esa-sceva/satcom-chunk-collection huggingface dataset:
# Enhanced Privacy-Preserving Architecture for Fundus Disease Diagnosis with Federated Learning
## 4 Results
### Statistical Significance of Model Performance  
To validate the performance improvements of the proposed FL architecture compared to previous approaches, we conducted statistical significance tests on the accuracy results presented in Table 2. Specifically, we computed 95% confidence intervals (CIs) for accuracy scores and performed paired _t_-tests to determine whether the observed improvements were statistically significant.  
For the accuracy results of our FL approach (84.88%) and the CL model (86.63%), we computed the confidence intervals using the standard error (SE) of accuracy values obtained from multiple training runs. The 95% confidence intervals are:  
Proposed FL approach (VGG-19 with wFedAvg and k-client selection training): 84.88% \(\pm\) 1.12% (CI: [83.76%, 86.00%]).  
CL (VGG-19): 86.63% \(\pm\) 1.05% (CI: [85.58%, 87.68%]).  
The overlap in confidence intervals suggests that, while there is a measurable difference, the reduction in accuracy in our FL approach remains within an acceptable range. To further confirm whether the difference in performance is statistically significant, we conducted a paired _t_-test between the accuracy distributions of the FL and CL models. The resulting _p_-value = 0.032, which is below the conventional threshold of 0.05, indicating that the accuracy difference between our FL approach and the centralized model is statistically significant.  
Additionally, compared to conventional FL approaches, which experience an average accuracy reduction of up to 55%, our proposed method showed a significantly smaller reduction of only 1.85%. Conducting a one-sample _t_-test comparing our FL accuracy reduction (1.85%) against the mean accuracy reduction in conventional FL setups (55%) resulted in \(p\) < 0.001, further confirming that our approach provides a statistically significant improvement.  
These results demonstrate that, while our FL architecture does exhibit a slight accuracy reduction compared to CL, this reduction is minimal and statistically significant when compared to conventional FL methods. Our approach successfully mitigates the trade-offs inherent in FL while maintaining privacy-preserving benefits.  
Thus, the proposed FL architecture achieves competitive accuracy with CL while preserving client data privacy and computational efficiency.
# Locating Infectious Sources Using Bluetooth System of Smart Devices
## 2 System Composition Architecture
### Low-Power Bluetooth Technology
#### 2.3.2 Received Signal Strength Indication  
The parameters related to distance estimation are mainly Received Signal Strength Indication (RSSI). The received Bluetooth signal RSSI has a certain mathematical relationship with the distance. According to the signal strength received by the transmitting end and the receiving end, the propagation loss of the signal is calculated and converted into the distance through the fitting regression function [32]. The calculation method of the distance between two smart mobile terminals may be given as  
\[\text{Dist}(\text{RSSI}_{1},\text{RSSI}_{2})=10^{\frac{[\text{RSSI}_{1}+\text{ RSSI}_{2}]/2-\text{A}_{1}}{10\text{A}_{\text{H}}}}+\delta \tag{1}\]  
where Dist is the distance between two devices, \(\text{RSSI}_{1}\) is the signal strength from the Bluetooth port of the second mobile terminal to the first one, \(\text{RSSI}_{2}\) is the signal strength from the Bluetooth port of the first mobile terminal to the second one, \(\text{A}_{1}\) is the Bluetooth channel attenuation coefficient under the standard 1-m distance, \(\text{A}_{\text{tt}}\) is the environmental attenuation factor, and \(\delta\) represents the environmental correction parameters. According to the inverse square law, RSSI is inversely proportional to the square of the distance.  
The above distance Dist is the estimated distance between two smart mobile terminals. Under normal circumstances, according to the surrounding environment, the value of \(\text{A}_{1}\) can be given as 59, the value of \(\text{A}_{\text{tt}}\) is 2, and the value of \(\delta\) is 0.2 [33].  
Different environments have different effects on the variation of RSSI, even if the distance between any two devices in these environments is the same. Therefore, environmental factors need to be considered when applying the path loss model to estimate the distance for a given RSSI. After a comparative analysis of filtering algorithms, [PERSON]'s algorithm is the most effective solution [34]. [PERSON]'s algorithm is a linear filter and the basic idea is to use the minimum mean square error as the best estimate of time and to complete the estimation of the state variables by the estimate of the previous moment and the observation of the current moment [35].  
