esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Literature Review
### LCA in Space Missions and Research Gap
The ESA LCA handbook addresses this gap by providing guidelines for space-specific LCAs focused on sustainability. However, using the ESA LCA handbook requires access to subsystem-level mass budget data for a specific satellite, which are often unavailable due to commercial confidentiality. Therefore, while the ESA handbook is beneficial for satellite manufacturers with access to this data, a gap remains for generic users who lack such information. A Method for Quantifying the Environmental Impacts of Manufacturing Low Earth Orbit (LEO) Satellite Constellations  
Figure 4 below illustrates a five-stage method for performing a life cycle assessment (LCA). The process begins with the acquisition of orbital data for the satellite from Federal Communications Commission (FCC) filings. The rationale for selecting FCC filings as the data source is discussed in detail in Section 2.2.2. Following this, the mass budget is partitioned using the European Space Agency (ESA) Life Cycle Inventory (LCI) database, as elaborated in Section 2.2.1. Parametric estimation is then employed to predict the propellant mass, with the justification for this method provided in Section 2.2.2. Afterward, the final mass budget is estimated, and midpoint indicators are calculated according to the system boundaries outlined in Section 2.3. In this approach, SimaPro 9.5 software is used to compute the midpoint indicators after the final mass budget has been established.
## 4 Case Study  
The long-distance water transfer project is located in southern China's Guangzhou Province. The total length of the water transmission line is 113.1 km, a deep-buried TBM tunnel is the main water transmission structure, accounting for 75% of the total water transmission line, and the tunnel is buried at a depth of 30 to 60 m. The location of the project and the layout of the TBM tunnel are shown in Figure 6a,b. The TBM tunnel of the project starts from the reservoir and ends at the water pond, with a total length of 28.3 km. Ten working shafts are arranged along the tunnel, numbered 1\(\#\)-10\(\#\), and the tunnel is correspondingly divided into ten parts by working shafts, numbered \(\textcircled{D}-\textcircled{\char 32}\).  
The three-dimensional model of the tunnel and working shafts and maintenance ventilation process are shown in Figure 6c. During the operation of the water transmission tunnel, the water stop valves in the working shaft are closed and in a state of pressure, and the inside of the tunnel is full of water. During maintenance, the water stop valves are opened to discharge the water flow inside the pipeline, and the pipe is kept dry. Ventilation equipment such as axial flow fans and air ducts are arranged in the working shaft for maintenance ventilation, supplying fresh air from the outside into the tunnel (as shown by the solid green line with an arrow in Figure 6c) to ensure maintenance work safety and the working comfort of maintenance personnel.  
Figure 5: Establishment process of the hybrid prediction model.
# Discrimination of Earthquake-Induced Building Destruction from Space Using a Pretrained CNN Model
## 4 Experimental Results and Discussion
### Effect of Fine-Tuning Different Layers for the Detecting Collapsed Buildings  
The comparison between incremental fine-tuning was applied to investigate the impact of the depth of fine-tuning on the performance of pretrained VGGNet model with dataset enlarged by 16 times (16\(\times\)). The results were shown in Figure 5. It is obvious that the performance of pretrained VGGNet model increases with the increased of fine-tuned convolutional blocks. The worst result was obtained by only fine-tuning the fully-connected layer. One of the reasons is that the dataset for pre-training and the dataset for fine-tuning are significantly distant. The pretrained VGGNet model with fine-tuning each layer performed best with 88.83% of OA, 75.33% of Kappa, and 11.18% of total disagreement values. The results also indicate that, the more layers that are used for backpropagation, the better the learning process could be obtained.  
Figure 4: Performance of dataset augmentation. (**A**) Loss curves. (**B**) Time consumption for training the model.
