esa-sceva/satcom-chunk-collection huggingface dataset:
Additionally, the methods proposed in [8,9] can reconfigure the group of robots into different formation patterns by coordinating, also in a decentralized way, the joint angles in the corresponding mechanical linkage. A self-reconfigurable robotic system that is capable of autonomous movement and self-assembly is introduced in [10]. The formation problem of multiple robots based on the leader-follower mechanism is investigated in [11]. A model based on Swarm Chemistry is used in [12] to investigate as interesting patterns can be detected. Finally, a three-dimensional (3D) model identification method based on weighted implicit shape representation (WISR) is proposed in [13]. **Localization and search methods for UAV and drone swarms**: This special issue presents papers to define the position information of the robot members in the system and real-time search to cover a broad search space. In [14], an algorithm for UAV path planning based on time-difference-of-arrival (TDOA) is proposed. In [15], the authors propose a decision-control approach with the event-triggered communication scheme for the problem of signal source localization. The authors of [16] present a novel search method for a swarm of drones--a PSO algorithm is used as mechanism to update the position. Furthermore, in [17], an integrated algorithm combining the potential field and the three degrees (the dispersion degree, the homodromous degree, and the district-difference degree) is proposed to deal with cooperative target hunting by multi-AUV team in a surface-water environment. Another search algorithm based on a multi-agent system with a behavioral network made up by six different behaviors, whose parameters are optimized by a genetic algorithm and adapt to the scenario, is present in [18]. **Intelligence techniques for solving optimization problems. ** An algorithm inspired by the process of migration and reproduction of flora is proposed in [19] to solve some complex, non-linear, and discrete optimization problems. An additional parallel technique for meta-heuristic algorithms designed for optimization purposes is presented in [20]. The idea was based primarily on the action of multi-threading, which allowed placing individuals of a given population in specific places where an extreme can be located. Finally, a distributed hybrid fish swarm optimization algorithm (DHFSOA) designed in order to optimize the deployment of underwater acoustic sensor nodes has been proposed in [21]. We would like to thank all authors, the many dedicated referees, the editor team of Applied Sciences, and especially [PERSON] (Assistant Managing Editor) for their valuable contributions, making this special issue a success. The author declares no conflicts of interest.
# Ohmic Heating in the Food Industry: Developments in Concepts and Applications during 2013-2020
## 3 Principles of Ohmic Heating  
The ohmic heating system consists mainly of heating unit, electrodes, data recording system, alternating current (AC) power source, voltage control unit, and thermocouples to measure temperature [36]. In most foods, there are large quantities of water and dissolved salts, which can increase the electrical conductivity, as with these ions that are exposed to the electric field, they will move towards the electrodes of the source of the opposite charges, generating ion collisions and thereby leading to increased temperature in the food [37]. For ohmic heating, the material must have electrical conduction. Non-conductive material may become conductive by adding an electrolyte such as NaCl (sodium chloride) or soluble organic salt (tetraalkyl ammonium), which does not interfere with the reaction, or ionic solvents can be used because they are naturally conductive [38].  
The basis of electrical conduction heating (ohmic heating or electrical resistance heating) is that the alternating electric current passes through the food, which causes the ions to move towards the electrodes of opposite charges. This movement of ions causes collisions with each other, which leads to resistance to the movement of ions and increases kinetic energy. The product is heated to convert food into electrical resistance, and the heat inside it is generated instantaneously and volumetrically as a result of the movement of ions. The amount of heat depends on the current, the voltage, the electric field, and electrical conductivity of food [39; 40].  
The passage of electrical current through the foodstuff is subject to [PERSON]'s law, as heat is generated as a result of the food becoming an electrical resistance.  
\[\begin{split} V\propto I\\ V=IR\end{split} \tag{1}\]  
where  
\(V\): Voltage (volts)  
\(I\): Current strength (amps)  
\(R\): Resistance (Ohm)  
Figure 1: Search criteria and evolution of publications on ohmic heating from 1997–2021.
## 4 Simulation Results
### Shading Pattern 1  
Figure 11b-e show the results of comparative analyses of power, voltages, currents, and duty cycles between the MPPT methods. These waveforms are drawn from the shading pattern 1 in which the GMPP location is on the right side of the PV curve. The proposed technique tracks the right GMPP because the searching process enables the rapid convergence of the operating point toward GMPP.  
