esa-sceva/satcom-chunk-collection huggingface dataset:
# Deep Directional Network for Object Tracking
## 3 Direction Deep Network Tracker
### Overall Framework  
Figure 1 shows the overall flow chart of the tracking algorithm in this paper, including pretraining, fine-tuning and online tracking stages. At the top of the picture is the pretraining phase. The network  
is pretrained using the specific tracking datasets. Direction labels indicate the category labels for 11 direction categories, and Pos-Neg Labels denote the category labels for positive and negative samples. We obtain a pretrained network and then use the first frame to fine-tune the network. In the fine-tuning phase, we first extract the positive and negative samples. Then, we classify the positive samples into direction categories and fine-tune the network. In the tracking phase, we extract a sample at the previous frame and input the sample into the fine-tuned network to obtain a score and a direction. If the sample is negative, we use the redetection mechanism to determine the final target position. If it is a positive sample, we determine whether the direction class is T (true-sample). If direction class is T, then we obtain the final target position. If it is not, we will perform window sliding to continue sampling. If the number of sliding windows reaches ten, we stop the sliding window operation to determine the final target position.  
The architecture of the direction of the deep network for target tracking proposed in this paper is shown in Figure 2. The architecture of the network mainly includes three convolutional layers (Conv1, Conv2 and Conv3) and three fully connected layers (FC4, FC5 and {FC6, FC7}). FC6 and FC7 are two independent branches of the same fully connected layer. They represent the direction classification layer and the pos-neg sample classification layers, respectively. There is a pooling layer behind layers conv1 and conv2, denoted Pool1 and Pool2, respectively. In the tracking phase, we extract a sample and then send it to the network to obtain the sample's direction and score. Direction represents the sample direction category and score represents the pos-neg sample score of the sample. We perform the corresponding sliding window operation according to direction. We judge the final target position according to direction and score.  
Figure 1: Overall flow chart of our tracking algorithm.  
Table 1 shows the filter sizes and the numbers of each type of layer in the deep directional network. Our network uses three convolutional layers. Our original intention is to reduce computation amount of the network. Too many layers will inevitably increase the amount of computations and too few layers will be difficult to obtain the deep semantic features of the target. So we use three convolutional layers and three fully connected layers. We use the Relu activation function after each convolutional layer and fully connected layer, as shown in Equation (1).  
\[Relu(x)=\left\{\begin{array}{ll}x&if\ x>0\\ 0&if\ x\leq 0\end{array}\right. \tag{1}\]
# SSTNDS: A SET-Based Non-Dominated Sorting Algorithm for Multi-Objective Optimization Problems
## 4 Proposed Non-Dominated Sorting Algorithm
### Algorithm Description
```
Input solution \(s\), objective index \(m\), objective set \(A\), position information \(PS\) Output dominant set\(\Omega(s)\)
1 Begin
2\(\Omega(s)\leftarrow\) empty;
3\(pos\_m\gets PS(s,m)\); % position \(s\) first appears
4 if\(pos\_m\) equals 1
5\(\Omega(s)\leftarrow\) empty;
6 else
7\(\Omega(s)\gets A(m,\)\(1\)\(:\)\(pos\_m-1)\); % the smallest preferential set
8 for\(j=1\) to \(M\)and\(j\) is not equal to \(m\)
9\(pos\_j(s)\gets PS(s,j)\); % get position in objective \(j\)
10\(AP(s,j)\gets A(j,\)\(1\)\(:\)\(pos\_j-1)\); % get preferential set
11\(\Omega(s)\leftarrow\)intersect(\(\Omega(s)\), \(AP(s,j)\)); % update dominant set
12 end
13
14 end for
15 return\(\Omega(s)\)
16
17 end for
```  
**Algorithm 2**Obtain Dominant Set  
The comparing process is realized in Algorithm 3 lines 3-8. This algorithm is used to check whether solution \(p\) dominates \(s\) or they are equal. If any objective value of \(p\) is better than \(s\), the returned value is 1 which means \(p\) dominates \(s\) and the final rank of \(s\) is \(r+1\); otherwise, if the returned value is 0 it means that \(s\) is identical to \(p\) and the rank of \(s\) is \(r\) (Algorithm 1 line 18). ```
