esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 The SPB/SFX Instrument  
### Scientific Scope and X-ray Techniques  
The Single Particles, Biomolecules and Serial Crystallography (SPB/SFX) scientific instrument's [61] primary goal is to enable three-dimensional imaging, or three-dimensional structure determination, of micrometer-scale and smaller objects. A particular focus is placed on biological objects--including viruses, biomolecules, and protein crystals--though the instrument will also be capable of investigating non-biological samples using similar techniques. This structure determination is not limited to static structures--three-dimensional time-resolved structures are within scope too. One of the main driving factors for such studies is to ultimately enable rational drug design through understanding the structure, and hence the function, of arbitrary biomolecules. Studies in structural biology with X-rays have a long history and have exploited ever-brighter X-ray sources as they have been developed [62].  
X-ray FELs, as the most recent phase in X-ray source development, offer yet additional benefits to structural biology with X-rays. In particular, they offer the possibility to investigate radiation damage sensitive samples (such as proteins with important metal centers), samples that scatter only weakly (small crystals or non-crystalline specimens), time-resolved processes that are irreversible, as well as other cases that inherently require many incident X-ray photons in a single pulse [63; 64]. Unprecedented possibilities are opening to observe weakly scattering samples, such as small crystals of proteins or perhaps even non-crystalline bio-samples such as viruses, which are largely unable to be seen at synchrotron or lab based X-ray sources. Nevertheless, techniques that are relatively simple at conventional sources, such as tomography, are not viable at an X-ray FEL where samples are typically destroyed by the act of illumination in a single projection. This reality means that many frames of data from different projections of a crystal or (reproducible) particle must be combined to form a complete three-dimensional diffraction volume that can be interpreted later as structure [65; 66]. These methods require as many as tens of thousands or hundreds of thousands of \"good\" hits for a single structure [67; 68]--and many more should one wish to look at a series of structures resolved in time for example.
# Dynamic Nearest Neighbor: An Improved Machine Learning Classifier and Its Application in Finances
## 4 Results
### Datasets
As in the Bank dataset, the purpose is to predict if a potential client will subscribe to a term deposit [35]. Also, the two classes are Yes/No, and the dataset patterns contain mixed attributes, that is, both numerical and categorical attributes, with no missing values. The \(IR\) index is 8.13, which indicates that the dataset is severely unbalanced. **Banknote authentication**  
The creation of this dataset is intended to detect fraudulent banknotes [34]. To do this, data were extracted from images that were taken from genuine and forged banknote-like specimens. A total of 1327 patterns were generated, where each pattern consists of four attributes: wavelet variance, wavelet skewness, wavelet kurtosis and image entropy, which were extracted from the images. The \(IR\) index is 1.24, which indicates that the dataset is balanced. **Credit approval**  
This dataset [34] classifies people described by a set of attributes representing cases of people who were granted credit (383 patterns) and who were not granted credit (307 patterns) in a bank. In total the dataset contains 690 patterns that contain 15 mixed attributes (numeric and categorical). It has no missing values and the \(IR\) index is 1.24, which indicates that the dataset is balanced. **German credit data**  
This dataset classifies people described by a set of attributes as good or bad credit risks [34]. There are 1000 patterns containing 24 mixed attributes (numeric and categorical). Class 1 contains 700 patterns, while class 2 contains 300 patterns. It has no missing values and the \(IR\) index is 2.33, which indicates that the dataset is imbalanced. **Iranian credit**  
This dataset classifies people described by a set of attributes that represent cases of people who are good customers (950 patterns) and bad customers in terms of paying their credit (633 patterns) in an Iranian private bank. In total the dataset contains 1583 patterns containing 28 mixed attributes (numeric and categorical) [28]. It has no missing values and the \(IR\) index is 1.50, which indicates that the dataset is balanced. **Qualitative bankruptcy**  
With this dataset it is possible to predict a future bankruptcy based on qualitative attributes [30]. There are two classes, bankruptcy cases (107 patterns) and non-bankruptcy cases (143 patterns). In total the dataset contains 250 patterns that contain six categorical attributes). It has no missing values and the \(IR\) index is 1.33, which indicates that the dataset is balanced. Table 1 contains a summary (in alphabetical order) of the specifications for the eight datasets.
