esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
### Previous Work
However, when the number of messages becomes large, traditional per-message authentication schemes may generate unaffordable delays [27]. The privacy can be enforced even further by avoiding transmissions of sensitive information (e.g., projected EV route) over the wireless channel. This can be achieved by making the EV responsible for determining which vehicles can potentially interfere with the EV in the near future. To the best of our knowledge, this approach has not been previously addressed in the literature. Furthermore, the unicast eliminates the need of frequent resource-heavy map exchanges between communicating vehicles. To communicate the vehicle-customized information, the network routing protocols have to be used. It is worth noting that the performance of ad hoc routing protocols in the specific conditions of VANETs has been questioned in some studies. For example, [PERSON] et al. [28] concluded that the ad hoc routing protocols fail to deliver the performance needed to reliably support the variety of proposed VANET applications and should not be used as a universal method for information dissemination in the VANETs. The authors of the study [29], dealing with the performance of the Greedy Perimeter Stateless Routing (GPSR) protocol in vehicular networks, conclude that due to its performance limitations, GPSR seems to be unsuitable for vehicular communication applications. The usage of certain network-layer protocols can be restricted for certain services in Europe. For instance, the European Telecommunications Standards Institute (ETSI) European Standard (EN) 302 636-3 [30] requires the use of ETSI GeoNetworking [31] as the only network-layer protocol for safety-related communications in ETSI ITS-G5A band. However, for a certain sort of application, e.g., warning applications, that have less strict delay requirements than the safety-related applications, deployment of ad hoc routing-based communication can still be justified and considered as a legitimate approach. Moreover, an ability to implement relatively reliable unicast communication can even be, for some applications, preferred over the sheer value of the end-to-end delay.
# The BCL Method for DSSC: Basis and Applications +
## 7 The Role of Lithium on the Photo Conversion Efficiency of Ruthenium Dyes
### Adduct Generation
#### 7.2.2 Molecular Orbitals  
Figure 33a shows the energy and composition diagram of the molecular orbitals of the complex and its adduct. To carry out the analysis, the system is described in terms of four fragments: {dmb}, {dcb}, {Ru}, and {Ti(OH)}3]. The stabilization of the HOMO and LUMO levels is observed; meanwhile, the LUMO + 1 and LUMO + 2 are not altered. This stabilization produces a decrease in the HOMO-LUMO band gap energy from 2.07 eV to 1.96 eV; it is manifested as a bathochromic shift in the absorption maxima of the MLCT band. The increase in HOMO energy and ionization potential is experimentally correlated with the increase in oxidation potential.  
In both cases, in the complex and the adduct, the HOMO and LUMO present the same composition; however, for LUMO + 1 and LUMO + 2, notorious differences appear, as shown in Figure 33b: the dmb fragment predominates in both orbitals in the free complex, but in the adduct the dcb fragment appears with a large contribution. This change in the composition is due to the presence of the Li\({}^{+}\) ion which stabilizes the {dcb} fragment.  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Compound** & \begin{tabular}{c} **Ti-O(CO)** \\ **d1** \\ \end{tabular} & \begin{tabular}{c} **C-O** \\ **d2** \\ \end{tabular} & \begin{tabular}{c} **C=O** \\ **d3** \\ \end{tabular} & \
\begin{tabular}{c} **(CO)O-O(CO)** \\ **d4** \\ \end{tabular} &
\begin{tabular}{c} **(Py-Py)** \\ **\(\pi\)** \\ \end{tabular} \\ \hline
1(S0) & 1.99 & 1.30 & 1.22 & 6.11 & 178 \\
1(T1) & 1.99 & 1.30 & 1.23 & 6.23 & 181 \\
1(T1)Li & 2.00 & 1.28 & 1.25 & 3.98 & 158 \\
1(S0)Li & 2.00 & 1.27 & 1.25 & 4.03 & 161 \\ \hline \end{tabular}
\end{table}
Table 6: Atomic distances (A) and bond angles (\({}^{\circ}\)) for the lithium adduct.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**Compound** & \begin{tabular}{c} **Electronic** \\ **Transition (I)** \\ \end{tabular} & \begin{tabular}{c} **Energy (eV)** \\ **Exp** \\ \end{tabular} & \begin{tabular}{c} **Oscillator** \\ **Calc** \\ \end{tabular} & \
