esa-sceva/satcom-chunk-collection huggingface dataset:
# A Cooperative Transmission Scheme in Radio Frequency Energy-Harvesting WBANs
## 4 Cooperation Scheme
### Cooperation Phase  
During the cooperation phase, the coordinator is responsible for selecting proper relays for common nodes. The coordinator calculates the optimal cooperation scheme and packs the related information in the command packets, which are broadcast during the download period. If relay \(r_{i}\) is selected as a cooperator to forward a packet uploaded by common node \(c_{j}\), the probability that the packet can be correctly received by the coordinator is  
\[\mathbb{P}_{ij}=a_{j}+b_{i}c_{ij}-a_{j}b_{i}c_{ij}. \tag{11}\]  
Compared with uploading without the cooperating relay, the increment of the probability of the packet being successfully received is  
\[\mathbb{P}_{ij}^{\Delta}=b_{i}c_{ij}-a_{j}b_{i}c_{ij}. \tag{12}\]  
Common nodes are relatively far from the coordinator; these nodes harvest less energy but need more energy to ensure that the uploaded packets can be successfully received by the coordinator. Compared with relay nodes, common nodes have poor network performance, which is the short board of the network. The main goal of the cooperative transmission is to upgrade the performance of the common nodes with the help of the relay nodes. Maximization of the sum value of the increment of the packet reception probability is desired when the coordinator schedules the cooperation plan.  
Let \(\xi_{ij}\) denote whether \(r_{i}\) acts as a relay node to forward packet transmitted by common node \(c_{j}\). The value \(\xi_{ij}\) equals one when \(r_{i}\) is assigned to the relay packet uploaded by \(c_{j}\); otherwise, \(\xi_{ij}\) is zero. Then, the problem can be formulated as  
\[max\sum_{i=1}^{m}\sum_{j=1}^{n}\xi_{ij}\mathbb{P}_{ij}^{\Delta} \tag{13}\] \[s.t.\sum_{i=1}^{m}\xi_{ij}\in\{0,1\},j=1,2,...,n\] \[\sum_{j=1}^{n}\xi_{ij}L_{j}\leq l_{i},i=1,2,...,m\]  
The cooperation plan among sensors can be obtained by solving Equation (13), which is a 0-1 multiple knapsack problem. The problem can be solved by binary search algorithm [32] or greedy algorithm, and a set of \(\xi_{ij}\) can be gained. If relay node \(r_{i}\) is assigned to the relay packet transmitted by common node \(c_{j}\), \(r_{i}\) will turn to receive mode during the time slot assigned to the common node \(c_{j}\) and forward the received packet to the coordinator.
## 4 Discussion
### Method Comparison  
Multiple linear regression (MLR) is a widely used method in supervised learning, especially for understanding and predicting linear relationships between variables. Owing to its optimal modelling strategy within the linear causal category, MLR often outperforms other standard models [60]. The motivation for using MLR stems from its established reputation as a simple, traditional model. Its strength lies in its capacity to capture linear relationships effectively between multiple predictors (independent variables) and a single response (dependent variable), making it particularly appealing for data analysis [29]. Additionally, MLR can provide an empirical equation for calculating shear strength using multiple inputs, offering utility in geotechnical engineering applications and practices.  
Despite these strengths, MLR has its limitations, including its inability to handle nonlinear correlations or complex interactions between input data and the target variable. In response to these limitations, random forest regression (RFR) was employed. This robust and versatile technique navigates these challenges, offering high prediction performance [61]. The RFR model is an ensemble of regression trees, building a large number of these trees before combining them for a final prediction [62]. Moreover, RFR provides insights into feature importance, thereby helping to unravel underlying relationships in the data.  
A comparison of the results and performance of both models can offer valuable insights into their respective strengths and weaknesses. This allows for an evaluation of how well each model captures the underlying patterns in the dataset and a determination of which model yields better results.  
