esa-sceva/satcom-chunk-collection huggingface dataset:
# IoT Implementation of Kalman Filter to Improve Accuracy of Air Quality Monitoring and Prediction
## 2 Related Work
### Prediction Model  
In recent years, many studies on air quality have focused on prediction air pollutant concentrations and assessing AQ in a given area. There are two main methods for constructing predictive models: traditional statistical methods and methods based on machine learning (ML) or multi-model fusion.  
At the statistical method level, [PERSON] et al. [29] propose a model of AQ prediction based on Simple Moving Average (SMA). The model predicts the concentration of pollutants in the next period or periods by the average of a set of recent actual data. Because of its simple calculation method, it is very suitable for immediate prediction. [PERSON] et al. [30] propose the Exponentially Weighted Moving Average model (EWMA). The principle of the model is the different weighting factors of the pollutant data in different historical periods on the prediction process. Without considering the periodicity, the influence of the variable far from the target period is relatively low. Therefore, the prediction results based on EWMA model are smoother and closer to recent data than the SMA. The Autoregressive Integrated Moving Average (ARIMA) [31; 32] model is widely used in AQ prediction. The model filters the non-stationary factors in the original sequence by using the data difference method, so that the model can obtain better prediction results.  
Although the traditional statistical-based prediction model has better performance in terms of interpretability and computational cost, it is limited by the single feature expression and lacks the ability to deal with complex prediction problems such as nonlinear processes. With the development of ML, many studies have chosen to use ML methods or multi-mode fusion [33; 34] to predict AQ. In the study of [PERSON] and [PERSON] [35], based on the J48 decision tree algorithm, two different decision models are constructed to predict the concentration of PM2.5 in the two adjacent regions. In the study of Raipure and Mehetre [21], the ID3 algorithm in the decision tree is applied to calculate the percentage of air pollutants. The algorithm is used to predict specific areas and provide early warning information for highly polluted areas. [PERSON] et al. [23] propose a multi-input and multi-output AQ prediction model based on ANN (Artificial Neural Network). Based on the relationship between current and past 24 h of pollutant concentration, a 24 h prediction network is established. To predict ozone concentrations,  
Figure 1: Functional model of the proposed system.  
[PERSON] uses multiple linear regression and ANN based on principal components [36]. [PERSON] and [PERSON] [37] propose ANN to predict the average daily concentration of PM2.5 two days in advance based on AQ trajectory analysis and wavelet transform.
# Graphene-Based Grid Patterns Fabricated via Direct Ink Writing for Flexible Transparent Electrodes
###### Abstract
With the help of DIW technology, fine micro-nano patterns of conductive films can be prepared so as to precisely adjust the square resistance and light transmittance to meet the requirements of various devices [24]. However, there are still challenges in preparing GFTEs via DIW techniques as the \"coffee ring\" effect occurs during evaporation, leading to the formation of graphene films with uneven thicknesses [25; 26]. In this study, graphene-based grid patterns were fabricated by DIW to address the above issues. The composition of the conductive graphene ink was carefully manipulated. A water/ethanol mixed solvent was used to prepare the graphene ink, and sodium dodecyl sulfate (SDS) was added to the graphene ink. SDS induces Marangoni flow to eliminate non-uniform deposition, while ethanol enhances flake alignment during hot-pressing. The resulting grid-patterned GFTE achieves a low square resistance of 21.3 \(\Omega\)/sq and a relatively high light transmittance of 68.5%. The overall optic and electronic performance exceeds most metal/graphene composite FTEs. More importantly, this DIW-based technique can be conducted without using toxic solvents or high-temperature steps, thus paving the way for scalable fabrication of flexible electronics.
