esa-sceva/satcom-chunk-collection huggingface dataset:
# Developing a Recommendation Model for the Smart Factory System
## Literature Review
### Recommendation Systems  
In order to solve the problem of big data, many studies have proposed related methods for recommendation systems. A recommendation system helps users obtain information that they seek, and a good recommendation system also can find out other relevant information for users. In general, recommendation systems can be divided into three types [23]: content-based, collaborative filtering, and hybrid methods, for reducing information overload. For content-based filtering, each item will be analyzed and ranked according to the score for a queried item, and a list of high-score items will be recommended for some queries. Meanwhile collaborative filtering is one of the most popular methods for recommendation systems [24]. The system calculates the analyzes the basis according to a user from a group that shares similar interests. It not only considers an item's contents but also the profiles of users and may discover the potential needs of users with two major approaches, user-based and item-based. Researchers further developed the hybrid method to predict the result and avoid the cold-start problem or new user problem [25]. For some specific circumstances, it is always hard to choose places to go from an endless number of options. Recommendation systems suggest items for users according to their explicit and implicit feedback information, such as ratings and reviews, which can help us make decisions that are more appropriate. The relevant studies and applications on recommendation systems are shown in Table 1.  
Based on these articles, we found that few studies have been conducted to establish a recommendation system for Smart Factories. Therefore, this research is based on the functions of the recommendation system to determine the key the dimensions and prime influencing factors of Smart Factories, with the aim to recommend suitable models with indicators to factories, according to the current situation and conditions.
## Appendix B  
**Proof of Theorem 2.** When \(\Theta=\) '11', the outage cannot happen at U\({}_{1}\) since U\({}_{1}\) has successfully acquired the desired signal at the end of the first time slot. In addition, the selected optimal relay must satisfy the QoS of U\({}_{1}\); that is, as long as the optimal relay exists, U\({}_{1}\) will not have an outage. Therefore, the outage event occurs at U\({}_{1}\) only when there is no relay in the candidate sets that meet the requirements to be the optimal relay. This also means that when all the power is allocated to the signal to be sent to U\({}_{1}\), the achievable rate still does not reach the target data rate.  
From the Law of Total Probability and the analyses above, the outage probability U\({}_{1}\) can be written as  
\[P_{1}=\sum_{\Theta\in\{10,01,00\}}P^{\Theta}P^{\Theta}_{out,1} \tag{102}\]  
where \(P^{\Theta}\) denotes the probability for the event that a signaling sent by U\({}_{1}\) is \(\Theta\), which is given by (103)-(105), and \(P^{\Theta}_{out,1}=\prod_{n=1}^{N}\Bigl{[}1-P^{\Theta}_{n,1}\Bigr{]}\) represents the outage probability for U\({}_{1}\) in the case of \(\Theta\), which can be rewritten as  
\[P^{\Theta}_{n,1}=\left\{\begin{array}{ll}Pr\Bigl{\{}R_{R_{n}U_{1}}^{x_{2}, \Theta}\geq R_{2}\Bigr{|}\beta_{n2}^{\Theta}=0\Bigr{\}}Pr\Bigl{\{}R_{SR_{n}}^{x _{2}}\geq R_{2}\Bigr{\}},&\Theta=01,\\ Pr\Bigl{\{}R_{R_{n}U_{1}}^{x_{1},\Theta}\geq R_{1}\Bigr{|}\beta_{n2}^{\Theta} =0\Bigr{\}}Pr\Bigl{\{}R_{SR_{n}}^{x_{2}}\geq R_{2},R_{SR_{n}}^{x_{1}}\geq R_{1 }\Bigr{\}},&\Theta\in\{10,00\}.\end{array}\right. \tag{103}\]  
where \(Pr\Bigl{\{}R_{SR_{n}}^{x_{2}}\geq R_{2}\Bigr{\}}\) and \(Pr\Bigl{\{}R_{SR_{n}}^{x_{2}}\geq R_{2},R_{SR_{n}}^{x_{1}}\geq R_{1}\Bigr{\}}\) are given by (100) and (101), respectively, and \(Pr\Bigl{\{}R_{R_{n}U_{1}}^{x_{1},\Theta}\geq R_{1}\Bigr{|}\beta_{n2}^{\Theta} =0\Bigr{\}}\) can be expressed as (104)-(105).  
