esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
We consider that collaborative search based on multi-sonar system is a dynamic process with real-time changes in relative position. Traditional efficiency evaluation methods are limited to static modeling, which lacks dynamic analysis and adaptive adjustment capability, and cannot cope with the dynamic situations with real-time changes. In addition, due to the confidence level of sonar detection probability, we consider that multi-sensor detection information fusion is also in a \"dynamic\" state of change, that is, a high detection probability should be given high confidence and high weight. To solve this problem, in this paper, we introduce the Monte Carlo sampling concept to establish a collaborative search efficiency evaluation model of multi-sonar system based on information fusion and dynamic deduction under uncertain target situations. To quantify the advantages of adaptive weight allocation, we compare it with the average weighting fusion method. According to the simulation results, the variance decreases from 0.01 to 0.0071, which proves that the proposed information fusion method has better stability. To further determine whether the mean of the detection probability has statistical significance, we design the one-sample \(t\)-test method. The simulation results show that at the a = 0.05 level, the null hypothesis \(H_{0}\) is rejected, and the mean of the detection probability is significantly different from the random probability of 0.5, indicating its statistical significance. Moreover, through simulation case analysis, we evaluate the collaborative search efficiency of multi-sonar system in fully-passive and multi-base working modes, and compare the impact of each sonar on the overall detection capability, which verifies the effectiveness of the proposed method. This work can provide strong support for the development of collaborative search schemes for multi-sonar system and a theoretical basis for their practical application.  
Conceptualization, S.W., W.C., Z.L. and X.C.; methodology, S.W., W.C., Z.L. and X.C.; software, S.W.; data curation, S.W., W.C., Z.L. and X.C.; writing--original draft preparation, S.W., W.C., Z.L. and X.C.; writing--review and editing, S.W., W.C., Z.L. and X.C.; visualization, S.W., W.C., Z.L. and X.C. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Natural Science Foundation of China (Grant No. 62201605).  
Not applicable.  
The data presented in this study are available on request from the corresponding author. The data are not publicly available due to confidentiality agreement.  
The authors declare no conflicts of interest.
# Predicting the Risk of Chronic Kidney Disease (CKD) Using Machine Learning Algorithm
## 3 Proposed Methodology
### Undersampling for Data Balancing  
#### 3.2.1 Extremely Unbalanced Data  
The distribution of target variable of creatinine is shown in Figure 5. Most of the samples are concentrated near the median, and the number of samples away from the median is very less. A machine learning model will fit better on the region with more samples and perform worse around the region where data is less. In another words, the confidence interval of prediction accuracy will be wider where the samples are less.  
For general regression tasks, the problem of unbalanced data can be ignored. If an interval contains less samples, we can say that the samples in that range are outliers. However, for this task, it is the opposite. Our target is to correctly predict those people who have a high creatinine value, though we have a few data for that range. From Figure 5, we observe that most samples are between 0.5 and 1.4. If we use the whole dataset to train a machine learning model, as the training algorithm will try to minimize the sum error for the whole dataset, samples with high creatinine value where data is less will be ignored.  
In order to show the impact of imbalance problem on regression more explicitly, we performed a simple experiment using XGBoost with Minimum Square Error (MSE) as loss function. The result is shown in Figure 6, where the \(y\)-axis represents the ground truth and the \(x\)-axis is the predicted value. We observed that the model failed to predict for the interval with less training samples.  
Generally speaking, the problem occurs because the number of samples with a high creatinine value is not enough for the machine learning model to learn from them. The data imbalance problem usually can be solved by data level methods and model level methods [19]. Oversampling is the most common data level method. However, this data is obviously not suitable for oversampling. Over-sampling refers to balance data distribution by resampling or generating new data from low number of available ones. In this problem, high creatinine data is extremely low. Oversampling will cause noise in the rare data to be amplified countless times. We used undersampling method and proposed a cost-sensitive mean-squared error (MSE) loss function to deal with this problem.
## 2 Materials and Methods
### Evaluation of Torque and Vertical Force  
Twenty J-shaped simulated resin root canal models (Endo Training Bloc, Dentsply Sirona) were used. Coronal flaring was performed to a depth of 4 mm from the apex with a NiTi rotary instrument (ProPaper SX, Dentsply Sirona, Ballagues, Switzerland), and the glide path was established manually with #10, #15, and #20 stainless steel K-files ([PERSON], Munich, Germany). The models were then randomly assigned to two test instruments, i.e., JZ and non-handed JZ (\(n=10\) each).  
