esa-sceva/satcom-chunk-collection huggingface dataset:
# Narrowband Internet of Things (NB-IoT):
## 6 Conclusions  
Due to the fact that most of the existing works are segmented and only consider one or two releases in their corresponding studies or simulations, this paper has presented a comprehensive overview of NB-IoT standard from Release 13 to Release 16 prospects to enhance and enable more realistic research. It further presented the detailed current state of the art of NB-IoT based on the ongoing discussion on NB-IoT protocol stack along with the related contributions and analyzed the knowledge gaps by using NB-IoT standard as a benchmark. It is observed that most of the articles focus on improving one or few features while neglecting others,it could be better to display the trade-offs between the improvement feature and the neglected ones, i.e., performance trade-off between PHY and MAC layer when one feature is changed in either of the layers, the impact of repetition on overall energy consumption, CFO on channel estimation quality etc. This paper also presented the NB-IoT deployment strategies to highlight the coexistence possibilities with other legacy technologies i.e., LTE, by considering the NB-IoT support for small cells in HetNet scenarios. Lastly, it discussed the open research challenges and the future common research focus on NB-IoT i.e., battery life, optimal resource usage, handover support during mobility, transmission latency, scheduling, etc. To the best of the author's knowledge, this is the first survey that covers broadly these mentioned contributions and hence this work will help the researchers get most of the needed information to accelerate their research by finding the relevant information and sources for deeper exploration of the research concepts as well as finding possible solutions.  
Conceptualization, C.B.M., H.M., M.M.A., Y.L.M., S.P. and S.M.; investigation, C.B.M., H.M., M.M.A., Y.L.M., S.P. and S.M.; resources, C.B.M., H.M., M.M.A., Y.L.M., S.P. and S.M.; writing--original draft preparation, C.B.M.; writing--review and editing, C.B.M., H.M.; formal analysis, C.B.M., H.M., M.M.A., Y.L.M., S.P. and S.M.; supervision, M.M.A., H.M., Y.L.M.  
This research received no external funding.  
This project has received funding partly from European Union's Horizon 2020 Research and Innovation Program under Grant 668995 and European Union Regional Development Fund in the framework of the Tallinn University of Technology Development Program 2016-2022. This material reflects only the authors' view and the EC Research Executive Agency is not responsible for any use that may be made of the information it contains.  
The authors declare no conflict of interest.
## 3 Results and Discussion  
### Pesticide Residues in Apples and Citrus Fruits--Comparison  
#### 3.1.1 Apples  
Figures 1 and 2 present the content of pesticide residues in apples from the harvests of 2012 and 2020, respectively. Table 1 presents data concerning the levels of the individual pesticides in the analysed apples, taking into account the values of MRL and the limit of quantification (LOQ). Figure 1: Content of pesticides in samples of whole apples from the harvest of 2012. In the samples of whole apples from 2012, the number of identified pesticides was 26: boscalid (fungicide), carbendazim (fungicide), chlorpyrifos (acaricide, insecticide), bupirimate (fungicide), difenoconazole (fungicide), diphenylamine (plant growth regulator), disulfoton (insecticide), hexythiazox (acaricide, insecticide), fenzaquin (acaricide), malathion (acaricide, insecticide), propagrite (acaricide), pyraclostrobin (fungicide, plant growth regulator), pyrimethanil (fungicide), thiophanate methyl (fungicide), thiolac-prid (insecticide), triflumuron (insecticide), flusiazole (fungicide), pirimicarb (insecticide), trifloxystrobin (fungicide), methoxyfenzoide (insecticide), thiodox (insecticide), epoxiconazole (fungicide), hexaflumuron (insecticide), triadimenol (fungicide), indoxacarb (insecticide), and cyprodinil (fungicide), as shown in Figure 1, Table 1. In contrast, only seven pesticides were identified in whole apples from the 2020 harvest: boscalid (fungicide), pyraclostrobin (fungicide, plant growth regulator), captan (fungicide), fluidoxonil (fungicide), tetrahydrophthalimide (THPI) (metabolite of captan-fungicyd), fluopyram (fungicide), tebuconazole (fungicide), as shown in Figure 2, Table 1. One can clearly note that the number of pesticides identified in apples in 2020 was lower. Only two pesticides, boscalid and pyraclostrobin, were identified in both analysed batches of whole apples from 2012 and 2020. The group of pesticides most often identified in 2012 was that of insecticides (50%), followed by fungicides (46%) and growth regulators (8%). In 2020, only fungicides were identified in whole apple samples. This may be due to the fact that at that time there was an increased interest in alternative methods of plant protection, including fruit production in a sustainable system, where one of the primary assumptions is conducting activities aimed at the creation of biological equilibrium. Numerous studies indicate that side effects of chemical control include the destruction of natural flora and fauna and the contamination of water, soil and air, which has an impact on plant resistance to diseases and pests [11, 60, 61]. Figure 2: Content of pesticides in samples of whole apples from the harvest of 2020.