\[Pr\Bigl{\{}R_{R_{n}U_{1}}^{x_{2},01}\geq R_{2}\Bigr{|}\beta_{n2}^{01}=0\Bigr{\}} =\frac{\sigma^{2}}{\lambda_{TU_{1}P_{T}}}exp\biggl{(}\frac{\sigma^{2}}{\lambda _{TU_{1}P_{T}}}\biggr{)}(Q_{11}-Q_{12}), \tag{104}\]  
\[Pr\Bigl{\{}R_{R_{n}U_{1}}^{x_{1},10}\geq R_{1}\Bigr{|}\beta_{n2}^{10}=0\Bigr{\}} =\frac{\sigma^{2}}{\lambda_{TU_{1}P_{T}}}exp\biggl{(}\frac{\sigma^{2}}{\lambda _{TU_{1}P_{T}}}\biggr{)}(Q_{9}-Q_{10}), \tag{105}\]  
\[Pr\Bigl{\{}R_{R_{n}U_{1}}^{x_{1},00}\geq R_{1}\Bigr{|}\beta_{n2}^{00}=0\Bigr{\}} =\frac{\sigma^{2}}{\lambda_{TU_{1}P_{T}}}exp\biggl{(}\frac{\sigma^{2}}{ \lambda_{TU_{1}P_{T}}}\biggr{)}(Q_{9}-Q_{10}). \tag{106}\]  
Finally, using (103)-(105), the outage probability for U\({}_{1}\) in cooperative CR-NOMA networks can be obtained as Equation (29), which completes the proof of Theorem 2. \(\square\)
# Counteracting Selfish Nodes Using Reputation Based System in Mobile Ad Hoc Networks
## 4 Results and Discussion
### Simulation Setup  
The proposed scheme is simulated using network simulator NS-2 and compared with OCEAN to check the efficiency of our scheme using the simulation parameters given in Table 2. The AWK scripts are developed for the collection of data from NS-2 simulator trace files. To automate the process to run extensive scenarios without user interference, we developed a bash script. The random waypoint model is used as a mobility model and CBR is used for UDP traffic generation. The simulation is conducted for different area sizes and pause times. The simulation time, speed of nodes, and number of selfish nodes are varied in order to perform an in-depth evaluation of the scheme. The simulation results are averaged over 30 randomly generated scenarios.
## 3 Results and Discussion  
### FTIR Analysis  
FTIR analysis of hybrid materials allowed us to obtain information about interactions between the individual components. Figure 2 shows the FTIR spectra of SiO\({}_{2}\) hybrid materials with increasing amounts of PEG. The silica spectrum in curve (a) shows asymmetric and symmetric stretching of Si-O-Si at 1080 cm\({}^{-1}\), with a shoulder at 1200 cm\({}^{-1}\). In addition, we can see the bending vibrations of Si-O-Si at 800 and 460 cm\({}^{-1}\), whereas the band at 948 cm\({}^{-1}\) is assigned to Si-OH bond vibrations. The intense band at 3450 cm\({}^{-1}\) and the peak at 1640 cm\({}^{-1}\) are due to stretching and bending vibrations of the OH of water. The broadness of the OH vibration suggests H-bonding between the OH of the solvent molecules and Si atoms. The addition of PEG to the silica matrix is confirmed by the increase in the intensity of the peaks at 2927 cm\({}^{-1}\) and 1454 cm\({}^{-1}\) that are associated with C-H asymmetric stretching and bending, respectively. In addition, we observe a shift of this peak in pure PEG to 2870 cm\({}^{-1}\)[25; 26; 27]. The presence of Fe(II)C (Figure 3) is detectable in curves (b-d) by an increase in the intensity of the peak at 1722 cm\({}^{-1}\), which is due to C=O stretching [28]. In addition, the increase in the intensity of the peak at 1560 cm\({}^{-1}\)ascribed to the COO\({}^{-}\) group, is another sign of the encapsulation of Fe(II) citrate in the system [23].
