esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Nanofertilizers
### Selenium Nanoparticles (SeNPs)  
Selenium can exist in different forms including oxyhalides, selenides, halides, oxides, acids, oxyacids, selenoenzymes and selenium nucleic acids [104]. Selenium occurs in the same group as sulfur and can exist in oxidation states of \(-2\), \(0\), \(+2\), \(+4\) and \(+6\). They differ in some reactions with the sulfur due to the unstable state of the \(+6\) oxidation state and the poor shielding effect of the d-orbitals introduced in the selenium. The most stable oxidation state of the selenides is the \(-2\) state and, in this form, they react with the highly electropositive elements.  
Over one billion humans are suffering from selenium malnutrition, thus its supplementation or fertilization in plants and animals is necessary for human wellbeing [105]. It had been reported to be toxic, but recent research has shown that its toxicity depends on the concentration [106; 107]. Selenium can be obtained as supplements from food or meat, and the best method of enrichment to satisfy human requirements is to enhance its level in agricultural crops by means of spraying or addition as selenite or selenates in fertilizers [108]. Phosphate fertilizers, sewage waste and farmyard manure are rich in selenium and can become sources of this micronutrient [106]. Selenium nanoparticles have interesting physicochemical properties and are greatly bioavailable with good physiological functions such as excellent antimicrobial, anti-cancer, antioxidant, etc. activities. They have been greatly applied as supplements in plants and food and are widely used in nanomedicines due to their low toxicity profiles [109]. They act as detoxifying agents when they contact heavy metals such as mercury, cadmium, and lead and such property has enabled them to guard living organisms against several diseases. Selenium compounds can easily functionalize with ligands. For example, selenocystin binding with glutathione peroxidase can neutralize the free radicals present in cells, thereby eliminating the harmful effect of the radicals. However, SeNPs hardly interact with most compounds and as a result are slowly released into living systems [104]. Inorganic forms of selenium seem to be more bioavailable than the organic ones, as demonstrated with tomatoes and strawberries. These crops scavenge for selenium in soils and store them in the edible parts, and the quantities of the inorganic forms are found to be more than the organic ones. Such actions reduce the inorganic selenium in the soils, which is more cost-effective [108].  
On a general note, selenium fertilization can interestingly improve the production of biochemical compounds such as amino acids, flavonoids, glucosinolates, protein, and phenolics compounds [107]. [PERSON] et al. [110] reported enhanced flavonoids and phenolic compounds in selenium-biofortified tomato fruit. [PERSON] also reported an increase in glucosinolates, which hydrolyzes to form isothi cyanates that possess excellent anticancer properties [111].
# Deterministic and Robust Optimization Approach for Single Artillery Unit Fire Scheduling Problem
## 6 Conclusions  
To the best of our knowledge, no study thus far has involved determining the firing sequence for enemy targets to minimize the total threat of the enemy to friendly forces until the end of the firing operation. In this paper, we proposed a single artillery unit fire scheduling model that minimizes the total enemy threat to friendly forces by simultaneously considering the threats of the enemy targets, enemy target destruction time, and firing preparation time.  
Most of the factors in a battlefield environment are uncertain, and it is especially difficult to assess the information related to the enemy. We considered the threat level of the enemy target to be an uncertain parameter and proposed an RO model for fire scheduling. However, the proposed RO model represents a semi-infinite problem that has infinitely many constraints. Therefore, we developed robust counterpart formulations for the proposed model. In the robust counterpart formulation with cardinality-constrained uncertainty, the conservativeness of the solution can be controlled by adjusting \(\Gamma\). Using this model, decision-makers in the battlefield can determine robust firing sequences and associated objective values by considering the uncertainty. Thus, various battlefield operations can be planned.  
Our experimental results verified that the robust counterpart formulation with cardinality-constrained uncertainty is equivalent to the deterministic optimization model for the firing sequence when the uncertainty degree is zero and that it is equivalent to the robust counterpart formulation with box uncertainty when the uncertainty degree is at its maximum. Therefore, the robust counterpart formulation with cardinality-constrained uncertainty can be used to obtain solutions for deterministic optimization modeling and worst-case modeling by changing the value of \(\Gamma\).  
