esa-sceva/satcom-chunk-collection huggingface dataset:
# Enhancing Privacy in Large Language Model with Homomorphic Encryption and Sparse Attention
The introduction of this technology provides new possibilities for protecting privacy in conversational systems. However, applying FHE technology to conversational large models is not without challenges. Firstly, traditional FHE schemes have significant limitations in computational efficiency, making them difficult to apply directly to online conversational systems requiring quick responses. Secondly, the complexity of conversational large models themselves and the high-dimensional data processing requirements further exacerbate this challenge. Against this backdrop, the present study proposes a privacy-preserving conversational large model framework based on FHE and attention mechanisms. The at tention mechanism, a technique that allows models to focus on important parts of the input sequence, has proven its utility in the field of natural language processing, particularly within Transformer models [4; 5]. It enables the identification of information segments most relevant to responding to user requests by allocating different weights. Our study not only explores how to combine the attention mechanism with FHE to process encrypted data but also proposes a dynamic sparse attention module aimed at enhancing the model's computational efficiency and processing speed. In this paper, we first provide a detailed review of the fundamental principles of FHE and its applications in privacy protection. We then discuss the attention mechanism, specifically its application in transformer models, and how it can contribute to performance improvements in conversational systems. Next, we introduce our proposed FHE-based privacy-preserving conversational large model framework, explaining how complex dialogues can be processed without sacrificing user privacy. Additionally, we propose a dynamic sparse attention module, an important enhancement to existing technology, which significantly improves the model's computational efficiency while maintaining privacy protection. The significance of this research lies in that it not only proposes a new model for privacy-safe conversational systems but also offers practical guidance on effectively integrating FHE technology into real-time, efficient conversational systems. Through the studies presented in this paper, we aim to provide a more robust theoretical foundation and technical support for privacy security in conversational systems, offer stronger data security for users, and provide direction for future research in this domain.
## 3 Proposed Method
### Problem Formulation  
To screen a patient for breast cancer detection, two commonly used views of a patient's mammogram are the MLO and CC views. The problem is identifying the breast density, severity level/pathology, and findings from the two views. Based on the BI-RADS (Breast Imaging Reporting and Data System) guidelines, there are four density levels, BI-RADS I, BI-RADS II, BI-RADS III, and BI-RADS IV, which are used to classify breast density, where BI-RADS I represents the category with the lowest density, while BI-RADS IV corresponds to the category with the highest density. Additionally, there are two main abnormality types, masses, and calcifications, which are important to identify. Finally, the severity level/pathology means whether the case for the abnormality type is benign or malignant.  
We represent an ROI as \(\mathbf{x}\)\(\in\)\(\mathbb{R}^{m\times n}\), where \(m\) and \(n\) represent the resolution of the ROI. \(x_{\text{MLO}}\) and \(x_{CC}\) stand for the ROIs extracted from the MLO view and the CC view, respectively. There are eight categories: BI-RADS I (1), BI-RADS II (2), BI-RADS III (3), BI-RADS IV (4), mass (5), calcification (6), benign (7), and malignant (8). The first four categories correspond to density types, the next two categories represent abnormality types, and the last two categories stand for severity levels (pathology). In view of this, the label for a pair of ROIs \((x_{\text{MLO}},\ x_{CC})\in\mathbb{R}^{m\times n}\times\mathbb{R}^{m\times n}\) corresponding to a patient is \(l=\left(l_{d},\ l_{f},\ l_{p}\right)\), where \(l_{d}\in Y_{d}=\left\{0,1\right\}^{4}\), \(l_{f}\in Y_{f}=\left\{0,1\right\}^{2}\), and \(l_{p}\in Y_{p}=\left\{0,1\right\}^{2}\); \(Y_{d}\), \(Y_{f}\), and \(Y_{p}\) are the label spaces of the density type, abnormality type/findings, and severity level/pathology, respectively, in one-hot encoding; and 0 means absent, and 1 means present. For example, if \(l=\left(l_{d},\ l_{f},\ l_{p}\right)\), where \(l_{d}=\left[0\ 1\ 0\ 0\right]\), \(l_{f}=\left[1\ 0\ 0\right]\), and \(l_{p}=\left[1\ 0\right]\), then the density level is 2, the finding is a mass, and the case is benign. It is a multi-label classification problem. We need to design a mapping \(\varphi:\mathbb{R}^{m\times n}\times\mathbb{R}^{m\times n}\to Y_{d} \times Y_{f}\times Y_{p}\), such that \(\varphi(x_{\text{MLO}},\ x_{CC})=\left(l_{d},\ l_{f},\ l_{p}\right)\).  
