esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Analysis
### Prediction Results
#### 4.3.2 Prediction Results Based on Relatively Balanced Training Dataset
Obviously, the improvement values of the ensemble classifiers are smaller than that of the individual classifier, which also reflects the advantage of the ensemble classifiers in processing imbalanced datasets. * The _kappa_ coefficients of all classifiers increased, with the LR classifier showing an improvement value of 0.121 and XGBoost showing an improvement value of 0.023, which is the smallest. This phenomenon not only reflects the enhanced separability \begin{table}
\begin{tabular}{c c c} \hline \hline
**Classifier** & **PRE** & **REC** \\ \hline SVM & \(0.913\pm 0.0809\) & \(0.943\pm 0.0530\) \\ KNN & \(0.930\pm 0.0865\) & \(0.921\pm 0.0583\) \\ DT & \(0.947\pm 0.0484\) & \(0.901\pm 0.0285\) \\ LR & \(0.934\pm 0.0827\) & \(0.909\pm 0.0527\) \\ GBDT & \(0.938\pm 0.0557\) & \(0.937\pm 0.0364\) \\ RF & \(0.931\pm 0.0531\) & \(0.943\pm 0.0317\) \\ XGBoost & \(0.945\pm 0.0612\) & \(0.943\pm 0.0250\) \\ Stacking & \(0.955\pm 0.0399\) & \(0.955\pm 0.0326\) \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Prediction results based on relatively balanced training dataset. \begin{table}
\begin{tabular}{c c} \hline \hline
**Classifiers** & **Hyperparameters** \\ \hline SVM & Kernel = '_rbf'_, \(C=0.9\), \(g=0.5\) \\ KNN & \(k=6\), weights = distance \\ DT & Min sample split = 5, min sample leaf = 4, criterion = '_gini_' \\ LR & \(C=0.9\), penalty = '_l_'2', solver = '_sagg_' \\ \multirow{2}{*}{GBDT} & Learning rate = 0.1, n estimators = 110, max depth = 4, \\  & max features = 5, min sample split = 4, min sample leaf = 4 \\ \multirow{2}{*}{RF} & n estimators = 110, max depth = 6, max features = 5, \\  & min sample split = 6, min sample leaf = 6 \\ XGBoost & Learning rate = 0.1, n estimators = 110, max depth = 5, \\ \multirow{2}{*}{} & max features = 5, min sample split = 5, min sample leaf = 5 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Optimization results of hyperparameters based on relatively balanced training dataset.
## 3 Results
### Estimating Yield Variability from LAI and Meteorology Data through Inference of Crop N Status  
In investigating the sensitivity of LNA retrieval to climate and LAI data, we interchanged the meteorological data and LAI observational inputs between the two seasons and repeated the yield retrieval. The accuracy of the DALEC-Crop yield estimates was more dependent on the time-series of LAI in comparison to that of the meteorological data (Experiment 2; see Supplementary Material, Figure S3). Specifically, when interchanging the meteorological driving data (i.e., Experiment 2\({}_{\text{MET}}\)) the mean NRMSE between the recorded and modelled yield was increased from 10 to 14%. When interchanging the LAI observations (i.e., Experiment 2\({}_{\text{LAI}}\)), however, the error inflation was greater with the mean NRMSE increasing from 10 to 22%.
