esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Si Micropillar Growth  
The slurry layer was irradiated with an Nd:YAG laser (Megaopto Co., Ltd, Wako, Saitama, Japan) having a wavelength of 532 nm. The excitation light source was a laser diode, and the laser system was air-cooled. The laser beam had a Gaussian profile with a diameter of 85 \(\upmu\)m. A pulse width of 48.4 ns was used at a repetition frequency of 10 kHz. According to our previous study, if the laser fluence is too high or the scan speed is too low, the pillars could be ablated due to successive laser irradiation [39]. On the other hand, if the laser fluence is too low or the scan speed too high, the Si waste powders could not be completely melted [39]. Different laser fluences and scan speeds were employed for slurry layers of different thicknesses, and the most suitable ones for each condition were chosen to be used in this study. The laser fluence was 529 mJ/cm\({}^{2}\) for a slurry layer of 8 \(\upmu\)m in thickness and 1057 mJ/cm\({}^{2}\) for slurry layers of 13 and 20 \(\upmu\)m thickness, respectively. The scan speed was 1 mm/s. Area laser irradiation was conducted on the slurry layer to fabricate Si micropillar anodes for electrochemical testing. The line spacing was 57 \(\upmu\)m. The laser irradiation conditions are summarized in Table 1.
# Achievable Rate of NOMA-Based Cooperative Spectrum-Sharing CRN over Nakagami-\(m\) Channels
## 3 System Model
### Achievable Rates at PR and SRs  
The PR applies MRC on the received signals of two phases, and the achievable data rate (in bits per second (bps)) at the PR can be expressed as  
\[R_{p}^{\text{NOMA}}=\frac{1}{2}\log_{2}\!\left(1+\rho_{p}|h_{p}|^{2}+\frac{ \rho_{s}|g_{p}|^{2}\alpha_{0}}{\rho_{s}|g_{p}|^{2}\sum_{i=1}^{K}\alpha_{i}+1} \right). \tag{6}\]  
where \(\rho_{s}=P_{s}/\sigma_{s}^{2}\). On the other hand, the achievable data rate (in bps) at the \(k^{\text{th}}\) SR is given by  
\[R_{s,k}^{\text{NOMA}}=\frac{1}{2}\log_{2}\!\left(1+\frac{\rho_{s}|g_{s,k}|^{2 }\alpha_{k}}{\rho_{s}|g_{s,k}|^{2}\sum_{i=k+1}^{K}\alpha_{i}+1}\right). \tag{7}\]  
The achievable data rate (in bps) of the last secondary user is provided as follows; i.e., the user \(K\) can be expressed as  
\[R_{s,K}^{\text{NOMA}}=\frac{1}{2}\log_{2}\!\left(1+\rho_{s}|g_{s,K}|^{2}\right) \tag{8}\]However, if the ST fails to decode \(x_{0}\), there will be no NOMA-based transmissions in the second stage. The achievable data rate (in bps) at the PR then becomes  
\[R_{p}^{\text{ST-silent}}=\frac{1}{2}\log_{2}\Bigl{(}1+\rho_{p}|h_{p}|^{2}\Bigr{)} \tag{9}\]  
and the achievable rate at the SR is zero, i.e., \(R_{s,k}^{\text{ST-silent}}=0\ \forall k\). This is due to the fact that the ST can use the resource of the PT only if it can improve or maintain the rate of the PT. Therefore, if it fails to decode \(x_{0}\) transmitted by this particular PT, it cannot transmit the symbols to its respective SRs in the next time slot.  
The SIC is implemented at the \(k^{\text{th}}\) SR to separate the superimposed signals in the power domain. In particular, the \(k^{\text{th}}\) SR detects the symbol of the \(j^{\text{th}}\) SR, where \(j<k\), which is then removed from the received signal in a successive manner, whereas, if \(j>k\), then the \(j^{\text{th}}\) SR symbol is treated as noise at \(k^{\text{th}}\) SR. Therefore, we can write the achievable data rate for \(k^{\text{th}}\) SR to detect the symbol of the primary receiver (\(j=0\)) as  
\[R_{0,k}=\frac{1}{2}\log_{2}\Biggl{(}1+\rho_{p}|h_{p,k}|^{2}+\frac{\rho_{s}|g_ {s,k}|^{2}\alpha_{0}}{\rho_{s}|g_{s,k}|^{2}\sum_{i=1}^{K}\alpha_{i}+1}\Biggr{)}.\]  
where \(h_{p,k}\) is the channel between the PT and the \(k^{\text{th}}\) SR. The achievable data rate for \(k^{\text{th}}\) SR to detect the symbol of the \(j^{\text{th}}\) SR, where \(1\leq j\leq k\), is given by  
\[R_{j,k}=\frac{1}{2}\log_{2}\Biggl{(}1+\frac{\rho_{s}|g_{s,k}|^{2}\alpha_{j}}{ \rho_{s}|g_{s,k}|^{2}\sum_{i=j+1}^{K}\alpha_{i}+1}\Biggr{)}. \tag{10}\]  
Note that symbol \(x_{j}\) can be decoded and removed by the \(k^{\text{th}}\) SR, if \(R_{j,k}\geq R_{j}\), where \(R_{j}\) is the target data rate of the \(j^{\text{th}}\) SR. This successive cancellation process continues until \(x_{k}\) is successfully decoded at the \(k^{\text{th}}\) SR.
