esa-sceva/satcom-chunk-collection huggingface dataset:
# Toward Questionnaire Complexity Reduction by Decreasing the Questions
## 2 The State of the Art
### Reducing Questions on Learning Style Questionnaires  
Generally, when managing surveys, one of the objectives is increasing the student participation rate in questionnaires (in our specific case, the CHAEA survey), and a possible solution to achieve that is by reducing the number of questions. It is well known that low-complexity questionnaires may mitigate respondent fatigue and increase completion rates, among other reasons.  
The impact of questionnaire length on the accuracy rate has widely been assessed in different fields, not only over academic branches of knowledge. For example, the accuracy rate as an indicator of response quality using a questionnaire with predefined answer options was analyzed, and it was observed that while the length of the questionnaire negatively impacted the response rate, it did not significantly affect the accuracy rate [42]. Reducing the length of a survey may enhance the participation rate, but splitting long questionnaires into two shorter parts may also increase that rate [43]. Another study investigated the frequent questionnaire request hypothesis by analyzing self-responses and suggested that increasing the number of surveys could decrease response rates [44].  
In the specific case of learning style surveys, the major issue in offering learning style adaptation about how many questions the student has to respond to has been assessed by several works. In particular, it is feasible to lower the number of versions by considering the general trend of the student and not the exact score achieved in each dimension. For example, in the case of the Felder-Silverman's Learning Style Model [12], it is possible to extract information about these personal preferences using a questionnaire such as the Index of Learning Styles (ILS) questionnaire, where the students are divided into three classes and then decision trees are applied to classify them [45].  
Another proposal [46] consists of an adaptive electronic questionnaire that uses association rules to predict part of the answers given for a Myers-Briggs Type Indicator (MBTI) questionnaire, which is a personality assessment model [13]. After that, it employs a question selection algorithm to determine the order of questions, and, finally, an algorithm predicts the learning style of a given person using the user-supplied answers and the predicted answers.  
Another approach that was also applied to the MBTI questionnaire proposed a dynamic electronic questionnaire that reduces the number of questions of and identifies the psychological types [47]. That method is composed of a question sorting algorithm, a learning type prediction algorithm, and an automatic parameter selection algorithm. The question sorting algorithm recognizes which questions are more important for identifying the preferences, where the importance of a question is its ability to discriminate between the two possible classes for each psychological type. That ability is represented by how many times one class is larger than the other.
# Clinical, Research, and Educational Applications of ChatGPT in Dentistry: A Narrative Review
## 4 Discussion
### Clinical Applications  
#### 4.2.1 Diagnostics and Radiology  
Although it is not possible to delegate complete diagnostic autonomy, ChatGPT can assist clinicians by providing information on possible diagnoses if symptom descriptions are inputted. ChatGPT-4 demonstrates potential as an auxiliary tool for diagnosing typical and mildly atypical presentations of common diseases, can reliably provide trustworthy and transparent recommendations according to current consensus guidelines, and can clarify doubts [27; 28; 29; 30]. When looking for treatment recommendations, LLMs generally aligned with the current literature up to 75% of the time, although data sources were frequently missing. Both GPT-4 and clinician reviews proposed procedures that might lead to overtreatment. Among the models, GPT-4 demonstrated the highest overall accuracy [26]. However, its performance has been shown to decline with greater atypicality [31].  
The performance of the GPT program in describing and providing differential diagnoses for radiolucent lesions in panoramic radiographs is variable and at this stage limited in its use for clinical application [32]. When inputting into the ChatGPT 4 chatbox \"If I provide you with images of intraoral X-rays, or orthopantomograms, would you be able to diagnose pathologies such as tooth decay, periodontitis, or other oral pathologies?\" the chatbot responds: \"I'm not able to diagnose medical conditions or interpret medical images like intraoral X-rays. It's important to consult a qualified healthcare professional, such as a dentist or radiologist, who can provide accurate diagnoses and appropriate treatment recommendations for tooth decay, periodontitis, or other oral pathologies.\" It is not possible to use the ChatGPT 4 chatbox directly as a diagnostic tool. Within the OpenAI GPT platform, it is possible to find numerous pre-trained AIs, or to train a new one, to overcome this limitation.
