esa-sceva/satcom-chunk-collection huggingface dataset:
# Optimization of DLTS Hinges for the Assembly of the Solar Arrays of a Communication CubeSat
## 2 Methodology
### Design of Double-Layer Tape Spring (DLTS) Hinge (Coupling Mechanism)  
#### 2.2.1 Thickness Accommodation Technique  
An origami-based pattern was selected and assumed for the generation of the transform of the solar array from the stowed to the deployment configuration. The flasher was implemented for this case as it initiates maximized stowage capabilities and ratios by using thick accommodation techniques, which permit each section of the analyzed system to rotate initially and fold synchronously from the stowed system.  
The array pattern was not considered a zero-thickness model as the thickness of the panels could not be neglected. Therefore, various thickness accommodation techniques were investigated to prevent the self-intersection between the panels and to attain the desirable flasher foldability and low degree of freedom in the kinematic motion among the coupling origami mechanisms [22]. The flexible membrane technique was selected as an ideal option to accommodate the panels' thickness and consequently to enable folding. The hinges were presumed to be almost zero thickness due to the usage of a thin layer of material. The technique maintained a full ROM (rough order of magnitude), indicating that the thick flasher origami pattern could maintain the parallel position of the panel faces from the stowed to the deployed state. The motion of the technique was examined to assure a low degree of freedom and perseverance of dihedral angles. It was observed that there was an inconsistent motion in the folding due to specified configurations and constraints. Thus, the problem was eliminated by minimizing the crease area of the flexible hinge, which resulted in the decrease in hinge points and degree of freedom and the obtainment of a better facilitation of the pattern without stretching or twisting the solar panels [23].  
Figure 2 showcases the position of flexible membrane hinges which implement the motion of a 90\({}^{\circ}\) valley and mountain fold.
## 3 Results
### Daylight Autonomy (DA)
#### 3.2.3 Influence of Orientation (North/South)  
For north-oriented windows, the \(DA_{300}\) values remain relatively stable across all sizes for bright coatings, except for the values for the 15% WWR with dark finishes, where the \(DA_{300}\) values decrease both near the facade and towards the back of the room, with peaks between 1.00 and 2.00 m from the facade. North-facing rooms display marked differences in \(DA_{500}\) behavior between centered and upper (C/U) window positions. For WWRs ranging from 25% to 45%, the \(DA_{500}\) values for bright coatings in centered configurations start between 70% and 90% in Seville, and 65% and 88% in Barcelona, decreasing linearly towards the room's rear. Upper configurations begin with slightly lower values (79% to 87%), but these decline less steeply, resulting in higher average values at the room's end.  
In south orientations, the average \(DA_{500}\) values for Seville range from 87% (45% WWR, centered) to 63% (25% WWR, upper), while Barcelona's values range from 95% (45% WWR, centered) to 48% (25% WWR, upper). Additionally, a south window lintel effect reduces DA levels within 0.50 m of the window, and this effect is more pronounced for dark coatings (30% reduction) compared to bright coatings (20%).