Each model was instrumented using an automated root canal instrumentation device, and torque and force generated during the instrumentation were monitored with a torque/force measuring unit attached to the root canal instrumentation device (Figure 2), as described previously [12]. Briefly, the device consisted of a test stand with a moving stage (MX2-500N; IMADA, Aichi, Japan), and the torque/force measuring unit consisting of a load cell (LUX-B-ID; Kyowa, Tokyo, Japan; rated capacity, \(\pm\)50 N; nonlinearity, within \(\pm\)0.15% of rated output) and strain gauges (KFG-2-120-D31-11; Kyowa; foil type; strain limits at room temperature, approximately 5%) was connected to the model to measure the apical/coronal vertical loads and clockwise torque, respectively. A liner correlation has been confirmed between the distortion values measured by the strain gauges and torque values [12]. An endodontic motor ([PERSON], Kyoto, Japan) attached to the moving stage of the stand was programmed to repeat an up-and-down movement at 50 mm/min for 2 and 1 s in the apical and coronal directions, respectively [10]. The maximum torque and force values were evaluated.  
Instrumentation was performed using a two-instrument sequence following the manufacturers' recommendations for curved canals, and size #25/0.04 and #25/0.06 taper instruments were used sequentially at a rotational speed of 500 rpm. In the first sequence (#25/0.04 taper), the canal was instrumented to 2 mm short of the apex and then to the full working length. In the second sequence (#25/0.06 taper), the canal was instrumented sequentially to 2 and 1 mm short of the apex and finally to the full working length. As a lubricant, RC-Prep (Premier, Plymouth Meeting, PA, USA) was used [9]. Before each instrument change, canal patency was checked with a #10 K-file followed by canal irrigation with 1 mL of distilled water. All instruments used for instrumentation were examined with a surgical operating microscope (OPMI pico, Carl Zeiss, Gottingen, Germany) at \(\times\)21.3 magnification for any possible defect or distortion (plastic deformation).
###### Abstract  
A novel analytical solution for the designing of the birdcage RF coil has been demonstrated in this paper. A new concept of dominant resonance path has been introduced in this paper which is used to identify the specific closed current loop in the birdcage RF coil which is responsible for the dominant resonance frequency mode. This concept is used to determine the precise numerical values of the lumped capacitance deployed in the legs and/or end-rings of the birdcage RF coil for its proper operation at the desired resonance frequency. The analytical solution presented in this paper has been established by performing the two-port network based equivalent circuit modeling of the birdcage RF coil. The proposed analytical solution uses T-matrix theory and develops a relationship between the input impedance of the birdcage coil and the impedances of its leg and end-ring segments. The proposed analytical solution provides the information about the resonance frequency spectrum of the birdcage RF coil and solves the issue of its interfacing with external circuits without affecting its resonance characteristics. Based upon the proposed analysis and designing strategy presented in this paper, the low pass, high pass and band pass configurations of the birdcage RF coil were successfully implemented with FPCB (Flexible Printed Circuit board) technique for small volume NMR imaging applications at 1.5 T and 3.0 T MRI system. The results obtained for the implemented birdcage coils using the proposed analysis and designing technique are in closed agreement with already established methods.  
MRI system; birdcage coil; birdcage configurations; coil capacitance; analytical solution; equivalent circuit modelling; T-matrix theory; 3D-EM simulation; small volume RF coil +
Footnote †: journal: Applied Physics Letters B
# Rogue Node Detection Based on a Fog Network Utilizing Parked Vehicles
## 1 Introduction
(3) To detect rogue nodes effectively, a machine learning method is firstly used to classify the moving vehicles according to the average moving speed of the vehicle. Then, a novel U-test rogue node detection method, which is performed by fog nodes, is proposed to further discover the bad nodes.
