esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Cobalt  
Co content in both red deer and wild boar meat proved to be negligible for both species. In the majority of the samples examined, the Co concentration in game meat closely approached the limit of quantification (LOQ). These Co levels align with findings reported by other researchers who studied these same species, such as [PERSON] et al. [25] (0.002 mg/kg) and [PERSON] et al. [26] (0.02 mg/kg). In contrast, [PERSON] et al. [27] and [PERSON] et al. [28] found higher Co contents in wild boar meat, measuring at 0.17 mg/kg and 0.44 mg/kg, respectively. [PERSON] et al. [29], studying the chemical composition of wild fallow deer in different types of muscle, found very low Co levels that varied from 0.002 to 0.003 mg/kg.  
Cobalt is part of the core of vitamin B12, and its main source comes from meat-based foods. The Adequate Intake (AI) recommended by the EFSA [22] is 65 \(\upmu\)g per person per  
\begin{table}
\begin{tabular}{c c c c} \hline
**Element** & **Statistic** & **Intake of Red Deer Meat** & **Intake of Wild Boar Meat** \\ \hline \multirow{4}{*}{Co} & 5 th & 0.003 & 0.003 \\  & 50 th & 0.046 & 0.043 \\  & 95 th & 0.197 & 0.188 \\ \hline \multirow{4}{*}{Cr} & 5 th & 0.010 & 0.026 \\  & 50 th & 0.669 & 0.353 \\  & 95 th & 2.893 & 1.526 \\ \hline \multirow{4}{*}{Cu} & 5 th & 0.198 & 0.581 \\  & 50 th & 3.583 & 3.162 \\  & 95 th & 27.80 & 29.33 \\ \hline \multirow{4}{*}{Fe} & 5 th & 10.24 & 9.150 \\  & 50 th & 138.3 & 76.45 \\  & 95 th & 597.8 & 638.8 \\ \hline \multirow{4}{*}{Ni} & 5 th & 0.090 & 0.157 \\  & 50 th & 0.748 & 0.855 \\  & 95 th & 6.229 & 7.878 \\ \hline \multirow{4}{*}{Se} & 5 th & 0.009 & 0.035 \\  & 50 th & 0.128 & 0.287 \\  & 95 th & 0.554 & 2.326 \\ \hline \multirow{4}{*}{Zn} & 5 th & 10.35 & 6.120 \\  & 50 th & 84.85 & 46.34 \\ \cline{1-1}  & 95 th & 695.8 & 350.9 \\ \hline \end{tabular}
\end{table}
Table 6: Estimated intake of Co, Cr, Cu, Fe, Ni, Se, and Zn by probabilistic assessment due to consumption of red deer and wild boar in hunter-consumers (mg/person/year).  
day. The estimated intake of cobalt through the consumption of deer and wild boar meat for 50% of hunter-consumers represents 1.4% and 1.3% of the AI, respectively.
# Permittivity of Undoped Silicon in the Millimeter Wave Range
###### Abstract  
With the rapid development of millimeter wave technology, it is a fundamental requirement to understand the permittivity of materials in this frequency range. This paper describes the dielectric measurement of undoped silicon in the E-band (60-90 GHz) using a free-space quasi-optical system. This system is capable of creating local plane wave, which is desirable for dielectric measurement in the millimeter wave range. Details of the design and performance of the quasi-optical system are presented. The principle of dielectric measurement and retrieval process are described incorporating the theories of wave propagation and scattering parameters. Measured results of a sheet of undoped silicon are in agreement with the published results in the literature, within a discrepancy of 1%. It is also observed that silicon has a small temperature coefficient for permittivity. This work is helpful for understanding the dielectric property of silicon in the millimeter wave range. The method is applicable to other electronic materials as well as liquid samples.  