Input solution \(p\), \(s\) Output value equals 1 if \(p\) dominates \(s\), equals 0 if \(p\) is identical to \(s\)
1 Begin
2\(value\gets 0\);
3 for\(j\)=1 to M
4 ifA(\(j,\)\(p\)) < \(A(j,\)\(s)\)
5\(value\)\(\leftarrow\)\(1\) % \(p\) dominates \(s\)
6 break;
7 end for
8
9 returnvalue
10 end
```  
**Algorithm 3**Dominate Comparision
# Fabric Defect Detection Method Using SA-Pix2 pix Network and Transfer Learning
## 1 Introduction
A systematic investigation into the impact of each loss function on defect detection accuracy reveals that the fusion of L1 loss and the enhanced structural loss significantly enhances the network's capacity to process image intricacies, thereby improving overall image reconstruction accuracy. Building upon this foundation, migration learning is introduced to constrain the training of the target network by aligning feature distributions, effectively addressing potential overfitting issues stemming from the limited influence of small sample sizes during training. This holistic approach aims to enhance the accuracy and robustness of fabric blemish detection by synergizing the SA-Pix2 pix network, an adapted loss function, and migration learning to surmount challenges posed by small sample sizes and complex fabric textures in blemish reconstruction.
# Performance Analysis of a Zero-Energy Building Using Photovoltaics and Hydrogen Storage
## 2 Material and Methods
### Building Description  
The present study investigates a building with a 400 m\({}^{2}\) floor area (20 m \(\times\) 20 m) with a height of 3.1 m. The building is located in Athens, Greece (37.984\({}^{\circ}\), 23.728\({}^{\circ}\)) and it is a residential one with six occupants. The present analysis is conducted with the TRNSYS tool [22] which is a dynamic simulation tool and the meteorological data were used from its libraries. The building includes a south wall, a west wall, a north wall, and an east wall. The south wall includes a window of 12 m\({}^{2}\), the east wall includes a window of 6 m\({}^{2}\), and the west wall has a window of 6 m\({}^{2}\), while the north wall has no windows. The windows are placed in the center of the respective walls, and they have a square shape.  
The ground slab exchanges heat with the ground, and the external walls and the roof with the external air. More specifically, the roof is a flat one without inclination. The composition of the structural materials and the thermal properties of the respective materials have been taken from Ref. [23]. It is useful to state that the U-values of the ground, roofs external walls, and windows were selected to be 0.304 W/m\({}^{2}\)K, 0.318 W/m\({}^{2}\)K, 0.365 W/m\({}^{2}\)K, and 1.4 W/m\({}^{2}\)K. The windows have a g-value of 0.59, while the building has no shadings. The aforementioned values for the U-values are acceptable values according to the Greek legislation for Zone B [24].  
The thermal comfort limit of the temperature was set at 20 \({}^{\circ}\)C for the winter and 26 \({}^{\circ}\)C for the summer period. The total infiltration rate and natural ventilation rate were set at 0.8 air changes per hour which is a reasonable value according to Greek legislation [24]. The specific gain for the appliances and the lighting were selected totally at 9 W/m\({}^{2}\)[24] and the daily variation in this load is depicted in Figure 1; which is a typical one according to the literature [25]. The specific thermal load of the occupants was chosen at 100 W per person (ISO-7730 [26]). Table 1 includes the basic data of the studied building.  
\begin{table}
\begin{tabular}{c c} \hline
**Parameter** & **Value** \\ \hline Temperature comfort limit in winter & 20 \({}^{\circ}\)C \\ Temperature comfort limit in summer & 26 \({}^{\circ}\)C \\ Building’s net floor area & 400 m\({}^{2}\) \\ Side width (South, West, North, East) & 20 m \\ Height & 3.1 m \\ Area of the south window & 12 m\({}^{2}\) \\ Area of the west window & 6 m\({}^{2}\) \\ Area of the east window & 6 m\({}^{2}\) \\ Infiltration and natural ventilation & 0.8 air change per hour \\ Appliances and lighting specific load & 9 W/m\({}^{2}\) \\ Residents & 6 occupants in the rest \\ Specific thermal load per person & 100 W/person (ISO 7730) \\ Ground U-value & 0.304 W/m\({}^{2}\)K \\ Roof U-value & 0.318 W/m\({}^{2}\)K \\ \hline \end{tabular}
\end{table}
Table 1: Basic data for the building description.
