esa-sceva/satcom-chunk-collection huggingface dataset:
# Experimental Evolution of Magnetic Nanoparticle Resistance in _Escherichia coli_
## 3 Results
### Metal Resistance  
Measurement of 24-h growth in excess metal (iron (II), iron (III), gallium (III), and silver (I)) was accessed for the FeNP, control, and ancestral populations at 25 days of evolution (Figure 2A-D). The FeNP showed highly statistically greater 24-h growth across concentration compared to the controls, which were greater than the ancestors in magnetite, iron (II), iron (III), gallium (III), and silver (I) (Table 2a). Iron (II) and iron (III) are of particular interest to this study because in solution, magnetite nanoparticles release both iron (II) and iron (III) ions into the medium. Measurements by ICP-OES determined how much of each of these species was released relative to the measured mass of that species added to DMB medium (Appendix A Table A2, Figures A1-A3). These figures show that the actual amount of iron entering solution was always considerably less than the measured amounts initially added to the medium. The iron contents in these solutions were in the following order magnetite NPs > iron (III) > iron (II). The method presented was assessed by utilizing aliquots and dilutions of magnetite NPs, iron (II), and iron (III). The emission line used for the quantification of Fe (II) was 259.9 nm. Results showed high precision with the relative standard deviation (RSD) values for most of the samples < 2%. The plotted calibration curve was linear with a R\({}^{2}\) value > 0.999.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & \multicolumn{3}{c}{**(a)**} \\ \hline
**Substance** & **Pop. Effect (F, p)** & **Conc. Effect (F, p)** & **Interaction (F, p)** \\ \hline magnetite & 383.9, 0.001 & 42.5, 0.001 & 25.9, 0.001 \\ ampicillin & 283.1, 0.001 & 17.1, 0.001 & 16.7, 0.001 \\ chloramphenicol & 71.1, 0.001 & 114.6, 0.001 & 7.9, 0.001 \\ rifampicin & 1285.1, 0.001 & 16.3, 0.001 & 16.5, 0.001 \\ tetracycline & 62.9, 0.001 & 12.6, 0.001 & 5.7, 0.001 \\ iron (II) & 18.6, 0.001 & 88.0, 0.001 & 1.7, 0.116 \\ iron (III) & 42.9, 0.001 & 77.4, 0.001 & 2.7, 0.011 \\ gallium (III) & 29.8, 0.001 & 111.8, 0.001 & 1.2, 0.278 \\ silver (I) & 7.1, 0.009 & 15.8, 0.001 & 2.3, 0.029 \\ \hline \hline  & \multicolumn{3}{c}{**(b)**} \\ \hline
**Substance** & **Pop. Effect (F, p)** & **Conc. Effect (F, p)** & **Interaction (F, p)** \\ \hline magnetite & 9.5, 0.003 & 22.7, 0.001 & 12.8, 0.001 \\ chloramphenicol & 159.1, 0.001 & 25.7, 0.001 & 22.6, 0.001 \\ silver (I) & 243.1, 0.001 & 10.4, 0.001 & 15.6, 0.001 \\ \hline \hline \end{tabular}
\end{table}
Table 2: (a) General linear model results for phenotypic assays, FeNP vs. Control. (b) General linear model results for phenotypic assays, control vs. ancestor.  
Figure 1: The mean and SE of 24 h growth for FeNP, Control, and Ancestral populations in increasing concentration of magnetite NPs are shown. There was no observable growth at 2000 mg/mL magnetite for the FeNP population.  
The controls also showed statistically greater growth in all metals across concentration compared to the ancestor.
## 5 Conclusions  
Aiming at the problem of difficult noise identification and feature extraction caused by the complex structure of rotating machinery and the strong coupling of vibration signals, this paper proposes an adaptive noise-complete set of empirical modal decomposition and an improved threshold screening SVD algorithm to be applied in the noise reduction in vibration simulation signals as well as the test signals of the combine harvester, and the following results are obtained:  
1. In comparison with EMD, EEMD, CEEMD, and CEEMDAN methods, it is concluded that the improved CEEMDAN method proposed in this paper can effectively decompose signals when performing signal decomposition, and at the same time, reduce false components and improve decomposition accuracy. The improved threshold screening SVD algorithm sets the difference between the singular value energy difference spectrum and the singular value difference spectrum as the discriminative threshold, which is more conducive to avoiding the threshold screening error caused by the sudden change of a certain order of singular value brought by noise.