# Centrifuge Modelling of the Impact of Excavation with Partition Piles on Adjacent Existing Tunnel
## 2 Design of the Centrifuge Modelling
### Model Preparation
#### 2.2.3 Heavy Liquid Discharge System  
The discharge system for heavy liquids implemented in the conducted experiment was purposefully devised to simulate the excavation process. For the heavy liquid, zinc chloride (ZnCl\({}_{2}\)) was meticulously prepared, possessing an equivalent density to that of the sand used in the experiment. The system consisted of three main parts: the heavy liquid bag, the discharge pathway, and the collection box. The discharge pathway included the discharge pipe, electromagnetic valve, and the associated power supply. The heavy liquid bag was made by stacking silicone membranes that were 0.3 mm thick and had holes on the side for liquid discharge. The discharge conduit utilized in the experiment was crafted from FEP plastic tubing, featuring an internal diameter of 4 mm and an external diameter of 6 mm. The electromagnetic valve was a small DC24V, normally closed type, and was fixed on top of the collection box, connected to the power supply through slip rings. Considering the dimensions of the centrifuge and the model box, the collection box was made of PP material with internal dimensions of 80 mm \(\times\) 400 mm \(\times\) 100 mm. To ensure complete discharge of the heavy liquid, a rubber bend pipe was used. One extremity of the conduit was linked to a hollow screw, while the opposite end was affixed to the base of the bag. This setup utilized the siphon effect to completely discharge the heavy liquid. The overall sequence for heavy liquid discharge was as follows: heavy liquid bag \(\rightarrow\) discharge pipe \(\rightarrow\) electromagnetic valve \(\rightarrow\) collection box. The specific arrangement is shown in Figure 4.
## 3 Physical Model and Parameter Settings
### Vibration and Boundary Conditions  
The emphasis of this paper is on the wear of pipes by the coupling of particles and vibrations. Previous studies have been made on the wear of seawater pipes by different vibration frequencies and amplitudes [35]. This paper analyzes the effects of different velocities, particle diameters and rotational characteristics on pipeline wear under the same vibration levels. In terms of initial boundary conditions, when seawater and ice particles are mixed and pumped into seawater pipes by centrifugal pumps, the velocities of ice particles at the inlet are the same as that of seawater, so the initial velocities of solid and liquid phases are the same. The particles are assumed to be homogeneous spherical particles with no-slip on the pipe wall. The boundary condition settings in FLUENT are shown in Table 2.  
In order to ensure the reliability of the simulation process, the control variable method was used to set the parameters of each physical property. When the vibration was coupled with other factors, the vibration condition was set as f = 40 Hz and A = 1 mm (the results of [PERSON]'s study [35] on the influence of vibration amplitude and vibration frequency on pipeline wear were taken into consideration). The specific parameter values are shown in Table 3.
# Research on Improved OEE Measurement Method Based on the Multiproduct Production System
## 3 Methodology
### Design Ideas of MPSE  
Considering the characteristics of the multiproduct production system and the disadvantages of current OEE measurement methods, we conclude the design ideas based on the research in other fields, such as project management, programming, flexible manufacturing, and statistics. This heuristic method could match the requirements in the multiproduct production system and improve the current OEE measurement methods.  
1. Integration idea. The idea of integration has been widely adopted in many other research fields, such as business, programming, and so on. In the process of manufacturing, the idea of integration can be effectively used in multitasking project management, system design, and the like [48; 49; 50]. Similarly, in the multiproduct production system, the idea of integration is often applied in the manufacturing industry by solving multiple subproblems to achieve an overall goal, such as the capacity improvement and quality improvement [51; 52]. Therefore, based on the integration idea, we can simplify the measurement for whole system indicators by measuring several subsystem indicators, which is more accessible in the multiproduct production environment. Hence, the integration idea should be applied into the design of MPSE.
2. Dynamic idea. The dynamic disturbance is commonly found in production systems because of unpredictable real-time events. In the environment of the production system, the occurrence of some random internal and external noises, such as machine tool failure, new order entry, would cause the infeasibility of original optimal scheduling [53; 54; 55]. Thus, we have to adjust the scheduling plan dynamically. If we still adopt the static measurement method, the data we collect would not be accurate. Therefore, the dynamic idea is often used to solve these problems by designing a dynamic management system to adapt to the manufacturing environment's change, especially in the multiproduct production system [56; 57]. In addition, the dynamic idea can still support the dynamic measurement index of MPSE although there are some changes in the bottleneck, load time, and so on.
