esa-sceva/satcom-chunk-collection huggingface dataset:
# Covert Anti-Jamming Communication Based on Gaussian Coded Modulation
## 4 Undetectability Analysis
### Undetectability under an Uncertain Noise Level  
In practice, [PERSON] must estimate the noise level, \(\sigma_{n}^{2}\), to determine the proper threshold, \(\alpha\). The estimation error affects the tradeoff between the detection probability and the false alarm probability. Let the estimated noise variance be \(\sigma_{est}^{2}:=\rho\sigma_{n}^{2}\) using an uncertainty parameter, \(\rho\in[\rho_{\text{min}},\rho_{\text{max}}]\). Under this definition, the uncertainty increases when the interval \([\rho_{\text{min}},\rho_{\text{max}}]\) grows. In contrast, the uncertainty becomes smaller when the interval becomes narrower, and finally becomes zero when \(\rho_{\text{min}}=\rho_{\text{max}}=1\). In this scenario, [PERSON] sets the parameter \(\alpha\) by using \(\sigma_{est}^{2}\) instead of \(\sigma_{n}^{2}\) to set the decision threshold, \(\alpha\sigma_{est}\). Then, the detection and false alarm probabilities in Lemma 2 change according to \(\rho\), as shown below.  
**Lemma 3**.: _Let [PERSON], using an energy-detecting strategy, estimate the noise level to be \(\sigma_{est}^{2}:=\rho\sigma_{n}^{2}\) with the uncertainty parameter \(\rho\). Then, for the target false alarm probability of [PERSON], \(P_{\text{F,target}}=2Q(\alpha)\), the per-symbol detection probabilities of the proposed signal \(P_{D,Gaussian}\) and BPSK-modulated signal \(P_{D,Binary}\) are_  
\[\begin{split}& P_{D,Binary}=Q\big{(}\alpha\sqrt{\rho}-\sqrt{ \beta}\big{)}+Q\big{(}\alpha\sqrt{\rho}+\sqrt{\beta}\big{)},\\ & P_{D,Gaussian}=2Q\bigg{(}\frac{\alpha\sqrt{\rho}}{\sqrt{1+ \beta}}\bigg{)},\end{split} \tag{35}\]  
_and the actual false alarm probability becomes \(P_{\text{F}}=2Q(\alpha\sqrt{\rho})\)._  
One can easily prove Lemma 3 by calculating the \(\alpha\sigma_{est}\)-tail probability using the same procedure as used in Equations (32)-(34). Lemma 3 shows that the uncertainty of the noise level of [PERSON] only causes a shift in the tradeoff between the detection probability and the false alarm probability. This implies that \(P_{\text{F}}\) increases and \(P_{\text{D}}\) decreases if [PERSON] underestimates (\(\rho<1\)) the noise level; in contrast, \(P_{\text{F}}\) decreases and \(P_{\text{D}}\) increases if [PERSON] overestimates (\(\rho>1\)) the noise level. The results for different \(\rho\) are illustrated in Figure 7.  
In conclusion, Lemma 1 shows that the proposed method is more undetectable than the finite-field modulation method, regardless of the eavesdropping scenario. Lemmas 2 and 3 show that the proposed method is more undetectable than the traditional binary modulation method, especially when the eavesdropping scenario is more threatening.
## 3 Methods
### Data Acquisition and Analysis  
All chemicals emitted from the sample vial headspace were extracted using a SPME fiber of 2 cm 50/30 \(\upmu\)m DVB/PDMS/Carboxen (57299-U, Supelco, Bellefonte, PA, USA). All samples were heated to 37 \({}^{\circ}\)C (close to body temperature) using a digital hotplate during extraction to enhance the emissions. A 60 min extraction time was used for all of the extractions. After the extraction, the SPME fiber loaded with odorants inserted to the 250 \({}^{\circ}\)C GC injector for thermal desorption of samples to the GC columns and separation and analysis using MS and olfactometer.  
