esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Conclusions  
To ensure a reliable synchronization in LTE-D2D, it is necessary to perform joint estimation of the IFO and SI with reduced complexity and high performance. To address this issue, a computationally effective joint detection method was proposed in the LTE-D2D system. Exploiting the correlation between the received PSSS and SSSS sequences, the proposed detection scheme was designed to remove the impact of symbol timing error on the synchronization performance. The inherent feature of the PSSS abd SSSS was used to perform sequential detection without any information on the transmitted synchronization signals. Simulation results have illustrated that the proposed sequential detection scheme is robust to symbol timing error in multipath frequency-selective fading channel and its performance is superior to the conventional methods.  
Figure 6: Performance of the proposed and reduced-complexity joint detection (RCJD) schemes using extended cyclic prefix (CP) versus SNR when the same complexity is considered.  
**Author Contributions:** [PERSON] and [PERSON] designed the proposed estimation algorithm. [PERSON] supplemented the proposed algorithm. [PERSON]-[PERSON] complemented the problems of the proposed scheme and suggested solutions. [PERSON]-[PERSON] gave feedbacks about an enhanced algorithm and all simulation results. All authors have read and agreed to the published version of the manuscript.  
**Acknowledgments:** This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (NRF-2018R1D1A1B07048819) and was supported by Unmanned Vehicles Advanced Core Technology Research and Development Program through the National Research Foundation of Korea (NRF), Unmanned Vehicle Advanced Research Center (UVARC) funded by the Ministry of Science and ICT, the Republic of Korea (NRF-2017M1B3A2A01049997).  
**Conflicts of Interest:** The authors declare no conflict of interest.
# A Machine Learning Model for Predicting Noise Limits of Motor Vehicles in UNECE R51 Regulations
## 1 Introduction
Noise limits of various categories of vehicles defined in UNECE R51 have important influences on noise control of automotive vehicles and automotive design. However, limit values of vehicle noises defined in current standards and regulations need to be reviewed because a newly launched vehicle with improved noise controls may not comply with these UNECE regulations. From automotive manufacturers' points of view, the future revisions of these noise limits are vital for improving their vehicle design, and the estimation of them would be very helpful to avoid being out-of-date of their products in the aspect of noise control. Therefore, in this study, by using the data collected on vehicle noise limits in current regulations, the machine learning model-BPNN (back-propagation neural network) is developed to determine noise limits of a vehicle by using time series data and to predict the vehicle's acceleration noise limits for next revision of UNECE R51. The proposed prediction model adopts the Levenberg-Marquardt algorithm which can automatically adapt its learning rate to train the model with input data, and at the same time randomly select the validation data and test data to verify the correlation and determine the accuracy of the prediction results. As a showcase to use the proposed prediction model, historic acceleration noise limits available from six versions of UNECE R51 are used for training the model, and then the acceleration noise limits for its incoming seventh version can be predicted and validated.
## 3 Methods
### Dimensionality Analysis of MT Data
#### 3.2.2 Geoelectric Strike Angle from Impedance Invariants  
Rotational invariants quantities computed from MT data have been extensively used in the past decades at various stages of MT data processing. Two common examples are the determinant invariant and the squared sum of components of the impedance tensor [26]. With the determination of seven invariants of impedance tensor, ref. [23] have proposed some tools and guidance for analyzing MT dimensionality, i.e., to assess whether a 1D, 2D or 3D conductivity structure interpretation is required prior to modeling or inverting MT data. Moreover, they presented some necessary conditions for considering a regional 2D resistivity structure and advanced that a mean geoelectrical strike angle can be easily calculated in the presence of dominant 2D resistivity structure. In this study, the geoelectric strike angle was determined following this approach to verify the possible extent of a 2D conductivity structure and determine the associated geoelectric strike.