Table 6 presents a comparative performance analysis of MLR and RFR models applied to training, testing, and 10-fold cross-validation datasets. When comparing MAE values, the RFR model shows superior performance, particularly with the training data, where it achieved an MAE of 3.79, compared to 8.31 with MLR. This trend of enhanced performance continues in the testing data, but not in the cross-validation, where MLR produced a slightly better MAE result. For RMSE and RMSLE, RFR consistently outperforms MLR across all datasets, but not in the cross-validation, where MLR outperformed RFR in RMSE. In terms of R\({}^{2}\) values, which indicate the goodness of fit, RFR shows a slight edge in the training and testing data, but MLR secures a slightly higher value in the 10-fold cross-validation data. Despite some minor variances, both models demonstrate robust predictive capabilities, although RFR generally exhibits stronger performance, particularly on the training and testing datasets.
## 3 Results and Discussion
### SCB Test
#### 3.2.3 Effect of NMAS on the SCB Test Result  
As it can be seen from Figure 19, the \(G_{\rm f}\) value of sample 3 is bigger than that of sample 7, and the \(G_{\rm f}\) value of sample 5 is bigger than that of sample 9 which indicates that the nominal maximum aggregate size is negatively correlated with the cracking resistance of mixtures. As it can be seen from Figure 20, the FI value of sample 3 is bigger than that of sample 7, and the FI value of sample 5 is bigger than that of sample 9, which shows that the nominal maximum aggregate size is positively correlated with the crack propagation speed of asphalt mixtures. Thus, the asphalt mixtures having smaller NMAS could get better anti-crack ability. It can be seen, from Table 4, that in the design of asphalt mixtures, the asphalt mixtures having bigger NMAS have lower optimum asphalt content. When the asphalt content is lower inside the asphalt mixtures, the stress transferring ability of the asphalt inside the asphalt mixtures will be weaker, which could make the asphalt mixtures having smaller NMAS have better anti-cracking ability.  
Figure 19: Effect of NMAS on \(G_{\rm f}\).  
Figure 18: Effect of gradation type on FI value.
## 1 Introduction
This adaptability makes the LO well-suited for dynamic environments, where the optimization landscape may change over time [28]. Furthermore, the LO efficiently identifies and exploits promising regions of the solution space, ensuring that the most relevant and high-quality model updates are selected for communication [29; 30]. This reduces communication overhead and contributes to the FL framework's overall effectiveness, enhancing the convergence speed and robustness of the learning process. This paper introduces a pioneering communication-efficient FL framework harnessing the power of the LO. The proposed framework significantly reduces communication overhead while preserving model accuracy by dynamically selecting the best client models to share and updating the global model accordingly. This breakthrough enhances the scalability and sustainability of FL in resource-constrained scenarios and extends its applicability to real-world sustainable development initiatives. Through empirical studies and comparisons, we delve into the intricate details of the proposed framework, contributing a comprehensive understanding of how the integration of FL, metaheuristic algorithms, and, specifically, the LO can advance the field and foster sustainable solutions in the era of ML. The primary contributions can be summarized as follows:  
* Introduces a novel FL framework employing the LO for efficient communication. * Achieves significant communication cost reduction compared to existing methods. * Maintains comparable or superior model performance on diverse datasets. * Enables more scalable and sustainable FL applications for real-world development goals. The structure of this paper is as follows. In Section 2, the relevant literature is reviewed. A detailed explanation of the Lemurs optimizer is provided in Section 3, and, in Section 4, the FedLO method is presented. The results are analyzed and discussed in Section 5, the findings are concluded, and an outline for future work is provided in Section 6.
