esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results
### Biomass Calculation  
Biomass can be divided into three parts: stem, foliage, and root biomass. We did not examine root biomass because empirical data were not available. Figure 6 shows the estimated stem and foliage biomass _versus_ the observed biomass at each site. Both relationships were close to one-to-one linear relationships. Linear regression showed a high correlation between the estimated and observed values (r\({}^{2}\) = 0.89 and 0.94 for stem and foliage biomass, respectively). These values were close to the one-to-one line, in which the estimated values should be equal to the observed values. Foliage and stem biomass at Takayama were slightly overestimated compared to those at Hitsuijgaoka. This difference was likely because the canopy at Takayama is younger than that at Hitsuijgaoka. A summary of regression statistics is given in Table 4.  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Parameters** & **Site** & **N** & **Equation** & **RMSE** & **Standard deviation** \\ \hline \(W_{s}\) & HTJ & 9 & 0.98X \(-\) 1.59 & 0.95 & 14.40 \\  & TKY & 9 & 1.02X \(+\) 3.79 & 0.96 & 12.66 \\  & All & 18 & 1.01X \(+\) 0.02 & 0.94 & 16.22 \\ \(W_{F}\) & HTJ & 9 & 1.15X \(-\) 0.04 & 0.96 & 0.25 \\  & TKY & 9 & 1.04X \(-\) 0.07 & 0.94 & 0.26 \\  & All & 18 & 1.09X \(+\) 0.02 & 0.89 & 0.28 \\ GPP & HTJ & 24 & 1.13X \(+\) 0.22 & 0.97 & 3.21 \\  & TKY & 60 & 0.86X \(-\) 0.34 & 0.85 & 4.01 \\ \hline \end{tabular}
\end{table}
Table 4: Summary of linear regression statistics of estimated _versus_ observed values of stem biomass (W\({}_{\rm s}\)), foliage biomass (W\({}_{\rm F}\)) and GPP at Hitsuijgaoka (HTJ) and Takayama (TKY). The error terms were presented as the standard deviation values.  
Figure 6: Validation of **(a)** estimated stem biomass _versus_ observed stem biomass at Hitsuijgaoka (open circle) and Takayama (solid circle); and **(b)** estimated foliage biomass _versus_ observed foliage biomass at Hitsuijgaoka (open triangle) and Takayama (solid triangle). The estimated values equal the observed values.
## 3 Space Trajectory Planning of Robot Joint (Driving Limb)  
The problem of the joint (driving limb) space trajectory planning pertains to the transition of a joint (driving limb) from an initial state \(\{\mathbf{p}_{k},\mathbf{v}_{k},\mathbf{\alpha}_{k}\}\) to a target state \(\{\mathbf{p}_{k+1},\mathbf{v}_{k+1},\mathbf{\alpha}_{k+1}\}\) within a specified movement time \(T_{k}\), while adhering to the speed \(\mathbf{v}_{\max}\), acceleration \(\mathbf{\alpha}_{\max}\), and jerk \(\mathbf{\gamma}_{\max}\) constraints imposed by the robot joint (driving limb). For Point-to-Point (PTP) tasks, the trajectory planning parameters can be determined after the completion of joint (driving limb) space path planning, while for continuous path (CP) tasks, the parameters can be determined based on the corresponding processing parameters. As an optimization problem, the objective parameters of trajectory planning can include time and acceleration, among others, with the optimization goals being time-optimal trajectory optimization and minimum acceleration trajectory optimization.  
In an in situ manufacturing processing system, the processing actuator is designed to be movable, which leads to dynamic coupling with the moving base. This can have consequential effects on the balance and stability of the actuator, ultimately impeding the processing effect of the entire system. To circumvent such undesirable effects, a reduction in the force between the actuator and moving base must be achieved, which can be accomplished by minimizing the acceleration in the process of joint (driving limb) movement, thus, making minimum acceleration trajectory planning the optimal goal of joint (driving limb) space trajectory planning in this domain.  
