esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Coherent FFLs
### C1-FFL: A Sign-Sensitive Delay Device
#### 3.1.3 Filtering Out Spurious Signals  
C1-FFLs--and, potentially, all types of coherent FFLs--act as sign-sensitive delay circuits. Delays are important because they permit to select the essential signals (filtering) to the FFL working. Signal filtering is asymmetric since it depends on the logic of the FFL and the sign of the inputs. Only persistent (ON sign) stimuli (i.e., longer that TON--the initial time needed by protein Y to build up and activate gene Z) can produce effects on an AND logic gate. In contrast, a delay in the input OFF step permits the OR-gate C1-FFL to maintain Z expression even in front of a sudden loss of one input (as observed in the _E. coli_ flagella system) [6]. The FFL dynamics of protein expression in the presence or absence of the inducers (Sx and Sy) explains the mechanism of filtering out short and redundant noises  
Figure 3: Natural and synthetic AND logic C1-FFL in _E. coli._ (**A**) The bacterial arabinose utilization system requires a second inducer, Sy (L-arabinose), beside the main one, Sx (cAMP). Moreover, Z consists of two targets—the two operons. (**B**) The synthetic C1-FFL engineered by [PERSON] et al. [38]. X (the RNA trigger) removes, first, the toehold switch from Y mRNA such that \(\sigma^{28}\) is produced. Then, X also removes the toehold switch from the green fluorescent protein (GFF) mRNA, which has been transcribed with the help of the \(\sigma^{28}\) factor. In this way, GFP can be finally synthesized.  
(see Figures 2 and 4). [PERSON] et al. [40] confirmed that the AND logic C1-FFL regulation emerges adaptively by filtering short spurious signals. Therefore, the configuration of three node genes with three edges and a logic gates gave the FFLs the capability to \"learn\" that short, fluctuating input signals shall be cleared out to keep a proper working [6].
## 3 Results and Discussion
### Toxicity Analysis
After 18 h of irradiation without PVDF, product X (see Figure 6a in Section 3.2.1.) was found with a proportion of 96% regarding all detected molecules via GC-MS. Only 38% of the phototransformation product X was found when PVDF was present during irradiation with UVA light. The amount of product X in the solution after UVA light treatment with PVDF is reduced, thus the toxicity decrease might be correlated to its enhanced removal. The treatment of diclofenac with UVA light leads to an increase of toxicity regardless of the presence of PVDF within the first 90 min. After 18 h of irradiation the diclofenac solution with PVDF shows almost no toxic effect towards _Vibrio fischeri_ anymore, while the toxicity is doubled without PVDF. The addition of PVDF during diclofenac UVA irradiation leads to a reduction of toxicity.
## 3 Methodology
### Cross-Validation  
The process of deciding whether the numerical results quantified by different interpolators are acceptable as descriptions of the data, is known as cross-validation, which tests the model's capability to predict data. Performing the cross-validation, some of the data are removed before training begins. Then when training is done, the data that were removed can be used to test the performance of the learned model on \"new\" data. The actual error incurred in this process is measured by the difference between the actual and estimated value [58; 59; 60].  
The accuracy of calculation was measured by the mean absolute error (MAE) and the root-mean-square error (RMSE). The MAE and RMSE are calculated for the data set as:  
\[MAE=\frac{1}{n}\sum_{i=1}^{n}\lvert e_{i}\rvert, \tag{6}\]  
\[RMSE=\sqrt{\frac{1}{n}\sum_{i=1}^{n}e_{i}^{2}}, \tag{7}\]  
where \(\lvert e_{i}\rvert=\lvert y_{i}-g_{i}\rvert\), actual = \(y_{i}\), and predicted = \(g_{i}\).  
The closer the values of RMSE are to zero the more accurate the model results will be.  
The RMSE value cannot be considered a performance criterion to define the reliability of the kriging estimate [12], even if it is used in all geostatistical methods. Many studies regarding spatial interpolation of rainfall [34], evaluation of the best interpolation methods for mapping climatic variables at regional scale [61], and spatial distribution of rainfall [62]have performed the MAE and RMSE calculation in order to compare the results. A graphical result of the goodness of fitting actual and estimated values is given by the scatterplot: the closer its slope is 1, the more accurate the estimation is.