## 2 Results and Discussion
### Thermal Shift Assay on Selected Fragments vs. Rel\({}_{Seq}\) Protein Constructs
Both constructs retain part of the central 3-helix bundle to ensure proper folding, especially in the case of the less stable SYNTH domain [50]. The four fragments that failed to show binding to the bifunctional protein (**B1**, **B4**, **A2**, and **BTI**) also failed to show dose-dependent effects on the two truncated constructs, while the biphasic curve initially observed for **B2** was determined to be a selective interaction with the HYD domain, with no affinity for the SYNTH domain. With the exception of **B3**, which shows comparable affinities for both catalytic domains, all the fragments binding to \(\mathrm{Rel}_{Seq}\) (1-385) showed a remarkable selectivity for the SYNTH domain, even with a generally increased absolute value for the measured \(K_{\mathrm{d}}\). This is probably due to the lower overall stability of the SYNTH domain compared to the full protein, as previously reported by [PERSON] et al. [50]. Indeed, \(\mathrm{Rel}_{Seq}\) SYNTH (79-385), despite being catalytically functional (data not shown), requires the use of a non-ionic surfactant in the purification steps in order to avoid precipitation. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Entry** & **Compound** & \(\mathbf{K_{d}}\) **RelSeq 1–385** & \(\mathbf{K_{d}}\) **RelSeq SYNTH** & \(\mathbf{K_{d}}\) **RelSeq HYD** \\ \hline
1 & **ATP** & 0.49 \(\pm\) 0.09 (0.39 \(\pm\) 0.04)1  & & \\
2 & **GDP** & 0.26 \(\pm\) 0.06 (0.15 \(\pm\) 0.01)1  & \\
3 & **B1** & _No binding_ & _No binding_ & _No binding_ \\
4 & **B2** & _Biphasic_ & _No binding_ & 1.9 \(\pm\) 0.7 \\
5 & **B3** & 3.4 \(\pm\) 0.3 & 4.3 \(\pm\) 0.4 & 3.3 \(\pm\) 0.9 \\
6 & **B4** & _No binding_ & _No binding_ & _No binding_ \\
7 & **A1** & 1.2 \(\pm\) 0.3 & 10.8 \(\pm\) 2.2 & _No binding_ \\
8 & **A2** & _No binding_ & _No binding_ & _No binding_ \\
9 & **A3** & 1.5 \(\pm\) 0.1 & 5.5 \(\pm\) 0.9 & _No binding_ \\
10 & **A4** & 6.6 \(\pm\) 0.1 & 9.8 \(\pm\) 2.8 & _No binding_ \\
11 & **A5** & 1.1 \(\pm\) 0.2 & 2.2 \(\pm\) 0.4 & _No binding_ \\
12 & **A6** & 4.3 \(\pm\) 1.1 & 6.5 \(\pm\) 1.2 & _No binding_ \\
13 & **A7** & 4.0 \(\pm\) 0.9 & 4.3 \(\pm\) 0.5 & _No binding_ \\
14 & **I1** & 6.5 \(\pm\) 1.1 & 9.6 \(\pm\) 1.5 & _No binding_ \\
15 & **I2** & 2.5 \(\pm\) 0.6 & 5.5 \(\pm\) 0.9 & _No binding_ \\
16 & **I3** & 4.0 \(\pm\) 0.5 & 9.9 \(\pm\) 4.5 & _No binding_ \\
17 & **I4** & 3.2 \(\pm\) 0.7 & \(>\)15 & _No binding_ \\
18 & **BO1** & 2.2 \(\pm\) 0.3 & 2.7 \(\pm\) 0.6 & _No binding_ \\
19 & **BT1** & _No binding_ & _No binding_ & _No binding_ \\
20 & **TP1** & 3.4 \(\pm\) 0.8 & 8.3 \(\pm\) 1.6 & _No binding_ \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Thermal shift assay. \(K_{\mathrm{d}}\) (mM) of protein–ligand complexes engaged by \(\mathrm{Rel}_{Seq}\) constructs with the tested fragments.
## 2 Materials and Experiments  
### Materials  
In this study, OPC and FA were used as binders to fabricate CNT-reinforced cement composites. Table 1 shows the chemical compositions of OPC and FA by X-ray fluorescence (XRF) analysis (ZSX Primus II, Rigaku, Tokyo, Japan). The densities of OPC and FA were 3.14 and 2.30 g/cm\({}^{3}\), respectively. For FA, the refined FA produced at thermal power plant Y in South Korea was used. The contents of CaO and SiO\({}_{2}\) + Al\({}_{2}\)O\({}_{3}\) + Fe\({}_{2}\)O\({}_{3}\) in FA were 3.93% and 84.76%, respectively, which corresponded to class F according to ASTM C 618. SEM images shown in Figure 1 were obtained using an ultra-high-resolution cold field-emission SEM (Hitachi SU-8230, Hitachi, Japan). Figure 1a shows the morphology of the FA observed using SEM. FA was found to be spherical. The CNTs used in this study were MWCNTs with an average diameter of 10 nm and an average length of 1.5 \(\upmu\)m. Figure 1b shows the SEM images of the CNTs used. To use CNTs in cement-based materials, the dispersion problem must be solved. As with powdered cement materials, CNT particles are aggregated during mixing owing to the application of the Van der Waals force. To prevent CNT aggregation, CNTs were prepared in the form of an aqueous solution. To improve dispersibility, an aqueous solution of 5% polycarbonate-based superplasticizer was prepared [32]. The solid content of the CNT added to the aqueous solution was 2.01% by weight. And CNTs in solution were dispersed through sonication for 2 h.  
