esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Principles and Methodologies
### OSP Methods
#### 2.3.2 Selection of Target Mode  
Many scholars suggest that for some high arch dams, lower modal orders are often considered to have large dynamic response. However, the modal energy is not always concentrated on the low-order modes. For some complex structures, higher modal orders also exhibit large dynamic response [14; 23]. Therefore, selecting only the first several modes as the modal shape matrix is not appropriate. To improve the modal energy at sensor locations, this paper employs a modified modal assurance criterion (MMAC) to address this issue. It uses the modal participation factor (MPF) [22] as the criterion for evaluating the dynamic responses of different modes. The new modal shape matrix is then composed of modes with relatively higher modal energy. The MPF is defined as:  
\[\varphi_{ir}=\frac{{{\Phi_{i}}^{T}MD_{r}}}{{{\Phi_{i}}^{T}M\Phi_{i}}} \tag{19}\]  
where \(\varphi_{ir}\) is the MPF in \(r\) direction for the \(i\)-th mode, \(\Phi\) is the modal shape matrix, \(M\) is the structural mass matrix, and \(D\) is a vector that describes the direction of excitation.
## 4 Results and Observations
### Further Validation on the Test Bearings under Condition C3
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Bearing**} & **Baseline** & **Baseline** & **Estimated** & **Baseline** & **Estimated RUL** & **RUL 95\% CI** \\
**ID** & **FDT \({}^{*}\)\(t_{TDT}\)** & **EOLT \({}^{*}\)\(t_{EOL}\)** & **/minute** & **End-of-Life** & **RUL** & **(Residual)** & **/minute** \\  & **/minute** & **/minute** & **/minute** & **Time \({}^{*}\)\(t_{EOL}\)** & **/minute** & **/minute** & **/minute** & **/minute** \\ \hline \multirow{4}{*}{5} & \multirow{4}{*}{327} & \multirow{4}{*}{353} & 343 & 355 & 10 & 12 (+2) & [10,14] \\ \cline{3-8}  & & & 345 & 350 & 8 & 5 (\(-3\)) & [4,6] \\ \cline{3-8}  & & & 346 & 349 & 7 & 3 (\(-4\)) & [2,4] \\ \cline{3-8}  & & & 347 & 352 & 6 & **5 (\(-1\))** & [4,6] \\ \hline \multirow{4}{*}{6} & \multirow{4}{*}{1418} & \multirow{4}{*}{1478} & 1428 & 1472 & 50 & 44 (\(-6\)) & [38,50] \\ \cline{3-8}  & & & 1429 & 1476 & 49 & 47 (\(-2\)) & [41,53] \\ \cline{3-8}  & & & 1430 & 1477 & 48 & 47 (\(-1\)) & [41,54] \\ \cline{3-8}  & & & 1441 & 1478 & 37 & **37 (0)** & [33,41] \\ \hline \hline \end{tabular}  
* Baseline FDT: first detection time of fault. Baseline EOLT: bearing end-of-life time of ground truth. PT: RUL prediction time.  
\end{table} 
Table 7: RUL Prediction Results of Bearing 5 and Bearing 6 (Condition 3). As for Bearing 6, the estimated RULs at the prediction times \(t_{PT}=1428\) and \(t_{PT}=1429\) are close to but earlier than their baselines. Note that the prediction times are merely about 10 minutes away from the detection of fault. Additionally, the RUL estimations at \(t_{PT}=1430\) and \(t_{PT}=1441\) are more precise since their residuals have reduced to less than one minute.
## 3 Monitoring Results and Analysis
### Comparison of Tunnel Displacement with Different Pre-Supports  
For the leading ductule section, the distribution ranges of the arch settlement and horizontal convergence were about 24-45 mm and 9-20 mm, respectively. Moreover, the mean values were calculated as around 33 mm and 13 mm, respectively. Whereas, the distribution ranges of the arch settlement and the horizontal convergence in the pipe roof section were circa 17-31 mm and 10-18 mm, respectively, with mean values of 23 mm and 12 mm, correspondingly (Figure 11). When the leading ductule was used as pre-support, the arch settlement and horizontal convergence in the leading ductule section were increased to different degrees compared to those in the pipe roof section, and a clearer increase of the arch settlement was observed. This suggests that for shallow-buried large cross-section loess tunnel, a better control on the arch settlement can be achieved through the pipe roof, in comparison with the leading ductule.  
