esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Removing the Influence of Climate Change  
The climatic background (e.g., climate warming and interannual climate variability) is known to modulate forest dynamics and post-disturbance alterations in the land surface energy balance [44; 45; 46]. To quantify the thermal signature of forest disturbances on LST, this study employed a sliding window approach conceptually adapted from space-for-time substitution methods [47]. The core assumption underpinning this technique is that local background climatic influences are spatially homogeneous across both the target disturbed pixel and the adjacent reference pixels within the analysis window. Consequently, the observed LST deviations between the disturbed pixel and its undisturbed neighbors are primarily attributed to biophysical feedback mechanisms triggered by disturbance-induced modifications of land surface properties [16; 17; 29]. The specific implementation steps are as follows: 1. Window initialization--create a \(3\times 3\) window centered on each disturbed forest pixel (Figure S1). 2. Pixel identification--identify the disturbed forest pixel and the undisturbed forest pixel in the window, respectively. 3. Climate baseline computation--compute the LST mean value corresponding to the undisturbed forest pixel in this window (LST\({}_{\text{undisturbed}}\)). 4. \(\Delta\) LST computation--compute the LST mean value corresponding to the disturbed forest pixel in this window (LST\({}_{\text{disturbed}}\)). The biophysical impact of forest disturbance on LST (\(\Delta\) LST) is subsequently quantified as the difference between the LST of the disturbed pixel and this baseline, expressed as follows:  
\[\Delta\text{LST}=\text{LST}_{\text{disturbed}}-\text{LST}_{\text{ undisturbed}} \tag{1}\]  
Based on this methodology, we separately computed the variations in \(\Delta\) NDVI, \(\Delta\) LAI, \(\Delta\) ET, \(\Delta\) Albedo, and other climatic factors.  
According to the characteristics of the disturbance dataset, when calculating \(\Delta\) FDS, we identified the pixel's disturbance severity and year within a sliding window, and \(\Delta\) FDS is the difference of the mean value between the pixel's disturbance severity of the current year and the disturbance severity of the other years.
# Wideband Lorenz Resonance Reconfigurable Metasurface for 5G+ Communications
## 2 Unit Cell Modeling and Design
### Equivalent Circuit Model
(**c**) EC model of the metal patch with a varactor diode; the varactor diode EC mode is in the red dash square. (**d**) Equivalent circuit model of the MSRRA unit cell. The complete EC model of the MSRRA unit cell connects the above two blocks in parallel, as shown in Figure 1b. \(Z_{in}\) is the total input impedance and \(\Gamma\) is the reflection coefficient, which can be calculated using the following formulas:  
\[Z_{in}=Y_{metal}\ //Z_{eq}, \tag{3}\]  
\[\Gamma=\bigg{|}\frac{\eta_{0}-Z_{in}}{\eta_{0}+Z_{in}}\bigg{|}. \tag{4}\]  
In the obtained EC model, the MSRRA unit cell can be regarded as a parallel resonant circuit. The capacitor is introduced by the metal patch (with the varactor) and the inductor is generated by the grounded substrate medium. Compared with the capacitance introduced by the metal patch and the varactor, the inductance of the metal patch and the varactor is relatively small. Therefore, the patch with the varactor is capacitive. So, if the patch capacitance is designed relatively large, the tuning range of the resonant frequency becomes small within the capacitance variation of the varactor. Conversely, the range of the resonant frequency becomes larger. It is very important to select a reasonable ratio between the patch capacitor and the varactor capacitor. The grounded substrate can appear capacitive or inductive as the thickness of the medium variates periodically. The periodic thickness is \(\lambda/2\). When the thickness is below \(\lambda/4\), it is inductive, and it is capacitive when the thickness is greater than \(\lambda/4\) and below \(\lambda/2\). So, adjusting the thickness of the medium can also adjust the resonant frequency shift within the range where the medium is inductive. On the other hand, since the MSRRA is equivalent to a parallel RLC resonant circuit, the Q factor is proportional to \(\sqrt{C/L}\). As the Q factor increases, the slope of the magnitude curve at the resonant point increases while the bandwidth decreases. Conversely, the bandwidth increases. The way to increase the bandwidth is to increase the thickness of the substrate, or reduce the capacitance of the unit cell.
