esa-sceva/satcom-chunk-collection huggingface dataset:
# Measurement of Visible Radiation through a _Sansevieria cylindrica_-Based \"Living Sensor\"
## 4 Conclusions  
In this paper, a living sensor based on a _Sansevieria cylindrica_ plant was presented. The main objective was to verify that the plant was sensitive to changes in light intensity on a regular basis and that this was related to the variation in a measurable electrical quantity. The measures performed made it possible to achieve this goal, and it is possible to state that the present paper proposed for the first time in literature a device capable of measuring visible radiation, and the approach pursued here evinces important features such as being environmentally friendly, self-generating, mimetic, battery-less sensor, simple,  
Figure 10: Calibration diagram of the _Sansevieria cylindrica_ used as living sensor.  
low-cost, non-toxic, and biodegradable. It is worth noting that the device proposed here also improves the state of the art in terms of greenness. Through its criterion, it is capable of going beyond \"classical\" silicon-based approaches that are not biodegradable nor eco-friendly and that produce CO\({}_{2}\) from the production step to the disposal phase. Furthermore, no toxicity can be associated with plants and, due to the natural process of photosynthesis, the living sensor is able to absorb carbon dioxide present in the environment. The working principle, together with the transduction mechanism, was presented, as well as the results, in terms of the spatial distribution of applied illuminance; the selectivity achievements and metrological characterization were addressed in order to demonstrate performance and the suitability of the proposed sensor, which can be applied in various fields such as indoor and outdoor monitoring for cultural heritage, smart home, smart agriculture, and smart cities.  
The measurements carried out until now showed the validity of the approach and enabled experimental procedures to be developed to verify the limits and potential of the device for the various hypothesized applications.  
Conceptualization, C.T.; methodology--engineering physics, C.T. and A.M.G.; methodology--agricultural chemistry, I.P. and A.B.; validation, C.T. and A.M.G.; investigation, C.T.; supervision, C.T. All authors have read and agreed to the published version of the manuscript.  
The research activity was partially funded by SAMOTHRACE \"SiciliAn MicronanOTecH Research And Innovation CEnter\"-Ecosistema dell'innovazione (PNRR, Mission 4, Component 2 Investment 1.5, Avviso n. 3277 del 30-12-2021, cup E63C2200090006), and Spoke 1-Universita di Catania-Work Package 6 Cultural Heritage.  
Not applicable.  
The raw data supporting the conclusions of this article will be made available by the corresponding author upon reasonable request.  
The authors would like to thank [PERSON] for the support regarding the source generator system. The authors would like to thank [PERSON] for the helpful guidance in SEM analysis.  
The authors declare no conflict of interest.
## 2 Theoretical Model
### Seawater Turbulence and Localized Wave of Bessel-Gaussian Amplitude Envelope
In Equation (3), we have changed the Bessel function of the first kind \(J_{m}(...)\) in [18; 21] to modified Bessel function of the first kind, through the following transformation [22]  
\[I_{m}(x)=\mathrm{i}^{-m}J_{m}(\mathrm{i}x), \tag{4}\]  
Applying the localized wave theory [17; 23] and using the frequency spectrum of the Gaussian pulse, we can express the localized wave of Bessel-Gaussian amplitude envelope (BGLW) transiting in turbulent seawater as  
\[u_{p}(\rho,\theta,z,t)= \frac{T_{0}}{2\sqrt{\pi}}\exp(-\mathrm{i}\Omega t)\int_{-\infty} ^{+\infty}\exp\left(-\frac{\omega^{2}T_{0}^{2}}{4}-\mathrm{i}\omega t\right) u(\rho,\theta,z;\omega)\mathrm{d}\omega, \tag{5}\]  
where \(T_{0}\) represents the half-modulated pulse width of the input pulse and \(\Omega\) is center frequency. Introducing new variables: the sum frequency \(\omega_{c}=(\omega_{1}+\omega_{2})/2\) and the difference frequency \(\omega_{d}=\omega_{1}-\omega_{2}\), in the case of narrowband, we have the approximate: \(\omega_{c}\approx\widetilde{\Omega}\), where \(\widetilde{\Omega}\) represents average frequency, meanwhile, \(\omega_{c}\) relates to the location of the center frequency [24]. That is to say  
\[\Omega\approx\widetilde{\Omega} \tag{6}\]  
For the Gaussian spectrum of the frequency of pulse, we have  
\[ \begin{split}\widetilde{\Omega}&=\frac{\int_{0}^{ \infty}\omega\exp\left(-\frac{T_{0}^{2}}{4}\omega^{2}\right)\mathrm{d}\omega }{\int_{0}^{\infty}\exp\left(-\frac{T_{0}^{2}}{4}\omega^{2}\right)\mathrm{d} \omega}\\ &=\frac{2}{T_{0}\sqrt{\pi}}\end{split}  \tag{7}\]