## 2 Materials and Methods
### Sensor and Irrigation System Installation  
In three layers, three trays were used in each, and in the first layer, a surface drip irrigation system was installed, while in the second layer, a subsurface drip irrigation system was installed and in the third layer both irrigation systems were installed. Individual trays were considered as a single treatment and block with three replications since each tray was partitioned into three equal size parts. Trays had small holes at the bottom to let extra water drain away and avoid water logging. Furthermore, each tray was covered by another tray to collect drainage water and consequently deduct the drained water from the total water applied during each watering time. A small water container was placed in the first layer of the chamber where irrigation lines were connected to supply water. A mainline hose crossed the first and second layers and connected to a three-way valve where various sub-mainline hoses originated from and supplied water to each tray lateral hose. Watering was carried out based on capacitive soil moisture sensor readings, and the three-way valve was manually controlled.  
Six capacitive soil moisture sensors were employed in all trays, where each tray had a single sensor. Sensors were installed in the tray at 6 cm deep and 0.6 cm from the dipper and plant. All the sensors were interfaced with a low-cost data logger or microcontroller using an Arduino Uno(tm) (ATmega328, Auduino LLC, New York, NY, USA) that was placed inside a small plastic box. In addition to the capacitive soil moisture sensors, other sensors, including a temperature and humidity sensor (DHT11), light intensity sensor (BH1750), and WiFi module (ESP8266), were connected to the microcontroller (Arduino Uno) (Figure 3). A 12 V power adopter was also connected to the relay to provide the required power for controlling the water pump. Data were uploaded onto the cloud server (ThingSpeak)  
Figure 2: Experimental arrangements of plots for indoor seedlings germination system.  
in one-minute time intervals. The cloud system was used to display and store data for accessing and retrieving later. Two LED lights were allocated to every tray or in the row of tomato seeds that was illuminated by a single light.
## 2 Experimental Section
### Synthesis of **1**  
A mixture of dichloromethane (350 mL), \(N\)-Boc-\(\beta\)-alanine (8.50 g, 45.0 mmol), 1-[3-(dimethylamino)propyl]-3-ethylcarbodiimide hydrochloride (EDCl) (8.6 g, 45.0 mmol) and 4-(dimethylamino)pyridine (5.49 g, 45.0 mmol) were stirred for 10 min at RT, followed by addition of octylamine (5.71 g, 45.0 mmol). The mixture was stirred for 12 h at RT. The mixture was washed with 5% HCl (2 \(\times\) 100 mL), saturated NaHCO\({}_{3}\) solution (1 \(\times\) 100 mL) and brine (1 \(\times\) 50 mL). The resulting organic layer was dried (MgSO\({}_{4}\)) and concentrated to provide \(N\)-Boc-\(\beta\)-ala-octylamide (**1**) as a white crystalline solid (11.6 g, 38.6 mmol, 86%): mp. 67.5-68.4 \({}^{\circ}\)C; \({}^{1}\)H NMR (methanol-\(d\)4): \(\delta\) 3.18 (t, J = 7.0 Hz, 2H), 2.90 (t, \(J\) = 6.5 Hz, 2H), 2.35 (t, J = 6.5 Hz, 2H), 1.51 (quintet, \(J\) = 7.0 Hz, 2H), 1.34 (m, 10H), 0.93 (t, distorted, \(J\) = 6.5 Hz, 3H):
## 3 Results
### Effect of the CFS on Biogenic Amine Production of Pacific White Shrimp  
Putrescine and cadaverine were the two major biogenic amines found in shrimp, and their changes were shown in Figure 5. During the cold chain logistics, the accumulations of biogenic amines were increased. Through the cold chain logistics, the contents of two biogenic amines in CK3 were higher than CK1 and CK2 (Figure 5). As shown in Tables 4 and 5, the contents of two biogenic amines in group SA3 were lower than that of group CK3 at the end of the storage. At the storage of 6 days, Table 5 shows that the content of cadaverine in group CK3 was significantly higher than group CK1, CK2 and SA3 (\(p<0.05\)), while there was not obviously difference among group CK1, CK2, SA1, SA2 and SA3 (\(p>0.05\)). Additionally, at the end of the storage, for group SA1, SA2, and SA3 treated with CFS from _A. sovria_, the levels of putrescine in shrimp samples reduced by 18.65%, 18.21% and 16.05%, and the levels of cadaverine decreased by 22.81%, 22.80% and 18.96%, respectively.  
