esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 PPDT Classification Model
### Computation Model  
To address the practical considerations of deploying our proposed algorithm in real-world cloud environments, we consider a scenario in which an organization that owns a decision tree classification model outsources the inference task by sending encrypted data to a cloud service provider (e.g., AWS and Google Cloud). Figure 2 illustrates the structure of our computational model, which involves three entities: the _client_, who possesses the feature vector to be classified; the _model holder_, who holds the trained decision tree; and the _cloud server_, which performs the encrypted computation on behalf of the model holder.  
* The client encrypts the feature vectors and sends them to the model holder, who then encrypts the information needed to calculate the decision tree's path cost and threshold.
* The client's encrypted feature vectors are sent to the server, relaying the model holder, to conceal the information (\(\lambda_{i}\)) about which elements of the feature vector threshold of the decision node should compare.
* On behalf of the model holder, the server performs the necessary calculation for the decision tree prediction and sends the client's encrypted classification results.
* The client decrypts the information and obtains the classification results.  
Even if the organization does not maintain its own servers, decision tree classification with privacy preservation is possible. The concrete process of the protocol is shown in Section 3.3.  
A Representative Application of Online Medical Diagnostics  
Machine-learned diagnostic models developed by medical institutions represent valuable intellectual property that is central to their competitive advantage. These institutions seek to offer diagnostic services without disclosing proprietary models, and cloud-based computation provides a practical solution to scale such services while reducing local computational costs.  
However, concerns related to data confidentiality and model privacy present challenges for real-world deployment. Clients demand privacy-preserving services that do not expose their sensitive health data, while cloud service providers typically prefer not to manage or store sensitive information due to increased compliance and security risks.  
Therefore, enabling secure and efficient inference over encrypted data and models aligns with the interests of all stakeholders. Our approach allows computations to be carried out on encrypted inputs and models, thereby mitigating privacy concerns and reducing the operational burdens associated with sensitive data management on cloud platforms.
## 2 The Heuristic Algorithms
### The Two-Step Algorithm: Constructive and Improvement (\(CH\_1 Pa\))
#### 2.1.2 1 Pa  
Improvement Phase  
The improvement Algorithm 4, here denoted _1 Pa_, is described below. The algorithm has been designed to improve the solution obtained by the constructive heuristic \(CHa\), by exploiting the tree stored in _prev_. In particular, this algorithm inversely visits the path from \(o\) to \(d\) produced by \(CHa\). Starting at node \(d\) (line 1), it enters a loop (line 3) that ends only when node \(o\) is reached. For each node \(n\) in the path, it tries to identify a new parent \(n^{\prime}\) (line 6) which allows it to improve the objective function (line 7). To avoid the creation of sub-cycles, it is tested that the new parent \(n^{\prime}\) is not a descendant of the current node \(n\) (line 8).  
```
1:\(n\gets d\)
2:\(currCost\gets cost[d]\)
3:while\(n\
eq o\)do
4:\(np\gets prev[n]\)
5:for each arc \((n^{\prime},n)\in A\)do
6:\(ds\gets cost[n^{\prime}]+c_{n^{\prime}n}+CostPath(n,d,prev)\)
7:if\(|ds|<currCost|\)then
8:if\(Path(n,d,prev)\cap Path(o,n^{\prime},prev)=\emptyset\)then
9:\(np\gets n^{\prime}\)
10:endif
11:endif
12:endfor
13:\(prev[n]\gets np\)
14:\(n\gets np\)
15:endwhile
16:return\(prev,currCost\)
```  
**Algorithm 4** IPA (\(G(N,A)\), \(o\), \(d\), \(cost\), \(prev\)).  
Analyzing the computational complexity of this algorithm, we have a complexity of \(O(|N|)\) in line 3, having to iterate over a maximum of \(|N|\) nodes, and for the loop of line 5 forces the algorithm to visit a maximum of \((|N|-1)\) arcs, reaching a complexity of \(O(|N|^{2})\). Finally, considering the complexity of the functions _Path_ and _CostPath_ we reach a computational complexity equal to \(O(|N|^{3})\).  
Just to give an idea of the _1 Pa_, in Figure 3 the starting solution and the first improvement iteration are reported.  