# A Novel Rolling Bearing Fault Diagnosis Method Based on MFO-Optimized VMD and DE-OSELM
## 4 VMD Optimization Based on the Improved MFO
### Simulated Analysis
The effective component IMF2, after being screened, underwent an envelope demodulation analysis and yielded an envelope spectrum displayed in Figure 5. The extracted frequency components included the conversion frequency (\(f_{r}\)), the fault characteristic frequency (\(f_{i}\)), and its double frequency (\(2f_{i}\)). These results demonstrate the successful application of the VMD optimization method proposed in this paper, in combination with EWCS index screening, for effectively decomposing the simulation signal \(x(t)\). To verify the superiority of the proposed method, we employed a VMD decomposition technique with fixed parameters of <5, 2000> and identified effective components as IMF3, IMF4, and IMF5 based on the EWCS (Method 1). Additionally, we utilized the screening method based on correlation coefficient and kurtosis value, presented in reference [43], along with a <3, 773> VMD decomposition, and we identified IMF1 and IMF2 as effective components (Method 2). Subsequently, we reconstructed and analyzed the selected effective components using envelope demodulation, and the outcomes are illustrated in Figure 6. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Index** & **IMF1** & **IMF2** & **IMF3** \\ \hline PSNR & 52.0958 & 52.8020 & 52.2489 \\ MSE & 0.4013 & 0.3411 & 0.3874 \\ EWCS & \(-\)0.0096 & 0.0276 & \(-\)0.0180 \\ Effective IMFs & & \(\surd\) & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The selection of effective IMF components. Figure 5: The effective component envelope spectrum. Figure 6: (**a**) The envelope spectrum by method-1. (**b**) The envelope spectrum by method-2. Figure 6 indicates that Method 1 and Method 2 were unable to extract \(f_{r}\) and \(f_{i}\). By contrast, the VMD optimization method and EWCS index utilized in the proposed method were successful in extracting signal components that carried the information on system faults. This finding reinforces the effectiveness of the VMD optimization method and the EWCS index in fault feature extraction.
# Future Industrial Applications: Exploring LPWAN-Driven IoT Protocols
## 3 Wireless Protocols Architecture
### RedCap  
5G consists of two radio technologies: the older LTE and the novel radio interface technology named New Radio (NR) [56]. While NB-IoT and LTE-M work on the modified LTE-based architecture, in Release 17 3 GPP focused on the IoT use criteria by reducing the existing capabilities of the NR architecture to meet the requirements falling between more extreme requirements defined by mMTC, URLLC, and eMBB [22]. Thus, we were introduced to NR-Lite, which later became named RedCap. Some of the reasons to look into RedCap, although NB-IoT and LTE-M offer cellular IoT solutions, are better system efficiency than LTE due to beam-formed operation, higher subcarrier spacing for latency reduction, massive MIMO coverage, mixed numerology, higher positioning accuracy, and low-overhead carriers. Deployment in the Frequency Range 2 (FR2) spectrum can be very useful for private networks due to its limited range and high spatial reuse. Also, the 5G core and architecture (Figure 4) offer network slicing, service-based architecture, and flow-based QoS [57]. RedCap can become a successor to NB-IoT, offering lower latency and higher reliability due to its 5G backbone [58]. RedCap uses Quadrature Amplitude Modulation schemes such as 256 QAM and 64 QAM to modulate the bits [22; 59].  
Figure 4: Network architecture for IoT protocol based on 3 GPP licensed spectrum 5G NR based RedCap.
###### Abstract  
Moderate to severe (M2S) atopic dermatitis (AD) is a chronic condition impacting individuals, society, and healthcare systems. Considering the changing M2S-AD treatment landscape, this study assesses the M2S-AD burden in patients reaching referral centers in Greece.  
This was a multicenter, cross-sectional study. Patients aged 12 years or older with clinically diagnosed M2S-AD were enrolled. Data collected included clinical practice assessments and the following validated patient-reported instruments: Dermatology Life Quality Index (DLOI); EuroQol-5 Dimensions-3 Level scale (EQ-5D-3L); Patient Oriented Eczema Measure (POEM); Peak Pruritus Numerical Rating Scale (PP-NRS); and Work Productivity and Activity Impairment: General Health (WPAI:GH). A pain frequency/intensity/cause questionnaire and a sleep disturbance scale were also used. **Results**: Outcomes of 184 adults (51.1% female) with M2S-AD based on the Eczema Area and Severity Index (EASI) are presented (n = 117 moderate; n = 67 severe). Among the patients, 14.8% were obese, 59.2% had allergic comorbidities, and 88.0% were receiving AD-specific therapy (systemic 38.6%). The median age, disease duration, body surface area, and total EASI scores were 38.8 years, 11.8 years, 30.0%, and 16.9, respectively. The median DLOI score was 12.0, with'symptons/feelings' being the most affected domain. EQ-5D dimensions 'anxiety/depression' and 'pain/discomfort' were also affected (65.2% and 64.1% reporting problems, respectively). The median POEM score was 17.0. Pain, severe pruritus (PP-NRS \(\geq\) 7), and sleep disturbance were reported by 80.4%, 62.0%, and 88.5%, respectively. The median WPAI:GH 'work productivity loss' and 'activity impairment' scores were 23.8% and 30.0%, respectively. **Conclusions**: Both moderate and severe AD patients reaching Greek specialized centers experience significant symptom burden and impairments in quality of life, sleep, work, and daily activities.  
