esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Related Work
### Privacy-Preserving Deep Learning (PPDL)
#### 3.1.1 Works Based on Differential Privacy  
Differential Privacy (DP) aims to provide strong privacy guarantees by adding noise to the data or model parameters. It ensures that the presence or absence of individual data points does not significantly affect the model's output. It has become a common approach for preserving privacy in machine learning [37]. [PERSON] et al. [1] is the first work considered privacy-preserving in collaborative deep learning where clients (training parties) learn a global model without sharing their data. In [1], each party obtains parameters from the server, runs the stochastic gradient descent (SGD) algorithm on its local dataset to update the local parameters, and then selectively uploads some of the gradients to the server. Sharing part of the gradients reveals some information about the training data; hence, they use differential privacy by adding noise to the parameter's updates. [PERSON]. [38] proposes a differentially private stochastic gradient descent (SGD) algorithm that calculates the gradients from a random sample and clips them. After that, it computes the average, adds noise, and descents in the opposite direction of the average noisy gradients. However, in this work, the accuracy is affected by using large privacy budgets. Different approaches suggest adding noise to the objective function [39] and the data [40] rather than gradients. Other works such as [41] suggest using local Differential Privacy to address scenarios where a centralized entity for data aggregation is not feasible or trustworthy, providing a decentralized alternative to protect individual data privacy without relying on a central curator. However, they are complicated approaches that demand much computation from the training party and result in lower accuracy even with low privacy budgets.  
To implement differential privacy mechanisms in deep learning, compromises are necessary to preserve utility. The choice of differential privacy variants and privacy budgets also affects the privacy and utility of machine learning models in practical applications. [PERSON] and [PERSON] [42] examined these matters in their work and discovered a significant difference between the upper bounds of privacy loss guaranteed by differential privacy mechanisms and the actual privacy losses measured through inference attacks.  
Overall, the application of differential privacy in deep learning requires careful consideration of the compromises needed to preserve utility while ensuring privacy. The choice of privacy mechanisms, privacy budget values, and evaluation of privacy losses through inference attacks are important aspects to address in privacy-preserving machine learning research.
# Anomaly Detection of Remote Sensing Images Based on the Channel Attention Mechanism and LRX
## 4 Experimental Results and Analysis
### Comparison with State-of-the-Arts
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{3}{c}{**Dataset**} \\ \cline{2-4}  & **HYDICE Urban** & **AVIRIS Airplane** & **Salinas Scene** \\ \hline LRX & 0.9737 & 0.9496 & 0.9291 \\ \hline BJSR & 0.7988 & 0.981 & 0.9533 \\ \hline LRaSMD & 0.779 & 0.9854 & 0.8187 \\ \hline MCRD & 0.9071 & 0.9512 & 0.8972 \\ \hline AE-based & 0.8871 & 0.9829 & 0.8929 \\ \hline AUTO-AD & 0.9646 & 0.9764 & 0.9582 \\ \hline Ours & 0.9871 & 0.9916 & 0.9642 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: AUC values of different algorithms on the three hyperspectral data. Figure 8: Visualized anomaly detection results of different algorithms on Salinas Valley data set: (**a**) LRX; (**b**) BJSR; (**c**) LRaSMD; (**d**) MCRD; (**e**) AE-based; (**f**) AUTO-AD; (**g**) ours; (**h**) ground truth. urban dataset, AVIRIS airplane dataset, and Salinas Valley dataset, respectively. The average running times for the AUTO-AD method for the HYDICE urban dataset, AVIRIS airplane dataset, and Salinas Valley dataset are 28.66, 38.57, and 35.97, respectively. On the other hand, the proposed method has average running times of 101.67, 78.81, and 131.97 for the corresponding datasets. The proposed method exhibits longer average running times due to the increased complexity introduced by the attention module and the time-consuming sliding window strategy employed in the LRX algorithm. Considering both the effectiveness of anomaly detection and the computational time required, the proposed method demonstrates satisfactory performance. The proposed hyperspectral anomaly detection algorithm based on channel attention mechanism and LRX aims to explore the effective bands of different land covers through the auto-encode network based on the channel attention mechanism, which makes the extracted features more discriminative. By utilizing the reconstructed image for anomaly detection, the impact of noise on the detection performance of the LRX algorithm is reduced. Therefore, on the HYDICE urban dataset, the AUC value of the proposed algorithm is 0.9871, while the AUC value of the LRX algorithm is 0.9737. On the AVIRIS airplane dataset, the AUC value of the proposed algorithm is 0.9916, while the AUC value of the LRX algorithm is 0.9496. On the Salinas Valley dataset, the AUC value of the proposed algorithm is 0.9642, while the AUC value of the LRX algorithm is 0.9291. These results highlight a significant improvement in the performance of the proposed algorithm compared to the LRX algorithm across all three hyperspectral datasets.
