esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Since ancient times, essential oils obtained from various aromatic plants have been utilized as bioactive ingredients in medicines, foods and cosmetics. The present study aimed to investigate the chemical composition and biological activities of St John's Wort (_Hypericum perforatum_ L.) essential oil (SJW EO) from Bulgaria, which is known to possess various biological properties. Gas chromatography and mass spectrometry (GC-MS) analysis, determination of antioxidant activity (by the ABTS method), an antimicrobial activity test and an _in vitro_ anti-inflammatory activity test were performed. The main classes of compounds identified by GC-MS analysis were monoterpens (43.55%), followed by sesquiterpenes (36.81%) and alkanes (16.92%). The predominant chemical components of SJW EO were \(\alpha\)-pinene (27.52%), followed by \(\beta\)-pinene (10.08%), \(\beta\)-caryophyllene (6.77%), germacrene D (6.37%) and carophyllene oxide (4.48%). The highest antibacterial activity was observed against the Gram-negative bacteria _Klebsiella pneumoniae_ ATCC 13883 (inhibition zone of 12.0 mm) and _Pseudomonas aeruginosa_ ATCC 9027 (inhibition zone of 11.0 mm). SJW EO exhibited significant _in vitro_ anti-inflammatory activity, as the results demonstrated that its anti-inflammatory effect was stronger than those of the conventional anti-inflammatory drugs Prednisolon Cortico and acetylsalicylic acid (Aspirin), which were used as controls (all in concentration of 1 mg/mL). The obtained results demonstrated that Bulgarian SJW EO can be used as an active ingredient in the composition of new products for the pharmaceutical and cosmetic industries.  
Keywords:essential oil; St John's Wort; _Hypericum perforatum_; biological activities +
Footnote †: journal: Applied sciences
## 4 Designing an Ontology Framework: A Case Study on Asthma
### Decision Tree Model for Asthma Detection: Implementation Details  
A Decision Tree classifier was trained to detect asthma using a structured pipeline where numerical features were standardized, categorical features one-hot encoded, and boolean features binary-encoded. The dataset of 1000 patients was split into 800 for training and 200 for testing to ensure generalizability. Using Information Gain, the top 16 features, such as FEV1 Score, Chest Tightness, and Night-time Symptoms, were selected, excluding less informative ones like Education Level. Decision rules were extracted in an interpretable if-else format, and numerical thresholds were converted back to clinical values (e.g., FEV1 \(\leq\) 0.87 \(\rightarrow\) FEV1 \(\leq\) 2.5 L) for usability. Table 3 reports the evaluation metrics for the algorithms: Accuracy, Precision, Recall, and F1 Score for Decision Tree, Logistic Regression, and Neural Network. While the Neural Network achieved the highest scores, the Decision Tree offered the best trade-off between accuracy and interpretability, making it ideal for clinical rule extraction. Logistic Regression, though lower in accuracy, remains valuable for its simplicity. Furthermore, the decision tree thresholds were validated using clinical guidelines such as GINA for FEV1 and the medical literature for BMI and age, ensuring that the extracted rules are not only data-driven but also clinically sound and applicable in real-world medical decision-making [13; 62].
## 3 Methodology
### Decision Tree Regression  
Since artificial intelligence technology has advanced, decision trees have evolved as a frequently utilized machine learning method. They are widely used in the medical area, supporting clinicians in diagnostic and treatment plan formulation [45; 46]. Decision trees are used in the financial sector for risk assessment and credit evaluation. Decision tree regression is also widely used in industrial production to anticipate product sales, optimize production processes, and detect production management concerns such as defects [47; 48; 49; 50; 51].  
When it comes to logistics and supply chain management, decision tree regression assists with demand forecasting, inventory optimization, and estimating traffic flow [52; 53]. For sales and marketing, decision tree regression proves valuable in market segmentation, hotspot analyses, sales forecasting, and other decision-making aspects. Environmental science also benefits from decision tree regression, such as air quality predictions, water quality assessment, climate change, and sports analyses [54; 55].  
Decision tree regression is a kind of regression method based on the decision tree algorithm, which is used to predict continuous target variables [56]. It creates a predictive model by dividing the feature space into rectangular areas, and at each leaf node, the prediction result is derived using the mean of the samples in the relevant region [57]. The pros and cons of the decision tree algorithm are illustrated in Table 2.
# Graph Representation-Based Deep Multi-View Semantic Similarity Learning Model for Recommendation
## 5 Experiment
### Settings  
To validate the effectiveness of the recommendation algorithm proposed in this paper, the following comparison method is selected.  