3. Standardization idea. In the statistical model, data should be standardized to eliminate the impact of dimension [58]. Similarly, the measurement indicators of different dimensions, which are caused by the different product's technologies, the different units of measurement for bottleneck velocity, and so on, should be converted in the same dimension since the data in manufacturing system may in different units, for example the output of product with different specifications. Therefore, the standardization idea should be used in MPSE to eliminate the negative effect caused by the variety of products in the multiproduct production system [59].
## 4 Evaluation and Discussion
### Time Analysis
#### 4.5.3 Embedding and Extraction Time Analysis  
The embedding process in this experiment is limited to the insertion of a secret image and excludes the generation of pixel distribution locations via the chaotic logistic map function and keystream shuffling through the Josephus permutation, as they have previously been performed separately. Similarly, the extraction process involves retrieving a secret image from a cover image using the LSB 3-3-2 embedding technique, with the pixel distribution locations retained without recalculation.  
In the same way as the previous time analyses, this analysis also comprises seven iterations, each involving the insertion and extraction of a secret image into and from a cover image. The average time taken across these seven iterations will yield aggregate results. The parameters and precision will also stay the same as those of the previous analysis.  
Table 15 demonstrates that the insertion and extraction times are solely contingent on the size of the secret image, remaining unaffected by the size of the cover image. Notably, there is a noteworthy escalation in the insertion and extraction times for varying secret image sizes.
## 2 UAV-Based Data Collections and Processing
### The GNSS-Reflectometry Sensor  
In this section, the original version of the GNSS-Reflectometry sensor used for data collection is described, then the modifications introduced to achieve the low-complexity version are explained. The complete details of the sensor were published in [48], however it is presented briefly here for sake of clarity of the experimental setup and the post-processing methodology.  
The original sensor was designed with the aim of detecting water surfaces and land water content. It is intended for small UAVs with weight of 3 kg and memory limit able to store 30 min of data collection. The sensor receives both direct and reflected GNSS sigmas using three separate antennas: (1) an up-looking RHCP antenna, (2) a down-looking LHCP antenna and (3) a down-looking RHCP antenna.  
The sensor features four synchronized RF channels connecting the direct signal and the LHCP reflected signal to a front end, while the RHCP reflected signal with the direct signal are connected to another front end. Both front-ends down convert the direct signals to baseband (BB), while the reflected signals are down converted to an intermediate frequency (IF) in sake of low noise levels for the already weak reflected signals. An embedded microprocessor controls the flow of the digital samples of all channels.  
In this work, in order to reduce the total mass of the payload, a simplified version of the sensor that embeds (1) the RHCP antenna for the direct signal and (2) the LHCP antenna for the reflected ray, was used.
## 3 Experiments and Discussion
### Experiments on VRM
#### 3.1.3 Computation of Pitching Moment Coefficient  
There exists a gap of longitudinal aerodynamic characteristics between the whole aircraft and the wing-tail combination. Thus, the incremental aerodynamic derivatives \(c_{L_{\text{W}}}\) and \(c_{m_{\text{W}}}\) as the supplement to existing aerodynamic model are developed in this paper. However, since the pitching moment change is mainly caused by the deflection of elevator or stabilizer, the computing results based on wing-tail combination should be comparable with authentic aerodynamic data. The modeling data of B737-800 aircraft provides longitudinal pitching moment changes caused by elevator and stabilizer deflections [34; 35]. According to the geometric parameters of wing and horizontal tail listed in ref [34], a nonplanar vortex ring model was built. Figure 10 shows the comparison results between computing results and the modeling data in three flight conditions. stands for the pitching moment change due to stabilizer deflection and \(c_{m\text{o}}\) represents the moment change due to elevator deflection. It should be noted that the flight condition \(M=0.76\), \(\alpha=2^{\circ}\), \(H=10\),\(0000\)\(m\) approximates the cruising flight condition of civil aviation aircraft.  
Figure 10: Comparison of computation and experimental pitching moment coefficients.  
Figure 9: Mean square error (MSE) of nonplanar and planar vortex ring method (VRM).  