# Monitoring and Forecasting Green Tide in the Yellow Sea Using Satellite Imagery
## 3 Method
### Identification of U. prolifera and Sargassum by RS  
We usually use the spectral properties of macroalgae, which have a distinct reflection peak (700-800 nm) in the near-infrared band, to separate them from seawater. Although this reflection peak separates _U. prolifera_ and Sargassum from seawater, the presence of _U. prolifera_ can be demonstrated by the other reflection peak appears near the green band (550-570 nm), a spectral feature asents from Sargassum [7; 34; 35]. The RS images usually contain the spectral information of the above bands (Landsat 5 has spectral bands 1-6: 485, 569, 660, 840, 1676, 2223 nm; GF-1 has spectral bands 1-4: 485, 555, 676, 774 nm; HY-1C has spectral bands 1-4: 460, 560, 650, 825 nm). Therefore, the spectral characteristics of different bands of RS images can distinguish _U. prolifera_ and Sargassum, taking GF-1 as an example.  
As shown in Figure 7, the black points were the remote sensing reflectance of GF-1 RS data in different bands, and the solid black line connects all these points. The black dotted line connects the mean reflectance of band 1 (_Red_ band) and band 3 (_Blue_ band), and the formula of the line was fitted by \(y=ax+b\). The green line segment represents the difference between \(y2\) and \(y1\) (\(d=y2-y1\)), where \(y2\) is the mean reflectance of band 2 (Green band) of each RS datum and \(y1\) is the \(y\) coordinate of the same \(x\) value (band 2) on the black dotted line (\(y=ax+b\)). If \(d>0\), it meant that there was a reflection peak near the green band, which means this pixel was _U. prolifera_; otherwise, if \(d<=0\), there was no reflection peak which means this pixel may be Sargassum.  
Figure 6: Schematic diagram of percentile points.
# A Location Privacy Preservation Method Based on Dummy Locations in Internet of Vehicles
## 6 Performance Evaluation and Discussion
### Communication Overhead  
Figure 7 shows the impact of privacy parameter \(k\) on communication overhead in terms of data traffic. From Figure 7, we observe that the communication overhead of proposed RR-DLS method is concentrated on RSU and the LBS server, and the communication overhead at the vehicle user side is small. As privacy parameter \(k\) increases, the communication overhead in terms of data traffic increases.
## 4 Simulations
### NC-RBF-DNN Modeling Accuracy  
In this section, we compare the accuracy of NC-RBF-DNN with that of previous solar power generation prediction methods. As long as the performance of NC-RBF-DNN is comparable to or better than other methods, the rationality of importance ranking for input dimensions is demonstrated. The proposed scheme was compared with a DNN model (NC-RBF-DNN without the RNF layer) and the random forest model [11; 12]. For all three models, the inputs were 16 weather variables, and the output was the power generation by solar panels. Table 2 lists the performance results in the experiment. The NRMSE of the two DNN-based models was smaller than that of the random forest. This is not surprising, considering that DNNs typically employ more parameters to enhance prediction accuracy. The NRMSE of NC-RBF-DNN was slightly lower than that of the DNN, indicating the benefits of the RBF layer in enhancing prediction accuracy.
