esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussions
### Synthesis
#### 4.4.4 Population Diversity: Summary of Results and Visualization  
The experimental results present the population diversity, defined in Equation (4), as a complement to the best so far objective function values. The pair of metrics, objective value and population diversity, was anticipated to allow us to discriminate between premature and global convergences. The results have further shown that a variety of scenarios occur in evolutionary optimization: the Wasserstein-based algorithms (WBGEA\(\_\)...) typically have low population diversities, while sequence-based algorithms (Ref\(\_\)...) maintain more diverse populations throughout the search.  
The objective function impacts the diversity: all versions of algorithms tested, whether Wasserstein or sequence-based, are slow at reducing the diversity when applied to \(F_{inert}\), the inertia function. In the other functions, the diversity of Wasserstein-based optimizers drops to near 0 after about 200 iterations.  
Diversity in itself is not indicative of an algorithm's performance. For example, in Figure 19, looking at \(F_{inert}\), WBGEA\(\_\)1t\(\_\)nc and Ref\(\_\)alg\(\_\)nc attain comparable objectives but WBGEA\(\_\)1t\(\_\)nc and Ref\(\_\)alg\(\_\)nc have a low and a high diversity, respectively.  
In addition to diversity, the variance of the functions in the populations has been recorded. The information provided by variance is similar to that of diversity, with the additional difficulty created by the varying functions scales. For this reason, we do not report variance results here.
# Multi-Class Intrusion Detection Based on Transformer for IoT Networks Using CIC-IoT-2023 Dataset
## 2 Methodology
### Data Segmentation  
Since the dataset lacks predefined training and testing sets, we used the holdout method for segmentation in this experiment. This technique involves dividing the dataset into a training-validation set and a testing set based on a specified ratio. In this study, we allocate 80% of the dataset to the training-validation set and the remaining 20% to the test set. This partitioning strategy aims to make the model generalizable. Furthermore, in the training-validation set, 80% is designated as the training set, including 37,349,263 records, while the remaining 20% is designated as the validation set, with a total of 9,337,316 records. This distribution corresponds to a proportion of approximately 80% and 20% for the entire dataset [6].
## 2 Positioning Method  
### Experimental Environment  
In this study, we developed a simulator that imitates GPS-shaded areas, as depicted in Figure 1. The color temperatures of LED illuminators depicted in Figure 1 equaled 3000, 4500, and 6000 K, and the said illuminators were installed 73 cm above the simulator floor. Each LED-lighting fixture measured 1 m long--a combination of two 50 cm long bar lamps. Individual fixtures were placed 20 cm apart. The simulator floor was marked by a grid comprising square elements measuring 5 cm each along the x- and y- directions, as depicted in Figure 1. On the grid, the red dots indicate points of intersections between the horizontal and vertical grid lines, and they represent chromaticity measurement points. Therefore, in this study, reference-data measurements were performed at 21 points.  
It is noteworthy that the front and rear bar lamps comprising the LED illuminator operated at different frequencies, thereby separating the illumination zones along the y-axis. Figure 1 shows the irradiation of 1.75 kHz light on the elliptical part of the LED illuminators and that of 4.75 kHz light on the rectangular part.  
By configuring the simulator in this manner, the positioning in the x-axis direction is calculated based on the chromaticity value, whereas that in the y-axis direction is calculated based on the different frequency-component ratios of the LED-irradiated light.  
Figure 2 shows the signal-processing procedure used in this study [24]. An RGB sensor (HDJD-822) is used for performing positioning on the mesh grid on the simulator floor. The RGB sensor detects the light irradiated by the three LED fixtures located on the ceiling of the simulator and outputs R, G, and B voltage signals. The LED illuminators irradiate 1.75 and 4.75 kHz light for each section, as shown in Figure 1. For this purpose, the LED illuminators are controlled using a power switching method that employs pulse width modulation.  
Figure 1: Indoor-environment simulator used in this study and size of testbed [24].  
