esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Effect of the Culture Conditions on the Pollutant Elimination
#### 3.2.5 Fate of CBZ
These results confirm that these algae may take up this substrate into the cell, and another mechanism, namely biodegradation, occurs intracellularly in parallel to the bioaccumulation of CBZ, even though biosorption of this molecule by the microalgae is possible, it is usually negligible [34]. In addition, to confirm the presence of the transformation products generated by the CBZ transformation by _A. braunii_, MS analyses were carried out in positive mode electrospray ionization. UHPLC/MS/MS data showed the qualitative presence of three different metabolites in the cell-free supernatants (Table 4), confirming the biotransformation role of _A. braunii_ cells on CBZ. The most abundant compound detected was 10-hydroxycarbamazepine (10-OH CBZ). From these results, 10-OH CBZ was the one mostly resembling CBZ; we could be sure that this metabolite is the one that appeared on the HPLC at earlier times at the same wavelength. The other detected metabolites did not appear in the HPLC chromatograms, despite the sensitivity of this analytical method. This suggests that these are unstable and/or are present in concentrations that are below the instrumental limit of detection (LOD) of the used HPLC method. Based on the chemical structure of the organic intermediates (degradation products), a possible mechanism of the formation of 10-OH CBZ consisting of a two-step reaction could be explained by the epoxidation of carbamazepine, followed by a direct ring-opening of the resulting epoxide and the formation of the resulting alcohol is proposed in Figure 7.  
Figure 6: CBZ concentration in BB and PP media with burst and non-burst cells for an initial CBZ concentration of 2.5 mg/L and an inoculum concentration of _A. braunii_ between 10\({}^{4}\) and 10\({}^{5}\) cells/mL. From these data, it can be concluded that in the case of _A. braunii_ cells, the major mechanism involved in the elimination of CBZ was bioaccumulation (at about 80%). It can be supposed that the other main mechanism of CBZ removal was the biometabolic oxidative transformation of CBZ mainly into 10-OH CBZ. To the authors' knowledge, this is the first study reporting on the detection CBZ intermediates formed during the CBZ biodegradation by _A. braunii_. However, more detailed analysis is needed in the future in order to clarify the mechanism of the biotransformation step and to extend the knowledge on the role of _A. braunii_ in the elimination of this refractory pharmaceutic compound.
## 5 Analysis and Results
### Path Model Assessment  
In this research work, structural equations were modelled on a variance basis using Smart PLS 3.0. The moderately complex model that was utilized in this investigation consists of seven independent variables, one mediation variable, and one dependent variable. In order to analyze the measurement and structural model, this work employed Partial Least Square-Structural Equation Modelling (PLS-SEM) with SmartPLS v. 3 ([PERSON] et al., 2015) as a statistical tool. As survey research is not normally distributed, this approach is especially well-suited for this paper because it allows for smaller sample sizes without making normality assumptions ([PERSON] et al., 2015). Therefore, it is necessary to evaluate both the measurement model and the structural model. While the structural model addresses the presumption that the hypotheses are accepted or rejected, the measurement model uses the PLS Algorithm to assess the consistency, convergence, and discriminating validity of the model.
## 2 Related Work  
### Parallel Transmission Opportunities in Underwater Environments  
There are mainly three types of parallel transmission opportunities in the underwater environments that have been noticed and utilized in the previous researches, i.e., the time domain, space domain, and multi-channel opportunities.  
The time domain parallel transmission opportunity arises from the large propagation delay among network nodes. The sound propagation speed in underwater is around 1500 m/s, which results in a long propagation delay in the underwater communication. This propagation delay has many negative effects, but it also leads to concurrent transmission opportunities in time domain. Figure 1 gives an example of the time domain parallel transmission in underwater environments, where data packets can be sent concurrently by the source S1 and source S2 without overlapping at the destination D1 and destination D2 if their transmission time is scheduled according to the propagation delay between S1 to D1 and S2 to D2.  
