esa-sceva/satcom-chunk-collection huggingface dataset:
# Spare Parts Made by Additive Manufacturing to Improve Preventive Maintenance
## 2 Methodology
### Availability of Materials and Processes  
The materials that can be applied in AM can vary depending on the technology used and the functional needs of the spare parts that have been selected. As shown in Figure 3, the AM process is based on two levels. The first level is the digital level which consists of obtaining a CAD file. This format must be translated into a format understandable by the slicer. The stereolithography (.stl) and 3 MF (.3 mf) formats are widely used as input to the slicer processor. The output of the slicer processor is the G-Code, which is sent by the machine controller to the selected technology. The second level is the physical level where the digital model is manufactured.  
Figure 2: Proposed systematic PM schedule using AM technologies.  
In the AM process, the piece can be manufactured in different positions,, most of the time the orientation is taken into consideration, particularly in extrusion material technology to avoid stresses in the built part. In powder bed fusion technology (PBF), this problem is avoided because the powder already does the job of the supports, counter to conventional machining where generally the orientation is limited and depends on the tool which removes the material and on the working trajectory because of its special geometry. The AM process can build any part with very complex geometry, so additive manufacturing is considered a fast-forming technology compared to conventional machining. The disadvantages compared to conventional manufacturing can be cost, functionality and accuracy. The AM has multiple varieties of materials which can be found in filaments (FDM), powders (SLS) and liquids (SLA). AM allows manufacturing with a wide variety of materials, the four known families are polymers, metals, ceramics and woods, but in this study, we did not consider woods and ceramics because they are not used in the industrial field. Metal was not considered either, as we selected parts that did not need to be manufactured in metal.  
There are many AM processes, and they are different from each other, their diversity being due to their characteristics and the raw material used. Figure 4 helps with choosing the right 3D printing material by first searching the adequate 3D printing technology and then looking for the materials available to print with. The figure also shows the process used for the 3D printing technology and there are six known processes: powder bed fusion, metal extrusion, vat polymerisation, material jetting, binder jetting and direct energy deposition [5,6]. In this case study, one material and two different processes for the same material were chosen:  
* PA12 (Polyamide 12) in form of powder. The spare parts were processed by HP Multi-Jet technology;
* PA12 (Polyamide 12). The spare parts were processed by powder bed fusion, by an SLS machine [33,34].  
Figure 3: The additive manufacturing process (AM) according to ISO/ASTM 52900.
# Survey of Localization for Internet of Things Nodes: Approaches, Challenges and Open Issues
## 6 Evaluation Metrics for Localization Techniques
### Coverage  
Coverage represents the proportion of the nodes which can be localized over the nodes positioned in the network, irrespective of the location accuracy. Node density, the ability of nodes to connect, and anchor nodes' position are the parameters influencing coverage in the network. To evaluate the localization algorithm for coverage, it must be tested for the distinct scenarios, viz. a diverse number of anchor nodes, different network dimensions, and distinct communication range. For the lower node densities, coverage may be less for localization algorithms with arbitrary topology due to connectivity issues.  
The performance comparison of evaluation metrics depicting their characteristics are as given in Table 4.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Measurement** & **Type of** & **Algorithm** & **Accuracy** & **Cost** & \begin{tabular}{c} **Anchor to** \\ **Node Ratio** \\ \end{tabular} & \begin{tabular}{c} **Communication** \\ **Expenses** \\ \end{tabular} & \begin{tabular}{c} **Realization** \\ **Cost** \\ \end{tabular} & \
\begin{tabular}{c} **Convergence** \\ **Time** \\ \end{tabular} &
\begin{tabular}{c} **Energy Consumption** \\ **sumption** \\ \end{tabular} & **Coverage** \\ \hline \multirow{4}{*}{Range-based} & AoA & Medium & High & High & High & High & High & High & High & Low \\  & ToA & High & High & High & High & High & Medium & High & Low \\  & TDoA & High & High & Medium & High & High & Medium & High & Medium \\  & RSSI & High & Low & Low & Low & Medium & Medium & High & High \\ \hline \multirow{4}{*}{Range-Free} & Hop Count & Low & Low & Low & Low & Low & Low & High & High \\  & Centroid & Low & Low & Low & Low & Low & Medium & Low & High \\  & AHT & Medium & Low & Medium & High & High & Medium & High & Medium \\  & Analytical & Low & Low & Medium & Medium & Medium & Medium & High & Medium \\  & Geometry & Mobile Anchor & & & & & & & & \\  & Node & High & High & High & High & High & Low & Low & Low \\ \hline \hline \end{tabular}
\end{table}
Table 4: Performance Comparison of Evaluation Metrics.