All carpet, urine, and water treated carpet, urine-microbe and water-microbe treated carpet, and cleaning product treated carpet samples were analyzed and completed using a custom multidimensional gas chromatograph (Microanalytics, Round Rock, TX, USA) built on Standard Agilent 6890N (G1530N) (Agilent Technologies, Santa Clara, CA, USA)-mass spectrometer (Agilent 5973N, Agilent Technologies, Santa Clara, CA, USA) olfactometer (GC-MS-O). The system Automation software was Multitrax v. 10.1 (MOCON, INC. Round Rock, TX, USA), the data acquisition software was Chemstation ver. D.02.00.275 (Agilent Technologies, Santa Clara, CA, USA), and aroma characterization was done using AromaTrax ver. 10.1 (Microanalytics, Round Rock, TX, USA).  
The GC oven temperature was programmed at the initial 40 \({}^{\circ}\)C for 3 min, followed by ramping up to 240 \({}^{\circ}\)C at a rate of 7 \({}^{\circ}\)C-min\({}^{-1}\), where it was maintained for 8.43 min. The quadrupole MS used the electron ionization mode with ionization energy of 70 eV during operation, and the full scan range was 34 to 350 \(m/\)z. The carrier gas used was ultra-high pure (UHP) helium (99.99%, Airgas, Des Moines, IA, USA). The GC used in this experiment has two columns in a series: the first one is a non-polar column with a fixed restrictor pre-column, and the second one is a polar column. The system was used in full heart cut mode with a total run time of 40 min. One part of the effluent from the polar column was simultaneously directed to the single quadrupole mass spectrometer and three parts of the effluent was directed to the sniff port of the Olfactometry.  
The peaks were identified using PBM-Benchtop software (Wiley7 library) and the NIST database library. Aromagrams for odors were generated using AromaTrax software, recorded and generated by the panelist. The odor intensity reported was on a scale of 0-100, where 0 was minimum, and 100 was the maximum. Odors characters recorded/reported by the panelist was verified with published odors descriptors [16; 17].
# Study on Active Tracking of Underwater Acoustic Target Based on Deep Convolution Neural Network
## 2 Kalman Filter Active Tracking Based on Deep Convolutional Neural Network
### The Structure of the DCNN Model
The parameters of the convo5 convolutional layer are as follows, the input is \(16\times 4\times 64\) feature input, the activation function 'ReLU', after convolution pooling, the output is \(8\times 2\times 128\). The fully connected layer is used to \"flatten\" the input, that is, to make the multidimensional input one-dimensional. It is often used in the transition from the convolutional layer to the fully connected layer. The fully connected layer has 2048 neuron nodes and is connected to the convo5 convolutional layer. The output layer uses the 'Softmax' function as the classifier. According to the number of tracking targets, set the corresponding number of nodes. The output \(y_{1},y_{2},\cdots,y_{n}\) from each node in the previous layer is used as the confidence level to generate a new output. The incentive function of each node is \(soft\text{max}\),  
\[soft\text{max}(y)_{j}=\frac{\text{e}^{y_{j}}}{\sum_{j=1}^{n}\text{e}^{y_{j}}} \tag{8}\]  
The output of the Softmax function is between 0 to 1, and the sum of the output values is 1, that is,  
\[\sum_{j=1}^{n}\text{e}^{y_{j}}=1 \tag{9}\]  
According to the Formula (9), the probability that a certain target exists on the beam azimuth can be calculated. After the DCNN model is built, the target data set can be input into the model for training, and the trained model can be used to identify the target.