## 3 Proposed LQRC and LQRIC for _Lc_-Hapf
### LQRC with Integral Action (LQRIC) for LC-HAPF  
The LQRIC adds the integral term of each state variable on the basis of LQRC, which aims to reduce the steady state error of the system. Thus, for the small-signal state-space model of LQRIC-controlled _LC_-HAPF, the state variable \(\widetilde{\mathrm{e}}\) is upgraded and shown in (23), whereas the control variable \(\widetilde{\mathrm{u}}\), disturbance variable \(\widetilde{\mathrm{z}}\), and output variable \(\widetilde{\mathrm{y}}\) of the LQRIC remain the same as the LQRC.  
\[\widetilde{\mathrm{e}}=\left[\widetilde{\iota}_{cd}^{\gamma}\ \ \ \widetilde{\iota}_{cq}\ \ \ \ \widetilde{\iota}_{c0}\ \ \ \int\widetilde{\iota}_{cd}\ \ \ \int\widetilde{\iota}_{cq}\ \ \ \int\widetilde{\iota}_{c0}\right]^{T} \tag{23}\]  
The matrices A, B, C, D and E for the LQRIC-controlled _LC_-HAPF are modified accordingly and expressed as:  
\[\mathrm{A}=\left[\begin{array}{cccccc}-\frac{R_{cx}}{L_{cx}}&\omega&0&0&0&0 \\ -\omega&-\frac{R_{cx}}{L_{cx}}&0&0&0&0\\ 0&0&-\frac{R_{cx}}{L_{cx}}&0&0&0\\ 1&0&0&0&0&0\\ 0&1&0&0&0&0\\ 0&0&1&0&0&0\\ \end{array}\right]\text{, }\mathrm{B}=\left[\begin{array}{cccc}\frac{1}{L_{cx}}&0&0\\ 0&\frac{1}{L_{cx}}&0\\ 0&0&\frac{1}{L_{cx}}\\ 0&0&\frac{1}{L_{cx}}\\ 0&0&0\\ 0&0&0\\ \end{array}\right]\text{, }\mathrm{C}=\left[\begin{array}{cccc}1&0&0&0&0\\ 0&1&0&0&0\\ 0&0&1&0&0\\ 0&0&0&0\\ 0&0&0\\ 0&0&0\\ \end{array}\right]\text{.}\]  
For LQRIC, the R matrix remains the same as LQRC, whereas the Q matrix has three additional integral weighting elements, \(q_{q4}\), \(q_{q5}\) and \(q_{q6}\), which are shown below:  
\[\mathrm{Q}=\left[\begin{array}{cccccc}q_{q1}&0&0&0&0&0\\ 0&q_{q2}&0&0&0&0\\ 0&0&q_{q3}&0&0&0\\ 0&0&0&q_{q4}&0&0\\ 0&0&0&q_{q5}&0\\ 0&0&0&0&0&q_{q6}\\ \end{array}\right] \tag{24}\]For the design of the Q matrix for LQRIC, the integral weighting elements are designed larger than the corresponding proportional weighting elements \(q_{q1}\), \(q_{q2}\) and \(q_{q3}\), i.e., \(q_{q4}\geq q_{q1}\), \(q_{q5}\geq q_{q2}\) and \(q_{q6}\geq q_{q3}\), in order to achieve a better steady-state performance compared with the LQRC. Finally, based on the updated variable \(\widetilde{\text{e}}\) and matrices A, B, C, D, E, Q and R, the optimal control gain K matrix of the LQRIC can be obtained by the design process of LQRC in Section 3.1.2, and therefore the controlled system in (21) of LQRIC can be acquired.
