esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Example of Application: Performance Simulations using the EIPS framework
### Performance Simulations Analysis Using EIPS Framework
#### 4.3.3 Retrieval Error Performance Simulations  
The retrieval error performance of all the MW system configurations (A, B and C) was estimated using the information and error analysis model and these are presented in Figure 8. This shows the improvements of the ATMS-type, AMSU-A-type and TES-based MW instruments over the background errors for temperature and humidity. The results in Figure 8 show that a TES-based MW sounding instrument (configuration C) outperforms the other two configurations (A: ATMS-type and B: AMSU-A-type) for both temperature and humidity profiling.  
The aforementioned improvements (as in Section 4.3.2) are mainly attributed to the TES-based MW instrument's (configuration C) excellent NEDT performance. We have also observed that in the  
Figure 8: Temperature (in left) and Humidity (in right) retrieval error performance estimation and comparison for all three MW instrument system configurations (A (ATMS-type), B (AMSU-A-type) and C (TES-based MW)).  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & \multicolumn{2}{c}{**Average DFS**} \\
**MW System Configuration** & \multicolumn{2}{c}{**(Over Eight Atmospheric Profiles)**} \\ \cline{2-3}  & **Temperature (T)** & **Humidity (q)** \\ \hline A & 0.35 & 2.24 \\ B & 1.15 & 0.67 \\ C & 6.27 & 5.29 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Information Content (DFS) Estimations.  
case of humidity retrieval, the ATMS-type instrument provides noticeable improvements compared to the AMSU-A-type instrument, due to the five additional 183 GHz humidity sounding channels. These results show the direct impact of instrument noise on the information content for each instrument. This also demonstrates the importance of achieving better instrument noise performance for improving the temperature and humidity retrievals.
# A Cooperative MAC Protocol for a M2M Heterogeneous Area Network
## 4 M2M Area Network Design
### IEEE 802.15.4 Standard
Moreover, after a transmission, an acknowledgement for the received packet is sent back immediately to provide a reliable communication service by the MAC layer. A packet transmission has to be finished within one inter-frame spacing (IFS), otherwise the transmission will be deferred to the next superframe. The 6 LoWPAN standard has been developed by the Internet Engineering Task Force (IETF) group by incorporating the IP (Internet Protocol) and associated layers on top of the IEEE 802.15.4 protocol stack [21]. The 6 LoWPAN standard is capable of supporting both M2M and IoT applications. Figure 4 shows the 6 LoWPAN protocol stack. The adaptation layer is mainly used to compress 40 bytes IP header in to a two-byte field to be accommodated within a small size 128-byte 802.15.4 packet. For an M2M area network, it is possible to use the 802.11/802.15.4 standards in a standalone mode. However, these standards have their own comparative limitations. The 802.11 standard's  
Figure 4: 6 LoWPAN protocol stack. main drawback is higher energy consumption compared to the IEEE 802.15.4 standard. The main advantages of the 802.11 standard are the increased data rate and a longer transmission range. The main relative advantage of the IEEE 802.15.4 is low power consumption. The main disadvantages of the 802.15.4 standard are low transmission data rate and a shorter transmission range. In this work, we propose a heterogeneous network architecture using both IEEE 802.11 and IEEE 802.15.4 standards to develop a high capacity, low-power, and long-range M2M area network exploiting the advantages of both standards.