# Advances of UAVs toward Future Transportation: The State-of-the-Art, Challenges, and Opportunities
## 4 Potential Research Directions
### Energy Optimization  
The energy limitations of UAVs are a great challenge to establish a long-term UAV-monitoring system in ITS application due to short battery life. The battery life of conventional UAVs is usually less than half an hour, which could create significant challenges for ITS operations with autonomous UAVs. Because of the short battery life, the average flight time also decreases. Although various energy consumption models for UAVs are developed, the energy consumption of autonomous UAVs is still to be explored.  
In future ITS, UAVs should consist of long flight time, more sensor operation capacity, continuous connection with the communication network, and emergency response capacity. All these operations require a considerable amount of energy, which is limited in typical UAVs. In addition to advances in battery materials and options, the investigation of maximizing the flight time, operability, and coverage of autonomous UAVs by optimizing energy can be explored in the future. In addition, the use of solar and renewable energies can also be investigated further.
## 4 Conclusions  
Carbon nanowalls show low electrical properties due to the amorphous buffer layer. In order to improve this portion, the MWCNT was used as a buffer layer, and the structural and electrical properties were studied. The structural characteristics by Raman spectra were not significantly different from those of general CNWs. This is because the Raman spectra only analyzes the edge part of a thin film surface, not bulk analysis, suggesting that the CNW has been successfully grown on MWCNT-based buffer layers. The dramatic changes were identified in the electrical characterization. As the CNT content increased, the carrier concentration was improved and the resistivity was dramatically decreased. In the I-V characteristics, all samples showed linearity, indicating that they were electrically stable, and a successful ohmic contact could be expected. CNW samples with MWCNT-based buffer layers showed improvement of I-R linearity compared with pristine CNW, and samples containing 70 \(\mu\)L of CNTs showed perfect straight lines and perfect stable properties. CNW is a carbon material which has characteristics that differ greatly by pre-treatment, unlike graphene and CNT. In this study, electrical characteristics of CNWs were improved by pre-treatment of MWCNT-based buffer layers. As such, the electrical characteristic conversion method, according to the pre-treatment, is expected to have a high influence on various devices such as biosensors and gas sensors, which will be used in the future.  
Figure 6: Electrical stability with I-R curve of all samples: (**a**) pristine CNW; (**b**) MWCNT (30 \(\mu\)L)/CNW; (**c**) MWCNT (50 \(\mu\)L)/CNW; (**d**) MWCNT (70 \(\mu\)L)/CNW; (**e**) average \(\Delta\)R with percentage.  
**Author Contributions:** Conceptualization, J.K. and C.P.; methodology, H.J., H.K. and H.C.; formal analysis H.K. and S.K.; software, S.K.; writing, J.K.; supervision, H.K. All authors have read and agreed to the published version of the manuscript.  
**Acknowledgments:** This research was supported by a grant from R&D Program of the Korea Railroad Research Institute, Republic of Korea and was also supported by the Korea Institute of Energy Technology Evaluation and Planning (KETEP) and the Ministry of Trade, Industry & Energy (MOTIE) of the Republic of Korea (No. 20184030201900).  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Diversity of Collagen Proteins and Their Biomedical Applications in Drug Delivery
## 2 Diversity of Collagens
### Mammalian Collagen
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Tissues or Organs** & **Collagen Types** & **Diseases** \\ \hline Brain & & \\ \hline \multirow{3}{*}{} & IV & Porencephaly, schizencephaly \\  & IV & Porencephaly \\ \hline \multirow{3}{*}{} & Cartilage & II & Chondodysplasias, achondrogenesis \\  & IX & Multiple epiphyseal dysplasia \\  & XI & Fibrochondrogenesis, chondodysplasias \\ \hline \multirow{3}{*}{} & Eye & I & Osteogenesis imperfeta \\  & II & Dysplasias, sticker syndrome \\  & IV & Alport syndrome \\  & XI & Stickler syndrome \\  & XXV & Congenital cranial dysinnervation \\ \hline \multirow{3}{*}{} & I & Osteogenesis imperfeta \\  & II & Stickler syndrome \\  & IV & Alport syndrome, X-linked deafness \\  & XI & Deafness \\ \hline \multirow{3}{*}{} & Joint & II & Stickler syndrome \\  & III & Ehlers-Danlos syndrome \\  & V & Ehlers-Danlos syndrome \\  & IX & Multiple epiphyseal dysplasia \\  & XI & [PERSON] syndrome \\ \hline \multirow{3}{*}{} & Kidney & \\  & IV & [PERSON] syndrome, hereditary angiopathy with nephropathy \\ \hline \multirow{3}{*}{} & Muscle & IV & Hereditary angiopathy with nephropathy \\  & VI & Bethlem myophthy, Ullrich congenital muscular dystrophy \\  & XII & Bethlem myophthy \\ \hline \multirow{3}{*}{} & III & [PERSON] syndrome \\  & V & [PERSON] syndrome \\  & VII & Epidermolysis bullosa \\  & XVI & Epidermolysis bullosa \\ \hline \multirow{3}{*}{} & Teeth & \\  & I & Osteogenesis imperfeta, dentinogenesis imperfeta \\ \hline \hline \multicolumn{1}{l}{Vasculature} & III & [PERSON] syndrome \\  & IV & Cerebral small-vessel disease, Hereditary angiopathy with nephropathy \\ \hline \end{tabular}
\end{table} 
Table 3: _Cont._The triple helix, which is a distinguishing feature of various collagen assemblies, guarantees tissue strength and influences cell behavior. This unique structure is analogous to the double helix of DNA. These features present the biological importance of collagen. In particular, collagen type IV was considered to be a representative of an evolutionary transition molecule from unicellular prokaryotic organisms to multicellular eukaryotic animals [23]. Despite the extensive research on the structure and function of triple helices, there are still many unanswered questions. For example, very little is known about the function of various prokaryotic collagens and the mechanism by which the triple helix structure affects cell function.
# Automated Anomaly Detection and Causal Analysis for Civil Aviation Using QAR Data
## 3 Preliminary Preparation for Data and Causal Analysis
### Preparations for Causal Analysis  
The workflow for flight anomaly event detection and causal analysis in the approach phase is as follows:  
First, we classify the causes of abnormalities into two categories--human factor and environmental factor. Specifically, the initial determination of whether the abnormality was caused by a human factor or environmental factor is based on the contribution of the features of the abnormal events. The parameters and parameter categories used for abnormality detection and causal analysis in the approach phase are shown in Table 2.
## 7 Experiments and Results
### Experiment 3: Linear Blending with the RORPO Filter  
Based on the results from the previous experiment, it is obvious that the linear blending with just the RORPO filter outperforms approaches where multiple filters are combined. Therefore, the focus was aimed on using only this method and elaborating whether the blending between the original and enhanced image was set to its optimum, or whether it could improve the results even further by setting it to a different value. As described by Equations (17) and (18), the blending is controlled by setting the \(\alpha\) value from the interval \(<0,1>\). The effect of different \(\alpha\) values is shown in Figure 11.
###### Abstract  
For embedded impedance spectroscopy, a suitable method for analyzing AC signals needs to be carefully chosen to overcome limited processing capability and memory availability. This paper compares various methods, including the fast Fourier transform (FFT), the FFT with barycenter correction, the FFT with windowing, the Goertzel filter, the discrete-time Fourier transform (DTFT), and sine fitting using linear or nonlinear least squares, and cross-correlation, for analyzing AC signals in terms of speed, memory requirements, amplitude measurement accuracy, and phase measurement accuracy. These methods are implemented in reference systems with and without hardware acceleration for validation. The investigation results show that the Goertzel algorithm has the best overall performance when hardware acceleration is excluded or in the case of memory constraints. In implementations with hardware acceleration, the FFT with barycentre correction stands out. The linear sine fitting method provides the most accurate amplitude and phase determinations at the expense of speed and memory requirements.  
AC signal processing; embedded impedance spectroscopy; impedance spectroscopy; multisine; Fast-Fourier transform; Goertzel algorithm; curve-fitting Article