To block the effects of other external lights and to select only the driving frequency components of the LED illuminators, the preprocessed signals were passed through a bandpass filter that allowed only the 1.75-kHz and 4.75-kHz signals to pass. The Direct  
Figure 2: Signal-processing workflow: (**a**) Schematic of signal-processing procedure; (**b**) Simulink model for fuzzy positioning.  
Form-II infinite-impulse-response bandpass filter was designed using MATLAB FDA Toolbox and implemented using MATLAB Simulink. The Simulink model of the bandpass filter works with the Micro-Autobox (ds-1401) DAQ to conduct signal filtering. The DAQ output signals were converted into DC via a smoothing circuit comprising a capacitor, following which the chromaticity value and frequency-component ratio were calculated using Arduino 2506. Finally, the x- and y-axis coordinates were estimated using the corresponding fuzzy systems. Processing in the Arduino board was implemented as a MATLAB Simulink model linked to the board via the MATLAB Embedded Coder Toolbox.
# Current Progress in Femtosecond Laser Ablation/Ionisation Time-of-Flight Mass Spectrometry
## 1 Introduction
Without advanced modelling tools, it would also be difficult to arrange an optimal optical system for the ion source and the optical microscope frequently combined with current LIMS systems [24; 27; 28]. The development of LIMS did not stop after a hibernation period in the late 1990s. Current studies show that the critical constraints, such as low accuracy and precision in the elemental and isotope quantification, low mass resolution and considerable interference by abundant polyatomic species have been improved [1; 25; 26; 29; 30; 31]. Furthermore, reduced mass spectrometric signal fluctuations, combined with high stability femtosecond laser ablation, pave the way to high-resolution chemical imaging and depth profiling analysis. The mass spectrometric analysis of these data allows for complex correlation analyses between isotopes and elements leading to improved quantification and identification of molecular parent compounds, and their location within the sample volume. These interesting new features can be of importance in analysing highly heterogeneous materials, including micrometre-sized mineralogical grains or identifying extant or fossilised microorganisms within rocks and soils [28; 32; 33; 34; 35]. The benefits of femtosecond LIMS are well-demonstrated in recent publications following numerous performance studies. Although fs-LIMS studies are still limited compared to extensive fs-LIBS [36; 37; 38] and fs-LA-ICP-MS [39] studies published in the last decade, this technique is rapidly developing and has the potential to compete with the latter techniques. Numerous advantages of femtosecond-pulse duration are known for both fs-LIBS and fs-LA-ICP-MS, and also for fs-LIMS which benefits from the resulting low ablation thresholds, absence of fractionation by vaporisation, improved spatial resolution for 3 Dmapping applications, small ablated mass, and reduced sample damage. All these benefits originate from the physics of femtosecond ablation and subsequent plasma formation, which are described in Section 2. Section 3 introduces highly sensitive fs-LIMS systems used for a variety of studies on chemical composition in the last two decades. In Section 4, we summarise the analytical performance of fs-LIMS. Several applications of fs-LIMS are discussed in Section 5. Special attention is given to the comparison of analytical figures of merit (sensitivity, accuracy, and precision), as well as unique applications to the analysis of heterogeneous samples and micrometre-sized objects.