## 4 Joint Power Allocation for Co3P JDT
However, Equation (29) contains several complicated operations such as exponentials, exponential integrals, and their products. We can solve Equation (29) by only using numerical techniques such as the bisection method. The solid lines in Figure 4a-c show \(P_{3}^{*}\) versus \(\tilde{\gamma}_{3}\)[dB], with \(\tilde{\gamma}_{1}\)[dB] as a parameter, when \(P_{S}=2\), \(4\), and \(8\) W, respectively, in the condition \(\tilde{\gamma}_{3}=\tilde{\gamma}_{2}\). In this study, we obtained these solid lines from Equation (29) by using the bisection method. From these solid lines, we see that in the condition \(\tilde{\gamma}_{1}\geq\tilde{\gamma}_{2}=\tilde{\gamma}_{3}\), \(P_{3}^{*}\) is almost quadratic with respect to \(\tilde{\gamma}_{3}\)[dB] over the Co3P JDT region (i.e., the region from the non-JDT boundary point dividing the Co3P JDT mode and the non-JDT mode, to the balanced point of \(P_{1}^{*}=P_{2}^{*}=P_{3}^{*}\) at \(\tilde{\gamma}_{1}=\tilde{\gamma}_{2}=\tilde{\gamma}_{3}\)). Thus, we use the LLA CTP power expression for \(P_{3}^{*}\) versus \(\tilde{\gamma}_{3}\)[dB] in the condition \(\tilde{\gamma}_{3}=\tilde{\gamma}_{2}\), which is a linear line based on the linearization at the balanced point of \(P_{1}^{*}=P_{2}^{*}=P_{3}^{*}\) at \(\tilde{\gamma}_{1}=\tilde{\gamma}_{2}=\tilde{\gamma}_{3}\). In addition, we can use an LQA CTP power expression for \(P_{3}^{*}\) versus \(\tilde{\gamma}_{3}\)[dB] in the condition \(\tilde{\gamma}_{3}=\tilde{\gamma}_{2}\), which is a quadratic line connecting the non-JDT boundary point of \(P_{2}^{*}=P_{3}^{*}=0\) at \(\tilde{\gamma}_{3}=\tilde{\gamma}_{2}\) and the balanced point of \(P_{1}^{*}=P_{2}^{*}=P_{3}^{*}\) at \(\tilde{\gamma}_{1}=\tilde{\gamma}_{2}=\tilde{\gamma}_{3}\), while satisfying the slope at the balanced point. We can approximate the optimum CTP3 power at the settled point of \(P_{3}^{*}=P_{2}^{*}\) at \(\tilde{\gamma}_{3}=\tilde{\gamma}_{2}\), using the LLA or LQA CTP power expression. To derive the LQA expression in the condition \(\tilde{\gamma}_{1}\geq\tilde{\gamma}_{2}=\tilde{\gamma}_{3}\), we first derive the non-JDT boundary point. Given \(\tilde{\gamma}_{1}\), we obtain Equation (30) by substituting \(P_{3}^{*}=0\), \(P_{1}^{*}=P_{S}-2P_{3}^{*}\), and \(dP_{1}^{*}/dP_{3}^{*}=-2\) into Equation (29).
# Use of Anionic Polysaccharides in the Development of 3D Bioprinting Technology
## 2 Overview of Natural Anionic Polysaccharides from Plant, Bacteria and Algae
### Alginate from Brown Algae and Bacteria  
#### 2.3.1 Alginate from Brown Algae  
Alginates are quite abundant in nature since they occur both as a structural component in marine brown algae (Phaeophyceas), comprising up to 40% of the dray algae such as _Laminaria digitata_, _L. hyperborea_ and _Macrocystis pyrifera_. Alginates are unbranched binary copolymers, consist of (1->4) linked \(\beta\)-p-mannuronic acid (M) and \(\alpha\)-t-guluronic acid (G) residues (Figure 3) [47; 48], of widely varying composition and sequence according to: (i) algae, (ii) where they are developed and, (iii) the organs used for extraction.  
