esa-sceva/satcom-chunk-collection huggingface dataset:
# tRNA and tsRNA: From Heterogeneity to Multifaceted Regulators
## 4 Revisit the Regulatory Mechanisms of tRNA and tsRNA  
### tRNA: Focus on Both Main and Side Businesses  
The indispensable role of tRNA in decoding mRNA and facilitating protein synthesis is undeniable. Alterations in tRNA pools, specific tRNA species, RNA modifications, and charging levels can significantly impact protein synthesis efficiency. These effects may arise from variations in tRNA availability or by influencing critical processes like codon-anticodon pairing [7; 8; 37]. While the primary function of tRNA is mRNA decoding during translation, emerging evidence from DNA/RNA sequence complementarity rules and structural biology suggests that tRNAs also possess functions beyond translation. This expanding understanding of tRNA mechanisms provides valuable insights into their broader biological roles.
# Ball Tracking Based on Multiscale Feature Enhancement and Cooperative Trajectory Matching
## 3 Methods
### Ball Tracking
#### 3.4.1 Multilevel Cooperative Matching
Considering computational efficiency and performance, this paper uses the method of manually constructing a ball HOG feature [36] library as a feature basis to calculate the matching degree of ball appearance features. First, in the training data set, soccer, basketball, and volleyball balls were intercepted in different motion states, i.e., ball instance, then the HOG features of each ball sample were extracted to construct the matrix \(h_{l}\), and then the same method was used to calculate the HOG feature \(h_{i}\) of each candidate detection. Equation (22) indicates that at this time, the candidate detection that has the minimum average distance and meets the threshold can be used as the final matching detection. \[d^{(3)}_{(i,l)}=min\{\frac{1}{N}\|h_{i}-h_{l}\|\} \tag{22}\]  
The pseudocode of multilevel collaborative matching is shown as Algorithm 1. ```
0: Detection indices \(\mathcal{D}=\{D_{1},\dots,D_{i},\dots,D_{M}\}\), Track indices \(\mathcal{T}=\{T_{1},\dots,T_{k-1}\}\)
0: New track \(\mathcal{T}\)
1: Initialization set of matches \(\mathcal{M}\leftarrow\emptyset\)
2: Initialization set of unmatched detections \(\mathcal{U}\leftarrow\mathcal{D}\)
3:for\(D_{i}\) in \(\mathcal{D}\)do
4: Compute KF matching distance \(d^{(1)}(i,k)\)
5: Compute LM matching distance \(d^{(2)}(i,k)\)
6:if\(d^{(1)}(i,k)>t^{(1)}\) and \(d^{(2)}(i,k)>t^{(2)}\)then
7:\(\mathcal{M}\gets D_{i}\)
8:endif
9:endfor
10:for\(D_{i}\) in \(\mathcal{M}\)do
11: Compute the minimize AF matching distance \(d^{(3)}(i,l)\)
12:\(T_{k}=D_{i}\)
13:\(\mathcal{T}\leftarrow T_{k}\)
14:endfor
15:return\(\mathcal{T}\)
```  
**Algorithm 1** Multilevel Cooperative Matching
# Low-Cost IoT and LoRaWAN-Based System for Laying Hen Identification in Family Poultry Farms
## 4 Results and Discussion
### Field Testing
In comparison with the traditional method using one-way doors, during the field trial, the farmer's routine was reduced to a single brief visit per day, thanks to the system's automation capabilities. This observation, based on direct experience during system deployment, highlights the practical benefit of reducing time and physical effort. Moreover, while the conventional approach requires the farmer to manually identify hens, register each laying event, and do so consistently during three daily visits, this process is prone to human error, omissions, or inconsistency over time. By contrast, the automated system ensures continuous, objective, and timestamped data collection without interrupting the animals or overburdening the farmer. Finally, this contributes to improved traceability and reliability of the information gathered, and enhances the integration of digital tools in everyday farming practices.