## 3 The ILDACP Algorithm
### Power Load Disaggregation
#### 3.3.2 Power Consumption Trajectory Estimating  
We have received the total power consumption trajectory when the appliance with largest power contribution is operating, which is written as \(A^{j}\). By removing 0 from \(A^{j}\), it is partition into some operation duration sub-sequences \(\left[A^{j}_{1},A^{j}_{2},...,A^{j}_{k}\right]\), where \(k\) is the number of operation sub-sequences. Since \(A^{j}_{r}\) represents an operation duration, and \(S^{j}\) include more than one operation durations, we use sub-sequence searching method based DTW (SSDTW) to find the best matched sub-sequence from appliance signatures. SSDTW is described as follows:  
Assume that \(S^{j}\) represents the closest signature to \(A^{j}\). We estimate each \(A^{j}_{r}\) according to following procedure.  
1. Sequentially scan \(S^{j}\) and find a time \(s(1\leq s\leq T)\) with \(S^{j}(s)\
eq 0\), compute DTW distance between \(S^{j}[s,T]\) and \(A^{j}_{r}\). In this procedure, \(\text{DTW}\left(S^{j}[s,t],A^{j}_{r}\right)\)\((s\leq t\leq T)\) are generated.
2. Scanning and compute iteratively DTW distance as above until \(s>T\), find such a sub-sequence \(S^{j}_{s}\), which bring about minimum DTW distance.
3. Replace \(A^{j}_{r}\) in \(A^{j}\) with the best matched signature subsequence \(S^{j}_{s}\).  
At last, the new total power consumption sequence is calculated according to Equation (9):  
\[P=P-A^{j} \tag{9}\]
# Loop-Back Quantum Key Distribution (QKD) for Secure and Scalable Multi-Node Quantum Networks
## 4 Discussion
### Optical Hardware  
Implementing the Loop-Back QKD protocol requires an optical setup capable of supporting bidirectional pulse reflection with polarization transformation. A key challenge arises when a node operates in reflection mode while ensuring that the polarization transformation is consistently applied, regardless of the incoming pulse direction. The protocol's  
Figure 5: Comparison of the security thresholds for BB84 and Loop-Back QKD with multiple pulses. The zero-crossing points indicate the maximum tolerable quantum bit error rate (QBER) before the protocol becomes insecure. While increasing \(n\) enhances resilience to errors, it simultaneously reduces the initial secret key rate. These results assume an ideal error correction process.  
reliance on multi-pulse interactions introduces the need for precise timing synchronization. Moreover, practical deployment in quantum networks will necessitate compatibility with existing infrastructure, which may impose additional constraints on system design.  
The configuration must include a beam splitter to divide incoming pulses into two internal paths, an optical switch to determine whether the node remains transparent or reflects, and a set of wave plate modulators to introduce the polarization transformation. A dielectric mirror or Faraday retroreflector redirects the pulse back toward its source while maintaining the intended polarization. The pulse is reinjected into the fiber by a polarization-sensitive optical coupler to minimize losses and maintain coherence.  
In normal operation, the node remains transparent, allowing pulses to traverse uninterrupted. When set to reflection mode, the optical switch directs the pulse to the polarization control unit, where it is routed based on its direction of arrival. The pulse then undergoes transformation using a quarter-wave plate (QWP) or half-wave plate (HWP), followed by reflection and reinsertion into the fiber. To measure the pulse, regardless of whether it arrived through reflection or after completing the full ring, the node leverages a measurement setup that allows it to track the state of the pulse.  
The measurement process involves routing the pulse through a polarization analyzer after the polarization transformation. The node can detect the polarization state by comparing it against a known reference basis (either \(X\) or \(Z\)) using a set of polarization beam splitters (PBS) or other suitable polarization-sensitive detectors. This reference basis corresponds to the basis in which the transmitting node initially polarized the pulse, regardless of the pulse's initial direction. An alternative approach involves using a Sagnac interferometer, ensuring bidirectional polarization transformation and simultaneous pulse measurement. Additionally, an electro-optic modulator (EOM) can be incorporated into the reflection path for real-time polarization adjustments.
## 4 Conclusions  
This study presents novel modification methods for coconut-fibre-based horticultural substrates aimed at extending their durability and enhancing their physicochemical properties. The main modifications included coating fibres with biopolymers through in-solution polymerisation and the addition of pelletised biochar derived from wood-processing biomonomers.  
