esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### H. lupulus Extract Chemical Composition  
The chemical profile of the hop extracts was performed by GC/MS technique (Table 3). In all extracts, the content of sesquiterpenes was significantly higher than that of monoterepenes and ranged from 9.5% in TPe up to 35.4% in STGe. Among the monoterepenes, linalool was found in STGe, HBKe, CCHe and PCe, while \(Z\)-methyl geranate (1.5%) and \(\alpha\)-copaene (0.2%) were observed only in STGe and PCe, respectively. Several sesquiterpene compounds were identified among which \(\beta\)-caryophyllene, humulene, \(\beta\)-eudesmene and caryophyllene oxide were the most representative. In detail, \(\beta\)-caryophyllene was present in STGe (4.7.0%), HBKe (3.6%), CCHe (3.6%), PCJe (3.7%), PCe (5.0%) and TPe (6.5%) while humulene was found in the same varieties (STGe: 12.0%, HBKe: 6.8%, CCHe: 11.0%, PCJe: 14.1% and PCe: 17.8%), except for TPe. On the contrary, \(\beta\)-eudesmene was present only in STGe (7.4%) and in HLBe (35.5%). Caryophyllene oxide, present in average percentages lower than the compounds mentioned, was missing only in HLBe hop. Other minor sesquiterpenes were present only in one sample and missing in all others, particularly \(\delta\)-cadinene (1.8% in STGe), selina-3,7(11)-diene (1.7% in HBKe) and \(\alpha\)-eudesmol (4.1% in HBKe). In all analyzed samples, the presence of lupulone was significant varying from 14.5% to 63.8%. The SPME technique was also used to analyze the beer volatile composition. Fifteen compounds were identified (Table 4), among which eight belong to the fatty acid family. Only two terpene components such as \(\beta\)-myrorene and linalool were detected and, in particular, both compounds were found in IPA (2.0% and 0.8%, respectively) while only \(\beta\)-myrorene was present in the ACD sample (7.7%); on the contrary, they were missing in the LKO sample. The highest content of fatty acids was in the IPA sample (89.4%) followed by LKO (78.6%) and ACD (67.3%). In particular, hexyl octanoate (32.9%, 23.7%) and ethyl decanoate (23.4% and 15.3%) were the most abundant in LKO and in ACD, respectively. Hexadecanoic acid detected only in IPA (73.2%) represented the principal component.
## 2 Results
### Gene Expression Analysis  
The effects of all the selected AB, NIC, and OAs on the expression of virulence genes in the _E. coli_ K88 strain employed in this study are presented in Figure 2A-C. Doxycycline and colistin reported a general downregulation for all the analyzed genes, except for _bssS_, that showed only a reduction trend (\(p<0.1\)) in its expression level, and _luxS_, which was not significantly influenced by doxycycline. Amoxicillin did not show significant differences in mRNA levels (Figure 2A). Thymol, carvacrol, and eugenol significantly reduced the expression of all the genes tested, with the unique exception of _faeG_ for eugenol, whose level did not differ (\(p>0.05\)) from the untreated control (Figure 2B). Benzoic acid reduced the expression of all genes (\(p<0.05\)), except for _bssS_, that was not different from the control, and _faeG_, which showed an increase in transcription levels (\(p<0.05\)). Sorbic acid upregulated the expression of all the examined genes (\(p<0.05\)), except for _faeG_ and _bssS_. Finally, hexanoic acid was not different from the control, except for an increase of _bssS_ mRNA levels (\(p<0.05\)) (Figure 2C).  
Figure 2: Effect of different antibiotics (**A**), nature-identical compounds (**B**), and organic acids (**C**) on relative expression levels of _E. coli_ K88 virulence genes related to cellular adhesion (_faeG_), heat-labile toxin (_eltA_ and _eltB_), heat-stable toxins (_estA_ and _estB_), motility (_motA_), quorum sensing (_luxS_), and biofilm formation (_bssS_ and _tnaA_). Data are expressed as means (n = 3) and SEM is represented by vertical bars. For each gene, significant differences between each substance and its control are marked by asterisks (\(p<0.05\)).  
Considering the strong modulatory effect by NIC, gene expression results were pooled by treatment (NIC vs. AB) and compared. Results showed that the efficacy of NIC was the same as that of AB (\(p>0.05\)).
