esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Our study reports for the first time, over a 12-month period, the seasonal variations in chemical composition and antibacterial and antioxidant activity of _Rosmarinus officinalis_ L. essential oil (RoEO) from Southwestern Romania (Ollenia region). To analyze the constituents of RoEO, a comprehensive gas chromatography/mass spectrometry (GC/MS) method was employed. The analysis aimed to identify and quantify the various components by comparing their mass spectra with reference spectra from the National Institute of Standards and Technology (NIST) Library 2020. The minimum inhibitory concentration (MIC) values of _Staphylococcus aureus_ minimum were determined using the microdilution method (96-well plates). The antioxidant activity was analyzed using 2,2-diphenyl-1-picrylhydrazyl (DPPH), 2,2\({}^{\prime}\)-azino-_bis_(3-ethylbenzothazoline-6-sulfonic acid) (ABTS), and hydrogen peroxide (H\({}_{2}\)O\({}_{2}\)) radical scavenging assays. This analysis provided a detailed profile of RoEO's constituents, revealing significant monthly variations. Key compounds, such as camphor, eucalypt, \(\alpha\)-pinene, camphene, and \(\alpha\)-mycrene, were quantified, alongside lesser-studied constituents like \(\beta\)-pinene, \(\alpha\)-terpinene, linalool, terpione, and carvarcd. Comparisons were made with a reference sample from Tunisia. Oxygenated monoterrenes reach the highest concentration (56.82-66.94%), followed by monoterrene hydrocarbons (30.06-40.28%), sesquiterpene hydrocarbons (0.90-2.44%), and oxygenated sesquiterpenes (0.02-0.23%). Camphor was found in high concentrations ranging from 29.41% to 40.03%. 1,8-Cineole was another dominant compound, ranging from 13.07% to 16.16%, significantly lower compared to the Tunisian reference (52.77%). \(\alpha\)-Pinene ranged from 11.36% to 19.33%, while \(\alpha\)-mycrene ranged from 1.65% to 3.08%. Correlations between specific compounds and their bioactivity were explored to understand their contributions to the overall efficacy of RoEO. This comprehensive analysis provides valuable insights into the potential applications and seasonal variability of RoEO from Romania.  
Rosmarinus officinalis L.; Romania flora; essential oil; antibacterial activity; antioxidant activity; seasonal variations +
Footnote †: journal: Applied sciences
## 1 Introduction  
Oral administration of drug delivery drugs is the most popular route, and different dosage forms have been developed for the oral cavity. Oral films offer advantages such as small size and thickness, administration without water, potential rapid onset of action, and overall high convenience of administration [1]. Films have been proved to be suitable to incorporate and promote a sustained release of several bioactive molecules and therapeutic agents [2]. Oral films are generally prepared from a polymeric matrix, which may be composed of one or more polymers. Guar gum is one of the polymers that can be used, being a water-soluble polysaccharide, biodegradable, and a hydrophilic matrix carrier for oral delivery drugs [3, 4, 5, 6, 7]. However, proteins and peptides are complex macromolecules that can change their native structure, since they are susceptible to denaturation/degradationwhen at the highly acidic and protease-rich environment of the human gastrointestinal tract (GIT). Additionally, the therapeutic effects of these macromolecules are limited by their poor stability, low bioavailability, and short half-lives [3; 5]. Thus, the micro- or nanoencapsulation of bioactive peptides is often performed to improve their physical stability, biological function, and storage stability but also bioavailability [8]. Several types of colloidal particles have been shown to be capable of encapsulating and improving the bioactivity of proteins.  
In the last decades, several proteins and peptides showed therapeutic properties, such as providing health-promoting benefits to the immune, cardiovascular, nervous, and gastrointestinal systems [9; 10; 11]. \(\alpha\)-casozepine is a peptide obtained from the hydrolysis of whey protein that presents anxiolytic activity, exerting benzodiazepine-like activity [12].  
In the present study, polymeric microparticles based on a natural polymer (chitosan) were prepared to encapsulate and improve the effectiveness of a relaxing peptide (benzodiazepepine-like properties). The excellent physicochemical properties of chitosan and its biological activities make chitosan microparticles a promising system to be used as active ingredient carriers [13; 14; 15; 16]. The incorporation of chitosan microparticles into the guar-gum films may increase the bioavailability of the carried bioactive peptide. Therefore, the main objective of the present study is to prepare an oral film incorporating chitosan microparticles to control the release of a relaxing peptide, being a more convenient and effective alternative to improve health and wellbeing.
