esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Short Chain Alcohols
### Isopropanol  
Isopropanol is a natural fermentation product from certain microbes. Industrially, isopropanol is widely used as a solvent and can be dehydrated into propylene, a petroleum-derived monomer for the production of the widely used plastic, polypropylene. The production pathway for isopropanol begins with acetyl-CoA condensation to acetoacetyl-CoA, which is followed by the removal of the CoA moiety to form acetoacetate. Acetoacetate is then decarboxylated into acetone, which is subsequently reduced to isopropanol (Figure 3). Isopropanol production pathway and genes were introduced into PCC 7942 [16]. Despite the expectation that the isopropanol pathway has a decarboxylation as driving force, the resulting strain was not able to produce isopropanol under photosynthetic conditions. Further analysis through acetate feeding showed that the acetyl-CoA level in PCC 7942 is insufficient to support isopropanol production. As an alternative approach, cyanobacteria expressing isopropanol production genes were incubated in the dark without nitrogen and phosphorus. Under nutrient limiting conditions, the acetyl-CoA flux was hypothesized to increase. As a result, 26.5 mg/L of isopropanol was observed [16]. With further optimization of cultivation conditions, isopropanol production has been increased to 146 mg/L [15].  
In another study aimed at producing acetone, the precursor to isopropanol, using engineered PCC 6803, acetate forming genes were disrupted to increase the pool of intracellular acetyl-CoA [64]. This approach increased the acetone production six-fold reaching a titer of 36 mg/L in four days. Together, these results indicated that in addition to decarboxylation, increasing the substrate pool is also an important design principle.
## 4 Experiments
### Experiments on CAI and Ablation Study  
To evaluate the quality of the Class Activation Intervals predicted by the network, we do not have any ground-truth values and therefore we asked for an opinion from experts. The result produced is in agreement with experts knowledge. For example, from Figure 3 we can see how, for the CAI of the maize class the model has predicted the time interval that goes from May to the beginning of November, while for the _winter wheat_ class the model has predicted a CAI that goes from late April until early July despite the NDVI values are still high. This high NDVI value is typically due to weed presence, re-growth after harvesting and a subsequent crop. Indeed, the model identified in these noisy and complex data the time domain that is uniquely related to wheat growth.  
To understand how the CAI changes with the variation of the patch on which it is calculated, we have taken into consideration the _winter wheat_ class and calculated all the CAI vectors on the entire test set. We show in Figure 8 an aggregate mean value for each patch, and we can see how the time interval associated with this class does not change according to the patch, even if the average activation values \(n_{i,j,t}^{c}\) change slightly. The network used in this experiment uses Resnet 101 as a bottom-up block and the numerical results are reported in Table 1.  
To understand the effect of learning the NDVI indices with the aim of predicting the CAI, we also conducted an ablation study in which we eliminate the MSE loss during training and compare this result with the same model that uses the loss function. From the numerical results reported in Table 3 we can see that the MSE Loss does not greatly affect the classification performance.
## 3 System Throughput and Packet Loss Rate  
In this section we derive expressions for the system throughput and the packet loss rate considering the system model in Section 2.  
Figure 1: System model topology: \(K=5\) satellites with different erasure probabilities \(\varepsilon\) for a spacing of \(s=4\) positions between consecutive satellites. The snapshot corresponds to the 17 th position of \(Sat_{1}\).
## 4 Discussion
### Model Accuracy vs. Computational Cost Trade Offs  
Our analysis revealed several important trade offs between the model accuracy and computational efficiency. Higher-fidelity models provide better accuracy but require significantly more computational resources. Simplified surrogate models can reduce the computation time by 60-70% while maintaining acceptable accuracy (within \(\pm\)0.2 mm). And the optimal balance point appeared to be achieved when using medium-fidelity models during optimization, followed by high-fidelity validation of the final solutions.  
