esa-sceva/satcom-chunk-collection huggingface dataset:
# A Scientific Document Retrieval and Reordering Method by Incorporating HFS and LSD
## 2 Related Work  
### Scientific Document Retrieval  
Relying solely on text-based retrieval methods for scientific documents often falls short of meeting the retrieval criteria. It is, in fact, more logical to integrate mathematical expressions during retrieval, given the substantial presence of formulas within scientific documents. [PERSON] et al. [8] pioneered the application of unsupervised embedding learning and graph convolutional neural networks to the task of learning mathematical representations. They ingeniously leveraged graph convolutional neural networks to extract mathematical expressions within documents. Experimental findings demonstrated that, in comparison to traditional vector space methods, this approach not only improves similarity precision but also facilitates the retrieval of interdisciplinary literature.  
An indispensable strategy for enhancing technology literature search involves obtaining semantically meaningful representations from academic documents. Language models built upon the Transformer architecture have brought about substantial progress in the realm of natural language processing. Notably, models such as SciBERT [9] and BioBERT [10] have showcased their prowess in acquiring comprehensive text representations. In pursuit of obtaining semantically meaningful document-level representations, [PERSON] et al. [11] proposed a straightforward approach. By amalgamating the structure of SciBERT with a novel loss function, they fine-tuned Transformer models to acquire high-quality representations of scientific literature. These representations were then utilized for predicting target journal categories based on titles and abstracts. Evaluation results across three distinct datasets showcased the efficacy of this approach, demonstrating that the learned high-quality representations are more conducive to information retrieval tasks.  
In the realm of combining mathematical expressions with contextual text, and in response to the challenge posed by pretrained models that tend to overlook the structural intricacies and semantic connections between mathematical formulas and their context, [PERSON] et al. [12] introduced MathBERT. This model was trained jointly on mathematical formulas and their corresponding context. In their efforts, they designed pretraining tasks aimed at more effectively capturing the semantic and structural features of formulas. [PERSON] et al. [13] utilized vectors comprising one-dimensional formula embeddings and generalized text for the execution of mathematical information retrieval tasks.  
In this study, scientific document retrieval places significant emphasis on both mathematical expressions and contextual semantics. We meticulously analyze each symbol within the expressions, focusing on individual components rather than attributes of multiple sub-expressions. Moreover, leveraging the language comprehension abilities learned by large models to enhance the overall performance of smaller models.
# Junction Temperature Optical Sensing Techniques for Power Switching Semiconductors: A Review
## 3 Junction Temperature Optical Sensing Techniques
### FBG Sensing Technique
In contrast to short FBG sensors, longer FBGs can deliver accurate temperature readings in areas with less extreme thermal gradients and favor placement with simple installation. The key benefits offered by FBG sensors, along with underlying challenges, are summarized in Table 7. So far, sampling rate, grating head length, and thermal conductivity are major factors influencing FBG accuracy, which depend on the manufacturer's specifications. Error due to sensor housing can be controlled with proper calibration. Smaller housing is necessary for compatibility, since FBG sensors will be incorporated into the power module. In addition, compact housing will allow quick response, which is also necessary for transient temperature measurement. The advantage offered by FBG over other optical-based techniques, if embedded adequately in MOSFET and IGBT modules, provides fast and accurate measurement at a low cost compared to the thermography technique counterpart.
## 3 Contribution of Selected Yeasts to Emerging Winemaking Demands
### Reduction of Ethanol Content
Moreover, an additional study using different species, namely _T. delbrueckii_ and _Z. bailii_, showed the greatest potential for producing wines with reduced ethanol concentration under limited aerobic conditions and on defined media, with over 2% [\(v/v\)] reductions, depending on the aeration regime, compared to the anaerobic control [122]. Interesting results were also obtained using _Pichia_ spp. In particular, _P. kuyveri_ is able to produce about 0.36 g of ethanol per g of sugar, with a yield that is about 22% lower than that of _S. cerevisiae_[79]. Wines with reduced ethanol concentrations without excessive acetic acid levels were produced by sequentially inoculating _P. kluyveri_ with _S. cerevisiae_ in high-sugar Merlot grape musts [123]. However, _S. cerevisiae_ must be used in conjunction with these NS strains since they usually exhibit a poor ability to drive the wine fermentation alone. Using only selected _S. cerevisiae_ strains, [PERSON] et al. [124] reported low ethanol yield and less acetic acid production under aerobic conditions at laboratory scale, replacing NS yeasts as reported by [PERSON] et al. [111]. From an ethanol-reduction perspective, Gene Modification (GM) techniques have been applied to partially redirect carbon metabolism from ethanol production by shifting carbon to other endpoints [125]. In detail, the overexpression of _GPD1_ and/or _GPD2_ genes, encoding the glycerol-3-phosphate dehydrogenase isozymes, increases the synthesis of glycerol while decreasing the synthesis of ethanol [126]. In addition, [PERSON] et al. [127] developed a low-alcohol producer strain through a genetic modification of the _S. cerevisiae_ Pdc2p transcription factor, encoded by the _PDC2_ gene, that is involved in the regulation of the availability of the PDC (pyruvate decarboxylase) isozymes, which catalyze the reaction of pyruvate to acetaldehyde in the ethanol biosynthetic pathway. Through homologous recombination, the strain can reduce the ethanol concentration up to 1.89% \(v/v\) without affecting the overall fermentation kinetics. More recently, it was shown that by mutating the general transcription factor Spt15p, the TATA-binding protein, a strain of _S. cerevisiae_ (YS59-409) was developed with an ethanol reduction of about 34.9% [128]. Nevertheless, only a few efforts have been made in this direction, probably because of consumer uncertainty about GM technologies.
