esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 System Analysis
### Security Analysis
#### 5.2.2 Security Analysis of the Presented Scheme  
**Conclusion 2:** The presented scheme can achieve privacy protection requirements \(<L_{(k,s)},Q_{l}>\).  
**Proof.** In a single query, the presented scheme is clearly able to meet the user's privacy protection requirements \(<L_{(k,s)},Q_{l}>\). In \(n\) times continuous queries, the \(k\) dummy locations included in each \(\mathit{Req}\) are selected from \(s\) different second-level grids, so the number of organizations that \(nk\) dummy locations belong to is \(s\) and then the probability that the user's real organization is recognized is no greater than \(\frac{1}{s}\). Thus, the presented scheme satisfies \(L_{(k,s)}\). Moreover, The number of each basic service category selected is \(\frac{nk\times k}{M}\) times, so the probability of each basic service category being recognized is \((\frac{nk}{M})/nk=\frac{1}{M}\leq\frac{1}{t}\), which satisfies \(Q_{l}\).  
**Conclusion 3:** The presented scheme can resist LSA.  
**Proof.** In this paper, the core idea for location privacy protection is to make the locations submitted to the LBS server as scattered as possible. The locations in each \(\mathit{Req}\) are different. As the number of query times \(n\) increases, the \(nk\) locations involved in the \(n\) query requests are always different from each other. \(nk\) locations disperse in no less than \(s\) different second-level grids. The distribution characteristics of the \(nk\) locations are consistent with the locations distribution characteristics of the locations in \(G_{sub}\), and there will be no situation where the user's locations are concentrated in some specific places. Therefore, the attacker cannot identify where the user often appears. In addition, because the user's real location is replaced by the other location in the same second-level grid that the real one is located in, the probability that the real location is recognized is 0. As to the query content protection, the idea of homogenization is adopted to ensure the number of times that each basic service category is selected in each query is the same, then the probabilities of each service category being selected are all \(\frac{1}{M}\). From the above, it can be proved that the presented scheme can resist LSA attacks.  
**Conclusion 4:** The presented scheme can resist RSA.  
Proof.: It can be seen from the proof of Conclusion 3 that the \(nk\) mutually different locations in the \(n\) queries are scattered in \(s\) different second-level grids, and there is no case where some specific locations are highly concentrated. Each query does not submit the user's real location, and the query content protection reaches \(l\)-diversity. So the attacker cannot obtain the user's real location and query content through association analysis. In a word, the presented scheme can resist RSA attacks.
# Enhancing Diffusion-Based Music Generation Performance with LoRA
## 3 Applying LoRA to AudioLDM
### Justification for Applying LoRA to Query and Value  
The AudioLDM framework is based on a latent diffusion model and uses a U-Net structure to generate music. Within the U-Net, both self-attention and cross-attention are used extensively to improve the accuracy of conditional generation. Each attention block includes layer normalization, self-attention, cross-attention, and a feedforward module, with skip connections after each step. These blocks help the model understand both short- and long-range patterns in the data while combining guidance information at different levels.  
Both self- and cross-attention mechanisms rely on three core linear projection layers: the query, key, and value. These layers model inter-dependencies by computing similarity scores using the dot product between the query and key and propagating contextual information via the value. Among them, the query and value serve as the primary conduits of information flow in each attention block, directly determining the focus of the model and which information is transmitted via U-Net.  
This structural design is critical in diffusion-based music generation, where the conditioning signal from text embeddings must precisely guide the denoising process at each step. The attention mechanism enables dynamic alignment between textual semantics and audio features across varying resolution levels in the U-Net. Therefore, modifying the most influential components (i.e., the query and value) offers an effective means of controlling and refining the generative process.  
The query determines which elements of the input the model should address, whereas the value projection dictates what information is delivered as a result of the attention layer. These layers directly affect the final output quality. In contrast, the key primarily serves to compute similarity with the query and plays a more auxiliary role. Similarly, the output projection layer following the cross-attention layer primarily post-processes the attention results without significantly influencing semantic alignment.  
Based on this architectural analysis, we adopt a strategy of applying LoRA exclusively to the query and value. This design choice preserves the expressive capacity of the model while minimizing the number of additional parameters. This choice enables performance gains or retention with only partial fine-tuning, avoiding the computational cost of updating the entire model.