# A Soft, Biocompatible Magnetic Field Enabled Wireless Surgical Lighting Patty for Neurosurgery  
[PERSON]  
1 Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX 77843, USA; [EMAIL_ADDRESS] (W.S.K.); [EMAIL_ADDRESS] (S.H.)1 Department of Neurosurgery, Barrow Neurological Institute, St. Joseph's Hospital and Medical Center, Phoenix, AZ 85013, USA; [EMAIL_ADDRESS] (C.M.); [PERSON]DDRESS] (P.N.); [EMAIL_ADDRESS] (M.T.L.)1 Center for Remote Health Sciences and Technologies, Texas A&M University, College Station, TX 77843,  
complicates the given procedure. There is an unmet need to develop a surgical tool that can provide enough light at the depth of a surgical field for a considerable amount of time during surgery.  
Neurosurgical patties (referred to as cottonoids or neurosurgical sponges) are widely used in neurosurgery, and they can manage biofluids and protect neural tissues from damaged ones in surgical fields [12]. Here, we present a versatile illuminated surgical patty that can compensate for deep, dark dissection surgical fields and simultaneously manage fluids in the field. Electronic components, including light emitting diodes, are mounted on a flexible polyimide substrate, and the device can make intimate contact with a surgical field [13; 14; 15; 16; 17]. Key aspects are multimodalities, delivery of light at the depth of a surgical field, and management of biofluids at the field. The proposed surgical lighting patty can provide miniaturized diffuse lighting to a surgical corridor for an improved surgical field of vision. Moreover, the surgical lighting patty can be used as a barrier for the management of fluids in a surgical cavity or corridor while providing enough light to the surgical field. For example, in neurosurgery, surgical patties are often used to line surgical cavities to protect normal, healthy nervous system tissues in a non-absorptive fashion during complicated surgical dissections.  
The surgical lighting patty is comprised of three layers. Two outer layers, surgical sponges, sandwich together a central lighting layer. The central lighting layer includes an electronic circuit with light-emitting diodes (LEDs) and a reed switch embedded in it. Semiconductor components and a reed switch are connected by copper interconnections on a thin polyimide substrate, and a soft polymer, such as Polydimethylsiloxane (PDMS), encapsulates the central lighting layer. The light-emitting element in the layer defines a light source oriented along a substrate, and a reed switch engages in power control so that it activates or deactivates the power supply or a set of batteries in response to a magnet to emit the light from the light source. This results in a biocompatible, wireless operation. Through this study, we advance the flexible electronics technology for successful integration of miniaturized, low power LEDs into a surgical patty, which yields a novel, versatile wireless platform for use in neurosurgery.
# Soft Decision Decoding with Cyclic Information Set and the Decoder Architecture for Cyclic Codes
## 4 The Architecture and Complexity Analysis of the Proposed Decoder
### Architecture of the Correlation Discrepancy Calculation Unit
The last PE serially outputs \(\xi_{n-k}\), which is all candidate codewords and their correlation discrepancy with \(\mathbf{r}^{(i)}\).
## 2 Theoretical Background
### FMEA  
The objective of FMEA is to identify all potential failure modes of a process or product. A failure mode occurs when the product does not perform as it should or when it malfunctions in some way. Even the simplest products have many opportunities for failure modes. In traditional FMEA, the risk of a failure mode and its effects are measured through three factors that are severity, occurrence, and detection [34]. Severity refers to the effect of the failure mode when it takes place; occurrence refers to the probability of the failure mode occurring; detection refers to the probability of the failure mode detecting. The RPN of a failure mode can be obtained by using Equation (1).  
\[RPN=O\times S\times D, \tag{1}\]where \(O\) signifies the occurrence, \(S\) denotes the severity, and \(D\) represents the detection. Through the values of \(RPN\), the failure modes can be prioritized.  
