esa-sceva/satcom-chunk-collection huggingface dataset:
# Boosting the Learning for Ranking Patterns
## 7 Discussion
### Real-World Applications
* **Content recommendation:** Content recommendation is a crucial aspect of media companies and content providers as it has a direct impact on user engagement and satisfaction. By using AHPRank, these companies can provide personalized content recommendations to their users based on their interests, preferences, and behaviors. This approach will not only enhance the user experience but also increase the likelihood of retaining users and increasing engagement. One of the key advantages of using AHPRank for content recommendation is its ability to incorporate multiple criteria in the decision-making process, such as content type, user preferences, and viewing history. This comprehensive approach allows for more accurate and relevant recommendations, ultimately leading to improved user satisfaction. Moreover, content recommendation systems can also be enhanced by incorporating real-time feedback from users, such as user ratings and reviews, to continually improve the recommendation process. This can be achieved by incorporating the feedback into the decision-making process of the AHPRank algorithm, allowing for more personalized and accurate recommendations. * **Supply chain optimization:** A crucial aspect for companies to reduce costs and increase efficiency. By using AHPRank, companies can rank suppliers, products, or logistical options based on multiple criteria, such as cost, delivery time, reliability, and quality. This approach enables companies to make informed decisions about suppliers and products, thereby reducing risks associated with supply chain management. In addition, AHPRank can also be used to optimize supply chain logistics by evaluating various transportation options, such as different routes or modes of transportation, and selecting the most efficient and cost-effective option. This can ultimately result in faster delivery times, reduced transportation costs, and improved customer satisfaction. In a more concrete way, here are five real-life tasks where AHPRank can prove to be interesting to use: 1. **Supplier evaluation and selection:** AHPRank can be used to rank and select suppliers based on multiple criteria, such as quality, cost, and delivery time. 2. **Investment portfolio management:** AHPRank can help portfolio managers to rank and select assets based on various factors such as return, risk, and liquidity. 3. **Employee performance evaluation:** AHPRank can be used to evaluate employee performance based on multiple criteria such as productivity, teamwork, and innovation. 4. **Product design and development:** AHPRank can be used to rank different design options based on factors such as customer preferences, cost, and manufacturability. 5. **Marketing campaign optimization:** AHPRank can be used to rank different marketing strategies based on criteria such as target audience, reach, and cost-effectiveness. Overall, the flexibility and scalability of the AHPRank approach make it a valuable tool for decision making in many industries.
# Study of Sea Fog Environment Polarization Transmission Characteristics
## 2 The Physical Attributes of a Complicated Environment such as Marine Fog  
### Marine Fog's Physical Qualities  
The production of marine fog depends on the temperature of the sea air, the amount of water vapor present, and the presence of condensation nuclei. Sea fog is significantly distinct from most clouds in that it achieves air supersaturation by adiabatic cooling, which lowers air pressure. Sea fog does not arise primarily as a result of air pressure in the lower atmosphere close to the ground. In further detail, if \(f\) represents the relative humidity of the air, it is defined as the proportion of the saturated water vapor pressure \(e_{a}\) at the actual temperature T to the real water vapor pressure \(e\), and its related representation is as follows [12]:  
\[f=\frac{e}{e_{a}} \tag{1}\]  
The derivative is derived by applying logarithms to both sides of the preceding formula:  
\[\frac{df}{f}=\frac{de}{e}-\frac{de_{a}}{e_{a}} \tag{2}\]  
Let \(L\) represent the latent heat of evaporation, \(A\) represent the heating equivalent, and \(R_{W}\) represent the specific gas constant of water vapor, which are put into the Clausius-Clapeyron equation.  
\[\frac{de_{a}}{e_{a}}=\frac{L}{AR_{W}}\frac{dT}{T^{2}} \tag{3}\]  
Combining (2) and (3):  
\[\frac{df}{f}=\frac{de}{e}-\frac{L}{AR_{W}}\frac{dT}{T^{2}} \tag{4}\]If \(T=273\) K and \(\frac{L}{\alpha R_{W}T}\approx 19.5\), then Formula (4) is:  
\[\frac{df}{f}=\frac{de}{e}-19.5\frac{dT}{T^{2}} \tag{5}\]  
Equation (5) demonstrates that the relative humidity in a marine environment can be raised to saturation by raising the water vapor pressure and decreasing the temperature. In the process of marine fog generation, two distinct processes often act in tandem. Hummidification in the marine environment occurs primarily through seawater evaporation and atmospheric precipitation, whereas cooling is primarily of two types: the sea surface temperature is lower than the atmospheric temperature, or there is a temperature difference between the sea surface and the atmosphere.
