esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Antimicrobial Activity  
According to the results shown in Table 3, none of the extracts exhibited antibacterial activity against Gram-negative bacteria (_E. coli_ and _S. enteritidis_). On the contrary, all _Hypericum_ extracts demonstrated some degree of inhibition (weak, moderate, or strong) and bactericidal effect against Gram-positive bacteria (_S. aureus_ and _E. faecalis_). More specifically, _H. perforatum_ demonstrated strong growth inhibition and bactericidal activity against both _S. aureus_ (MIC: 0.06 mg/mL; MBC: 0.51 mg/mL) and _E. faecalis_ (MIC: 0.13 mg/mL; MBC: 0.51 mg/mL). Similarly, _H. delplicum_ also demonstrated a strong growth inhibition against both Gram-positive species. On the other hand, _H. cycladicum_ and _H. olympicum_ exhibited strong bacterial inhibition activity against _S. aureus_ (MIC: 0.31 mg/mL) but moderate inhibition against _E. faecalis_ (MIC: 0.63 mg/mL). All the other _Hypericum_ species demonstrated moderate bacterial inhibition activity (MIC: 0.13 mg/mL), except the _H. fragile_ extract, which exhibited weak inhibition activity against both _S. aureus_ and _E. faecalis_ (1.75 mg/mL and 3.50 mg/mL, respectively).  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Species** & **TPC \(\pm\) SD** & **TFC \(\pm\) SD** \\ \hline \multirow{3}{*}{\(H\). _perfolatum_} & mg GAE/g dry plant material & mg RE/g dry plant material \\  & 59.31 \(\pm\) 16.47 \({}^{\text{a}}\) & 0.21 \(\pm\) 0.14 \({}^{\text{A}}\) \\  & 54.87 \(\pm\) 2.73 \({}^{\text{a}}\) & 1.18 \(\pm\) 0.22 \({}^{\text{B}}\) \\  & 26.92 \(\pm\) 10.32 \({}^{\text{b}}\) & 0.30 \(\pm\) 0.12 \({}^{\text{C}}\) \\  & 54.59 \(\pm\) 8.55 \({}^{\text{a}}\) & 1.34 \(\pm\) 0.05 \({}^{\text{D}}\) \\  & 86 \(\pm\) 13.34 \({}^{\text{c}}\) & 0.76 \(\pm\) 0.11 \({}^{\text{E}}\) \\  & 51.26 \(\pm\) 21.36 \({}^{\text{a}}\) & 1.58 \(\pm\) 0.08 \({}^{\text{F}}\) \\  & 39.72 \(\pm\) 6.05 \({}^{\text{d}}\) & 0.54 \(\pm\) 0.12 \({}^{\text{G}}\) \\  & 32.63 \(\pm\) 17.42 \({}^{\text{b}}\) & 0.64 \(\pm\) 0.09 \({}^{\text{H}}\) \\  & 54.09 \(\pm\) 4.29 \({}^{\text{a}}\) & 0.6 \(\pm\) 0.17 \({}^{\text{I}}\) \\ \hline \hline \end{tabular} \({}^{\text{*}}\) Results are presented as mean \(\pm\)standard deviation (SD) (\(n=3\)) (\(p<0.05\)). \({}^{\text{a},\text{d}}\) and \({}^{\text{A},\text{I}}\) values with the same letter are non-significantly different (\(p<0.05\)).  
\end{table}
Table 2: Total phenolic and total flavonoid content of the _Hypericum_ species examined.  
Figure 2: (a) UV-Vis spectra of \(H\). _cycladicum_, _H. delplicum_, _H. fragile_, and _H. perfontum_ extracts. (b) UV-Vis spectra of _H. cycladicum_, _H. delplicum_, _H. fragile_, and _H. perfontum_ extracts after complexing with AlCl\({}_{3}\) reagent.