## 5 Discussion
### Meltwater Infiltrating Crevasses  
Many melt ponds decrease the albedo of the glacier tongue, which suffers the risk of potential glacial collapse [55]. The presence of englacial liquid water can weaken ice, and it is plausible that surface melt at the suture zone could percolate into the ice, weaken shear margins, and allow the ice tongue to speed up as a result of reduced rigidity [17; 18]. The storage capacity and estimated water depths of these crevasses are large enough to induce hydro-fracturing if there is sufficient meltwater supply. The efflux from rivers and melt ponds into water-filled crevasses may triggered hydro-fracturing [32]. The coincident coordinated drainage occurs during the peak in meltwater production, which means that lake drainage events occurred during a high melt period [56; 57].  
Generally, ice speeds along flow lines gradually increases as the melt season progresses, particularly within the shear margins in grounded ice. However, on floating ice, surface meltwater may also influence ice calving by percolating through crevasse and hydrofracture drainage, weakening the ice shelf shear margins [18]. Certainly, the complex process and feedback between calving and floating ice hydrology-driven forcing may need to be examined. Our efforts focused on the evolution of melt pond area that shows a short-term increase in surface velocity correlated with an ice terminate disintegration. In addition, the observation demonstrated that the time at which there is a maximal area of melt ponds is preceded by the moment when ice flow reaches peak values; some time is spent in the high melt period to destabilize the fractures and penetrate crevasses. Additionally, the meltwater infiltrating crevasses may not accelerate ice flow immediately, so it cannot directly induce an instantaneous ice calving event. Therefore, the surface meltwater has a  
Figure 11: Monthly series of ocean temperature below 450 m at the Petermann ice shelf from 2015 to 2017 at position (59.9671 W, 80.5804 N) from [PERSON].  
potential impact on glacier terminus stability, due to hydrofracturing under a warmer and continued high melt season, which eventually drive ice calving.
# Botnet Detection Technology Based on DNS
## 3 Botnet Detection Technology  
Now, we turn from the botnet evading techniques to focus on the botnet detecting techniques. The data source of botnet detection techniques is mainly the network traffic used, as well as some application data (such as mail records and DNS logging). This article is mainly based on DNS detection technology. Moreover, the definition of abnormal patterns are mainly based on the characteristics of content (signature-based) anomalies and based on specific behavior (behavior-based) anomalies.  
Bots are often detected through their communication with a C and C infrastructure [16]. To evade detection, botmasters increasingly obfuscate the C and C communications, for example, by utilizing fast flux or P2P protocols. At present, a botmaster that gives out instructions often monitor and control bots. The transmission of these orders, which are known as C and C messages, can be centralized, peer-to-peer or hybrid. In the centralized structure, the bots contact with the C and C servers to receive instructions from the botmaster. In comparison with other architecture, the speed of message propagation and convergence is faster. It is not difficult to carry out, maintain, and monitor. However, a single point of failure limits it. While the access to the C and C server is blocked, such botnets can be taken down. In the course of communication, centralized infrastructure is often adopted by IRC or HTTP botnets.  
In addition to the network communication, some of the botnet behavior characteristics, such as community similarity, also reflected in the use of some network services (such as DNS or email log records). The researchers attempted to find the characteristics of abnormal records to identify possible botnets from this kind of data.
# Computer-Aided Surgical Simulation for Correcting Complex Limb Deformities in Children
## 4 Discussion
This implies a drastic reduction in costs and the potential diffusion of this technological innovation into the routine clinical practice, improving the quality of care even in peripheral hospitals or in developing countries. Our study must be seen in light of some limitations. First, we aimed to investigate the feasibility of an in-house low-cost CASS process on a small heterogeneous case series; thus, the study was not designed to assess CASS in terms of risk-benefits and cost-effectiveness, in comparison with traditional planning procedures. To this purpose, case control studies and prospective cohort studies are required in the near future. Second, the final correction was evaluated on plain radiographs, since we could not obtain postoperative CT scans from our patients, due to ethical issues. This prevented us from verifying the final correction in a 3D fashion that is more reliable. Third, PO surgery frequently regards the correction of immature bone with open physics. Despite the fact that the correction may be satisfactory at the time of surgery, recurrent deformities may present at follow-up, due to the abnormal growth of the physics. This issue could be overcome by the combination of corrective osteotomies and growth modulation procedures in staged, multistep surgeries, but the current technology is still far from including growth modulation in the CASS process. Fourth, the CASS process essentially involved the correction of the bones, thus, not considering the role of soft tissues. This issue implies that we could not simulate the surgical approach, the repair, transfer, or reconstruction of capsules or ligaments. Moreover, we superimposed rigid constraints, for example, in case of radial relocation in forearm deformities. In the future, the application of specific constraints mimicking the elastic modules of capsules, tendons, and ligaments will increase the accuracy and effectiveness of the CASS process.
