esa-sceva/satcom-chunk-collection huggingface dataset:
# Experimental Study on the Surface Properties of Nanoalumina-Filled Epoxy Resin Nanocomposites
From the AFM topographic images, roughness coefficients can be computed, which represent the ratio of the real surface area to the geometrical surface area for identical external dimensions of the sample. Wettability and roughness are important properties to be ensured for many applications, such as for painting and gluing components, for which the bonding to adhesives and coating substances are increased for wettable and roughened surfaces. This article presents an experimental study on the wettability and the surface roughness of EPNCs manufactured with a thermosetting EP, which in turn is manufactured with two different curing agents (DDM and DDSA+MNA) and filled with functionalized alumina (Al\({}_{2}\)O\({}_{3}\)) NPs with different weight ratios, namely 1, 3, and 5 wt. (%). To study the wettability, CAs were measured. To study the surface roughness, AFM topographic images were first obtained. Next, two amplitude parameters were evaluated, namely the arithmetic average roughness (\(R_{a}\)) and the root mean square roughness (\(R_{q}\)). These parameters were considered as measurements of the surface roughness, because AFM surface profiles can also be used to assess the nanofiller dispersion [32]. The obtained results, namely the mean values of CAs, the images of surface roughness from AFM, and the related amplitude parameters, are presented and discussed with respect to the influence of the NP loading. The results are differentiated for the two different thermosetting EP systems, which were obtained by using the two different curing agents (DDM and DDSA+MNA). It should be noted that no previous studies focused on the wettability and surface roughness of EPNCs systems similar to those studied herein were found in the open literature. For this reason, the presented study can be considered original, and the obtained results are of great importance for future studies.
# Optimization of Replaced Grinding Wheel Diameter for Minimum Grinding Cost in Internal Grinding
## 2 Cost Analysis
It is observed from the figures that the grinding cost depends on various factors, such as the initial diameter, the grinding wheel width, the ratio between the length and the diameter of the workpieces, the total depth of dressing cut, the wheel life, the radial grinding wheel wear per dress, the replaced wheel diameter and so on. In addition, among these parameters, the replaced wheel diameter is the unique factor holding an optimum value at which the grinding cost is minimum. Hence, the optimum replaced wheel diameter has been selected to be the objective of the cost optimization problem. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Coefficient depends on** & **Code** & **Value** & **Reference** \\ \hline Work-piece material and tolerance grade & c\({}_{1}\) & \(c_{1}=0.0288\cdot t_{g}1^{.4153}\) & [18] \\ Grinding wheel diameter & c\({}_{2}\) & \(c_{2}=0.5657\cdot d_{s}^{0.153}\) & [18] \\ Measurement type & c\({}_{3}\) & 1 (using micrometer) & [19] \\ Ratio of length to diameter of work-piece & c\({}_{4}\) & \(c_{4}=1.0642\cdot R_{ld}^{-0.5079}\) & [18] \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Coefficients for determining the radial wheel feed. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Name** & **Code** & **Equation** \\ \hline Grinding time & \(t_{c}\) & \(t_{c}=l_{w}\cdot a_{e,tot}/\left(v_{fa}\cdot v_{fr}\right)\) \\ Dressing time & \(t_{d,p}\) & \(t_{d,p}=t_{d}/n_{p,d}\) \\ Wheel replacing time per work-piece & \(t_{wr,p}\) & \(t_{wr,p}=t_{wr}/n_{p,w}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Time components. From the above analyses, the cost optimization problem to determine the optimum replaced wheel diameter \(\rm D_{e,op}\) is expressed by:  
\[\rm minC_{ig}=\rm minf(D_{e}) \tag{11}\]  
With the following constraint:  
\[D_{e,\rm min}\leq D_{e}\leq D_{e,\rm max} \tag{12}\]  
Additionally, as reported above, the optimum replaced wheel diameter is affected by a number of parameters. In this study, seven main input factors, including the initial wheel diameter \(\rm D_{0}\), the width of wheel \(\rm W_{gw}\), the ratio of work-piece length per work-piece diameter \(\rm R_{ld}\), the dressing depth of cut \(\rm a_{ed}\), the wheel life \(\rm T_{w}\), the radial grinding wheel wear per dress \(\rm W_{pd}\), the cost of the grinding machine \(\rm C_{m,h}\) and the wheel cost \(\rm C_{gw}\) were carefully selected to evaluate their effects on the optimum replaced diameter. Hence, the optimum replaced wheel diameter can be described as follows:  
\[D_{e,op}=f(D_{0},W_{gw},R_{ld},a_{ed},T_{w},W_{pd},C_{m,h},C_{gw}) \tag{13}\]
###### Abstract  
In this paper, we introduce a new kind of Service Level Agreement(SLA) Template to better control dynamically quality of medical monitoring platform service. Our approach is based on Health care system and Health Information Technology (HIT) research area, specifically the field of telemonitoring system for patients who suffer from chronic obstructive pulmonary disease (COPD). According to WHO statistics, COPD is the third leading cause of death worldwide. To this end, several solutions or platforms exist today to monitor COPD. Most of these platforms manage large volume of patient data. This can bring about quality and lost data problems. To address these issues, control mechanisms must be proposed and designed to improve the quality of service (QoS) on these platforms. A platform with continuously monitored QoS can save patients' lives and reduce data quality risk. In this article, we propose an ontology that uses SLAs data from COPD monitoring platforms with dynamic data from a patient context. We dynamically calculate the number of patient data incidents and the number of service request incidents from two dynamic contexts: SLA and the patient context. If the number of incidents is higher than what is expected in the SLA, then alerts are sent to the interface parties in real time. Finally, the contribution of this article is the proposed virtual SLA template to better control SLA violation and improve quality of medical monitoring platforms services.  