* **Baseline1** uses the average of all scored items scored by the current active users as the predicted scores of the target items by the current active users;
* **Baseline2** The user-based collaborative filtering algorithm finds users with similar interests to the current active users based on their historical ratings, and then uses the weight of similar users' ratings on target items to predict the average of the current active users' ratings on target items;
* **PMF**[40] was proposed by [PERSON] and [PERSON], which can be considered a probabilistic extension of the SVD model. PMF learns the implicit feature vector of users and items from the user-item rating matrix and uses the inner product of the implicit feature vector of users and items to predict the missing items in the user-item rating matrix;
* **TrustSVD**[25] proposes an advanced SVD++ algorithm. Based on the original SVD++, it takes both explicit trust relationship and rating information of users into model construction;
* **DeepCoNN**[41] proposes a parallel framework by jointly using the users' feedbacks, two parallel neural networks are used to deal with users' and items' information synchronously.  
The user-item scoring matrix is randomly partitioned five times, with 80% of the data extracted each time as the training set and the remaining 20% as the test set. The final run takes the average of the algorithm's results across the five test sets. For fair comparison, the parameters of different algorithms are set with reference to the corresponding literature or experimental results of the comparison algorithms, and the optimal performance of each comparison algorithm is obtained under these parameter settings. In Baseline2, the cosine is used to calculate the similarity between users; in PMF, the hidden feature vector dimension is 10 and the regularization factor \(\lambda_{u}=\lambda_{v}=0.001\); in GraphSAGE-CF, the cosine is used to calculate the similarity between users.  
The experiment was run on a quad-core Intel(R) Core(TM) i5-7400H CPU with 3.00 GHz main frequency, 8 GB RAM, Window10 operating system and J2 SE1.8.
# Self-Configuring IoT Service QoS Guarantee Using QBAIoT
## 2 State of the Art
### Internet of Things
#### 2.1.1 Definitions  
Different standardization organizations had presented the definition, architecture, recommendations, and application domains of the IoT. The International Telecommunication Union-Telecommunication Standardization Sector (ITU-T) presents a definition of the IoT environment and its applications through the SG20 study group. According to the ITU-T Y.2060 document, IoT is a ubiquitous network available anywhere, at any time and to anyone [6]. It is a global infrastructure allowing to offer advanced services by interconnecting objects through various communication technologies [7]. In addition, the International Organization for Standardization/International Electrotechnical Commission (ISO/IEC) proposes a definition and specifies the vocabulary used in the IoT environment in [8]. According to this organization, the IoT is a network of physical objects that collect and transmit data. It is an infrastructure of interconnected objects, humans, and information resources that can process the information reported and react accordingly [8; 9]. Moreover, the Internet Engineering Task Force (IETF), defines the general idea of the IoT as the fact of connecting objects to provide contextual services, and this through different technologies to achieve a service accessible from anywhere and at any time [7].
## 1 Introduction
There is a paucity of data for cervical IVD tissue response, and there are positive implications for understanding IVD response as it relates to overall cervical spine kinematics, especially for more widespread application to injury biomechanics. Cervical IVD tissue response data could be utilized to assess and supplement the characterization of the head/neck complex motion, and data could facilitate the continued improvement of injury prediction tools (ATDs and HMBs) [20; 21]. Previous studies have attempted to establish a relationship between IVD pressures and cervical spine motion/loading [22; 23; 24; 25; 26; 27]; however, to date, the exact relationship of cervical IVD pressures to loading is still uncertain and remains the focus of this research. Many research initiatives have made efforts to evaluate PMHS head and cervical spine kinematics [5; 8; 28; 29; 30; 31; 32; 33; 34; 35; 36; 37], yet there has not been a robust attempt to characterize the biomechanical response of cervical IVDs due to anatomical and kinematic limitations for instrumentation. Access to the cervical spine IVDs is extremely limited due to the requirement for an anterior surgical approach, the small size of the cervical spine, and impediments created by surrounding anatomical structures (notably the mandible and thorax). The extreme and multi-directional head/neck motions enabled by the cervical spine itself ultimately limit instrumentation visibility and viability during motion (i.e., instrumentation often negatively interacts with the chin/chest). Previous experimental options for assessing spinal loads in a PMHS involved the invasive implantation of multi-axis load cell instrumentation (i.e., replacing an IVD or vertebral body with a load cell) to directly measure the load path. This method is less than ideal, as it disrupts the integrity of the primary and surrounding anatomical structures and negatively and artificially affects the overall PMHS kinematics/kinetics [38]. A more favourable option for quantifying cervical spine injury potential is to measure loads indirectly through the IVD pressure, which has been correlated to axial spine loading [39; 40]. Investigating IVD pressure measurements using miniature pressure sensors preserves anatomical and structural spine integrity while providing biomechanical response data necessary to quantify injurious insults to the spine. In this study, we investigated the potential feasibility of cervical IVD pressure sensors in performing biomechanical research. We hypothesized that a relationship between IVD pressures and cervical spine loading exists, and cervical spine motion is correlated with changes in cervical IVD pressure; thus, the goal of this study was to capture cervical IVD pressure responses during quasistatic motion of the head and neck. Cervical IVD tissue response data could be utilized to assess and supplement the characterization of the head/neck complex motion during both non-injurious quasistatic motion testing as well as during dynamic injurious experimentation, and data could facilitate the continued improvement of injury prediction tools.
