esa-sceva/satcom-chunk-collection huggingface dataset:
# Analysis of Dust Detection Algorithms Based on FY-4A Satellite Data
## 4 Results and Validation
### Validation with Ground-Based PM10 Measurements
#### 4.2.2 Validation Results of the BTD Overlay IDDI Algorithm  
We integrated the BTD and IDDI algorithms that only used thermal infrared channels to verify the accuracy of the dust monitoring algorithms. Table 7 shows that the results of POCD after integration were similar to those for BTD verification. The improvement was indicated by PoFD being greatly reduced to 56.17%. At 04:00 UTC and 06:00 UTC, the integration results were the best with a PoFD of 63.15% and 62.39%, respectively, which were higher than the POCD for any single algorithm.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**DST** & **YY** & **YN** & **NY** & **POCD (\%)** & **POFD (\%)** \\ \hline
02:00 UTC & 440 & 802 & 1192 & 35.43\% & 73.04\% \\
04:00 UTC & 560 & 523 & 2221 & 51.71\% & 79.86\% \\
06:00 UTC & 511 & 481 & 2324 & 51.51\% & 81.98\% \\
08:00 UTC & 161 & 172 & 1086 & 48.35\% & 87.09\% \\ Average & & & & 46.75\% & 80.49\% \\ \hline \hline \end{tabular}
\end{table}
Table 6: Statistical metrics for the comparison of the DST dust identification and the PM10 observations.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**STD** & **YY** & **YN** & **NY** & **POCD (\%)** & **POFD (\%)** \\ \hline
02:00 UTC & 724 & 622 & 2023 & 53.79\% & 73.64\% \\
04:00 UTC & 638 & 433 & 1630 & 59.57\% & 71.87\% \\
06:00 UTC & 628 & 415 & 1965 & 60.21\% & 75.78\% \\
08:00 UTC & 469 & 450 & 1452 & 51.03\% & 75.59\% \\ Average & & & & 56.15\% & 74.22\% \\ \hline \hline \end{tabular}
\end{table}
Table 3: Statistical metrics for the comparison of the BTD dust identification and the PM10 observations.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**NDDI** & **YY** & **YN** & **NY** & **POCD (\%)** & **POFD (\%)** \\ \hline
02:00 UTC & 490 & 856 & 7309 & 36.40\% & 93.72\% \\
04:00 UTC & 466 & 605 & 13,098 & 43.51\% & 96.56\% \\
06:00 UTC & 398 & 645 & 11,812 & 38.16\% & 96.74\% \\
08:00 UTC & 363 & 556 & 5025 & 39.50\% & 93.26\% \\ Average & & & & 39.39\% & 95.07\% \\ \hline \hline \end{tabular}
\end{table}
Table 4: Statistical metrics for the comparison of the NDDI dust identification and the PM10 observations.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**IDDI** & **YY** & **YN** & **NY** & **POCD (\%)** & **POFD (\%)** \\ \hline
02:00 UTC & 441 & 441 & 1001 & 50.00\% & 69.42\% \\
04:00 UTC & 575 & 532 & 1714 & 51.94\% & 74.88\% \\
06:00 UTC & 547 & 479 & 1968 & 53.31\% & 78.25\% \\
08:00 UTC & 308 & 511 & 886 & 37.61\% & 74.20\% \\ Average & & & & 48.22\% & 74.19\% \\ \hline \hline \end{tabular}
\end{table}
Table 5: Statistical metrics for the comparison of the IDDI dust identification and the PM10 observations.
# Adsorption of Ni2+ and Cd2+ from Water by Calcium Alginate/Spent Coffee Grounds Composite Beads
## 3 Results and Discussion
### Adsorbent Dose Effect (Beads)  
In Figure 8, at the lowest dose of adsorbent, 0.025 g in 100 mL of a solution with a concentration of 20 ppm of metal, the CA beads showed a larger adsorption capacity than the CA-SCGs beads (s = standard deviation). At 0.025 g of adsorbent, the CA beads reached an absorption of cadmium of 71.15% (s = 1.83%), whereas the adsorption for CA-SCGs beads was 62.67% (s = 2.13%). The values for adsorption of nickel were 29.11% (s = 1.71%) for the CA beads and 24.67% (s = 2.01%) for the CA-SCGs beads. This result was expected because the CA beads have a uniform structure with a larger number of carboxylic functional groups. Nevertheless, when the dose of adsorbent increased at the same concentration of metal, the CA-SCGs beads reached higher levels of adsorption.  