In particular, starting from node 6, the algorithm searches for a node connected to 6, reached during the execution of \(CHa\) and not belonging to the current solution. The first (and unique) candidate is node 5. Before accepting node 5 and modifying the solution, the algorithm checks if passing through node 5 to reach node 6 from node 1 is cheaper than the current solution. This is not the case, thus node 5 is not selected. Moreover, going forward from node 6 to node 4, there is a candidate node 3. Connecting node 3 to 4 is convenient: the new path from the origin node 1 to node 3, plus the cost of the new arc connecting node 3 to node 4, and the cost from node 4 to the destination node 6 permits to improve the objective function that passes from 191 to 131. This selection is accepted and the search continues going forward from node 3 to the origin. There are no more possibilities to improve the solution, thus the algorithm stops.
## 2 Related Works and Contribution
### Time-Driven Cluster-Head Rotation Methods
Stable Election Protocol (SEP) [22] was intended for heterogeneous sensor networks, where sensor nodes have different initial levels of energy. In SEP new CH nodes are randomly selected at the beginning of each round, just like in LEACH. An advantage of this protocol is that sensor nodes with higher energy become CHs more often than the nodes with lower energy resources. This approach prolongs the time interval before the death of the first sensor node. A modified Stable Election Protocol, named Prolong-SEP (P-SEP) was presented in [23] to extend the lifetime of fog-supported sensor networks by maintaining balanced energy consumption. P-SEP enables uniform nodes distribution and introduces two energy levels for normal and advanced nodes. The nodes with energy level above the threshold are nominated as CH candidates and then the CHs are randomly selected from the candidates, by taking into account their distance to fog nodes. It should be noted that in both SEP and P-SEP algorithm, the selection of CH node is an element of a complex clustering procedure. Thus, those methods can be ineffective in frequent rotating CH role for predetermined clusters, where changes of cluster membership are not required. In [24] a protocol was presented called HEER (Hamilton Energy-Efficient Routing Protocol). HEER uses greedy algorithm to construct Hamilton path between sensor nodes in each cluster. This path is used for data transmission purpose. Sensor nodes on the path take the role of CH successively in turns. As for other time-driven methods, the change of CH occurs after operating for a predetermined round time.
# Fault Detection for Pitch System of Wind Turbine-Driven Doubly Fed Based on IHHO-LightGBM
###### Abstract  
To address the issue of a large calculation and difficult optimization for the traditional fault detection of a wind turbine-based pitch control system, a fault detection model, based on LightGBM by the improved Harris Hawks optimization algorithm (light gradient boosting machine by the improved Harris Hawks optimization, IHHO-LightGBM) for the wind turbine-based pitch control system, is proposed in this article. Firstly, a trigonometric function model is introduced by IHHO to update the prey escape energy, to balance the global exploration ability and local development ability of the algorithm. In this model, the fault detection false alarm rate is used as the fitness function, and the two parameters are used as the optimization objects of the improved Harris Hawks optimization algorithm, to optimize the parameters, so as to achieve the global optimal parameters to improve the performance of the fault detection model. Three different fault data of the pitch control system in actual operations of domestic wind farms are used as the experimental data, the Pearson correlation analysis method is introduced, and the wind turbine power output is taken as the main state parameter, to analyze the correlation degree of all the characteristic variables of the data and screen the important characteristic variables out, so as to achieve the effective dimensionality reduction process of the data, by using the feature selection method. Three established fault detection models are selected and compared with the proposed method, to verify its feasibility. The experimental data indicate that compared with other algorithms, the fault detecting ability of the proposed model is improved in all aspects, and the false alarm rate and false negative rate are lower.  
pitch control system; LightGBM; Harris Hawks optimizer; data acquisition and monitoring control system; Pearson correlation coefficient; fault detection; wind turbine +
Footnote †: journal: Applied Sciences
## 4 NLP Model Optimization for ClaimsBERT
### Fine-Tuning BERT  
Although adapting a pre-trained language model such as BERT for a specific task can improve performance significantly, an appropriate fine-tuning strategy must be developed [5]. In this paper we followed the same techniques we utilized in our previous paper [8] in order to maximize the performance we gain from the fine-tuning process. These techniques are utilized for any of the model improvements presented throughout the remainder of this paper in order to ultimately successfully train our ClaimsBERT classifier. Specifically, these techniques are employed to avoid the risk of catastrophic forgetting, overfitting and any randomness effects, which can occur when training a language model on a small dataset.  
To ensure that the model will be able to modify BERT's pre-trained weights during the training process based on our given downstream task, the chosen hyperparameters, and our curated dataset, we unfreeze all of the transformer layers during the fine-tuning process [33].  