Figure 5: Experimental validation of the flexible nozzle design through detailed measurements and comparisons. (**a**) The experimental setup of the laser tracker measurement process inside the nozzle, highlighting our rigorous data collection methodology. (**b**) The high-resolution mesh curves obtained from both laser tracker measurements and flexible sensors, providing comprehensive shape data. (**c–f**) Detailed profile deviation analyses for each of the Mach number configurations (1.3, 1.2, 1.1, and 1.0, respectively), allowing for a quantitative assessment of our design’s performance against theoretical predictions. The results demonstrate strong agreement between the designed and achieved profiles, with deviations falling within acceptable engineering tolerances.  
These findings suggest that, while our approach is highly effective for current applications, future work should focus on developing more efficient optimization algorithms and exploring advanced parallel computing strategies to handle increasingly complex nozzle configurations.
## 1 Introduction
Moreover, our group developed bOCT technology by integrating OCT technology with biospeckle phenomena, and demonstrated the micronutrient and toxic effects of increasing Zn concentrations (0, 5, 10 and 100 mg/L) on seed germination and seedling growth [12], the individual effect of four different PEMPs concentrations (0, 10, 50 and 100 mg/L) on lentil seed germination [13], the synergic effect of 100 m/L PEMPs and Zn on seed germination [14] and the impact of Acid Mine Drainage (AMD) on seed germination and seedling growth [15]. In these studies, the observed bOCT results were compared with some conventional measurements to observe the reliability and observation speed of the bOCT technique. Interestingly, the conventional measurements are in agreement with the bOCT results. However, the effect of an external agent was successfully observed within 6 h of exposure using the proposed bOCT technique, in contrast to conventional measurements which took a minimum of 40 h to exhibit significant effects, emphasizing the relative reliability and observation speed of the bOCT technique. In this study, a novel optical technology called bOCT was deployed to examine the possible size-dependent effect of AlNPs on lentils (_Lens culinaris_), a popular leguminous crop consumed and cultivated around the world. It has been estimated that the demand for lentils will increase from 6.3 million tons in 2018 to grow up to 8.4 million tons in 2024 globally, owing to the important properties of lentils, such as possessing a substantial amount of protein and fiber, low calories, essential amino acids, fatty acids, and trace minerals [16].
# Could _Gnomoniopsis castaneae_ Be Used as a Biological Control Agent against Insect Pests?
## 3 Results
### Virulence of the Tested Fungi in Adults of M. persicae  
Significant differences in _M. persicae_ adult's mortality were detected among Treatments (F = 71.062, df = 1.1832, \(p\) < 0.001), Exposure time (F = 6.347, df = 6.1832, \(p\) < 0.001), and used Concentrations (F = 18.105, df = 6.1832, \(p\) < 0.001). The main effects and interactions for all factors proved to be significant (Treatment * Exposure time: F = 7.994, df= 6, \(p\) < 0.001; Treatment * Concentration: F = 4.430, df = 5, \(p\) < 0.001; Exposure time * Concentration: F = 4.668, df = 30, \(p\) < 0.001; Treatment * Exposure time * Concentration: F = 4.317, df = 30, \(p\) < 0.001).  
On day 7 after exposure, the final mortality percentages of _M. persicae_ adults were 26.7 to 86.7% with _I. fumosorosea_ and 6.7 to 36.7% with _G. castaneae_ (Figure 3). For the control, larvae mortality was 6.7% at the end of the experiment. The mortality percentage was dose dependent. The survival time of _M. persicae_ adults treated with _I. fumosorosea_ and _G. castaneae_ was statistically significant in comparison to the survival time of control adults. More specifically, the survival time of adults was 6.43-4.46 days after exposure to _I. fumosorosea_ and 6.56-5.96 days after exposure to _G. castaneae_. For control adults, the survival time was 6.76 days (F: 6.117; df: 12; \(p\) = 0.017).  