## 3 Results
### Physical Properties
#### 3.2.1 Dry Matter (DM)
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **DM (\%)** & **AW** & **\(\rho\)b (kg:m\({}^{-}\)3)** \\ \hline C0 & 0.071 \(\pm\) 0.007 \({}^{\text{a}}\) & 0.988 \(\pm\) 0.001 \({}^{\text{f}}\) & 250.41 \(\pm\) 3.66 \({}^{\text{f}}\) \\ C0\_FD & 0.956 \(\pm\) 0.001 \({}^{\text{c}}\) & 0.214 \(\pm\) 0.013 \({}^{\text{b}}\) & 14.97 \(\pm\) 2.61 \({}^{\text{a}}\) \\ C0\_VD & 0.969 \(\pm\) 0.013 \({}^{\text{c}}\) & 0.320 \(\pm\) 0.004 \({}^{\text{f}}\) & 41.89 \(\pm\) 0.10 \({}^{\text{b}}\) \\ CB & 0.075 \(\pm\) 0.001 \({}^{\text{a}}\) & 0.985 \(\pm\) 0.001 \({}^{\text{f}}\) & 374.25 \(\pm\) 6.88 \({}^{\text{h}}\) \\ CB\_FD & 0.965 \(\pm\) 0.002 \({}^{\text{c}}\) & 0.159 \(\pm\) 0.004 \({}^{\text{a}}\) & 19.59 \(\pm\) 3.38 \({}^{\text{a}}\) \\ CB\_VD & 0.978 \(\pm\) 0.012 \({}^{\text{c}}\) & 0.308 \(\pm\) 0.003 \({}^{\text{d,e}}\) & 39.94 \(\pm\) 2.71 \({}^{\text{b}}\) \\ B0 & 0.151 \(\pm\) 0.001 \({}^{\text{b}}\) & 0.989 \(\pm\) 0.002 \({}^{\text{f}}\) & 207.47 \(\pm\) 4.19 \({}^{\text{e}}\) \\ B0\_FD & 0.967 \(\pm\) 0.001 \({}^{\text{c}}\) & 0.203 \(\pm\) 0.011 \({}^{\text{b}}\) & 47.78 \(\pm\) 2.28 \({}^{\text{b}}\) \\ B0\_VD & 0.973 \(\pm\) 0.018 \({}^{\text{c}}\) & 0.293 \(\pm\) 0.004 \({}^{\text{d}}\) & 90.78 \(\pm\) 5.58 \({}^{\text{d}}\) \\ BB & 0.154 \(\pm\) 0.002 \({}^{\text{b}}\) & 0.985 \(\pm\) 0.003 \({}^{\text{f}}\) & 341.15 \(\pm\) 3.25 \(\lx@sectionsign\) \\ BB\_FD & 0.969 \(\pm\) 0.001 \({}^{\text{c}}\) & 0.173 \(\pm\) 0.002 \({}^{\text{a}}\) & 49.12 \(\pm\) 1.30 \({}^{\text{b}}\) \\ BB\_VD & 0.974 \(\pm\) 0.003 \({}^{\text{c}}\) & 0.273 \(\pm\) 0.002 \({}^{\text{c}}\) & 53.73 \(\pm\) 3.32 \({}^{\text{b}}\) \\ \hline \hline \end{tabular} Values (mean of three replications) \(\pm\) standard deviation followed by different letters (a–h), are different (\(p\leq 0.05\)) according to [PERSON]’s test.  