millieter wave; quasi-optics; free space method; undoped silicon; permittivity; temperature +
Footnote †: journal: Accepted: 8 August 2019; Published: 10 August 2019
## 3 Results and Discussion
### Questionnaire Results
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Parameters of Lockdown Aspects (I: Fully Disagree to 5: Fully Agree)** & **Mean Score** & **Overall Comfort** & **Thermal Comfort** & **Air quality Overall** & **Light Overall** & **Noise Overall** & **SBS** \\ \hline I understand the recently updated organizational health and safety policies and procedures that have been put in place in maintaining a healthy and safe environment at work & 3.71 & 0.185 * & 0.236 ** & 0.153 * & 0.171 & 0.112 & 0.002 \\ I am comfortable networking to my work site & 4.12 & 0.179 * & 0.247 ** & 0.271 ** & 0.281 ** & 0.240 ** & \(-0.119\) \\ I am comfortable networking to my work site & 3.56 & 0.294 ** & 0.320 ** & 0.246 ** & 0.302 ** & 0.042 & \(-0.246\) * \\ I am comfortable travelling for work if required & 3.78 & 0.280 ** & 0.353 ** & 0.229 * & 0.304 ** & 0.137 & \(-0.205\) * \\ I would prefer to work from home if that option was extended to me Systems are in place to enable social distancing at our organization My colleagues are taking the COVID-19 virus seriously & 2.54 & 0.118 & 0.038 & 0.056 & \(-0.036\) & \(-0.172\) \\ \hline \hline \end{tabular}
* Correlation is significant at the 0.05 level ** Correlation is significant at the 0.01 level.  
\end{table} 
Table 3: Correlations between IEQ comfort and degree of acceptance of the lockdown aspects. Figure 7: Comparison of IEQ parameters perception in workplace before and after COVID-19 countermeasures.
## 2 Materials and Methods
### Monitoring System Design
#### 2.2.1 System Hardware and Software Architecture  
The proposed IAQ monitoring system architecture is comprised of two major components as shown in Figure 1. The hardware component consists of the IoT sensors, network devices, and connectivity equipment. The software component encompasses a range of device specific IoT cloud platforms, unified data management services, and the end-user applications that provide data visualization or dashboarding.  
A key aspect of this proposed architecture is the use of multiple sensor devices that communicate directly to independent IoT cloud platforms. The IoT cloud platforms often require little to no configuration. They handle the sensor devices to cloud communication and serve as an intermediary data store or relay service point. It is also a feature of such platforms that an Application Programming Interface (API)--a software intermediary that allows two applications to talk to each other--is provided for users to programmatically interact with their devices or data without using the platform's graphical interface directly. With APIs, users can query data from IoT devices in a near-real time manner and aggregate the data from multiple platforms into a single, unified location. This can be achieved through low-code and open-source solutions such as Node-RED for performing data query or ingest and InfluxDB for time-series data storage. Alternative services can also be implemented using Amazon Web Services or Azure IoT services that adopt an optional subscription-based models.  
Figure 1: An architecture-level diagram of the monitoring system from sensor and network devices to cloud platforms for data storage and visualization.
## 4 Experiment Results  
### Datasets  
Experiments were performed on the KITTI dataset. The KITTI stereo dataset [26] is a collection of rectified stereo pairs made up of 61 scenes (containing about 42,382 stereo frames) mainly concerned with driving scenarios. The predominant image size is 1242 \(\times\) 375 pixels. Here, only image frames from the left camera (so as the right camera) are used as the testing data in our method. Instead of obtaining images from the front vehicle and host vehicle simultaneously, we use two frames (with interval \(\Delta\)t) in the video to imitate the cooperation of the front and back vehicles. \(\Delta\)t is a random value within 3-20. To simulate the occlusion situation, we selected some vehicles in the picture as the blind spot, and blocked these objects with the white panel in the picture of the back vehicle (shown in Figure 6d).  
Figure 6 gives an example of how to use images of KITTI datasets to simulate V2V in reality. In the left column, (a) and (b), two images in KITTI with an interval of 15 frames are chosen. In the right column, (c) and (d), these two images are pretended to be images from the front and host vehicle, respectively. The vehicle with the red rectangle is supposed to be the occluded object. The bottom host image is processed by using a white panel to block the vehicle. We used these image couples to test our method's effectiveness and flexibility.