# Generative Adversarial Networks in Histological Image Segmentation: A Systematic Literature Review
## 3 Fundamentals and Basic Definitions
### Types of GANs
#### 3.3.6 Pix2 Pix  
Pix2 Pix is a conditional generative adversarial network (cGAN) architecture proposed by [PERSON] et al. [52], specifically designed for image-to-image translation tasks. Unlike traditional GANs that learn to generate images from random noise, Pix2 Pix learns a mapping from an input image to an output image, making it suitable for tasks such as image colorization, edge-to-photo translation, and semantic segmentation.  
The core idea of Pix2 Pix is to train a generator \(G\) that produces an output image \(G(x,z)\), given an input image \(x\) and optional noise \(z\), such that the output image resembles a target image \(y\). A discriminator \(D\) is simultaneously trained to distinguish between real image pairs \((x,y)\) and fake pairs \((x,G(x,z))\). The conditional adversarial loss that guides this process is defined as:  
\[\mathcal{L}_{cGAN}(G,D)=\mathbb{E}_{x,y}[\log D(x,y)]+\mathbb{E}_{x,z}[\log(1- D(x,G(x,z)))] \tag{8}\]To further encourage the generator to produce outputs close to the ground truth, a traditional \(L_{1}\) loss is added:  
\[\mathcal{L}_{L1}(G)=\mathbb{E}_{x,y,z}[\|y-G(x,z)\|_{1}] \tag{9}\]  
The final objective function for the Pix2 Pix model is a weighted combination of the adversarial loss and the \(L_{1}\) loss:  
\[G^{*}=\arg\min_{G}\max_{D}\mathcal{L}_{cGAN}(G,D)+\lambda\mathcal{L}_{L1}(G) \tag{10}\]  
where \(\lambda\) is a hyperparameter that balances the two loss terms.
## 3 Proposed Methods
### Offline Data Engineering
#### 3.3.2 Construction of the User-Similarity Matrix  
In addition to mining patterns, the other main process is the construction of the user-similarity matrix. The main purpose of this process is to compute the user personality similarities that support the online course degree prediction. That is, the users on similar personalities will be considered while predicting the unknown degrees. Hence, all user personalities are transformed into a binary vector. For example, assume the personality only contains two main sets: sex = {Male, Female} and blood type = {A, B, O, AB}. If the sex and blood type for a user are female and O, respectively, the binary vector for {[Male, Female], {A, B, O, AB}} is {0, 1, 0, 0, 1, 0}. Accordingly, the user similarity in this paper is defined in Definition 1.  
**Definition 1**.: _Assume \(z\) unique personalities exist in the personality table. Given two users {x, y} with bit vectors {p\({}_{x,1}\), \(p_{x,2}\), \(\ldots\), \(p_{x,z}\)} and {p\({}_{y,1}\), \(p_{y,2}\), \(\ldots\), \(p_{y,z}\)}, where \(p\) {0, 1}, the user similarity \(S_{x,y}\) is defined as_  
\[S_{x,y}=\frac{\sum_{0\leq i\leq z}p_{x,i}\times p_{y,i}}{\sqrt{\sum_{0\leq i \leq z}\left(p_{x,i}\right)^{2}}\times\sqrt{\sum_{0\leq i\leq z}\left(p_{y,i} \right)^{2}}} \tag{1}\]  
where \(p_{x,i}\) and \(p_{y,i}\) are the \(i\)th vectors of users \(x\) and \(y\), respectively.  
Based on the user similarities, the similarity matrix is constructed to accelerate the online prediction.
# Process Development of Low-Loss LPCVD Silicon Nitride Waveguides on 8-Inch Wafer
## 5 Conclusions  
In order to solve the problem of high stress caused by the preparation of thick silicon nitride waveguides (\(\geq\)300 nm) via LPCVD, this work used the filling of silicon nitride in the BOX layer trench to prepare waveguides. For the deposition of the core layer, we used a vertical furnace tube with a rotating holder during the deposition of the film, which prevented the accumulation of stress in one direction. At the same time, in order to reduce the stress, we set up a checkerboard stress relief pattern in the BOX layer; to make the CMP more uniform and reduce the warpage of the wafer, we removed all the silicon nitride in the nonwaveguide area before the CMP. Based on all these optimizations, we successfully demonstrated Si\({}_{3}\)N\({}_{4}\) waveguides with LPCVD on an 8-inch wafer. This showed brilliant loss properties with a propagation loss of 0.157 dB/cm at 1550 nm and 0.06 dB/cm at 1580 nm. The bending loss is also characterized by 0.013 dB at a radius of 50 \(\upmu\)m and only 0.0095 dB at a radius of 80 \(\upmu\)m. All the results meet the state-of-the-art specifications with a similar process.  