## 3 Description of QLM-ChPs Matrix Approach
### The RC Methodology  
In this part, we concentrate on the linearized boundary value problems (BVPs) (13) and (14). We first define the associated residual function of the linear operators. We then correct the current approximated solution by the technique of residual correction (RC). To do so, we first fix the iteration \(e\) and rewrite the BVPs (13) as  
\[\begin{cases}\mathfrak{L}[M_{e}(x)]=\pi_{e-1}(x),\quad x\in[0,1],\\ \frac{d}{dx}M_{e}(0)=0,\quad\frac{d}{dx}M_{e}(1)+s_{h}\,M_{e}(1)=s_{h},\end{cases} \tag{34}\]  
where \(\mathfrak{L}[M_{e}(x)]:=\kappa_{e-1,1}(x)\,\frac{d^{2}}{dx^{2}}M_{e}(x)+\kappa _{e-1,2}(x)\,\frac{d}{dx}M_{e}(x)+\kappa_{e-1,3}(x)\,M_{e}(x)\) is a linear differential operator. We also define the error term  
\[E_{e,H}(x):=M_{e}(x)-M_{H}^{(e)}(x), \tag{35}\]  
where \(M_{e}(x)\) is the exact solution of (34), and \(M_{H}^{(e)}(x)\) shows the approximate solution obtained by the QLM-ChPs approach as given in (15). The associated residual function is defined by  
\[\mathfrak{R}_{H}^{(e)}(x):=\mathfrak{L}[M_{H}^{(e)}(x)]-\pi_{e-1}(x). \tag{36}\]  
It is evident that we have  
\[\mathfrak{L}[M_{H}^{(e)}(x)]=\pi_{e-1}(x)+\mathfrak{R}_{H}^{(e)}(x). \tag{37}\]  
We now arrive at the error problem by subtracting (34) from (37), followed by using the definition of the error term given in (35). It follows that  
\[\begin{cases}\mathfrak{L}[E_{e,H}(x)]=-\mathfrak{R}_{H}^{(e)}(x),\quad x\in[0,1],\\ \frac{d}{dx}E_{e,H}(0)=0,\quad\frac{d}{dx}E_{e,H}(1)+s_{h}\,E_{e,H}(1)=0.\end{cases} \tag{38}\]  
We now solve the error differential Equation (38) with the aid of the previously illustrated QLM-ChPs technique. Here, we take \((H^{\prime}+1)\) basis functions such that \(H^{\prime}\) is greater than \(H\). Therefore, we obtain the following approximation  
\[E_{e,H}(x)\approx E_{e,H,H^{\prime}}(x):=\sum_{h=0}^{H^{\prime}}\partial_{h} \,\mathcal{S}_{h}(x),\quad x\in[0,1]. \tag{39}\]  
The preceding approximate solution \(M_{H}^{(e)}(x)\) will now be corrected by adding the former error term (39) to it. By denoting the new approximate solution of model (34) by \(M_{H,H^{\prime}}^{(e)}(x)\), we obtain the following improvement for the approximate solution  
\[M_{H,H^{\prime}}^{(e)}(x):=M_{H}^{(e)}(x)+E_{e,H,H^{\prime}}(x),\quad x\in[0,1]. \tag{40}\]  
Analogous to (32), we can define the REFs related to the new improved solution \(M_{H,H^{\prime}}^{(e)}(x)\). Let us denote it by \(\mathfrak{R}_{H,H^{\prime}}^{(e)}(x)\) for \(x\in[0,1]\).
## 3 Axis TF in the Area Coordinate System
### Area Coordinate System Based on Axis DOFs  
Equation (7) is equivalent to expressing the Cartesian coordinates of points in the triangle by coordinate interpolation of triangle vertexes. Since axis TF in the cylinder tolerance zone has four DOFs, interpolation of the ultimate state on each DOF can be used to express axis pose.  
The cylinder tolerance zone in Figure 1 is extracted and amplified to Figure 4. Base lines \(\$_{2}\) and \(\$_{3}\) in Figure 4 are pose of axis TF when the translational displacement along \(X\) and \(Y\) axes in \(O\)-\(XYZ\) are at the maximum. Under this circumstance, the corresponding area coordinate values \(\lambda_{2}\) and \(\lambda_{3}\) are equal to 1. Their contribution to axis variation on the translational DOF are \(u\) and \(v\). Base lines \(\$_{4}\) and \(\$_{5}\) are the pose of axis TF when the rotations around \(X\) and \(Y\) axes in \(O\)-\(XYZ\) are at the maximum. At this time, the corresponding area coordinate values \(\lambda_{4}\) and \(\lambda_{5}\) are equal to 1. Their contributions to axis rotation on the rotational DOF are \(\alpha\) and \(\beta\). \(\$_{1}\) is pose of axis TF when there is no error. \(\$\) is the axis of any pose in the tolerance zone and can be expressed by linear Equation (8) [19].  
