esa-sceva/satcom-chunk-collection huggingface dataset:
# SFBKT: A Synthetically Forgetting Behavior Method for Knowledge Tracing
## 2 Related Work and Motivation
The model leverages student features, such as prior knowledge, answer rate, and practice time, as well as exercise features, such as textual information, question difficulty, skill level, and skill dependency [28]. To enhance the capacity of the original loss function and mitigate inconsistencies in answering predictions for questions that share similar knowledge components (KC), the DKT+ model introduces two regularization terms [29]. Furthermore, to capture the intricate KCs that students learn, key-value memory has been introduced as a representation of the knowledge state, inspired by memory-augmented neural networks [30]. The use of an external memory structure provides greater representational power than the hidden variables used in the original DKT model, thus enhancing the model's ability to indicate proficiency. Several studies have endeavored to integrate attention mechanisms into KT models by adopting the Transformer architecture [31]. The common goal of these studies is to optimize prediction performance by redistributing the attention weight of the problem. Although each work adopts a unique approach to incorporating the attention mechanism, the underlying idea remains consistent [18; 32; 33]. Moreover, graph-based KT models utilize graph neural networks to model the interrelatedness and interdependence among KCs. Despite some previous research addressing the limitations of existing KT models and advancing the field of deep learning-based KT, the feature extraction methods used in these models have tended to be relatively simplistic, resulting in suboptimal utilization of student practice record information. Table 2 provides a summary of the key descriptive features of the main KT methods employed in the field. Based on current knowledge, it appears that students' learning sequences are embedded with complex information forgetting. The probability of a student providing a correct answer in the next exercise is influenced not only by their learning history but also by the time lag of the previous exercise. Traditional knowledge tracing models and deep knowledge tracing models have rarely comprehensively considered forgetting behavior. Most models only account for forgetting factors in past exercise records, such as the DKT-forgetting model, and ignore the forgetting process after practice. The introduction of the neural Hawkes process is a significant step in addressing this issue, as it expands the traditional multivariate Hawkes process by incorporating the time decay effects of past times and LSTM nodes [25]. Building on this concept, we aim to combine traditional forgetting factors with the Hawkes process to enable dynamic changes in students' knowledge states over time while also considering past practice records, which align more closely with the forgetting curve. Collaborative filtering can further enhance the full utilization of information by better integrating individual and group data.
## 3 Results and Analysis
### Elimination of Electrode Lead Jitter Interference  
The results which were obtained based on the EMG recordings (muscles relaxed) acquired in the shielded room session are presented in Figure 10. By closely observing the plots in Figure 10, one can  
Figure 8: The comparison of the time waveforms (**a**) and spectra (**b**) of the EMG signals (subject #4) under different conditions. The 50 Hz notch condition (2 nd row) was obtained by applying 50 Hz notch filter (software) to raw data of the No-Shield condition (1 st row).  
see that the waveform across all four of the tested methods exhibited almost the same characteristics in the first second, which is because the electrode leads were maintained in a fixed state while recording the signals. After the first second, the electrode lead cables were individually shaken at about the same amplitude and the waveform of Method 1 changed greatly while that of Method 2 is changed slightly in comparison to that of the first second. Additionally, Method 3 could be seen to exhibit a slightly lower waveform compared to Method 1 while the waveform of Method 4 has almost no change with respect to that of the first second. This implies that Method 4 has substantially eliminated the interference of the electrode lead jitter on the target signal. Therefore, it could be said that the proposed shielded drive approach would significantly eliminate the interference of the electrode lead jitter, especially when implemented with Method 4.  
Figure 10: The time waveforms of the recorded signals (subject #15) with the electrode lead cables initially kept still (0–1 s) and then shaken simultaneously (1–2 s) for the four different shielded conditions.  
Figure 9: Mean spectral amplitude of 50 Hz interference (averaged across all subjects) of the four different shielded methods under three different experimental sessions: ECG, EOG and EMG experiments.