Figure 1: Serial transmission and time domain parallel transmission. (**a**) serial transmission; (**b**) time domain parallel transmission.  
The space domain parallel transmission opportunity comes from the fact that two parts of the networks can send packets simultaneously when they are all located out of the interference regions of each other. Usually the interference region of a node is determined by its transmission power and the attenuation of the links, so the space domain parallel transmission opportunity can not only be explored from the location of nodes, but also be created by changing the transmission power of nodes. In Figure 2, the source S1 and source S2 can communicate with their corresponding destination D1 and destination D2 simultaneously when they reduce their transmission power.  
The multi-channel parallel transmission opportunity resides in the multi-channel networks, where multiple parallel channels are created by CDMA or FDMA. Different from the pure CDMA and FDMA, the channel number is usually less than the node number in a multi-channel MAC protocol. Therefore, these kinds of protocols are mostly content-based, which means that the nodes need to compete for the channels if they have data to send.
# Utility-Centric Service Provisioning in Multi-Access Edge Computing
## 3 System Model and Problem Formulation
### Service Latency Estimation
#### 3.3.3 Service Latency  
Combining communication and processing latency, the service latency of user \(u\), denoted as \(t_{u}\), can be expressed as follows.  
\[t_{u}=\sum\limits_{n\in\mathcal{N}\cup\{l\}}y_{un}\left(\tau_{u,n}^{comm}+ \tau_{u,n}^{proc}\right),\quad\forall u\in\mathcal{U} \tag{4}\]  
Service latency is a vital factor that greatly affects users' QoE. In our work, instead of using raw service latency, we convert it to a MOS by means of a utility function, which is presented in the next section.
# Assessing Drivers' Physiological Responses Using Consumer Grade Devices
### Devices Used to Measure Driver's Physiological Responses
It is presented in detail along with different models in the next section. Different versions of Faros are widely used in research studies for detection of various medical conditions, which affect humans' cardiovascular activity [57; 58; 59; 60]. Since Faros 360 is certified as a medical device, it is also often used as a reference for calibration or validation of new wearable devices [61; 62; 63]. However, Faros has been used also for monitoring different profiles of users' states. For example, it has been used for the assessment of mental stress when interacting with robots, where, based on HRV data, it was found that human operators show higher engagement levels when being successful at completing a task [64]. It has also been used formonitoring effects of air pollution and noise due to traffic, and the results showed that HRV and blood pressure (BP) drop when noise increases [65]. Other studies have shown that the ECG signal from Faros can also be used to calculate other variables such as BP [66], or to extract core body temperature with respect to clothing and persons' activity [67]. Since with wearable devices such as Faros, the experimental conditions differ and are not always performed in a controlled laboratory environment, the gathered data can be affected by motion artefacts. [PERSON] et al. found that motion can describe the high frequencies in HRV up to 40% [68]. However, the noise of motion can be eliminated by adding an additional accelerometer on each electrode [69]. With this, it is possible to eliminate the baseline wandering without affecting the ECG signal. Contrary to E4, Faros has already been used in a driving environment. [PERSON] et al. used HRV data gathered with Faros to observe the effects of semi-automated driving in Tesla model S [36]. The effects of different levels of automation on drivers' state were studied also in a driving simulator environment by [PERSON], et al. [38] and [PERSON] et al. [37]. Their findings were however different, as [PERSON] et al. report on a decreased level of arousal and sympathetic nervous system activity when switching from automation level 2 to level 3, while [PERSON] et al. did not find any effects on the ECG channel for different levels of autonomous driving [37].
## 3 Results and Discussion
### Workflow from Data Acquisition to Location Tracking
#### 3.1.1 Signal Processing of Physiological Parameters
The de-noised, corrected signal shown overlaying the noisy signal. The ECG signal was used to derive the heart rate and arterial blood pressure using Physionet WFDB library.
