esa-sceva/satcom-chunk-collection huggingface dataset:
# Microstructural Changes Influencing the Magnetoresistive Behavior of Bulk Nanocrystalline Materials
## 1 Introduction
[5] found the highest GMR (16.5% measured at 5 K) when annealing Cu\({}_{80}\)Co\({}_{20}\) for 10 min at 500 \({}^{\circ}\)C. Screening the GMR for different melt-spun Cu-Co-compositions by [PERSON] and [PERSON] [12] yielded highest GMR at 10 K for 10 at% Co after annealing at 420 \({}^{\circ}\)C for 30 min. In summary, the ideal ferromagnetic content for highest GMR has to be sought for at low ferromagnetic compositions, with dedicated annealing treatments adjusted to the production route and thus to the starting microstructure. In case of the occurrence of GMR, the drop in resistance of a magnetized material in comparison to a non-magnetized one is due to a decrease in spin-dependent scattering at interfaces of ferromagnetic and non-ferromagnetic materials. For increased granular GMR-effect, a high number of small particles (i.e. interfaces) is needed. SPD by HPT [17; 18; 19] is capable of providing these small particles, as it was already shown in [9; 10; 11]. Furthermore, it was presented in [11] that the HPT processing conditions and thus the evolving microstructure directly influence the magnitude of the drop in resistance. For example, compositions of medium ferromagnetic content provide higher GMR due to microstructural changes, when being deformed at elevated temperatures, compared to RT deformation [11]. During SPD, non-equilibrium, supersaturated, metastable phases can develop [20]. Consequently, not only coarsening but also segregation processes will take place upon subsequent annealing treatments. While growth of ferromagnetic particles will lead to a decrease in the GMR-effect, segregation of new, small ferromagnetic particles out of the supersaturated phase will increase the effect. It has not yet been investigated whether the severely deformed state already constitutes the ideal state, i.e., maximal drop in resistance in magnetic fields, or whether there is an optimum temperature treatment to achieve this state and if so--what is it for different compositions. In this study, adequate subsequent thermal treatments for maximizing the GMR are determined and related to the underlying microstructure. Furthermore, it will be demonstrated that the SPD process by using HPT is capable of producing samples of large volume (diameter: 30 mm, height: 7 mm) with the severely deformed material exhibiting a very high microstructural stability as well as a high temperature stability of GMR properties.
## 2 Theory
The detection probability of the OAM mode for a non-uniformly correlated high-order Bessel-Gaussian beam propagating under conditions of atmospheric turbulence is defined as follows [13]:  
\[P=\frac{I(z)}{\sum\limits_{\Delta l=-\infty}^{\infty}I(z)} \tag{15}\]  
where \(I(z)\) describes the intensity of the signal OAM beam, and the term \(\sum\limits_{\Delta l=-\infty}^{\infty}I(z)\) denotes the intensity of both the signal OAM beam and the crosstalk OAM beams. To describe the channel loss and crosstalk of the OAM communication system, a signal-to-noise ratio (SNR) per bit of the OAM mode for a non-uniformly correlated high-order Bessel-Gaussian beam was introduced; it is shown below [18]:  
\[SNR=\frac{I(z)}{\sum\limits_{\Delta l=-\infty}^{\infty}I(z),(\Delta l\
eq 0)} \tag{16}\]  
here, the term \(\sum\
olimits_{\Delta l=-\infty}^{\infty}I(z),\ (\Delta l\
eq 0)\) denotes the intensity of the crosstalk OAM beams. The average bit error rate (BER) is an important parameter used to measure signal quality for FSO communication systems. The average BER of non-uniformly correlated high-orderBessel-Gaussian beams under conditions of atmospheric turbulence can be expressed as follows:  
\[BER=\frac{1}{2}erfc\Big{(}\sqrt{SNR/2}\Big{)} \tag{17}\]  
Equations (15)-(17) are the main analytical expressions used in this study. From Equation (14), it can be gleaned that the detection probability, SNR, and BER are affected by the difference of the signal OAM state and the crosstalk OAM states, beam width wavelength, receiver aperture diameter, and the strength of the turbulent atmosphere. The numerical results provide a convenient way of analyzing the performance of orbital angular momentum communication for a non-uniformly correlated high-order Bessel-Gaussian beam in a turbulent atmosphere.