* The results demonstrated that biopolymer coatings significantly increased substrate porosity by 12-24%, which improves aeration and root respiration. Bulk density was reduced by up to 15%, enhancing substrate lightness and ease of handling. Water retention capacity increased by approximately 18%, contributing to better moisture availability during cultivation. Moreover, electrical conductivity values rose, indicating enhanced ion retention.
* Crucially, nutrient dynamics were improved: the modified substrates exhibited a greater capacity to adsorb and gradually release essential macronutrients, notably phosphate ions (PO4\({}^{3-}\)) and potassium ions (K\({}^{+}\)), with their availability increased by 20-30% compared to unmodified substrates. This improvement translated into better nutrient uptake by plants.
* Physiological measurements of raspberries confirmed these benefits. The relative chlorophyll content in leaves increased by 15%, indicating improved plant health and nutrient status. These outcomes suggest that the modified substrates provide a more favourable growing medium for horticultural plants.
* Additionally, the enhanced surface properties and structural stability of the modified coconut fibre substrates resulted in an operational lifespan exceeding two growing seasons, reducing the need for frequent substrate replacement and contributing to sustainability and cost-effectiveness in horticultural production.  
The integration of biopolymer coatings and pelletised biochar into coconut fibre substrates effectively improved their physical and chemical characteristics, enhanced nutrient availability, and supported healthy plant growth over multiple cycles. These findings highlight the potential for commercial application and warrant further investigation into crop-specific optimisation and environmental impacts.  
Conceptualisation, M.B. and N.M.; methodology, M.B., N.M. and M.S.; investigation, M.B., N.M. and M.S.; formal analysis, M.K.; resources, M.K.; data curation, M.B., N.M., M.S. and M.K.; writing--original draft preparation M.B., N.M. and M.S.; writing--review and editing, M.B. and N.M.; visualisation, N.M. and M.S.; supervision, M.B. and M.K. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The data presented in this study are available on request from the corresponding author due to various file formats.  
The authors declare no conflicts of interest.
## 3 Results and Discussion
### Similarity Analysis of Responses  
Figure 4 shows the cosine similarity index (CSI) between responses from ChatGPT and Gemini across 30 distinct queries. The CSI values fluctuated significantly across the queries, ranging approximately from 0.5 to 0.8. This suggests a varying degree of similarity in the responses between ChatGPT and Gemini, indicating that while sometimes the models provide highly similar answers, at other times their responses are substantially different. Peaks around Queries 5, 14, 16, and 24 suggest instances where both models provide highly similar responses, possibly due to shared training data or algorithmic similarities. Conversely, lower points at Queries 7, 17, 21, and 30 indicate significant divergence in responses, likely stemming from differing interpretations of the queries or prioritization strategies.  
It is important to acknowledge that CSI using TF-IDF might not yield perfect results when comparing similarities between paragraphs. TF-IDF calculates the importance of a word within a document relative to its frequency across a corpus. However, it does not account for semantic meaning or synonyms of words, and it does not capture the semantic meaning or synonyms of words. Despite this limitation, TF-IDF remains an effective method for quantifying text overlaps [12].
## 2 Materials and Methods
### RP-HPLC-DAD Analyses and Methods Validation  
Chromotographic analyses of the different withdrawals were carried out on a 1200 series HPLC system (Agilent Technologies, Santa Clara, CA, USA), equipped with a quaternary gradient pump, a degasser, an autosampler, a thermostatted column oven set at 30.0 \(\pm\) 0.5 \({}^{\circ}\)C, and a diode-array detector (DAD). Data acquisition was performed using  
Figure 2: LiveFlow® bioreactor in our set-up.  
ChemStation software (B.04.01). Separations were carried out on a Gemini C-18 column (150 \(\times\) 2.1 mm, i.d.; 5 \(\upmu\)m; 110 A, Phenomenex, Torrance, CA, USA) with an XSelect HSS T3 Vanguard column (5 \(\times\) 2.1 mm i.d.; 3.5 \(\upmu\)m; 100 A, Waters Milford, MA, USA) at a constant flow rate of 0.3 mL/min (injection volume: 10 \(\upmu\)L). A binary mobile phase consisting of 0.1% formic acid aqueous solution and methanol was used at different volume ratios according to the tested molecule: caffeic and quinic acids 65/35, \(v/v\); rosmarinic acid 55/45, \(v/v\); quercetin and rutin 35/65, \(v/v\). All the elutions were carried out in isocratic mode. Chromatograms were recorded at 325 nm for caffeic acid, quinic acid, and rosmarinic acid and at 360 nm for quercetin and rutin. Each withdrawal at each monitoring time was analyzed in triplicate.  