Figure 2 shows the thermogravimetric (TG) and differential thermogravimetric (DTG) curves of the CNTs. Multiple peaks were observed in the graph, indicating that multiple components such as nanotubes, amorphous carbon, and graphitic nanoparticles were present inside the CNTs [33]. The largest weight change occurred between 350 and 500 \({}^{\circ}\)C, and the residue content was found to be 50% after 500 \({}^{\circ}\)C. During the production of CNTs, the section in which the weight change occurs varies with the production method used and the proportion of raw materials [34; 35].  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Oxide** & **OPC (wt.\%)** & **FA (wt.\%)** \\ \hline SiO\({}_{2}\) & 20.5 & 54.5 \\ Al\({}_{2}\)O\({}_{3}\) & 4.69 & 24.3 \\ Fe\({}_{2}\)O\({}_{3}\) & 3.3 & 5.96 \\ CaO & 62.2 & 3.93 \\ MgO & 3.06 & 1.46 \\ K\({}_{2}\)O & 1.67 & 1.56 \\ Na\({}_{2}\)O & 0.17 & 0.98 \\ TiO\({}_{2}\) & 0.39 & 1.24 \\ P\({}_{2}\)O\({}_{5}\) & 0.17 & 0.66 \\ SO\({}_{3}\) & 1.51 & 2.15 \\ SrO & 0.09 & 0.12 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Chemical compositions of OPC and FA.  
Figure 1: SEM images of raw materials: (**a**) FA; (**b**) CNTs.  
Figure 3 shows the particle size distributions (PSDs) of OPC and FA. The PSDs of the raw materials were measured by laser diffraction analysis (Beckman Coulter LS 230). OPC showed a monomodal distribution, while FA exhibited a bimodal distribution.
## 2 Materials and Methods
### Dataset
GDSD data was obtained by interpolation based on the snow depth data observed by more than 700 meteorological observation stations in China [46]. This interpolation method divides the 200 km range into one unit, calculates the orientational relationship (O), distance (D), and correlation coefficient (C) of all observation stations in each unit, and finally determines the interpolation weight of each grid point based on the relationship between O, D, and C. This interpolation method fully considers the spatial representation of snow depth at each station and its functional relationship with the snow depth at surrounding stations. The gridded snow depth obtained by this interpolation method was also compared with the snow depth data obtained by arithmetic average method and inverse distance weight method. The results show that the difference of snow depth data obtained by the three methods is very small. The GDSD data has a temporal resolution of about 5 days and a spatial resolution of 0.5 \(\times\) 0.5\({}^{\circ}\). This data can be downloaded from the National Cryosphere Desert Data Center ([[http://www.ncdc.ac.cn/](http://www.ncdc.ac.cn/)]([http://www.ncdc.ac.cn/](http://www.ncdc.ac.cn/)) (accessed on 31 May 2021)) and a detailed description of the data can also be found on this website. The error of snow depth simulated by the Noah-MP model was also compared with two sets of snow depth data retrieved based on microwave remote sensing. The first was the long-term series of daily snow depth dataset in China (LSD) released by [PERSON] and [PERSON] [47], and the second was China's daily snow depth and snow water equivalent products (CSS) released by [PERSON] et al. [48]. These two sets of data were both produced using SMMR, SSM/I, and SSMIS satellite remote sensing brightness temperature data with a spatial resolution of 25 km \(\times\) 25 km and a temporal resolution of 1 day. The LSD data can be downloaded from the National Tibetan Plateau Data Center ([[http://data.tpdc.ac.cn/](http://data.tpdc.ac.cn/)]([http://data.tpdc.ac.cn/](http://data.tpdc.ac.cn/)) (accessed on 23 May 2021)) and the CSS data can be downloaded from the National Cryosphere Desert Data Center ([[http://www.ncdc.ac.cn/](http://www.ncdc.ac.cn/)]([http://www.ncdc.ac.cn/](http://www.ncdc.ac.cn/)) (accessed on 31 May 2021)).