# Hybrid Energy Routing Approach for Energy Internet
## 7 Simulation and Results
### Results of the Proposed Energy Routing Approach
With this value, \(ER_{17}\) selects the producer \(D4\) with the fitness value for consumer \(D7\). The same process is repeated for consumer \(D3\) with \(ER_{10}\). The calculation results including costs, efficient paths, power losses and selected sources, transmission power and the max capacity of paths are shown in Table 11 and Figure 6. The selected producers with the efficient paths for consumer \(D7\) and \(D3\) satisfied the energy routing constraints. Figure 6: Optimization results of case 1 (\(\alpha=0.5\)): (**a**) energy-efficient path from \(D4\) (\(ER_{13}\)) to \(D7\) (\(ER_{17}\)); (**b**) energy-efficient path from \(D4\) (\(ER_{13}\)) to \(D3\) (\(ER_{10}\)). _Case 2 analysis: mono-source consumer with overlapping transmission time_  
Consumers _D3_ and _D7_ have an overlapping transmission time (case 2 in Table 10). Thus, the pre-existing power over certain power lines may be different from zero. This will lead to major changes of the power loss in those lines, which directly affect the efficient path selection; therefore, affecting the fitness value responsible for the producer selection. By comparison to case 1, as shown in Table 12 and Figure 9, the selected producer for consumer _D7_ is _D4_ with the same energy efficient path, power loss and fitness value since, at the beginning, the pre-existing power in this path is equal to zero. However, across the overlapping time, the energy-efficient transmission paths from _D3_ to _D2_ and _D4_ have changed from the original paths \(9\to 1\to 17\to 11\to 10\), and \(13\to 8\to 9\to 1\to 17\to 11\)\(\to 10\), to \(9\to 1\to 3\to 2\to 10\) and \(13\to 6\to 7\to 3\to 2\to 10\), respectively. Because of the change in the pre-existing power in \(13\to 8\to 9\to 1\to 17\), from 0 to 12 kw, which results in increasing in the power losses of those power lines, has led to an augmentation in the power loss of the original efficient paths, which have become \(5.61296\times 10^{-1}\) kw and \(8.0264\times 10^{-1}\) kw, respectively, which influenced the fitness value. The results of cases 1 and 2 demonstrated that the power loss in the power system was not only affected by the energy transmitted, but also by the pre-existing power. Figure 9: Optimization results of case 2 (\(\alpha=0.5\)): (**a**) energy–efficient path from _D4_ (_ER\({}_{13}\)_) to _D7_ (_ER\({}_{17}\)_); (**b**) energy-efficient path from _D4_ (_ER\({}_{13}\)_)to _D3_ (_ER\({}_{10}\)_).
# Current Challenges in _Yersinia_ Diagnosis and Treatment
## 6 Animal Reservoirs of _Yersinia_
### Reservoirs of Yersinia enterocolitica  
_Y. enterocolitica_ is a ubiquitous microorganism, but most isolates from asymptomatic carriers, food, and the environment are not pathogenic [48]. The meta-analysis from [PERSON] highlighted food exposures as the significant risk factors of yersiniosis. It confirmed that the predominant reservoirs are pigs (the consumption of raw or undercooked pork, occupational contact with pigs). Infection can also be caused by untreated drinking water. Thus, food and water are significantly associated with sporadic _Y. enterocolitica_ infections [61].  