At 0.500 g of adsorbent, CA-SCGs beads reached an adsorption of cadmium of 93.29% (s = 2.25%), while adsorption for the CA beads was 84.08% (s = 2.05%). The values for adsorption of nickel at this dose were 75.62% (s = 2.31%) for CA-SCGs beads and 55.97% (s = 1.92%) for CA. Even though the CA beads have more adsorption sites in their structure, the adsorption sites of the CA-SCGs beads have more affinity to the metal than the adsorption sites of the CA beads. As was discussed earlier, the presence of phenolic groups in CA-SCGs can explain the greater affinity of these beads and their better performance when the adsorbent dose increased. As a result, the metal solution had a greater affinity with the adsorption sites of the CA-SCGs beads during the experiment, and at higher doses of CA-SCGs beads, the number of adsorption sites did not pose a problem.
# IoT-Based Intelligent Modeling of Smart Home Environment for Fire Prevention and Safety
## 7 Conclusions  
The primary objective of the proposed work was to design an intelligent analysis of smart home for fire prevention. Two major flaws of the currently used systems are: (a) the fire prevention systems mostly use a single sensor for event detection but problems arise if the target sensor does not detect the event; (b) false alarms can be generated. Overall our proposed method provides a solution to these problems. We introduced an efficient technique to overcome these problems. We used multi-sensors for each region in smart homes. To reduce the false alarms, we used the GSM communication system. The purpose of GSM communication was to alert the user at the very initial time of the fire. Fire detection decisions were made by the main home sink connected with all the sensors wirelessly. The decision was made on the basis of the sensor's values or the user's response. We simulated fire in FDS that was designed by NIST, and the generated results of the simulation were analyzed by our proposed algorithm that we implemented in Visual Studio using C++ libraries programming language. The simulators were installed on a machine with the following specification: Intel(R) Core(TM) i5-3570 CPU @ 3.40 GHz 3.80 GHz, and RAM 16 GB. The energy consumption of the deployed sensors was also computed, and we noticed that it was within an acceptable limit. The results and other evaluations showed that our proposed work fulfills all the desired requirements. In the future, as we used multi-sensors for the detection of fire and the amount of data generated by the sensors during a fire was high, we will work to find a method that deals with this high amount of data efficiently.  
This study was supported by a National Research Foundation of Korea (NRF) grant funded by the Korean government (NRF-2017R1C1B5017464). This study was also supported by a National Research Foundation of Korea (NRF) grant funded by the Korean government (MSIP) (NRF-2016R1A2A1A05005459). The grant covered the research work and the publication costs for open access publishing.  
[PERSON] prepared the comparative analysis report. [PERSON] established the entire framework of the study and selected the tools to be used. [PERSON] reviewed this work and suggested improving it by making use of various datasets. [PERSON] and [PERSON] used the selected tools to perform the simulations.  
The authors declare no conflict of interest.
# RoSe-Mix: Robust and Secure Deep Neural Network Watermarking in Black-Box Settings via Image Mixup
## 3 Proposed Method
### Motivation  
Although RoSe and Mixer offer strong protection against usurpers, where any drop in watermark recovery accuracy is closely tied to a drop in the main task accuracy, each method has inherent drawbacks:  
* Trigger set vulnerability: RoSe's main limitation lies in its trigger set, which acts as the Owner's key. Since the same trigger set is used for both watermark embedding and verification, it is vulnerable to a potentially untrustworthy verifier. Furthermore, increasing the number of watermark samples from the training data can degrade the model's performance on the main task, limiting the scalability of RoSe [23].
* Domain space overlap: In Mixer, the verification trigger set differs from the trigger samples used during watermark embedding. However, since both sets are derived from the same data domain, represented as a sphere in the feature space, there remains a risk that an attacker could generate keys \(\lambda\) and \(\mu\) that are very similar to the Owner's keys. This could place the attacker in the same domain space, making it difficult for the verifier to distinguish between the legitimate Owner and the attacker in case of a dispute [25].  
Although some of the limitations persist, our proposed method does not aim to resolve all of them. Instead, it combines the strong security features of both RoSe and Mixer, capitalizing on their complementary strengths to enhance the security robustness of the watermarking process. By merging their protective mechanisms, the new approach offers a higher level of security against potential attacks.  