# Basins of Convergence in a Multi-Perturbed CR3 BP +
## 4 Application to the Sun-Mars System: Adding the Effects of Phobos and Deimos
### The Sun-Mars Problem: The Classical Formulation  
Firstly, the classical CR3 BP is studied. The Lagrange points in this case are obtained assuming that Mars orbits circularly around the Sun. Taking into account the initial data given in Table 8, it can be estimated the value of the mass parameter \(\mu=3.22710\times 10^{-7}\left[-\right]\). The BCs of the problem configured with these data are shown in Figures 8-10, where different domain sizes have been considered. The coordinates of the Lagrange points estimated in this case are those shown in Table 9.  
As can be seen, the region covered by the BCs is significantly larger than that obtained in the validation process. This is fundamentally due to the fact that the dimensionless parameter of the masses is remarkably smaller. However, it can also be seen that the libration points are located in a region very close to the primary masses. Moreover, it is possible to observe that all of the points outside of the regions resembling fractals converge towards Lagrange point \(L_{1}\).  
Figure 7: Basins of convergence for the Sun–Earth system considering relativistic effects with \(\mu=0.5\left[-\right]\), \(n=1\left[-\right]\), and \(\epsilon=1\left[-\right]\).  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Mass of Sun [kg] & \(1.98850\times 10^{30}\) \\ Mass of Mars [kg] & \(6.41710\times 10^{23}\) \\ Sun–Mars distance [km] & \(2.27923\times 10^{8}\) \\ \hline \hline \end{tabular}
\end{table}
Table 8: Sun–Mars problem data [31; 32].  
Figure 8: Basins of convergence for the Sun–Mars system in the classical formulation and for a broad size domain.  
Figure 10: Basins of convergence for the Sun–Mars system in the classical formulation, zooming in on Mars.  
Figure 9: Basins of convergence for the Sun–Mars system in the classical formulation and for a reduced size domain.  
Finally, it is also worth noting the proximity of libration points \(L_{1}\) and \(L_{2}\) to Mars (see Figure 10). Considering Equations (2) and (3), it can be deduced that this fact is also related to the very low mass parameter. As the mass parameter decreases, the collinear points \(L_{1}\) and \(L_{2}\) tend to approach the smallest mass (\(m_{2}\)), while point \(L_{3}\) moves away from it. The Lagrange points \(L_{4}\) and \(L_{5}\) remain at the midpoint between the primary masses, as can be seen in Table 9.
# A Computational Platform for Automatic Signal Processing for Bender Element Sensors +
## 5 Robustness of GeoHyTE Predictions  
As opposed to most conventional interpretation techniques, the model updating technique encoded into GeoHyTE is objective and physically consistent. Objectivity means that the interpretation of the same output signal should yield similar results independently of the analyst, provided that some simple routine is properly executed. Physical consistency is grounded in the use of a finite element model representative of the experiment, which enables the correlation of two signals of the same nature, namely the output signal obtained in the lab and the output signal obtained with the model. This is an important difference compared, for instance, with the cross-correlation technique presented in Section 2.1.4, which is also objective but physically inconsistent since it correlates signals of different natures (input and output).  
However, the objectivity can be compromised if the final value of the shear modulus is influenced by the (subjective) choice of the first estimate of the arrival time. This is a typical risk of conventional extremum finding techniques which are unable to distinguish between local and global extrema. In reference [15], the authors showed that the fixed-point technique has a very large attraction basin, meaning that it converges to the global  
Figure 23: Average shear moduli in the context of the International Parallel Test [19].  
maximum correlation for a very wide choice of starting points and very fast convergence. The study of the solution convergence presented in Section 5.1 corroborates that conclusion and, in addition, offers the mathematical explanation of this behaviour.  
Also, the physical consistency may be hindered by poor finite element results, especially given the complexity of the mechanical behaviour of porous materials and the relatively high excitation frequencies. This issue is studied in Section 5.2, where it is shown that the convergence value of the shear modulus is very insensitive to imperfect finite element results obtained, for instance, using very coarse meshes and poor approximation bases.