**Keywords: atopic dermatitis; cross-sectional; DLQI; EASI; HCRU; work impairment**
## 4 Experimental Analysis
### Datasets
#### 4.1.2 CHB-MIT EEG Dataset  
The CHB-MIT dataset is an openly accessible database [50] containing EEG recordings of epilepsy patients that were recorded using surface EEG electrodes at Children's Hospital Boston (CHB-MIT) and provided by the Massachusetts Institute of Technology [51,52]. The dataset contains 23 cases collected from 22 pediatric patients suffering from intractable seizures, of whom 18 are female and 5 are male, with ages ranging from 1.5 to 22 years old. A total of 686 EEG data records are collected, of which 198 contain seizure events. The dataset has been recorded with a sampling frequency of 256 Hz and contains 23 or more EEG channels. Figure 4 shows a snapshot of 5 s of raw data in 23 EEG channels in the CHB-MIT dataset.
## 2 Experimental Procedures
### Contact Angle and Surface Energy Determination  
A computer-controlled KSV 101 optical contact angle meter (CAM) (KRUSS GmbH, Hamburg, Germany) was used to determine the contact angle and surface energy of the 5 hemp/PCL biocomposite samples. It captured and analysed video images automatically in order to measure the dynamic or static contact angles, the surface tension of liquids, surface free energies and absorption of liquids. Both water and glycerol were used as the test fluids or liquid probes, with known polar and dispersive components of surface tension. The thermodynamics of solid-liquid interactions are analysed from the measurements of contact angle data obtained. From [PERSON]'s equation [42], solid surface tension from contact angle is obtained in Equation (6):  
\[\gamma_{s} =\gamma_{l}\mathcal{C}os\theta+\gamma_{sl} \tag{6}\]  
where \(\theta\) represents the probe liquid contact angle, \(\gamma_{s}\) and \(\gamma_{l}\) denote the solid and liquid surface tensions or free energy respectively and \(\gamma_{sl}\) indicates the solid-liquid interfacial free energy. \(\theta\) and \(\gamma_{l}\) are the only measurable quantities in [PERSON]'s equation. An additional relationship known as geometric mean is required to obtain both \(\gamma_{s}\) and \(\gamma_{sl}\). This relationship is obtained by combining [PERSON]'s equations using [PERSON]'s proposed model or approach [35; 43; 44]:  
\[\gamma_{l}(1+\mathcal{C}os\theta)=2\left[\sqrt{(\gamma_{l}p*\gamma_{s}p)}+ \sqrt{(\gamma_{l}^{d}*\gamma_{s}{}^{d})}\right] \tag{7}\]  
where \(\gamma_{l}p\) and \(\gamma_{s}{}^{p}\) represent the liquid and solid polar surface energy, respectively and \(\gamma_{l}^{d}\) and \(\gamma_{s}{}^{d}\) liquid and solid dispersive surface energies, respectively. The total surface energy is the addition of the polar and dispersive surface tensions or energies. Therefore, Equation (7) was used to determine both two components (dispersive and polar) of the total surface free energy of all the 5 biocomposite samples. This was based on the measurements from the contact angle of 20 drops averaged for each of the liquids, using KSV contact angle meter (CAM) surface free energy (SFE) software. Similarly, the solid surface was characterised by determining free surface energy.
