esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### The Thermal Conductivity of the Composites  
The in-plane and out-of-plane TC of PTFE composites with different FGi contents are shown in Figure 3a. As the FGi content increases, the in-plane TC increases from 0.14 for pure PTFE to 1.21 W\(\cdot\)m\({}^{-1}\cdot\)K\({}^{-1}\) for PTFE-0.5, and then decreases to 0.47 W\(\cdot\)m\({}^{-1}\cdot\)K\({}^{-1}\) for PTFE-0.75. Meanwhile, the out-of-plane TC remains almost stable. In order to analyze the effect of molecular orientation and crystallinity on TC, EMD simulations based on the Green-Kubo method are used to calculate the TC of PTFE with various orientations and crystallinities. The models for various orientation degrees and crystallinity are shown in Figure S4, and the corresponding TC for each model is shown in Figure 3b. It is well known that the phonon transport efficiency in molecular chains determines the TC of polymers [20]. The oriented arrangement reduces the disordered entanglement among the molecular chains and increases the mean free path of phonons, thereby significantly enhancing the TC [17; 19]. Therefore, the introduction of FGi enhances the in-plane orientation of PTFE molecules, leading to a rapid increase in the in-plane TC of PTFE-0.25 and PTFE-0.5. When the FGi content increases to 0.75 wt%, the reduced crystallinity leads to an increase in phonon scattering in the amorphous region, which results in a decrease in TC [37]. These phenomena align with the varying trends of in-plane TC observed in different models presented in Figure S5, indicating the synergistic effect of crystallinity and orientation degree on the enhancement of TC.
# Blood Pressure Monitoring Based on Flexible Encapsulated Sensors
## 3 Flexible Packaging and Testing of Sensors
### Performance Testing of Sensors  
In order to compare the performance of the sensor before and after packaging (the ratio of PDMS curing agent to base was 1:40), this study conducted a cyclic experiment of applying and releasing pressure to the sensor three times while controlling the external pressure. During the application of pressure, the external pressure was increased from 104 kPa to 114 kPa in 2 kPa steps. During the release of pressure, the external pressure was decreased from 114 kPa to 104 kPa in 2 kPa steps. The relationship curve between the external applied pressure and the output value of the packaged sensor is shown in Figure 5.  
From the above figure, it can be observed that the encapsulated sensors exhibited hysteresis and good repeatability. Furthermore, the output values of the sensors were directly proportional to the applied external pressure. According to our calculations, the sensitivity of the encapsulated sensor was 0.9972, linearity was 0.26%, hysteresis was 0.021%, repeatability was 0.127%, and accuracy was 0.1962. To compare the performance of the sensors before and after encapsulation, the accuracy and sensitivity of sensors before and after encapsulation were calculated and are presented in Table 1.  
According to Table 1, it can be seen that the changes in the precision and sensitivity of the sensor after encapsulation were small, and the encapsulated sensor still retained high precision and sensitivity.
## 3 Results
### Amylose and Amylopectin Content  
The amylose and amylopectin content are key factors influencing the properties and performance of starch-based thermoplastics. These two polysaccharides differ in their molecular structure and behavior, directly impacting the mechanical, thermal, and barrier properties of starch-based materials.  
According to [PERSON]. [PERSON] et al. (2013), a higher amylose content leads to greater rigidity and tensile strength, as amylose molecules have a linear structure that promotes strong intermolecular interactions and film formation ([PERSON] et al., 2013). In contrast, higher amylopectin content enhances flexibility and elasticity, due to its highly branched molecular structure, which disrupts ordered packing and increases plasticity ([PERSON] et al., 2013).  
For the purple Peruvian potato, the amylose content was determined to be 23.76%, while the amylopectin content was 76.24%. These values are consistent with those reported in the literature for various potato species ([PERSON] et al., 2013; [PERSON] et al., 2013; [PERSON] et al., 2013). The moderate amylose content in purple Peruvian potato starch suggests that it can form strong yet somewhat flexible films, making it a promising candidate for applications in biodegradable packaging and starch-based thermoplastics.  
Additionally, the high amylopectin content enhances the gelatinization properties, influencing factors such as swelling power, solubility, and retrogradation behavior. This balance between amylose and amylopectin makes purple Peruvian potato starch particularly suitable for film formation, bioplastic applications, and edible coatings where both strength and flexibility are desirable.