If the number of targets is large, then the solution of the model presented in this paper cannot be found in a short time. Because the available time for operation preparation in a war scenario is always limited, prompt operation preparation is critical. In the future, we will develop heuristics to find the optimal solution in a short time for a large number of targets. In addition, uncertain factors besides the threat level of the enemy targets exist in war scenarios. Therefore, to obtain more realistic solutions, we will consider a more general uncertainty set for the uncertain factors, such as the central limit theorem uncertainty set [22].  
[PERSON] developed the model and wrote the majority of the manuscript. [PERSON] performed the experiments and wrote the experimental section of the paper. [PERSON] developed the overall idea and the basic outline of the paper.  
The authors declare no conflict of interest.  
Figure 2: Comparison of objective values corresponding to the minimum and maximum values of the uncertainty degree (\(n=12\)).
# Pressure Drop and Particle Settlement of Gas-Solid Two-Phase Flow in a Pipe
## 4 Results and Discussion
### Relationship between the Pressure Drop and Mixture Inlet Velocity
#### 4.5.3 Effect of Particle Diameter  
The relationship between the pressure drop per unit length \(p/L\) and mixture inlet velocity \(V\) for different particle diameters is shown in Figure 12, where we can see that the values of \(p/L\) decrease as the particle diameter increases. The reason for this is that under conditions where the particle volume concentration is constant, the smaller the particle, the more particles there are, and the stronger the effect of particle gas interaction. The stronger particle and gas interactions makes the required pressure drop larger due to the drag effect of the gas on the particles. It is obvious that the effect of particle diameter on pressure drop is less significant than that of the particle volume concentration and mass flow.  
Figure 10: Relationship between \(p/L\) and \(V\) for different concentrations (\(d_{p}\) = 100 \(\upmu\)m, \(m_{\rm s}\) = 15 t/h). \(\upmu\)s: 1%; \(\upmu\)s: 5%; \(\upDelta\): 10%; \(\upmu\)s: 15%; \(\upphi\): 20%.
# An Experimental Study of the Performance of a Crossed Rib Diffuser in Room Acoustic Control
## 2 Specification and Acoustic Properties of CRD
### Absorption Characteristics
The CRD-A had a broadband high absorption coefficient of larger than 0.6 at 800 Hz and higher, showing that the CRD can be altered to a practical absorber easily. Additionally, the measured result of CRD-A agreed well with theoretical absorption coefficient with LR assumption up to a peak frequency of 1.25 kHz. Thereafter, the measured value converged gradually to the theoretical value with an ER assumption. These results indicate that, for CRD-A, the ribs behind the absorbing layer play an identical role to that of a honeycomb structure at low-mid frequencies, resulting a local reacting behavior with a weak sound  
incident angle dependency. Then, the sound incident angle dependency became stronger with increasing frequency because the wavelength is smaller than the spaces between ribs. Figure 5: Laying a sound-absorbing layer on R-1 (**Upper**), and a sound absorption measurement of the CRD-A (**Lower**). Figure 6: Measured absorption coefficients of R-1, R-2, CRD and CRD-A compared with the theoretical absorption coefficients of a single permeable membrane absorber based on an assumption of an extended reaction (ER) and a local reaction (LR).
## 5 Cooperative SiM-RPO  
In the SiM-RPO algorithm, delivery of the data through a single path has a high chance of packet loss. This is because of the underwater noisy channel, harsh environment, path losses, and attenuation. Therefore, the data reception at the MS may be corrupted and more erroneous. Therefore, for satisfactory data reception, the data are analyzed by the MS. If the data have a high BER than a defined value, then the data are retransmitted only one time on the demand of the MS by a relay. Otherwise, the data reception at the MS is considered successful.  
In the CoSiM-RPO algorithm, data exchanging of a node \(S\) with the MS in a cooperative manner through a relay \(R\) is as shown in Figure 4. When the \(S\) broadcasts data \(x\) toward the MS, then the effected data \(y_{M}\) and \(y_{R}\) by the channel gain \(g\) and noise \(n\) are received at the MS and neighbors, respectively. The received data \(y_{M}\) at the MS are analyzed for errors. If the errors in the data are less than a defined value, it is considered successful at the MS and acknowledged by the MS. Otherwise, in case of errors greater than the defined value, a request by the MS is broadcasted. In response, the relay broadcasts its data \(y_{R}\) that it received from the sender. The MS receives the relaying data \(y_{RM}\) and analyzes further if they satisfy the BER threshold, and it is considered that the data are delivered successfully. Otherwise, the data are discarded by the MS. The Algorithm 1 and flowchart in Figure 5 also explain the proposed algorithm.  