# Centralized vs. Decentralized: Performance Comparison between BigchainDB and Amazon QLDB
## 7 Discussion and Conclusions
Overall, BigchainDB's CPU utilization, has greater fluctuations than QLDB, meaning it has more local minimums and maximums, as can be observed in case 3 (10,000 inserts), where BigchainDB has five local peaks, while in the same scenario, QLDB has only two. Furthermore, analyzing the use of the same technology (BigchainDB) in two separate instances (case 1 and case 3), we can see that for a small number of requests, such as in case 1, the CPU utilization during the inserts is almost constant, with no significant fluctuations. In case 3, however, we register sharp fluctuations. The conclusion of this study shows that QLDB has an overall better performance compared to BigchainDB based on the metrics that have been considered (CPU and memory usage) (see Figures 32 and 33). Figure 32: CPU and memory usage—minimum values. From the perspective of database ledger implementation, Amazon QLDB proved to be an integrated solution and easier to use, whilst BigchainDB involves a complex system to be implemented and developed, but with the advantage of being more flexible. Although both systems are almost ready-to-use solutions for local environments, when it comes to configuring and setting up the communication between nodes within a production environment, BigchainDB adds a layer of complexity from a DevOps perspective, while Amazon QLDB completely overcomes this issue. Depending on the area considered and the identified needs, both BigchainDB and Amazon QLDB can be considered as suitable solutions for a ledger database.
## 3 Proposed Solutions
### Robust Iterative Solution
The strategy above conducts an improved iteration with a dumping factor \(\kappa>0\):  
\[\mathbf{\mathcal{W}}^{-1}=\mathbf{Q}_{\varepsilon}+\left(\frac{\partial\mathbf{r} ^{o}}{\partial\mathbf{s}^{oT}}\mathbf{Q}_{s}\frac{\partial\mathbf{r}^{T}}{ \partial\mathbf{s}^{o}}\right). \tag{41}\]2 \[\mathbf{\mathcal{W}}^{-1}=\mathbf{Q}_{\varepsilon}+\left(\frac{\partial\mathbf{r} ^{o}}{\partial\mathbf{s}^{oT}}\mathbf{Q}_{s}\frac{\partial\mathbf{r}^{T}}{ \partial\mathbf{s}^{o}}\right). \tag{41}\]3  
where \(\widetilde{\mathbf{X}}_{k}^{(k)}=\sum_{i=1}^{M}\mathbf{a}_{i}^{(k)}w_{i} \mathbf{a}_{i}^{(k)^{T}}\), \(\widetilde{\mathbf{w}}_{k}^{(k)}=\sum_{i=1}^{M}\mathbf{a}_{i}^{(k)}w_{i}(r_{ i}-b_{i}^{(k)})\), \(\mathbf{a}_{i}^{(k)}=\partial\overline{r}_{i}(\mathbf{\mu})/\partial\mathbf{\mu}|_{ \mathbf{\mu}=\hat{\mathbf{\mu}}^{(k-1)}}\), and \(b_{i}^{(k)}=\overline{r}_{i}(\tilde{\mathbf{\mu}}^{(k-1)})-\mathbf{a}_{i}^{(k)^{T }}\tilde{\mathbf{\mu}}^{(k-1)}\). Obviously, the values of \(\widetilde{\mathbf{X}}_{\mathbf{\mu}}^{(k)}\) and \(\widetilde{\mathbf{w}}_{\mathbf{\mu}}^{(k)}\) increase with the iterative progress, creating a potential risk of overflow. In order to avoid the numerical problem during the iteration, (60) and (61) shall be replaced by  
\[\mathbf{\mathcal{W}}^{-1}=\mathbf{Q}_{\varepsilon}+\left(\frac{\partial\mathbf{r} ^{o}}{\partial\mathbf{s}^{oT}}\mathbf{Q}_{s}\frac{\partial\mathbf{r}^{T}}{ \partial\mathbf{s}^{o}}\right). \tag{41}\]4 \[\mathbf{\mathcal{W}}^{-1}=\mathbf{Q}_{\varepsilon}+\left(\frac{\partial\mathbf{r} ^{o}}{\partial\mathbf{s}^{oT}}\mathbf{Q}_{s}\frac{\partial\mathbf{r}^{T}}{ \partial\mathbf{s}^{o}}\right). \tag{41}\]5  
where \(\rho_{k}\) is a modified dumping factor updating during the iteration. \[\rho_{k}=\left\{ \begin{aligned} (1-\kappa)/(1-\kappa^{k}),& \kappa\
eq 1,\\ 1/k,&\kappa=1.\end{aligned} \right. \tag{64}\]  
As a result, the improved iteration solution, referred to as the robust iterative solution (RIS), is  
\[\tilde{\mathbf{\mu}}^{(k)}=\mathbf{X}_{\mathbf{\mu}}^{(k)}{}^{-1}\mathbf{w}_{\mathbf{\mu} }^{(k)}. \tag{65}\]  
There are two conditions for exiting the iteration: (1) the residual threshold is reached, \(\|\tilde{\mathbf{\mu}}^{(k)}(1:2N)-\tilde{\mathbf{\mu}}^{(k-1)}(1:2N)\|<\epsilon\), where \(\epsilon\) is a small number; or (2) the number of iterations exceeds the set of upper bound \(K\). Finally, the estimation is then given by \(\tilde{\mathbf{\mu}}=\tilde{\mathbf{\mu}}^{(k)}\). The introduction of \(\kappa\) into (60)-(61) alleviates the risk of divergence, although it may decelerate the convergence. Compared to the GN method that suffers from rapid divergence to the optimal if the initial guess is poor or the noise/error is relatively large, the proposed RIS provides a better guarantee of convergence. An example is shown in Figure 3 when \(\kappa=1\).