## 3 Results  
Regarding the VJZE-RMS, there is a clear trend in its median values, decreasing from the early proliferative phase to the late proliferative phase (from (247(91) \(\mu\)V to 158(252) \(\mu\)V)), where it reached its minimum value. During the early luteal phase, an upward trend was observed (from (158(252) \(\mu\)V to 374(397) \(\mu\)V, \(p<0.05\))), reaching its highest value during the late luteal phase (from (374(397) \(\mu\)V to 477(304) \(\mu\)V, \(p<0.05\), when compared to the late proliferative phase). The fJZE-mean clearly shows exactly the opposite trend, increasing from 2.5(0.54) cycles/min during the early proliferative phase to 2.96(0.97) cycles/min during the late proliferative phase, reaching its maximum value. During the early luteal phase, a downward trend was observed (from 2.96(0.97) cycles/min to 2.37(1.05) cycles/min), continuing to fall to its lowest value of 1.33(0.55) cycles/min in the late luteal phase, with \(p<0.05\) when compared to the previous three phases. Boxplots of the derived features across the menstrual cycle are shown in Figure 2.  
The VJZE-RMS divided by a factor of ten closely follows the Intrauterine Pressure (IUP) values, creating an electrically derived IUP index. The same situation applies between the uterine contractility shown both with an IUP device and with US and the fJZE-mean, without the need to multiply or divide with a constant factor, for three of the four phases of the menstrual cycle, creating an electrically derived uterine contractility index. The comparison bar plots are shown in Figure 3.  
Figure 3: Comparison bar plots across the menstrual cycle (**a**) mechanically and electrically derived intrauterine pressure between ElectroUteroGraph (EUG) and Intrauterine Pressure (IUP) device and (**b**) mechanically and electrically derived uterine contractility between ElectroUteroGraph (EUG), Ultrasound (US) and Intrauterine Pressure (IUP) device.  
Figure 2: Boxplots of the derived features (**a**) Root-Mean-Square Voltage-Amplitude of JZE’s Electrical Activity and (**b**) Mean Frequency of JZE’s Electrical Activity, across the menstrual cycle.  
The EUG demonstrated the detection of changes in the peristaltic activity among the phases of the menstrual cycle that coincide with the hormonal changes of estrogen and progesterone. A qualitative representation comparing the measured electrical activity using the EUG and the change in the hormonal levels across the menstrual cycle is shown in Figure 4.
## 3 Results and Discussion
### Partial Least Squares (PLS) Regression  
Table S1 presents the R\({}^{2}\), RMSE, and RPD values for calibration and validation sets of the PLS regression model applied to the full dataset. This model effectively predicted the quality properties of red, yellow, and orange varieties, achieving R\({}^{2}\) values exceeding 0.83, 0.58, and 0.73, respectively. These results demonstrate the robust predictive capability of the PLS regression model for all quality parameters across the three varieties. Previous studies have successfully employed NIR spectroscopy for non-destructive assessment of an apple's internal quality. Partial least squares regression (PLSR) models have been developed and validated to predict internal quality attributes, such as total soluble solids (TSSs) and titratable acidity (TA), using external test samples under various conditions [59; 60].  
Near-infrared spectroscopy (NIRS), combined with partial least squares (PLS) analysis, was used to non-destructively assess antioxidant activity and water content in red pepper powder [27]. The PLS model was developed using Unscrambler X version 11.0 software. Results indicate that the PLS method is precise and holds promise for quantifying antioxidant activity and water content in red pepper powder samples [27].
## 3 Trail-Based Search Strategy  
### Problem Statement  
In this paper, we consider the wireless sensor and actor network which has the following properties.  
* The network area is a square with side length \(H\). There are \(N_{S}\) static sensor nodes deployed in the network, and one mobile actor moves in the network to perform actions. The mobility of the actor follows Random Waypoint model.
* The transmission range of both the sensor nodes and the actor is denoted as \(R\), and the connectivity of the network is guaranteed with given \(R\).
* Two sensor nodes \(i\) and \(j\) are _neighbors_ if \(d_{i,j}\leq R\). We define _neighbor table_, denoted as \(N(i)\), as the set of nodes in the node \(i\)'s transmission range. Each sensor node in the area obtains a neighbor table when the network is initialized.
* The time is divided into slots.  