## 3 Results and Discussion  
Four categories of microbial interactions, functional to the discussion, can be identified among those tested in this work: yeast-yeast, yeast-bacteria, bacteria-yeast, and bacteria-bacteria. As a whole, negative and neutral interactions have been detected, while no examples of positive interactions have been reported. This is in contrast to the literature in the field, which identifies different behaviors attributable to mutualism/synergism and commensalism reported in matrices of oenological interest [1,54] (e.g., between _S. cerevisiae_ and _L. plantarum_ in grape juices [55], _Kloeckera apiculata_/_S. cerevisiae_ in grape juice integrated with yeast extract [56]). The findings highlight that plate screening did not seem adequate to detect positive interactions between microorganisms in the wine sector.  
Inhibitions representative of all the possible typologies of antagonisms have been observed, including all tested microbial categories (i.e., _Saccharomyces_/non-_Saccharomyces_/ yeasts and lactic acid bacteria). According to the extent of their antagonistic behaviors, the different microorganisms used in this study have been classified as strains of mild (\(\pm\)), middle (+), or strong (++) inhibitory activity showing halos of inhibition, surrounding the respective spots, with a diameter lower than 3 mm, ranging from 3-6 mm or more than 6 mm, respectively. Evidence has been detected in all three media agar used as substrates, resulting in a more frequent on culture medium, followed by grape juice and, finally, wine. In particular, referring to the exerted (Figure 1a) and suffered (Figure 1b) inhibitions, non-_Saccharomyces_ yeasts and LAB showed activity on culture medium and grape juice while _S. cerevisiae_ strains on culture medium and--in only a few cases--wine. These results confirmed, in the oenological field, that in vitro antagonisms do not necessarily correlate with the same behavior in in situ studies [57]. Highlighted trends indicate a selective maintenance of the antagonistic character shifting from tests on a culture medium to evaluation on the edible matrix. Intriguingly, in the case of the yeasts, this selectivity seemed to correspond to the phase of dominance during winemaking: grape juice for non-_Saccharomyces_ and wine for _S. cerevisiae_[58,59].  
Therefore, selected strains belonging to all the microbial categories tested in this plate screening [non-_Saccharomyces_, _S. cerevisiae_, and Lactic Acid Bacteria (LAB)] have interacted on culture medium agar (examples in Figure S1).  
Evidence highlights only a certain connection with the clear succession of microbial dominance (i.e., non-_Saccharomyces_ in grape must and in early Alcoholic fermentation (AF), _Saccharomyces_ in the middle/late AF, LAB during malolactic fermentation) [30], underlining how the dominance phenomenon is only one of the complex WMC interactions.
# Digitalization of the Ex-Core Neutron Flux Monitoring System for APR1400 Nuclear Power Plant
## 2 Design of the Digital ENFMS for APR1400
### Linear Power Signal Generation  
#### 2.2.1 Data Acquisition  
The front-end electronics for the linear power signal generation of the wide-range monitoring instrumentation acquire mean values of current signals from neutron flux detectors for a fixed response time which is much longer than the average time between individual current pulses. Analog linear amplifiers acquire, amplify and then convert the current signals generated by detectors into voltage signals within the range fit to the respective input span of A/D converter. The rate of A/D conversion applicable to process component controls in nuclear power plants is also sufficient to this application.
## 2 Methodology
### Establishment and Derivation of Prediction Model of Surface Subsidence  
#### 2.3.1 Surface Subsidence Prediction Based on Equivalent Mining Height Theory  
Under conditions of grouting into overburden separation, the uneven subsidence of key strata can be treated as equivalent mining height, allowing the use of the probability integral method to predict ground surface subsidence. The schematic diagram for predicting surface subsidence using equivalent mining height is illustrated in Figure 4. The core approach involves treating the overlying strata above the key strata as a random medium and dividing the uneven subsidence of the key strata into micro-units. By employing the concept of integration, the cumulative influence of all these units on the strata or surface is calculated, ultimately yielding the predicted surface subsidence.  
The strata subsidence caused by the 1 \(\times\) d\(u\) mining unit can be obtained according to the probability integral method  
\[\mathrm{d}W(x)=\frac{1}{r}\mathrm{exp}(-\frac{\pi(x-u)^{2}}{r^{2}})\mathrm{d}u \tag{22}\]  
where \(r\) represents the main influence radius, which can be calculated by the ratio of the mining unit buried depth \(h_{z}\) to the tangent of the main influence angle \(\mathrm{tan}\beta\). \(W(x)\) is the subsidence value of the strata at the position \(x\) from the origin of the coordinate.  