\begin{tabular}{c} **Main Excitation** \\ **Strength** \\ \end{tabular} &
\begin{tabular}{c} **Nature** \\ \end{tabular} \\ \hline
1(S0) & 1 & 2.55 & 2.55 & 0.05 & \
\begin{tabular}{c} 61\% H2 \(\rightarrow\) L2 \\ 15\% H0 \(\rightarrow\) L3 \\ 12\% H0 \(\rightarrow\) L2 \\ 10\% H2 \(\rightarrow\) L1 \\ 59\% H2 \(\rightarrow\) L2 \\ 10\% H2 \(\rightarrow\) L2 \\ 10\% H2 \(\rightarrow\) L1 \\ 59\% H2 \(\rightarrow\) L2 \\ 26\% H0 \(\rightarrow\) L1 \\ 6\% H0 \(\rightarrow\) L2 \\ \end{tabular} &
\begin{tabular}{c} Ru \(\rightarrow\) dcb \\ \(\pi\)dcb \\ \(\pi\)dmb \\ \end{tabular} \\
1(S0)Li & 1a & 2.43 & 2.44 & 0.07 & \
\begin{tabular}{c} 59\% H2 \(\rightarrow\) L2 \\ 26\% H0 \(\rightarrow\) L1 \\ 6\% H0 \(\rightarrow\) L2 \\ \end{tabular} &
\begin{tabular}{c} Ru \(\rightarrow\) dcb \\ \(\pi\)dcb \\ \(\pi\)dmb \\ \end{tabular} \\ \hline \end{tabular}
\end{table}
Table 7: TDDFT results for complexes 1 and 1 + Li.
# A Real-Time Nut-Type Classifier Application Using Transfer Learning
## 3 Results and Discussion
The F1-score calculated for ResNet-50 is again the highest among the others. The F1-score values also validate the performance superiority of TL models with regard to the custom CNN model. Figure 6: The changes in the evaluation metrics with respect to the number of epochs when different TL models are used. Figure 7: Best validation accuracy and best validation loss values obtained with respect to different learning models. The evaluation results show that a practical application for classifying nuts can be implemented using appropriate AI models. Learning-based solutions have become prevalent in almost every area of daily life. Day by day, new requirements emerge from different problem domains. CNN and DL are often consulted to develop trustworthy applications. However, finding a successful solution can be time consuming and effortful due to the complex dynamics of such approaches. In particular, finding suitable hyperparameters may not be the best option to train a new learning model on another dataset with similar problem domains. Therefore, adaptive and intelligent strategies should be used to respond to the requirements. The TL can be proposed as a possible solution for training new learning models because it is time saving and effortless. The basic idea of TL is to use the hyperparameters of previous successful models and create new models based on the pre-trained ones. In this study, the feasibility of different TL approaches is interrogated using a practical application for classifying unpackaged nuts. A newly created dataset is used throughout the evaluations. A custom CNN model is also trained for classification. The results clearly show that the TL is a promising candidate for new problems that need to be solved using successful learning models. More specifically, there are well-known TL architectures that contain a huge amount of data. Therefore, the TL-based nut-type classification outperforms the solution generated via the custom CNN model. Moreover, the evaluations show that the accuracy ratio obtained by applying TL models reaches the limits. In other words, determining a proper TL model is adequate to develop a successful application. The process of using TL is much less time consuming and makes it effortless to seek out a CNN model with optimal hyperparameters. Hence, this study can motivate further research that would try to employ AI in different problem domains.
# ABPCaps: A Novel Capsule Network-Based Method for the Prediction of Antibacterial Peptides
## 3 Results and Discussion
### Evaluation of ABPCaps on the Subsets with Functions against Different Bacteria  
Further experiments were performed to verify the accuracy and effectiveness of the APCaps. Based on the functions against different bacteria such as _Escherichia coli_ and _Staphylococcus aureus_, the ABP dataset was divided into several subsets. A negative set of non-AMPs was also created for each subset using UniProt. Then, in an 8:2 ratio, the subsets were randomly divided into training and test sets. Table 4 shows the number of sequences for each subset.  