Based on an analysis of a specific joint actuator, the preferred acceleration curve, which meets the desired conditions, is represented by a mathematical expression \(\alpha(t)\). The resulting mathematical model for joint actuator space minimum acceleration trajectory planning can be described using Equations (36)-(41):  
\[\min\max\{|\alpha(t)|,t\in[0,T_{k}]\} \tag{36}\]  
\[\text  
Here, \(0<t_{a}\leq t_{b}<t_{c}\leq t_{d}<t_{e}\leq t_{f}<T_{k}\), \(0\leq|\alpha_{\rm ma}|\), \(|\alpha_{\rm mi}|\leq\alpha_{\rm max}\).  
According to the above analysis, the acceleration curve can be made up of Bernstein function segments, and the piecewise linear interpolation is a special case, as shown in Equation (44):  
\[\alpha(\tau)=\sum_{k=0}^{n}\beta_{k}b_{k}^{n}(\tau) \tag{44}\]  
where  
\[\tau=\frac{t}{T_{k}}\in[0,1] \tag{45}\]  
\(\tau\) is the normalized time variable; \(b_{k}^{n}(\tau)=C_{n}^{k}(1-\tau)^{n-k}\tau^{k}\) is the base function, and \(\beta_{k}\) is the corresponding coefficient. Therefore, the trajectory planning problem of joint (driving limb) space, converts to the optimization problem with \(\beta_{k}\) as the optimization object, Equation (36) as the objective function, and Equations (37)-(41) as the constraint condition.
# A Review of Document Image Enhancement Based on Document Degradation Problem
## 3 Document Image Degradation Methods
### Handwriting Fading Problem
proposed an encoder-decoder architecture based on Vision Transformer [41], as shown in Figure 7. The degraded image is first divided into several patches, which are then fed into the encoder, where the patch is mapped to a potential representation of each token during the encoding process, where each token corresponds one-to-one. The token is then passed to the decoder for patch enhancement. The model uses a self-attentive mechanism to obtain high-level global information for better performance. Since the images are trained in pixel blocks, there may be incomplete extraction of information from edge junction regions.
# A Novel Spectral-Spatial Classification Method for Hyperspectral Image at Superpixel Level
## 2 The Proposed HSI Classification Method at Superpixel Level
### Superpixel-To-Superpixel Similarity
Compared with the similarity between a pair of superpixels introduced in [40] (an affine hull model and the singular value decomposition), the similarity suggested above is easy to understand since only the sorting rule is used. Contrasted to similarity defined in [41; 47], our method is simple to calculate, and has no use of parameters. These advantages of the proposal make it easier to be applied in the field of remote sensing.
## 1 Introduction  
Since the beginning of 2020, the SARS-CoV-2 virus (Severe Acute Respiratory Syndrome-Corona Viruses-2) has triggered the outbreak of a world-wide pandemic, leading to restrictive measures of isolation and closure. To face the health emergency, hospitals increased the number of beds in intensive care units (ICUs) and introduced novel indicators for prioritizing patient admission and predicting patient outcome.  
A reverse-transcriptase polymerase chain reaction (RT-PCR) assay from nasopharyngeal swabs or bronchoalveolar lavage is the reference test for diagnosing SARS-CoV-2 infection [1]. Chest Computed Tomography (CT) has recently been considered to be a potential non-invasive method for independently confirming the diagnosis of suspected COVID-19 patients with a sensitivity of 97%, specificity of 25%, and accuracy of 68% [2]. Consequently, many COVID-19 patients underwent CT scans to evaluate the extent of the damage and improve prognosis estimation thus increasing the possibility of an overdiagnosis.  
In addition, CT-based radiological findings (e.g., Ground Glass Opacity [GGO], Crazy Paving, Lung Consolidation) can detect SARS-CoV-2 virus based on 2D/3D imaging techniques in one or both lungs and can be used as a surrogate of disease severity. These findings were reached by means of a consensus in the European Society of Radiology (ESR) [3].  