## 4 Triggering Mechanism
### Events Triggering Actions
#### 4.1.1 Planned Changes  
Planned changes are associated with evolving requirements, such as the need to incorporate additional features into the network. For instance, consider the example of adding a CO\({}_{2}\) level detection capability, which would require the inclusion of an additional sensor and specific processing algorithms capable of triggering notifications. In this case, implementing the CO\({}_{2}\) level detection capability necessitates the integration of multiple features within the network. We would like to mention that triggering notifications based on external changes has been extensively covered in various publications and will not be a focal point of this work [24].
# Internal Backpressure for Onboard Crospoint-Buffered Switches with Port Multiplexing
## 4 Traffic Module  
Firstly, to simulate traffic arrival at each port, the ON-OFF model [41] is employed for traffic generation. During the ON state, the system continuously sends a high volume of data packets, resulting in distinct traffic peaks. Conversely, in the OFF state, data are not  
Figure 4: Internal structure of crossbar of port multiplexing.  
sent. In this context, the average duration of the ON state is discerned, referred to as the burst length.  
The lengths of the ON and OFF periods in the ON-OFF model follow a periodic geometric distribution, resulting in the following:  
\[\begin{cases}burst\_length=\sum_{i=1}^{\infty}i(1-p_{1})^{i-1}=1/p_{1},\\ off\_length=\sum_{j=1}^{\infty}j(1-p_{2})^{j-1}=1/p_{2}.\end{cases} \tag{1}\]  
In this context, \(p_{1}\) represents the probability of terminating a burst in each time slot, while \(p_{2}\) represents the probability of initiating a new burst. Assuming the average probability of a data packet arriving at a given time slot is denoted by \(\rho\), we can derive the following:  
\[\begin{cases}p(ON)=\rho,\\ p(OFF)=1-\rho.\end{cases} \tag{2}\]  
Based on the global equilibrium equation, we derive the following:  
\[p(ON)p_{1}=p(OFF)p_{2}. \tag{3}\]  
From Equations (1) to (3) above, the duration of the OFF cycle can be derived as follows:  
\[off\_length=burst\_length\times(1-\rho)/\rho. \tag{4}\]  
Secondly, in order to simulate the traffic distribution from input to output, the non-uniform traffic distribution model [42] is used. Let \(p_{i,j}\) denote the traffic load from port \(i\) to port \(j\), which can be formulated as follows:  
\[p_{i,j}=\begin{cases}\Big{(}\frac{\omega}{N_{h}}+\frac{1-\omega}{N}\Big{)}\rho,j\in S_{H},\\ \frac{1-\omega}{N}\rho,j\in S_{L},\end{cases} \tag{5}\]  
where \(S_{H}\) represents the set of high-load ports and \(S_{L}\) represents the set of low-load ports. \(\omega\) is employed as the imbalance factor, ranging from 0 to 1.  
When \(\omega\) equals 0, the traffic is uniformly distributed across all ports. As \(\omega\) gradually increases, the traffic begins to concentrate towards the hot ports, indicating a more uneven distribution. When \(\omega\) reaches 1, all traffic is directed to the hot ports [43]. It can be observed that for any given \(i\), we have the following:  
\[\begin{split}\sum_{j}p_{i,j}&=N_{h}(\frac{\omega}{N _{h}}+\frac{1-\omega}{N})\rho+N_{l}\frac{1-\omega}{N}\rho\\ &=\omega\rho+(1-\omega)\rho\\ &=\rho.\end{split} \tag{6}\]  
In the aforementioned equation, \(N_{h}\) denotes the number of hot ports, with \(N\) being the total number of ports. In the simulations, the value of \(N_{h}\) is taken as half of the total number of ports. Among them, Figure 4(a) shows the state transition diagram of the ON-OFF model, and Figure 4(b) shows the non-uniform traffic distribution model diagram.