Figure 4: Feature comparison between five hand-crafted features and features extracted by ABPCaps using four basic classifiers (KNN, RF, SVM, and GNB). Models are trained with these features and accuracy, F1-score and AUCROC are chosen as evaluation metrics. Based on the features extracted by ABPCaps and the hand-crafted features, the graph shows the performance of the different classifiers in distinguishing between ABP and non-ABP.  
For each subset, the performance of the ABPCaps was compared to the baseline models. Table 5 shows the average performance metrics over the ten subsets, showing that ABPCaps outperformed all baseline models in terms of precision, accuracy, F1-score, AUROC, and recall. The full evaluation metrics of ABPCaps in each subset are summarized in Supplementary Table S1. Furthermore, ROC curves were drawn for the models trained on every subgroup, as shown in Supplementary Figure S3, and the trained models achieved the best AUROC on every subgroup. Through extensive experiments on multiple datasets, ABPCaps outperformed all baselines and demonstrated excellent predictive abilities on various ABP datasets of different sizes and function against different bacteria. Overall, the results of the experiments demonstrated the accuracy and effectiveness of the ABPCaps and their ability to predict the antibacterial activity of peptides against a wide variety of bacteria.
###### Abstract
Except for the engineering application, researchers used finite element method (FEM) model to simulate the PCCV and analyzed the failure model. As PCCV was designed to withstand certain inner pressure even in the case of a loss of coolant accident (LOCA), [PERSON] and [PERSON] [11] used the FEM model with a nonlinearity constitutive model to predict the ultimate pressure capacity and failure mode. The results found that the ultimate pressure capacity of the PCCV was 0.886 MPa, which was 86% higher than the design pressure. Cracks were likely to occur near the apex of the dome, the junction of the dome and cylinder and the bottom of the cylinder. [PERSON] et al. [12] used an axisymmetric model and a 3D model to simulate 1:4 scale model of the PCCV. Both high temperature loading and internal pressure were considered in the analysis. The analysis results indicated the temperature affected the behavior of liner plate, but had little impact on the ultimate pressure capacity of the PCCV. [PERSON] [13] used a degenerated shell element in FEM model for ultimate pressure capacity evaluation. For precise simulation, this FEM model involved many material properties including a tension stiffening model, shear transfer mechanism, crack occurrence criteria and so on. The results showed the initial crack occurred at the internal pressure 0.83 MPa. In contrast to paper [11], the crack began in the middle part of the wall and then spread to the upper part of the wall at pressure 1.14 MPa. Other than the whole PCCV FEM model, the simplified model was also used. [PERSON] and [PERSON] [14] used part of the cylindrical wall of PCCV for sensitivity analysis. The sensitivity analysis of seven types of geometry and five kinds of load step was conducted. Results showed that the steel liner and temperature effect were the most important factors. [PERSON] et al. [15] compared the bonded PCCV with unbonded PCCV in FEM modeling and predicted the failure at the extreme inner pressure. The results indicated that the cylinder wall deformations were larger with bonded tendons and so were the peak strains during the internal pressurization. [PERSON] and [PERSON] [16] adopted a nonlinear FEM model to analyze the thermal and mechanical behaviors of the PCCV that was subjected to temperature loading and internal pressure. The analysis showed that the temperature had little impact on the ultimate pressure capacity of the PCCV. In this paper, we adopted FBG to monitor the PCCV during the ILRT. Moreover, the FEM model was built to simulate the pressurization and depressurization process. Firstly, the monitoring results were compared to the variation trend of pressure platform. Secondly, the rationality of the monitoring results was discussed because of abrupt temperature drop. Thirdly, the FEM model calculation results were compared with the monitoring results to testify the FEM model. Last, the calculation results were analyzed for sensor placement.