## 4 Clustering Refinement Framework
### Clustering Refinement with Member Based Stability
* If there exist multiple majority labels, that means objects are shifting back and forth between clusters, and therefore those packaged unstable members are assigned to a new cluster, determined by their shifting range. * Otherwise, object occurrences in clusters are random. That is, each time we apply an alternative clusterer, its assignment changes correspondingly. Indeed, random assignment among all clusterers is probably outliers. For example, suppose \(K^{(0)}=\{2,3,4\},|D|=10,|M|=6\), and the clustering vectors are:  
\[ \begin{array}{ccccccccccccc}&x_{1},&x_{2},&x_{3},&x_{4},&x_{5},&x_{6},&x_{7},&x_{8},&x_{9},&x_{10}\\ M_{1}:&1,&1,&1,&1,&1,&1,&2,&2,&2,&2\\ M_{2}:&1,&1,&2,&3,&1,&2,&2,&2,&2,&2\\ M_{3}:&1,&2,&2,&1,&1,&1,&2,&2,&2,&2\\ M_{4}:&1,&2,&1,&1,&1,&2,&3,&2,&2,&2\\ M_{5}:&1,&1,&2,&4,&1,&4,&4,&2,&2,&2\\ M_{6}:&1,&2,&1,&1,&1,&4,&4,&2,&2,&2\\ \end{array} \]  
Figure 7: Illustration of clustering vector alignment process, where \(C_{1},C_{2},\ldots\) denote centroids of clusters with different point annotations, \(p_{1},p_{2}\) are two example objects, and dashed lines denote distances between \(p_{i}\) and \(C_{j}\). The upper figures are original clustering labels of (**a**) \(K=2\), (**b**) \(K=3\), and (**c**) \(K=4\), while the lower figures are relabeled clusterings. For each individual \(x_{i}\), count the vote of each label among all clusterers in the format of _cluster_ : _vote_ as following:  
\[x_{1}:\ 1:6\to 1\] \[x_{2}:\ 1:3,\ 2:3\to\text{frequent shifting between }1,2\] \[x_{3}:\ 1:3,\ 2:3\to\text{frequent shifting between }1,2\] \[x_{4}:\ 1:4,\ 3:1,\ 4:1\to 1\] \[x_{5}:\ 1:6\to 1\] \[x_{6}:\ 1:2,\ 2:2,\ 4:2\to\text{no majority, outlier}\] \[x_{7}:\ 2:3,\ 3:1,\ 4:2\to 2\] \[x_{8}:\ 2:6\to 2\] \[x_{9}:\ 2:6\to 2\] \[x_{1}:\ 1:6\to 1\]0  
If vote \(>6/3=2\), majority rule is applied. However, for \(x_{2},x_{3}\), the majorities are not unique, and they frequently shift between Clusters 1 and 2. The reason behind could be that the role of \(x_{2},x_{3}\) is fuzzy between Clusters 1 and 2, and in order to differentiate them from explicit stable members, we assign new labels. Otherwise, if no majority exists, such as \(x_{6}\), an outlier is detected since it does not belong to any clusters explicitly. Therefore, the final clustering vector is \(1,3,3,1,1,4,2,2,2,2\). In this way, clustering results are refined by integrating good enough results from multiple established clusterers.
# Efficient Lattice-Based Cryptosystems with Key Dependent Message Security
###### Abstract
Through the above comparison, we can observe three urgent problems in KDM-secure public key encryption schemes: (1) how to securely encrypt the complex functions of the secret key (not only itself); (2) how to construct the public-key encryption scheme with compact ciphertexts, and independent of the challenge functions, and (3) The existing compact cryptosystems are all based on lattice problems. A question is raised of how to modify the LWE problem to improve the efficiency of encryption and decryption. Our Results. In this paper, we analyse the KDM security of the public-key encryption scheme, and choose to use the RLWE (Ring-Learning with error) problem to build the compact cryptosystems. First, we apply the invertible technique [18] to obtain the RLWE\({}^{*}\) problem, then provide a new version by scaling the noise, and proving that it remains hard after the above modification. After that, we give a useful transformation to obtain the RLWE\({}^{*}\) assumption-Hermite normal form (HNF), namely, the secret chooses form error distribution. As it happens, through noise scaling, the secret key just fits into the message space \(R_{t}\), so our scheme can securely encrypt the linear functions of its secret key. Therefore, we easily construct a compact public-key scheme, analyze its correctness, and prove its key-dependent message security under the RLWE\({}^{*}\) assumption. In particular, through the proof of KDM-CPA security, we observe that the ciphertexts are pseudorandom with encrypting the secret key directly. If we do not expand the message space by scaling the noise, then it is possible to construct a symmetric-key scheme for KDM security by directly encrypting the secret key to its linear functions. Therefore, we further improve the RLWE\({}^{*}\) problem, propose its variant \(k\)-RLWE\({}^{*}\) problem, and demonstrate its hardness. For the message space \(R_{2}\), given a small enough Hamming weight \(h\) and making \(\left( \begin{array}{c}n\\ h\end{array} \right)\) large enough, we can obtain a binary secret symmetric-key scheme with less ciphertext noise. Finally, we prove that our scheme is KDM-CPA secure under the special \(k\)-RLWE\({}^{*}\) assumption and the cost of encryption and decryption is only \(\mathrm{ploylog}(n)\) bit operations per message symbol. Organization. In Section 2, we describe some important lemmas and give the formal definition of KDM-secure cryptosystems. In Section 3, we first introduce the RLWE\({}^{*}\) problem and a HNF (Hermite normal form) transformation, then construct a compact public-key scheme with KDM-CPA security. In Section 4, similarly to the previous section, the variant \(k\)-RLWE\({}^{*}\) problem and symmetric-key scheme are presented. In Section 5, we provide a detailed performance comparison. Finally, the conclusion is given in Section 6.