Therefore, we preprocess the RSSI of the collected broadcast packets to obtain the corrected RSSI values. The [PERSON] filter model includes the following equations.  
The [PERSON] filter time update equations are  
\[\begin{array}{c}\hat{\text{x}}_{\text{k}+1}^{-}=\text{A}_{\text{k}}\hat{ \text{x}}_{\text{k}}^{-}+\text{Bu}_{\text{k}}\\ \hat{\text{P}}_{\text{k}+1}^{-}=\text{A}_{\text{k}}\text{P}_{\text{k
# Fog over Virtualized IoT: New Opportunity for Context-Aware Networked Applications and a Case Study
## 5 Performance Evaluation
### Test Scenario  
In order to guarantee the accuracy and timeliness of the VC information transmission, it is necessary to analyze and act upon the data in less than a second. Data are sent to the Cloud for historical analysis, BD analytics, and long-term storage with less latency. For example, each of the thousands or hundreds of thousands of FNs might send periodic summaries of the data grid to the Cloud for historical analysis and storage.  
In the test carried out, the FNs are assumed to be placed over a spatially limited Intranet. We consider a physical topology with four heterogeneous FNs. In order to model the CPU consumptions of the FNs, we refer to the Intel R-CoreTM 2 CPU Q6700 with 2.67 GHz frequency rate and 4 GB of RAM memory. In order to confirm the correctness of the proposed infrastructure in Figure 1, we require an evaluation to explore several resource management and scheduling techniques such as operator, application, task placement and consolidation. We evaluate some resource management policies applicable to the Fog environment with respect to their impact on latency, network usage, and energy consumption. In this framework, the VCs publish data to SVF; applications are run on Fog devices (FN) to process data coming from VCs. Indeed, it is important to test the solution with a real-world input workload and compare it with the corresponding solutions of the recent FDC-based techniques. So, in the next subsection, we will move on to this issue and address it with several simulated testbed cases.
# Predictive Modelling for Blasting-Induced Vibrations from Open-Pit Excavations
## 4 Prediction Models
### Multivariable Regression Analysis  
Multivariable regression analyses were carried out using IBM SPSS Statistics Version 26.0 (SPSS), which is a powerful statistical software package [36] that generates a simple equation for estimating output. Many researchers have performed multivariable regression analyses with ANN to compare the performance of prediction methods [15; 16; 37]. In this study, two types of multivariable regression analysis were carried out using training and validation datasets from 50 open-pit blasting construction sites to identify linear or non-linear relationships between influential factors and PPV. One was multivariable linear regression analysis (MLRA) and the other was multivariable non-linear regression analysis (MnLRA). The developed MLRA is represented by Equation (18). From seven influential factors, SW and VoP were excluded, since their partial regression coefficients had higher \(p\)-values than the significant level, 0.05. After the two factors were removed, the F and \(p\)-values of the MLRA model showed approximately 49 and 0, respectively. In addition, constant and five influential factors had \(p\)-values that were near 0. These F and \(p\)-values mean that the MLRA model is statistically significant. However, this model showed a low R of 0.495. The developed MnLRA is represented by Equation (19). This equation has been developed in exponential form following the form of the conventional empirical formula. \(p\)-values of all partial regression coefficients except for VoP were shown to be lower than the significant level, 0.05. Therefore, we removed the VoP from the input parameters. F and \(p\)-values of the MnLRA model showed approximately 898 and 0, respectively. Besides, the R of this model was high, 0.927. Here, the influential factor m was converted to -m in Equation (19) because all influencing factors and PPV are positive, while m is negative.  
\[\text{PPV}=-0.588+1.2\times 10^{-4}\text{VoD}+0.092\text{W}-0.003\text{D}-1.45 \times 10^{-6}\text{K}-0.193\text{m} \tag{18}\]  
\[\text{PPV}=0.034\text{VoD}^{0.79}\text{W}^{0.741}\text{ SW}^{-0.37}\text{D}^{-1.602}\text{K}^{0.375}(-\text{m})^{-2.248} \tag{19}\]  
Figure 5: Peak particle velocity versus scaled distance for 893 datasets.  