However, it is still organized as homo- (MM blocks and GG blocks) and/or heteropolymeric blocks (MG blocks) [49]. Distribution, quantity and length of the MM, MG and GG blocks determine the rheological properties of alginates. Thus, high levels of G blocks stiffen the alginate chains [49] and lead to more viscous solutions. It is in the presence of cations and especially Ca\({}^{2+}\) ions that alginate forms gel (called egg box model) whose resistance is strongly influenced by the content and the length of the G blocks [48; 49]. Compared with other gelling polysaccharides, the most striking features of alginate's physical properties are the selective binding of multivalent cations, being the basis for gel formation and the fact that the sol/gel transition of alginates is not particularly influenced by temperature. Commercial alignates are produced mainly from _Macrocystis pyrifera_, _Ascophyllum nodosum_, _Laminaria_ species, _Eclonia maxima_, _Lessonia nigrescens_, _Durillea Antarctica_ and _Sargassum_ species [50]. Many processes are used to extract alginate [51; 52; 53; 54]. In a general way, algae powder was first demineralized using acid solution such as HCl (0.1%-0.5%) and secondly treated with alkaline solution using NaHCO\({}_{3}\) or NaOH (0.5 to 2%; pH 10-11) at 60-70 \({}^{\circ}\)C which easily allows the release of alignates in sodium salt form [51; 53]. To increase yield, the alkali treatment is repeated 2-3 times. After filtration to eliminate insoluble residue, CaCl\({}_{2}\) solution (5%-15% (w/v)) is added to the filtrate to precipitate alginate in calcium salt. Then, calcium alginate could be bleached using aqueous oxygen peroxide or sodium hypochlorite (1%-10% (v/v)) treatment followed by mixing with HCl solution (1%-5% (v/v)) to obtain alginic acid which is purify by repeated water washing to completely remove  
Figure 3: Mains structure of alginates. From algae with R=H and from bacteria with R= H or COCH\({}_{3}\) (acetyl group).  
calcium salt [48; 49; 51; 52]. Finally, the highly purified alginic acid form is usually: (i) converted to sodium alginate using carbonate or NaOH solution treatment, (ii) precipitated using alcohol (ethanol, isopropanol... ), (iii) dried and (iv) ground in fine powder.
###### Abstract  
The proliferation of smartphones and internet connectivity has provided the opportunity to use crowdsourced data in traffic management. Nowadays, many people use navigation apps such as _Google Maps, Waze_, and _Filtsmeister_ to obtain real-time travel information and provide feedback on road conditions, such as reporting police speed checks. As an accurate traffic speed prediction is of great significance for road users and traffic managers, different models have been proposed and widely used to predict traffic speed considering the spatio-temporal dependence of traffic data and external factors such as the weather, accidents and points of interest. This study investigates the impact of crowdsourced data about police enforcement from navigation apps on traffic speed. In addition, we examine whether the police enforcement report affects the accuracy of the deep learning prediction model. The authors extract crowdsourced police enforcement information from navigation apps, collect the corresponding historical traffic speed data, and predict traffic speed in several corridors in The Netherlands using a GCN-GRU traffic speed prediction model. The results show that the crowdsourced data for police enforcement cause the average vehicle speed to drop between 1 [km/h] and 3 [km/h] when passing the road segments marked with police activity. Moreover, the prediction performance of the GCN-GRU model during the periods without police enforcement is better than the periods with reported police activity, showing that police speed check reports can decrease the accuracy of speed prediction models.  
Keywords:crowdsourced data; traffic speed prediction; traffic enforcement; police enforcement +
Footnote †: journal: 2023, 13, 11822. [[https://doi.org/10.3390/app132111822](https://doi.org/10.3390/app132111822)]([https://doi.org/10.3390/app132111822](https://doi.org/10.3390/app132111822))
# Retransmission Avoidance for Reliable Data Delivery in Underwater WSNs
## 3 FLMPC: Proposed Protocols
### Network Configuration and Data Transmission
#### 3.6.1 Potential Neighbor Selection
By focusing on \(E\) in Figure 5(a), we can see, it has \(D\) and \(I\) in range which confirms \(h-2\) hops information with respect to \(C\). However, its not enough for avoiding the energy hole occurrence in the network. Both \(D\) and \(I\) are computed to ensure that there is no void hole. \(D\) and \(I\) has \(H\) and \(J\) in their transmission ranges, respectively, and based upon them \(C\) forwards data packet to \(E\). Now, \(E\) is the current forwarder and also a cross node, so data packet needs to be multicasted. \(E\) follows complete selection mechanism and spot two relay nodes \(D\) and \(I\). \(D\) further has \(H\) in its range. However, \(D\) can not be the \(PFN\) because \(E\) has only \(h-2\) hops information in case of \(D\). \(D\) is leading to void region and because of no backward transmission, \(H\) has only choice of packet drop. Node \(J\) lies in \(I\)'s transmission range and \(K\) resides in range of \(J\). \(h-3\) hops information is confirmed and \(E\) now forwards data packet to \(I\). Being cross node \(E\) needs to multicast data packet, however, only one \(PFN\) can ensure reliable transmission. FLMPC-One and FLMPC-Two make energy efficient decisions. If there is no cross node in transmission range or the existing cross node is leading to a void hole, data packet transmission to such node results in packet drop. As shown in Figure 6b, \(A\) is cross node and it has \(I\) in range. However, \(A\) leads the data packet to a void hole. Now, \(C\) looks in the neighbor table to locate another cross node. In the case shown in Figure 6b, this search results in negation. \(C\) now searches for relay nodes and spots \(B\) which ensures \(h-3\) hops information. The data packet is forwarded to \(PFN\)\(B\).
