esa-sceva/satcom-chunk-collection huggingface dataset:
# Design, Challenges and Developments for 5G Massive MIMO Antenna Systems at Sub 6-GHz Band: A Review
## 1 Introduction
This indicates that the majority of new 5G phones, if not all of them, feature four antennas for cellular coverage. These antennas almost definitely will include automatic antenna adjusting capabilities [13]. To avoid interference, each individual antenna in a mobile phone should be placed as far away from the other antennas as possible. On a small phone, this is quite difficult. Fortunately, the operating frequency is high, as are the wavelength and antenna length. Here, it increases the number of antennas that must be integrated into a phone, especially since the majority of these standards support large-scale multi (MIMO) antennas [14]. Deploying 5G networks below 6 GHz requires a wide range of up to 20 Gbps and higher data rates. The 5G bands released are 3.3-4.2 GHz and 4.4-5 GHz, increasing the bandwidth to 100 MHz. In addition, to cover large areas, the base station has a mMIMO or 3D configuration that covers both the azimuth and vertical planes. Both the smartphone and the base station must increase the number of antenna elements below 6 GHz. At the higher frequencies of 5G, mMIMO arrays with significantly more radiating antenna elements will significantly minimize the network congestion and increase its capacity and throughput [15]. ITU-R has designated the mid-band of the sub-6 GHz (FR1) spectrum for the deployment of 5G communications, which comprises LTE n71 (470-698 MHz), n81-n83, n91-n94 (698-960 MHz), n74-n76(1.427-1.518 GHz), n65, n66(2.11-2.2 GHz), n30, n40, n38, n41, n 90 (2.3-2.69 GHz), n 77, n78, n79 (3.3-5 GHz), and LTE42/43/46/47 (3.3-5.925 GHz). Most of the conventional cellular mobile communication traffic is expected to be carried by FR1 bands. The large-scale sub-6 GHz MIMO antenna array is anticipated to be deployed inside the radiation aperture of the original 3G, 2G, and 4G antennas [15; 16]. In the specified use scenarios, the 5G specification enables a maximum downlink data rate that is twice as fast as the uplink data rate. 5G requires at least a 4 \(\times\) 4 downlink MIMO and at least a 2 \(\times\) 2 uplink MIMO is recommended in the current deployment phases below 2.6 GHz [17; 18].
## 4 Movement of the Driving Tip  
Then, the vibration amplitude of the driving tip was calculated by transient analysis. A FEM model with \(L_{1}\) = 11 mm, \(L_{2}\) = 20 mm and \(A\) = 11 mm was used for the calculation, whose first bending series resonance frequency was 53.762 kHz. We choose these dimensions based on the high electromechanical coupling factor and the size miniaturization. In fact, the proposed transducer can accomplish rotary driving under any dimensions but a symmetrical structure. This is a merit of the proposed design, which allows us to choose its dimensions freely to satisfy the different demands of space, speed and torque. Sine and cosine voltages with a value of 140 V\({}_{0\text{-P}}\) were applied on the PZT elements, and the transient analysis continued for about 5.58 ms. One particle on the end rim was selected, and its movements was extracted and plotted, see Figure 5.  
The transducer took about 3.5 ms to reach a steady vibration. The calculated result states that the steady transverse vibration amplitude of the driving tip is about 3.69 \(\upmu\)m, and the superimposing of the horizontal and vertical bending forms a nearly circular movement. The horizontal and vertical displacements are individually controlled by the horizontal and vertical bending. Therefore, the trajectory of this motion can be adjusted freely by the amplitudes of the sine and cosine voltages.  
It should be noted that Figure 5 was obtained under a free boundary condition. However, when used in the motor, the fixing of the clamper must bring some changes. Figure 6a gives another FEM model, in which four bolts were added, and fixed boundary conditions were applied on the ends of the bolts. The first bending resonance frequency was calculated to be 54.142 kHz by modal analysis, which experienced a little increase of 0.38 kHz. The vibration characteristic of the driving tip by transient analysis is shown is Figure 6. Under the resonance frequency, the new model with fixed boundaries achieved higher transverse displacement of about 3.76 \(\upmu\)m. Thus, the clamping of the transducer causes the increases of both resonance frequency and vibration amplitude. These two factors can lead to an improvement of the vibration velocity of the driving tip.  
