esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Due to the ever-increasing data demand of end users, the number of information and communication technology (ICT)-related devices and equipment continues to increase. This induces large amounts of heat emissions, which can cause serious environmental pollution. In recent times, signal transmission systems such as cellular base stations (BSs) have been constructed everywhere and these emit a large carbon footprint. Large-scale antenna systems (LSASs) that use a large amount of transmission antennas to serve a limited number of users can increase energy efficiency (EE) of BSs based on the beamforming effect, and thus can be a promising candidate to reduce the carbon footprint of the ICT field. In this paper, we discuss the necessary schemes to realize LSASs and show the expected EE gain of the LSAS with enough practicality. There are many obstacles to realize the high EE LSAS, and even though several studies have shown separate schemes to increase the EE and/or throughput (TP) of LSASs, few have shown combinations of schemes, and presented how much EE gain can be achieved by the schemes in the overall system. Based on the analysis in this paper, we believe more detailed work for the realization of high energy efficient BSs with LSASs is possible because this paper shows the necessary schemes and the maximum achievable energy efficiency gain as a reference. Extensive analysis and simulation results show that with proper implementation of the power amplifier/RF module and a robust channel estimation scheme, LSASs with 600 transmitter (TX) antennas can achieve 99.4 times more EE gain compared to the current systems, thereby resulting in significant reduction of carbon footprints.  
green information and communication technology; energy efficiency; large-scale antenna systems; cellular base station +
Footnote †: journal: _Sustainability_
## 2 Materials and Methods
### Extraction of Aroma Volatile Compounds  
Aroma volatile compounds were isolated by head space solid phase microextraction (HS-SPME) and identified and quantitated by GC-MS. For this purpose, immediately after harvest, approximately 50 g of fresh mushroom samples from each treatment (in triplicates) were wrapped in aluminum foil, sealed in plastic bags and kept at \(-40\)\({}^{\circ}\)C until analysis, which was conducted within one week. Prior to analysis, samples were thawed, finely chopped and approximately 1 g was weighed into 15 mL screw capped glass vials (Supelco, Bellefonte, PA, USA), followed by the addition of 4 mL of saturated aqueous NaCl solution to inhibit enzymatic degradation and boost the release of volatiles [17; 29] and 50 \(\upmu\)L of internal standard (4-methyl-1-pentanol) methanolic solution (\(\upmu\)g/mL). A magnetic stir bar was immersed in each vial, which was sealed with PTFE-faced/silicone septum (Supelco, Bellefonte, PA, USA). Sealed SPME vials were kept in a water bath at 40 \({}^{\circ}\)C, under magnetic stirring (250 rpm, Phoenix Instrument RSM-10 HS/HP, Garbsen, Germany) for 5 min to equilibrate. Then the SPME assembly was inserted, and fiber was exposed in the headspace and left to incubate for 40 min under continuous stirring. The volatiles were extracted with 50/30 \(\upmu\)m divinylbenzene/carboxen/polydimethylsiloxane (DVB/CAR/PDMS) StableFlex fibers (Supelco Inc., Bellefonte, PA, USA), reported exhibiting good selectivity and high efficiency for the extraction of volatiles from wild and cultivated mushrooms [15; 17; 27; 30; 31; 32]. They are additionally more selective for the identification of aldehydes such as metholand phenylacetaldehyde, considered important for mushroom flavor [32].
## 3 Results and Discussions
### Return Loss Response  
Figure 2 illustrates the return loss curve of the antenna. The return loss result of this antenna was extracted from the simulation result in CST software. From Figure 2, it is noted that the antenna works in the range of UWB frequency band (3.1 GHz to 10.6 GHz), and it shows band notch characteristics in the frequency range of 7 GHz to 7.85 GHz, which was one of the goals of this paper. In the frequency range of 7 GHz to 7.85 GHz, the return loss values are above -2 dB, which will allow interference-free communication in the UWB system for low-power BSNs. The presented return loss results meet the estimated aim of this paper. At other frequency bands, except band notch regions, the return loss values show very good results --especially at a higher frequency.