health care system; adaptation; SLA; violation; adaptive SLA template; ontology; context-aware; virtual; dynamic adaptation; context-aware application Article
# Enhancing Malware Detection via RGB Assembly Visualization and Hybrid Deep Learning Models
## 1 Introduction
### Current Challenges in Malware Detection
#### 1.1.2 Limitations of Traditional Analysis Methods  
Conventional static analysis approaches face significant limitations when confronting obfuscated binaries. Traditional signature-based detection relies on known patterns and heuristics, rendering it ineffective against zero-day threats and previously unseen malware variants. Dynamic analysis, while capable of observing runtime behavior, suffers from sandbox evasion techniques where malware can detect virtualized environments and remain dormant to avoid detection. Furthermore, the computational overhead associated with comprehensive dynamic analysis makes real-time detection challenging in production environments.
# Cross-Project Defect Prediction Method Based on Manifold Feature Transformation
## 3 Cross-Project Defect Prediction Method Based on Manifold Feature Transformation
### Method Framework  
The framework of the MFTCPDP method is shown in Figure 1. The input is a source project and a target project. After manifold feature transformation, new source projects and target projects were obtained. Then, we used the machine learning model to build a defect prediction model on the transformed source project and predict defects on the transformed target project to obtain a prediction label. Finally, the performance of the MFTCPDP method was compared with real labels. The core of the MFTCPDP method lies in the process of manifold feature transformation. We introduce the concept and implementation details of manifold feature transformation in the next section.
## 2 TDC Architecture
### Tmped Delay Line  
The tapped delay line is the main module of the TDL-TDC in charge of the fine count. In fact, it can be formed by cascading any logic gates. For the purpose of high precision measurement, it is necessary to select a gate with the smallest delay to build a delay line in the FPGA. Previous research has shown that the CARRY4 block can be used as a delay element [26; 27]. As shown in Figure 2, there are four multiplexers (MUXCPs) in the CARRY4 block, which can be perfectly used as the delay element. The hit signal propagates from the bottom to the top, which means that adjacent slices can be connected into carry chains in the vertical direction. In this paper, each CARRY4 module leads out CO0-CO3 as four taps. The TDL states are logic zeros if there is no signal. When bringing a pulse to the input, the states of the TDL propagate from \"0\" to \"1\". When the next clock comes, D flip-flops to capture the TDL states in the form of a thermometer code. This means that it can accurately measure the time interval between the rising edge of the input signal and the next rising edge of the clock. As the clock and signal are asynchronous, more pipelined registers are created to eliminate metastability.  
All delay elements of the TDL must be modified and located in the same clock region to avoid additional mismatch. The STA shows the delay of a CARRY4 block in the ZYNQ-7020 is 100 ps. Considering the ones-counter encoder in this paper, the number of taps should be a multiple of 6. In this work, 48 CARRY4 blocks are used to ensure the total delay is longer than 2.857 ns (350 MHz clock).
# Comprehensive Review of RF MEMS Switches in Satellite Communications
## 2 Advanced Technologies and Mechanisms in RF MEMS Switches  
RF MEMS switches are devices that operate in the RF to the millimeter-wave frequency range, consisting of two distinct parts: the mechanical and the electrical. The mechanical part is driven by one of four actuation mechanisms: electrostatic, piezoelectric, electromagnetic, or electrothermal, facilitating the lateral or vertical movement of the switch. The electrical part is categorized by the contact type, as either capacitive (metal-insulator-metal contact) or ohmic (metal-to-metal contact), and by the circuit configuration, as either series or parallel. The following sections further elaborate on these types of switches and their respective research.