## 3 Results and Discussion
### Adsorption of Reactive Orange 16 (RO 16) as an Indicator for Fabric Sorption Properties
#### 3.3.3 Isotherm Experiments
The theoretical monolayer maximum adsorption capacity of this fabric accounted for 2.525 after the chitosan treatment due to the previously discussed higher content of chitosan on the fabric surface as well as a different binding mechanism. Parameter \(q_{m}\) is also very useful for the comparison of the adsorption capacities of different adsorbents for the same dye. Besides the complicated route for obtaining cotton fabric loaded with metal-organic frame material UiO-66, long reaction time (12 h), used toxic DMF, and high operation temperature (80 \({}^{\circ}\)C), its \(q_{m}\) for RO 16 is only 5.17 mg/g [48], which is even lower than that observed for untreated jute (5.996 mg/g, Table 2). Furthermore, although activated carbons studied in the earlier literature [28; 33] have higher \(q_{m}\) for RO 16, they cannot compete with J1.0% CS since during their preparation, high temperatures \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Adsorption**} & \multirow{2}{*}{**Isotherm**} & \multicolumn{4}{c}{**Adsorbents**} \\ \cline{3-6}  & & **Parameters** & **J** & **J0.5\% CS** & **J1.0\% CS** & **J2.0\% CS** \\ \hline \multirow{2}{*}{Langmuir} & \(K_{L}\) & 0.008 & 0.044 & 0.080 & \(-\)0.093 \\  & \(q_{m}\) & 5.996 & 10.128 & 14.075 & 2.525 \\ \hline \multirow{2}{*}{Freundlich} & \(K_{f}\) & 1.719 & 1.394 & 2.838 & 15.035 \\  & \(1/n\) & 0.254 & 0.404 & 0.351 & \(-\)0.375 \\ \hline \hline \end{tabular} \(K_{L}\) (L/mg) is Langmuir constant, \(K_{f}\)(mg/g)/(L/mg)\({}^{1/n}\) is Freundlich constant, and \(1/n\) is a constant related to the fabric surface heterogeneity.  
\end{table} 
Table 2: Isotherm parameters for RO 16 adsorption onto studied adsorbents. Figure 9: Modeling the isotherm data presented in Figure 8 using: (**a**) [PERSON] and (**b**) Freundlich isotherm models and the linear fit of experimental data for RO 16 adsorption. and non-environmental friendly chemicals (like 85% ortho-phosphoric acid) were used, raising the question regarding the adsorbent cost-effectiveness. The economic impact of the proposed approach can be considered beneficial for possible real applications since the associated costs are very low because 1.0% of chitosan solution was used for the functionalization of fabric that possessed the highest \(q\) for RO 16.
## 4 Results
### Irradiated Blocks  
The correlation between MAT descriptors and neutron fluence is shown in Figure 5a in the case of block samples, while Figure 5b shows the correlation between MAT descriptors and Vickers hardness when using normalized parameters, similar to the case of Charpy samples. In these measurements \(\mu^{\prime}\) (\(h_{a}\) = \(-\)600 mA, \(h_{b}\) = 1200 mA) was used as optimal.  
These two graphs (Figure 5a,b) and similar graphs in case of Charpy samples (Figure 4a,b) show very well the large scatter of measured points. Explanation of this phenomenon is the main purpose of the present work. One can even question the presence of any correlation at all. However, in our opinion the tendency of the increase in magnetic parameters is clearly seen, especially if we compare the modification of parameters generated by neutron irradiation with the values of unirradiated reference samples. On the average, about 20% increase in magnetic descriptors can be detected. In other words, some correlation definitely exists, but it is questionable if this correlation is linear or not, which is also reflected by the low values of the regression coefficients.  
Figure 5: Optimally chosen MAT descriptor as a function of neutron fluence in block samples (**a**), and the ratio of MAT descriptors of individual irradiated and reference samples as a function of Vickers hardness (**b**).