The duty cycle (0.458) also converges to the best location of the GMPP in the proposed algorithm, whereas the HC MPPT schemes cannot stop the fluctuation of the duty cycle values even after tracing the path of the GMPP locus. Power fluctuations under SSC in both the INC and P&O algorithms in this shading condition are 117 and 125 W, respectively, compared with the zero power loss in the proposed coordinated techniques (Figure 11a).  
To track the GMPP, the proposed technique requires a 0.13 s tracking speed. Figure 12b shows no occurrence of voltage oscillation in the proposed algorithm, whereas the INC and P&O consist of 24.5 V and 27.9 V oscillations around GMPP.  
Figure 11c depicts a comparative analysis of the PV currents of different algorithms. The steady-state oscillations in PV currents are measured at 0.462 and 0.518 A in both the former and latter conventional MPPT techniques. However, the proposed MPPT scheme removed the current fluctuation when its operating point converged toward the GMPP.  
Nevertheless, the accuracy of the proposed system improved because no noticeable perturbation was observed in the duty cycle at the GMPP location. The proposed method acquired 0.45% and 0.27% tracking efficiencies higher than those of the INC and P&O MPPT techniques, respectively.  
Figure 10: Simulation results of INC, P&O, and Coordinated PI-MPPT algorithms at STC are listed as: (**a**) STC waveforms; (**b**) power; (**c**) voltage (**d**); current; and (**e**) duty cycle waveforms.  
Figure 11: Simulation results of INC, P&O, and Coordinated PI-MPPT algorithms at shading pattern 1, are listed as: (**a**) pattern 1; (**b**) power; (**c**) voltage; (**d**) current; and (**e**) duty cycle characteristics curves.  
Figure 12: Simulation results of INC, P&O, and Coordinated PI-MPPT algorithms at shading pattern 2, are listed as: (**a**) pattern 2; (**b**) power; (**c**) voltage; (**d**) current; and (**e**) duty cycle.
## 3 Results and Discussion
### Experimental Results of Thermo-Physical Properties
#### 3.4.3 Thermal Effusivity  
Thermal effusivity characterizes the ability of a material to exchange an important amount of thermal energy in a short time with the external environment in an unsteady  
Figure 14: Density variation of concrete reinforced with different volume percentages of RWG at 28 th and 90 th days.  
Figure 15: Thermal conductivity variation of concrete incorporated with different volume rates of RWG, at dry and saturated states.  
state of heat flow. The results of this quantity at dry and saturated states with average measurement errors for all samples respectively less than 6% and 3% are presented in Figure 16. It can be seen that the thermal effusivity of concrete decreased with the volume content increase of RWG in concrete by about 8.06% and 21.28% respectively at dry and saturated states. These results can be attributed to the fact that air in pores is replaced by water and the thermal effusivity of water (1588 J.K\({}^{-1}\).s\({}^{-1/2}\).m\({}^{-2}\), at 20 \({}^{\circ}\)C) is higher than that of air (6 J.K\({}^{-1}\).s\({}^{-1/2}\).m\({}^{-2}\), at 20 \({}^{\circ}\)C) [59]. Moreover, the thermal effusivity value at saturated state is greater than the one at dry state, similarly to thermal conductivity. This difference diminished significantly from 19.04% to 5.45% respectively for RC and RWC50. These findings could also be related to the absorptivity of the studied composite which deceased with the increase volume content of RWG in concrete, due to the nonabsorbent characteristic of glass (Table 2). This characteristic leads to the absorption decrease of the composite as the RWG is incorporated in concrete [60], which in turn affects positively the thermal effusivity of the studied sample.
## 4 Data Understanding and Preparation
### Features Representation  
We also used the different text representation methods. We used the standard vector space representation using TF-IDF (Term Frequency-Inverse Document Frequency) weighting [57], special tokenizers of popular transformers models (BERT, DistilBERT and XLNet tokenizer) and the representation of the text documents using the following word embeddings:  
* word2 vec-- provides direct access to vector representations of words. It is a combination of two techniques, two neural networks--Continuous bag of words (CBOW) and Skip-gram model [58].