2. Compared to the individual denoising methods of improved CEEMDAN and SVD, the combined denoising approach of improved CEEMDAN with threshold-based SVD demonstrates significant effectiveness in handling vibration signals from combine harvesters. The SNR increased by 9.9675 dB and 6.5233 dB, respectively, indicating that the proposed combined denoising method in this study exhibits an excellent denoising performance.
3. The improved CEEMDAN-improved threshold screening SVD noise reduction method effectively preserves the characteristic signal information of the noise-reduced combine harvester vibration signals and the vibration simulation signals, as observed from the improved time-frequency diagrams. By reducing the noise in the vibration signals, effective vibration features can be extracted, and the vibration signals can subsequently be used to more accurately analyze combine harvester bearing failures.
4. Through this paper on simulation signals, which adds to Gaussian white noise research, we found that the method in this paper for reducing Gaussian white noise is more effective, can effectively reduce Gaussian noise, and attenuate white noise, while retaining underlying signal information.
## 1 Introduction
### Paper Organization  
This paper is organized as follows. The system model is presented in Section 2. The description of the computation of the precoding matrix compliant with the 5G-NR standard is provided in Section 3 for SU-MIMO systems based on Type I single-panel and multi-panel codebooks. Then, Type II codebook design for MU-MIMO systems is detailed in Section 4. Numerical results are presented in Section 5 considering a realistic deployment of a 5G-NR scenario by using the CDL channel model defined in TR 38-901. Conclusions are finally drawn in Section 6.  
Notation: boldface lower-case letters are used for vectors, while boldface upper-case letters are used for matrices. \(\mathbf{A}^{T}\), \(\mathbf{A}^{H}\), and \(\mathbf{A}^{-1}\) represent the transpose, conjugate transpose, and inverse of matrix \(\mathbf{A}\), respectively. \(\mathbb{E}(\mathbf{A})\) stands for the expected value of \(\mathbf{A}\). \(\left[\mathbf{A}\right]_{d}\) represents the \(d\)th column vector of matrix \(\mathbf{A}\) while \(\mathbf{A}(m,n)\) is the \((m,n)\)th element. \(A\otimes B\) represents the Kronecker product on matrices \(A\) and \(B\). \(\left(\begin{subarray}{c}n\\ k\end{subarray}\right)\) denotes the binomial coefficient. \(\left\|\cdot\right\|\), \(\left\|\cdot\right\|_{F}\) are used to denote the Euclidean and Frobenius norm, respectively. \(\mathbb{C}^{N\times M}\) is the space of complex \(N\times M\) matrices. \(\mathcal{CN}(\mu,\sigma^{2})\) is the complex Gaussian distribution with mean \(\mu\) and variance \(\sigma^{2}\). \(\mathbf{I}_{d}\) is the \(d\times d\) identity matrix.