Figure 3: MS prioritization.  
Figure 4: Data cooperation.  
Figure 5: Flowchart for the cooperative sink mobility for reliable and persistence operation (CoSiM-RPO) scheme.  
```
1 Network deployed;
2 Partitioned into four squares;
3 Place nodes;
4 N\({}_{m}\) = Node m;
5 MS\({}_{i}\)(x,y) = Coordinates of MS\({}_{i}\);
6 ID\({}_{N_{m}}\) = Node ID;
7 ID\({}_{MS_{i}}\) = MS ID;
8 whilePacket reached == falsedo
9 MS\({}_{i}\)(x,y) = MS\({}_{i}\)(\(x\pm z,y\pm z\)) // Diagonal movement
10 Or MS\({}_{i}\)(x,y) = MS\({}_{i}\)(\(x\pm z,y\)) // Horizontal movement
11 Or MS\({}_{i}\)(x,y) = MS\({}_{i}\)(\(x,y\pm z\)) // Vertical movement
12 Send an info packet;
13 ifInfo packet == receivedthen
14 Forward data;
15 Request = 0;
16 if\(BER\)\(<\) 0.5 then
17 Data read by MS;
18 Packet reached = true;
19
20 else
21 if\(Request\)\(<\) 1 then
22 Send request;
23 Forward data again;
24 Request = Request + 1;
25 if\(BER\)\(<\) 0.5 then
26 Data read by MS;
27 Packet reached = true;
28 else
29 Packet dropped;
30 Packet reached = true;
31
32 end if
33 else
34 Packet dropped;
35 Packet reached == true;
36
37 end if
38
39 end if
40 else
41 Hold data;
42 Wait for MS;
43
44 end if
45
46 end if
```  
**Algorithm 1**Algorithm for the CoSiM-RPO scheme.
# Assessment of Daytime HONO Emission Source from Asphal Surface to Urban Air
## 3 Results
### Conversion Rate of NO\({}_{2}\) to HONO  
According to Equations (1) and (2) under the conditions of 11.3 W m\({}^{-2}\) of UV intensity and 500 ppbv of toluene, we calculated \(k_{HONO}\) (the first-order NO\({}_{2}\) conversion rate to HONO) of \(1.23\times 10^{-4}\) s\({}^{-1}\) for the asphalt surface, which is much smaller than a commercial TiO\({}_{2}\)-doped paint surface [34]. The HONO emission fluxes in our experiment range between zero and 3.1 \(\times\) 10\({}^{9}\) molecules cm\({}^{-2}\) s\({}^{-1}\) along with NO\({}_{2}\) concentrations (Figure 3). In Table 1, the observed HONO fluxes with the asphalt surface are compared with other common surfaces found in urban conditions. The HONO emission fluxes on TiO\({}_{2}\) photocatalytic paint surface studies are 10 times higher than that of the asphalt surface.  
Several studies reported high HONO production on aerosol surfaces with large components of photoreactive humic acids and soots [41]. Although a few high fluxes of HONO on soil, forest canopy, and TiO\({}_{2}\) paint studies were reported, HONO emission flux from asphalt surfaces in this study is generally in a similar range as those on other common urban surfaces such as glass, aerosol, soil, and forest canopies. This implies that HONO production with heterogeneous photolytic NO\({}_{2}\) conversion from asphalt surfaces is likely an important source of daytime HONO, especially in urban areas.
# A Hybrid Genetic Algorithm for Ground Station Scheduling Problems
## 3 Ground Resource Scheduling Model
### Optimization Objective
#### 3.2.1 Maximizing Task Execution Rate  
If task k can be successfully scheduled to equipment that meets the constraint conditions, then \(T_{k}\cdot x_{k}=1\); otherwise, \(T_{k}\cdot x_{k}=0\). The formula for calculating the task execution rate (TER) is as follows:  
\[\mathrm{TER}=\frac{\sum\limits_{1\leq k\leq n}T_{k}\cdot x_{k}}{n} \tag{2}\]  
\(\{k|1\leq k\leq n,n\in Z\}\) represents the range of task id, \(n\) denotes the total number of tasks, and \(x_{k}\) indicates whether task \(k\) is executed, taking values of 0 or 1.