## 4 Experiments, Results, and Discussion
### Experimental Results
#### 4.3.1 Horizontal Object Detection
This is due to the inclusion of the effective channel attention module in the DCE_YOLOX, which allows the network to make full use of feature information and focus more on targets with distinct features. At the same time, the optimized network input size allows the network to improve the detection performance for small objects. Figure 6: Comparison chart before and after improvement. The upper part is before improvement, and the lower part is after improvement. To further evaluate the performance of the DCE_YOLOX algorithm, using the DOTA dataset with the same training and testing samples, this paper compares Faster RCNN [43], R-FCN [44], SSD [45], YOLOv2, YOLOv3, and YOLOv5 series, and the comparison results are shown in Table 4; the best results are highlighted in bold. From the table, we can see that DCE_YOLOX has the highest mAP compared to other algorithms, with a detection accuracy of 83.9%. In addition, the DCE_YOLOX algorithm improves 73.0%, 63.1%, 23.4%, 36.7%, 62.5%, 30.2%, and 9.5% in average accuracy mAP, respectively, compared with SSD300, SSD512, Faster R-CNN, R-FCN, YOLOv2, YOLOv3, and the optimal YOLOv5. From these experiments, we can conclude that our algorithm has a better focus on small objects, while it has an equally good focus on other objects and is well suited for applications in remote sensing.
## 6 Discussion  
The proposed protocol offers a hybrid cryptography approach, integrating both asymmetric and symmetric cryptography to leverage the advantages of each. Unlike other solutions that impose high costs in communication and computation in rekeying every session, the protocol provides a lightweight mechanism to reuse the asymmetric key pair  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Proposed Protocol** & \multicolumn{1}{c}{**Computation**} & \multicolumn{1}{c}{**\#Cycle**} & \multicolumn{1}{c}{**Time (ms)**} \\ \hline  & 1.a & \(T_{r}+T_{mod}\) & 105,810 \(\pm\) 24 & 2.11 \\  & 1.b & \(T_{sm}\) & 130,443,888 \(\pm\) 56,314 & 2608.88 \\  & 1.c & \(T_{r}\) & 80,649 \(\pm\) 20 & 1.78 \\ Level 2 ECQV & 3.a & \(T_{puf}\) & 135,810 \(\pm\) 14 & 2.72 \\ Asymmetric key pair & 3.b & \(T_{h}+T_{mod}\) & 39,103 \(\pm\) 28 & 0.78 \\  & 3.c & \(T_{bm}+T_{h}+T_{mod}\) & 30,867 \(\pm\) 20 & 0.62 \\  & 3.d & \(T_{sm}+T_{a}\) & 130,789,946 \(\pm\) 56,350 & 2615.80 \\ \hline  & & Subtotal & 261,626,073 & 5232.69 \\ \hline  & 5.a & \(T_{r}+T_{mod}\) & 105,810 \(\pm\) 20 & 2.11 \\ Level 1 SKEM & 5.b & \(T_{sm}\) & 130,443,888 \(\pm\) 56,314 & 2608.88 \\ Symmetric session key & 5.c & \(T_{h}+T_{mod}\) & 39,103 \(\pm\) 28 & 0.78 \\  & 5.d & \(T_{bm}+T_{hu}+T_{h}+T_{sm}\) & 130,463,538 \(\pm\) 56,326 & 2609.27 \\ \hline  & & Subtotal & 261,052,339 & 5221.04 \\ \hline  & & Total & 522,678,412 & 10,453.73 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Timing measurement of individual step on RISC-V 32-bit IMAC.  
while updating fresh session keys. With these functions, the protocol can still offer essential security properties and prevent common attacks in IoT networks. However, it lacks support for anonymity, which is a critical requirement in highly sensitive data transmission scenarios, such as healthcare, e-payment, and e-voting. Anonymity ensures that the identities of communicating parties remain concealed, thereby protecting privacy and preventing unauthorized tracking. Furthermore, to maximize the benefit of the level 1 security channel, deciding the lifetime of a node's key pair is critical. An optimal update period can maximize the benefit of the fast rekeying mechanism and mitigate the risk of particular attacks like replay attacks. Additionally, despite the intended limitation in computation at the node side, the time required to compute elliptic curve cryptography remains relatively high. Consequently, this limitation restricts the frequency of sensor data transmission periods, particularly in applications requiring the transmission of sensor data every minute. Therefore, we aim to improve the protocol by addressing such limitations in future work at the system's architecture level. Furthermore, a systematic approach to designing a secure system for sensor nodes that encompasses both network and hardware device security is needed. Trusted execution environment (TEE) mechanisms can be integrated to improve a node's security against physical attacks, like side-channel attacks. Safeguarding random sources and protecting the memory storage of keys are mandatory.