## 1 Introduction
However, more complex DL methods typically provide better results in tasks, such as natural language processing and image recognition tasks, which is why it could be a reasonable task to evaluate the applicability of these methods for anomaly and malware detection on a host level. Experiments were performed using the attack-caused Windows OS system calls traces dataset (AWSTD) [15], which is currently considered to be the biggest collection of system calls, generated by more than 12,000 of malware samples and exploits, running on Windows OS. Two complex dual-flow models, long short-term memory fully convolutional network (LSTM-FCN) and gated recurrent unit (GRU)-FCN, were tested; these models were previously proved to be the most efficient in tests on many datasets [16; 17]. In addition, two newly introduced models, AWSTCD-CNN-LSTM and AWSTD-CNN-GRU, were tested. The results obtained were compared with simpler single-flow CNN models, both static and dynamic. Although results by the dual-flow models were good (up to 99.2%), the performed analysis showed that they are, in fact, the same as single-flow models (99.3%) if error rate (1%) is evaluated. Training and anomaly detection times, which are crucial in the intrusion containment process, of dual-flow models, were higher--up to 3- to 7-fold in several cases--compared to single-flow models, making practical use of complex models non-recommendable for the anomaly and malware detection task on system call data presented as univariate times series data. On the other hand, the newly tested AWSTD-CNN- static (S) single-flow model demonstrated 30% better training and testing times, preserving identically high detection accuracy (99.3%).
## 3 Results and Discussion
### Design Process
#### 3.2.3 Design for Manufacturing  
The in-house manufactured parts were slightly modified to achieve draft angles that would facilitate the manufacturing process. In areas where it was not possible to modify the negative draft angle, molds were devised to allow a smoother removal of the part. The tolerances were defined using GD&T as a result of stack up tolerances for the assembled parts. The carbon fiber parts presented in Figure 6 were created from soft molds, which, in turn, were created from hard molds by a Roland model MDX-40A CNC.
Furthermore, according to previous studies, meteorological factors such as radiation, water vapor, etc., and vegetation photosynthesis-related parameters provided the sources and energy for NAI formation [15; 17]. Human activities and air pollutants are potentially quenching the existence of NAIs [2; 7]. Topographic factors indirectly influence the NAIs via changing the vegetation and meteorological factors [17]. Thus, these five categories are incorporated into our NAI model. As an important part of the large urban agglomeration in China [19], in the past decades, Zhejiang Province has experienced rapid urbanization and industrialization with economic growth, which has led to a large number of air-pollution-related health problems [25; 26]. The rising demands for clean air have stimulated the need for NAI-related ecosystem services assessment. In this study, we used a large number of site-based NAI data that were continuously monitored to build the NAI inversion model. Meanwhile, satellite-based SIF was introduced in the model as an alternative to plant photoelectric progress. With the help of geographic information technologies, our study will realize the spatial-temporal modeling of regional NAI dynamics with higher resolutions in Zhejiang Province. We aimed to (1) evaluate the sensitivity of the bio-geophysical parameters to NAI dynamics, (2) generate satellite-based NAI estimation based on the RF model, and (3) explore the NAI dynamics across the Zhejiang Province, China.
# Speaker Diarization: A Review of Objectives and Methods
## 3 Analysis of Speech Signals
### Advanced Spectral Measures  
A common approach for speech spectral analysis is the _Mel-frequency cepstral coefficients_ (MFCCs) [27]. They use similar filtering as BFs (as well as the Mel scale), but reduce to a smaller set of parameters, typically 13 for telephone speech. This small number is sufficient to discriminate formants to within 100 Hz (which is approximately the smallest amount used by speakers to distinguish among vowels in most languages). For the broad spectral purposes of SD (which need not identify individual sounds), precision of analysis can be lower, thus allowing fewer parameters, if cost reduction is important.  
Another common spectral analysis method, _linear predictive coding_ (LPC), uses an all-pole model of the speech spectrum [28], to directly focus on the resonances of the VT (spectral poles, or intense resonances, are far more important perceptually than other frequency aspects). Standard LPC is common in cellular speech telephony, but is rarely used for SD, as the other speech measures we discussed suffice for speech continuity decisions and SV.
## 2 Cold Plasma Technology
### Plasma Production  
CP can be generated using various gases and produced by a wide range of methods. Each distinct method has a broad range of uses. Plasmas are formed by providing power to a neutral gas, which induces charge transporters. When high-energy electrons or photonsinteract with neutral molecules and atoms in the feed gas, electrons and ions are formed inside the gaseous phase (electron-impact ionization as well as photoionization) [17; 18].  