When the mining unit is \(w(u)\times\mathrm{d}u\), the subsidence of the strata can be obtained as follows:  
\[\mathrm{d}W(x)=\frac{w(u)}{r}\mathrm{exp}(-\frac{\pi(x-u)^{2}}{r^{2}})\mathrm{ d}u \tag{23}\]  
Assuming that the mining boundary is from \(x_{1}\) to \(x_{2}\), the subsidence curve of strata can be obtained as follows:  
\[W(x)=\int_{x_{1}}^{x_{2}}\frac{w(u)}{r}\mathrm{exp}(-\frac{\pi(x-u)^{2}}{r^{2} })\mathrm{d}u \tag{24}\]  
Figure 4: Schematic diagram of predicting surface subsidence by equivalent mining height theory.  
Considering the cushioning and rebound effects of the loose layer on surface subsidence, the subsidence coefficient \(q_{0}\) needs to be introduced. Subsequently, the prediction formula for ground surface subsidence with grouting into overburden separation is as follows:  
\[W(x)=q_{0}\int_{0}^{W+2b}\frac{w(u)}{r}\text{exp}(-\frac{\pi(x-u)^{2}}{r^{2}}) \text{d}u \tag{25}\]
## 6 Results and Discussion
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline
**H/S** & **H** & **H** & **H** & **H** & **H** & **S** & **S** & **S** & **S** & **S** \\ \hline Number of hidden neurons & 3 & 30 & 50 & 80 & 100 & 3 & 30 & 50 & 80 & 100 \\ \hline training sum of squares error & 0.288 & 0.021 & 0.008 & 0.125 & 2.268 & 0.094 & 0.022 & 0.015 & 0.018 & 0.015 \\ \hline training time (ms) & 2 & 2 & 6 & 5 & 9 & 0 & 0 & 2 & 2 & 2 \\ \hline training relative error & 0.037 & 0.002 & 0.001 & 0.025 & 0.288 & 0.045 & 0.012 & 0.01 & 0.013 & 0.01 \\ \hline testing sum of squares error & 0.386 & 0.272 & 0.415 & 0.733 & 1.939 & 0.158 & 0.018 & 0.023 & 0.034 & 0.023 \\ \hline testing relative error & 0.135 & 0.019 & 0.135 & 0.094 & 0.678 & 0.278 & 0.018 & 0.017 & 0.027 & 0.017 \\ \hline MSE & 107.12 & 42.206 & 61.051 & 110.04 & 668.56 & 160.063 & 23.487 & 14.711 & 33.224 & 22.3799 \\ \hline MAE & 7.2494 & 3.2004 & 3.1415 & 6.6091 & 21.095 & 8.01854 & 3.5317 & 2.9691 & 4.2829 & 3.62697 \\ \hline MAPE & 1.3001 & 0.5720 & 0.5574 & 1.1800 & 3.6681 & 1.43662 & 0.6339 & 0.5260 & 0.7671 & 0.63997 \\ \hline RMSE & 10.35008 & 6.496684 & 7.813519 & 10.49037 & 25.85701 & 12.6516305 & 4.846391 & 3.835549 & 5.764049 & 4.73075014 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: PCA–ELM models using a hyperbolic tangent function and sigmoid function with different ranges of hidden neurons. Figure 5: Comparison of the errors for various models.
# Research on Grain Moisture Model Based on Improved SSA-SVR Algorithm
## 3 System Modeling Method Construction
### Sparrow Search Algorithm  
The Sparrow Search Algorithm (SSA) is a relatively new swarm intelligence optimization algorithm, inspired by the foraging and predator evasion behaviors of sparrows. The SSA possesses commendable local search capabilities. During foraging, sparrows are divided into discoverers, who are responsible for providing the direction of foraging for the population, and joiners, who are responsible for following to obtain food. When sparrows perceive danger, they exhibit anti-predatory behavior, which updates the population's position [24; 25]. It is based on six hypothetical rules: 1. leaders and followers, 2. discoverers and sentinels, 3. safe areas and food sources, 4. sparrow vigilance behavior, 5. response to sudden events, and 6. flight behavior patterns, thereby simulating the social behavior and survival strategies of sparrows.  
SSA uses these behavioral assumptions to simulate the social behavior and foraging strategy of sparrows, guiding the process of finding the global optimum. The algorithm updates the positions of the sparrows through iteration, eventually converging to the optimal or near-optimal solution.  