# Machine Learning in Beyond 5G/6G Networks--State-of-the-Art and Future Trends
## 3 Machine Learning
### Unsupervised Learning
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**ML Approach** & **Advantages** & **Limitations** \\ \hline \multirow{4}{*}{ANN} & \(\bullet\) & High fault tolerance & \(\bullet\) & Hardware dependence \\  & \(\bullet\) & Distributed memory & \(\bullet\) & Reduced trust \\  & \(\bullet\) & Parallel processing capability & \(\bullet\) & Structure through trial and error \\  & \(\bullet\) & Robust to noise & \(\bullet\) & Computationally expensive \\ \hline \multirow{4}{*}{knn} & \(\bullet\) & One hyperparameter (k) & \(\bullet\) & Sensitive to noise \\  & \(\bullet\) & Non-parametric & \(\bullet\) & Curse of dimensionality \\  & \(\bullet\) & No training step & \(\bullet\) & Needs homogenous features \\  & \(\bullet\) & Easy to implement in multi-class problems & \(\bullet\) & \\ \hline \multirow{4}{*}{Naive Bayes} & \(\bullet\) & Fast and can be used in real-time & \(\bullet\) & Not so accurate \\  & \(\bullet\) & Insensitive to irrelevant features & \(\bullet\) & Zero-frequency problem \\  & \(\bullet\) & Performs well with high dimensional data & \(\bullet\) & Assumes independent features \\  & \(\bullet\) & Scalable with large datasets & \(\bullet\) & Several level-data variables \\ \hline \multirow{4}{*}{Decision tree} & \(\bullet\) & Does not require normalization or scaling of data & \(\bullet\) & \(\bullet\) \\  & \(\bullet\) & Missing values in data do not affect process & \(\bullet\) & High complexity \\  & \(\bullet\) & Simple implementation & \(\bullet\) & Instable for data variation \\  & \(\bullet\) & High accuracy & & \\ \hline \multirow{4}{*}{Random Forest} & \(\bullet\) & Accurate and robust & \(\bullet\) & Low correlation between trees \\  & \(\bullet\) & Insensitive to overfitting & \(\bullet\) & High complexity \\  & \(\bullet\) & Offers feature importance & \(\bullet\) & \\ \hline \multirow{2}{*}{CNN} & \(\bullet\) & Automatically detects important features & \(\bullet\) & Lacks ability to be spatially invariant from \\  & \(\bullet\) & Weight sharing & & input data \\  & \(\bullet\) & Minimizes computation & \(\bullet\) & Slow training procedure \\ \hline \multirow{2}{*}{RNN} & \(\bullet\) & Can process inputs of any length & \(\bullet\) & Computationally expensive \\  & \(\bullet\) & Model size does not increase with larger input & \(\bullet\) & \(\bullet\) \\ \cline{1-1}  & \(\bullet\) & Minimizes computation & & \(\bullet\) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Advantages and limitations of supervised ML methods. up, followed by a supervised learning method. In this way the top layer is trained based on known input, and so fine-tuning the whole architecture. In our study, many different algorithms were applied for unsupervised ML, but all of them were based and inspired from the previously mentioned algorithms. The advantages and limitations of the most common unsupervised ML methods that were introduced [16; 25; 26; 27; 28], are analyzed in Table 3:
# Hierarchical Coordinated Predictive Control of Multiagent Systems for Process Industries
## 4 Case Study: Multisource High-Power Microwave Heating Process
### Microwave Heating Process and Device
As shown in Table 2, the energy-conversion efficiencies and the ability to heat the material varied among the six microwave sources. The microwave sources were ranked as follows from highest to lowest heating capacity yields: 1, 3, 5, 4, 6, 2. This ranking also corresponds to the normalized costs of the microwave sources from lowest to highest. Note that the energy-conversion efficiency does not directly determine the heating capacity of a microwave source; e.g., microwave source 5, which has the lowest energy-conversion efficiency, provides a greater heating capacity than microwave source 2, which has the highest energy-conversion efficiency. The above characteristics of the microwave heating process indicate its complex physical properties on the one hand, and on the other hand, they make it possible to use the proposed HCPC method to reduce energy consumption and optimize economic performance.
## 5 Results and Discussion
### DDoS Attacks Classification  
We use the LSTM model designed in Section 4.2 to classify the network flows in the CIC dataset, as mentioned in Table 3. In this experiment, we conduct binary and multiclass classification. Figure 4a-c show the confusion matrices of the binary classification on CICIDS2017, CICIDS2018, and CICDDoS2019 test sets, respectively. We can note that almost 100% of the true attack and benign instances are predicted correctly.  