# Remediation of Metal/Metalloid-Polluted Soils: A Short Review
## 4 Removal Techniques
### Physical Processes
#### 4.1.3 Thermal Treatment  
Thermal desorption, such as calcination, is the process of heating a medium under controlled temperature to eliminate a volatile substance. The main techniques for heating soil are conductive heating, electrical resistive heating, steam-based heating, and radio-frequency heating [60]. Volatile pollutants are desorbed from soil and are collected using vacuum-negative pressure or a carrier gas. The process can occur at high-temperaturedesorption (320-560 \({}^{\circ}\)C) or low-temperature desorption (90-320 \({}^{\circ}\)C), depending on the pollutant boiling point [11]. For example, this technique is widely used to treat contaminated soils with mercury. This metal boils at 357 \({}^{\circ}\)C, therefore thermal desorption is a good option to remove this metal from soil [68].  
[PERSON] et al. [67] studied thermal stabilization to reduce the mobility and availability of zinc and copper in contaminated soils. The metals are fixed in the treated soil. They found that the residual concentrations in tested soils decreased as the temperature increase to 700 \({}^{\circ}\)C.  
The advantages of using this method are safety, little secondary pollution production, and less energy consumption compared with other processes [68]. However, it requires high capital costs and gas emission control, but it is only really effective high pollutant concentrations and it can damage soil structure.
## 4 Simulation Results  
### Training of TD3  
A VSG-MMC model on the MATLAB/Simulink platform is implemented to serve as a testing system for the purpose of determining whether the suggested DRL-based agent is effective. Figure 10 shows the layer graph of the critic/actor network proposed by the article.  
Both the actor and critic networks have the same size with five layers, which are one input layer, one output layer, and three hidden layers. Each hidden layer is formed from a non-saturated function. It is possible to think of TD3 training as an iterative process of finding the best possible control strategy. The goal of the TD3 training in this research is to create an agent that can provide the best MMC-VSG parameter settings under various damping and inertia conditions on the MATLAB/Simulink platform, the parameters of which are displayed in Table 2.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Simulation Parameter** & **Value** \\ \hline Training Episode & 600 \\ Discount Factor & 0.995 \\ MiniBatch Size & 512 \\ Learning Rate (Actor) & 0.0001 \\ Learning Rate (Critic) & 0.001 \\ Soft Update Discount Factor & 0.01 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Parameters of the TD3-VSG training setting.  
Figure 9: TD3-based control framework diagram for MMC-VSG.  
Figure 11 depicts the average rewards with regard to training iterations using the framework developed in Section 3. In the same picture, a DDPG comparison technique with identical parameters is also displayed. At the training stage, the convergence patterns of the two methods are very similar. The blue/red star represents every 10 episodes of average rewards while training, whereas the line represents the reward \(R\)'s tendency from beginning to finish. During the first 100 training sessions, the TD3 reward amount varies between \(-400\) and \(-600\), while the DDPG reward value varies between \(-400\) and \(-500\), and the matching output changes greatly.  
The TD3-VSG's reward amount grows considerably after 200 episodes, in contrast with the DDPG-VSG, which retains the value of a modest reward until roughly 240 training sessions. The TD3 algorithm employs the approach of double Q-learning, resulting in a quicker convergence speed. As the number of training episodes increases, the reward value eventually converges between \(-30\) and \(0\), indicating that the agent has adopted the best control strategy.  
