esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion  
### Effects of Electrolyte Concentration  
The electrolyte concentration of NaCl directly influenced the resistance between the electrodes and changed the output current [30]. In the EC process, increasing the electrolyte concentration could increase the phosphate removal efficiency and reduce the electrolysis operation time. The aluminum-air fuel cell system's phosphate removal performance was monitored with different concentrations of NaCl ranging from 10 mmol/L to 2 mmol/L with a resistor of 10 \(\Omega\). With the decrease in the electrolyte concentration, the phosphate removal rate in the aluminum-air fuel cell showed a downward trend, and the required reaction time was prolonged. The phosphate removal in the aluminum-air fuel cell containing 8 mmol/L of NaCl was slightly lower than that of the cell containing 10 mmol/L of NaCl. When the NaCl concentration decreased to 2 mmol/L, the phosphate removal reached 97.8%, 93.3%, 91.8%, and 86.9% at 60 min, respectively (Figure 2a). White flocculent precipitates were observed with different operating concentrations of electrolytes. At 10 mmol/L of NaCl, the phosphate removal was 98.0% at 50 min, and the rate of removal decreased with longer operation times, which demonstrated that the operation time could be reduced with a higher electrolyte concentration. When the NaCl concentration was decreased from 10 mmol/L to 2 mmol/L, the average output current density of the aluminum-air fuel cell decreased from 0.20 mA/cm\({}^{2}\) to 0.06 mA/cm\({}^{2}\) (Figure 2b). The low conductivity of the solution led to an increase in the internal resistance of the fuel cell, slowing down the mass transfer in solution and the dissolution rate of the aluminum anode [24]. When the NaCl concentration declined from 10 mmol/L to 2 mmol/L, the calculated dissolutions of aluminum ions were 33.7 mg/L, 32.2 mg/L, 20.3 mg/L, and 9.5 mg/L, respectively. A higher conductivity electrolyte increased the anode dissolution and produced larger amounts of aluminum hydroxides for complex precipitation reactions in the solution; thus, a higher electrolyte concentration was favorable for phosphate removal [20]. In a traditional EC system, an increase in the electrolyte concentration can improve energy consumption, leading to an increase in the EC operating costs. On the contrary, a higher electrolyte concentration will generate more electricity for the aluminum-air fuel cell.  
To analyze the influence of electrolyte concentrations on the electrochemical performance of the aluminum-air fuel cell, polarization curves and electrode potentials were tested with different electrolyte concentrations. As the electrolyte conductivity decreased, the maximum power density of the aluminum-air fuel cell reduced from 265.7 mW/m\({}^{2}\) to 76.6 mW/m\({}^{2}\) (Figure 3a). The electrode potentials of the aluminum anode and the air cathode both decreased with a decline in conductivity (Figure 3b). With a higher electrolyte concentration, more aluminum ions dissolved, generating a higher current output.
# Influence of Baffle Location on the Diodicity of Tesla Valves
## 2 Problem Setup  
### Geometric Model and Design Proposal  
Figure 1 illustrates the geometric dimensions of the TV utilized in this study. Optimization cases incorporating baffles are depicted in Figures 2 and 3. Baffles are strategically placed on the upper and lower walls of the inlet and outlet channels, respectively, and are indicated by dashed-line circles in the figures. The design parameters encompass baffle thickness, baffle height, and the distance from the baffle to the channel bifurcation. The baffle parameters specified in this study are as follows: the baffle width is \(w\) = 0.1 mm, the baffle height is \(h\) = 1 mm, and the distance from the baffle to the channel bifurcation is \(d\) = 1.25 mm.