Figure 5: Movement of the end tip by transient analysis: (**a**) FEM model of the transducer; (**b**) the displacement response of the driving tip along OX direction; (**c**) the steady motion of the driving tip in XOY plane.  
Figure 6: Movement of the end tip under fixed boundary condition: (**a**) FEM model; (**b**) the displacement response along OX direction; (**c**) the steady motion in XOY plane.
## 5 Limitations and Discussion  
In contrast to the goals of methods in previous security-related studies, the goal of our method is prevention, and we hence propose a novel approach for extracting attack data from previously built knowledge units. We also proposed a method for recommending requirements based on the relevant security measures. However, because these measures all continue to evolve and improve, the following limitations have been identified and should be considered in further research.  
First, a full understanding of the domain knowledge of the field is required, and the knowledge base should be updated and maintained throughout its life cycle. Although it is assumed that the provided ontology-based knowledge framework includes attack and security requirements, domain information, and associated risk areas, and these are assumed to be enough to generate some meaningful results, this may not be the case since the problem-related data, information, and knowledge are continuously changing in the real world. Therefore, it is necessary to develop a mechanism to measure the quality of the case-based knowledge base as well as the way to continuously revise and maintain the knowledge base to retain the benefits of reuse. Otherwise, the generated outcomes are no longer trustworthy and eventually may interfere with and harm the related decision-making process.  
Second, the number of survey samples was insufficient and more thoroughly designed experiments are needed. To evaluate a newly developed method, the subject matter experts should be fully familiar with it. In this regard, although a half-day tutoring session was provided with the tool demo, we believe the pool of experts was not sufficient. For this reason, although the preliminary results show the potential of the method, the objectivity and reliability of the overall response may be insufficient. In addition, we plan to improve the tools and their user interfaces to help users obtain the required knowledge of basic ontology usage and the analysis process used in the method.  
Finally, to perform some of the processes in our proposed method, expert opinions and subjective judgments are required. Appropriate accommodation of experts' opinions is required for case reuse, and a revision plan should be considered for the weakening of security measures due to reuse. Therefore, subjective opinions from security experts are required to reflect and retrieve the final security requirements. To support and facilitate the cycle of CBR, and most importantly assure the correctness of the cases, completeness and consistency of the system as well as management of the updates should be considered in the further development of the method and tools.  
Figure 19: Average of each software quality characteristic.  
On the basis of the feedback provided in the questionnaire, we also confirmed the improvement in the system, which reflects the representation of surrounding environmental factors that take into account future policies or security measures.
# Optimized Implementation of Argon2 Utilizing the Graphics Processing Unit
## 3 Implementation
### Previous Implementation [16]  
The Argon2 encryption algorithm consists of three stages: initialization (init), filling memory blocks (fill memory blocks), and termination (final). In the previous GPU implementation, as shown in the left of Figure 2, only the 'fill memory blocks' stage was processed on the GPU.  
A key advantage of this approach is that the Blake hash function works quickly on the CPU, making it efficient for processing small amounts of passwords. However, this does not hold true when processing multiple passwords. Even though Blake2b operations  
Figure 2: Changes in workload distribution between CPU and GPU.  
are performed quickly on the CPU, when handling a large number of passwords, more computations can be done on the GPU.  
In the 'init' stage of Argon2, various parameters (such as password and salt) are inputted and the blake2b hash function is applied to obtain a 64-byte hash value. This hash value is then used to generate the initial memory block for further computations. The size of the generated memory block is determined by the following formula.  
\[Lanes\times 2\times ArgonBlockSize(1024-Byte)\]  
'Lanes' refers to the number of rows in the memory block and 'ArgonBlockSize' is 1024 bytes. Therefore, if 'lanes = 1', the size of the initial memory block generated in the 'init' stage is 2048 bytes.  
As mentioned, the size of the initial memory block generated after the 'init' stage is larger compared to the input parameters, resulting in an increased amount of data transferred from the CPU to the GPU. Consequently, the overall computational speed is slowed down. In the 'final' stage, multiple blocks are also selected and copied to the CPU for the calculation of the final hash. Similar to the previous stages, the problem of significant copying costs arises as multiple memory blocks are selected and copied to the CPU for processing.  