# Energy-Aware 3D Path Planning by Autonomous Ground Vehicle in Wireless Sensor Networks
## 2 Related Work
It is expected that under these circumstances, autonomous ground vehicles will be able to perform more tasks more efficiently while using less electricity. While most studies have created an effective methodology for designing dynamically feasible and energy-efficient trajectories for skid steering or differential steering vehicles, there are few studies known on path planning for Ackermann steering autonomous ground vehicles. The paper [36] presents a completeness-guaranteed, energy-efficient path planning technique for autonomous ground vehicles with Ackermann steering, based on the \(A^{*}\) search algorithm. The energy cost model of the autonomous ground vehicle is initially built using its kinematic constraints. Next, given the start and target states, the energy cost model is utilized to build the energy-aware motion primitives offline and calculate the cost of each primary trajectory. Lastly, a completeness property analysis is presented along with a proposal for an energy-efficient path planner. The effectiveness of the proposed energy-efficient journey planner is validated by means of simulation on over 150 randomly generated maps and real car testing. The results show that a small departure from the distance-optimal path can save energy expenses by around 26.9% in simulation and 21.09% in an actual test scenario for autonomous ground vehicles with Ackermann steering. In the paper [37], the Nearest Neighbour algorithm is used to solve the energy-aware path planning problem associated with an Autonomous Underwater Vehicle (AUV). The simulations indicate that the Nearest Neighbour approach achieves faster convergence and produces superior solutions in comparison to the Genetic Algorithm and Grey Wolf Optimizer algorithm. They facilitate rapid gathering of sensor data while minimising energy usage. The paper [38] employs the closest neighbour technique to solve the energy-aware path planning problem of an autonomous underwater vehicle. Path planning simulations indicate that the Nearest Neighbour technique achieves faster convergence and produces superior results when compared to the genetic algorithm. They successfully collect sensor data while minimising energy usage. This allows the monitoring system to quickly discover and respond to anomalies. This study also considers scenarios in which it is recommended to avoid selecting direct travel routes between certain pairs of nodes for various reasons. A modified Nearest Neighbour-based approach is proposed to address this challenging scenario. The direct connections between these nodes are restricted in situations where the modified version of the Nearest Neighbour-based technique shows significant effectiveness. Most papers dealing the energy-aware path planning problem with an AGV consider the problem in a 2D environment. On the other hand, the vehicles can face mounds or even small hills, which can even cause altitude difference. Therefore, we consider the energy-aware path planning problem with an AGV in a 3D environment.
# Application of Machine Learning in the Identification and Prediction of Maritime Accident Factors
## 2 Materials and Methods
### Clustering
#### 3.3.1 Clusters 1 and 2  
As we can see, cluster 1 is the one with the highest number of accidents associated. It represents the cluster number versus the two main variables like complying with the minimum crew members and ship dimensions (beam) or ship type or year of construction (Figures 6-8). From Figure 6, it can be concluded that cluster 2 is related to the accidents that do not meet the minimum crew members (2) associated with accidents from 4 to 10. In consequence, it can be concluded that tugboats, yachts and recreational vessels are too often undermanned. What is more, Figure 7 shows that clusters 1 and 2 are associated with capsizing accidents (6) of ship types tugboats and auxiliary boats of the port service, commercial yachts and recreational boats. What is more, it is associated with the lowest beam dimensions (Figure 8) and the recent year of construction (Figure 9).