Table 3 compares the training time for both methods. The training time for the TD3 algorithm was 14.7 h compared to 11.2 h for the DDPG method.  
\begin{table}
\begin{tabular}{c c c} \hline
**Parameter** & **TD3** & **DDPG** \\ \hline training Time & 14.7h & 11.2h \\ \hline \end{tabular}
\end{table}
Table 3: The comparison experiment parameters.  
Figure 10: Layer graph. (**a**) Actor network. (**b**) Critic network.
# Concept Justification of Future 3 DPVS and Novel Approach towards its Conceptual Development
## 2 Laws of System Evolution and Concept Justification for 3 DPVS  
In the previous section the background knowledge and limitations of current 3D scaffold, vibrations and cell culture unity have been discussed. The novel concept 3 DPVS was brought out from a technical vision to address several limitations, to make best use of some common benefits of scaffolding and vibration, as well as to remain within the future scaffolding trend. However, to scientifically bring out a novel concept and prove it as practical and rooted, this reasoning is considered as insufficient. A thorough systematic analysis which can give more evidence is necessary. In this connection, we will further justify and solidify the concept feasibility of 3 DPVS using Laws of System Evolution (LSE), which have been validated for a significant amount of product innovation by scientists, engineers and designers in previous decades. It is interesting that, while using LSE to study and justify 3 DPVS concepts, some design inspiration or possible direction might also be generated. This indeed helps the future conceptual design process.
# A Carrier-Based Gardner Timing Synchronization Algorithm for BPSK Signal in Maritime Communication
## 2 Problem Formulation
### Solutions to Doppler  
To eliminate the Doppler effects, two methods are usually used.  
Method 1: Resampling the passband signal directly. This method requires estimating the Doppler factor. One approach is to calculate the Doppler factor by measuring the change of the length of signal through a synchronization method in the time domain and resample the passband signal. This method assumes a constant Doppler factor throughout the signal time, which may not hold true for time-varying Doppler. As a result, the performance of this method may degrade.  
Method 2: Carrier frequency offset (CFO) estimation and compensation, and bit synchronization. The former eliminates the frequency offset of the signal, allowing for accurate bit synchronization, which eliminates the spreading effect of the signal in the time domain. The Gardner algorithm is a canonical example of bit-synchronization algorithms.  
Figure 1 shows the closed-loop feedback structure of the Gardner algorithm [32]. The loop consists of an interpolator, a timing error detector (TED), a loop filter (LF) and an NCO. The input signal is the baseband signal after down-conversion, and the output is the baseband symbol after decimation.  
Figure 1: The structure of Gardner algorithm.  
Figure 2 shows the principle of the Gardner algorithm for TED on the baseband signal. The figure depicts a baseband signal containing two symbols, where \(y(n-1)\), \(y(n-1/2)\) and \(y(n)\) are the three detection points required to detect timing error. \(y(n-1)\) and \(y(n)\) are the optimal sampling instants for the two adjacent symbols in the baseband, and \(y(n-1/2)\) is the midpoint of these two adjacent symbols. Therefore, the Gardner algorithm performs two resampling points per symbol on average.  
Let \(x(n-1)\) and \(x(n)\) be the symbols adjudicated based on \(y(n-1)\) and \(y(n)\). The error detection equation of Gardner algorithm is  
\[err_{n}=y(n-\frac{1}{2})*[x(n-1)-x(n)] \tag{5}\]  
where \(err_{n}\) contains the information that the signal is extended or compressed and therefore Doppler can be detected.  
As seen in Figure 2, the shape of the baseband signal bears some similarity to the cosine signal. This similarity serves for timing error detection in CB-Gardner, which can eliminate the impact of Doppler directly on the passband while maintaining the real-time iterative property of the Gardner algorithm.
