esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 E-Nose: Principles and Main Agri-Food Applications
As for every approach discussed herein, the need for a rapid, reliable and affordable method for early screening of contaminated agri-food is nowadays particularly important, not just to protect consumers' health, but also to avoid economic losses. In this context, the capability of some E-nose tools to discriminate among infected and healthy fruits and vegetables is often declared to reach classification rates up to 99.9% [64; 70]. Behind that, defects such as over-ripening, damage on product surface and biochemical physiological post-harvest mechanisms can lead to changes in aroma. E-nose is reliable and sensitive enough to correctly predict different common changes naturally occurring in post-harvest [64; 91; 92]. E-nose tools can possibly be replaced, in some instances, by specific sensors for the quantitative assessment of the presence of a given volatile compound or a small group of them. Indeed, in some cases, the degradation of food quality can be controlled through the production of one, or few waste products, as occurring, for example, in the case of ripened pork salami, where flavor deterioration is associated with abnormal levels of 2-heptenal and methyl esters of heptanoic, pentanoic and hexanoic acids [93]. Another common example is that related to nuts, where the rancid flavor associated with the deterioration of walnut oils can be associated with the production of 2-octenal, hexanal, 2-heptenal, 1-octen-3-ol, hexanoic acid and nonanal, different from the almond oils, where lipid oxidation is more related to 1-pentanol, hexanal and hexanoic acid, and from the peanuts, whose degradation is marked by octanal, nonanal, hexanal and 2-pentlypyridine [94].
# An Orthodontic Treatment Case of a TMD Patient with Maximly Posterior Intrusion Using TsADs
## 1 Introduction  
When using orthodontics to treat patients with temporomandibular disorders (TMDs), clinicians have found it difficult to establish a decisive treatment protocol. The etiologies are varied, and it is not easy to find the cause of TMDs [1, 2]. When the etiology is due to problems with a patient's occlusion, there is a dramatic change in the occlusion after treatment of TMDs, as the position of the patient's condyles can be significantly altered. Therefore, the treatment of an orthodontic patient with TMDs should start with the stabilization of the condylar position, and a stabilization splint is approved to be the most reliable tool for stabilizing the surrounding musculoskeletal structures of a temporomandibular joint (TM) [3, 4]. To record the occlusal relationship of a patient whose condyles are properly positioned in their most stabilized position and to fabricate the stabilization splint, it is essential to use an articulator [5, 6].  
After applying a stabilization splint in patients with TMDs caused by occlusal disorders, some show bite openings accompanied by premature contacts on their posterior teeth [7]. This can be caused by the clockwise rotation of the mandibular bodies, followed by a positional change of the mandibular condyles, generally to a more superior position in the articular fossae [8]. In the past, orthognathic surgery with maxillary posterior impaction was necessary to improve a patient's open bite when the intrusion of maxillary posteriorsegments was required [9]. If orthognathic surgery could not be included in the treatment option, up-and-down elastics were alternatives for correcting anterior open bites, and the treatment effect was mainly due to the extrusion of the maxillary and mandibular anterior teeth. Consequently, these treatment mechanics can induce excessive exposure of the anterior teeth and an undesirable condylar positional change by providing a retraction force on the TMJ from the articular fossae.  
With the use of temporary skeletal anchorage devices (TSADs), anchorage control is improved, and the efficiency of orthodontic treatment is better. Moreover, TSADs enable some types of tooth movement that were regarded as impossible with conventional orthodontic mechanotherapy. Although the mechanical behavior of TSADs could be affected by some variables like diameter and length of them, the crowning achievement of TSADs might be their ability to intrude the posterior teeth, which means the orthodontic treatment of anterior open bite with posterior early contacts can be resolved without orthognathic surgery or undesirable changes in the condylar position and esthetics [2; 10]. This article presents an appropriate protocol and considerations of orthodontic treatment for patients with TMD.