Note that test datasets were never used prior to the performance evaluation of the prediction methods. This means only the training and validation datasets were used to develop the ANN model, EF-1, 2 (K and m), MLRA, and MnLRA.
\begin{table}
\begin{tabular}{c c c c c} \multicolumn{5}{c}{**Logical Operators and Quantifiers**} \\ \hline \(\forall:\) for all & \(\forall_{i}:\) for any & \(\forall_{u}:\) for almost all & \(\forall:\) for each & \(\forall:\) for some & \(\forall_{u}:\) for (a) relatively small \\  & & \(\forall_{l}:\) for (a) relatively large & \(\exists:\) there exist(s) & \\ \multicolumn{5}{c}{**Comparison and Relation Symbols**} \\ \hline \(\ll:\) much less than & \(\sim:\) asymptotically equivalent & \(\gtrsim:\) asymptotically less than & \(\
ot\approx:\) not sufficiently close to \\  & & \(f(n)=O(g(n)):\exists n_{o},c>0:\delta f(n)\leq cg(n)\forall n\geq n_{0}\) & \\  & & \(f(n)=o(g(n)):\lim_{n\to\infty}\frac{(n)}{g(n)}=0\) & \\ \multicolumn{5}{c}{**Sets and Number Systems**} \\ \hline \(\mathbb{C}:\) set of complex numbers & \(\mathbb{R}:\) set of real numbers & \(\mathbb{R}_{0}:\) set of nonzero real numbers & \(\mathbb{R}_{0}^{+}:\) set of non-negative real numbers \\ \(\mathbb{R}_{-1/2}^{-}:\{x\in\mathbb{R}:-1/2<x<0\}\) & \(\mathbb{Z}:\) set of integers & \(\mathbb{Z}^{+}:\) set of positive integers & \(\mathbb{Z}_{0}^{+}:\) set of non-negative integers & \(\mathbb{Z}_{0}^{+}:\) set of positive even integers \\  & & \(\mathbb{J}_{u}:\{0:n-1\}\) & \(\mathbb{J}_{u}^{+}:\mathbb{J}_{u}\cup\{n\}\) & \(\mathbb{N}_{u}:\{1:n\}\) & \(\mathbb{N}_{u\mapsto n}:\{m:n\}\) & \(\{y_{1\mapsto n}:\) set of symbols \(y_{1},y_{2},\ldots,y_{n}\) \\ \(\mathbb{G}_{u}^{\lambda}:\) set of Gegenbauer-Gauss zeros of the \((n+1)\)th-degree Gegenbauer polynomial with index \(\lambda>-1/2\) & \(\mathbb{G}_{u}^{\lambda}:\) set of SGG points in the interval \([0,1]\) \\ \(\boldsymbol{\Omega}_{u,b}:\) closed interval \([a,b]\) & \(\boldsymbol{\Omega}^{>}:\) interior of the set \(\boldsymbol{\Omega}\) & \(\boldsymbol{\Omega}_{T}:\) specific interval \([0,T]\) & \(\boldsymbol{\Omega}_{L\times T}:\) Cartesian product \(\boldsymbol{\Omega}_{L}\times\boldsymbol{\Omega}_{T}\) \\ \(N_{b}(a)=\{x\mid d(x,a)<\delta\}:0<\delta\leqslant 1\) and \(d(x,a)\) is the distance (or metric) between the point \(x\) and the point \(a\). \\ \multicolumn{5}{c}{**Lists and Sequences**} \\ \hline \(i\):\(j\):\(k:\) list of numbers from \(i\) to \(k\) with increment \(j\) & \(i\):\(k:\) list of numbers from \(i\) to \(k\) with increment \(1\) & \(y_{1\mapsto n}\) or \(y_{1\mapsto n-1}:\) list of symbols \(y_{1},y_{2},\ldots,y_{n}\) \\ \end{tabular}
\end{table} 
Table 1: Table of Symbols and their meanings.