We have discovered that the size of the data to be copied is large and we have focused on optimizing this aspect.
# Using Convolutional Neural Networks for Blocking Prediction in Elastic Optical Networks
## 6 Discussion  
Figure 12 represents the confusion matrix of the CNN 2D model. In binary classification tasks like this research, where the outputs are a sequence of zeros and ones (indicating whether the situation is leading-to-blocking or not), one of the most appropriate assessment metrics is the confusion matrix. The confusion matrix, also referred to as an error  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Model Name** & **Model Configuration** & **Accuracy** \\ \hline \multirow{4}{*}{Proposed CNN1D} & No. of convolutional layer: 5 & \\  & No. of FC layers: 2 & \\  & Max-pooling patch size: 2 & \\  & Activation Function: ReLU & \\ \hline \multirow{4}{*}{Proposed CNN2D} & No. of convolutional layer: 5 & \\  & No. of FC layers: 2 & \\  & Max-pooling patch size: 2 \(\times\) 2 & \\  & Activation Function: ReLU & \\ \hline \multirow{2}{*}{KNN} & n neighbors: 5 & 0.7729 \\ \hline SVM & Kernel: Linear & 0.9091 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Selected model comparison.  
Figure 11: Comparison of different models.  
matrix, is a tabular representation commonly utilized to evaluate the performance of a classification model on a known set of test data. This matrix is very helpful in gauging the effectiveness of binary classification, by providing a detailed breakdown of predictions into different categories.  
The entries of this matrix demonstrate the model's predictions compared to the actual states. In this matrix, the horizontal axis represents the actual values and the vertical access represents the values predicted by the CNN 2D model. The prediction statuses are broken down into four categories: True Positives (TP), True Negatives (TN), False Positives (FP), and False Negatives (FN).  
For the evaluation of the proposed CNN 2D model in this study, a dataset of 1750 samples is utilized. Among the 1750 samples, 942 samples were leading-to-blocking and the rest 808 samples were not leading-to-blocking.  
Among the 942 samples, 901 samples were correctly predicted as leading-to-blocking (True Positive) when Similarly, 712 samples out of 808 were accurately predicted as not-leading-to-blocking (True Negative). Therefore, the model could predict the leading and not-leading-to-blocking samples with, respectively, 92.65% and 88.12% accuracy.  
However, there were errors, as indicated by the misclassification of 91 samples as not-leading-to-blocking when they are leading-to-blocking and 41 samples as leading-to-blocking when they are not-leading-to-blocking, which respectively represent 4.35% and 11.88% of error. Overall, 1613 out of 1750 samples are correctly predicted, representing 92.17% of the dataset.
# NDVI or PPI: A (Quick) Comparison for Vegetation Dynamics Monitoring in Mountainous Area
## 3 Results
### Seasonality Parameters Retrieval  
The seasonality parameters indicate close concordance between the two indices (PPI and NDVI) in determining the SOS and EOS for deciduous trees, grasslands, and shrublands (Table 2). The mean differences (PPI\(-\)NDVI) for the SOS are 1.4 days for deciduous trees and grasslands and 4.2 days for shrublands. For the EOS, the mean differences are \(-1.4\) days for deciduous trees, \(-9.8\) days for grasslands, and \(-11.8\) days for shrublands. However, for coniferous trees, the differences are more significant, with a mean difference of 32.8 days for the SOS and \(-12.2\) days for the EOS. Regarding the length of the season, the divergences are minimal for deciduous trees (\(-2.8\) days), followed by grasslands (\(-11.2\) days), shrublands (\(-16\) days), and coniferous trees (\(-45\) days). For the POS retrieval, significant differences are observed across all vegetation classes, with PPI detecting the peak earlier than NDVI.  
Figure 6: PPI and NDVI time-series for each vegetation class.
## 4 Methods of Quantum Multiplexing  
There are several ways to couple a quantum channel with others by using WDM, and all of them vary in efficiency and complexity. The main problem that these techniques address is the suppression of the influence of SpRS. The following overview contains the basic techniques known to the authors, but which may always need to be adapted to the desired application and combined with each other.  