# Multiple Observations for Secret-Key Binding with SRAM PUFs
## 6 The Soft-Decision Helper Data Scheme
### Achievable Performance  
We are interested in the maximum achievable secret-key rate of the binary SD scheme. As before we should first show that the scheme is secure, i.e., the uniformity condition of Definition 1 is satisfied since the key is generated uniformly at random. Furthermore, the leakage condition is satisfied as \(I(S;W^{n},\hat{\Psi}_{t}^{n})=0\) as is shown in Appendix A.4. Now, we can derive the achievable rate over the channel from the encoder to the decoder. The channel is described by the conditional distribution \(\Pr(R=r,\hat{\Psi}_{t}=\hat{\psi}_{t}|C=c)\), and it follows from the channel coding theorem (see, e.g., [20], Chapter 3) that the maximum achievable rate is given by the mutual information maximized over the input distribution. Furthermore, \(C\) is uniform here, since we are using a linear code. We therefore evaluate this mutual information next  
\[\begin{array}{rcl}R_{\text{SD},t}^{*}&\stackrel{{\Delta}}{{=} }&I\big{(}C;R,\hat{\Psi}_{t}\big{)}\stackrel{{(a)}}{{=}}I\big{(} C;R|\hat{\Psi}_{t}\big{)}=H\big{(}R|\hat{\Psi}_{t}\big{)}-H\big{(}R|\hat{\Psi}_{t},C \big{)}\\ &\stackrel{{(b)}}{{=}}&1-H\big{(}\hat{U}_{t}\oplus Y|\hat{\Psi}_{t }\big{)}\stackrel{{(c)}}{{=}}1-H\big{(}\hat{U}_{t}\oplus Y|M_{t} ^{\text{\tiny SD}},\hat{U}_{t}\big{)}=1-H\big{(}Y|M_{t}^{\text{\tiny SD}},\hat{ U}_{t}\big{)}\\ &\stackrel{{(d)}}{{=}}&1-H(Y|w_{H}(X_{1},X_{2},\ldots,X_{t})) \stackrel{{(e)}}{{=}}H(Y)-H(Y|X_{1},X_{2},\ldots,X_{t})\\ &=&I(Y;X_{1},X_{2},\ldots,X_{t}).\end{array} \tag{37}\]  
In \((a)\) we used the fact that the key is generated independently from the SRAM-PUF observations and thus \(I(C;\hat{\Psi}_{t})=0\). In \((b)\) we used that \(r=c\oplus(\hat{u}_{t}\oplus y)\) and furthermore the key (and codebook) is generated uniformly and independently from the SRAM-PUFobservations (and thus also independently from \(\hat{\psi}_{t}\)). Furthermore, \((c)\) follows by the Markov Chain  
\[Y\oplus\hat{U}_{t}\leftrightarrow\hat{\Psi}_{t}\leftrightarrow(M_{t}^{\text{\tiny SD }},\hat{U}_{t}), \tag{38}\]  
and \((d)\) holds since (by (18)) \(w_{H}(x_{1},x_{2},\ldots,x_{t})\) is uniquely determined by the tuple \((m_{t}^{\text{\tiny SD}},\hat{a}_{t})\) and vice-versa. Finally, \((e)\) follows from the fact that the symmetric SRAM-PUF is unbiased (5), and by the fact that the conditional probability of the next observation given \(t\) previous observations \((x_{1},x_{2},\ldots,x_{t})\) only depends on the number of observed ones (7).  
It follows from the derivations above that the binary SD scheme achieves secret-key rate \(R_{\text{SD},t}^{\text{\tiny SD}}\), where achievable secret-key rate is defined in Definition 1. Furthermore, \(R_{\text{SD},t}^{\text{\tiny SD}}=C_{t}\) is equal to the secret-key capacity for \(t\) enrollment observations as given by Theorem 1, which shows that the binary SD scheme is optimal.