# Bistatic Forward ISAR with DVB-T Transmitter of Opportunity
## 4 BFISAR Image Reconstruction Algorithm
For the fast time, expression (15) is rewritten as:  
\[\hat{\Phi}(k,p)=2\pi\frac{k}{T_{U}}\Bigg{(}\frac{R_{ijk\min}(p)}{c}+k.\Delta T -\frac{R_{ijk}(p)}{c}\Bigg{)}. \tag{20}\]  
Multiply and divide the right part of the expression Equation (20) with \(\Delta T\), and denote \(\Big{(}\frac{R_{ijk\min}(p)}{c}-\frac{R_{ijk}(p)}{c}\Big{)}=-\frac{\Delta R_{ ijk}(p)}{c}\), i.e.,  
\[\hat{\Phi}(k,p)=\frac{2\pi.k.\Delta T}{T_{U}.\Delta T}\Bigg{[}-\frac{\Delta R _{ijk}(p)}{c}+k.\Delta T\Bigg{]}=-\frac{2\pi.k.\Delta T}{T_{U}.\Delta T.c} \Delta R_{ijk}(p)+\frac{2\pi.(k.\Delta T)^{2}}{T_{U}.\Delta T}. \tag{21}\]  
The first derivative of the phase \(\hat{\Phi}(k,p)\) in respect of the fast time is written as:  
\[\frac{d}{d\tau}\hat{\Phi}(k,p)=-\frac{2\pi.}{T_{U}.\Delta T.c}\Delta R_{ijk}(p )+\frac{4\pi.(k.\Delta T)}{T_{U}.\Delta T}. \tag{22}\]  
For \(p=0,k=0\), the second linear term is expressed by:  
\[\hat{\Phi}^{(1)}(0,0).(k.\Delta T)=\frac{d\hat{\Phi}(0,0)}{d\tau}(k\Delta T)=- \frac{2\pi.}{T_{U}.\Delta T.c}\Delta R_{ijk}(0).(k.\Delta T). \tag{23}\]  
Denoting the number of range samples as \(K=\frac{T_{U}}{\Delta T}\), the range resolution as \(\Delta R=c\Delta T\), and the dimensionless range coordinate of the scattering point as \(\hat{k}=-\frac{\Delta R_{ijk}(0)}{\Delta R}\), the second linear term can be written as:  
\[\hat{\Phi}(k,p)=2\pi\Bigg{\{}.(f_{0}+k\Delta f)\Bigg{(}t-\frac{R_{ijk}(p)}{c} \Bigg{)}-k\Delta f.\tilde{t}\Bigg{\}}. \tag{15}\]0  
Based on Equations (19) and (24), the phase term Equation (15) can be written as:  
\[\hat{\Phi}(k,p)=2\pi\Bigg{\{}.(f_{0}+k\Delta f)\Bigg{(}t-\frac{R_{ijk}(p)}{c} \Bigg{)}-k\Delta f.\tilde{t}\Bigg{\}}. \tag{15}\]1  
where \(\Phi(k,p)\) is the sequence of higher-order terms. Then, the image reconstruction Equation (14) can be expressed as:  
\[\hat{\Phi}(k,p)=2\pi\Bigg{\{}.(f_{0}+k\Delta f)\Bigg{(}t-\frac{R_{ijk}(p)}{c} \Bigg{)}-k\Delta f.\tilde{t}\Bigg{\}}. \tag{15}\]2  
for each \(\hat{p}=\overline{0,\ N-1}\hat{k}=\overline{0,\ K-1}\). Based on Equation (26), it can be concluded that the image extraction is a procedure of a total phase compensation applied to the BFISAR signal. Phases defined by distances to scattering points at the moment of imaging only remain in the image. Expression (25) can be interpreted as a 2-D spatial transformation of the signal \(\hat{S}(p,k)\) to the image \(a(\hat{k},\hat{p})\). Operations of linear phase compensations and higher-order phase compensations can be divided. Then, Equation (26) can be rewritten as:  
\[\hat{\Phi}(k,p)=2\pi\Bigg{\{}.(f_{0}+k\Delta f)\Bigg{(}t-\frac{R_{ijk}(p)}{c} \Bigg{)}-k\Delta f.\tilde{t}\Bigg{\}}. \tag{15}\]3  
Based on Equation (27), stages of image extraction from the BFISAR signal can be derived.