## 2 Materials and Methods
### Methods
#### 2.2.2 LC Measurement  
An electrochemical detector (ECD: SU-300, DKK-TOA, Tokyo, Japan) was used, with 0.1 mol/L NaOH as the mobile phase, a column temperature of 25 \({}^{\circ}\)C, a flow rate of 0.3 mL/min, an AS8020 autosampler (Tosoh), and a sample injection volume of 20 \(\upmu\)L. The columns were ion-exchange columns with a core-shell type filler and reacted with amines (S-30/70=St (styrene)/DVB (divinylbenzene)-STMDAH (tetramethyldiaminohex-ane)); \(\upmu\)4.6 mm \(\times\) 150 mm as described by [PERSON] [21]. The theoretical plate numbers (N) of the samples were determined using the internal processing program of the system. Standard solutions were prepared by weighing approximately 20 mg each of glucose, allose, and raffinose and preparing a 200 \(\upmu\)g/mL solution using 100 mL of distilled water. Each concentration (1.25 \(\upmu\)g/mL, 2.5 \(\upmu\)g/mL, 5 \(\upmu\)g/mL, 10 \(\upmu\)g/mL, 20 \(\upmu\)g/mL, and 40 \(\upmu\)g/mL) was then prepared for calibration curve measurements.  
A sample solution of 5 g of each rice wine was weighed and diluted 25,000-fold with distilled water to determine the carbohydrate content. As evaluation of the validity of LC measurements, the glucose, allose, and raffinose levels were quantified using standard solutions and evaluated by calculating the linearity as well as the square of the correlation coefficient (R) of the calibration curve for each sample (R\({}^{2}\)) (\(n\) = 12).  
The limits of detection (LOD) and quantification (LOQ) were calculated. Reproducibility and precision were assessed using the relative standard deviation (RSD) with known concentrations of glucose, allose, and raffinose, with each measured repeatedly for \(n\) = 12. The LOD and LOQ were calculated using the following equations:  
\[\text{LOD}=3.3\times(\text{s}/\text{a}) \tag{1}\]  
\[\text{LOQ}=10\times(\text{s}/\text{a}) \tag{2}\]  
s: SD of the intercept for the calibration curve  
a: Slope of the calibration curve
## 6 Conclusions  
In this paper, we construct the extended trajectory state space under the background of unknown measurement noise covariance, derive the VB-TPHD closed solution by introducing the variational Bayesian approximation into the TPHD filter framework and obtain the joint estimation of the noise covariance sequence and the posterior trajectory state. Considering the filtering efficiency, we only include the noise parameters at the current time in the updating process.  
Figure 10: Comparison of RMS TM error under different lengths of update window.  
Figure 9: Comparison of VB-TPHD and VB-PHD using EK and UK two nonlinear implementation forms under the decomposition of RMS TM metric with time varying measurement noise. (**a**) RMS TM error of localization. (**b**) RMS TM error of false targets. (**c**) RMS TM error of missed targets. (**d**) RMS TM error of switching cost (\(L=5\)).  
Under the Gaussian model, the extended trajectory state can be modeled as the product of the inverse gamma with the Gaussian mixture. We use the AGM-VB-TPHD filter to complete the trajectory estimation at a constant velocity under the linear model. In addition, AEK-VB-TPHD and AUK-VB-TPHD also have good application effects on the trajectory estimation of the constant angular velocity turns under the nonlinear model. It can be seen from the simulation that VB-TPHD can obtain the statistical information of the dynamic trajectory state with measurement noise effectively and accurately. It improves the estimation of trajectory state and number compared with the adaptive filter based on tag-PHD. The results show the validity and effectiveness of our algorithm along with the implementation and prove that TPHD is an excellent filtering framework.  
We suppose that the proposed filter can be applied into maneuvering target detection and the tracking of a harsh and unknown environment in radar and sonar systems. Additionally, the fault-tolerant systems with low-cost integrated GPS/INS positioning systems would be suitable. The next research direction is to apply extended targets and group targets to the framework of TPHD so as to achieve better and more accurate multi-target tracking from the perspective of trajectory.  