Table 8 presents the outputs of ACC, PR, RE, and F1-score of binary classification on the three CIC datasets. All the performance metrics return values above 0.99 for the binary classification model on the three datasets. We also perform multiclassification on the CIC  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Metric** & **Binary Classification** & **Multiclassification** \\ \hline
**Accuracy (ACC)** & \(\frac{TP+TN}{TP+FP+TN+TN}\) & \(\frac{1}{T}\sum\limits_{i=1}^{C}TP_{i}\) \\ \hline
**Precision (PR)** & \(\frac{TP}{TP+FP}\) & \(\frac{1}{c}\sum\limits_{i=1}^{C}\frac{TP_{i}}{TP_{i}+FP_{i}}\) \\ \hline
**Recall (RE)** & \(\frac{TP}{TP+TN}\) & \(\frac{1}{c}\sum\limits_{i=1}^{C}\frac{TP_{i}}{TP_{i}+TN_{i}}\) \\ \hline
**F1-score** & \(\frac{2\times PRx+RC}{PR+RC}\) & \(\frac{1}{c}\sum\limits_{i=1}^{C}\frac{2\times PRx+Rx}{PR_{i}+RR_{i}}\) \\ \hline \hline \end{tabular}
\end{table}
Table 6: Classification model metrics.  
Figure 4: Confusion matrices of binary classification: (**a**) CICIDS2017, (**b**) CICIDS2018, and (c) CICDDoS2019.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Descriptive Accuracy (DA)** & **Descriptive Sparsity (DS)** \\ \hline \(\mathbf{DA}_{k}(x,f)=f(x|x_{1}=0,\cdots,x_{k})_{e}\) & MAZ\((r)=\int_{r}^{r}h(x)dx\) for \(r\in[0,1]\) \\ \hline \hline \end{tabular}
\end{table}
Table 7: Definition of evaluation metrics of explanation methods.  
datasets using the LSTM model. We classify 17 classes of DDoS attacks extracted from CIC 2017, 2108, and 2019 in addition to the benign class. Table 9 shows the classification performance of the LSTM model on these classes. We can note from this table that all performance metrics show very high values of all classes from CICIDS107 and CICIDS2018 datasets and MSSQL, NTP, NetBIOS, and TFTP classes from CICDDoS2019, while the DNS, LDAP, SNMP, SSDP, SYN, UDP, and UDP-Lag classes return low values of the performance metrics.  
Figure 5 presents the confusion matrix obtained by the CICDDoS2019 dataset classification. This matrix introduces more detailed information on the classes and shows that the LSTM model confuses DNS, LDAP, and SNMP and between UDP, SSDP, SYN, and UDP-Lag classes.
## 4 Resource Management in Heterogeneous Network (RM HetNet)
### Power Allocation (PA)
#### 4.1.4 Open Issues and Proposed Solutions  
The UE capability has been investigated as one of the main factors in the next generation of mobile communications. Devices in 6G will be AI-guided and require substantial computational power to operate AI algorithms. Thus, new devices must be designed using new strategies and materials that support emerging networks. On the other hand, low-power SCs enhance the coverage and capacity of HetNets when SC locate underlying or overlaying MCs and utilize frequency reuse. There are still many problems the 5G networks to overcome. Cooperative and non-cooperative game theory can be modeled and analyzed in 6G networks. EE and SE are challenges in a 6G network, particularly in NOMA. An adaptive power allocation scheme is a key component of a 6G network, and NOMA has shown promise for improving system efficiency. This power allocation scheme enables developers to adjust transmit power levels to improve throughput, increasing system efficiency. Since BSs are implemented at fixed locations, the air-ground link distance mostly depends on the UAV position. UAV-assisted communication networks typically allocate more power when the UAV is close to the target to reduce path loss. Power allocation can be more productive in this way.