# Mapping Smallholder Yield Heterogeneity at Multiple Scales in Eastern Africa
## Materials and Methods
### Satellite Image Processing  
We employed remote sensing data from three different satellite constellations, including 2-m resolution Skysat imagery from Terra Bella, 5-m resolution RapidEye imagery from Planet, and 10-m resolution Sentinel-2 imagery from the European Space Agency. A summary of the bands used in this study is given in Table 1. Among the three, Sentinel-2 data were publicly available through the GEE, while Skysat and RapidEye data were generously provided by Terra Bell and Planet, respectively, and uploaded to GEE for processing. All images were delivered as Top-Of-Atmosphere (TOA) product. We did not further apply radiometric corrections to the satellite data, because there is no existing algorithm onboard GEE that can efficiently accomplish the task, and therefore we used the  
Figure 1: Study region in west Kenya (**a**). Grey area is where One Acre Fund crop cut yields are available; (**b**) individual crop cut yields (black boundaries delineate counties and grey boundaries delineate districts); and (**c**) aggregated crop cut yields at District level with star symbols showing the hypothetical sites for crop model simulations. (**d**) Sub-region from where household survey data come. (**e**) Apr-July mean temperature; and (**f**) total precipitation in 2016 is given with the colored rectangle indicating the surveyed sub-region.  
TOA product rather than Surface-Reflectance (SR) in yield estimation. Cloud contamination is often a serious issue in this tropical area. For the B-K sub-region, we identified one relatively cloud-free image from Skysat on 17 May and two cloud-free images from RapidEye on June 1 st and June 4 th (Figure S1). Images from Sentinel-2 collection were either heavily contaminated by cloud or very hazy. For the broader 1 AF region, we identified two relatively cloud-free Sentinel-2 images on 27 May and 6 June (Figure S2), while the other two sensors only cover a small fraction of the study area. Clouds and shadows remaining in these images were masked using a random forests classifier trained on manually delineated samples throughout the region. Collectively, six commonly used vegetation indices (VIs) were calculated and investigated as the candidate explanatory variables for yields in the sub-region (Table 2). The preferred VIs were then used in the 1 AF region for yield estimation. Temperature data on a regional scale was approximated by the 1-km MODerate-resolution Imaging Spectroradiometer (MODIS) Aqua MYD11A2 (8-day composite) version 5 land surface temperature (LST) products on board the GEE. We used LST data from the Aqua satellite because its overpass times of 1:30 am and pm at local was closer to the timing of daily minimum and maximum temperature than the Terra satellite [10].
# O2O: An Underwater VLC Approach in Baltic and North Sea
## 4 Outage Performance of the System  
Generally, the outage probability defines as the probability in which the instantaneous SNR (\(\gamma\)) falls below the expected threshold SNR (\(\gamma_{th}\)). To obtain the outage performance of the proposed system the transmitted \(\gamma\) should be below the threshold \(\gamma_{th}\) value of the communication link as follows,  
\[P_{out}=F_{\gamma}(\gamma)=Pr(\gamma\leq\gamma_{th}). \tag{19}\]  
We can rewrite (19) by substituting (11) and obtained the outage performance expression for the whole system as in (21). Additionally, for the single-layer the outage probability could be formulated as,  
\[P_{out}^{SL}(\gamma)=\frac{2^{(\alpha+\beta-3)}\alpha\beta}{2\pi\sqrt{\mu} \Gamma(\alpha)\Gamma(\beta)}G_{1,5}^{4,1}\left[\frac{\alpha^{2}\beta^{2}\gamma_ {th}}{16\mu}\left|\begin{array}{c}\frac{1}{2}\\ \frac{\alpha-1}{2},\frac{\alpha}{2},\frac{\beta-1}{2},\frac{\beta}{2},-\frac{1 }{2}\end{array}\right.\right]. \tag{20}\]  
\[P_{out}=z\sqrt{\frac{\gamma_{th}}{\mu}}G_{1,9}^{8,1}\left(\frac{\kappa\gamma_{ th}}{\mu}\left|\begin{array}{c}\frac{1}{2}\\ \frac{\alpha_{N}-1}{2},\frac{\alpha_{N}}{2},\frac{\alpha_{B}-1}{2},\frac{\alpha_ {B}}{2},\frac{\beta_{N}-1}{2},\frac{\beta_{N}}{2},\frac{\beta_{B}-1}{2}, \frac{\beta_{B}}{2},-\frac{1}{2}\end{array}\right.\right). \tag{21}\]