[PERSON] performed the simulations and wrote the paper; [PERSON]. ([PERSON]), [PERSON] and [PERSON]. ([PERSON]) supervised the task and revised the paper with constructive suggestions; [PERSON], [PERSON] and [PERSON] analyzed the data. [PERSON] and [PERSON] offered revision during the whole process. All authors have read and agreed to the published version of the manuscript.  
This research was funded by National Natural Science Foundation of China under grant 61971179 and the Fundamental Research Funds for the Central Universities under grant B200202165.  
Not applicable.  
The authors declare no conflict of interest.
# Semi-Supervised Medical Image Classification Based on Attention and Intrinsic Features of Samples
## 2 Related Work
### Semi-Supervised Learning Based on Consistency Regularization  
The consistency-based method involves using the information of unlabeled data effectively by making two prediction results for an image consistent through random enhancement. Most previous work has tried to design an effective method to generate a consistent target close to the real mark of the image. For example, the \(\pi\) model directly takes the network output as the consistency target [18]. The Temporal Ensembling method [29] uses the exponential moving average (EMA) prediction result for unlabeled data as the consistency target, the quality of which can be improved because it comes from the set information of the previous period. However, the Temporal Ensembling method needs to integrate the information of the previous period, which requires a huge prediction matrix and makes training data-heavy. Therefore, the Mean Teacher [30] was brought into being. Instead of maintaining the exponential moving average (EMA) prediction, the Mean Teacher framework uses the exponential average weight of the student model to reconstruct the teacher model. Since the teacher model comes from the integration of the student model, its prediction can also produce a reliable consistency goal. Recently, some work has studied more effective image enhancement functions and how to generate more reliable consistency targets to improve the benefits of consistency regularization. For example, [PERSON] et al. [32; 33; 34] proved that using better image enhancement methods to create undisturbed samples can greatly improve the performance of model classification. By enhancing the local smoothness of the given input label distribution, the proposed virtual anti-disturbance [10] can prevent the model from straying further and further from the correct path because of the bad target by generating a more reliable consistency target [35] and can improve the existing consistency-based method by exploring the internal relationships among input data [36]. Different from the above work, our goal is to effectively strengthen the performance of the classifier by paying more attention to the main characteristics of differences between different types of samples and introducing a parameter into the supervision loss to make the prediction result for each labeled sample closer to the real label.
## 3 System Models and Assumptions  
### Network Model  
The network will be assumed to consist of \(N\) nodes that are uniformly randomly distributed within an \(M\times M\) square area. Nodes are heterogeneous in terms of energy, i.e., there are two types of nodes: normal nodes and advanced nodes. Normal nodes have energy \(E_{0}\), while advanced nodes have \(a\) times more energy than normal nodes \(E_{0}(1+a)\). This will mean that some nodes are equipped with more energy than other nodes. Sensor nodes are formed into multiple clusters. Clusters will each have \(n\) sensing nodes and a cluster head \(CH\). These two types of nodes are shown in Figure 1. Nodes that belong to the same cluster will know the IDs of each other and can communicate with each other. Routing the data to the base station is done through the \(CH\) nodes.
## 3 Results and Discussion
### Nutritional Screening and Morphofunctional Assessment
#### 3.2.2 What Nutritional Screening Tool Should Be Used?  
**Recommendation 6**.: _GARIN advises the use of any validated screening tool, as there is no specific tool for detecting malnutrition in patients diagnosed with COPD._  
Grade of recommendation O, (98%) Strong consensus.  
Comment:  
It is essential to determine weight change as a percentage since increased mortality has been reported in patients with weight loss of >5% in the previous 6 months [15].  
Validated nutritional screening tools most commonly used in studies of COPD patients include the Mini-Nutritional Assessment (MNA) [15; 57; 61; 62] and MNA-SF [63], Geriatric Nutritional Risk Index (GNRI) in patients >65 years [64], Nutritional Risk Screening (NRS 2002) [35; 60; 65; 66], Malnutrition Universal Screening Tool (MUST) [15], and Icelandic Screening Tool (ISS) [35].  
The MNA questionnaire has been widely used in elderly patients for detecting those patients at risk of malnutrition even before weight changes occur, and has been found to be a predictive marker of mortality and hospital costs [62].  
An association has been demonstrated with the number of exacerbations and the MNA-SF questionnaire, as well as with other pulmonary assessment parameters, including %FEV1 (forced expiratory volume in 1 s), %VC (vital capacity), %RV (residual volume), %DLCO (diffusing capacity for carbon monoxide) [63].  
An association has been found between a low GNRI and a decreased 6 min walk distance (6 MWD) value in COPD patients.  
