esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Simulations and Discussion
### The Fitting Accuracy of the BA-ELM  
There are some key parameters influencing the BA-ELM performance, such as the hidden neuron number (_HN_num_), activation function \(g_{h}(x)\), population size (_P_num_), and iteration number (_N_max_). During the process of optimizing the parameters, the RMSE in \(N\) training samples was adopted as the evaluation criterion:  
\[\begin{array}{ll}RMSE&=\sqrt{\frac{1}{N}\sum\limits_{i=1}^{N}\left(\hat{x}_{ g}-x_{0}-\delta_{BA-ELM}(\hat{x}_{g})\right)^{2}}\\ &=\sqrt{\frac{1}{N}\sum\limits_{i=1}^{N}\left(\hat{x}_{g}-x_{0}-\sum\limits_{j =1}^{L}\hat{\theta}_{j}^{*}\delta_{h}(w_{j}^{*}\cdot\hat{x}_{g}+b_{j}^{*}) \right)^{2}}\end{array}\quad i=1,2,3\ldots N\enspace. \tag{42}\]  
where \(\delta_{BA-ELM}(\hat{x}_{g})\) denotes the prediction output of the BA-ELM with input \(\hat{x}_{g}\).  
In consideration of the accuracy and computation time of the BA-ELM for estimating the function, we set _HN_num_ = 25, _P_num_ = 25, and _N_max_ = 50 in the model. The prediction accuracies of the _sig_ activation function and the _sin_ activation function were compared by calculating their RMSE values, and the RMSE of the _sin_ function was larger than that of the _sig_ function by two orders of magnitude, so the _sig_ function was selected as the activation function \(g_{h}(x)\). Figure 8 shows the performance of the BA-ELM in the function estimation.  
The relationship functions in Figure 8 almost overlap with the fitting curves estimated by the BA-ELM model; this illustrates that the BA-ELM can achieve excellent fitting accuracy under Gaussian widths \(\sigma_{PSF}\) of 0.3 and 0.671. Detailed results on the fitting errors of the BA-ELM model are shown in Figure 9. Here, we defined the fitting error as the difference between the actual systematic error \(\sigma_{\hat{x}_{g}}\) and the predicted systematic error \(\delta_{BA-ELM}(\hat{x}_{g})\) at the sampling positions. From Figure 9, it can be seen that when the \(\sigma_{PSF}\) values were 0.3 and 0.671, the maximum fitting errors were 2.75 \(\times 10^{-7}\) pixels and 2.88 \(\times 10^{-7}\) pixels--both smaller than \(3\times 10^{-7}\) pixels.  
Figure 8: The fitting performance of the extreme learning machine optimized by the bat algorithm (BA-ELM). **(a)** Gaussian width \(\sigma_{PSF}\) = 0.3. **(b)** Gaussian width \(\sigma_{PSF}\) = 0.671.
## 3 Results and Discussion
### Quantitative Differences between FKBT and 1Y-KBT  
The standard addition method could be used for the accurate quantitation of volatile compounds, which could avoid the matrix effect and mimic losses during extraction and concentration [19]. The SIM mode of GC-MS improved the accuracy of quantification. The concentrations of the aroma volatiles with an FD value \(\geq\)16 in both KBT infusions are presented in Table 3A. The regression correlation coefficients (R\({}^{2}\)) of compounds quantified through standard addition ranged from 0.9888 to 0.9999 (benzeneacetic acid was semi-quantified using an internal standard because of the lack of a reference chemical), which was acceptable for precise quantification. Table 3B contains the OAVs of these compounds. Compared with FD, the quantitative analysis more directly indicated the differences between FKBT and 1Y-KBT. The concentrations of aroma volatiles changed dramatically after 1 year of storage; the ratios of the compound concentrations in FKBT to those in 1Y-KBT ranged from 0.3 to 10.9. After OAV calculation, FKBT and 1Y-KBT had 15 and 14 compounds, respectively, with an OAV > 1; all of the compounds, apart from (Z)-methyl epijasmonate were shared between them. Furthermore, all the aroma volatiles with an OAV > 1, apart from \(\beta\)-damascenone, had a higher OAV in FKBT than in 1Y-KBT. Dimethyl sulfide, 2-methylpropanal, 3-methylbutanal, and 2-methylbutanal, which were detected through SPME-GC-O-MS, were measured at \(>\)300 \(\mu\)g/L in the FKBT infusion much higher than in the 1Y-KBT infusion. The ratios of the concentrations of these four volatiles in FKBT to those in 1Y-KBT were between 4.0 and 10.9. After OAV calibration, the OAVs of all four of these volatiles exceeded 100 in the FKBT infusion. In particular, dimethyl sulfide had an OAV > 1000. Although the concentrations of these volatiles were much lower in the 1Y-KBT infusion, they contributed greatly to the aroma. These four compounds are commonly detected in various teas and products that undergo the Maillard reaction and have clear metabolic pathways [20]. Some evidence indicates that these compounds exhibit an increasing trend with infusion time [8; 16].