## 3 Rolling Bearing Remaining Useful Life (RUL) Prediction Method  
### Signal Preprocessing  
For rolling bearing vibration signals, an enhanced IU-Net model is employed to perform noise reduction on their time-frequency-domain signals. Initially, the time-domain signals undergo Fourier transform to generate frequency-domain signals, which are subsequently processed by the signal processing model. In this enhanced model, the standard U-Net's 2D convolutions and pooling operations are replaced with their 1D counterparts. An end-to-end learning framework is utilized, wherein the encoder module extracts multi-scale signal features through downsampling. Each layer comprises two dilated convolutional kernels and ReLU activation functions, effectively expanding the receptive field to capture long-range dependencies. The decoder employs deconvolution and concatenation techniques to restore approximate representations of edge details through interpolation, thereby accomplishing effective signal preprocessing. A set of data is selected from the full-life data signals and processed using the method described in this paper. The specific processing results are shown in Figure 4.
## 1 Introduction
The performance of each solution was assessed in two scenarios (with and without jamming) with 10 simulation runs per solution per scenario (i.e., 20 runs per solution). In summary, the main contributions of this paper are:  
* The definition of a rule-set for a new RPL OF called the Dual-Radio interface routing Protocol for LLNs Objective Function (DRiPLOF), which enables true multi-interface operation and provides a good balance between reliability and end-to-end latency, as well as an implementation of DRiPLOF and of another multi-interface capable OF derived from the work of [PERSON] et al. [9,10] (see Section 3). * Modifications and additions to the Contiki-NG [12] operating system (see Section 5.1), which not only bring it closer to 100% compliance with relevant standards [4,11], but also enables tracking of interface-level statistics, making it easier to develop new OFs for RPL networks with multi-interfaced devices. * Modifications and additions to the Cooja network simulator [13] and the Cooja radio driver for Contiki-NG, allowing anyone to simulate multi-interfaced nodes running a complete protocol stack of their own design. * Radio drivers for certain physical platforms (e.g., the Zolertia ReMote [14]), such that a newly developed OF may be deployed across a network of multi-interfaced RPL nodes immediately after the simulation stage. This paper first explains how RPL works in Section 2 to better understand the related work in Section 3. Next, it outlines a rule-set for a new RPL OF and describes how we evaluated its performance in Sections 4 and 5, respectively. Simulation statistics, and their relevance towards better harnessing the capabilities of multi-interfaced devices, with a special focus on RPL's resilience against external stressors (such as jamming), are presented in Section 6. Finally, we summarize our findings and discuss future work in Section 7.
# Communication Manager for Hyper-Connected RPAS Environments
## 3 Communication Infrastructure Manager: Design and Deployment  
The CIM is a comprehensive communications operator/manager designed for RPAS environments. The CIM allows the creation and establishment of networks between the different RPAS actors (RPA and RPS) and the selection among the accessible communication channels.  
Traditional RPAS communication systems used to be static designs that used a single RF interface to communicate between the RPA and the RPS. The evolution and miniaturization of electronics, in combination with the emergence of new requirements and needs, have resulted in fully connected systems with several communication interfaces available.  
The CIM is a software entity deployed in each RPAS element (RPA and RPS). The deployment of an entity such as the CIM allows one to take advantage of all current resources automatically and efficiently by addressing the problem from two different approaches: (i) taking advantage of transmission interfaces; (ii) establishment of networks, not only point-to-point but multi-hop networks and also multi-technology paths (e.g., first hop using Wi-Fi, second hop using RF).  
The architecture of the CIM can be summarized in Figure 2. As the picture shows, the CIM relies on different technologies for its correct operation. On the first hand, the CIM employs SDN. Taking advantage of SDN, we acquire a centralized and global management plane that makes it possible to decide and program the behavior of the RPAS network, bringing with it many of the advantages inherent to SDN networks, such as better control and management of data, efficiency in the optimal management of available resources, or unifying data and infrastructure. On the other hand, it uses virtual networks. Virtual networks are created to complete dynamic tunnels over the existing RPA infrastructure. In this way, the selected virtual network changes thanks to the decisions made by the SDN controller, and these changes are made transparently for both RPA and RPS applications.  
Figure 2: Communication Infrastructure Manager (CIM) architecture.  