## 4 Experiment and Analysis
### Impact of the Number of Measurement Points  
The measurement error was \(\sigma\) = 0.1 \({}^{\circ}\)C, and the measurement points were taken as \(M\) = 11, 21, and 41. The inversion results of different numbers of measurement points are shown in Figures 6 and 7, and the average relative error is shown in Table 2.  
The results are as follows:  
1. When \(M\) = 11, the average relative error was 1.6%; when \(M\) = 21, the average relative error was 1.1%; and when \(M\) = 41, the average relative error was 1.4%.
2. When \(M\) = 11, the average relative error was 2.5%; when \(M\) = 21, the average relative error was 1.6%; and when \(M\) = 41, the average relative error was 1.4%.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Measurement Points** & **Case 1 Average Relative Error (\%)** & **Case 2 Average Relative Error (\%)** \\ \hline
11 & 1.6 & 2.5 \\
21 & 1.1 & 1.6 \\
41 & 1.0 & 1.4 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Impacts of different measurement points of cases 1 and 2.  
Figure 6: Identification results of different measurement points for inversion results of case 1.  
Figure 7: Identification results of different measurement points for inversion results of case 2.  
By increasing the measuring points, the average relative error decreased and the inversion accuracy improved.
# A Survey of Candidate Waveforms for beyond 5G Systems
## 4 Performance Results and Discussion
### Computational Complexity
Concluding, for DL transmissions, where this KPI is the most critical, due to the low processing capabilities of mobile terminals, the most suitable waveform is F-OFDM, since,  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Waveform** & **Complex Multiplications** \\ \hline OFDM & \(3N_{s}N\log_{2}(N_{s}N)+4N_{s}N\) \\ \hline F-OFDM & \(3N_{s}N\log_{2}(N_{s}N)+4N_{s}N+2I(N_{s}N+N_{cp})\) \\ \hline GFDM & \(N_{s}N(\log_{2}(N)+2\log_{2}(N_{s})+4+\log_{2}(N_{s}N)+\log_{2}(N_{s})+\) \\  & \(LL_{GFDM}(2\log_{2}(N_{s})+1))\) \\ \hline TIBWB-OFDM with WTO \\ \hline \(T_{x}+R_{x}\) A & \(4N_{x}\log_{2}(N_{x})+4N_{x}+2N_{s}N(1+\beta)+2\beta N_{s}N\) \\ \hline \(T_{x}+R_{x}\) C & \(4N_{x}\log_{2}(N_{x})+4N_{x}+(L+1)(N_{s}N(1+\beta))+L(2\beta N_{s}N)+(L-1)(N_{x} \log_{2}(N_{x})+N_{s}N(1+\beta))\) \\ \hline \when compared to OFDM, it only requires the sub-band filtering operations. On the other hand, GFDM and TIBWB-OFDM with and without WTO rely on transmission/reception of large signals with multiple OFDM-based sub-symbols and may include iterative interference cancellation algorithms. Therefore, along with the filtering and windowing operations, respectively, multiple FFTs and IFFTs must be employed to recover the original data, increasing significantly the receiver complexity.