Figure 4: Effect of CFS from _A. sovria_ on TVB-N content of Pacific white shrimp (_L. vannamei_) during different cold chain logistics.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \multirow{2}{*}{**Time (days)**} & \multicolumn{5}{c}{**The Content of TVB-N**} \\ \cline{2-6}  & **CK1** & **SA1** & **CK2** & **SA2** & **CK3** & **SA3** \\ \hline
0 & 5.13 \(\pm\) 0.16 \({}^{\rm a}\) & 4.86 \(\pm\) 0.58 \({}^{\rm b}\) & 5.13 \(\pm\) 0.16 \({}^{\rm a}\) & 4.86 \(\pm\) 0.58 \({}^{\rm b}\) & 5.13 \(\pm\) 0.16 \({}^{\rm a}\) & 4.86 \(\pm\) 0.58 \({}^{\rm b}\) \\
2 & 8.56 \(\pm\) 0.36 \({}^{\rm c}\) & 7.05 \(\pm\) 0.29 \({}^{\rm d}\) & 8.76 \(\pm\) 0.30 \({}^{\rm c}\) & 7.21 \(\pm\) 0.11 \({}^{\rm d}\) & 12.48 \(\pm\) 0.17 \({}^{\rm a}\) & 10.97 \(\pm\) 0.08 \({}^{\rm b}\) \\
4 & 10.34 \(\pm\) 0.06 \({}^{\rm f}\) & 10.575 \(\pm\) 0.11 \({}^{\rm e}\) & 12.17 \(\pm\) 0.06 \({}^{\rm d}\) & 12.975 \(\pm\) 1.12 \({}^{\rm c}\) & 16.54 \(\pm\) 0.03 \({}^{\rm a}\) & 14.61 \(\pm\) 0.00 \({}^{\rm b}\) \\
6 & 19.26 \(\pm\) 1.08 \({}^{\rm c}\)\({}^{\rm d}\) & 12.59 \(\pm\) 1.06 \({}^{\rm f}\) & 25.27 \(\pm\) 1.73 \({}^{\rm a,b}\) & 16.98 \(\pm\) 1.12 \({}^{\rm d,e}\) & 28.26 \(\pm\) 1.64 \({}^{\rm a}\) & 20.90 \(\pm\) 1.67 \({}^{\rm c}\) \\
9 & 30.87 \(\pm\) 0.10 \({}^{\rm c}\)\({}^{\rm d}\) & 20.09 \(\pm\) 0.06 \({}^{\rm f}\) & 37.67 \(\pm\) 0.25 \({}^{\rm b}\) & 26.41 \(\pm\) 0.11 \({}^{\rm e}\) & 40.67 \(\pm\) 1.31 \({}^{\rm a}\) & 33.41 \(\pm\) 0.08 \({}^{\rm c}\) \\ \hline \end{tabular} Note: 1. Different letters (a–f) at each column denote significant differences (\(p<0.05\)) between means according to [PERSON]’s multiple range test.  
\end{table}
Table 3: Effect of CFS from _A. sovria_ on TVB-N content of Pacific white shrimp (_L. vannamei_) during different cold chain logistics by F test.
## 2 Results and Discussion
### Fingerprinting of Cinnamon Drugs Using UV/Vis
#### 2.5.2 Supervised Multivariate Data Analysis of Cinnamon UV/Vis Dataset  
Three models were attempted in this part focused mainly on the analysis of authenticated samples, including for all authenticated samples (Supplementary Figure S9A,B), CI against other authenticated samples (Supplementary Figure S10A-C), and finally, CA against CV and CVM (Supplementary Figure S10D-F).  
Firstly, modeling of all authenticated samples showed that CI replicates positioned the most distant quarter in the score plot (Supplementary Figure S9A) far from other samples, with negative PC2 score values and in agreement with the GC-MS model, Figure 3B. Additionally, CA replicates were along the right side with CV, while CVM was positioned on the left side (Supplementary Figure S9A) and opposite to results from GC/MS showing clear segregation of CV and CVM from CA, Supplementary Figure S3. The model parameters showed weak validation parameters, i.e., R\({}^{2}\) = 86.2%, Q\({}^{2}\) = 60.2%, and \(p\)-value = 0.303, as calculated by its permutation, Supplementary Figure S9B, compared to GC/MS similar model.  