## 4 Experimental Results and Discussion
The cumulative hydrogen production was 1.3997 m\({}^{3}\) and 1.4012 m\({}^{3}\), respectively, indicating a slight increase in production. Previous experimental results demonstrated that short-term thermal control did not lead to uncontrolled temperature variations, allowing for extended operation to assess system stability, reliability, and performance improvements. Figure 8 illustrates the temperature response of the electrolysis stack in the hydrogen production system during a long-term experiment, where the temperature of the electrolysis stack was consistently maintained within a range of 72.5 \(\pm\) 1 \({}^{\circ}\)C. The experimental result also demonstrates stable power and energy consumption, confirming the feasibility, effectiveness, and reliability of the thermal control strategy using FLC with the LSTMNN. The experimental results investigated the integration of the FLC and LSTMNN strategies in the thermal control system to achieve more precise temperature regulation of the electrolysis stack in the hydrogen production system. Table 2 presents a detailed perfor  
Figure 6: Power response of the hydrogen production system under two different thermal control strategies, displaying variations in power output when using different approaches and illustrating their impact on system performance. Figure 7: Current response of the electrolysis stack using two thermal control strategies, presenting current variations under these strategies and highlighting differences in electrical performance. mance analysis comparing the FLC method alone with the combined FLC and LSTMNN strategies. Using the FLC method alone, the temperature of the electrolysis stack could be regulated within a \(\pm\)2 \({}^{\circ}\)C range, yet significant variations persisted due to the slow response of the electric three-way valve. This delay caused discrepancies between the actual and target temperatures as the FLC signal failed to promptly adjust the valve. To overcome these limitations, the designed LSTMNN algorithm predicts temperature based on measured data before feeding it into the FLC controller. This approach reduces steady-state error variations in the electrolysis stack temperature, enhancing thermal control effectiveness. As shown in Table 2, the combined hydrogen production thermal control system, employing the FLC and LSTMNN strategies, demonstrates robust stability during long-term experiments. It consistently maintained temperatures within \(\pm\)1 \({}^{\circ}\)C and stabilized the power output at 10 kW. This integrated approach, highlighted in Table 2, enhances overall system performance, as confirmed by extended experimental testing, emphasizing its feasibility, effectiveness, and reliability. Therefore, the thermal control strategy in this study, integrating FLC with an LSTMNN, significantly improves the thermal control capabilities in a hydrogen production system. Its high stability in long-term experiments underscores its potential and practicality in actual applications.
## 4 Mission S-Net
### S Band Isl System
#### 4.3.2 S Band Radio  
TUB, in co-operation with IQ wireless Ltd., has developed a highly integrated S band transceiver (SLINK, see Figure 11 for an overview and Table 9 for the parameters) with a maximum of 169 kbps crosslink capability suitable for nanosatellites. SLINK offers two operating modes, selectable by SNC: An up-/down-link mode, and an ISL mode. The downlink is characterized by 1 Mbps at a symbol rate of 1.4 MHz, the uplink by 32 kbps, and the ISL by 100 kbps nominal. Both ISL and UL have a symbol rate of 80 kHz. The use of phase modulation and effective channel coding (convolutional) lead to a good SNR, with only a small transmission power of 27 dBm possible. The ground link is designed for a 3 m ground station reflector at a minimal elevation angle of 10 deg at LEO. The nominal downlink data rate of 1 Mbps can be improved by adapting the bandwidth--QPSK and 8 PSK are optional--or the channel coding, with variable code rate of 0.25-0.75. In contrast, the uplink bit rate is constant and better than 32 kbps. The up- and down-links are separated in frequency (2.0 and 2.2 GHz, respectively) and in time.