# Quasi-Passive Resistive Exosuit for Space Activities: Proof of Concept
## 2 Materials and Methods
### Proof of Concept
#### 2.2.2 Control Strategy  
To control the QPA and the XoSoft exosuit, the main information required is the gait phase, since the clutches have two states (engaged and disengaged) that typically occur once per cycle. Consequently, the core of the control system can be a state machine that keeps track of the gait of the user. The gait cycle (time between two consecutive hell-ground contacts, heel strikes, of the same lower limb) consists in two main phases: (i) stance phase, that is the time between the heel strike of the foot and the toe off (from 0% to about 60%); and (ii) swing phase, time between the toe off and its consecutive heel strike (from about 60% to 100%). The transition between these phases is marked by the heel-strike and by the toe-off respectively. This of course, would not occur in space. To further refine this segmentation of the gait cycle, it is common to consider up to six events [90]. By fusing the sensor outputs from the ground contacts and knee angles, in real-time, the system segments the gait cycle to identify different events such as: heel strike (HS), flat foot (FIF), front foot (FrF), toe off (TO), positive speed inflection (PSI) and negative speed inflection (NSI). Using sensory data and sequencing, the specific control signals can be applied to provide the required assistance and actuator action. Once the gait has been segmented, it is possible to program the engagement and disengagement of the clutches by selecting the gait cycle percentage. The control strategy for the XoSoft exosuit can be selected based on the required resistance to be provided to the specific muscle and task. Figure 3 shows a gait cycle, corresponding segmentation and engage/disengage events. Key events during this cycle include: the interval during which the front knee actuation is engaged (\(QPA_{\textit{RneeFlexion}}\)), which generates a resistive torque at the knee during flexion, and the rear knee actuation (\(QPA_{\textit{RneeFlexion}}\)) phase, which generates resistance at the knee joint during extension. \(QPA_{\textit{RneeFlexion}}\) is actuated between the 5% and 70% of the gait cycle, while (\(QPA_{\textit{RneeFlexion}}\)) is operational between the 60% and 100% of the gait cycle. The choice of these particular values is due to the angular displacement that the knee covers during walking. In particular, the maximum value of the knee angle is reached at mid swing (as shown in Section 3.2.3) and since the EB elongation is a function of the joint angle, the initial or final engagement instants of both actuators are aligned with the mid swing point.
## 4 Simulations, Results and Discussion
### Equivalence Among Complex Wald, Rao Test and GIRT for Linear Model
#### 4.2.1 Complex Classical Linear Model Testing Problem  
First, we apply complex Wald test to the complex linear model problem. Assume the data are modeled according to [23]:  
\[\tilde{\mathbf{x}}=\mathbf{H}\tilde{\boldsymbol{\theta}}+\tilde{\mathbf{w}},\]  
where \(\tilde{\mathbf{H}}\in\mathbb{C}^{N\times p}\) is a known matrix with \(N>p\) and full rank, \(\tilde{\boldsymbol{\theta}}\) is an unknown complex \(p\times 1\) parameter vector, and \(\tilde{\mathbf{w}}\) is a complex \(N\times 1\) random vector with PDF \(\tilde{\mathbf{w}}\sim\mathcal{CN}(\boldsymbol{0},\tilde{\mathbf{C}})\), with \(\tilde{\mathbf{C}}\in\mathbb{C}^{N\times N}\). The testing problem is equivalent to deciding between two hypotheses:  
\[\mathcal{H}_{0}:\tilde{\boldsymbol{\theta}}=\boldsymbol{0}\] \[\mathcal{H}_{1}:\tilde{\boldsymbol{\theta}}\
eq\boldsymbol{0}\]  
Figure 7: Complex Rao and Wald test performance with “smaller” \(\mathbf{D}_{B}\).  