Furthermore, images can convey a large amount of information which the human eye cannot objectively quantify, providing other potential predictive or prognostic factors related to the COVID-19 disease. For this reason, the field of radiomics uses rigorous mathematical definitions and well-defined approaches [4] to quantitatively describe the image-based properties contained within radiological images, such as texture and shape/volumetric information.  
Semi-automatic segmentation has recently been suggested as a tool for quickly sectioning the lungs or the COVID-19 lesions, enabling the extraction of the radiomic features in order to improve the prediction of several clinical endpoints, including ICU admission, need for ventilators [5; 6; 7; 8; 9; 10], and severe vs. critical conditions [9]. However, only a limited number of papers have investigated patient mortality as an outcome, often having only a relatively limited patient cohort or a short patient follow-up [5; 6; 7; 8; 9; 10].  
The former limitation is likely related to the manual nature of the segmentation methods used in the papers published, which represents a very time-consuming task. During the pandemic, various semi-automatic segmentation COVID-19-dedicated tools became available; therefore, the performance of Machine Learning models built on the radiomic features extracted was investigated, using one of these tools for predicting mortality in a high-risk COVID-19-positive group.
## 5 Discussion
### Limitations and Future Work  
The RAG system developed in this study has limitations due to its reliance on the performance of the LLM. Additionally, the QA system extracts context based on the similarity of the question to generate responses. In cases where the context is not established in the database, the response is limited. Although this reduces hallucinations, it may be perceived as a limitation of the system from the user's perspective. This could potentially limit the generalization of performance across databases of varying sizes and structures. Therefore, this study aims to improve the QA system's performance by adjusting various thresholds.  
Moreover, the data used in the experiment were selected specifically for research purposes from a customized database optimized for a particular domain. This implies that the same level of performance may not be guaranteed with datasets from other domains or with different structures, which could introduce potential bias in the study. Nonetheless, the domain used in our actual experiment is already a structured database from a company currently in operation. Consequently, multiple users in this domain can expand their personalized databases with their own information and use the RAG QA system to derive answers without hallucinations, which is a significant contribution of this study. In the future, we plan to leverage the accumulated information from this company's system to increase the potential for generalization.  
In the future, we intend to focus our research on overcoming the limitations identified in this study by utilizing the accumulated information from the company's system. Specifically, we will work on enhancing the integration of data from various domains and improving the ability to process unstructured data, enabling the system to handle more complex and diverse queries.  
Conceptualization, J.B., B.K., K.-A.C. and E.L.; methodology, J.B., B.K. and K.-A.C.; software, J.B., B.K. and E.L.; validation, J.B. and B.K.; investigation, J.B., B.K., K.-A.C. and E.L.; data curation, J.B. and B.K.; writing--original draft preparation, J.B., B.K. and K.-A.C.; writing--review and editing, J.B., B.K. and K.-A.C.; visualization, J.B. and B.K.; supervision, K.-A.C. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The data are contained within the article.  
Author [PERSON] was employed by the company Textway Inc. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## 4 Results
### Evaluation of Recommendation Results  
The proposed Topic-HGNN framework incorporates the topic model into a hypergraph neural network for enhancing user and item embedding representation. Five typical topic model-based recommendation techniques and two state of art neural network-based recommendation methods are selected as baselines.  
In order to examine whether the user and the item feature identified by the Topic-HGNN can achieve better personalized recommendations, we utilized precision and diversification to evaluate the recommendation results in detail. The experiment was conducted on the Retailrocket dataset.  
* CTR provides an interpretable latent structure for users and items by combining the merits of traditional collaborative filtering and probabilistic topic modeling [54].
* SVD-LDA improves SVD-based recommendations for items with textual content with topic modeling of this content [55].
* CoAWILDA relies on an adaptive online Latent Dirichlet Allocation to model newly available items arriving as a document stream and incremental matrix factorization for collaborative filtering [56].  
Figure 4: Comparison of user’s preference inferring performance.  
* AR-LDA uses topic modeling and sequential association rule mining to capture the preference of the user's product changes over time [57].