## Materials and Methods
### Statistical Analysis  
The Shapiro-Wilk and Levene tests were performed to analyze the normality of the data and its homoscedasticity. Analysis of variance based on Kruskal-Wallis ranks was performed. [PERSON]'s posterior tests were then performed to identify which treatment made the difference. All statistical analyses were performed using the statistical software Sigma Plot 12.0 [31] and Stat-graphics of Nau [32]. \(p\leq 0.05\) indicated significance. In _Ambystoma mexicanum_, the tail moment (Figure 1B) and tail length also indicated genetic damage. Both EMS and UV-C induce significant genetic damage with respect to the negative control (\(p<0.05\)). EMS presented a behavior similar to that reported in _Oreochromis niloticus_ only in the tail moment parameter. No dose-response relationship is observed in the tail length. Regarding UV-C, the tail moment and the tail length were proportional to the exposure time. MGs did not reveal EMS or UV-C-induced genetic damage (Figure 1F). Only the 10 mM EMS treatment presented significant differences compared to the negative control (\(p<0.05\)). The genetic damage induced by EMS and UV-C and the protective effect of ascorbic acid after the exposure of _Oreochromis niloticus_ erythrocytes to these agents is presented in Table 1. The genotoxic treatment with UV-C in _Oreochromis niloticus_ erythrocytes increases significantly (\(p<0.05\)) and in direct proportion to the exposure time, the average length of the tail, and the number of MGs with respect to the negative control. No significant differences were observed with respect to the negative control (NC) in the migration group containing the highest number of comet cells. The EMS showed the same behavior except for the 10 mM EMS concentration where the number of MGs (6) and the average tail length (9.2 \(\upmu\)m) were obtained. A significant decrease (\(p<0.05\)) compared to NC was also shown in the number of cells in the migration group that contained the highest number of comet cells. The antigenotoxic treatment with different concentrations of AA after exposing the cells to 5 min of UV-C showed significant differences in the number of MGs only in UV-C 5 min + 5 mM and in the case of tail length in UV-C 5 min + 5 mM and 15 mM AA. The tail length detected an increase in genetic damage compared to positive control (PC), but not a decrease, while MGs detected a significant decrease at least for UV-C 5 min + AA 5 mM. The number of MGs containing the highest number of comet cells increases significantly (\(p<0.05\)) compared to the PC. The antigenotoxic effects of AA on blood cells _Oreochromis niloticus_ and _Ambystoma mexicanum_ exposed to UV-C are shown in Figure 2. In _Oreochromis niloticus_, both the tail moment (Figure 2A) and tail length (Figure 2C) show that the AA treatments at 5, 10, and
# Microwave NDT of Smart Composite Structures with Embedded Antennas
###### Abstract  
The integration of antennas in composite structures is gaining popularity with advances in wireless communications and the ever-increasing demands for efficient smart structures. Efforts are ongoing to ensure that antenna-embedded composite structures are robust and resilient to inevitable impacts, loading and other external factors that threaten the structural integrity of these structures. Undoubtedly, the in situ inspection of such structures to identify anomalies and predict failures is required. In this paper, the microwave non-destructive testing (NDT) of antenna-embedded composite structures is introduced for the first time. The objective is accomplished using a planar resonator probe operating in the UHF frequency range (~525 MHz). High-resolution images of a C-band patch antenna fabricated on an aramid paper-based honeycomb substrate and covered with a glass fiber reinforced polymer (GFRP) sheet are presented. The imaging prowess of microwave NDT and its distinct advantages in inspecting such structures are highlighted. The qualitative as well as the quantitative evaluation of the images produced by the planar resonator probe and a conventional K-band rectangular aperture probe are included. Overall, the potential utility of microwave NDT for the inspection of smart structures is demonstrated.  