\[\$=\lambda_{1}\$_{1}+\lambda_{2}\$_{2}+\lambda_{3}\$_{3}+\lambda_{4}\$_{4}+ \lambda_{5}\$_{5} \tag{8}\]  
Equation (8) reflects that any pose of axis TF correspond to one point in the four-dimensional space in the area coordinate value (\(\lambda_{1}\), \(\lambda_{2}\), \(\lambda_{3}\), \(\lambda_{4}\), \(\lambda_{5}\)). In other words, any axis pose in the tolerance zone in Figure 1 can establish linear relationships with tolerance and clearance through Equation (8). Here, (\(\lambda_{1}\), \(\lambda_{2}\), \(\lambda_{3}\), \(\lambda_{4}\), \(\lambda_{5}\)) might be negative and is used to express situations when variables \(u_{i}\), \(v_{i}\), \(\alpha_{i}\) and \(\beta_{i}\) are negative. Therefore, with reference to Equation (4), the constraint relation between area coordinate values of axis TF can be improved as:  
\[|\lambda_{1}|+|\lambda_{2}|+|\lambda_{3}|+|\lambda_{4}|+|\lambda_{5}|=1 \tag{9}\]  
For instance, \(\$_{1}\) expresses the ideal location of axis and (\(\lambda_{1}\), \(\lambda_{2}\), \(\lambda_{3}\), \(\lambda_{4}\), \(\lambda_{5}\)) = (1, 0, 0, 0, 0). When the axis is at \(\$_{4}\), (\(\lambda_{1}\), \(\lambda_{2}\), \(\lambda_{3}\), \(\lambda_{4}\), \(\lambda_{5}\)) = (0, 0, 0, 1, 0). When the axis is at \(\$_{7}\), (\(\lambda_{1}\), \(\lambda_{2}\), \(\lambda_{3}\), \(\lambda_{4}\), \(\lambda_{5}\)) = (0, 0, -1, 0, 0).  
Figure 4: Cylinder tolerance zone and baselines of area coordinate system corresponding to axis TF.  
This constraint relation ensures the coordinated relations among ultimate errors of axis TF on multiple DOFs.
# The Impact of Underlying Opaque White Coating Parameters on Flexographic Print Quality
###### Abstract  
Opaque white ink is highly important when printing on transparent substrates. The purpose of the white ink is to completely or partially cover the content of the packaging as well as to ensure the printing of other colors over it according to the appropriate printing specifications. The main goal of this study was to research the impact of anilox roller volume of opaque white on its opacity, and, consequently, on opacity, as well as the CIELAB values of colors that are printed over it. The research was conducted in three printing stages, during which the volume of the anilox roller was increased in linear steps of 3 cm\({}^{3}\)/m\({}^{2}\) while other parameters remained constant. The analysis of white ink covering properties was carried out on microscopic print images using ImageJ software. The results showed that a linear increase in the anilox roller volume resulted in an exponential change in white ink opacity as well as the opacity of color samples. It was also determined that a volume increase of 6 cm\({}^{3}\)/m\({}^{2}\) was required to visually notice color difference. Namely, medium lightness colors more strongly reacted to changes in white ink opacity compared to light and dark colors.  
flexographic print quality; opaque white ink; covering property; anilox volume Article
## 2 Materials and Methods
### Pre-Processing  
As the method that was used for this study was originally designed for radiological (specifically, magnetic resonance) images, the histology images needed to be processed to make the characteristics like them. Firstly, taking into consideration the large size of the images used in the evaluation of the proposed approach, we resampled the images on the basis of a factor (\(f\)) of 1/25 (4%), resulting in image sizes where the minimum and maximum size of each side was between 200 and 700 pixels, respectively, which made the size like that of radiological images, and also helped reduce computation time and memory requirement. To prevent potential aliasing caused due to the large resampling factor, we smoothed the images using a Gaussian kernel (\(\sigma=f/2\)) before resampling (Figure 4). The size of the Gaussian kernel was chosen using the Nyquist-Shannon sampling theorem [PERSON] (1941), according to which if we want to preserve the invertibility of a transform, the sampling frequency needs to be at least twice the highest frequency of a signal, thereby ensuring that smoothing occurs without loss of structural information within the tissue region.  