## 3 Geophysical Techniques and Geophysical Characteristics
### Geophysical Techniques
#### 3.1.2 The Controlled-Source Audio-Magnetotelluric Method  
Because the detection depth did not penetrate the low resistance overburden, the controllable source audio-frequency magnetotelluric method was completed on the basis of the detection profile and high-density electrical method. The controlled-source audio-frequency magnetotelluric method (CSAMT) is developed based on magnetotelluric exploration using an artificial source method with frequency domain sounding, the signal strength is large with a detecting depth of 1.5 km, but it is commonly used for \"far\" area data when dealing with field measured data; thus, its transceiver distance setting is one of the preconditions for effective use of the method. The profile is consistent with the high-density electrical method, with a distance of 20 m. Based on the new understanding of [PERSON] et al. [17] in 2017 regarding the geological survey project of the Changsha-Zhuzhou-Xiangtan urban agglomeration, the power supply AB is arranged in the southwest section of the survey line near Yangjiaxiang Village, Chou Town. The power supply line AB is 1.04 km long, parallel to the profile of the measuring line, and the transmission distance is 9.8 km. The observation device is in TM mode, and the instrument is a GDP32 series multi-functional electrical workstation developed by the Zonge Company in the United States. The generator transmitting power is 30 kW. The emission current presents the characteristics of high frequency with low current and low frequency with high current. The maximum emission current is 16 A and the minimum emission current is 3.5 A. The data collection frequency is 0.125-8192 Hz, and the data processing software uses Scs2D. The processing process includes flying point elimination, static correction, terrain correction and inversion, etc. In this study, one-dimensional Rostick inversion results are used as the initial model of the two-dimensional Occam inversion method.
## 1 Introduction
One of the particularities of the thermal pyrolysis process is the flexibility to achieve the product of interest by changing the operating parameters, mainly temperature, residence time and heating rate [4]. Unlike mechanical recycling, thermal pyrolysis can handle highly contaminated waste, which is also economically viable [5]. There have been numerous studies on the thermal pyrolysis of particular types of plastic waste. For example, authors such as [PERSON] et al. [10], [PERSON] et al. [11] and [PERSON] et al. [12] carried out thermal pyrolysis tests to convert PE plastic waste into valuable oils. The main disadvantage of this process is that the oil obtained has high wax content with too much energy consumed. One possibility for improving the performance of pyrolysis is the use of a specific catalyst (catalytic pyrolysis). The catalyst allows the use of less stringent reaction conditions, lowering the temperature and residence time of the overall process and affecting the total operating cost. In addition, the use of the catalyst allows the product spectra to be directed towards fuel and valuable chemicals, depending on the process conditions and being more selective [5; 13]. In short, the catalyst plays an important role in energy consumption and the composition and properties of pyrolysis products [4; 12]. A wide variety of catalysts have been assayed in the pyrolysis of plastic waste, placed in a pyrolysis reactor (in situ) or an independent catalytic bed (ex situ) [14; 15; 16]. Most of the used catalysts are structures of aluminum-silicate minerals with high surface area and high acid strength like HY, HUSY, HBeta and HZSM-5 zeolites. There are numerous works, such as [PERSON] et al. [14], where the catalytic pyrolysis of plastic waste was carried out in situ in the reactor. One of the disadvantages of this process is that the presence of contaminants like additives (plasticizers, stabilizers, antioxidants, etc.) and other materials (organic matter, papers, etc.) can have a direct effect on the acid centers of the catalyst, reducing its catalytic activity or favoring the formation of coke [17]. Problems related to global waste generation and management and plastic waste release, in particular, have been increasing in recent years. Therefore, there is a growing need to search for integrated solutions to respond to this problem. Achieving a model based on zero waste includes different actions such as sustainable design, reduction, reuse and recycling of waste and product responsibility [18]. In this sense, this work aims to give added value to PE film waste through catalytic pyrolysis, helping to reduce the amount of waste and avoiding environmental problems. For this, the characterization of different oils obtained through the catalytic in situ pyrolysis of PE film from the non-selectively collected fraction from municipal solid waste in a fixed-bed batch-type pyrolysis reactor with different commercial catalysts has been carried out. Additionally, the characterization of the catalyst was carried out using different techniques, including X-ray powder diffraction (XRD) and N\({}_{2}\) adsorption isotherms.