We employ deep learning techniques to design the mapping \(\varphi\) that extracts discriminative features from the input ROIs and associates them with three labels in an end-to-end manner. In the following subsections, we give the details of the deep-learning-based method for modeling \(\varphi\).
# Survey on Security Threats in Agricultural IoT and Smart Farming
## 1 Introduction
These challenges are often hard to deal with effectively, due to their range of possible cyber and physical security threats. Nowadays, ICT systems are the number one cyber-criminal means worldwide for stealing money, intellectual properties, business secrets and other assets. The associated term of cyber-security refers to the combination of techniques, skills and processes in order to ensure a high degree of protection for network, computers, programs and data, against malware, attacks, damage and unauthorized access. In this context, on a daily basis, new types of cyber threats are emerging, including ransomware, endpoint attacks, phishing, third party attacks, supply chain attacks, artificial intelligence and Machine Learning-driven attacks, crypto-jacking, cyber physical attacks, state sponsored attacks, IoT attacks, threats to smart devices, attacks on connected, semi-autonomous or autonomous, vehicles [8]. The significant increase in terrorism, financial devastation and physical injuries or deaths are the result of these types of cyber-attacks. The aforementioned types of threats, alongside the existing physical hazards in agriculture [9], form the basis for creating models that can be used to detect system and network vulnerabilities. The application of cyber tools and (cyber) physical infrastructures as well as of new stronger cybersecurity measures and techniques are of the utmost importance in order to encounter these attacks successfully. This paper is an extensive literature review of the benefits, the security issues and threats as well as theeffective mitigation strategies posed by the introduction of modern ICT solutions in the domain of agriculture. Advantages, security issues, cybersecurity and IoT-related threats as well as mitigation measures are organized and presented into distinct sections. The remainder of this paper is structured as follows: Section 2 presents an overview of ICT applications and benefits in the agricultural domain; Section 3 focuses on the vulnerabilities, risks and threats in Agriculture 4.0; Section 4 focuses on mitigation strategies and techniques concerning the risks presented in the previous section and finally, Section 5 concludes the paper.
# Research Progress on Router Devices for the OAM Optical Communication
## 4 OAM Channel Filtering
The team verified the OAM filtering in the subsequent experiments, and the experimental results are highly consistent with the theoretical results, and the blocked output port power is suppressed by more than 23 dB by the filter, as shown in Table 4.  
In order to realize the high-fidelity output of OAM mode, in 2019, [PERSON] et al. [136] combined the advantages of the interferometer and proposed another novel OAM filter using the interferometer. In this scheme, multi-beam interference filtering is realized by controlling the relative phase difference in the different OAM beams. The principle is shown in Figure 5b. First, the polarized beam splitter (PBS) is used to divide the input \(n\) OAM multiplexed beams into \(n\) equal parts, and then the relative phase difference in the different OAM beams is controlled by the Dow prism. Additionally, a half-wave plate (HWP) is utilized to counteract the half-wave loss due to the reflection at each branch interface. Moreover, a series of phase compensators are placed to adjust for the phase discrepancies arising from the different optical path branches. Since the relationship between OAM mode and phase difference satisfies \(\theta=2\pi l\), only a specific OAM mode value can achieve 100% passing, while other OAM modes will be suppressed, so as to realize the OAM filtering function as presented in Table 4. This filtering reduces the overlapping effect of adjacent OAM modes and improves the filtering effect. It can be used in the fields of single photon recognition and optical switching. In 2022, [PERSON] et al. [137] proposed and experimentally demonstrated a chiral long-period fiber grating (CLPG)-based scheme for filtering spin-entangled