# En-Face Optical Coherence Tomography Angiography for Longitudinal Monitoring of Retinal Injury
## 1 Introduction
Recently, OCTA was used to monitor capillary response and choroid vasculature [27] in laser photocoagulation therapy, which is used to modulate the oxidative demand in proliferative diabetic retinopathy [28; 29] and diabetic macular edema (DME). The photoreceptor damage of modern photocoagulation is at the limit of detection using conventional fundus imaging, and OCT only detects macular thinning after several weeks [17]. FA and OCTA have been well correlated in the inner retina when OCTA was used in a diabetic mouse model [30]. Few trials were performed with OCTA in L-CNV [31; 32; 33], but longitudinal studies were limited to only the chronic phase's impact on the choroid. To better understand the difference in inflammatory response between the acute injury and CNV, we used two distinct laser models. In these procedures, a surgical photoablation laser operating at 532 nm was used. The classical L-CNV model is used here to study the chronic phase of the inflammatory response [34]. The acute injury model photoreceptor ablation induces a predominantly acute inflammatory response [35]. Histological analysis of lesions precludes monitoring the dynamics of lesion formation in longitudinal studies; therefore, the method we developed uses high-resolution data cubes that were processed for multiple co-registered analysis techniques orthogonal (cross sections and en-face views) [36] and OCTA. Simply put, one single scan replicates the information of multimodal scanning, providing depth-resolved angiography and perfect co-registration between views. While previous CNV studies focused solely on visualization of the choriocapillaris [32; 37], in the current study we are screening for imaging markers of the phases of retinal injury. Therefore, we build upon previous work to show that high-resolution orthagonal OCT and OCTA are a multimodal combination that is highly suitable for longitudinal retinal imaging studies.
# State-of-the-Art Techniques in RF Energy Harvesting Circuits
## 5 Conclusions  
In this work, we focus on RF Energy Harvesting and wireless power transfer. We have summarized the state-of-the-art RF energy scavenging technology during the last 10 years. A basic RF energy scavenging unit is composed of the following: the antenna, the impedance matching network, and the rectifier. All modules were presented, but we emphasized mostly on the rectifier's design, which can be designed with diodes or with MOSFETs (BTMOS, DTMOS). Additionally, the power conversion efficiency (PCE) depends on the load of the system and characterizes the quality of a rectenna system. After this literature research, we concluded that most authors use distributed elements to design their impedance matching networks and choose the doubler topology in a rectifier circuit.  
The conceptualization of the paper was done by [PERSON], A.D.B., M.S.P. and S.K.G.; I.D.B. writing--original draft preparation, I.D.B. and K.K. performed the data curation, S.K.G. supervised the process. [PERSON], [PERSON] and K.K. analyzed the methodology and results and contributed to writing the manuscript. All authors contributed to reviewing the manuscript. All authors have read and agreed to the published version of the manuscript.  
This research has been co-financed by the European Union and Greek national funds through the Operational Program Competitiveness, Entrepreneurship and Innovation, under the call RESEARCH-CREATE-INNOVATE (project code: T1 EDK-05274).  
Data Availability Statement: Data sharing not applicable.  
The authors declare no conflict of interest.
## 4 Results and Discussion
### Experiment I: The Performance of the XLNet Model  
The primary purpose of Experiment I is to verify the effectiveness of using XLNet to extract text semantic features using a combination of pretrained models and hierarchical attention. We select pretrained models in the experiment, such as BERT base, BERT-WWM, and ERNIE3.0, as baseline models. First, we train 27 classifiers for each of the four classifier trees, totaling 108 classifiers. The corresponding P, R, and F1 values are then calculated for each classifier based on the metrics described in Section 4.4. The detailed results are presented in Table 3. Among the four models listed in Table 4, no pretrained model has a 100% overwhelming absolute advantage for the 27 classifiers. However, from a relative perspective, the advantage of XLNet's training effect is still significant (Figure 7). In Figure 7, the horizontal axis represents each model's performance metrics P, R, and F1. In contrast, the vertical axis represents the proportion of classifiers with the best training performance compared to other models for each metric. Among each group of the 27 classifiers for the four classifier trees, from the perspective of P, the group using XLNet has 16 (59%) classifiers outperform the groups using the other three baseline models in terms of training performance. The best classifiers using BERT, WWM, and ERNIE3.0 are 10 (37%), 4 (19%), and 2 (7%), respectively.  
Based on Table 3, we calculated the mean values of P, R, and F1 for the 27 classifiers in each model. The model using XLNet demonstrates the best performance in the training of the classifier tree, with an average of P value as 93.7%, R value as 93.3%, and F1 value as 93.2%. Moreover, the results show that the performance of XLNet is 2.7%, 5%, and 15% higher than the other baseline models on P values, 4.4%, 5.9%, and 18.1% higher on R values, and 4.3%, 6%, and 18.8% higher on F1 values, respectively. Therefore, for RQ1 \"How does the XLNet pretrained model perform in extracting text semantic features?\", we can conclude that the training results using the XLNet pretrained model in the classifier tree are significantly better than those of the other three models.  