Interestingly, _Y. enterocolitica_ 4/O:3 is the most common cause of sporadic human yersiniosis caused by contaminated pork meat in Finland and Germany [56]. Italian data have shown that the pathogenic biotypes of _Y. enterocolitica_ are uncommon in foods and are mainly isolated from animal sources (including pork and poultry meat and their derivatives) [57]. Chinese data indicated that domestic farm dogs can also transmit _Yersinia_ infection [62]. Similarly, the suspected route of transmission is through contact with domestic animals (Sweden, Denmark, China) [59; 63]. _Y. enterocolitica_ has been detected in unpasteurized milk and cow feces in the United Kingdom and the United States. However, the prevalence in cows is lower than in pigs [64; 62]. Pathogenic serotypes have also been detected in sheep and goats [64; 62]. A Swiss study showed that _Y. enterocolitica_ and _Y. pseudotuberculosisis_ are also commonly found in wild boars, with a higher incidence in younger individuals [65].  
Yersiniosis can also be transmitted by wild animals such as brown hares, bats, and wild rodents [66; 67; 68]. _Y. enterocolitica_ has been isolated from the stool of wild boars, red deer, and roe deer (the _All_ gene was isolated, suggesting a virulence gene for _Y. enterocolitica_) [69]. In Poland, _Y. enterocolitica_ is frequently isolated from game animals, which poses a risk of spreading to other animal species and humans [70]. In Spain, wild boars are very common reservoirs of _Y. enterocolitica_ and _Y. pseudotuberculosisis_[71].
## 4 Mathematical Description of the Principles of ED Using SLC in MIMO-OFDM Systems
### Mathematical Formulation of the Role of Detection Threshold on ED Process
When an ED process employing the SLC technique exploits the DDT adjustment in the MIMO communication system, the detection (\(P_{d}^{DDT}\)) and false alarm probabilities (\(P_{fa}^{DDT}\)) can be defined as:  
\[P_{d}^{DDT}=\min_{\lambda^{DDT}\epsilon\big{[}\lambda_{d}^{DDT}/ \rho^{\prime},\ \rho^{\prime}\lambda_{d}^{DDT}\big{]}}Q\Bigg{(}\frac{\lambda^{DDT}- RN^{DDT}\big{(}2\sigma_{w}^{2}\big{)}\left(1+\frac{P}{2\sigma_{w}^{2}} \right)}{\sqrt{RN^{DDT}\left(1+\frac{P}{2\sigma_{w}^{2}}\right)}\left(2\sigma_ {w}^{2}\right)}\Bigg{)}=Q\Bigg{(}\frac{\frac{\lambda_{d}^{DDT}-RN^{DDT}\big{(}2 \sigma_{w}^{2}\big{)}\left(1+\frac{P}{2\sigma_{w}^{2}}\right)}{\sqrt{RN^{DDT }\left(1+\frac{P}{2\sigma_{w}^{2}}\right)}\left(2\sigma_{w}^{2}\right)}}{ \sqrt{RN^{DDT}\left(2\sigma_{w}^{2}\right)}}\Bigg{)} \tag{12}\] \[P_{fa}^{DDT}=\max_{\lambda_{i}^{DDT}\epsilon\big{[}\lambda_{d}^{ DDT}/\rho^{\prime},\ \rho^{\prime}\lambda_{fa}^{DDT}\big{]}}Q\Bigg{(}\frac{\lambda^{DDT}-RN^{DDT} \big{(}2\sigma_{w}^{2}\big{)}}{\sqrt{RN^{DDT}\left(2\sigma_{w}^{2}\right)}} \Bigg{)}=Q\Bigg{(}\frac{\rho^{\prime}\lambda_{fa}^{DDT}-RN^{DDT}\big{(}2 \sigma_{w}^{2}\big{)}}{\sqrt{RN^{DDT}\left(2\sigma_{w}^{2}\right)}}\Bigg{)}, \tag{13}\]  
where \(N^{DDT}\) represents a number of sensing samples, \(\lambda_{d}^{DDT}\) the detection threshold and \(\lambda_{fa}^{DDT}\) the false alarm threshold. Based on (12) and (13), the probability of detection can be formulated in relation to the probability of false alarm in CR systems, where the PU signal is detected by employing DDT adjustments:  
\[P_{d}^{DDT}=Q\Bigg{(}\frac{Q^{-1}\big{(}P_{fa}^{DDT}\big{)}-\sqrt{RN^{DDT}} \big{(}\rho^{\prime 2}\frac{P}{2R\sigma_{w}^{2}}+\big{(}\rho^{\prime 2}-1\big{)} \big{)}}{\sqrt{\rho^{\prime 2}\left(1+\frac{P}{R\sigma_{w}^{2}}\right)}}\Bigg{)} \tag{14}\]  
If the DDT factor is fixed and set to 1 (\(\rho^{\prime}=1\)), Equation (14) is identical to Equation (9) and the DDT adjustment does not exist. If the DDT factor is greater than 1.00 (\(\rho^{\prime}>\) 1.00), some level of DDT adaptation is implemented in the process of ED. A greater DDT factor leads to a higher scope of DDT level for potential selection.