## 3 Methodology
### Ssa-Cnn-LSTM Fault Prediction Model  
#### 3.4.1 Cnn-LSTM Model  
Due to the small feature dimension of the system-level fault data of platform screen doors, these low-dimensional data will lead to difficulty in neural network feature extraction and affect the final prediction results. The architecture of the CNN-LSTM model proposed in this study is illustrated in Figure 5. The input data undergo a structured processing sequence, which includes a convolutional layer, a pooling layer, a long short-term memory (LSTM) layer, a flattening layer, and a fully connected layer, ultimately culminating in the final output. The CNN used in the model consists of one-dimensional convolutional (Conv 1D) layers and max pooling layers; Conv 1D is commonly used to process sequence data, including time-series data [44].  
The number of hyperparameters within a neural network significantly influences the accuracy of its results. Consequently, the sparrow search algorithm was chosen as a method for the automatic optimization of the hyperparameters with the aim of enhancing the predictive accuracy of the model.  
Figure 5: CNN-LSTM model structure.
## 2 Data and Methodology
### Selected Model and Improvements  
The basic model selected for our detection task contains several changes based on the yolov5m6 [12] baseline model. Here '6' denotes 'P6', which means an extra P6 object output layer is added in the yolov5-P6 models for the detection of larger objects. Synchronized batch normalization [26], multi-scale training, and cosine learning rate are applied to assist the process of training, and the merge-NMS (non-maximum suppression) algorithm is used to remove redundant boxes for the same target during inference. The Mosaic method is used as an efficient technique that randomly selects three additional images for each  
Figure 1: Pie chart of data distribution of the targets from the ten categories in the training and validation sets.  
Figure 2: Image samples from the training set. These samples differ in their resolutions and have been rescaled to the same size here for convenient demonstration.  
image sample with cropping and scaling and combines them into one image, which will then be input into the network. The technique of label smoothing can be introduced in the computation of cross-entropy loss to prevent the model from being overconfident in classifying different types of objects which can be a source of over-fitting [27]. Mixup is an augmentation method that stacks two images into one with different weights. We will incorporate the methods of label smoothing and mixup optionally in some of our tests and analyze their effectiveness.  
Considering the characteristics of our dataset, a main concern of this study is the problem of an unbalanced distribution of classes and hard examples in our dataset, which makes training difficult for certain objects. As an attempt to tackle this problem, the data pre-processing algorithms are strengthened by including combinations of auxiliary augmentation methods, which can enrich the features of each class to be learned by the model and improve the generalizability of the network. Another adjustment is to modify the loss function based on the idea of guiding the training process by taking the quality of samples into account. These practices will be described in more detail in the rest of this section.
## 1 Introduction
[PERSON] et al. [18] investigate the impact of joint orientation and spacing on rock fragmentation mechanisms using RIATs. They found that a joint orientation of about 30\({}^{\circ}\) is optimal for boring, and thrust increases and torque decreases as joint spacing increases. Similarly, [PERSON] et al. [19] used the RIAT to investigate the effects of joint orientation and spacing on TBM rock cutting efficiency and cracking behaviour. The experimental results showed that the optimal rock cutting conditions are achieved with smaller joint spacings and joint orientations between 45\({}^{\circ}\) and 60\({}^{\circ}\). In a similar vein, indentation tests provide another effective approach for understanding the impact of joints on TBM performance. [PERSON] et al. [20] analyzed the impact of joint orientation on breakage behaviour using indentation tests, reporting that joint orientation could change the relative magnitudes of tensile and shear stresses along the trajectories of median cracks. [PERSON] et al. [21] conducted disc cutter indentation tests with joint spacing, joint orientation, and cutter spacing as variables, and found that rock breaking efficiency is highest when the joint orientation is 60\({}^{\circ}\). [PERSON] et al. [22] investigated the rock breaking mechanisms of disc cutters with different joint shear strengths. Rock cutting with disc cutters predominantly involves compressive fracturing induced by normal force, while drag picks primarily tear off rock through cutting force. Due to this difference, the effect of joints on the cuttability of conical picks equipped with roadheaders may differ from that on disc cutters. However, while some researchers have investigated the effect of joint orientation on the cuttability of drag picks using numerical simulations [23; 24], studies on how joint properties affect roadheader performance remain limited. In this study, an experimental approach was employed to investigate the effect of joints on roadheader performance. A series of laboratory-scale LCM tests was conducted on both jointed rock mass specimens and intact rock specimens using a conical pick. The joint rock mass specimens were prepared with joint spacings of 30 mm, 60 mm, and 90 mm, with the joint orientation fixed at 90\({}^{\circ}\). Cutting tests were performed at various cutting depths and line spacings, considering both parallel and perpendicular cutting directions relative to the joint plane. The cuttability of the conical pick was then analyzed based on these conditions.