Figure 7: Comparison of training advantages between XLNet and baseline models such as BERT, WWM, and ERNIE3.0.
# Network Allocation Vector (NAV) Optimization for Underwater Handshaking-Based Protocols
## 4 Performance Analysis
### Simulation Conditions  
An event-driven simulation was carried out in a network simulator developed in Matlab that has been used in our previous published works [22; 23; 29; 30; 31; 32; 33; 34] and we considered [PERSON]'s underwater channel model [35; 36]. We consider a 3-km \(\times\) 3-km underwater plane at a certain depth, where 25 static nodes are randomly distributed. The main parameters used in the simulation are summarized in Table 1.
## 3 Wave Head Calibration Based on Parameter Optimization MVMD and Morphological Gradient
### Morphological Gradient  
In order to highlight the amplitude mutation characteristics of the traveling wave signal, the morphological gradient [21] is introduced. The basic idea is to use the flat structural element-like 'probe' to perform expansion and corrosion operations in the mathematical morphology of the traveling wave signal, and to highlight the change area in the traveling wave signal by calculating the difference. The steps of using morphological gradient to calibrate the arrival time of the wave head are as follows:  
For the traveling wave signal \(x(t)\), its definition domain is \(D_{x}\), and for the flat structure element \(g(p)\), its definition domain is \(D_{g}\), which this paper selects as 3, with the expansion calculation formula shown in (17) and the corrosion calculation formula shown in (18).  
\[(x\oplus g)(t)=\max_{m\in D_{\mathrm{g}}}(x(t-p)+g(p)),(t-p)\in D_{\mathrm{x}} \tag{17}\]  
\[(x\ominus g)(t)=\min_{m\in D_{\mathrm{g}}}(x(t+p)-g(p)),(t+p)\in D_{\mathrm{x}} \tag{18}\]  
The morphological gradient calculation formula is shown in (19).  
\[G(t)=(x\oplus g)(t)-(x\ominus g)(t) \tag{19}\]  
The corresponding time of the maximum morphological gradient \(G_{\mathrm{max}}(t)\) is the arrival time of the wave head.  
Analysis of Formula (19) reveals that morphological gradient computation involves only basic mathematical operations--addition, subtraction, and extremum determination. This computational simplicity yields high execution efficiency and avoids complex integral transformations while maintaining robust resistance to noise interference. The method's calculation of differences between local maxima and minima makes it particularly sensitive to traveling wave signal mutation points, making it well suited for determining wave head arrival times.
## 1 Introduction
In order to develop the new NN-based estimation method, many different types of transfer functions that can affect the quality of estimation results should be considered. For this reason, DFs can be utilized as a transfer function family because DFs can represent all three different types (i.e., L-type, S-type, and N-type) of quality characteristics. Among these transfer functions based on DFs, the best transfer function for experimental data using the identified learning and validation approaches is then investigated in this proposed approach. This proposed best transfer function determination procedure for a given NN structure based on DF is derived for the proposed DR estimation method. DFs with their associated weight parameters can provide a variety of transfer function families. In addition, the process mean is often kept at the target value, and the process standard deviation is minimized to the least possible in the RD methodology. Therefore, the \"nominal is best\" and \"smaller is better\" DFs are proposed as new NN transfer functions. Next, the best transfer functions for the process mean response function and standard deviation response function are determined based on the optimal weight parameters of the proposed transfer functions using MLE. The associated confidence intervals for the optimal weight parameters are also determined. The results of a case study are presented to support the view that better solutions are obtained from the proposed feed-forward NN-based DR estimation method compared with those from the conventional LSM method and the feed-forward NN estimation approach by using the conventional log-sigmoid transfer function. An overview of the proposed NN-based DR function estimation method is presented in Figure 1.
