esa-sceva/satcom-chunk-collection huggingface dataset:
# Blind Watermarking of Color Medical Images Using Hadamard Transform and Fractional-Order Moments
## 5 Experimental Results
### Visual Imperceptibility  
We utilized peak signal-to-noise ratio to measure and evaluate the proposed watermarking's imperceptibility. Increasing the PSNR values leads to the high invisibility of the watermarking model. The PSNR is defined as  
\[PSNR(f,f_{w})=10*log_{10}\left(\frac{MAX{{}_{f}}^{2}}{MSE}\right) \tag{28}\]  
where \(f\) is the host color image and \(f_{w}\) is the color watermarked image; \(MSE\) is the mean square error, and it is represented as  
\[Mse=\frac{1}{N^{2}}\sum_{i=1}^{N}\sum_{j=1}^{N}[f_{w}(i,j)-f(i,j)]^{2} \tag{29}\]  
The invisibility of the proposed methodology was evaluated at the value of \(\alpha=1.1\) and \(\Delta\) values ranged from 0.2 to 1.0 in comparison with other existing moment-based methods [28; 29; 41; 60; 61], which in turn embedded binary watermarks of size \(32\times 32\) into the standard color images. The average PSNR values are indicated in Figure 7. As we notice, there is a reverse operation between the PSNR values, and PSNR values increased as \(\Delta\) values decreased. The average PSNR values of the proposed watermarking method and other similar methods are shown in Table 1. From these values, we note the proposed method's success in attaining high PSNR values up to \(\sim\)68 dB compared with other similar methods, which ensures the high imperceptibility of our method compared to other similar methods.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Quantization** & **QEMs [60]** & **QPHTs [61]** & **QRSCMs [28]** & **QLFMs [29]** & **MfrEMs [41]** &
\begin{tabular}{c} **Proposed** \\ **Method** \\ \end{tabular} \\ \hline
0.2 & 44.20 & 50.03 & 54.03 & 58.25 & 59.27 & 68.84 \\
0.4 & 37.78 & 44.12 & 50.67 & 53.21 & 54.24 & 66.42 \\
0.6 & 34.31 & 41.08 & 47.46 & 48.86 & 50.21 & 61.03 \\
0.8 & 31.51 & 38.25 & 44.67 & 45.05 & 47.05 & 57.32 \\
1.0 & 29.81 & 36.78 & 43.97 & 44.91 & 46.41 & 54.21 \\ \hline \hline \end{tabular}
\end{table}
Table 1: PSNR values using varying values of \(\Delta\).  
Figure 7: Average PSNR values using varying values of \(\Delta\).  
The PSNR values for a chosen color medical image (named IMG 1-5) are also indicated in an illustrative example (Figure 8) with high PSNR values, ensuring that our proposed method is suitable for application on medical images in a reasonable manner.
# Propagation, Scattering and Defect Detection in a Circular Edge with Quasi-Edge Waves
## 4 Directivity Pattern of Scattered Waves
### Influence of the Size of Through-Thickness Edge Defect  
The SDPs of defect depths at 2, 8 and 14 mm were investigated. The corresponding defect depth-to-wavelength ratios were 0.08, 0.32 and 0.56, respectively. Figure 14a-c present the SDPs of edge defects with depths of 2, 8 and 14 mm, respectively. The results indicate that the maximum amplitude of scattered waves increased with the defect size. The energy of scattered waves was mainly located in the forward direction as well as directions that were perpendicular to the wave propagation direction, while the energy of backward scattered waves in the \(0^{\circ}\) direction was relatively weak. Meanwhile, the forward components of scattered waves have a relatively larger amplitude. It can also be observed that the amplitudes of backward scattered waves in the direction of \(-67.5^{\circ}\) significantly increased with the defect depth-to-wavelength ratio.
# A Novel RBFNN-CNN Model for Speaker Identification in Stressful Talking Environments
## 6 Conclusions  
In this paper, we focused on improving speaker identification performance in stressful talking environments by introducing a novel RBFNN-CNN model. The proposed model was compared against shallow and deep learning models using a local Arabic Emirati-accent dataset and a global English Speech Under Simulated and Actual Stress (SUSAS) corpus. MFCCs were used as the extracted features of the database based on classical machine learning algorithms SVM, MLP, and KNN as classifiers, as well as deep learning techniques, such as RBFNN-CNN, CNN, and RNN in stressful talking conditions. Some conclusions can be presented. First, the proposed RBFNN-CNN model outperformed all other models based on both datasets. Second, the results attained based on SVM were better than those achieved based on each of the MLP and KNN classifiers using the Emirati-accent dataset. Third, the MLP had better performance as compared to the other classifiers using the SUSAS speech corpus. Fourth, the CNN_BILSTM classifier came second in terms of performance after the proposed model using both datasets. Furthermore, the proposed model surpassed the models used in studies in related work based on the same datasets.  