OAM of photons by utilizing the inherent spiral characteristics of a CLPG. The authors demonstrated both theoretically and experimentally that co-handed OAM, characterized by the same chirality as the helical phase wavefront of a CLPG, couples with higher-order cladding modes and experiences a loss. Conversely, cross-handed OAM with the opposite chirality propagates freely through the CLPG. To input \(\ket{L,\,1}\) and \(\ket{R,\,-1}\), for example, on the right side of CLPG, a transmission loss is observed, as shown in Figure 5c. Since the spatial light coupling at the input end to the fiber will inevitably excite the fundamental mode (**HE**11), there will be not only the input vector vortex beam (VVB) but also the **HE**11 mode in the grating. In addition, the excited **HE**11 mode will be coupled with the cladding **HE**21 mode, and the loss will occur near the resonant wavelength. The **HE**11 modes excited by input \(\ket{L,\,1}\) and \(\ket{R,\,-1}\) are the same. Excluding the loss from **HE**11 coupling, there will be no extra loss as the input VVB \(\ket{L,\,1}\) will freely pass, while the input VVB \(\ket{R,\,-1}\) will couple to
# Spectrum Allocation Based on an Improved Gravitational Search Algorithm
## 3 Technologies
### Algorithms  
#### 3.2.1 Basic Thoughts  
Some studies have shown that GSA could outperform other metaheuristic algorithms [20], especially in global search. Still, similar to other swarm intelligence algorithm, GSA can easily fall into local optimum and lack fine searchability. Based on the above, GSA and PSA are respectively chosen as coarse search and fine search of the spectrum allocation mechanism. Therefore, the position of the particle with the heaviest mass in the GSA iterations represents a close-to-optimal solution for spectrum assignment, which is used as the initial value of PSA for further local search.
# Effect of Blue Light Intensity During Spreading on the Aroma of Green Tea
## 3 Results
### Validation of Important Differential Volatile Compounds  
A prior study indicated that the impact of aroma compounds in the system is influenced by the ratio of their concentration to the threshold [9]. Thus, we used the 53 compounds identified through OPLS-DA analysis in rOAV analysis to differentiate green tea samples exposed to various light conditions. Table 2 shows that 16 compounds were identified using GC-MS, each with rOAVs > 1. The rOAV of linalool exceeded 800, indicating a significant impact on the aroma, while hexanoic acid ethyl ester, geraniol, (Z)-2-octen-1-ol, naphthalene, and \(E\)-cital (geraniol) (rOAV > 10), were identified as a key aroma component. The 16 volatile compounds that contribute significantly to the aroma of green tea under different light conditions during spreading were shown in Figure 7. MBL samples had the highest concentrations of coumarin and hotrienol from fatty acid-derived volatiles. The content of 5-ethyldihydro-2(3H)-furanone and (\(Z\))-2-octen-1-ol were significantly increased under LBL treatment. Hexanoic acid ethyl ester significantly accumulated under HBL and LBL treatments, while delta-decalactone significantly accumulated under DARK treatment (Figure 7A). After spreading, the concentrations of geraniol, cis-linalool oxide (_furanoid_),  
Figure 6: Analysis of 53 differential volatile compounds with VIP > 1 in green tea samples with different spreading light treatments.  
_trans_-linalo oxide (_furanoid_), linalool, and \(E\)-citral (geranial) from volatile terpenoids were significantly higher in MBL samples, whereas levels of enrol were higher in HBL samples (Figure 7B). Indole and methyl salicylate were two amino acid-derived volatiles; indole significantly accumulated under LBL treatment, while methyl salicate accumulated under MBL treatment (Figure 7C). In addition, the content of 2,6,6-trimethyl-1,3-cyclohexadiene-1-carboxaldehyde from carotenoid-derived volatiles was significantly higher in MBL than other treatments (Figure 7D). Naphthalene from other sources was accumulated only under increased blue light treatment after spreading (Figure 7E).  
Figure 7: Effect of different light conditions on the contents of 16 important differential volatile compounds contributing to the aroma of green tea. (**A**) FADVs. (**B**) VTs. (**C**) AADVs. (**D**) CDVs. (**E**) Other sources of volatiles. Red indicates upregulation, and blue indicates downregulation.