## 3 Proposed Credibility Evaluation Methodology
### Parameter-Based Reliability Evaluation  
We adopted the parameter-based reliability evaluation method proposed by [PERSON] [31], which enables the accuracy of the simulation component models used in XILS to be verified indirectly. For each scenario, we conducted \(N\) identical trials of both the XILS test and the real-vehicle test and compared the obtained parameter values to calculate three types of consistency indices: intra-XILS consistency (\(P_{m}(v)\)), intra-real-vehicle consistency (\(P_{m}(r)\)), and XILS-real-vehicle consistency (\(P_{m}(v,r)\)). Based on these indices, the correlation index \(C_{m}\) and the application index \(A_{m}\) are calculated as shown in Equations (7) and (8), respectively, as follows:  
\[C_{m}=\frac{P_{m}(v,r)}{P_{m}(r)}\times 100\,(\%), \tag{7}\]  
\[A_{m}=\frac{P_{m}(v)}{P_{m}(r)}\times 100\,(\%). \tag{8}\]  
The correlation index \(C_{m}\) represents the similarity between the results of the XILS tests and the real-vehicle tests, with values close to 100% indicating highly accurate simulation models. The applicability index \(A_{m}\) exceeding 100% means that the repeatability and reproducibility of the XILS test are better than those of the real-vehicle test. The XILS platform is considered to achieve parameter-based reliability \(R_{m}\) when all criteria in Equation (9) are met:  
\[R_{m}=(C_{m}>\alpha_{P})\cap(A_{m}>\beta_{P}),\ m\in\{1,2,\ldots,M\} \tag{9}\]  
where \(\alpha_{P}\) and \(\beta_{P}\) denote the correlation evaluation criterion and the applicability evaluation criterion, which are defined in Equations (10) and (11), respectively:  
\[\alpha_{P}=C_{P}-\sigma_{P}, \tag{10}\]  
\[\beta_{P}=Q-\sigma_{P},\ (Q=1) \tag{11}\]  
where \(C_{P}\) denotes the minimum value among the maximum consistency indices for the real-vehicle tests, and \(\sigma_{P}\) is the average deviation between the maximum and minimum consistency indices of these tests. These criteria imply that the normalized consistency of the XILS trials must exceed that of the real-vehicle trials to guarantee superior repeatability and reproducibility.  
The evaluation criteria \(\alpha_{P}\) and \(\beta_{P}\) are established based on empirical data analysis to ensure statistical robustness. As detailed in Oh ([31], Equations (19)-(24)), \(\alpha_{P}\) is derived from the principle that XILS-to-real-world consistency must exceed the normalized consistency observed within real-world tests alone, with \(\sigma_{P}\) accounting for inherent experimental variability. The criterion \(\beta_{P}\) assumes ideal simulation repeatability \((Q=1)\) while incorporating the same variability measure, ensuring XILS demonstrates superior reproducibility compared to real-world testing conditions.  
This method enables prior identification of any specific parameters that could undermine the XILS platform's reliability, whereby the accuracy of the corresponding simulation models can be refined accordingly. As the complete derivation of the parameter-based evaluation equation is provided by [PERSON] [31], it is omitted herein.
## 3 Numerical Parametric Study
### Cable with a Single TM-HDR-D  
For a cable-TM-HDR-D system, the characteristic Equation (10) can be simplified to  
\[\sin(\beta L)-\sin(\beta x_{2}^{*})\sin(\beta x_{2})\alpha\mu\beta L=0 \tag{14}\]  
Then, the modal damping ratio of the system can be calculated via Equation (12) after solving the complex wavenumber \(\beta_{n}\) from Equation (13).  
Figure 3 shows the 1 st and 2 nd modal damping curves with the dependence of the installation location of the TM-HDR-D, with \(\mu=0.005\), 0.01, 0.02, and 0.03.  