Despite the advantages of the SSA over other optimization algorithms, the search for the optimal solution still relies on discoverers, which can easily get trapped in local optima. Additionally, followers and sentinels, although they make up the majority of the population, have weaker optimization abilities. Therefore, an improved method has been proposed [26].
# Protecting Sensitive Data in the Information Age: State of the Art and Future Prospects
## 5 Future Prospects
Furthermore, the question arises how an efficient and trustworthy key management can be implemented in such a scenario. This concerns both the key generation and the key provisioning. On the other hand, misinformation about a data subject can also be disseminated by third parties. This is well known in the context of OSNs such as Facebook and commonly referred to as _fake news_. Here, as well, information is disseminated by means of word-of-mouth and not directly from a data subject to the intended recipient. For OSNs, there are approaches that can be used to restrain the dissemination of misinformation. Contradictory information, i.e., misinformation and credible information, is identified, and its propagation in the network is monitored [193]. Using a greedy approach, users in the network can be identified who are considered trustworthy and can therefore be assumed to contribute to minimize the spread of misinformation by sharing only credible information in the network. In the case of contradictory information, this allows one to determine which version represents the misinformation--namely, the one which is not shared by a trustworthy user--and then to remove it from circulation [194]. However, this procedure was developed specifically for social networks. It must therefore be studied whether it can also be applied to the data infrastructure of smart services. In particular, it must be assessed to what extent this approach has to be adapted and extended in order to be able to deal with heterogeneous raw data from a wide variety of domains instead of purely textual information. Furthermore, the IoT is a much more dynamic structure than a social network. Whereas in the latter case, users usually remain part of the network for a long time, in the IoT, new data sources are constantly being added or removed. Therefore, it is also important to research how the approach can be adapted to such an ever-changing environment. We consider these seven challenges to be the most critical ones related to the protection of sensitive data in the information age that need to be addressed in future work.
# A Novel Method of Calibrating Micro-Scale Parameters of PFC Model and Experimental Validation
## 2 Microscopic and Macroscopic Parameters of PFC Model  
### Key Microscopic Parameters of Parallel-Bond Model  
In this study, the main purpose is to find an approach to determine the micro-scale parameters of parallel-bond model, which can be used to simulate fracture propagation in rock specimens under uniaxial loads. As the internal fracture in thin specimens can be well represented by the surface cracks, the rock specimens used for lab tests are usually machined into prisms. Accordingly, the PFC2D software is commonly adopted to study the rock fracture behavior [33]. The microscopic parameters of the parallel-bond model can be classified as: specimen-genesis control parameters, particle-based material parameters, and parallel-bond parameters. To avoid boundary effects, dimensions of the rock model were set to 100 mm in width and 150 mm in length. The final platen velocity, namely, axial loading speed, was maintained at 0.05 m/s, and the test-termination criterion was assigned a value of 0.6. For the other specimen-genesis control parameters, their values were default according to the PFC manual [34]. Besides, Table 1 shows the detailed descriptions concerning the parameters of the particle and the bond.  
Table 1 shows that the slope of the post-peak stress-strain curve is closely related to the particle friction coefficient. After several trial and error attempts, its value can be easily determined based on the experimental stress-strain curve. Overall, the uniaxial compressive strength of the model is controlled by \(\sigma_{c}\), \(\tau_{c}\), \(\overline{\sigma}_{c}\), and \(\overline{\tau}_{c}\), the Young's modulus is jointly influenced by \(E_{\text{c}}\) and \(\overline{E}_{c}\), and the Poisson's ratio is highly correlated with \(k_{\text{n}}\)/\(k_{\text{s}}\) and \(\overline{k}_{n}\)/\(\overline{k}_{\text{s}}\)[35; 36; 37]. Besides, interactions between the micro-scale parameters also affect them to varying degrees [24]. In respect of \(\overline{\sigma}_{c}\) and \(\overline{\tau}_{c}\), they are used for small-scale adjustments and have little impact on the macro-mechanical properties. To quickly determine the values of these parameters, the following simplifications are made: \(\sigma_{c}=\tau_{c}\), \(E_{\text{c}}=\overline{E}_{c}\), and \(k_{\text{n}}\)/\(k_{\text{s}}=\overline{k}_{n}\)/\(\overline{k}_{\text{s}}\)[36]. Therefore, the number of independent variables to be optimized are reduced to three, i.e., \(\sigma_{c}\), \(E_{\text{c}}\), and \(k_{\text{n}}\)/\(k_{\text{s}}\).