* EUU-CF extracts topics in Wikipedia by using the LDA model and then uses the topics on user browsing history to extract user preferences [58].
* Graph-CNN is a graph convolutional neural network-based approach to recommend products to users by analyzing their previous interactions [42].
* HyperCTR learns item representations based on multi-modal information interactions among users and items [48].
## 2 Grid-Connected PV Generation System
### PV Array Model under PSC  
A PV array consists of a number of solar panels according to the load capacity requirements. When \(R_{s}=0\) and \(R_{sh}=\infty\), the mathematical model [19; 30] of the PV array under uniform irradiance can be described by the following equation:  
\[I_{apv}=I_{scn}N_{p}\{1-C_{1}[\exp(\frac{V_{apv}/N_{s}}{C_{2}V_{ocn}})-1]\}, \tag{1}\]  
where \(C_{1}=(1-I_{m}/I_{scn})e^{-V_{m}/(C_{2}V_{ocn})}\), \(C_{2}=(V_{m}/V_{ocn}-1)[\ln(1-I_{m}/I_{scn})]^{-1}\), \(I_{scn}\), \(V_{ocn}\), \(I_{m}\), \(V_{m}\), \(N_{s}\), \(N_{p}\), \(I_{apv}\), and \(V_{apv}\) are the parameters of the PV panels under standard test conditions (STCs), the short-circuit current, open-circuit voltage, MPP current, MPP voltage, number of PV modules in series, number of PV modules in parallel, PV array output current, and PV array output voltage, respectively. Although the model in Equation (1) is no longer applicable under shaded conditions, the mathematical model for the PV array under local shadow conditions can be established based on a similar principle. A series of PV modules in a PV array is described as a substring, and is shown in Figure 2a. For the PV substring in Figure 2a under irradiance, the solid curve (blue and pink) in Figure 2b is the actual output curve. It can be seen that the \(V\)-\(I\) characteristic curve is clearly divided into two sections. Under non-uniform irradiance conditions, the current \(I_{sc1}\) generated by the substring \(N_{s1}\) is not equal to the current \(I_{sc2}\) generated by the substring \(N_{s2}\), and \(I_{sc1}>I_{sc2}\). For example, a single-string array current equation with two substrings connected in series can be represented by the following piecewise function [16]:  
\[I_{pv}=\left\{\begin{array}{l}I_{sc1}\{1-C_{1}[\exp(\frac{V_{pv}/(N_{sl})}{C_ {2}V_{ocn}})-1]\},I_{sc2}\leq I\leq I_{sc1}\\ I_{sc2}\{1-C_{1}[\exp(\frac{V_{pv}/(N_{2}+N_{sl})}{C_{2}V_{ocn}})-1]\},0\leq I \leq I_{sc2}\end{array}\right., \tag{2}\]  
where \(I_{sc1}\) and \(I_{sc2}\) represent the short-circuit currents at different irradiances, as shown in Figure 2b. \(V_{pv}\) is the PV substring voltage and \(I_{pv}\) is the PV substring current.  
Figure 2: Schematic model of PV array: (**a**) PV substring under shading condition; (**b**) \(V\)–\(I\) characteristic curve corresponding to (**a**); (**c**) PV array model.  
The PV array distribution into rows and columns is shown in Figure 2c below. From (1) and (2), the mathematical model of the array under PSC can be expressed as follows:  
\[\left\{\begin{array}{c}I_{apv}=\sum\limits_{1}^{N_{p}}I_{pv}=M(V_{apv})\\ V_{apv}=\max\{V_{pv}\}\end{array}\right., \tag{3}\]  
where \(I_{apv}\) and \(V_{apv}\) are the total output current and voltage in one series, provided by Equation (3).