# Generative Image Steganography via Encoding Pose Keypoints
## 3 The Proposed Method
### Generation of Human Pose Keypoint Sequences
#### 3.1.2 Model Training  
LSTM is a type of recurrent neural network (RNN) designed to address the gradient vanishing problem in long sequences by introducing gating mechanisms, including forget, input, and output gates. Compared to traditional RNNs and GRU, LSTM's dedicated memory cell offers finer control over information retention and forgetting, making it more efficient at capturing long-term dependencies and ensuring greater stability, especially in complex sequence generation tasks. In the LSTM network, the output at any time correlates with information from all previous times. At time \(t\), the simplified output formula of the model can be represented as:  
\[\begin{split}& h_{t}=\text{LSTM}(x_{t},h_{t-1};\theta_{LSTM})\\ & output=\text{Softmax}(h_{t}\times W+b_{o})\end{split} \tag{2}\]  
Specifically, \(h_{t}\) is the hidden state at time \(t\), \(x_{t}\) is the input at time \(t\), \(\theta_{LSTM}\) represents the weight parameters of the LSTM network, \(W\) represents the weight matrix of the output layer, and \(b_{o}\) represents the bias in the output layer.  
According to Formula (2), the first \(i-1\) elements of the keypoint index sequence \(S\) are input into the model individually. At time \(i\), the model's output is the conditional probability distribution of \(s_{i}\), represented as \(\text{Pr}(s_{i}\,|\,s_{1},\,s_{2},\,\ldots,\,s_{i-1})\), where the range of the \(s_{i}\)'s value is any index value in the index table. This paper adopts the MLE strategy to train the LSTM to generate the sequence of keypoint data consistent with the true human pose distribution. The MLE strategy achieves this by defining a loss function, which measures theprobability of the model predicting the current observation given the previous observations. The loss function can be expressed as:  
\[Loss=-\sum_{i=1}^{I}\log\Pr(s_{i}|s_{1},s_{2},\ldots,s_{i-1}) \tag{3}\]  
Specifically, \(I\) is the sequence length, and \(s_{i}\) is the observed value at time \(i\). By minimizing the loss function, we are searching for model parameters that can maximize the probability of the observed data sequence, thereby obtaining the sequence generation model consistent with the real human keypoint data distribution.
## 2 Materials and Methods
### The Proposed Method  
Our application has been implemented in MATLAB(r) R2017. The toolboxes that have been used are _Statistics and Machine Learning_ and _Computer Vision System_. As mentioned in Section 1, for the development of the tool, the Infant COPE database [3] has been used. This is a database that is composed of 195 color images of 26 neonates, 13 boys, and 13 girls, with an age between 18 hours and 3 days. For the images, the neonates have been exposed to the pain of the heel test and to three non-painful stimuli: a corporal disturbance (movement from one cradle to another), air stimulation applied to the nose, and the friction of a wet cotton in the heel. In addition, images of resting infants have been taken.  
Figure 5: Representation of the transformed space for nonlinear SVM.  
As mentioned before, this implementation could be applied to a mobile device and/or a wearable system, so that, on the one hand, a baby monitor would continuously analyze the images it captures. On the other hand, the parents or medical staff would wear a bracelet or have a mobile application to warn them when the baby is suffering pain. The diagram in Figure 6 shows a possible example of the implementation stages.  
The first step is pre-processing the input image by detecting infants' faces and then resizing the resulting images and converting them into grey scale. All images are normalized to a size of \(100\times 120\) pixels. Afterwards, features have been extracted using the texture descriptors mentioned before. The NFCS scale will be followed, so descriptors have been applied only to relevant facial areas to the NFCS scale: right eye, left eye, mouth, and brow. These areas are manually selected with sizes \(30\times 50\) pixels for eyes, \(40\times 90\) pixels for mouth, and \(15\times 40\) pixels for brow. It was possible to make an analysis to find the ideal sizes for each part due to the small size of the used database. Feature vectors from each area have been concatenated to obtain the global descriptor.  
Finally, a previously trained SVM classifier decides if the input frame corresponds with a baby in pain or not. The system will be continuously monitoring the video frames obtained and sending an alarm to the mobile device if a pain expression is detected.
## 3 Results
### Dynamic Mechanical Test Results
#### 3.2.4 Effects of Heat Treatment Time on the Hysteresis Behavior of WMD  
To evaluate the effect of heat treatment time, NiTi WMDs were heat-treated at 200 \({}^{\circ}\)C for 15 min, 30 min, and 45 min, then subjected to sinusoidal excitations at 1 Hz with amplitudes of 0.3 mm, 0.4 mm, 0.5 mm, and 0.6 mm. Figure 14 compares the hysteresis loops of NiTi WMDs heat-treated for various times. As can be seen in Figure 14, the area enclosed within the symmetric hysteresis loop is slightly enlarged when the heat treatment time becomes longer. To quantify this phenomenon, the dynamic stiffness and loss factors are calculated and plotted as a function of heat treatment time and displacement amplitudes, as shown in Tables 9 and 10 and Figure 15, respectively.  