The proportion of the tunnel displacement at each excavation stage in the total tunnel displacement is presented in Tables 4 and 5. The requirements of TSEM were strictly followed in the excavation of each section. Therefore, the tunnel displacement proportion at different excavation stages can reflect the ground response at each excavation stage. In this regard, the excavation stage with the largest tunnel displacement proportion was defined as the maximum deformation stage. The maximum arch settlement stage and the maximum horizontal convergence stage of all monitoring sections were calculated, as shown in Figure 15. The maximum arch settlement stages within the pipe roof sections and the leading ductule sections are UB with four monitoring sections and MB stages with five monitoring sections, respectively. The ground response time of the pipe roof section was earlier than that of the leading ductule section. However, no obvious difference in the horizontal convergence was observed in regard to this phenomenon. This suggests that the pipe roof had greater disturbance to the ground outside the reinforcement ring when it reinforced the ground in advance. This was mainly reflected vertically, rather than horizontally. This is due to the setting of the pipe roof and the leading ductule outside the arch, hence the disturbance was mostly concentrated in the ground above the arch, and less disturbance occurred within the ground on the side of the tunnel.
## 4 Discussion
### Weighting Analysis of Impact Factors  
In this study, the relative importance of each influencing factor in the prediction of collapse disaster risk was analyzed using weighting through the convolutional neural network model. Figure 13 demonstrates the weight distribution of each factor in the evaluation of collapse disaster risk, showing the degree of contribution of the different factors to the collapse risk.  
As can be seen from the figure, the slope has the largest weight, indicating that the slope has the most significant effect on the risk of collapse. Soils in high-slope areas are less stable and prone to sliding and collapse, so the weight of the slope factor was ranked first among all factors. The lithology factor followed closely, with a high weight, indicating that the physical properties of rocks (e.g., hardness, weathering resistance) directly affect the stability of the soil body, and the risk of collapse is higher, especially in areas with soft rocks or fissure development. In addition, the weight of vegetation cover was equally high, indicating that vegetation plays an important role in reducing the risk of collapse. In particular, in areas with better vegetation cover, the stability of the soil body is enhanced. The weight of the precipitation factor was also higher; in areas with high precipitation, the soil body is affected by water saturation and is easily destabilized, thus increasing the risk of collapse. The graphs also show that some other factors, such as the aspect, curvature, population size, soil moisture, and distance from roads, have relatively low weights, but they play an auxiliary role in predicting the results of collapse hazards by combining to influence the stability of geological conditions.
## 3 Control System Architecture
### Speed Controller  
To ensure the motor consistently operates as a synchronous motor, virtual Hall position signals (\(H_{abc}\)) are used to synchronize the stator currents (\(I_{abc}\)) with the back-EMFs. This approach maximizes torque output while minimizing ripple by maintaining constant currents during the intervals when the back-EMFs are also constant. Since back-EMFs exhibit a trapezoidal shape with a flat region of 120\({}^{\circ}\), a six-step inverter operation is employed, where only two phases are active at any given time.  
The speed controller, as illustrated in Figure 7, compares the motor speed (\(N^{*}\)) to the speed reference (\(N\)) to generate the required additional torque (\(T^{*}\)). Three current references are then generated (\(T^{*}_{abc}\)) and supplied to a hysteresis control system with an adjustable band to produce gating signals. The hysteresis control system generates gating signals for the three-phase inverter, with a variable switching frequency determined by the hysteresis band value and the dynamics of the stator circuit. By restructuring the stator currents using virtual Hall pulses and maintaining an optimized current profile, significant reductions in current ripple are achieved. Consequently, the torque profile is also improved, resulting in enhanced motor performance.
## 2 Original SIFT Algorithm  
The traditional SIFT algorithm is divided into four parts: scale space extreme value detection, key localization, orientation determination, and key point description.