\end{table} 
Table 4: Dry matter (DM), water activity (AW), bulk density (\(\rho\)b). Figure 4: Changes in the VOCs profile of processed courgetti. impregnation process to treat cowgette [8], sweet potato [21], and kale [40]. Table 5 shows the regression equation describing the dry weight, which depends on the drying method used, the vacuum impregnation process, and the type of vegetable. The model presented explains only 22% of the dependence of the three predicates used.
## 4 Equalization Technologies  
To mitigate the impairments in high-speed PAM4 system mentioned above, various digital equalization technologies have been studied. In this section, we introduce the most popular equalizers, including FFE, DFE, Volterra nonlinear equalizer (VNLE) and machine learning based equalizer, in detail to compensate for these impairments. Moreover, some improved equalization methods are also described to handle specific issues here.
## 3 Results
### Thermodynamics of SOM Microbial Decomposition
#### 3.2.1 Thermodynamic State Variables and Soil Microbial Diversity
That yields lower \(\Delta_{\text{r}}\)G\({}_{\text{SOM}}\) (more negative) than the other samples. Samples with the lowest \(\Delta_{\text{c}}\)G\({}_{\text{SOM}}\) are those with SOM more reduced than carbohydrates, with just one exception. Those samples remain the ones with the lowest bacteria diversity (lower Taxa_S values). The Cambisol sample under pasture with \(\Delta_{\text{c}}\)H\({}_{\text{SOM}}\) closer to that of carbohydrates is the sample with the highest bacterial diversity, in both TAXA_S and Shannon_H index. The Taxa_S of fungi is lower in the Cambisol sample under pasture than those in the samples with more reduced substrates. Leonardite and Cambisol pine, with similar CR values and the highest \(\Delta_{\text{r}}\)G\({}_{\text{SOM}}\), both have a similar microbiological composition characterized by higher Taxa_S of fungi than that of bacteria. Pahokee peat and Cambisol under pasture have the lowest \(\Delta_{\text{r}}\)G\({}_{\text{SOM}}\). Pahokee peat presents a little higher microbial diversity than Leonardite (based on the Shannon_H index) and higher bacteria diversity than Cambisol pine. Although the Cam \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \multirow{2}{*}{**Samples**} & **Taxa\_S** & **Taxa\_S** & **Shannon** & **Shannon** & \(-\Delta_{\text{c}}\)H\({}_{\text{SOM}}\) & \(-\Delta_{\text{c}}\)G\({}_{\text{SOM}}\) & \(-\)CR & \(-\Delta_{\text{r}}\)G\({}_{\text{SOM}}\) \\  & **Bacteria** & **Fungi** & **Bacteria** & **Fungi** & **kJmol\({}^{-1}\)C** & **kJmol\({}^{-1}\)C** & **kJmol\({}^{-1}\)CO\({}_{2}\)C** & **kJmol\({}^{-1}\)C** \\ \hline Leonardite & 7 & 21 & 1.92 & 2.07 & 538 & 554 & 273 \(\pm\) 14 & 276 \\ Pahokee peat & 8 & 15 & 1.93 & 2.19 & 545 & 551 & 463 \(\pm\) 20 & 468 \\ Cambisol pasture & 28 & 11 & 3.07 & 2.20 & 489 & 495 & 495 \(\pm\) 21 & 500 \\ Cambisol pine & 6 & 21 & 1.67 & 2.90 & 539 & 544 & 239 \(\pm\) 18 & 242 \\ \hline \end{tabular}
\end{table} 
Table 5: Biological and thermodynamic properties determined for four different mineral soil samples. Microbial diversity was determined by ARISA and is given by the Taxa_S and Shannon_H diversity indices for soil bacteria and fungi. Reproducibility for \(-\Delta_{\text{c}}\)H\({}_{\text{SOM}}\) is 5%. The residual standard error is 21.5 kJ mol\({}^{-1}\). CR values are the average of duplicates with their standard deviation. bisl under pasture have the highest bacteria diversity, the low Gibbs energy change of this sample is not reflected in the fungi diversity data. Figure 4 shows that there are two clear different groups based on the thermodynamic state variables (Figure 4a): the peat and CambisolP1 under pasture also form a different group from those of CambisolPi1 under Pine and Leonardite based on the microbial diversity (Figure 4b). Although the set of samples is too low for showing robust results, there is evidence of the possible connection between these thermodynamic variables and the soil microbial composition.