## 3 Results
### Impact of Tap Water Quality on Yeast Biomass Yield and Lipid Content
\begin{table}
\begin{tabular}{c c c c c} \hline
**Water Used for the** & **Time** & **Yeast Biomass Yield** & **Lipid Content [\%]** & **Residual Oil** \\
**Culture Medium** & **Time** & **[g/dm\({}^{3}\)]** & **Lipid Content [\%]** & **in Medium [\%]** \\ \hline Tap water (U) & 43 & 8.17 (\(\pm\)0.73) & 27 & 0.44 \\  & 65 & 22.33 (\(\pm\)4.56) & 33 & 1.44 \\ Distilled water & 43 & 9.21 (\(\pm\)0.76) & 26 & 4.44 \\  & 65 & 24.89 (\(\pm\)0.94) & 30 & 2.89 \\ Tap water (U) & 20 & 16.44 (\(\pm\)1.57) & 39 & 36.22 \\ \hline \end{tabular}  
* U—tap water collected at the right bank of the Vistula River in Warsaw.  
\end{table} 
Table 2: Comparison of yeast biomass yield, cell lipid content, and residual oil content of the culture medium as a function of the type of water used in the medium and the time of biomass sampling (\(p<0.05\)). different carbon sources and interfering with regulating the selected culture parameters in the bioreactor. In the culture conducted with distilled water-based medium, the concentration of the lipid carbon source remaining in the culture medium was lower at the end of the culture. In contrast, in the second variant, the residual oil content in the culture medium was higher in the last hours of culture. The amount of carbon source dosed in the distilled water-based culture was three times higher than in the second culture (Table 2). The total was 300 g of waste oil supplementation (the lipid substrate concentration in the medium was 4.44 after 43 h and 2.89 g/dm\({}^{3}\) after 65 h). In the tap water-based culture, where the carbon source was a single addition, these contents were lower (0.44 after 43 h and 1.44 g/dm\({}^{3}\) after 65 h). It should be noted that although the added waste substrate was significantly consumed in both cultures, the amount of substrate added did not significantly affect the biomass yield and intracellular lipid content. In the 20 h culture, the residual oil content of the culture medium was high (36.22%) due to the rapidly interrupted culture and the high C/N ratio, as the carbon source was not consumed in the logarithmic phase. Lipid accumulation is influenced by the substrate used in the medium. [PERSON] et al. (2022) [42] studied lipids produced in media with three carbon sources: linoleic and oleic acids, glucose, and volatile fatty acids. In the linoleic and oleic acid medium, the lipid content decreased by half after 72 h. Conversely, the glucose medium showed intensive lipid production, with a fivefold increase in content, reaching 37% after 72 h. No significant changes were noted in the volatile fatty acid medium. The authors concluded that yeast readily assimilates glucose, promoting growth and lipogenesis, particularly when there is a high carbon-to-nitrogen ratio [42].
# Fault Detection and Isolation for Redundant Inertial Measurement Unit under Quantization
## 3 Experiments Results and Analysis
### Experiments of Step Fault
#### 3.2.2 The Monte Carlo Simulation  
The first-order channel filter and the second-order channel filter use the same principles for parameter selection. Take the second-order channel filter as an example. The parameters of time constant, fault magnitude and sample time are considered in the Monte Carlo analysis.  
With the different parameters (based on the ratio of cut-off frequency to rising time) of the second-order channel filter, its false alarm rate, false isolation rate and positive detection rate are calculated as 1000 times the Monte Carlo simulation. The statistical results are shown in Table 3 (typical fault). In this table, the false alarm rate is defined as the probability of failure alarm without fault injection; the false isolation rate is defined as the probability of correctly detecting fault but not isolating it correctly. Their formulas are as follows. PCD 1 refers to the probability that a fault is detected within 1 s. PCD 2 refers to the probability that a fault is detected within 1 to 10 s. PCD 3 refers to the probability that a fault is detected within 10 to 100 s. PCD 4 the probability that a fault is detected within more than 100 s.  
Figure 10: Second-order channel filter (the fault amplitude is 0.5\({}^{\circ}\)/h). (**a**) Fault detection; (**b**) Fault isolation.  
Table 3 helps draw the conclusion that with the increase of the parameters of the second-order channel filter, its false alarm rate and false isolation rate decreases, whereas the channel filter cannot detect any fault in a short time. Hence, we design and select the appropriate time constant according to actual conditions of RIMU.  
When the parameter of the second-order channel filter is 3.85, we conduct the Monte Carlo simulations 1000 times to verify the detection effect of the same parameter on detecting the fault of different amplitudes.  
Table 4 shows that the same parameter of the second-order channel filter has different FDI effects on the faults of different amplitudes. The fault of a smaller amplitude leads to a worse FDI effect. In other words, to detect or isolate the fault of smaller amplitude, the FDI method may need a larger parameter.  