The main limitation in our study is that we used two different datasets for model evaluation. In the future, we plan to extend the Emirati-accent speech dataset to include more speakers from different emirates and genders.  
A.B.N.: Conceptualization, Methodology, Supervision, Writing--Original Draft, Writing--Review and Editing. N.A.: Funding Acquisition, Methodology, Writing--Review and Editing. I.S.: Data Curation, Methodology, Writing--Review and Editing. S.A.S.: Investigation, Writing--Original Draft. N.H.: Methodology, Software. M.L.: Methodology, Writing--Review and Editing. A.E.: Methodology, Writing--Review and Editing. All authors have read and agreed to the published version of the manuscript.  
This project was funded by the \"Machine learning and Arabic language processing research group\" at the University of Sharjah.  
The authors have authorization from the University of Sharjah to gather speech databases from UAE nationals based on the competitive research project titled Emirati-Accented Speaker and Emotion Recognition Based on Deep Neural Network, No. 19020403139.  
This study did not involve any experiments on animals.  
This project was funded by the \"Machine learning and Arabic language processing research group\" at the University of Sharjah.  
The authors have contributed from the University of Sharjah to gather speech databases from UAE nationals based on the competitive research project titled Emirati-Accented Speaker and Emotion Recognition Based on Deep Neural Network, No. 19020403139.  
**Acknowledgments:** The authors would like to convey their thanks and appreciation to the University of Sharjah for supporting the work through the research group, Machine Learning and Arabic Language Processing. Additionally, the authors would like to thank engineers [PERSON] and [PERSON] for assisting the research team.
## 4 Conclusions  
We proposed a novel way of automatically planning cellular networks supported by linear programming and cloud computing and applied a test case to the GSM protocol. It was implemented with the help of cloud-services, easing up our process of joining each module and both speeding up execution time and lessening the amount of resources used.  
For a realistic scenario with a problematic site, where 83.7% of the serving area had interference, the proposed planning algorithm was capable of removing interference, guaranteeing excellent conditions for communication in the whole serving area. This process was 12 times faster and used four times less space than a planning tool running in a physical machine (which can only plan and optimize one site). Additionally, both time and space complexity were discussed after planning the whole scenario of 70 cells; we noticed that optimizing larger networks might take an unrealistic amount of time.  
Results show that the cloud-based implementation ends up being slightly slower than a physical implementation. However, there are still noticeable advantages to using cloud services, such as the pay-per-use approach, the non-necessity of buying specific hardware, and the fact that other features can use your modules by simply triggering them.  
For future work, we propose an extension of this work pattern to other network technologies, including UMTS and LTE, and even the modern 5G. We also propose an evaluation of the working pattern using other linear programming solvers that might simplify or speed up the process. Finally, investigating a way of splitting the world map into smaller problems and sending them away for optimization, might prove worthwhile to larger scenarios.  
Conceptualization, A.G. and L.S.F.; methodology, A.G. and L.S.F.; software, A.G., D.F, P.P.; validation, A.G.; formal analysis, G.S. and L.S.F.; investigation, A.G. and L.S.F.; resources, D.F. and P.P.; data curation, D.F. and P.P.; writing--original draft preparation, A.G.; writing--review and editing, P.S., A.C. and L.S.F.; visualization, G.S.; supervision, P.S. and L.S.F.; project administration, L.S.F. All authors have read and agreed to the published version of the manuscript. This research received no external funding.  
This work is part of the OptiNET-5G project, co-funded by Centro2020, Portugal2020, European Union (project 023304).  
The authors declare no conflict of interest.  
Figure 20: Comparison of the CPSP algorithm execution times between a physical machine and the cloud-based implementation.