# Reliability of a Portable Fixed Dynamometer During Different Isometric Hamstring Assessments
## 2 Methodology  
### Experimental Approach to the Problem  
A cross-sectional, repeated measures design was used for comparative analysis of reliability for peak force, mean force, RFD, contraction duration and impulse during three different maximal voluntary isometric contractions (MVICs) of the hamstrings, as measured with a portable fixed dynamometer device (Hawkin TruStrength, Portland, ME, USA) at a sampling rate of 1200 Hz. Participants attended a familiarization session and three testing sessions exactly seven days apart, at the same time of day to minimize biological variability. Reliability was established using changes in the mean, ICCs and CVs.
## 4 Result and Discussions
### Comparison of XGBoost with Other Algorithms  
The best features observed were a combination of the numeric features and features extracted using GLCM and CNN. Four other models were developed based on the features using different algorithms, and the performances of the models were compared with XGBoost. The algorithms considered are Random Forest (RF), Extreme Learning Trees (ET), Gradient Boosting (GB), and K Nearest Neighbor (KNN), with hyper-parameter optimization conducted in each case. The average RMSE of each algorithm is presented in Figure 4. It is observed that XGBoost (together with CNN and GLCM extracted features and numeric features) had the lowest RMSE, followed by GB, and ET had the highest value. XGBoost was better because of the regularization term added to its loss function and the column subsampling technique employed in it, which minimizes overfitting. ET had the least performance because it does not employ any optimization process for the determination of splits; rather it chooses the splits randomly.  
Figure 4: RMSE values of the ML models for a combination of CNN + GLCM features and non-image features.  
Figure 3: Percentage improvement over non use of images for the top three feature extraction methods; CNN, CNN + LBP, and CNN + GLCM.
# Infrastructure Elements for Smart Campuses: A Bibliometric Analysis
## 3 Results: Overview of Smart Campus Research
### Discussion of Smart Campus Infrastructure Themes
#### 3.2.3 Energy Management System (EMS)  
The development of auto-analysers as part of a smart campus network grid can be integrated with an energy management system (EMS). The EMS cluster theme was produced from modularity class 1, as shown in Figure 6 and Table 4. In Table 4, the dominant drivers are renewable energy with harmonic closeness centrality measure of 0.396; BIM to BEM (0.399); Photovoltaic (0.399); energy renovation (0.397); laser scanner (0.394); and net-zero energy building (0.359). All the publications that produced this theme were journals except for one conference article and the cluster year of publication is 2015.  
Renewable energy and Photovoltaic devices as the main drivers of the energy management system continually recurred in 25% of analysed documents for this group. Net-zero energy buildings consume energy created on-site, mostly through renewable sources [56; 57]. Net-zero energy buildings or retrofitted existing buildings on campuses can use photovoltaic solar panels and renovate an existing building to become energy efficient [58]. In achieving an EMS system on campus, building information modelling (BIM) can be applied in building energy model (BEM) when retrofitting or renovating existing campus buildings [59; 60]. EMS can work effectively in a smart campus grid where there are machine learning-enabled analysers and cloud computing.