antenna; composites; conformal load-bearing antenna structures; microwaves; non-destructive testing; smart structures +
Footnote †: journal: Sensors
Earthworm treatment increases dissolved oxygen content due to the worms' burrowing action [14; 16] and removes organic matter from different types of waste and wastewater: up to 58 to 63% TOC [17; 18], 98 to 99% BOD5, and 70 to 100% COD [19; 20; 21]. Earthworm treatments have also been shown to remove ammonia up to 67% by vermicomposting [22] and up to 97% by vermifiltration [20] and increase nitrate up to 75% by vermicomposting and 187% by vermifiltration [22]. The C:N ratio has been reported to decrease in solid and liquid wastes treated by earthworms due to the loss of carbon as CO2 [12; 17]. Earthworms alter microbial communities, favoring betaproteobacteria [23; 24; 25], which include ammonia-oxidizing bacteria (AOB), nitrite-oxidizing bacteria (NOB) and some denitrifiers [26]; on the other hand, fungi such as _Candida albicans_, _Aspergillus flavus_ and _Aspergillus niger_[27] and pathogenic bacteria _Bacillus sp._, _E. coli_, _Serratia marcescens_[28], _Pseudomonas aeruginosa_, _Klebsiella pneumoniae_, _Staphylococcus aureus_ and _Streptococcus pyogenes_[27] are suppressed. A drawback of earthworm waste and wastewater treatments is the reported increase of nitrate [22; 29] and phosphorus [22; 30; 31], increasing the risk of eutrophication by the effluents. Thus, further downstream treatment is needed to remove these contaminants. Hydroponic (soil-less) plant-based treatments have been shown to effectively remove nitrate and inorganic phosphorus, among other contaminants, from different types of wastewaters and, in particular, swine wastewaters [32; 33; 34]. Combining earthworms with downstream hydroponic cultures has been recently proposed [35] and is a promising technology for efficient treatment of wastewaters such as those from hog farms. The present study aimed to build and test an indoor pilot-scale swine wastewater treatment system comprising a vermifiltration unit for the removal of ammonia, nitrite, organic matter, and pathogens with a downstream hydroponic plant cultivation unit for further removal of nitrate and phosphorus with the concomitant production of marketable crops.
# Effect of IoT Capabilities and Energy Consumption behavior on Green Supply Chain Integration
## 2 Background and Related Work
### Internet of Things  
In business management literature, IoT is used to facilitate organizations' modern needs [27; 28], IoT can enable organizations to align their resources on a single platform and track all their processes and activities efficiently [11; 27]. In supply chain management, IoT has practical implications. It can be used to track products, maintain inventory, keep a record of buyers and suppliers, reduce theft and mishandling, in addition to many other benefits. Nowadays, most of the organizations have their own customized software to integrate their activities and to align their customers, suppliers, and team members, for sharing information, enhancing communication, and to smooth organizational activities. The supply chain ranges from traditional inventory registers and activities to a computerized system. Finally, IoT systems based on barcode scanners and RFID technology enable people to track, monitor, record, and analyze inventory details. This is achieved only due to IoT applications in the supply chain and to enhance the supply chain performance [29; 30].  