## 2 Proposed Coverage Enhancement Modeling
### Coverage Description and Node Motion Model
Therefore, the following analytical formulae are used to define the speeds of motion in the \(X\) and \(Y\) directions:  
\[v_{X}=-\frac{\partial\varphi(x,y,t)}{\partial y} \tag{6}\]  
\[v_{Y}=\frac{\partial\varphi(x,y,t)}{\partial x} \tag{7}\]  
To simplify the formula, \(a=y-B(t)\sin(k(x-ct))\) and \(b=1+k^{2}B(t)^{2}\cos^{2}(k(x-ct))\). The two velocity components of the node motion to the east and north can be simplified to  
\[v_{X}=\left[1-\text{tanh}\Big{(}\frac{a}{b}\Big{)}\right]\cdot(b)^{0.5} \tag{8}\]  
\[v_{Y}=\left[1-\text{tanh}^{2}\Big{(}\frac{a}{b}\Big{)}\right]\cdot\left[\frac {B(t)k\cos(k(x-ct))}{b^{0.5}}-\frac{k^{3}B^{2}(t)\sin(2k(x-ct))\cdot a}{2\cdot b ^{1.5}}\right] \tag{9}\]  
Based on the above node motion model, when the time passes \(\Delta t\), the node moves \(\sum_{i}^{t}v_{X}\cdot\Delta t\)\(m\) in the \(X\) direction and \(\sum_{i}^{t}v_{Y}\cdot\Delta t\)\(m\) in the \(Y\) direction. The node is mainly affected by gravity and buoyancy in the \(Z\) direction. Initially, the gravity of the node is greater than its buoyancy, and the node is in a sinking state. However, considering that the final state of the node is located in water, that is, gravity equals buoyancy, the displacement of the node in the \(Z\) direction does not change under the strong impact of the horizontal direction flow. After \(\Delta t\), the position of underwater node \(i\) is  
\[\left(x^{\prime}_{i},y^{\prime}_{i},z^{\prime}_{i}\right)=\left(x_{i}+\sum_{ 1}^{l}v_{X}\cdot\Delta t,y_{i}+\sum_{1}^{l}v_{Y}\cdot\Delta t,z_{i}\right) \tag{10}\]
## 5 Some Applications of Antiresonance Assignment through Active Control
### Antiresonance Assignment and Energy Harvesting  
In recent years, the TMDs have been extended to applications simultaneously dealing both with the harmonic vibration suppression and the energy harvesting, leading to the so-called energy-regenerative TMD [105; 106; 107; 108; 109]. Several theoretical studies have been proposed (see e.g., [107; 109; 110]) and various applications have been developed.  
In [105] the semi-active series multi-DOF TMD is exploited to perform both vibration absorption and energy harvesting. Numerical simulations are carried out based on the model of a tall building equipped under random and harmonic excitations. The results corroborate the effectiveness of the SATMD to control the vibration while harvesting a large amount energy.  
The feasibility of simultaneous energy harvesting and vibration control is experimentally demonstrated in [106]. Energy is harvested from the prototype laboratory building, when it is excited by a harmonic force, exploiting the regenerative electromagnetic TMD with energy harvesting function.  
Robust electro-mechanical design criteria for dual-TMDs for vibration absorption and energy harvesting in bridges are proposed in [108]: effectiveness of the design is corroborated through numerical simulations.  
A numerical comparison between the TMD and the TMD-inert has been proposed in [111] for both harmonic vibration absorption and energy harvesting. This device has been investigated in [112] through numerical analyses for wind-induced vibration control and energy harvesting in building structures.  
The delayed resonator (see Section 3.3) has been employed as well to perform energy harvesting [113; 114]. The authors show, by simulation and experiments, that the energy harvesting capability of vibration absorbers is improved by exploiting feedback control laws.