###### Abstract  
The advancement of deep neural networks (DNNs) has prompted many cloud service providers to offer deep learning as a service (DLaaS) to users across various application domains. However, in current DLaaS prediction systems, users' data are at risk of leakage. Homomorphic encryption allows operations to be performed on ciphertext without decryption, which can be applied to DLaaS to ensure users' data privacy. However, mainstream homomorphic encryption schemes only support homomorphic addition and multiplication, and do not support the ReLU activation function commonly used in the activation layers of DNNs. Previous work used approximate polynomials to replace the ReLU activation function, but the DNNs they implemented either had low inference accuracy or high inference latency. In order to achieve low inference latency of DNNs on encrypted data while ensuring inference accuracy, we propose a low-degree Hermite deep neural network framework (called LHDNN), which uses a set of low-degree trainable Hermite polynomials (called LotHps) as activation layers of DNNs. Additionally, LHDNN integrates a novel weight initialization and regularization module into the LotHps activation layer, which makes the training process of DNNs more stable and gives a stronger generalization ability. Additionally, to further improve the model accuracy, we propose a variable-weighted difference training (VDT) strategy that uses ReLU-based models to guide the training of LotHps-based models. Extensive experiments on multiple benchmark datasets validate the superiority of LHDNN in terms of inference speed and accuracy on encrypted data.  
DLaaS; homomorphic encryption; privacy protection; CKKS FHE scheme; deep neural networks; hermite polynomials +
Footnote †: journal: Applied  
Article  
LHDNN: Maintaining High Precision and Low Latency Inference of Deep Neural Networks on Encrypted Data.  
doi:10.3390/app13084815
## 2 Conceptual Framework  
### Vulnerability Index  
The vulnerability index represents the susceptibility of a system to loss or damage or its propensity to be adversely affected by something else. It also contains a range of concepts such as sensitivity and lack of adaptive capacity [29]. Closely linked to vulnerability is the concept of risk, which is based on of the interactions of vulnerability, exposure, and hazard [29]. Hazards on the other hand represent the potential occurrence of a natural or human-induced physical event or physical impact that may cause loss of life, injury, or other health impacts, as well as damage and loss of property, livelihoods, services, ecosystems, and environmental resources. In other words, hazards refer to physical events and trends or physical impacts [29]. To further support the above, the vulnerability sourcebook [30] reaffirms the definition of vulnerability as, \"The propensity or predisposition to be adversely affected. Vulnerability encompasses a variety of concepts and elements including sensitivity or susceptibility to harm and lack of capacity to cope and adapt.\" The vulnerability assessment reveals that tailored support can play an important role in assisting the rural poor in adapting to climate change [30]. Vulnerability is frequently influenced by the amount and rate of climate change, as well as the variability to which any specific cropping system is subject (wheat, barley, and maize yields) [20]. Vulnerability is a function of (1) the exposure index, which describes the effects of climate change on components of the climate such as precipitation and temperature; (2) the sensitivity index, which describes the extent to which climate change has a detrimental effect on a cropping system; and (3) the adaptive capacity index, which measures a production system's ability to react to shocks produced by precipitation reduction. Literacy and poverty rates, for example, can serve as proxies for adaptive ability [20; 31] (Figure 1).  
In this study, the vulnerability index is based on time series crop yields, precipitation, and socio-economic proxies of adaptive capacity [21; 32; 33]. The approach used by this study for framing vulnerability improves the quantification of variables, such as adaptive capacity, that cannot be easily quantified. Data on adaptive capacity are difficult to come by throughout most of Africa, forcing scientists to rely on proxies. Literacy and poverty rates are the two most important proxies of adaptive capacity in Africa in general and Morocco in particular [21] (Figure 1). This is because poverty reduction and enhancement of literacy have been identified as key drivers of development across Africa, including Morocco.
