esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Ghg
\begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**Pretreatment** & **Power Plant—Type of Fuel** & **Equation for CO2** & **Equation for NOx** & **_p_-Value** \\ \hline \multirow{4}{*}{Control} & SP-NG & 14,074.16 + 2625.24A \(-\) 2363.16B \(-\) 682.08B\({}^{2}\) & 65.5 + 12.22A \(-\) 11B \(-\) 3.17B\({}^{2}\) & \textless{}0.0001 \\ \cline{2-5}  & SP-HO & 22,682.41 + 4230.94A \(-\) 3805.56B \(-\) 1099.27B\({}^{2}\) & 55.77 + 10.4A \(-\) 9.36B \(-\) 2.70B\({}^{2}\) & \textless{}0.0001 \\ \cline{2-5}  & GT-NG & 17,305.02 + 3227.9A \(-\) 2905.65B \(-\) 838.66B\({}^{2}\) & 42.27 + 7.88A \(-\) 7.1B \(-\) 2.05B\({}^{2}\) & \textless{}0.0001 \\ \cline{2-5}  & GT-GO & 23,191.38 + 4325.88A \(-\) 3894.02B \(-\) 1123.94B\({}^{2}\) & 128.13 + 23.9A \(-\) 21.51B \(-\) 6.21B\({}^{2}\) & \textless{}0.0001 \\ \cline{2-5}  & CC-NG & 9958.13 + 1857.48A \(-\) 1672.05B \(-\) 482.61B\({}^{2}\) & 65.28 + 12.18A \(-\) 10.96B \(-\) 3.16B\({}^{2}\) & \textless{}0.0001 \\ \cline{2-5}  & CC-GO & 13,764.35 + 2567.46A \(-\) 2311.14B \(-\) 667.07B\({}^{2}\) & 83.65 + 15.60A \(-\) 14.05B \(-\) 4.05B\({}^{2}\) & \textless{}0.0001 \\ \hline \multirow{4}{*}{US \({}^{\star}\)} & SP-NG & 12,304.77 + 2563.27A \(-\) 2518.20B & 56.27 + 11.93A \(-\) 11.72B & \textless{}0.0001 \\ \cline{2-5}  & SP-HO & 19,830.81 + 4131.06A \(-\) 4058.43B & 48.75 + 10.16A \(-\) 9.98B & \textless{}0.0001 \\ \cline{2-5}  & GT-NG & 15,129.45 + 3151.7A \(-\) 3096.28B & +36.95 + 7.70A \(-\) 7.56B & \textless{}0.0001 \\ \cline{2-5}  & GT-GO & 20,275.79 + 4223.76A \(-\) 4194.49B & 112.02 + 23.34A \(-\) 22.93B & \textless{}0.0001 \\ \cline{2-5}  & CC-NG & 8706.21 + 1813.64A \(-\) 1781.75B & 57.07 + 11.89A \(-\) 11.68B & \textless{}0.0001 \\ \cline{2-5}  & CC-TGO & 12,033.91 + 2506.85A \(-\) 2462.77B & 73.13 + 15.23A \(-\) 14.97B & \textless{}0.0001 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Fitting the effect of different levels of microwave power and sample thickness on independent parameters in a microwave dry without a pretreatment. Figure 7: Three-dimensional plots of NOx in CC-NG for carrot drying (effect of microwave power and thickness): (**a**) control, (**b**) ultrasonicated, and (**c**) blanched samples. The results suggested a significant reduction in \(GHG\) production upon a pretreatment of the samples. The reason could be the prevention from the formation of the hard layer upon a pretreatment, which can help in the fast removal of moisture, hence, declining the drying duration and the energy required for the drying process. Upon a decrement in the energy consumption, the amount of the required fuel and hence the \(GHG\) emission due to the combustion of the fossil fuels in the plants will be decremented [59]. In line with this research, the results of other studies on drying blackberries [60] and goldenberries [21] also indicated a decline in \(GHG\) emission upon a pretreatment of the samples.