# Cost-Benefit Analysis of Small-Scale Rooftop PV Systems: The Case of Dragotin, Croatia
## 4 Conclusions  
The paper analyzed the cost-effectiveness of applying the PV roof system on a family house in the continental rural part of Croatia. For this purpose, a daily curve of electricity consumption was recorded with a step of 1 h for the observed object and the production of electricity from the PV system for a given location of the object was calculated. Due to routine daily activities that are repeated throughout the year, it was possible to apply the curves of the average daily electricity consumption for the whole month.  
In general, the daily load profile and the electricity generation profile do not match, so it is necessary to calculate the \(SS\) and \(SC\) factors. For the observed object, the \(SC\) factor is in the range of 51.9% to 82.9%, which can be assessed as satisfactory. However, as the purchase price of surplus electricity produced (in case more energy is injected into the network than is taken from it) is low, the load profile needs to be further improved by using electricity consumers at a time when there is surplus electricity produced. However, it is not realistic to expect that the consumption profile could be significantly improved by manual regulation and changes in inhabits. An in-house energy management system, mentioned in the introductory part, can yield much better results related to self-consumption. The amount of \(SS\) and \(SC\) can be significantly affected by the use of battery systems for energy storage. However, such systems are still not cost-effective. The increasing use of air conditioners due to climate change has a positive effect on the daily load profile in summer but also in spring and autumn when there are certain needs for less heating of the building, and there is surplus electricity produced in the PV system.  
For the analyzed facility, with the existing electricity prices, which are among the lowest in the EU and with the existing legislation related to net metering, the roof PV system can be paid for in 10.5 years without government incentives.  
In addition to reducing electricity bills, installing PV roofing systems provides benefits, such as energy autonomy, reduced carbon emissions, and the creation of new, local jobs.  
As a continuation of this research, it would be interesting to analyze PV systems of different capacities (for example, in the range of 2 to 5 kW) for current legislation and  
Figure 14: Degree-days of heating/cooling for Dragotin [24].  
determine the optimal system not only by the criterion of the payback period but also by the total profit over the life of the PV system.  
M.B.: conceptualization, methodology, writing--original draft preparation, visualization; A.C.: validation, supervision; B.Z.: software, data curation. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest.
# Studies on Seismic Performance of Precast Concrete Columns with Ground Splice Sleeve
## 2 Test Setup
### Test Apparatus and Loading Laws  
#### 2.3.1 Test Apparatus  
There are many test loading methods to study the seismic performance of frame columns, among which the simply supported beam loading and the cantilever beam loading are the most common ways. In tests of high strength concrete square-section columns carried out at the University of Nebraska Lincoln, the simply supported beam loading was chosen [39], while in tests of high strength concrete columns conducted at Toronto University and Clarkson University, the cantilever beam loading was employed [40; 41]. In this test, the cantilever beam loading was adopted and an actuator with a maximum loading of 10 tons was utilized to exert the cyclic load. The loading equipment is shown in Figure 3.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Specimen Number** & **Concrete (MPa)** & **Grout (MPa)** \\ \hline
1\# (cast in situ) & 52.1 & 125.6 \\
2\# & 53.8 & 128.0 \\
3\# & 57.1 & 121.6 \\
5\# & 38.8 & 118.1 \\
6\# (cast in situ) & 52.7 & 125.3 \\
7\# & 51.1 & 127.7 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Actual cubic compressive strength \(f_{cu}\) of the concrete and grout of the specimens.
## 3 Classification of Optimization Objectives
### Multi-Objective Optimization Focussed on Design Related Problems in WSNs
#### 3.1.2 Energy Conservation  
Wireless sensor nodes are inherently energy constrained devices. Furthermore, most of the times these devices are deployed in hard to reach areas where recharge or replacement of batteries is not possible. Therefore, energy conservation through efficient utilization of available energy helps to prolong the operation of the network. Maximization of energy conservation is one of the desirable objectives which has been addressed in various articles, for example in [49], a multi-objective evolutionary algorithm has been proposed to jointly optimize two conflicting objectives namely, maximization of energy conservation and maximizing the accuracy of spectrum sensing. Coverage performance and the energy conservation have been jointly optimized in [50] by formulating a multi-objective optimization problem and using evolutionary algorithm based decomposition approach. The authors showed that their algorithm performed better than the Non-dominated Sorting Genetic Algorithm II (NSGA-II) [50].  