## 2. Adsorptive Removal of Contaminants
### Organic Pollutants  
SDB has been extensively explored for removing organic contaminants, including dyes, pharmaceuticals, and industrial chemicals. Its porous carbon framework and oxygenated functional groups promote hydrogen bonding and \(\pi\)-\(\pi\) interactions with these contaminants [9; 20]. Dye adsorption is one notable area: a sludge biochar removed various textile dyes with capacities of 20-60 mg/g (e.g., up to \(\sim\)59 mg/g for Direct Red dye) [21]. SDB used for dye removal retained approximately 85% of its initial capacity after three cycles, underscoring the need for regeneration optimization [22]. Surface activation can greatly enhance these capabilities. For example, novel oxidative activation increased the adsorption of tetracycline antibiotics from 34 mg/g (original biochar) to 154 mg/g [9]. Researchers have also tailored sludge biochar with specific functional groups to broaden pollutant affinity. For instance, introducing nitrogen through ammonia or amino-rich additives can generate amine and cyano functional groups, enhancing the adsorption of both cationic and anionic organic contaminants [23]. Tannery SDB has been engineered as a versatile adsorbent capable of simultaneously removing dyes, phenols, and antibiotics [24]. Some  
Figure 1: Schematic diagram of the adsorption mechanism of SDB. Reprinted with permission from copyright [14], Elsevier.  
studies demonstrate the effective removal of many pharmaceutical compounds through \(\pi\)-electron interactions and the partial adsorption of short-chain PFASs, while longer-chain variants necessitate SDB surface modifications [25; 26]. These advances indicate that SDB can be engineered for the high-capacity removal of diverse organic contaminants from wastewater. Overall, electrostatic attraction, \(\pi\)-\(\pi\)interactions, and hydrogen bonding are the three main adsorption mechanisms that have been widely investigated, especially for the removal of organic contaminants.
## 5 Case Study: Experimental Validation in a Representative Smart Building Scenario
### Implementation of the JFML-IoT-Based Energy Management System
#### 5.4.1 Sensor and Actuator Object Instantiation
_IEEE Access_**2024**, _12_, 56097-56109. [CrossRef]
* [PERSON] et al. (2025) [PERSON]; [PERSON]; [PERSON] Deep-Fuzzy Logic Control for Optimal Energy Management: A Predictive and Adaptive Framework for Grid-Connected Microgrids. _Energies_**2025**, _18_, 995. [CrossRef]
* [PERSON] et al. (2023) [PERSON] [PERSON]; [PERSON]; [PERSON] Fuzzy Controlled Wavelet-Based Edge Computing Method for Energy-Harvesting IoT Sensors. _IEEE Internet Things J._**2023**, _10_, 18909-18918. [CrossRef]
* [PERSON] et al. (2024) [PERSON]; [PERSON]; [PERSON] Fuzzy Machine Learning: A Comprehensive Framework and Systematic Review. _IEEE Trans. Fuzzy Syst._**2024**, _32_, 3861-3878. [CrossRef]
* [PERSON] and [PERSON] (2023) [PERSON]; [PERSON]; [PERSON] Integration of IoT sensor data and BIM for real-time energy performance simulation. _Autom. Constr._**2023**, _149_, 104931. [CrossRef]
* [PERSON] et al. (2023) [PERSON]; [PERSON]; [PERSON]; [PERSON] A hybrid BIM and fuzzy logic model for optimizing building energy performance. _Sustainability_**2023**, _15_, 16436. [CrossRef]

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###### Abstract  
The humid highlands of the Galapagos are the islands' most biologically productive regions and a key habitat for endemic animal and plant species. These areas are crucial for the region's food security and for the control of invasive plants, but little is known about the spatial distribution of its land cover. We generated a baseline high-resolution land cover map of the agricultural zones and their surrounding protected areas. We combined the high spatial resolution of PlanetScope images with the high spectral resolution of Sentinel-2 images in an object-based classification using a RandomForest algorithm. We used images collected with an unmanned aerial vehicle (UAV) to verify and validate our classified map. Despite the astounding diversity and heterogeneity of the highland landscape, our classification yielded useful results (overall Kappa: 0.7, \(R^{2}\): 0.69) and revealed that across all four inhabited islands, invasive plants cover the largest fraction (28.5%) of the agricultural area, followed by pastures (22.3%), native vegetation (18.6%), food crops (18.3%), and mixed forest and pioneer plants (11.6%). Our results are consistent with historical trajectories of colonization and abandonment of the highlands. The produced dataset is designed to suit the needs of practitioners of both conservation and agriculture and aims to foster collaboration between the two areas.  
agriculture; conservation; galapagos; image fusion; invasive species; land cover; planestcope; random forest; sentinel-2; uav 2020  
control the spread of invasive plants to nearby native dominated patches or to the protected lowlands while simultaneously contributing to local food security [5; 6; 7].  