## 4 Discussion of Results
The criteria in the AASM manual were also employed for scoring an event after a drop in peak thermal airflow signal excursions by \(\geq\) 90% of the corresponding baseline for a duration \(\geq\)10 s [11]. Nevertheless, there are many sources of uncertainty in the criteria defined in literature. First, the AASM doesn't provide a precise definition for the respiratory baseline. Second, the published criteria for mathematical scoring apnea are not consistent and vary with different standards [60]. Moreover, the high intraobserver and interobserver variability due to human scoring and human errors [17] significantly affect the robustness and performance of automated sleep systems. Adopting a probabilistic framework in the proposed study provides an efficient approach to propagate different sources of uncertainty using a data-driven modeling framework optimized with respect to the ability to detect apnea events. Interestingly, using the proposed GMM-based classification system showed an overall improved performance in apnea detection and a more consistent and generalizable performance across patients with different severity levels. Finally, the presented approach focuses on automated detection for apnea events using oronasal airflow respiration signal. Future work many extend the algorithm by adding an additional input through the nasal pressure signal in order to study dynamical changes in this signal during hypopnea as recommended by the AASM. Using the oronasal airflow and the nasal pressure signals will enable detecting both apnea and hypopnea events needed to estimate the AHI in order to provide a diagnosis for sleep apnea severity. Additionally, this will improve the ability to estimate respiratory baseline by incorporating two signals instead of one leading potentially to a decreased number of false positive detections. Moreover, future work may consider adding input from the respiratory effort signal to provide diagnosis for the type of detected events (OSA, OSA, or MSA).
## 5 Conclusions  
As a preliminary study for BPMSM development, this paper provides an effective means for BPMSM mechanical structure optimization by developing a virtual prototype based in Ansoft Maxwell. Using the sensitivity evaluation process, the candidate mechanical structural parameters for specific performances are determined. Then, orthogonal tests are performed to determine the dominant mechanical structural parameters to be optimized utilizing simulated sample datasets. The optimization model for the BPMSM  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**BPMSM Performance** & **Before** & **After** & **Improvement/\%** \\ \hline \(k_{z}\)/(N/mm) & 2.12 & 3.3158 & 56.4 \\ \(k_{x}\)/(N-m/rad) & 0.31 & 0.531 & 71.29 \\ \(k\)/(N/A) & 2.6527 & 3.1725 & 19.6 \\ \(T\)/(mN\(\cdot\)m) & 143.08 & 173.3752 & 21.17 \\ \(\eta\)/\% & 69 & 75 & 8.7 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Comparison of BPMSM performance, before and after optimization.  
mechanical structure is developed by using regression model and solved with a GA. The simulation results show that the performance of the optimized BPMSM has been comprehensively improved. To be specific, the passive axial stiffness, passive tilting stiffness, force-current coefficient, and motor efficiency increased by 56.4%, 71.3%, 19.6%, and 8.7%, respectively. The results verify the correctness and rationality of the optimization method, which can provide a novel method for the structural optimization of BPMSMs.  
Conceptualization, H.S., L.H. and L.Y.; methodology, Y.Y.; software, F.B.; validation, Y.Y. and F.B.; writing--original draft preparation, H.S. and F.B.; writing--review and editing, H.S.; supervision, B.L. and G.C.; project administration, P.Y.; funding acquisition, G.C. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Key Research and Development Program of China, grant number 2017 YFB1104600 and the National Natural Science Foundation of China, grant numbers 51605291 and 51905383.  
Not applicable.  
Thanks to [PERSON] for helpful discussions.  
The authors declare no conflict of interest.
# Surface Defect Detection Methods for Industrial Products: A Review
## 2 Traditional Feature-Based Machine Vision Algorithm for Surface Defect Detection
### Color Feature-Based Method
#### 2.2.2 Color Moments  
The main idea of color moment is that any color distribution in the image can be represented by its moments of each order. Since the information of color distribution is mainly concentrated in low-order moments, usually only the first-order moment (mean), the second-order moment (variance) and the third-order moment (offset) of the color are enough to show the color of the image surface distributed.  
