esa-sceva/satcom-chunk-collection huggingface dataset:
# Tensor-Based Approaches for Nonlinear and Multilinear Systems Modeling and Identification
###### Abstract  
Nonlinear (NL) and multilinear (ML) systems play a fundamental role in engineering and science. Over the last two decades, active research has been carried out on exploiting the intrinsically multilinear structure of input-output signals and/or models in order to develop more efficient identification algorithms. This has been achieved using the notion of tensors, which are the central objects in multilinear algebra, giving rise to tensor-based approaches. The aim of this paper is to review such approaches for modeling and identifying NL and ML systems using input-output data, with a reminder of the tensor operations and decompositions needed to render the presentation as self-contained as possible. In the case of NL systems, two families of models are considered: the Volterra models and block-oriented ones. Volterra models, frequently used in numerous fields of application, have the drawback to be characterized by a huge number of coefficients contained in the so-called Volterra kernels, making their identification difficult. In order to reduce this parametric complexity, we show how Volterra systems can be represented by expanding high-order kernels using the parallel factor (PARAFAC) decomposition or generalized orthogonal basis (GOB) functions, which leads to the so-called Volterra-PARAFAC, and Volterra-GOB models, respectively. The extended Kalman filter (EKF) is presented to estimate the parameters of a Volterra-PARAFAC model. Another approach to reduce the parametric complexity consists in using block-oriented models such as those of Wiener, Hammerstein and Wiener-Hammerstein. With the purpose of estimating the parameters of such models, we show how the Volterra kernels associated with these models can be written under the form of structured tensor decompositions. In the last part of the paper, the notion of tensor systems is introduced using the [PERSON] product of tensors. Discrete-time memoryless tensor-input tensor-output (TITO) systems are defined by means of a relation between an \(N\)th-order tensor of input signals and a \(P\)th-order tensor of output signals via a \((P+N)\)th-order transfer tensor. Such systems generalize the standard memoryless multi-input multi-output (MIMO) system to the case where input and output data define tensors of order higher than two. The case of a TISO system is then considered assuming the system transfer is a rank-one \(N\)th-order tensor viewed as a global multilinear impulse response (IR) whose parameters are estimated using the weighted least-squares (WLS) method. A closed-form solution is proposed for estimating each individual IR associated with each mode-\(n\) subsystem.  
block-oriented nonlinear systems; multilinear systems; parameter estimation; tensor decompositions; tensor systems; Volterra systems; Wiener-Hammerstein systems
# Toward Integrated Satellite Operations and Network Management: A Review and Novel Framework
## 3 Integrated Management Framework
The operator can configure the optimization algorithm parameters, the constraints used in the optimization loop, and their relative weights in the solution evaluation. This module is further explained in Section 3.2 and is presented in detail in [90].
# A New DGNSS Positioning Infrastructure for Android Smartphones
## 4 Experimental Studies
### Effects of Ionosphere in the Low-Latitude Region  
It is well known that the ionospheric activity is affected by many factors, such as daytime, season, and solar activity. Thus, these factors may affect the positioning accuracy of the standalone GNSS. When using the DGNSS positioning infrastructure, most of the ionospehric errors can be mitigated. In this paper, we mainly compared the performance of DGNSS in the morning and that in the afternoon.  
Macau is located in low-latitude regions, with the latitude ranging from 22.1 degrees to 22.3 degrees. A comparison test was performed in Macau on 8 December 2016, when the solar activity was low. The first data set was collected from 10:30 a.m. to 11:00 a.m., during which the ionospheric delay was still small. The other was collected from 2:00 p.m. to 2:30 p.m., during which the ionospheric delay reached the diurnal maximum [10]. In two tests, we walked along a predefined track of a trapezoid shape. Both trajectories are in the open area, and the effects of multipath on smartphones can be assumed to be same for two data sets. The distance between the smartphones to the reference station is approximately 4 km.  