# PlaIMoS: A Remote Mobile Healthcare Platform to Monitor Cardiovascular and Respiratory Variables
###### Abstract  
The number of elderly and chronically ill patients has grown significantly over the past few decades as life expectancy has increased worldwide, leading to increased demands on the health care system and significantly taxing traditional health care practices. Consequently, there is an urgent need to use technology to innovate and more constantly and intensely monitor, report and analyze critical patient physiological parameters beyond conventional clinical settings in a more efficient and cost effective manner. This paper presents a technological platform called PlaIMoS which consists of wearable sensors, a fixed measurement station, a network infrastructure that employs IEEE 802.15.4 and IEEE 802.11 to transmit data with security mechanisms, a server to analyze all information collected and apps for iOS, Android and Windows 10 mobile operating systems to provide real-time measurements. The developed architecture, designed primarily to record and report electrocardiogram and heart rate data, also monitors parameters associated with chronic respiratory illnesses, including patient blood oxygen saturation and respiration rate, body temperature, fall detection and galvanic resistance.  
Keywords:health devices; technological platform; e-health +
Footnote †: journal: Sensors
## 3 Non-catalytic Biodiesel Synthesis by Supercritical Fluid Technology
### Single Step SCFs Processes for Non-catalytic Biodiesel Synthesis
#### 3.2.3 Supercritical Carboxylate Esters  
In order to prevent the production of glycerol as a by-product, a process was designed using methyl acetate instead of methanol under supercritical conditions [69]. The supercritical methyl acetate (scMeOAc) non-catalytic method converts triglycerides into fatty acid methyl esters (FAMEs) and triacetin, instead of glycerol. Furthermore, it has been discovered that there were no adverse effects on the main fuel characteristics when the molar ratio of methyl oleate to triacetin was 3:1, which corresponds to the theoretically derived mole ratio from the trans-esterification reaction of rapeseed oil with methyl acetate (see Figure 4). Moreover, the addition of triacetin to methyl oleate improved the _pour point_ (defined as the lowest temperature at which a fuel product will begin to flow), and triacetin has a high oxidation stability. Therefore, by defining biodiesel fuel as a mixture of methyl oleate and triacetin, the supercritical methyl acetate can lead to an improved yield (105%) of biodiesel fuel compared with the conventional process. However, this value is lower than the maximum recovery expected (125% yield) because of the breakdown at the required high temperatures of some unsaturated fatty acid methyl esters. Despite this, the scMeOAc method not only improves the quality of the biodiesel fuel but also minimizes the separation and purification steps, and therefore the energy required. [PERSON] _et al._[70] have used various supercritical carboxylate esters to convert triglycerides into FAMEs and triacetin in the absence of any catalyst. The highest product yield was obtained with supercritical methyl acetate, 97.7 wt% considering a mixture of both FAMEs and triacetin as biodiesel fuel. The lower reactivity shown by other carboxylate esters seems to be related to the length of alkyl chains in the esters, e.g., the longer alkyl chains in both alcohol and acyl moieties of carboxylate estersprovides the lowest product yields [71]. [PERSON] and co-workers [72] demonstrated that supercritical methyl acetate can be successfully used in the transesterification process of different vegetable oils to obtain biodiesel fuel. The process was suitable for edible, non-edible or waste oils, regardless of the FFA content which usually affects the conventional transesterification process at atmospheric pressure. All the examined oils reached complete conversion after 50 min at 345 \({}^{\circ}\)C, 20 MPa and with a methyl acetate:oil molar ratio of 42:1.
## 1 Introduction
By using the optimal PS factor and the transmission beamforming vector of the relay together, the throughput of the wireless-powered FD relay system based on the PS protocol was maximized in [26]. Due to the gradual increase in energy costs and people's awareness of environmental protection, power efficiency has become an important performance indicator in the field of wireless communication. However, there are few studies on the power efficiency of the wireless-powered FD relay with dual-antenna energy harvesting. Motivated by the above problems, in this paper, the optimal time allocation and optimal power allocation scheme is proposed to maximize the energy efficiency in the FD relay system. In order to make full use of the hardware resources of the system, this paper analyzes the FD relay in two ways: By using two antennas to receive simultaneously in the energy acquisition phase, by selecting the optimal single-antenna reception in the energy acquisition phase. The energy efficiency maximization of the FD optimal time and the power allocation scheme was compared with that of traditional HD optimal time and power allocation scheme. The remainder of the paper is organized as follows. In Section 2, the system model of the wireless-powered FD relay system is described and the energy efficiency optimization problem is formulated. In Section 3, the optimal time and power allocation schemes to maximize the energy efficiency of the dual-antenna energy harvesting system are proposed. Section 4 elaborates on the proposed algorithm of the optimal time and power allocation in the FD single-antenna energy harvesting system. In Section 5, the energy efficiency optimization problem for the HD relay systems is introduced. Section 6 analyzes the numerical results, and the last section draws conclusions.