In this study, the relationship between IoT and SI and IoT and CI has been established. Since IoT can enable both suppliers and customers to integrate with the manufacturer, it can also help to enhance the interaction between manufacturer and supplier to understand the needs of the manufacturer, production planning, and inventory level. The manufacturer can order online or via email using information and communication technologies or with the use of IoT to order the products according to a product's design and its cost. All in all, IoT can positively affect SI, which can lead to GSCP [11; 27]. In the same way, IoT also affects CI, since IoT can enable customers to interact and communicate with the manufacturers. IoT can also facilitate customers to book their products online and check the specification of products by using the internet. Furthermore, the customer can track their products in real time. Customers can get in contact with the manufacturer and can subsequently develop a strong relationship. This can result in customer satisfaction, loyalty, and the reordering of products from the same manufacturer which can result in certain manufacturers having a competitive advantage and improved financial performance. Therefore, the use of IoT can increase customer interaction, and CI can lead to enhanced supply chain performance and GSCP [11; 27].
## 3 DWA Algorithm and Improvements
### Speed Sampling  
In the process of running the AGV, to make the obtained speed group \((v_{x},v_{y},\omega)\) approach the optimal value as much as possible, it is necessary to sample multiple groups of data for simulation. However, due to some performance and the environment of AGV itself, the sampling range is limited.  
(1) The speed constraint of the AGV is expressed as in Equation (5).  
\[V_{m}=\left\{(v,\omega)\big{|}v_{x},v_{y}\in[v_{\min},v_{\max}],\omega\in[ \omega_{\min},\omega_{\max}]\right\} \tag{5}\]  
In the above formula, \(v_{\min}\) and \(v_{\max}\) refer to the minimum and maximum linear velocities, respectively; \(\omega_{\min}\) and \(\omega_{\max}\) represent the minimum and maximum angular velocities, respectively.  
(2) In the predicted time range, the speed set under the constraint of acceleration and deceleration of the motor is expressed as in Equation (6).  
\[V_{d}=\left\{(v,\omega)\big{|}v_{x},v_{y}\in[v_{t}-\hat{v}_{1}\Delta t,v_{t}+ \hat{v}_{2}\Delta t],\omega\in[\omega_{t}-\dot{\omega}_{1}\Delta t,\omega_{t} +\dot{\omega}_{2}\Delta t]\right\} \tag{6}\]  
In the above formula, \(v_{t}\) and \(\omega_{t}\) denote the linear velocity and the angular velocity at the current moment, respectively; \(\hat{v}_{1}\) and \(\hat{v}_{2}\) represent the minimum and maximum decelerations, respectively; \(\dot{\omega}_{1}\) and \(\dot{\omega}_{2}\) indicate the minimum and maximum accelerations, respectively.  
Figure 5: Fusion algorithm flowchart.  
When the AGV encounters a moving obstacle, it is necessary to ensure a safe distance between the AGV and the obstacle so that the speed of the AGV is reduced to zero before hitting the obstacle and its speed constraint is expressed as in Equation (7).  
\[V_{a}=\left\{(v,\omega)\Big{|}v_{x},v_{y}\leq\sqrt{2 dist(v,\omega)\dot{v}_{1}}, \omega\leq\sqrt{2 dist(v,\omega)\dot{\omega}_{1}}\right\} \tag{7}\]  
In the above formula, \(dist(v,w)\) represents the shortest distance from the end of the simulated trajectory to the obstacle.  