The humid highlands are also an irreplaceable foraging habitat for Galapagos giant tortoises and crucial nesting habitat for several endemic bird species [8; 9]. Adult tortoises migrate every year from their nesting grounds in the lowlands to the highlands seeking food, and at high elevations, their seasonal diet has become dominated by introduced and invasive species [10]. Thus, some landowners of Santa Cruz island that are located in the tortoises' migratory path have turned to tourism for their primary source of income, allowing tortoises to wander freely through their land and charging tourists to see tortoises in their \"natural\" environment [11]. However, land parcels are divided by barbed wire and living fences made up mainly of _Erythrina smithiana_. _E. smithiana_ grows into dense thickets that act as barriers for tortoises and are known to interrupt their migratory paths [12].
# Adaptive Pre/Post-Compensation of Cascade Filters in Coherent Optical Transponders
## 7 Discussions of Results and Research Findings
### Comparison of Contribution to Other Published Methods
#### 7.1.3 Jitter Effects  
Based on the literature review, we noticed that the jitter at the Tx DAC is not being considered. While thermal/transistor noise has a white spectral profile at DAC, the noise at low frequencies is dominated by quantization effects, while the high-frequencies signal components are more susceptible to timing uncertainty. The effects of aperture and sampling clock jitter on an ideal DAC SNR can be forecasted by the following simple analysis. Assuming s is signal, as a function of time t, S is itsfrequency transform as a function of frequency f and \(\Delta\) is the rms of the jitter. Therefore, the degradation in DAC SNR due to clock jitter:  
\[DAC_{SNR, jitter}\coloneqq\frac{E\Big{[}s^{2}(t)\Big{]}}{E[n^{2}(t)]}=\frac{ \int_{f=0}^{W}S(f)df}{4\pi^{2}\Delta^{2}.\int_{f=0}^{W}S(f)f^{2}df}. \tag{20}\]  
From the electronics side or RF (radio frequency), on both the transmitter and receiver clocks, the dominant sources of jitter in PLLs are the VCO (voltage controller oscillator) and CP (charge pump). With respect to the VCO, it is subject to jitter accumulation which manifests itself as high phase noise at low-frequency offsets with respect to the carrier frequency. This can be expressed with the following simplified [PERSON]'s frequency domain equation [46]. Other sources, as shown in [47], are thermal noise, and flicker noise in clock buffers, the internal aperture of the ADC, supply variation and electromagnetic coupling. The latter is due to the decrease in electronic channel lengths when circuits are integrated on the same substrate. Some jitter, or ripple effects, is caused by closed-loop control. The design of the loop filter order and bandwidth plays a major role in controlling the total jitter induced. Since in frequency-domain the signal is treated as a summation of sinusoidal tones, we will illustrate, in Figure 9, the impact of jitter the SNR (signal to noise (error) ratio) as the frequency and amplitude increase. The latter is key in our case since the less pre-compensation performed at the transmitter, the launched SNR is more resilient to jitter due to Tx PLL and timing misalignment of DAC sub-channels. It does not apply to the receiver since the processing is posterior to ADC and the associated clock wandering.
## 2 Materials and Methods
### Pelton Bucket Geometry Optimization  
To obtain the bucket's optimal geometry for improving hydraulic efficiency \(\eta_{h}\) and reducing energy losses \(P_{wi}\), metaheuristic optimization methods have been considered. These methods search for an approximate optimal solution to a problem without considering its convergence in only one value (or set of values), providing accurate results with fewer computational costs [23].  
Genetic algorithms are metaheuristic-based evolutionary algorithms capable of generating high-quality solutions for optimization problems. These algorithms are an analogy of the natural behavior of living things and evolutionary theory, and start with an initial population of unique random candidate solutions with their own properties or genome. Every solution's suitability is evaluated as an objective function of the optimization problem being solved. As in natural system evolution, over generations, the new offspring are obtained by the crossover and mutation of the previous population's individual characteristics, to make the new candidates more suitable to solve the problem.  