## 3 Results and Discussion
### Characterizations of CDs/g-C\({}_{3}\)N\({}_{4}\) Composites
The high-resolution spectrum of C 1 s (Figure 2d) can be deconvoluted into three peaks at 284.9, 288.1, and 288.6 eV, which are attributed to C-C, C-(NH\({}_{X}\)), and C-(N\({}_{3}\)), respectively [37]. The N 1 s spectrum (Figure 2e) is deconvoluted into four peaks at 398.6, 399.1, 400.9, and 404.8 eV corresponding to the triazine ring of nitrogen (N-(C\({}_{2}\))), tertiary nitrogen atoms (N-(C\({}_{3}\))), N-H\({}_{X}\), and \(\pi\)-\(\pi^{*}\) stacking [38]. The O 1 s spectrum (Figure 2f) can be divided into two peaks at 531.7 and 532.6 eV, which can be ascribed to the C-OH group and adsorbed O on the surface [39]. The abundant chemical groups formed on CDs/g-C\({}_{3}\)N\({}_{4}\) composites can provide huge active adsorption sites for analytes. Figure 3a,b show the N\({}_{2}\) adsorption-desorption and pore size distribution isotherms of pristine g-C\({}_{3}\)N\({}_{4}\) and CDs/g-C\({}_{3}\)N\({}_{4}\) composites. The BET specific surface area of CDs/g-C\({}_{3}\)N\({}_{4}\) was calculated to be 20.67 m\({}^{2}\) g\({}^{-1}\), which was higher than that of pristine g-C\({}_{3}\)N\({}_{4}\) (13.34 m\({}^{2}\) g\({}^{-1}\)). The average pore diameters of pristine g-C\({}_{3}\)N\({}_{4}\) and CDs/g-C\({}_{3}\)N\({}_{4}\) were obtained by the BJH method with values of 33.7 and 39.3 nm, respectively, indicating the formation of mesoporous structures. More details referring to the BET and BJH results are listed in Table S2. A higher specific surface area and mesoporous structures improved the adsorption sites and analyte diffusion [40]. Furthermore, the thermal stability of as-prepared materials was also evaluated by TG analysis, because that SPME fiber needs to be inserted into the GC-MS inlet for desorption at 260 \({}^{\circ}\)C. The TG curve (Figure 3c)  
Figure 2: XRD patterns (**a**) and FTIR spectra (**b**) of g-C\({}_{3}\)N\({}_{4}\) and CDs/g-C\({}_{3}\)N\({}_{4}\), XPS survey spectrum (**c**), and high-resolution XPS spectra of C 1 s (**d**), N 1 s (**e**), and O1 s (**f**) for CDs/g-C\({}_{3}\)N\({}_{4}\). demonstrates that as-prepared CDs/g-C\({}_{3}\)N\({}_{4}\) composites have no obvious weight loss within 500 \({}^{\circ}\)C, indicating that the composites possess excellent thermal stability.
## 2 Related Work  
### Baseball Spectator Assistance  
In sports games, augmented reality (AR) and virtual reality (VR) technologies are designed to enhance the spectator experience. As an application of AR technology, research has been conducted to create virtual scenes that can be viewed from any angle by synchronizing and synthesizing multiple sports videos [9; 10; 11]. Research has also been conducted to generate virtual sports scenes from TV videos [12; 13; 14], and systems have been proposed that can display these scenes in real-time [15]. In addition, research has been conducted on AR systems that display statistical and supplementary information about teams and players on images of sports games [3; 16; 17].  
In a study using VR technology, [PERSON] et al. [18] proposed an \"Interactive Instant Replay\" system that allows users to experience past recorded sports plays as a 360-degree spherical image with tactile feedback. Systems using AR or VR technology can reproduce a sense of presence similar to that of a game in a stadium and will likely enhance the spectator experience. In addition, the information provided in the virtual space, which is not available in conventional spectating, can contribute to the understanding of sports. However, it has been pointed out that spectators should interact with others who share their viewing experience rather than approach the game itself [19].  
There is research on the use of robots as companions for sports spectators. [PERSON] et al. [7] proposed a group robot that can speculate about baseball in a VR space. [PERSON] et al. [6] proposed a robot that can watch sports programs together with a spectator and share in their excitement. However, in these studies, the behavior of the robots was limited to expressing emotions and enthusiasm, which is insufficient for spectator behavior. Therefore, the robots need to understand the game situations and behave accordingly. Although studies have generated automatic commentary for sports games based on the situation [20; 21; 22; 23; 24; 25; 26; 27], the commentary is in the position of an indirect third party with respect to spectators, hindering the shared experience.  
In this study, we focused on robots watching games together with humans to create a shared viewing experience. The robots demonstrate their role as spectators by not only expressing emotions but also behaving according to the situation.