## 2 Related Work
### P2P-Based Distributed Training  
In P2P-based distributed training, the training results of workers are aggregated via peer-to-peer (P2P) communication without PS. In AllReduce-SGD (AR-SGD) [27; 30], a synchronous P2P-based distributed algorithm, the parameters of all workers are synchronized via AllReduce communication at every iteration. The synchronization overhead, however, can be a big problem like BSP. In decentralized parallel SGD (D-PSGD) [21], each worker exchanges its parameters with another worker and updates its parameters by averaging them at every iteration. D-PSGD proved that the convergence rate of P2P-based distributed training is comparable to that of SGD through the theoretical analysis, which promoted to study many P2P-based distributed training algorithms. AD-PSGD [22], the asynchronous version of D-PSGD, improves the training performance of D-PSGD by allowing workers to communicate with each other asynchronously. The stochastic gradient push (SGP) [23], a state-of-the-art P2P-based distributed training algorithm, adopts the push-sum gossip algorithm [31] to efficiently aggregate parameter aggregation of workers. To further accelerate the aggregation, SGP uses a directed exponential graph where a worker communicates with another worker placed in \(2^{i}\) hops away at iteration \(i\). The Cooperative-SGD [32] proposes a unified framework for generalizing existing distributed training algorithms and provides its convergence analysis within the generalized framework, where fully-synchronous SGD (BSP, AR-SGD), EASGD, and D-PSGD are represented as its special cases. Ref. [33] provides a general consistency condition that covers existing asynchronous distributed training algorithms.
# FEAROL: Aging Flow Entries Based on Local Staircase Randomized Response for Secure SDN Flow Tables
## 3 Aging Flow Entries with Staircase Randomized Response
Specifically, \(A_{j}=0\) means that flow entries will keep their current existence and idle time unchanged, and \(A_{j}=1\) means that the existing and idle time of the flow entry will age normally as listed in Table 3.  
Since \(E(FA_{i})=1\) implies how flow entry \(i\) ages normally, indicating the real lifespan of flow entry \(i\), while \(E(FA_{i})\) is computed by the sets of \(A\) and \(P\), we can adjust \(A\) and \(P\) to change \(E(A_{i})\). Consider the following three scenarios: \(A=[1,0,0,0]\) and \(P=[0.75,0.25/3,0.25/3,0.25/3]\), \(A=[1,0,2,3]\) and \(P=[0.75,0.25/3,0.25/3,0.25/3]\) and \(A=[1,0,-1,2]\) and \(P=[0.75,0.25/3,0.25/3,0.25/3]\). We compute their \(E(FA_{i})\) as 0.75, 1.417 and 0.833, respectively, using Equation (10). These imply that flow entry \(i\) ages 0.75-, 1.417- and 0.833-times normal. \[E(FA_{i})=A_{1}p+\sum_{j=2}^{|A|}A_{j}(1-p)/(|A|-1) \tag{10}\]  
Since we can identify the actions in the set \(A\) as \(A_{1}\) and not \(A_{1}\) with the probability of \(p\) and \(1-p\), respectively, we guarantee \(\epsilon\)-differential privacy for the lifespan of each flow entry in the flow table, independent of the attacker's prior knowledge, and \(\epsilon\) is given by Equation (7). In particular, \(\epsilon\) is 1.1 for \(p=0.75\). Since \(e\) increases as \(p\) increases, and the smaller the \(\epsilon\) is, the more protection it provides, we need to reduce \(p\) to provide \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter** & **Probability** & **Value** & **Description** \\ \hline \multirow{4}{*}{\(A_{1}\)} & \multirow{4}{*}{\(p\)} & 0 & flow entry will not age \\  & & 1 & flow entry is aging normally \\  & & \(+n\) & flow entry is aging \(n\) times faster than normal \\  & & \(-n\) & flow entry is aging \(n\) times slower than normal \\ \hline \multirow{4}{*}{\(A_{j}(j>1)\)} & \multirow{4}{*}{\((1-p)/(|A|-1)\)} & 0 & flow entry will not age \\  & & 1 & flow entry is aging normally \\ \cline{1-1}  & & \(-n\) & flow entry is aging \(n\) times slower than normal \\ \cline{1-1}  & & \(+n\) & flow entry is aging \(n\) times faster than normal \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Parameters and their descriptions: \(p\) is the probability of action \(A_{1}\), and \(n\) is a positive integer. more protection. In reality, we might adjust \(A\) and \(P\) to balance the network performance and security.
# Prediction of Curing Time/Shear Strength of Non-Conductive Adhesives Using a Neural Network Model
## 2 Materials and Methods
### Specimens and Analysis
#### 2.2.2 Shear Strength Measurement  
The shear strength of adhesive was measured using the die shear test. Specimens were made of an FR-4 PCB without solder resist and a dummy chip. The dimension of the PCB was 132 mm \(\times\) 30 mm \(\times\) 0.5 mm, and the dimension of the chip was 5.3 mm \(\times\) 7.3 mm \(\times\) 0.7 mm. Before flip-chip bonding, the PCB and chip were cleaned in a microwave plasma machine (TePla 660, PVA Tepla, Wettenberg, Germany). Adhesives were applied to the FR-4 PCB, and the thickness between the PCB and the dummy chip was evenly maintained using a 100 \(\upmu\)m-thick polyimide film.  