Figure 2: Mixer method applied to the MNIST dataset for trigger generation with \(k=2\). From a Dirichlet distribution parameterized by \(\alpha\), two vectors are generated, \(\lambda\) and \(\mu\), that are used to mix up, respectively, \((x_{i},x_{j})\) and \((y_{i},y_{j})\) to generate \((\tilde{x},\tilde{y})\).
## 5 Performance Analysis and Results
### Reduction of Hallucinations in Complex Responses  
The RAG method significantly reduced hallucinations, particularly in complex and multi-step queries. The model's success in avoiding hallucinations--in which details are fabricated without a basis in the query or context--was notably higher when relevant contextual information was provided. It was shown through analysis that hallucinations were present in 12.72% of responses utilizing the RAG method, which is significantly lower than the rate that was observed in context-free predictions. The role of the RAG method in grounding the model's output is confirmed by providing pertinent data, enhancing the model's ability to handle complex queries accurately.  
The influence of relevant context as opposed to irrelevant context was identified as another critical factor. When relevant context was provided for generating responses, hallucinations were observed in only 2 out of 74 queries. In contrast, hallucinations occurred in 33% of cases when irrelevant context was present. The stark contrast is demonstrated, indicating that while the retrieval function of the RAG method generally mitigates hallucinations, its effectiveness depends on the retrieved context's relevance. The model showed reduced proficiency in differentiating between relevant and irrelevant contexts where precise semantic alignment was absent, leading to inaccuracies, as can be seen in Figure 6:  
Challenges were observed in handling queries requiring enumeration or listing items, such as steps in procedures or lists of documents. Hallucinations were observed in approximately 30.3% of responses in which the model listed items despite incorporating an RAG-based context. This frequency was exacerbated in cases where context was deemed irrelevant, highlighting the necessity for refinement in handling enumerative queries. Enumerative responses lacking adequate context heightened the likelihood of hallucinations,  
Figure 5: Prediction Accuracy with and without Context.  
and errors that could potentially mislead users in procedural or administrative tasks were also introduced.  
Further statistical validation of these findings indicated a significant improvement in hallucination reduction was observed when relevant context was utilized. The \(p\)-value calculations from the thesis support this outcome, indicating that the probability of hallucination-free responses was considerably higher with the application of RAG context (\(p<0.001\)). Consequently, the null hypothesis, which posits no significant difference between context-free and context-augmented predictions, is rejected.  
The effectiveness of the RAG method in limiting hallucinations in complex responses has been demonstrated; however, challenges persist with queries that require structured lists or involve ambiguous context. The relevance of context retrieval is improved, and the approach to enumerative data is refined, enhancing the VA's response accuracy further, thereby reducing risks associated with complex academic queries.
## 2 Hybrid Framework Description
### Adaptive Fusion Module
#### 2.2.2 Magnetic Disturbance Detector  
In this section, an MDD--namely, a magnetic element detector (MED)--is proposed to determine the quality of the magnetic field. Since magnetic fields are easily disturbed, the MED is necessary in the framework. Magnetic disturbances can be approximately divided into static disturbances (from constant disturbances, such as hard or soft iron disturbances) and time-varying disturbances (from time-varying disturbances, such as from electronic devices or power lines). A static magnetic disturbance can be detected when the total magnetic field intensity (\(F\)) is not equal to 1 (the magnetometer must be calibrated). The difference between \(F\) and 1 could indicate the quality of the magnetic field. To determine the time-varying disturbance, the average of the three-axis magnetic field strength variances (\(\bar{\sigma_{m}^{2}}\)) is introduced. Therefore, the multi-class MED is designed as  
\[MED=\left\{\begin{array}{ll}2&|\bar{F}-1|<\gamma_{F}\text{ \emph{and} }\bar{\sigma_{m}^{2}}<\gamma_{mv}\\ 1&\gamma_{F}\leqslant|\bar{F}-1|<\gamma_{F}^{\prime}\text{ \emph{and} }\bar{\sigma_{m}^{2}}<\gamma_{mv}\\ 0&\text{ \emph{otherwise}}\end{array}\right., \tag{3}\]  
where 2 denotes a pure magnetic field, 1 denotes a low-quality magnetic field, and 0 denotes a magnetic disturbance. \(\bar{F}=\frac{1}{W^{\prime}}\sum_{i=k}^{k+W^{\prime}-1}F_{i}\) denotes the average total magnetic field intensity in the sample window with a size of \(W^{\prime}\). \(\gamma_{F}\) and \(\gamma_{F}^{\prime}\) denote the validation gate thresholds for the total magnetic field intensity, and \(\gamma_{mv}\) denotes the variance threshold. In our experiment, \(\gamma_{F}\) is set to 0.05, \(\gamma_{F}^{\prime}\) is set to 0.2, \(\gamma_{mv}\) is set to 0.005, and \(W^{\prime}\) is set to 5.