## 3 Experimental Section  
Figure 6 shows the experimental set-up for recording and reading of the micrograting. The photopolymer was not sensitive to red wavelength; thus, a He-Ne laser (633 nm) was used for the exact focusing and optical alignment. A Nd:YAG laser (532 nm) was used for recording and reading of the micrograting. The beam was split into signal and reference beams using a polarization beam splitter (PBS). Shutter 2 was open during alignment with the red laser. Shutter 3 was open during the recording of two counter-propagating beams, and closed during the reading of the recorded micrograting. The incident plane waves were converted into circularly polarized light using a QWP. To obtain a high quality reading signal, a confocal filter was used to remove the noise and cross-talk of the adjacent recording layer. An OL mounted to a precision linear stage provided 17 nm/1 pulse resolution.  
Figure 5: Comparison of the full width at half maximum (FWHM) in the photopolymer for scalar and vector diffraction theory: (**a**) scalar and transversal component in vector diffraction theory and (**b**) the total, transversal, and longitudinal component in vector diffraction theory.  
The TGI shown in Figure 7 was designed to find the exact focal position and optical alignment. In Figure 7, the entrance pupil signal shows the micrograting in the focal region. The TGI set-up allows high resolution for good optical imaging quality, enabling the detection of defocus, astigmatism, coma, and SA resulting from the interference of the object beam and tilt of the reference beam. The exact optical focusing and alignment can be checked using the interference fringe pattern; a straight-line pattern indicates good optical alignment. In Figure 8, we confirmed the optical alignment by comparing the numerical results with those obtained experimentally.  
To verify the recording and reading of the micrograting, OL of 0.65 NA is used. The OL was an aspherical, aberration free lens, commonly used for high diffraction efficiency and quality. The Aprilis photopolymer (HMC-050-G-06) was used for the recording and reading of MHDS. The thickness of the photopolymer was 400 \(\upmu\)m, and the two glass substrates had thickness of 600 \(\upmu\)m. The shutter had an exposure time of 100 ms. The recording energy (for are cording power signal of 240 \(\upmu\)W and  
Figure 6: Experimental set-up for MHSS. BE: beam expander, M, mirror; L, lens; BS, beam splitter; PBS, polarization beam splitter; HWP, half wave plate; QWP, quarter wave plate. Green, recording and reading beam; Red, guide beam for optical alignment.  
Figure 7: Proposed experimental set-up: Twyman-Green interferometer (TGI) for exact focusing and optical alignment.  
reference beam of 240 \(\upmu\)W) was 24 \(\upmu\)J. During the reading process, a reflected power of 2 \(\upmu\)W was detected. The calculated diffraction efficiency was 0.83%; note that this value is calculated by confined volume of the micrograting and limitations of the optical system (NA\({}_{lens}<n_{photopolymer}\)).
## 5 Discussion
The evolution of the soil structure and the solid-liquid interaction are both different in this scenario. When the water content decreased from 26% to 18%, the NMR test indicated the predominant loss of the capillary water amount. At a higher water content, the cohesive force of the soil particles primarily arises from the capillary attraction which increases with a decrease in the capillary water content [48]. Furthermore, the dispersive clay platelets become aggregated at the contact points of the skeleton particles as the water content reduces, which adds to the overall cementation effect (Figure 15e). Therefore, the tensile strength gradually increases at this stage. With the further drying of the specimens, the water that is present in the loess is mainly from the decreasing amount of adsorbed water that is firmly attached to the clay particles, as is indicated in Figure 14. The dehydration of the clay significantly enhances the clay's cementation and induces a quick increase in the soil suction due to the short-range clay-water interaction. It can be noted that the developed soil suction within the adsorption-dominated regime is generally several orders of magnitude higher than that which is within the capillarity-dominated regime [48]. Therefore, the tensile strength increased at a higher rate when \(w<18\%\), which explains the two distinct stages of tensile strength variation that are shown in Figure 11a. Moreover, the energy dispersive X-ray analysis that was performed on the specimens with water contents of 6% revealed that the Ca and Na elements were detected at the contact points between the particles (Figure 16), indicating the precipitation of soluble and less soluble salts at very low water content. Obviously, the recovery of soluble and less soluble bonding agents at low water contents contributes to the further improvement of the resistance of loess to tensile failure. Furthermore, the transition from brittle failure to ductile failure of the DM specimens at the critical water content of 18% (Figure 6) is ascribed to the fact that the consistency of the loess changes at the onset of the adsorbed water-dominated stage. It is worth mentioning that the studied loess is a clayey loess containing a clay faction as high as 21.5%, thus the clay cementation, clay-water interaction and adsorbed water content play important roles in analyzing the effect of the water content on the loess' tensile behavior. As for loess with diminished clay composition, such as sandy loess, capillary effects are a dominating factor affecting the tensile behavior over the unsaturated water content range, in addition to the inherent salt and carbonate cementations. Figure 16: Energy dispersive spectrometer (EDS) analysis of the DM specimen at \(w=6\%\).