Compared with modeling data, the MSE of \(c_{m\otimes stab}\) is 0.0045 and the MSE of \(c_{m\otimes e}\) is 0.0055. This shows that the pitching moment change caused by the deflection of stabilizer or elevator approximates the modeling data under the above flight conditions. Since the geometric boundary of the control surface approaches the boundary of each vortex ring, VRM can be used to accurately describe the pitching moment change.  
The experiments in Section 3.1 prove that the VRM has satisfactory computing accuracy--which provides a good basis for further estimation of aircraft acceleration and turbulence intensity. In particular, the accurate computation of pitching moment coefficient is significant to separate maneuvering flight effects on EDR estimation.
# Alginate Modification and Lectin-Conjugation Approach to Synthesize the Mucoadhesive Matrix
## 3 Alginate Application in Drug Delivery
### Alginate Ester  
Alginate ester is synthesized through esterification. Esterification is the earliest functionalization of alginate, and it is generally carried out in non-aqueous systems. This functionalization could perform to both available functional groups, either carbonyl or hydroxyl. An additional catalyst is needed for esterification of the hydroxyl groups for a more selective reaction [41].  
A full chemoselective carbonyl alginate ester was introduced with the use of tetrabutylammonium (TBA) salt of alginate [42]. This amphiphilic alginate is suggested for use as an amorphous solid dispersions (ASDs) matrix. The presence of alginate esters, ethyl, butyl, and benzyl are aimed at enhancing drug solubility. The introduction of butyl groups to alginate through esterification forms hydrophilic alginate without losing their gelation ability in the presence of calcium chloride [43]. By using the same functionalization method, a methyl alginate ester was synthesized and used as an excipient for direct compression in immediate drug-release tablet production [44]. Another study created a nanoparticle platform based on oleate alginate ester for curcumin delivery [45]. Alginate is active with formamide so it can actively react with methyl oleate; alginate ester is formed after 48 h of reaction, and the excess formamide can be removed with soxhlet apparatus.  
The attachment of the hydrophobic moieties on the alginate backbone creates a micelle carrier system. Such a system is one way to enhance the solubility of poorly water-soluble drugs and drug loading encapsulation, and is a controlled release mechanism [46]. However, further investigation is necessary to improve the solubility enhancement factor and to find a compatible way to facilitate ASD formation. Moreover, the development of a specific esterification method is needed to create a tunable hydrophobicity-hydrophilicity balance characteristic [42].
## 4 Evaluation Criteria and Experimental Results
### Automatic Labelling (SentiWordNet)
\begin{table}
\begin{tabular}{c c c c} \hline
**Domain** & **Aspect** & **LDA Model** & **SA-LDA Model** \\ \hline Movie & Act & (actor, show, play, character, art, life, movie, appear, sound, only) & (performance, act, actor, character, play, actress, part, scene, role, do) \\ Movie & Plot & (story, play, series, know, role, line, set, sound, voice,say) & (story, series, play, script, sequence, role, scenario, line, do, text) \\  & Soundtrack & (music, song, sound, great, play, musical, product,show,story,movie) & (music, sound, audio, song, show, effect, playlist, soundtrack, play, movie) \\ \hline Restaurant & Food & (food, restaurant, menu, good, chicken, dishes, street, visit, dinner, service) & (restaurant, menu, food, taste, appetizer, dinner, dishes, view, cook, flavor) \\ Restaurant & Service & (staff, manager, ask, service, food, said,friendly,restaurant,told,eat) & (service, staff, order, internet, view, location, delivery, parking, menu, seating) \\ Price & Price & (price, card, feel, money, charged, payment, cheap, night, really, like) & (price, cost, payment, offer, card, discount, pay, bill, charge, worth) \\ \hline \multirow{4}{*}{Domestic Saudi Airlines} & \multirow{2}{*}{Flight} & (ticket,check, & (flight, seat, meal, internet, food, entertainment, movie, clean, drink, ticket) \\  & & flight, bad,lounge,schedule,time,only, food,hour) & (flight, seat, meal, internet, food, entertainment, movie, clean, drink, ticket) \\  & & (staff, desk, check, & (ticket, service, lounge, flight, baggage, schedule, staff, upgrade, offer, punctuality) \\  & & Staff & (staff, friendly, service, direct, talk,helpful,front,desk,time, late) & (staff, service, facility, schedule, time, crew, ticket, pilot, flight,plane) \\ \hline \end{tabular}
\end{table} 
Table 3: Comparison between the proposed topic modelling results and the based topic modelling algorithm.