# EC-Kad: An Efficient Data Redundancy Scheme for Cloud Storage
## 4 Experiment Evaluation
### Experimental Result Evaluation
Figure 7 shows the comparison of the redundant computation overhead values for different redundancy encoding schemes when processing a 100 MB data file with 10 chunks under various redundant factors. When the redundant factor was 1.2, the redundant computation overhead values of our EC-Kad scheme and GMR-RC were 2.1 \(\times\) 10\({}^{-6}\) J and 2.4 \(\times\) 10\({}^{-6}\) J, respectively. As the redundant factor increased, the gap in redundant computation overhead between these two schemes gradually narrowed. When the redundant factor reached 1.6, the redundant computation overhead values of the two schemes were almost the same. Overall, our EC-Kad scheme outperformed the other comparative schemes. This is because our EC-Kad scheme uses a Cauchy matrix to implement erasure codes, which can reduce the computational complexity in the encoding and decoding processes, thereby significantly decreasing the redundant computation overhead. In contrast, NEC-CRC had the highest redundant computation overhead among all comparative schemes. This is because the NEC-CRC scheme needs to generate more encoding vectors during the encoding process, resulting in a higher redundant computation overhead. Figure 6: Recovery success rate. Figure 7: Redundant computation overhead.
# Real-Time Tension Distribution Design for Cable-Driven Parallel Robot
## 4 Optimization of Cable Distribution
### Analysis for Tracking the Stability of the Desired Control Wrench  
Similar to the derivation of Equation (19), the derivative of \(E_{1}\) can be calculated as follows:  
\[\hat{E}_{1}=e_{1}^{T}\epsilon_{1}=e_{1}^{T}(\dot{A}^{T}\texttt{Tanh}(\hat{L}) +A^{T}\frac{\text{d}}{\text{dt}}(\texttt{Tanh}(\hat{L}))-\dot{\tau}) \tag{35}\]  
In order to examine the tracking stability of the desired control wrench, it is necessary to determine the variation of \(\frac{\text{d}}{\text{dt}}(\texttt{Tanh}(\hat{L}))\) and \(\tau_{d}\). With the updating law selected as shown in Equations (33) and (34), \(\frac{\text{d}}{\text{dt}}(\texttt{Tanh}(\hat{L}))\) becomes  
\[\begin{split}&\frac{\text{d}}{\text{dt}}(\texttt{Tanh}(\hat{L}))=( \boldsymbol{U}_{\boldsymbol{m}\times\boldsymbol{m}}-\texttt{Tanh}2(\hat{L})) \hat{L}\\ &=(\boldsymbol{U}_{\boldsymbol{m}\times\boldsymbol{m}}-\texttt{ Tanh}2(\hat{L}))(\hat{S}\hat{L}_{a}+\hat{L}_{a}\hat{S})\\ &=-\alpha_{1}H_{Tan}\hat{S}\hat{S}^{T}H_{Tan}^{T}Ae_{1}-\alpha_{ 2}\lambda H_{Tan}\hat{L}_{a}\hat{L}_{a}^{T}H_{Tan}^{T}e_{2}\end{split} \tag{36}\]  
Equation (35) can be written as  
\[\hat{E}_{1}=e_{1}^{T}z-\alpha_{1}\bar{d} \tag{37}\]  
where \(\bar{z}=z-\alpha_{2}\lambda H_{Tan}\hat{L}_{a}\hat{L}_{Ta}^{T}H_{Tan}^{T}e_{2}\) and \(\bar{d}=\hat{e}_{1}^{T}\bar{A}^{T}H_{Tan}^{T}\hat{S}\bar{S}^{T}H_{Tan}^{T}Ae_{1}\).  
Figure 2: Relation between wrench space and tension space.  