## 1 Introduction
Calcium silicate cements have been recently introduced in endodontics for their enhanced properties compared to conventional sealers: high biocompatibility (monoxicity); high chemical stability within the biological environment; shrinkage absence; expansion after setting; the absence of an inflammatory response in the case of extrusion into the periapical space; HA formation; high pH (strongly antibacterial); hydrophilicity; excellent sealing ability; and ease to use. Calcium silicate cements can be classified based on their chemical composition or their clinical application. According to the clinical context, hydraulic cements can be divided into intracoronal, intraradicular, and extraradicular materials whilst, considering the chemical constitution, they can be classified as hydraulic aluminate cement or hydraulic calcium silicate-based cement. Moreover, the latter can be divided into two different categories according to the cement components: Portland cement and calcium silicate-based cement [25; 26]. The most commonly used spacers include cotton pellets and polytetrafluoroethylene (PTFE) tapes [16; 18]. Cotton pellets have been widely used as an endodontic spacer and can be soaked with medicaments. Although, some authors described impairment in stability and displacement of the overlying restorative material during masticatory loading. Furthermore, the cotton fibers may interpose to the cavity walls-restoration interface, affecting the marginal seal of the provisional restoration and acting as a sponge by drawing fluids from the oral cavity to the internal cavity walls and root canal system [7]. PTFE tape is a versatile material that has been increasingly used for various purposes in dentistry and has been considered a suitable alternative spacer material with the potential to overcome the disadvantages of using cotton pellets. being a non-fibrous polymer, it reduces absorption and provides better support the provisional restoration [17; 27]; although, PTFE cannot be soaked with medicaments such as cotton pellets. The use of PTFE tape as a spacer is associated with less tooth's contamination compared with cotton pellets, though the evidence available is limited and heterogeneous [16]. Aim of this in vitro study was to evaluate the inhibiting effect of zinc oxide/calcium sulphate filling material, to bacterial infiltration in temporary restorations, with and without different types of spacers placed apically to the restoration.
## 3 Materials and Methods
### Training Process  
The overall training process is described in Figure 3. Using domain-annotated datasets from multiple domains in training, the overall process is divided into 3 stages.  
In the first stage, the base model is trained with multiple datasets without considering the input domain. The domain category can include \"visible light\" and \"infrared\" images sourced from \"real condition\" and \"synthesized condition\". Based on the MS COCO pre-trained weights, the model is trained to detect objects using only the detection loss. Domain classifier layers are frozen at this stage.  
In the second stage, all the base modules, including the backbone, neck, and head, are frozen. The domain classifier module is the only trainable part at this stage. Using the domain classification loss, the classifier learns to identify the source domain of the input data using the extracted features of the backbone.  
The third stage can be summarized as a domain adaptation process. In this stage, the head module and domain classifier are frozen. However, the loss of the domain classifier is substituted to domain confusion loss at this stage. With the total loss being the sum of the detection loss and the domain confusion loss, the model learns to extract domain-invariant features while maintaining the detection performance of the base model. In our approach, a data domain is classified into two sets of two binary categories of spectrum and data source. Therefore, the model learns the ability to extract shape-derived features regardless of input domain while dealing with the domain shift between real and synthetic data to obtain high accuracy. After the three stages of training, the domain classifier is detached for calculation efficiency.
