esa-sceva/satcom-chunk-collection huggingface dataset:
# Artificial Intelligence, Sensors and Vital Health Signs: A Review
## 4 Internet of Things (IoT) for Healthcare Delivery
### Monitoring of physiological parameters  
In recent years, quite a lot of approaches to vital health signs monitoring as key parameters to analyze patient health has been reported. In [50], the system was able to monitor the heart rate and activity recognition using wireless sensor network technology. The system is capable of communicating with medical professionals in the event of anomalies such as falls, tachycardia, or bradycardia through mobile devices. In a similar approach, [51] developed a system for monitoring and tracking patient activities with medical issues. The work of [52] investigates the application of the Blue-mix cloud technique to archive vital signs measured data, enabling remote use by doctors and presenting the vital health sign results via IBM Watson. Similarly, in [53], a real-time IoT-based ECG telemetry system is proposed. The study presented a high-performance quality assessment algorithm implemented on Android mobile devices. The study also showed the effectiveness of the approach under distinct physical actions. Motionless monitoring can influence the use of field sensors that enable the gathering of contextual data for action recognition. An exciting study is proposed in [54] where environmental sensors, an optical-track camera, and smartwatch-embedded IoT-based sensors are used to gather motion, video, and audio signals data conjointly with a specialized wearable for vital parameters measurement.
# UAV Attitude Angle Measurement Method Based on Magnetometer-Satellite Positioning System
## 3 Magnetometer Calibration Based on Least-Squares Fitting Algorithm
### Magnetometer Calibration  
The ellipsoid parameters obtained by fitting are used to calibrate the data measured by the magnetometer. First, we calibrate the origin of the ellipsoid coordinates as:  
\[h_{bt}=h_{b}-C \tag{14}\]  
After this correction, the measurement data are distributed in an ellipsoid with the coordinate origin as the center of the sphere, which also eliminates the zero-point error.  
Simultaneously, to eliminate the difference in the sensitivity of each axis, spherical calibration is required. When performing spherical calibration, a reference axis must be determined first. In the magnetometer-satellite measurement of the UAV attitude angle, the \(Y\)-axis is more important. Therefore, the \(Y\)-axis can be selected as the reference for spherical correction, namely:  
\[h_{b}=y_{scale}*h_{bt}*[x_{scale}\quad y_{scale}\quad z_{scale}] \tag{15}\]
## 4 Interference Analysis
### Mathematical Models for Interference Calculation  
This subsection details the mathematical models employed in our analysis. We will present the formulations used to calculate the power ratios of various interference types, including adjacent satellite interference and, most importantly, the interference originating from MVDS side lobes.
# A Photonic Label-Free Biosensor to Detect _Salmonella_ spp. in Fresh Vegetables and Meat
## 2 Materials and Methods
### Microbiological Enumeration  
Culture Method  
_Salmonella_ spp. quantification in food samples adhered to the guidelines provided by UNE-EN ISO 6579-1:2017/A1:2021 [33]. This standard specifies a horizontal method for the detection, enumeration, and serotyping of _Salmonella_. A Xylose Lysine Deoxycholate agar (XLD agar) medium was used as a key component in the process. Enumeration was conducted following 48 h of incubation at 37 (\(\pm\)1) \({}^{\circ}\)C.  
_Salmonella_ spp. quantification in food samples followed the guidelines provided by EN ISO UNE-6579-1:2017/A1:2021. This standard specifies a horizontal method for the detection, enumeration, and serotyping of _Salmonella_. In accordance with the ISO protocol, 25 g of each meat or vegetable sample was weighed and placed in a sterile bag. Subsequently, 225 mL of buffered peptone water was added to the bag. The contents were shaken for one minute using a Stomacher and then incubated at 37 \({}^{\circ}\)C for 18 h. After the incubation period, a 0.1 mL aliquot of the primary enrichment was transferred into 10 mL of Rappaport Vassiliadis medium with soya (RVS), and 1.0 mL was transferred into 9 mL of Muller-Kauffmann tetridionate-novobicin (MKTTn) broth. The RVS broth and the MKTTn broth were incubated at 41.56 \({}^{\circ}\)C for 24 h and at 37 \({}^{\circ}\)C for 24 h, respectively. From each secondary enrichment, a loopful was streaked onto the XLD agar plates.  