\begin{table}
\begin{tabular}{p{14.2 pt} p{142.3 pt} p{142.3 pt} p{142.3 pt} p{142.3 pt}} \hline \hline
**Ref.** & **Objectives** & **Equation** & **Details** \\ \hline
[135] & Energy efficiency, packet error rate, average delay & maximize & \(\Phi.\mu\) & (6) & Where \(\mu\) denotes the optimal weights vector; \(\Phi\) denotes the real time vector. \\ \hline
[136] & Optimum structure of heat exchanger & \(\max:I=\dfrac{1000R^{2}}{\Delta P}\) & (7) & & The internal diameter of three tubes set as optimization variable and these variable are \(D_{tube1}\),\(D_{tube2}\) and \(D_{tube3}\). Its unit is millimeters. \\ \hline
[137] & Minimum energy consumption, uniform battery power depletion, and minimum delay & minimize & \(F(x)=(F_{1}(x),.....,F_{n}(x))^{T}\) & (8) & Where \(F_{i}(x)\) is an objective function, for \(1\leq i\leq n\); \(w_{i}\) denotes a weight selected by a network designer to reflect the relative importance of the objective function: \(c_{i}\) denotes a coefficient that not only scales \(F_{i}(x)\) but also helps produce a one-dimensional function. \\ \hline
[138] & Maximizing the network lifetime subject to QoS constraints & \(\max B_{s_{o}s_{d}}(\
u)\) & (9) & Multi-objective function. Objective 1 maximizes the residual energy of the selected nodes. Objective 2 maximizes the residual energy of the forwarding set. \(B_{s_{o}s_{d}}\) represents end-to-end path battery cost; \(\wp\) represents the set of paths between the nodes; \(L_{i}^{b}\) represents local battery cost for each node. \\ \hline \hline \end{tabular}
\end{table}
Table 2: _Cont._Design of routing algorithm has been proposed in [51; 52; 53] to optimize various conflicting objectives including energy conservation, packet delivery ratio, jitter, delay and robustness. The solution for energy conservation has also been searched in optimal cluster formation, for example in [54], a multi-objective optimization technique has been proposed to maximize energy conservation and to minimize the delay in data collection process.
## 5 Performance Evaluation  
We evaluate the proposed MVHS-LSTM in terms of both loss and time. The simulation environment and simulation results analysis is also discussed in this section. The evaluation aims to answer the following two questions: (1) Can the simulation results verify the theoretical analysis? and (2) Does the proposed algorithm outperform the comparing algorithms, i.e., the PSO-LSTM algorithm, Support Vector Machines algorithm and ARIMA algorithm?
## 2 Materials and Methods
### Models and Evaluation Criteria for ANN and BBD-RSM  
The significance and performance of the ANN and BBD-RSM models were assessed using the following statistical performance indices: coefficient of determination (\(R^{2}\)), mean relative error (\(MRE\)), and roots mean-square error (\(RMSE\)), as follows:  
\[R^{2}=1-\left(\frac{\sum_{i=1}^{n}(S_{i}-F_{i})^{2}}{\sum_{i=1}^{n}{(F_{i})}^ {2}}\right) \tag{5}\]  
\[RMSE=\sqrt{\frac{1}{n}\sum_{i=1}^{n}(S_{i}-F_{i})^{2}} \tag{6}\]  
\[MRE(\%)=\frac{1}{n}\sum_{i=1}^{n}\left|100\frac{S_{i}-F_{i}}{S_{i}}\right| \tag{7}\]where, \(S_{i}\) and \(F_{i}\) represent the actual parameter and predicted parameter values, respectively, as determined by the ANN and BBD-RSM models, and n denotes the total sample size or number of parameters.