# A Critical Review of SCWG in the Context of Available Gasification Technologies for Plastic Waste
## 6 Gasification of Plastic Waste: An Overview
### Supercritical Water Gasification (SCWG)
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Reactor** & **Feedstock** & **Optimal Reaction Conditions** & **Catalyst** & **Reference** & **Year** \\ \hline \multirow{3}{*}{Batch reactor} & \multirow{3}{*}{XPIE} & 700 \({}^{\circ}\)C, 30 MPa, 30 min, 20 of & & & \\  & & molar ratio of water to carbon & [PERSON] & Okajima et al. [140] & 2004 \\  & & in XPLE & & & \\ \hline batch reactor & LDPE, HDPE, PP, PS & 450 \({}^{\circ}\)C, 10-38 MPa, 60 min & RuO\({}_{2}\) & [PERSON] and [PERSON] [141] & 2016 \\ \hline \multirow{3}{*}{quartz tube reactor} & \multirow{3}{*}{HIPS} & 800 \({}^{\circ}\)C, 60 min, 23 MPa, & & \\  & & feedstock concentration of 3 & - & [PERSON] et al. [142] & 2018 \\  & & wt\% & & & \\ \hline \multirow{3}{*}{quartz tube reactor} & \multirow{3}{*}{ABS} & 450 \({}^{\circ}\)C, 60 min, 23 MPa, & & & \\  & & water/ABS (15:1) & - & [PERSON] et al. [143] & 2018 \\ \hline \multirow{3}{*}{quartz tube reactor} & \multirow{3}{*}{PP} & 750 \({}^{\circ}\)C, 60 min, 23 MPa, & & & \\  & & feedstock concentration was & & & \\  & & 5-25 wt\% & & & \\ \hline \multirow{3}{*}{quartz tube reactor} & \multirow{3}{*}{ABS} & 800 \({}^{\circ}\)C, 60 min, 23 MPa, a & & & \\  & & feedstock concentration of 3 & & & \\  & & wt\% and a solution of 1 wt\% & & & \\  & & formic acid & & & \\ \hline \hline quartz tube reactor & PET & 800 \({}^{\circ}\)C, 10 min, 23 MPa & inorganic salts in seawater & [PERSON] et al. [146] & 2020 \\ \hline \multirow{3}{*}{batch reactor} & \multirow{3}{*}{food waste and LDPE} & 420-480 \({}^{\circ}\)C, 35 MPa, 30-75 min &
\begin{tabular}{c} food additives (NaCl, \\ Na\({}_{2}\)CO\({}_{3}\), and NaHCO\({}_{3}\)) \\ \end{tabular} & [PERSON] et al. [147] & 2020 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Supercritical water gasification summary table.
## 3 System Performance
### Load  
Load represents the total load (in bits/s) submitted to wireless LAN layers by all higher layers in all WLAN nodes in the network. In this case we can see that the simulated network provides an average global load of approximately 65,000 bps for all patients.  
The average load of each patient is approximately 7000 bps. This statistic represents the total data traffic received by the entire WLAN station from the higher layers of the MACs that are accepted and queued for transmission. This statistic does not include any higher layer data, which are rejected without queuing due to full queue or large size of the data packet. Any data traffic that is relayed by the Access Point (AP) from its source to its destination within the Basic Service Set (BSS) is counted twice for this statistic, since such data packets are double-loads for the BSS, as both the source node and the AP have to contend for their transmission via shared medium. Figure 5 shows the traffic data load in the network for each athlete and the global load, made up from all the contributions.  
In Figure 6, we can observe the load provided by our system to a patient and the load provided by one of the sensors that is monitoring that patient. As we can see the average patient load is around 7000 bps with many peaks. These peaks in traffic are because each sensor is sampling at various times and so several sensors may be sending information producing traffic peaks. However, as we can see, there are times when the sensors are not picking or sending information, but are in standby mode to save energy. In all cases, the information captured or sent is low.  
Figure 7 shows the end-to-end delay and WLAN-Media Access Delay. The end-to-end delay represents the delay of data received by traffic sinks across all nodes. In this case, we can see that this delay is lower than 1 millisecond in all samples, so suggesting that there is an acceptable delay period for real time monitoring of patients.  
WLAN - Media access delay represents the global statistic for total queuing and contention delays of the data, management, and delayed request frames transmitted by all WLAN MACs in the network. For each frame, this delay is calculated as the duration from the time when it is inserted into the transmission queue, which is arrival time for higher layer data packages and creation time for all other frame types, to the time when the frame is sent to the physical layer for the first time. Hence, it also includes the period for successful Reset To Send/Clear To Send (RTS/CTS) exchange, backoff periods and collision delays. This delay should be less than the previous one as it is calculated on layer 2. For this reason, the average value is 0.00018 s, as we can see in Figure 7.  
Figure 4: Network architecture. AP: Access point, WAN: Wide Area Network.  
Figure 5: Load in the network in bits per second.  
Figure 6: Load produced by a patient and a sensor node.  
Figure 7: End-to-end delay & Media Access delay.