Finally, the CIM uses a network monitoring application that collects information and metrics from the network (e.g., RTT, packet loss, available bandwidth, jitter, power consumption) and reports this information to the SDN controller as input for making decisions affecting the network. This system, therefore, incorporates significant flexibility and offers many deployment possibilities. This article presents the reference scenario used for the use cases of the Labyrinth project.  
The following subsections describe the steps to bring the system up, covering all the implementation details and explaining the scenario components in depth.
## 2 Materials and Methods
### Fourier Synthesizer  
This subsection explains the Fourier synthesizer. The Fourier synthesizer generates \(y_{F}\)(t) using the estimated locations and amplitudes of the extrema of the input GAW and the Fourier coefficients obtained by transforming the pulse model. The location of two quasi unit pulse trains approximate the maxima and minima of the individual pulses of the input GAW. They are the supporting points at which the instantaneous phase is equal to odd and even integer multiples of \(\pi\), i.e., (\(\pi\), 3 \(\pi\), 5 \(\pi\)... _and 2 \(\pi\), 4 \(\pi\), 6 \(\pi\)..._ ), respectively. These supporting points are sorted and interpolated to obtain \(\Theta\)(t). Instantaneous phase \(\Theta\)(t) and the Fourier coefficients \(R_{k}\) obtained by transforming prototype pulses are provided to the Fourier synthesizer to synthesize the GAWs.  
The amplitude of the modelled GAW is modulated by multiplying the carrier \(y_{F}\)(t) by an estimated modulator \(m(t)\). The modulator is obtained as a superposition of the estimated upper envelope \(m_{u}(t)\) and the lower envelope \(m_{l}\) (\(t\)), i.e.,  
\[m(t)=\left(\frac{1+y_{F}(\text{t})}{2}\right)\cdot m_{u}(t)-\left(\frac{-1+y_{ F}(\text{t})}{2}\right)\cdot m_{l}\left(t\right) \tag{12}\]  
The envelopes are estimated by interpolating the pulse heights of the modulated unit pulse trains using shape preserving piecewise cubic interpolation. The carrier \(y_{F}\)(t) is obtained as the negative cosine of the estimated instantaneous phase \(\Theta\)(t). Hence, at times when \(y_{F}\)(t) is \(+1\) and \(-1\), the modulator is set equal to the upper and lower envelope respectively. At times in between, (12) enables smooth transition.  
To obtain the signals \(y_{F}\)(t) used in the generation of the synthesized corpus, the same synthesizer is used, but instead of using as input the parameters estimated from an input GAW, parameters drawn from the distributions explained in Section 2.3 are used.
## 4 Experiments and Analysis
### Results Analysis and Discussion
#### 4.4.2 Analysis of Results on the US-Airline Dataset  
Table 4 concludes the accuracy of different model performances on the US Airline dataset compared to our proposed model. We found that our proposed model has a significant accuracy of 94.01% for this dataset, with RNN-TF-IDF [34] remaining the lowest compared to all other models. Similarly, we found that TF-IDF-Glove's performance with different neural networks for the US Airline dataset is noteworthy. For example, TF-IDF-Glove-BiLSTM-CNN attained 93.5% accuracy.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Proposed** & **Narrative** \\ \hline TF-IDF-Glove-CNN & \\ TF-IDF-Glove-LSTM & \\ TF-IDF-Glove-BiLSTM & Methods used TF-IDF-Glove word embedding. \\ TF-IDF-Glove-CNN-LSTM & \\ TF-IDF-Glove-CNN-BiLSTM & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Proposed method using a pre-trained word vector GloVe.  