## 5 Experimental Results
### Hardware Used  
The tests were realized using the TelosB [31] and SHIMMER2R platforms. The TelosB and SHIMMER2R employ the CC2420 chip radio and the MSP430F11 processor. Importantly, the CC2420 implements the PHY layer of the IEEE 802.15.4  
The TelosB were used as the network devices while The SHIMMER2R acted as the coordinator. Finally, a Texas Instruments CC2531 USB dongle (Texas Instruments CC2531 USB Dongle [32]; was used as a sniffer tool. This dongle uses the CC2531 radio chip and can receive IEEE 802.15.4 frames.
## 4 Results and Performance Evaluation
### Effectiveness of the DLFBR Impulse Detection Algorithm
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Dataset**} & **Soft Threshold** & **CFAR** & **Proposed Method** \\  & **(IOU = 0.2)** & **(IOU = 0.2)** & **(IOU = 0.3)** \\ \hline Dataset 1 & 0.7562 & 0.6767 & 0.9154 \\ Dataset 2 & 0.6333 & 0.7111 & 0.8100 \\ Dataset 3 & 0.4882 & 0.6948 & 0.9331 \\ Dataset 4 & 0.7640 & 0.8037 & 0.9336 \\ Average & 0.6604 & 0.7215 & 0.8980 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Precision performance for deep learning flexible boundary regression (DLFBR) compared to the soft threshold and constant fault alarm rate (CFAR) methods.
## 1 Introduction
[PERSON] and [PERSON] [15] presented the results of investigations based on ambient vibrations carried out to evaluate the structural condition of a masonry bell tower built in the XVII century and about 74 m high. The assessment procedure included full-scale vibration testing, modal identification from the ambient vibration responses, finite element modelling, and dynamics-based identification of the uncertain structural parameters of the model. [PERSON] et al. [17] presented the results of a vibration test and operational modal analysis performed on the historic masonry bell tower of Hagia Sophia Church in Trabzon, Turkiye. The experimental measurements were performed using two measurement setups at different times. In addition to the experimental measurements, an analytical model of the tower was developed. The authors report that good harmony is achieved between the mode shapes, but there are some differences between the natural frequencies. [PERSON] et al. [24] presented the results of a study carried out on four different isolated masonry bell towers in the province of Ferrara, Italy. The parameters of the materials were derived by calibrating finite element models (FEM), using data collected during a study campaign carried out with an OMA application. Recently, [PERSON] et al. [29] presented the results of a dynamic on-site investigation of a stone minaret in Mostar and provided a seismic evaluation. The on-site investigation included dynamic identification of the minaret through environmental vibration tests and qualitative assessment of the masonry through sound pulse velocity measurements. In addition to the modal analysis, the authors performed a time history analysis using the Applied Element Method. The authors reported good agreement between the first natural frequency obtained by the on-site investigation and the modal analysis. [PERSON] and [PERSON] [31] presented a new element-level damage detection framework for truss railroad bridges that combines acceleration and strain response analysis. They determined operational acceleration and strain responses to train passage and developed a new damage index by formulating a strategy to combine damage features obtained individually from acceleration and strain analysis. [PERSON] and [PERSON] [32] presented the application of OMA to gears and induction motors, based on the random decrement technique and the extended Ibrahim time method. This work shows how far the application of OMA goes beyond monitoring the condition of structures in civil engineering. In this work, ambient vibration tests and OMA were applied to determine the modal parameters of the masonry bell tower. The determination of modal parameters is necessary for updating, i.e., tuning, a numerical model that can be used to simulate the behaviour of the structure. The results of the OMA are then used for the identification of the structure. The ultimate goal of this study is to develop a finite element (FE) model in which the modal analysis results agree with the experimental results and which reliably simulates the behaviour of the tower.