## 4 Discussion  
The aim of the current study was to investigate whether the risk of falls, as determined by predictors of functional ability, may be related to cognitive function in older individuals at risk of MCI. Our findings suggest that older individuals with higher dynamic balance, handgrip strength, or gait speed, as well as increased confidence in their balance or higher levels of education, demonstrated better cognitive function across most of the dimensions we assessed. Additionally, diminished dynamic balance emerged as a significant predictor of lower MMSE scores and decreased semantic fluency, while reduced gait speed was a strong predictor of reduced semantic fluency and language skills, even when controlling for educational level. Notably, the population showed a mean MMSE score that reflected  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **MMSE** & \
\begin{tabular}{c} **Phonologic** \\ **Fluency** \\ \end{tabular} &
\begin{tabular}{c} **Semantic** \\ **Fluency** \\ \end{tabular} & **Language** \\ \hline TUG & \(-0.236\)** ** & \(-0.122\) & \(-0.303\)** ** & \(-0.219\)** ** \\ Handgrip strength & \(0.140\) & \(0.201\)** ** & \(0.190\) * & \(0.264\)** ** \\ ABC & \(0.135\) & \(0.161\)** & \(0.277\)** ** & \(0.205\)** ** \\ Gait speed & \(-0.150\)** & \(-0.139\) & \(-0.352\)** ** & \(-0.297\)** ** \\ \hline Age (years) & \(-0.149\)** & \(-0.144\) & \(-0.385\)** ** & \(-0.353\)** ** \\ Sex & \(-0.095\) & \(-0.207\)** & \(-0.157\)** * & \(-0.246\)** ** \\ Educational level & \(0.330\)** ** & \(0.509\)** & \(0.318\)** ** & \(0.542\)** ** \\ BMI (kg/m\({}^{2}\)) & \(-0.068\) & \(0.046\) & \(-0.110\) & \(-0.066\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Pearson’s correlations among variables analyzed in this study.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Variable** & **B** & \(\beta\) & **t** & **95\% CI** & _p-Value_ \\ \hline MMSE & TUG & \(-0.538\) & \(-0.275\) & \(-2.254\) & \(-1.09\) & \(-0.67\) & \(0.025\) \\  & Educational level & \(1.268\) & \(0.319\) & \(3.838\) & \(0.616\) & \(1.920\) & \(<0.001\) \\ \hline Phonologic fluency & Educational level & \(7.360\) & \(0.041\) & \(5.965\) & \(4.921\) & \(9.797\) & \(<0.001\) \\  & Age & \(-0.554\) & \(-0.244\) & \(-3.551\) & \(-0.862\) & \(-0.246\) & \(<0.001\) \\ \hline Semantic fluency & Educational level & \(3.678\) & \(0.273\) & \(3.364\) & \(1.520\) & \(5.873\) & \(0.001\) \\  & Gait speed & \(-4.931\) & \(-0.241\) & \(-1.970\) & \(-9.873\) & \(0.011\) & \(0.049\) \\  & TUG & \(-1.378\) & \(-0.207\) & \(-2.572\) & \(-2.437\) & \(-0.320\) & \(0.011\) \\  & ABC & \(0.077\) & \(0.163\) & \(2.045\) & \(0.003\) & \(0.152\) & \(0.042\) \\ \hline Language & Educational level & \(1.796\) & \(0.476\) & \(6.613\) & \(1.260\) & \(0.233\) & \(<0.001\) \\  & Gait speed & \(-1.654\) & \(-0.290\) & \(-2.661\) & \(-2.882\) & \(-0.427\) & \(0.009\) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Multivariate linear regression analyses for variables related to cognitive function factors.  
that a proportion of the sample may have MCI, although these data should be treated with caution.