# Time-Sensitive Networking in IEEE 802.11 be: On the Way to Low-Latency WiFi 7
## 3 IEEE 802.11 be
### MAC Layer
In consequence, time-sensitive and best-effort traffic could be provided with differentiated RUs to meet timely delivery requirements. * Coordinated beamforming (CBF) enables simultaneous transmissions from different APs within the same coverage area while ensuring spatial radiation nulls to non-targeted devices [30]. * Distributed MU-MIMO allows APs to perform joint data transmissions (also known as JTX) to multiple STAs by reusing the same time/frequency resources [31]. Spatial diversity can then be exploited to increase frame reception probability. Thanks to the multi-AP coordination, multiple overlapping BSSs (OBSSs) can turn channel contention in their favor, resulting in a better use of shared resources. Beyond the latency reduction obtained by using the spectrum more efficiently, new solutions to protect time-critical traffic across the cooperating BSSs may be enabled. For instance, APs dealing with best-effort traffic may agree on reducing transmission power to provide spatial reuse opportunities, so that STAs from other BSSs can successfully transmit their short-duration, time-sensitive packets at the same time. Advanced transmission schemes such as hybrid automatic repeat request (HARQ) offer notable performance gains in varying channels compared to the traditional stop & wait approach, but it is not yet clear if such gains will also be achieved in WLANs due to the severity of collisions. Be that as it may, HARQ still retains the goal of reducing average latency, because of its performance improvements in PER [32]. In short, the new IEEE 802.11 be MAC functionalities will help to use more efficiently the spectrum resources and allocate them in a more flexible way to optimize throughput, latency, or reliability, depending on the scenario requirements. Furthermore, these functionalities can be better exploited for low-latency purposes if some core TSN features (e.g., admission control and scheduled operation) are integrated on top of them, as we will see in Section 4. Last but not least, the lack of legacy devices operating in the upcoming 6 GHz band also offers the possibility of rethinking channel access for future WiFi 7 adopters. In this sense, traditional channel access schemes based on contention might be partially replaced by others able to offer higher levels of determinism, thus facilitating the management of real-time deterministic traffic and the inclusion of TSN mechanisms.
## 3 Results and Discussion
### In Vitro Release  
In vitro release studies were performed in different pH media without enzymes: PBS, SGF (pH 2) and SIF (pH 7). The studies have been conducted to evaluate if and how different pH conditions can influence the release of SLM from the NLCs. The in vitro release profiles are shown in Figure 2. In PBS, the absence of the burst effect is evident: the release of SLM is not immediate but gradual, up to a maximum percentage of 66.3% in the 24 h.  
In the gastric environment during the first 2 h, corresponding to transit through the stomach, only the 9.6% of the SLM, was released. Thus, the developed NLCs remain intact in SGF, according to the previous data of the stability study, and they prevent degradation of the encapsulated extract and can be promising carriers for the transport of a large amount of SLM into the intestine.  
In simulated intestinal environment, a controlled release of SLM with a maximum of 15.4% released within 24 h was observed. These results suggest that, following the gastrointestinal tract transit, 84.6% of SLM remains encapsulated in NLCs, allowing greater intestinal absorption.  
The lower release obtained in simulated gastro-intestinal conditions suggests that the formulation protects SLM from degradation and guarantees a slow release of the extract due to the lipid matrix. NLCs demonstrated promising potential to prevent burst release and degradation of SLM which remained entrapped into nanoparticles and could be absorbed and reach the target site after oral administration [41]. The absence of the burst effect can be justified by the low percentage of liquid lipid which does not lead to the formation of an outer shell containing high concentrations of SLM, but the oil is distributed randomly in the solid lipid matrix [42]. Such a prolonged release can allow increased absorption of the drug, maintaining a constant concentration in the plasma.  
Different theoretical models were considered to examine the nature of release. Drug release mechanism in PBS was defined by fitting SLM release data during 24 h to various kinetics models. By comparing the regression coefficient values (Zero-order: 0.8263; First-order: 0.9304; Korsmeyer-Peppas: 0.7578; Hixson: 0.8998; Higuchi: 0.9664), the Higuchi model was shown to be the most adequate to describe the kinetics of NLCs. Thus, the diffusion process controlled the release mechanism. Furthermore, if analyzed according to Korsmeyer-Peppas model, the release exponent \(n\) was >0.89, and it confirms that the release follows a super case II drug-transport mechanism and the mechanism can be inferred to follow a non-F  
Figure 2: In vitro release studies of SLM-NLCs in phosphate buffered saline (PBS) pH 7.4 (black line) and in simulated gastrointestinal fluids (dark grey line and light grey dotted line). Results are expressed as means \(\pm\) standard deviation, \(n=3\).
# Advances and Challenges in IoT-Based Smart Drug Delivery Systems: A Comprehensive Review
## 3 Applications of Smart Drug Delivery Devices
### Implantable Smart Drug Delivery Devices
#### 3.2.1 Implantable Infusion Pumps  
These devices are implantable pumps that can deliver drugs, such as pain medications or chemotherapy, directly to the site of action. The pumps can be controlled wirelessly using a smartphone or other device, allowing healthcare providers to adjust the delivery rate as needed.  