Figure 7 compares the smartphone positioning performance in the morning and that in the afternoon. In the figure, the blue line and the green line represent the smartphone GNSS and DGNSS trajectories respectively, and the red line represents the ground truth. As shown in the figure,  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Mode** & **Average/m** & **Root Mean Squares (RMS)/m** \\ \hline Standalone GNSS & 2.3 & 2.6 \\ DGNSS & 0.8 & 1.5 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Performance comparison between standalone GNSS and DGNSS in the smartphone.  
Figure 6: Performance comparison between two smartphones.  
the standalone GNSS positioning error in the morning was much smaller than that in the afternoon. When using the positioning infrastructure, the positioning error was reduced significantly for both tests.  
Table 3 shows the smartphone performance in the morning and that in the afternoon. In the morning, the ionospheric delay was smaller, and the average of the standalone GNSS positioning error was 0.9 m, and the RMS error was 1.7 m. In the afternoon, the ionospheric delay was much larger; the performance of the traditional model used in smartphones was not good, especially for low latitude areas. During this period of time, the average of the standalone GNSS positioning error reached 4.0 m, and the RMS error was as large as 4.4 m. When using the proposed DGNSS, the positioning error was reduced significantly in both tests. Specifically, the RMS error was reduced to 1.5 m, and 1.8 m in the morning and the afternoon, respectively. The average of horizontal positioning error was reduced to 0.4 m in both tests.
# Zeroize: A New Method to Improve the Utilization of 5G Networks When Running VoIP over IPv6
## 3 Zeroize Method Design
### Source IPv6 Address  
In packet-based networks, the purpose of the source IPv6 address is to identify the sender of the data. The receiver uses this source IPv6 address to send back a response to the sender [19]. In VoIP applications, the call is started over two stages, namely signaling and media transfer. In the signaling stage, a specific protocol (e.g., SIP protocol) is utilized to start the call and agree on the call parameters, including the IPv6 address of each call  
Figure 1: Zeroize method topology.  
end. In other words, each of the calls knows the IPv6 address of the other end at this stage. In the media transfer stage, a different specific protocol is utilized (e.g., RTP protocol) to carry the voice data [20, 21]. The RTP, with the help of other protocols (i.e., UDP/IPv6), uses the call parameters from the first stage, including the IPv6 address, to send the voice data to the intended destination. The point is that the source IPv6 address is included in every voice data packet (media packets). However, the VoIP media transfer session is not request-response, and the callee client does not send back a response to the sender by itself. In case the callee replies to the caller, the callee client uses the source IPv6 address, which is known from the first stage [14, 15, 19]. In addition, the other network devices (e.g., switches, routers, or firewalls) are not affected. Typically, the switch is a layer two device while the IPv6 protocol is layer three, where the router is using the destination IPv6 address to perform its main function of routing and the firewall denies a VoIP call during the call setup by SIP or H323 VoIP signaling protocols, but not while transferring the voice media data using RTP/UDP/IP protocols. That aside, none of the other protocols will be affected by this design. Accordingly, the source IPv6 address in the voice data packets is superfluous and can be assigned a different purpose, such as carrying the voice data itself. This condition reduces the consumed bandwidth by each call and increases the channel call capacity.