Following incubation, different concentrations of previously obtained pure culture (ranging from 10 CFU/mL to 10\({}^{8}\) CFU/mL) of _Salmonella enterica Typhimurium_ and _Salmonella enterica Enteritidis_ were added to fresh chicken and Batavia samples. The food samples were subsequently incubated for varying durations of 3, 6, and 24 h, at a temperature of 37 (\(\pm\)1) \({}^{\circ}\)C. After the incubation period, the first inoculum was quantified by performing decimal dilutions of buffered peptone water. Each experimental procedure was conducted in duplicate to enhance the reliability and consistency of the findings. The samples at each enrichment stage were plated on a selective agar medium (XLD agar). These agar plates were placed in an incubator at 37 \({}^{\circ}\)C for a specific duration ranging from 3 to 24 h. After the incubation period, the plates were carefully examined for the presence of characteristic _Salmonella_ colonies. This examination enabled enumeration and identification of _Salmonella_ spp. based on the distinct colony characteristics.  
Similar enumeration and identification of _Salmonella_ spp. on culture plates were performed for fresh salad and fresh chicken naturally contaminated with the pathogen. These samples were diluted 1:10 to achieve concentrations similar to those in the spiked samples of fresh chicken and red Batavia (ranging from 10 to 10\({}^{10}\) CFU/mL).
## 3 Results and Discussion
As shown in Figure 5, permeability changes due to the creation of perforation channels and subsequent transformation of the stress distribution. We can conclude that even with the initial pressure, highly stressed zones arise on the lateral surface of the perforation due to the influence of the vertical load from the overlying rock stratum. In such zones,  
Figure 7: Dependence of the normalized well productivity index on the differential pressure drawdown. the permeability immediately decreases by about 15%. Moreover, with an increase in differential pressure drawdown to 9 MPa, the decrease in permeability can reach 25% of the initial value. On the contrary, in the upper and lower parts of the perforation channels, permeability can be restored by reducing the magnitude of stresses. Thus, when we create perforations, the permeability increases by 20% of the initial value without pressure drawdown. With an increase in pressure drawdown, the zone of decreased stresses becomes smaller, and the maximum increase in permeability reaches 10-15%. Figure 6 reveals the possible areas of rock destruction near the perforation channel. The compressive stress areas are marked in a red color, while tensile stress areas are highlighted in green. As shown in the figure, the compressive stress area lies in parallel to the tensile stress area, which extends along the lateral surface of the perforation. It is important to note that both kinds of destruction areas are close to each other, because we applied the effective stresses when using the Mohr-Coulomb criterion. It means that the pressure value, multiplied by the Biot coefficient (Ratio (8)), was subtracted from the total stresses. When we increase the pressure drawdown, the tensile stress areas decrease because the effective stresses grow. With a pressure drawdown of 9 MPa, these zones completely disappear, while compressive stress areas can increase. Furthermore, we assessed the dependence of the normalized productivity index on differential pressure drawdown, as shown in Figure 7. Although we consider a well section with a small thickness, it is possible to predict a change in the well productivity index caused by a decrease in permeability and an increase in pressure drawdown. This result is a consequence of the change in permeability under the influence of effective stresses shown in Figure 2:0 As shown in Figure 7, a decrease in the well productivity index can reach 15% with a maximum pressure drawdown of 12 MPa. This change indicates the need to optimize well production.