When applying traditional FMEA in real-world cases, there exist some important weaknesses. One significant weakness is that it is difficult to evaluate risk factors of failure modes in terms of exact numbers from 1 to 10. Furthermore, traditional FMEA does not consider the relationship of the risk factors. Moreover, traditional FMEA lacks a mechanism for systematically identifying all failure modes. In addition, different combinations of risk factor scores can produce the same RPN value, disregarding the whole different risk levels of the failure modes. Therefore, many studies have been devoted to mitigating these shortcomings of traditional FMEA [35; 36; 37]. In this study, we integrated fuzzy set theory, HTA, and SHERPA with traditional FMEA to overcome these shortcomings.
## 4 SNR Characterization
### Electrical Bandwidth of the RSOA  
We start our investigation by measuring the small signal modulation frequency response of the RSOA itself. We chose the externally seeded configuration to exclude the impact of dispersion and the external cavity. The frequency response for 50-120 mA bias current and at an input power of \(-\)3 dBm are plotted in Figure 5, which shows that the modulation response improves with higher bias current. At 100 mA, the RSOA exhibits \(-\)3 and \(-\)6 dB bandwidths of 2.4 and 4.55 GHz, respectively.
# Bandwidth-Enhanced Circularly Polarized Crescent-Shaped Slot Antenna via Circular-Patch Loading
###### Abstract  
This paper presents the crescent-shaped slot antenna loading of a circular patch that greatly enhances a 3 dB axial-ratio (AR) bandwidth. A bent feeding line was introduced and attached to a 50-\(\Omega\) microstrip feed line to achieve circular polarization operation. By loading the circular patch into a crescent-shaped slot at a proper position, the 3 dB AR bandwidth is considerably broadened. An antenna prototype with overall dimensions of 50.8 \(\times\) 50.8 \(\times\) 0.5 mm\({}^{3}\) is experimentally demonstrated. Measurement results of the fabricated antenna showed a wide \(-\)10 dB reflection bandwidth of 92.33% (1.79-4.86 GHz) and a broad 3 dB AR bandwidth of 84.40% (1.89-4.65 GHz). The measured peak gain of the antenna was approximately 4.09 dBic at 2.9 GHz. Good agreement was also achieved between measurement and simulation results.  
broad bandwidth; circular polarization; crescent-shaped slot antenna; microstrip feed line; patch loading +
Footnote †: journal: Applied
## 3 Methodology
### Training Scheme  
In the previous subsection, we described how to make the reconstructed image from the Bernoulli-sampled projection image and the theoretical properties of the proposed method. In this subsection, we describe the scheme for training a denoising neural network using this method. To train a denoising neural network only on the images from LDCT scans, we generated a set of binary vectors using binary Bernoulli sampling. Depending on the probability \(k\), the distribution of the randomly generated binary Bernoulli vector \(b\) has a value of 0 or 1.  
In the training scheme, the denoising neural network is trained to restore the residual part of the image reconstructed without Bernoulli sampling on the projection image from the image reconstructed using Bernoulli sampling on the projection image. The residual part of the reconstructed image \(\overline{f}\) can be expressed as follows:  
\[\overline{f}(x,y)=\frac{2\pi}{N_{\theta}}\sum_{i=0}^{N_{\theta}-1}((\hat{q}*g_ {\infty})\odot(1-b))[i,ycos\theta-xsin\theta]. \tag{17}\]  
Using these methods to obtain the necessary image pairs \(\left\{\hat{f}_{m},\overline{f}_{m}\right\}_{m=0}^{M}\) for learning, the denoising neural network is learned to minimize the following loss function:  
\[L=\sum_{m=0}^{M-1}\sum_{s=0}^{S-1}\parallel F_{\theta}\big{(}f^{\prime}_{m,s} \big{)}-\overline{f}_{m,s}\parallel\frac{2}{2}, \tag{18}\]  
where \(F_{\theta}\) is a denoising neural network parameterized by \(\theta\) and \(s\) is the slice index of the reconstructed image. The denoising neural network trained by the proposed method can avoid convergence to an identity mapping. In addition, due to the randomly generated binary Bernoulli vector, the overfitting problem can be avoided even if the denoising neural network is trained on a small number of LDCT scans.