## 4 Results and Discussion
### Research Questions' Findings
This method strives to optimize the identification of relevant patents (true positives, TP) and minimize the misidentification of irrelevant patents as relevant (false positives, FP) or the oversight of pertinent patents (false negatives, FN), ensuring the collection's most relevant patents are precisely retrieved. Recall, precision, F1-score, Mean Average Precision (MAP), and accuracy are performance measures utilized in the literature for patent retrieval tasks. The definitions of the different parameters are given below, with Section S3.1 in the Supplementary Material providing a more thorough discussion on the topic [100, 101, 102, 103, 104]. Very few studies have shared comprehensive performance parameters pertinent to their proposed patent retrieval techniques. Different studies have employed different performance measures to evaluate the efficacy of their patent retrieval approaches. Some studies have focused solely on Mean Average Precision (MAP), while others have prioritized maximizing recall and enhancing precision by minimizing false positives. Additionally, several studies employ a variety of measures to provide a comprehensive evaluation of their methodologies, including precision, recall, F1 score, and others. The selection of the performance measures usually depends on the unique objectives of the research, the specific features of the dataset, and the anticipated application of the retrieval system. Table 7 summarizes the recall, precision, MAP, F1 score, and accuracy as reported by various studies. References [50, 51] reported a perfect recall rate by utilizing general information retrieval-based dynamic ranking techniques. The best precision of 99% was achieved using a nearest-neighbor-based technique [54]. The top MAP score of 92% was reported using an ML-based patent retrieval technique [54], while the highest F1 score of 95% has been obtained using an NLP-based technique [62]. Only a handful of studies have reported the accuracy of their proposed patent retrieval techniques, with the highest accuracy of 96% obtained using an ensemble-based retrieval method [56]. \begin{table}
\begin{tabular}{l c c c} \hline \hline \multicolumn{1}{c}{**Techniques**} & \begin{tabular}{c} **Number of** \\ **Studies** \\ \end{tabular} & \
\begin{tabular}{c} **Study** \\ \end{tabular} &
\begin{tabular}{c} **General Category** \\ \end{tabular} \\ \hline \hline \multicolumn{1}{c}{**Semantic Trees**} & 1 & [71] & Semantic Analysis-Based \\ \hline \multicolumn{1}{c}{**Cosine Similarity**} & 1 & [90] & Techniques \\ \hline \multicolumn{1}{c}{**Claim Tree Structure**} & 1 & [90] & Others \\ \hline \multicolumn{1}{c}{**Patent Ontology**} & 2 & [92, 93] & \\ \hline \hline \end{tabular}
\end{table} 
Table 6: _Cont._Figure 4: Patent retrieval techniques proposed by different studies.
# Aggregation Dispatch and Control Strategies for Multi-Type Loads in Industrial Parks
## 4 Case Study
### Simulation Analysis
#### 4.2.4 Analysis of Scenario Four
There may be differences between a single fixed-response characteristic curve and the actual response characteristics of the controllable load. Therefore, controllable loads may not be able to accurately track the target power curve without errors. To address this limitation, the following countermeasures can be considered: (1) Using parameter models, such as state-space equations, to more accurately model controllable loads. (2) In the feedback correction stage, dynamically modifying the predictive model of controllable load based on real-time control error. (3) Allocate a small-capacity energy storage system for deviation control.
