esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Performance of Original RFs and Percentage of Tumor Tissue Necrosis  
The results of the different analysis approaches varied slightly according to the data being analyzed and followed similar patterns (Figure 2). For the first approach, when Dataset 1 was analyzed, the performance of RFs (C-index) across the 1000 repeats ranged between 0.33 and 0.83, with an average (\(\pm\)SD) of 0.55 \(\pm\) 0.08. The RF \"Intensity minimum\" was selected in 91.8% of the times, followed by intensity skewness (77.3% of the times), intensity peak position (70.6%), and intensity kurtosis (64.8%). The remaining features were selected in less than 50% of the times.  
In Dataset 2, the c index values across the 1000 repeats ranged between 0.07 and 0.98, with an average (\(\pm\)SD) of 0.50 \(\pm\) 0.18. None of the features was selected more than 50% of the times. Intensity minimum and intensity kurtosis were the most selected features (49.5 % and 46%, respectively).  
In Dataset 3, the C-index values ranged between 0.31 and 0.95, with an average (\(\pm\)SD) of 0.62 \(\pm\) 0.09. None of the features was selected more than 50% of the times. Intensity minimum and intensity kurtosis were the most selected features (49.5 % and 46%, respectively). Intensity minimum was the most selected RF (79.2%) followed by intensity skewness (61.4%) and intensity peak position (52.2%).  
For the second approach, we assessed the ability of the clinical variables \"percentage of tumor tissue necrosis\" to stratify the patients in long and short survivors. The C-index values ranged (average \(\pm\) SD) between 0.20 and 0.82 (0.64 \(\pm\) 0.07), 0.12 and 1.00 (0.58 \(\pm\) 0.12), and 0.11 and 0.91 (0.67 \(\pm\) 0.09) in Dataset 1, Dataset 2, and Dataset 3, respectively.  
For the third approach, combining the percentage of tumor tissue necrosis with the RFs resulted in C-indices with an average (\(\pm\)SD) of 0.58 \(\pm\) 0.07, 0.52 \(\pm\) 0.16, 0.61 \(\pm\) 0.09 in Dataset 1, Dataset 2 and Dataset 3 respectively.  
\(T\)-tests showed that on average, the percentage of tumor tissue necrosis significantly (\(p\)-value \(<\) 0.05) outperformed the RFs-based models as well as the combination of RFs and the percentage of tumor tissue necrosis across all the three datasets.  
Figure 2: Boxplot comparison of the C-indices of the clinical and original RF models.
# Processing of AZ91D Magnesium Alloy by Laser Powder Bed Fusion
###### Abstract  
Magnesium alloys are perspective materials for use in transportation, aerospace and medical industries, mainly because of their good load-to-weight ratio, biocompatibility and biodegradability. For the effective production of magnesium components by the laser powder bed fusion (LPBF) process, the process parameters with verified mechanical properties need to be determined. In this paper, we prepared bulk samples with a high relative density of AZ91D magnesium alloy. Tensile tests were then performed on LPBF samples to evaluate the mechanical properties. Our results show that the bulk samples achieved a relative density >99%, in multiple planes over the full sample height, while the mechanical properties reached values of \(Y_{S}=181\) MPa, \(UTS=305\) MPa and \(A_{5,65}=5.2\%\). The analysis by scanning electron microscope revealed fine \(\beta\)-Mg\({}_{17}\)Al\({}_{12}\) particles in the microstructure, which have a positive effect on the mechanical properties. The chemical composition of magnesium alloy AZ91D changed slightly during processing by LPBF due to the evaporation of the Mg content. However, the resulting composition still corresponds to the range specified by the ASTM standard for the AZ91D alloy.  
AZ91D; magnesium alloy; laser powder bed fusion; energy dispersive spectroscopy; scanning electron microscope; process parameters; single tracks; porosity 2023  
are segregated in the matrix of Mg, which is caused by the wide range of liquid phases between Mg and Al or Mg and Zn. Therefore, the produced part needs to be optimized by heat treatment. The disadvantages of this process are high cost and long cycle time, which are needed for the production of parts. These issues can be solved by using additive manufacturing technology, which allows the production of complex parts [7].  
One of the additive technologies that is possible for the production of magnesium alloys is the LPBF process. LPBF uses a high-power laser to melt the metal powder into the shape of parts layer by layer [8; 9]. Production of complex parts without imperfections requires an optimal combination of LPBF process parameters, i.e., laser power, laser speed, hatch distance and production strategy. In the case of magnesium alloys, the right combination of process parameters is more important because the high energy of laser power can cause the evaporation of magnesium, which leads to the formation of imperfections such as porosity [10; 11; 12].  