## 4 Proposed Detection Scheme
### Algorithm Description
For \(|m|\leq M\) and \(n=0,1\), obtain the reduced search space based metric, \(\Phi_{d}(m,n)\), by formula (15). 2. The IFO estimate \(\vartheta\) is firstly obtained by finding the peak of \(\Phi_{d}(m,n)\) over \((m,n)\), by formula (20). 3. If \(\hat{n}=0\) in step 2, decide that the transmitted PSS is \(P_{0}(k)\), and complete the joint detection of IFO and SID. Otherwise, go to the next step to determine that the transmitted PSS is either \(P_{1}(k)\) or \(P_{2}(k)\). 4. Using the IFO estimate \(\vartheta\) obtained in step 2, calculate \(\Phi_{a}^{I}(\vartheta,1)\) and \(\Phi_{a}^{I}(\vartheta,2)\), by formula (14). 5. If \(\Phi_{a}^{I}(\hat{v},1)>\Phi_{a}^{I}(\hat{v},2)\), decide that the transmitted PSS is \(P_{1}(k)\). Otherwise, decide that the transmitted PSS is \(P_{2}(k)\).
## 1 Introduction  
Natural (biological) materials are a valuable source of inspiration for the design and fabrication of high-performance synthetic materials [1]. Through research on the complex structure and extraordinary (mechanical) properties of biological materials [2, 3], high-performance bio-inspired materials can be developed.  
The most ubiquitous biological material, wood, is a lightweight cellular composite with strong anisotropic mechanical properties that rivals contemporary synthetic materials [4]. Wood can not only bear substantial self-weight and wind load, but can also transport nutrients over long distances to sustain tree growth through its interior structure [5]. At the micro level, wood structure is primarily formed by parallel hollow tubes (tracheids in softwood or vessels in hardwood) that are composed of wood cells. This alignment of tracheids/vessels along the stem axis of a tree is the source of the special properties of wood [2, 4, 6, 7]. Lightweight wood-structure-mimicking composites could not feasibly be fabricated without the use of 3D printing and the assistance of X-ray computed tomography technology [8].  
Micro X-ray computed tomography (\(\upmu\)-CT) is a powerful tool for visualizing material structures and is often used to explore the structural properties of biological materials [9; 10; 11; 12; 13]. Images acquired by a \(\upmu\)-CT which correspond to the 3D structure of the parenchyma could be transformed into models for finite element analysis [14] and additive manufacturing (AM)/3D printing [9; 15]. AM/3D printing allows for the fabrication of parts exhibiting complex geometries, such as interconnected porous structures [16]. Although existing studies have explored the scanning and recreation of wood material structures through \(\upmu\)-CT scanning and 3D printing, the potential of utilizing the anisotropic nature of wood's elementary features to construct mechanically customizable lightweight composites has been scarcely discussed.  
In this paper, the three-dimensional porous structure of wood was reconstructed from \(\upmu\)-CT images. An elementary model selected from the reconstructed structure was combined to form the composite models. Numerical analyses were performed to demonstrate the relation between the mechanical properties of the composites and the arrangements of elementary models. The obtained elementary model and composites could be fabricated by AM/3D printing.
## 4 Performance Evaluation
### Transfer Learning Tasks
#### 4.2.2. NSL-KDD to UNSW-NB15 Transfer Learning  
In this task, we train the model on the NSL-KDD dataset (source domain) with abundant labeled samples and transfer the knowledge to the UNSW-NB15 dataset (target domain) with limited labeled data. Different from the previous experiments, as the datasets change, due to a relatively small dataset of NSL-KDD, we only configure one hidden layer for the source and target feature transform networks, and the classification network has three hidden layers. The dimension of common feature space, i.e., the number of units of output layer in the feature transform networks, is configured to 256. The training process includes three epochs, and each epoch contains multiple iterations, which depend on the batch size (set to 1024) and how much data we have in the dataset.  
We compare the proposed method with several methods that are mentioned in Ref. [41] to validate the superiority of our proposed method (denoted as _dthetl_), including:  
1. The _hemap_ method, which employs linear projection to transform the diverse source and target feature space into a shared latent space, concurrently minimizing projection errors and sample distances across different domains.