# ChatGPT as a Virtual Dietitian: Exploring Its Potential as a Tool for Improving Nutrition Knowledge
## 3 ChatGPT as a Nutrition Tool
### Can ChatGPT Effectively Replace Dietitians and Nutrition Professionals?
#### 3.3.8 Wearable Device Integration  
Numerous nutrition applications allow for the integration of wearable devices to collect real-time data on physical activity levels, vital signs, and other health metrics [48]. This seamless integration enables users to track their progress, gain insights into their overall health and wellness, and make informed decisions about their nutrition and lifestyle. For instance, a user with a wearable fitness tracker might be able to receive real-time feedback on their daily steps, heart rate, or calories burned through a nutrition app integrated with such a device. The app can tailor dietary recommendations based on this data, promoting healthier choices and better alignment with the user's fitness goals. However, as ChatGPT operates as a text-based model, it cannot directly interact with or interpret data from wearable devices. This limitation prevents ChatGPT from leveraging real-time information and offering tailored feedback based on physiological data.
# Natural Photosensitizers in Clinical Trials
## 4 Natural Photosensitizers Pending Clinical Trials
### Cercosporin  
Cercosporin is an important perylenequinone that plays a role in fungal pathogenicity of the genus _Cercospora_[129; 130]. Cercosporin is a light-activated toxin and also acts as a photosensitizer [131]. The subcellular localization of cercosporin includes mitochondria and the endoplasmic reticulum. The ROS generated during PDT, together with the photosensitizer in question, cause damage to cell membranes, including mainly the organelles described earlier. Damage to mitochondria leads to disruption of the respiratory chain, resulting in a decrease in ATP production and subsequent bioenergetic breakdown of the cell. The maximum absorption peak of cercosporin is at about 470 nm. Due to this short wavelength, this photosensitizer can be used mainly for the treatment of superficial cancerous lesions. Previous work has shown that PDT with cercosporin induces cell death in glioblastoma multiforme (T98G and U87) and breast adenocarcinoma (MCF7 and T47D) cells [132; 133].
# Storing Set Families More Compactly with Top ZDDs
## 3 Top Tree and Top DAG
### DAG Compression  
DAG compression is a scheme to represent a labelled rooted tree by a smaller DAG by sharing identical subtrees appearing repeatedly. See Figure 4 for an example.  
When we repeat sharing common sub-structures until the sub-structure rooted at each node \(u\) becomes unique, the resulting structure is called the minimal DAG. The minimal DAG is unique and it can be obtained in \(\mathrm{O}(n)\) time for a tree with \(n\) nodes [19]. We call representing a tree by the minimal DAG as DAG compression. After DAG compression, the number of nodes may be exponentially smaller than the original tree. On the other hand, in the worst case, the number of nodes never decreases. This is because DAG compression can share only identical subtrees. Here a subtree means the tree consisting of all the descendants of a node. For example, a path in which all nodes have the same label can be represented by just storing the length of the path and the label, DAG compression cannot capture this repeated structure and cannot compress the path.
## 3 Data-Collection Platforms and Instruments  
The development of digital datasets, estimation models, and products related to the biophysical characteristics of crops is critically dependent upon the acquisition of science quality spectral-reflectance data in a field setting. Our emphasis is on acquiring those data in a systematic manner with a high level of control, precision, and repeatability.
# SecureLLM: A Unified Framework for Privacy-Focused Large Language Models
## 2 Background
### Comparison with Existing References  
While our discussion in Section 2.2 outlined several methods for protecting privacy in large language models (LLMs), it is useful to compare these methods side by side and underscore how _SecureLLM_ differs from or improves upon them.  
* **Differential privacy (DP):** Prior works have shown how DP can protect the privacy of individual data points during training by injecting noise into gradients [8; 24; 29]. For instance, [PERSON] et al. [32] pioneered per-sample gradient clipping and noise addition to bound the privacy loss in neural network training. These methods, however, typically focus solely on training-time privacy rather than providing security at inference or preventing advanced prompt-based attacks. In contrast, _SecureLLM_ incorporates DP _both_ at training/fine-tuning and inference time, which helps reduce membership inference threats _after_ model deployment.