## 4 Approaches to the reconstruction problem from protein contact maps  
In this section we review the approaches reported in literature for the contact map reconstruction problem. To be useful, a reconstructing algorithm from contact maps must satisfy some non-trivial requirements. First of all it must be fast enough to be used for large scale reconstructions while we showed in the previous section that the problem to realize a contact map in the three-dimensional space is generally intractable. The difficulty of the problem is moreover increased by the fact that a reconstructing algorithm must be designed in order to deal also with highly blurred contact maps. Given the impossibility to find an optimal solution to this problem in reasonable time, all algorithms developed so far are heuristic. An heuristic algorithm is not required to produce the best possible solution but only a good solution in the average case. One necessary requirement of a reasonable solution is that it must satisfy at least some minimal biological constraints such as the _backbone constraint_.  
Existing algorithms proceed in two phases: in the first phase they generate an initial set of 3D coordinates which are refined in the second phase in order to obtain a 3D structure as consistent as possible with the input contact map. We can coarsely identify three approaches to the reconstruction problem. In the first approach (Section 4.1.) the realization problem is defined as the search in the three-dimensional space of the set of coordinates which minimize an opportune cost function. The search in the coordinates space is based on _gradient descent_ methods. The main problem of gradient descent minimization approach is that, during the computation, the procedure can be trapped in local minima of the cost function from which it cannot escape. This is much more likely to happen when the contact map is not physical (i.e. when the constraints imposed are contradictory). For this reason, the other methods developed so far adopt a _local search heuristic_ approach. A local search algorithm starts from a candidate solution and then iteratively moves to a neighbor solution until the best solution found has not been improved in a given number of steps or until the time bound given to the procedure is elapsed. The local search avoidsthat the procedure gets trapped in a local minimum. Our second class of methods (Section 4.2.) is based on a local search approach known as _simulated annealing_. To finish, a third and recent method, uses a Distance Geometry (Section 4.3.) approach to generate an initial solution and then it uses a local search technique to refine such an initial solution. After a detailed description, we discuss the computational complexity of these methods (Section 4.4.).
## 2 Fundamental Concepts
### Wiretap Channel Models
#### 2.3.1 MIMO Wiretap Channels  
A MIMO network structure consists of multiple transmit and receive antennae that allow for simultaneous secure broadcasts with less inference and noise. Providing physical layer secrecy via adaptations to the multi-antenna technique is a typical and essential type of security method, which can be applied in practical wireless communication systems [45; 46; 47].  
In such multi-antenna scenarios, a typical MIMO wiretap channel model includes SISO, SIMO and MISO channels. In this review paper, we consider the more general setting that was studied in [39], in which the transmitter ([PERSON]) wants to reveal the secret information to the intended receiver ([PERSON]) over the main channel while the eavesdropper ([PERSON]) acquires a noisy version of the transmitted signal via the wiretap channel, as shown in Figure 2.  
In this setup, the [PERSON], [PERSON] and [PERSON] are equipped with multi-antenna systems, which are denoted by \(N_{A}\), \(N_{B}\)_and_\(N_{E}\), respectively. Furthermore, the CSI of the main transmission and eavesdropper channels are denoted by \(H\) and \(G\), respectively. The general representations of the signals that are received by [PERSON] and [PERSON], respectively, are described by the following equations:  
\[y_{B} =\mathbf{H}x_{s}+\mathbf{n}_{B} \tag{1}\] \[y_{E} =\mathbf{G}x_{s}+\mathbf{n}_{E} \tag{2}\]  
where \(x_{s}\) is the \(n_{A}\times 1\) transmitted signal with a covariance matrix of \(Q_{X}=E\big{[}x_{s}x_{s}{}^{H}\big{]}\) for \(Q_{X}>0\). \(H\in\mathbb{C}^{N_{B}\times N_{A}}\) and \(G\in\mathbb{C}^{N_{E}\times N_{A}}\) are the MIMO channel matrices from [PERSON] to Bob and Eve, respectively. Since the channels are discrete memoryless channels at the transmitter, each element of \(\mathbf{H}\) and \(\mathbf{G}\) obey the complex Gaussian distribution with channel gain coefficients of \(h^{ij}\) and \(g^{ij}\) between the \(i^{th}\) source antenna and the \(j^{th}\) intended receiver and eavesdropper antennae. The noise vector of the received signal antennae is assumed to be a complex Gaussian noise vector, with each element of \(\mathbf{n_{B}}\) and \(\mathbf{n_{E}}\) designed as an independent and identically distributed (IID) complex noise vector with the variance of \(\sigma_{H}^{2}\) and \(\sigma_{G}^{2}\), where \(h^{ij}\sim\mathbb{C}N(0,\sigma_{\text{F}_{2}}^{2})\) and \(g^{ij}\sim\mathbb{C}N(0,\,\sigma_{\text{G}}^{2})\).  