## 3 Data
### Acceleration Computation  
Because one's instant acceleration of is not directly available in the dataset, the \"ground truth\" for training and evaluation needs to be computed by velocity differences. We designed the formula below to calculate the acceleration between two consecutive video frames, where the acceleration of the initial frame is set to zero:  
\[\mathbf{a}_{t}=\left\{\begin{array}{ll}0,&t=0\\ \frac{\mathbf{v}_{t}-\mathbf{v}_{t-1}}{\Delta t},&t>0\end{array}\right.\]  
where \(t\) is the frame index, and \(\mathbf{a}_{t}\) and \(\mathbf{v}_{t}\) are the acceleration and velocity of the AV at the time frame \(t\), respectively.
## 2 Theory Background and Methodology
These indices are expressed as follows:  
\[R^{2}=1-\frac{\sum_{i=1}^{N}\left(S_{p}-\overline{S}\right)^{2}}{\sum_{i=1}^{N }\left(S_{m}-\overline{S}\right)^{2}} \tag{6}\]  
\[RMSE=\sqrt{\frac{\sum_{i=1}^{N}\left(S_{p}-S_{m}\right)^{2}}{N}} \tag{7}\]  
\[MAE=\frac{1}{N}\sum_{i=1}^{N}\left|S_{p}-S_{m}\right| \tag{8}\]  
where \(S_{p}\) and \(S_{m}\) are the predicted and measured values, respectively; N is the number of samples and \(\overline{S}\) denotes the mean value of the observed response. On the other hand, to estimate the dynamic characteristics (frequency and damping ratio), fast Fourier transformation (FFT) was used to extract the dominant frequency mode of the signals'  
Figure 2: Model process and evaluation. measurements. FFT theory and its applications are presented in [35, 36]. In the present study, the acceleration signals measurements and the predicted dynamic strains were used to evaluate the performance of the developed method. In addition, the Hilbert transform method was used to extract the instantaneous frequency and measurements phase and to predict accelerations [36]. The instantaneous signals frequency and phase were extracted to evaluate the performance of the presented method. The instantaneous frequency and phase of complex signals (\(a_{x}(t)\)) can be extracted using Hilbert transform as following Equation (9) [36]:  
For the systems that can be used to extract complex signals using input (\(x(t)\)) and output (\(\hat{x}(t)=1/\pi t(x(t))\)[36]:  
\[\{\varepsilon_{d}(z,t)\}=[D]\{a_{d}(z,t)\} \tag{2}\]0  
where \(A_{x}(t)=\sqrt{x^{2}(t)+\hat{x}^{2}(t)}\) is the signal instantaneous amplitude, and \(\varphi_{x}(t)=\tan(\hat{x}(t)/x(t))\) is the instantaneous phase. Therefore, the instantaneous frequency, \(\omega(t)=d\varphi_{x}(t)/dt\), was calculated. The measurement and predicted acceleration were used as an input to the Hilbert system. In addition, [PERSON] and [PERSON] [36] found that the autocorrelation function can be used to estimate the damping ratio of signals measurements. The authors observed high correlation between the normalized autocorrelation and the impulse response function (IRF) of the structures' responses. Equation (8) was used to select the signal value of time (\(t\)) for two response channels (\(x_{1}\) and \(x_{2}\)) of the dynamic response. \[\{\varepsilon_{d}(z,t)\}=[D]\{a_{d}(z,t)\} \tag{2}\]1  
The autocorrelation function is shown in Equation (11). \[\{\varepsilon_{d}(z,t)\}=[D]\{a_{d}(z,t)\} \tag{2}\]2  
where \(\theta\) is a random variable with a uniform probability density function, and \(\tau\) is the time lag. Then, the least square method was used to estimate the coefficients of a polynomial fit for the signals' IRF. The linear regression was used to calculate the slope of the IRF. Then, the damping ratio was calculated as follows Equation (12):  
\[\{\varepsilon_{d}(z,t)\}=[D]\{a_{d}(z,t)\} \tag{2}\]3  
where \(p(1)\) is the first coefficient or slope of the IRF bridge response and \(\omega_{n}\) is the structure natural frequency.