## 2 Material and Methods
### Formulation of the Distribution Area  
The process of determining the distribution area and the distance of tsunami waves involved measurements through distribution area formulations. These were generated through a comparison between the DEM of the first building, which was closest to the shoreline; the maximum height of the building closest to the shoreline; the maximum height of the tsunami wave; and the degree of the tsunami wave slopes after arriving on land and hitting buildings, trees, or stones.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Stages** & **Description** \\ \hline Add Photos & 4853 Photos with TIF and BMP format \\ \hline “Reference Settings” & WGS 84 (EPSG:-4326) \\  & Camera accuracy (m): 10 \\  & Camera accuracy (deg): 2 \\  & Marker accuracy (m): 0.005 \\  & Scale bar accuracy (m): 0.001 \\  & Marker accuracy (pix): 0.1 \\  & The point accuracy (pix): 4 \\ \hline “Camera Calibration” & FC220 (4.73 mm) 4853 image, 1600 \(\times\) 1200 pix \\  & Camera type: Frame \\  & Pixel size (mm): 0.0039356 \(\times\) 0.0039356 \\  & Focal length (mm): 4.73 \\  & Type: Auto \\  & F: 1201.85 \\  & ccx 3000 \\  & Cy: 2000 \\ \hline Align Photos & Accuracy: High \\  & Generic preselection (check list) \\  & Reference preselection (check list) \\  & Key point limit: 150.000 \\  & Tie point limit: 70.000 \\  & Adaptive camera model fitting (check list) \\ \hline Build Dence Cloud & Quality: High \\ \hline Input GCP & GCP point data in TXT format (tab delimited) \\  & Depth filtering: Mild \\ \hline Build Mesh & Surface Type: Height field, \\  & Source data: Sparse cloud \\  & Face count: Medium (51,257) \\  & Interpolation: Enabled (default) \\  & Calculate vertex colors (check list) \\ \hline Build Texture & Mapping mode: Orthophoto \\  & Blending mode: Mosaic (default) \\  & Texture size/count: 160000 \(\times\) 1 \\  & Enable color correction (check list) \\  & Enable hole filing (check list) \\ \hline Build DEM & Projection: Geographic WGS 84 (EPSG:-4326) \\  & Source data: Spres cloud \\  & Interpolation: enabled (default) \\ \hline Build Orthomosaic & Projection: Geographic WGS 84 (EPSG:-4326) \\  & Surface: DEM \\  & Blending mode: Mosaic (default) \\  & Enable hole filing (check list) \\ \hline Export Model & Save as type: Wavefront OBJ (*.obj) \\ \hline \hline \end{tabular}
\end{table}
Table 4: Stages and settings used for the 3D orthomosaic process conducted with Agisoft metashape professional software.  
The method used to measure the tsunami area distribution was the Simple Additive Weighting Method (SAW), which has the following equation:  
WD = P1:P2:P3:P4  
where  
WD is the tsunamis wave distribution area (tsunamis endpoint);  
P1 is the DEM of the building closest to the beach;  
P2 is the height of the building closest to the beach;  
P3 is the tsunami wave height prediction;  
P4 is the slope degree of the tsunami waves.