## 2 Results
### O. vulgaris Terpenoids Exert Toxic Effects on S. littoralis larvae

Table 1: Chemical composition of _Origanum vulgare_ terpenoids in undamaged control plants, in _Spodoptera littoralis_ herbivore wounded (HW) plants and in _S. littoralis_ grass upon feeding on _O. vulgare_ leaves. The results are expressed as \(\mu\)g g\({}^{-1}\) fresh weight and are the mean of at least three replicates \(\pm\) SD. Asterisks indicate significant differences between control and HW: *_p_ < 0.05; **_p_ < 0.01. (Figure 2A). When larvae were offered a diet containing the _O. vulgare_ terpenoid extract, a significant reduction of larvae survival was found with respect to ACD + Tween 20 (Figure 2B). We preliminary tested the effects of several _O. vulgare_ terpenoids and we found that carvacrol (the major terpenoid of _O. vulgare_, Table 1) and _p_-cymene induced a significant (_p_ < 0.05) reduction of survival percentage with respect to ACD + Tween 20, whereas the effect of \(\gamma\)-terpinene (the precursor of carvacrol) was not different from controls (Figure 2B). _O. vulgare_ terpenoids also affected the larval weight with a significant (_p_ < 0.05) weight reduction, with respect to ACD (Figure 3). In particular, during the larval development from the 2 nd to the 3 rd instar, the caterpillar weight increased less rapidly in caterpillars feeding on _O. vulgare_ leaves, with respect to controls (Figure 3A). Caterpillars feeding on \(\gamma\)-terpinene and on _O. vulgare_ terpenoid extracts showed a significant (_p_ < 0.05) weight reduction with respect to ACD + Tween 20 (Figure 3B), whereas neither carvacrol nor _p_-cymene affected the larvae weight (Figure 3B). Figure 2: Percentage of _S. littoralis_ survival upon feeding on _O. vulgare_ leaves and terpenoids. (**A**) Comparison of _S. littoralis_ survival upon feeding on artificial diet (ACD) or _O. vulgare_ leaves. (**B**) Effect of terpen extracted from _O. vulgare_ leaves and of some _O. vulgare_ monoterpenes on _S. littoralis_ survival. ACD + Tween 20 was used as control. Bars indicate standard deviation. Different letters indicate significant (_p_ < 0.05) differences. To better assess the effects of _O. vulgare_ terpenoids on _S. littoralis_ growth, the increase of larval size was also monitored (Figure 4). Feeding of _S. littoralis_ on _O. vulgare_ leaves was ineffective on larvae size during the 2 nd instar. However, a significant size reduction was found at the 3 rd instar, with respect to controls (Figure 4A). As for the larvae weight increase, \(\gamma\)-terpinene and the _O. vulgare_ terpenoids extracts were the only treatments that were able to significantly reduce the larvae size, with respect to ACD + Tween 20 (Figure 4B).
## 3 Results and Discussion
### Effect of Different Permeabilities  
Permeability is a property of the reservoir rock that measures the capacity and ability of the formation to transmit fluid [11]. The core samples with different permeabilities are conducted to understand the effects of water invasion. The gas production rate and original saturation pressure are 1000 mL/min and 30 MPa, respectively. The experiment results without aquifer and with 3 times, 7 times and infinite aquifer are illustrated in Figure 8a. From the result of Figure 8a, it can be seen that the recovery factor increases gradually with the increase of permeability when there is no aquifer [21]. This is because the gas flow resistance in the low permeability core is far greater than its resistance in  
Figure 6: Bottom hole pressure and producing pressure drop versus recovery factor for different production rates. (**a**) Bottom hole pressure; (**b**) producing pressure drop.  
Figure 7: Water invasion versus time for different production rates and recovery factor versus gas production rate (**a**) Water invasion; (**b**) recovery factor.  
high permeability core, which is determined by the micro-pore structure characteristic of reservoirs. The recovery factor of the low permeability core with 0.001 mD is 54.09%, and it is considerably lower than that of 94.60% in high permeability with 10 mD. When the permeability is less than 0.1 mD, the 3 times aquifer promotes gas production and the recovery factor can increase by 3.7-8.5%. Once permeability exceeds 0.1 mD, water invasion reduces greatly, increasing the recovery factor, and the decrease is between 20.07% and 35.90%. This shows that the greater the permeability is, the more the aquifer size has an obvious effect on recovery factor at the same gas production rate. The gas reservoirs with different permeabilities correspond to an optimum aquifer, which can maximize the recovery factor. The lower the permeability is, the larger the corresponding aquifer is. The 0.1 mD core is 3 times aquifer while the 0.01 mD core is 7 times aquifer.  
Figure 8b shows water invasion with different production rates between 500 mL/min and 4000 mL/min in the 7 times aquifer. When the permeability is 0.001 mD, gas production rate increases from 500 mL/min to 4000 mL/min and the related recovery factor reduces from 64% to 45%. While the permeability is 10 mD, gas production rate increases from 500 mL/min to 4000 mL/min and recovery factor is almost kept at about 54%. This indicates that the increase of gas production rate reduces the recovery factor of gas reservoirs, but the impact on recovery factor decreases with the increase of permeability. Thus, in the low and extra-low permeability reservoirs, we should control gas production rate in the development of gas reservoirs and extend production time so as to achieve the high recovery factor while we should properly speed up gas production rate in the medium-high permeability reservoirs.