Figure 2: Average (% \(\pm\) sd) larval mortality of _T. granarium_ treated with conidial suspensions of _I. fumosorosea_ and _G. castaneae_ at six concentrations (1:10\({}^{3}\), 2:10\({}^{4}\), 3:10\({}^{5}\), 4:10\({}^{6}\), 5:10\({}^{7}\), 6:10\({}^{8}\)).  
P. interpuctella, 2.1 \(\times\) 10\({}^{8}\) conidia per _M. persicae_). On the other hand, relevant _G. castaneae_ post-mortem conidial production was 1.5 \(\times\) 10\({}^{4}\) conidia per _T. granarium_, 2.3 \(\times\) 10\({}^{4}\) conidia per _M. persicae_ cadavers, and 1 \(\times\) 10\({}^{5}\) conidia per _P. interpuctella_ cadaver.
# Consumer's Attitude towards Display Google Ads
## 1 Introduction
### Hypotheses Development and Research Model
#### 1.2.3 Display Google Ads Credibility  
Credibility is the extent to which a consumer has perceived information and background with the advertisement [38]. Ad credibility was defined as the credible content of advertisements that made the ad more persuasive and convincing [39]. The trustworthiness and attractiveness of an ad can highly impact a consumer's attention [40], for example, a consumer's first impression of an ad can extremely influence its credibility of it, thus making it an important variable to be examined [39]. Positive thinking about the ad's credibility enhances customers' attitudes toward the ad [41]. Google ads create a positive image of products and/or services, and increase customer engagement and sales [12]. Trust in social media tools affects consumer attitudes [13], brand awareness, and customers' buying decisions [15]. Confidence and personal experience are important for customers' trust [18]. In the current study, credibility is described as the extent to which Display Google ads are believable to the user. It is noted that limited studies examined the effect of ads' credibility in terms of consumer attitude towards Display Google ads. Consequently, it is posited that:  
**H5.**_Display Google ads credibility has a significant positive influence on consumer attitude towards Display Google ads._
## 2 LPD Signal Design and Detection Method
### Practical Receiver Design  
As illustrated in the last section, the feasibility of the under-sampling method has been proved. However, the proposed LPD signal waveform does not exhibit a constant envelope. The sampling phase plays an important role in the sampling process. Here, a practical receiver-based on a multiphase clock [25; 26] is presented. The block diagram of the receiver is shown in Figure 3. At the front end of the receiver, the pass band of the analog band  
Figure 1: Comparison diagram of the spectrum between \(s(t)\) and \(x(t)\). **(a)** Spectrum of modulated signal \(s(t)\). **(b)** Spectrum of the proposed LPD signal \(x(t)\).  
pass filter (BPF) is \([(L-1/2)\omega_{c_{s}},(L+N-1/2)\omega_{c}]\), and the bandwidth of the pass band is \(N\omega_{c}\). Then, frequency contents out of the pass band will be filtered out by the analog BPF. The SNR of signal \(y(t)\) can be given by \(SNR_{y}=P_{s}/NN_{0}\omega_{c}\), where \(P_{s}\) denotes the average transmit power. Thereafter, the multiphase clock, which can produce several sampling clocks with the same frequency but different phases, are employed. They can be modeled as \(p_{m}(t)=\sum_{n=-\infty}^{+\infty}\delta(t-nT_{s}-m\Delta T_{s})\) where \(m=0,1,2,\cdots,M-1\) and \(\Delta=1/M\). Thus, a total of \(M\) sampled signals can be obtained. Comparing the mean power of these sampled signals, we can select the sampled signal with the maximum mean power as input for the LPF. The LPF is considered to be an ideal LPF with cut-off frequency \(\omega_{B}\). It's important to note that artificial noise (if it exists) will be filtered out by the LPF.  