# Combining the Transformer and Convolution for Effective Brain Tumor Classification Using MRI Images
###### Abstract  
In the world, brain tumor (BT) is considered the major cause of death related to cancer, which requires early and accurate detection for patient survival. In the early detection of BT, computer-aided diagnosis (CAD) plays a significant role, the medical experts receive a second opinion through CAD during image examination. Several researchers proposed different methods based on traditional machine learning (TML) and deep learning (DL). The TML requires hand-crafted features engineering, which is a time-consuming process to select an optimal features extractor and requires domain experts to have enough knowledge of optimal features selection. The DL methods outperform the TML due to the end-to-end automatic, high-level, and robust feature extraction mechanism. In BT classification, the deep learning methods have a great potential to capture local features by convolution operation, but the ability of global features extraction to keep Long-range dependencies is relatively weak. A self-attention mechanism in Vision Transformer (ViT) has the ability to model long-range dependencies which is very important for precise BT classification. Therefore, we employ a hybrid transformer-enhanced convolutional neural network (TECNN)-based model for BT classification, where the CNN is used for local feature extraction and the transformer employs an attention mechanism to extract global features. Experiments are performed on two public datasets that are BraTS 2018 and Figshare. The experimental results of our model using BraTS 2018 and Figshare datasets achieves an average accuracy of 96.75% and 99.10%, respectively. In the experiments, the proposed model outperforms several state-of-the-art methods using BraTS 2018 and Figshare datasets by achieving 3.06% and 1.06% accuracy, respectively.  
brain tumor; classification; convolutional neural network; MRI; SVM; Vision Transformers 2023
# Cyber Resilience Limitations in Space Systems Design Process: Insights from Space Designers
## 1 Introduction
### Challenges in Achieving Cyber Resilience for Space Infrastructure
#### 1.2.4 Unique Cyber Threats in the Space Domain  
While both terrestrial and space systems face common cyber threats such as data breaches, malware, and denial-of-service attacks, space systems are exposed to additional, domain-specific threats [15]. For instance, adversaries may deploy signal jamming, spoofing, or anti-satellite (ASAT) weapons to disrupt communications or disable space assets [15]. Furthermore, the remote nature of space assets makes them especially vulnerable to sophisticated cyber-physical attacks that target control systems or communication links. The introduction of state actors into space-based warfare further complicates the threat landscape, as space assets are increasingly seen as strategic targets [32]. This distinguishes space systems from other critical infrastructures where cyber resilience is primarily concerned with protecting against network breaches and data loss rather than physical disablement through cyber means [31; 33].
# Exploiting Data Analytics and Deep Learning Systems to Support Pavement Maintenance Decisions
## 4 Description of Case Study--Palermo, Italy
#### Characteristics of Available Data
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Data Group** & **Source** & **Data Obtained** \\ \hline Census data & ISTAT [67] & Population of Circumscription, \\ Traffic and commuting data & ISTAT, Municipality of Palermo [67; 71; 72] & Employment, commuting statistics, industrial \\  & activities, commercial activities \\ Economic activity data & Palermo Urban Transport Plan [68] & PUT zone activity rate, traffic rate, workers, arrivals \\ Historical maintenance and & and departures, Industrial activities in the zone \\ road network data & RAP and Municipality of Palermo [73] & Year of maintenance, road lengths and area \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Summary of Data Sources. The task was, therefore, to utilize these factors to provide analyses on road maintenance and predict the next instances of interventions. Consequently, using this workflow could provide an easier pathway for the road authority to plan interventions. Within the dataset, it is worth mentioning that a significant number of the features are categorical as they relate to the circumscription or the neighborhood in which the feature occurs.
## 6 Discussion
### Imprecation of Results
#### 6.1.6 Visibility Constraint  
Visibility constraints owing to bad weather such as heavy rain and fog make the MWL much higher than good weather conditions, especially in high-congested areas. Visibility is the main tool for recognising the situation outside the bridge [46]. Multiple interviewees confessed that the limited visibility within one mile with many crossing ships feels so severe as to affect safe navigation in many cases, even when the RO and lookout are onboard. The prompt decision-making in a very short time is inevitable under these situations; thus, the time pressure of ROs rises significantly in addition to mental demands and frustration. All interviewees expressed that they could not imagine remote navigation without the crew's support onboard with maintaining safety and stable mental conditions, even if highly reliable autonomous navigation systems were installed. An interviewee suggested that he could not judge whether the information from autonomous systems was accurate under the limited data transform condition, because the situation outside a ship changes rapidly. Another interviewee commented that he would turn off the autonomous navigation system under these situations if he was onboard. The most reliable tool is his \"eye\", and unexpected intervention by autonomous systems would induce confusion. According to him, this feeling does not change as far as the RO has final responsibility for navigation.  