* GloVe--is one of the newest methods for calculating the vector representation of words. However, this approach does not use the whole corpus. It is learned only based on global statistics on the occurrence of words in a current context. The method captures various linguistic patterns and can successfully solve problems based on the principle of analogy [59].
* fastText--is a library created by Facebook's research team to learn and calculate the word representation and sentence classification. Its principle is to assign a vector representation to n-grams of characters that contain individual words [60].  
We used GloVe embeddings pre-trained on Common Crawl (300 dimensions) and Twitter (200 dimensions), for the fastText we used pre-trained word vectors for 157 languages, trained on Common Crawl and Wikipedia (also in dimension 300).  
Compared to word embeddings, BERT model includes an attention mechanism which is able to learn contextual relations between words in text. BERT consists of an encoder which processes the text input and decoder used to perform the prediction. Since BERT's goal is to generate a language model, only the encoder mechanism is necessary. BERT, which uses the Transformer encoder is able to learn the context of a word based on its entire surrounding (both left and right context of the words). When comparing to other directional models, that read the text input sequentially, transformer models read it as an entire sequence at once (from that point of view, it can be considered as non-directional). We used following tokenizers from Transformers ([[https://huggingface.co/transformers/](https://huggingface.co/transformers/)]([https://huggingface.co/transformers/](https://huggingface.co/transformers/)):  
* BERT tokenizer --bert-base and bert-large cased/uncased tokenizer--based on word piece;
* DistilBERT Tokenizer--distilbert-base-cased tokenizer--is identical to BertTokenizer and runs end-to-end tokenization: punctuation splitting and wordpiece;
* XLNet tokenizer--xlnet-base-cased tokenizer--based on sentence piece.
## 3 Testbed Description
### System Testing  
Three different metrics were observed to investigate the performance of each routing protocol. The first investigation observed the optimal bandwidth of each protocol in an outdoor scenario and in an indoor scenario. The bandwidth was measured using iperf3 with UDP packets over a period of 86400 s for indoor and 1000 s for outdoor testbeds. The second and third investigation examined other performance characteristics of the protocols, such as jitter and PDR for the indoor testbed.
## 3 Results and Discussion
### Source of As
#### 3.3.3 Mineralogy of the Regolith  
In Ganzhou city, IAREEMs are divided into heavy and light rare earth types, with significant As content in the semi-differentiated layer of the heavy rare earth deposit in Longnan county, exceeding China's agricultural land soil safety standards [22; 25]. The minerals in the regolith are laid in Table 7. The main minerals in the regolith are kaolinite\(\backslash\)illite (46.00%) and quartz (45.89%). Among them, is a type of As-containing mineral called chernovite-(\(\Upsilon\)) occuring as micron-sized pseudocubic crystals in the regolith (Figure 8a). The chemical composition of chernovite-(\(\Upsilon\)) is shown in Figure 8b. It is clear that there is a high content (over 25%) of As in chernovite-(\(\Upsilon\)) of semi-weathered regolith samples. This results in a high background value of As in the heavy rare earth deposit. Under the ongoing surface fluid-mineral interaction, As and REEs precipitate to generate chernovite-(\(\Upsilon\)) [22; 48]. Additionally, the fully weathered layer of IAREEM contains 46% kaolinite and other clay minerals. Studies have shown As being readily adsorbed by clay minerals such as kaolinite [22; 49]. Therefore, the As in atmospheric deposition in Longnan county and surrounding areas is highly likely to come from heavy rare earth minerals. Rare earth mining activities have led to vegetation destruction and soil erosion [3; 4; 11], exposing weathered layers beneath the soil humus and resulting in wind-borne transport of fine  
Figure 6: (**a**) Boxplot of atmospheric deposition fluxes for PTEs. The box reflects the 25 th and 75 th quantiles. Whiskers are the maximum and minimum values within 1.5 times the interquartile range. The dark line inside the box represents the median. Dark solid circles outside the whisker are outliers. (**b**) The correlation of Fractional Vegetation Cover with suspended particulate matter collected from atmospheric deposition. The remote sensing images used for the FVC calculations and the calculation process are shown in Table 1.  
grained arsenic-adsorbing clay minerals and chernovite-(Y), thereby increasing the risk of As exposure.  