# Augmented Reality in Surgical Navigation: A Review of Evaluation and Validation Metrics
## 3 Evaluation and Validation of AR Surgical Technology
The framework used here can serve as a user-oriented approach in evaluating the amount of perceived information using image-guided clinical systems by surgeons and medical students and supporting their design from the viewpoint of cognitive engineer. Figure 9 summarizes the suggested parameters which are relevant in evaluating and validating the current AR systems. To have the required precision of any of the AR system under consideration, visualization is the key factor which is further dependent upon many factors, mainly the human-computer interface. The source of real data and the virtual image are important factors affecting the visualization. With an efficient hand-eye coordination and display type, proper spatial reasoning and depth perception are obtained. Various other factors such as the registration technique, tracking modality, rendering time, etc. also contribute towards a reliable visualization. The system being user friendly will ensure the easy acceptance from the surgical community. The surgeon's ability to use the equipment in the operating room may be constrained by a lack of intuitive design. For a human-centered AR system, the user experience is important additional to the user interface and should be in line with the requirements of the surgeon, the major stakeholder. The overall functionality of the system and patient's safety during the whole procedure are the useful factors to evaluate and validate the usage of the AR based navigation in surgery. As already discussed in this section, the surgeons' skills (such as sensory, motor, and cognitive abilities) are also crucial in achieving a precise surgical result as well as managing the stress and exhaustion that arise during the procedure. Another crucial element in the validation of the AR systems should be the patient's post-operative recovery period, success, and relapse rate. The effective approach to validate a surgical procedure is to look at the clinical outcome in terms of operating time and efficiency, which is the \begin{table}
\begin{tabular}{l l l l} \hline \hline
**Operator** & **Location** & **Medical Process** & **Patient** \\ \hline Engineer & Laboratory & Technical Scenario & Simulations \\ Medical Student & Simulated OR & Simulated Procedure & Physical Phantoms \\ Surgeon & Operation Room & Real Procedure & Clinical Dataset \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Validation workflow based on the operator level (from low to high clinical realism). Figure 8: Evaluation metrics for augmented reality surgical technology. result of all these elements. Currently, it is challenging to compare accuracy measurements between various systems due to differences in evaluation techniques. However, feedback from surgeons can help determine whether the most advanced and current AR assisted navigation systems are effective or not [66; 93; 94].
## 5 Evaluation Metrics and Cross-Validation for Cardiovascular Disease Prediction
### Model Interpretability and Explainability  
There is an essential need for making machine learning models more interpretable and transparent, especially in the healthcare system. In particular, one of the main challenges in machine learning methods is dealing with complex models that are often considered black boxes [187; 172]. While machine learning methods have demonstrated exceptional predictive power, understanding their decision-making processes can be a very challenging issue [188; 189]. This is a major concern in healthcare systems, where decisions need to be justified and trusted. In other words, it is important to understand why a machine-learning model makes certain predictions. Therefore, model interpretability and explainability become particularly essential in healthcare, especially for cardiovascular disease prediction. Specifically, In healthcare systems, the significance lies not only in the quantitative algorithmic performance but also in the essential features that the algorithm employs for disease detection [187]. Hence, incorporating interpretability and explainability for machine learning models enhances the practical application of such models in real-world scenarios [190]. Understanding which features had the most influence on a prediction is a fundamental form of interpretability.  
Techniques like feature importance scores can help in identifying the most significant predictors in cardiovascular disease prediction [191]. Considering simpler and more interpretable models like Decision Trees or Logistic Regression, especially when clinical decision-making requires transparency. Employ local interpretable models like Local Interpretable Model-Agnostic Explanations (LIME) or SHapley Additive exPlanations (SHAP), which is a game theoretic approach, to explain individual predictions [192; 193; 194; 195; 196; 197; 198]. These models provide explanations and visualizations for specific instances, which can be valuable in healthcare decision-making since they also help healthcare professionals to understand model outputs [199; 200]. Applying such models can involve creating rule-basedsystems that align with medical expertise and regulations, ensuring that model predictions are consistent with established healthcare standards. If a model produces predictions that contradict established clinical knowledge, it should raise a red flag and prompt further investigation. Models may drift or degrade over time, and ongoing vigilance, especially in real-time systems, is essential to ensure they remain trustworthy [201].  
In a nutshell, interpretability, and explainability are crucial for ensuring trust and accountability in clinical decision-support systems. Ensuring that models can be understood and trusted is vital for making responsible and effective clinical decisions [202].