# SEHIDS: Self Evolving Host-Based Intrusion Detection System for IoT Networks
## 4 Methods
### Problem Formulation and High Level Abstraction for the Proposed Model
Another important criterion that needs to be carefully specified is the learning scheme used for training SEHIDS during its evolution. Training the entire model after adding a new layer can aid the model in encoding all possible cases in the model's parameter space, but this may make it less sensitive to the temporal variations in traffic during the node's lifetime and overburden the resources of the devices. To overcome the above challenges, several approaches and techniques have been devised to develop SEHIDS. First, the conditions for adding a new layer are set based on the divergence between the errors produced by the model due to the new input and the residual errors produced by the model so far. This makes these conditions flexible for varying the learning capacity with respect to their true outcomes and avoids the shortcomings associated with setting mild and hard conditions for adding a new layer. Second, the SEHIDS can add the new layers as a hidden layer and adjust the number of neurons to the number of remaining parameters to achieve the optimal degrees of freedom. This gives the SEHIDS the tremendous ability to consolidate its acquired knowledge without expanding its hypothesis space beyond what is required or overburdening the resources of the devices. Finally, the principles of the cascade correlation learning algorithm [29] are used as a means to train the model. This algorithm was chosen because it can use the knowledge gained from training one variant of the same model for subsequent variants without having to start the entire training process from scratch or even train the entire layers of the model. This is hugely important for the SEHIDS model, as its structure changes over time and the training patterns are scattered over irregular intervals (e.g., intended traffic is received incrementally over the lifetime of a node). In addition, limiting the training of the model to the newly added layers can reduce the resource requirements of the model, which is also critical for IoT devices. To visually illustrate this approach, Figure 2 shows the high-level abstraction of the evolution of the SEHIDS architecture. Figure 2a shows the SEHIDS architecture at time \(t=1\), i.e., at the time when the marked node joins the IoT network, at this time, the SEHIDS comprises two layers: Input and Output, with no hidden layer. Figure 2b, on the other hand, shows SEHIDS at time \(t\) when a new hidden layer has been added to the SEHIDS architecture.
# Modulus Stretch-Based Circular SAR Imaging with Contour Thinning
## 4 Conclusions  
This paper presents a contour thinning imaging method. Based on the backprojection algorithm, the method performs modulus stretch on each sub-aperture image to make the target's perimeter area ratio larger and the target contour thinner and clearer. The experimental results using the Goctha dataset show that the contour of the image after modulus stretch using piecewise linear transformation is thinner and clearer than the original. The degree of contour thinning is 134% higher than the original average. Based on this method, the target classification algorithm based on contour thinning imaging will be further studied.  
Figure 11: Comparison of contour thinning degree between traditional backprojection imaging and modulus stretch imaging: (**a**) 5\({}^{o}\) aperture imaging; (**b**) 10\({}^{o}\) aperture imaging; (**c**) Full aperture imaging.  
Figure 10: The degree of contour thinning varies with threshold \(T\) on different models: (**a**) Chevrolet Impala LT; (**b**) Mitsubishi Galant ES; (**c**) Toyota Highlander; (**d**) Chevrolet HHR LT.  
**Author Contributions:** Conceptualization, R.H.; Formal analysis, R.H. and K.Z.; Funding acquisition, Z.P.; Investigation, R.H. and K.Z.; Methodology, R.H.; Project administration, Z.P.; Software, R.H.; Supervision, Z.P.; Visualization, R.H.; Writing--original draft, R.H.; Writing--review and editing, Z.P.  
**Funding:** This work is supported by National Natural Science Foundation of China (61571096, 61775030), the Key Laboratory Fund of Beam Control, Chinese Academy of Sciences (2017 LBC003) and Sichuan Science and Technology Program (2019 YJ0167).  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Design of a Novel Linear Shaped Charge and Factors Influencing its Penetration Performance
## 4 Results
### Numerical Simulation Results  
Typical jet formation and penetration processes of an LSC and BLSC are shown in Figure 9. After the explosion, the copper liner elements gradually moved to the symmetry plane and collapsed under the action of the detonation wave. When the copper liner elements arrived at the collision point, the velocity of the outer liner elements was lower than that of the inner liner elements; hence, the outer liner elements moved to the left of the collision point and formed a slug. At the same time, the inner liner elements moved to the right of the collision point and formed a jet. The remaining copper liner elements, which did not arrive at the collision point, forming an alae. Therefore, the colliding liner elements were divided into three parts: jet, slug, and alae.  
The penetration depths, as well as the DOP test results for the LSC and BLSC, are graphically illustrated in Figure 8, and Figure 9 shows the typical numerical results of jet formation and penetration processes described by Autodyn. Furthermore, Table 3 shows the good agreement between the experiment and numerical simulation outputs. These indicate that the adopted numerical constitutive models and corresponding parameters are able to give reliable predictions of penetration performance of LSCs and BLSCs.  
Figure 8: Penetration depth of shaped charges: (**a**) LSC; (**b**) BLSC.  