## 2 Materials and Methods
### Raman Microscopy  
To capture the condition of the skin into which the allergen enters, four-week-old female BALB/c mice were treated with tape-stripping or the application of 4% SDS to the dorsal skin to establish a sensitized skin condition on days 0, 7, and 15. The application of SDS solution is a commonly used technique for epicutaneous sensitization as well as tape-stripping [18]. After the third treatment with tape-stripping or 4% SDS, the mice were immediately sacrificed, and skin from the back was collected. Raman spectra were acquired with a home-built laser-scanning confocal Raman microscope. A single-mode frequency-doubled Nd:YAG laser (MSL-FN-532-S-100 mW; CNI Laser, Changchun, China) operating at the wavelength of 532 nm was used as an excitation laser light. The excitation laser light was focused on a sample through a 60\(\times\) objective lens (CFI Plan Apo Lambda 60 XC, 60\(\times\), NA = 1.2; Nikon, Tokyo, Japan). The back-scattered Raman signal was collected with the same objective lens and detected by a spectrometer (IsoPlane 320, Princeton Instruments, Trenton, NJ, USA) with a cooled CCD image sensor (Pixis 400 BR, \(-\)70 \({}^{\circ}\)C, 1340 \(\times\) 400 pixels; Princeton Instruments, Trenton, NJ, USA). The Raman microscope was controlled with imaging software (MwMapper, version 1.4.5; ScienceEdge Inc., Shizuoka, Japan, [[https://scienceedge.com](https://scienceedge.com)]([https://scienceedge.com](https://scienceedge.com)), accessed on 30 December 2021). The Raman spectrum from \(-\)30 to 3588 cm\({}^{-1}\) was simultaneously obtained with a single exposure. The excitation laser power and the exposure time were 25 mW on the sample plane and 5 s, respectively. Three independent sites were analyzed at a depth of 5, 10, and 15 \(\upmu\)m from the skin surface.
## 2 Related Work
### SIN Local-World Phenomenon  
With deeper research on complex networks, people gradually find a phenomenon. That is, a node is close to some nodes, while it is loosely connected to other nodes. Therefore, a local-world is formed between the close nodes. Within the local-world, there is a preferential connection mechanism, and this phenomenon is called the local-world phenomenon. This phenomenon is ubiquitous, such as economic networks, trade networks, and mobile communication networks, etc.  
Study of local-world phenomena can be traced back to the year 2003, when the article named 'a local-world evolving network model' opened the prelude [5]. In this article, it considered the entire network in the previous modeling process when selecting a connection node, but selected nodes as the local-world in the entire network. The benefit of this improvement was that each node only occupies the local-world network connection information, in line with the characteristics of the local-world network. Compared with the establishment process of scale-free networks, the probability of preferential connection of the newly joined nodes has changed to some extent.  
Since the local-world evolution model was proposed, it immediately gained the attention of some scholars. Some scholars have studied the virus propagation, robustness, and sequential failures of the model, and other scholars have put forward some corresponding improvement models based on the deficiencies in the local-world evolution model [29; 30]. The improved network model is more in line with the characteristics of the actual network and is more worthy of promotion.  
Combining the compositional structure of SIN and the overall structural topology, the heterogeneity of the network structure and business scope, and the multi-layer and cross-domain of space distribution make the SIN show a more obvious local-world phenomenon. In the four-layer structure, there is a local-world phenomenon with a priority connection mechanism within each layer. There is also a local-world phenomenon with preferential connections between different levels. This requires that when studying the topology evolution of SIN, we must consider the nuances. Simultaneously, due to the different compositional mechanisms within different levels, the extent of local-world phenomena is also different. In the synchronous orbit layer, since the satellites that make up the system each operate on a certain orbit, and the constellation of three GEO satellites can realize all-weathers, full-coverage investigations, and achieve corresponding tasks, its internal operating mechanism is simple. By comparison, because of the large number of nodes in the adjacent space layer and the complexity and uncertainty of the formation of the aircraft, the hot air balloon network, and the trajectory, the local-world phenomenon is more obvious, and the overall performance of the network is also affected.