## 1 Introduction
Furthermore, DSD obtained from other studies can significantly vary depending on rain type and geographical location [41]. With the development of optical laser techniques (disdrometer), the continuous and direct measurement of raindrop size and velocity has become easily available to assess rainfall \(KE\). A recent study with splash erosion measurements under natural rainfall, using the splash cup technique and rainfall monitoring with a disdrometer, was performed by [PERSON] et al. [42]. They used a funnel and cup installed directly in the field for splash erosion measurements, and found a good correlation between splash erosion and rainfall \(KE\); however, their findings were based on only nine sampling periods. [PERSON] et al. [43] conducted a study in Spain where the splash erosion of three soil types was measured with Morgan splash cups [23]. A significant relationship was found between splash erosion and the rainfall erosivity index \(EI_{30}\), and high variabilities between the replicates indicated the heterogeneity in splash erosion spatial distribution. According to the results reported from these studies, there are still many uncertainties concerning the changes in surface condition and spatial distribution of splash erosion. The studies investigating splash erosion under natural rainfall are limited to local conditions. Consequently, monitoring of the rainfall characteristics on higher temporal and spatial resolution is crucial for describing the dominant rainfall parameters on splash erosion related to a specific location. Apart from the field studies of [PERSON] et al. [42] and [PERSON] et al. [43], there are very few experiments that include both the monitoring of splash erosion and rainfall characteristics, including DSD, in the same location. Considering the local influences and lack of the data sets on rainfall DSD, it is difficult to define the role of splash in soil erosion process and predict it relative to local conditions. Furthermore, [PERSON] [44] pointed out that many rain events in Central Europe do not generate overland flow, but splash erosion is initiated already from the first drop impact, which emphasizes the importance of this soil degradation process. Lack of knowledge about the effect of erosive rainfall events on splash detachment in the agriculturally active Central European area was the main motivation for the present study. This study presents the results from the splash erosion measurements collected during three consecutive summer seasons at three sites in Central Europe. Together with splash erosion, rainfall parameters, including rainfall intensity and _KE_, were monitored at the sites, with the aim of analyzing performance of the most common rainfall erosivity parameters (_KE_, intensity, and rainfall erosivity (_EI\({}_{30}\)_)), in order to predict splash erosion under natural rainfall.
# miRNA-Gene Association Prediction Method Based on Deep Learning Model
## 5 Conclusions  
In this study, we propose a unique deep learning approach to predict associations between human miRNAs and genes. The performance of our method was measured using five-fold cross-validation, achieving the highest AUC of 0.9834 and an average AUC of 0.9804. This method outperformed the SOTA miRNA-gene association prediction methods. The outstanding performance of the proposed model can be attributed to the following:  
* Extensive training data: our model benefited from training on the largest sequence dataset ever constructed for miRNA-gene associations.
* Optimal data embedding: we employed a sophisticated vectorization technique, transforming complex miRNA and gene sequence features. This was based on a model specifically designed for protein sequence embedding.
* Logical negative data construction: by considering Euclidean distance, cosine similarity, and Mahalanobis distance, we defined a set of criteria that allowed for the logical construction of negative data.
* Optimized model architecture: drawing from the above data, we designed an effective miRNA-gene LSTM deep learning model.  
We also conducted two case studies using our method. Initially, to identify potentially associated pairs, we performed experiments to predict pairs with unknown associations. Furthermore, we conducted experiments to predict miRNAs closely associated with _BRCA2_, a gene linked to various cancers. Through these experiments, we demonstrate that our research exhibits outstanding generalization performance and has a wide range of practical applications.  
There are limitations in this study as well. Specifically, when selecting the negative set randomly from the Negative Candidate Pool to match the number of positives, the data that is randomly chosen as the negative data can have a minor impact on performance; however, this is considered an inevitable part of deep learning research that requires the prediction of associations, as data with a clear negative relationship are almost non-existent in the field of bioinformatics. The method of setting negatives in this way is expected to significantly influence the advancement of future bioinformatics deep learning research.  
Conceptualization, S.Y. and K.L.; methodology, S.Y. and I.H.; software, S.Y. and I.H.; validation, S.Y., I.H., J.C. and H.Y.; formal analysis, I.H. and H.Y.; investigation, H.Y. and J.C.; resources, S.Y., I.H., J.C. and H.Y.; data curation, I.H. and J.C.; writing--original draft preparation, S.Y.; writing--review and editing, S.Y. and K.L.; visualization, J.C. and H.Y.; supervision, K.L.; project administration, K.L.; funding acquisition, K.L. and S.Y. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korean Government (MSIT) (No. 2021R1F1A106151313).  
Not applicable.  
All data produced or examined during this research can be found in the article. Further inquiries can be directed to the corresponding author.  