Figure 7: This figure depicts the regional distribution of seven potentially toxic elements (PTEs) in atmospheric deposition. The dots in the legend indicate the locations of sampling sites. The color coding of each dot corresponds to PTE fluxes, which are classified into five tiers based on quartile analysis.  
Therefore, it is necessary to carry out ecological restoration and restore vegetation in mining areas, which can inhibit the migration of pollutants in mining areas, and reduce the risk to children, especially those exposed to As.
## 1 Introduction
From the above literature, different NOMA topologies have given considerable attention in academic research. However, few studies focus on the energy efficiency of MCMC-NOMA networks. The authors of Reference [14] have investigated energy minimization via determination of the optimal transmit power of the BS, which can achieve the optimal operating load of the cell. Hence, the motivations of this paper can be concluded as follows:  
* Energy efficiency in MCMC-NOMA is not well studied and that gives more motivation to contribute on investigating this problem. * Since such a topology entails inter-cell interference, as a consequence of inter-cell interference mitigation, the energy efficiency performance of the network can be significantly improved and that helps in the full exploitation of the potentials of NOMA. It is desirable to design a system that mitigates the inter-cell interference prior to the final power allocation. Based on these motivations, the contributions of this paper are:
* The inter-cell interference mitigation is designed using \(k\)-means graph-based clustering. Thus, the network topology is divided into isolated interference regions. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Study** & [4] & [6] & [7] & [8] & [10] & [13] & [3] & **This Study** \\ \hline User Association & No & No & Yes & Yes & Yes & No & Yes & Yes \\ \hline Interference Management & No & No & No & No & No & No & No & Yes \\ \hline Network Topology & MC & HetNet & MC & MC & MC & SC & MCMC & MCMC \\ \hline Approach & O & O & O & O & O/GT & GT & O & O/GT \\ \hline \hline \end{tabular} (O): Optimization, (GT): Game Theory, (ML): Machine Learning.  
\end{table} 
Table 1: Comparison of different related works. * Based on the aforementioned aspects of game theory, we consider auction mechanism to design a distributed power allocation scheme. Finally, the simulation results demonstrate that the proposed algorithm outperforms the existing NOMA-based fractional transmit power allocation (NOMA-FTPA) and conventional OFDMA system in terms of energy efficiency.
## 4 Proposed QLHH-II Algorithm
### QLHH-II Detailed Steps
#### 4.2.3 Update  
For each action made, the following tuple is stored in memory \(\mathcal{M}\): UAV performing the action; state in which the action is made; action considered; the cumulative reward of the \(\tau\) following actions; state after these \(\tau\) actions. In CCUS3O, at each time step \(t\), the tuple \((u,S_{t-\tau},v_{t-\tau},r_{t-\tau},S_{t})\) is thus added in \(\mathcal{M}\). Every such item \(i=\left(u^{(i)},S^{(i)},v^{(i)},r^{(i)},S^{(i)}\right)\in\mathcal{M}\) makes it possible to compute the following two values depicted in Figure 6:  
\[pred_{i}(\Theta)=Q_{u^{(i)}}\Big{(}S^{(i)},v^{(i)};\Theta\Big{)}\] \[targ_{i}=r^{(i)}+\gamma\cdot\max_{v\in V}Q_{u^{(i)}}\Big{(}S^{(i )},v;\Theta\Big{)}\]  
with \(\gamma\in[0,1]\) the discount factor; \(pred_{i}(\Theta)\) the value of the evaluation obtained by choosing \(v^{(i)}\) from \(S^{(i)}\) with the current \(\Theta\), and \(targ_{i}\) the value of the target evaluation, i.e., the evaluation that should have been made according to the obtained reward and future reward. After every movement of a UAV, \(\Theta\) is, thus, updated by processing a Stochastic Gradient Descent (SGD) step for a mini-batch \(\mathcal{B}\subset\mathcal{M}\) randomly selected from the memory to minimise the loss, formally defined below, being the squared mean error.  
\[loss=\sum_{i\in\mathcal{B}}(pred_{i}(\Theta)-targ_{i})^{2}\]  
It should be noted that the target value _targ_ is considered a constant. It means that during its calculation, there is no trace of the variable \(\Theta\) for the SGD, unlike the predicted value, which depends on \(\Theta\).