## 3 Methodology  
The methodology developed is structured in the following five main phases (Figure 1):The methodology follows five main phases: analysis of the current scenario, establishment of the baseline, calculations to evaluate key outcomes, analysis of blockchain implementation, and discussion of results. Each phase is structured to systematically assess the impact of IoV and blockchain on Ro-Ro terminals.  
**Phase 1: Work Scenario**  
In this initial stage, the current situation of the Ro-Ro terminals is analyzed, identifying the fundamental variables for the study. Dependent variables include wait time, vehicular flow, CO\({}_{2}\) emissions, and operating costs per hour. These variables directly reflect the results of technological impact, such as the use of the Internet of Vehicles (IoV) and automation. On the other hand, independent variables, such as average time per vehicle, number of AGVs or drivers, energy consumption, and traffic volume, represent controllable factors that affect performance. This comprehensive approach makes it possible to evaluate how technological solutions can optimize the efficiency, sustainability, and profitability of terminals.  
**Phase 2: Establishing the Baseline**  
Initial values are assigned to key variables in manual and automated scenarios. This includes calculations of average time per operation, vehicular flow, CO\({}_{2}\) emissions, costs per hour, and maximum operating capacity. These values will serve as a point of comparison to measure the benefits of the proposed technologies.  
**Phase 3: Implementation of Calculations**  
The necessary calculations are made to evaluate the key results: reduction of waiting times, increase in vehicular flow, decrease in CO\({}_{2}\) emissions, and economic savings, both total and per vehicle. In addition, the mean and standard deviation of the variables are analyzed to evaluate the consistency of the results.  
**Phase 4: Blockchain Implementation Analysis**  
At this stage, a focus group evaluates critical aspects for blockchain implementation, such as software development, infrastructure, integration with existing systems, training, and ongoing operational costs.  
**Phase 5: Results and Discussion**  
Figure 1: Methodological scheme. Source: own elaboration.  
The results obtained are consolidated, discussing the impacts and benefits of the use of advanced technologies in Ro-Ro terminals. This comprehensive analysis provides a strategic vision for the improvement of port operations, focusing on productivity, sustainability, and profitability.
# Optimization of Acoustic Communication Links for a Swarm of AUVs: The COMET and NEMOSENS Examples
## 5 Physical Layer
### Adaptive Parameters
In reference [28], the bound for \(P_{\text{ICI}}\) is derived for an OFDM system considering a time-variant channel, and its resulting expression is given by  
\[P_{\text{ICI}}=1-\int_{-f_{d}}^{f_{d}}P(f)\text{sinc}^{2}(fT_{\text{sym}}) \text{d}f, \tag{8}\]  
with \(f_{d}\) being the value of the maximum Doppler frequency, and \(P(f)\) the Doppler spectrum. The variable \(f_{d}\) is defined as  
\[f_{d}=\frac{v}{c}\cdot f_{c}, \tag{9}\]  
where \(v\) is the relative velocity between transmitter and receiver, and \(c\) is the sound speed propagation. Since the actual Doppler spectrum \(P(f)\) is unknown, it is predicted. This prediction is made using the maximum entropy principle [29]. The main motivation for using this approach is to avoid adding random and unknown information to the system. These models are specially useful in underwater applications [29; 30; 31]. For this work, we assume that the Doppler spectrum \(P(f)\) has a known bounded normalized support. In other words, we consider as known the maximum Doppler effect. The computation of \(P(f)\) follows the guidelines provided at [29], in which is employed a numerical approach. Knowing that we utilize a full redundancy receiver, we can compute the interference power due to IBI as follows:  
\[P_{\text{IBI}}=\int_{T_{\text{CP}}}^{+\infty}P_{dp}(\tau)\text{d}\tau, \tag{10}\]  
with \(P_{dp}(\tau)\) being the channel power-delay profile. For obtaining a prediction of the power delay-profile, we utilize a wideband ray tracer for shallow water [32]. In order to also consider the frequency dependent attenuation phenomenon, [PERSON]'s formula [33] is added to this procedure. For solving the optimization problem of Equation (7), we compute the interference power for all predefined pairs of parameters \((N_{\text{sub}},T_{\text{CP}})\). The pair which leads to the minimum interference, and that obeys the overhead constraint is selected. For summarizing, Table 1 shows all the input parameters of the optimization problem. Notice that the optimization only requires knowledge about the channel geometry. As output, the optimization algorithm provides the number of subcarriers and the CP length \((N_{\text{sub}},T_{\text{CP}})\), as shown in Table 2.