esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Biological Activities
### Antiproliferative Effect against Cancer Cells  
The effect of citral against the proliferation of cancer cells has been extensively reported (Table 6). Studies on colorectal cancer cells showed that citral inhibits the growth of HCT116 and HT29 cell lines in a dose- and time-dependent manner without inducing effect in the non-cancerous colon cells CCD841-CoN [9]. Citral reduces the proliferation of the colon cancer cells Caco-2 notoriously [9]. [PERSON], [PERSON] [88] reported that the nano-emulsifying drug delivery system loading with citral (CIT-SNEDDS) was more effective than citral in inhibiting the proliferation of SW620 colorectal adenocarcinoma cells after 72 h of treatment; however, at short times (24 and 48 h), citral was more effective than CIT-SNEDDS to reduce the growth of this cell line. In HT29 cells, citral was more effective than CIT-SNEDDS in reducing cell proliferation. On the other hand, citral was more effective than the antineoplastic drug Cisplatin in reducing the proliferation of the human stomach cancer cells AGS; shrunken cells and generation of shapeless cells were observed after the treatment of AGS cells with citral, which could be correlated with the activation of cell death by apoptosis, where contraction, cell rounding, and the formation of membrane blebs are characteristic. Furthermore, citral showed less cytotoxicity than Cisplatin against the lung fibroblast non-cancerous MRC-5 [9]. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Compound/** & **Doses** & **Effect** & **Ref.
## 2 Dynamic Model of the Feed System
### Calculation of Restoring Force of Bearings
#### 2.2.1 Restoring Force of Angular Contact Ball Bearing
\begin{aligned} &\left(\frac{\sqrt{\sigma_{L\text{Aj}}^{ 2}+\left(z_{\text{Rij}}^{\text{A}}-z_{L\text{oj}}^{\text{A}}\right)^{2}-R_{ \text{0}}+\delta_{A\text{0}}}}{C_{\text{Aj}}+C_{\text{Aoj}}}\right)^{2/3}& \delta_{L\text{Aj}}+\delta_{A\text{0}}>0\\ & 0&\delta_{L\text{Aj}}+\delta_{A\text{0}}\leq 0\end{aligned} \right. \tag{37}\]  
\[\alpha_{L\text{Aj}}=\cos^{-1}\left(\frac{\sigma_{L\text{Aj}}}{B_{0}+\delta_{ L\text{Aj}}+\delta_{A\text{0}}}\right) \tag{38}\]  
where \(\delta_{A\text{0}}\) is contact deformation under the preload; \(\delta_{L\text{Aj}}\) is contact deformation between the \(j\)th ball and the raceways for the left angular contact ball bearing; \(B_{0}\) is the initial distance of the raceway centers; \(C_{\text{Aj}}\) and \(C_{\text{Aoj}}\) are the Hertz contact constant of the inner and outer rings. The contact force and contact angle for the right angular contact ball bearing can be expressed as:  
\[Q_{RAj}=\left\{ \begin{aligned} &\left(\frac{\sqrt{\sigma_{RAj}^{2}+ \left(z_{R\text{ij}}^{\text{A}}-z_{R\text{oj}}^{\text{A}}\right)^{2}-R_{0}+ \delta_{A\text{0}}}}{C_{\text{Aj}}+C_{\text{Aoj}}}\right)^{2/3}&\delta_ {RAj}+\delta_{A\text{0}}>0\\ & 0&\delta_{RAj}+\delta_{A\text{0}}\leq 0\end{aligned} \right. \tag{39}\]  
\[\alpha_{RAj}=\cos^{-1}\left(\frac{\sigma_{RAj}}{B_{0}+\delta_{RAj}+\delta_{A \text{0}}}\right) \tag{40}\]  
where \(\delta_{RAj}\) is contact deformation between the \(j\)th ball and the raceways for the right angular contact ball bearing. The restoring forces along the \(x\), \(y\) and \(z\) directions for the left angular contact ball bearing can be expressed as:  
\[F_{\text{LA}x}=-\sum_{j=1}^{N_{\text{bb}}}Q_{\text{LA}j}\cos\theta_{\text{bj}} \cos\alpha_{\text{LA}j} \tag{41}\]  
\[F_{\text{LA}y}=-\sum_{j=1}^{N_{\text{bb}}}Q_{\text{LA}j}\sin\theta_{\text{bj}} \cos\alpha_{\text{LA}j} \tag{42}\]  
\[F_{\text{LA}z}=-\sum_{j=1}^{N_{\text{bb}}}Q_{\text{LA}j}\sin\alpha_{\text{LA}j} \tag{43}\]  
The restoring forces along the \(x,y\) and \(z\) directions for the right angular contact ball bearing can be expressed as:  
\[F_{\text{RA}x}=-\sum_{j=1}^{N_{\text{bb}}}Q_{\text{RA}j}\cos\theta_{\text{bj}} \cos\alpha_{\text{RA}j} \tag{44}\]  
\[F_{\text{RA}y}=-\sum_{j=1}^{N_{\text{bb}}}Q_{\text{RA}j}\sin\theta_{\text{bj}} \cos\alpha_{\text{RA}j} \tag{45}\]  
\[F_{\text{RA}z}=\sum_{j=1}^{N_{\text{bb}}}Q_{\text{RA}j}\sin\alpha_{\text{RA}j} \tag{46}\]
## 4 Discussion
### Application for Phytomanagement  
_Miscantthus x giganteus_ is a fast-growing C4 plant with good nutrient management and high yields of suitable biomass [3]. Therefore, the recent research has also been oriented to its cultivation on contaminated or marginal land, resulting in sustainable joint phytomanagement/phytoremediation with biomass production. Miscanthus was confirmed as a plant suitable for such applications in metal contaminated soils as reported previously [5; 65; 66; 5]. Much less experimental work was done with organic contaminants, particularly petroleum, one of the most abundant environmental pollution sources.  