* **Large spacing method**--Commonly used by QKD system manufacturers. It is based on the largest possible spacing between quantum and classical channels (while respecting suitable wavelengths in the fiber). Typically, quantum channels are placed in the O-band (1310 nm), and classical channels are placed in the C-band (1550 nm). The total spacing is thus approximately 240 nm, and thus, Raman noise has minimal effect  
Figure 1: Sketch of a graph showing the approximate shape of the reflected noise of the classical channel in the band at approximately 1550 nm. In the middle is the central wavelength to which Rayleigh scattering corresponds. Then, at a distance of approximately 0.1 nm is the Brillouin scattering, and at a distance of 100 nm is the Raman scattering (both temperature dependent). In terms of noise, it is best to place the quantum channel in the anti-Stokes region 1 to 5 nm from the classical channel (red circle). Alternatively, a similar area might be used in the Stokes region.  
on the quantum channel. Such a technique is simple to implement and is described with an example of a possible metropolitan network in [29].
* **Narrow filtering method--**Procedure described in [30]. Both types of channels are located in the C-band in relative proximity. Most of the Raman noise is filtered out by using the narrowest possible filters (DWDM 50 GHz, 25 GHz, 12.5 GHz).
* **Attenuation method--**Our method, which is described in detail in this article. Like the second one, the quantum and service channels are positioned close to each other. Here, however, conventional 100 GHz filters are used for filtering. For this reason, it is thus necessary to attenuate the other classical channels as much as possible. This also reduces the Raman noise power.
## 4 Conclusions  
GAC, initially identified by its microporous nature, underwent a four-cycle process combining adsorption and the Fenton reaction for caffeine removal. The surface area of GAC increased post-Fenton reaction, signifying its efficacy as a regenerating process. Analyses of XRD and FTIR spectra affirmed the retention of the GAC structure, showcasing its capability to uphold textural attributes and functional groups throughout cycles.  
Individual studies of adsorption and Fenton reactions revealed optimized parameters, yielding high caffeine removal rates. The combined process demonstrated efficiency in all four cycles, achieving substantial caffeine removal rates (average of 93.7%). Applying the Fenton reaction for 5 and 60 min enabled final degradation efficiencies of 99.2% and 99.6%, respectively, resulting in final caffeine concentrations of 0.2 mg/L and 0.07 mg/L, respectively. The reduction in TOC was significant, especially within 5 min of the Fenton reaction, showcasing its effectiveness in reducing TOC (96.1%, 66.0%, and 87.5% for the second, third, and fourth cycles). The GAC regeneration efficiency remained high, ranging from 91.8% to 99.6%, indicating the preservation of the solid's textural characteristics.  
In comparison to previous studies, the proposed method in this work excelled in efficiency, with shorter times and lower reagent concentrations during GAC regeneration. These promising outcomes, supported by numerical results, indicate the potential application of the combined process for treating water and effluents containing caffeine. However, ecotoxicity and solid disposal issues must be considered for future studies aiming to implement this approach on an industrial scale.  
Conceptualization, N.P.Z. and V.J.D.; Methodology, N.P.Z. and K.H.; Software, N.P.Z.; Validation, N.P.Z. and K.H.; Formal analysis, N.P.Z. and V.J.D.; Investigation, N.P.Z.; Resources, L.A.F.; Writing--original draft, N.P.Z.; Writing--review & editing, V.J.D., K.H. and L.A.F.; Supervision, V.J.D. and L.A.F.; Project administration, L.A.F.; Funding acquisition, L.A.F. All authors have read and agreed to the published version of the manuscript.  
The authors thank the National Council of Technological and Scientific Development (CNPq) and the Coordination for the Improvement of Higher Education Personnel (CAPES) of the Brazilian Government for the financial support granted to carry out this work.  
Not applicable.  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author/s.  
The authors declare no conflict of interest.