Features: no consideration of pixel spatial position; no need to vectorize the color features; no need for color quantization, smoothing, and other subsequent processing.  
Literature [30] proposed a method of weighted fusion of color moment features and FSIFT features according to the magnitude of influence, which solved the problem that a single feature does not express the content of tile surface defects obviously. Literature [31] used cosine similarity to test the periodic law of the magneto-optical images, which proved the correctness of the law summarized by the color moment feature, so as to select the appropriate magneto-optical image for welding defect detection and location.
# Multi-Tier Cellular Handover with Multi-Access Edge Computing and Deep Learning
## 5 Proposed Algorithm
### Benchmarking Scheme
#### 5.1.2 Handover Procedure  
Key communication delay parameters for the handover procedure are given below. All latency values are obtained from [23, 25]:  
* Handover request from source gNB to target gNB: 2 ms between their respective distributed units;
* Admission control: 1 ms for admission control at the target gNB;
* UE handover initiation message: 1 ms for data transmission over air interface;
* UE handover configurations: \(\bigcirc\) Handover request processing: 5 ms;
* Status transfer from source gNB to target gNB: 1 ms;
- Target gNB and UE synchronization messages: 2 ms.  
Additionally, two handover failure (HOF) types are identified:  
1. If at any point during the handover procedure, the desired BS's CQI is \(<\) 1, the handover is stopped, and the UE is moved to the connected state;
2. If 16 or more communication failures occur in a set handover period, these are considered gross handover failures [26], then the UE will be disconnected from the BS and become idle.
## 2 Study Area and Datasets
### Remote Sensing Observation  
Estimation of LAI from remote sensing data is the feasible method for generating LAI products at regional and, in particular, global scales. The LAI products, such as the MODIS LAI product (MOD15) and the CYCLOPES LAI product, are almost spatially incomplete and temporally discontinuous owing to cloud contamination and are not accurate for certain vegetation types [43,44]. The use of multi-temporal data could be a better strategy for addressing problem of information missing, which is a major feature of Global Land Surface Satellite (GLASS) products. The GLASS product, generated and released by the Center for Global Change Data Processing and Analysis of Beijing Normal University [45], has a temporal resolution of eight-day intervals and spans the period of 1981-2014.  
In the GLASS LAI algorithm, general regression neural networks (GRNNs) are trained over the fused LAI from MODIS and CYCLOPES LAI products and pre-processed MODIS/AVHRR surface reflectance of BELMANIP sites. The LAI is retrieved from time-series reflectance data with the trained GRNNs. Direct validation and inter-comparative studies have demonstrated that GLASS LAI has significantly longer temporal ranges, higher spatio-temporal resolution, and improved accuracy compared with other products, such as MODIS LAI and CYCLOPES LAI [44,46].  
The GLASS LAI product employed in this study spans the period 2003-2012 at 1 km and eight-day resolution in a sinusoidal projection system. For integrating into the data assimilation modeling framework, we processed the LAI for China and resampled it to 0.5\({}^{\circ}\)\(\times\) 0.5\({}^{\circ}\) resolution for consistency with the climatic datasets applied within the LPJ model.
# Multi-Objective Optimization of Drilling Trajectory Considering Buckling Risk
###### Abstract  
Directional drilling involves three sections--vertical, curved, and horizontal--and is used for drilling offshore wells and mining unconventional resources. The initial design of a well trajectory is important because the total length of the well trajectory is associated with the drilling cost; furthermore, the drag force and torque may cause buckling and damage the drill pipe. The well trajectory should be optimized considering the length and load of drill pipes. In this study, a new method of optimizing directional well trajectory is used to formulate the objective function considering the length, drag, and torque. To verify the applicability of this method, we applied it to an actual oil well and a theoretical oil well. The results obtained show that the use of the proposed method in the initial design of drilling trajectory can reduce the torque by up to 15%, drag by 2.6%, and length by 8.5% for the two models used in this study. This method is safer as it reduces the risk of buckling compared to the design that relies on the previous designer's experience, and it reduces the trajectory length; thus, it can save time and costs of drilling.  