Hyperparameter Selection  
The two primary hyperparameters that affect fine-tuning BERT are the learning rate (LR) to avoid catastrophic forgetting, and selecting an appropriate epoch limit in order to avoid overfitting. More detailed information on these are available in our previous paper [8], but both are summarized below for convenience:  
* **Catastrophic Forgetting**: The cyclic method we utilized was first presented by [PERSON] in [53], and it allows us to determine the optimal learning rate for our model training that avoids catastrophic forgetting. This method initially selects a low learning rate, which is then increased exponentially for each subsequent batch. The LrFinder function [53] was used to determine the best learning rate for each architecture.
* **Overfitting**: A common problem when training a neural network is the determination of an appropriate number of training epochs to use. An overfitted training dataset can result from too many epochs, while underfitting may be caused by a lack of iterations. If the monitored metric does not improve after a certain number of epochs, then we can stop the training process through selection of an appropriate early-stopping method. By implementing a data-driven automation approach, it eliminates the need to manually select the number of epochs. Our model monitors validation loss values, and if it does not show any improvement after two epochs, we stop training.  
Fine-tuning BERT allowed us to improve its performance from the 76% maximum accuracy obtained prior to fine-tuning, to a significantly higher 92% accuracy [8]. This is a very significant improvement. It allowed our research to progress on the implementation of all downstream tasks for the CYVET framework. However, we needed to explore how to further improve the accuracy of this claims classifier. The achieved accuracy of 95% stillindicated that 1 out of every 20 sequences would be misclassified. It was our goal, therefore, to explore architectural changes that would help us further improve this accuracy.
## 2 Materials and Methods  
The hyperspectral image classification and segmentation tasks are related problems. The goal of image classification is to assign a class label to each pixel in the hyperspectral image whilst segmentation refers to the partition of the set of image pixels into the collection of sets. These sets are declared regions where the pixels in each set are close to each other in some sense. Notably, the term classification is used when there is no spatial information taken into account. On the other hand, the term segmentation is used when spatial prior information is being considered. Let P is a set of n pixels of HI as given by \(\text{P}\equiv\{1,\ 2,\ \dots,\ n\}\). Similarly, \(\text{M}\equiv\{1,\ 2,\ \dots,\ N\}\) where M is a set of integers consisting of dataset labels. Further, let X is a set of d-dimensional vectors of image pixels represented as \(\text{X}=(\text{x}_{1},\ \text{x}_{2},\ \dots,\ \text{x}_{n})\in\Re^{d\times n}\). Y is shown below as the set of labels for a dataset \(\text{Y}=(\text{y}_{1},\ \text{y}_{2},\ \dots,\ \text{y}_{n})\). The purpose behind this problem formulation is to classify each pixel \(\text{x}_{i}\in\text{P}\) and assign them their best label \(\text{y}_{i}\in\text{M}\) and also segment them into different regions \(\text{R}_{i}\subset\text{P}\) based on labels \(\text{y}_{i}\) for pixel \(\text{X}_{i}\)[28]. The flow chart for the proposed methodology, namely the hybrid learning system (HLS), is presented in Figure 1. After training on AL-based samples, posterior probabilities were generated by a generalized composite kernels algorithm. On these probabilities, the maxim-a-posteriori algorithm was employed to find the best class probability/label against each training sample. On these classification labels, an \(\alpha\)-expansion algorithm is run to generate the segmentation map. Generally in classification, spectral-spatial kernels are computed over a sample with its immediate and farther neighbors to assign it the probabilities, but in segmentation, the best label is assigned based on immediate neighbors. We have used two datasets to conduct our experimentation. Initially, each dataset is passed to extended attribute profiles (EAPs) for the components' selection. After selecting the components, the MBT algorithm was applied for AL and multinomial logistic regression is applied for unlabeled HI data. Then, the output from multinomial logistic regression is used for segmentation. The method is iterated over ten Monte Carlo runs to get optimal results.