# RepFTI: Representation-Fused Function-Type Inference for Vehicular Secure Software Systems
## 1 Introduction
Existing representation methods can be mainly divided into two categories: (1) text-based methods [22; 23; 24; 25], which mainly extract features from assembly codes and control flow information that are reserved from binary files, and (2) graph-based methods [26; 27; 28; 29], whereby software engineers can build graph-representation models at the function, basic block, instruction, and other levels. For example, a control flow graph-based embedding method is proposed in [28] for efficient binary code-similarity detection. And also, the authors in [30] model the call relationship between basic blocks as a graph network to detect software vulnerabilities. But, they do not fuse text semantic features and graph features, which leads to the absence of global data dependencies [31]. In the software of vehicles, the function-type inference has two sub-problems: recovering the semantics within the function and the control flow between functions. At first, the abstract-level functions are often met when we try to trace the execution process of in-vehicle software, but the general dynamic binary analysis tools are hardly useful to identify the abstract-level interface. And then the semantic feature can only find the similarity execution process within functions, but how can we filter some similarity local execution processes with different global dependency relationships? These are mainly problems in existing works. In this work, we extract the features of functions and recognize the function type by the semantics and the control flow of functions. Our starting point is a list of assembly instructions at the function level, and the goal is to produce the observable function type without any compilation information to obtain early and clear findings of the vulnerabilities and code defects. **Our Approach. ** In this paper, we propose a novel representation-fused function-type inference (RepFTI) framework, which focuses on using multi-representation fusion to recover global data dependencies that were destroyed during the model-training process. The RepFTI mainly consists of the following two parts: (1) generating semantic representations and graph representations, and (2) fusing multiple representations to reconstruct the whole data dependency. For semantic representations, we generate word embeddings by refining the token classification and modifying the embedding model. For node representation, we traverse each node in the function call graph and extract the call relationship feature between the target node and its neighbor nodes as the node representation. Corresponding to the reconstruction process of complete data dependency, short-span data dependencies are recovered using a Bidirectional Long Short-Term Memory (Bi-LSTM) network, and long-span data dependencies are recovered with a GAT model.
## 4 Survey Findings
### Drivers and Benefits
\begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline  & \multicolumn{4}{c}{**Environmental Drivers**} & \multicolumn{4}{c}{**Environmental Benefits**} \\ \cline{2-9}  & **Tot** & **P.A.** & **P.** & **Gap** & **Tot** & **P.A.** & **P.** & **Gap** \\ \hline Average score for drivers and benefits & 3.42 & 4.05 & 3.30 & 0.75 & 3.20 & 3.64 & 3.12 & 0.52 \\ Reduction of emissions to air & 3.51 & 3.84 & 3.45 & 0.39 & 3.31 & 3.36 & 3.30 & 0.06 \\ Reduction of emissions into water & 3.66 & 4.39 & 3.53 & 0.86 & 3.50 & 4.14 & 3.38 & 0.76 \\ Reduction of waste generation & 4.09 & 4.94 & 3.92 & 1.02 & 3.98 & 4.60 & 3.86 & 0.74 \\ Reduction in the use of soil and soil contamination & 3.21 & 4.10 & 3.05 & 1.05 & 2.95 & 3.75 & 2.80 & 0.95 \\ Reduction in the use of resources, raw materials, and energy & 3.90 & 4.72 & 3.75 & 0.97 & 3.58 & 4.00 & 3.51 & 0.49 \\ Reduction of noise, smell, vibration, visual impact & 3.11 & 3.35 & 3.07 & 0.28 & 3.01 & 3.14 & 2.98 & 0.16 \\ Reduction in risk of industrial accidents & 3.52 & 3.58 & 3.51 & 0.07 & 3.23 & 3.24 & 3.22 & 0.02 \\ Effects on biodiversity & 2.37 & 3.48 & 2.16 & 1.32 & 2.06 & 2.90 & 1.90 & 1 \\ \hline \hline \end{tabular} Source: Author’s elaboration on survey database.  