## 2 Materials and Methods
### Preparation of Extracts  
The samples were lyophilized for 24 h (chamber pressure 0.190 mbar, shelf temperature \(\mathrm{T_{min}}=-35\)\({}^{\circ}\)C, \(\mathrm{T_{max}}=20\)\({}^{\circ}\)C, condenser temperature \(-85\)\({}^{\circ}\)C) using a Beta 2-8 LSC plus lyophilizer (Martin Christ Gefnertrockungsamlagen GmbH, Osterode Am Harz, Germany). One gram of lyophilized samples were mixed with 50 mL of methanol/water solution (7:3 \(v\)/\(v\)), then extracted in an ultrasonic bath (Elmasonic S30H, Elma Schmidbauer GmbH, Singen, Germany) for 15 min. The obtained extracts were centrifuged at 14,000 rpm for 10 min at 20 \({}^{\circ}\)C (Centrifuge 5418 Eppendorf, Warsaw, Poland) and subsequently filtered using 0.22 \(\upmu\)m nylon membrane filters (Sigma-Aldrich, Darmstadt, Germany). The resulting clear extracts served for further analyses.  
Determination of the Reducing Sugars Content (RSC), Total Free Amino Acids (TFAA) and Total Phenolic Content (TSC)  
The RSC was determined by the DNS (3,5-dinitrosalicylic acid) method as described elsewhere [19]. Briefly, 1 mL of each extract was mixed with 1 mL of 0.05 M acetate buffer (pH 4.8) and 3 mL of DNS reagent, then shaken vigorously. The mixtures were incubated in boiling water for 5 min, and cooled at room temperature. The absorbance value was measured at 540 nm using a microplate reader (Synergy LX, BioTek, Winooski, VT, USA) by placing the samples in a 96-well microplate. Glucose (0.01-10 mg/mL) in acetate buffer was used for the calibration curve.  
Total free amino acid (TFAA) were analyzed following previously described methodology using Cd-ninhydrin reagent [4]. Exactly 1 mL of extracts were mixed with 2 mL of a Cd-ninhydrin reagent. The samples were shaken, heated at 84 \({}^{\circ}\)C for 5 min, cooled in ice water and the absorbance at 507 nm was determined by the use of UV-VIS Thermo Scientific Evolution 220 spectrophotometer (Waltham, MA, USA). The results were expressed as mg glycine (Gly) per gram of sample with respect to the standard curve including the dilution factor. The standard curve was first prepared using glycine (0.01-10 mg/mL).  
TPC was determined by the Folin-Ciocalteu method as described by [PERSON] et al. [28]. To 100 \(\upmu\)L of extracts, 6 mL of distilled water and 0.5 mL of Folin-Ciocalteu reagent were added. After 3 min of incubation, 1.5 mL of saturated Na2 CO3 solution was added. In the next step, the mixtures were incubated for 30 min in the dark at 40 \({}^{\circ}\)C. The absorbance of the mixtures was measured at 765 nm. TPC was expressed as mg gallic acid equivalents (GAE) per mL of sample (mg GAE/mL).