# A Survey of Machine Learning Models in Renewable Energy Predictions
## 1 Introduction
### Review Studies of Applying Machine-Learning Techniques to Renewable Energy
Aiming at a broad survey of machine-learning models in renewable-energy predictions based in recent years, this investigation elucidates categories of renewable energy sources, machine-learning models, data pre-processing techniques, parameter selection of machine-learning models, and performance measurements of machine-learning models in renewable-energy predictions. Moreover, directions of potential or possible future work were pointed out as well. The structure of this study is as follows. Section 2 depicts data preprocessing methods, machine-learning models, and parameter selection techniques; Section 3 presents measurements of model performances; Section 4 draws conclusions and provides directions of future work.
# Recent Advances in Tunable External Cavity Diode Lasers
## 2 Basic Theory of External Cavity Diode Lasers  
The external cavity diode laser is generally composed of a gain chip, a collimator, and an optical feedback element. Various external cavity configurations have been developed for ECDLs, among which the planar mirror external cavity represents the most fundamental and straightforward structure. The basic configuration is illustrated in Figure 1. The right facet of the laser is combined with the reflective mirror to form a compound reflector. The effective reflectivity is \(r_{\mathit{eff}}\). The ECDLs take the semiconductor lasers as the active region. The optical feedback element feeds the light back into the gain chip to achieve mode
## 6 Experimental Results
### Results
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**BenFun** & **Name** & **Search Subset** & **Optimal** \\ \hline \multirow{6}{*}{\(CF_{4}\)} & Composition Function 4 (Test Function No. 24) & \multirow{6}{*}{2400} \\  & N = 4 & & \\  & \(\sigma=[10,20,30,40]\), \(\lambda=[10,1e-6,10,1]\) & \\  & \(bias=[0,100,200,300]\) & & \\  & \(g_{1}\): Ackley’s Function \(F_{13}\)\({}^{\prime}\) & \multirow{2}{*}{\([-100,100]^{10}\)} & \multirow{2}{*}{2400} \\  & \(g_{2}\): High Conditioned Elliptic Function \(F_{11}\)\({}^{\prime}\) & \\  & \(g_{3}\): Girewank Function \(F_{15}\)\({}^{\prime}\) & \\  & \(g_{4}\): Rastrigin’s Function \(F_{5}\)\({}^{\prime}\) & \\ \hline \multirow{6}{*}{\(CF_{5}\)} & Composition Function 5 (Test Function No. 25) & \multirow{6}{*}{2500} \\  & N = 5 & & \\  & \(\sigma=[10,20,30,40,50]\), \(\lambda=[10,1,10,1e-6,1]\) & \\  & \(bias=[0,100,200,300,400]\) & & \\ \cline{1-1}  & \(g_{1}\): Rastrigin’s Function \(F_{5}\)\({}^{\prime}\) & \([-100,100]^{10}\) & 2500 \\ \cline{1-1}  & \(g_{2}\): Happycat Function \(F_{17}\)\({}^{\prime}\) & \\ \cline{1-1}  & \(g_{3}\): Ackley Function \(F_{13}\)\({}^{\prime}\) & \\ \cline{1-1}  & \(g_{4}\): Discus Function \(F_{12}\)\({}^{\prime}\) & \\ \cline{1-1}  & \(g_{5}\): Rosenbrock’s Function \(F_{4}\)\({}^{\prime}\) & \\ \hline \multirow{6}{*}{\(CF_{6}\)} & Composition Function 6 (Test Function No. 26) & \multirow{6}{*}{2600} \\  & N = 5 & & \\ \cline{1-1}  & \(\sigma=[10,20,20,30,40]\), \(\lambda=[1e-26,10,1e-6,10,5e-4]\) & \\ \cline{1-1}  & \(bias=[0,100,200,300,400]\) & & \\ \cline{1-1}  & \(g_{1}\): Expanded Scaffer’s \(F_{6}\) Function \(F_{5}\)\({}^{\prime}\) & \([-100,100]^{10}\) & 2600 \\ \cline{1-1}  & \(g_{2}\): Modified Schwefel’s Function \(F_{10}\)\({}^{\prime}\) & \\ \cline{1-1}  & \(g_{3}\): [PERSON]’s Function \(F_{15}\)\({}^{\prime}\) & \\ \cline{1-1}  & \(g_{4}\): [PERSON]’s Function \(F_{4}\)\({}^{\prime}\) & \\ \cline{1-1}  & \(g_{5}\): [PERSON]’s Function \(F_{5}\)\({}^{\prime}\) & \\ \hline \hline \end{tabular}
\end{table} 