## 4 Analysis and Findings of Research Questions
### Research Question 2: What Are the Detection Mechanisms of APT Attacks on Mobile Phones?
#### 4.2.1 Personal Computer APT Detection Mechanisms
Moreover, the suggestion to employ the graph2 vec model has yielded noteworthy efficacy. The process profile has been standardized and represented in a graph format, which has been further embedded to display comprehensive features. This aids in the efficient detection of APT malware. Additionally, the researchers of [135] conducted a thorough literature review and analyzed publicly available APT samples using multiple analysis techniques, including static, dynamic, and reverse engineering. The study has provided a detailed overview of various techniques used by APT malware to evade detection, such as stealth mechanisms, anti-analysis measures, and covert communication methods. The evasive maneuver re-engineering framework (EMRF) is a newly developed framework by the authors. It comprises several modules, including process injector, DLL hijacker, fileless mechanism augmenters, code obfuscators, anti-debug augmenters, anti-virtualization augmenters, anti-sandbox augmenters, and custom evaders. The framework aims to demonstrate how evasion techniques can be used to bypass modern security solutions and prove their effectiveness. \begin{table}
\begin{tabular}{c c c c} \hline
**Platform** & **Detection Mechanism** & **Description** & **Article** \\ \hline \multirow{6}{*}{IoT} & MITRE Framework & Utilizing the TTPs framework of MITRE for mobile for & [150] \\  & & mitigate the occurrence of false positives. & \\ \hline \multirow{6}{*}{IoT} & Kalman Backpropagation & Kalman backpropagation is used to design a dynamic & [151] \\  & & predictive model. & \\  & Machine Learning Algorithm & The suggested method is dependent on machine & \\  & & learning algorithms. & \\  & Local Outlier Factor (LOF) & Recognizes suspicious behavior that differs from what & \\  & Intelligent APT & & is expected. & \\  & & & \\  & & & \\  & & & \\ \hline \end{tabular}
\end{table} 
Table 10: _Cont._Finally, the study of [118] has successfully devised a system that consolidates and leverages alerts from pre-existing systems for the purpose of identifying APTs. The proposed system, POIROT, utilizes causal correlation-aided semantic analysis to detect multistage threats over an extended period of time based on alerts from pre-existing systems. The study utilized causality analysis to autonomously identify the logical connections among the alerts, resulting in the restructuring of the initial alerts into alert chains without prior knowledge of APT processes. This approach significantly decreased the number of irrelevant alerts present in the extensive logs.
## 3 Results and Discussion
### Intact Mass Analysis Can Guide Small-Scale Test Expression of Multiple Clones
[PERSON] [15] describes intact mass measurement of small photosystem II components using LC-MS with acetonitrile as the organic solvent. In our laboratory, we have been unable to generate membrane protein spectra using this solvent. Other authors [16, 17, 18, 19, 20] have used acetone precipitation prior to removing detergent prior to LC-MS. This procedure requires relatively a large amount of protein, which is seldom available in an IMP structural genomics context and never available from test expressions. Moreover, this method is not easily adapted for high throughput. We have previously described methods in which detergents are separated from IMPs via in-line LC-MS [21]. However, these methods require more \begin{table}
\begin{tabular}{c c} \hline
**Delta Mass (Da)** & **Tentative Interpretation** \\ \hline +(22) n & Sodium adduct and proton loss \\ \(-89\) & N-terminal methionine loss and acetylation \\ \(-131\) & N-terminal methionine loss \\ +178 & Gluconoylation [13] \\ +256 & Phosphogluconylation \\ +(80) n & Phosphorylation \\ \(-18\) & Pyroglutamic acid from N-terminal glutamine \\ +42 & Acetylation \\ +(16) n & Oxidation \\ +14 & Methylation \\ \(<\)\(-131\) & (1) N-terminal or C-terminal truncation \\ \(<\)\(-131\) & (2) N-terminal and C-terminal truncation \\ \(<\)\(-131\) & (3) Different construct \\ \(<\)\(-131\) & (4) Different protein \\ +305 & Glutathione \\ +56 & Nickel adduct and 2 proton loss \\ +227 & Biotismlation \\ +1216 & Glycan core \\ +1216 + (161) n & Glycoslation envelope \\ various & Point mutation \\ + theoretical mass & (1) Deconvolution artefact \\ + theoretical mass & (2) Dimer \\ - theoretical mass/2 & Deconvolution artefact \\ +29 & S-nitroslation \\ +70 & N-pyruvate acid 2-iminyl \\ +119 & Cysteinylation \\ +454 & FMN \\ +48 (n) & Selenomethonine \\ \(-14\) & Lysine demethylation \\ \(-28\) & Arginine-Lysine substitution \\ \hline \end{tabular}
\end{table} 
Table 2: Commonly observed deviations from theoretical protein mass in test expression and scale-up. (Deconvolution artefacts are identified by presence of the wrong (+11 or +44) sodium mass interval). prolonged elution protocols which need to be varied based on the specifics of the protein and the detergents used. Furthermore, they are not easy to integrate on an instrument devoted to high-throughput standardized analysis. Hence, we have found it more useful to use MS/MS as a means of identifying membrane proteins. This is of crucial importance as the expression levels of membrane proteins are 1-2 orders of magnitude lower than those of soluble proteins, and there is an operational need to identify protein bands in the complex mixes of contaminating proteins during the first steps of purification.