As reflected in Figure 15a, little improvement in the dynamic stiffness can be noticed when the heat treatment time is increased from 15 min to 30 min. Nevertheless, a significant decrease in stiffness occurs if the heat treatment time is extended to 45 min. This softening behavior can be attributed to the abnormal grain growth within the NiTi wire. For all of the heat treatment times involved, 30 min proves to be optimal for achieving the best damping capacity of the NiTi WMD, as shown in Figure 15b. The loss factor reaches its peak as the heat treatment time is extended from 15 min to 30 min and then experiences a significant drop after the time is prolonged to 45 min.  
Figure 13: Characterization of the dynamic hysteresis behavior of NiTi WMDs heat-treated at different temperatures: (**a**) dynamic stiffness; (**b**) loss factor.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Amplitude**} & \multicolumn{4}{c}{**Heat Treatment Temperature (\({}^{\circ}\)C)**} \\ \cline{2-5}  & **25** & **150** & **200** & **250** \\ \hline
0.3 & 0.48 \(\pm\) 0.01 & 0.46 \(\pm\) 0.03 & 0.53 \(\pm\) 0.04 & 0.39 \(\pm\) 0.06 \\
0.4 & 0.47 \(\pm\) 0.01 & 0.41 \(\pm\) 0.01 & 0.49 \(\pm\) 0.05 & 0.36 \(\pm\) 0.03 \\
0.5 & 0.44 \(\pm\) 0.01 & 0.38 \(\pm\) 0.04 & 0.45 \(\pm\) 0.01 & 0.33 \(\pm\) 0.02 \\
0.6 & 0.42 \(\pm\) 0.01 & 0.36 \(\pm\) 0.01 & 0.43 \(\pm\) 0.02 & 0.31 \(\pm\) 0.01 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Loss factor parameters at different heat treatment temperatures.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Amplitude (mm)**} & \multicolumn{3}{c}{**Heat Treatment Time (min)**} \\ \cline{2-4}  & **15** & **30** & **45** \\ \hline
0.3 & 0.39 \(\pm\) 0.04 & 0.53 \(\pm\) 0.04 & 0.34 \(\pm\) 0.05 \\
0.4 & 0.36 \(\pm\) 0.01 & 0.49 \(\pm\) 0.05 & 0.33 \(\pm\) 0.05 \\
0.5 & 0.32 \(\pm\) 0.02 & 0.45 \(\pm\) 0.01 & 0.31 \(\pm\) 0.03 \\
0.6 & 0.32 \(\pm\) 0.01 & 0.43 \(\pm\) 0.02 & 0.29 \(\pm\) 0.03 \\ \hline \hline \end{tabular}
\end{table}
Table 10: Loss factor parameters at different heat treatment times.  
Figure 14: Effect of heat treatment time on the NiTi WMD’s dynamic hysteresis behavior with various displacement amplitudes: **(a)** 0.3 mm; **(b)** 0.4 mm; **(c)** 0.5 mm; **(d)** 0.6 mm.
# Characterisation of Dust Particles Deposited on Photovoltaic Panels in the United Arab Emirates
## 3 Results and Discussion
### Dust Chemical Composition (XRD Characterisation)
As can be seen, all the samples contained calcium carbonate and silicon oxide regardless of the site; however, each individual site further possessed unique compositions containing Magnesium Sodium, Aluminium, Titanium and Rubidium in compositions of Carbonates, Silicates and Oxides. However, calcium carbonate and silicon oxide are the predominant compounds in all locations. In another research [22], XRD analysis of dust samples from South Africa revealed a molecular composition potentially comprising quartz (SiO\({}_{2}\)), quartz-a (SiO\({}_{2}\)-alpha), calcite (CaCO\({}_{3}\)), iron oxide chloride (FeOCl), kaersutite (Al\({}_{2}\)Ca\({}_{2}\)Mg\({}_{6}\)NaO\({}_{24}\)Si\({}_{6}\)), and mantienneite (Al\({}_{2}\)FeH\({}_{33}\)K\({}_{0.5}\)Mg\({}_{3}\)O\({}_{34}\)P\({}_{4}\)Ti). Further research is needed on the interaction between sand particle composition/morphology, PV surface morphology, wind speed, and relative humidity to help devise suitable cleaning technologies specific to UAE weather environmental conditions.