# Auditory Localization in Low-Bitrate Compressed Ambisonic Scenes
## 5 Discussion
The 5 th-order diffused sound scene was added to investigate if the spatial distortion of the single sound source presentation within the scene will be affected by feeding additional non-directional information to each of the encoded channels. This condition was supposed to mimic a recording done with an Ambsonic microphone, although maintaining the highest possible spatial resolution of the single sound source by synthesizing the Ambsonic sound field. The impact of the sound scene complexity on the localization error has not been revealed. The chosen data collection method might have contributed to a high variance in the participants' overall localization performance. The average duration of the test session was about 45 minutes with a single break in between. Some of the participants reported a mild psycho-physical fatigue after the experiment which suggests that responses collected using a less challenging test methodology could give more consistent results across participants. As the participants' heads were not constrained and the head movements were not recorded by the optical tracking system, it is unknown to what degree the small head rotations also contributed to the measured localization performance. Another factor affecting localization precision in the experiment is the acoustic pointer response collection method. It is possible that the auditory localization precision measured using the acoustic pointing method gives higher error estimates than the source discrimination methods used for the MAA measurements, which is focused more on the change of the perceived acoustic signal rather than spatial analysis of the sound field [48]. However, once the virtual pointer and target directions are perceptually matched, participants might compare both signals using other features than perceived spatial locations. The degree in which the presented mechanism has contributed to the experimental results remains unknown. Further studies should consider different response collection techniques adequate to the proposed application of the coding system. The continuation of research will investigate the development of efficient indirect perceptual evaluation methods for the assessment of binaural-based spatial audio systems, including bitrate compression schemes. We will also investigate localization accuracy and precision for frontal presentations in more depth given the importance of this region for immersive content consumption in VR and AR as well as teleconferencing services.
## 3 Results
### Taxonomy  
Family Aphididae Latreille, 1802  
Figure 2: Phylogenetic tree showing the phylogenetic position of _Takecallis nigroantennatus_ sp. nov. among _Takecallis_ species, inferred from maximum likelihood (ML) and Bayesian Interference (BI) analysis based on elongation factor 1 (EF1\(\alpha\)) gene sequences. The first number on the branch is posterior probabilities from MrBayes; the second, SH-aLRT support (%); and third, ultrafast bootstrap support (%) from IQ-TREE. Scale bar: expected substitutions per site.  
Subfamily Calaphidinae Oestlund, 1919  
Tribe Therioaphidini Borner, 1944  
Genus _Takcallis_ Mastumura, 1917  
Species _Takcallis nigroantennatus_ Wieczorek sp. nov.  
Materials examined: 28 adult viviparous females and 14 alatoid nymphs (collected in the type locality, Poland); specimens were mounted on microscope slides (20 adult viviparous females + 6 alatoid nymphs) and processed for DNA sequencing (8 + 8). Additional fresh material for comparative morphological study was collected by the first author on 4 May 2023 in the Meise Botanic Garden, Belgium: 10 adult viviparous females of _T. arundiclens_ from _Pseudosasa japonica_ ([PERSON] and [PERSON] ex [PERSON].) [PERSON] ex [PERSON]; 10 adult viviparous females of _T. arundinariae_ from _Sasa meagolphyllla_ [PERSON] and [PERSON]; and five adult viviparous females of _T. nigroantennatus_ sp. nov. from _Fargesia initita_ x _muirelae_.  
Type of locality and host plant: 54\({}^{\circ}\)27\({}^{\circ}\)52.19\({}^{\prime\prime}\) N, 17\({}^{\circ}\)02\({}^{\prime}\)01.55\({}^{\prime\prime}\) ES; Supusk, Poland; coll. from _Fargesia rufa_ T.P.Yi on 9 July, 1 August, and 3 September 2022 by [PERSON].  
Type of repository: Holotype (Supusk, Poland, _Fargesia rufa_ 1 August 2022, leg. [PERSON], slide DZUS 1/8.22_7) and three paratypes (slides DZUS 9/7.22_8, DZUS 1/8.22_9, DZUS 3/10.22_10 same data as holotype) were deposited in the Entomology Collection of the University of Silesia in Katowice, Poland (DZUS).  
Etymology: The species name refers to the antennae black, except for the antennal segments I-II, and the very base of segment III, clearly visible in live, fresh fixed, and slide-mounted adult viviparous females.