Conceptualization, Z.L.; data curation, Z.L., Q.C. and J.Z.; formal analysis, Z.L. and Z.F.; investigation, Z.L. and X.Z.; methodology, Z.L., Y.Z. and L.J.; resources, L.J. and Z.L.; software Z.L. and Z.F.; supervision, L.J. and Y.Z.; writing--original draft, Z.L.; writing--review and editing, L.J., Z.F. and X.Z. All authors have read and agreed to the published version of the manuscript.  
This document is the result of the research project funded by the National Key Research and Development Program Funding Project (2018 YFB2200503).  
Not applicable.  
We thank [PERSON], [PERSON], and [PERSON] for their technical support and suggestive discussions.  
The authors declare no conflict of interest.
# Parallel Makespan Calculation for Flow Shop Scheduling Problem with Minimal and Maximal Idle Time
## 3 Problem Formulation
This is corrected by lines 8-9, where the completion time \(C_{i}^{a}\) is increased appropriately if the difference between \(C_{i}^{a}\) and \(S_{i+1}^{a}\) is more than \(d_{a}\). It should be noted that, this time, the jobs are processed from \(\pi(n-1)\) down to \(\pi(1)\), so the jobs completing later are corrected first. Due to this, there is a guarantee that each job will have to be shifted at most once. The resulting schedule is always feasible. Moreover, the schedule is always left-shifted as any decrease in any of the completion times would either violate one of the constraints (3)-(5) or would not correspond to the solution \(\pi\). It should also be noted that Algorithm 1 is also sequential and that its running time is \(O(nm)\) using a single processor. ```
1\(C_{1}^{1}\leftarrow\rho_{1}^{1}\);
2 for\(i=2,3,\ldots,n\)do
3\(C_{i}^{1}\gets C_{i-1}^{1}+r_{1}+\rho_{1}^{1}\);
4 for\(a=2,3,\ldots,m\)do
5\(C_{1}^{a}\gets C_{1}^{a-1}+\rho_{1}^{a}\);
6 for\(i=2,3,\ldots,n\)do
7\(C_{i}^{a}\leftarrow\max\{C_{i-1}^{a}+r_{a},C_{i}^{a-1}\}+\rho_{i}^{a}\);
8 for\(i=n-1,n-2,\ldots,1\)do
9\(C_{i}^{a}\leftarrow\max\{C_{i}^{a},C_{i+1}^{a}-\rho_{i+1}^{a}-d_{a}\}\);
10
```  
**Algorithm 1**Constructing left-shifted schedule \(C\) from the solution \(\pi\)  
Finally, let \(C_{\max}(\pi)\) be the completion time of the job that completes as last in the schedule described by solution \(\pi\), meaning that:  
\[C_{\max}(\pi)=\max_{j\in\mathcal{J}}C_{j}^{m}=C_{n}^{m}. \tag{6}\]  
The value \(C_{\max}(\pi)\) is commonly referred to as makespan. The goal of optimization is to find a solution \(\pi^{\star}\) that minimizes the makespan, meaning that:  
\[C_{\max}(\pi^{\star})=\min_{\pi\in\Pi}C_{\max}(\pi), \tag{7}\]  
where \(\Pi\) is the set of all possible solutions. Solution \(\pi^{\star}\) is called the optimal solution. To summarize, the mathematical model of the problem can be stated as follows:  
\[\textbf{\emph{minimize}} C_{\max}(\pi), \tag{8}\] \[\textbf{\emph{subject to}} C_{i}^{a}-S_{i}^{a}-\rho_{i}^{a}=0 \forall a\in\mathcal{M}\ \ \forall i\in\mathcal{J},\] (9) \[C_{i-1}^{a}-S_{i}^{a}\leq 0 \forall a\in\mathcal{M}\setminus\{1\}\ \forall i\in\mathcal{J},\] (10) \[S^{a}=(S^{a}_{1},S^{a}_{2},\ldots,S^{a}_{n}). \tag{2}\]0 (11) \[S^{a}=(S^{a}_{1},S^{a}_{2},\ldots,S^{a}_{n}). \tag{2}\]1 (12) \[S^{a}=(S^{a}_{1},S^{a}_{2},\ldots,S^{a}_{n}). \tag{2}\]2  
where values \(S\), \(C\) and \(\rho\) are calculated from \(\pi\), as shown in Algorithm 1.