In the network stated above, we consider the problem of searching the mobile actor. When a sensor node detects an event, it becomes a _source_ and generates a _search packet_ which includes an event report. Generally, the actors' mobility pattern is not available to the sensor nodes. Therefore, the search packet has to be forwarded to the mobile actor without its location information.  
Our goal is to design a search strategy that exploits the trail information of the actor to improve the search efficiency. In the rest of this section, a distributed mechanism is firstly proposed to maintain the actor's trail with low overhead. Then we introduce the details of trail-based search (TS) strategy which is composed of two phases: the search phase and the chase phase.
# An Efficient and Fast Hybrid GWO-JAYA Algorithm for Design Optimization
## 3 Test Problems and Optimization Results
### Optimal Crashworthiness Design of a Vehicle Subject to Side Impact
The 10 inequality constraint functions stated by Equation (27) define limitations on the force transferred to the dummy's abdomen (\(G_{1}\)), velocities of the dummy's upper/middle/lower chest (respectively, \(G_{2}\), \(G_{3}\), and \(G_{4}\)), deflections of the dummy's upper/middle/lower ribs (respectively, \(G_{5}\), \(G_{6}\), and \(G_{7}\)), force transferred to the dummy's cubic symphysis (\(G_{8}\)), velocity at B-pillar middle-point (\(G_{9}\)), and velocity at B-pillar front door (\(G_{10}\)). Forces are expressed in kN, velocities in mm/s, and deflections in mm. The side constraints on optimization variables are as follows: \(0.5\leq\chi_{1}\),\(\chi_{2}\),\(\chi_{3}\),\(\chi_{4}\),\(\chi_{5}\),\(\chi_{6}\),\(\chi_{7}\)\(\leq 1.5\) mm (continuous variables); \(\chi_{8}\),\(\chi_{9}\)\(\in\){0.192;0.345} (discrete variables); \(-30\leq\chi_{10}\),\(\chi_{11}\)\(\leq 30\) mm (continuous variables). Figure 13: (**a**) Finite element model of vehicle subject to side impact (taken from [108]); (**b**) schematic of vehicle parts to be optimized. The best solution available in the literature for the optimal crashworthiness problem is that obtained with the multi-strategy fusion improved gray wolf optimization (IGWO) algorithm with a structural weight of 21.39473 kg [69]. In particular, IGWO outperformed the chaotic GWO, CSO, and tunicate swarm algorithm (TSA) that converged to optimized structural weights between 21.46164 and 22.70296 kg. The adaptive dynamic self-learning grey wolf optimization algorithm (ASGWO) [70] obtained a slightly higher optimized weight than TSA, 22.87188 kg. The starling murmuration optimizer (SMO) of Ref. [47] converged to the optimized weight of 22.84298 kg, practically the same as the improved continuous ant colony optimization algorithm (LIACO\({}_{\text{R}}\)) that obtained 22.84299 kg. The other metaheuristic algorithms tested in [47] (i.e., the krill herd algorithm (KH), the best variant of artificial bee colony (ABC), the Harris hawks optimizer (HHO), the comprehensive learning particle swarm optimizer (CLPSO), and the whale optimization algorithm (WOA)) found optimized weights ranging between 22.88596 and 23.12717 kg. Table 7 compares the optimal solution obtained with the FHGWJA algorithm developed in this study with those of the aforementioned optimizers. The results are grouped as follows: (i) FHGWJA and its component algorithms, the standard GWO and JAYA; (ii) IGWO and its three best competitors compared in [69]; (iii) ASGWO [70]; (iv) SMO and its six best competitors compared in [47]. All designs reported in the table are practically feasible as the maximum constraint violation was 0.001%. Details on computational cost and statistical performance for independent optimization runs are reported below the table when available. It can be seen that FHGWJA again was the best optimizer also for this test case.