It has been shown that the term \(-\alpha_{2}\lambda H_{Tan}\hat{L}_{a}\hat{L}_{a}^{T}H_{Tan}^{T}e_{2}\) generated by the updating law of \(\hat{S}\) has an effect on the tracking stability of the desired wrench. Here, we select \(\alpha_{1}\) as follows to ensure consistent tracking of the desired wrench:  
\[\alpha_{1}\Bigg{\{}\begin{array}{rcl}&\geqslant 0&(\tilde{E}_{r}>0)\\ &\geqslant\frac{-\tilde{E}_{r}}{\tilde{d}}&(\tilde{d}>\delta,\tilde{E}_{r}<0) \end{array}\]  
where \(\tilde{E}_{r}\) is defined as \(\tilde{E}_{r}:=-e_{1}^{T}z-\beta e_{1}^{T}e_{1}\). As a result, we obtain  
\[\dot{E}_{1}\leqslant-\beta e_{1}^{T}e_{1} \tag{38}\]  
which indicates that \(E_{1}\) continues to decrease with the suggested selection law for \(\alpha_{1}\). Here, because the selection of \(\alpha_{1}\) takes into account the effect of \(\tilde{E}_{r}\), which includes the effect caused by the variation of \(\hat{S}\), it can be seen that \(A^{T}f\) is able to consistently track the desired wrench even when \(\hat{S}\) varies. Due to the updating of \(\hat{S}\), \(E_{2}\) is minimized and the tension \(\hat{f}\) finally reaches \(f_{m}\), which is close to the desired \(f_{0}\) illustrated in the feasible tension solutions with respect to \(\tau_{d}\) (for example, \(f_{m}\) in Figure 2).
# Antenna Arrays for Line-of-Sight Massive MIMO: Half Wavelength Is Not Enough
## 9 Conclusions  
In this paper, we have discussed the impact of the array on the performances of a massive MIMO system.  
In particular, we have analyzed the LoS case, showing that increasing the size of the array can be very beneficial to the condition number of the MU-MIMO channel matrix: since the BS antenna is working in the near-field region of the array, a larger element distance allows one to exploit the spherical shape of the wavefronts.  
We have also shown that using a non-equispaced array, we are able to further improve the resulting condition number, since by getting rid of the grating lobes, we are able to achieve a significant lowering of the average condition number also for small increases of the antenna array dimension. Such an improvement in the conditioning of the channel matrix results in an improvement in the spectral efficiency of the massive MIMO systems when using linear processing of the signals.  
We have also analyzed the results achievable by means of circular arrays and a full circle coverage, finding results perfectly aligned with the ones of the non-equispaced linear array.  
It is worth underlining that all of the performed analyses have been done considering a fixed BS complexity. It is trivial to understand that with a proper sparse array design, we could reduce the number of antennas at the BS, achieving the same average conditioning of the channel matrix for a desired number of user terminals.  
It is fundamental to underline that the improvement that can be achieved by optimizing the antenna array geometry has a minimum impact on the overall system cost and does not increase the computational effort of the communication schemes in any way.  
As a development of the present work, we are working on the design of more complex planar and conformal antenna array geometries; we are also working towards the use of a ray-tracing simulator, in order to check the achieved results when the line of sight is the dominant, but not the only, propagation term present. Finally, we will investigate the effect of a joint optimization of the antenna array when using a smart user scheduling approach.  
[PERSON] conceived of the approach and wrote the numerical code to perform the simulations. [PERSON], [PERSON] and [PERSON] helped with the discussion and comparison of the results. [PERSON], [PERSON] and [PERSON] wrote the paper.  
The authors declare no conflict of interest.
## 3 Results and Discussion
### Influence of the NaOH Concentration in the Aqueous Solution  
Previous experiments have shown that from Cr(III)-bearing alkaline solutions, i.e., 0.01 g L\({}^{-1}\) Cr(III) in 0.1 M NaOH, there was no Cr(III) removal-precipitation over a period of 3 h and under agitation at 1000 min\({}^{-1}\) in the absence of the carbon nanotubes; this indicated that the removal of Cr(III) from these alkaline solutions was solely due to the presence of carbon nanotubes acting as an adsorbent material of the heavy metal. In order to assess the influence of this variable during the adsorption of chromium(III), NaOH concentration variations in the range 0.1-0.5 M NaOH were carried out with aqueous solutions containing 0.01 g L\({}^{-1}\) Cr(III). The adsorbent dosage was 1 g L\({}^{-1}\) and the temperature was 20 \({}^{\circ}\)C. The results from this experiment (Table 3) showed that the percentage of metal adsorption (and metal uptake) was dependent of the NaOH concentration of the aqueous solution, increasing the chromium(III) loaded onto the adsorbent from 0.1 to 0.35 M NaOH solutions, and decreasing as the NaOH concentration in the solution was further increased.