# Terahertz Emission Enhanced by a Laser Irradiating on a T-Type Target
## 2 Simulation Setup  
In order to demonstrate the enhancement effect of the TTT on THz emission and the physical reasons for it, we used the PIC code of open source EPOCH [52] and carried out a series of 2D simulations. For the TTT scheme, the initial setup in the simulations is displayed in Figure 1. The simulation box was located at the \((x,y)\) plane, and the simulation region was set as \(200\lambda_{0}~{}\times~{}100\lambda_{0}\) with \(6000~{}\times~{}2000\) grids, where \(\lambda_{0}=1.0~{}\upmu\)m was the laser's wavelength. In addition, the quasi-particle number of every cell was 50 per kind of particle. Both targets I and II were composed of a material (we assume gold, Au) and electrons were utilized as the TTT. The TTT-I was distributed from \(5\lambda_{0}\leq x\leq 75\lambda_{0}\) along the \(x\)-axis and \(-2\lambda_{0}\leq y\leq 2\lambda_{0}\) along the \(y\)-axis, with number density \(n_{e0}=50n_{c}\), while the TTT-II was distributed from \(75\lambda_{0}\leq x\leq 80\lambda_{0}\) with \(n_{e0}=50n_{c}\), where \(n_{c}=m_{e}\omega_{0}^{2}/4\pi e^{2}\) was the critical plasma number density, with \(m_{e}\) being the electron mass, \(e\) being the electron charge, and \(\omega_{0}\) being the laser frequency. Since the laser intensity was much larger than the ionization potential of the Au ions [44; 53; 54], they were assumed to be ionized to Au\({}^{50+}\) due to the limitation of the computing resources. Furthermore, the ions were mobile in all of the simulations. As a comparison, the SLT located at \(75\lambda_{0}\leq x\leq 80\lambda_{0}\) with only target II \(n_{e0}=50n_{c}\) was set in the simulations. Meanwhile, it should be noted that we also performed a series of simulations on other material (CH), and the obtained results were qualitatively consistent with the case of Au. Moreover, it is appropriate not to include collisions since the collisional absorption is dominant at a much lower intensity [55; 56; 57; 58].  
A transverse Gaussian and p-polarized laser with temporal and spatial profile \(I=I_{0}\exp[-(t-t_{0})^{2}/\tau_{0}^{2}]\exp(-y^{2}/\tau_{0}^{2})\) was normally incident from the left boundary of the simulation box. The peak intensity was \(I_{0}=6.0\times 10^{20}\) W/cm\({}^{2}\), corresponding to the normalized vector potential \(a_{0}=eE_{0}/m_{e}c\omega_{0}=21\) for \(\lambda_{0}=1.0\)\(\upmu\)m. Furthermore, the laser parameters \(r_{0}=6.0\lambda_{0}\) and \(t_{0}=\tau_{0}=5T_{0}\) were set to fixed values in all simulations, where \(T_{0}\) was the laser period. In addition, the \(x\)-direction and \(y\)-direction were the absorption boundary for both fields and particles, respectively.
# Simulation-Based Model-Updating Method for Linear Dynamic Structural Systems
###### Abstract
[14] proposed a method based on FRF functions to update the FE model using the Sherman-Morrison Formula. [PERSON] et al. [15] applied the improved Inverse Eigen-sensitivity Method (IEM) to update the FE model of a plane truss structure. This method calculates the required insensitivity coefficients, which closely approximate their actual values, using analytical and experimental modal data. In recent years, new methods have been applied to model updating, incorporating neural networks and genetic optimization algorithms [16; 17]. Implementing these methods is laborious due to the requirement for complex solutions. The complex nature of damping mechanisms in structural systems makes parameterizing the damping matrix more challenging than mass and stiffness matrices [18]. This paper aims to present a simulation-based model-updating method that effectively corrects the stiffness and damping matrices without imposing a high computational burden. The matter is formulated as an optimization problem that is solved by the parameter estimation tool of MATLAB/Simulink. This model-updating strategy is based on transferring the test data to the Simulink environment and updating the damping and stiffness coefficients in the system simulation model. The dynamic behavior of the building model under the effect of the El Centro earthquake and the results of the free vibration test were considered when selecting the parameters to be updated. The presented updating method demonstrates high accuracy in reproducing the measured dynamic responses of the building model. The subsequent sections of this paper are structured as follows: Section 2 presents the establishment of the structural system model and a comparative analysis of natural frequencies, mode shapes, and dynamic responses through experimental and theoretical approaches. In Section 3, the free vibration of the building model is formulated as a Simulink block diagram. Section 4 introduces and applies the simulation-based model-updating method to the structural system, effectively illustrating the efficiency of the proposed technique. Lastly, Section 5 draws comprehensive conclusions based on the findings.