## 4 Measurement/Calibration of \(R_{s}\)
### In Silico Method  
The laboratory- or field-based calibration method on the \(R_{s}\) would have some limitations because the alteration of diverse environment factors caused differences in the \(R_{s}\) data obtained by both determination methods. Considering the time-intensive nature of experimental determination on the \(R_{s}\), computational modelling methods might potentially overcome these limitations if modelling data were from the in situ calibration method and also provide a solution for the calibration of that \(R_{s}\) value that cannot be estimated by field experiments due to the poor correlation between concentrations of compounds in water and on the sampling device [130].  
The empirical method has been used to estimate the \(R_{s}\) in some previous investigations. [PERSON] et al. [131] applied the dimensionless Sherwood correlation developed for the calculation of dynamic parameters in laminar flow to estimate the \(R_{s}\) for some passive sampling devices. [PERSON] et al. [50] found a linear relationship (\(R^{2}\) = 0.84) for the laboratory-measured \(R_{s}\) with a \(log\)\(K_{ow}\) of 0-4, which could be used to predict the \(R_{s}\).  
In addition to the empirical method, some specific models have been increasingly applied to estimate related parameters of the \(R_{s}\). [PERSON] et al. [132], on the basis of a quantitative structure-activity relationship (QSAR) model, explored the effect of DOM on sampling dynamics. With the development of machine learning methods, the regression models can be varied from simple linear relation to complex nonlinear functions. [PERSON] et al. [130] used artificial neural networks (ANNs) for the first time to model and predict the \(R_{s}\) of organic compounds for the POCIS.
# Modelica-Based Energy Management of PEMFC Hybrid Power System of Vehicle
## 4 Simulation and Evaluation
### Results of Power Allocation for SM-MPC
#### 4.3.2 Fluctuation of SOC for Lithium Battery  
Based on the allocation results of the three methods under the NEDC and HWFET conditions, the corresponding SOC variation curves are shown in Figures 9 and 10 below. From the figures, it can be seen that under the NEDC condition, the SOC allocated by the state machine exhibits a gradually increasing trend. This is primarily because this condition spends most of its time in a low power demand phase, and the SM1 method is able to continuously charge the lithium battery, resulting in its SOC being higher than the reference  
Figure 6: Output voltage and power curves of PEMFC and lithium-ion battery at the different loading current: (**a**) PEMFC, and (**b**) lithium-ion battery.  
point. In contrast, the MPC method shows SOC values below the reference point most of the time. However, the SM-MPC method is able to maintain the SOC near the reference value. In the HWFET driving cycle, it can still be observed that the SM-MPC method more effectively maintains the SOC value of the lithium battery.
# Efficacy of High-Dose Polyclonal Intravenous Immunoglobulin in COVID-19: A Systematic Review
## 1 Introduction  
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) caused the coronavirus disease (COVID-19) pandemic beginning in 2019 [1, 2]. At the time of writing, more than 4 million people have died from coronavirus disease (COVID-19), and more than 200 million have been infected [3]. Clinicians and researchers have struggled to develop an effective therapeutic protocol to treat and contain the spread of this infectious disease, and more than 300 drugs have been or are being investigated under clinical trials in different parts of the world [4, 5]. Among the various therapeutic and prophylactic strategies developed to contain the COVID-19 epidemic, passive immunization by COVID-19 convalescent plasma (CCP) transfusion has been proven effective when CCP was administered early (within 72 h from symptom onset) and with a high titer ( \(>\)1:160) of anti-SARS-CoV-2 neutralizing antibodies (nAb) [6; 7]. Late disease stages are characterized by an exaggerated immune response, which responds to immunosuppressive: high-dose intravenous immunoglobulin (IVIG), based on previous positive experiences in autoimmune, inflammatory and other infectious diseases, including coronavirus-induced infections [8; 9], has been also proposed for COVID-19 [10; 11; 12; 13].  