Plasma technology is classified into thermal and low-temperature plasma methods on the basis of how the plasma is generated. According to various authors, thermal plasma comprises thermodynamically balanced ions, electrons, and gas molecules. Low-temperature plasma is generally categorized as semi-equilibrium plasma, in which there is a local thermodynamic equilibrium among species such as electrons and gas molecules, and non-equilibrium plasma, in which electrons have higher temperatures and gas molecules have moderate temperatures, with lower temperatures for the whole system [19; 20; 21].  
The structure of the fed gas implemented for CP influences the generation of reactive species. These substances are primarily in charge of antimicrobial activity. The mechanism of food preservation differs significantly because of the formation of various reactive species by the various gases and generators used [21]. Plasma reactive species can separate covalent bonds and initiate various reactions crucial for numerous technological applications [22].
## 2 Material and Methods
### Data Sources  
In this study, MODIS GPP (MOD17A2H) and MODIS ET (MOD16A2) products from 2003 to 2022 were utilized to calculate WUE. The MOD17A2H provides cumulative GPP data with an 8-day composite period at a resolution of 500 m, while the MOD16A2 product provides global terrestrial evapotranspiration data at the resolution and temporal frequency [6]. Additionally, we used the MODIS land cover type version 6.1 (MCD12Q1 v061), which also has a resolution of 500 m. Data from multiple sources were incorporated spanning the same period for the purpose of detecting the drivers of WUE. Air temperature at 2 m, and VSWC at 7 cm below the ground, with a resolution of 0.1 degrees, were obtained from ERA5-Land monthly averaged dataset ([[https://cds.climate.copernicus.eu](https://cds.climate.copernicus.eu)]([https://cds.climate.copernicus.eu](https://cds.climate.copernicus.eu))). The net radiation was included in the ERA5-land dataset. The Climate Hazards Group Infrared Precipitation with station (CHIRPS) precipitation dataset was selected in our study, as the EAR5-land dataset has a relatively large deviation from the mainstream precipitation products in mainland China ([PERSON] et al., 2021; [PERSON] et al., 2021) [42; 43]. The CHIRPS is a 40-year quasi-global (50\({}^{\circ}\) S-50\({}^{\circ}\) N, 180\({}^{\circ}\) E-180\({}^{\circ}\) N), with a resolution of 0.05\({}^{\circ}\) at monthly  
Figure 1: The location of study area, with samples from the vegetation.  
scale. Monthly VPD data from Terra-Climate, with a resolution of 1/24\({}^{\circ}\) (\(\sim\)4 km) was also used. The 8-day MODIS LAI product (MOD12A2H), with a 500 m resolution, along with the 16-day MODIS EVI product (MOD13A1) at the same resolution, were incorporated to evaluate the effect of vegetation phenology on WUE. The yearly Standardized Precipitation Evapotranspiration Index (SPEI) was used to identify the drought period. Detailed information on the datasets is depicted in Table 1.
# Cooperative Terrestrial-Underwater FSO System: Design and Performance Analysis
## 1 Introduction
These fluctuations give rise to ocean turbulence which is one of the main factors affecting the quality of UWOC links. Among several distributions, the Weibull distribution provides excellent accuracy for the scintillation generated in underwater links [9; 23]. This work presents a novel communication system that extends optical wireless networks from terrestrial to underwater environments, considering different channel degradation factors that reflect a practical scenario. In particular, this paper proposes and evaluates the design of a cooperative terrestrial-underwater FSO (FSO-TU) system. The FSO-TU system is a combination of terrestrial and underwater FSO links that form a hybrid network. In this work, a new closed-form expression for calculating the average bit error rate (BER) in FSO-TU systems is derived. This new expression carefully considers various network degradation factors for a realistic evaluation of hybrid networks not previously considered in other works such as [24]. Therefore, in order to include these new degradation factors, a novel channel model for the end-to-end FSO-TU system is developed. In particular, our new model considers atmospheric attenuation, geometric spread, pointing errors and atmospheric turbulence for the terrestrial link and oceanic attenuation, geometric spread and oceanic turbulence for the undersea link. In this respect, Gamma-Gamma distribution is used to model the effect of atmospheric turbulence while the Weibull distribution is chosen to model ocean turbulence. Finally, performance evaluation of the proposed cooperative FSO-TU system is carried out and discussed under various conditions and real scenarios. The rest of the paper is organised as follows. Section 2 presents a model of the proposed system. The main channel degradation phenomena for the terrestrial and underwater link are discussed in Section 3. Then, in Section 4, the closed-form expression for the average BER is derived. The analysis of different realistic scenarios is performed in Section 5. Finally, the most relevant conclusions are summarized in Section 6.