* **Homomorphic encryption (HE):** Fully homomorphic encryption techniques enable computation on encrypted data [9; 25]. Although this approach theoretically offers strong data confidentiality, it remains computationally demanding for multi-billion-parameter LLMs. Emerging research on _leveled_ and _approximate_ HE [26] has improved its efficiency somewhat, yet its production-scale adoption is still hindered by a heavy overhead. By contrast, _SecureLLM_ employs _lightweight_ encryption (AES-GCM combined with ephemeral key exchange), which is more tractable for real-time, large-scale gradient exchanges and inference.
* **Secure enclaves and TEE:** Security frameworks built on hardware enclaves [10; 27] provide strong isolation but require specialized hardware and can still be susceptible to side-channel attacks if not properly configured. Moreover, enclaves alone do not inherently mitigate membership inference attacks on model outputs. _SecureLLM_ instead places primary emphasis on decentralized fine-tuning via multi-party computation (MPC), which does not rely on trusted hardware at every node.
* **Federated and decentralized learning:** Federated or decentralized learning methods keep the data local and transmit model updates (gradients) to a central or distributed aggregator [28]. They prevent raw data from leaving local nodes, yet naive gradient sharing can still reveal sensitive patterns if an adversary intercepts them. By adding an _encryption layer_ for gradients _plus_ an MPC-based aggregator, _SecureLLM_ ensures that no single party observes others' plaintext updates. Additional defenses--like top-\(k\) sparsification--help curb communication overheads.  
* **Our added value:**_SecureLLM_ is distinguished from these earlier works by _holistically_ weaving together the following: 1. _Lightweight cryptography_ for gradient and key management; 2. _Decentralized fine-tuning_ under MPC; 3. _Differentially private inference_ for post-training protection. In this way, _SecureLLM_ covers multiple phases of the LLM lifecycle (training, fine-tuning, and inference) in a single framework with a tunable performance overhead.
## 4 Network Creation and Energy-Efficient Clustering
### Optimized DCNN  
The extracted features are subjected to DCNN [30], which include 3 varied layers, namely \"convolutional layer, pooling layer, and fully-connected layers\". All neurons in the feature map are connected to neighboring neurons in the previous layer. At a position \((a\,\ b)\) in \(l^{th}\) layer of \(c^{th}\) feature map, the values of features are evaluated as per Equation (16).  
\[K_{a,b,c}^{l}=W_{c}^{lT}J_{a,b}^{l}+F_{c}^{l} \tag{16}\]  
In Equation (17), \(W_{c}^{l}\) denotes the weight that is tuned optimally using the HEM-AL scheme and \(F_{c}^{l}\) denotes the bias term of \(c^{th}\) filter related to \(l^{th}\) layer. In addition, at the centered location \((a\,\ b)\) of \(l^{th}\) layer, the patch input is indicated by \(J_{a,b}^{l}\). The activation value \(\left(act_{a,b,c}^{l}\right)\) related to convolutional features \(K_{a,b,c}^{l}\) is evaluated as given in Equation (17).  
\[act_{a,b,c}^{l}=act\left(K_{a,b,c}^{l}\right) \tag{17}\]  
**Pooling layer:** Pooling layers in the DCNN perform the down sampling operations with the outcomes acquired from the convolutional layers. For each pooling function \(pool\ (\bullet)\) related to \(act_{m,h,c}^{l}\), the \(G_{a,b,c}^{l}\) value is computed as given in Equation (18), where, \(A_{a,b}\) refers to a neighborhood near a location \((a\,\ b)\).  
\[G_{a,b,c}^{l}=pool\left(act_{m,b,c}^{l}\right),\forall(m.h)\in A_{a,b} \tag{18}\]  
The output layer is the last layer of DCNN, in which the prediction results occur. The loss of DCNN is symbolized by \(Loss\) and is computed as in Equation (19).  
\[Loss=\frac{1}{U}\sum_{h=1}^{U}l\left(\theta;G^{(h)},O^{(h)}\right) \tag{19}\]The general constraint related with \(W_{c}^{l}\) and \(F_{c}^{l}\) is specified by \(\theta\). Here exist \(U\) counts of output-input relation \(\left\{\left(J^{(h)}\,\ G^{(h)}\right)\ ;\ h\ \in\ [1,\ \cdots,U]\right\}\). The \(h^{th}\) input feature, the labels and output are specified by \(J^{(h)}\), \(G^{(h)}\) and \(O^{(h)}\), respectively.
