposing a method of improving the load profile during peak times through kernel smoothing (Section 4.1.2);
* The formulation of an optimal configuration methodology, determining the optimal values for the maximum limits of pods utilized by horizontal pod autoscalers (Section 4.2); the method's advantage lies in its proposal to reduce the dimensionality of the autoscaler parameter space (Section 4.2.3); the problem is streamlined from the multidimensional to one-dimensional by distributing limit values for all deployments proportionally (see Equation (10)) to a singular limit value for the highest-load deployment;
* The successful application of the proposed method to derive optimal upper limits for the autoscaler (Table 7) under real-world conditions and in the context of a real system (Clinical Data Repository). However, the presented approach is not confined to the specific CDR system deployed in Poland. While the case study centers on the Polish medical system, the underlying principles and methodologies discussed herein are designed for broader application across diverse scenarios and geographic locations. Emphasizing cloud resource optimization through autoscaler parameter tuning in microservice architecture in Kubernetes deployments, our findings contribute to a comprehensive understanding of resource optimization strategies in cloud-based microservice architectures, transcending any specific industry or geographical constraints. Following this introduction, the paper is structured into key sections to provide a comprehensive view of the research. The subsequent sections include Background and Related Work, which explains the context of the considerations presented in this paper and reviews the research related to the topic, Materials and Methods, where the research methodology is detailed, Results, where the findings are presented, and Conclusions, which summarize the significance of the research and its implications.
## 6 Conclusions  
In this paper, a security protocol was proposed in order to solve the vulnerability of the unique identification value being transmitted as clear text, which is constantly being pointed out in LTE standards and related technical documents, and to be used as a basic study of mobile communication technology to be used in the future cyber world.  
The proposed security protocol was designed to safely transmit IMSI, RNTI and GUTI in a variety of initial attach processes when the UE accesses the LTE network. The proposed security protocol generated a key through the challenge-response method to encrypt and transmit the unique ID and support error detection and verification. As a result of a performance analysis, the security protocol encrypted and safely transmitted vulnerability parameters and turned out to have a performance of an average of 32.0% based on VoIP 100%, demanding a lower rate of delay, so that the safety and performance of the proposed encryption algorithm were found to be efficient.  
After all of the analyses, the proposed security protocols had less overhead and fully satisfied the privacy requirements and the maximum permissible delay defined in LTE standards.
## 5 Evaluation
### Performance for the Data Extraction Using Office2/SON in Terms of Time  
As previously indicated in Section 3.2, the Office2 JSON script requires a processing time of between 0.02 and 0.4 s to extract data from an Office document, including the utilisation of OLEVBA, and to save it in a JSON file.  
The times recorded for the API request vary considerably, which can be attributed to the diverse factors influencing the request's performance. The factors contributing to this variability include the hardware configuration of the host on which the LLM-Sentinel script is being executed. Primarily, this includes the central processing unit (CPU) clock speed and disk read/write speed, as well as the internet bandwidth and the package round trip time. Additionally, the status of the Anthropic API and its current workload must be considered. In the testing phase, the maximum recorded delay between the transmission of the request and the receipt of the response, which was written into the response JSON file, was 25 s. The minimum recorded delay was three seconds, with a median of approximately five to eleven seconds. This illustrates that the response time may be insufficient for automated usage in a high-availability environment, where a 25 s delay may be unacceptable. Nevertheless, for an assistant of manual static analysis, the fewseconds will not be a significant issue, given that the alternative of manual static analysis will take many minutes, and requires high concentration and a substantial degree of prior knowledge.  
In summary, a large language model is capable of identifying the most relevant indicators of malicious among a vast array of information extracted from a document. The average per-use cost of USD 0.19 is not high for purposeful usage, but it can reach considerable levels when used for automatic assessment. It has been demonstrated that a large language model can be employed to perform a rapid and dependable static analysis of a given Office document, facilitating a textual and numerical assessment. The concluding section presents a summary of the proposal and its outcome, as well as an analysis of future developments and the potential legal implications for companies.
# RNN-Based Sequence to Sequence Decoder for Run-Length Limited Codes in Visible Light Communication
## 4 Results and Analysis
### Simulation Setup  
For the two benchmark methods, we implemented them on Matlab R2019a. For the seq2 seq decoder, it was built on Python with Keras libraries [21], and NVIDIA GTX 1070 Ti has been used to train the model. Further, the VLC model and corresponding dataset were generated on Python, and the testing data size was set to \(10^{4}N\) bits per SNR for all subsequent simulations.