For the noise component, \(w(t)\) can be written by summation of \(N\) sub-band noise elements as \(w(t)=\sum_{k=0}^{N-1}w_{k}(t)\exp[(k+L)\cdot j\omega_{c}t]\), where \(w_{k}(t)\) is an independent zero-mean band-limited AWGN with bandwidth \(\omega_{c}\) and power spectrum density \(N_{0}\). After sampling, these sub-band noises are added incoherently, and the power spectrum density becomes \(NN_{0}\). Following that, the SNR of signal \(\bar{s}[n]\) can be given by  
\[SNR_{d}=\frac{E\left[\bar{s}^{2}[n]\right]}{NN_{0}\omega_{B}}=\frac{\left| \sum\limits_{k=1}^{L+N-1}\sqrt{1/N}\cdot\exp(j2\Delta k\pi)\right|^{2}P_{s}}{ NN_{0}\omega_{B}} \tag{3}\]  
Figure 2: Frequency domain representation of proposed sampling method. (**a**) Spectrum of \(x(t)\). (**b**) Spectrum of sampling function. (**c**) Spectrum of sampled signal with \(f_{s}=f_{c}\). (**d**) Spectrum of sampled signal with \(f_{s}\
eq f_{c}\).  
and then the receiving gain can be achieved as  
\[\eta=\frac{SNR_{d}}{SNR_{y}}=D\cdot\left|\sum_{k=L}^{L+N-1}\sqrt{1/N}\exp(j2\Delta k \pi)\right|^{2} \tag{4}\]  
As a result, the maximum receiving gain becomes \(ND\) if the sampling phase is synchronized perfectly when \(\Delta=0\). Followed by the LPF, signal \(\widehat{s}[n]\) can be demodulated in traditional ways.
# The Development of a Prototype Solution for Detecting Wear and Tear in Pedestrian Crossings
## 3 Prototype
### Hardware Components  
A Raspberry PI was utilized to construct the IoT device for integration into the vehicles. A Raspberry PI 5 with 8 GB RAM was employed to assess the performance of the final device [22]. Additionally, it was essential to equip the system with a GPS device, with the NEO-6M GPS model [23] being utilized in this instance. Finally, for image acquisition, it was necessary to integrate a camera, in this case, a Wide-Angle 1080 p UVC-Compliant USB Camera Module with a Metal Case [24]. To interconnect all these components, an electrical circuit was designed using the EasyEDA online tool [25]. Figure 17 shows the schematic of this electrical circuit, which includes all the components described.  
* Raspberry PI (U1)
* GPS NEO-6M (U2)
* Wide-angle 1080 p UVC-Compliant USB Camera Module with Metal Case (U3)  
As previously stated, the Raspberry Pi serves as the processing core, executing the YOLOv4-tiny model to detect wear and tear on crosswalks. The GPS device is tasked with providing the precise geographical coordinates of the location of the crosswalks. The camera is responsible for capturing high-quality images of the crosswalks. The camera's wide viewing angle allows the YOLOv4-tiny model to obtain detailed images of the surface of crosswalks, which facilitates the identification of signs of wear.  
Figure 16: The architecture of the prototype was developed.  
A detailed examination of Figure 17 reveals that the connection between the Raspberry Pi and the camera is established via a Universal Serial Bus (USB) port. Regarding the link between this device and the GPS module, a serial connection is employed via the Raspberry Pi's RX and TX pins. This connection is powered by the 3.3 V and ground connections on the Raspberry Pi. Figure 18 illustrates the various hardware components developed based on the electrical circuit shown in Figure 17. This circuit employs a cell phone holder, designated as the \"Phone Mount\"(Figure 18 (1)), which is adapted to support the camera. Additionally, it utilizes the Arducam 1080 P Low Light (Figure 18 (2)). This is connected via a USB 2.0 port to the Raspberry PI 5 (Figure 18 (3)); the GPS module is also used, GPS Neo-6m (Figure 18 (4)), via the Raspberry PI's RX serial port, which is connected to the GPS module's TX port. Finally, the power supply employs a Bluetooth adapter (Figure 18 (5)), which, in addition to containing Bluetooth technology, also contains two USB ports. This is plugged into the cigarette lighter socket in the car and then connected to the Raspberry Pi via a USB-C cable. The subsequent section will describe the software solutions developed to operate this hardware.  