## 2 Materials and Methods
### Data Processing and Comparison Method
#### 2.3.3 Data Filtering and Comparisons  
The kinematic trajectories comparisons were executed calculating epoch by epoch the differences between the GS14 positions and the reference trajectory. Since special attention was given to the novel Galileo constellation, its results, for the combinations of four, five, and six satellites, were also compared with the analogous results of GPS and GLONASS systems and statistically assessed. From the POSPac(tm) solution (Figure 1--reference trajectory), and by using the ZUPD feature, the kinematic epochs were identified. These epochs, for the purposes of this study and according to Sections 2.2 and 2.3.1, were used to extract only the kinematic part of the solutions both for the MMS system and for all the previously computed trajectories (see Section 2.3.1 and Table 2). For each epoch of each kinematic RNX2 RTKP solution, the deviations of the GS14 trajectories and the reference trajectory were calculated in terms of \(\Delta_{\text{Ei}}\), \(\Delta_{\text{Ni}}\), \(\Delta_{\text{Hi}}\), and \(\Delta_{\text{Di}}\) (Equations (2)-(5)--the \"i\" subscript indicates that these quantities are scalar and referred to the i-th epoch, otherwise they have to be considered as vectors). E, N, and H are the East and North coordinates and the ellipsoidal height, respectively. \[\Delta_{\text{E,i}} =\text{E}_{\text{MMS,i}}-\text{E}_{\text{RTKLIB,i}}; \tag{2}\] \[\Delta_{\text{N,i}} =\text{N}_{\text{MMS,i}}-\text{N}_{\text{RTKLIB,i}};\] (3) \[\Delta_{\text{H,i}} =|\text{H}_{\text{MMS,i}}-\text{H}_{\text{RTKLIB,i}}|;\] (4) \[\Delta_{\text{D,i}} =\sqrt{\left(\Delta_{\text{E,i}}\right)^{2}+(\Delta_{\text{N,i}} )^{2}}. \tag{5}\]  
\(\Delta_{\text{D,i}}\) and the \(\Delta_{\text{H,i}}\) were used to compare the planimetric and altimetric performances of the Galileo system with regard to GPS and GLONASS. In particular, all the computed GS14 kinematic trajectories featuring at least one \(|\Delta_{\text{D,i}}|>5\) m were excluded to select only the potentially best combinations. Indeed, in this phase, a planimetric deviation of 5 m from the reference trajectory was considered an acceptable threshold to include float comparisons in the next computations. According to Section 2.3.1, it is worth noting that this constraint caused a further exclusion of some tested satellites combinations for the three constellations. The last filtering process was introduced to select the best solution, among the nine potential solutions produced for each selected satellite combination (see Section 2.3.1), by considering the deviations \(\Delta_{\text{D}}\) and the \(\Delta_{\text{H}}\) and the quantity of produced fixed solutions (%Q\({}_{1}\)) for each considered GS14 derived trajectory. In this case, \(\Delta_{\text{D}}\) and \(\Delta_{\text{H}}\) were considered as the vectors constituted by the \(\Delta_{\text{D,i}}\) and the \(\Delta_{\text{H,i}}\) computed for each epoch. Lastly, the percentage of fixed solutions %Q\({}_{1}\) was always calculated considering the number of kinematic epochs (1376 see Section 2.2).
###### Abstract  
The diagnosis of primary open-angle glaucoma (POAG) progression based on structural imaging such as color fundus photos (CFPs) is challenging due to the limited number of early biomarkers, as commonly determined by clinicians, and the inherent variability in optic nerve heads (ONHs) between individuals. Moreover, while visual function is the main concern for glaucoma patients, and the ability to infer future visual outcome from imaging will benefit patients by early intervention, there is currently no available tool for this. To detect glaucoma progression from ocular hypertension both structurally and functionally, and identify potential objective early biomarkers associated with progression, we developed and evaluated deep convolutional long short-term memory (CNN-LSTM) neural network models using longitudinal CFPs from the Ocular Hypertension Treatment Study (OHTS). Patients were categorized into four diagnostic groups for model input: healthy, POAG with optic disc changes, POAG with visual field (VF) changes, and POAG with both optic disc and VF changes. Gradient-weighted class activation mapping (Grad-CAM) was employed for the post hoc visualization of image features, which may be associated with the objective POAG biomarkers (rather than the biomarkers determined by clinicians). The CNN-LSTM models for the detection of POAG progression achieved promising performance results both for the structural and functional models, with an area under curve (AUC) performance of 0.894 for the disc-only group, 0.911 for the VF-only group, and 0.939 for the disc and VF group. The model demonstrated high precision (0.984) and F1-score (0.963) in the both-changes group (disc + VF). Our preliminary investigation for early POAG biomarkers with Grad-CAM feature visualization signified that retinal vasculature could serve as an early and objective biomarker for POAG progression, complementing the traditionally used optic disc features and improving clinical workflows.  