The final speed of the DWA is the intersection of these three constraints; the speed \(V\) is expressed as in Equation (8).  
\[V=V_{m}\cap V_{d}\cap V_{l} \tag{8}\]
# Comparison of the Properties and Structure of Montmorillonite Modified with Polyvinylpyrrolidone
## 2 Materials and Methods
### Preparation of Modified MMT Samples  
MMT samples are prepared using a previously developed MMT modification method using PVP in an aqueous colloidal solution with the subsequent selection of three separate solution fractions [46]. Colloidal solutions of MMT and PVP are prepared as described in the previous work [46]. However, this work, unlike the previous one, studied nanocomposites obtained with different ratios of MMT/PVP in the initial solution. Therefore, aqueous colloidal solutions of MMT and PVP were mixed under the influence of ultrasound in such proportions that the mass ratio of MMT and PVP in the suspension was 4:1, 3:1, 2:1, 1:1, and 1:2, respectively. After mixing, each resulting suspension is separated into three layers (fractions). As a result, after appropriate treatment of these fractions, three modified montmorillonites are obtained from each suspension: the bottom layer (MMT/PVP-bottom)--finely dispersed light gray powder, the middle layer (MMT/PVP-middle)--finely dispersed white powder, and the top layer (MMT/PVP-top)--light-yellow film, which was ground using a laboratory mill.  
The yield of each type of modified MMT is presented in Table 1. As the content of MMT in the initial mixture increases, the yield of the bottom and middle layers increases. This indicates that most of the original MMT is contained in the bottom and middle layers, and the top layer consists mainly of PVP. The XRD, TGA, and FTIR analysis results also confirm these conclusions.  
A nanocomposite with a mass ratio of MMT/PVP = 1:12 is also obtained for comparative studies. Colloidal solutions of MMT and PVP are prepared similarly. They are mixed under the influence of ultrasonic waves with a frequency of 40 kHz (in the Sonic-0.5 apparatus (Polsonic, Warsaw, Poland) for 25 min in quantities so that the mass ratio of MMT/PVP is 1:12, respectively. This colloidal solution is homogeneous and does not separate into any layers. The resulting mixture is dried at 70-80 \({}^{\circ}\)C in an air dryer, and then under vacuum to completely remove water. Next, the target product is ground in a laboratory mill.
## 3 Results
### Measurement Setup  
The two-span reinforced concrete bridge is part of the federal road B303 and crosses a service road in Sesslach, Germany. Each bridge field is 28 m long and has five longitudinal beams. The GBR is set up orthogonally to these beams angled upwards at the bridge's underside, as discussed in Section 2.1. This setup necessitates a projection to receive the vertical component of the displacement vector. Since the projection can be influenced by the other vector components, we apply a second GBR in line with the longitudinal beams (see also [2; 38]). As a result, GBR 1 receives good signal reflection from the beams, while GBR 2 requires the installation of corner reflectors due to insufficient reflection from the smooth concrete surface (see Figure 4). Both GBRs achieve an average SNR for all measurement days of 34 dB to 40 dB for reflectors 2, 3, and 4. The average SNR for reflectors 1 and 5 is lower at 22 dB to 26 dB since the signal attenuation of the GBR antennas increases with the beam width. Consequently, the displacement precision for the vertical component varies between the reflectors. For reflectors 2, 3, and 4, the estimates range between 0.02 and 0.05 mm. The precision for reflectors 1 and 5 is estimated in the range of 0.12 mm to 0.29 mm.  
In addition, strain sensors installed on the underside of the beams can provide reference measurements to evaluate the GBR performance for natural frequency estimation. The one-dimensional linear strain sensors from HBM (Darmstadt, Germany) have a sampling rate of 100 Hz and their background noise during the measurements is estimated at about 0.1 um m\({}^{-1}\). Lastly, the bridge's temperature is measured by a sensor placed on the north side of a pillar. In the following sections, we discuss the analysis of a measurement campaign with a total measurement duration of 16 h. An overview of the campaign and the particular temperature ranges per day are shown in Table 2.  
Figure 4: Vertical view of the bridge with sensor and reflector installation.