The authors declare no conflict of interest.
## 5 Feasibility Study  
### Performance Evaluation of Raspberry Pi Boards as Asterisk Server for LTC Applications  
#### 5.1.1 Overview  
This subsection evaluates the performance of Raspberry Pi boards that run Raspberry Pi OS (previously called Raspbian) when acting as VoIP communication servers using the open-source Asterisk 16 IP-PBX, which is installed on top of the Raspberry Pi OS. This comparative study was performed to promote the use of tiny embedded Linux platforms as suitable candidates for LTC applications in home and small business domains or as an integral part of the UC solutions in large enterprises to empower them with LTC capability.  
We gauged the resource utilization and the operating system responsiveness of different Raspberry Pi boards with differentiated capabilities while initiating an increasing number of simultaneous VoIP calls. Both passthrough and transcoded VoIP calls were implemented, tested, and contrasted to give the reader a comprehensive overview of the boards' performance at different VoIP setups. We also monitored the VoIP call quality metrics, namely, jitter and packet loss, throughout each scenario to ensure it falls within the acceptable limits set by Cisco [33].  
We utilized the following Python libraries in the developed Python testing scripts: (1) pycall: it was used for creating and using Asterisk files, and it enables programmatic origination of VoIP calls through the testing script, (2) psutil: it was used to retrieve information on running processes and the system performance measurements (%CPU utilization and load average), (3) os: it was used to run Linux bash commands from within the testing scripts and send remote commands to the peer PoT gateway used during the transcoding VoIP testing, and (4) subprocess: it was used to spawn new processes, connect to their input/output/error pipes, and get their return codes. The outputs of the running processes were saved within the gateway and used for further analysis and visualization using a data visualization software, namely, Tableau desktop.  
Figure 5: The door entry node (breadboard view).
## 5 Experimental Investigations
### Experimental Procedure
#### 5.2.2 Long-Term Tests  
The long-term durability tests are carried out in especially developed test rigs (Figure 3a). Those rigs allow a long-term loading of the test specimens as well as a readjustment of the forces by hollow piston cylinders, which are operated hydraulically. In addition to the constant load, the specimens are exposed to tempered water (40 or 60 \({}^{\circ}\)C).  
The tap water used is not exchanged during the entire test period. The pH-value of the water is 8 and rises rapidly to approximately 11 after dissolution of alkalis from the concrete of the test specimens. This results in an alkaline solution which, besides local attacks by the concrete pore solution on the reinforcement, leads to an attack on the reinforcement material. Accordingly, a simultaneous exposure of stress, moisture, temperature and alkalinity can be examined (Figure 3b).  
The load is again applied via clamping devices made of steel plates (clamping length 250 mm). To ensure a permanent clamping of the test specimens, the clamping length increased by 50 mm compared to the short-term tests. The contact pressure is applied by lateral mechanically pretensioned threaded rods. Since the test specimens slipped out during first trial tests, the contact pressure increased to approximately 4.0 MPa (short-term tests 3.0 MPa). The reason for the slipping of the specimens is the thermal expansion of the threaded rods in the tempered water, which leads to a reduced contact pressure compared to the short-term tests.  
To measure the creep deformations of the specimens or rather the carbon reinforcement, inductive displacement sensors are placed on the formwork and the filling side of the test specimens. Due to the length of the clamping devices, a test area of 500 mm and a measuring area of 450 mm were chosen.  
Figure 2: (**a**) Specimen in testing machine; (**b**) schematic (according to [10]) of the test setup for short-term tests.  
While recording the deformations, the test specimens are loaded slowly in the tempered water, up to half of the reference load from the short-term tests. However, due to the high tensile strength of the concrete, no cracks will appear at this load level. Therefore, the specimens are loaded up to a final crack pattern before the load level is reduced back to half of the reference load and kept constant for 24 h.  