## 3 Analysis of Results
### Compression Mechanisms of Anchor Rods  
[PERSON] et al. measured the force condition on the anchor rods and found that many anchor rods were under extension; additionally, tension and compression segments coexisted on some anchor rods [28]. [PERSON] et al. concluded that tension and compression alternately appeared in the surrounding rocks based on field-measured results [29]. [PERSON] et al. also conducted field measurements and found that both loosening and compression zones existed in the roadway-surrounding rocks; moreover, they derived that the coexistence of loosening and compression regions was tightly related to the appearance of zonal disintegration in the surrounding rocks [8]. Explaining the alternate appearance of tension and compression in the roadway-surrounding rocks has become a basic problem that should be addressed in investigating zonal disintegration mechanisms.  
Figure 19 shows the numerical simulation results obtained via the discrete element method for the failure of the surrounding rocks in the roadway reinforced by anchor rods and the forces acting on the anchor rods of 2.2 m in length and a 100.0 kN bearing capacity.  
After the reinforcement of the roof and two sides by the anchor rods, the initial failure of the surrounding rocks occurred on the floor. The conjugate failure plane was formed and extended from two shoulders of the roadway in the vertical or horizontal directions, as illustrated by the AD, BC, and BE fracture planes in Figure 19a. As the stress in the roof increased, the compression-shear failure began to appear on two sides and developed deeper toward the surrounding rocks in a conjugate-failure pattern (see Figure 19b). By monitoring the forces acting on the anchor rods, it can be concluded that the anchor rods were mainly subjected to tension. This occurred because the radial displacement of the shallow surrounding rocks was larger than that of the deep rocks when the surrounding rocks at different depths underwent deformation and movement toward the roadway, thus putting each anchor rod as a whole in a tensile state. When the surrounding rocks were fractured, the integrated rocks moved along the shear-fractured zones, and the anchor rods were subjected to bending moments. In the bending deformation of the anchor rods, the bulk of the bent segment was under compression, as shown by stress conditions near points H and G in Figure 19b. Therefore, the compression of the anchor rods in the roadway-surrounding rocks in the zonal disintegration state could be measured via strain gauges attached to the anchor rod surface.  
The compression phenomenon in the roadway-surrounding rocks was closely related to zonal disintegration. However, the compression state was not the result of radial compression of the surrounding rocks but was induced by the bending of the anchor rods or the testing devices when the integrated rocks after compression-shear cutting were loosened toward the roadway.  
Figure 19: Stress state of anchor rods. (**a**,**b**) indicate the different stages of the lane, respectively.
## 3 Results and Discussion
### Using PAN Network for Defect Segmentation  
In this experiment, the PAN network [27] was trained with the original real images and with the optimal synthetic augmentation dataset to validate the benefits in a complete different segmentation architecture compared with DeepLabV3+.  
PAN is proposed to exploit the impact of global contextual information in semantic segmentation. This network combines attention mechanism and spatial pyramid to extract precise dense features for pixel labeling instead of dilated convolution and artificially designed decoder networks. PAN uses a Feature Pyramid Attention (FPA) module to perform spatial pyramid attention structure on high-level output and combining global pooling to learn a better feature representation. This network also has a Global Attention Upsample module on each decoder layer to provide global context as a guidance of low-level features to select category localization detail [27].  
With this network and the original dataset, we obtain an IoU metric of 75.31%, while, in the case of the augmented dataset, we get 82.29%. This repetition in the IoU increment shows that the benefits of augmenting the dataset using synthetic images are also repeated using another segmentation network. The comparison of the obtained results with both segmentation networks with the previously-mentioned datasets is shown in Figure 11. Moreover, apart from the metric, the segmentation quality results are also better. The segmentation performed by the network trained with synthetic data are better adapted to the geometry of the defects. Due to the FPA module, this architecture uses the information at different scales, so the segmentation output is better in terms of pixel-level attention.
# A Robust and Secure Watermarking Approach Based on Hermite Transform and SVD-DCT
## 5 Experiments and Results
### Computational Complexity  
Since the watermarking insertion/extraction processes are composed of several stages, we give the complexity for those stages that involved the host image in the insertion process, and we did not include neither the pre-processing of the LOGO watermark and image MetaData because of their small dimensions compared to the host image. Thus, the computational complexity for a grayscale image of \(M\times N\) px is given as follows:  
* Hermite transform (for both the decomposition and reconstruction stages): \(\mathcal{O}(2\times N\mathcal{O}\times(N+1)^{2}\times M\times N)\), where \(N\mathcal{O}\)c is the number of coefficients and \(\mathcal{N}\) represents the size of the binomial window.