Therefore, in this study, the LPBF process parameters are developed for the production of volume parts from magnesium alloy AZ91D. The DoE method is used to produce the cubic samples with different process parameter combinations. The best combination of process parameters is used for the production of tensile samples to obtain the mechanical properties of magnesium alloy AZ91D processed by LPBF technology.
# Explainable AI for Alzheimer Detection: A Review of Current Methods and Applications
## 3 Overview of Explainable Artificial Intelligence (XAI)
### Types of XAI Methods
#### 3.1.1 Model-Specific Techniques in XAI  
Model-specific approaches are closely related to an AI model's architecture and are made to capitalize on certain features or functions. While model-specific offers better integration with the model structure, they still focus primarily on machine-learned features, which may not align with domain-specific clinical knowledge. For example, Grad-CAM techniques are employed in CNNs to view picture regions that impact the model's judgment, offering insights into the inner workings of the network. A model described in [55] uses techniques like principal component analysis and Grad-CAM to decode a neural network's layers and expose decision-making processes. However, the main drawback of procedures that are exclusive to a model is that they are not transferable. These methods might not work with other models because they are specialized to certain architectures. Model-specific techniques are nevertheless quite effective even in XAI when explanations closely match the model's internal operations. One such example is the VGG16 model, which uses its architecture to make precise and understandable judgments when combined with transfer learning for early AD in [13]. While model-specific approaches offer accurate and tailored insights, they are not universally applicable across many models. The article [32] emphasizes how flexible in explaining different models, but it also exposes the possibility of explanations that are too general or imprecise.
# From Dormancy to Eradication: Strategies for Controlling Bacterial Persisters in Food Settings
## 5 Mechanisms of Persister Cell Formation
### Toxin-Antitoxin System-Induced Persisters  
The mediation of persistence has been proposed as a function of toxin-antitoxin (TA) systems [22]. TA systems are widespread across bacteria and archaea, defined as ubiquitous small operons containing two genes that separately express a stable toxin molecule to slow down/block certain metabolic processes and a labile antitoxin to neutralize this toxicity, which can be RNA or a protein [23; 24]. There are eight groups (types I to VIII) of TA systems. Except for type VIII, in which the toxins are RNAs, in all other TA classes, the toxins are proteins. Antitoxins are either non-coding RNAs (types I, III, and VIII) or proteins (types II, IV, V, VI, and VII) [25; 26]. Persisters are mostly associated with type I and type II TA systems.  
[PERSON] et al. [27], in their review, consider that type II is probably the most abundant, as well as the best characterized class of TAs. These contain many families of toxins with different molecular activities such as kinases, ribonucleases (ribosome and ribosome-independent), acetyltransferase, and gyrase inhibitors. Type I TA systems are made of a small regulatory RNA, as an antitoxin, and a mRNA as an antitoxin (Figure 1) [28]. The better-characterized type I TA systems are Hok/Sok, TsB/IstR, and LdrD/RdID, SymE/SymR, which are usually involved in the disruption of the cell wall and transcription and translation, respectively [28]. Type II TA systems, known for their redundancy, have been linked to persister cell formation through protein-protein interactions. The HipA-HipB complex was the first TA system associated with persistence, specifically in _E. coli_, where the overexpression of the _hipA_ gene increases persistence, while its deletion reduces it [4]. Other TA systems, including mqsRA and yafQ, have also been implicated in the reduction in _E. coli_ survival during antibiotic treatment. Recent reviews by [PERSON] et al. [27]have linked other TA complexes, such as RelE, MazF, YafO, and VapC, to persistence. Other knockout library studies have identified additional candidate genes associated with persisters, including global regulators like DskA, DnaKJ, or IhfAB; this topic has been reviewed by [PERSON] et al. (2015).
# A Physical-Layer Watermarking Scheme Based on 5G NR
## 2 Watermark System Model on 5G NR
### Performance Analysis for Bit Error Rate
#### 2.3.1 Symbol Error Rate of the User's Symbol  
For the Gaussian channel, the fading coefficient of the signal is constant; for the Rayleigh fading channel, in the case of slow fading [23], it can be considered that the fading coefficient in one symbol period is also approximately constant, and the attenuation coefficient is marked as \(\mu\).  
Figure 8: Watermark authentication schemes for scheme I, scheme II and scheme III.  
Under ideal conditions, although the watermark signal is embedded into the phase of DMRS, due to the symmetry of the watermark, it will not affect the channel estimation of DMRS, so it will not affect the symbol error rate of the user's signal. In theory, the symbol error rate of the user's signal should be the same as the symbol error rate of standard 16 QAM.  