The interruption of visual information that ROs acquire during remote navigation significantly increases the stress. This trend is outstanding in the congested area. ROs should manage a ship by the information of only navigation equipment during the interruption that changes the situation around a ship a lot. For instance, ROs cannot confirm the movement of small fishing boats that do not have AIS on the screen during the interruption, even though the delay of decision-making for a few seconds would directly lead to an accident. According to interviewees, autonomous systems will not be alternated with visibility information, even if they are highly reliable. The case study used visual data that supplied static images every 10 s. All interviewees were surprised at the time length and stressed the irritated feeling and time pressure due to the lack of visual information while waiting for the next visual image. One unique comment was that 10 s of interruption under non-congested (e.g., ocean) and clear weather conditions would not lead to a significant rise of MWL because ROs can easily predict future situations.
## 4 Big Data
### Big Data Analytics in Smart Grid
#### 4.3.10 Load Disaggregation  
Non-intrusive load monitoring (NILM) is a type of load that separates general load profiles at the home standard from the power usage of specific machines [297]. NILM, out of just one smart meter, placed in the house is effortless to accept by clients than direct appliance monitoring framework [298]. The various types of residential electric machines possess varying possibilities for participation in the DR program, leading to a better understanding of their customers' behavior and a more energy-efficient approach [299; 300]. NILM early approaches were mostly centered on detecting an edge in power transmission to indicate whether a recognized device is on or off [301; 302].
# Towards a Collection of Security and Privacy Patterns
## 3 Security Patterns
### Authorization
#### 3.5.1 Single Access Point  
The Single Access Point [14] grants or denies entry to the system after checking the client requiring access. In that way, external access is provided along with protection from misuse or damage. The Single Access Point pattern is easy to apply, defines a clear entry point to the system, and can be assessed when implementing the desired security policy. Through a Single Access Point, we can provide authorization and also support the overall system security posture.  
Concrete implementations are elements such as a Login Window or a Validation Screen. The client logs in at the Single Access Point and then uses the protected system. As an entry point is created, it can be used for the enforcement of different policies such as authentication, authorization, validity of incoming data, known offenders, and replay policies. Therefore, from an implementation perspective, the establishment of a Single Access Point must be supported with the introduction of a check point [14] (including, e.g., a Policy Decision Point and a Policy Enforcement Point, as typically found in policy-based authorization management solutions [50]).
## 3 Materials and Methods
### Anomaly Classification  
To perform anomaly classification, we used a two-stream architecture. Contrary to existing approaches that rely on the optical flow for one stream and the RGB image for the other stream, we used a simple but effective strategy that eliminates the need for expensive optical flow computation. The proposed two-stream architecture is depicted in Figure 5, whereas the details of both the spatial and temporal streams as well as late temporal modeling are provided later in this section.  
Figure 5: Proposed two-stream architecture for spatiotemporal learning for anomaly classification.
## 4 Analytical Methods
### Major and Trace Element Determinations  
For whole-rock major and trace element analysis, the samples were initially crushed using a jaw crusher and subsequently pulverized to 200 mesh with an agate ball mill at the Langfang Regional Geological Survey in Hebei Province, China. The analyses of major and trace elements were conducted at ALS Chemex (Guangzhou, China) Co., Ltd. Major elements were measured using a Rigaku ZXS100e X-ray fluorescence spectrophotometer (XRF) (Tokyo, Japan), achieving an analytical precision of better than 2%. Trace elements were quantified using an Agilent 7500a (Agilent, Santa Clara, CA, USA) inductively coupled plasma-mass spectrometer (ICP-MS). To ensure accuracy, two standard reference materials (AGV-2 and GSR-1) were employed, with the analytical precision maintained within 5-10%.