Implantable sensor-based infusion pumps are medical devices that are surgically implanted into the body to deliver precise doses of medication, such as insulin for the treatment of diabetes or pain medication for the management of chronic pain. These devices are equipped with sensors that can monitor the patient's physiological parameters and adjust the delivery of medication accordingly. Some examples of implantable sensor-based infusion pumps are given in Table 5.
# A Room Monitoring System Using Deep Learning and Perspective Correction Techniques
## 5 Experimental Results
### Evaluation of Schemes Using MAE and RMSE  
We evaluated and compared the four schemes using other metrics including mean absolute error (MAE) and root mean squared error (RMSE), defined by Equations (8) and (9), respectively. Here, \(N\) is the total number of testing images, and \(y_{i}\) and \(y_{i}^{\prime}\) are the number of ground-truth and predicted people, respectively, in the \(i\)th image.  
\[MAE=\frac{1}{N}\sum_{i=1}^{N}\left|y_{i}-y_{i}^{\prime}\right| \tag{8}\]  
\[RMSE=\sqrt{\frac{1}{N}\sum_{i=1}^{N}\left(y_{i}-y_{i}^{\prime}\right)^{2}} \tag{9}\]  
The MAE shows the average of the absolute differences between the actual and predicted values. This metric only indicates the magnitude of the error; however, it does not consider false positives or negatives. The higher the MAE value, the worse is the scheme. Figure 11 shows the MAE of the four schemes. Our proposed Mask R-CNN + Tiles scheme had a relatively low MAE and it outperformed the MobileNet-SSD and YOLO v3 schemes for all five datasets. Further, it significantly outperformed the Mask R-CNN scheme for the reading room and YouTube-8M datasets.  
RMSE is a quadratic scoring rule that measures the error of a model in predicting quantitative data. Similar to the MAE metric, the higher the RMSE value, the worse is the scheme. Figure 12 shows the RMSE of the four schemes. Our proposed Mask R-CNN + Tiles scheme achieved the lowest RMSE value for all datasets except the PIROPO dataset, where the Mask R-CNN scheme had the lowest RMSE value and the Mask R-CNN + Tiles scheme had the second-lowest one. Further, the Mask R-CNN + Tiles scheme exhibited considerably lower RMSE values than the other schemes for the reading room and YouTube-8M datasets owing to the variety of activities, such as walking, standing, and sitting, presented in these two datasets. The variation in the object sizes and densities also contributed to this remarkable result; the higher the number of people, the better is the image resolution, which is the highlight of our proposed scheme.  
Figure 11: Mean absolute error of four schemes.  
Figure 13 illustrates the detection results of the proposed Mask R-CNN + Tiles scheme on five datasets. We observe that our proposed scheme achieved good detection results for all five datasets. The classroom, MICC people counting, and PIROPO datasets contain images with uniform or simple activities, therefore, the MAE and RMSE values are low in these datasets. While in the reading room and YouTube-8M datasets, the MAE and RMSE values are higher owing to the multiple activities presented in the images.
# Improved Likelihood Probability in MIMO Systems Using One-Bit ADCs
###### Abstract  
This study considers an improved likelihood probability in multi-input multi-output (MIMO) systems using one-bit analog-to-digital converters (ADCs). MIMO systems using one-bit ADCs are known to exhibit from performance degradation because of inaccurate likelihood probabilities. To overcome this degradation, the proposed method leverages the detected symbols to estimate the true likelihood probability by combining the initial likelihood probability. An optimization problem is formulated to minimize the mean-squared error between the true and combined likelihood probabilities, and a solution is derived using the least-squares method. Simulation results show that the proposed method obtains a signal-to-noise gain of approximately 0.3 dB to achieve a frame error rate of \(10^{-1}\) compared to conventional methods. This improvement in performance is attributed to the enhanced reliability of the likelihood probability.  