## 4 Results
### Assessment of Exposed Assets
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Piombino-Follonica**} & \multicolumn{3}{c}{**Marina di Campo**} & \multicolumn{3}{c}{**Alghero-Fertilia**} \\ \hline
**Terrain Under Sea Level** & **2010** & **2040** & **2070** & **2099** & **2010** & **2040** & **2070** & **2099** & **2010** & **2040** & **2070** & **2099** \\ \hline Cropland & 26.60 & 35.20 & 39.60 & 44.60 & 1.54 & 1.03 & 0.96 & 1.52 & 2.83 & 0.68 & 1.92 & 6.36 \\ Woodland and Forest & 12.30 & 18.30 & 17.30 & 18.30 & 1.54 & 4.38 & 4.19 & 6.69 & 0.53 & 0.45 & 1.52 & 5.37 \\ Grassland & 0.00 & 0.01 & 0.05 & 0.10 & - & - & - & - & 2.49 & 0.59 & 0.98 & 2.39 \\ Healthland and Scribus & 0.01 & 0.03 & 0.30 & 0.46 & 0.14 & 0.21 & 0.88 & 17.90 & - & 0.12 & 0.52 & 1.49 \\ Open Space \({}^{1}\) & 2.98 & 2.64 & 2.74 & 3.11 & 43.40 & 48.80 & 45.70 & 31.20 & 11.60 & 1.40 & 2.57 & 4.07 \\ Wetland & 17.60 & 18.00 & 23.00 & 19.50 & - & - & - & - & - & - & - & - \\ Water & 35.30 & 21.20 & 12.40 & 8.15 & 45.90 & 39.60 & 36.90 & 22.00 & 68.80 & 95.40 & 88.50 & 73.30 \\ Urban & 5.19 & 4.57 & 4.55 & 5.83 & 7.55 & 6.03 & 11.40 & 20.60 & 13.70 & 1.34 & 3.96 & 6.98 \\ \hline \hline \multicolumn{11}{c}{**Rome**} & \multicolumn{3}{c}{**Latina-Sabaudia**} & \multicolumn{3}{c}{} \\
**Terrain Under Sea level** & **2010** & **2040** & **2070** & **2099** & **2010** & **2040** & **2070** & **2099** & & \\ \hline Cropland & 31.70 & 42.70 & 43.20 & 38.60 & 29.30 & 27.80 & 25.80 & 24.50 & & \\ Woodland and Forest & 12.10 & 8.71 & 6.85 & 7.75 & 2.04 & 3.62 & 5.21 & 6.50 & & \\ Grassland & 0.13 & 0.24 & 0.96 & 3.07 & 15.30 & 22.30 & 28.40 & 31.20 & & \\ Heathland and Scribus & 0.01 & 0.12 & 0.36 & 0.39 & - & 0.00 & 0.00 & 0.00 & 0.00 & & \\ Open Space \({}^{1}\) & 5.35 & 6.25 & 6.84 & 6.14 & 0.41 & 0.64 & 1.14 & 1.58 & & \\ Wetland & 2.10 & 3.72 & 5.61 & 5.14 & 3.66 & 3.16 & 2.74 & 2.42 & & \\ Water & 35.80 & 25.20 & 17.90 & 11.80 & 48.00 & 39.80 & 32.30 & 26.60 & & \\ Urban & 12.80 & 13.10 & 18.20 & 27.10 & 1.28 & 2.73 & 4.44 & 7.16 & & \\ \hline \hline \end{tabular} \({}^{1}\) Open space with little or no vegetation.  
\end{table} 
Table 5: Breakdowns in percentage values of the most exposed assets due to the RSLR during the 21 st century.
# Methods for Detecting Picric Acid--A Review of Recent Progress
## 3 Sensors for Detecting and Measuring PA
### Carbon Dots  
Carbon dots (C-dots), comprising graphene quantum dots (GQDs), carbon nanodots (CNDs) and carbon quantum dots (CQDs) [90; 91] belong to a relatively new class of carbon nanomaterials, which are less than 10 nm in size [92]. Currently, C-dots are obtained by laser ablation of graphite powder and cement [93; 94], and the thermal cracking of organic compounds [95; 96], electro-oxidation of graphite [97]. C-dots are generally characterised by simple and inexpensive preparation methods and strong electromagnetic wave absorption properties, especially in the UV range, and depending on the method of obtaining them, this range can be extended to visible light wavelengths [98; 99; 100; 101; 102; 103; 104]. To date, it has not been possible to propose a unified mechanism for the fluorescence of C-dots. There are currently three main theories on the subject, these are the surface state theory [105; 106; 107; 108; 109], the quantum confinement effect theory [110; 111; 112; 113] and the molecular fluorescence theory [114; 115; 116].  