# Research on Side-Channel Analysis Based on Deep Learning with Different Sample Data
## 2 Four Deep Learning Models
### Long Short-Term Memory Network  
To improve the gradient explosion or gradient disappearance problem of the simple recurrent neural network, the Long Short-Term Memory Network (LSTM) based on gating is introduced, which is a variant of a cyclic neural network [27; 28] and is widely used in speech recognition, text generation, machine translation, image description, and video marking. Due to its complex structure, it has high computational complexity and is difficult to carry out in-depth learning. To control the path of information transmission, the three gates in the gating mechanism introduced by the LSTM network are the input gate \(i_{t}\), the forgetting gate \(f_{t}\), and the output gate \(o_{t}\). The input gate controls the amount of information to be stored in the candidate state \(\widetilde{c_{t}}\) at the current time, the output gate determines the amount of information transmitted from the internal state \(c_{t}\) at the current time to the external state \(h_{t}\), and the forgetting gate controls whether the internal state \(c_{t-1}\) information at the previous time is retained or discarded. The expression is as follows:  
\[\begin{cases}\begin{aligned} i_{t}=\sigma(W_{i}x_{t}+U_{t}h_{t-1}+b _{i})\\ f_{t}=\sigma(W_{f}x_{t}+U_{f}h_{t-1}+b_{f})\\ o_{t}=\sigma(W_{o}x_{t}+U_{o}h_{t-1}+b_{o})\end{aligned}\end{cases} \tag{12}\]  
Among them, the input of the current time is \(x_{t}\), \(\sigma(\cdot)\) is the logistic function, and the corresponding interval of the value range is (0, 1). The recurrent cell structure of the LSTM network is shown in Figure 4 below.  
The timing dependency of the LSTM network can be expressed by the following formula:  
\[\begin{bmatrix}\widetilde{c_{t}}\\ o_{t}\\ i_{t}\\ f_{t}\end{bmatrix}=\begin{bmatrix}tanh\\ \sigma\\ \sigma\\ \sigma\end{bmatrix}\left(W\begin{bmatrix}x_{t}\\ h_{t-1}\end{bmatrix}+b\right) \tag{13}\]  
\[c_{t}=f_{t}\odot c_{t-1}+i_{t}\odot\widetilde{c_{t}} \tag{14}\]  
\[h_{t}=o_{t}\odot tanh(c_{t}) \tag{15}\]  
Figure 4: Recurrent structure of LSTM network.
## 3 Results Analysis
### Experimental Results for Time Measurements
#### 3.1.2 CPU-Bound Load Test Results  
CPU-bound background workload can be expected to result in worsened latencies due to limited hardware resources such as memory bandwidth and shared caches [20]. The results of the experiment with CPU-bound load are shown in Figures 6 and 7. They show that there is no big effect on median values in comparison to the Idle system testbed. However, it causes significantly longer tails in the CCDF plot (Figure 7), which imply significant influence on the mean of the distributions (dashed lines). The means are shifted to the right, to the region of 1 \(\upmu\)s. However, the 99.9% percentile for the _TSC_ based timer overhead is, as in the idle system case, about 27 ns for all virtualization platforms, besides the Virtual Box case, for which the value is approximately 2 \(\upmu\)s. The shape of CCDF for this experiment is quite similar to the idle case, however this load affects mostly on rare and heavy tails.  
Figure 5: CCDF representation of measurements of time cost for the idle testbed.  
Figure 6: Measurements of timer cost for the CPU testbed on: (**a**) Host OS; (**b**) Xen; (**c**) Virtual Box; (**d**) QEMU; and (**e**) VMware ESXi.  
Figure 7: CCDF representation of measurements of time cost for the CPU testbed.