\end{table} 
Table 5: Environmental drivers and benefits. consistent with existing literature [53; 57; 58; 62]. On the other hand, the difference in environmental and economic drivers and benefits is less evident, respectively 0.22 and 0.20 (Tables 5 and 6). Moreover, a slight difference exists between public administration and private organizations. In general, public administrations appear as less satisfied concerning environmental aspects (Tables 4-6). Even though benefits are generally lower that drivers, the most significant in the strategic category are \"legislative compliance\" (4.64) and \"improved image\" (4.59) (Table 4). Moreover, EMAS is not only useful for private organizations to improve legislative compliance, but also for public administrations. In fact, public administrations consider the EMS very important (5.10) to ensure that all operations are conducted in accordance with existing regulations. It is probably due to the fact that Italian environmental legislation is particularly complex and also for public bodies it is often hard to maintain the compliance, with the risk of criminal penalties for public administrators. Concerning the \"environment\" category, the main benefit is \"reduction of waste generation\" (3.98) (Table 5). Finally in relation to \"economic factors\" is \"energy savings\" (3.40) (Table 6). These results agree with findings of scholars that previously investigated EMAS (see Paragraph 2).
# Simulative and Experimental Investigation of Vibration Transfer Path at Gearboxes
## 5 Results and Discussion
### Experimental Results
The influence of the torque on the peak-to-mean values of the signal after post-processing from the rotating sensors has also been investigated, as shown in Figure 14. The relation between the peak-to-mean value and the torque is unclear from these three test scenarios (1000 rpm, 1500 rpm, and 2000 rpm). In contrast, the influence of the rotational speed on the ratio of the peak-to-mean value is quite linear. This is also obvious because the rotational speed only influences the vibration excitation. At the same time, different external torques can generate different displacements of the entire gearbox. Because of the nonlinear stiffness of the bearings and gears, the displacements can affect their stiffness, which changes the dynamic behavior of the gearbox. Hence, a strong excitation because of a large external torque does not mean a better peak-to-mean ratio.
## 3 Model and Calculation Method
### Model Validation  
In order to verify the validity of the vehicle-track dynamic coupling model, the simulation results for the composite sleeper-ballasted track are compared with the results in the literature [17] (as shown in Table 4) under the same conditions. It can be observed that the dynamic responses calculated by the model in this paper are relatively close to those in the literature [17]. Therefore, the model in this paper can effectively describe the dynamic response of the track structure.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Dynamic Responses** & **[PERSON] [17]** & **This Paper** \\ \hline Rail displacement (mm) & \(1.41\) & \(1.38\) \\ Sleeper displacement (mm) & \(0.90\) & \(0.87\) \\ Sleeper von Mises stress (MPa) & \(1.45\) & \(1.12\) \\ Ballast bed displacement (mm) & \(0.24\) & \(0.22\) \\ \hline \hline \end{tabular}
\end{table}
Table 4: The maximum dynamic responses.  
Figure 5: Track irregularity with distance.
## 4 Discussions and Results
### Consumption Characterization  
At the end of analyzing and optimizing energy consumption, a solution is reached that reflects the actual needs and climatic conditions of the geographical context in which the study is conducted. Below, Figure 6 shows the annual percentage values. Specifically, the percentage value related to heating has been reduced to 20.24%. In comparison, the air conditioning value has increased to 12.96%, more accurately reflecting the high consumption during the hot summer months. Additionally, there has been a slight increase in the consumption of household appliances to maintain an overall balance in consumption.  
The percentage values for each end use and each month are presented in Table 1 and in Table 2 below. These values represent the estimation of the annual energy consumption for each category, optimized to balance the overall energy demand without compromising daily functionality. This approach allows us to meet the monthly energy constraints and adapt to the specific seasonal and behavioral dynamics of the users.  
Figure 4: Monthly consumption in January based on standard data from [42].  
Figure 5: Daily consumption in January based on standard data from [42].  
Figure 3: Annual consumption based on standard data from [42].  
As can be seen from the obtained values, the consumption of kitchen appliances, standby, and refrigerators remains almost constant throughout the year. Lighting shows lower values during the summer months. Heating and cooling consumption have zero values during the months when they are not used. Heating is absent in the summer, while air conditioning does not appear in the winter.