## 2 Materials and Methods
### Multiplex PCR-Based Next Generation Sequencing  
DNA fragments were amplified and barcoded in two steps using KAPA2G Fast Multiplex Mix (Merck, Darmstadt, Germany). In the first step, fragments coding for mutation hotspot regions were amplified with gene specific primers (Table 2) containing primer binding sites for universal Multiplicom MID Dx primers (Agilent technologies, Santa Clara, CA, USA) in a multiplex PCR. 1 \(\upmu\)L WGA product and in total 0.5 \(\upmu\)M gene specific primers were applied (concentrations of all primers are shown in Table S1). The concentration of each primer is shown in Table S1. In the second step, Multiplicom MID Dx primers were used to attach unique barcodes for sequencing to the PCR products. 0.5 \(\upmu\)L of Multiplicom MID Dx primers and 0.5 \(\upmu\)L of first round PCR product were used. The applied PCR protocols are listed in Table S2.  
Final PCR products were purified using Agencourt AMPure XP beads (Beckmann Coulter, Brea, CA, USA) and quantified with the Qubit 2.0 Fluorometer (Thermo Fisher Scientific, Waltham, MA, USA) using the Broad Range Quant-iT dsDNA Assay Kit (Thermo Fisher Scientific).  
DNA libraries of 0.0143 pM per sample were sequenced on a MiSeq system (Illumina, San Diego, CA, USA) with 151 bp paired end reads.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Characteristics** & **Total** & **in \%** \\ \hline Patients & 13 & 100 \\  & Age at blood draw & \\ Mean & 61.2 & \\ Median & 61 & \\ Range & 46–78 & \\ Tumor size & & \\ pT1 & 4 & 30.8 \\ pT2–4 & 8 & 61.5 \\ na & 1 & 7.7 \\  & Nodal status at time of diagnosis & \\
0 & 4 & 30.8 \\
1–3 & 7 & 53.8 \\ na & 2 & 15.4 \\  & Metastasis status at time of diagnosis & \\
0 & 10 & 77.0 \\
1 & 2 & 15.4 \\ na & 1 & 7.7 \\ Histology & & \\ NST & 8 & 61.5 \\ Invasive-lobular & 5 & 38.5 \\ Grading & & \\
1–2 & 9 & 69.2 \\
3 & 2 & 15.4 \\ na & 2 & 23.1 \\ Subtype & & \\ Luminal & 11 & 84.6 \\ Triple negative & 2 & 15.4 \\  & CTC count (per 7.5 mL blood) & \\ Median & 94 & \\ Range & 6–15,340 & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Patient characteristics.  
FASTQ files were analyzed with the Galaxy web platform, using the public server at usegalaxy.org (last access for the Galaxy web platform: March 2020) [18]. Reads were aligned to the human reference genome (hg38) with BWA-MEM (Galaxy Version 0.7.17.1) [19]. Standard parameters were applied. Aligned sequences were screened for mutations using the IG Viewer (Version 2.3.25) [20]. Identified mutations were covered by \(>\)100\(\times\) and had a variant allele frequency (VAF) \(>\)12.5%.  
As controls, DNA from the reference cell lines mentioned below as well as a negative control was included in every run.
## 1 Introduction
For this reason, parametric relationships are used to correct flow stress for adiabatic heating and frictional effects [10]. Frictional effects during hot working are carefully controlled using combinations of lubricants during experimental setup [11], while adiabatic heating may be controlled by selecting low strain rates that allow enough time for heat dissipation. However, this is not always practicable under industrial conditions like forging and extrusion, which could require very high strain rates. Nevertheless, the experimental setup plays a significant role in obtaining accurate flow stress data. Uniaxial compression testing is one of the most utilized experimental testing methods for determining flow stress. The test is conducted under different conditions of strain, strain rates, and deformation temperatures. The common testing facilities include screw-driven machines, servo-hydraulic machines, and split Hopkinson pressure bars (SHPBs). The conventional screw-driven and servo-hydraulic machines like the Instron universal testing machine and Gleeble 1500 thermomechanical simulator are suitable for conducting tests up to a strain rate of 5/s, whereas specifically designed testing machines with higher servo-hydraulic and fast data acquisition systems like the Gleeble 3500 and Gleeble 3800 achieve strain rates of about 100/s [12]. However, these strain rates are much lower than those of typical industrial processes like forging. The SHPB can achieve higher strain rates between \(10^{2}\) and \(10^{4}\)/s, which covers a wide range of industrial processing conditions but is limited to a max strain of 0.3 [12; 13]. Given the varying capacity of the different testing machines, it is quite challenging to test a wider range of parameters using a single machine, which affects the reliability and validity of the flow stress data. The existing machines are based on slightly different principles and data acquisition systems, which may influence the results. Testing metallic alloys for a broader range of strain rates involves using two different machines [14; 15]. Even in situations where a single machine is used, as in the case of Gleeble 3500 or 3800 thermomechanical simulators, testing at strain rates above 10/s not only results in frequent wear and tear of the anvils and wastes time, test samples, and consumables, but it also gives jerky flow stress patterns that are not easily reproducible, and their origin can be misleading. These jerky flow stress patterns could result from metallurgical phenomena or vibrating load cells in the machine. These challenges associated with high strain rate uniaxial compression testing led the authors to explore an alternative method to generate flow stress data. This method involves using constitutive parameters obtained from low strain rates in uniaxial compression testing as an input code in DEFORM-3D. Thereafter, DEFORM-3D numerical simulation was used to generate flow stress data under high strain rate uniaxial compression conditions. The method's reliability was then evaluated by comparing the generated flow stress data with experimental data.