The thermo-compression bonding process was conducted using a flip-chip bonder (FCB-3, Panasonic, Osaka, Japan), and the thermos-profile is shown in Figure 2. The bonding process was conducted under a pre-heating temperature of 90 \({}^{\circ}\)C, a bonding temperature of 280 \({}^{\circ}\)C, a bonding time of 10 s, and a compression load of 100 N. The shear strength was measured using a bond tester (Dage BT-4000, Nordson, Manchester, UK) with a test load of 100 kg, a land speed of 167 \(\upmu\)m, a shear height of 75 \(\upmu\)m, and an overtravel of 100 \(\upmu\)m. The shear strength was measured twice per condition.  
Figure 1: DEA measurement (**a**) equipment setup with impedance Analyzer, heating stage and DEA sensor, and variation of dielectric loss and (**b**) DEA sensor.
## 3 Circuit Implementation  
The complete schematic of the proposed power amplifier in this article is shown in Figure 14. It consists of three parts: the matching networks, the PA power cell, and the integrated bias circuits. The power cell employs a differential structure to suppress even-order harmonics and reduce linearity degradation caused by the ground bonding wires.
## 4 Experimental Simulations and Results
### Simulation Experiment Settings  
In the first case, the D1-D2 section was selected for the simulation to evaluate the effectiveness of the proposed algorithm. The performance of ESS-NSGA-III was evaluated against NSGA-III and NSGA-II. The parameters for these three algorithms are listed in Tables 2 and 3.  
In the second case, the entire operational route from D1 to D9 was simulated using the ESS-NSGA-III algorithm. The feasibility of the driving strategy optimization method was further validated by comparing the different running time strategies.
## 3 Avocado's Composition in Nutrients and Bioactives  
### General Composition/Chemical Composition  
The fruit of avocado has three structural parts: peel, pulp, and seed, and it is categorized into different types based on the geographical area it is domesticated in. Under these circumstances, avocado belongs either in Guatem (_quilaoacatl_), Mexican (_aoacatl_), and West Indian (_llacacolacatl_) varieties [23]. Each variety has individual characteristics such as color and texture of the peel features, size and shape of the fruit, ripening time, oil composition, salinity, and frost tolerance. The commercially dominant cultivated variety is Hass avocado, a hybrid of Guatem and Mexican varieties, due to its coarseness and firmness of the peel, as well as its richness in bioactive oils that can be harvested [24].  
The compositional analysis of avocado clarifies this fruit as a nutritional powerhouse. In particular, it is high in fiber, vitamins, minerals, monounsaturated fatty acids, and other compounds that are indicated to promote valuable health effects. In this review, data of five types of different cultivars were selected (Haas, Fuerte, Bacon, Ettinger, Pinkerton) and displayed in Table 1.
## 2 Signal Division Method of Bearing Health State
### Health Status Division Results  
Based on the bearing vibration signal in the XJTU-SY dataset, the health status is divided, and the division results are shown in Figure 4. The long red line in the figure shows the boundary line of the healthy stage. It can be seen that the vibration signal tends to be stable in the healthy stage on the left side of the bearing vibration signal. After the failure point, the characteristic amplitude of the vibration signal shows an increasing trend with the running time, which accurately reflects the bearing degradation process.  
Taking Working Condition 1 as an example, the bearing under Working Condition 1 is divided into four types of bearing states: normal, cage fault, outer-race fault, and mixed fault of inner and outer race.  
Bearing normal data is the health status data before the FOT point of bearing1_1 to bearing1_5; outer ring failure data is the degradation data after the FOT point of bearing1_1 to bearing1_3; inner ring failure data is the degradation data after the FOT point of bearing1_4, and mixed inner and outer ring failure data is the degradation data after the FOT point of bearing1_5.  
Since the vibration signal at the fault occurrence point is at the critical value of the fault and normal signals, and the signal points collected within 1.28 s have not been further divided, to use more reliable data, this paper discards the vibration signal at the bearing health fault occurrence point. The specific bearing health status division results of the three working conditions are shown in Table 3.  