## 3 Experimental Results
### Experimental Results  
In this subsection, the quantitative evaluation of the propose approach on different types of dynamic loads are give. The quantitative metrics utilized in the experiments are the root mean square error (RMSE) and the correlation coefficient (CC). Suppose that \(Q_{i},i=1,2,\cdots,N_{Q}\), is identified loads at timestamp \(i\), and \(N_{Q}\) is the total time. \(\hat{Q}_{i}\), i = 1,2, \(\cdots\), \(N_{Q}\) is the ground truth values of the loads. The RMSE is given by  
\[RMSE=\sqrt{\frac{1}{N}\sum_{i=1}^{N_{Q}}(Q_{i}-\hat{Q}_{i})^{2}}. \tag{8}\]  
The metric CC is given by  
\[CC=\frac{1}{N_{Q}-1}\sum_{i=1}^{N_{Q}}(\frac{Q_{i}-\mu_{Q}}{\sigma_{Q}})(\frac {\hat{Q}_{i}-\mu_{Q}}{\sigma_{Q}}), \tag{9}\]  
where \(\mu_{Q}\) and \(\sigma_{Q}\) are the mean value and standard deviation of the identified loads, respectively. \(\mu_{Q}\) and \(\sigma_{\hat{Q}}\) are those of the ground truths of the loads, respectively. A lower RMSE or higher CC would reflect better identification results of the load.
# Enhancing Privacy in Large Language Model with Homomorphic Encryption and Sparse Attention
## 2 Related Work
### Transformer  
The transformer model, introduced by [PERSON] et al. [4] in 2017, has precipitated a revolutionary shift in the field of NLP. This architecture abandons the traditional recurrent neural network (RNN) [27; 28] and convolutional neural network (CNN) [29; 30; 31] frameworks, being constructed entirely on the attention mechanism, which facilitates more efficient processing of sequence data and better captures long-distance dependencies, as shown in Figure 2.  
The core concept of the transformer is the parallel processing of all elements in a sequence, a stark departure from the sequential processing required by RNNs. It consists of an encoder and a decoder, each comprising multiple identical layers, with each layer containing two primary sub-layers: the multi-head attention mechanism and the position-wise feed-forward networks. Mathematically, the overall structure of the transformer can be described by the following formula:  
\[\begin{split}\text{Transformer}(Q,K,V)&=\text{ LayerNorm}(\text{FFN}(\text{LayerNorm}(\text{Attention}(Q,K,V)+Q))\\ &+\text{Attention}(Q,K,V))\end{split} \tag{13}\]  
Here, \(\text{Attention}(Q,K,V)\) denotes the output of the multi-head attention mechanism, FFN represents the position-wise feed-forward networks, and LayerNorm refers to the layer normalization operation.  
The encoder is composed of N identical layers, each with two sub-layers. The first sub-layer is the multi-head attention mechanism, and the second is a simple, position-wise fully connected feed-forward network. For each sub-layer, the transformer employs a residual connection, followed by layer normalization. This means that the output of each sub-layer is \(\text{LayerNorm}(x+\text{Sublayer}(x))\), where \(\text{Sublayer}(x)\) is the operation of the sub-layer itself. The multi-head attention mechanism allows the model to focus on different parts of the input at different positions. Specifically, multi-head attention maps \(Q\), \(K\), and \(V\) to the dimensions \(d_{k}\), \(d_{k}\), and \(d_{v}\), respectively, \(h\) times, then performs independent attention function computations on each mapped triplet, concatenates the outputs of all heads, and performs a final linear mapping to produce the final output.  
Due to its superior performance and flexibility, the transformer model has achieved remarkable success in NLP tasks and has been applied to various language models and tasks, such as BERT and GPT. In this work, the exploration focuses on integrating the transformer architecture with Homomorphic Encryption technology to provide high-quality conversational generation services while preserving user privacy. The challenge lies in designing an effective encryption-compatible transformer model that ensures computational efficiency and model performance do not suffer significantly due to encryption operations.