It assumed that \(\mathbf{n_{B}}\in\mathbb{C}^{N_{B}\times 1}\) and \(\mathbf{n_{E}}\in\mathbb{C}^{N_{E}\times 1}\) are zero-mean AWGN vectors at the legitimate receiver, as well as at an eavesdropper, with \(\mathbf{n_{B}}\sim\mathbb{C}N(0,\,I)\) and \(\mathbf{n_{E}}\sim\mathbb{C}N(0,\,I)\), respectively. The maximum power of the transmitted signal from Alice is assumed to be P, where \(E\big{\{}\big{|}\big{|}x_{s}\big{|}\big{|}^{2}\big{\}}\leq P\) for a total power P. Initiatively, Equations (1) and (2) are used as a basic optimization tool in PLS. Hence, the typical MIMO wiretap channel model has been widely considered in PLS to ensure secure data transmission.
# Transfer Convolutional Neural Network for Cross-Project Defect Prediction
## 6 Discussion  
### Why Does TCNN Work?  
The experimental results in Section 5 showed that the TCNN method had better predictive performance than methods that directly adopted handcrafted features or DL-generated features in our investigated CPDP tasks. The possible reasons are summarized as follows:  
(1) Compared with the CPDP methods based on handcrafted features (e.g., NNFilter, DG, TCA, TCA+), the TCNN model considered the semantic and structural features of the project source code. Our method maps the ASTs into an integer vector and feeds them into the TCNN for transferable features generation. After that, it concatenates DL-generated with the handcrafted features to make sure the defect knowledge carried in the handcrafted and DL-generated features can be utilized simultaneously when training the predictor.  
(2) Compared with the previous SDP methods that use DL-generated features (e.g., DBN, DPDBN, CNN, DPCNN), our TCNN model considers the data distribution divergence between the source and target datasets in the CPDP task. By adding the distribution matching layer in the process of training the CNN model, the hidden representations of the source and target project-specific layers are embedded into an RKHS where the different distributions can be matched [34]. The implementation of this matching process is that we adjusted the loss function calculation during TCNN training by merging the classification error with the distribution divergence. Finally, TCNN can obtain transferable DL-generated features for constructing the defect predictor. Although the literature [9] assumes that DBN-generated features can capture the common characteristics of defects between projects, we believe that TCNN-generated features could capture not only the common characteristics, but also the transferable components among different projects.
## 1 Introduction  
Carrier phase tracking is an important but very vulnerable operation in a GNSS receiver, due to the short GNSS carrier wavelength and the low transmitted signal power [1, 2]. Utilizing a narrow loop bandwidth is one solution for tracking weak carrier phases. However, the bandwidth should be wide enough to accommodate the satellite/receiver dynamics and the clock frequency variations to avoid loss of locks [3]. Thus, in a standalone GNSS receiver, the loop bandwidth value is a compromise choice between sensitivity and signal dynamics [4].  