Then, based on the properties of the complex Gaussian PDF  
\[\tilde{\mathbf{x}}\sim\mathcal{CN}(\tilde{\mathbf{\mu}},\tilde{\mathbf{C}})\]  
so that \(\tilde{\mathbf{\mu}}=\mathbf{H}\tilde{\mathbf{\theta}}\) and \(\tilde{\mathbf{C}}(\tilde{\mathbf{\theta}})=\tilde{\mathbf{C}}(\text{not dependent on }\tilde{\mathbf{\theta}}\text{ or }\tilde{\mathbf{\theta}}^{*})\). The PDF is  
\[p(\tilde{\mathbf{x}};\tilde{\mathbf{\theta}})=\frac{1}{\pi^{N}\det(\tilde{\mathbf{ C}})}\exp{[-(\tilde{\mathbf{x}}-\tilde{\mathbf{H}}\tilde{\mathbf{\theta}})^{H} \tilde{\mathbf{C}}^{-1}(\tilde{\mathbf{x}}-\tilde{\mathbf{H}}\tilde{\mathbf{ \theta}})]}.\]  
We have  
\[\frac{\partial\ln p(\tilde{\mathbf{x}};\tilde{\mathbf{\theta}})}{ \partial\tilde{\mathbf{\theta}}^{*}}=-\frac{\partial(\tilde{\mathbf{x}}-\tilde{ \mathbf{H}}\tilde{\mathbf{\theta}})^{H}\tilde{\mathbf{C}}^{-1}(\tilde{\mathbf{x}}- \tilde{\mathbf{H}}\tilde{\mathbf{\theta}})}{\partial\tilde{\mathbf{\theta}}^{*}}\] \[=\tilde{\mathbf{H}}^{H}\tilde{\mathbf{C}}^{-1}(\tilde{\mathbf{x}} -\tilde{\mathbf{H}}\tilde{\mathbf{\theta}})\]  
Therefore, the MLE of \(\tilde{\mathbf{\theta}}\) under \(\mathcal{H}_{1}\) is \(\hat{\mathbf{\theta}}_{1}=(\tilde{\mathbf{H}}^{H}\tilde{\mathbf{C}}^{-1}\tilde{ \mathbf{H}})^{-1}\tilde{\mathbf{H}}^{H}\tilde{\mathbf{C}}^{-1}\tilde{\mathbf{x}}\). Also we have, as shown in [23],  
\[\tilde{\mathbf{I}}(\tilde{\mathbf{\theta}})=\tilde{\mathbf{H}}^{H}\tilde{\mathbf{ C}}^{-1}\tilde{\mathbf{H}},\]  
and the real FIM has the special form  
\[\mathbf{\Gamma}^{-1}(\xi)=\frac{1}{2}\bigg{[}\begin{array}{cc}\text{Re}\{( \tilde{\mathbf{H}}^{H}\tilde{\mathbf{C}}^{-1}\tilde{\mathbf{H}})^{-1}\}&- \text{Im}\{(\tilde{\mathbf{H}}^{H}\tilde{\mathbf{C}}^{-1}\tilde{\mathbf{H}}) ^{-1}\}\\ \text{Im}\{(\tilde{\mathbf{H}}^{H}\tilde{\mathbf{C}}^{-1}\tilde{\mathbf{H}})^{- 1}\}&\text{Re}\{(\tilde{\mathbf{H}}^{H}\tilde{\mathbf{C}}^{-1}\tilde{\mathbf{ H}})^{-1}\}\end{array}\bigg{]}.\]
## 4 Conclusions  
This study proposed a method of image-based matching network modeling to analyze the color characteristics of the textiles found along the Silk Road from the 4 th to 8 th centuries. Two clustering methods, including K-means clustering and octree clustering, were compared for the datasets containing 165 images of brocade patterns. The cluster numbers for the primary and secondary colors were determined using the SSE method and elbow method to obtain three primary and nine secondary representative colors. The clustering results showed that most fabrics exhibit red-yellow tones, while the secondary colors tend toward yellow and green tones. By comparing the results with the PCSS color system, it was found that light and soft colors account for 27%, contributing to an overall style characterized by simplicity, elegance, and low contrast. The Apriori algorithm was used to derive eight association rules, and the matching network model was established. The results showed that the most frequently used two-color scheme is 1a-3c, with a confidence rate of 47%, while the three-color rule is 1a-3c-6f, with a confidence level of 61%. These color schemes were applied for a classic pattern style with a pair of dragons and pearls found in Astana, demonstrating various styles. This study can quantify the colors for understanding, and has significant implications for historical and cultural heritage. Since the experimental samples are brocade textiles found along the Silk Road, the sample size may be limited and the accuracy of the algorithm also need to be improved for further research.  
Conceptualization, B.G.; Methodology, L.Z. and B.G.; Validation, L.Z.; Formal analysis, L.Z., Y.L. and B.G.; Investigation, Y.L.; Resources, H.F. and Y.L.; Writing--original draft, X.S. and L.Z.; Writing--review & editing, B.G.; Supervision, H.F. and B.G.; Project administration, H.F.; Funding acquisition, H.F. All authors have read and agreed to the published version of the manuscript.  