According to ICH guidelines [32], different parameters were tested. To confirm specificity, the chromatographic profiles of the medium and of each compound in the medium were compared and no peak was observed in the medium at the retention time of each compound. Calibration curves produced on the same day and over three consecutive days by plotting the peak area of the medium spiked with each compound at five different experimental concentrations (range 0.02-0.2 mg/mL) injected three times vs. the theoretical concentration were used to evaluate the linearity. All R\({}^{2}\) values were higher than 0.9900. Limit of detection (LOD) and limit of quantification (LOQ) for all the compounds were in the range of 0.002-0.004 mg/mL and 0.01-0.02 mg/mL, respectively.  
The method's accuracy (intra- and inter-day) was calculated for each analyte by analyzing in triplicate the three different concentration levels (0.02, 0.1, and 0.2 mg/mL) over three consecutive days; it ranged from 92.10% to 103.85% for all the compounds. To determine the method's precision, the same three concentration levels for each analyte were analyzed six times within a single day (intra-day assay) and in triplicate each day for three consecutive days (inter-day assay). Precision values were lower than 2.0%.
###### Abstract  
Exploring and monitoring the underwater world using underwater sensors is drawing a lot of attention these days. In this field cooperation between acoustic sensor nodes has been a critical problem due to the challenging features such as acoustic channel failure (sound signal), long propagation delay of acoustic signal, limited bandwidth and loss of connectivity. There are several proposed methods to improve cooperation between the nodes by incorporating information/game theory in the node's cooperation. However, there is a need to classify the existing works and demonstrate their performance in addressing the cooperation issue. In this paper, we have conducted a review to investigate various factors affecting cooperation in underwater acoustic sensor networks. We study various cooperation techniques used for underwater acoustic sensor networks from different perspectives, with a concentration on communication reliability, energy consumption, and security and present a taxonomy for underwater cooperation. Moreover, we further review how the game theory can be applied to make the nodes cooperate with each other. We further analyze different cooperative game methods, where their performance on different metrics is compared. Finally, open issues and future research direction in underwater acoustic sensor networks are highlighted.  
Keywords:underwater acoustic sensor networks; UASNs; cooperation in UASNs; taxonomy of UASNs cooperation; game theory-based cooperation +
Footnote †: journal: Sensors
# Predicting Wheat Potential Yield in China Based on Eco-Evolutionary Optimality Principles
## 5 Conclusions  
In this study, we harmonized three methods founded on the principles of the EEO theory: the simulation of wheat growth, the projection of environmental limitations, and the optimization of the sowing date. These were amalgamated to predict variations in China's wheat PY across two representative SSPs. From our findings, several conclusions can be drawn: 1. A consistent rising atmospheric CO\({}_{2}\) concentration universally benefits the enhancement of PY, while elevating temperatures negatively impact PY in the majority of regions if we neglect the optimization of future sowing date. The evolution of PY exhibits a discernible two-stage pattern under both SSPs, with a turning point converging around the year 2060. 2. Warmer environmental conditions afford agricultural practitioners the opportunity to opt for an earlier sowing date, thus facilitating a more synchronized crop growth cycle with the availability of resources. Notably, a substantial advancement of the sowing date is projected if farmers intend to optimize yield outcomes. 3. Through the simultaneous integration of wheat growth and optimized sowing date predictions, a notable increase in wheat PY is anticipated from 2021 to 2060 under SSP126, followed by an insignificant trend. In contrast, under SSP370, no substantial variation is foreseen prior to 2060, with a significant increase projected thereafter. As our model does not include type-specific parameters and provides a more reliable response of the wheat planting system to climate change, the wheat PY predictions from this study can be made with greater confidence. A reasonable prediction of the resulting data demonstrates this success. Future PY predictions that encompass a more comprehensive range, such as global-scale predictions, hold great promise for implementation.  