The theoretical symbol error rate of 16 QAM can be calculated as:  
\[P_{e}(\eta)=1-\left(1-\frac{3}{4}\text{erfc}\left[\left(\frac{\eta E_{s}}{10n _{0}}\right)^{1/2}\right]\right)^{2} \tag{13}\]  
where \(\eta=|\mu|^{2}\), \(\mu\) is the attenuation coefficient, \(E_{s}\) is the average symbol energy, and \(n_{0}\) is the power spectral density of noise. For the Gaussian channel, \(\eta\) is the constant 1, so the symbol error rate of the user's signal in the Gaussian channel can be expressed as  
\[P_{e}=1-\left(1-\frac{3}{4}\text{erfc}\left[\left(\frac{E_{s}}{10n_{0}}\right)^ {1/2}\right]\right)^{2} \tag{14}\]  
For Rayleigh fading channels, \(\eta\) obeys the distribution:  
\[f(\eta)=e^{-\eta},\;\eta\geq 0 \tag{15}\]  
Therefore, in fading channel, the average symbol error rate can be calculated as:  
\[P_{e}=\int_{0}^{\infty}P_{e}(\eta)f(\eta)d\eta \tag{16}\]
## 3 Results and Discussion
### The SSVEP Signal Dataset  
In this study, an open-access dataset, HS-SSVEP, which was approved by the Research Ethics Committee of Tsinghua University, is used to train and evaluate the proposed approaches [21]. In this dataset, a 40-target BCI speller is used to design an offline BCI experiment, which contains 40 characters including the 26 letters in the English alphabet, 10 digits, and four symbols. The 40 stimuli are flickered at frequencies between 8-15.8 Hz with an interval of 0.2 Hz and coded using a joint frequency and phase modulation approach [30].  
In this dataset, 35 healthy subjects (17 females and 18 males) aged between 17-34 years (mean age: 22 years) were asked to participate in the offline BCI experiment. For each subject, each target frequency was presented six times, and then 240 trials were collected. Each trial lasted for 6 s, in which the first and last 0.5 s were used for visual cues and rest, respectively.  
The EEG signal was recorded using 64 channels at a 1 kHz sampling rate. Out of the 64 electrodes, eleven channels from the occipital and parietal areas, namely Pz, PO-3/4/5/6/7/8/z, and O-1/2/z, were used. To reduce storage and computation costs, the collected EEG signal was simply downsampled to 250 Hz and filtered using a 6 th-order Butterworth filter between 1 Hz and 40 Hz. Then, the filtered EEG signals were segmented and the first and last 0.5 s were removed. The first dt seconds were selected as a data epoch, which contains 250 dt time samples by 11 channels. In this study, the dt is examined using four, three, two, and one.
## 3 Results and Discussion
### Enzymatic Studies  
Inhibition of Collagen and Tyrosine  
Measurements aimed to demonstrate the collagenase and tyrosinase inhibition capacity of the investigated extracts lyophilized from leaves and roots of _A. reptans_ (samples L_L--freeze-dried leaves and R_L--freeze-dried roots).  
The extract derived from the lyophilized root of _A. reptans_ displays the highest inhibition of collagenase capacity. Extracts originating from the root of exhibit comparatively lower values. From the values (Table 8), we can observe that the strongest inhibition of tyrosinase properties is shown by the extract of the root of the bugle herb obtained from the plant dried by lyophilization (47.52%). Similar values were obtained by extracts of the leaf (41.33%).  
The investigations into cytotoxicity, tyrosinase activity, and collagenase activity have revealed the multifaceted potential of _A. reptans_ extracts. The findings suggest that these extracts exhibit versatility, holding promise not only as antioxidants and chelating agents but also showcasing attributes with broad applications in skincare. These attributes include the potential to impact skin pigmentation and collagen metabolism. It is worth noting that, in terms of enzymatic activity, the root extract of ajuga demonstrates higher catalytic efficiency than leaf extract (17.59% of collagenase inhibition and 6.19% of tyrosinase inhibition, respectively), prompting further investigation of this raw material for its rejuvenating properties. This component induces natural skin renewal processes by promoting the synthesis of structural protein, such as collagen. Additionally, the root extract inhibits melanin synthesis, which may result in skin whitening effects.