When the cable is equipped with a single TM-HDR-D, as the damper is located near the middle of the cable span, the first modal damping ratio gradually increases and reaches the maximum value. Moreover, increasing the damper mass will significantly enhance its damping effect, as depicted in Figure 3a. When the damper's installation mass is relatively small, in order to maximize the cable's 2 nd modal damping ratio, the optimal damper location is near the cable's quarter point. However, when the damper's mass is larger, i.e., \(\mu=0.03\), the optimal damper location is closer to the cable's anchor point, as illustrated in Figure 3b. The main reason for this phenomenon is that the larger mass of the damper causes obvious changes to the modal shape of the cable.  
Figure 2: Modal damping curves of a cable with a single HDR damper for (**a**) different loss factor \(\varphi\) (\(x_{1}/L=0.02\)) and (**b**) different location \(x_{1}/L\).  
Similarly, for the enhancement of cable's modal damping, it is efficient to increase the TM-HDR-D's mass. Therefore, considering the convenience of installation and maintenance of the TM-HDR-D, it is feasible to select a damper with a large mass to be installed near the cable's anchor point to ensure the damping effect.  
Figure 4 shows the relationship between the cable's first modal damping ratio and the tuning frequency ratio \(\rho\) of the TM-HDR-D for different mass ratios (\(\mu\)). Note that the TM-HDR-D is located at \(x_{2}/L=0.5\) with \(\varphi=0.5\).  
As can be seen from Figure 4, the TM-HDR-D, as a kind of TMD-type device, is also sensitive to frequency. The best frequency tuning condition is about \(\rho=1.06\). By comparing the curves under different mass ratios, it can be seen that the damper's mass has a great influence on the modal damping and leads a small change in the best tuning frequency ratio \(\rho\). In practice, in order to improve the robustness of the TM-HDR-D, the damper's mass should be increased reasonably.
###### Abstract  
Precipitation typically leads to the accumulation of soil moisture, which causes slope instability and triggers landslides. However, due to the lag nature of this process, landslides usually do not occur on the day of heavy rainfall. Therefore, it is essential to incorporate antecedent effective precipitation as a factor in landslide prediction models that allow for the creation of more comprehensive landslide susceptibility maps. In this study, six machine learning models are compared, with antecedent effective precipitation included as a conditioning factor for model training. The optimal model is selected to simulate landslide susceptibility maps under four return periods (5, 10, 20, and 50 years). Additionally, the mean decreases in the Gini and SHAP values are employed to identify the most significant factors contributing to landslides. The results indicate the following: (1) Effective antecedent precipitation is the most influential factor in landslide occurrence, ranging from one to two times higher than other factors. (2) Most meteorological stations in the study area show antecedent effective precipitation that follows a lognormal distribution, mainly in coastal areas, with a secondary fit to the general extreme value distribution. The spatial distribution of antecedent effective precipitation is more prominent in the coastal and western mountainous regions, with lower values that then increase with longer return periods in central areas. (3) The XGBoost model achieves the best performance, with an area under the curve of 0.96 and an accuracy of 89.02%. (4) The landslide susceptibility maps for the four return periods reveal three high-risk zones: the southern coastal mountains, the western Zhejiang mountains, and the areas surrounding the hilly region of Shaoxing to Taizhou in central Zhejiang. This study provides dynamic decision-making support for the prevention and control of rainstorm-induced landslide risks.  
landslide; susceptibility assessment; machine learning; effective antecedent precipitation; return period 2025  
6
# Optimal Design and Fish-Passing Performance Analysis of a Fish-Friendly Axial Flow Pump
## 5 Fish-Passing Performance Analysis of the Fish-Friendly Axial Flow Pump
### Comparative Analysis of Fish Subjected to Pressure and Shear Damage  
From the point of view of the variation in the average pressure of the fish body with the motion path in Figure 16, the lowest negative pressure in the inlet section of the fish-friendly axial flow pump was higher than \(-40\) kPa when the runner pressure was \(-59.2\) kPa. The inlet passage of the prototype pump was at a pressure lower than \(-40\) kPa, and the lowest negative pressure was \(-64.7\) kPa. The average and maximum negative pressures of the fish friend pump were higher than those of the prototype pump. The maximum positive pressure on the front of the blade on the path of the fish was \(11.8\) kPa, which was significantly lower than that of the prototype axial flow pump at \(43.8\) kPa. This showed that the level of pressure gradient caused by the pressure change in the fish body in the fish-friendly pump was lower, so it was less vulnerable to pressure gradient damage. This means that the degree of negative pressure damage to the passing fish body was reduced, and it was more beneficial for the fish to pass through the axial flow pump.  