# Process-Oriented Stream Classification Pipeline: A Literature Review
## 6 Classifier Maintenance
### Drift Detection Algorithms
#### 6.2.2 Distribution Based  
The second-largest class of drift detection methods is distribution based [36], i.e., the distribution of newly arriving instances is compared to the historical distribution by a suitable distance metric. For comparing two distributions, well-known and often used metrics are, e.g., the Kullback-Leibler or the Jensen-Shannon divergence. An example of the first is the _Information Theoretic Approach_ (ITA). Historical and new data records are divided into bins using a \(k\)-dimensional tree, and then the frequencies within the bins are compared using the distance metric [161]. Large deviations indicate a change in the data stream. The data-driven approach pursues the idea of detecting drifts directly at the source and does not suffer from model intrinsic errors. A drawback of comparing large datasets is  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Work** & **Drift Types** & **Drift Detection Categories** \\ \hline
[4] & Sudden/abrupt, incremental Gradual, & Sequential analysis, control charts, \\  & reoccurring & Distribution, contextual \\
[153] & Real, virtual, abrupt, incremental, Gradual, & \\  & reoccurring, cyclical, full, sub & \\
[154] & Sudden, incremental, gradual, Recurring, & Offline, online, number of classifiers, \\  & concept evolution & Supervised, unsupervised \\
[155] & Statistical test, error, distribution \\
[36] & Sudden,gradual, Incremental, reoccurring & Error, distribution, multiple hypothesis \\
[112] & Real, virtual, class prior, Abrupt, gradual, local, & Sequential, window-based \\
[118] & Global, cyclic, acyclic, Predictable, unpredictable & \\
[118] & Sudden,gradual, Incremental, reoccurring & \\
[85] & Real, virtual & Statistical methods, window-based, \\  & & Block-based ensembles, incremental \\
[156] & Local, global & Unsupervised batch, unsupervised online \\
[34] & Abrupt, gradual, Incremental, real, virtual & Distribution, sequential analysis, Statistical \\  & process control & \\ \hline \hline \end{tabular}
\end{table}
Table 6: Overview of summarizing drift detection work and used terminologies of drift types, as well as detection algorithm categories.  
the high computational complexity [36]. Nevertheless, these algorithms are generally also capable of detecting the severity of a drift. Similar to error-driven approaches, two samples, namely the historical and the new dataset, must be carefully defined. Typically, this is implemented using two sliding windows. Again, choosing the right size is, of course, decisive. For example, recurring concepts must be recognizable in the data and not be ignored by the wrong choice of window size.
# Using ChatGPT in Software Requirements Engineering: A Comprehensive Review
## 2 Background
### Overview of ChatGPT  
ChatGPT is an AI chatbot built using OpenAI's large language models (LLMs), such as GPT-4 and earlier versions [10]. It has set new standards (tasks, metrics, etc.) in artificial intelligence by demonstrating machines' ability to understand the intricacies of human language and communication.  
OpenAI unveiled an initial demonstration of ChatGPT on 30 November 2022, sparking widespread interest on social media as users demonstrated its capabilities. Examples ranged from travel planning to creating fables to coding computer programs. Within five days, the chatbot had more than one million users. ChatGPT's evolution has been one of continuous improvement, with each version building on the foundation laid by its predecessors.  
This evolution is also characterized by an exponential increase in the number of parameters used to train the model. This increase in parameters generally leads to significant improvements in the effectiveness of the solutions presented by the models. This is because models with more parameters have a greater ability to learn and capture complex nuances and patterns in the training data.  
Chat GPT-4, the latest version, continues this trend of exponential improvement with changes such as improved model alignment, Internet connectivity, better steerability, and more [10].  
The GPT model and software engineering intersect by applying natural language processing (NLP) techniques to various tasks within the software development lifecycle. GPT's language generation capabilities provide valuable support and enhancements to software engineering processes [11].  
ChatGPT can streamline the process of gathering, analyzing, and documenting requirements in the context of software requirements. It uses its extensive knowledge base and language understanding capabilities to engage stakeholders in conversations, generate accurate documentation, and provide real-time feedback on requirements. Through its ability to continuously learn and adapt, ChatGPT improves stakeholder collaboration, accelerates the requirements gathering process, and ultimately contributes to more efficient and successful software development outcomes. However, ChatGPT should not be viewed as a replacement for human expertise and judgment. Instead, ChatGPT should be viewed as a complement to it [12].