\begin{table}
\begin{tabular}{l l c} \hline \hline
**Author/Year** & **Models** & **Accuracy** \\ \hline [PERSON] et al. [53] & RNTN & 0.8070 \\ [PERSON] et al. [37] & CharSCNN & 0.8570 \\ [PERSON] et al. [38] & CRNN & 0.7987 \\ [PERSON] \& [PERSON] [40] & CNN autoencoder & 0.7931 \\ [PERSON] \& [PERSON] [40] & BiLSTM autoencoder & 0.7911 \\ [PERSON] et al. [36] & IWV & 0.8052 \\ [PERSON] et al. [19] & SAPCP & 0.8522 \\ [PERSON] et al. [34] & TF-IDF-CNN & 0.7668 \\ [PERSON] et al. [34] & TF-IDF-RNN & 0.5695 \\ [PERSON] et al. [34] & Word embeeding -CNN & 0.8006 \\ [PERSON] et al. [34] & Word embeeding -RNN & 0.8281 \\ [PERSON] et al. [44] & ABCDM & 0.8182 \\ [PERSON] and [PERSON] [54] & BERT-GloVe-Word2 Vec-BiRNN & 0.84 \\ [PERSON] et al. [48] & EFL & 0.863 \\ \hline \hline \end{tabular}
\begin{tabular}{l l} \hline \hline  & TF-IDF-Glove-LSTM & 0.8234 \\  & TF-IDF-Glove-BiLSTM & 0.8345 \\  & TF-IDF-Glove-CNN & 0.8322 \\  & TF-IDF-GloveLSTM+CNN & 0.8432 \\  & TF-IDF-Glove-BILSTM-CNN & 0.8530 \\ Our model & ACL-SA & 0.8712 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Sentiment140 dataset and the baseline models’ accuracy comparison.
## 3 Quadrotor Controller
### Tracking Controller  
The objective of the tracking controller is to follow the original trajectory of the quadcopter and the additional trajectory that is generated from the anti-swing controller. So, to follow the quadcopter trajectory, the six outputs of the quadrotor \((x,y,z,\phi,\theta,\psi)\) must be controlled. However, selecting all these six outputs will induce an under-actuated system, as there are only four inputs. To overcome this problem, a double-loop architecture method is adopted as shown in Figure 3[23; 24]. The function of the inner loop is to control the attitude angles and the altitude \((\phi,\theta,\psi,z)\), while the function of the outer loop is to follow the desired trajectory in the x and y directions by providing the inner loop with the desired angle values. As a result, the quadrotor is stabilized at a desired height and yaw angle by the inner loop controller, while the outer loop stabilizes it in the x and y directions. To achieve stability, the inner loop must be substantially quicker than the outer loop.  
The controller used in the open-source flight controller (PX4) is the PID controller. More details about the structure and design of the tracking controller implemented in the open-source PX4 autopilot can be found in [25]. In this controller, it is assumed that the motion is just a perturbation from the hovering condition. The PX4 firmware used in this work is the V1.12.3 release [26].
# A Cloud Microservices Architecture for Data Integrity Verifiability Based on Blockchain
## 2 Preliminaries and Related Work
### Data Integrity with Blockchain  
Blockchain, firstly introduced by [PERSON] [13] in the design of Bitcoin, is described as a distributed database, where the information contained is allocated in blocks; each block is chained with its previous one by adding the hash address of the previous block, among other data, establishing a chain. Each block can also include smart contracts, a set of computer instructions that are triggered to run specific tasks. Blockchain is an excellent option for data integrity; it is resistant to malicious data modification.  
Some initiatives have explored the use of blockchain to provide integrity in audit issues. [PERSON], et al. [31], proposed a scheme using blockchain technology as a request-response channel for a client-server system in a control access application to record both client requests and the server responds in an auditable manner. [PERSON] et al. [32] argued that traditional log systems are vulnerable and subject to a series of attacks; they searched for a system able to avoid audit logs being \"tamped\" by adversaries; then, they proposed a system called BlockAudit, which used Hyperledger blockchain. There are other works based on Hyperledger, focusing on auditing data integrity using schemes involving a third-party auditor [33]. [PERSON] et al. [34], use a mobile agent technology to deploy distributed virtual machine agent model in the cloud; the machine agent enables to cooperate to store data, then they built an integrity protection mechanism with their virtual machine proxy model; they trust in the security mechanism of the blockchain technology to improve the performance of the cloud computing concerning secure storing and secure computing. Even though there are works focused on blockchain in data integrity aspects, we aimed to provide a data verifiability architecture that can be integrated with other systems, in particular with cloud-based.  
A closely related work is presented in [35], an audit mechanism that saves all the events of a supply chain in a blockchain in a hashed form; then users of the supply chain system can query integrity, data provenance, and traceability from a blockchain through an intermediate server that establishes communication between blockchain and the supply chain. Recently, _RootLogChain_ was proposed in [36], it is an audit mechanism that is built upon a security protocol to create a root user in a blockchain, and from there, all root events are stored as logs within a blockchain.