Due to their luminescent properties and low toxicity [97; 117] C-dots have shown potential for, among other things, fluorescence bioimaging [118] and multimodal bioimaging of cells or tissues [119]. C-dots can also be used as biosensors, for example, for visual monitoring of elemental content in cells [120; 121], glucose levels [122] or pH values [123]. Nanomaterials of this type are optically sensitive to compounds that are chromophores.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Material** & **Sensing Mechanism** & **K\({}_{\text{SV}}\) (M\({}^{-1}\))** & **LOD** & **Ref.** \\ \hline MAEC-PMA & static quenching & \(2.95\times 10^{4}\) & 93.3 nM & [85] \\ COP-401 & PET (dynamic quenching) & \(8.3\times 10^{4}\) & \(<\)1 ppm & [82] \\ COP-301 & PET (dynamic quenching) & \(2.6\times 10^{5}\) & \(<\)1 ppm & [82] \\ A-NS & static quenching & \(8\times 10^{5}\) & 90 nM & [86] \\ SNW-1 & quenching & \(9.5\times 10^{4}\) & 50 nM & [81] \\ COP-3 & PET (static quenching) & \(1.45\times 10^{4}\) & \(<\)1 ppm & [87] \\ DL-COF & static quenching & \(2.24\times 10^{6}\) & 57.31 nM & [88] \\ PI-COF & PET, IFE & \(1\times 10^{7}\) & 0.25 uM & [89] \\ \hline \hline \end{tabular}
\end{table}
Table 4: Example of COPs and COFs used as nitro high-energetic compounds sensors.  
Based on the ability of C-dots to undergo fluorescence quenching in the presence of PA, they have attracted significant interest as potential probes for the detection of picric acid and other nitroaromatic compounds (Table 5).
## 2 Related Work
### Hardware Event Counters  
HPCs are special-purpose registers located inside the processor that count the number of occurrences of microarchitectural events, such as L1 cache miss, instruction retired, bus access, branch misprediction, etc. In most modern processors, the number of events that can be monitored ranges from twenty to more than a few hundreds, but only a limited set can be selected at a time, around 2 to 8 for many processors. The HPCs were mostly designed to measure the performance of software running on top of a given processor and to help in optimizing the software. Although HPCs are not designed for security, there is significant use of HPCs for microarchitectural attack detection in several research works since they are easily accessible by software [7; 10]. The proposed approaches involve profiling the software with the counted events in order to either identify the characteristics of attacks (so-called signature-based approach [11; 12]), identifying the characteristics of a legitimate software (so-called anomaly-based approach [13]), or a mix of them [14]. The profiling can be performed with machine learning algorithms [10; 11; 13; 14] or using some heuristics [12].  
Similar to HPC, [PERSON] et al. proposed CC-Hunter [15], which adds event monitoring logic in shared hardware and counts whenever one process uses specific hardware resources that are already occupied by another process. It allows the detection of Covert Timing Channels (CTCs), which use the leak of microarchitectural timing information to secretly communicate between different processes. PMUe [16] proposed the detection of ROP attacks based on modified HPCs. It contains a hardware logic recognizing short instructions separated by branch misprediction events, which is a typical ROP pattern.  
However, existing hardware event counters approaches only focus on counters in a small and predefined list, without discussing a systematic approach to evaluate various microarchitectural events and find what events can efficiently contribute to detect attacks.
# Advances in Toughening Modification Methods for Epoxy Resins: A Comprehensive Review
## 3 Thermoplastic Resin Toughening Method
### Advancements in Thermoplastic Resin Toughening  
In 1983, [PERSON] and [PERSON] [63] introduced the concept of using thermoplastic resins to modify and toughen epoxy resins. Currently, the thermoplastic resins employed for toughening epoxy resins include polyetheretherketone (PEEK), polyetherimide (PEI), polysulfone (PSF), and polyethersulfone (PES). These materials are favored not only for their excellent toughness but also for their high modulus and thermal resistance, making them well-suited as toughening agents for epoxy resins.