drilling trajectory; drill pipe buckling; directional drilling; multi-objective optimization; particle swarm optimization Article
## 5 Summary Comparisons: Functionality and Performance
### Functionality Comparison  
The comparative outcomes of our proposed protocol with respect to other protocols in [10; 11; 12] are displayed in Table 3. From our analyses, we can observe that the protocol presented in [10] is vulnerable to the verifier loss attack, resulting in an inability to achieve \(EC_{8}\).  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Notation** & **Description** & **Notation** & **Description** \\ \hline \(EC_{1}\) & User anonymity and un-traceability & \(EC_{6}\) & Key agreement provision \\ \(EC_{2}\) & Sound repairability & \(EC_{7}\) & Mutual authentication \\ \(EC_{3}\) & Password exposure is avoidable & \(EC_{8}\) & Resist known attacks \\ \(EC_{4}\) & Password friendly & \(EC_{9}\) & Forward secrecy \\ \(EC_{5}\) & No password verifiers in GWN & \(EC_{10}\) & No smart card loss attack \\ \hline \hline \end{tabular}
\end{table}
Table 2: Criteria for evaluating authentication schemes.  
\begin{table}
\begin{tabular}{l l l l l l l l l l l l} \hline \hline \multirow{2}{*}{**Protocols**} & \multirow{2}{*}{**Ref.**} & \multicolumn{8}{c}{**Evaluation Criteria**} \\ \cline{3-11}  & & EC\({}_{1}\) & EC\({}_{2}\) & EC\({}_{3}\) & EC\({}_{4}\) & EC\({}_{5}\) & EC\({}_{6}\) & EC\({}_{7}\) & EC\({}_{8}\) & EC\({}_{9}\) & EC\({}_{10}\) \\ \hline [PERSON] et al. & [10] & ✔ & ✔ & ✔ & ✔ & ✔ & ✔ & ✔ & ✗ & ✔ & ✔ \\ \hline [PERSON] et al. & [11] & ✔ & ✔ & ✔ & ✔ & ✔ & ✗ & ✗ & ✗ & ✔ \\ \hline [PERSON] et al. & [12] & ✗ & ✔ & ✔ & ✔ & ✗ & ✗ & ✗ & ✔ & ✗ \\ \hline Ours & — & ✔ & ✔ & ✔ & ✔ & ✔ & ✔ & ✔ & ✔ & ✔ \\ \hline \hline \end{tabular}
\end{table}
Table 3: Functionality comparison among relevant AKA protocols.  
The protocol shown in [11] falls short of achieving \(EC_{7}\), \(EC_{8}\), and \(EC_{9}\). Specifically, neither the physician nor the sensor node can authenticate the gateway, aligning with \(EC_{7}\). Also, the protocol of [11] is susceptible to user impersonation attacks, which is in line with \(EC_{8}\). Additionally, it falters in attaining forward security, corresponding to \(EC_{9}\).  
For the protocol in [12], the server or GWN in [12] retains many more password-related parameters, which threatens the security of passwords (\(EC_{5}\)). As for \(EC_{7}\), mutual authentication (\(EC_{7}\)) cannot be met because the messages do not guarantee that BASN can realize mutual authentication with respect to the GWN. Additionally, the users in [12] directly submit their bare identities to the GWN or the registration center in order to complete the registration phase, and once the gateway is corrupted by the adversary, the anonymity (\(EC_{1}\)) of the user will not be respected.  
Only our proposal fulfills all the stipulated security prerequisites. It is evident that our proposal exhibits resistance against known attacks, enabling the attainment of optimal security and usability objectives. Notably, since no smart card has been used in our proposal, our proposal can meet criterium \(EC_{10}\) naturally.
# Enhanced Speech Emotion Recognition Using Conditional-DCGAN-Based Data Augmentation
## 3 Proposed Methodology
### Data Preprocessing
#### 3.1.2 Mel-Spectrogram Extraction  
To build an emotion speech recognition model, the key features extracted from voice data include the fundamental acoustic characteristics and variability of the speech. In this study, we utilized Mel-spectrograms among these features. A Mel-spectrogram is an effective tool for analyzing the characteristics of specific sounds, as it visually represents the changes in signal frequency over time.  
Spectrograms are generated based on the Short Time Fourier Transform (STFT), and Mel-spectrograms are the result of applying the Mel-scale to these spectrograms to better reflect human hearing characteristics. The Mel-scale structures human hearing characteristics so that below 1 kHz the center frequency is evenly divided and above 1 kHz it follows a logarithm scale [34]. Equation (1) is the Mel-scale conversion function for frequency.  
\[Mel(f)=2595\ *\ log_{10}\bigg{(}1+\frac{f}{100}\ \bigg{)} \tag{1}\]  
A Mel-filter bank is a set of filters designed based on the Mel-scale, which transforms the frequency spectrum of speech to match the Mel-scale and extracts the energy from each frequency band. These filters divide the frequency spectrum into multiple bands and calculate the energy in each band, which is then used to generate the Mel-spectrogram. The filters are narrower at lower frequencies and wider at higher frequencies, reflecting the characteristic of the Mel-scale that humans are more sensitive to lower frequencies and less sensitive to higher frequencies. The Mel-spectrogram accurately represents these auditory characteristics.  