Table 6: _Cont._to provide a measure of central tendency that was more representative of the distribution of the data obtained in each experiment. A simple inspection of the values of the average fitness (Avg) of the objective function and of the corresponding standard deviation (StdDev), as it is shown in Table 9, permits us to establish a priori that VLE can compete successfully with a considerable part of this type of optimization problem, which is the scope of the present research. In other words, it can been seen that the results obtained by VLE are competitive. We define the true percentage deviation (\(TPD\)) (34) as the measure of the search success. However, several of the benchmark functions tested have the optimal solutions as zero, and it is not possible to divide by zero. The (\(TPD\)) indicator was employed only for those functions whose optima (\(Opt\)) were different than zero. For the remaining functions, as the success indicator, we calculated the (\(DMO\)), the difference between the minimum achieved by VLE (\(Min\)) and the optimal value published (\(Opt\)) (35).
## 4 Conclusions  
This study demonstrated the feasibility of forecasting biochemical oxygen demand (BOD5) concentrations five days in advance using a combination of machine learning (ML) and artificial neural network (ANN) models. Among the evaluated models, the ARIMA-LSTM architecture yielded the best performance, effectively identifying short-term trends associated with irregularities in incoming wastewater. The achieved SMAPE of 1.052% confirms the high predictive accuracy and the potential for integrating ML techniques with mechanistic models to optimize wastewater treatment operations. This predictive capability enables timely adjustments to aeration regimes and sludge dosage, contributing to enhanced process efficiency.  
The ARIMA-LSTM model improves prediction quality by combining linear and nonlinear dependencies, capturing both trend-based and complex temporal relationships in wastewater characteristics. Given that many wastewater parameters are inherently interrelated, future enhancements to this model should involve incorporating additional measurable predictors, such as variations in flow rate, ammonium nitrogen concentration, phosphates, and chemical oxygen demand (COD). These features are critical for accurately reflecting the interactions within this system. Integrating such predictors would allow this model to better account for the multidimensional nature of wastewater dynamics, further improving its predictive performance.  
To implement these improvements effectively, the operation of online sensors and the developed forecasting model can be unified within SCADA systems, creating an automated  
Figure 8: Residual Analysis (ARIMA–LSTM): (**a**) Histogram of Residuals; (**b**) Residual Scatter Plot.  
feedback loop at the treatment plant. For example, based on the forecasted quality of incoming wastewater, the SCADA system could proactively adjust the dose of activated sludge in the bioreactor or modify the intensity of aeration. This approach would enhance operational stability, prevent process disruptions, and improve overall treatment efficiency, particularly in facilities that lack advanced real-time monitoring systems. These advancements hold significant promise for reducing energy costs, optimizing treatment processes, and supporting predictive maintenance strategies in wastewater treatment plants.  
Conceptualization, I.G.; methodology, I.G.; software, I.G.; validation, I.G. and N.M.; formal analysis, I.G.; investigation, I.G.; resources, I.G.; data curation, I.G.; writing--original draft preparation; writing--review and editing, I.G.; visualization, I.G.; supervision, N.M.; project administration, N.M.; funding acquisition, N.M. 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 author declares no conflicts of interest.