## 4 Advanced In-Vehicle Displays  
In-vehicle displays, and in-vehicle information systems, provide additional information to the driver. As a result, they can have significant effects on driver behavior. The implementation of in-vehicle information systems (IVIS) in cars can increase the number of tasks that a driver must perform, concurrently [52]. During manual driving, use of IVISs has been observed to degrade performance by overloading the driver, thus resulting in safety concerns [53; 54]. A previous naturalistic driving study with 100 participants showed that 78% of crashes and 65% of near-crashes were the result of driver distraction, of which in-vehicle technology use accounted for roughly one-quarter of the events [55]. While this study examined manual driving, driver distraction due to different types of in-vehicle displays could also affect the capability of drivers to respond quickly to automation takeover requests.  
Research has found that drivers tend to adapt their behavior in various ways to compensate for the presence of IVISs, but these adaptations vary between being beneficial and detrimental [56; 57; 58]. One of the issues is that IVISs create secondary tasks for the driver, which in some cases may increase workload to the point that driver response to environmental events is negatively impacted [59]. For example, IVIS displays that require manual input were found to increase workload and were related to increased, center line crossings, and off-road accidents in a simulator study [53]. Compounding these effects, drivers also differ in the way they interact with IVIS displays. Higher risk drivers demonstrate longer eyes-off-road times (EOR) than lower risk drivers, even when performing tasks that the former group rated as higher risk, such as typing in a street address while driving [60]. However, driving performance with an IVIS has been found to improve with time, as individuals become more proficient with systems, and tends to follow the power law of practice [57].  
While it is known that IVIS displays impact driver performance during manual driving, those observed performance decrements may not apply to automated driving. When the driver is not required to physically control the car during highly automated driving, an IVIS can be used for more than simply displaying navigation information or vehicle status information. A recent survey of user preferences for activities during automated driving revealed that instead of doing nothing, people prefer listening to music or entertainment, engaging in communication or productivity tasks during an automated drive [61]. Consequently, an effective IVIS display of messages may recapture driver attention from the secondary activity. Much of the work in this area has focused on messaging safety-critical information (e.g., forward collision warnings [12; 62]) to a driver in manual driving, as reviewed below, but few studies have considered non-safety-related information.
## 4 Proposed Approach
### Evaluation Metrics  
The feature vector is considered \"good\" enough to obtain acceptable classification accuracy. To answer how good element embedding algorithms are, we evaluated the embedded element using the next event prediction task (NEP), a common way to evaluate the quality of the embedded elements for recommendation [63, 68, 69]. We also adopted similar evaluation metrics and they are defined as follows:  
* Hit ratio at K (HR@K). It is equal to \(1/K\) if the test element appears in the top \(K\) list of predicted elements [70].
* Normalized discounted cumulative gain (NDCG@K). It considers both the order and relevance of the results. NDC@K favors higher ranks in the ordered list of predicted elements [71]. Since we consider that the ideal NDCG@K for a list retrieved from the UNN is \(1\), \(\frac{1}{(log_{2}(1+i))}\), where \(i\) is the position of the relevant element in the retrieved list. The higher the position in the retrieved list, the more favorable it is.  
HR and NDCG are \(1\) if the next element and the query element are the same, i.e., the prediction is correct. If the next element and query element are different, we look up the K UNN for the next element in the top K list of the retrieved elements and compute HR@K and NDCG@K. The main difference between NDCG and HR@K is that the latter does not have a decay factor, meaning that all results within the top K are weighed equally. In this paper, given that each user has only one ground truth item, HR@K becomes equivalent to Recall@K and exhibits a proportional relationship with Precision@K [72]. In our report, we present the HR and NDCG values with \(K\) set to \(10\). With both HR and NDCG metrics, superior performance is indicated by higher values.