# Transitioning to Society 5.0 in Africa: Tools to Support ICT Infrastructure Sharing
## 1 Introduction
### Contribution and Structure  
Although several scholars have made contributions to improving the Nigerian telecommunication infrastructure sharing, there is to date no proposed rigorous method or design guidelines on how to achieve success. The issue of which sites, BTSs or masts to select as the host(s) for colocation still poses significant hindrances and challenges for the telecommunication industry. The quality and amount of information are considered critical to support the basis for suitable decision-making. Good decision-making requires informative formulation, clear evaluation and quick reformulation of alternatives. Current research in the area is predominately top-down in nature and does not convince the telecoms industry that the benefits of mast colocation outweigh what they see as the potential negative impacts of mast colocation on their bottom line/profits. This study employed a bottom-up approach--using real world cases of mast locations in a particular area--in order to illustrate the economic and environmental benefits of mast colocation to the telecoms industry directly. In order to achieve this goal, mathematical models were developed and the colocation problem rigorously defined as an optimization problem, allowing development of an associated ICT tool to support decision-making. The remaining parts of this paper are organized as follows: Section 2 presents background/related work, specifically outlining the barriers and enablers of BTS infrastructure sharing in Nigeria, and existing work on tools for infrastructure sharing. Section 3 outlines the required mathematical models and optimization framework for infrastructure sharing, suitable to be embedded into a prototype ICT tool. Section 4 presents a real-world case study based in Nigeria, while Section 5 concludes the work.  
Figure 4: Fallen telecommunication mast and damage to buildings.  
Figure 3: Fallen telecommunication mast and damage to cars.
# A Novel Deep Learning Method for Recognition and Classification of Brain Tumors from MRI Images
## 3 Proposed Methodology
### Loss Function  
During training, the Mask-RCNN model [37] employs a multi-task loss L on each sampled RoI, defined as:  
\[L(Mask\ RCNN)=L_{class}+L_{bbox}+L_{mask} \tag{1}\]  
where \(L_{class}\), \(L_{bbox}\), and \(L_{mask}\) represent the class label prediction loss, bounding box refinement loss, and segmentation mask prediction loss, respectively.  
\(L_{class}\) is defined as follows:  
\[L_{class}=-\log P_{u} \tag{2}\]  
where \(P\) is a (k + 1) dimensional vector corresponding to the likelihood of a pixel going to the k class or background. For each RoI, \(P=P_{0},...,P_{k}\) and \(P_{u}\) is the probability related to class \(u\).  
\(L_{bbox}\) is defined as follows:  
\[L_{bbox}(v_{i},\ v_{i}{}^{*})=\sum_{\text{i}\in\{\text{x, y,w,h\}\}6}smooth_{L1}(v_{i}-v_{i}^{*}) \tag{3}\]  
where  
\[smooth_{L1}(x)=\left\{\begin{array}{ll}0.5x^{2}&\text{if }|\text{x}|<1\\ |\text{x}|-0.5&\text{otherwise,}\end{array}\right. \tag{4}\]  
Vector vi represents four parameters coordinate of the predicted bounding box, and \(v_{i}{}^{*}\) is the coordinate of the GT relating to the positive anchor. The smooth-\(L_{1}\) function is a robust \(L_{1}\) loss that is less sensitive to outliers over \(L_{2}\) loss. When regression targets are infinite, training with \(L_{2}\) loss can necessitate the careful modification of learning levels to avoid exploding gradients. For the training of the mask network, the average binary cross-entropy loss is employed that is given as follows:  
\[L_{mask}=-\frac{1}{n^{2}}\sum_{1\leq i,\ j\leq n}\left[x_{ij}\cdot logP_{ij}^ {k}+\left(1-x_{ij}\right)\cdot log\left(1-P_{ij}^{k}\right)\right] \tag{5}\]  
where \(x_{ij}\) represents the value of a pixel \((i,j)\) in a GT mask of size \(n\times n\) and \(P_{ij}^{k}\) is the predicted value of the same pixel in the mask learned for class \(k\).