Figure 7: Experimental results of shaped charges: (**a**) LSC; (**b**) BLSC.
# Analytical-Numerical Model for Temperature Prediction of a Serpentine Belt Drive System
## 2 Thermal Model
### Establishment of Thermal Model
This heat is also strongly related to the ambient temperature, belt geometry, speed, and other parameters, which can be simply represented by a function, as follows:  
\[\sum\limits_{n=1}^{N}{(1-\xi_{n})P_{fn}+P_{h}}=k(\mathbf{T_{b}},\mathbf{D_{b}},V_{b},\Lambda_{b},T_{a},\cdots) \tag{4}\]  
where the belt temperature plots belong to the matrix \(\mathbf{T_{b}}\), belt geometry dimensions are stored in the matrix \(\mathbf{D_{b}}\), belt velocity can be represented as \(V_{b}\), and the material thermal conductivity is \(\Lambda_{b}\). \(N\) is the total number of pulleys in the belt drive system. By combining Equations (2)-(4), a simple thermal model can be established, as follows:  
\[\left\{ \begin{array}{c}\xi_{1}P_{f1}=f(\mathbf{T_{p1}},\mathbf{D_{p1}}, \omega_{p1},\Lambda_{p1},h_{p1},T_{a},\cdots)\\ \vdots\\ \xi_{n}P_{fn}=f(\mathbf{T_{pn}},\mathbf{D_{pn}},\omega_{pn},\Lambda_{pn},h_{ pn},T_{a},\cdots)\\ \xi_{1}P_{f1}=g(\mathbf{T_{p1}},T_{b},P_{f1})\\ \vdots\\ \xi_{n}P_{fn}=g(\mathbf{T_{pn}},T_{b},P_{fn})\\ \sum\limits_{n=1}^{N}{(1-\xi_{n})P_{fn}+P_{h}}=k(\mathbf{T_{b}},\mathbf{D_{b}},V_{b},\Lambda_{b},h_{b},T_{a},\cdots)\end{array} \right. \tag{5}\]  
The subsequent subsections describe the analysis of the thermal flows inside the components and belt-pulley heat exchanges to provide clear information regarding the functions \(f(\mathbf{T_{pn}},\,\ldots\,)\), \(g(\mathbf{T_{pn}},\,\ldots\,)\) and \(k(\mathbf{T_{b}},\,\ldots\,)\) and solve this set of equations.
## 6 Conclusions
### Limitations  
* The model assumes fixed active power factors, and reactive power support was excluded. Including reactive power control could further enhance voltage stability.
* Real-time operational changes were not considered; a day-ahead schedule was assumed.
* The computational effort, although not critical in offline scenarios, may be significant for larger networks or real-time applications.
* Energy losses and CO\({}_{2}\) emissions, although consistent, are modest in absolute terms. This outcome is not due to deficiencies in the proposed methodology but rather reflects the physical and operational constraints of the test microgrid. Specifically, the system is characterized by limited photovoltaic generation capacity, and the BESS units have fixed location, power rating, and charge/discharge operation schedules. These restrictions significantly narrow the search space available to the optimization algorithm, thus constraining the potential for further performance improvements. As such, the reported results should be interpreted as the best possible outcomes within a constrained infrastructure scenario.
# Feasibility of Optical Flow Field Measurements of the Coolant in a Grinding Machine
## 3 Experimental Setup and Signal Processing
### Image Enhancement and Cross-Correlation Algorithm  
For the velocity field calculation, the captured images for SIV and PIV are visually enhanced and analysed with the software DynamicStudio, developed by the company Dantec Dynamics. The image enhancement process uses a spatial median and a top-hat image filter for eliminating noise in the images. For increasing the detectability of the particles, the Sobel operator is used to highlight horizontal intensity changes and to eliminate visible stationary flow structures from the edges. The velocity field is then calculated with an adaptive PIV algorithm with an overlap of 75% for an interrogation area size of 32 px \(\times\) 32 px. For the velocity field determination for a certain set of cooling parameters (e.g., MWF volume flow, nozzle height), 1000 single measurements are taken for SIV and 350 for PIV. For each of these measurement series the averaged velocity field is calculated after automated outlier deletion with a range validation to minimize the residual impact of measurement outliers. Additionally, the standard deviation of the measurements is determined as a measure of the random measurement deviation.