## 2 Results and Discussion
### Electrochemical Corrosion Measurement
#### 2.3.1 Potentiodynamic Polarization (PDP) Measurement
The IE % values are listed in Table 2. The corrosion inhibition efficiency of the coated CRS was higher and increased as the length of the chain extender increased (A to C). The CRS coated with WPU-g-BTA-C showed extremely higher corrosion inhibition efficiency (\(99.972\%\)). In the present study, the order of inhibition efficiency was found to be  
\[\mathrm{WPU-g-BTA-A<WPU-g-BTA-B<WPU-g-BTA-C}\]  
Long Term Anti-Corrosion Study (3 Days)  
In Figure 4 and Table 3, it can be seen that, compared with the bare CRS, self-corrosion potentials of the samples, which were covered with organic coating, shifted more significantly to the right and their corrosion currents also had a downward trend. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multicolumn{2}{c}{**Sample Code**} & \multicolumn{1}{c}{**CRS**} & \multicolumn{1}{c}{**WPU-g-BTA-A**} & \multicolumn{1}{c}{**WPU-g-BTA-B**} & \multicolumn{1}{c}{**WPU-g-BTA-C**} \\ \hline
**Coating thickness** & (\(\mu\)m) & 60 \(\pm\) 2 & 60 \(\pm\) 2 & 60 \(\pm\) 2 & 60 \(\pm\) 2 \\ \hline \multirow{3}{*}{
\begin{tabular}{c} **Electrochemical** \\ **parameters** \\ \end{tabular} } & Ecorr (V) & \(-0.81121\) & \(-0.3311\) & \(-0.25208\) & \(-0.19831\) \\ \cline{2-6}  & icorr (\(\mu\)A/cm\({}^{2}\)) & 71.773 & 0.079 & 0.068 & 0.020 \\ \cline{2-6}  & IE \% & - & 99.889 & 99.905 & 99.972 \\ \hline \multirow{3}{*}{**EIS (Nyquist)**} & \(Z^{\prime}\) (k\(\Omega\)) & 0.16 & 978.84 & 1552.69 & 2055.23 \\ \cline{2-6}  & -Z\({}^{\prime\prime}\) (k\(\Omega\)) & 0.09 & 346.95 & 462.90 & 582.83 \\ \hline
**BODE** & log(Z) & 2.22 & 5.99 & 6.21 & 6.33 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Electrochemical parameters, the corresponding inhibition efficiency (IE %), EIS, and BODE results for raw CRS and CRS electrode coated with WPU-g-BTA-A, WPU-g-BTA-B, and WPU-g-BTA-C in 3.5 wt% NaCl (\({}_{\text{aq}}\)) at 298 K.  
The corrosion potentials of the samples shifted from \(-\)0.634 V for the bare steel to \(-\)0.633 V for WPVU\(-\)g\(-\)BTA\(-\)A, \(-\)0.576 V for WPVU\(-\)g\(-\)BTA\(-\)B, and \(-\)0.550 V for WPVU\(-\)g\(-\)BTA\(-\)C. The corrosion current (icorr) decreased from 4.98 uA.cm\({}^{-2}\) for CRS to 4.17 uA.cm\({}^{-2}\) for WPVU\(-\)g\(-\)BTA\(-\)A, 4.08 uA.cm\({}^{-2}\) for WPVU\(-\)g\(-\)BTA\(-\)B, and 3.41 uA.cm\({}^{-2}\) for WPVU\(-\)g\(-\)BTA\(-\)C, where WPVU\(-\)g\(-\)BTA\(-\)C had the smallest corrosion current. Further, it is also noted that the CRS exhibited Rp value of 2.587 k\(\Omega\) whereas the Rp value for WPVU\(-\)g\(-\)BTA\(-\)C coated CRS is 3.127 k\(\Omega\). This increase in Rp value clearly indicates that the coating acts as a physical barrier and appreciably resists the corrosion of the CRS from corrosion medium. Moreover, the highest corrosion protection efficiency (P\({}_{\rm EF}\)) % achieved for WPVU\(-\)g\(-\)BTA\(-\)C coating was 31.56% after 3 days' immersion in 3.5 wt% NaCl (aq).
## 4 Experimental Results and Analysis
### Experimental Design  
Experiment 1 verified the effectiveness of the data enhancement method proposed by the model in this paper.  
Experiment 2 verified whether the parameter selection in this paper was reasonable.  
Experiment 3 verified the effectiveness of the proposed model in this paper, and compared the classification performance with Bi-LSTM [7], Bi-GRU [9], GRNN [21], IoMET, and BERT-Base [22]--provided by Google as reference models. The hypernatremia settings of each model are shown in Tables 4 and 5.
## 5 Example
### Introducing the Networking Layer
In this table, the data sent to _B_\({}^{\prime}\) will also be available to \(J\), \(B\), \(D\), \(K\), and _K_\({}^{\prime}\), and the data sent to _G_\({}^{\prime}\) will also be available to \(G\), \(K\), and _K_\({}^{\prime}\). On the other hand, it is easy to check that no new flows have been added. Figure 9: An implementation configuration for Figure 4.  