Many factors could contribute to miscanthus growth starting from macro and micronutrients in the soil, their availability, climate and weather, water availability, quantity and quality of contamination, including synergic effects, other stresses, etc. It is very complicated to uncover all factors when aiming at real contaminated soils. Therefore, it is common in bioremediation practice that preliminary case experiments similar to ours are carried out to obtain data for the field experiment [36; 46]. Information about plant and microbial stress response is very useful for planning complex phytomanagement of contaminated sites. The tolerance to specific contamination is one of the essential requirements for choosing a suitable plant, and stimulation of soil microorganisms is crucial for rhizodegradation.  
In AGR soil with agricultural contamination by Cd and MIL soil with low-level Pb contamination, Zn and Cd _Mxg_ produced a satisfactory amount of biomass. The good physiological state of plants, was also confirmed by chlorophyll fluorescence measuring and microscopy. Additionally, reduction of microbial stress was recorded in these two variants. The opposite result was obtained for miscanthus grown in real soil from petroleum sludge lagoon with cross-contamination. Plants could survive, but biomass production was deficient, and no microbial stress reduction was determined.  
Our previous study also determined a significant decrease of calorific value for the biomass grown in petroleum industry contaminated soil [67]. This notion implicates it would be less useful to use the biomass for combustion compared to AGR and MIL soils, which had values comparable with middle-quality brown coal or pine wood.  
Based on these results, _Mxg_ should not be considered a good remediation agent for the tested OIL soil contaminated by the petroleum industry. However, we could not attribute these negative results to petroleum contamination since we were neither able to cover other possible pollutants nor evaluate likely synergies. More research and meta-analyses of many other case-studies will be needed to identify significant general factors or their combinations affecting the _Mxg_ and effective measures to compensate them. Field experiments are also required to verify the results in real-scale conditions and to receive long-term data that cannot be collected in short-term pot experiment with young plants.