# Echelle Grating Spectroscopic Technology for High-Resolution and Broadband Spectral Measurement
## 3 Optical Layout
### Cross-Dispersion Layout  
At OQLM, the grating equation can be expressed as:  
\[2d\sin\theta_{B}\cos\gamma=m\lambda \tag{18}\]  
Due to the large blaze angle and large grating period of the echelle, the blazed wavelengths of all orders are diffracted in the same direction, causing the overlap of the diffraction wavelengths from different orders. In order to separate the overlapping orders, we need to perform secondary dispersion in the direction perpendicular to the grating dispersion, forming the cross-dispersion to obtain a two-dimensional spectrum [30], as shown in Figure 6. The cross disperser can be a prism or a grating. Commercial spectrometers usually use prisms, and astronomical instruments employ gratings more. We can define the grating dispersion as the primary dispersion and the dispersion perpendicular to the primary one as the auxiliary dispersion. The resolution of the auxiliary dispersion should be greater than the free spectral range. The arrangements of the primary and auxiliary dispersion have three modes, as shown in Figure 7. Taking a prism as the auxiliary disperser, for example, the position of the prism can be before or after the echelle, or the rays pass through the prism twice. When the rays are pre-dispersed by the prism (Figure 7a) [31,32], the incidence angles on the echelle are not uniform, which makes the calibration difficult. The layout in which rays pass through the prism twice (Figure 7c) [33, 34, 35, 36] suffers from the  
Figure 5: (**a**) In-plane Quasi-Littrow mode (IQLM); (**b**) Off-plane Quasi-Littrow mode (OQLM). The gray plane is the principal plane of the echelle, N is the normal of the echelle plane, and Na is the normal of the reflective facet. \(i\) and \(\theta\) are the angles of incidence and diffraction, respectively. \(i_{d}\) is the angle between the incident rays and Na. \(\gamma\) and \(\gamma^{\prime}\) are the angles at which the incident and diffraction rays deviate from the principal plane, respectively. \(W_{g}\times L_{g}\) is the size of the echelle.  
same problem, but it has a higher resolving power between orders. The most common mode is that the prism is after the grating (Figure 7b) [15; 37], which is easier for calibration and can meet the resolving demands for orders in most cases.
## 5 Results and Discussions
### Network Resource Efficiency  
Next, a simulation is performed to evaluate the efficiency of network resources (RE) in serving the received NSRs. Figures 3 and 4 show the results considering the maximum number of nodes and the network states.  
The RE calculated based on the total number of nodes (100, 200, 300), are shown in Figure 4. At N = 300, the network can maintain a value above the median for RE, although the resource availability gradually decreases as the number of NSRs increases. In contrast, the network encounters resource shortages when N = 100, leading to a sharp decline in RE when the number of NSRs peaks. Therefore, effective network resource allocation schemes are required to keep the network operational regardless of the surge of service requests. During simulations, the proposed DCB-NS scheme has maintained a higher RE than the Thompson Sampling and Epsilon-Greedy methods. The proposed scheme improves the slice allocation process by utilizing the RIF values that determine the current network resource utilization. Additionally, the integrated maximum flow computation ensures an efficient mapping of network slices. These processes allow the NSA to intelligently select the best possible network slices by considering the current state of the network.  
The network RE based on the current network state is also considered, as shown in Figure 5. For this simulation, the average REs for network states 1, 2, and 3, are taken as shown in Figure 4a-c. The results indicate that the proposed scheme can achieve higher RE regardless of the different states of the network. Moreover, the proposed scheme achieves  
Figure 4: Network Resource Efficiency according to the maximum number of nodes: (a) N = 300, (b) N = 200, and (c) N = 100.  
favorable results compared to the other algorithms for all network states and the maximum number of network nodes.
# The Helical Resonator: A Scheme for Radio Frequency Plasma Generation
###### Abstract  
The helical resonator is a scheme for the production of high voltage at radio frequency, useful for gas breakdown and plasma sustainment, which, through a proper design, enables avoiding the use of a matching network. In this work, we consider the treatment of the helical resonator, including a grounded shield, as a transmission line with a shorted end and an open one, the latter possibly connected to a capacitive load. The input voltage is applied to a tap point located near the shorted end. After deriving an expression for the velocity factor of the perturbations propagating along the line, and in the special case of the shield at infinity also of the characteristic impedance, we calculate the input impedance and the voltage amplification factor of the resonator as a function of the wave number. Focusing on the resonance condition, which maximizes the voltage amplification, we then discuss the effect of the tap point position, dissipation and the optional capacitive load, in terms of resonator performance and matching to the power supply.  
plasma; helical resonator; radio frequency; RF plasma source +
Footnote †: journal: Applied Sciences
## 4 Energy Efficiency  
In this section, we discuss the precoding for a downlink CF-mMIMO-assisted LEO SATCOM system based on the EE maximization criterion. Due to the effect of NPAs, the signal received by a single GU contains interference from other GUs and signal distortion except for the desired signal. In this paper, we treat the signal distortion as noise and utilize the model of NPAs introduced in Section 2. In addition to this, we have to consider the connection between the satellites and the GUs. Moreover, the investigation of EE maximization is more complex than the investigation of SE maximization.