# A Heterogeneous Battlefield Situation Information Sharing Method Based on Content
## 4 Construction of the Unified Battlefield Situation Information Space  
The unified battlefield situation information space (UBSIS) is the virtual space constituted by the information of each combat information system, that is the information space obtained after the entity description information in each system is described consistently and stored centrally. The internal description information about entities is stored in the XML documents in an entity oriented manner, and the external information about entities is stored in the semantic relationship matrix. In the process of constructing and updating the UBSIS, the information describing the same entity needs to be aggregated so as to eliminate information redundancy effectively. The way of realizing the aggregation of the internal and external description information about entities is introduced below, respectively.
# Improved Defect Detection Using Adaptive Leaky NLMS Filter in Guided-Wave Testing of Pipelines
## 5 Results
### Results Comparison  
The performance of the algorithm was assessed in experimental trials by detecting defects of CSA sizes between (and including) 3% and 8%. It is expected that the algorithm would be more effective at lower frequencies since they are more dispersive; furthermore, the excitation power of lower frequencies is higher than those of higher frequencies, both due to the duration of the signals as well as the systems transfer function. Therefore, frequencies between 30 to 50 kHz with a step of 2 kHz were tested. The SNR achieved from the general propagation routine is demonstrated in Appendix B. The peak SNR is always achieved using 40 kHz excitation frequency where the SNR decreases by moving toward either side of that frequency. Furthermore, the SNRs achieved from each frequency are reduced almost linearly when the defect size is decreased; thus, the noise energy is almost constant while the defect energy is decreasing.  
Figure 13: Comparison of the result achieved from NLMS (black) vs leaky LMS (orange) where (**a**) shows the time-domain results and (**b–f**) show the normalized magnitudes of filter order 1 to 5, respectively.
# Measurement of Bridge Vibration by UAVs Combined with CNN and KLT Optical-Flow Method
## 2 Methods
### Convolutional Neural Networks  
The false displacement caused by UAV measurement can be removed by a CNN. As a feedforward neural network, each neuron of the CNN only extracts the local information of the input data, and the information are collected at a higher level of the network to obtain the global information [34]. The complexity of the model is reduced by weight sharing [35], which accelerates the computing speed and improves the calculation accuracy. The following is an introduction to the function layer involved in this paper.  
The convolution layer is the core of the whole CNN, and the convolution process is shown in Figure 5. Suppose that there is a \(2\times 2\) convolution kernel, the sub area consistent with the size of convolution kernel is found in the input data. An element of the new matrix can be obtained by multiplying and summing each corresponding element in the sub area and convolution kernel, and a new matrix can be generated by navigating all of the input data in steps according to the above method.  
The feature-extraction ability of the network and the approximation ability of complex functions can be enhanced by activation-function layer. The activation function used in this paper is Leaky Relu, and its expression is as follows:  
\[f(x)=\left\{\begin{array}{ll}x,&x\geq 0\\ scale\times x,&x<0\end{array}\right.. \tag{10}\]  
The regression layer generally deals with regression problems, and its loss function is expressed as follows:  
\[J=\frac{1}{2}\sum_{i=1}^{R}\frac{(t_{i}-y_{i})^{2}}{R}. \tag{11}\]  
where \(R\) is the number of samples, \(t_{i}\) is the target output, and \(y_{i}\) is the prediction output. The value of \(J\) will decrease with the training of the network until it converges to the target value.  
The network used in this paper is a deep-convolution neural network including input layer, convolution layer, activation-function layer, full-connection layer, normalization layer, and output layer. The architecture of the CNN is shown in Figure 6.  
Figure 5: Convolution process.