# Assessment of Hull and Propeller Degradation Due to Biofouling Using Tree-Based Models
###### Abstract  
A hull and propeller biofouling assessment framework is presented and demonstrated using a bulk carrier as a case study corresponding to an operational period of two and a half years. The aim is to support the decision-making process for optimizing maintenance related to hull and propeller cleaning actions. For the degradation assessment, an appropriate key performance indicator is defined comparing the expected shaft power required with the measured power under the same operational conditions. The power prediction models are data-driven based on machine learning algorithms. The process includes feature engineering, filtering, and data smoothing, while an evaluation of regression algorithms of the decision tree family is performed. The extra trees algorithm was selected, presenting a mean absolute percentage error of 1.1%. The analysis incorporates two prediction models corresponding to two different approaches. In the first, the model is employed as a reference performance baseline representing the clean vessel. When applied to a dataset reflecting advanced stages of biofouling, an average power increase of 11.3% is predicted. In the second approach, the model entails a temporal feature enabling the examination of scenarios at different points in time. Considering synthetic data corresponding to 300 days since hull cleaning, it was derived that the fouled vessel required an average 20.5% increase in power.  
ship energy efficiency; hull and propeller biofouling; data analysis; data-driven models; maintenance optimization +
Footnote †: journal: Applied Sciences
## Data and Methods
### Method  
#### Accessibility Using Network Analysis  
We used network analysis to evaluate vulnerable areas for EMS in urban and rural areas. The network analysis for calculating accessibility was performed using the Network Analyst extension of the Geographic Information System (ArcGISTM), which provides various techniques for network-based spatial analysis, such as optimal path analysis on a road network, identification of the nearest facility, analysis of service ranges, and other accessibility functions [46]. Among the various network analyst functions, we used the origin-destination cost matrix (O-D cost matrix) to create a matrix of the origins  
and destinations. The optimal path was extracted to determine the least time to arrive at the destinations. The transfer path for emergency patients was determined as the route from the community center (through 119-safety centers or community health center) to the nearest emergency center.
## 9 Objective 4: Relationship between Local and Global Ductility  
In experimental studies the ductility capacity is usually obtained for individual members (local ductility), for this reason, as stated earlier, it is suggested [10] considering local ductility as the basis for design. Thus it results convenient relate local to global ductility. In this section the ratio of global to local ductility, denoted by the \(R_{LG}\) parameter is presented and discussed. The ratio is calculated only for bending local ductility (\(\mu_{L\phi}\)) and global ductility, according to the two definitions under consideration (\(\mu_{GS}\) y \(\mu_{GI}\)), for dynamics and pushover analysis. The results are summarized in Table 10.  
It is observed that the \(R_{LG}\) values associated to \(\mu_{GI}\) and dynamic analysis give unreasonable larger values particularly for the 10-story building; the reason for this is that, as mentioned in Section 7, the normalizing quantity \(D_{y,t}\) in Equation (5) may be very small. These values are larger than that of pushover analysis which in turn are larger than those of \(\mu_{GS}\) and dynamic analysis. Considering that: (a) \(\mu_{GI}\) of dynamic analysis fails addressing the normalizing quantity (\(D_{y,t}\) in Equation (5)) giving unreasonable large values of \(\mu_{GI}\), (b) pushover does not take into account dissipation of energy, (c) SCWB concept was followed in the building design, and (d) the local axial ductility is not important in framed steel building structures, the ratio of global to local ductility capacity, proposed in this study, is calculated as the ratio of \(\mu_{GS}\) to \(\mu_{L\phi}\) of beams for dynamic analysis. Results from the table indicate that, for this definition, the mean values of \(R_{LG}\) and the uncertainty in the estimation are larger for the 3- than for the 10-level building and larger for the \(EW\) than for the \(NS\) direction. Based on the values obtained in this study (0.41, 0.34, 0.35 and 0.32) a value of 1/3 is proposed for the \(R_{LG}\) ratio. Thus, if local ductility capacity is stated as the basis for the design, say 15 or 12, the global ductility capacity can be estimated as 5 or 4.