## 2 Distributed PV System and Proposed MPPT Algorithm
### Distributed PV System
#### 2.1.2 Description of DC-DC Boost Converter  
This article uses a boost circuit, as shown in Figure 4.  
When the circuit operates stably, the energy stored by the inductor, L in one cycle T, is equal to the energy released by it. This relationship can be represented by the following equation:  
\[Ei_{1}t_{on}=(U_{\text{out}}-E)i_{1}t_{off} \tag{6}\]  
The output voltage \(U_{\text{out}}\) is:  
\[U_{\text{out}}=\frac{t_{on}+t_{off}}{t_{off}}E=\frac{T}{t_{off}}E \tag{7}\]  
Figure 4: Boost circuit diagram.  
Figure 3: The characteristic curve of PV module. (**a**) Current–voltage characteristic curve; (**b**) power–voltage characteristic curve.  
From Equation (7), it can be determined that:  
\[\frac{U_{\text{out}}}{E}=\frac{T}{t_{off}}=\frac{T}{T-t_{on}}=\frac{1}{1-D} \tag{8}\]  
where, \(D\) is the duty ratio, \(D=t_{on}/T\).  
According to Equations (7) and (8), the equivalent internal resistance of the boost circuit is:  
\[R_{s}=\frac{E}{i_{1}}=\frac{U_{\text{out}}(1-D)}{\frac{t_{0}}{1-D}}=R(1-D)^{2} \tag{9}\]  
The output capacitance equivalent formula is:  
\[C_{\text{out}}=\frac{D}{R_{o}\gamma_{Vup}f} \tag{10}\]  
\[C_{in}=\frac{D}{8L\gamma_{Vup}f^{2}} \tag{11}\]  
where \(R_{o}\) is output resistance, \(\gamma_{Vup}\) is the output voltage ripple factor, and \(f\) is the switching frequency.  
A DC/DC boost converter was applied to the MPPT system, and the corresponding data are presented in Table 2.