2. The _hetl_[15], which is similar to _hemap_ but includes clustering target data before each iteration.
3. The _base_ approach, which entails the direct training of the source domain while subsequently applying predictions to target domain data. This is accomplished by orchestrating the transformation of both source and target data into a shared feature space through principal component analysis (PCA).
4. The _hemmd_ method [41], which is similar to _hemap_ but minimizes cross-domain distribution distance with measurement of MMD.  
In total, seven transfer learning tasks are constructed. In each task, data samples belong to normal and an attack class are selected to represent source and target domain  
Figure 2: Influence of ReLU activation functions to model training.  
data from the NSL-KDD dataset and UNSW-NB15 datasets. To compare with existing methods, we employ several standard evaluation metrics for binary classification tasks, including accuracy. The results are shown in Table 4.  
From the results, we can see that the proposed _dhetl_ has the highest prediction accuracy in the given transfer learning scenarios. Except for _dhetl_, _hemmd_ has the highest accuracy compared with other methods, which has been analyzed in [41]. Compared with _hemmd_, the main improvement of _dhetl_ is attributed to the nonlinear projection of feature spaces to common space, which is more expressive than the linear projection in _hemmd_. In addition, _dhetl_ trains the network by optimizing the classification loss, which is directly related to the learning task, while _hemap_ is not since it optimizes projection loss. Furthermore, the compared methods only utilize partial data from the dataset to optimize their model, while the proposed method uses all available data to train a model. Therefore, we have verified that the proposed method outperformed other methods.
## 3 Optical Receiver Filter Equation-MGF Optimisation Procedure
### MGF-Based Model for Time-Invariant Rx System
#### 3.2.2 Use Case II: \(r_{\text{sig}}\)(t) with White AGTN Noise and ISI  
For a linear time-invariant Rx with PIN photodetector and a white AGTN noise, the LMGF in Equation (10) takes the following form [11; 15]:  
\[L_{r_{f(m)}}=(-1)^{m}\int\lambda_{m}(Exp[\ h_{f}]-1)dt+\frac{N_{0}}{4}\ \int\ h_{f}^{2}\ dt. \tag{16}\]  
Then, once we apply the same steps from Equations (11)-(13), the filter equation in Equation (14) changes to the following form:  
\[\lambda_{0}Exp[\ h_{f}]-\lambda_{1}Exp[-h_{f}]+\frac{N_{0}}{2}\text{h}_{f}^{ 2}+(\lambda_{1}-\lambda_{0})=0. \tag{17}\]  
Equation (17) accounts for the white AGTN noise alongside the main signal \(r_{sig}\). It is possible to conclude a reliable analytical solution of Equation (17) by taking the first three terms of the exponential power series:  
\[h_{f}=\frac{2(\lambda_{1}+\lambda_{0})}{(\lambda_{1}-\lambda_{0}-N_{0})}. \tag{18}\]
# Research on Model-Based Fault Diagnosis for a Gas Turbine Based on Transient Performance
## 2 Methodology
### Diagnostic System  
The diagnostic scheme was designed as in Figure 4. The input of the real gas turbine is the working condition, and the input of the simulation model is the working condition and health condition represented by the health parameters. The health condition consists of two parts, the degradation of component working ability and inertial coefficients. A detailed description of the degradation is shown in reference [23].  
The diagnostic process can be divided into three steps.  
1. Model simulation  
At the beginning of the calculation, we set the gas turbine faults in the gas turbine and obtained the measurable parameters after the operation of the gas turbine. By inputting the same input parameters as the measurable parameters of the fault model and inserting the health initial values diagnosed by the calculation model, the measurable parameters of the fault model can be estimated through the calculation model.
2. Objective function calculation  
The estimated parameters given in the initial calculation are generally incorrect, therefore the deviation exists between the model and the measurable parameters in the actual gas turbine. The deviation can be determined by the objective function in a certain calculation way. The object function of this paper is defined as:  
\[f_{obj}=\int\limits_{t=t_{i}}^{t_{i}}|Z_{t}-\overline{Z_{t}}| \tag{10}\]  
3. Optimization of the estimation parameters  
If the value of the objective function is small, the estimate can recognize and isolate the fault to achieve the goal of the fault diagnosis. However, when the deviation does not achieve the desired accuracy, the estimated initial value needs to be corrected through the optimization algorithm to return to the first process calculation. Such a process takes place recursively until the objective function reaches an acceptable accuracy.  
Figure 4: Scheme of fault diagnosis based on transient process measuring data.