In this study, the hydraulic efficiency, friction, and windage losses expressions provided by [24] have been taken into account for the optimization of the Pelton bucket geometry. While [PERSON]'s hydraulic efficiency formula considers geometrical features of the bucket such as the exit angle \(\beta_{1}\), inlet angle \(\beta_{2}\), and peripherial speed coefficient \(K_{u}\), the EIC60041 standard [25] friction and windage losses formula considers the length \(L\), width \(B\), and the casing dimensions \(B_{io}\), \(B_{iu}\), \(R_{io}\), better described in Figure 4.  
\[\eta_{h}=2k_{u}(1-k_{u})\bigg{(}1-cos\frac{\beta_{2}}{2}\bigg{)}+2k_{u}(1-k_{u })\bigg{(}cos\frac{\beta_{2}}{2}cos\beta_{1}\bigg{)} \tag{5}\]  
\[P_{wi}=15w^{3}(D_{P}+L)^{5}\bigg{(}\frac{B}{D_{P}+L}\bigg{)}^{\frac{1}{4}} \bigg{(}\frac{Bio}{D_{P}+L}\bigg{)}^{\frac{3}{2}}\bigg{(}\frac{Biu}{D_{P}+L} \bigg{)}^{\frac{5}{4}}\bigg{(}\frac{Bio}{D_{P}+L}\bigg{)}^{\frac{7}{4}} \tag{6}\]  
Figure 3: [PERSON] buckets’ graphic design method.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \(B\) & \(L\) & \(D\) & \(f\) & \(M\) & \(E\) & \(\beta_{1}\) & \(\beta_{2}\) & \(L\) & \(\beta_{3}\) & \(\beta_{4}\) \\ \hline \(2.5d\) & \(2.5d\) & \(0.85d\) & \(0.8d\) & \(d\) & \(0.3d\) & \(10^{\circ}\) & \(
# RDSP: Rapidly Deployable Wireless Ad Hoc System for Post-Disaster Management
## 4 Performance Evaluation
### Performance Metrics  
To investigate the performance of the RDSP scheme, the following metrics were used:  
* Distance covered: refers to the total distance covered by intermediate relays using wireless transmission
* End-to-end delay: refers to the average time it takes for a request message sent from the client device to reach the server device.
* Round-trip delay: refers to the average time it takes for a message sent from the client device to reach the server device and an acknowledgment message sent back from the server device to reach the client device.
* Message delivery ratio: refers to the percentage of messages successfully received by the server device.
* Network overhead: refers to the total number of control messages sent by relays on different paths.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Parameter** & **Value** \\ \hline Deployment scenario & COMSATS University Campus \\ Deployment area & 153,000 m\({}^{2}\) \\ Total relays & 23 \\ Total Paths & 4 \\ Relays Transmission range & 90 m \\ Routing protocol (RDSP) & DIA and MMN \\ Routing protocol (UF) & DSDV \\ Hello interval (RDSP) & 2 sec \\ Hello inerval (UF) & 1 sec \\ Request messages & 100 \\ Experiment duration & 550 s \\ \hline \hline \end{tabular}
\end{table}
Table 1: Experimental Parameters  
Figure 8: Deployment of the RDSP system.
## 2 Materials and Methods
### Machine Learning Models
#### 2.7.3 Support Vector Machine (SVM)  
The SVM algorithm is a non-parametric classifier. In dynamic modeling, an SVM offers a simple structure, fast convergence speed, and high generalization ability, making it a favorable choice over neural networks [27]. The SVM creates an optimal boundary to separate the classes in the feature space and maximize the separation between classes [28]. The surface of the boundary is called the hyperplane. All the data points nearest to the hyperplane are called support vectors. In the SVM algorithm, classes are separated using a geometric criterion to find the optimum separation [29]. SVMs were initially introduced to separate linear class boundaries. This limitation was overcome through the projection of the feature space to a higher dimension using the kernel trick [30].