# Performance Analysis of D2D Communication with Retransmission Mechanism in Cellular Networks
## 5 Queuing Stationary Analysis and Performance Parameters
### The Optimal Number of Retransmission Attempts  
In this section, we discuss the approach to choose the optimal number of retransmission attempts \(K^{*}\) such that:  
\[K^{*}=\arg\max_{K\in(0,\infty)}\ \overline{Thr}, \tag{26}\]  
where \(\overline{Thr}\) is given in (21). We can see that the average throughput is a function of the number of retransmission attempts \(K^{*}\) if the other system parameters are fixed. Nevertheless, it is hard to obtain a closed-form solution of \(K^{*}\), and the optimal \(K^{*}\) can be numerically obtained. Theoretically, the value space of \(K^{*}\) can be any nonnegative integer, but if it is too large, it leads to lower average throughput. Thus, we took a group of numbers \(K\in(0,K_{\max})\) and obtained the optimal solution as:  
\[K^{*}=\arg\max_{K\in(0,K_{\max})}\ \overline{Thr} \tag{27}\]
## 3 The Proposed Method
### Feature Extraction and Classification Based on the Dual-CNN Model  
The Dual-CNN model consists of two CNNs, i.e., the 6 Ch-CNN and PauliRGB-CNN. The 6 Ch-CNN contains two convolution layers (Conv61, Conv62), two pooling layers, and two fully connected layers (FC6_200, FC6_84). It is used to acquire the polarization data feature. The PauliRGB-CNN also includes two convolution layers (Conv31, Conv32), two pooling layers and two fully connected layers (FC3_200, FC3_84), and it is applied to obtain the spatial features. More specifically, as shown in Figure 2, \"Conv61, 500@3\(\times\)3\" represents the first 6-channel convolution layer depending upon the 3\(\times\)3 convolution kernel and generates 500 feature maps. \"FC6_200\" represents the 6-channel fully connected layer consisting of 200 neurons. Notice that the ReLu is used as the activation function for all the hidden layers and the 2\(\times\)2 max-pooling is used for the pooling layers. PauliRGB-CNN is constructed as done in the 6 Ch-CNN.  
In the training process, the FP and BP are two vital procedures for updating the network. By training the network, the 6 Ch-CNN can obtain the features with the property of polarization, while PauliRGB-CNN obtains the features which contain spatial characteristics. Then, the Softmax function is employed to implement the classification.
# Adaptive Wavelet Coding Applied in a Wireless Control System
## 3 System Description
### Implementation of an Adaptive Wavelet Coding
In particular, it can be observed that the proposed adaptive wavelet coding allows for doubling the transmission rate when the channel is in a good state without degrading the system's average performance in terms of bit error rate, wh  
Figure 7: (**a**) Illustration of the behavior of the implemented wavelet adaptive encoder; (**b**) system performance with adaptive wavelet coding. send auxiliary data; for example, the status of the battery without compromising the basic data flow for the control loop.
# Overrelaxed Sinkhorn-Knopp Algorithm for Regularized Optimal Transport
## 1 Introduction
### Accelerations of the Sinkhorn-Knopp Algorithm  
In the literature, several accelerations of the Sinkhorn-Knopp algorithm have been proposed, using for instance greedy coordinate descent [13] or screening strategies [14]. In another line of research, the introduction of relaxation variables through heavy ball approaches [15] has recently gained popularity to speed up the convergence of algorithms optimizing convex [16] or non-convex [17; 18] problems. In this context, the use of regularized nonlinear accelerations (RNAs) [19; 20; 21] based on Anderson mixing has led to important numerical improvements, although the global convergence is not guaranteed with such approaches, as is shown further. In this paper, we also investigated another approach related to the successive overrelaxation (SOR) algorithm [22], which is a classical way to solve linear systems. Similar schemes have been empirically considered to accelerate the SK algorithm in [9; 23]. The convergence of these algorithms has nevertheless not been studied yet in the context of regularized optimal transport.  