multi-input multi-output systems; one-bit analog-to-digital converters; maximum likelihood probability; weighted combining +
Footnote †: journal: Sensors
## 3 Results and Discussion
### Antinutritional Compounds  
Antinutrients are naturally occurring compounds in plant-based food that can interfere with nutrient absorption, as they bind with minerals in the body and prevent them from active metabolism [25]. Antinutritional compositions (tannin, phytate (phytic acid), and oxalate (oxalic acid)) of _Physalis peruviana_ L. fruit samples under different experimental conditions were analyzed, as shown in Table 3. The phytate contents in all dried conditions were higher than those of oxalate and tannin, whereas the oxalate contents were the lowest among the other antinutrients for all drying conditions. The estimated threshold oxalate toxicity in humans is 2-5 g/100 g [26]. There is no standardized safe tannin limit; however, tannin is a water-soluble polyphenol compound which can be reduced through soaking the plant material in water [27]. A tannin content of about 1.75 mg/100 g for this fruit was reported by [PERSON] et al. [5]. Their reported result is similar to the finding in this study. The presence of phytate in food decreases the bioavailability of divalent cations such as zinc, iron, and calcium in the digestion tract [28].  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Drying** & \multicolumn{4}{c}{**Minerals**} \\ \cline{2-6}
**Condition** & **K (mg/100 g)** & **Ca (mg/100 g)** & **Zn (mg/100 g)** & **Fe (mg/100 g)** & **Mg (mg/100 g)** \\ \hline Shade-dried & 313.66 \(\pm\) 0.05 a & 19.72 \(\pm\) 0.12 a & 0.27 \(\pm\) 0.02 a & 0.09 \(\pm\) 0.15 a & 17.67 \(\pm\) 0.03 a \\ Sun-dried & 204.88 \(\pm\) 0.41 a & 13.43 a & 0.15 \(\pm\) 0.18 a & 0.07 \(\pm\) 0.07 a & 12.38 \(\pm\) 0.24 a \\ Oven-dried & 91.75 \(\pm\) 0.01 b & 7.15 \(\pm\) 0.06 a & 0.11 \(\pm\) 0.05 a & 0.06 \(\pm\) 0.10 a & 8.09 \(\pm\) 0.74 b \\ \hline \hline \end{tabular}  
Means with the same superscript letters within the same column show no significant difference at \(p>0.05\). The values are reported in the form of the mean \(\pm\) SD taken from the triplicated trials.  
\end{table}
Table 2: Mineral contents in the fruit of _Physali peruviana_ L.
# Optimal Non-Integer Sliding Mode Control for Frequency Regulation in Stand-Alone Modern Power Grids
## 5 Overview of the Original SCA
### The Hybrid SCA and HS  
In spite the fact that the SCA has exhibited an efficient accuracy more often than other well-known heuristic methodologies, the native algorithm is not fitting for highly complex problems because of its poor exploration capability. To ameliorate the diversification of the standard SCA, the improvisation strategy used in HS is integrated into the SCA. In this way, a component of each search agent \(X=(x_{1},x_{2},\ldots,x_{D})\) is generated by using the SCA mechanism, with a probability of the harmony memory consideration rate (_HMCR_). While with the rate of \((1-HMCR)\), a new component is randomly generated within the range of [_LB UP_], where _LB_ and _UP_ are the lower and upper bounds of the search agent space. Moreover, with a probability of _HMCR_ multiplying the pitch adjustment rate (_PAR_), the surrounding space of a search agent is searched by the coefficient distance bandwidth (_bw_). To guarantee a quick convergence and to guarantee the quality of the search agents, the design coefficients of the SCA-HS algorithm (_PAR_, _bw_) are dynamically updated during the evaluation procedure [35], given as:  
\[PAR_{t}=PAR_{min}+(PAR_{max}-PAR_{min})\times t/T_{max} \tag{28}\]  
\[bw_{t}=bw_{max}e^{(\frac{\ln(\frac{bw_{min}}{T_{max}})}{T_{max}})} \tag{29}\]  
where \(PAR_{min}\) and \(PAR_{max}\) are the minimum and maximum pitch adjustment, respectively. Likewise, \(bw_{min}\) and \(bw_{max}\) are the minimum and maximum bandwidths, respectively.  
The computational procedure of combination of the SCA with HS is depicted in Figure 4.  
Figure 4: The pseudo-code of the proposed optimization algorithm.  
The evolutionary algorithms (e.g., genetic, firefly, cuckoo search, etc.) merely require information about the objective function. For online setting of the controller coefficients, a proper objective function should be defined for the guidance of its search of heuristic methodologies. In this study, the objective function of Equation (30) is adopted to adjust optimally the coefficients embedded in the FOSMC controller optimally.  
\[J=\int_{0}^{\infty}t.\ e_{set-point}^{2}(t)+\Delta u^{2}(t).dt \tag{30}\]  
where \(e_{set-point}\) is the error signal and \(\Delta u\) is the control signal.