A Mel-spectrogram can be considered a three-dimensional image that visually represents the changes in frequency and amplitude over time by adding a time axis. Therefore, this study proposes a method for data augmentation by utilizing the Mel-spectrograms extracted from speech data as images. To achieve this, the cleaned data, which was preprocessed using the trim function, was converted into a Mel-spectrogram using the'melspectrogram' function from the librosa library [33]. During this process, the power spectrum was converted into decibel units to compress the dynamic range of the data. This conversion is done using the decibel scale, and the formula is presented in Equation (2). _power_ represents the original spectrum value, and _ref_ is the maximum amplitude of the Mel-spectrogram. This transformation reduces the difference between very large and small values, ensuring that the intensity of the data is handled uniformly and that the various intensities of the speech signals are standardized. The data normalization process adjusts all input features to the same scale, setting the data values within the range [0, 1] based on the maximum and minimum values of each data point.  
\[S_{db}=10\ *\ \log_{10}\bigg{(}\begin{matrix}power\\ ref\end{matrix}\bigg{)} \tag{2}\]  
After going through these preprocessing steps, the data converted into Mel-spectrogram form is used for experiments. Figure 1 shows an example of a Mel-spectrogram.
# Evaluation of Eccentric Compressive Behavior Between Active and Passive Preloading in CFST
## 2 Finite Element Modeling
### Calculation Models  
To investigate the effect of various preloads on the eccentric compressive performance of CFST columns, four representative models simplified from practice (Figure 7) were developed, with preloads applied in different directions for different objects.  
First, as shown in Figure 7a, the concrete of CFST-SP relied on the hoop stresses generated in the steel tube by the steel flanges and bolts to obtain an active radial preload (Figure 3b). CFST-SP is a new structure proposed by us. Its manufacturing process is not complicated. First, the steel plate is cut according to the design size and bent into the target shape, then assembled into a tube with high-strength bolts and filled with concrete, finally, the bolts are tightened to apply preload [34]. Figure 7b, however, showed a self-stressing concrete filled steel tube column, for which the concrete had an active three-way compressive preload during its outward expansion. The application of CFST-CP is relatively mature, and many scholars have also conducted relevant research on self  
Figure 6: Specific meshing components: (**a**) General view; (**b**) Concrete core; (**c**) Steel tube; (**d**) Steel plate.  
stressing concrete [35; 36]. In this study, the temperature was applied directly to the steel tube of CFST-SP and the concrete of CFST-CP, with reliance on the temperature difference to achieve the active preloads; so, it was not necessary to simulate the flange and bolts of CFST-SP. Similarly, as shown in Figure 7c, for the passively preloaded column CFST-CC, only the concrete was subjected to pre-compression. Additionally, as shown in Figure 7d, for CFST-SC, only the steel tubes were subjected to pre-compression. These two models were derived from the engineering cases in Figure 1b,c.
## 2 Methodology  
Relevant works published up to March 2024 were included in this review. Databases used in the search were PubMed, Web of Science, Cochrane Library, and Google Scholar. Combinations of the following terms were applied in the search: \"obesity\", \"weight loss\", \"supplementation\", \"prebiotic\", \"probiotic\", \"micronutrients\", \"vitamins\", \"minerals\", and individual keywords representing each vitamin and mineral of interest. To gather data on the efficacy of micronutrients and microbiota intervention in supporting weight loss and metabolic health, as well as the micronutrient status of humans, respectively, the strongest research evidence was sought. Therefore, meta-analyses and systematic reviews were prioritized over randomized controlled trials (RCTs). Further selection criteria for these studies were recency, number of reported outcomes of interest, focus on people with excess weight, and reported effect sizes (weighted mean difference--WMD, or mean difference--MD, over standardized mean difference--SMD) to enable comparing outcomes between the works.  
Although this review focused on the effects of the mentioned interventions in humans, in cases where no sufficient data were available, some complementary evidence from animal studies was included. Such practice was only applied in cases of insufficient data from human studies or important findings, showing responses in living organisms that for ethical (experiments where obesity or related health disorders were induced in animals on purpose) or physical reasons (for example, in the case of germ-free animals) could not be replicated in humans.