## 5 Discussions
### Implications of UAV Selection  
The key prerequisite for conducting this work is to find efficient, accurate, and rapidly deployable UAV measurement technology. Professional-level UAV systems have undergone significant advancements, particularly with the integration of the RTK positioning system. This development has allowed for the production of a DSM with considerable accuracy, even without the use of GCPs. In the case of UAVs equipped with cameras, the DSM error obtained through traditional SFM methods ranged from 0.3-0.54 m. However, with the incorporation of the LiDAR module, the DSM error decreased to the 0.1 m level. The reconstruction of the DSM using the SFM method was affected by light spots formed in the original image due to sunlight reflecting off water puddles on the beach. This reflection had little impact on the DSM reconstruction using LiDAR data from the UAV. Through an evaluation of three different UAVs, we identified the optimal configuration featuring RTK and LiDAR technology, achieving a correlation coefficient of >90% when compared with in situ data, superior to UAVs carrying only RTK based on SFM technology.  
Due to the relatively narrow scope covered in this research, a multirotor UAV equipped with RTK and LiDAR sensors and capable of flying for 45 min is an appropriate choice because of its less expensive cost and high resolution and accuracy. However, it is essential to note that if the study area expands significantly, for instance, reaching dimensions on the order of magnitude larger than the LGTF, the utilization of fixed-wing UAVs may become a necessity to effectively carry out monitoring operations over the extended geographical expanse. There is another limitation when utilizing drones for tidal flat monitoring and integrating them with digital twin applications. A UAV is capable of monitoring the tidal channel as the water in the tidal channels completely recedes after low tide, exposing the bottom of the channel. However, if the bottom of the channel is not exposed at the end of the ebb tide period, this method has certain limitations. Light-based LiDAR, as opposed to bathymetric LiDAR, lacks the capability to penetrate the existing water layers to reach the seabed, requiring further manual measurements to be taken at the tidal channel.
## 4 Applications in Food Analysis
### Off-Flavors  
As described in Section 4.1, aroma belongs to the most important sensory characteristics of food. Food products with an inappropriate aroma impression are often rejected by consumers and may lead to a loss of consumer confidence [304]. The awareness of off-flavors in food relies mainly on their concentration occurring in the food matrix and odor threshold values. Off-flavors are defined as unpleasant odors or flavors transmitted to food through internal impairing changes [304]. Many volatile compounds are associated with unpleasant odor notes such, e.g., putrid, musty, rotten, skunk, _etc_. However, the received off-flavor impression often depends on the concentration of the related aroma compounds. The most common off-flavor compounds are those that cause musty, earthy off-flavors (haloanisoles, halophenols, geosmin and methylisoborneol) and medicinal off-flavors (phenolic compounds, sulfur-containing compounds and carbonyl compounds) [304]. Other off-flavor compounds are generally classified according to their origin, including microbially derived off-flavors, compounds originating from packaging materials, from cleaning agents, and compounds resulting from Maillard reactions [304].  
The impact of the cork composition on the sensory properties of wines was investigated by means of SPME GC-MS [272]. Cork was shown to have an active role in the sorption of volatile phenols from wine. In particular, the sorption properties of 4-ethylphenol and 4-ethylguaiacol phenols in hydro-alcoholic medium were investigated by [PERSON] and [PERSON] [272]. Through the high sorption activity of corks the concentration of 4-ethylphenol and 4-ethylguaiacol was decreased. High concentration of these compounds can lead to the formation of off-flavor. Both, in models and real wine samples the tested cork leads to a significant reduction of the mentioned off-flavor compounds in wine [272].  
The oxidative stability of conventional high-oleic sunflower oil and analytical and sensory lipid oxidation parameters in conventional and high-oleic rapeseed oil were reported by [PERSON] _et al_. [260,261].  
It was concluded that the combination of volatile compound analysis with HS-SPME-GC and multivariate statistical methods provides a sensitive tool in differentiating conventional sunflower oil and high-oleic sunflower oil as by means of volatile lipid oxidation marker compounds. The same applies to differentiating rapeseed oil and high-oleic rapeseed oil.