* HVS stage: \(\mathcal{O}(b^{2}\times(M/b)\times(N/b))\), where \(b\times b=b^{2}\) is the size of each block.
* DCT and inverse DCT: \(\mathcal{O}(2\times(M/b)\;(N/b)\times(b^{2}\log_{2}b))\).
* SVD, which is applied several time in the insertion process: \(\mathcal{O}((2\times 4)\times(2\times M/b)\times(2\times N/b))\times max(2 \times M/b,2\times N/b))\), and it could be simply \(\mathcal{O}((2\times 4)\times(2\times M/b)\times(2\times N/b)\times(2 \times M/b))\) considering that \(M\geq N\).
* SVD reconstruction: \(\mathcal{O}(4\times 2\times(2\times M/b\times 2\times N/b\times max(2 \times M/b,2\times N/b)))=\mathcal{O}(4\times 2\times(2\times M/b\times 2 \times N/b\times 2\times M/b))\)\), for \(M\geq N\).  
Figure 15: Histogram equalization (128): (**a**) Equalized _[PERSON]_ image. (**b**) Recovered watermark (LOGO). (**c**) Recovered MetaData. Histogram equalization (4): (**d**) Equalized _[PERSON]_ image. (**e**) Recovered watermark (LOGO). (**f**) Recovered MetaData. Translation (100): (**g**) Translated _[PERSON]_ image. (**h**) Recovered watermark (LOGO). (**i**) Recovered MetaData.  
Finally, fixing \(NOC=9\), \(\mathcal{N}=2\), and \(b=4\), the simplified computational complexity is given by: \(\mathcal{O}(M\times N+M\times N+M\times N+(M^{2}\times N))+(M^{2}\times N))\), and resolving it, we obtain the following total computational complexity: \(\mathcal{O}(M\times N\times(M+1))\).  
Figure 16: Rotation (90): **(a)** Rotated _[PERSON]_ image. (b) Recovered watermark (LOGO). (c) Recovered MetaData. Rotation (45): **(d)** Rotated _[PERSON]_ image. (e) Recovery Watermark (LOGO). (f) Recovered MetaData. Cropping (30): **(g)** Cropped _[PERSON]_ image. (h) Recovered watermark (LOGO). (i) Recovered MetaData. Cropping (45): **(j)** Cropped _[PERSON]_ image. (k) Recovered watermark (LOGO). (l) Recovered MetaData.
# Deep Learning for Intrusion Detection Systems (IDSs) in Time Series Data +
## 5 Experiments
### Experimental Set-Up  
Prior to conducting experiments on our model, we engage in feature selection and present outcomes for various feature sets.  
To utilize our dataset, we employ 5-fold cross-validation and average the results across all 5 folds. We note that after the time series formatting--instead of individual samples--we have windows of size \(W\) and, thus, the shuffling and splitting to folds are performed on such independent windows. To demonstrate the robustness of our proposed approach, we make a comparison with the standard binary IDS classification approach. We also show the effect of the input window size on the results and, thus, exhibit the change in accuracy while the value of this variable is ascending.  
Apart from experiments concerning the input window size, we also experiment with the prediction horizon. Based on the aforementioned methodology, we still predict a single label, which is not always the next packet. For each window, we create a new label that indicates the existence of an attack in the next \(T\) time steps.  
To ensure the effectiveness of our predictive models, we evaluate their performance using the F1 score, precision, and recall. These metrics are necessary due to the imbalanced nature of our dataset, where simple accuracy could produce inaccurate results. F1 score, precision, and recall provide a more accurate measure of the model's performance by accounting for the imbalanced dataset and ensuring a fair evaluation.  
We train our models for 40 rounds, applying early stopping with a patience parameter of \(p=5\). All the models are trained using the Adam Optimizer with a learning rate of \(lr=0.001\), a weight decay of \(w=1\times 10^{5}\), and a batch size of \(B=32\). A summary of the hyperparameter setup is shown in Table 2.