## 7 Conclusions, Limitations, and Future Works  
Aiming to explore the potentials for incorporating physiological signals for detecting music-induced emotion and the role of users' personality in group-wise modeling, this study constructed a thorough dataset with aligned music stimuli audio and listeners' physiological signals, ratings on emotion, and personalities. To bridge the gap between physiological computing and MIR, this study leveraged techniques in physiological signal processing to extract physiological signals that are potentially interpretable. Our findings on multimodal music emotion classification make it evident that users' physiological responses to music is an informative modality for the MER task on music-induced emotion and made more significant contributions to valence than arousal classification. The physiology modality was found to be complementary to the acoustic modality for valence prediction. However, for arousal classification, the acoustic modality alone was comparable with the multimodal feature set. Additionally, the results of our user-group-wise classification experiment imply that both music acoustic and listeners' physiological signals might be more effective in predicting music-induced emotions for users of certain personality traits than other users. These observations imply moderating effects of personality in individuals' emotional response to music. Through a theoretical lens, our findings call for a more holistic perspective when studying music-induced emotion. Despite the proved effect of music characteristics on listeners' arousal levels, its effect on valence response can be largely individual-dependent. Beyond psychoacoustic analysis, there are multiple factors contributing to inducing emotion in music listeners, some of which are relatively changeable and short-term (e.g., physiological signals), while others are more stable and long-term (e.g., personality traits). Practically, through demonstrating the potential of physiological sensing techniques in emotion-aware MIR, this study opens up a number of possibilities in the future for emotion-aware or context-based music retrieval systems, such as recommending music based on physiological metrics of users and maintaining emotion-sensitive playlists which can be adjusted in real time in accordance with users' changing physiological response to music. In addition, findings of this study also highlight the importance of multimodal and personalized modeling that exploits not only content-based MER but also various forms of user-dependent information sources (e.g., personality, physiological signals). Findings of this line of research may also prompt academic and industrial advancement in closely related areas such as human physiological response, human-computer interaction, music perception, and music therapy. This study has several limitations. Although the HKU956 dataset is of a decent size, samples with neutral labels were finally discarded, which resulted in smaller samples, especially in the group-wise experiments. Moreover, our dataset also faced the issue of
# A Framework for Cloud Based E-Government from the Perspective of Developing Countries
## 4 Case Study
### Electronic Transaction Processing Model  
Since there is no payment gateway available in Nepal, we have approached some of the private companies to discuss how their services could be used in e-government. Among the companies, E-sewa, Khalti and Pravu Bank agreed to discuss the possibility of expanding their services in e-government as shown in Table 6. E-sewa has been providing electronic wallet service to the individual and allows them to pay different types of bill i.e., internet bill, telephone bill, purchase cinema ticket and online payment to some supermarket. After the initial discussion with the CEO we arranged another meeting that included the government official and representative from E-sewa. The representatives have presented their business model that shows how they provide their service for a small amount of commission. They agreed to have further meeting to discuss the terms and condition to handle the driving licensing fees from the customer. The commission for the handling fees for the services needs to be discussed between the company and the driving licensing department. The company currently charges 2.5 percent commission per transaction of the total amount, however this could be discussed in detail and could be reached in mutual understanding in relation to the fee handling for the government department. The government official and bank employee agreed to have next meeting to discuss and agreed the handling fee and sign and memorandum of understanding.  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Current Practice** & & **Agreed Delivery Channels** \\ \hline - & e-government portal & - & e-government portal \\ - & Manual service provided from the government office (Paper based) & - & Rural municipalities \\  & - & Tele-centre \\  & - & Libraries \\  & - & Private business \\ \hline \hline \end{tabular}
\end{table}
Table 5: Delivery channels for e-government services.  
To see how these transaction modes perform and what impact they make on stakeholders' decisions to use e-government services, we embedded the services of E-sewa for payment handling within the driving licensing portal. While in the current e-government portal the user could only fill the form online and have to go to the local government office or to the bank in order to complete the transaction, use of E-sewa would allow the stakeholders to pay the application fee instantly. It saves both stakeholders and government time that is required to process the transaction. Although use of E-sewa in portal certainly attracted more user, there are some serious concern has been raised by some of the stakeholders. The stakeholders require opening an account with the E-sewa and deposit fund in order to pay by using E-sewa service. Therefore, this could not be as effective as debit or credit card payment.
## 4 Discussion
### The Existing Limitations and Future Prospects  
Currently, obtaining accurate leaf-sun angle values is difficult, especially over mountainous areas. Most GPP models assume the mean leaf-sun angle as a constant, which might create uncertainties in the final GPP estimates. Our future work would try to find improvements in GPP estimation with the use of more accurate leaf-sun angle values in the carbon modeling process. In this work, several topographic maps were obtained from the 30 m SRTM DEM, and it should be noted that any uncertainties in the original DEM data  
Figure 10: Color-coded correlation matrixes for annual GPPML-LUE, GPPTL-LUE, GPPMOD17, GPPMTG, GPPTG, GPPPBL, and GPPBEPS over Wanglang Reserve. The correlation matrixes were calculated from the annual GPP maps of MOD17, TL-LUE, MTL-LUE, TG, MTG, BEPS, and BTL.  
could lead to bias in the results. Besides the surface topography, vegetation heterogeneity (LAI and LC) is also important in GPP estimation [94]. Over mountainous areas, LAI and LC maps have been reported to present high uncertainties because of the high levels of cloud, aerosol, and snow in remote sensing observations [95; 96]. Future work should develop reliable algorithms to obtain reliable LAI and LC maps at fine resolutions.
## 5 Evaluation
### Experiments  
#### 5.2.1 Cabinet Environmental Deployment  
CENet monitors the temperature of the isolated contacts in high voltage cabinets. The cabinet is steel and has a plastic window for the operator to see the equipment running state, so the RF signal can transmit through the window. Slave nodes are deployed in the cabinets for temperature sensing. Master nodes are placed outside the cabinet.  