## 3 Results
### Uncertainty of Satellite Data in MODIS GPP Simulation over Ecosystems in the Arid Region  
#### 3.2.1 Impacts of the Accuracy of the Land Cover Classification on MODIS GPP Simulation  
One of the first MODIS products used in the MOD17 algorithm is the Land Cover Product, MOD12Q1. The importance of this product cannot be overstated as the MOD17 algorithm relies heavily on land cover type through use of the BPLUT [15]. Based on the locations of the flux tower sites, we obtained the land cover type of each site from the MCD12Q1 results, and compared them with the actual land cover types. We found that MCD12Q1 misclassified most of the sites downstream of the HRB (Figure 7), which is an artificial oasis ecotone with sparse vegetation in the extremely arid region of China. For example, in our study, the land cover type of the NTZ site is a cropland ecosystem; however, it was classified as grassland in the MCD12Q1 land cover data (Figure 7). In addition, MCD12Q1 also misclassified the forest types at the sites of HHL, SDQ, and HYL as grassland.
# Applying Topological Information for Routing Commercial Vehicles Around Traffic Congestion
## 4 Methodology and Model Development  
### Simulation Platform  
The ArcGIS platform and Network Analyst extension were utilized to design, create, and build a transportation network dataset. This environment allowed the evaluation of alternative routes according to predefined criteria such as heavy vehicle restrictions, neighborhood impacts, road geometry, and traffic conditions.  
The geographic datasets used in this analysis were collected from the FDOT's Unified Basemap Repository (UBR) database. The UBR provides quarterly street and traffic datasets from the network provider NAVSTREET's street data by HERE Technologies.  
Additionally, the UBR database contains data suitable for routing applications with a complete navigable road network.
# Compact Sub 6 GHz Slot Multiantenna System for 5G Laptops
## 7 Conclusions  
This paper presented a closed-slot sub 6 GHz dual-band MIMO six-antenna system for 5G laptops with metal back covers. This six-antenna system is arranged with long and straight closed slots that have the same structure and size in a continuous and compact manner, which not only has a simple structure but can also meet the requirements of a large screen with a high screen-to-body ratio. Employing an asymmetric T-shaped feed-in section with a chip capacitor, the closed slots can be excited to produce half-wavelength resonant modes that cover the 3300-3600 MHz range required for the sub 6 GHz low-frequency band. Additionally, full-wavelength resonant modes can be excited to cover the 4800-5000 MHz range required for the sub 6 GHz high-frequency band that is not part of the sub 6 GHz low-frequency band's octave range. The six-antenna system can be set up in a compact, continuous manner in 5 mm intervals without requiring any isolation elements, which not only achieves good space utilization, multiple antenna efficiency, ECC and isolation, but also has a beautiful metal look. Conceptualization, S.-C.C. ; methodology, S.-C.C. and C.-S.W. ; software, C.-S.W., S.-H.C. and C.-C.L. ; validation, S.-H.C.; visualization, C.-C.L. ; writing--original draft preparation, C.-S.W. ; writing--review and editing, S.-H.C.; supervision, S.-C.C. ; project administration, S.-C.C. and S.-H.C.; funding acquisition, S.-C.C. and S.-H.C. All authors have read and agreed to the published version of the manuscript. This work was sponsored by the Ministry of Science and Technology (MOST) of Taiwan under grants MOST 111-2221-E606-003 and MOST 111-2221-E606-004.