###### Abstract  
HSPs demonstrate a strong association with gamma-delta (\(\gamma\)s) T cells. Most of the studies regarding interactions between the parameters were conducted in the 1990s. Despite promising results, the concept of targeting \(\gamma\)s T cells by HSPs seems to be a forgotten direction due to potent non-peptide phosphoantigens rather than HSPs have been found to be the essential stimulatory components for human \(\gamma\)s cells. Currently, with greater knowledge of lymphocyte diversity, and more accurate diagnostic methods, we decided to study the correlation once again in the neoplastic condition. Twenty-one children with newly diagnosed acute lymphoblastic leukaemia (ALL) were enrolled on the study. Serum HSP90 concentrations were evaluated by an enzyme-linked immunosorbent assay (ELISA), subsets of \(\gamma\)s T cells (CD3+ \(\gamma\)s, CD3+ \(\gamma\)s HLA/DR+, CD4+ \(\gamma\)s and CD8+ \(\gamma\)s) by flow cytometry. We have shown statistically relevant correlations between serum HSP90 and CD3+ HLA/DR+ \(\gamma\)s T cells in paediatric ALL at diagnosis (R = 0.53, \(p<0.05\)), but not after induction chemotherapy. We also have demonstrated decreased levels of both serum HSP90 and CD3+ HLA/DR+ \(\gamma\)s T cells before treatment, which may indirectly indicate dose-dependent unknown interaction between the parameters. The results of our study may be a good introduction to research on the association between HSPs and CD3+ HLA/DR+ \(\gamma\)s T cells, which could be an interesting direction for the development of anti-cancer strategies, not just for childhood ALL.  
serum HSP90; gamma-delta T cells; acute lymphoblastic leukaemia +
Footnote †: journal: Appl. Sci. 2021
## 2 Methodology
### Direct Method for Determining Recptance Coupling Matrix  
In this method, the receptance matrix \(\left[\mathrm{H}(\omega)\right]\) is derived from the motion equations in the frequency domain:  
\[\left[\mathrm{H}(\omega)\right]\{\mathrm{F}(\omega)\}=\{\mathrm{U}(\omega)\} \text{where }\left[\mathrm{H}(\omega)\right]=\left(\left[\mathrm{K}\right]+\mathrm{j} \omega\mathrm{[C]}-\omega^{2}\mathrm{[M]}\right)^{-1} \tag{2}\]  
where [M], [K], and [C] are the mass, stiffness, and damping matrices, respectively, and \(\omega\) is the angular frequency. \(\mathrm{U}(\omega)\) and \(\mathrm{F}(\omega)\) represent the displacement response and external excitation force in the frequency domain, respectively. This formula provides the exact relationship between force and displacement, accounting for mass, damping, and stiffness.  
The receptance matrix \(\mathrm{H}(\omega)\) contains the frequency response functions (FRFs) of the structure, which describe the displacement response to a unit input force. The elements of the receptance matrix at any frequency of interest are calculated by substituting the corresponding elements of stiffness K, damping C, and mass M as follows:  
\[\mathrm{H}_{\mathrm{ii}}(\omega)=\frac{\mathrm{U}_{\mathrm{i}}(\omega)}{ \mathrm{F}_{\mathrm{j}}(\omega)}=\left(\left[\mathrm{K}_{\mathrm{ij}}\right]+ \mathrm{j}\omega\left[\mathrm{C}_{\mathrm{ij}}\right]-\omega^{2}\mathrm{[M_{ ij}]}\right)^{-1} \tag{3}\]  
This formula provides the exact relationship between force and displacement for each frequency \(\omega\), accounting for the effects of mass, damping, and stiffness in the system. The term \(\left[Z(\omega)\right]=\left[\mathrm{H}(\omega)\right]^{-1}=\left[\mathrm{H}( \omega)\right]=\left(\left[\mathrm{K}\right]+\mathrm{j}\omega\mathrm{[C]}- \omega^{2}\mathrm{[M]}\right)^{-1}\) represents the dynamic stiffness matrix of the system, which is frequency-dependent.
# Measures and Methods for the Evaluation of ATO Algorithms
## 4 Demonstration of Approach  
This section describes a brief demonstration of the outlined approach. The simulation framework that has been used for an exemplary ATO algorithm evaluation consists of two components depicted in Figure 4:  
1. Driver Advisory System (DAS) named BEAOnline by TU Dresden (used as ATO algorithm),
2. Train simulator named NEO by ProRail.  
BEAOnline was originally developed as a driver advice system and based on the offline functionality of BEA (Basic Energy Analysis), which can be used for energy consumption estimation [43]. The main functionality of ATO and DAS algorithms, namely the calculation of the optimal speed curve, coincides. With BEAOnline, a DAS algorithm was refined for application as part of an ATO software.  