Thirty six nodes are deployed in high voltage cabinets in a substation. There are one BS node, 10 master nodes, and 25 slave nodes. The BS connects to the computer through UART. The nodes with ID numbers from 1 to 10 are master nodes, and 11 to 35 are slave nodes. The slave node mounts a DS75 temperature sensor. It senses environment temperature and transmits the data every 5 minutes. Master nodes are not equipped with any sensors and are only responsible for data packet forwarding. We set the request beacon interval to be 0.5 s. The RF transmission power for slave node is \(-\)20 dBm, and for a master node it is \(-\)10 dBm. CENet drops a data packet after four retransmissions, which can be increased by the user to have a more reliable data yield.  
The routing tree construction is completed within 30 seconds. The maximum number of hops of a routing tree is 4. Two criteria are adopted to estimate the link quality. The first one is the number of retransmissions, as described in Section 3.2.1, where the time window size is 12 data transmissions, \(a=0.5\), and \(b=0.2\). The second one is packet drop rate. The threshold of the packet drop rate is 10% in a time window.  
The network was running for 17 days. The BS received 115,056 data packets which are stored in a PostgreSQL [21] database. Because every data packet includes an attribute of sequence number, BS  
Figure 14: Average energy consumption of CENet and standard stack for sample interval between 30 s and 300 s.  
can calculate packet loss rate, as shown in Figure 15. The data packet loss rate varies between 0% and 15% with an average of 5.6%. From Figure 15, one can see that nodes with more hops have greater packet loss rate. This is because the retransmission mechanism in CENet only provides a certain reliability of transmission at each hop (hop-by-hop reliability). In other words, it can't ensure end-to-end reliable packet delivery.
## 2 Background
Another major issue with medical telemetry is the data transmission reliability and security [18]. When monitoring a patient, we must have a high level of trust in the quality of the data. Thus, it is very important that the system collects and transmits data reliably, and in a timely manner to the monitoring entity. Reliability in a medical system network can be defined in terms of delay profile, delay jitter and information loss rate whereas efficiency of a network can be defined as the ratio of information bits to the total transmitted bits [19]. Also, it is important to emphasise that, in the case of medical monitoring applications, simply wearing the device may disclose to the adversaries such as patient's employer or \begin{table}
\begin{tabular}{l c c c c c c} \hline \hline
**Type** & **Min** & **Max** & **Unit of measure** & **Data type** & **\# Octets** & **Example** \\ \hline Temperature & 0 & \(\sim\)50 & \({}^{\circ}\)C & Binary & 1 & 00100101 \\ Heart Rate & 0 & \(\sim\)200 & Beats per minute & Binary & 1 & 00111100 \\ Blood Pressure & 0 & \(\sim\)200 & mmHg (x 2 & & 01111000 \\ Respiration rate & 0 & \(\sim\)50 & Breaths per minute & Binary & 1 & 00001110 \\ Blood oxygen & 0 & 100 & Percentage Oxygen & & & \\ concentration & & & & & & \\ Blood glucose & 0.0 & \(\sim\)50.0 & Mmol/L–a decimal value & Binary coded & 3 & 8.2 \\ concentration & & & & (to 1 decimal place) & ASCII & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Vital signs data-summary of ranges and types of data. insurer or acquaintances that the patient is suffering from a medical condition [18]. Consequently, the wearable monitoring device has to be as unobtrusive as possible, to preserve patient's privacy.
# Hydrological Drought Frequency Analysis in Water Management Using Univariate Distributions
## 2 Methodology
### Probability Distributions
#### 2.1.4 Wilson-Hilferty (WH3)  
The three-parameter Wilson-Hilferty distribution is a generalized form of the two-parameter Wilson-Hilferty distribution. Both are cases of Amoroso distribution [20; 29].  
\[f(x)=\frac{3\cdot\exp\biggl{(}-\left(\frac{x-\gamma}{\beta}\right)^{3}\biggr{)} }{|\beta|\cdot\Gamma(\alpha)}\cdot\biggl{(}\frac{x-\gamma}{\beta}\biggr{)}^{3 \cdot\alpha-1} \tag{14}\]  
\[F(x)=1-\int\limits_{\gamma}^{x}\frac{3\cdot\exp\biggl{(}-\left(\frac{x-\gamma }{\beta}\right)^{3}\biggr{)}}{|\beta|\cdot\Gamma(\alpha)}\cdot\biggl{(}\frac{ x}{\beta}\biggr{)}^{3\cdot\alpha-1}dx=\frac{\Gamma\biggl{(}\alpha,\left(\frac{x- \gamma}{\beta}\right)^{3}\biggr{)}}{\Gamma(\alpha)} \tag{15}\]  
\[x(p)=\gamma+\beta\cdot\sqrt[3]{qgamma(1-p,\alpha)} \tag{16}\]where \(\alpha,\beta,\gamma\) are the shape, the scale, and the position parameters, respectively; \(\alpha,\beta>0\); \(x\) can take any values in the range \(\gamma<x<\infty\).