This constant load serves as a preload for the test specimens, as it was specified in the project C\({}^{3}\)-V2.1. The influence of the preloading will be explained in detail in Section 6. After 24 h, the load level is applied and kept constant by manual readjustment over the entire test period until the failure of the specimens. The test time generally depends on the height of the load level.
## 4 Results
### Reproducibility of Single Subject Inference (Spearman Correlation)  
A Spearman correlation was calculated using and compared between two EEG trials to verify whether the proposed BSL features are reproducible between the trials. The highest correlation between the trials was found in the alpha band (0.98), followed by gamma (0.92), theta (0.89), and beta (0.82) bands (see Figure 4). The Spearman correlation was compared with state-of-the-art functional-connectivity features: leakage corrected amplitude envelope correlation (AEC-c), graphical least absolute shrinkage and selection operator (GLASSO), phase transfer entropy (PTE), and imaginary coherence (ImCoh). BSL features had consistent results in all four frequency bands compared to state-of-the-art functional-connectivity features.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Frequency Band** & **Difference (Rest vs. Task)** & \(t\)**-Statistic** & \(p\)**-Value** \\ \hline Delta & 0.094613317431321 & 2.9064863124854 & 0.0984324783123 \\ Theta & 0.130153031853969 & 3.1567605365934 & 0.00235352056178 \\ Alpha & 0.076217499646317 & 1.8087360091388 & 0.07478653690259 \\ Beta & 0.184088564061622 & 4.5157062410558 & \textless{}0.01 \\ Gamma & 0.103185265750143 & 2.4786212696908 & 0.01560225463094 \\ \hline \hline \end{tabular}
\end{table}
Table 1: BSL two-sided \(t\)-test for frequency bands.  
Figure 4: Comparison of Spearman correlation of BSL features in theta, alpha, beta, and gamma frequency bands with state-of-the-art connectivity features.
## Materials and Methods  
### Model Description  
The physically-based 2D TREX model [15], the 1D pipe routing, and the sub-basin components of the CANOE model [16] are integrated in the TRENOE platform. As for the land-phase hydrology, TRENOE uses the diffusive wave approximation of shallow water equations (SW) for surface runoff modeling, the Green & Ampt method [17] for infiltration calculations, and the canopy storage for interception estimations. An explicit finite-difference scheme ([PERSON]'s method) is applied for numerical solution. Otherwise, the building roofs are represented separately from the 2D surface model. Since the real elevation of roofs are generally not described in digital terrain model (DTM) data for urban areas, a realistic adaptation consists in raising the building elevations above the land surface at the step of input data pre-treatment. Besides, as most roofs are directly connected to the sewer network in the studied catchment, the grid cells of roofs that are linked to the same sewer node (the nearest) are assembled as virtual sub-basins in the CANOE model. The areas of these sub-basins are equal to the total area of the connected roof cells. The non-linear reservoir method is then applied to simulate the rainfall-runoff relations for each conceptual sub-basin. These sewer nodes are the connecting points between the 2D surface module and the 1D network module in the TRENOE platform, and are called \"junction nodes\". At each time-step, TRENOE removes water from the grid cells of \"manholes\" (sewer inlets, sewer drain grills) and simultaneously makes it enter into the corresponding \"junction nodes\". Additionally, the flow routing process in the sewer networks is computed by the 1D kinematic wave approximation of the shallow water equations and solved by a four-point implicit finite-difference scheme due to [PERSON] [18] (Equation (1)). The model scheme is illustrated in Figure 1.  
\[\frac{dA}{dt}\ +\ \frac{dQ}{dx}\ =\ q\ -\ i \tag{1}\]  
With \(Q\) the discharge (m\({}^{3}\) s\({}^{-1}\)), \(A\) the cross section of the flow (m\({}^{2}\)), \(i\) the infiltration (m\({}^{3}\) s\({}^{-1}\)) and \(q\) the rainfall (m\({}^{3}\) s\({}^{-1}\)).