Conceptualization, S.Q., H.W. and S.T.; methodology, S.Q., H.W. and S.T.; software, S.Q.; validation, S.T.; formal analysis, S.Q. and S.T.; investigation, S.C. and H.W.; resources, S.C. and H.W.; data curation, S.C. and H.W.; writing--original draft preparation, S.Q. and S.T.; writing--review and editing, S.C. and H.W.; visualization, S.Q. and S.T.; supervision, H.W. and S.C.; project administration, H.W. and S.C.; funding acquisition, H.W. and S.C. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Natural Science Foundation of China under grants 42271406 and 72140005, and in part by the National Natural Science Foundation of China (42001356 and 32301392).  
Not applicable.  
All input data used in this study can be collected with open access. The codes for PY generation in this study are available on reasonable request.  
We acknowledge the contribution from reviewers.  
The authors declare no conflicts of interest.
## 2 Related Works
### Gnmf  
In [7], [PERSON] et al. developed a graph regularized non-negative matrix factorization (GNMF) method to obtain a compact data representation that discovers hidden concept, and respects the intrinsic geometric structure simultaneously. GNMF minimizes the objective function as follows:  
\[\begin{array}{c}J_{GNMF}=\ ||\mathbf{X}-\mathbf{U}\mathbf{V}||_{F}^{2}+ \lambda Tr(\mathbf{V}\mathbf{L}\mathbf{V}^{T})\\ \text{s.t.}\ U_{ik}\geq 0,V_{kj}\geq 0,\ \forall i,j,k.\end{array} \tag{3}\]  
where \(\mathbf{L}=\mathbf{D}-\mathbf{W}\) is called graph Laplacian, \(\mathbf{W}\) denotes the weight matrix constructed by finding the \(k\) nearest neighbors for each data point, and \(\mathbf{D}\) is a diagonal matrix whose entries are column sums of \(\mathbf{W}\), i.e., \(\mathbf{D}_{ii}=\sum_{j}\mathbf{W}_{ij}\).  
The objective function \(J_{GNMF}\) is also not convex when both \(\mathbf{U}\) and \(\mathbf{V}\) are taken as variables simultaneously. Therefore it is unlikely to find the global minima. The Using the following update rules [7], local minima of the objective function \(J_{GNMF}\) can be obtained:  
\[\begin{array}{ll}U_{ik}^{t+1}=&U_{ik}^{t}\frac{(\mathbf{X}(\mathbf{V}^{t})^{ T})_{ik}}{(\mathbf{U}^{t}\mathbf{V}^{t}(\mathbf{V}^{t})^{T})_{ik}}\\ V_{kj}^{t+1}=&V_{kj}^{t}\frac{((\mathbf{U}^{t+1})^{T}\mathbf{X}+\lambda V^{t} \mathbf{W})_{kj}}{((\mathbf{U}^{t+1})^{T}\mathbf{U}^{t+1}\mathbf{V}^{t}+ \lambda V^{t}\mathbf{D})_{kj}}\end{array}, \tag{4}\]  
[PERSON] et al. [7] has proved that the objective function \(J_{GNMF}\) is non-increasing under the above updating rules.  
In this section, a novel \(\ell_{2,1}\) norm and Hessian Regularized Non-negative Matrix Factorization with Discriminability (\(\ell_{2,1}\)HNMFD) model is developed, which performs Hessian regularized Non-negative Matrix Factorization (HNMF) and preserves discriminative information, as well as maintaining row sparsity for encoding matrices simultaneously. Then, an alternating optimization scheme is developed to solve its objective function.
## 2 Related Work
[17] improved upon DeepLabV3+ by integrating MobileNetV2 as the backbone feature extractor and employing the Dice Loss function, achieving higher overall accuracy in road extraction. However, modifications to the core ASPP module, crucial for multi-scale feature extraction, remain largely unexplored in the context of road extraction. [PERSON] et al. [29] proposed a Dense and Global Spatial Pyramid Pooling (DGSPP) module inspired by the ASPP module but did not take advantage of the encoder-decoder architecture of DeepLabV3+. Building on these contributions and addressing the limitations of previous studies, this study proposes advancements in road extraction by replacing the ASPP module with DenseDDSSPP in the DeepLabV3+ model [13]. In this approach, the output from a preceding depthwise separable convolution layer, after applying a dilation rate, is merged with the input of the next layer in an iterative procedure. Additionally, our study incorporatesSqueeze-and-Excitation [30] in the decoder module to support the selection of relevant features for road extraction decisions. We hypothesize that this approach will efficiently generate denser features that capture intricate and useful road patterns, potentially leading to improved road extraction from high-resolution satellite imagery.