Figure 17: Schematic of the electrical circuit of the various hardware components.
## 2 Methodology  
### Description of the Innovative Refrigerant-PCM-Water Heat Exchanger  
In this paper, an innovative compact 3-fluids refrigerant-PCM-water heat exchanger (RPW-HEX) shown in Figure 2a is proposed, as a solution to reduce bulkiness and complexity of integration of latent heat TES into vapour compression systems, especially while assembling the final components. The RPW-HEX was designed and manufactured by AKG (Hofgeismar, Germany) and it contains 7 sections: the central part of the RPW-HEX (block A), one distributor for the HTF and one for the refrigerant (blocks W1 and R1), one collector for the HTF and one for the refrigerant (blocks W2 and R2), and two PCM storage containers (block P1 and P2) to provide space for expansion of the PCM. The main focus of the design of the RPW-HEX is the centre section (block A), which is designed to incorporate refrigerant, PCM, and HTF in an array of parallel channels integrated in a unique container, hence eliminating the need of having extra components (i.e., TES tank, HEX, valves, etc.) in the overall system. The channels are distributed in a regular way following the order of refrigerant--PCM--HTF, as shown in Figure 2b. This configuration allows simultaneous direct heat transfer between the refrigerant and both the HTF and the PCM. To enhance the heat transfer, the refrigerant and the HTF flow in counter-flow configuration.  
The RPW-HEX is made of 600 \({}^{\circ}\)C dip-brazed 3000 aluminium core alloy, which provides high mechanical stability to withstand the volume expansion of the PCM and the refrigerant pressure inside the RPW-HEX. The low density of aluminium allows construction of relatively light structure of the RPW-HEX. Moreover, the high thermal conductivity and low cost of aluminium over other metals encourages even more its use in construction of the heat exchangers.  
Table 1 presents the main structural characteristics of the RPW-HEX in study. The RPW-HEX external structural size is 300 mm \(\times\) 272 mm \(\times\) 94 mm, while internally it contains a total of 45 channels (15 PCM channels, 14 refrigerant channels, and 16 HTF channels). An extra channel of the HTF is designed to isolate the refrigerant channel from the external surface to lessen the possible cold losses to the ambient, thus both the top and the bottom of the tank contain the HTF channel. Fins are embedded  
among the HTF, refrigerant, and PCM channels to enhance heat transfer. Each refrigerant and HTF channel contains 43 fins, while each PCM channel contains a total of 33 fins. The RPW-HEX channels containing the PCM are constructed of dense geometry of aluminium offset strip fins to increase heat transfer through the PCM taking advantage of the high thermal conductivity of aluminium.
## 4 Results
### Data Preprocessing  
The training/testing subset separation ratio has a substantial impact on a model's accuracy and generalizability. The ratio depends on the issue's features and available data. For complex problems or limited data, a larger portion of the data should be allocated to the training set. Common separation ratios applied are 70/30, 80/20, or 90/10. Figure 5 displays the RMSE sensitivity analysis results of an RBFNN model for various training/testing subset separation ratios. The results show that a 90/10 separation split is most effective, which is applied in this study.  
Figure 6 displays outliers identified applying the MD method with a Gaussian process regression (GPR) model with an automatic relevance determination (ARD) exponential  
Figure 4: Heatmap correlation matrix displaying the relationships between independent and dependent parameters for the compiled dataset.  
Figure 5: RMSE results for the FV dataset evaluated with the RBFNN model with three training/testing subset separation ratios.  
kernel function. In accordance with the problem's evaluation criteria, this kernel provided the best results. Based on high residual errors [36], a total of 48 outliers (4.2% of the training subset) were identified and eliminated by the MD process, combined with 10-fold cross-validation, before proceeding with the modelling process.