Furthermore, noting that the provided pairs of images were of varying sizes, we padded each image to ensure that (i) the size of paired images were the same (this step is not mandatory but simplifies the application of the transformation on landmarks) and (ii) the target tissue was in the image center. Once all image pairs were padded such that they were of the same size, we further padded them (4\(\times\) the size of the similarity metric's kernel, Equation (1)) to ensure that the apparent tissue was far enough from the image boundaries, and hence accommodated appropriate calculation of the deformation field after changes caused by the affine registration step. A binary mask, computed by excluding the padded portions of the image (size of the similarity metric kernel, Equation (1)) was also used during the affine registration process.  
The mask defined the area that computations should be performed, which resulted in improved computational efficiency and no mismatches in terms of boundaries. The padded areas were filled with Gaussian noise matched to the distribution of image intensity in the 4 corners (the size of the similarity metric kernel, Equation (1)) of the unpadded image, which lowered the response of the normalized cross-correlation (NCC) metric along the border between slide background and the padded area (Figure 5).  
Figure 4: Gastric image resampled before (**A**) and after (**B**) smoothing with a Gaussian kernel.
# Evaluation of Coastal Sea Level Offshore Hong Kong from Jason-2 Altimetry
## 3 Data Sets
### Coastal Altimetry Data Sets
#### 3.1.3 Geophysical Corrections  
In PISTACH and X-TRACK, state-of-the-art geophysical corrections other than those of the official GDR are provided. For X-TRACK, only the ocean tide solution and the DAC are provided individually, while in PISTACH, two to three values are given for each correction. Different sets of correction terms obviously lead to different coastal sea level estimates.
## 5 Discussion
* Finally, the testbed deployment discussed here is based on IoT microcontrollers of marginal speed and memory size; a step beyond this, more powerful units of larger memory size can be used to execute machine learning models in line with the future trend for on-device intelligence and decision making [49; 50] by running tailored machine learning algorithms and reducing data amounts to be transferred to the cloud. All these improvements should be developed with respect to the support of higher layer interoperability, security, and privacy modalities [51] toward a sustainable future of agricultural operations.
## 2 Materials and Methods
### Implementation of a Voice Assistant with Cache Storage  
In this experimental phase, a device capable of utilizing natural language recognition and processing techniques was created, providing a highly energy-efficient and much faster VA compared to conventional VAs. It is essential to clarify that the tests conducted in this work are independent of those used for the system's design and creation.  
Implementation of a VA on Raspberry Pi Using Python  
One of the processes required by VAs is synthesizing voice traffic into text. This requires proprietary or standard protocols to perform this task, either on the local device or the ISP cloud servers. To implement a VA system that allows receiving queries, obtaining responses from an AI system in the cloud, and storing this information in a cache to return it to the user later, a state-of-the-art device with sufficient hardware capabilities was used for the proper functioning of a VA and the implementation of local cache. In this case, a Raspberry Pi 4 with 4 GB of RAM and a 1.5 GHz processor was used, programmed with Python version 3.11 and an omnidirectional USB 2.0 microphone with 1 dB sensitivity.  
For this experimental phase, the following configurations and implementations were carried out:  
_Implementation of an STT system using Vosk (vosk-model-es-0.42)_[13]: this tool allows a pre-trained model to recognize multiple languages, interpret voice with its different tones, and convert it into text.  
_Interaction with a Natural Language Processing (NLP) system_[41]: TensorFlow Text library was used to create a model for interpreting user queries. Since humans can ask the same thing in various ways in natural language, an AI, which is capable of calculating the similarity between two texts in percentage, was necessary. This helps determine if a query associated with a response had been previously made. If so, the response is retrieved from the cache. The AI can validate if two requests are equivalent using a probabilistic method based on the cosine function, which defines a similarity value. If the defined threshold is met, the AI compares it with stored responses using the same cosine function to find the appropriate answer for the user.  
_Use of Generative AI (cloud service)_[42]: the text converted from voice represents a question/query. If it is not in the cache, a Generative AI resolves it. OpenAI (gpt-4) was chosen for its reliability, high recognition, and usability in the current world.  
_Storage of questions and answers in a cache_: pairs of questions and their answers are stored in memory, and a file with this information is created, allowing it to be preserved over time.  
_Translation of text responses to speech (TTS) (yyttsx3)_[43]: the response delivered to the user is synthesized into speech using the text-to-speech system.