## 3 Proposed Methodology
### Focal Node Identification Using Distributed Fuzzy Cognitive Map
#### 3.1.3 Parallelization of FCM  
This FCM learning process was parallelized in the proposed work. The weight matrix RDD was provided as an input to the _parallelize_ function. By employing the _parallelize_ function, the weight matrix RDD was partitioned into distinct subsets, each representing specific causal relations within the system. This division led to the creation of multiple new RDDs, each containing a specific subset of weight matrix values. These RDDs were then distributed across individual nodes within the distributed system.  
The process of FCM learning entails operating on both the weight matrix and the state vector. In order to enable this process, it is necessary for the state vector to be readily available across all the nodes in which the weight matrix is spread out. To accomplish this task, the broadcast function was utilised on the state vector RDD, thereby replicating the state vector spanning every node in the network. Moreover, the state vector was cached on each of the distributed nodes. The state vector RDD was a unidimensional vector; thus, its replication across nodes resulted in negligible effects on the memory capability of each node. In contrast, the RDD for the weight matrix was characterised by a considerable number of rows and columns, resulting in a substantial spatial requirement. As a result, it was disseminated across the various nodes within the cluster. At each node, the FCM learning process was applied using Equation (1), generating partial results. The final state vector, representing the final global solution, was derived by amalgamating the aforementioned partial outcomes. The parallel learning of the FCM is depicted in Figure 3, which was adopted from [60].
# Visible Light Communication System Technology Review: Devices, Architectures, and Applications
## 4 Modulation Technology in VLC System  
Due to the low bandwidth of the light-emitting diodes (LEDs) used in visible light communication (VLC), complex modulation schemes are a potentially attractive approach to offer high-speed data transmission. Different from other types of communication, the modulation scheme of VLC must achieve a higher rate and meet the requirements of human perception of light. The following two attributes can characterize these requirements for perceiving light.  
* Dimming:  
Performing different types of activities that require different levels of illuminance. For example, the illuminance in the range of 30-100 lux is sufficient to perform simple visual tasks in most public places. If the LED can be adjusted to any level, it is necessary to understand the impact of human perception of light, meaning that the human eye expands to adapt to a lower illuminance at the pupil to allow more light to enter the eye. The characteristics of the perceived light of the human eye are shown in Figure 8.  
* Flicker reduction:  
The VLC modulation scheme requires that humans should not perceive the brightness of the flicker. The flicker can cause harmful and severe physiological changes in the human body, as shown in [115]. Therefore, it is necessary to change the light intensity faster than the human eye can perceive the flicker or change the light intensity faster than 200 Hz to avoid any harmful effects, according to the IEEE 802.15.7 standard [116]. This means that the VLC Modulation scheme should reduce flicker while providing a higher data rate.  
After preprocessing, encoding and modulation, a binary bitstream drives the LD (or LED), and the electrical signal implemented the intensity modulation to convert into an optical signal. Each modulation technique has a finite number of symbols in which data can be encoded. Having more symbols allows the representation of more bits by a single symbol. For example, if an eight-symbol modulation technique is used, each symbol can represent a set of three bits because each set can have one of eight possibilities. In general, each symbol of an M-symbol scheme can represent k 1/2 log2 M bits, and these k bits are mapped such that adjacent symbols differ by only one bit (gray encoding). Therefore, the incorrect selection of adjacent symbols results in a single bit error. However, the use of such techniques comes with an increase in the power required or a decrease in the immunity of error.  
Figure 8: Human eye perceives the actual light differently due to enlargement/contraction of pupil. © 2015 IEEE. Reprinted, with permission from [114].  
The five common modulation schemes of VLC are:  
1. Multilevel pulse amplitude modulation (M-PAM)
2. Phase shift keying (PSK) modulation
3. M-ary quadrature amplitude modulation (M-QAM)
4. Orthogonal frequency division modulation (OFDM)
5. Color shift keying (CSK) modulation
## 2 Literature Review
### Summarized Literature Review  
The focus of the researchers is on EC minimization through different routing protocols. All protocols are designed for different objectives according to the scenario. Depth-based routing uses single path which emphasis on energy efficacy by overlooking void hole regions and other network procedures. In multi-path routing protocols, the focus is on reliability of data from source to destination. EC is not taken into account in this scenario. Void hole avoidance considered both parameters simultaneously: energy use and data reliability. However, some compromises in terms of latency and EC also need to be faced because of void nodes origination which lead the network towards maximum EC and degrades the network lifetime.  