## 3 Results
\right.\]  
Let us insert the equations into the balance equation:  
\[\text{C}_{\text{Ln}}\,=\,\Big{[}\text{Ln}^{3+}\Big{]}\,+\,\frac{\big{[}\text {Ln}^{3+}\big{]}\cdot\big{[}\text{Cl}^{-}\big{]}}{\text{K}_{\text{n},\,\text {Cl}}}\,+\,\frac{\big{[}\text{Ln}^{3+}\big{]}\cdot\big{[}\text{OH}^{-}\big{]} }{\text{K}_{\text{n},\,\text{h}}}\]  
Let us express the concentration of hydroxo complexes:  
\[\big{[}\text{LnOH}^{2+}\big{]}=\frac{\text{C}_{\text{Ln}}}{1+\frac{\text{K} _{\text{n},\,\text{h}}}{\big{[}\text{OH}^{4}\big{]}}+\frac{\text{K}_{\text{n},\,\text{h}}\big{[}\text{Cl}^{-}\big{]}}{\text{K}_{\text{n},\,\text{Cl}} \big{[}\text{OH}^{-}\big{]}}}\]  
where \(\text{K}_{\text{n},\,\text{h}}\) and \(\text{K}_{\text{n},\,\text{Cl}}\)--instability constants of REE hydroxo and chloro complexes, respectively. Results of calculations of instability constants and Gibbs energies are presented in Table 5.  
Calculations of values presented in Table 5 showed that chloro complex LaCl\({}^{2+}\) is less stable than chloro complex YCl\({}^{2+}\). Similar dependence is observed for hydroxo complexes La(OH)\({}^{2+}\) and Y(OH)\({}^{2+}\). We base our conclusions on the values of constants stability. Gibbs energies of La and Y chloro and hydroxo complexes' formation have almost similar values. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Compound** & \(\text{K}_{\text{n},\text{Cl}}\) & \(\text{\Delta}_{\text{compl}}\text{G}_{298}^{0}\)**, kJ/moL** & **Compound** & \(\text{K}_{\text{n},\text{h}}\) & \(\text{\Delta}_{\text{compl}}\text{G}_{298}^{0}\)**, kJ/mol** \\ \hline LaCl\({}^{2+}\) & 0.077 & \(-6.34\) & La(OH)\({}^{2+}\) & \(1.70\times 10^{-8}\) & \(-44.35\) \\ YCl\({}^{2+}\) & 0.054 & \(-7.22\) & Y(OH)\({}^{2+}\) & \(1.56\times 10^{-8}\) & \(-44.56\) \\ \hline \hline \end{tabular} where \(\text{K}_{\text{n},\text{h}}\)--instability constants of REE hydroxo complexes, \(\text{K}_{\text{n},\text{Cl}}\)--instability constants of REE chloro complexes, and \(\text{\Delta}_{\text{compl}}\text{G}_{298}^{0}\)--Gibbs energies of formation complexes.  
\end{table} 
Table 5: The yttrium (III) and lanthanum (III) hydroxo and chloro complexes instability constants.
# Application of Microbially Induced CaCO\({}_{3}\) on the Reinforcement of Rock Discontinuity
###### Abstract  
Microbially induced calcium carbonate precipitation (MICP) is a technique used in geotechnical engineering to reinforce soil and rock. While it is commonly used for soil reinforcement, its application for rock reinforcement in saline-alkaline environments is limited. In order to improve the reinforcement effect of microbially induced calcium carbonate on rock joints in saline-alkaline environments, experiments were conducted to cultivate _Sporosarcina pasteuri_. The strengthening effects of MICP on rock joints were evaluated using the direct shear test. Samples of sandstone with rough surfaces were reinforced by MICP. The shear strength characteristics of rock joints reinforced by CaCO\({}_{3}\) were then assessed. The results showed that after being domesticated in a saline-alkaline environment, the bacterial concentration reached over 96% of that in a neutral environment. The domesticated _Sporosarcina pasteuri_ performed well at temperatures between 10-30 \({}^{\circ}\)C in saline-alkaline conditions. In the saline-alkaline environment, the shear strength of rock joints and the production rate of CaCO\({}_{3}\) were higher, and the _Sporosarcina pasteuri_ with domestication showed better joint repair performance. The peak shear strength of rock joints reinforced by MICP increased with curing time, with a quicker strength development in the early stage and a slower increase later on. The peak shear strength of cemented rock joints significantly surpassed that of uncemented rock joints. This research can provide valuable insights for the application of MICP technology in reinforcing rock joints in saline-alkaline environment.  