Literature studies show that the main evaluation index of shear damage of fish in a strong shear environment is the shear strain rate. When the shear strain rate is less than \(500\) s\({}^{-1}\), it is considered that the fish will not be subjected to shear damage. Figure 16 shows the change in the average shear strain rate on the body surface as a fish passes through the axial flow pump. Both the prototype pump and the fish-friendly pump reach the maximum shear strain rate at the front of the runner. As far as the situation seen in the diagram is concerned, when the fish body passes through the prototype axial flow pump and the fish-friendly axial flow pump, it does not reach the damage threshold of \(500\) s\({}^{-1}\), so it will not be subjected to shear damage. The actual situation still needs to be analyzed by the distribution of the shear strain rate on the fish surface.  
Thus, it can be seen that changing the blade thickness and cutting angle can effectively reduce the impact damage to fish. This is consistent with [PERSON]'s view [37] that the non-right angle between the leading edge of the blade and the trajectory of the fish can reduce the strike speed or damage rate. The pressure injury of fish is mainly negative pressure injury, and [PERSON] et al. [7; 8] also reached a similar conclusion. Therefore, the impact damage caused by blades should be given priority in the design of fish-friendly hydraulic machinery. [PERSON] [38], based on CFD streamline assessment of Kaplan turbines, also found that collision is the most serious cause of fish death. Secondly, the negative pressure and pressure gradient need to be considered, while the priority of shear stress injury is slightly lower.  
Figure 15: \(C_{dual}\) distribution of the impact death index obtained from the fitting equation.
moldavica_ L.  
Some authors suggest that the use of _D. moldavica_ L. essential oil could provide a powerful tool in control of pathogenic microorganisms in the food and pharmaceutical industry. The antimicrobial potential of the essential oil of Egyptian Moldavian balm against six bacterial (_Bacillus subtilis, Bacillus cereus, Staphylococcus aureus, Microcous luteus, Klebsiella pneumoniae, Serratia marcenscens_) and four fungal strains (_Aspergillus niger, Penicillium notatum, Mucora heimalis, Fusarium oxysporum_) was investigated by [PERSON] and [PERSON] [16]. [PERSON] et al. [13] studied the antioxidative activity of total flavonoids extracted from _D. moldavica_ L. The total flavonoids showed remarkable scavenging effects against 1,1-diphenyl-2-picrylhydrazyl, hydroxyl, and superoxide anion radicals in vitro. Moldavian dragonhead (_D. moldavica_ L.) is used in traditional medicine as a stimulant and antiseptic [19]. Extracts and oil from this plant are used widely in the food, pharmaceutical, flavouring, and cosmetic industries [20]. So far, the addition of _D. moldavica_ L. leaves has been tested in bread [21], or corn snacks [2]. Snacks were also enriched with _D. moldavica_ L. seeds [3; 22]. These seeds were used in the 3, 12, and 22% snacks recipes. Moldavian dragonhead bagasse (MDB) (Figure 1b) is waste product from the process of pressing oil from the seeds (Figure 1a) of _D. moldavica_ L.  
The use of bagasse from these seeds has not yet been extensively tested in food production. Recently, MDB were just an addition to the production of pasta [23]. The addition of MDB to extruded products was also noted by [PERSON] et al. [24]. Figure 1: The appearance of seeds (**a**) and bagasse from _D. moldavica_ L. (**b**). The pasta was supplemented with MDB added to the recipe up to 25%. It was found that this waste could be used in an amount of 10% for pasta; consequently, an acceptable product with high nutritional value was obtained. There was an increase in the nutritional value of protein by 5.8%, 145% of dietary fiber, and 50% of minerals. Moreover, MDB has a good fatty acid composition with a high proportion of linolenic acid (62.21%) and linoleic acid (20.39%) [23]. _D. molavica_ L. bagasse after the cold oil pressing were added as supplementation to corn snacks in the amount of 5-30%. In these studies, the increasing amount of MDB had a significant effect on the physical properties of the extrudates. A decrease in the bulk density was noticed, but also an increase in the apparent density and the cutting force. Moreover, water absorption decreased and water solubility increased [24]. Sustainable development of waste management allows the use of bagasse, especially from seeds, as a source of valuable nutrients (fiber, protein, fat, minerals, vitamins and dyes) to enrich food products, especially ice cream. In the present study, for the first time, we proposed a waste additive from the pressing of dragonhead seed for the production of ice cream. The scope of the work included the determination of thermal and physicochemical properties of ice cream with this additive.