###### Abstract  
An improved adaptive constant false alarm rate (CFAR) detector based on fuzzy theory is proposed to address the issue of poor detection performance and robustness of the variability index (VI) class CFAR detectors due to the misjudgment of the background environment and other reasons. The integration of the order statistic threshold adjustable detection algorithm (OSTA) into the adaptive CFAR detector has the potential to address the aforementioned issue. Therefore, in a clutter edge environment, the ratio of the means of the leading and lagging windows is calculated separately, and the differences between these mean ratios and predefined thresholds are used as inputs to the fuzzy inference machine, and the background clutter estimation of the OSTA is determined based on the fuzzy output, which can extend the range of values of the background clutter estimation, and improve the detection performance of the OSTA in this environment. The Kaigh-Lachenbruch quantile detection algorithm (KLQ) exhibits robust detection performance in multiple-target environments. Therefore, KLQ is used to detect targets in this environment, further improving the detection performance of the detector. The experimental results show that in multiple-target environments with an average misjudgment rate of 27.48%, the proposed detector increases the detection probability by 15.58% compared to the recently proposed variability index heterogeneous clutter estimate modified ordered statistics CFAR detector (VIHCEMOS-CFAR), and in a clutter edge environment, the false alarm rate of the proposed detector was reduced by approximately 89.64% compared to VIHCEMOS-CFAR. Additionally, the effectiveness of the proposed detector is also validated by real clutter data. It can be seen that the proposed adaptive CFAR detector has better robustness to the misjudgment of the background environment and better overall detection performance regardless of the environment.  
a pacs: 12 May 2025
## 2 System Modeling and Optimization Problems
### Channel Model
\(P_{MR}\) is the relay forwarding power. Note that (10) can only be used under two assumptions: (1) the transmit amplifier operates in its linear region, and (2) the last three power consumptions do not depend on the rate of the communication link [11]. Energy efficiency is defined as the data rate per unit of energy. This is the ratio of the data rate to the total power consumption [32]:  
\[EE=\frac{R}{P_{all}}, \tag{11}\]  
Energy efficiency (\(EE\)) is measured in bit/Joule.
## 2 Methodology
### Denoise Methods
#### 2.5.2 CEEMDAN-IWT Denoising Algorithm  
Threshold selection is a key factor affecting the performance of wavelet denoising methods. If the threshold is too small, the processed signal will contain too many noise-dominated wavelet components against the purpose of denoising. On the contrary, if it is too large, partial signal components will be discarded and cause apparent signal distortion after reconstruction. In addition, these wavelets with small coefficients indeed carry the information at a higher frequency range, making them relevant for high bridge modes. The optimal threshold is determined by the improved wavelet threshold [33].  
\[\lambda=\sigma\sqrt{2\lg N},\sigma=\text{median}\frac{\left|\omega_{j,k} \right|}{0.6745} \tag{35}\]  
where \(\omega_{j,k}\) is the k-th coefficient after waveform decomposition, N is the signal length, \(\sigma\) is the signal variance, and \(\lambda\) is the threshold.  
\[\hat{\omega}_{j,k}=\left\{\begin{array}{l}0,\;\left|\omega_{j,k}\right|< \lambda_{k}\\ \omega_{j,k}\right|\omega_{j,k}\right|\geq\lambda_{k}\end{array}\right\}, \lambda_{k}=\frac{\lambda}{\sqrt[3]{k}} \tag{36}\]  
where \(\hat{\omega}_{j,k}\) is the waveform coefficient of the reconstituted signal and \(\lambda_{k}\) is the newly set threshold. The sym4 wavelet is used to decompose and denoise the high-frequency IMF component from CEEMDAN. To remove the noise components of the IMF component, the wavelet with a coefficient of less than this newly set decision threshold will be discarded, and the rest are retained. The final signal is reconstructed by the superposition of the recovered IMF component and the low-frequency IMF component decomposed by CEEMDAN through a wavelet transform. Figure 3 shows the flowchart of the proposed denoising algorithm of CEEMDAN combined with the improved wavelet threshold denoising algorithm (CEEMDAN-IWT).