# Multifunction RF Systems for Naval Platforms
###### Abstract  
The evolving role of modern navies has required increasingly higher levels of capability in the Radio Frequency (RF) shipboard systems that provide radar, communications, Electronic Attack (EA) and Electronic Support (ES) functions. The result has been a proliferation of topside antennas and associated hardware on naval vessels. The notion of MultiFunction RF (MFRF) systems has drawn considerable interest as an approach to reversing this trend. In a MFRF system, RF functions are consolidated within a shared set of electronics and antenna apertures that utilize Active Electronically Scanned Array (AESA) technology. This paper highlights a number of issues to be considered in the design and implementation of a naval MFRF system. Specifically, the key requirements of the RF functions of interest are first reviewed, and MFRF system design trade-offs resulting from costs and/or performance limitations in existing hardware technology are then discussed. It is found that limitations in hardware technology constrain the implementation of practical MFRF systems. MFRF system prototype development programs that have been conducted in other countries are described. MFRF resource allocation management is identified as an important future research topic.  
n aval systems; multifunction RF systems; active electronically steered arrays; radar; electronic support; electronic attack; communications +
Footnote †: journal: Sensors
## 4 Discussion
On the contrary, if an excess of laser fluence is adopted during the SLM process, the parameter m' is higher than zero; therefore, vaporization can take place, having Ni at a higher vapor pressure than \(\mathrm{T}_{\mathrm{i}}\). The peak of the relative density vs. laser fluence curve exhibits a compromise between the defects related to lack of fusion and gas porosities, as extensively discussed in the literature [28]. The schematic of the laser beam/material interaction during the powder scanning, representative of the two extreme conditions previously mentioned, is depicted in Figure 8[30]. Under low laser fluence, limited penetration depth can be achieved (see Figure 8a), and the potentially associated defect is correlated to the lack of fusion due to insufficient energy irradiated from the laser beam. The fusion of the powder bed takes place, creating a stable and uniform path of the liquid pool. On the contrary, upon an increase in the laser fluence, the penetration depth can be significantly increased, thanks to the vaporization of a limited amount of liquid metal (proportional to the coefficient m'), and the principal defect is related to the formation of spherical pores, due to the entrapment of the residual vaporized material (see Figure 8b). It can be seen that from Figure 7, the shape and the size of the liquid pools versus the increase in the laser fluence follow the description suggested by Figure 8. In fact, low laser fluence (63 J/mm\({}^{3}\)) induced a melting with limited depth, while the increase in fluence provoked a deeper liquid pool, also having an irregular shape. The high fluence values can promote vaporization, leading not only to gas porosities but also to a possible compositional variation. This is in good agreement not only with the evolution of the Ni and Ti content versus the laser fluence, as shown in Figure 5, but also with the trend on the relative density, listed in Table 1. According to this, the transformation temperatures and the corresponding transformation enthalpies are subjected to a variation, which is due to the Ni content shift and to residual stresses, too. The achieved results are also confirmed by a previous work, in which the laser parameters were varied with the aim of obtaining austenite or martensite in different portions of the same built sample. In that case, functionally graded NiTi SMAs were produced by the LPBF process by the local evaporation of Ni under the irradiation of the laser beam, incident under different energetic values [23]. The passage from martensite to austenite at room temperature becomes a potentiality for designing and producing functionally graded parts.