# Bearing Fault Diagnosis Method Based on Improved VMD and Parallel Hybrid Neural Network
## 4 Results
### Fault Diagnosis Model Based on Parallel Hybrid Neural Network
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Network Layer Name** & **Parameter Setting** & **Output Shape** \\ \hline Input Layer & / & [batch, 1024, 1] \\ BiGRU Layer 1-1 & Units = 32 & [batch, 1024, 64] \\ BiGRU Layer 1-2 & Units = 16 & [batch, 1024, 32] \\
1D-CNN Layer1-3 & Filters = 32, Kernel\_size= 64 & [batch, 961, 32] \\
1D-CNN Layer1-4 & Filters = 16, Kernel\_size= 16 & [batch, 946, 16] \\ BiLSTM Layer2-1 & Units = 32 & [batch, 1024, 64] \\ BiLSTM Layer2-2 & Units = 16 & [batch, 1024, 32] \\
1D-CNN Layer2-3 & Filters = 32, Kernel\_size= 64 & [batch, 961, 32] \\
1D-CNN Layer2-4 & Filters = 16, Kernel\_size= 16 & [batch, 946, 16] \\ Concatenate Layer & / & [batch, 1892, 16] \\
1D-GAP Layer & / & [batch, 16] \\ Output Layer & SoftMax classifier & [batch, 6] \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Hyperparameters of model. After using the test data set to evaluate the training model, the diagnosis results are presented in the form of a confusion matrix in Figure 11. The confusion matrix shows that the accuracy of the test is 99.72%. Experimental results verify the effectiveness of the proposed fault detection method and show that the method can accurately diagnose faults with high accuracy.
## 3 Results
### Performance Analysis of Individual Models and Proposed Ensemble Method  
The training and validation accuracy curves for each base model are displayed in Figure 11. The dotted red vertical lines in Figure 11 represent the best validation accuracy for each model, for which the checkpoints are saved and used to obtain the prediction result. The test accuracies for WRN-40, DenseNet-121, EfficientNet-B7, ResNet-152, and ResNet-200 are found as 85.4%, 88.43%, 91.21%, 62.9% and 64.17%, respectively, as given in Table 4, whereas, for the proposed approach, it is 94.62%, which is superior to all the base models.  
It can be observed from Table 4 that EfficientNet-B7 shows better performance compared to ResNet-152, ResNet-200, WRN-40, and DenseNet-121. However, the proposed method outperforms all individual base classifiers in terms of accuracy, specificity, sensitivity, and F1-score with 94.62%, 93.51%, 95.29%, and 93.16%, respectively. The confusion matrices shown in Figure 12 provide more insight into the model's performance. The confusion matrix obtained from the proposed method demonstrates excellent performance with the best _TP_ and _TN_, values along with the least _FP_ and _FN_ values compared to the base models. The receiver operating characteristic (ROC) curve in Figure 13 displays the area under the curve (_AUC_), which demonstrates the base models' and proposed model's ability to classify the fracture and non-fracture classes correctly. The proposed model achieves an AUC score of 99%, the highest of all.  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Model** & _Acc (\%)_ & _Spec (\%)_ & _Sens (\%)_ & _F1 (\%)_ & _AUC (\%)_ \\ \hline WRN-40 & 85.4 & 86.6 & 81.02 & 82.72 & 94.00 \\ DenseNet-121 & 88.43 & 84.2 & 90.81 & 86.38 & 96.00 \\ EfficientNet-B7 & 91.21 & 88.42 & 92.03 & 89.19 & 98.00 \\ ResNet-152 & 62.9 & 58.37 & 61.09 & 59.72 & 87.2 \\ ResNet-200 & 64.17 & 56.02 & 60.93 & 61.4 & 85.06 \\
**Proposed** & **94.62** & **93.51** & **95.29** & **93.16** & **99.00** \\ \hline \hline \end{tabular}
\end{table}
Table 4: Analysis of results obtained from the individual CNNs and the proposed model.  
Figure 11: Training and validation accuracy curves of base models: (**a**) WRN-40 (**b**) DenseNet-121, and (**c**) EfficientNet-B7.  
Figure 12: Confusion matrices obtained from: (**a**) WRN-40; (**b**) DenseNet-121; (**c**) EfficientNet-B7; and (**d**) Proposed method.