Figure 4: Result of bearing health status division.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Fault Location** & **Bearing1\_1** & **Bearing1\_2** & **Bearing1\_3** & **Bearing1\_4** & **Bearing1\_5** \\ \hline Normal & 1:76 & 1:43 & 1:56 & 1:77 & 1:32 \\ Cage & & & & 79:122 & \\ Inner-Outer-race & & & & 34:52 \\ Outer-race & & & & & \\ \hline  & Bearing2\_1 & Bearing2\_2 & Bearing2\_3 & Bearing2\_4 & Bearing2\_5 \\ \hline Normal & 1:449 & 1:44 & 1:322 & 1:28 & 1:118 \\ cage & & & 324:533 & & \\ Inner-race & & 451:491 & & 30:42 & 120:339 \\ Outer-race & & & & & \\ \hline  & Bearing3\_1 & Bearing3\_2 & Bearing3\_3 & Bearing3\_4 & Bearing3\_5 \\ \hline Normal & 1:2376 & 1:912 & 1:338 & 1:1414 & 1:4 \\ Cage-Inner- & & & 914:2496 & & \\ outer-race & & & & & \\ Inner-race & & & & 340:371 & 1416:1515 & \\ outer-race & & & & & 6:114 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Time division of health status.
## 2 Literature Review and Related Work
### Imperialist Competitive Algorithm  
The imperialist competitive algorithm (ICA) is an effective evolutionary algorithm framework [21]. It facilitates population evolution through mechanisms such as assimilation, colonial revolutions, and imperial competition, demonstrating efficient global and local search capabilities. For the past few years, the ICA has been successfully applied to solving various multi-objective energy-efficient scheduling problems.  
[PERSON] et al. [22] designed a two-phase meta-heuristic (TPM) algorithm based on the ICA and variable neighborhood search (VNS) for a multi-objective flexible job shop scheduling problem (FJSP) with energy consumption thresholds, aiming to minimize the makespan and total tardiness. [PERSON] et al. [23] proposed a two-level imperialist competitive algorithm (TICA) for the energy-efficient hybrid flow shop scheduling problem. In this algorithm, the strongest empire and other empires are divided into two levels, with the strongest empire not participating in the competition. To generate high-quality solutions, different assimilation and revolution operations are performed during various search phases within the empire. [PERSON] et al. [24] addressed the energy-efficient hybrid flow shop scheduling problem with uncertain processing times by proposing an Empire Grouping Imperialist Competitive Algorithm (EGICA). This algorithm constructs initial empires through the normalized total cost under interval scenarios and groups them into empire groups. By executing assimilation operations on different parts of the encoding and performing adaptive revolutions, it enhances performance. Additionally, a two-phase imperialist competition operation is used to maintain population diversity.  
From the above literature, it is evident that many researchers have made significant improvements to the ICA and successfully applied it to various scheduling problems. For the assimilation in the global search of the algorithm, existing studies mostly adopt the uniform crossover (UX) operator [17], with the probability parameters preset through experiments. However, applying traditional static parameters to the ICA may not ensure optimal search efficiency and solution quality due to the complexity of the EEMsMIAJSP [25]. Therefore, it is necessary to design a reasonable dynamic parameter adaptive adjustment mechanism to balance the exploration and exploitation of the algorithm.
However, this study was extended to the observation of multi-constellation systems and the use of a Xiaomi Miss Android smartphone, two low-cost u-blox receivers--ZED-F9P and ZED-F9R--and a Septentrio AsteRx-U geodetic GNSS receiver in terms of the reference trajectory of the Applank Corporation GPS/INS MMS system. Thus far, this article has shown the importance of positioning quality in geospatial data collection, using low-cost sensors from u-blox or smartphones that are affordable for all. The main contribution of this article is the comparison of trajectories acquired by using different GNSS devices and a comparison with the reference trajectory. Although the authors are aware that both smartphones and u-blox devices can receive both GNSS and INS measurements, the focus of this study was on the performance of GNSS receivers in urban and semi-urban environments. The goal was to gain information on the quality of GNSS positioning and to compare the performance of GNSS-based geodetic receivers and the much cheaper u-blox and dual-frequency smartphones operating in kinematic mode. All the findings obtained in this study should be extended and improved in the future to also provide deeper insights into GNSS/INS observations and post-processing quality.