# Credible Navigation Algorithm for GNSS Attack Detection Using Auxiliary Sensor System
## 3 Credible Navigation Algorithm
### Algorithm Design
Set the initial global position (converted to the navigation coordinate system) according to the GNSS signal acquired when the mobile terminal is initially at stationary. Initialize parameters \(n_{j}\), \(Th_{j}\), \(n_{s}\), \(Th_{s}\), \(\text{flag}_{j}\) and \(\text{flag}_{s}\). Figure 4: GNSS slow-change verification. Figure 5: Algorithm flowchart. Step 2: Decide whether there is a navigation and positioning requirement. If so, the GNSS receiver and auxiliary sensor will enter the working state and go to step 3; otherwise, go to step 8. Step 3: Determine whether the GNSS and auxiliary sensor system have new and valid input data. If the acquired information of GNSS and auxiliary sensor system is valid, the information of GNSS from global coordinates is converted to navigation coordinates, and the position unity with the auxiliary sensor system is realized to obtain \(\rho_{k}^{\text{G}}\) and \(\rho_{k}^{\text{DI}}\). Then, we go to step 4; if the effective information of the auxiliary navigation sensor system is obtained, and the GNSS positioning result is interrupted, the auxiliary update model is used to maintain the terminal position prediction, and we go to step 3 again; if no valid information is obtained, we go back to step 2. Step 4: Calculate GNSS drift \(\Delta\rho_{k}\) at time \(k\). If \(\Delta\rho_{k}>T\text{h}_{\text{j}}\), then we have \(\text{flag}_{\text{j}}(k-1)+1\), and we go to step 5; otherwise, we have \(\text{flag}_{\text{j}}(k)=0\), and we go to step 6. Step 5: Determine the size of \(\text{flag}_{\text{j}}(k)\). If \(\text{flag}_{\text{j}}(k)<\text{n}_{\text{j}}\), we proceed to Step 6; if \(\text{flag}_{\text{j}}(k)\geq\text{n}_{\text{j}}\), it is determined that there is a GNSS jump attack, the GNSS is set as an untrusted navigation source, and the auxiliary update model is used to maintain the terminal position prediction at this time. If this happens, we reverse back to step 3. Step 6: Calculate \(\text{flag}_{\text{s}}(k)\). If \(\text{flag}_{\text{s}}(k)\geq T\text{h}_{\text{s}}\), it is determined that there is a GNSS slow-change attack, the GNSS is set as an untrusted navigation source, and the auxiliary update model is used to maintain the terminal position prediction. Then, we go back to step 3; otherwise, we continue to step 7. Step 7: GNSS is in a credible stage. The credible update model is used to maintain the terminal position output, and then we go to step 3 again. Step 8: End.
# Research on a 3D Seismic Horizon Automatic-Tracking Method Based on Corrugated Global Diffusion
## 3 Case Analysis
#### Limitations of the Algorithm  
(1) Scene restrictions: Due to the DTW algorithm (based on correlation calculations) being the main algorithm in this paper, representing the assumption that data points from the same horizon have similar waveforms that are reflected in the correlation coefficients, the correlation coefficients are relatively high. When calculating similarity, it is necessary to scan a certain search window for data-sampling points as potential horizon candidates. However, if there are faults with large gaps, this method cannot scan the corresponding horizon points since the search window cannot be too large to avoid the problem of excessive calculation complexity due to frequent similarity calculation. Therefore, when  
Figure 21: Automatic 3D seismic horizon-tracking results based on global ripple diffusion in the Qinan area (xline175).  
Figure 22: Stereoscopic display of 3D seismic automatic horizon-tracking results for horizon Q1.  
there are faults with large gaps, the algorithm in this paper is limited in its application and needs to be further developed and improved in the future.  
(2) Parameter sensitivity: As mentioned above, the algorithm relies on the calculation of similarities between the same horizon, and it is well known that the calculation of similarities of waveforms depends on the parameters. This is also a major flaw in the calculation of similarity, such as the length of the correlation window and the search range, which will result in changes in the similarity value and affect the horizon-tracking result. Fixed correlation window selection cannot adapt to data changes, such as longer or shorter waveform periods. Ideally, different window lengths should be selected to achieve the best correlation window length. However, the data dynamics show complex changes, and it is difficult to form a clear relationship between the window width and data dynamics, so it is difficult to dynamically determine the correlation window.  
(3) Time complexity: Due to the innovative use of the DTW algorithm based on wave diffusion, this algorithm requires a diffusion calculation regarding all the surrounding points for the diffusion of the seed points, meaning that it requires more correlation calculations when compared to traditional methods, and the calculation complexity of correlation is also relatively high, resulting in a greater time calculation complexity for this algorithm. Of course, with the advancement of computer hardware technology, the introduction of highly parallel computing and GPU devices will mitigate this defect to some extent.