The project was supported by National Natural Science Foundation of China (Grant No. 61702461), \"Textile Light\" Applied Basic Research Project of China National Textile Industry Federation (Grant No. J202007), Zhejiang Province Philosophy and Social Science Planning Art Research Project (Grant No. 24 NDJC171 YB), and the Special Fund for Scientific Research of Zhejiang Sci-Tech University (Grant No. 24076114Y).  
Not applicable.  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
Figure 8: Application of the matching color scheme in a classic pattern.
## 4 System Architecture and Performance Analysis  
### Reference High-rate Modulation Scheme  
#### 4.1.1 DS8-PSK Encoder  
Figure 3 shows the reference architecture of the DS8-PSK transmitter. The DS8-PSK waveform is transmitted using a pair of light sources, which comprise a reference LED group and a data LED group. Clusters of three bits from the input high-rate data stream are modulated based on the phase-shift value between two transmitted waveforms, which are driven by a pair of light sources. In order to enable the mapping of the clusters of three bits of an input bit string to a phase-shift value, the DS8-PSK duty cycle must be separated into eight time-slots. Additionally, the number of LEDs in each light source should be eight. The waveform generated by each LED in a light source is a square wave. Within a light source, the \(\left({\rm i}+1\right)^{\text{th}}\) waveform is delayed by 1/8 of a duty cycle compared to the \({\rm i}^{\text{th}}\) waveform.  
When transmitting Tx data, all phases of the waveforms which are generated by the reference LED group are maintained from 0, \(T\)/8, to 7\(T\)/8, and all phases of the waveforms generated by the data LED group are shifted in comparison with the waveforms in the first LED group based on the  
Figure 3: Reference architecture of the dimmable spatial 8-phase shift keying (DS8-PSK) transmitter.  
phase-shift value. The phase-shift value of a pair of light sources is called _S_Phase_Shift_. It is calculated using (5) by considering the value of \(i\), where \(T\) is the duty cycle of a LED waveform. Moreover, Table 1 shows the mapping of three bits onto the _S_Phase_Shift_ value by considering the value of \(i\).  
\[S\_Phase\_Shift=\frac{T}{8}i \tag{5}\]  
DS8-PSK supports dimming in steps of 1/8 (12.5%) of a duty cycle \(T\). The dimming level can be calculated by the sum of \"1\" states among eight time slots in a duty cycle \(T\) as expressed by (6). Thus, the DS8-PSK waveform can support seven dimming levels from 1/8 (12.5%) and 2/8 (25%) to 7/8 (87.5%). The data rate of each data LED group is calculated by (7). For example, with an optical clock rate and a symbol rate of 10 kHz and 10 symbols/s, respectively, the transmission data rate that can be achieved by a data LED group is 30 bps.  
\[Dimming\_level=\frac{\sum``1\"}{8} \tag{6}\]  
\[Data\_rate(bps)=(bit/symbol)\times(symbol\_rate) \tag{7}\]
## 3 Proposed Method
### Validation Stage  
The last stage consists in evaluating the \(k\) approximate and hardened programs obtained in the previous stages. Validation is done by comparing each version with the original program in terms of fault coverage, the percentage of error in the results and the execution time of overheads. This step is performed by means of fault injection tools to analyze the fault coverage, and simulators/emulators to determine the execution times and the percentage of error in the results.  
The effectiveness of the hardening step is validated by fault injection campaigns. The fault model is the bit-flip model, in which only a single bit is affected during each program execution. This model is useful to simulate the effects of a single event upset (SEU). Injected faults are classified according to their effect in the system as follows [42]:  
* SDCs (silent data corruption): When the program completes its execution with erroneous output results.
* unACEs (unnecessary for architecturally correct execution): When the program obtains the expected results.
* Hangs: For abnormal terminations of the program or infinite loops.  
Target bits of the system that, when affected by a fault, cause faults classified as SDC and 'hangs' are considered together as ACE, i.e., necessary for the architecturally correct execution of the system.  
To estimate reliability improvements, we propose to use the mean work to failure (MWTF) metric [43]. The MWTF takes into account the trade-off between performance and reliability, and can be calculated as follows:  
\[MWTF=\frac{amount\,of\,work\,completed}{number\,of\,errors\,encountered} \tag{1}\]  
\[MWTF=\frac{1}{raw\,error\,rate\cdot AVF\cdot execution\,time} \tag{2}\]  
Where the AVF (architectural vulnerability factor) is the probability that a fault becomes an error. It is calculated as follow:  
\[AVF=\frac{number\,of\,ACE\,bits}{total\,number\,of\,bits} \tag{3}\]