## 2 Theoretical Background
### Fault Detection Using an Artificial Neural Network for Regression
\(t_{r_{ij}}\) is the center time of \(r_{ij}(t)\) and \(t_{s}\) is the center time of the reference signal \(s(t)\) which are expressed as follows:  
\[t_{r_{ij}} =\int t|r_{ij}(t)|^{2}dt \tag{7}\] \[t_{s} =\int t|s(t)|^{2}dt. \tag{8}\]  
The output parameters were the fault determination index (\(F(t)\)) and the unbalanced signal index (\(U(t)\)). The fault determination index estimates the fault location, and the unbalanced signal index estimates the type of the fault. The dataset to train the MLP according to the averages of TFCC, normalized energy variance, and time delay were obtained by the simulated measured data considering the thresholds for each cable sample. The dataset was randomly divided into two separate  
Figure 1:0 Structure of the neural network used for fault detection and classification  
sets, each for training (85%) and testing (15%). We employed a scaled conjugate gradient (SGC) back-propagation algorithm with a cross-entropy performance function [24]. Furthermore, the training process was ceased under the following conditions: (1) the maximum number of iterations (1000) is reached; (2) the maximum amount of time is exceeded; (3) the performance is minimized to the goal; or (4) the performance gradient falls below the target.
## 1 Introduction
Let us take the European Space Agency (ESA)'s Sentinel-1 mission as an example. Its repeat cycle is 12 days, while the repeat cycle of the ESA's previous EnviSAT mission is 35 days. Moreover, the Sentinel-1 mission is designed as a two-satellite constellation [30], which implies that the corresponding repeat cycle can be as low as 6 days. That is to say, when MT-InSAR techniques are applied to such kind of data, the dimension of the corresponding SAR stacks could be very high, in particular in long-term deformation monitoring applications. [PERSON] et al. [31] pointed out that the MLE based phase reconstruction methods have a super-nonlinear computational complexity and proposed a sequential estimator to accelerate the reconstruction process on high-dimensional SAR stacks. However, as this method achieves high computational efficiency at the expense of losing coherence information, the reliability of reconstruction might not be assured. [PERSON] et al. [32] further pointed out that the EVD method is superior to the MLE based methods in the aspect of computational efficiency. In addition, they suggested us to use the EVD method to process a high-dimensional SAR stack. Indeed, the computational complexity of the EVD method can be approximately denoted as \(O\big{(}N^{3}\big{)}\)[33; 34]. It indicates that the required execution time will be considerably increased when the dimension of a SAR stack is getting higher. In other words, the EVD method is still not an ideal method to efficiently reconstruct interferometric signals from a high-dimensional SAR stack. In this paper, a new approach is developed in order to efficiently reconstruct interferometric signals. This method is inspired by the theory of probabilistic principle component analysis (PPCA), which defines a complex valued probability generative model depicting the phase reconstruction process. Further, a targeted expectation maximization process is designed and implemented to resolve the model. On the premise that the accuracy of the proposed method is at the same level as that of the traditional EVD method, the computational complexity can be reduced to \(O(MN)\), where \(M\) is the number of SHPs. The phase reconstruction process can be therefore significantly accelerated, especially in the case of dealing with a high-dimensional SAR stack. This paper is arranged as follows. Traditional phase reconstruction methods, in particular the EVD method, are reviewed in Section 2. Next, the complex valued probability generative model and the corresponding resolution strategy are discussed in Section 3. In Section 4, the proposed method is applied to test data, and its performance is carefully investigated. This paper is concluded in Section 5. Most of the problems in this paper are discussed from the perspective of probability theory. The readers are referred to [35] for more details.
## 3 System Model and Preliminaries
### Traffic Classification  
One of the objectives of TLQoS is the QoS provisioning for diverse traffic types having different QoS requirements. Considering delay and reliability as the QoS metric, we classify the traffic as follows:  
* _Critical(Cr)_ traffic: This type of traffic has both the delay and reliability constraints. Examples include electroencephalogram (EEG), electrocardiogram (ECG) and Electromyography (EMG) _etc_. that generate real time continuous data which need to be delivered with a lower delay and higher reliability.
* _Delay constrained (Dc)_ traffic: This type of traffic needs to be delivered with lower delay, however, it can tolerate some packet losses. Tele-medicine video streaming application traffic possesses such requirement.
* _Reliability constrained (Rc)_ traffic: The traffic belong to this type requires higher reliability, without having any delay constraints. Example of this type includes respiration monitoring, pH-level monitoring _etc_. which can be processed offline, but packet losses for this type may cause severe consequences.
* _Regular (Rg)_ traffic: Traffic of this type has no delay or reliability constraints. Traffic generated from a patient's regular vital sign monitoring applications such as temperature, pressure _etc_. corresponds to this class of traffic.