# Area of Operation Planning for Free-Floating Car Sharing Systems
## 1 Introduction
### Related Work  
Free floating car sharing systems have been gaining increasing attention due to the flexibility and the global trend of reducing private car ownership. So far, research on optimization at the strategic level has mostly focused on the optimal placement of stations within its area of operation. Conversely, the problem of finding an optimal relocation strategy has been the primarily studied optimization problem at the operational level. [PERSON] et al. (2016) and [PERSON] et al. (2016) give an overview of the literature pertaining to the optimal design and operation of electric car sharing systems. In more recent articles, [PERSON] et al. (2017) and [PERSON] et al. (2018) focus on optimizing charging and relocating aspects, but not the AoO. In terms of location optimization, [PERSON] et al. (2019) use travel demand to optimize the locations of stations. However, they consider a station-based system and not a free-floating one. To the best of our knowledge, the aspects of business model, infrastructure and fleet management are well-covered in existing literature. However, optimizing the area of operation is an under-researched topic.  
[PERSON] et al. (2018) describe a combined integer linear programming (ILP) formulation for the placement of charging stations and the relocation of vehicles within a station-based electric car sharing system and apply the resulting algorithm to a system in Nice, France. More recently, [PERSON] et al. (2019) provide multiple exact (ILP-based) and heuristic algorithms for placing charging stations in a station-based one-way electric car sharing system. The authors evaluate their algorithms on both artificial and real-world instances from Vienna, Austria, and give insights into their performance characteristics, as well as the structure and quality of the resulting solutions.  
Optimization problems related to the optimal design of an area by selecting its constituent parts are, of course, also common in other domains. For example, [PERSON] et al. (2019) describe an ILP-based algorithm for determining the optimal layout of wildlife reserves with optional connectivity and buffer zone requirements and evaluate it on a variety of artificial and real-world instances.
## 6 Plate Partitioning Optimization  
### Problem Definition  
The second step of the BTB optimization addresses the plate partitioning (see Figure 4). The objective of this optimization problem is to minimize the number of welding seams along the boom while fulfilling a set of constraints, mostly concerning manufacturability. The underlying motivation is that for every additional plate, an additional welding seam has to be made, which increases the costs since welding seams are typically more expensive than material. Fewer welding seams means fewer plates (i.e., better usage of metal sheets) and fewer steps within the manufacturing of the boom. However, these two points are not reflected in the cost function (Equation (4)).  
Recalling the parameter definitions in Section 4, the plate partitioning optimization problem is formalized as  
\[\min_{n_{b},n_{s},n_{t}}g(n_{b},n_{s},n_{t})=n_{b}+n_{s}+n_{t} \tag{17}\]  
subject to  
\[u_{i} >MinLength \tag{18}\] \[u_{i} <MaxLength\] (19) \[\sum_{i=1}^{n_{b}}u_{i} =TotalLength\] (20) \[\sum_{i=1}^{m_{b}}u_{i} \
otin[l\cdot DistBulkheads-MinGap,l\cdot DistBulkheads+MinGap]\] (21) \[1\leq m_{b}\leq n_{b},1\leq l\leq n-1\] \[\sum_{i=1}^{m_{b}}u_{i} \
otin\left[\sum_{j=1}^{m_{s}}v_{j}-MinGap,\sum_{j=1}^{m_{s}}v_{j }+MinGap\right]\] (22) \[1\leq m_{b}\leq n_{b},1\leq m_{s}\leq n_{s}\]  
and equivalently for side plates \(\mathbf{v}\) and top plates \(\mathbf{w}\).  
Figure 7: Decision Matrix for Boom Configuration before optimization ((**left**) with Demanded Quality Criteria) and after optimization (**right**). Note that, for a better representation, only the scoring for comparing the algorithms are shown.  
The constraint in Equations (18) and (19) define the minimum and maximum length of the bottom plates, respectively (the latter one is shown in Figure 8). Equation (20) states that the lengths of all single bottom plates have to sum up to the total length of the middle section (denoted as _TotalLength_, see also Figure 8). In Equation (21), it is described that no welding seam of the bottom plates is within _MinGap_ to any of the bulkheads. An example of this is shown in Figure 8 for the two side plates and the third bulkhead. Finally, Equation (22) states that no welding seam of two bottom plates is within _MinGap_ to any welding seam of two side plates (see again Figure 8 for an example).  
For the side plates, a set of similar constraints has to be defined. The equivalent of Equation (22), however, includes the variables of the side and top plates, ensuring that no seam of any two side plates lies within _MinGap_ of any seam of two top plates. The constraints for the top plates only have to be defined equivalently to Equations (18)-(20); Equation (21) is not necessary since the bulkheads are only welded to the bottom and side plates and not to the top plates, and Equation (22) is not needed since this is already covered by the constraints of the side plates.  