The aim of this review is to systematically analyze the safety and efficacy of the use of high-dose IVIG in patients with COVID-19 (including new primary research) and grade the quality of the available evidence following the Cochrane guidance for methodology.
# Neural Graph Similarity Computation with Contrastive Learning
## 2 Related Works
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Methods** & **Type** & **Year** & **Ref.** & **Methods** & **Type** & **Year** & **Ref.** \\ \hline A\({}^{*}\) & Classical & 1968 & [8] & SimGNN & Neural & 2019 & [7] \\ Beam & Classical & 2006 & [15] & GMN & Neural & 2019 & [26] \\ Hungarian & Classical & 2009 & [21] & GraphSim & Neural & 2020 & [13] \\ VJ & Classical & 2011 & [22] & MGMN & Neural & 2021 & [14] \\ HED & Classical & 2015 & [10] & H2 MN & Neural & 2021 & [27] \\ Noah & Hybrid & 2021 & [29] & GOTSim & Neural & 2021 & [20] \\ GENN-A\({}^{*}\) & Hybrid & 2021 & [28] & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of existing graph similarity computation methods. The Type column represents the type of the method, which can be classical, neural, or hybrid. Year refers to the publication year, and Ref. provides the reference. trend that uses contrastive learning to facilitate graph-related tasks [31; 32], in this work, we also devise an unsupervised contrastive loss to guide graph similarity learning. **Graph matching. ** Discovering the equivalent nodes among graphs, i.e., graph matching, has been intensively studied by different domains, such as social networks [33; 34], computer vision [35; 36] and knowledge graphs [37; 38; 39]. Since the graphs are usually in a large scale, state-of-the-art solutions utilize GNN embeddings to approximate the matching results. To learn the parameters of neural models, either node-level or graph-level matching label is used, and graph matching approximates the GED when GED is used as a graph-level annotation [20]. However, it should be noted that the goal of neural graph matching is to generate the fine-grained node matching result, while graph similarity learning merely needs to predict the similarity score. There are also some other related works [40; 41; 42; 43; 44; 45; 46].
# Recent Advances and Insights into Bromelain Processing, Pharmacokinetics and Therapeutic Uses
## 6 Bromelain Studies
### Osteoarthritis and Rheumatid Arthritis  
The anti-inflammatory and analgesic properties of bromelain were first reported in 1964 for the treatment of both osteoarthritis (OA) and rheumatoid arthritis [87]. Several RCTs underlined the potential ability of this nutraceuticaltical to decrease pain and inflammation in people with knee and hip OA [88]. In one of the most recent RCTs, 16-week supplementation with bromelain (500 mg/day) and diclofenac (100 mg/day) was tested in mild-to-moderate knee OA patients. At the end of treatment, the effects of bromelain and diclofenac in mitigating the symptoms of mild-to-moderate knee OA were comparable [Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC)] [89]. Similar results have been obtained in people with hip OA [81]. Another study included forty men and women with lumbar spine osteoarthritis who were divided into two groups to receive, for six weeks, either accelofenac 100 mg b.i.d or aecclofenac 100 mg b.i.d with enzyme supplements (bromelain) 250 mg b.i.d. The patients treated with bromelain reported significantly diminished pain scores (visual analogue scale and Oswestry low back pain questionnaire, respectively \(p\) = 0.001 and \(p\) = 0.000) and improved life quality, compared with patients treated with accelofenac alone [90]. Bromelain's potent analgesic and antiinflammatory properties were highlighted when administitated to patients suffering from chronic osteoarthritis (both pain-associated and not), even when used in association with other nutraceuticalials including _Curcuma longa, Harpagophytum procumbens_ and/or Boswellia serrata [91]. Bromelain potentially acts as a non-steroidal anti-inflammatory drug (NSAIDS) painkiller, as it reduces the dosage and/or the number of administrations of conventional treatments [92].