# Removal of Phosphate from Aqueous Solution by
## 3 Results and Discussion
### Adsorption Kinetics and Isotherm Adsorption  
#### 3.3.1 Adsorption Kinetics  
As shown in Figure 7, the phosphate adsorption capacity of zeolite, biochar, and PZC 7:3 varied with contact time under constant temperature conditions. In the dynamics of phosphate adsorption, three phases of adsorption, such as rapid, equilibrium, and relatively slow, were displayed. As exhibited in Figure 7, the initial stage of adsorption, which lasted from 0 to 60 min, was called rapid adsorption, and phosphate was adsorbed at 76.88%, 72.05%, and 3.18% by biochar, PZC 7:3, and zeolite, respectively. It was demonstrated that the abundant adsorption sites on the surface of sorbent in the initial stage could interact with phosphate, which resulted in a quick increase in the adsorption capacity [62]. Based on the relationship between the phosphate adsorption capacity and time at this stage, it was hypothesized that zeolite, biochar, and PZC 7:3 mostly adsorbed phosphate on their exterior surfaces by either external surface adsorption or fast boundary layer diffusion [63]. For 60-480 min, the adsorption capacity kept going up because a lot of phosphorus was being absorbed and more of the adsorption sites on the adsorbent were being used [64,65]. After 4 h, the adsorption reached its equilibrium adsorption capacity.  
Figure 6: The effects of pH values on the adsorption capacity of zeolite, biochar, and PZC 7:3.  
Figure 7: Fitting of pseudo-first-order and pseudo-second-order kinetic models.  
The adsorption process of phosphate on the adsorbent was investigated with the assistance of pseudo-first-order and pseudo-second-order kinetic models [66]. As represented in Table 3, \(R^{2}\) values of the pseudo-first-order and pseudo-second-order models of zeolite, biochar, and PZC 7:3 were greater than 0.87, showing that the adsorption process was dominated by chemisorption [67], such as ion exchange, precipitation, and ligand exchange [68; 69]. In addition, the impacts of physical adsorption cannot be neglected.
## 3 Nutritional and Therapeutic Interventions
### Preserving Gut Microbiota in a Uremic Milieu  
For many years, the microbiota has been underestimated; however, it is now an acknowledged fact that in advanced uremic stages the microbiota is significantly affected. Chronic kidney disease is characterized by an accumulation of protein-bound uremic toxins (PBUTs) such as p-cresyl sulfate (pCS), p-cresyl glucuronide (pCG), indoxyl sulfate (IxS), and indole-3-acetic acid (IAA). Each of these uremic retention solutes exerts toxic effects, and several have been associated with worsening outcomes in CKD patients, in particular with cardiovascular morbidity and mortality. All four PBUTs originate from the intestinal microbial metabolism, mainly from the aromatic amino acids (AAAs) tyrosine, phenylalanine and tryptophan [122; 123; 124; 125]. These outcomes stem from a state of dysbiosis in bacteriological equilibrium with pathobionts overcoming symbiotic germs and becoming deranged in the tight gastrointestinal junction barrier resulting in an increasingly toxic milieu such as PBUTs, [126] which is highly toxic on a cardiovascular level. This in turn may elicit cardiac damage and nephrotoxicity, endothelial damage and diffuse endothelial injury [127]. Effective, targeted therapies for HD patients have not yet been well defined, with the majority of research work aimed at decreasing the levels of PBUTs rather than specifically curing the uremic microbiota [128]. Some authors have recently suggested the addition of nuts and/or vegetables to the diet [129; 130], whilst others have performed studies on the efficacy of a new intestinal charcoal adsorbent [131]. A recent review by [PERSON] et al. describes the ability of different polyphenols, such as anthocyanin, catechchin, chlorogenic acid, and resveratrol, to regulate intestinal microorganisms, inhibit pathogenic bacteria, and reduce inflammation [132]. Another study group conducted a randomized, placebo-controlled pilot study in HD patients to establish whether administration of a symbiotic, either individually or in association with divinylbenzene-polyvinylpyrrolidone (DVB-PVP) cartridge, could reduce the production of uremic toxins [133]. In view of the complexity in establishing full composition of the microbiota, therapeutic trials aimed at correcting uremic dysbiosis are few, unsatisfactory and inconclusive. Despite a series of attempts to date, no effective therapy using prebiotics, probiotics and symbiotics to maintain a healthy microbiota in hemodialysis has yet been defined [134; 135]. However, tailored amino acid supplementation may produce a certain rebalancing of the microbiota in the course of chronic diseases including CKD [136].