# Evaluation of Satellite Imagery for Monitoring Pacific Walruses at a Large Coastal Haulout
## 2 Materials and Methods
### Analysis  
We quantified variability between observers for each imagery type in terms of (1) consensus in recognizing which satellite images contained detectable walnves, (2) agreement on the digitized areal extent, and (3) spatial agreement on the digitized peripheries. Based on the area digitized by each observer for all images with detected walnves, we calculated the root mean square error (RMSE) among observer pairs, then divided the RMSE by the observer average to attain a CV (coefficient of variation) RMSE. We averaged CV-RMSE by imagery type to evaluate how image resolution influenced disagreement among observers. We also quantified spatial agreement among the observers for each image by ratioing the area of 3-way geographic intersection among the digitized aggregations to the area of the corresponding 3-way geographic union. We averaged this ratio (the area of the geographic union against the area of the geographic intersection) for each imagery type, plotted it, and derived the linear regression slope forced through zero (and r-square) to compare, among imagery types, the degree of 3-way spatial observer agreement. The linear regression relies on the interpretation of each image independently by the three observers. Higher slopes indicated greater 3-way agreement, and higher r-squares indicated a greater consistency over the range of different aggregation areas.  
We compared satellite image and UAS imagery interpretations collected within 24 h of one another and assumed that the UAS surveys provided the most accurate delineation of an aggregation's periphery. When satellite imagery was collected within 24 h of a UAS survey, we compared the area occupied by walnvs aggregations identified in the satellite images with that observed in UAS imagery. For each pair of satellite and UAS images, we calculated the percent deviation in the area occupied by walnves as determined in the satellite imagery (the 3-observer average) compared to that determined from the UAS image ((Satellite-UAS)/UAS*100).
# A New LoRa-like Transceiver Suited for LEO Satellite Communications +
## 3 Proposed Transceiver
### Differential Chirp Spread Spectrum  
#### 3.1.1 Principle  
Based on the previous analysis, we propose an enhancement of the LoRa symbol generation process and we then show how it makes the detection of the received symbols robust to synchronization errors. Our idea, inspired by the principle of differential digital modulation techniques, consists of transmitting the value of the symbols, not directly, but rather their cumulative sum, so that, at the receiver, they can be retrieved by differentiation. In the following, we call this method of digital modulation the differential chirp spread spectrum (DCSS). Based on this, the DCSS transmitter consists of sequentially generating chirps based on the symbols \(D_{p}\) obtained as follows:  
\[D_{p}=(S_{p}+D_{p-1})\mod M\text{ for }p\geq 0, \tag{19}\]where \(S_{p}\) has been defined as the LoRa symbol transmitted at time \(pT\). Without \"loss of\", generally, we suggest setting \(D_{-1}=0\) to initiate the integration processing. At the receiver side, the estimation of \(\left\{\hat{S}_{p}\right\}_{p\geq 0}\) is obtained as:  
\[\hat{S}_{p}=(\hat{D}_{p}-\hat{D}_{p-1})\mod M \tag{20}\]  
where the estimation of the DCSS symbols \(\left\{\hat{D}_{p}\right\}\) are based on the periodogram method presented in Section 2.2. Thus, as expressed in (14) and (15), the differential process performed by (20), limits the impact of \((-L+C)\) on the symbol estimations.  
However, it is necessary to estimate and compensate the fractional CFO \(\
u\) and the fractional STO \(\lambda\) to prevent performance degradation. Furthermore, to insure high robustness of DCSS to the variation of Doppler shift over time, the latter technique is combined with more precise estimation of the frequencies that maximize the module of the FFTs, as described in the next paragraph.
# Study of System Integral Energy Efficiency of a Hybrid Pneumatic Power System
## 2 Materials and Methods
### Efficiency
#### 2.3.4 System Efficiency  
Herein, we present the theoretical calculation of integral efficiency. The ICE efficiency, compressor efficiency, and air motor efficiency have been defined. We apply the Bernoulli equation to the Y junction, and the energy at inlet and outlet of merger pipe is measured as:  
\[Q_{in}=Q_{loss}+Q_{out} \tag{10}\]  
where \(Q_{in}\) is the energy of flow at the merger inlet (\(Q_{1}\) (compressed airflow inlet) and \(Q_{2}\) (exhaust-gas flow inlet)); \(Q_{out}\) is the energy of flow at the merger outlet (\(Q_{3}\)); and \(Q_{loss}\) is the energy loses in pipe; we assume that \(Q_{loss}=0\).  