## 5 Discussions and Research Framework
### Positive Attitude towards BIM Integration with Emerging Digital Technologies  
BIM integration with emerging digital technologies, such as photogrammetry, AR, VR, scanning approach, simulation, GPS, and RFID, has promising results for construction safety in high-rise buildings, for instance, computer vision-based methods, monitoring, tracking, drone technology, cameras, images, videos, and digital modeling. In addition, they believed that computer vision methods can be used in engineering management of construction projects to transform processes and to allow the collection and analysis of digital images and extraction of high-dimensional data from the real world to generate knowledge to enhance management decision-making. The literature study also supports their viewpoint that computer-vision methods have the potential to investigate the special issues during construction, such as progress monitoring [113], efficiency enhancement and analysis [114], and health and safety monitoring [115]. Normally, the inspection and assessment of infrastructure (bridges, tunnels, underground pipe, and asphalt pavements) is manually checked by structural engineers. It has been found that by using a 3-D digital image correlation technique and closed circuit television (CCTV) imaging, the structural integrity of tunnels, crack detection, and structural condition of bridges can be assessed and monitored [116]. Hence, there is a path for future researchers to integrate the computer-vision techniques for assessment, monitoring, and inspection of high-rise building projects during construction and in pre-construction phases. In addition, there is a lack of sufficiently large databases in literature studies to ensure the accuracy of the computer vision. The constraint of sufficiently large datasets, therefore, prevents the development of computer vision in construction.
## 4 Results and Analysis
### Data Preparation
#### 4.1.5 Observation  
Figure 16, depicts the real number of employees before the optimization methodology is applied in the left y-axis, and the number of employees in each category after medical benefit optimization methodology is applied is depicted by the right y-axis. The number of employees in each category is reduced after the optimization methodology is applied. This proves that not all insured employees are using healthcare services. The observation is that organization is paying extra premium amounts as not all employees are using healthcare services frequently.  
It can be seen from Figure 17 that the allocated benefit amount is not fully utilized.The x-axis is representing categories and the y-axis is depicting the percentage of amount utilization in each category.  
In Figure 18, it can be seen that after optimization total amount for each category is reduced. It is observed that in the three categories B, D and F, there are out of pocket employees. They paid an additional amount from their pocket for using healthcare services.  
\begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**Category** & \(\phi\) & \(\beta\) & \(\mu+\sigma\) & \(\mu+2\sigma\) & \(\mu+3\sigma\) \\ \hline A & 2,722,499 & 6,636,057 & 14,224,155 & 20,743,556 & 27,262,958 \\ \hline B & 126,989 & 87,333 & 231,184 & 335,380 & 439,575 \\ \hline C & 21,274,512 & 31,442,546 & 72,638,057 & 97,809,596 & 122,981,136 \\ \hline D & 4,347,796 & 3,295,044.144 & 12,159,063 & 17,871,775 & 23,584,488 \\ \hline E & 35,067,873 & 52,380,635 & 161,637,982 & 237,239,625 & 312,841,268 \\ \hline F & 19,504,878 & 14,347,412 & 60,059,019 & 89,927,607 & 119,796,195 \\ \hline G & 4,113,456 & 8,257,405 & 11,036,095 & 15,387,108 & 19,738,122 \\ \hline \hline \end{tabular}
\end{table}
Table 19: Optimization of total medical benefit for each Category A,B,C,D,E,F and G.  
Figure 16: Number of employees in each category.  
Figure 17: Percentage of Utilization Amount in each category.  
Figure 18: Comparison of Utilization Amount in each category.  
Figure 19 depicts the data-driven analysis of amounts for each category using a gamma distribution. After the optimization, it is necessary to compute the percentage of optimization achieved. Optimization percentage is computed by using Equation (6):  
\[P=\frac{(\phi-T)}{T}*100=\frac{29288429.57}{116446432.6}*100=25\% \tag{6}\]  
\(\phi\) is computed optimized amount and \(T\) is current total amount, both amounts are shown in Figure 12. The \(T\) is optimized to \(\phi\) by 25% with the help of proposed methodology. This optimization is achieved as we have divided each category into two groups, each group has different sum amount and different premium amounts. By using these dynamic amounts for each category, need based packages are derived.