# Lung Ultrasound Imaging, a Technical Review
## 3 Ultrasound Multiple-Scattering Characterization
### Measurement of the Diffusion Constant Using a Conventional Ultrasound Probe  
The diffusion constant can be measured using a conventional ultrasound probe connected to a multiple channel ultrasound scanner, in an unconventional way. As an example, in a recent study, a open research platform, i.e., Verasonics Vantage 128 (Verasonics, Kirkland, WA, USA), was used to test these concepts [48]. No beamforming was performed. Instead, the 128 elements were used one by one to transmit 2 cycle Gaussian pulses (centered at 5 MHz or at 7.8 MHz). For each transmit, the signals backscattered from the lung parenchyma were collected on the whole array. With 128 elements on the array, this led to the acquisition of a 128X128 XN inter-element response matrix (IRM), N being the number of time points. The IRM matrix is symmetric due to reciprocity. The antisymmetric of the IRM was calculated and both the IRM and the anti-symmetric IRM were split into overlapping time windows. This allowed to extract only the incoherent part of the backscattered intensity. Details of the method are described in [48]. In a diffusive medium, the spatial spread of the incoherent intensity grows over time, and this rate of growth is determined by the diffusion constant \(D\) according to the following equation:  
\[I_{incoherent}(r,T)=I(T)\times exp\left(-\frac{r^{2}}{4 DT}\right), \tag{1}\]  
\(r\), being the emitter-receiver distance and \(T\) the time window. To calculate \(D\), the incoherent intensity for each time window was fitted with a Gaussian curve. The width (variance) of this Gaussian, when plotted against time, gives a linear trend, whose slope is equal to 2D.  
Figure 6: (**Left**): Two heterogeneous media with different scattering properties (top: weakly scattering medium, bottom: highly scattering medium). (**Center**) and (**right**): propagation of a plane in these two media at two different times. In the medium with a lower scatterer density, the wave makes rapid progress (**top**). In the medium with a dense scatterer distribution (**bottom**), the wave makes slower progress.
## 6 Conclusion  
The effect of concurrent transmission on the performance, in terms of chip, symbol and packet error probability, of IEEE 802.15.4 systems was investigated. To this aim, an analytical model, which starts from the PHY characterization of the IEEE 802.15.4 standard, was proposed, and the results were validated using simulations. Since the system performance does not depend on the number of interfering devices, but rather, on the overall amount of interfering power [18; 20], the case of one single interferer was considered. Both non-coherent and coherent demodulation were analyzed in the absence of thermal noise. The joint effect of thermal noise and interference was taken into account by means of simulations. For the case of non-coherent demodulation, we found that \(0\) dB is the absolute minimum necessary value of SIR for the successful detection of the useful packets. We have also shown that the proposed analysis is in agreement with the measurement results of the literature [16; 18; 19; 20] under realistic working conditions. We conclude that the mathematical framework can be used to provide analytical explanations to the experimental results shown in [16; 18; 19; 20].
## 2 Background  
### Dynamic Device Identity  
There are two approaches to defining the identity of a hardware device. The first of these is to issue the device with a fixed identity at the time of manufacture. This may be carried out either by permanently associating a key to the hardware by burning it into Read Only Memory (ROM) or storing it within a cryptographic storage device [3], or by deriving the identity using physical characteristics of the hardware--such as Physical Unclonable Functions (PUFs) [4].  
The alternative method, which alleviates the need to rely on pre-determined fixed identities, is to use dynamically generated identities. This technique creates a unique identity at the time that a device is registered with the system, and allows that identity to be revoked and subsequently a new identity may be created.  
Adopting this approach has a number of advantages. It eliminates the requirement to securely generate and distribute the keys associated with a fixed identity conferred at the time of manufacture. Given that it also enables the identity associated with a device to be revoked, and for a new identity to be assigned, this approach makes it much easier to ascertain that any previous access to a device has been revoked, since the identity to which any previous permissions applied no longer exists. This concept is especially important for high-value Internet-connected devices, such as cars, which may be expected to have more than one owner during their lifetime. When utilising a fixed identity it is not possible for the new owner of a device to be certain that all previous owners have relinquished all access to the device; but when dynamic identities are used, the new owner may simply generate a new identity for the device and delete the old identity, guaranteeing that no other user has the security credentials for the new identity.  
Dynamic identity also has the advantage that a device may be joined to any compatible system--with the minimum prior knowledge of that system. Providing the device knows the end-point address for the system's key registration service, all other data can be bootstrapped, and the C2 service does not require any previous relationship with the device manufacturer or originator.