# A New Approach to Production Process Capability Assessment for Non-Normal Data
## 3 Problem Formulation  
The indicators of process quality capability, \(C_{p}\) and \(C_{pk}\), are widely used to measure process capability. Traditional methods of estimating indicators require process data to be explicit and have a normal distribution [55; 56]. Often, however, actual data on measurements of quality features are ambiguous and do not have a normal distribution, and therefore using traditional methods to estimate them can lead to erroneous results. Therefore, in order to determine these indicators, incomplete data and data that do not have a normal distribution are not analyzed, because the results of the analysis may contain errors and high uncertainty [45; 57]. In addition, in the case of quality features, where a bilateral specification limit has been defined and the basic principles of sampling and measurement have been observed, the determination of these indicators should not be very difficult. However, a problem can arise with products where only a unilateral specification limit, i.e., USL or LSL, is specified.  
The article is structured as follows: Section 4 describes the methods used. Section 5 discusses in detail the proposed data transformation method. Section 6 contains the results and analysis of the empirical data. The article closes with conclusions and directions for further research.  
\begin{table}
\begin{tabular}{c c} \hline
**Quality Condition** & \(C_{p}\) \\ \hline Super Excellent & \(C_{p}\geq 2.00\) \\ Excellent & \(1.67\leq C_{p}<2.00\) \\ Satisfactory & \(1.33\leq C_{p}<1.67\) \\ Capable & \(1.00\leq C_{p}<1.33\) \\ Inadequate & \(0.67\leq C_{p}<1.00\) \\ Poor & \(C_{p}<0.67\) \\ \hline \end{tabular}
\end{table}
Table 1: Quality conditions and \(C_{p}\) values.
## 2 Experimental Section
### Design of the Sensor System  
The sensor system introduced in this publication is designed to be assembled in between the cutting head and the collimator unit. The laser beam (dot-dashed line in Figure 2) is guided from the collimator, through the sensor, onto the lens of the cutting head, which focuses the beam onto the workpiece (Figure 2). The temperature in the process zone during laser cutting of steel has to be higher than 1500 \({}^{\circ}\)C to melt the material. This high temperature results in the emission of thermal radiation with the intensity maximum of this radiation being in the range of 1 \(\upmu\)m, _i.e._, in the same spectral region of the laser emission. The thermal radiation partly propagates into the cutting head (dashed line, Figure 2). The lens in the cutting head collimates this thermal radiation and guides it backwards in direction to the collimator. The radiation on the brink is reflected by a ring mirror onto an elliptical mirror. The inside diameter of the ring mirror and the elliptical mirror are both larger than the laser beam diameter in order not to influence the laser in its propagation to the workpiece. The elliptical mirror has two focal points, one of which is identical to the center point of the ring mirror, _i.e._, the center of the primary laser beam. In the other focal point of the elliptical mirror, a cone is placed to guide the radiation that is collected by the ring mirror via the elliptical mirror in a vertical direction out of the ellipse. This optical design is patented by the co-authors' company [20]. To facilitate measurements of the thermal radiation collected by the described mirror combination without the signal radiation (process radiation) being outshined by any reflected or scattered laser radiation (primary radiation), a sophisticated filter combination, consisting of a reflective calflex-X(r) and a laser protection filter for infrared lasers with OD9, is situated in front of a Si and InGaAs photodiode sandwich. The sensor system uses a logarithmic amplifier to provide a voltage output signal proportional to the logarithm of the photodiode current. The diode current is digitalized with a sample rate of 20 kHz, which in turn enables a signal evaluation by digital signal processor or PC.  
Figure 1: Geometry of the test pattern cut clockwise (dimensions in mm).
## 3 Methods
### Time Series Forecasting Models
#### 3.1.4 Bilst  
Recurrent neural networks (RNNs) are a sequence-to-sequence model where the model does not change depending on the length of the sequence. However, when dealing with long sequences, it forgets information from further back in time, leading to problems such as gradient loss and overfitting. The long short-term memory (LSTM) model improves on the RNN by adding \"forgetting gates\", \"input gates\", and \"output gates\" to control the retention and rejection of information through the function of gates. This optimizes the shortcomings of RNN's short-term memory and can effectively handle the transfer of information in long-time sequences [43; 44]. Figure 4 shows a structural diagram of the LSTM model.  
The BILSTM model, a bidirectional long short-term memory neural network, consists of two independent LSTM models, a forward model and a backward model. The output combines the results of both models. To some extent, the BILSTM model can compensate for information that may be missed by the unidirectional LSTM [45]. Figure 5 shows a structural diagram of the BILSTM model.