# Finite Element Analysis of a Novel Approach for Knee and Ankle Protection during Landing
## 2 Materials and Methods
### Material Properties and Interactions  
The material properties of the model parts were all selected from previous studies. Except for the encapsulated soft tissues, skin, and heel fat pad, all tissues were idealized as homogeneous, isotropic, and linear-elastic materials, and the ligaments were defined to be subjected only to tension [21; 22; 23]. For the encapsulated soft tissues and heel fat pad, we used the same hyperelastic material, for which the stress-strain data were adopted from an in vivo ultrasonic measurement [24]. The skin was modeled as a hyperelastic material with the first-order Ogden model (\(\mu\) = 0.122 kPa, \(\alpha\) = 18) [7]. Regarding the brace, material properties were derived from the actual exoskeleton equipment. The elastic modulus of the rigid connecting rod and fixed components was assigned as 72 GPa, whereas Poisson's ratio was 0.3. Moreover, the elastic modulus of the semi-rigid ankle support was 36 GPa and Poisson's ratio was also 0.3. The spring constant at the knee joint was set as 4 N/mm.  
The meniscus and articular cartilages in the knee joint were connected to each other via contact behavior, assuming a hard contact and frictionless contact in the normal and  
Figure 1: Finite element model of the ankle and knee complex, showing a detailed view of the segments and demonstrating the braced conditions used in the simulation.  
tangential directions, respectively. The bones were connected to each other via contact behavior, which was assumed frictionless [24]. Moreover, the cartilaginous layers were resembled by nonlinear contact stiffness between the bones [7]. The encapsulated soft tissues, ligaments, and heel fat pad were tied to the bony structures. Furthermore, the skin was tied to the constructed geometry, and the brace was connected to the skin via the fixed components tied to the thigh and shank, as shown in Figure 1.
## 5 Implementation and Experimental Setup
### Evaluation Metrics  
The performance of our system was measured by using the following evaluation metrics.  
1. Accuracy: the following formula was used to calculate the model's accuracy, as shown in Equation (10). \[Accuracy=1-\frac{\parallel Y-\hat{Y}\parallel F}{\parallel Y\parallel F}\] (10) where \(Y\) represents the actual data and \(\hat{Y}\) represents the predicted data.
2. Root Mean Square Error (RMSE): the RMSE was calculated using Equation (11). \[RMSE=\sqrt{\frac{1}{MN}\ \sum_{j=1}^{M}\sum_{i=1}^{N}\left(y^{ij}-\hat{y^{ij}} \right)^{2}}\] (11) where \(y^{ij}\)_and_\(\hat{y^{ij}}\) are subsets of \(Y\) and \(\hat{Y}\) and represent the actual data and predicted data for the \(i\)th time sample in the \(i\)th session, respectively. \(M\) is the total time samples, and \(N\) is the number of products. The RMSE is precisely used to evaluate the prediction error. The smaller the value of the RMSE is, the better the prediction rate or score is according to Equation (12). \[\text{Prediction rate}\ \propto\ \frac{1}{\text{RMSE}}\] (12)  
While the accuracy is used to detect the predictions' precision, it has an opposite effect to the RMSE on the prediction rate, as shown in Equation (13). The higher the value of the accuracy is, the better the prediction rate is.  
\[\text{Prediction rate}\ \propto\ \text{Accuracy} \tag{13}\]
3. Recall@20: This refers to the total number of correctly recommended items among the first 20 items, making this one of the essential evaluation metrics for building accurate recommendation systems.
4. F-Score: This analyzes the classification of a recommendation model and provides the model's test accuracy computed from the precision and recall score.
5. MRR@20: This represents the mean reciprocal rank, and we computed the average reciprocal rank of the top 20 products. A higher value of MRR indicates that the model has higher accuracy for correctly recommending products.
6. ROC: The ROC curve (receiver operating characteristic curve) is a graph used to present the model's classification performance. The two key parameters, known as the true-positive rate and false-positive rate, are plotted against each other. We used this performance metric to evaluate our model's performance and compare it with a few traditional models.