# A Clutter Loss Model for Satellite Communication Systems
## 6 Model Results
These observations are even more evident for the antenna height of 18 m and for Melbourne, which is characterized by taller and more distant buildings (see Figures 2 and 3). The reason behind the differences in the results highlighted above is likely due to the fact that, in the proposed Monte Carlo simulation, more realistic reflections coefficients are used, as well as an unlimited number of wave reflections and diffractions. As expected, for negative elevation angles, the clutter loss values are much larger; however, concerning frequency interference studies, this result is compensated by the usual larger power radiated by the base station towards the users on the ground. As expected, the analytical model described in Section 5.2 is close to the simulations results especially in the low probability range which is the most important for interference studies.
# Crystal Plasticity Finite Element Modeling on High Temperature Low Cycle Fatigue of Ti2 AlNb Alloy
## 2 Experiment and Modeling Theory
### Chaboche Cyclic Deformation Constitutive Model  
The Chaboche model is a time-independent model, including yield criterion, flow method, hardening criterion and strain decomposition. On the basis of the A-F follow-up hardening model, [PERSON] proposed a follow-up hardening model with three back stress components called the Chaboche model [24]. The Chaboche follow-up hardening, and the Vocc isotropic hardening criteria are be combined to describe the cyclic deformation behavior of Ti2 AlNb base alloy at high temperature, the equations are as follows.  
The total strain rate can be decomposed into elastic part and plastic part as follows:  
\[\dot{\mathbf{\varepsilon}}=\dot{\mathbf{\varepsilon}}^{\mathbf{\varepsilon}}+\dot{\mathbf{ \varepsilon}}^{\mathbf{p}}, \tag{1}\]  
\[\sigma=\mathbf{D}:\mathbf{\varepsilon}^{\mathbf{\varepsilon}}, \tag{2}\]  
\[\dot{\mathbf{\varepsilon}}^{\mathbf{p}}=\frac{3}{2}\frac{\mathbf{\sigma}^{\prime}-\mathbf{ \alpha}}{Q}\dot{\mathbf{p}}, \tag{3}\]  
\[F=\frac{3}{2}\sqrt{(\mathbf{\sigma}^{\prime}-\mathbf{\alpha}):(\mathbf{\sigma}^{\prime}- \mathbf{\alpha})}-Q^{2}, \tag{4}\]  
where \(\mathbf{D}\) is the elasticity tensor, \(p\) is the equivalent plastic strain, \(F\) is the yield function, \(\alpha\) is back stress tensor and \(\sigma^{\prime}\) is deviatoric stress tensor. The nonlinear follow-up hardening criterion decomposes the back stress tensor \(\mathbf{\alpha}\) into three back stress components \(\alpha_{i}\):  
\[\begin{array}{c}\alpha=\sum\limits_{i=1}^{3}\alpha_{i}\\ \dot{\alpha}_{i}=C_{i}\dot{\mathbf{p}}\frac{1}{Q}(\sigma-\alpha)-\gamma_{i}\alpha _{i}\dot{\mathbf{p}}\end{array} \tag{5}\]  
where \(C_{i}\) is initial value of follow-up hardening parameters, \(\gamma_{i}\) is rate of change of following hardening parameters, \(p\) is the equivalent plastic strain. The size of yield surface \(Q\) is:  
\[Q=Q_{0}+Q_{\infty}(1-e^{-bp}), \tag{6}\]  
where \(Q\) is the yield surface size, \(Q_{0}\) is the initial value of the yield surface size, \(Q_{\infty}\) is the maximum change value of the yield surface size, and \(b\) is the change rate of the yield surface size.
# Joint Cell Association and User Scheduling in Carrier Aggregated Heterogeneous Networks
## 3 Joint Cell Association Scheme for Multi-Flow Carrier Aggregation
### Summary  
The pseudo code of the proposed joint cell association scheme with multiple flow CA is summarized as in Algorithm 1.