# Semi-CNN Architecture for Effective Spatio-Temporal Learning in Action Recognition
## 3 Proposed Architecture
### Base Models
#### 3.1.2 Residual Networks (ResNets)  
When developing deeper networks by stacking more and more convolution layers, a critical problem arises during back-propagation where the gradients decrease and become too small or vanish while propagating to the lower layers. This prevents weights learning and updating in the layers, which prevents the network from converging. Hence, adding more layers without any remedy will result in stagnant accuracy or even poorer performance [14]. [PERSON] et al. [14] proposed a residual learning framework, which incorporates a residual learning formulation in the stacked layers to allow better optimization. Residual learning is implemented using \"shortcut connections\" where the output of a previous layer can be mapped and added to the output of the connecting stacked layers. Another benefit of residual learning is that it does not incur additional complexity or parameters to the network, which makes the development of extremely deep network (up to 152 layers) possible, with complexity much lower than VGG nets.
## 2 Materials and Methods
### Data Set for ML  
The data were specially prepared for ML analysis and were subject to data audit (checking for outliers and incomplete or uncertain data). In addition, the data were aggregated into one large set in a.csv file. This is due to the fact that some ML algorithms require sufficiently large data sets (from several hundred to several thousand per class) to function properly and achieve good results that are generalizable to other data sets of a similar type.  
The total number of samples (1266, corresponding to the number of cases both with defects and lack of defect--not to the samples in the time record) was divided into three sets balanced in classes: (1) training set (70% of samples: 886), (2) evaluation set (20% of samples: 253), (3) validation set (10% of samples: 127).  
Based on the data set (the input data were position and amplitude and the output data were the defect length), we formulated two research questions to be solved using ML models--each of them was to be answered by a different group of ML models. The first research question for the ML part was as follows: is it possible to detect a defect based on the data set, i.e., determine whether the defect occurs? The answer to this question would allow the board to be directed to further, manual technical inspection without stopping the production line. The second research question for the ML part was as follows: is it possible to predict the place where the defect occurs (e.g., to indicate it to the production line operator, which would shorten the duration of manual inspection to a minimum). Both of these research questions and the answers to them can constitute the basis for building two different technical control systems based on ML or one comprehensive system providing answers to both of them. Such formulation of research problems also answers the applicability of the article, i.e., the potential application of the work is automatic ML-based quality control and defect detection systems within the Industry 4.0/5.0 paradigm.
## 3 Accelerated Corrosion Testing of the Reactor Concrete Block
### Results and Discussion  
#### 3.2.1 Electrochemical Measurements  
Initial electrochemical measurements known as the half-cell potential were taken prior to the initiation of the conditioning period. These measurements indicated a passive state of steel reinforcement. The NaCl solution was then placed in the cored areas on 2 December 2015 and electrochemical readings were recorded weekly thereafter. As shown in Figure 17, three weeks after conditioning, half-cell potential values were observed to be more negative than \(-350\) mV (referred to as the corrosion threshold) at all three locations. At the conclusion of the wet/dry cycles, half-cell potential readings indicated high corrosion risk in one of the three locations (0.25 inch cover location) and severe corrosion damage (more negative than \(-500\) mV) in the other two locations (0.125 inch and 1.0 inch cover locations). The 1.0 inch cover location is known to have leakage associated with it since the NaCl solution drained continuously from this location from the commencement of the testing. While chloride diffusion is often assumed to be the primary initiator of corrosion damage, the presence of cracking in the concrete matrix may have a more profound effect on corrosion in some instances. The 0.125 inch cover and the 0.25 inch cover locations did not experience similar issues with leakage. The bottom of the hole at the 1.0 inch cover location was sealed with epoxy in the first week of April in 2015.  
Figure 18 shows linear polarization resistance results at the three locations with a logarithmic fit of the data points. The x-axis in Figure 18 represents the number of days after the solution was placed in the cored areas (initiated on 2 December 2014). The results indicate that all locations had relatively high corrosion rates since the polarization resistance was less than 100 ohms [4]. As seen in the figure, data was not collected between 24 December 2014 and 19 February 2015 (between 22 and 79 days) due to a malfunction with the potentiostat/galvanostat cable over that time period. This was addressed and the testing was resumed after 29 February, 2015. Since these readings are taken weekly over a time span of 300 days and due to the instantaneous nature of the readings, trends in the data set are more important than readings taken on a particular day. Therefore, trend lines with both upper and lower estimates are shown in the figures. A statistical method was used to eliminate outliers with low values to obtain the upper estimate and eliminate outliers with high values to obtain the lower estimate.