automated detection; early glaucoma biomarkers; primary open-angle glaucoma; structural and functional progression; longitudinal color fundus images; artificial intelligence
## Appendix A Custom Tool for Simulating Different Sized \(\Delta T\)_Vectors_  
This section investigates the software tool used to generate the simulated \(\Delta T_{i}\) values, with all variables with their initialised values being displayed in Table A1. To enable the interested reader to faithfully reproduce this tool, the documented pseudo-code showing the creation of a \(\Delta T_{i}\)_Vector_ during both the RREQ broadcast and corresponding RREP response phases is provided in Tables A2 and A3 respectively.  
\begin{table}
\begin{tabular}{|p{142.3 pt}|p{142.3 pt}|} \hline
**Code Section** & **Comments** \\ \hline FOR \(I=\) HC to \(I\): & As it is a RREP broadcast, the iteration starts at \(I=HC\). \\ \hline \multirow{4}{*}{\(\rho=[\)\(0\)\(,\rho_{max}\)\(]\)} & Random traffic load is assigned for each node to reflect a potential change in network traffic conditions between processing RREQ and RREP. \\ \cline{2-3}  & RREP processing time at each \(I\) follows the M/D/1 queuing model. Since the service time of both RREQ and RREP is assumed constant, \(T_{S\_I}\) from Table 1 is used. \\ \hline \multirow{4}{*}{\(\Delta T_{\_}i=\Delta T_{\_}{RREQ\_I}+\Delta T_{\_}{RREP\_I}\)} & Processing delays of both RREQ and RREP added to the \(\Delta T\)_Vector_ (\(\Delta T_{\_}i\)) \\ \hline IF \(I<M_{\_}2\) AND \(I>M_{\_}1\) THEN \(t_{\_}{RREP}=t_{\_}{RREP}+d_{\_}{f}S+\Delta T_{\_}{RREP\_I}\) & RREP tunnelled propagation delay through the wormhole is the sum of all \(\Delta T_{\_}{RREP}\) at intermediate nodes and the PTT between \(M_{\_}2\) and \(M_{\_}1\). \\ \hline ELSE & If \(I\) is a legitimate intermediate node then \(\Delta T\)_Vector_ index is incremented. \\ \hline IF \(I=M_{\_}2\) THEN \(t_{\_}{RREP}=t_{\_}{RREP}+d_{\_}{f}S\) \(\Delta T_{\_}i=\Delta T_{\_}i+\Delta T_{\_}{F2}\) & The PTT between \(I\) (\(M_{\_}2\)) and \(I\)-\(I\) is added to \(t_{\_}{RREP}\) and \(M_{\_}2\) increments its entry in \(\Delta T\)_Vector_ with \(\Delta T_{\_}{F2}\) which was calculated during the RREQ broadcast process. \\ \hline IF \(I=M_{\_}1\) THEN \(t_{\_}{RREP}=t_{\_}{RREP}+R/S\) \(\Delta T_{\_}i=\Delta T_{\_}i+\Delta T_{\_}{F1}\) & \(M_{\_}1\) calculates \(\Delta T_{\_}{F1}\) with which \(\Delta T_{\_}i\) is incremented. \\ \hline END FOR & \\ \hline \end{tabular}
\end{table}
Table 10: Routing packet processing delay generation, time tampering and generation of the \(\Delta T\)_Vector_ during the RREP response phase.