Table 5 recommends the parameters of the channel filters to detect or isolate a typical fault in different sampling time (the Monte Carlo simulation performed 1000 times; the step fault is \(5^{\circ}/\mathrm{h}\); the false alarm rate and false isolation rate are 0.05 respectively).
## 4 Results
### The Effect of Factor Triplets  
The next step was to analyze the triplets of factors. Similar to before, all possible combinations of factors were created. These combinations and their Intercept levels are the following as shown in Table 3, while the significant pairs resulting from the comparison can be seen in Figure 4 in the form of 95% CIs. As can be seen in Table 3, when the contrast ratio of 1.5:1 is present in the intercept levels, the estimates become smaller. Usually, when either the desktop display or the turned off shadows factor is in the intercept levels, the estimates also become smaller. However, when either the 45\({}^{\circ}\) FoV or the \(-15^{\circ}\) camera rotation is beside them, the estimate increases. Therefore, it can be suspected from Table 3, that the 45\({}^{\circ}\) FoV and \(-15^{\circ}\) camera rotation can increase completion times when they are in a triplet. According to the results, there are 179 significant differences when triplets are involved. There are 23 significant decreases in completion times, while the remaining 156 differences are significant increases. The largest significant decrease with an estimate of \(-132.228\) s is when 75\({}^{\circ}\) FoV and \(-45^{\circ}\) camera rotation is used with turned on shadows (\(p\)-value = 0.029173), while the largest significant increase with an estimate of 420.88 s on average is when 75\({}^{\circ}\) FoV, 45\({}^{\circ}\) camera rotation and 3:1 contrast ratio are used (\(p\)-value = 0.000768). \begin{table}
\begin{tabular}{c c c c c} \hline
**Pair** & **Interaction** & **Estimate** & **Standard Error** & **Significance** \\ \hline  & undefined \& \(-45^{\circ}\) & (235.153 s) \(-\) 116.335 s & 34.498 s & 0.000756 \\  & \(60^{\circ}\) \& \(0^{\circ}\) & (235.153 s) \(+\) 87.491 s & 37.625 s & 0.020127 \\  & \(75^{\circ}\) \& \(0^{\circ}\) & (235.153 s) \(+\) 113.522 s & 42.469 s & 0.007563 \\ FoV \& ROT & \(90^{\circ}\) \& \(0^{\circ}\) & (235.153 s) \(+\) 106.576 s & 38.876 s & 0.006158 \\  & undefined \& \(30^{\circ}\) & (235.153 s) \(-\) 83.108 s & 20.514 s & 5.24 \(\times\)\(10^{-5}\) \\  & \(60^{\circ}\) \& \(45^{\circ}\) & (235.153 s) \(-\) 109.940 s & 50.618 s & 0.029947 \\  & undefined \& \(45^{\circ}\) & (235.153 s) \(-\) 100.112 s & 31.145 s & 0.001323 \\ \hline FoV \& Display device & undefined \& GVR & (189.449 s) \(-\) 38.779 s & 12.773 s & 0.002420 \\ \hline  & \(60^{\circ}\) \& 21:1 & (142.204 s) \(-\) 94.178 s & 39.031 s & 0.015900 \\ FoV \& CR & \(60^{\circ}\) \& 7:1 & (142.204 s) \(-\) 139.055 s & 32.592 s & 2.05 \(\times\)\(10^{-5}\) \\  & \(75^{\circ}\) \& 7:1 & (142.204 s) \(-\) 122.968 s & 30.117 s & 4.58 \(\times\)\(10^{-5}\) \\  & \(90^{\circ}\) \& 7:1 & (142.204 s) \(-\) 149.376 s & 35.079 s & 2.13 \(\times\)\(10^{-5}\) \\ \hline ROT \& Display device & \(0^{\circ}\) \& GVR & (223.473 s) \(-\) 61.777 s & 22.405 s & 0.005870 \\ \hline ROT \& CR & \(30^{\circ}\) \& 21:1 & (155.049 s) \(-\) 87.817 s & 43.652 s & 0.044300 \\ \hline CR \& Display device & 7:1 \& GVR & (138.634 s) \(+\) 26.223 s & 11.871 s & 0.027255 \\ \hline \end{tabular}
\end{table} 
Table 2: Significant interactions among variable pairs.