# FPGA-Based Frequency Tracking Strategy with High-Accuracy for Wireless Power Transmission Systems
## 4 Experimental Verification  
### Experimental Platform  
In order to verify the feasibility of the above frequency-tracking control method, Figure 12 is used as an experimental platform for WPT system design. The experimental platform mainly consists of a high-frequency power supply, a transceiver coil, an oscilloscope, current probes, a rectifier circuit, and an electronic load. Built into the high-frequency power supply are current and voltage acquisition circuits. The FPGA chip controls the output voltage frequency of the power supply; the operating frequency is 48 kHz at a fixed frequency. Table 1 shows the circuit parameters of the platform.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameters** & **Symbol** & **Value** \\ \hline transmitting coil self-inductance & \(L_{1}\) & 275 \(\mu\)H \\ receiving coil self-inductance & \(L_{2}\) & 275 \(\mu\)H \\ transmitter compensation capacitance & C\({}_{1}\) & 40 nF \\ receiver compensation capacitance & C\({}_{2}\) & 40 nF \\ internal resistance of transmitting coil & \(R_{1}\) & 0.18 \(\Omega\) \\ internal resistance of receiving coil & \(R_{2}\) & 0.16 \(\Omega\) \\ inherent resonating frequency & \(f_{0}\) & 48 kHz \\ \hline \hline \end{tabular}
\end{table}
Table 1: Main parameters of WPT system.  
Figure 11: Simulation waveform of frequency-tracking control experiment.  
Figure 12: WPT system experimental platform.
# Characteristics of Acoustic Emission Caused by Intermittent Fatigue of Rock Salt
## 1 Introduction  
Rock salt is a near-perfect material for waste disposal and oil storage repositories because of its properties of excellent ductility [1; 2], good self-healing features after being damaged [3; 4; 5; 6; 7], and low permeability [8; 9]. Stressed states in underground engineering are extremely complicated: gas injection occurs periodically, and production is not continuous. Therefore, time intervals exist among the circles during the operation of gas storage caverns. The rheological responses of rock salt [10; 11] determine the time interval duration that affects the mechanical fatigue behavior.  
Since [PERSON] identified the Kaiser effect in rock materials [12], acoustic emission (AE) has been widely used in research because of its unique ability to explore microscopic mechanisms of rock behavior. This technique is very effective in identifying micro- and macro-defects and their temporal evolution due to fatigue in several materials [13]. Scholars around the world have studied the AE characteristics of rock salt and other materials in fatigue experiments. In experiments on rock salt, [PERSON] et al. [14] studied the microstructural variations and damage evolvement of salt rock under cyclic loading. [PERSON] et al. [15] observed that AE counts exhibit obviously distinct characteristics in different stages ofcrack propagation. [PERSON] et al. (2016) applied AE to examine and classify different deformation mechanisms. [PERSON] et al. (2017) discovered marble specimens under various loading schemes using acoustic emission in terms of F-function. [PERSON] et al. (2018) used AE to develop a statistical model of crack propagation that assesses the distance and angular relationship of neighboring cracking events arranged in their temporal order of occurrence. [PERSON] et al. (2019) demonstrated the capability of AE to predict the fracture of marble specimens using F-function. [PERSON] et al. (2020) studied the fatigue damage to a reinforced concrete beam using AE during fatigue. Although application of AE to fatigue studies appears mature and complete, little work has been carried out on the effect of fatigue applied with intermittent time intervals.
## 4 Results and Analysis
### Prediction Results  
In this section, we employed a total of eight different classifiers, including KNN, SVM, DT, LR, RF, GBDT, XGBoost, and our constructed stacking ensemble model. We separately trained and tested these eight models on both the imbalanced dataset and the dataset oversampled using SMOTE. All classifiers were trained and tested separately based on the 5-fold cross-validation dataset divided in Section 3.3. Section 4.3.1 presents the predictive results of various classifiers on the imbalanced original dataset. In Section 4.3.2, we provide the predictive results for different classifiers on the balanced dataset, as well as analyze the impact of data balance on classifier performance.