## 4 Conclusions  
In this research, we presented a two-stage deep convolutional neural network to predict the off-design performance of a S-CO\({}_{2}\) turbine based on field reconstruction. The concrete results are listed as following:  
1. The design and optimization of a 60,000 rpm S-CO\({}_{2}\) turbine were completed based on our previous research. The output power of the designed turbine is 1019 kW and the total static efficiency is 89.44%.
2. At stage 1, the field reconstruction was conducted on 1000 off-design cases with varying design variables. The physical fields were plausibly predicted and all key typical phenomena in turbine were captured. The average relative error of the field is less than 1.5%, while the maximum relative error is less than 15%.
3. Based on the reconstructed physical field, the off-design performance of the S-CO\({}_{2}\) turbine was predicted accurately at stage 2. The relative error of predicted power and efficiency are between \(-\)5% and +5%. Moreover, the relative error of efficiency is concentrated in the \(\pm\)1% range.
4. Compared with other five classic data prediction methods, XGboost, KNN, RF, SVR, and MLP, the off-design power and efficiency prediction in this method clearly outperforms classical methods and comparable to a state-of-the-art model.
5. In addition, once the deep model is well-trained, the calculation with GPU-accelerated can quickly predict the physical fields on the blade surface and turbine performance.  
Compared to the conventional off-design analysis methods, our method can provide more mechanism explanations for designers due to accurate prediction of physical fields. Our method relies  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Model** & **XGboost** & **KNN** & **RF** & **SVR** & **MLP** & **Our Study** \\ \hline R\({}^{2}\) & 0.6784 & 0.7020 & 0.7446 & 0.8447 & 0.9072 & 0.9851 \\ MAE & 0.0184 & 0.0133 & 0.0148 & 0.0076 & 0.0066 & 0.0027 \\ RMSE & 0.0297 & 0.0288 & 0.0267 & 0.0208 & 0.0161 & 0.0054 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison with five classic data prediction methods.  
Figure 15: Prediction results under different methods: (**a**) power; (**b**) efficiency.  
on less human intervention and has the advantages of being effective, universal, flexible, and easy to implement, showing a good promise for real-time control and design optimization of turbines.  
Conceptualization, D.S. and Y.X.; investigation, D.S. and L.S.; methodology, D.S. and L.S.; resources, Y.X.; software, D.S. and Y.X.; supervision, Y.X.; validation Y.X.; writing--original draft preparation, D.S. and L.S.; writing--review and editing, D.S. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors declare no conflict of interest.
## 1 Introduction
However, the captured thermal image had relatively low spatial resolutions. Most of the investigations focused on the evaporation characteristics of the droplet without concerning the distribution of solid-liquid interface temperature. It is important to measure the interface temperature, which has a significant role in the droplet evaporation process. The sensitivity to measure the droplet interface temperature should also be enhanced to analyze the droplet evaporation phenomenon accurately. This paper develops a novel method to measure the interface temperature of a sessile droplet using thermoresponsive polymer and a surface plasmon resonance imaging (SPRi) technique. Poly(\(N\)-isopropylacrylamide) (pNIPAM) is a thermoresponsive polymer that shows phase transitions at a low critical solution temperature (LCST) [23]. The phase transition is a reversible process with coil structures below the LCST and globule structures above the LCST [24]. Its physical properties and refractive index are also changed according to the temperature [25]. Particularly, we focus on the refractive index variation of thermoresponsive polymer depending on the temperature. Refractive index changes can be sensitively detected using the SPRi technique that is a promising method in sensing applications.