# The Numerical Study for the Effect of Stiffness Matching on Wheel-Rail Curve Squeal Noise
###### Abstract  
This study delves into the phenomenon of high-frequency squeal noise occurring as trains traverse small-radius curved tracks and investigates the factors influencing wheel-rail curve squeal noise, particularly focusing on stiffness matching. To achieve this, we initially construct a finite element model of the wheel-rail friction system using finite element software ABAQUS 2022, validating its accuracy against Coulomb's friction law. Subsequently, we employ complex eigenvalue analysis to extract the complex eigenvalues and vibration modes of the wheel-rail system, enabling us to study the positions and vibrational patterns associated with squeal noise by analyzing the amplitudes of unstable modes. Finally, we assess the impact of wheel-rail stiffness matching on curve squeal noise, using wheel-rail material stiffness and rail support stiffness as key variables. The outcomes of this study reveal the following insights: (1) Unstable modes closely align with the resonant frequency and mode shape of the wheel and rail. (2) Curve squeal noise primarily emanates from vibrations at the rim, railhead, and rail foot. (3) Wheel and rail stiffness significantly affect squeal noise, with a significant deviation in the elastic modulus between rail and wheel increasing the likelihood of squeal noise, while an optimal ratio of about 1.2 is observed. (4) Rail support stiffness plays a discernible role in controlling curve squeal noise. Theoretically, maintaining an appropriate support stiffness level can minimize the negative damping ratio of unstable modes, providing a viable avenue for curve squeal noise control.  
curve squeal noise; finite element; stiffness matching; complex mode +
Footnote †: journal: Applied sciences
## 1 Introduction  
Currants (_Ribes_ L.) are shrubs of the order _Saxifragacae_, belonging to the gooseberry family (_Grossulariaceae_) [1]. They are cultivated throughout Northern Europe, Asia, North America, in the mountainous areas of South America and North-West Africa [2]. Red and white currant shrubs, and above all, blackcurrant, are cultivated mainly for their tasty and rich bioactive fruits. The fruit is eaten in both fresh and unprocessed forms, as well as in preserves such as frozen berries, juices, jams, jellies, etc. [1]. Other parts of the currant shrubs, such as seeds, buds and leaves, are an underappreciated source of numerous bioactive ingredients. Currant leaves contain more polyphenatic compounds than their fruits and are considered a good source of these compounds [3]. Although currant phenolics were the subject of many studies, these constituents in currant leaves are best characterized in blackcurrant leaves, which are a very good source of these bioactives [4; 5; 6]. The main compounds from this group that are detected in the leaves are quercetin (1.359 mg/g DM), myricetin (0.831 mg/g DM),rutin (0.266 mg/g DM) and catechins. The presence of phenolic acids was also detected. This included salicylic (0.236 mg/g DM), p-coumaric (0.266 mg/g DM), gallic and quinic as well as small amounts of chlorogenic, singmic, ferulic and coffee acids. It has been reported that the average concentration of the total amount of phenolics in blackcurrant leaves is in the range of 32 to 44 mg/1 DM [1; 5; 6; 7; 8]. Currant leaves have greater antioxidant activity and stronger anti-inflammatory properties than the fruit and other parts of this plant. As with the leaves of other berries, such as strawberries, blackberries and raspberries, they contain large amounts of polyphenolic compounds [5; 7]. Moreover, they contain valuable fatty acids, minerals and essential oils [5].  
The fatty acids contained in blackcurrant leaves are mainly unsaturated fatty acids such as \(\alpha\)-linolenic, \(\gamma\)-linolenic, _cis-_7,10,13-hexadecatiene and stearidine, as well as phosphatidylcholine and phosphatidylglycerol. They have anti-cancer, antibacterial and anti-inflammatory properties [9]. Blackcurrant leaves contain significant amounts of micronutrients such as iron, manganese and boron. They have also shown a dietary advantage in the potassium to sodium ratio. The high amount of potassium in relation to the amount of sodium causes the diuretic effect of the leaves [7]. The aromatic smell of fresh blackcurrant leaves is related to their content of essential oils that includes monoterenes and sesquiterpenenes [10]. According to our best knowledge there are no references concerning the basic chemical composition and antioxidant capacity of various types of currant leaves.  
The aim of this study was to investigate the basic chemical composition, total polyphenolic compounds content and antioxidant activity of black (_Ribes nrigrum_), red (_Ribes rubrum_) and white currant (_Ribes nrigrum_) leaves.