## 6 Discussion
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Name**} & \multirow{2}{*}{**Models**} & \multicolumn{4}{c}{**RMSE**} \\ \cline{2-5}  & & **Short** & **Tall Grass** & **People** \\  & & **Grass** & **\& **Weed** & **Blockage** \\ \hline Optimized & \(AITU-R_{\mathit{foliuge}}+PL_{\mathit{PlameEarth}}=\) & & & & \\ FITU-R NG [17] & \(0.2\ f^{0.3}d^{0.6}+(40\log d)-(20\ log\ ht)-(20\ log\ hr)\) & 25.2 & 27.7 & 24.7 \\ \hline FITU-R MA & \(PL_{MA-ITU-R}(dB)=\) & 4.3 & 10.9 & 8.1 \\ FSPL [31] & \(A_{M}\Big{(}1-e^{-Rd/A_{M}}\Big{)}+32.44+20\ log\ d+20\ log\ f\) & & & \\ \hline One Slope PL & \(PL_{\mathit{LOS}(433 MHz)}=PL_{0}+10\mathit{nlog}(d)\) & 5.7 & 11.8 & 8.3 \\ LOS, 433 MHz [19] & (\(n=2.37\) for SF7 and BW 125 kHz) & & & \\ \hline Proposed & - short grass: \(RSSI_{1}(d)=-53.79-19.42 log(d)\); \(d<\) & & & \\ \(\mathit{d_{bp}}\) & 4.6 & 11.4 & 8.0 \\ (Based on & - tall grass: \(RSSI_{1}(d)=-56.04-18.37 log(d)\); \(d<d_{bp}\) & 4.4 & 10.9 & 7.8 \\ measurement) & \(RSSI_{2}(d)=-43.76-26.95 log(d)\); \(d>d_{bp}\) & & & \\ \hline Proposed & \(RSSI(d)=[-55.4,\ -18.2]+[-16.2,\ 0.2]log(d)\), \(d<d_{bp}\) & & & \\ (MBFLR) & \(RSSI(d)=[-42.9,\ -22.5]+[-26.8,\ 4.9]log(d)\), \(d>d_{bp}\) & 0.8 & 1.2 & 2.6 \\ \(\mu_{A1}=0.4\) & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Statistical evaluation for each path loss model using RMSE. Figure 14: Comparisons between the proposed model and the other models.
## 3 Conceptual Models and Numerical Considerations  
The study aims to characterize the SGI behavior in the PPGB and quantify the influence of such interactions induced by the natural and anthropogenic activities. There were selected components in the water balance to assess the variations of interaction dynamics in dry and wet seasons. The study conducted model calibration and validation by using the data from January 2015 to December 2017. More specifically, the data in 2015 were for the model calibration, while the data in 2016 and 2017 were for the model validation.  
Figure 3 shows the workflow of the study for the GSFLOW simulations. In the study, the preparation of computational parameters for the MODFLOW-2005 model is based on the software environment Groundwater Modeling System (GMS). The calibration and validation processes of the groundwater model were conducted first with the GMS interface. With the cell-based input parameters of MODFLOW-2005 prepared by the GMS, the MODFLOW-2005 input files were then integrated with the PRMS input files for the GSFLOW simulations. Note that the calibration of the MODFLOW relied on the observations from the groundwater monitoring network, while the calibration and validation of the PRMS model used data from the streamflow stations. In the study, the monthly time-step of MODFLOW-2005 could be converted to the daily time-step in GSFLOW simulation by using a variable TSMULT in the MODFLOW-2005 in the GSFLOW simulation. In a typical MODFLOW simulation, TSMULT is the multiplier for the length of successive time steps. However, for a GSFLOW simulation, the parameter TSMULT must be set to a value of 1. The number of time steps in a stress period and the length of a stress period must be specified  
such that the time step length is equal to one day and the period length is one day. Iterative procedures between MODFLOW-2005 and PRMS are required to adjust the parameters in both the MODFLOW and PRMS models. Following the report of [PERSON] et al. (2008), the GSFLOW model was calibrated using the independent MODFLOW-2005 and PRMS models by running these models separately [11]. In the study, similar processes were conducted to calibrate and validate the GSFLOW model. The calibration and validation of the GSFLOW model then focus on the best-fit of streamflow at the monitoring stations Li-lin Bridge and Xing She Da Qiao. During the calibration of the GSFLOW model, the parameters in the PRMS model might need further adjustment to obtain the best-fit of the streamflow. Note that the assessments of the interactions between surface water and groundwater used the daily flow rates obtained from the GSFLOW model. The following sections briefly describe the conceptual model and the numerical considerations for scenarios used for quantifying the behavior of SGIs for the full PPGB, upstream, and downstream fan models.