# Surface Roughness Analysis in the Hard Milling of JIS SKD61 Alloy Steel
## 5 Conclusions  
This study presented the optimization of the cutting conditions for \(Ra\) when hard milling JIS SKD61 alloy steel (40-50) HRC with coated carbide tool based on a combination of the Taguchi technique and RSM method. Based on this work, conclusions were drawn as follows:  
* Through ANOVA, the results indicate that the control factors such as cutting speed, feed rate, depth of cut, and material hardness have a significant effect on \(Ra\) at a reliability level of 95%. The most influential factor on \(Ra\) among the investigated factors is the feed rate followed by depth of cut, cutting speed and, finally, material hardness.
* According to the response surface analysis, the predicted result of the model is in reasonable alignment with the observations taken from the experiments. Thus, the established model can be utilized to estimate the \(Ra\) in the hard milling of JJS SKD61 steel with a 95% confidence interval within the range of machining conditions investigated.
* The optimized cutting parameters for \(Ra\) are a cutting speed of 75 m/min, a feed rate of 0.01 mm/tooth, a depth of cut of 0.2 mm, and material hardness of 40 HRC, with predicted \(Ra\) of 0.118 \(\upmu\)m.
* The percentage error between the experimental and predicted values of the minimum \(Ra\) is 3.2%, and is found to be insignificant.
* The milled surface roughness under the optimized machining parameters is 0.122 \(\upmu\)m, which can be justified by the fact that the finish hard milling is able to replace the finish grinding in the mold and die manufacturing industry.  
The authors thank the Department of Mechanical Engineering, and the Department of Mold and Die Engineering of the National Kaohsiung University of Applied Sciences for their instrumental support to insure measurement of surface roughness and the cutting forces required.  
[PERSON] performed all the cutting experiments, analyzed the data and wrote the paper. [PERSON] developed the research orientation and checked the quality of paper.  
The authors declare no conflict of interest.
# Multi-Antenna Jammer-Assisted Secure Short Packet Communications in IoT Networks
## 5 Conclusions  
In this paper, we investigated the secrecy performance of IoT networks with finite blocklength coding. To exploit the available multi-antenna jammer for secrecy enhancement, we proposed three different secure transmission schemes. Specifically, we derived approximate closed-form expressions for the secrecy throughput of all the proposed schemes. Moreover, we presented the asymptotic secrecy throughput in the case of infinite blocklength to gain further insights. In addition, we determined the optimal blocklength that  
Figure 9: (**a**) The optimal blocklength and (**b**) the corresponding maximum secrecy throughput versus \(P_{j}\) for \(B=100\), \(N_{j}=8\), \(P_{a}=10\) dBm, \(\epsilon_{\text{max}}=0.35\), and \(N_{\text{max}}=400\).  
maximizes the secrecy throughput subject to the latency and reliability constraints. Numerical results demonstrated that both the ZFB scheme and the NAN scheme strictly outperform the TAS scheme in terms of the secrecy throughput, and the performance loss from short packet transmissions can be compensated for by increasing the number of antennas at the jammer.  
Methodology, D.C., J.L., J.H., and X.Z.; formal analysis, D.C., J.L., J.H., X.Z., and S.Z.; writing--original draft preparation, D.C. and J.L.; writing--review and editing, J.H., X.Z., and S.Z.; supervision, X.Z. and S.Z. All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by the Doctoral Research Start-up Funding of Nanyang Normal University under Grant 2022 ZX017; in part by the Cultivating Fund Project for the National Natural Science Foundation of China of Nanyang Normal University under Grant 2022 PY024; in part by the open research fund of Key Lab of Broadband Wireless Communication and Sensor Network Technology (Nanjing University of Posts and Telecommunications), Ministry of Education, under Grant JZNY202107; in part by the Key Scientific Research Projects of Colleges and Universities in Henan Province of China under Grant 21A520033, 23A520038, and 23A510001; and in part by the Key Scientific and Technological Research Projects in Henan Province under Grant 222102320369, 232102210121, and 232102220101.  
Not applicable.  
The authors declare no conflict of interest.