\begin{table}
\begin{tabular}{p{56.9 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Names of Protocol (s)** & **Features of Protocol (s)** & **Achievements of Protocol (s)** & **Limitation of Protocol (s)** \\ \hline GEDAR [9] & Multi-hoping & Void hole coverage & High EC during mobility in nodes \\ \hline HYDROCAST [10] & Multi-hoping & High packet delivery ratio with Reliable transmission of packets & High delay and EC \\ \hline LMPC [11] & Multi-hoping and Binary tree generation & High packet delivery ratio with minimum delay & Void holes with long term transmission \\ \hline FLMPC-one and FLMPC-two [12] & Multi-hoping and Binary tree generations & High delay, high packet delivery ratio, low EC and High packet received ratio & Sparse regions effect its processing time \\ \hline WDFAD-DBR [13] & Multi-hoping & Less delay and high packet delivery ratio & Void holes and wastage of energy with long communication \\ \hline GFGD (GGFGD) [14] & Multi-hoping between small cubes & Energy efficacy and least delay & No \\ \hline SHORT [15] & Multi-hoping & High throughput of network and long life of network & High delay \\ \hline RE-PRR [16] & Multi-hoping & Energy efficient and reliability in data transmission & Sparse regions effects the processing time of network \\ \hline OR [17] & Opportunistic routing & Interference avoidance & Trade-off between energy and distance is not discussed \\ \hline GDGOR [18] & Multi-hoping and depth adjustment & Void node avoidance & Trade-off between high EC is persisted \\ \hline SEEC, CSEEC CDSEEC [19] & Clustering in the square and circular fields & Energy minimization is achieved successfully & Distance dependency on energy is not considered and communication overhead is not focused \\ \hline EAMR [20] & Multi-hoping & Reduction in excessive overhead and reduce cluster head changes with increase in network lifetime & In sparse region, cluster heads cannot bear the load \\ \hline \hline \end{tabular}
\end{table}
Table 3: Summarized Literature Review.
## 3 Results and Discussion
### Elemental Profile of SN Extracts  
Obtained elemental profile of the SE, MAC, UAE, and MAE extracts are presented in Table 3. Contents of investigated elements and minerals demonstrated certain diversity among tested extract \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Element** & **Linearity** & **Concentration Range (mg/L)** & **Calibration Curve** & **IDL (ug/L)** & **MDL (ug/L)** \\ \hline Pb & 0.9988 & 0.00–1.00 & Y = 1164.63x + 9.2436 & 5.8 & 7.0 \\ Cd & 0.9993 & 0.00–1.00 & Y = 1273.34x + 4.0666 & 0.8 & 1.0 \\ As & 0.9994 & 0.00–1.00 & Y = 557.79x + 43.289 & 4.8 & 5.0 \\ Cu & 0.9998 & 0.00–1.00 & Y = 34203.24x + 77.3832 & 0.7 & 1.0 \\ Fe & 0.9987 & 0.00–1.00 & Y = 20583.09x + 120.7146 & 2.5 & 3.0 \\ Zn & 0.9992 & 0.00–1.00 & Y = 2474.83x + 92.4606 & 1.3 & 2.0 \\ Mn & 0.9992 & 0.00–1.00 & Y = 145407.53x + 733.941 & 1.1 & 2.0 \\ Ni & 0.9983 & 0.00–1.00 & Y = 4195.05x + 24.0310 & 2.2 & 3.0 \\ Cr & 0.9986 & 0.00–1.00 & Y = 21768.48x + 63.1700 & 0.8 & 1.0 \\ K & 0.9970 & 0.00–1.00 & Y = 1251.794x + 204.4287 & 99.9 & 100.0 \\ Na & 0.9994 & 0.00–1.00 & Y = 7787.27x + 456.4494 & 30.0 & 30.0 \\ Ca & 0.9969 & 0.00–6.00 & Y = 4675.28x + 463240.0555 & 15.5 & 30.0 \\ Mg & 0.9977 & 0.00–7.00 & Y = 362638.66x + 37444.2176 & 9.0 & 20.0 \\ \hline \hline \end{tabular}  
* Accuracy, precision, inter-day, and intra-day precisions for both spiking levels (S\({}_{1}\) and S\({}_{2}\)) are given in the Table, RSD-relative standard deviation.  
\end{table} 
Table 1: Obtained values for linearity, limits of detection and quantitation, accuracy, and precision of the applied method.