## 2 Feature Extraction
### GLCM and Haralick Features
#### 2.2.2 Haralick Features  
Thirteen features can be extracted from the GLCM for an image. These features are presented as follows:  
* Energy feature: This is also called angular second moment (ASM) or uniformity. It describes the uniformity of an image. When the gray levels of pixels are similar, the energy value will be large. \[f_{1}=\sum_{i=0}^{l-1}\sum_{j=0}^{l-1}P_{d,\theta}{}^{2}(i,j).\] (4)
* Entropy feature: This concept comes from thermodynamics, which is a field of physics concerned with heat, temperature, and their relationship with energy and work. In our case, it could be considered a chaotic or disordered quantity. \[f_{2}=-\sum_{i=0}^{l-1}\sum_{j=0}^{l-1}P_{d,\theta}(i,j)\cdot\log P_{d,\theta} (i,j).\] (5)
* Contrast feature: This measures the intensity variations between the pixels with a fixed direction and distance \((d,\theta)\). With the same gray level, the contrast value will be equal to 0. If \(|i-j|=1\), there is little contrast so the weight is just 1. If \(|i-j|=2\), the contrast of the gray level is higher; therefore, the weight is larger at 4. This means that the weight increases exponentially. \[f_{3}=\sum_{i=0}^{l-1}\sum_{j=0}^{l-1}\lvert i-j\rvert^{2}\cdot P_{d,\theta}( i,j).\] (6)
* Correlation feature: This feature describes the linear dependency of the gray levels in the co-occurrence matrix. It shows how a center pixel relates to others. \[f_{4}=\sum_{i=0}^{l-1}\sum_{j=0}^{l-1}P_{d,\theta}(i,j)\frac{(i-\mu_{x})(j-\mu _{y})}{\sigma_{x}\sigma_{y}}.\] (7)  
Figure 7: Calculation of gray-level co-occurrence matrix. where \(\mu_{x},\mu_{y}\) and \(\sigma_{x},\sigma_{y}\) are the mean and standard deviations, which are calculated as \[\sigma_{x} =\sqrt{\sum_{i=0}^{l-1}\sum_{j=0}^{l-1}P_{d,\theta}(i,j)\cdot(i-\mu_{x })^{2}},\] (8) \[\sigma_{y} =\sqrt{\sum_{i=0}^{l-1}\sum_{j=0}^{l-1}P_{d,\theta}(i,j)\cdot(i-\mu_{y })^{2}},\] (9) \[\mu_{x} =\sum_{i=0}^{l-1}\sum_{j=0}^{l-1}i\cdot P_{d,\theta}(i,j),\] (10) \[\mu_{y} =\sum_{i=0}^{l-1}\sum_{j=0}^{l-1}j\cdot P_{d,\theta}(i,j).\] (11) In a symmetrical gray-level co-occurrence matrix, \(\mu_{x}=\mu_{y}\) and \(\sigma_{x}=\sigma_{y}\). * Homogeneity feature: This feature is also known as inverse difference moment (IDM). It describes the local similarity of an image. \[f_{5}=\sum_{i=0}^{l-1}\sum_{j=0}^{l-1}\frac{1}{1+(i-j)^{2}}P_{d,\theta}(i,j).\] (12) The weight of IDM is the inverse of the weight of contrast; therefore, it is lower at locations farther away from the diagonal of the GLCM. This means that positions nearer to the GLCM diagonal will have larger weights.
## 2 Materials and Methods
### Materials and Samples Preparation
Figure 5: Adding short reinforcing fibers to the geopolymer mass. Figure 6: Reinforcement of geopolymers with long fibers: (**a**) method of placement; (**b**) application of the remainder of the geopolymer mixture to the reinforcement. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Sample** & \multicolumn{5}{c}{**Ingredients**} \\ \hline Fly Ash & Metakaolin & Sand & Alkaline Solution & Mix Proportion (L/S) & Number of Layers \\ \hline BMF.1.10 & + & - & + & 10 M & 0.4:1 & 1 \\ \hline BMF.2.10 & + & - & + & 10 M & 0.4:1 & 2 \\ \hline BMF.1.5 & + & - & + & 5 M & 0.4:1 & 1 \\ \hline BMF.2.5 & + & - & + & 5 M & 0.4:1 & 2 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Composition of prepared geopolymer composites reinforced with basalt grid. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Sample** & \multicolumn{5}{c}{**Ingredients**} \\ \hline Fly Ash & Sand & Cement & Alkaline Solution & Mix Proportion (L/S) & Class Fiber Type E & Addition of Fibers (wt \%) \\ \hline F5 & + & + & – & 5 M & 0.4:1 & – & – \\ \hline GF.1 & + & + & – & 5 M & 0.4:1 & Long & 1 \\ \hline GF.2 & + & + & – & 5 M & 0.4:1 & Long & 2 \\ \hline GF.1.4.5 & + & + & – & 5 M & 0.4:1 & 4.5 mm & 1 \\ \hline GF.2.4.5 & + & + & – & 5 M & 0.4:1 & 4.5 mm & 2 \\ \hline GF.1.12 & + & + & – & 5 M & 0.4:1 & 12 mm & 1 \\ \hline H5 & + & + & + & 5 M & 0.4:1 & – & – \\ \hline CGF.1 & + & + & + & 5 M & 0.4:1 & Long & 1 \\ \hline CGF.2 & + & + & + & 5 M & 0.4:1 & Long & 2 \\ \hline CGF.1.4.5 & + & + & + & 5 M & 0.4:1 & 4.5 mm & 1 \\ \hline CGF.2.4.5 & + & & + & 5 M & 0.4:1 & 4.5 mm & 2 \\ \hline CGF.1.12 & + & + & + & 5 M & 0.4:1 & 12 mm & 1 \\ \hline CGF.2.12 & + & + & + & 5 M & 0.4:1 & 12 mm & 2 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Composition of prepared geopolymer composites reinforced with glass fiber type E. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Sample** & \multicolumn{5}{c}{**Ingredients**} \\ \hline Fly Ash & Sand & Cement & Alkaline Solution & Mix Proportion (L/S) & Carbon Fiber & Addition of Fibers (wt \%) \\ \hline F10 & + & + & – & 10 M & 0.4:1 & – & – \\ \hline CF.0.5 & + & + & – & 10 M & 0.4:1 & Long & 0.5 \\ \hline CF.1 & + & + & – & 10 M & 0.4:1 & Long & 1 \\ \hline CF.0.5.5 & + & + & – & 10 M & 0.4:1 & 5 mm & 0.5 \\ \hline CF.1.5 & + & + & – & 10 M & 0.4:1 & 5 mm & 1 \\ \hline H10 & + & + & + & 10 M & 0.4:1 & – & – \\ \hline CCF.0.5 & + & + & + & 10 M & 0.4:1 & Long & 0.5 \\ \hline CCF.1 & + & + & + & 10 M & 0.4:1 & Long & 1 \\ \hline CCF.0.5.5 & + & + & + & 10 M & 0.4:1 & 5 mm & 0.5 \\ \hline CCF.1.5 & + & + & + & 10 M & 0.4:1 & 5 mm & 1 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Composition of prepared geopolymer composites reinforced with carbon fiber. For the compressive strength test, 8 specimens were prepared for each type of geopolymer presented in Tables 4-6 (a total of 256 specimens). For flexural testing, a minimum of 4 specimens was prepared for each type of geopolymer material presented in the tables above (a total of approximately 128 specimens).
## 5 Research Findings
### RQ1: What Techniques Are Utilized to Abuse the CPLS as C&C Communication Channels?
#### 5.3.4 Negative Reputation Scores  
[PERSON] et al. [115] propose SocialClymene, which calculates negative reputation scores based on user history and identifies suspicious group activity on online social networks. This approach can detect stego-botnets that utilize steganographic images for C&C communication.
## 3 Background
### Creation of New Transactions and Blocks  
When a transaction on a node in the network is finished, the node tells other nodes in the network about the transaction. A list of temporary transactions, called a transaction or memory pool, is also kept and maintained by nearly every node in the network. When the contents of a node's transaction pool are updated, they are transmitted to its nearby nodes [56]. However, a network's nodes do not need to form a complete network; all nodes do not have to be connected directly. Therefore, transmitting a given node's transaction list to all nodes in the network may require many steps. If a node receives transaction information that contradicts a transaction already stored in the specified node's transaction pool, the additional information is deleted [56].  
A disagreement may occur if two contradictory transactions occur simultaneously at different nodes in the network. Both nodes will make every effort to inform as many other nodes as possible about their transaction, and the other nodes will regard the first valid transaction they receive as the correct one. However, network nodes must agree on a single blockchain state. Therefore, miner nodes must choose which transactions should be included in the next block. Miner nodes, like full nodes, keep track of all incoming transactions in their transaction pool [55].