Modeling of CI in one class group against other samples showed clear clustering of CI from other samples along the \(x\)-axis. The model resulted in strong validation parameters, i.e., R\({}^{2}\) = 99.7%, Q\({}^{2}\) = 97.1%, and \(p\)-value = 0.0026, Supplementary Figure S10A, and permutation results, Supplementary Figure S10B. The corresponding OPLS-DA line-plot was Supplementary Figure S10C revealed CI richness in compounds possessing a UV \(\lambda_{\text{max}}\) at 260 nm typical for cinnamic acid and confirmed their abundance in CI and in agreement with NMR results (Table 2). Such results are presented for the first time using UV/Vis analysis.  
Furthermore, modeling of CA vs. CV and CVM to assess the possible adulteration of CV or CVM by the lower grade CA. OPLS-DA score plot was successful in showing a model of clearly segregated groups, Supplementary Figure S10D,E. The model also showed validation parameters of R\({}^{2}\) = 99.4%, Q\({}^{2}\) = 96.9%, though non-significant with \(p\)-value = 0.09. These findings were in agreement with that of GC/MS results (Figure 3A), confirming the  
Figure 6: Unsupervised multivariate data analysis, including HCA (**A**) and PCA score plot (**B**) for all authenticated and commercial cinnamon products based on UV/Vis analysis, in addition to PCA score plot of authenticated cinnamon drugs (**C**). The samples code is listed in Table 1.  
authenticity of the officially used cinnamon species, _C. verum_. Additionally, the line-plot revealed that CV and CVM encompass higher proanthocyanidins and catechins than CA, attributed to the two most prominent \(\lambda_{\max}\) at 258 and 290 nm [21], see Supplementary Figure S10F. These results are preliminary and should be confirmed using LC/MS analysis to identify exact structures.
## 4 Design of Proposed Scheme: FAPR
### Algorithms and Formulation
#### 4.5.5 Bandwidth Estimation Method  
In SDN networks, link bandwidth can be obtained via the OpenFlow protocol or through third-party measurement technology, such as sFlow (Version 5). In the FAPR scheme, we obtain link bandwidth through the OpenFlow protocol. The bandwidth of a link is determined by the capabilities of its two endpoints. Therefore, we can obtain the link bandwidth from traffic information of ports. In the OpenFlow protocol, the traffic information contains the statistical information of ports, flow tables, group tables, etc., and we can obtain the number of packets sent and received, and the number of bytes during the statistical time. Finally, the link bandwidth can be calculated.  
Assume that at time \(t_{1}\), the controller sends the first port request message to a switch that connects to the target link, and the statistical message returned by the switch contains the number of bytes sent and received by port \(S_{1}\) at time \(t_{1}\), and the time of duration is \(d_{1}\). Assume that at time \(t_{2}\), the controller sends the second port request message to the switch again, and the number of bytes and the time of duration are \(S_{2}\) and \(d_{2}\), respectively. As the result, the used bandwidth of this port, or the used bandwidth of this link in this period of time, represented by \(UB_{t_{1}-t_{2}}\), can be calculated, and the corresponding formula is shown as follows:  
\[UB_{t_{1}-t_{2}}=\frac{8(S_{2}-S_{1})}{t_{2}+d_{2}-t_{1}-d_{1}} \tag{4}\]  
The remaining bandwidth of a link can be calculated through subtracting the used bandwidth from the maximum bandwidth of the port (link). Similarly, the statistical traffic of flow table entries or group table entries can be calculated. Note that the data of the remaining bandwidth will not only be used in the backup path allocation module, but also can support subsequent traffic optimization projects such as load balancing.
## 2 Research Context
### Complexity Management  
Increasing product variety incurs additional costs at several levels: extra time in design [45], a reduction in economies of scale, and the need for more service parts [35]. This product variety increase leads to a greater variety of parts in production, which increases uncertainties in the manufacturing process [5]. These uncertainties result in a loss of transparency in operations tracking, leading to difficult production control. This additional management, thus, incurs extra costs due to complexity management [35].  