A decrease in weight and BMI has been described in those patients with a higher nutritional risk as measured by the NRS 2002 scale, together with worse lung function and lower exercise capacity [60], higher one-year mortality, and higher hospital readmissions (cut-off point < 3 points) [65].  
Albumin level (within automated nutritional risk screening) is a good inflammatory marker associated with increased in-hospital mortality (cut-off point < 30.5 g/L) and increased risk of readmission (cut-off point < 30.1 g/L) [65].
## 4 Experiment and Results
### Results
#### 4.3.2 Stage 2: Surface Pixel Defect Mapping
Figure 13 illustrates the step-by-step outcomes from the bounding box to the segmentation process; (a) shows the original image from the dataset, (b) displays the ground truth bounding box labeled image, (c) represents the initial mask image from GrabCut with the applied shrunken bounding box, (d) shows the outcome after 5 rounds of training, and (e) presents the results after 10 rounds of training. The visualization demonstrates that, starting from the initial bounding box-based mask, the defect boundaries become progressively more refined through recursive learning. A comparison of images (d) and (e) in the second row indicates a reduction in noise over the rounds. Although the final round did not capture all defect pixels perfectly, the segmentation mask delineates the defect's shape and extent more accurately compared to the bounding box annotation.
## 2 Operating Principle
### Stage 1: \(0<t\leq t_{on}\)  
As can be seen in Figure 2, at this stage only \(S_{MAIN_{1}}\) is activated. The energy extracted from PV panel 1 is stored in the magnetizing inductance \(L_{m}\) of the flyback transformer. The maximum and average currents in the switch are defined by (1) and (2), respectively. On the other hand, the snubber capacitor \(C_{snb_{1}}\) is charged (from the previous cycle) to a voltage defined by Equation (3)  
\[I_{S_{MAIN_{MAX}}}=\frac{V_{P1}\ d_{1}}{(L_{m}+L_{I1})f_{s}} \tag{1}\]  
\[I_{S_{MAIN_{AVG}}}=\frac{V_{P1}\ d_{1}^{2}}{2\big{(}L_{m}+L_{I_{1}}\big{)}f_{s}} \tag{2}\]  
\[V_{C_{snti}}(0)=\frac{\Big{(}\frac{V_{P1}\ d_{1}}{(L_{m}+L_{I})f_{s}}\Big{)}L_{ I}}{\tan\Big{(}\frac{d_{2}\ T_{s}}{2\sqrt{L_{I}}\ c_{snti}}\Big{)}}+V_{out} \tag{3}\]  
The average current of \(S_{snb1}\) and the \(C_{snb1}\) are zero for energy balance of the snubber circuit that absorbs energy from \(L_{I_{1}}\) and delivers energy to \(C_{in_{1}}\). The output diode \(D_{out}\) is turned off and does not transfer energy to the output capacitor \(C_{out}\).
## 3 Materials and Methods
### Sensors
#### 3.2.3 Feeding Parameters  
For monitoring the feeding parameters, the fish behavior and the falling pellets must be measured. Again, low-cost components must be used to ensure the low-cost of the system.  
Firstly, the system for monitoring the fish behavior is described. The system is composed of three transparent Plexiglas tubes placed in different points of the inside of the tank. Inside each tube, there are 9 LDR sensors placed at 15 cm from each other. The operation of this sensor is based on the light refraction caused by the fish scales when they are swimming. Most of the currently available systems are based on the use of acoustic waves or machine vision. However, the systems based on acoustic waves have high energy consumption. On the other hand, the systems based on machine vision require from high computation. Furthermore, the systems based on machine vision usually do not give information about the swimming depth and swimming velocity of the fish. To control the stress of the fish and their feeding behavior, it is necessary to monitor the swimming depth and swimming velocity. Our system can determine the swimming depth and estimate the changes in the swimming velocities. The LDR employed in this system are the NSL 19M51 form Advanced Photonix [47]. The resistance of this LDR varies from 5 k\(\Omega\) to 20 M\(\Omega\). One limitation of our system is that it only can operate when the halogens are turned on.  
Nevertheless, as we pretend to monitor the behavior for feeding purposes, the feeding is only provided when the halogens are turned on. During the night, if an abnormal situation occurs and causes stress on the fish resulting in changes in their swimming depth and/or velocity it will not be detected by our system.  
The sensor to control the falling pellets is detailed in this paragraph. There are different options for pellet detection. While some authors use acoustic methods others use methods based on cameras. In our proposal, a camera will be used to obtain pictures of the pellets in the effluent pipe. Using the same process shown by [PERSON] et al. in [48] we will obtain the histograms. Then, we can quantify the amount of pellets according to the number of pixels with a selected value of brightness. The camera will be placed at the bottom of the water drainage system. It will be necessary to change the elbow pipe by a T shaped one and add a Methacrylate separator as shows Figure 8.