Finally, we want to note that some further details (e.g., the taper of the boom or a welding gap between any two plates) were neglected from the formulation for clarity.
# A Compact Ultra-Thin 4 \(\times\) 4 Multiple-Input Multiple-Output Antenna
## 3 Working Mechanism
### Application Scenario  
This paragraph presents a comprehensive analysis of the devices used in right-hand mode. Figure 7 depicts the simulated S-parameters when the proposed AP system held in a single right hand. A slight frequency offset caused by the inequable distance to the hand tissue of each element is observed, but the reflection coefficients of four ports can cover the desired bands. The isolation performance of this scene is also plotted in Figure 7. There was almost no significant deviation on \(S_{12}\) and \(S_{14}\) curves under this operation mode compared with Figure 2, while the deterioration of approximately 5 dB happened to \(S_{13}\) within the wanted band. Total Radiated Power (TRP) of the proposed AP system when a different port was excited with input power of 1 W at 3.5 GHz and radiating efficiency being portrayed in Figure 8. The TRP of Ant. 1 and Ant. 2 generally range from 600 mW to 700 mW in the target band, while Ant. 3 and Ant. 4 show poorer radiating ability due to their relative adjacent distance to the hand tissue. The hand tissue absorbs part of the input power. A relative flat radiating efficiency of the proposed AP was obtained. AP. 1 and AP. 2 achieved 70% and 50% radiating efficiency, respectively. The simulated three-dimension (3D) and two-dimension (2D) radiation patterns of this device held in a single hand are presented in Figure 9.  
Figure 6: Simulated (**a**) S-parameters and (**b**) Smith chart with various _Lcl_ values.  
Figure 7: Simulated S-Parameters when this AP system is in SHM mode.  
Specific absorption rate (SAR) is the definition of electromagnetic power absorption or consumption per unit of human tissue, which numerous electronics manufacturers have specified. Figure 10 displays the simulated SAR field of the hand model when Ant. 1 is excited with an input power of 100 mW at 3.5 GHz. It can be observed that the average SAR value of 1 Kg of human tissue was less than 1.166 W/Kg, which is inferior to the American and European standard values of 1.6 W/Kg and 2.0 W/kg.  
Figure 8: Simulated TRP and radiating efficiency of every antenna element.  
Figure 10: Simulated SAR field when Ant. 1 is excited at 3.5 GHz.  
Figure 9: Simulated 3D and 2D radiating patterns of the proposed AP when Port 1 is excited at 3.5 GHz.
# Biogenic Nanoparticles: Synthesis, Characterisation and Applications
## 2 Biogenic Synthesis of Nanoparticles
### Fungi
AgNPs showed uniform size and spherical form in FESEM images. As well as metal nanoparticles, fungi are capable of synthesising metal oxide nanoparticles. Magnetite is a widespread iron oxide that exhibits magnetic properties. Fungi such as _Aspergillus_ are capable of producing such nanoparticles [59]. Nanomaterials are being used to remove heavy metals and radioactive materials, due to their high (and fast) surface area to volume ratio [72]. Magnetite NPs are more efficient compared to other metal nanoparticles for this particular application because their superparamagnetic behaviour allows them easily to be separated from wastewater due to electrostatic and surface complexation. Bionanocomposites of fungus-Fe\({}_{3}\)O\({}_{4}\) for nuclear waste management have been reported [73]. Nano Fe\({}_{3}\)O\({}_{4}\) particles were obtained from _Penicillium_ Shijie, a fungus that grows on wood. SEM results revealed that the particles were uniformly decorated with a thin coating of Fe\({}_{3}\)O\({}_{4}\) on the fungus' surface. FTIR results revealed that the iron oxide particles being chemically bonded to the surface of the fungus. They found that in the case where ions were present, fungus-Fe\({}_{3}\)O\({}_{4}\) sorption was not affected by ion strengths, implying that its surface complexion that was assumed to influence sorption. Magnetite NPs have found wide uses in applications such as MRI [51], and for oscillation damping and position sensing [52], and recording machines. Like bacteria, using fungi also has a major drawback with regards to biosafety. Fungus like _F. oxysporum_ is dangerous since they are mostly pathogenic and thus are a concern to health. However, there are several non-pathogenic fungi examined that are suitable to synthesise nanoparticles. Variations of the _Trichoderma_ fungus are such examples that have been utilised in applications related to food [74], medicinal and paper industries.