In order to reduce the loop bandwidth for weak signal tracking, various Doppler aiding methods have been proposed [5]. One of the most common methods is vector tracking [6, 7], which tracks signals of all satellites jointly and allows aiding among satellites for more robust signal tracking. Another technology is GNSS/Inertial Navigation System (INS) deep integration. The key of this technology is to reduce the carrier phase tracking loop bandwidth by utilizing the received/satellite contributed Doppler estimates from a GNSS/INS integrated navigation solution [8, 9]. This integration method is very suitable for highly dynamic or strong interference applications [10]. However, inertial measurement units (IMUs) cannot provide good estimates on the receiver clock frequency instability [11], which is the key limiting factor of carrier tracking in low dynamic applications. To solve the problem, a dual antenna GNSS tracking loop was designed. This tracking loop has two inputs. One is from the master antenna in open sky, while the other one is from the slave antenna on the point to be measured. Master tracking loops are used for strong signal tracking and provide Doppler assistance to slave tracking loops. Since master and slaves loops are driven by the same oscillator in the receiver, and master and slave loops are tracking the signals from the same satellites, the slave tracking loop can track signals with much reduced bandwidth for improved tracking sensitivity. In this paper, the theoretical model and performance analysis of this proposed carrier tracking loop are presented. The impacts of signal dynamics, clock phase noise and thermal noise on carrier phase tracking are analyzed. The applications of the proposed tracking loop for RTK are also introduced.  
The paper is organized as follows: Section 2 recalls the carrier tracking principles and summarizes the impacts of signal dynamics, clock phase noise and thermal noise in carrier phase tracking. Section 3 presents the tracking loop structure of the dual antenna receiver, and analyzes the tracking errors of the slave tracking loop and differential carrier phase induced by different error sources. Section 4 gives the simulation results compared with the standard independent tracking architecture. Section 5 describes the test results of a software receiver using the new loop structure. Finally, some conclusions are drawn in the last section.
## 3 Grey Model with Seasonal Index Adjustment
### Model Accuracy Inspection  
Prediction model tests generally include an errors test, correlation test and posterior error test. In this subsection, we define these tests and list the reference of accuracy inspection grade.  
(1) Errors test  
The errors are defined as absolute error \(\Delta^{(0)}(i)=\left|x^{(0)}(i)-\widetilde{x}^{(0)}(i)\right|\), relative error \(e(i)=\frac{\Delta^{(0)}(i)}{x^{(0)}(i)}\), and average relative error (ARE) \(\overline{e}=\frac{1}{n}\underset{i=1}{n}e(i)\). (2) Correlation test  
Based on the absolute error, the minimum level difference is \(\Delta_{\min}=\underset{i}{\text{minmin}}\Delta_{i}(k)\), the maximum level difference is \(\Delta_{\max}=\underset{k}{\text{maxmax}}\Delta_{i}(k)\), and the calculation formula of the correlation coefficient is \(\zeta(k)=\frac{\Delta_{\min}+\zeta\Delta_{\max}}{\Delta(k)+\zeta\Delta_{\max}}\), in which \(\zeta\) is the resolution coefficient with the value between 0 and 1. The larger the value of \(\zeta\), the larger the value of the correlation, generally \(\zeta=0.5\). Grey correlation degree (GCD) is defined as \(r=\frac{1}{n}\underset{k=1}{\overset{n}{\sum}}\xi(k)\). (3) Posterior error test  
A posteriori error test is carried out according to the mean variance ratio and small error probability. Mean variance ratio (MVR) is defined as \(C=\frac{S_{2}}{S_{1}}=\sqrt{\frac{\sum\left[\Delta^{(0)}(i)-\overline{x}^{(0) }\right]^{2}}{n-1}}\bigg{/}\sqrt{\frac{\sum\left[x^{(0)}(i)-\overline{x}^{(0) }\right]^{2}}{n-1}}\), where \(\overline{x}^{(0)}=\frac{1}{n}\underset{i=1}{\overset{n}{\sum}}x_{i}^{(0)}\) and \(\overline{\