# The Recent Advancement in Unmanned Aerial Vehicle Tracking Antenna: A Review
## 3 Antenna Overview
### Travelling Antenna
#### 3.5.1 Yagi-Uda Antenna  
The Yagi-Uda antenna, also known as Yagi antenna or Yagi, has guided-course arrangement, with dipole and some extra parasitic components firmly coupled together, known as the reflector and directors. The main components of a Yagi antenna are the reflectors, directors, and dipole. The Yagi antenna has seen wide application in many areas, such as UHF/VHF radars, wind profiler systems, and phased Doppler radars. Its performance strongly depends on reflector, dipole, feeder, and director [53; 54]. Figure 5 demonstrates the structure of the Yagi-Uda antenna [55].  
Figure 4: Biconical antenna.  
Figure 5: Yagi–Uda antenna [55].
# Sub-Milliwatt Transceiver IC for Transcutaneous Communication of an Intracortical Visual Prosthesis
## 5 Results  
In this section, we will show the relevant measurement results for the IC, for both the uplink and downlink subsystems. Furthermore, to show the performance of the subsystem in the context of the overall system requirements, link budgets for the two subsystems are also given.
###### Abstract  
The Ocean Cleanup introduces a Digital Twin (DT) describing the cleanup systems made of netting to extract marine litter from our oceans. It consists of two wings forming a \"U-shape\" and a retention zone. During operation, the system is towed and drag-driven with a span-to-length ratio of \(0.6\leq SR^{*}\leq 0.8\). The twine Reynolds number is \(\Re e_{t}^{*}\in[800:1600]\), making it experience various local drag coefficients. The DT was built with OrcaFlex (OF) aiming at: (i) avoiding over - or under-designing the system; (ii) supporting the scale-up of the system; and (iii) estimating the costs and/or the impact of our offshore operations. Therefore, we present an attempt to build an accurate DT using data from the Great Pacific Garbage Patch (GPGP). We developed a three-cycle validation: (i) initial guess applying [PERSON]'s semi-empirical drag coefficient to define the OF drag coefficients without the influence of the angles of attack \(\theta\) of the wings; (ii) adjustment of the OF drag coefficients using AquaSim (AS) with its twine-by-twine drag correlation for various \(\theta\); (iii) re-adjustment of the OF drag coefficients from two-dimensional CFD simulations using Direct Numerical Simulation (DNS) for a twine-by-twine establishment of a drag correlation on a 1 m plane net, highlighting the shielding effects for \(\theta<24^{\circ}\). Consequently, an initial underestimation of \(-3\%\) in the combined towline tension, for a nominal span (\(SR^{*}=0.6\)), was corrected to a slight overestimation of \(+7\%\) compared to the GPGP data. For a wide span (\(SR^{*}=0.8\)), the deviation remained between \(+1\%\) and \(+15\%\) throughout the validation process. For a narrow span (\(SR^{*}\sim 0.02\)), mostly exhibiting low \(\theta\), the first cycle showed a \(+276\%\) deviation, whereas at the end of the third cycle, it showed a \(+43\%\) deviation.  
digital twin; drag coefficients; net; twine-by-twine; OrcaFlex; AquaSim; CFD simulation; shielding effects; ocean cleanup system +
Footnote †: journal: Marine Science and Engineering
## 5 Interoperability, Standardization and Security
### Cybersecurity Considerations for Integrated Automated Irrigation Systems
#### 5.2.1 Unique Security Risks and Vulnerabilities  
These vulnerabilities are further amplified by the dynamic and distributed nature of agricultural environments, with limited connectivity, reliance on wireless communication, and the remote location of many farms. IoT-based automated irrigation systems are susceptible to various cybersecurity threats, including the following:  
* Unauthorized Access and Data Breaches: remote access capabilities create potential entry points for unauthorized individuals, leading to manipulation of irrigation schedules, disruption of water flow, and unauthorized access to sensitive data [144].
* Data Tampering and Injection: malicious actors may tamper with or inject false data into the system, leading to sub-optimal irrigation practices and potentially harming crops or wasting water [144].
* Malware and Ransomware: the interconnectedness of devices increases the risk of malware and ransomware attacks, disrupting system functionality, compromising data integrity, and potentially crippling agricultural operations [145].
* Supply Chain Vulnerabilities: a compromise at any point in the complex supply chain of IoT-based irrigation systems can introduce vulnerabilities or backdoors exploitable by attackers [146].