The literature primarily distinguishes between two types of complexity: static and dynamic. Static or structural complexity is defined by the number and variety of the products, components, or production system elements such as workstations and equipment. Dynamic complexity concerns the uncertainty and variation in company behavior, such as fluctuating product demand, production times, etc. [44; 45].  
Other more nuanced types of complexity are specific to manufacturing: choice complexity, transfer complexity, and feed complexity [44].  
A complexity management approach aims to understand, control, and modify the design or planning in order to become more productive. To understand complexity, five groups of methods allow for its measurement and analysis: chaos and non-linear dynamic theory, information theory, hybrid, enumeration, and other methods [45]. In addition to modularity, other techniques can reduce complexity:  
* Modifying the assembly sequence or the supply chain configuration [44].
* Using the Variant Mode and Effect Analysis (VMEA), a method for designing a modular product focused on market needs with a minimum number of variants [50].
* Reducing the number of platforms [34].
* Standardizing parts to reduce complexity, costs, and product development time [2; 12].
* Grouping parts into sub-systems or a larger number of modules [2].  
Understanding product complexity is essential for developing an effective modular strategy, as reducing complexity impacts the productivity of the manufacturing system [44]. Modular design is not the only way to manage and reduce complexity in a business [45].
## 4 Proposed FFT Nulling with Fine Frequency Shift
When the NBI frequency is \(f_{I}=50.4/256T\) and interference is canceled using the proposed method described in Equations (10)-(16) with \(P=16\); the resulting spectrum is shown in Figure 6. As seen in the figure, when using the proposed fine frequency shift technique, the interference is completely removed compared to Figure 4, demonstrating the effectiveness of the proposed technique. After interference cancellation, the resulting signal's frequency is reversely shifted back to restore the original signal's frequency. If this step is not performed, the received signal will contain a carrier frequency offset equal to the fine frequency shift, which will degrade reception performance. The final signal, after the fine frequency shift is restored as shown in Figure 5, undergoes equalization and the remaining reception processes, as described in Figure 1.
## 3 Results and Discussion
### Reduction in pH during Gas-Neutralization  
As seen from Figure 1b, after 30 min of 100% CO\({}_{2}\)-neutralization, the pH of lime-treated CS decreased significantly (\(p<0.05\)) from 10.5 to 8.5 (solid-state condition) and 7.3 (liquid-state condition), respectively. In particular, the pH of the CS in the solid condition nearly stopped changing from the 5 th min onwards, while the pH of the CS in  
Figure 1: Changes in (**a**) pH and VFAs during LD-neutralization; and (**b**) pH during CO\({}_{2}\)-neutralization. LS- and SS-denote liquid state and solid state, respectively. Data are shown as mean \(\pm\) standard deviation (SD), \(n=3\).  
the liquid condition had been further continuously decreasing over the observed time range, despite the trend becoming increasingly slow. By comparison, it has been reported that 100% CO\({}_{2}\)-neutralization in the liquid state can reduce the pH to 6.8 and 7.0, respectively [18; 34]. This is probably because the alkali-treated lignocellulosic releases alkaline substances more easily after immersion in water, and CO\({}_{2}\) dissolved in it completes the acid-base neutralization reaction.  
Figure 1b illustrates the results of 50% CO\({}_{2}\)-neutralization. A similar phenomenon to those mentioned above with a slightly less effective pH reduction was identified. For the liquid condition, nearly the same pH endpoint was shown (\(p>0.05\)), but the slope changed more slowly, while for the solid state, the pH endpoint was higher, approximately 8.9 (\(p<0.05\)). This finding indicates that the simulated biogas (containing 50% CO\({}_{2}\)) was not suitable for neutralization reactions under solid-state conditions.  
To the best of our knowledge, the CO\({}_{2}\)-neutralization process under solid-state conditions was used for the first time. Although the final lower pH limit was higher than that of the liquid method reported earlier by other studies, it still meets the requirements for the AD initiation if operated with 100% CO\({}_{2}\). It was also more convenient for SS-AD as no additional water was required.