Equation (10) becomes:  
\[Q_{1}+Q_{2}=Q_{3} \tag{11}\]  
Base on the energy being released when the fuel is burnt, Equation (11) can eb rewritten as:  
\[\eta_{t}\eta_{C}W+\eta_{exhaust}W=\text{Q}_{3} \tag{12}\]  
where \(\eta_{t}\) denotes the ICE efficiency; \(\eta_{C}\) denotes the compressor efficiency; and \(\eta_{exhaust}\) denotes the efficiency loses in the exhaust gas of ICE.  
Following the construction of the system (Figure 1), the integral efficiency can be calculated as:  
\[\eta_{t-HPPS}W=\text{Q}_{3}\eta\ \Leftrightarrow\ \eta_{t-HPPS}W=(\eta_{t}\eta_{C}W+ \eta_{exhaust}W)\eta \tag{13}\]where \(\eta\) denotes the air motor efficiency.  
In the HPPS, system efficiency is known as fuel efficiency during operation. It is the efficiency with which the potential energy of the fuel is converted into its kinetic energy. The overall fuel efficiency varies depending on the equipment, especially for fossil fuels in combustion power plants. Fuel economy is the energy efficiency of a particular system in the HPPS, and is related to engine, transmission, and compressor efficiencies. Fuel consumption allows for a more accurate measurement of the HPPS performance, owing to the fact that fuel consumption exhibits a linear relationship whereas fuel economy results in a distorted improvement of efficiency.  
Here, we can define the HPPS efficiency:  
\[\eta_{t-HPPS}=\frac{W_{out}}{Q_{in\text{-}\eta_{tunnmission}}} \tag{14}\]  
where \(\eta_{t-HPPS}\) denotes HPPS efficiency and \(W_{out}\) denotes the outlet power of the air motor; \(Q_{in}\) is the energy including fuel consumption; \(\eta_{tunnmission}\) is the efficiency of the mechanical setup on the HPPS.  
\[\eta_{tunnmission}=\eta_{chain\text{ }transmission\eta\text{ }transmission\eta\text{ }roller} \tag{15}\]  
where \(\eta_{chain\text{ }transmission}\) is the chain transmission efficiency; \(\eta_{belt\text{ }transmission}\) is the belt transmission efficiency; \(\eta_{roller}\) is the efficiency of the roller.
## 2 Numerical Formulation
### Displacement Field  
Displacement inside the element at a point lying above or below mid-surface is interpolated using Equation (2)  
\[\mathbf{u}=\sum_{i=1}^{8}N_{i}(\xi,\eta)\mathbf{u}_{i}+\sum_{i=1}^{8}N_{i}(\xi, \eta)\xi\frac{t_{i}}{2}(-\mathbf{V}_{2i}\alpha_{i}+\mathbf{V}_{1i}\beta_{i}) \tag{2}\]  
Here, \(\mathbf{u}_{i}=[u_{i},v_{i},w_{i}]^{T}\) is the nodal displacement vector in the global cartesian co-odinate system. \(\mathbf{V}_{1i}\), \(\mathbf{V}_{2i}\), \(\mathbf{V}_{3i}\) are unit vectors orthogonal to each other (Figure 1). \(\mathbf{V}_{3i}\) is normal to the shell mid-surface. Therefore, each node has 3 translational degrees of freedom, i.e., \([u_{i},v_{i},w_{i}]\) and 2 rotational degrees of freedom, i.e., \([\alpha_{i},\beta_{i}]\)  
Equation (2) can be replaced with Equation (3) to model delaminated element  
\[\mathbf{u}=\sum_{i=1}^{8}N_{i}(\xi,\eta)\mathbf{u}_{i}+\sum_{i=1}^{8}N_{i}(\xi,\eta)\xi\frac{t_{i}}{2}(-\mathbf{V}_{2i}\alpha_{i}+\mathbf{V}_{1i}\beta_{i})+ \sum_{j=1}^{nd}H^{j}\mathbf{U}^{j} \tag{3}\]  
Here, \(\mathbf{U}^{j}=[U^{j},V^{j},W]^{T}\) is the jump in the displacement due to delamination in the \(j^{th}\) interface. \(nd\) is the number of delamination. \(H^{j}\) is the heaviside step function calculated by the following expression  
\[H^{j}=\hat{H}(z-z_{j})=\begin{cases}0,&z\leq z_{j}\\ 1,&z\geq z_{j}\end{cases} \tag{4}\]  
Here, \(z_{j}\) is the height of the \(j^{th}\) interface measured from the bottom.