## 5 Conclusions
Through the derivation and analysis of multiple models, it was determined that the adequate choice was the model encompassing \(\mathrm{GHI},\theta_{\mathrm{z}},\mathrm{T},\mathrm{k}_{\mathrm{t}},\mathrm{ \Psi}\), due to its high performance and the availability of the required inputs from most ground meteorological facilities. Future research endeavors should incorporate the deeply impactful total ozone column (TOC) variable among the model inputs. Addressing the scarcity of ground-based experimental TOC data could be achieved by using daily interpolated satellite observations. Incorporating other time scales (hourly and daily) and employing corrections based on site adaptation techniques will be imperative to extend the developed models to diverse locations accurately. Conceptualization, C.A.-T.; Formal analysis, S.G.-R., A.G.-R., D.G.-L. and I.G. ; Funding acquisition, C.A.-T.; Investigation, S.G.-R., D.G.-L. and I.G. ; Methodology, S.G.-R., A.G.-R. and I.G. ; Software, S.G.-R., A.G.-R., D.G.-L. and I.G. ; Supervision, C.A.-T.; Validation, C.A.-T.; Visualization, S.G.-R., A.G.-R. and I.G. ; Writing--original draft, S.G.-R. and A.G.-R.; Writing--review and editing, C.A.-T. and I.G. All authors have read and agreed to the published version of the manuscript. This research was funded by MCIN/AEI/ 10.13039/501100011033 and the \"European Union Next Generation EU/PRTR grant numbers TED2021-131563B-I00 and PID2022-13947/OB-I00 and Junta de Castilla y Leon, grant number INVESTUN/19/BU/0004. Not applicable. **Data Availability Statement:** Copies of the original dataset used in this work can be downloaded from [[http://hdl.handle.net/10259/7778](http://hdl.handle.net/10259/7778)]([http://hdl.handle.net/10259/7778](http://hdl.handle.net/10259/7778)) (accessed on 18 September 2023). **Conflicts of Interest:** The authors declare no conflict of interest. **Nomenclature** \begin{tabular}{l l} B\({}_{\mathrm{sc}}\) & Solar constant (=1361.1 W/m\({}^{2}\)) \\ CC & Cloud cover (\%) \\ D & Diffuse fraction \\ DHI & Diffuse horizontal irradiance (W/m\({}^{2}\)) \\ DNI & Direct normal irradiance (W/m\({}^{2}\)) \\ GHI & Global horizontal irradiance (W/m\({}^{2}\)) \\ GHUVE & Global horizontal UV erytal irradiance (W/m\({}^{2}\)) \\ k\({}_{\mathrm{t}}\) & Clearness index \\ k\({}^{\prime}\)d & Diffuse to extraterrestrial irradiance \\ n & Number of data points \\ nRMSE & Normalized root mean square error (\%) \\ nMBE & Normalized mean bias error (\%) \\ r & Pearson correlation coefficient \\ RH & Relative humidity (\%) \\ R\({}^{2}\) & Determination coefficient \\ T & Air temperature (\({}^{\circ}\)C) \\ \end{tabular}
## 3 Results and Discussion
### Method Comparison  
An assessment of the performance metrics between the Multiple Linear Regression (MLR) and Random Forest Regression (RFR) models reveals that RFR consistently outperforms MLR in predicting both peak and residual interface shear strength, as shown in  
Figure 14: Comparison of actual and predicted peak interface shear strength using RFR for (**a**) the training dataset, (**b**) the testing dataset, and (**c**) 10-fold cross-validation.  
Figure 15: Comparison of actual and predicted residual interface shear stress using RFR for (**a**) the training dataset, (**b**) the testing dataset, and (**c**) 10-fold cross-validation.  
For residual shear stress, RFR again shows higher performance with lower MAE, RMSE, and RMSLE values across all datasets, maintaining high R\({}^{2}\) values. Specifically, in the training phase, RFR achieves an MAE of 1.53, RMSE of 2.13, RMSLE of 0.09, and an R\({}^{2}\) of 0.97, compared to MLR's MAE of 3.61, RMSE of 4.72, RMSLE of 0.23, and R\({}^{2}\) of 0.89. In the testing phase, RFR scores an MAE of 3.77, RMSE of 4.82, RMSLE of 0.23, and an R\({}^{2}\) of 0.88, whereas MLR achieves an MAE of 3.39, RMSE of 4.52, RMSLE of 0.19, and an R\({}^{2}\) of 0.89. In the 10-fold cross-validation, RFR achieves an MAE of 3.97, RMSE of 5.16, RMSLE of 0.24, and an R\({}^{2}\) of 0.87, compared to MLR's MAE of 3.45, RMSE of 4.47, RMSLE of 0.21, and R\({}^{2}\) of 0.90. Overall, RFR proves to be a more reliable and accurate model for predicting both peak and residual interface shear strength.  
It is important to note that training with different seeds can yield varying results, and evaluating models based on random seeds alone may not be fair. The 10-fold cross-validation performed in this study provided a more robust comparison. Both MLR and RFR showed nearly identical results, with MLR occasionally outperforming RFR.