## 3 Demand Forecasting Models and Power Theft Detection Strategy
### ARIMA Model
**Step 7:**: _Determination of accuracy_: Finally, the various erroneous parameters are considered to determine the efficiency of the model. Various error parameters are tabulated in the results section. Generally, a time series data \((S_{m})\) is a combination of seasonality \((L_{m})\), trend \((T_{m})\), and noise \((N_{m})\) components, which can be determined as \(S_{m}=L_{m}+T_{m}+N_{m}\), known as an additive model. This additive model does not show good performance for the household electricity consumption data prediction due to its dependence on seasonality. Therefore, a multiplicative model, given as \(S_{m}=L_{m}\times T_{m}\times N_{m}\), is preferable. The multiplicative model can be transformed into an additive model with the introduction of logarithms, given as:  
\[\log S_{m}=L_{m}+\log T_{m}+\log N_{m} \tag{2}\]
## 2 Materials and Methods
### Cell Viability  
The human cervical cancer HeLa cells from the American Type Culture Collection ATCC (Rockwell, MD, USA) were employed to study _cell viability_. The cells were maintained under 37 \({}^{\circ}\)C, 95% humidity, and 5% CO\({}_{2}\) in culture medium composed of Dulbecco's modified Eagle's medium (DMEM) supplemented with penicillin (100 U/mL), amphoteric (250 ng/mL), streptomycin (10 ug/mL), and 10% fetal bovine serum.  
The MTT (3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazololium bromide) assay described by [PERSON] et al. (2011), with some modifications, was used to measure the impact of different concentrations of NEP extract on _cell viability_. First, HeLa cells were seeded in 96-well plates (10,000 cells/well), and they were incubated for 24 h. After, the cell medium was interchanged using DMEM together with different concentrations of NEP extract (0.025-0.500 mg/mL) diluted in water, employing a total volume of 200 \(\upmu\)L/well and using DMEM medium without samples as a control. In addition, milli-Q water was added to the NaDES in the same amount as the extract dilutions to determine the solvent cytotoxicity. Afterward, 10 \(\upmu\)L of 5 mg/mL MTT stock solution in phosphate buffer was added to each well and incubated for 3 h. Next, the medium was detached, and 200 \(\upmu\)L of dimethyl sulfoxide was added to dissolve formazan crystals.  
The absorbance of formazan solutions was determined at 570 nm (with a reference wavelength of 630 nm). Percentage of _cell viability_ was determined as follows:  
\[\%\;cell\;viability=\frac{Abs\;sample}{Abs\;control}\times 100 \tag{4}\]
## 3 Results
### Evaluation Indicators  
The shadow detection results of images can be evaluated using the producer's accuracy, user's accuracy, committed error, omitted error, overall accuracy, and kappa coefficient from the confusion matrix [44], as summarized in Table 1.  
A higher producer's accuracy and user's accuracy for shadows, as well as a lower omitted error, indicate the better ability of the shadow detection method to recognize shadows. A higher producer's accuracy and user's accuracy for non-shadows and a lower committed error indicate its better ability to distinguish non-shadows [45]. Overall accuracy refers to the ratio of the number of correctly categorized pixels to the total number of pixels in the image, with higher levels indicating a better overall capability of the shadow detection method. A higher kappa coefficient indicates the shadow mask is closer to the ground truth. Ideal shadow detection methods should usually have a higher producer's accuracy, user's accuracy, overall accuracy, and kappa coefficient, as well as a lower committed error and omitted error.