## 5 Conclusions
* An optimal seismic separation gap distance \(\left(\mathrm{d_{g,min}^{P_{t}}}\right)\) based only on the acceptable POs-\(\mathrm{\delta_{max}}\) is not always a safe prediction at a target \(\mathrm{P_{t}}\). * The minimum acceptable level of bounding risk \(\mathrm{P_{min}^{d_{g,t}}}\) of the eight-story RC frame is decreased when the separation gap distance \(\mathrm{d_{g,t}}\) is increased. * In the case of considering the acceptable POs-\(\mathrm{IDR_{max}}\), the seismic risk \(\mathrm{P_{dg,t}^{PO}}\) corresponding to \(\mathrm{d_{g,t}}\) depicts the capacity level of the EDP. Based on POs-\(\mathrm{\delta_{max}}\), the corresponding bounding risk at \(\mathrm{d_{g,t}}\) is \(\mathrm{P_{dg,t}^{\mathrm{max}}}\). If \(\mathrm{P_{dg,t}^{\mathrm{max}}}\) is lower than the \(\mathrm{P_{dg,t}^{PO}}\), the capacity level of the \(\mathrm{IDR_{max}}\) is exceeded. * Thus, POs-\(\mathrm{\delta_{max}}\) does not always lead to an acceptable risk-targeted solution when a certain capacity level of an EDP has to be verified. In future work, the bounding risk imposed on RC structures when local demand parameters are incorporated into the proposed method will be studied. Conceptualization, M.G.F. and M.J.F. ; methodology, M.G.F. and M.J.F. ; formal analysis, M.G.F. ; writing--original draft, M.G.F. and M.J.F. ; writing--review and editing, M.J.F. All authors have read and agreed to the published version of the manuscript. This research received no external funding. Not applicable. The raw data supporting the conclusions of this article will be made available by the authors on request. The authors declare no conflicts of interest. List of Greek symbols  
\(\beta_{\mathrm{EDP}|\mathrm{IM}}\) & Logarithm standard deviation of the linear regression model  
\(\varepsilon|\mathrm{IM}\) & Random error  
\(\lambda_{\mathrm{IM}}(\mathrm{im})\) & MAF of exceedance of im  
\(\lambda_{\mathrm{S_{e,T}}}\) & MAF of exceedance of \(\mathrm{S_{a,T1}}\)  
\(\lambda_{\mathrm{EDP}}(\mathrm{edp})\) & MAF of exceeding a given edp  
\(\delta_{\mathrm{max}}\) & Peak relative displacement demand (without structural bounding) at the examined top level of the contact point  
\(\delta_{\mathrm{max,t}}\) & \(\delta_{\mathrm{max}}\) at the target probability \(\mathrm{P_{t}}\)  
List of Latin symbols  
EDP & Engineering Demand Parameter  
IM & Intensity Measure  
EDP & Median Engineering Demand Parameter  
MAF & Mean Annual Frequency  
a, b & Parameters of linear regression analysis  
k, k\({}_{\mathrm{o}}\) & Slope and intercept of the seismic hazard curve  
GEDP\(|\mathrm{IM}(\mathrm{edp}|\mathrm{im})\) & Complementary Cumulative Distribution Function (CCDF)  
d\((\cdot)\) & Differentiation operator  
P\({}_{t}\) & Targeted structural bounding seismic risk (targeted probability per year)  
P\({}_{t,crit}\) & Critical value of P\({}_{t}\)
# Integrated Replay Spoofing-Aware Text-Independent Speaker Verification
###### Abstract
\begin{table}
\begin{tabular}{l c c c} \hline \hline  & **Target** & \
\begin{tabular}{c} **ZE** \\ **Non-Target** \\ \end{tabular} &
\begin{tabular}{c} **Replay** \\ **Non-Target** \\ \end{tabular} \\ \hline ZE-EER & 1 & 0 & \\ PAD-EER & 1 & & 0 \\ ISV-EER & 1 & 0 & 0 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Three types of EERs reported in this paper: Enrollment utterance is always bona fide (i.e., genuine, not replayed). Target: enrollment and test utterances are uttered by an identical speaker and are bona fide; zero-effort (ZE) non-target: enrollment and test utterances are uttered by different speakers and are bona fide; Replay non-target: enrollment and test utterances are uttered by an identical speaker and test utterance is replay spoofed. Figure 1: (**a**) An end-to-end architecture that trains embeddings (used for speaker identification (SID) and presentation attack detection (PAD)). LCNN and MTL refer to the light cnn and multi-task learning, concurrently; (**b**) a separate architecture that inputs speaker embeddings from SID and PAD results and outputs the Integrated result of speaker verification (SV) and PAD. Based on our hypothesis, we propose a novel modular approach using a separate DNN. This approach inputs two speaker embeddings (for enrollment and test each) and a PAD prediction to make the ISV decision. It adopts a two-phase approach. In the first phase, the speaker identifier and PAD system are trained separately. In the second phase, speaker embeddings are extracted from a pretrained speaker identifier [21], and the embeddings and PAD prediction results are fed to a separate DNN module. Using this framework, we achieved a 21.77% relative improvement in terms of ISV-EER. (We use the trial in [[https://www.asvspoof.org/index2017.html](https://www.asvspoof.org/index2017.html)]([https://www.asvspoof.org/index2017.html](https://www.asvspoof.org/index2017.html)) for calculating ISV-EER.) The contributions of this paper are as follows. 1. Propose a novel E2E framework that jointly optimizes SID, PAD, and the ISV task. 2. Experimentally validate the hypothesis that the discriminative information required for the SV and the PAD task may be distinct, requiring separate front-end modeling. 3. Propose a separate modular back-end DNN that takes speaker embeddings and PAD predictions as an input to make ISV decisions. The remainder of the paper is organized as follows. Section 2 details related work on the integrated system of SV and PAD. Section 3 introduces the two proposed frameworks. Section 4 presents our experiments and results, and the paper is concluded in Section 5.