The NEO simulator by ProRail allows the microscopic simulation of one (or multiple) train drives. Thereby, train models and detailed virtual track images are necessary. The system is based on the original version named _MATRICS_ following the concept of gaming simulation. It was used for several projects within ProRail either for demonstration or research purposes. [44] Furthermore, NEO is capable of generating comprehensive logging data, which is necessary to calculate the KPIs and thus evaluate the performance of the algorithm under test. By reading and analyzing all simulation log files, the KPIs for ATO algorithms are calculated and can be compared with other tests.  
The case study provides a brief evaluation of the impact of different timetable definitions including CTP variations to demonstrate the approach. For a complete evaluation of the given ATO algorithm more profound analyses are necessary.
# On Mitigating the Effects of Multipath on GNSS Using Environmental Context Detection
## 4 Context-Aware Navigation (CAN)
In almost all of the previous work on context detection, signal strength or its variants were used as the fundamental environment recognition parameters; however, this may not result in accurate context detection in the case of multi-constellation GNSS because: (1) the strength of the received signal is highly affected by NLOS and/or multipath; however, the severity of the effects varies with the frequencies [43]; (2) signal strength is majorly influenced by elevation angle and can also be affected by the receiver efficiency and antenna design; (3) the combination of multiple navigation systems in multi-constellation GNSS mode results in increased satellite density, and therefore monitoring the strength of each satellite can lead to a huge processing load. Hence, the signal strength may not be considered as an accurate recognition parameter when using multi-constellation and multi-frequency GNSS. In addition, most of the previous models relied on fixed-environment context definitions, giving little or no considerationto the dynamics of environment. However, considering the on-ground vehicle navigation, the GNSS receiver is not static and continuously encounters changing environmental contexts while navigating through cities. In dynamic mode, the GNSS receiver experiences a wide range of changing environmental contexts in urban or sub-urban areas, thereby, making context detection extremely difficult. In order to cope up with detecting the rapidly changing environmental context and to improve the positioning accuracy, a new context-aware navigation (CAN) algorithm was proposed that utilizes the globally and equally distributed property of GNSS, i.e., the satellite availability (the average number of satellites in a given geo-location) and a new feature--named the change factor (CF)--which estimates the normalized change in satellite availability in a pre-determined interval of time for decision making. The detailed description of working, implementation and environment characterization of the proposed CAN algorithm along with the working model of the CAN receiver is given in the subsequent sections.
# A Review of Counter-UAS Technologies for Cooperative Defensive Teams of Drones
## 6 Technological Challenges
### Team Coordination  
The coordination mechanism (i.e., the underlying mechanism to achieve a cooperative behavior) may concern: the imposition of an artificial interaction structure as a control or communication structure, aspects of the task specification, interaction dynamics of agent behaviors, etc. This may be seen as the \"society design\" or \"macro design\" of a multi-agent system, that is, the synthesis of a logical and physical protocol to ensure that the single agents interact to successfully achieve the global tasks and to avoid pursuing conflicting strategies or plans [108]. Coordination may be also defined as the \"process by which an agent reasons about its local actions and the (anticipated) actions of others to try and ensure the community acts in a coherent manner\" [109]. These definitions highlight that an effective coordination requires that each agent has to consider the actions of the other agents and that the main achievement is related to coherency, i.e., the goodness of the joint behavior according to the performance of interest for the given problem.  
In regard to the design of the coordination mechanism of a team for a given application, one critical point concerns the decision-making architecture, centralized or decentralized. Note that, for the distinction between a centralized and decentralized coordination from an algorithmic perspective, stricter criteria should be adopted, which establish that a coordination of a planning problem is centralized if [110]:  
* A single agent solves the overall problem; or
* All the agents solve the same overall problem; or
* The agents employ a wide number of communications (or a wide communication band) to plan their coordinated actions; or
* The agents exchange full plans.  
To the contrary, a coordination is decentralized if the agents make their decisions independently and if they employ limited communications (i.e., to exchange positions, maps, etc.). This algorithmic classification introduces a degree of decentralization and influences the theoretical and technical challenges to deal with for the coordination of teams of unmanned vehicles. Indeed, the maximization of the degree of decentralization represents a crucial aspect, looking also at the most recent works. However, such maximization should also consider additional issues, often overlooked in the modelling, including the following realistic scenarios: failures and cyberattacks, sensing noise and modelling uncertainties, intermittent or limited communication, etc.