Recent progress has been made on computational complexity guarantees for the Sinkhorn-Knopp algorithm and accelerated versions [13; 24; 25; 26]. Since the methods we discuss in this paper are based on asymptotic acceleration techniques, it is challenging to show their efficiency via global computational complexity guarantee, and we do not cover these aspects here.
# Analysis of Shield Tunnels Undercrossing an Existing Building and Tunnel Reinforcement Measures
###### Abstract
Many scholars have conducted studies on grouting reinforcement technology, but many of these focused on the effects of grouting reinforcement technology on shield tunnels and soils and a few on the effects of grouting reinforcement technology on complete buildings with basements and piles. In view of this, this paper studies the effect of double-shield tunnel excavation on a complete building with a basement and piles by using a mathematical model and combining engineering measurement data. We add grouting reinforcement measures to the model to study the effect of grouting reinforcement technology on a complete building with a basement and piles, which will make up for the shortcomings of existing research and provide a theoretical basis for future excavation of shield tunnels undercrossing existing buildings. Against a background of a section of Changsha Metro Line 6 where double-shield tunnels cross under an existing building, as shown in Figure 1, this paper establishes the model of the eight-story building with a double basement and piles, with shield tunnel excavation carried out under the building. Using the established model, the effects of the excavation of double-shield tunnels on soil displacement, segment displacement and internal force, pile displacement, and building subsidence were studied. In addition, the influence of grouting pressure on soil displacement, segment displacement and internal force, pile displacement, and building subsidence was analyzed, and reasonable grouting pressure parameters were further explored. At the same time, grouting reinforcement technology was used in the model, and the control effect of grouting reinforcement on pile displacement and building subsidence was analyzed. Grouting reinforcement parameters that can effectively reduce pile foundation displacement and building subsidence caused by shield tunnel excavation are further proposed.
# Energy Consumption Analysis of LPWAN Technologies and Lifetime Estimation for IoT Application
## 9 Conclusions  
Energy consumption is one of the most crucial requirements for design and implementation of an IoT application. In this paper, we have analysed the energy consumption and quantified the energy performance of the LPWAN technologies. Also, we calculated the battery lifetime for different technologies in respect to the divergent battery capacity. Results show that message transmission and current consumption of idle period is the main contributor for the overall battery lifetime. Based on the analysis of our experiment results, it can be concluded that LoRaWAN and DASH7 are more energy-efficient as compare to NB-IoT and Sigfox. However, LoRaWAN attracts application with better coverage distance and DASH7 covers the application space demanding mid range communication. Further, NB-IoT is more efficient than other technologies where availability is of utmost importance. Sigfox has the lowest energy consumption performance as compared to other LPWAN technologies. We demonstrated the lifetime of LPWAN technologies with different battery capacity and message transmission frequency. This can give insights and help in picking the right LPWAN technology, managing the frequency of communication, and finally selecting the appropriate battery capacity.  
IoT requires LPWAN technology and battery powered sensors to be compatible with the lifetime demand of the application. In ideal case, the battery lifetime of the LPWAN network stretches overseveral years, as it is multiple years in our evaluation. However, as shown later in the paper for the real-time case study on greenhouse deployment, the battery life decreases drastically. This gives three level of energy-consumption analysis: first level with simulation, second with the real-time hardwares, and finally the real time analysis of battery life post deployment. As we proceed in the above sequential analysis, a more realistic energy consumption evaluation can be achieved that can improve the future designs. Our future work will focus on the evaluation of the impact of LPWAN configuration parameters towards energy efficiency and also to propose a feedback model for calculating the lifetime of the network prior to actual deployment.  
[PERSON] and [PERSON] conceived and wrote the paper; [PERSON] and [PERSON] supervised the project. All authors have read and agreed to the published version of the manuscript.  
This research is funded by (1) EU-Interreg project on greenhouse monitoring (Grow!) [43]; (2) The SmartWaterGrid project, co-funded by imec, a research institute founded by the Flemish Government, with project support from VLAIO (contract number HBC.2019.0057). The SmartWaterGrid industry partners are De Waterroep, Itineris, Aloxy, and Hydroscan [44]; and (3) The European Union's Horizon 2020 Innovative Training Networks program under the [PERSON] grant agreement No 813114 (FutureArctic project) [45].  
The authors declare no conflict of interest.