Clearly, these transformations can be implemented by simple algorithms. The final configuration of Figure 9 seems to have no relation to the partial order of equivalence classes we started from (Figure 8), the only similarity being the fact that the sensors are at the bottom layer in both. However, according to the contents of the routing tables, the data flows between entities are the same, although not along the same paths. This means the first-given policies of secrecy, integrity, and conflict are properly implemented. For example:  
1. In Figure 3, we have a flow \(H{\rightarrow}A{\rightarrow}C{\rightarrow}K\). By looking at Figures 7-9, we see that the data of \(H\) can go to \(A\), \(C\), and \(K\) through the _Cloud Router_ and server \(A^{\prime}\). 2. Similarly, in Figure 3, we have the flow \(J{\rightarrow}G{\rightarrow}K\). In the cloud configuration data can go from \(J\) to \(G\) through \(G^{\prime}\) and from \(J\) to \(K\) through \(B^{\prime}\) and \(G^{\prime}\). 3. On the other hand, unwanted flows are clearly impossible. For example, the reader can check easily that there is no way for data in \(H\) to end up in \(D\) or data in \(C\) to end up in \(D\). Hence, conflict requirements are satisfied. We see that our solution uses multicasting, through which the same data can arrive at its destination through multiple paths. Multicasting is a well-known technique in networks, and methods exist to eliminate duplicate packets received from several sources. It makes our solution fault-tolerant to some extent, e.g., in the second example, the failure of one of \(B^{\prime}\) or \(G^{\prime}\) will not affect the path \(J{\rightarrow}K\). Clearly, this example can be scaled up by introducing many more sensors, many more wards, many more workstations, etc. To make this possible, the entities would have to be parameterized or indexed, such as _PulseDetect1_, _PulseDetect2_, etc. Evidently, real-life networks will not be able to be shown in the simple graphic format used here. Graphic interfaces, showing high-level representations that can be manipulated by administrators, should be the subject of future research.
## 1 Introduction
### Paper Contribution  
The contributions of this paper are twofold. First, a decentralized control scheme based on IADRC configuration was proposed to deal with the input couplings and subsystem couplings, where they are all refined into the generalized disturbance. This generalized disturbance was reflected in the output signal, and a novel ESO was designed to measure the generalized disturbance. Second, in the proposed decentralized control scheme, the generalized disturbance was estimated from the output signal through a nonlinear higher order ESO (NHOESO), which is different from the conventional ESOs due to two main features: (1) possessing a nonlinear error-correcting term to satisfy the rule, \"big error,small gain or small error, big gain\", (2) having one more augmented state for better estimation of the generalized disturbances, specifically with higher-order derivative disturbances.
## 4 Simulation and Results Discussions
### Comparison with Other Studies  
Table 4 gives a comparative analysis based on accuracy levels achieved in some recent research studies on malware detection, a topic closely related to the topic of our research work. We see that [PERSON] et al. [41] achieved an accuracy level of 95%, while [PERSON] et al. [42] achieved a slightly higher level of 96.6%. These findings point toward excellent performance levels in solving present-day cybersecurity issues related to malware. It is important to note that although the studies listed in Table 4 operate in different domains such as IoT device data and general-purpose malware detection, they have a common goal of improving security against malware threats by using deep learning models. These works vary in terms of dataset types, model architectures, and evaluation settings; however, they  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Metrics** & **ANN** & **CNN** & **GAT** \\ \hline Accuracy & 97.60\% & 80\% & 96.84\% \\ Precision, recall, & High value after & High value after & High value after \\ and F1-score & 20 epochs & 80 epochs & 20 epochs \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of models outputs.  
all report performance outcomes using standard classification metrics, particularly accuracy. In this comparison, we focus on the accuracy metric as a common point of reference to position our approach.  
Hence, our suggested methodology, which combines blockchain technology with deep learning architectures to enhance the security of intelligent systems, achieved a remarkable accuracy rating of 97.6%. This not only outperforms what has been reported in the above-discussed studies, but it also proves the efficacy and resilience of our hybrid approach. Through an integration of the complementing capabilities of blockchain and deep learning, our model proves to excel in improving security and enhancing optimizing performances within complex smart environments.