# On Ad Hoc Communication in Industrial Environments
## 4 Tracing Protocol
### Protocol Description  
The basic idea of the protocol is to use beacons to indicate the possibility of data exchange between a transmitter \(T\) and a receiver \(R\). A number of nodes is placed in the examined environment. All nodes send beacons with a certain frequency \(\frac{1}{dt}\). If any beacon is received by any receiver, it is logged to a log file. After the recording is completed, the log files of all nodes are processed, and a NEP is created.  
The nodes are all identical in function. The protocol defines a time step \(dt\). This is the time resolution of the resulting NEP. A smaller \(dt\) leads to a higher resolution in the NEP and to a higher bandwidth usage by the protocol. For the channel to be non-changing within \(dt\), \(dt\) must be chosen to be smaller than the coherence time of the communication channel. In the case of wireless communication at 2.4 GHz and a node speed of 1.2 m/s, the time resolution should be chosen to be smaller than 25 ms. An address \(A_{n}\) is assigned to every node, and a counter \(I_{n}(t)\) is incremented every time the node sends a beacon. Beacons are send every \(dt\). The beacon contains the address of the transmitter \(A_{T}\) and the current index of the transmitter \(I_{T}\).  
This beacon is send by the wireless interface (e.g., WiFi IEEE802.11 b/g/n) of the node. Any receiver (\(A_{r}\)) logs this beacon as an encounter tuple, with \(r\) being the receiver and \(s\) the transmitter. All recorded encounter tuples can be concatenated to form an encounter recording \(L_{R}\), which is a set of encounter tuples recorded by node \(R\).  
\[C=\left(\begin{array}{cc}A_{R}&T_{R}=I_{R}\cdot dt&A_{T}&T_{T}=I_{T}\cdot dt \end{array}\right)\in L_{r} \tag{5}\]  
Any entry in the recording describes the start or persistence of an encounter of the nodes \(T\) and \(R\
## 4 Simulation Results
### Computational Complexity Performance
Moreover, although the computational complexity of the existing STPAP algorithm in [31] is almost equal to that of the proposed STPAP beamforming algorithm, its output C/N\({}_{0}\) performance is not better than that of the proposed STPAP beamforming algorithm.
# Photocatalytic Inactivation as a Method of Elimination of _E. coli_ from Drinking Water
## 3 Inactivation Process by Photocatalysts
### Metal Sulphides as Photocatalysts for the Inactivation of _E. coli_  
Different types of sulphides have been reported for the inactivation of _E. coli_. For example, binary indium sulphide with band gap energy of 2.25 eV was used as the visible light sensitive photocatalyst for inactivating _E. coli_. Instead of hydroxyl radical causing the inactivation, hydrogen peroxide, peroxyl radicals, holes and electrons were reported as the cause of inactivation as confirmed by the scavenger investigation. These reactive species caused 5 log inactivation of bacteria cell by destroying enzymes and DNA, which are cytoplasmic components of the bacteria after 180 min of visible light irradiation [61]. Zinc sulphide has also been used for _E. coli_ inactivation. When the photocatalyst was exposed to UV light, 99% of _E. coli_ was inactivated within 120 min [62]. Apart from the binary sulphides, ternary sulphides were also used for _E. coli_ inactivation. [PERSON] et al. [63] reported the use of ternary CdIn\({}_{2}\)S\({}_{4}\) as a visible light-active photocatalyst. The ternary compound was found to be effective because it could be recycled via partition without losing its performance. This photocatalyst was able to inactivate 7 log of the bacteria within 180 min. The efficiency of the bacteria inactivation was also found to improve with increase in the photocatalyst loading. A novel nanocrystal ternary sulphide (Cu\({}_{2}\)WS\({}_{4}\)) was used to inactivate more than 99% (5 log) of Gram-negative _E. coli_ in the presence of light. Further investigation into the mechanism of antibacterial inactivation of this ternary sulphide photocatalyst showed that it has the ability to selectively bound to bacteria. In addition, it has peroxidase and oxidase properties, which enhanced the generation of reactive oxygen species that are active against _E. coli_[64].