# Review of Weed Detection Methods Based on Computer Vision
## 3 Traditional Machine Learning Weed Detection Methods
### Traditional Features and Their Advantages and Disadvantages for Common Weed Detection
#### 3.1.3 Spectral Features  
Spectral features are an effective method to distinguish plants with different leaf colors. When the spectral reflectance of weeds is remarkably different from that of crops [70], weeds and crops can be distinguished using spectral features. The spectral features are robust to partial occlusion and tend to decrease in calculation [71]. Some scholars have applied visible light and near-infrared spectra (Vis-NIR) [72; 73], multispectral/hyperspectral imaging [74], and fluorescence [75] in the detection of different plants.  
[PERSON] et al. [76] distinguished corn crops and weeds by using the contents of chlorophyll and carotenoid retrieved using spectral indices or by inverting PROSAIL (coupled PROSPECT and SAIL radiative transfer models, [77]), as well as the species of weeds. Some scholars have also used Vis-NIR to classify weeds in crops, but studies are limited to laboratory feasibility studies and rely extensively on stoichiometry to select effective wavelengths and establish calibration models [78; 79]. [PERSON] et al. [80] achieved good results in the identification of weeds and crops by using RGB and multispectral images in a lettuce field. However, weeds in plateau tropical conditions have different shapes and grow in large blocks, such that detecting them is difficult. Spectral sensors (spectrometers) can be used to measure the reflection intensity of multiple wavelengths and provide sufficient information to distinguish vegetation from soil. Nevertheless, they hardly distinguish species, especially in the early growth stages when crops and weeds have similar reflective characteristics [81; 82].  
During the growth and development stages of plants, the interaction between light and observed geometry and leaf angle distribution, as well as the variability of the spectral features of plant species, can affect hyperspectral detection. Capturing a multispectral image, hence, depends on the climatic conditions of the day, which changes the reflectivity of plants due to the amount of light absorbed. Although research on the identification of crop weeds by using sensitive spectral bands has achieved encouraging results, the accuracy is low under the condition that the spectral difference between crops and weeds is unobvious or the leaf reflection is affected by moisture, plant disease, growth period, and other factors [83]. Therefore, a combination of multiple features, such as shape and texture features, should be considered [84].
# An Active-Clamp Forward Inverter Featuring Soft Switching and Electrical Isolation
## 3 Operation Principles
### State 2 (\(t_{1}<t<t_{2}\), Figure 6b)  
When the main switch \(S_{m1}\) is switched off, the leakage inductor \(L_{r1}\), the magnetizing inductor \(L_{m1}\), and the parasitic capacitance \(C_{ds1}\) are resonant in series. The current \(i_{Lr1}\) of leakage inductor starts to charge the capacitor \(C_{ds1}\), the voltage \(v_{ds1}\) starts to rise, and the primary voltage \(v_{p1}\) starts to drop. At this time, the forward diode \(D_{1}\) remains on, and the freewheel diode \(D_{2}\) remains off. The equivalent circuit of this state is as shown in Figure 6b. The voltage \(v_{ds1}\) across the main switch and the current \(i_{Lr1}\) through the leakage inductor can be expressed as  
\[v_{ds1}(t)\ =\ V_{in}[1-\cos\omega_{1}(t-t_{1})]+i_{Lr1}(t)Z_{1}\sin\ \omega_{1}(t-t_{1}), \tag{10}\]  
\[i_{Lr1}(t)\ =\ i_{Lr1}(t_{1})\cos\omega_{1}(t-t_{1})+\frac{V_{in}}{Z_{1}}\sin\ \omega_{1}(t-t_{1}), \tag{11}\]  
where \(\omega_{1}\ =\ \frac{1}{\sqrt{C_{ds1}(L_{r1}+L_{m1})}}\), and \(Z_{1}\ =\ \sqrt{\frac{(L_{r1}+L_{m1})}{C_{ds1}}}\). The time the state 2 required can be expressed as follows:  
\[t_{2}-t_{1}\ =\ \frac{1}{\omega_{1}}\tan^{-1}\left[\frac{V_{in}}{i_{Lr1}(t_{1} )Z_{1}}\right]. \tag{12}\]  
At the time \(t=t_{2}\), \(v_{ds1}\) rises to be equal to \(V_{in}\), this state ends and goes to the next state.