Sporosarcina pasteuri; five-gradient domestication; saline-alkaline environment; direct shear test; rock joint; shear strength +
Footnote †: journal: Applied Sciences
## 2 Materials and Methods  
Figure 1 highlights the scheme of the geothermal-driven system used to satisfy the energy requests of a commercial centre located in Southern Italy. In particular, the investigated area refers to the Phlegraean Fields where high temperature geothermal aquifers are present at a very shallow depth [77; 78]. The system consists of an organic Rankine cycle (ORC) for the electric production and a heat exchanger (HEX) able to provide the thermal energy. The two components work in parallel and simultaneously and the total geothermal mass flow rate can be arranged between the ORC and HEX units according to the user energy demand. To this purpose, the geofluid is pumped to the two components from the geothermal reservoir and afterwards is reinjected into the ground.  
The adopted parallel configuration guarantees high flexibility to the CHP system, owing to the possibility to split the geothermal mass flow rate in the ORC and HEX units, depending on the hourly user energy request [52]. Furthermore, the parallel arrangement permits one to meet the high temperature heat demand for the space heating and also when the ORC condensation temperature is set to low values to maximise the electric power and efficiency [43; 52].  
The integrated system was able to exchange the electric energy with the grid and a traditional natural gas packaged firetube boiler was adopted to meet the thermal load when the geothermal production was not sufficient.  
In particular, the system can operate adopting a thermal-driven, an electric-driven or a mixed strategy. In the first two cases, the priority is given to the fulfilment of the heat and electricity request, respectively; as a consequence, the geothermal mass flow rate is divided between HEX and ORC sub-systems on the basis of the privileged load. In the mixed operation, the thermal-driven strategy is adopted when the heat demand is present in order to minimise the heat dumping. On the other hand, the geofluid is completely exploited in the ORC apparatus when the thermal request is absent to maximise the electric efficiency and production.  
The global performance of the combined system is expressed in terms of electric efficiency (\(\eta_{el}\)), energy utilisation factor (\(EUF\)) and marginal efficiency of electric production (\(\eta_{mar}\)) [28,79], as follows:  
\[\eta_{el}=\frac{P_{el}}{\dot{m}_{g}\left(h_{g,\ in}-h_{g,\ out}\right)} \tag{1}\]  
\[EUF=\frac{P_{el}\ +\ P_{th}}{\dot{m}_{g}\left(h_{g,in}-h_{g,out}\right)} \tag{2}\]  
\[\eta_{mar}=\frac{P_{el}}{\dot{m}_{g}\left(h_{g,\ in}-h_{g,\ out}\right)-\frac {P_{th}}{\dot{m}_{b,\ out}}} \tag{3}\]  
where \(P_{el}\) is the ORC electric power, \(\dot{m}_{g}\) corresponds to the geothermal mass flow rate, \(h_{g,in}\) and \(h_{g,out}\) represent the enthalpy of the geothermal fluid at the entrance and exit of the integrated apparatus, respectively, and \(\eta_{th,ref}\) is thermal reference efficiency, based on the European harmonised efficiency reference value for separate heat production [80].
## 2 Materials and Methods
### Datasets
#### 2.2.2 CRU TS  
The Climate Research Unit Time-Series (CRU TS) datasets are widely used high-resolution climate data, produced at the University of East Anglia by the Climate Research Unit. CRU TS was first published in 2000 using angular distance weighting (ADW) to interpolate 7 climate variables onto a 0.5\({}^{\circ}\) grind over a land surface excluding Antarctica [34]. Since the first release, it has been widely used for different purposes. CRU TS regularly updates the monthly land station observations for each variable (precipitation, temperature, vapor pressure, wet days, and cloud cover) from several sources, including CLIMATE messages, US National Oceanographic and Atmospheric Administration (NOAA) via its National Climate Data Center (NCDC), and data exchanged internationally between WMO countries. To further enhance the data quality and data-processing methodologies, CRU TS releases new versions periodically, with modifications and updates. The latest major new version (CRU TS v4) was published in 2020, updated by the inclusion of additional station observations for the period 1901-2018. Since the release of CRU TS version 4 in 2020, CRU has been updated annually [11]. The last version (CRU TS v. 4.08), released on 27 June 2024, covers the period from 1901 to 2023 and provides monthly precipitation with a 0.5\({}^{\circ}\) grid. In the current study, monthly precipitation data were downloaded for the required duration from the official website [[https://crudata.uea.ac.uk/cru/data/hrg/](https://crudata.uea.ac.uk/cru/data/hrg/)]([https://crudata.uea.ac.uk/cru/data/hrg/](https://crudata.uea.ac.uk/cru/data/hrg/)) (accessed on 19 July 2024).